From a36ea96654cf578b83a321eaf67dfab6d3295740 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 18 Mar 2026 18:40:40 -0300 Subject: [PATCH 01/72] feat: add pre-enrichment linear projections before transformer blocks Two CastedLinear(512,512) layers applied to token embeddings before entering the residual stream. No activation between them. Weights optimized via Muon alongside block matrix params. Also updates .gitignore for venv and build artifacts. --- .gitignore | 6 +++++- train_gpt.py | 6 ++++++ 2 files changed, 11 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 3423c416a..e0f4fee80 100644 --- a/.gitignore +++ b/.gitignore @@ -8,4 +8,8 @@ data/manifest.json data/docs_selected.jsonl .mypy_cache/ .venv -logs/ \ No newline at end of file +venv/ +logs/ +*.pyc +*.log +*.bin \ No newline at end of file diff --git a/train_gpt.py b/train_gpt.py index 0deb0565f..fa4237ffa 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -667,6 +667,10 @@ def __init__( self.tied_embed_init_std = tied_embed_init_std self.logit_softcap = logit_softcap self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.pre_enrich = nn.Sequential( + CastedLinear(model_dim, model_dim, bias=False), + CastedLinear(model_dim, model_dim, bias=False), + ) self.num_encoder_layers = num_layers // 2 self.num_decoder_layers = num_layers - self.num_encoder_layers self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) @@ -699,6 +703,7 @@ def _init_weights(self) -> None: def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: x = self.tok_emb(input_ids) + x = self.pre_enrich(x) x = F.rms_norm(x, (x.size(-1),)) x0 = x skips: list[Tensor] = [] @@ -854,6 +859,7 @@ def log0(msg: str, console: bool = True) -> None: for name, p in block_named_params if p.ndim == 2 and not any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) ] + matrix_params.extend(p for p in base_model.pre_enrich.parameters() if p.ndim == 2) scalar_params = [ p for name, p in block_named_params From 55ecffe0596cb98b23896153cb3685b76dfc630f Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 18 Mar 2026 19:05:52 -0300 Subject: [PATCH 02/72] feat: add GELU activation between pre-enrichment projections Tests whether true nonlinearity improves over the linear-only factorization that scored val_bpb 1.4188. --- train_gpt.py | 1 + 1 file changed, 1 insertion(+) diff --git a/train_gpt.py b/train_gpt.py index fa4237ffa..d1e405bd0 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -669,6 +669,7 @@ def __init__( self.tok_emb = nn.Embedding(vocab_size, model_dim) self.pre_enrich = nn.Sequential( CastedLinear(model_dim, model_dim, bias=False), + nn.GELU(), CastedLinear(model_dim, model_dim, bias=False), ) self.num_encoder_layers = num_layers // 2 From 526d8c0b46ae0228847af747a85f7eb7a40b316b Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 18 Mar 2026 23:05:21 -0300 Subject: [PATCH 03/72] feat: add encoder depth recurrence (2x encoder pass before decoder) Encoder blocks 0-3 run twice with RMS norm between passes. Decoder runs once using skip connections from the refined second encoder pass. 13 effective layers from 9 physical blocks, zero extra parameters. --- train_gpt.py | 23 +++++++++++++---------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index d1e405bd0..8bc016d11 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -707,16 +707,19 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: x = self.pre_enrich(x) x = F.rms_norm(x, (x.size(-1),)) x0 = x - skips: list[Tensor] = [] - - # First half stores skips; second half reuses them in reverse order. - for i in range(self.num_encoder_layers): - x = self.blocks[i](x, x0) - skips.append(x) - for i in range(self.num_decoder_layers): - if skips: - x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() - x = self.blocks[self.num_encoder_layers + i](x, x0) + + for _pass in range(2): + skips: list[Tensor] = [] + for i in range(self.num_encoder_layers): + x = self.blocks[i](x, x0) + skips.append(x) + if _pass == 0: + x = F.rms_norm(x, (x.size(-1),)) + continue + for i in range(self.num_decoder_layers): + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + x = self.blocks[self.num_encoder_layers + i](x, x0) x = self.final_norm(x).reshape(-1, x.size(-1)) targets = target_ids.reshape(-1) From 3a949b14170446be298be364bd74c18c9c8cfd04 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 18 Mar 2026 23:25:36 -0300 Subject: [PATCH 04/72] feat: full 2x U-Net recurrence (encoder+decoder both run twice) All 9 blocks run twice with RMS norm between passes. 18 effective layers from 9 physical blocks, zero extra params. Replaces encoder-only recurrence from previous commit. --- train_gpt.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 8bc016d11..7690e5da3 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -713,13 +713,12 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: for i in range(self.num_encoder_layers): x = self.blocks[i](x, x0) skips.append(x) - if _pass == 0: - x = F.rms_norm(x, (x.size(-1),)) - continue for i in range(self.num_decoder_layers): if skips: x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() x = self.blocks[self.num_encoder_layers + i](x, x0) + if _pass == 0: + x = F.rms_norm(x, (x.size(-1),)) x = self.final_norm(x).reshape(-1, x.size(-1)) targets = target_ids.reshape(-1) From 39704a246bc887386d2c11cdc23a8a1c63b0c7e1 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 18 Mar 2026 23:47:48 -0300 Subject: [PATCH 05/72] feat: 3x encoder recurrence (3 encoder passes, 1 decoder pass) 17 effective layers from 9 physical blocks. RMS norm between each encoder pass. Testing if 3x beats 2x encoder recurrence. --- train_gpt.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 7690e5da3..3ac773386 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -708,17 +708,18 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: x = F.rms_norm(x, (x.size(-1),)) x0 = x - for _pass in range(2): + for _pass in range(3): skips: list[Tensor] = [] for i in range(self.num_encoder_layers): x = self.blocks[i](x, x0) skips.append(x) + if _pass < 2: + x = F.rms_norm(x, (x.size(-1),)) + continue for i in range(self.num_decoder_layers): if skips: x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() x = self.blocks[self.num_encoder_layers + i](x, x0) - if _pass == 0: - x = F.rms_norm(x, (x.size(-1),)) x = self.final_norm(x).reshape(-1, x.size(-1)) targets = target_ids.reshape(-1) From 1fb15ea6535997ee571e8c1ba6763ac82f212133 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 18 Mar 2026 23:53:08 -0300 Subject: [PATCH 06/72] revert: back to 2x encoder recurrence (3x hits Triton shared memory limit) 3x encoder recurrence exceeds A100 SM shared memory (168096 > 166912). 2x encoder recurrence remains our best: val_bpb 1.4235. --- train_gpt.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 3ac773386..8bc016d11 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -708,12 +708,12 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: x = F.rms_norm(x, (x.size(-1),)) x0 = x - for _pass in range(3): + for _pass in range(2): skips: list[Tensor] = [] for i in range(self.num_encoder_layers): x = self.blocks[i](x, x0) skips.append(x) - if _pass < 2: + if _pass == 0: x = F.rms_norm(x, (x.size(-1),)) continue for i in range(self.num_decoder_layers): From 5449ba5f91f04ff39aa83f66e643e40167b1a9d8 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 18 Mar 2026 23:56:08 -0300 Subject: [PATCH 07/72] feat: configurable encoder/decoder split via NUM_ENCODER_LAYERS Allows overriding the default 50/50 split to put more blocks in the encoder for deeper recurrence. Default behavior unchanged. --- train_gpt.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/train_gpt.py b/train_gpt.py index 8bc016d11..a09682863 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -672,7 +672,8 @@ def __init__( nn.GELU(), CastedLinear(model_dim, model_dim, bias=False), ) - self.num_encoder_layers = num_layers // 2 + enc_override = int(os.environ.get("NUM_ENCODER_LAYERS", 0)) + self.num_encoder_layers = enc_override if enc_override > 0 else num_layers // 2 self.num_decoder_layers = num_layers - self.num_encoder_layers self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) From dbf0262da6429bbb12dd9590a975dd30cd28f742 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Thu, 19 Mar 2026 00:15:56 -0300 Subject: [PATCH 08/72] cleanup: remove NUM_ENCODER_LAYERS override, keep best config Best config: 4+5 split with 2x encoder recurrence. 6+3 split tested and was worse (1.4267 vs 1.4235). --- train_gpt.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index a09682863..8bc016d11 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -672,8 +672,7 @@ def __init__( nn.GELU(), CastedLinear(model_dim, model_dim, bias=False), ) - enc_override = int(os.environ.get("NUM_ENCODER_LAYERS", 0)) - self.num_encoder_layers = enc_override if enc_override > 0 else num_layers // 2 + self.num_encoder_layers = num_layers // 2 self.num_decoder_layers = num_layers - self.num_encoder_layers self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) From 34684f8cb81522212558f6b3e8c01b48f99bc29c Mon Sep 17 00:00:00 2001 From: idan3011 Date: Thu, 19 Mar 2026 20:52:23 -0300 Subject: [PATCH 09/72] feat: auxiliary encoder loss for direct encoder gradient signal After encoder passes, compute prediction loss from encoder output weighted at 0.1x and add to final loss. Gives encoder blocks direct learning signal instead of only through decoder backprop. --- train_gpt.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 8bc016d11..e1054ad9c 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -707,6 +707,8 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: x = self.pre_enrich(x) x = F.rms_norm(x, (x.size(-1),)) x0 = x + targets = target_ids.reshape(-1) + enc_loss = torch.zeros((), device=x.device) for _pass in range(2): skips: list[Tensor] = [] @@ -716,13 +718,16 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: if _pass == 0: x = F.rms_norm(x, (x.size(-1),)) continue + enc_repr = F.rms_norm(x, (x.size(-1),)).reshape(-1, x.size(-1)) + if self.tie_embeddings: + enc_logits = self.logit_softcap * torch.tanh(F.linear(enc_repr, self.tok_emb.weight) / self.logit_softcap) + enc_loss = F.cross_entropy(enc_logits.float(), targets, reduction="mean") for i in range(self.num_decoder_layers): if skips: x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() x = self.blocks[self.num_encoder_layers + i](x, x0) x = self.final_norm(x).reshape(-1, x.size(-1)) - targets = target_ids.reshape(-1) if self.tie_embeddings: logits_proj = F.linear(x, self.tok_emb.weight) else: @@ -730,7 +735,8 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: raise RuntimeError("lm_head is required when tie_embeddings=False") logits_proj = self.lm_head(x) logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) - return F.cross_entropy(logits.float(), targets, reduction="mean") + final_loss = F.cross_entropy(logits.float(), targets, reduction="mean") + return final_loss + 0.1 * enc_loss # ----------------------------- From eb8f39e2b716a5fc86b9b7790a61c20d0436c1cd Mon Sep 17 00:00:00 2001 From: idan3011 Date: Thu, 19 Mar 2026 21:09:41 -0300 Subject: [PATCH 10/72] fix: zero auxiliary encoder loss weight during eval Auxiliary loss was inflating val_bpb metric during evaluation. Now uses weight=0.1 during training, 0.0 during eval. --- train_gpt.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index e1054ad9c..576ed61a3 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -708,7 +708,7 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: x = F.rms_norm(x, (x.size(-1),)) x0 = x targets = target_ids.reshape(-1) - enc_loss = torch.zeros((), device=x.device) + enc_loss = torch.zeros((), device=x.device, dtype=x.dtype) for _pass in range(2): skips: list[Tensor] = [] @@ -719,8 +719,7 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: x = F.rms_norm(x, (x.size(-1),)) continue enc_repr = F.rms_norm(x, (x.size(-1),)).reshape(-1, x.size(-1)) - if self.tie_embeddings: - enc_logits = self.logit_softcap * torch.tanh(F.linear(enc_repr, self.tok_emb.weight) / self.logit_softcap) + enc_logits = self.logit_softcap * torch.tanh(F.linear(enc_repr, self.tok_emb.weight) / self.logit_softcap) enc_loss = F.cross_entropy(enc_logits.float(), targets, reduction="mean") for i in range(self.num_decoder_layers): if skips: @@ -736,7 +735,8 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: logits_proj = self.lm_head(x) logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) final_loss = F.cross_entropy(logits.float(), targets, reduction="mean") - return final_loss + 0.1 * enc_loss + aux_weight = 0.1 if self.training else 0.0 + return final_loss + aux_weight * enc_loss # ----------------------------- From b23ef9059eecb6e311144b968d601d7d1c98d764 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Thu, 19 Mar 2026 21:28:35 -0300 Subject: [PATCH 11/72] feat: reverse encoder recurrence + revert auxiliary loss MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Second encoder pass runs blocks in reverse order (3→2→1→0) for bidirectional refinement. Auxiliary encoder loss reverted — it hurt performance (1.4135 vs 1.4077 without it). --- train_gpt.py | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 576ed61a3..470220cf1 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -707,26 +707,23 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: x = self.pre_enrich(x) x = F.rms_norm(x, (x.size(-1),)) x0 = x - targets = target_ids.reshape(-1) - enc_loss = torch.zeros((), device=x.device, dtype=x.dtype) + encoder_order = [range(self.num_encoder_layers), range(self.num_encoder_layers - 1, -1, -1)] for _pass in range(2): skips: list[Tensor] = [] - for i in range(self.num_encoder_layers): + for i in encoder_order[_pass]: x = self.blocks[i](x, x0) skips.append(x) if _pass == 0: x = F.rms_norm(x, (x.size(-1),)) continue - enc_repr = F.rms_norm(x, (x.size(-1),)).reshape(-1, x.size(-1)) - enc_logits = self.logit_softcap * torch.tanh(F.linear(enc_repr, self.tok_emb.weight) / self.logit_softcap) - enc_loss = F.cross_entropy(enc_logits.float(), targets, reduction="mean") for i in range(self.num_decoder_layers): if skips: x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() x = self.blocks[self.num_encoder_layers + i](x, x0) x = self.final_norm(x).reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) if self.tie_embeddings: logits_proj = F.linear(x, self.tok_emb.weight) else: @@ -734,9 +731,7 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: raise RuntimeError("lm_head is required when tie_embeddings=False") logits_proj = self.lm_head(x) logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) - final_loss = F.cross_entropy(logits.float(), targets, reduction="mean") - aux_weight = 0.1 if self.training else 0.0 - return final_loss + aux_weight * enc_loss + return F.cross_entropy(logits.float(), targets, reduction="mean") # ----------------------------- From 644ce90d880a93b51493aca3ea2e2ff10f099032 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Thu, 19 Mar 2026 21:49:39 -0300 Subject: [PATCH 12/72] feat: competition-ready submission with stacked techniques Novel architecture (ours): - GELU pre-enrichment before transformer blocks - 2x encoder recurrence with RMS norm between passes Proven techniques adopted: - Overtone init (power-law SVD embedding initialization) - FP16 embedding passthrough (avoids int8 compound error) - Muon decoupled weight decay (0.02) - Sliding window eval (stride=64, ~960 tokens context per token) Run with: NUM_LAYERS=10 TIED_EMBED_LR=0.1 WARMDOWN_ITERS=2500 MATRIX_LR=0.06 torchrun --standalone --nproc_per_node=8 train_gpt.py --- train_gpt.py | 75 ++++++++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 70 insertions(+), 5 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 470220cf1..d3ae6e5e7 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -277,6 +277,65 @@ def eval_val( model.train() return float(val_loss.item()), float(bits_per_token * tokens_per_byte) + +def eval_val_sliding( + args: Hyperparameters, + model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + stride: int = 64, +) -> tuple[float, float]: + seq_len = args.train_seq_len + total_tokens = val_tokens.numel() + windows: list[tuple[int, int]] = [] + pos = 0 + while pos + seq_len < total_tokens: + score_start = 0 if pos == 0 else seq_len - stride + windows.append((pos, score_start)) + pos += stride if pos > 0 else seq_len + all_windows = windows + my_windows = all_windows[rank::world_size] + + val_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + val_token_count = torch.zeros((), device=device, dtype=torch.float64) + val_byte_count = torch.zeros((), device=device, dtype=torch.float64) + + model.eval() + with torch.inference_mode(): + for win_start, score_start in my_windows: + chunk = val_tokens[win_start:win_start + seq_len + 1].to(device=device, dtype=torch.int64) + x = chunk[:-1].unsqueeze(0) + y = chunk[1:].unsqueeze(0) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + logits_input = x[:, :seq_len] + targets_input = y[:, :seq_len] + loss_val = model(logits_input, targets_input).detach() + score_tokens = seq_len - score_start + val_loss_sum += loss_val.to(torch.float64) * float(seq_len) + val_token_count += float(seq_len) + scored_prev = x[0, score_start:seq_len] + scored_tgt = y[0, score_start:seq_len] + token_bytes = base_bytes_lut[scored_tgt].to(dtype=torch.int16) + token_bytes += (has_leading_space_lut[scored_tgt] & ~is_boundary_token_lut[scored_prev]).to(dtype=torch.int16) + val_byte_count += token_bytes.to(torch.float64).sum() + + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM) + + val_loss = val_loss_sum / val_token_count + bits_per_token = val_loss.item() / math.log(2.0) + tokens_per_byte = val_token_count.item() / val_byte_count.item() + model.train() + return float(val_loss.item()), float(bits_per_token * tokens_per_byte) + + # ----------------------------- # POST-TRAINING QUANTIZATION # ----------------------------- @@ -370,7 +429,7 @@ def quantize_state_dict_int8(state_dict: dict[str, Tensor]): # Small float tensors are cheap enough to keep directly. We still downcast # fp32/bf16 passthrough tensors to fp16 so metadata does not dominate size. - if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL: + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL or "tok_emb.weight" in name: kept = keep_float_tensor(name, t, passthrough_orig_dtypes) passthrough[name] = kept stats["int8_payload_bytes"] += tensor_nbytes(kept) @@ -698,6 +757,10 @@ def __init__( def _init_weights(self) -> None: if self.tie_embeddings: nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std) + with torch.no_grad(): + U, S, V = torch.linalg.svd(self.tok_emb.weight.data, full_matrices=False) + target_S = S[0] * (1.0 / torch.arange(1, S.shape[0] + 1, dtype=S.dtype)) ** 0.5 + self.tok_emb.weight.data = (U * target_S[None, :]) @ V for module in self.modules(): if isinstance(module, nn.Linear) and getattr(module, "_zero_init", False): nn.init.zeros_(module.weight) @@ -708,10 +771,9 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: x = F.rms_norm(x, (x.size(-1),)) x0 = x - encoder_order = [range(self.num_encoder_layers), range(self.num_encoder_layers - 1, -1, -1)] for _pass in range(2): skips: list[Tensor] = [] - for i in encoder_order[_pass]: + for i in range(self.num_encoder_layers): x = self.blocks[i](x, x0) skips.append(x) if _pass == 0: @@ -1042,6 +1104,10 @@ def lr_mul(step: int, elapsed_ms: float) -> float: torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) for opt in optimizers: opt.step() + with torch.no_grad(): + muon_lr = optimizer_muon.param_groups[0]["lr"] + for p in matrix_params: + p.mul_(1.0 - 0.02 * muon_lr) zero_grad_all() step += 1 @@ -1110,13 +1176,12 @@ def lr_mul(step: int, elapsed_ms: float) -> float: base_model.load_state_dict(dequantize_state_dict_int8(quant_state), strict=True) torch.cuda.synchronize() t_qeval = time.perf_counter() - q_val_loss, q_val_bpb = eval_val( + q_val_loss, q_val_bpb = eval_val_sliding( args, model, rank, world_size, device, - grad_accum_steps, val_tokens, base_bytes_lut, has_leading_space_lut, From cc200510c9835db63c629235ad4d528f600a92e8 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Thu, 19 Mar 2026 22:08:26 -0300 Subject: [PATCH 13/72] fix: sliding window eval only on multi-GPU, regular eval on single GPU Sliding window with stride=64 is too slow unbatched on single GPU (~30 min). Falls back to regular eval on single GPU for testing. Multi-GPU distributes windows across ranks. --- train_gpt.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index d3ae6e5e7..3da5d903e 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -1176,17 +1176,17 @@ def lr_mul(step: int, elapsed_ms: float) -> float: base_model.load_state_dict(dequantize_state_dict_int8(quant_state), strict=True) torch.cuda.synchronize() t_qeval = time.perf_counter() - q_val_loss, q_val_bpb = eval_val_sliding( - args, - model, - rank, - world_size, - device, - val_tokens, - base_bytes_lut, - has_leading_space_lut, - is_boundary_token_lut, - ) + use_sliding = world_size > 1 + if use_sliding: + q_val_loss, q_val_bpb = eval_val_sliding( + args, model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + ) + else: + q_val_loss, q_val_bpb = eval_val( + args, model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + ) torch.cuda.synchronize() log0( f"final_int8_zlib_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} " From 356d403049b0b7a17855b3c610bd1fdbfc7ee31c Mon Sep 17 00:00:00 2001 From: idan3011 Date: Fri, 20 Mar 2026 01:57:46 -0300 Subject: [PATCH 14/72] competition run: disable unbatched sliding window, use regular eval --- train_gpt.py | 15 ++++----------- 1 file changed, 4 insertions(+), 11 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 3da5d903e..d57c954a2 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -1176,17 +1176,10 @@ def lr_mul(step: int, elapsed_ms: float) -> float: base_model.load_state_dict(dequantize_state_dict_int8(quant_state), strict=True) torch.cuda.synchronize() t_qeval = time.perf_counter() - use_sliding = world_size > 1 - if use_sliding: - q_val_loss, q_val_bpb = eval_val_sliding( - args, model, rank, world_size, device, - val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, - ) - else: - q_val_loss, q_val_bpb = eval_val( - args, model, rank, world_size, device, grad_accum_steps, - val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, - ) + q_val_loss, q_val_bpb = eval_val( + args, model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + ) torch.cuda.synchronize() log0( f"final_int8_zlib_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} " From 2224bcd9f8a7ba5e9feac8b4690401369986af9d Mon Sep 17 00:00:00 2001 From: idan3011 Date: Fri, 20 Mar 2026 02:56:02 -0300 Subject: [PATCH 15/72] feat: batched sliding window eval + int8 embed + encoder recurrence flag 1. Batched sliding window eval (stride=64, batch=256) with proper per-token scoring via forward_logits method 2. Reverted FP16 embedding passthrough to fit 16MB cap 3. Encoder recurrence behind ENCODER_RECURRENCE=1 env var for A/B testing recurrence vs no-recurrence" --- train_gpt.py | 140 ++++++++++++++++++++++++++++++++++----------------- 1 file changed, 95 insertions(+), 45 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index d57c954a2..8336c8980 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -69,6 +69,7 @@ class Hyperparameters: tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) + encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "0"))) # Optimizer hyperparameters. embed_lr = float(os.environ.get("EMBED_LR", 0.6)) @@ -280,7 +281,7 @@ def eval_val( def eval_val_sliding( args: Hyperparameters, - model: nn.Module, + base_model: nn.Module, rank: int, world_size: int, device: torch.device, @@ -289,6 +290,7 @@ def eval_val_sliding( has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, stride: int = 64, + batch_size: int = 256, ) -> tuple[float, float]: seq_len = args.train_seq_len total_tokens = val_tokens.numel() @@ -297,43 +299,53 @@ def eval_val_sliding( while pos + seq_len < total_tokens: score_start = 0 if pos == 0 else seq_len - stride windows.append((pos, score_start)) - pos += stride if pos > 0 else seq_len - all_windows = windows - my_windows = all_windows[rank::world_size] + pos += stride + my_windows = windows[rank::world_size] - val_loss_sum = torch.zeros((), device=device, dtype=torch.float64) - val_token_count = torch.zeros((), device=device, dtype=torch.float64) - val_byte_count = torch.zeros((), device=device, dtype=torch.float64) + total_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + total_scored_tokens = torch.zeros((), device=device, dtype=torch.float64) + total_byte_count = torch.zeros((), device=device, dtype=torch.float64) - model.eval() + base_model.eval() with torch.inference_mode(): - for win_start, score_start in my_windows: - chunk = val_tokens[win_start:win_start + seq_len + 1].to(device=device, dtype=torch.int64) - x = chunk[:-1].unsqueeze(0) - y = chunk[1:].unsqueeze(0) + for batch_start in range(0, len(my_windows), batch_size): + batch_windows = my_windows[batch_start:batch_start + batch_size] + x_list = [] + y_list = [] + for win_start, _ in batch_windows: + chunk = val_tokens[win_start:win_start + seq_len + 1] + x_list.append(chunk[:-1]) + y_list.append(chunk[1:]) + x = torch.stack(x_list).to(device=device, dtype=torch.int64) + y = torch.stack(y_list).to(device=device, dtype=torch.int64) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): - logits_input = x[:, :seq_len] - targets_input = y[:, :seq_len] - loss_val = model(logits_input, targets_input).detach() - score_tokens = seq_len - score_start - val_loss_sum += loss_val.to(torch.float64) * float(seq_len) - val_token_count += float(seq_len) - scored_prev = x[0, score_start:seq_len] - scored_tgt = y[0, score_start:seq_len] - token_bytes = base_bytes_lut[scored_tgt].to(dtype=torch.int16) - token_bytes += (has_leading_space_lut[scored_tgt] & ~is_boundary_token_lut[scored_prev]).to(dtype=torch.int16) - val_byte_count += token_bytes.to(torch.float64).sum() + logits = base_model.forward_logits(x) + per_token_loss = F.cross_entropy( + logits.float().reshape(-1, logits.size(-1)), + y.reshape(-1), + reduction="none", + ).reshape(len(batch_windows), seq_len) + + for idx, (_, score_start) in enumerate(batch_windows): + scored_loss = per_token_loss[idx, score_start:] + total_loss_sum += scored_loss.to(torch.float64).sum() + total_scored_tokens += float(scored_loss.numel()) + scored_prev = x[idx, score_start:] + scored_tgt = y[idx, score_start:] + token_bytes = base_bytes_lut[scored_tgt].to(dtype=torch.int16) + token_bytes += (has_leading_space_lut[scored_tgt] & ~is_boundary_token_lut[scored_prev]).to(dtype=torch.int16) + total_byte_count += token_bytes.to(torch.float64).sum() if dist.is_available() and dist.is_initialized(): - dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM) - dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM) - dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM) + dist.all_reduce(total_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(total_scored_tokens, op=dist.ReduceOp.SUM) + dist.all_reduce(total_byte_count, op=dist.ReduceOp.SUM) - val_loss = val_loss_sum / val_token_count - bits_per_token = val_loss.item() / math.log(2.0) - tokens_per_byte = val_token_count.item() / val_byte_count.item() - model.train() - return float(val_loss.item()), float(bits_per_token * tokens_per_byte) + val_loss = (total_loss_sum / total_scored_tokens).item() + bpb = (total_loss_sum / (total_byte_count * math.log(2.0))).item() + base_model.train() + return float(val_loss), float(bpb) # ----------------------------- @@ -429,7 +441,7 @@ def quantize_state_dict_int8(state_dict: dict[str, Tensor]): # Small float tensors are cheap enough to keep directly. We still downcast # fp32/bf16 passthrough tensors to fp16 so metadata does not dominate size. - if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL or "tok_emb.weight" in name: + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL: kept = keep_float_tensor(name, t, passthrough_orig_dtypes) passthrough[name] = kept stats["int8_payload_bytes"] += tensor_nbytes(kept) @@ -725,6 +737,7 @@ def __init__( self.tie_embeddings = tie_embeddings self.tied_embed_init_std = tied_embed_init_std self.logit_softcap = logit_softcap + self.encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "0"))) self.tok_emb = nn.Embedding(vocab_size, model_dim) self.pre_enrich = nn.Sequential( CastedLinear(model_dim, model_dim, bias=False), @@ -765,36 +778,60 @@ def _init_weights(self) -> None: if isinstance(module, nn.Linear) and getattr(module, "_zero_init", False): nn.init.zeros_(module.weight) - def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: - x = self.tok_emb(input_ids) - x = self.pre_enrich(x) - x = F.rms_norm(x, (x.size(-1),)) - x0 = x - - for _pass in range(2): + def _run_blocks(self, x: Tensor, x0: Tensor) -> Tensor: + if self.encoder_recurrence: + for _pass in range(2): + skips: list[Tensor] = [] + for i in range(self.num_encoder_layers): + x = self.blocks[i](x, x0) + skips.append(x) + if _pass == 0: + x = F.rms_norm(x, (x.size(-1),)) + continue + for i in range(self.num_decoder_layers): + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + x = self.blocks[self.num_encoder_layers + i](x, x0) + else: skips: list[Tensor] = [] for i in range(self.num_encoder_layers): x = self.blocks[i](x, x0) skips.append(x) - if _pass == 0: - x = F.rms_norm(x, (x.size(-1),)) - continue for i in range(self.num_decoder_layers): if skips: x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() x = self.blocks[self.num_encoder_layers + i](x, x0) + return x - x = self.final_norm(x).reshape(-1, x.size(-1)) - targets = target_ids.reshape(-1) + def _compute_logits(self, x: Tensor) -> Tensor: if self.tie_embeddings: logits_proj = F.linear(x, self.tok_emb.weight) else: if self.lm_head is None: raise RuntimeError("lm_head is required when tie_embeddings=False") logits_proj = self.lm_head(x) - logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + + def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: + x = self.tok_emb(input_ids) + x = self.pre_enrich(x) + x = F.rms_norm(x, (x.size(-1),)) + x0 = x + x = self._run_blocks(x, x0) + x = self.final_norm(x).reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + logits = self._compute_logits(x) return F.cross_entropy(logits.float(), targets, reduction="mean") + def forward_logits(self, input_ids: Tensor) -> Tensor: + x = self.tok_emb(input_ids) + x = self.pre_enrich(x) + x = F.rms_norm(x, (x.size(-1),)) + x0 = x + x = self._run_blocks(x, x0) + x = self.final_norm(x) + return self._compute_logits(x) + # ----------------------------- # TRAINING @@ -1187,6 +1224,19 @@ def lr_mul(step: int, elapsed_ms: float) -> float: ) log0(f"final_int8_zlib_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}") + torch.cuda.synchronize() + t_slide = time.perf_counter() + sw_val_loss, sw_val_bpb = eval_val_sliding( + args, base_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + ) + torch.cuda.synchronize() + log0( + f"final_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{sw_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" + ) + log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f}") + if distributed: dist.destroy_process_group() From c2e9b1e48d0d380b0e2a04105edd534c1b4daac7 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Fri, 20 Mar 2026 04:11:45 -0300 Subject: [PATCH 16/72] Record: Pre-Enrichment + Encoder Recurrence (val_bpb=1.1855) --- .../README.md | 100 ++ .../submission.json | 17 + .../train.log | 106 ++ .../train_gpt.py | 1245 +++++++++++++++++ 4 files changed, 1468 insertions(+) create mode 100644 records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md create mode 100644 records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json create mode 100644 records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log create mode 100644 records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md new file mode 100644 index 000000000..b2f33c139 --- /dev/null +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md @@ -0,0 +1,100 @@ +## Pre-Enrichment + Encoder Recurrence + +Two architectural modifications to the baseline transformer: (1) a GELU pre-enrichment block that transforms raw embeddings before they enter the residual stream, and (2) 2x encoder recurrence that runs the encoder blocks twice with RMS norm stabilization between passes. Combined with sliding window evaluation (stride=64), overtone embedding initialization, and decoupled Muon weight decay, this achieves **val_bpb 1.1855** in a 15.75MB artifact trained in 10 minutes on 8xH100. + +--- + +### Key Contributions + +#### GELU Pre-Enrichment + +Raw token embeddings are a poor starting point for the residual stream. A 1024-token vocabulary maps each token to a 512-dimensional vector initialized from a normal distribution — these vectors carry no relational structure and every transformer layer must compensate for this weak initialization. + +I add two `CastedLinear(512→512)` projections with a GELU activation between them, applied after the embedding lookup and before the first transformer block: + +``` +embedding → Linear(512→512) → GELU → Linear(512→512) → RMS Norm → transformer blocks +``` + +This gives the model a learned nonlinear transformation to produce richer representations before the residual stream begins. Cost: 0.5M extra parameters (~3% of total), negligible step time overhead. + +#### 2x Encoder Recurrence + +Depth recurrence is a known technique (ALBERT, Universal Transformers). My contribution is applying it to only the encoder half of a U-Net transformer architecture, with RMS norm stabilization between passes, and providing A/B data showing it beats additional training steps. + +The baseline uses a U-Net architecture with encoder and decoder halves connected by skip connections. I reuse the encoder blocks for a second pass before running the decoder. + +With 10 layers (5 encoder + 5 decoder), the forward pass becomes: +1. Run encoder blocks 0-4 (first pass, build initial features) +2. RMS norm (stabilize between passes) +3. Run encoder blocks 0-4 again (second pass, refine features) +4. Run decoder blocks 5-9 with skip connections from the refined second encoder pass + +This produces **15 effective layers from 10 physical blocks** with zero extra parameters. The only cost is step time: ~75ms vs ~50ms without recurrence (~50% overhead from running 5 extra blocks). + +The critical question: does the architectural depth advantage justify 50% fewer training steps? + +**A/B Comparison (8xH100, 10 minutes, identical config except recurrence):** + +| Metric | With recurrence | Without recurrence | +|---------------------|--------------------|-----------------------| +| Steps completed | 8,004 | 11,955 | +| Step time | 75ms | 50ms | +| Standard BPB | 1.2211 | 1.2299 | +| Sliding window BPB | **1.1855** | 1.1947 | +| Submission size | 15.75MB | 15.82MB | + +50% more training steps could not overcome the depth advantage of encoder recurrence. At step 8000 (where the recurrence run stopped), the pre-quant val_bpb was 1.2065 vs 1.3020 for the no-recurrence run — a 0.0955 gap that the extra 4,000 steps narrowed but never closed. + +I find encoder recurrence to be a parameter-efficient alternative to adding physical layers: it doubles the effective encoder depth with zero parameters and predictable step time overhead. + +--- + +### Additional Techniques + +Overtone embedding init, decoupled Muon weight decay (0.02), batched sliding window eval (stride=64), 10 layers, MATRIX_LR=0.06, TIED_EMBED_LR=0.1, WARMDOWN_ITERS=2500. + +--- + +### What Didn't Work + +- **FP16 embedding passthrough**: Keeping the tied embedding in fp16 instead of int8 reduced quantization error by ~0.006 BPB (the tied embedding is used twice — input and output — so int8 errors compound). However, the extra ~520KB pushed the artifact over the 16MB cap. I had to revert to int8. + +- **3x encoder recurrence**: The tripled computation graph exceeded Triton's per-SM shared memory limit on both A100 (168,096 > 166,912 bytes) and RTX 4050. A compiler limitation, not an architectural one. + +- **Warmdown scheduler on A100**: The wallclock-aware warmdown schedule (`WARMDOWN_ITERS=1200`) estimates remaining time as `warmdown_iters × avg_step_time`. On A100 (~1100ms/step), this exceeds the total 600-second budget from step 0, causing the learning rate to decay throughout the entire run. Not relevant to 8xH100 submissions but was a significant debugging finding. + +- Also tried: full U-Net recurrence (too slow), reverse encoder pass order (worse), auxiliary encoder prediction loss (hurt performance). + +--- + +### Configuration + +``` +VOCAB_SIZE=1024 NUM_LAYERS=10 MODEL_DIM=512 NUM_HEADS=8 NUM_KV_HEADS=4 MLP_MULT=2 +TIE_EMBEDDINGS=1 TIED_EMBED_LR=0.1 MATRIX_LR=0.06 SCALAR_LR=0.04 +WARMDOWN_ITERS=2500 WARMUP_STEPS=20 TRAIN_BATCH_TOKENS=524288 TRAIN_SEQ_LEN=1024 +ENCODER_RECURRENCE=1 +``` + +Model parameters: 19,421,776 +Submission size (int8+zlib): 15,753,781 bytes (code: 53,089 bytes) + +### Reproduction + +All defaults are baked into the script: +```bash +torchrun --standalone --nproc_per_node=8 train_gpt.py +``` + +### Key Metrics + +| Metric | Value | +|---|---| +| Pre-quant val_bpb | 1.2065 | +| Post-quant val_bpb (standard) | 1.2211 | +| Post-quant val_bpb (sliding window) | **1.1855** | +| Training time | 599,979ms (8,004 steps at ~75ms) | +| Peak memory | 16,592 MiB | +| Submission size (int8+zlib) | 15,753,781 bytes | +| Model parameters | 19,421,776 | diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json new file mode 100644 index 000000000..5abb44808 --- /dev/null +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json @@ -0,0 +1,17 @@ +{ + "author": "Idanr", + "github_id": "idan3011", + "name": "Pre-Enrichment + Encoder Recurrence", + "blurb": "GELU pre-enrichment + 2x encoder recurrence + sliding window eval (stride=64) + overtone init + Muon WD, 10L 512d. 15 effective layers from 10 physical blocks via encoder-only depth recurrence.", + "date": "2026-03-20T06:00:00Z", + "val_loss": 2.00170864, + "val_bpb": 1.18552460, + "pre_quant_val_loss": 2.0372, + "pre_quant_val_bpb": 1.2065, + "step_stop": 8004, + "wallclock_seconds": 599.979, + "eval_time_seconds": 105.017, + "bytes_total": 15753781, + "bytes_model_int8_zlib": 15700692, + "bytes_code": 53089 +} diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log new file mode 100644 index 000000000..bdf7907b1 --- /dev/null +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log @@ -0,0 +1,106 @@ +W0320 05:59:38.326000 938 torch/distributed/run.py:803] +W0320 05:59:38.326000 938 torch/distributed/run.py:803] ***************************************** +W0320 05:59:38.326000 938 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +W0320 05:59:38.326000 938 torch/distributed/run.py:803] ***************************************** +logs/3d960f9f-a8f8-4aad-95cf-9c64179f6c7b.txt +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:10 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +model_params:19421776 +world_size:8 grad_accum_steps:1 +sdp_backends:cudnn=False flash=True mem_efficient=False math=False +attention_mode:gqa num_heads:8 num_kv_heads:4 +tie_embeddings:True embed_lr:0.1 head_lr:0.0 matrix_lr:0.06 scalar_lr:0.04 +train_batch_tokens:524288 train_seq_len:1024 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 +seed:1337 +warmup_step:1/20 +warmup_step:2/20 +warmup_step:3/20 +warmup_step:4/20 +warmup_step:5/20 +warmup_step:6/20 +warmup_step:7/20 +warmup_step:8/20 +warmup_step:9/20 +warmup_step:10/20 +warmup_step:11/20 +warmup_step:12/20 +warmup_step:13/20 +warmup_step:14/20 +warmup_step:15/20 +warmup_step:16/20 +warmup_step:17/20 +warmup_step:18/20 +warmup_step:19/20 +warmup_step:20/20 +step:0/20000 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step_avg:84.06ms +step:1000/20000 train_loss:2.3751 train_time:84532ms step_avg:84.53ms +step:1000/20000 val_loss:2.3386 val_bpb:1.3850 train_time:84560ms step_avg:84.56ms +step:1200/20000 train_loss:2.3931 train_time:101420ms step_avg:84.52ms +step:1400/20000 train_loss:2.4481 train_time:118283ms step_avg:84.49ms +step:1600/20000 train_loss:2.1157 train_time:135084ms step_avg:84.43ms +step:1800/20000 train_loss:2.2179 train_time:151937ms step_avg:84.41ms +step:2000/20000 train_loss:2.2564 train_time:166386ms step_avg:83.19ms +step:2000/20000 val_loss:2.2592 val_bpb:1.3380 train_time:166414ms step_avg:83.21ms +step:2200/20000 train_loss:2.3746 train_time:180844ms step_avg:82.20ms +step:2400/20000 train_loss:2.3884 train_time:195284ms step_avg:81.37ms +step:2600/20000 train_loss:2.2447 train_time:209738ms step_avg:80.67ms +step:2800/20000 train_loss:2.1986 train_time:224193ms step_avg:80.07ms +step:3000/20000 train_loss:3.2235 train_time:238641ms step_avg:79.55ms +step:3000/20000 val_loss:2.2425 val_bpb:1.3281 train_time:238670ms step_avg:79.56ms +step:3200/20000 train_loss:2.3045 train_time:253084ms step_avg:79.09ms +step:3400/20000 train_loss:2.1390 train_time:267525ms step_avg:78.68ms +step:3600/20000 train_loss:2.2635 train_time:281966ms step_avg:78.32ms +step:3800/20000 train_loss:2.1984 train_time:296409ms step_avg:78.00ms +step:4000/20000 train_loss:2.3154 train_time:310842ms step_avg:77.71ms +step:4000/20000 val_loss:2.2161 val_bpb:1.3125 train_time:310871ms step_avg:77.72ms +step:4200/20000 train_loss:2.2615 train_time:325396ms step_avg:77.48ms +step:4400/20000 train_loss:2.2156 train_time:339837ms step_avg:77.24ms +step:4600/20000 train_loss:2.2478 train_time:354286ms step_avg:77.02ms +step:4800/20000 train_loss:2.1844 train_time:368719ms step_avg:76.82ms +step:5000/20000 train_loss:2.2848 train_time:383154ms step_avg:76.63ms +step:5000/20000 val_loss:2.2044 val_bpb:1.3056 train_time:383184ms step_avg:76.64ms +step:5200/20000 train_loss:2.3330 train_time:397586ms step_avg:76.46ms +step:5400/20000 train_loss:2.2813 train_time:412010ms step_avg:76.30ms +step:5600/20000 train_loss:2.1798 train_time:426437ms step_avg:76.15ms +step:5800/20000 train_loss:2.2131 train_time:440862ms step_avg:76.01ms +step:6000/20000 train_loss:2.1233 train_time:455298ms step_avg:75.88ms +step:6000/20000 val_loss:2.1635 val_bpb:1.2814 train_time:455326ms step_avg:75.89ms +step:6200/20000 train_loss:2.1008 train_time:469733ms step_avg:75.76ms +step:6400/20000 train_loss:1.8779 train_time:484176ms step_avg:75.65ms +step:6600/20000 train_loss:2.0861 train_time:498603ms step_avg:75.55ms +step:6800/20000 train_loss:2.1251 train_time:513036ms step_avg:75.45ms +step:7000/20000 train_loss:2.0666 train_time:527470ms step_avg:75.35ms +step:7000/20000 val_loss:2.1076 val_bpb:1.2482 train_time:527498ms step_avg:75.36ms +step:7200/20000 train_loss:1.9345 train_time:541905ms step_avg:75.26ms +step:7400/20000 train_loss:1.8621 train_time:556345ms step_avg:75.18ms +step:7600/20000 train_loss:2.1033 train_time:570782ms step_avg:75.10ms +step:7800/20000 train_loss:2.0515 train_time:585219ms step_avg:75.03ms +step:8000/20000 train_loss:1.9788 train_time:599665ms step_avg:74.96ms +step:8000/20000 val_loss:2.0372 val_bpb:1.2065 train_time:599693ms step_avg:74.96ms +step:8004/20000 val_loss:2.0372 val_bpb:1.2065 train_time:599979ms step_avg:74.96ms +stopping_early: wallclock_cap train_time:599979ms step:8004/20000 +peak memory allocated: 16592 MiB reserved: 16888 MiB +Serialized model: 76676839 bytes +Code size: 53089 bytes +Total submission size: 76729928 bytes +Serialized model int8+zlib: 15700692 bytes (payload:19556672 raw_torch:19607921 payload_ratio:3.92x) +Total submission size int8+zlib: 15753781 bytes +final_int8_zlib_roundtrip val_loss:2.0618 val_bpb:1.2211 eval_time:2332ms +final_int8_zlib_roundtrip_exact val_loss:2.06182994 val_bpb:1.22113183 +final_sliding_window val_loss:2.0017 val_bpb:1.1855 eval_time:105017ms +final_sliding_window_exact val_loss:2.00170864 val_bpb:1.18552460 diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py new file mode 100644 index 000000000..6dc4d1826 --- /dev/null +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py @@ -0,0 +1,1245 @@ +""" +The `train_gpt.py` and `train_gpt_mlx.py` scripts are intended as good launching-off points for new participants, not SOTA configs. We'll accept PRs that tune, improve, or simplify these scripts without significantly increasing complexity, but competitive submissions should stay in the `/records` folder. + +Hard stop: `train_gpt.py` and `train_gpt_mlx.py` must never be longer than 1500 lines. +""" + +from __future__ import annotations + +import copy +import glob +import io +import math +import os +import random +import subprocess +import sys +import time +import uuid +import zlib +from pathlib import Path + +import numpy as np +import sentencepiece as spm +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn.parallel import DistributedDataParallel as DDP + +# ----------------------------- +# HYPERPARAMETERS +# ----------------------------- +# Default Simple Baseline run: +# - 9 transformer blocks at width 512 +# - 8 attention heads with 4 KV heads (GQA) and 2x MLP expansion +# - vocab size 1024, sequence length 1024, tied embeddings +# - 524,288 train tokens per step for 20,000 iterations with a ~10 minute cap + +class Hyperparameters: + # Data paths are shard globs produced by the existing preprocessing pipeline. + data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") + train_files = os.path.join(data_path, "fineweb_train_*.bin") + val_files = os.path.join(data_path, "fineweb_val_*.bin") + tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model") + run_id = os.environ.get("RUN_ID", str(uuid.uuid4())) + seed = int(os.environ.get("SEED", 1337)) + + # Validation cadence and batch size. Validation always uses the full fineweb_val split. + val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) + val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 1000)) + train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 200)) + + # Training length. + iterations = int(os.environ.get("ITERATIONS", 20000)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 2500)) + warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 524_288)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 1024)) + max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) + qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) + + # Model shape. + vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) + num_layers = int(os.environ.get("NUM_LAYERS", 10)) + num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) + model_dim = int(os.environ.get("MODEL_DIM", 512)) + num_heads = int(os.environ.get("NUM_HEADS", 8)) + mlp_mult = int(os.environ.get("MLP_MULT", 2)) + tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) + rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) + logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) + encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) + + # Optimizer hyperparameters. + embed_lr = float(os.environ.get("EMBED_LR", 0.6)) + head_lr = float(os.environ.get("HEAD_LR", 0.008)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.1)) + tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.06)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.04)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.95)) + muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) + muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.85)) + muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 500)) + beta1 = float(os.environ.get("BETA1", 0.9)) + beta2 = float(os.environ.get("BETA2", 0.95)) + adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) + grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.0)) + +# ----------------------------- +# MUON OPTIMIZER +# ----------------------------- +# +# As borrowed from modded-nanogpt +# Background on Muon: https://kellerjordan.github.io/posts/muon/ + +def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor: + # Orthogonalize a 2D update matrix with a fast Newton-Schulz iteration. + # Muon uses this to normalize matrix-shaped gradients before applying them. + a, b, c = (3.4445, -4.7750, 2.0315) + X = G.bfloat16() + X /= X.norm() + eps + transposed = G.size(0) > G.size(1) + if transposed: + X = X.T + for _ in range(steps): + A = X @ X.T + B = b * A + c * A @ A + X = a * X + B @ X + return X.T if transposed else X + + +class Muon(torch.optim.Optimizer): + def __init__(self, params, lr: float, momentum: float, backend_steps: int, nesterov: bool = True): + super().__init__( + params, + dict(lr=lr, momentum=momentum, backend_steps=backend_steps, nesterov=nesterov), + ) + + @torch.no_grad() + def step(self, closure=None): + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + distributed = dist.is_available() and dist.is_initialized() + world_size = dist.get_world_size() if distributed else 1 + rank = dist.get_rank() if distributed else 0 + + for group in self.param_groups: + params = group["params"] + if not params: + continue + lr = group["lr"] + momentum = group["momentum"] + backend_steps = group["backend_steps"] + nesterov = group["nesterov"] + + total_params = sum(int(p.numel()) for p in params) + updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16) + + curr = 0 + for i, p in enumerate(params): + if i % world_size == rank and p.grad is not None: + g = p.grad + state = self.state[p] + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros_like(g) + buf = state["momentum_buffer"] + buf.mul_(momentum).add_(g) + if nesterov: + g = g.add(buf, alpha=momentum) + g = zeropower_via_newtonschulz5(g, steps=backend_steps) + # Scale correction from Muon reference implementations. + g *= max(1, g.size(0) / g.size(1)) ** 0.5 + updates_flat[curr : curr + p.numel()] = g.reshape(-1) + curr += p.numel() + + if distributed: + dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM) + + curr = 0 + for p in params: + g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype) + p.add_(g, alpha=-lr) + curr += p.numel() + + return loss + + +# ----------------------------- +# TOKENIZER-AGNOSTIC EVALUATION SETUP +# ----------------------------- +# +# It's common for small models have a large fraction of their parameters be embeddings, since the 2 * d_model * d_vocab vectors can be gigantic. +# Instead of locking the tokenizer, we let you bring your own and calculate our validation metrics on the average compression of the validation set. +# We calculate BPB (bits-per-byte) instead of validation loss, so we need methods to count the number of bits per token in the tokenizer. +# Note: Submissions that edit the tokenizer will be examined more carefully, since screwing this up might unjustly improve your score. + +def build_sentencepiece_luts( + sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device +) -> tuple[Tensor, Tensor, Tensor]: + sp_vocab_size = int(sp.vocab_size()) + table_size = max(sp_vocab_size, vocab_size) + base_bytes_np = np.zeros((table_size,), dtype=np.int16) + has_leading_space_np = np.zeros((table_size,), dtype=np.bool_) + is_boundary_token_np = np.ones((table_size,), dtype=np.bool_) + for token_id in range(sp_vocab_size): + if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id): + continue + is_boundary_token_np[token_id] = False + if sp.is_byte(token_id): + base_bytes_np[token_id] = 1 + continue + piece = sp.id_to_piece(token_id) + if piece.startswith("▁"): + has_leading_space_np[token_id] = True + piece = piece[1:] + base_bytes_np[token_id] = len(piece.encode("utf-8")) + return ( + torch.tensor(base_bytes_np, dtype=torch.int16, device=device), + torch.tensor(has_leading_space_np, dtype=torch.bool, device=device), + torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device), + ) + + +def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: + files = [Path(p) for p in sorted(glob.glob(pattern))] + if not files: + raise FileNotFoundError(f"No files found for pattern: {pattern}") + # The export pipeline writes the fixed first-50k-doc validation set to fineweb_val_*. + tokens = torch.cat([load_data_shard(file) for file in files]).contiguous() + usable = ((tokens.numel() - 1) // seq_len) * seq_len + if usable <= 0: + raise ValueError(f"Validation split is too short for TRAIN_SEQ_LEN={seq_len}") + return tokens[: usable + 1] + + +def eval_val( + args: Hyperparameters, + model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + grad_accum_steps: int, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, +) -> tuple[float, float]: + # Validation computes two metrics: + # - val_loss: token cross-entropy (natural log) + # - val_bpb: tokenizer-agnostic compression metric used by the challenge + local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps) + if local_batch_tokens < args.train_seq_len: + raise ValueError( + "VAL_BATCH_SIZE must provide at least one sequence per rank; " + f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, " + f"GRAD_ACCUM_STEPS={grad_accum_steps}, TRAIN_SEQ_LEN={args.train_seq_len}" + ) + local_batch_seqs = local_batch_tokens // args.train_seq_len + total_seqs = (val_tokens.numel() - 1) // args.train_seq_len + seq_start = (total_seqs * rank) // world_size + seq_end = (total_seqs * (rank + 1)) // world_size + val_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + val_token_count = torch.zeros((), device=device, dtype=torch.float64) + val_byte_count = torch.zeros((), device=device, dtype=torch.float64) + + model.eval() + with torch.inference_mode(): + for batch_seq_start in range(seq_start, seq_end, local_batch_seqs): + batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end) + raw_start = batch_seq_start * args.train_seq_len + raw_end = batch_seq_end * args.train_seq_len + 1 + local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True) + x = local[:-1].reshape(-1, args.train_seq_len) + y = local[1:].reshape(-1, args.train_seq_len) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + batch_loss = model(x, y).detach() + batch_token_count = float(y.numel()) + val_loss_sum += batch_loss.to(torch.float64) * batch_token_count + val_token_count += batch_token_count + prev_ids = x.reshape(-1) + tgt_ids = y.reshape(-1) + token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16) + token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16) + val_byte_count += token_bytes.to(torch.float64).sum() + + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM) + + val_loss = val_loss_sum / val_token_count + bits_per_token = val_loss.item() / math.log(2.0) + tokens_per_byte = val_token_count.item() / val_byte_count.item() + model.train() + return float(val_loss.item()), float(bits_per_token * tokens_per_byte) + + +def eval_val_sliding( + args: Hyperparameters, + base_model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + stride: int = 64, + batch_size: int = 256, +) -> tuple[float, float]: + seq_len = args.train_seq_len + total_tokens = val_tokens.numel() + windows: list[tuple[int, int]] = [] + pos = 0 + while pos + seq_len < total_tokens: + score_start = 0 if pos == 0 else seq_len - stride + windows.append((pos, score_start)) + pos += stride + my_windows = windows[rank::world_size] + + total_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + total_scored_tokens = torch.zeros((), device=device, dtype=torch.float64) + total_byte_count = torch.zeros((), device=device, dtype=torch.float64) + + base_model.eval() + with torch.inference_mode(): + for batch_start in range(0, len(my_windows), batch_size): + batch_windows = my_windows[batch_start:batch_start + batch_size] + x_list = [] + y_list = [] + for win_start, _ in batch_windows: + chunk = val_tokens[win_start:win_start + seq_len + 1] + x_list.append(chunk[:-1]) + y_list.append(chunk[1:]) + x = torch.stack(x_list).to(device=device, dtype=torch.int64) + y = torch.stack(y_list).to(device=device, dtype=torch.int64) + + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + logits = base_model.forward_logits(x) + per_token_loss = F.cross_entropy( + logits.float().reshape(-1, logits.size(-1)), + y.reshape(-1), + reduction="none", + ).reshape(len(batch_windows), seq_len) + + for idx, (_, score_start) in enumerate(batch_windows): + scored_loss = per_token_loss[idx, score_start:] + total_loss_sum += scored_loss.to(torch.float64).sum() + total_scored_tokens += float(scored_loss.numel()) + scored_prev = x[idx, score_start:] + scored_tgt = y[idx, score_start:] + token_bytes = base_bytes_lut[scored_tgt].to(dtype=torch.int16) + token_bytes += (has_leading_space_lut[scored_tgt] & ~is_boundary_token_lut[scored_prev]).to(dtype=torch.int16) + total_byte_count += token_bytes.to(torch.float64).sum() + + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(total_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(total_scored_tokens, op=dist.ReduceOp.SUM) + dist.all_reduce(total_byte_count, op=dist.ReduceOp.SUM) + + val_loss = (total_loss_sum / total_scored_tokens).item() + bpb = (total_loss_sum / (total_byte_count * math.log(2.0))).item() + base_model.train() + return float(val_loss), float(bpb) + + +# ----------------------------- +# POST-TRAINING QUANTIZATION +# ----------------------------- +# +# It's silly to export our model, which is trained in bf16 and fp32, at that same precision. +# Instead, we get approximately the same model (with a small hit) by quantizing the model to int8 & zlib compressing. +# We can then decompress the model and run in higher precision for evaluation, after closing in under the size limit. + +CONTROL_TENSOR_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "CONTROL_TENSOR_NAME_PATTERNS", + "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights", + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS), + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 +INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 +INT8_PER_ROW_SCALE_DTYPE = torch.float16 +INT8_CLIP_PERCENTILE = 99.99984 +INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0 + +def tensor_nbytes(t: Tensor) -> int: + return int(t.numel()) * int(t.element_size()) + +def keep_float_tensor(name: str, t: Tensor, passthrough_orig_dtypes: dict[str, str]) -> Tensor: + if any(pattern in name for pattern in INT8_KEEP_FLOAT_FP32_NAME_PATTERNS): + return t.float().contiguous() + if t.dtype in {torch.float32, torch.bfloat16}: + passthrough_orig_dtypes[name] = str(t.dtype).removeprefix("torch.") + return t.to(dtype=INT8_KEEP_FLOAT_STORE_DTYPE).contiguous() + return t + +def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + # Matrices get one scale per row, which usually tracks output-channel + # ranges much better than a single tensor-wide scale. + clip_abs = ( + torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) + if t32.numel() + else torch.empty((t32.shape[0],), dtype=torch.float32) + ) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0) + q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous() + return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + + # Vectors / scalars use a simpler per-tensor scale. + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() + return q, scale + +def quantize_state_dict_int8(state_dict: dict[str, Tensor]): + # Single supported clean-script export format: + # - per-row int8 for 2D float tensors + # - per-tensor int8 for other float tensors + # - exact passthrough for non-floats + # - passthrough for small float tensors, stored as fp16 to save bytes + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + + # Small float tensors are cheap enough to keep directly. We still downcast + # fp32/bf16 passthrough tensors to fp16 so metadata does not dominate size. + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + + obj: dict[str, object] = { + "__quant_format__": "int8_clean_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats + +def dequantize_state_dict_int8(obj: dict[str, object]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + qmeta = obj.get("qmeta", {}) + passthrough_orig_dtypes = obj.get("passthrough_orig_dtypes", {}) + for name, q in obj["quantized"].items(): + dtype = getattr(torch, obj["dtypes"][name]) + s = obj["scales"][name] + if qmeta.get(name, {}).get("scheme") == "per_row" or s.ndim > 0: + s = s.to(dtype=torch.float32) + # Broadcast the saved row scale back across trailing dimensions. + out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous() + else: + scale = float(s.item()) + out[name] = (q.float() * scale).to(dtype=dtype).contiguous() + for name, t in obj["passthrough"].items(): + # Restore small tensors, undoing the temporary fp16 storage cast if needed. + out_t = t.detach().to("cpu").contiguous() + orig_dtype = passthrough_orig_dtypes.get(name) + if isinstance(orig_dtype, str): + out_t = out_t.to(dtype=getattr(torch, orig_dtype)).contiguous() + out[name] = out_t + return out + + +# ----------------------------- +# DATA LOADING +# ----------------------------- + +def load_data_shard(file: Path) -> Tensor: + header_bytes = 256 * np.dtype(" None: + self.file_idx = (self.file_idx + 1) % len(self.files) + self.tokens = load_data_shard(self.files[self.file_idx]) + self.pos = 0 + + def take(self, n: int) -> Tensor: + chunks: list[Tensor] = [] + remaining = n + while remaining > 0: + avail = self.tokens.numel() - self.pos + if avail <= 0: + self._advance_file() + continue + k = min(remaining, avail) + chunks.append(self.tokens[self.pos : self.pos + k]) + self.pos += k + remaining -= k + return chunks[0] if len(chunks) == 1 else torch.cat(chunks) + + +class DistributedTokenLoader: + # Each call consumes a contiguous chunk from the shared token stream, then slices out + # one disjoint span per rank. The extra "+1" token lets us build (x, y) by shifting. + def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): + self.rank = rank + self.world_size = world_size + self.device = device + self.stream = TokenStream(pattern) + + def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]: + local_tokens = global_tokens // (self.world_size * grad_accum_steps) + per_rank_span = local_tokens + 1 + chunk = self.stream.take(per_rank_span * self.world_size) + start = self.rank * per_rank_span + local = chunk[start : start + per_rank_span].to(dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) + +# ----------------------------- +# TRANSFORMER MODULES +# ----------------------------- + +class RMSNorm(nn.Module): + def __init__(self, eps: float | None = None): + super().__init__() + self.eps = eps + + def forward(self, x: Tensor) -> Tensor: + return F.rms_norm(x, (x.size(-1),), eps=self.eps) + + +class CastedLinear(nn.Linear): + # Keep weights in fp32 for optimizer/state quality, cast at matmul time for bf16 compute. + def forward(self, x: Tensor) -> Tensor: + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, self.weight.to(x.dtype), bias) + + +def restore_low_dim_params_to_fp32(module: nn.Module) -> None: + # Keep small/control parameters in fp32 even when the model body runs in bf16. + with torch.no_grad(): + for name, param in module.named_parameters(): + if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32: + param.data = param.data.float() + + +class Rotary(nn.Module): + # Caches cos/sin tables per sequence length on the current device. + def __init__(self, dim: int, base: float = 10000.0): + super().__init__() + inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self._seq_len_cached = 0 + self._cos_cached: Tensor | None = None + self._sin_cached: Tensor | None = None + + def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]: + if ( + self._cos_cached is None + or self._sin_cached is None + or self._seq_len_cached != seq_len + or self._cos_cached.device != device + ): + t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype) + freqs = torch.outer(t, self.inv_freq.to(device)) + self._cos_cached = freqs.cos()[None, None, :, :] + self._sin_cached = freqs.sin()[None, None, :, :] + self._seq_len_cached = seq_len + return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) + + +def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor: + half = x.size(-1) // 2 + x1, x2 = x[..., :half], x[..., half:] + return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + + +class CausalSelfAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + rope_base: float, + qk_gain_init: float, + ): + super().__init__() + if dim % num_heads != 0: + raise ValueError("model_dim must be divisible by num_heads") + if num_heads % num_kv_heads != 0: + raise ValueError("num_heads must be divisible by num_kv_heads") + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = dim // num_heads + if self.head_dim % 2 != 0: + raise ValueError("head_dim must be even for RoPE") + kv_dim = self.num_kv_heads * self.head_dim + self.c_q = CastedLinear(dim, dim, bias=False) + self.c_k = CastedLinear(dim, kv_dim, bias=False) + self.c_v = CastedLinear(dim, kv_dim, bias=False) + self.proj = CastedLinear(dim, dim, bias=False) + self.proj._zero_init = True + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rotary = Rotary(self.head_dim, base=rope_base) + + def forward(self, x: Tensor) -> Tensor: + bsz, seqlen, dim = x.shape + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + q = F.rms_norm(q, (q.size(-1),)) + k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin) + k = apply_rotary_emb(k, cos, sin) + q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None] + y = F.scaled_dot_product_attention( + q, + k, + v, + attn_mask=None, + is_causal=True, + enable_gqa=(self.num_kv_heads != self.num_heads), + ) + y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) + return self.proj(y) + + +class MLP(nn.Module): + # relu^2 MLP from the original modded-nanogpt setup + def __init__(self, dim: int, mlp_mult: int): + super().__init__() + hidden = mlp_mult * dim + self.fc = CastedLinear(dim, hidden, bias=False) + self.proj = CastedLinear(hidden, dim, bias=False) + self.proj._zero_init = True + + def forward(self, x: Tensor) -> Tensor: + x = torch.relu(self.fc(x)) + return self.proj(x.square()) + + +class Block(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + rope_base: float, + qk_gain_init: float, + ): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.mlp = MLP(dim, mlp_mult) + self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + + def forward(self, x: Tensor, x0: Tensor) -> Tensor: + mix = self.resid_mix.to(dtype=x.dtype) + x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out = self.attn(self.attn_norm(x)) + x = x + self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out + x = x + self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x)) + return x + + +class GPT(nn.Module): + def __init__( + self, + vocab_size: int, + num_layers: int, + model_dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + tie_embeddings: bool, + tied_embed_init_std: float, + logit_softcap: float, + rope_base: float, + qk_gain_init: float, + ): + super().__init__() + if logit_softcap <= 0.0: + raise ValueError(f"logit_softcap must be positive, got {logit_softcap}") + self.tie_embeddings = tie_embeddings + self.tied_embed_init_std = tied_embed_init_std + self.logit_softcap = logit_softcap + self.encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.pre_enrich = nn.Sequential( + CastedLinear(model_dim, model_dim, bias=False), + nn.GELU(), + CastedLinear(model_dim, model_dim, bias=False), + ) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + self.blocks = nn.ModuleList( + [ + Block( + model_dim, + num_heads, + num_kv_heads, + mlp_mult, + rope_base, + qk_gain_init, + ) + for i in range(num_layers) + ] + ) + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + if self.lm_head is not None: + self.lm_head._zero_init = True + self._init_weights() + + def _init_weights(self) -> None: + if self.tie_embeddings: + nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std) + with torch.no_grad(): + U, S, V = torch.linalg.svd(self.tok_emb.weight.data, full_matrices=False) + target_S = S[0] * (1.0 / torch.arange(1, S.shape[0] + 1, dtype=S.dtype)) ** 0.5 + self.tok_emb.weight.data = (U * target_S[None, :]) @ V + for module in self.modules(): + if isinstance(module, nn.Linear) and getattr(module, "_zero_init", False): + nn.init.zeros_(module.weight) + + def _run_blocks(self, x: Tensor, x0: Tensor) -> Tensor: + if self.encoder_recurrence: + for _pass in range(2): + skips: list[Tensor] = [] + for i in range(self.num_encoder_layers): + x = self.blocks[i](x, x0) + skips.append(x) + if _pass == 0: + x = F.rms_norm(x, (x.size(-1),)) + continue + for i in range(self.num_decoder_layers): + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + x = self.blocks[self.num_encoder_layers + i](x, x0) + else: + skips: list[Tensor] = [] + for i in range(self.num_encoder_layers): + x = self.blocks[i](x, x0) + skips.append(x) + for i in range(self.num_decoder_layers): + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + x = self.blocks[self.num_encoder_layers + i](x, x0) + return x + + def _compute_logits(self, x: Tensor) -> Tensor: + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + if self.lm_head is None: + raise RuntimeError("lm_head is required when tie_embeddings=False") + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + + def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: + x = self.tok_emb(input_ids) + x = self.pre_enrich(x) + x = F.rms_norm(x, (x.size(-1),)) + x0 = x + x = self._run_blocks(x, x0) + x = self.final_norm(x).reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + logits = self._compute_logits(x) + return F.cross_entropy(logits.float(), targets, reduction="mean") + + def forward_logits(self, input_ids: Tensor) -> Tensor: + x = self.tok_emb(input_ids) + x = self.pre_enrich(x) + x = F.rms_norm(x, (x.size(-1),)) + x0 = x + x = self._run_blocks(x, x0) + x = self.final_norm(x) + return self._compute_logits(x) + + +# ----------------------------- +# TRAINING +# ----------------------------- + +def main() -> None: + global zeropower_via_newtonschulz5 + + code = Path(__file__).read_text(encoding="utf-8") + args = Hyperparameters() + zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5) + + # ----------------------------- + # DISTRIBUTED + CUDA SETUP + # ----------------------------- + + distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ + rank = int(os.environ.get("RANK", "0")) + world_size = int(os.environ.get("WORLD_SIZE", "1")) + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + if world_size <= 0: + raise ValueError(f"WORLD_SIZE must be positive, got {world_size}") + if 8 % world_size != 0: + raise ValueError(f"WORLD_SIZE={world_size} must divide 8 so grad_accum_steps stays integral") + grad_accum_steps = 8 // world_size + grad_scale = 1.0 / grad_accum_steps + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is required") + device = torch.device("cuda", local_rank) + torch.cuda.set_device(device) + if distributed: + dist.init_process_group(backend="nccl", device_id=device) + dist.barrier() + master_process = rank == 0 + + # Fast math knobs + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp + + enable_cudnn_sdp(False) + enable_flash_sdp(True) + enable_mem_efficient_sdp(False) + enable_math_sdp(False) + + logfile = None + if master_process: + os.makedirs("logs", exist_ok=True) + logfile = f"logs/{args.run_id}.txt" + print(logfile) + + def log0(msg: str, console: bool = True) -> None: + if not master_process: + return + if console: + print(msg) + if logfile is not None: + with open(logfile, "a", encoding="utf-8") as f: + print(msg, file=f) + + log0(code, console=False) + log0("=" * 100, console=False) + log0(f"Running Python {sys.version}", console=False) + log0(f"Running PyTorch {torch.__version__}", console=False) + log0( + subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout, + console=False, + ) + log0("=" * 100, console=False) + + # ----------------------------- + # TOKENIZER + VALIDATION METRIC SETUP + # ----------------------------- + + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + torch.cuda.manual_seed_all(args.seed) + + if not args.tokenizer_path.endswith(".model"): + raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}") + sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) + if int(sp.vocab_size()) != args.vocab_size: + raise ValueError( + f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}" + ) + dataset_dir = Path(args.data_path).resolve() + actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin"))) + val_tokens = load_validation_tokens(args.val_files, args.train_seq_len) + base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts( + sp, args.vocab_size, device + ) + log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}") + log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}") + log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}") + + # ----------------------------- + # MODEL + OPTIMIZER SETUP + # ----------------------------- + + base_model = GPT( + vocab_size=args.vocab_size, + num_layers=args.num_layers, + model_dim=args.model_dim, + num_heads=args.num_heads, + num_kv_heads=args.num_kv_heads, + mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, + tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, + rope_base=args.rope_base, + qk_gain_init=args.qk_gain_init, + ).to(device).bfloat16() + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.float() + restore_low_dim_params_to_fp32(base_model) + compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) + model: nn.Module = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False) if distributed else compiled_model + + # Optimizer split: + # - token embedding (Adam) uses EMBED_LR + # - untied lm_head (Adam) uses HEAD_LR + # - matrix params in transformer blocks use MATRIX_LR via Muon + # - vectors/scalars use SCALAR_LR via Adam + block_named_params = list(base_model.blocks.named_parameters()) + matrix_params = [ + p + for name, p in block_named_params + if p.ndim == 2 and not any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) + ] + matrix_params.extend(p for p in base_model.pre_enrich.parameters() if p.ndim == 2) + scalar_params = [ + p + for name, p in block_named_params + if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) + ] + if base_model.skip_weights.numel() > 0: + scalar_params.append(base_model.skip_weights) + token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr + optimizer_tok = torch.optim.Adam( + [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}], + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + fused=True, + ) + optimizer_muon = Muon( + matrix_params, + lr=args.matrix_lr, + momentum=args.muon_momentum, + backend_steps=args.muon_backend_steps, + ) + for group in optimizer_muon.param_groups: + group["base_lr"] = args.matrix_lr + optimizer_scalar = torch.optim.Adam( + [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + fused=True, + ) + optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] + if base_model.lm_head is not None: + optimizer_head = torch.optim.Adam( + [{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}], + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + fused=True, + ) + optimizers.insert(1, optimizer_head) + + n_params = sum(p.numel() for p in base_model.parameters()) + log0(f"model_params:{n_params}") + log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") + log0("sdp_backends:cudnn=False flash=True mem_efficient=False math=False") + log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}") + log0( + f"tie_embeddings:{args.tie_embeddings} embed_lr:{token_lr} " + f"head_lr:{args.head_lr if base_model.lm_head is not None else 0.0} " + f"matrix_lr:{args.matrix_lr} scalar_lr:{args.scalar_lr}" + ) + log0( + f"train_batch_tokens:{args.train_batch_tokens} train_seq_len:{args.train_seq_len} " + f"iterations:{args.iterations} warmup_steps:{args.warmup_steps} " + f"max_wallclock_seconds:{args.max_wallclock_seconds:.3f}" + ) + log0(f"seed:{args.seed}") + + # ----------------------------- + # DATA LOADER & MODEL WARMUP + # ----------------------------- + + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + + def zero_grad_all() -> None: + for opt in optimizers: + opt.zero_grad(set_to_none=True) + + max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None + + def lr_mul(step: int, elapsed_ms: float) -> float: + if args.warmdown_iters <= 0: + return 1.0 + if max_wallclock_ms is None: + warmdown_start = max(args.iterations - args.warmdown_iters, 0) + return max((args.iterations - step) / max(args.warmdown_iters, 1), 0.0) if warmdown_start <= step < args.iterations else 1.0 + step_ms = elapsed_ms / max(step, 1) + warmdown_ms = args.warmdown_iters * step_ms + remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0) + return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0 + + # Warmup primes the compiled forward/backward/optimizer paths, then we restore the + # initial weights/optimizer state so measured training starts from the true init. + if args.warmup_steps > 0: + initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} + initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] + model.train() + for warmup_step in range(args.warmup_steps): + zero_grad_all() + for micro_step in range(grad_accum_steps): + if distributed: + model.require_backward_grad_sync = micro_step == grad_accum_steps - 1 + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + warmup_loss = model(x, y) + (warmup_loss * grad_scale).backward() + for opt in optimizers: + opt.step() + zero_grad_all() + if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps: + log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}") + base_model.load_state_dict(initial_model_state, strict=True) + for opt, state in zip(optimizers, initial_optimizer_states, strict=True): + opt.load_state_dict(state) + zero_grad_all() + if distributed: + model.require_backward_grad_sync = True + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + + # ----------------------------- + # MAIN TRAINING LOOP + # ----------------------------- + + training_time_ms = 0.0 + stop_after_step: int | None = None + torch.cuda.synchronize() + t0 = time.perf_counter() + + step = 0 + while True: + last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) + + should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) + if should_validate: + torch.cuda.synchronize() + training_time_ms += 1000.0 * (time.perf_counter() - t0) + val_loss, val_bpb = eval_val( + args, + model, + rank, + world_size, + device, + grad_accum_steps, + val_tokens, + base_bytes_lut, + has_leading_space_lut, + is_boundary_token_lut, + ) + log0( + f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} " + f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms" + ) + torch.cuda.synchronize() + t0 = time.perf_counter() + + if last_step: + if stop_after_step is not None and step < args.iterations: + log0( + f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms " + f"step:{step}/{args.iterations}" + ) + break + + elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + scale = lr_mul(step, elapsed_ms) + zero_grad_all() + train_loss = torch.zeros((), device=device) + for micro_step in range(grad_accum_steps): + if distributed: + model.require_backward_grad_sync = micro_step == grad_accum_steps - 1 + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = model(x, y) + train_loss += loss.detach() + (loss * grad_scale).backward() + train_loss /= grad_accum_steps + + frac = min(step / args.muon_momentum_warmup_steps, 1.0) if args.muon_momentum_warmup_steps > 0 else 1.0 + muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum + for group in optimizer_muon.param_groups: + group["momentum"] = muon_momentum + + for opt in optimizers: + for group in opt.param_groups: + group["lr"] = group["base_lr"] * scale + + if args.grad_clip_norm > 0: + torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) + for opt in optimizers: + opt.step() + with torch.no_grad(): + muon_lr = optimizer_muon.param_groups[0]["lr"] + for p in matrix_params: + p.mul_(1.0 - 0.02 * muon_lr) + zero_grad_all() + + step += 1 + approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + should_log_train = ( + args.train_log_every > 0 + and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None) + ) + if should_log_train: + log0( + f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} " + f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms" + ) + + # Needed to sync whether we've reached the wallclock cap. + reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms + if distributed and max_wallclock_ms is not None: + reached_cap_tensor = torch.tensor(int(reached_cap), device=device) + dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX) + reached_cap = bool(reached_cap_tensor.item()) + if stop_after_step is None and reached_cap: + stop_after_step = step + + log0( + f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" + ) + + # ----------------------------- + # SERIALIZATION + ROUNDTRIP VALIDATION + # ----------------------------- + # Save the raw state (useful for debugging/loading in PyTorch directly), then always produce + # the compressed int8+zlib artifact and validate the round-tripped weights. + + if master_process: + torch.save(base_model.state_dict(), "final_model.pt") + model_bytes = os.path.getsize("final_model.pt") + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model: {model_bytes} bytes") + log0(f"Code size: {code_bytes} bytes") + log0(f"Total submission size: {model_bytes + code_bytes} bytes") + + quant_obj, quant_stats = quantize_state_dict_int8(base_model.state_dict()) + quant_buf = io.BytesIO() + torch.save(quant_obj, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = zlib.compress(quant_raw, level=9) + quant_raw_bytes = len(quant_raw) + if master_process: + with open("final_model.int8.ptz", "wb") as f: + f.write(quant_blob) + quant_file_bytes = os.path.getsize("final_model.int8.ptz") + code_bytes = len(code.encode("utf-8")) + ratio = quant_stats["baseline_tensor_bytes"] / max(quant_stats["int8_payload_bytes"], 1) + log0( + f"Serialized model int8+zlib: {quant_file_bytes} bytes " + f"(payload:{quant_stats['int8_payload_bytes']} raw_torch:{quant_raw_bytes} payload_ratio:{ratio:.2f}x)" + ) + log0(f"Total submission size int8+zlib: {quant_file_bytes + code_bytes} bytes") + + if distributed: + dist.barrier() + with open("final_model.int8.ptz", "rb") as f: + quant_blob_disk = f.read() + quant_state = torch.load(io.BytesIO(zlib.decompress(quant_blob_disk)), map_location="cpu") + base_model.load_state_dict(dequantize_state_dict_int8(quant_state), strict=True) + torch.cuda.synchronize() + t_qeval = time.perf_counter() + q_val_loss, q_val_bpb = eval_val( + args, model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + ) + torch.cuda.synchronize() + log0( + f"final_int8_zlib_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms" + ) + log0(f"final_int8_zlib_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}") + + torch.cuda.synchronize() + t_slide = time.perf_counter() + sw_val_loss, sw_val_bpb = eval_val_sliding( + args, base_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + ) + torch.cuda.synchronize() + log0( + f"final_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{sw_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" + ) + log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f}") + + if distributed: + dist.destroy_process_group() + + +if __name__ == "__main__": + main() From 6ee7458daf1f509b495958b46240e927514805c7 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Fri, 20 Mar 2026 04:21:37 -0300 Subject: [PATCH 17/72] Record: Pre-Enrichment + Encoder Recurrence (val_bpb=1.1855) --- .../README.md | 100 ++ .../submission.json | 17 + .../train.log | 106 ++ .../train_gpt.py | 1245 +++++++++++++++++ 4 files changed, 1468 insertions(+) create mode 100644 records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md create mode 100644 records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json create mode 100644 records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log create mode 100644 records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md new file mode 100644 index 000000000..b2f33c139 --- /dev/null +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md @@ -0,0 +1,100 @@ +## Pre-Enrichment + Encoder Recurrence + +Two architectural modifications to the baseline transformer: (1) a GELU pre-enrichment block that transforms raw embeddings before they enter the residual stream, and (2) 2x encoder recurrence that runs the encoder blocks twice with RMS norm stabilization between passes. Combined with sliding window evaluation (stride=64), overtone embedding initialization, and decoupled Muon weight decay, this achieves **val_bpb 1.1855** in a 15.75MB artifact trained in 10 minutes on 8xH100. + +--- + +### Key Contributions + +#### GELU Pre-Enrichment + +Raw token embeddings are a poor starting point for the residual stream. A 1024-token vocabulary maps each token to a 512-dimensional vector initialized from a normal distribution — these vectors carry no relational structure and every transformer layer must compensate for this weak initialization. + +I add two `CastedLinear(512→512)` projections with a GELU activation between them, applied after the embedding lookup and before the first transformer block: + +``` +embedding → Linear(512→512) → GELU → Linear(512→512) → RMS Norm → transformer blocks +``` + +This gives the model a learned nonlinear transformation to produce richer representations before the residual stream begins. Cost: 0.5M extra parameters (~3% of total), negligible step time overhead. + +#### 2x Encoder Recurrence + +Depth recurrence is a known technique (ALBERT, Universal Transformers). My contribution is applying it to only the encoder half of a U-Net transformer architecture, with RMS norm stabilization between passes, and providing A/B data showing it beats additional training steps. + +The baseline uses a U-Net architecture with encoder and decoder halves connected by skip connections. I reuse the encoder blocks for a second pass before running the decoder. + +With 10 layers (5 encoder + 5 decoder), the forward pass becomes: +1. Run encoder blocks 0-4 (first pass, build initial features) +2. RMS norm (stabilize between passes) +3. Run encoder blocks 0-4 again (second pass, refine features) +4. Run decoder blocks 5-9 with skip connections from the refined second encoder pass + +This produces **15 effective layers from 10 physical blocks** with zero extra parameters. The only cost is step time: ~75ms vs ~50ms without recurrence (~50% overhead from running 5 extra blocks). + +The critical question: does the architectural depth advantage justify 50% fewer training steps? + +**A/B Comparison (8xH100, 10 minutes, identical config except recurrence):** + +| Metric | With recurrence | Without recurrence | +|---------------------|--------------------|-----------------------| +| Steps completed | 8,004 | 11,955 | +| Step time | 75ms | 50ms | +| Standard BPB | 1.2211 | 1.2299 | +| Sliding window BPB | **1.1855** | 1.1947 | +| Submission size | 15.75MB | 15.82MB | + +50% more training steps could not overcome the depth advantage of encoder recurrence. At step 8000 (where the recurrence run stopped), the pre-quant val_bpb was 1.2065 vs 1.3020 for the no-recurrence run — a 0.0955 gap that the extra 4,000 steps narrowed but never closed. + +I find encoder recurrence to be a parameter-efficient alternative to adding physical layers: it doubles the effective encoder depth with zero parameters and predictable step time overhead. + +--- + +### Additional Techniques + +Overtone embedding init, decoupled Muon weight decay (0.02), batched sliding window eval (stride=64), 10 layers, MATRIX_LR=0.06, TIED_EMBED_LR=0.1, WARMDOWN_ITERS=2500. + +--- + +### What Didn't Work + +- **FP16 embedding passthrough**: Keeping the tied embedding in fp16 instead of int8 reduced quantization error by ~0.006 BPB (the tied embedding is used twice — input and output — so int8 errors compound). However, the extra ~520KB pushed the artifact over the 16MB cap. I had to revert to int8. + +- **3x encoder recurrence**: The tripled computation graph exceeded Triton's per-SM shared memory limit on both A100 (168,096 > 166,912 bytes) and RTX 4050. A compiler limitation, not an architectural one. + +- **Warmdown scheduler on A100**: The wallclock-aware warmdown schedule (`WARMDOWN_ITERS=1200`) estimates remaining time as `warmdown_iters × avg_step_time`. On A100 (~1100ms/step), this exceeds the total 600-second budget from step 0, causing the learning rate to decay throughout the entire run. Not relevant to 8xH100 submissions but was a significant debugging finding. + +- Also tried: full U-Net recurrence (too slow), reverse encoder pass order (worse), auxiliary encoder prediction loss (hurt performance). + +--- + +### Configuration + +``` +VOCAB_SIZE=1024 NUM_LAYERS=10 MODEL_DIM=512 NUM_HEADS=8 NUM_KV_HEADS=4 MLP_MULT=2 +TIE_EMBEDDINGS=1 TIED_EMBED_LR=0.1 MATRIX_LR=0.06 SCALAR_LR=0.04 +WARMDOWN_ITERS=2500 WARMUP_STEPS=20 TRAIN_BATCH_TOKENS=524288 TRAIN_SEQ_LEN=1024 +ENCODER_RECURRENCE=1 +``` + +Model parameters: 19,421,776 +Submission size (int8+zlib): 15,753,781 bytes (code: 53,089 bytes) + +### Reproduction + +All defaults are baked into the script: +```bash +torchrun --standalone --nproc_per_node=8 train_gpt.py +``` + +### Key Metrics + +| Metric | Value | +|---|---| +| Pre-quant val_bpb | 1.2065 | +| Post-quant val_bpb (standard) | 1.2211 | +| Post-quant val_bpb (sliding window) | **1.1855** | +| Training time | 599,979ms (8,004 steps at ~75ms) | +| Peak memory | 16,592 MiB | +| Submission size (int8+zlib) | 15,753,781 bytes | +| Model parameters | 19,421,776 | diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json new file mode 100644 index 000000000..5abb44808 --- /dev/null +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json @@ -0,0 +1,17 @@ +{ + "author": "Idanr", + "github_id": "idan3011", + "name": "Pre-Enrichment + Encoder Recurrence", + "blurb": "GELU pre-enrichment + 2x encoder recurrence + sliding window eval (stride=64) + overtone init + Muon WD, 10L 512d. 15 effective layers from 10 physical blocks via encoder-only depth recurrence.", + "date": "2026-03-20T06:00:00Z", + "val_loss": 2.00170864, + "val_bpb": 1.18552460, + "pre_quant_val_loss": 2.0372, + "pre_quant_val_bpb": 1.2065, + "step_stop": 8004, + "wallclock_seconds": 599.979, + "eval_time_seconds": 105.017, + "bytes_total": 15753781, + "bytes_model_int8_zlib": 15700692, + "bytes_code": 53089 +} diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log new file mode 100644 index 000000000..bdf7907b1 --- /dev/null +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log @@ -0,0 +1,106 @@ +W0320 05:59:38.326000 938 torch/distributed/run.py:803] +W0320 05:59:38.326000 938 torch/distributed/run.py:803] ***************************************** +W0320 05:59:38.326000 938 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +W0320 05:59:38.326000 938 torch/distributed/run.py:803] ***************************************** +logs/3d960f9f-a8f8-4aad-95cf-9c64179f6c7b.txt +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:10 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +model_params:19421776 +world_size:8 grad_accum_steps:1 +sdp_backends:cudnn=False flash=True mem_efficient=False math=False +attention_mode:gqa num_heads:8 num_kv_heads:4 +tie_embeddings:True embed_lr:0.1 head_lr:0.0 matrix_lr:0.06 scalar_lr:0.04 +train_batch_tokens:524288 train_seq_len:1024 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 +seed:1337 +warmup_step:1/20 +warmup_step:2/20 +warmup_step:3/20 +warmup_step:4/20 +warmup_step:5/20 +warmup_step:6/20 +warmup_step:7/20 +warmup_step:8/20 +warmup_step:9/20 +warmup_step:10/20 +warmup_step:11/20 +warmup_step:12/20 +warmup_step:13/20 +warmup_step:14/20 +warmup_step:15/20 +warmup_step:16/20 +warmup_step:17/20 +warmup_step:18/20 +warmup_step:19/20 +warmup_step:20/20 +step:0/20000 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step_avg:84.06ms +step:1000/20000 train_loss:2.3751 train_time:84532ms step_avg:84.53ms +step:1000/20000 val_loss:2.3386 val_bpb:1.3850 train_time:84560ms step_avg:84.56ms +step:1200/20000 train_loss:2.3931 train_time:101420ms step_avg:84.52ms +step:1400/20000 train_loss:2.4481 train_time:118283ms step_avg:84.49ms +step:1600/20000 train_loss:2.1157 train_time:135084ms step_avg:84.43ms +step:1800/20000 train_loss:2.2179 train_time:151937ms step_avg:84.41ms +step:2000/20000 train_loss:2.2564 train_time:166386ms step_avg:83.19ms +step:2000/20000 val_loss:2.2592 val_bpb:1.3380 train_time:166414ms step_avg:83.21ms +step:2200/20000 train_loss:2.3746 train_time:180844ms step_avg:82.20ms +step:2400/20000 train_loss:2.3884 train_time:195284ms step_avg:81.37ms +step:2600/20000 train_loss:2.2447 train_time:209738ms step_avg:80.67ms +step:2800/20000 train_loss:2.1986 train_time:224193ms step_avg:80.07ms +step:3000/20000 train_loss:3.2235 train_time:238641ms step_avg:79.55ms +step:3000/20000 val_loss:2.2425 val_bpb:1.3281 train_time:238670ms step_avg:79.56ms +step:3200/20000 train_loss:2.3045 train_time:253084ms step_avg:79.09ms +step:3400/20000 train_loss:2.1390 train_time:267525ms step_avg:78.68ms +step:3600/20000 train_loss:2.2635 train_time:281966ms step_avg:78.32ms +step:3800/20000 train_loss:2.1984 train_time:296409ms step_avg:78.00ms +step:4000/20000 train_loss:2.3154 train_time:310842ms step_avg:77.71ms +step:4000/20000 val_loss:2.2161 val_bpb:1.3125 train_time:310871ms step_avg:77.72ms +step:4200/20000 train_loss:2.2615 train_time:325396ms step_avg:77.48ms +step:4400/20000 train_loss:2.2156 train_time:339837ms step_avg:77.24ms +step:4600/20000 train_loss:2.2478 train_time:354286ms step_avg:77.02ms +step:4800/20000 train_loss:2.1844 train_time:368719ms step_avg:76.82ms +step:5000/20000 train_loss:2.2848 train_time:383154ms step_avg:76.63ms +step:5000/20000 val_loss:2.2044 val_bpb:1.3056 train_time:383184ms step_avg:76.64ms +step:5200/20000 train_loss:2.3330 train_time:397586ms step_avg:76.46ms +step:5400/20000 train_loss:2.2813 train_time:412010ms step_avg:76.30ms +step:5600/20000 train_loss:2.1798 train_time:426437ms step_avg:76.15ms +step:5800/20000 train_loss:2.2131 train_time:440862ms step_avg:76.01ms +step:6000/20000 train_loss:2.1233 train_time:455298ms step_avg:75.88ms +step:6000/20000 val_loss:2.1635 val_bpb:1.2814 train_time:455326ms step_avg:75.89ms +step:6200/20000 train_loss:2.1008 train_time:469733ms step_avg:75.76ms +step:6400/20000 train_loss:1.8779 train_time:484176ms step_avg:75.65ms +step:6600/20000 train_loss:2.0861 train_time:498603ms step_avg:75.55ms +step:6800/20000 train_loss:2.1251 train_time:513036ms step_avg:75.45ms +step:7000/20000 train_loss:2.0666 train_time:527470ms step_avg:75.35ms +step:7000/20000 val_loss:2.1076 val_bpb:1.2482 train_time:527498ms step_avg:75.36ms +step:7200/20000 train_loss:1.9345 train_time:541905ms step_avg:75.26ms +step:7400/20000 train_loss:1.8621 train_time:556345ms step_avg:75.18ms +step:7600/20000 train_loss:2.1033 train_time:570782ms step_avg:75.10ms +step:7800/20000 train_loss:2.0515 train_time:585219ms step_avg:75.03ms +step:8000/20000 train_loss:1.9788 train_time:599665ms step_avg:74.96ms +step:8000/20000 val_loss:2.0372 val_bpb:1.2065 train_time:599693ms step_avg:74.96ms +step:8004/20000 val_loss:2.0372 val_bpb:1.2065 train_time:599979ms step_avg:74.96ms +stopping_early: wallclock_cap train_time:599979ms step:8004/20000 +peak memory allocated: 16592 MiB reserved: 16888 MiB +Serialized model: 76676839 bytes +Code size: 53089 bytes +Total submission size: 76729928 bytes +Serialized model int8+zlib: 15700692 bytes (payload:19556672 raw_torch:19607921 payload_ratio:3.92x) +Total submission size int8+zlib: 15753781 bytes +final_int8_zlib_roundtrip val_loss:2.0618 val_bpb:1.2211 eval_time:2332ms +final_int8_zlib_roundtrip_exact val_loss:2.06182994 val_bpb:1.22113183 +final_sliding_window val_loss:2.0017 val_bpb:1.1855 eval_time:105017ms +final_sliding_window_exact val_loss:2.00170864 val_bpb:1.18552460 diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py new file mode 100644 index 000000000..6dc4d1826 --- /dev/null +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py @@ -0,0 +1,1245 @@ +""" +The `train_gpt.py` and `train_gpt_mlx.py` scripts are intended as good launching-off points for new participants, not SOTA configs. We'll accept PRs that tune, improve, or simplify these scripts without significantly increasing complexity, but competitive submissions should stay in the `/records` folder. + +Hard stop: `train_gpt.py` and `train_gpt_mlx.py` must never be longer than 1500 lines. +""" + +from __future__ import annotations + +import copy +import glob +import io +import math +import os +import random +import subprocess +import sys +import time +import uuid +import zlib +from pathlib import Path + +import numpy as np +import sentencepiece as spm +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn.parallel import DistributedDataParallel as DDP + +# ----------------------------- +# HYPERPARAMETERS +# ----------------------------- +# Default Simple Baseline run: +# - 9 transformer blocks at width 512 +# - 8 attention heads with 4 KV heads (GQA) and 2x MLP expansion +# - vocab size 1024, sequence length 1024, tied embeddings +# - 524,288 train tokens per step for 20,000 iterations with a ~10 minute cap + +class Hyperparameters: + # Data paths are shard globs produced by the existing preprocessing pipeline. + data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") + train_files = os.path.join(data_path, "fineweb_train_*.bin") + val_files = os.path.join(data_path, "fineweb_val_*.bin") + tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model") + run_id = os.environ.get("RUN_ID", str(uuid.uuid4())) + seed = int(os.environ.get("SEED", 1337)) + + # Validation cadence and batch size. Validation always uses the full fineweb_val split. + val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) + val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 1000)) + train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 200)) + + # Training length. + iterations = int(os.environ.get("ITERATIONS", 20000)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 2500)) + warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 524_288)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 1024)) + max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) + qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) + + # Model shape. + vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) + num_layers = int(os.environ.get("NUM_LAYERS", 10)) + num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) + model_dim = int(os.environ.get("MODEL_DIM", 512)) + num_heads = int(os.environ.get("NUM_HEADS", 8)) + mlp_mult = int(os.environ.get("MLP_MULT", 2)) + tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) + rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) + logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) + encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) + + # Optimizer hyperparameters. + embed_lr = float(os.environ.get("EMBED_LR", 0.6)) + head_lr = float(os.environ.get("HEAD_LR", 0.008)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.1)) + tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.06)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.04)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.95)) + muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) + muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.85)) + muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 500)) + beta1 = float(os.environ.get("BETA1", 0.9)) + beta2 = float(os.environ.get("BETA2", 0.95)) + adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) + grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.0)) + +# ----------------------------- +# MUON OPTIMIZER +# ----------------------------- +# +# As borrowed from modded-nanogpt +# Background on Muon: https://kellerjordan.github.io/posts/muon/ + +def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor: + # Orthogonalize a 2D update matrix with a fast Newton-Schulz iteration. + # Muon uses this to normalize matrix-shaped gradients before applying them. + a, b, c = (3.4445, -4.7750, 2.0315) + X = G.bfloat16() + X /= X.norm() + eps + transposed = G.size(0) > G.size(1) + if transposed: + X = X.T + for _ in range(steps): + A = X @ X.T + B = b * A + c * A @ A + X = a * X + B @ X + return X.T if transposed else X + + +class Muon(torch.optim.Optimizer): + def __init__(self, params, lr: float, momentum: float, backend_steps: int, nesterov: bool = True): + super().__init__( + params, + dict(lr=lr, momentum=momentum, backend_steps=backend_steps, nesterov=nesterov), + ) + + @torch.no_grad() + def step(self, closure=None): + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + distributed = dist.is_available() and dist.is_initialized() + world_size = dist.get_world_size() if distributed else 1 + rank = dist.get_rank() if distributed else 0 + + for group in self.param_groups: + params = group["params"] + if not params: + continue + lr = group["lr"] + momentum = group["momentum"] + backend_steps = group["backend_steps"] + nesterov = group["nesterov"] + + total_params = sum(int(p.numel()) for p in params) + updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16) + + curr = 0 + for i, p in enumerate(params): + if i % world_size == rank and p.grad is not None: + g = p.grad + state = self.state[p] + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros_like(g) + buf = state["momentum_buffer"] + buf.mul_(momentum).add_(g) + if nesterov: + g = g.add(buf, alpha=momentum) + g = zeropower_via_newtonschulz5(g, steps=backend_steps) + # Scale correction from Muon reference implementations. + g *= max(1, g.size(0) / g.size(1)) ** 0.5 + updates_flat[curr : curr + p.numel()] = g.reshape(-1) + curr += p.numel() + + if distributed: + dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM) + + curr = 0 + for p in params: + g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype) + p.add_(g, alpha=-lr) + curr += p.numel() + + return loss + + +# ----------------------------- +# TOKENIZER-AGNOSTIC EVALUATION SETUP +# ----------------------------- +# +# It's common for small models have a large fraction of their parameters be embeddings, since the 2 * d_model * d_vocab vectors can be gigantic. +# Instead of locking the tokenizer, we let you bring your own and calculate our validation metrics on the average compression of the validation set. +# We calculate BPB (bits-per-byte) instead of validation loss, so we need methods to count the number of bits per token in the tokenizer. +# Note: Submissions that edit the tokenizer will be examined more carefully, since screwing this up might unjustly improve your score. + +def build_sentencepiece_luts( + sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device +) -> tuple[Tensor, Tensor, Tensor]: + sp_vocab_size = int(sp.vocab_size()) + table_size = max(sp_vocab_size, vocab_size) + base_bytes_np = np.zeros((table_size,), dtype=np.int16) + has_leading_space_np = np.zeros((table_size,), dtype=np.bool_) + is_boundary_token_np = np.ones((table_size,), dtype=np.bool_) + for token_id in range(sp_vocab_size): + if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id): + continue + is_boundary_token_np[token_id] = False + if sp.is_byte(token_id): + base_bytes_np[token_id] = 1 + continue + piece = sp.id_to_piece(token_id) + if piece.startswith("▁"): + has_leading_space_np[token_id] = True + piece = piece[1:] + base_bytes_np[token_id] = len(piece.encode("utf-8")) + return ( + torch.tensor(base_bytes_np, dtype=torch.int16, device=device), + torch.tensor(has_leading_space_np, dtype=torch.bool, device=device), + torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device), + ) + + +def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: + files = [Path(p) for p in sorted(glob.glob(pattern))] + if not files: + raise FileNotFoundError(f"No files found for pattern: {pattern}") + # The export pipeline writes the fixed first-50k-doc validation set to fineweb_val_*. + tokens = torch.cat([load_data_shard(file) for file in files]).contiguous() + usable = ((tokens.numel() - 1) // seq_len) * seq_len + if usable <= 0: + raise ValueError(f"Validation split is too short for TRAIN_SEQ_LEN={seq_len}") + return tokens[: usable + 1] + + +def eval_val( + args: Hyperparameters, + model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + grad_accum_steps: int, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, +) -> tuple[float, float]: + # Validation computes two metrics: + # - val_loss: token cross-entropy (natural log) + # - val_bpb: tokenizer-agnostic compression metric used by the challenge + local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps) + if local_batch_tokens < args.train_seq_len: + raise ValueError( + "VAL_BATCH_SIZE must provide at least one sequence per rank; " + f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, " + f"GRAD_ACCUM_STEPS={grad_accum_steps}, TRAIN_SEQ_LEN={args.train_seq_len}" + ) + local_batch_seqs = local_batch_tokens // args.train_seq_len + total_seqs = (val_tokens.numel() - 1) // args.train_seq_len + seq_start = (total_seqs * rank) // world_size + seq_end = (total_seqs * (rank + 1)) // world_size + val_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + val_token_count = torch.zeros((), device=device, dtype=torch.float64) + val_byte_count = torch.zeros((), device=device, dtype=torch.float64) + + model.eval() + with torch.inference_mode(): + for batch_seq_start in range(seq_start, seq_end, local_batch_seqs): + batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end) + raw_start = batch_seq_start * args.train_seq_len + raw_end = batch_seq_end * args.train_seq_len + 1 + local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True) + x = local[:-1].reshape(-1, args.train_seq_len) + y = local[1:].reshape(-1, args.train_seq_len) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + batch_loss = model(x, y).detach() + batch_token_count = float(y.numel()) + val_loss_sum += batch_loss.to(torch.float64) * batch_token_count + val_token_count += batch_token_count + prev_ids = x.reshape(-1) + tgt_ids = y.reshape(-1) + token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16) + token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16) + val_byte_count += token_bytes.to(torch.float64).sum() + + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM) + + val_loss = val_loss_sum / val_token_count + bits_per_token = val_loss.item() / math.log(2.0) + tokens_per_byte = val_token_count.item() / val_byte_count.item() + model.train() + return float(val_loss.item()), float(bits_per_token * tokens_per_byte) + + +def eval_val_sliding( + args: Hyperparameters, + base_model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + stride: int = 64, + batch_size: int = 256, +) -> tuple[float, float]: + seq_len = args.train_seq_len + total_tokens = val_tokens.numel() + windows: list[tuple[int, int]] = [] + pos = 0 + while pos + seq_len < total_tokens: + score_start = 0 if pos == 0 else seq_len - stride + windows.append((pos, score_start)) + pos += stride + my_windows = windows[rank::world_size] + + total_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + total_scored_tokens = torch.zeros((), device=device, dtype=torch.float64) + total_byte_count = torch.zeros((), device=device, dtype=torch.float64) + + base_model.eval() + with torch.inference_mode(): + for batch_start in range(0, len(my_windows), batch_size): + batch_windows = my_windows[batch_start:batch_start + batch_size] + x_list = [] + y_list = [] + for win_start, _ in batch_windows: + chunk = val_tokens[win_start:win_start + seq_len + 1] + x_list.append(chunk[:-1]) + y_list.append(chunk[1:]) + x = torch.stack(x_list).to(device=device, dtype=torch.int64) + y = torch.stack(y_list).to(device=device, dtype=torch.int64) + + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + logits = base_model.forward_logits(x) + per_token_loss = F.cross_entropy( + logits.float().reshape(-1, logits.size(-1)), + y.reshape(-1), + reduction="none", + ).reshape(len(batch_windows), seq_len) + + for idx, (_, score_start) in enumerate(batch_windows): + scored_loss = per_token_loss[idx, score_start:] + total_loss_sum += scored_loss.to(torch.float64).sum() + total_scored_tokens += float(scored_loss.numel()) + scored_prev = x[idx, score_start:] + scored_tgt = y[idx, score_start:] + token_bytes = base_bytes_lut[scored_tgt].to(dtype=torch.int16) + token_bytes += (has_leading_space_lut[scored_tgt] & ~is_boundary_token_lut[scored_prev]).to(dtype=torch.int16) + total_byte_count += token_bytes.to(torch.float64).sum() + + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(total_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(total_scored_tokens, op=dist.ReduceOp.SUM) + dist.all_reduce(total_byte_count, op=dist.ReduceOp.SUM) + + val_loss = (total_loss_sum / total_scored_tokens).item() + bpb = (total_loss_sum / (total_byte_count * math.log(2.0))).item() + base_model.train() + return float(val_loss), float(bpb) + + +# ----------------------------- +# POST-TRAINING QUANTIZATION +# ----------------------------- +# +# It's silly to export our model, which is trained in bf16 and fp32, at that same precision. +# Instead, we get approximately the same model (with a small hit) by quantizing the model to int8 & zlib compressing. +# We can then decompress the model and run in higher precision for evaluation, after closing in under the size limit. + +CONTROL_TENSOR_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "CONTROL_TENSOR_NAME_PATTERNS", + "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights", + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS), + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 +INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 +INT8_PER_ROW_SCALE_DTYPE = torch.float16 +INT8_CLIP_PERCENTILE = 99.99984 +INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0 + +def tensor_nbytes(t: Tensor) -> int: + return int(t.numel()) * int(t.element_size()) + +def keep_float_tensor(name: str, t: Tensor, passthrough_orig_dtypes: dict[str, str]) -> Tensor: + if any(pattern in name for pattern in INT8_KEEP_FLOAT_FP32_NAME_PATTERNS): + return t.float().contiguous() + if t.dtype in {torch.float32, torch.bfloat16}: + passthrough_orig_dtypes[name] = str(t.dtype).removeprefix("torch.") + return t.to(dtype=INT8_KEEP_FLOAT_STORE_DTYPE).contiguous() + return t + +def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + # Matrices get one scale per row, which usually tracks output-channel + # ranges much better than a single tensor-wide scale. + clip_abs = ( + torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) + if t32.numel() + else torch.empty((t32.shape[0],), dtype=torch.float32) + ) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0) + q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous() + return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + + # Vectors / scalars use a simpler per-tensor scale. + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() + return q, scale + +def quantize_state_dict_int8(state_dict: dict[str, Tensor]): + # Single supported clean-script export format: + # - per-row int8 for 2D float tensors + # - per-tensor int8 for other float tensors + # - exact passthrough for non-floats + # - passthrough for small float tensors, stored as fp16 to save bytes + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + + # Small float tensors are cheap enough to keep directly. We still downcast + # fp32/bf16 passthrough tensors to fp16 so metadata does not dominate size. + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + + obj: dict[str, object] = { + "__quant_format__": "int8_clean_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats + +def dequantize_state_dict_int8(obj: dict[str, object]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + qmeta = obj.get("qmeta", {}) + passthrough_orig_dtypes = obj.get("passthrough_orig_dtypes", {}) + for name, q in obj["quantized"].items(): + dtype = getattr(torch, obj["dtypes"][name]) + s = obj["scales"][name] + if qmeta.get(name, {}).get("scheme") == "per_row" or s.ndim > 0: + s = s.to(dtype=torch.float32) + # Broadcast the saved row scale back across trailing dimensions. + out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous() + else: + scale = float(s.item()) + out[name] = (q.float() * scale).to(dtype=dtype).contiguous() + for name, t in obj["passthrough"].items(): + # Restore small tensors, undoing the temporary fp16 storage cast if needed. + out_t = t.detach().to("cpu").contiguous() + orig_dtype = passthrough_orig_dtypes.get(name) + if isinstance(orig_dtype, str): + out_t = out_t.to(dtype=getattr(torch, orig_dtype)).contiguous() + out[name] = out_t + return out + + +# ----------------------------- +# DATA LOADING +# ----------------------------- + +def load_data_shard(file: Path) -> Tensor: + header_bytes = 256 * np.dtype(" None: + self.file_idx = (self.file_idx + 1) % len(self.files) + self.tokens = load_data_shard(self.files[self.file_idx]) + self.pos = 0 + + def take(self, n: int) -> Tensor: + chunks: list[Tensor] = [] + remaining = n + while remaining > 0: + avail = self.tokens.numel() - self.pos + if avail <= 0: + self._advance_file() + continue + k = min(remaining, avail) + chunks.append(self.tokens[self.pos : self.pos + k]) + self.pos += k + remaining -= k + return chunks[0] if len(chunks) == 1 else torch.cat(chunks) + + +class DistributedTokenLoader: + # Each call consumes a contiguous chunk from the shared token stream, then slices out + # one disjoint span per rank. The extra "+1" token lets us build (x, y) by shifting. + def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): + self.rank = rank + self.world_size = world_size + self.device = device + self.stream = TokenStream(pattern) + + def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]: + local_tokens = global_tokens // (self.world_size * grad_accum_steps) + per_rank_span = local_tokens + 1 + chunk = self.stream.take(per_rank_span * self.world_size) + start = self.rank * per_rank_span + local = chunk[start : start + per_rank_span].to(dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) + +# ----------------------------- +# TRANSFORMER MODULES +# ----------------------------- + +class RMSNorm(nn.Module): + def __init__(self, eps: float | None = None): + super().__init__() + self.eps = eps + + def forward(self, x: Tensor) -> Tensor: + return F.rms_norm(x, (x.size(-1),), eps=self.eps) + + +class CastedLinear(nn.Linear): + # Keep weights in fp32 for optimizer/state quality, cast at matmul time for bf16 compute. + def forward(self, x: Tensor) -> Tensor: + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, self.weight.to(x.dtype), bias) + + +def restore_low_dim_params_to_fp32(module: nn.Module) -> None: + # Keep small/control parameters in fp32 even when the model body runs in bf16. + with torch.no_grad(): + for name, param in module.named_parameters(): + if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32: + param.data = param.data.float() + + +class Rotary(nn.Module): + # Caches cos/sin tables per sequence length on the current device. + def __init__(self, dim: int, base: float = 10000.0): + super().__init__() + inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self._seq_len_cached = 0 + self._cos_cached: Tensor | None = None + self._sin_cached: Tensor | None = None + + def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]: + if ( + self._cos_cached is None + or self._sin_cached is None + or self._seq_len_cached != seq_len + or self._cos_cached.device != device + ): + t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype) + freqs = torch.outer(t, self.inv_freq.to(device)) + self._cos_cached = freqs.cos()[None, None, :, :] + self._sin_cached = freqs.sin()[None, None, :, :] + self._seq_len_cached = seq_len + return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) + + +def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor: + half = x.size(-1) // 2 + x1, x2 = x[..., :half], x[..., half:] + return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + + +class CausalSelfAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + rope_base: float, + qk_gain_init: float, + ): + super().__init__() + if dim % num_heads != 0: + raise ValueError("model_dim must be divisible by num_heads") + if num_heads % num_kv_heads != 0: + raise ValueError("num_heads must be divisible by num_kv_heads") + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = dim // num_heads + if self.head_dim % 2 != 0: + raise ValueError("head_dim must be even for RoPE") + kv_dim = self.num_kv_heads * self.head_dim + self.c_q = CastedLinear(dim, dim, bias=False) + self.c_k = CastedLinear(dim, kv_dim, bias=False) + self.c_v = CastedLinear(dim, kv_dim, bias=False) + self.proj = CastedLinear(dim, dim, bias=False) + self.proj._zero_init = True + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rotary = Rotary(self.head_dim, base=rope_base) + + def forward(self, x: Tensor) -> Tensor: + bsz, seqlen, dim = x.shape + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + q = F.rms_norm(q, (q.size(-1),)) + k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin) + k = apply_rotary_emb(k, cos, sin) + q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None] + y = F.scaled_dot_product_attention( + q, + k, + v, + attn_mask=None, + is_causal=True, + enable_gqa=(self.num_kv_heads != self.num_heads), + ) + y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) + return self.proj(y) + + +class MLP(nn.Module): + # relu^2 MLP from the original modded-nanogpt setup + def __init__(self, dim: int, mlp_mult: int): + super().__init__() + hidden = mlp_mult * dim + self.fc = CastedLinear(dim, hidden, bias=False) + self.proj = CastedLinear(hidden, dim, bias=False) + self.proj._zero_init = True + + def forward(self, x: Tensor) -> Tensor: + x = torch.relu(self.fc(x)) + return self.proj(x.square()) + + +class Block(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + rope_base: float, + qk_gain_init: float, + ): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.mlp = MLP(dim, mlp_mult) + self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + + def forward(self, x: Tensor, x0: Tensor) -> Tensor: + mix = self.resid_mix.to(dtype=x.dtype) + x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out = self.attn(self.attn_norm(x)) + x = x + self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out + x = x + self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x)) + return x + + +class GPT(nn.Module): + def __init__( + self, + vocab_size: int, + num_layers: int, + model_dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + tie_embeddings: bool, + tied_embed_init_std: float, + logit_softcap: float, + rope_base: float, + qk_gain_init: float, + ): + super().__init__() + if logit_softcap <= 0.0: + raise ValueError(f"logit_softcap must be positive, got {logit_softcap}") + self.tie_embeddings = tie_embeddings + self.tied_embed_init_std = tied_embed_init_std + self.logit_softcap = logit_softcap + self.encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.pre_enrich = nn.Sequential( + CastedLinear(model_dim, model_dim, bias=False), + nn.GELU(), + CastedLinear(model_dim, model_dim, bias=False), + ) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + self.blocks = nn.ModuleList( + [ + Block( + model_dim, + num_heads, + num_kv_heads, + mlp_mult, + rope_base, + qk_gain_init, + ) + for i in range(num_layers) + ] + ) + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + if self.lm_head is not None: + self.lm_head._zero_init = True + self._init_weights() + + def _init_weights(self) -> None: + if self.tie_embeddings: + nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std) + with torch.no_grad(): + U, S, V = torch.linalg.svd(self.tok_emb.weight.data, full_matrices=False) + target_S = S[0] * (1.0 / torch.arange(1, S.shape[0] + 1, dtype=S.dtype)) ** 0.5 + self.tok_emb.weight.data = (U * target_S[None, :]) @ V + for module in self.modules(): + if isinstance(module, nn.Linear) and getattr(module, "_zero_init", False): + nn.init.zeros_(module.weight) + + def _run_blocks(self, x: Tensor, x0: Tensor) -> Tensor: + if self.encoder_recurrence: + for _pass in range(2): + skips: list[Tensor] = [] + for i in range(self.num_encoder_layers): + x = self.blocks[i](x, x0) + skips.append(x) + if _pass == 0: + x = F.rms_norm(x, (x.size(-1),)) + continue + for i in range(self.num_decoder_layers): + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + x = self.blocks[self.num_encoder_layers + i](x, x0) + else: + skips: list[Tensor] = [] + for i in range(self.num_encoder_layers): + x = self.blocks[i](x, x0) + skips.append(x) + for i in range(self.num_decoder_layers): + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + x = self.blocks[self.num_encoder_layers + i](x, x0) + return x + + def _compute_logits(self, x: Tensor) -> Tensor: + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + if self.lm_head is None: + raise RuntimeError("lm_head is required when tie_embeddings=False") + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + + def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: + x = self.tok_emb(input_ids) + x = self.pre_enrich(x) + x = F.rms_norm(x, (x.size(-1),)) + x0 = x + x = self._run_blocks(x, x0) + x = self.final_norm(x).reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + logits = self._compute_logits(x) + return F.cross_entropy(logits.float(), targets, reduction="mean") + + def forward_logits(self, input_ids: Tensor) -> Tensor: + x = self.tok_emb(input_ids) + x = self.pre_enrich(x) + x = F.rms_norm(x, (x.size(-1),)) + x0 = x + x = self._run_blocks(x, x0) + x = self.final_norm(x) + return self._compute_logits(x) + + +# ----------------------------- +# TRAINING +# ----------------------------- + +def main() -> None: + global zeropower_via_newtonschulz5 + + code = Path(__file__).read_text(encoding="utf-8") + args = Hyperparameters() + zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5) + + # ----------------------------- + # DISTRIBUTED + CUDA SETUP + # ----------------------------- + + distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ + rank = int(os.environ.get("RANK", "0")) + world_size = int(os.environ.get("WORLD_SIZE", "1")) + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + if world_size <= 0: + raise ValueError(f"WORLD_SIZE must be positive, got {world_size}") + if 8 % world_size != 0: + raise ValueError(f"WORLD_SIZE={world_size} must divide 8 so grad_accum_steps stays integral") + grad_accum_steps = 8 // world_size + grad_scale = 1.0 / grad_accum_steps + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is required") + device = torch.device("cuda", local_rank) + torch.cuda.set_device(device) + if distributed: + dist.init_process_group(backend="nccl", device_id=device) + dist.barrier() + master_process = rank == 0 + + # Fast math knobs + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp + + enable_cudnn_sdp(False) + enable_flash_sdp(True) + enable_mem_efficient_sdp(False) + enable_math_sdp(False) + + logfile = None + if master_process: + os.makedirs("logs", exist_ok=True) + logfile = f"logs/{args.run_id}.txt" + print(logfile) + + def log0(msg: str, console: bool = True) -> None: + if not master_process: + return + if console: + print(msg) + if logfile is not None: + with open(logfile, "a", encoding="utf-8") as f: + print(msg, file=f) + + log0(code, console=False) + log0("=" * 100, console=False) + log0(f"Running Python {sys.version}", console=False) + log0(f"Running PyTorch {torch.__version__}", console=False) + log0( + subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout, + console=False, + ) + log0("=" * 100, console=False) + + # ----------------------------- + # TOKENIZER + VALIDATION METRIC SETUP + # ----------------------------- + + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + torch.cuda.manual_seed_all(args.seed) + + if not args.tokenizer_path.endswith(".model"): + raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}") + sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) + if int(sp.vocab_size()) != args.vocab_size: + raise ValueError( + f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}" + ) + dataset_dir = Path(args.data_path).resolve() + actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin"))) + val_tokens = load_validation_tokens(args.val_files, args.train_seq_len) + base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts( + sp, args.vocab_size, device + ) + log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}") + log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}") + log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}") + + # ----------------------------- + # MODEL + OPTIMIZER SETUP + # ----------------------------- + + base_model = GPT( + vocab_size=args.vocab_size, + num_layers=args.num_layers, + model_dim=args.model_dim, + num_heads=args.num_heads, + num_kv_heads=args.num_kv_heads, + mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, + tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, + rope_base=args.rope_base, + qk_gain_init=args.qk_gain_init, + ).to(device).bfloat16() + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.float() + restore_low_dim_params_to_fp32(base_model) + compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) + model: nn.Module = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False) if distributed else compiled_model + + # Optimizer split: + # - token embedding (Adam) uses EMBED_LR + # - untied lm_head (Adam) uses HEAD_LR + # - matrix params in transformer blocks use MATRIX_LR via Muon + # - vectors/scalars use SCALAR_LR via Adam + block_named_params = list(base_model.blocks.named_parameters()) + matrix_params = [ + p + for name, p in block_named_params + if p.ndim == 2 and not any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) + ] + matrix_params.extend(p for p in base_model.pre_enrich.parameters() if p.ndim == 2) + scalar_params = [ + p + for name, p in block_named_params + if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) + ] + if base_model.skip_weights.numel() > 0: + scalar_params.append(base_model.skip_weights) + token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr + optimizer_tok = torch.optim.Adam( + [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}], + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + fused=True, + ) + optimizer_muon = Muon( + matrix_params, + lr=args.matrix_lr, + momentum=args.muon_momentum, + backend_steps=args.muon_backend_steps, + ) + for group in optimizer_muon.param_groups: + group["base_lr"] = args.matrix_lr + optimizer_scalar = torch.optim.Adam( + [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + fused=True, + ) + optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] + if base_model.lm_head is not None: + optimizer_head = torch.optim.Adam( + [{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}], + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + fused=True, + ) + optimizers.insert(1, optimizer_head) + + n_params = sum(p.numel() for p in base_model.parameters()) + log0(f"model_params:{n_params}") + log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") + log0("sdp_backends:cudnn=False flash=True mem_efficient=False math=False") + log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}") + log0( + f"tie_embeddings:{args.tie_embeddings} embed_lr:{token_lr} " + f"head_lr:{args.head_lr if base_model.lm_head is not None else 0.0} " + f"matrix_lr:{args.matrix_lr} scalar_lr:{args.scalar_lr}" + ) + log0( + f"train_batch_tokens:{args.train_batch_tokens} train_seq_len:{args.train_seq_len} " + f"iterations:{args.iterations} warmup_steps:{args.warmup_steps} " + f"max_wallclock_seconds:{args.max_wallclock_seconds:.3f}" + ) + log0(f"seed:{args.seed}") + + # ----------------------------- + # DATA LOADER & MODEL WARMUP + # ----------------------------- + + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + + def zero_grad_all() -> None: + for opt in optimizers: + opt.zero_grad(set_to_none=True) + + max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None + + def lr_mul(step: int, elapsed_ms: float) -> float: + if args.warmdown_iters <= 0: + return 1.0 + if max_wallclock_ms is None: + warmdown_start = max(args.iterations - args.warmdown_iters, 0) + return max((args.iterations - step) / max(args.warmdown_iters, 1), 0.0) if warmdown_start <= step < args.iterations else 1.0 + step_ms = elapsed_ms / max(step, 1) + warmdown_ms = args.warmdown_iters * step_ms + remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0) + return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0 + + # Warmup primes the compiled forward/backward/optimizer paths, then we restore the + # initial weights/optimizer state so measured training starts from the true init. + if args.warmup_steps > 0: + initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} + initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] + model.train() + for warmup_step in range(args.warmup_steps): + zero_grad_all() + for micro_step in range(grad_accum_steps): + if distributed: + model.require_backward_grad_sync = micro_step == grad_accum_steps - 1 + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + warmup_loss = model(x, y) + (warmup_loss * grad_scale).backward() + for opt in optimizers: + opt.step() + zero_grad_all() + if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps: + log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}") + base_model.load_state_dict(initial_model_state, strict=True) + for opt, state in zip(optimizers, initial_optimizer_states, strict=True): + opt.load_state_dict(state) + zero_grad_all() + if distributed: + model.require_backward_grad_sync = True + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + + # ----------------------------- + # MAIN TRAINING LOOP + # ----------------------------- + + training_time_ms = 0.0 + stop_after_step: int | None = None + torch.cuda.synchronize() + t0 = time.perf_counter() + + step = 0 + while True: + last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) + + should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) + if should_validate: + torch.cuda.synchronize() + training_time_ms += 1000.0 * (time.perf_counter() - t0) + val_loss, val_bpb = eval_val( + args, + model, + rank, + world_size, + device, + grad_accum_steps, + val_tokens, + base_bytes_lut, + has_leading_space_lut, + is_boundary_token_lut, + ) + log0( + f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} " + f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms" + ) + torch.cuda.synchronize() + t0 = time.perf_counter() + + if last_step: + if stop_after_step is not None and step < args.iterations: + log0( + f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms " + f"step:{step}/{args.iterations}" + ) + break + + elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + scale = lr_mul(step, elapsed_ms) + zero_grad_all() + train_loss = torch.zeros((), device=device) + for micro_step in range(grad_accum_steps): + if distributed: + model.require_backward_grad_sync = micro_step == grad_accum_steps - 1 + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = model(x, y) + train_loss += loss.detach() + (loss * grad_scale).backward() + train_loss /= grad_accum_steps + + frac = min(step / args.muon_momentum_warmup_steps, 1.0) if args.muon_momentum_warmup_steps > 0 else 1.0 + muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum + for group in optimizer_muon.param_groups: + group["momentum"] = muon_momentum + + for opt in optimizers: + for group in opt.param_groups: + group["lr"] = group["base_lr"] * scale + + if args.grad_clip_norm > 0: + torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) + for opt in optimizers: + opt.step() + with torch.no_grad(): + muon_lr = optimizer_muon.param_groups[0]["lr"] + for p in matrix_params: + p.mul_(1.0 - 0.02 * muon_lr) + zero_grad_all() + + step += 1 + approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + should_log_train = ( + args.train_log_every > 0 + and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None) + ) + if should_log_train: + log0( + f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} " + f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms" + ) + + # Needed to sync whether we've reached the wallclock cap. + reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms + if distributed and max_wallclock_ms is not None: + reached_cap_tensor = torch.tensor(int(reached_cap), device=device) + dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX) + reached_cap = bool(reached_cap_tensor.item()) + if stop_after_step is None and reached_cap: + stop_after_step = step + + log0( + f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" + ) + + # ----------------------------- + # SERIALIZATION + ROUNDTRIP VALIDATION + # ----------------------------- + # Save the raw state (useful for debugging/loading in PyTorch directly), then always produce + # the compressed int8+zlib artifact and validate the round-tripped weights. + + if master_process: + torch.save(base_model.state_dict(), "final_model.pt") + model_bytes = os.path.getsize("final_model.pt") + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model: {model_bytes} bytes") + log0(f"Code size: {code_bytes} bytes") + log0(f"Total submission size: {model_bytes + code_bytes} bytes") + + quant_obj, quant_stats = quantize_state_dict_int8(base_model.state_dict()) + quant_buf = io.BytesIO() + torch.save(quant_obj, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = zlib.compress(quant_raw, level=9) + quant_raw_bytes = len(quant_raw) + if master_process: + with open("final_model.int8.ptz", "wb") as f: + f.write(quant_blob) + quant_file_bytes = os.path.getsize("final_model.int8.ptz") + code_bytes = len(code.encode("utf-8")) + ratio = quant_stats["baseline_tensor_bytes"] / max(quant_stats["int8_payload_bytes"], 1) + log0( + f"Serialized model int8+zlib: {quant_file_bytes} bytes " + f"(payload:{quant_stats['int8_payload_bytes']} raw_torch:{quant_raw_bytes} payload_ratio:{ratio:.2f}x)" + ) + log0(f"Total submission size int8+zlib: {quant_file_bytes + code_bytes} bytes") + + if distributed: + dist.barrier() + with open("final_model.int8.ptz", "rb") as f: + quant_blob_disk = f.read() + quant_state = torch.load(io.BytesIO(zlib.decompress(quant_blob_disk)), map_location="cpu") + base_model.load_state_dict(dequantize_state_dict_int8(quant_state), strict=True) + torch.cuda.synchronize() + t_qeval = time.perf_counter() + q_val_loss, q_val_bpb = eval_val( + args, model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + ) + torch.cuda.synchronize() + log0( + f"final_int8_zlib_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms" + ) + log0(f"final_int8_zlib_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}") + + torch.cuda.synchronize() + t_slide = time.perf_counter() + sw_val_loss, sw_val_bpb = eval_val_sliding( + args, base_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + ) + torch.cuda.synchronize() + log0( + f"final_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{sw_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" + ) + log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f}") + + if distributed: + dist.destroy_process_group() + + +if __name__ == "__main__": + main() From 317cca52e7a02f02e1ccbc6101e4f93b524b2239 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Fri, 20 Mar 2026 10:21:15 -0300 Subject: [PATCH 18/72] feat: int6 QAT + lzma + MLP 3x + SWA + WD 0.04 + dual run configs --- train_gpt.py | 166 ++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 137 insertions(+), 29 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 8336c8980..895811847 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -16,6 +16,7 @@ import sys import time import uuid +import lzma import zlib from pathlib import Path @@ -36,8 +37,9 @@ # - vocab size 1024, sequence length 1024, tied embeddings # - 524,288 train tokens per step for 20,000 iterations with a ~10 minute cap +_RUN_CONFIG = os.environ.get("RUN_CONFIG", "A") + class Hyperparameters: - # Data paths are shard globs produced by the existing preprocessing pipeline. data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") train_files = os.path.join(data_path, "fineweb_train_*.bin") val_files = os.path.join(data_path, "fineweb_val_*.bin") @@ -45,47 +47,46 @@ class Hyperparameters: run_id = os.environ.get("RUN_ID", str(uuid.uuid4())) seed = int(os.environ.get("SEED", 1337)) - # Validation cadence and batch size. Validation always uses the full fineweb_val split. val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 1000)) train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 200)) - # Training length. iterations = int(os.environ.get("ITERATIONS", 20000)) - warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 1200)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 2100 if _RUN_CONFIG == "A" else 2600)) warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 524_288)) - train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 1024)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048 if _RUN_CONFIG == "A" else 1024)) max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) - # Model shape. vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) - num_layers = int(os.environ.get("NUM_LAYERS", 9)) + num_layers = int(os.environ.get("NUM_LAYERS", 10)) num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) model_dim = int(os.environ.get("MODEL_DIM", 512)) num_heads = int(os.environ.get("NUM_HEADS", 8)) - mlp_mult = int(os.environ.get("MLP_MULT", 2)) + mlp_mult = int(os.environ.get("MLP_MULT", 3)) tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) - encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "0"))) + encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) - # Optimizer hyperparameters. embed_lr = float(os.environ.get("EMBED_LR", 0.6)) head_lr = float(os.environ.get("HEAD_LR", 0.008)) - tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.05)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.035)) tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) - matrix_lr = float(os.environ.get("MATRIX_LR", 0.04)) - scalar_lr = float(os.environ.get("SCALAR_LR", 0.04)) - muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.95)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.035 if _RUN_CONFIG == "A" else 0.025)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.025)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) - muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.85)) - muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 500)) + muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92)) + muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500)) beta1 = float(os.environ.get("BETA1", 0.9)) beta2 = float(os.environ.get("BETA2", 0.95)) adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.0)) + muon_wd = float(os.environ.get("MUON_WD", 0.04)) + adam_wd = float(os.environ.get("ADAM_WD", 0.04)) + swa_every = int(os.environ.get("SWA_EVERY", 200)) # ----------------------------- # MUON OPTIMIZER @@ -410,6 +411,70 @@ def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() return q, scale +def quantize_float_tensor_int6(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + clip_abs = ( + torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) + if t32.numel() + else torch.empty((t32.shape[0],), dtype=torch.float32) + ) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + scale = (clip_abs / 31.0).clamp_min(1.0 / 31.0) + q = torch.clamp(torch.round(clipped / scale[:, None]), -31, 31).to(torch.int8).contiguous() + return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 31.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -31, 31).to(torch.int8).contiguous() + return q, scale + +def quantize_state_dict_int6(state_dict: dict[str, Tensor]): + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL or "tok_emb.weight" in name: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor_int6(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + obj: dict[str, object] = { + "__quant_format__": "int6_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats + def quantize_state_dict_int8(state_dict: dict[str, Tensor]): # Single supported clean-script export format: # - per-row int8 for 2D float tensors @@ -577,11 +642,34 @@ def forward(self, x: Tensor) -> Tensor: return F.rms_norm(x, (x.size(-1),), eps=self.eps) +class _FakeQuantInt6(torch.autograd.Function): + @staticmethod + def forward(ctx, w: Tensor) -> Tensor: + if w.ndim != 2: + return w + row_max = w.abs().amax(dim=1, keepdim=True).clamp_min(1e-12) + scale = row_max / 31.0 + q = (w / scale).round().clamp(-31, 31) + return q * scale + + @staticmethod + def backward(ctx, grad: Tensor) -> Tensor: + return grad + +def fake_quant_int6(w: Tensor) -> Tensor: + return _FakeQuantInt6.apply(w) + class CastedLinear(nn.Linear): - # Keep weights in fp32 for optimizer/state quality, cast at matmul time for bf16 compute. + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.use_qat = False + def forward(self, x: Tensor) -> Tensor: + w = self.weight + if self.use_qat and self.training: + w = fake_quant_int6(w) bias = self.bias.to(x.dtype) if self.bias is not None else None - return F.linear(x, self.weight.to(x.dtype), bias) + return F.linear(x, w.to(x.dtype), bias) def restore_low_dim_params_to_fp32(module: nn.Module) -> None: @@ -949,6 +1037,9 @@ def log0(msg: str, console: bool = True) -> None: if isinstance(module, CastedLinear): module.float() restore_low_dim_params_to_fp32(base_model) + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.use_qat = True compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) model: nn.Module = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False) if distributed else compiled_model @@ -972,10 +1063,11 @@ def log0(msg: str, console: bool = True) -> None: if base_model.skip_weights.numel() > 0: scalar_params.append(base_model.skip_weights) token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr - optimizer_tok = torch.optim.Adam( + optimizer_tok = torch.optim.AdamW( [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}], betas=(args.beta1, args.beta2), eps=args.adam_eps, + weight_decay=args.adam_wd, fused=True, ) optimizer_muon = Muon( @@ -986,10 +1078,11 @@ def log0(msg: str, console: bool = True) -> None: ) for group in optimizer_muon.param_groups: group["base_lr"] = args.matrix_lr - optimizer_scalar = torch.optim.Adam( + optimizer_scalar = torch.optim.AdamW( [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], betas=(args.beta1, args.beta2), eps=args.adam_eps, + weight_decay=args.adam_wd, fused=True, ) optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] @@ -1076,6 +1169,7 @@ def lr_mul(step: int, elapsed_ms: float) -> float: training_time_ms = 0.0 stop_after_step: int | None = None + swa_checkpoints: list[dict[str, Tensor]] = [] torch.cuda.synchronize() t0 = time.perf_counter() @@ -1144,10 +1238,12 @@ def lr_mul(step: int, elapsed_ms: float) -> float: with torch.no_grad(): muon_lr = optimizer_muon.param_groups[0]["lr"] for p in matrix_params: - p.mul_(1.0 - 0.02 * muon_lr) + p.mul_(1.0 - args.muon_wd * muon_lr) zero_grad_all() step += 1 + if scale < 1.0 and args.swa_every > 0 and step % args.swa_every == 0: + swa_checkpoints.append({k: v.detach().cpu().clone() for k, v in base_model.state_dict().items()}) approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) should_log_train = ( args.train_log_every > 0 @@ -1179,6 +1275,18 @@ def lr_mul(step: int, elapsed_ms: float) -> float: # Save the raw state (useful for debugging/loading in PyTorch directly), then always produce # the compressed int8+zlib artifact and validate the round-tripped weights. + if swa_checkpoints: + log0(f"swa: averaging {len(swa_checkpoints)} checkpoints") + avg_state = {} + for key in swa_checkpoints[0]: + avg_state[key] = torch.stack([ckpt[key].float() for ckpt in swa_checkpoints]).mean(dim=0) + base_model.load_state_dict(avg_state, strict=True) + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.float() + restore_low_dim_params_to_fp32(base_model) + del swa_checkpoints + if master_process: torch.save(base_model.state_dict(), "final_model.pt") model_bytes = os.path.getsize("final_model.pt") @@ -1187,29 +1295,29 @@ def lr_mul(step: int, elapsed_ms: float) -> float: log0(f"Code size: {code_bytes} bytes") log0(f"Total submission size: {model_bytes + code_bytes} bytes") - quant_obj, quant_stats = quantize_state_dict_int8(base_model.state_dict()) + quant_obj, quant_stats = quantize_state_dict_int6(base_model.state_dict()) quant_buf = io.BytesIO() torch.save(quant_obj, quant_buf) quant_raw = quant_buf.getvalue() - quant_blob = zlib.compress(quant_raw, level=9) + quant_blob = lzma.compress(quant_raw, preset=6) quant_raw_bytes = len(quant_raw) if master_process: - with open("final_model.int8.ptz", "wb") as f: + with open("final_model.int6.ptz", "wb") as f: f.write(quant_blob) - quant_file_bytes = os.path.getsize("final_model.int8.ptz") + quant_file_bytes = os.path.getsize("final_model.int6.ptz") code_bytes = len(code.encode("utf-8")) ratio = quant_stats["baseline_tensor_bytes"] / max(quant_stats["int8_payload_bytes"], 1) log0( - f"Serialized model int8+zlib: {quant_file_bytes} bytes " + f"Serialized model int6+lzma: {quant_file_bytes} bytes " f"(payload:{quant_stats['int8_payload_bytes']} raw_torch:{quant_raw_bytes} payload_ratio:{ratio:.2f}x)" ) - log0(f"Total submission size int8+zlib: {quant_file_bytes + code_bytes} bytes") + log0(f"Total submission size int6+lzma: {quant_file_bytes + code_bytes} bytes") if distributed: dist.barrier() - with open("final_model.int8.ptz", "rb") as f: + with open("final_model.int6.ptz", "rb") as f: quant_blob_disk = f.read() - quant_state = torch.load(io.BytesIO(zlib.decompress(quant_blob_disk)), map_location="cpu") + quant_state = torch.load(io.BytesIO(lzma.decompress(quant_blob_disk)), map_location="cpu") base_model.load_state_dict(dequantize_state_dict_int8(quant_state), strict=True) torch.cuda.synchronize() t_qeval = time.perf_counter() From 9b7c19c0f017a42a747f6587022e82e5b3b275db Mon Sep 17 00:00:00 2001 From: idan3011 Date: Fri, 20 Mar 2026 12:12:19 -0300 Subject: [PATCH 19/72] fix: encoder recurrence default ON + log header --- train_gpt.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/train_gpt.py b/train_gpt.py index 895811847..698208d87 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -825,7 +825,7 @@ def __init__( self.tie_embeddings = tie_embeddings self.tied_embed_init_std = tied_embed_init_std self.logit_softcap = logit_softcap - self.encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "0"))) + self.encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) self.tok_emb = nn.Embedding(vocab_size, model_dim) self.pre_enrich = nn.Sequential( CastedLinear(model_dim, model_dim, bias=False), @@ -1097,6 +1097,7 @@ def log0(msg: str, console: bool = True) -> None: n_params = sum(p.numel() for p in base_model.parameters()) log0(f"model_params:{n_params}") + log0(f"encoder_recurrence:{'ON' if base_model.encoder_recurrence else 'OFF'}") log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") log0("sdp_backends:cudnn=False flash=True mem_efficient=False math=False") log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}") From c1bde37a62073fbb15c57d338dc59659dd303950 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Fri, 20 Mar 2026 12:44:50 -0300 Subject: [PATCH 20/72] Record: Pre-Enrichment + Encoder Recurrence (val_bpb=1.1709) --- .../README.md | 65 +++---- .../submission.json | 24 +-- .../train.log | 150 ++++++++-------- .../train_gpt.py | 163 +++++++++++++++--- 4 files changed, 255 insertions(+), 147 deletions(-) diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md index b2f33c139..56091b09c 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md @@ -1,6 +1,6 @@ ## Pre-Enrichment + Encoder Recurrence -Two architectural modifications to the baseline transformer: (1) a GELU pre-enrichment block that transforms raw embeddings before they enter the residual stream, and (2) 2x encoder recurrence that runs the encoder blocks twice with RMS norm stabilization between passes. Combined with sliding window evaluation (stride=64), overtone embedding initialization, and decoupled Muon weight decay, this achieves **val_bpb 1.1855** in a 15.75MB artifact trained in 10 minutes on 8xH100. +Two architectural modifications to the baseline transformer: (1) a GELU pre-enrichment block that transforms raw embeddings before they enter the residual stream, and (2) 2x encoder recurrence that runs the encoder blocks twice with RMS norm stabilization between passes. Combined with int6 QAT, lzma compression, MLP 3x, SWA, sliding window evaluation (stride=64), and overtone embedding initialization, this achieves **val_bpb 1.1709** in a 15.57MB artifact trained in 10 minutes on 8xH100. --- @@ -16,11 +16,11 @@ I add two `CastedLinear(512→512)` projections with a GELU activation between t embedding → Linear(512→512) → GELU → Linear(512→512) → RMS Norm → transformer blocks ``` -This gives the model a learned nonlinear transformation to produce richer representations before the residual stream begins. Cost: 0.5M extra parameters (~3% of total), negligible step time overhead. +This gives the model a learned nonlinear transformation to produce richer representations before the residual stream begins. Cost: 0.5M extra parameters (~2% of total), negligible step time overhead. #### 2x Encoder Recurrence -Depth recurrence is a known technique (ALBERT, Universal Transformers). My contribution is applying it to only the encoder half of a U-Net transformer architecture, with RMS norm stabilization between passes, and providing A/B data showing it beats additional training steps. +Depth recurrence is a known technique (ALBERT, Universal Transformers). My contribution is applying it to only the encoder half of a U-Net transformer architecture, with RMS norm stabilization between passes, and providing A/B data showing it consistently beats additional training steps across two different model configurations. The baseline uses a U-Net architecture with encoder and decoder halves connected by skip connections. I reuse the encoder blocks for a second pass before running the decoder. @@ -30,11 +30,19 @@ With 10 layers (5 encoder + 5 decoder), the forward pass becomes: 3. Run encoder blocks 0-4 again (second pass, refine features) 4. Run decoder blocks 5-9 with skip connections from the refined second encoder pass -This produces **15 effective layers from 10 physical blocks** with zero extra parameters. The only cost is step time: ~75ms vs ~50ms without recurrence (~50% overhead from running 5 extra blocks). +This produces **15 effective layers from 10 physical blocks** with zero extra parameters. -The critical question: does the architectural depth advantage justify 50% fewer training steps? +**A/B Comparison — Config 2 (MLP 3x, seq 2048, int6 QAT, SWA):** -**A/B Comparison (8xH100, 10 minutes, identical config except recurrence):** +| Metric | With recurrence | Without recurrence | +|---------------------|--------------------|-----------------------| +| Steps completed | 6,423 | 8,950 | +| Step time | 93ms | 67ms | +| Standard BPB | 1.1929 | 1.1959 | +| Sliding window BPB | **1.1709** | 1.1740 | +| Submission size | 15.57MB | 15.54MB | + +**A/B Comparison — Config 1 (MLP 2x, seq 1024, int8+zlib):** | Metric | With recurrence | Without recurrence | |---------------------|--------------------|-----------------------| @@ -44,57 +52,56 @@ The critical question: does the architectural depth advantage justify 50% fewer | Sliding window BPB | **1.1855** | 1.1947 | | Submission size | 15.75MB | 15.82MB | -50% more training steps could not overcome the depth advantage of encoder recurrence. At step 8000 (where the recurrence run stopped), the pre-quant val_bpb was 1.2065 vs 1.3020 for the no-recurrence run — a 0.0955 gap that the extra 4,000 steps narrowed but never closed. - -I find encoder recurrence to be a parameter-efficient alternative to adding physical layers: it doubles the effective encoder depth with zero parameters and predictable step time overhead. +Encoder recurrence wins across both configurations — different model sizes, different sequence lengths, different step counts. In both cases, 30-40% fewer training steps could not overcome the depth advantage. The pattern is consistent: deeper processing per step beats more gradient updates with shallower processing. --- ### Additional Techniques -Overtone embedding init, decoupled Muon weight decay (0.02), batched sliding window eval (stride=64), 10 layers, MATRIX_LR=0.06, TIED_EMBED_LR=0.1, WARMDOWN_ITERS=2500. +Int6 quantization-aware training (fake quant with STE in CastedLinear), lzma compression, MLP 3x expansion, stochastic weight averaging (11 checkpoints during warmdown), overtone embedding init, decoupled Muon weight decay (0.04), AdamW weight decay (0.04), batched sliding window eval (stride=64), fp16 embedding passthrough in quantization. + +Hyperparameters: NUM_LAYERS=10, TRAIN_SEQ_LEN=2048, MATRIX_LR=0.035, SCALAR_LR=0.025, TIED_EMBED_LR=0.035, MUON_MOMENTUM=0.99, WARMDOWN_ITERS=2100. --- ### What Didn't Work -- **FP16 embedding passthrough**: Keeping the tied embedding in fp16 instead of int8 reduced quantization error by ~0.006 BPB (the tied embedding is used twice — input and output — so int8 errors compound). However, the extra ~520KB pushed the artifact over the 16MB cap. I had to revert to int8. +- **FP16 embedding passthrough (without int6)**: Keeping the tied embedding in fp16 instead of int8 reduced quantization error by ~0.006 BPB but pushed the int8+zlib artifact over 16MB. Switching to int6 quantization solved this — fp16 embedding fits comfortably in the int6+lzma budget. -- **3x encoder recurrence**: The tripled computation graph exceeded Triton's per-SM shared memory limit on both A100 (168,096 > 166,912 bytes) and RTX 4050. A compiler limitation, not an architectural one. +- **3x encoder recurrence**: The tripled computation graph exceeded Triton's per-SM shared memory limit on A100 (168,096 > 166,912 bytes). A compiler limitation, not an architectural one. -- **Warmdown scheduler on A100**: The wallclock-aware warmdown schedule (`WARMDOWN_ITERS=1200`) estimates remaining time as `warmdown_iters × avg_step_time`. On A100 (~1100ms/step), this exceeds the total 600-second budget from step 0, causing the learning rate to decay throughout the entire run. Not relevant to 8xH100 submissions but was a significant debugging finding. +- **Warmdown scheduler on A100**: The wallclock-aware warmdown schedule estimates remaining time as `warmdown_iters × avg_step_time`. On A100 (~1100ms/step), this exceeds the total 600-second budget from step 0, causing the learning rate to decay throughout the entire run. Not relevant to 8xH100 but was a significant debugging finding during development. -- Also tried: full U-Net recurrence (too slow), reverse encoder pass order (worse), auxiliary encoder prediction loss (hurt performance). +- Also tried: full U-Net recurrence (too slow), reverse encoder pass order (worse), auxiliary encoder prediction loss (hurt performance), 6+3 encoder/decoder split (worse than 5+5). --- ### Configuration ``` -VOCAB_SIZE=1024 NUM_LAYERS=10 MODEL_DIM=512 NUM_HEADS=8 NUM_KV_HEADS=4 MLP_MULT=2 -TIE_EMBEDDINGS=1 TIED_EMBED_LR=0.1 MATRIX_LR=0.06 SCALAR_LR=0.04 -WARMDOWN_ITERS=2500 WARMUP_STEPS=20 TRAIN_BATCH_TOKENS=524288 TRAIN_SEQ_LEN=1024 -ENCODER_RECURRENCE=1 +RUN_CONFIG=A +VOCAB_SIZE=1024 NUM_LAYERS=10 MODEL_DIM=512 NUM_HEADS=8 NUM_KV_HEADS=4 MLP_MULT=3 +TIE_EMBEDDINGS=1 TIED_EMBED_LR=0.035 MATRIX_LR=0.035 SCALAR_LR=0.025 +MUON_MOMENTUM=0.99 MUON_MOMENTUM_WARMUP_START=0.92 MUON_MOMENTUM_WARMUP_STEPS=1500 +WARMDOWN_ITERS=2100 WARMUP_STEPS=20 TRAIN_BATCH_TOKENS=524288 TRAIN_SEQ_LEN=2048 +ENCODER_RECURRENCE=1 MUON_WD=0.04 ADAM_WD=0.04 SWA_EVERY=200 ``` -Model parameters: 19,421,776 -Submission size (int8+zlib): 15,753,781 bytes (code: 53,089 bytes) - ### Reproduction All defaults are baked into the script: ```bash -torchrun --standalone --nproc_per_node=8 train_gpt.py +RUN_CONFIG=A torchrun --standalone --nproc_per_node=8 train_gpt.py ``` ### Key Metrics | Metric | Value | |---|---| -| Pre-quant val_bpb | 1.2065 | -| Post-quant val_bpb (standard) | 1.2211 | -| Post-quant val_bpb (sliding window) | **1.1855** | -| Training time | 599,979ms (8,004 steps at ~75ms) | -| Peak memory | 16,592 MiB | -| Submission size (int8+zlib) | 15,753,781 bytes | -| Model parameters | 19,421,776 | +| Pre-quant val_bpb | 1.1730 | +| Post-quant val_bpb (standard) | 1.1929 | +| Post-quant val_bpb (sliding window) | **1.1709** | +| Training time | 600,034ms (6,423 steps at ~93ms) | +| Peak memory | 18,506 MiB | +| Submission size (int6+lzma) | 15,567,990 bytes | +| Model parameters | 24,664,656 | diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json index 5abb44808..3e784e3d5 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json @@ -2,16 +2,16 @@ "author": "Idanr", "github_id": "idan3011", "name": "Pre-Enrichment + Encoder Recurrence", - "blurb": "GELU pre-enrichment + 2x encoder recurrence + sliding window eval (stride=64) + overtone init + Muon WD, 10L 512d. 15 effective layers from 10 physical blocks via encoder-only depth recurrence.", - "date": "2026-03-20T06:00:00Z", - "val_loss": 2.00170864, - "val_bpb": 1.18552460, - "pre_quant_val_loss": 2.0372, - "pre_quant_val_bpb": 1.2065, - "step_stop": 8004, - "wallclock_seconds": 599.979, - "eval_time_seconds": 105.017, - "bytes_total": 15753781, - "bytes_model_int8_zlib": 15700692, - "bytes_code": 53089 + "blurb": "GELU pre-enrichment + 2x encoder recurrence + int6 QAT + lzma + MLP 3x + SWA + sliding window eval (stride=64), 10L 512d seq2048. 15 effective layers from 10 physical blocks via encoder-only depth recurrence.", + "date": "2026-03-20T15:15:00Z", + "val_loss": 1.97704181, + "val_bpb": 1.17091552, + "pre_quant_val_loss": 1.9805, + "pre_quant_val_bpb": 1.1730, + "step_stop": 6423, + "wallclock_seconds": 600.034, + "eval_time_seconds": 231.603, + "bytes_total": 15567990, + "bytes_model_int6_lzma": 15510344, + "bytes_code": 57646 } diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log index bdf7907b1..dda487723 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log @@ -1,17 +1,18 @@ -W0320 05:59:38.326000 938 torch/distributed/run.py:803] -W0320 05:59:38.326000 938 torch/distributed/run.py:803] ***************************************** -W0320 05:59:38.326000 938 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. -W0320 05:59:38.326000 938 torch/distributed/run.py:803] ***************************************** -logs/3d960f9f-a8f8-4aad-95cf-9c64179f6c7b.txt +W0320 15:15:09.903000 689 torch/distributed/run.py:803] +W0320 15:15:09.903000 689 torch/distributed/run.py:803] ***************************************** +W0320 15:15:09.903000 689 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +W0320 15:15:09.903000 689 torch/distributed/run.py:803] ***************************************** +logs/ab1171b2-63ed-4963-8580-c8635931513a.txt val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model train_loader:dataset:fineweb10B_sp1024 train_shards:10 val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 -model_params:19421776 +model_params:24664656 +encoder_recurrence:ON world_size:8 grad_accum_steps:1 sdp_backends:cudnn=False flash=True mem_efficient=False math=False attention_mode:gqa num_heads:8 num_kv_heads:4 -tie_embeddings:True embed_lr:0.1 head_lr:0.0 matrix_lr:0.06 scalar_lr:0.04 -train_batch_tokens:524288 train_seq_len:1024 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 +tie_embeddings:True embed_lr:0.035 head_lr:0.0 matrix_lr:0.035 scalar_lr:0.025 +train_batch_tokens:524288 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 seed:1337 warmup_step:1/20 warmup_step:2/20 @@ -33,74 +34,65 @@ warmup_step:17/20 warmup_step:18/20 warmup_step:19/20 warmup_step:20/20 -step:0/20000 val_loss:6.9313 val_bpb:4.1051 train_time:0ms step_avg:0.02ms -step:1/20000 train_loss:6.9314 train_time:60ms step_avg:59.71ms -step:2/20000 train_loss:9.7327 train_time:131ms step_avg:65.57ms -step:3/20000 train_loss:9.6676 train_time:209ms step_avg:69.66ms -step:4/20000 train_loss:10.0966 train_time:287ms step_avg:71.70ms -step:5/20000 train_loss:9.0540 train_time:365ms step_avg:72.95ms -step:6/20000 train_loss:8.2739 train_time:443ms step_avg:73.80ms -step:7/20000 train_loss:6.8085 train_time:519ms step_avg:74.12ms -step:8/20000 train_loss:6.3637 train_time:596ms step_avg:74.52ms -step:9/20000 train_loss:5.8905 train_time:674ms step_avg:74.84ms -step:10/20000 train_loss:5.5385 train_time:751ms step_avg:75.06ms -step:200/20000 train_loss:2.7947 train_time:16880ms step_avg:84.40ms -step:400/20000 train_loss:2.3387 train_time:33694ms step_avg:84.23ms -step:600/20000 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val_bpb:1.22113183 -final_sliding_window val_loss:2.0017 val_bpb:1.1855 eval_time:105017ms -final_sliding_window_exact val_loss:2.00170864 val_bpb:1.18552460 +step:0/20000 val_loss:6.9314 val_bpb:4.1051 train_time:0ms step_avg:0.02ms +step:1/20000 train_loss:6.9314 train_time:73ms step_avg:73.31ms +step:2/20000 train_loss:6.9295 train_time:158ms step_avg:78.86ms +step:3/20000 train_loss:6.3120 train_time:251ms step_avg:83.60ms +step:4/20000 train_loss:6.6233 train_time:347ms step_avg:86.71ms +step:5/20000 train_loss:6.3365 train_time:440ms step_avg:87.95ms +step:6/20000 train_loss:6.0246 train_time:535ms step_avg:89.15ms +step:7/20000 train_loss:5.4888 train_time:630ms step_avg:89.96ms +step:8/20000 train_loss:5.3087 train_time:726ms step_avg:90.80ms +step:9/20000 train_loss:5.0899 train_time:826ms step_avg:91.81ms +step:10/20000 train_loss:4.9072 train_time:934ms step_avg:93.38ms +step:200/20000 train_loss:2.7525 train_time:18047ms step_avg:90.23ms +step:400/20000 train_loss:2.2634 train_time:36091ms step_avg:90.23ms +step:600/20000 train_loss:2.4781 train_time:56712ms step_avg:94.52ms +step:800/20000 train_loss:2.2380 train_time:76975ms step_avg:96.22ms +step:1000/20000 train_loss:2.3345 train_time:97179ms step_avg:97.18ms +step:1000/20000 val_loss:2.2920 val_bpb:1.3575 train_time:97210ms step_avg:97.21ms +step:1200/20000 train_loss:2.3638 train_time:117462ms step_avg:97.89ms +step:1400/20000 train_loss:2.4064 train_time:138047ms step_avg:98.60ms +step:1600/20000 train_loss:2.0756 train_time:157916ms step_avg:98.70ms +step:1800/20000 train_loss:2.1807 train_time:178455ms step_avg:99.14ms +step:2000/20000 train_loss:2.2001 train_time:199128ms step_avg:99.56ms +step:2000/20000 val_loss:2.2001 val_bpb:1.3030 train_time:199170ms step_avg:99.58ms +step:2200/20000 train_loss:2.3071 train_time:217203ms step_avg:98.73ms +step:2400/20000 train_loss:2.3187 train_time:235267ms step_avg:98.03ms +step:2600/20000 train_loss:2.1834 train_time:253343ms step_avg:97.44ms +step:2800/20000 train_loss:2.1361 train_time:271461ms step_avg:96.95ms +step:3000/20000 train_loss:3.1833 train_time:289539ms step_avg:96.51ms +step:3000/20000 val_loss:2.1657 val_bpb:1.2826 train_time:289568ms step_avg:96.52ms +step:3200/20000 train_loss:2.2437 train_time:307590ms step_avg:96.12ms +step:3400/20000 train_loss:2.0670 train_time:325634ms step_avg:95.77ms +step:3600/20000 train_loss:2.1901 train_time:343674ms step_avg:95.46ms +step:3800/20000 train_loss:2.1431 train_time:361734ms step_avg:95.19ms +step:4000/20000 train_loss:2.2545 train_time:379779ms step_avg:94.94ms +step:4000/20000 val_loss:2.1454 val_bpb:1.2706 train_time:379823ms step_avg:94.96ms +step:4200/20000 train_loss:2.2027 train_time:398141ms step_avg:94.80ms +step:4400/20000 train_loss:2.1344 train_time:416531ms step_avg:94.67ms +step:4600/20000 train_loss:2.1774 train_time:434692ms step_avg:94.50ms +step:4800/20000 train_loss:2.0967 train_time:452827ms step_avg:94.34ms +step:5000/20000 train_loss:2.1739 train_time:470971ms step_avg:94.19ms +step:5000/20000 val_loss:2.0970 val_bpb:1.2420 train_time:470997ms step_avg:94.20ms +step:5200/20000 train_loss:2.2172 train_time:489137ms step_avg:94.06ms +step:5400/20000 train_loss:2.1638 train_time:507257ms step_avg:93.94ms +step:5600/20000 train_loss:2.0482 train_time:525386ms step_avg:93.82ms +step:5800/20000 train_loss:2.0885 train_time:543545ms step_avg:93.71ms +step:6000/20000 train_loss:1.9862 train_time:561681ms step_avg:93.61ms +step:6000/20000 val_loss:2.0151 val_bpb:1.1935 train_time:561709ms step_avg:93.62ms +step:6200/20000 train_loss:1.9534 train_time:579806ms step_avg:93.52ms +step:6400/20000 train_loss:1.7174 train_time:597947ms step_avg:93.43ms +step:6423/20000 val_loss:1.9805 val_bpb:1.1730 train_time:600034ms step_avg:93.42ms +stopping_early: wallclock_cap train_time:600034ms step:6423/20000 +peak memory allocated: 18506 MiB reserved: 18888 MiB +swa: averaging 11 checkpoints +Serialized model: 97648359 bytes +Code size: 57646 bytes +Total submission size: 97706005 bytes +Serialized model int6+lzma: 15510344 bytes (payload:25332032 raw_torch:25383027 payload_ratio:3.85x) +Total submission size int6+lzma: 15567990 bytes +final_int8_zlib_roundtrip val_loss:2.0142 val_bpb:1.1929 eval_time:2939ms +final_int8_zlib_roundtrip_exact val_loss:2.01421533 val_bpb:1.19293177 +final_sliding_window val_loss:1.9770 val_bpb:1.1709 eval_time:231603ms +final_sliding_window_exact val_loss:1.97704181 val_bpb:1.17091552 diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py index 6dc4d1826..698208d87 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py @@ -16,6 +16,7 @@ import sys import time import uuid +import lzma import zlib from pathlib import Path @@ -36,8 +37,9 @@ # - vocab size 1024, sequence length 1024, tied embeddings # - 524,288 train tokens per step for 20,000 iterations with a ~10 minute cap +_RUN_CONFIG = os.environ.get("RUN_CONFIG", "A") + class Hyperparameters: - # Data paths are shard globs produced by the existing preprocessing pipeline. data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") train_files = os.path.join(data_path, "fineweb_train_*.bin") val_files = os.path.join(data_path, "fineweb_val_*.bin") @@ -45,47 +47,46 @@ class Hyperparameters: run_id = os.environ.get("RUN_ID", str(uuid.uuid4())) seed = int(os.environ.get("SEED", 1337)) - # Validation cadence and batch size. Validation always uses the full fineweb_val split. val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 1000)) train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 200)) - # Training length. iterations = int(os.environ.get("ITERATIONS", 20000)) - warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 2500)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 2100 if _RUN_CONFIG == "A" else 2600)) warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 524_288)) - train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 1024)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048 if _RUN_CONFIG == "A" else 1024)) max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) - # Model shape. vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) num_layers = int(os.environ.get("NUM_LAYERS", 10)) num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) model_dim = int(os.environ.get("MODEL_DIM", 512)) num_heads = int(os.environ.get("NUM_HEADS", 8)) - mlp_mult = int(os.environ.get("MLP_MULT", 2)) + mlp_mult = int(os.environ.get("MLP_MULT", 3)) tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) - # Optimizer hyperparameters. embed_lr = float(os.environ.get("EMBED_LR", 0.6)) head_lr = float(os.environ.get("HEAD_LR", 0.008)) - tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.1)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.035)) tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) - matrix_lr = float(os.environ.get("MATRIX_LR", 0.06)) - scalar_lr = float(os.environ.get("SCALAR_LR", 0.04)) - muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.95)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.035 if _RUN_CONFIG == "A" else 0.025)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.025)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) - muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.85)) - muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 500)) + muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92)) + muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500)) beta1 = float(os.environ.get("BETA1", 0.9)) beta2 = float(os.environ.get("BETA2", 0.95)) adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.0)) + muon_wd = float(os.environ.get("MUON_WD", 0.04)) + adam_wd = float(os.environ.get("ADAM_WD", 0.04)) + swa_every = int(os.environ.get("SWA_EVERY", 200)) # ----------------------------- # MUON OPTIMIZER @@ -410,6 +411,70 @@ def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() return q, scale +def quantize_float_tensor_int6(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + clip_abs = ( + torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) + if t32.numel() + else torch.empty((t32.shape[0],), dtype=torch.float32) + ) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + scale = (clip_abs / 31.0).clamp_min(1.0 / 31.0) + q = torch.clamp(torch.round(clipped / scale[:, None]), -31, 31).to(torch.int8).contiguous() + return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 31.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -31, 31).to(torch.int8).contiguous() + return q, scale + +def quantize_state_dict_int6(state_dict: dict[str, Tensor]): + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL or "tok_emb.weight" in name: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor_int6(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + obj: dict[str, object] = { + "__quant_format__": "int6_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats + def quantize_state_dict_int8(state_dict: dict[str, Tensor]): # Single supported clean-script export format: # - per-row int8 for 2D float tensors @@ -577,11 +642,34 @@ def forward(self, x: Tensor) -> Tensor: return F.rms_norm(x, (x.size(-1),), eps=self.eps) +class _FakeQuantInt6(torch.autograd.Function): + @staticmethod + def forward(ctx, w: Tensor) -> Tensor: + if w.ndim != 2: + return w + row_max = w.abs().amax(dim=1, keepdim=True).clamp_min(1e-12) + scale = row_max / 31.0 + q = (w / scale).round().clamp(-31, 31) + return q * scale + + @staticmethod + def backward(ctx, grad: Tensor) -> Tensor: + return grad + +def fake_quant_int6(w: Tensor) -> Tensor: + return _FakeQuantInt6.apply(w) + class CastedLinear(nn.Linear): - # Keep weights in fp32 for optimizer/state quality, cast at matmul time for bf16 compute. + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.use_qat = False + def forward(self, x: Tensor) -> Tensor: + w = self.weight + if self.use_qat and self.training: + w = fake_quant_int6(w) bias = self.bias.to(x.dtype) if self.bias is not None else None - return F.linear(x, self.weight.to(x.dtype), bias) + return F.linear(x, w.to(x.dtype), bias) def restore_low_dim_params_to_fp32(module: nn.Module) -> None: @@ -949,6 +1037,9 @@ def log0(msg: str, console: bool = True) -> None: if isinstance(module, CastedLinear): module.float() restore_low_dim_params_to_fp32(base_model) + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.use_qat = True compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) model: nn.Module = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False) if distributed else compiled_model @@ -972,10 +1063,11 @@ def log0(msg: str, console: bool = True) -> None: if base_model.skip_weights.numel() > 0: scalar_params.append(base_model.skip_weights) token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr - optimizer_tok = torch.optim.Adam( + optimizer_tok = torch.optim.AdamW( [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}], betas=(args.beta1, args.beta2), eps=args.adam_eps, + weight_decay=args.adam_wd, fused=True, ) optimizer_muon = Muon( @@ -986,10 +1078,11 @@ def log0(msg: str, console: bool = True) -> None: ) for group in optimizer_muon.param_groups: group["base_lr"] = args.matrix_lr - optimizer_scalar = torch.optim.Adam( + optimizer_scalar = torch.optim.AdamW( [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], betas=(args.beta1, args.beta2), eps=args.adam_eps, + weight_decay=args.adam_wd, fused=True, ) optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] @@ -1004,6 +1097,7 @@ def log0(msg: str, console: bool = True) -> None: n_params = sum(p.numel() for p in base_model.parameters()) log0(f"model_params:{n_params}") + log0(f"encoder_recurrence:{'ON' if base_model.encoder_recurrence else 'OFF'}") log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") log0("sdp_backends:cudnn=False flash=True mem_efficient=False math=False") log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}") @@ -1076,6 +1170,7 @@ def lr_mul(step: int, elapsed_ms: float) -> float: training_time_ms = 0.0 stop_after_step: int | None = None + swa_checkpoints: list[dict[str, Tensor]] = [] torch.cuda.synchronize() t0 = time.perf_counter() @@ -1144,10 +1239,12 @@ def lr_mul(step: int, elapsed_ms: float) -> float: with torch.no_grad(): muon_lr = optimizer_muon.param_groups[0]["lr"] for p in matrix_params: - p.mul_(1.0 - 0.02 * muon_lr) + p.mul_(1.0 - args.muon_wd * muon_lr) zero_grad_all() step += 1 + if scale < 1.0 and args.swa_every > 0 and step % args.swa_every == 0: + swa_checkpoints.append({k: v.detach().cpu().clone() for k, v in base_model.state_dict().items()}) approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) should_log_train = ( args.train_log_every > 0 @@ -1179,6 +1276,18 @@ def lr_mul(step: int, elapsed_ms: float) -> float: # Save the raw state (useful for debugging/loading in PyTorch directly), then always produce # the compressed int8+zlib artifact and validate the round-tripped weights. + if swa_checkpoints: + log0(f"swa: averaging {len(swa_checkpoints)} checkpoints") + avg_state = {} + for key in swa_checkpoints[0]: + avg_state[key] = torch.stack([ckpt[key].float() for ckpt in swa_checkpoints]).mean(dim=0) + base_model.load_state_dict(avg_state, strict=True) + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.float() + restore_low_dim_params_to_fp32(base_model) + del swa_checkpoints + if master_process: torch.save(base_model.state_dict(), "final_model.pt") model_bytes = os.path.getsize("final_model.pt") @@ -1187,29 +1296,29 @@ def lr_mul(step: int, elapsed_ms: float) -> float: log0(f"Code size: {code_bytes} bytes") log0(f"Total submission size: {model_bytes + code_bytes} bytes") - quant_obj, quant_stats = quantize_state_dict_int8(base_model.state_dict()) + quant_obj, quant_stats = quantize_state_dict_int6(base_model.state_dict()) quant_buf = io.BytesIO() torch.save(quant_obj, quant_buf) quant_raw = quant_buf.getvalue() - quant_blob = zlib.compress(quant_raw, level=9) + quant_blob = lzma.compress(quant_raw, preset=6) quant_raw_bytes = len(quant_raw) if master_process: - with open("final_model.int8.ptz", "wb") as f: + with open("final_model.int6.ptz", "wb") as f: f.write(quant_blob) - quant_file_bytes = os.path.getsize("final_model.int8.ptz") + quant_file_bytes = os.path.getsize("final_model.int6.ptz") code_bytes = len(code.encode("utf-8")) ratio = quant_stats["baseline_tensor_bytes"] / max(quant_stats["int8_payload_bytes"], 1) log0( - f"Serialized model int8+zlib: {quant_file_bytes} bytes " + f"Serialized model int6+lzma: {quant_file_bytes} bytes " f"(payload:{quant_stats['int8_payload_bytes']} raw_torch:{quant_raw_bytes} payload_ratio:{ratio:.2f}x)" ) - log0(f"Total submission size int8+zlib: {quant_file_bytes + code_bytes} bytes") + log0(f"Total submission size int6+lzma: {quant_file_bytes + code_bytes} bytes") if distributed: dist.barrier() - with open("final_model.int8.ptz", "rb") as f: + with open("final_model.int6.ptz", "rb") as f: quant_blob_disk = f.read() - quant_state = torch.load(io.BytesIO(zlib.decompress(quant_blob_disk)), map_location="cpu") + quant_state = torch.load(io.BytesIO(lzma.decompress(quant_blob_disk)), map_location="cpu") base_model.load_state_dict(dequantize_state_dict_int8(quant_state), strict=True) torch.cuda.synchronize() t_qeval = time.perf_counter() From 4162e89cf46b00987f7090006b4be5819f996955 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Fri, 20 Mar 2026 13:32:33 -0300 Subject: [PATCH 21/72] feat: phase-transition resid_mix + Late-K passthrough + grad clip + 12L config Phase-transition sigmoid init for resid_mix (from rank 1). Late-K: last 2 layers c_k.weight kept fp16 during quantization. GRAD_CLIP_NORM=1.0 default. RUN_CONFIG=C: 12L MLP 2x (18 effective layers with recurrence). --- train_gpt.py | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 698208d87..e298cbcc3 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -60,11 +60,11 @@ class Hyperparameters: qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) - num_layers = int(os.environ.get("NUM_LAYERS", 10)) + num_layers = int(os.environ.get("NUM_LAYERS", 12 if _RUN_CONFIG == "C" else 10)) num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) model_dim = int(os.environ.get("MODEL_DIM", 512)) num_heads = int(os.environ.get("NUM_HEADS", 8)) - mlp_mult = int(os.environ.get("MLP_MULT", 3)) + mlp_mult = int(os.environ.get("MLP_MULT", 2 if _RUN_CONFIG == "C" else 3)) tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) @@ -83,7 +83,7 @@ class Hyperparameters: beta1 = float(os.environ.get("BETA1", 0.9)) beta2 = float(os.environ.get("BETA2", 0.95)) adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) - grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.0)) + grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 1.0)) muon_wd = float(os.environ.get("MUON_WD", 0.04)) adam_wd = float(os.environ.get("ADAM_WD", 0.04)) swa_every = int(os.environ.get("SWA_EVERY", 200)) @@ -429,6 +429,8 @@ def quantize_float_tensor_int6(t: Tensor) -> tuple[Tensor, Tensor]: return q, scale def quantize_state_dict_int6(state_dict: dict[str, Tensor]): + all_ck = sorted([n for n in state_dict if "attn.c_k.weight" in n]) + late_k_names = set(all_ck[-2:]) if len(all_ck) >= 2 else set() quantized: dict[str, Tensor] = {} scales: dict[str, Tensor] = {} dtypes: dict[str, str] = {} @@ -449,7 +451,7 @@ def quantize_state_dict_int6(state_dict: dict[str, Tensor]): passthrough[name] = t stats["int8_payload_bytes"] += tensor_nbytes(t) continue - if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL or "tok_emb.weight" in name: + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL or "tok_emb.weight" in name or name in late_k_names: kept = keep_float_tensor(name, t, passthrough_orig_dtypes) passthrough[name] = kept stats["int8_payload_bytes"] += tensor_nbytes(kept) @@ -865,6 +867,12 @@ def _init_weights(self) -> None: for module in self.modules(): if isinstance(module, nn.Linear) and getattr(module, "_zero_init", False): nn.init.zeros_(module.weight) + num_layers = len(self.blocks) + for i, block in enumerate(self.blocks): + with torch.no_grad(): + phase = torch.sigmoid(torch.tensor(3.0 * (i / max(num_layers - 1, 1) - 0.5))) + block.resid_mix.data[0] = phase * torch.ones(block.resid_mix.shape[1]) + block.resid_mix.data[1] = (1 - phase) * torch.ones(block.resid_mix.shape[1]) def _run_blocks(self, x: Tensor, x0: Tensor) -> Tensor: if self.encoder_recurrence: From ba49938e591e7ee2be09d4ac593637fdd99881c5 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Fri, 20 Mar 2026 23:49:26 -0300 Subject: [PATCH 22/72] revert phase-transition/Late-K/grad-clip, prep batch size test --- train_gpt.py | 12 ++---------- 1 file changed, 2 insertions(+), 10 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index e298cbcc3..da9fc26be 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -83,7 +83,7 @@ class Hyperparameters: beta1 = float(os.environ.get("BETA1", 0.9)) beta2 = float(os.environ.get("BETA2", 0.95)) adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) - grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 1.0)) + grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.0)) muon_wd = float(os.environ.get("MUON_WD", 0.04)) adam_wd = float(os.environ.get("ADAM_WD", 0.04)) swa_every = int(os.environ.get("SWA_EVERY", 200)) @@ -429,8 +429,6 @@ def quantize_float_tensor_int6(t: Tensor) -> tuple[Tensor, Tensor]: return q, scale def quantize_state_dict_int6(state_dict: dict[str, Tensor]): - all_ck = sorted([n for n in state_dict if "attn.c_k.weight" in n]) - late_k_names = set(all_ck[-2:]) if len(all_ck) >= 2 else set() quantized: dict[str, Tensor] = {} scales: dict[str, Tensor] = {} dtypes: dict[str, str] = {} @@ -451,7 +449,7 @@ def quantize_state_dict_int6(state_dict: dict[str, Tensor]): passthrough[name] = t stats["int8_payload_bytes"] += tensor_nbytes(t) continue - if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL or "tok_emb.weight" in name or name in late_k_names: + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL or "tok_emb.weight" in name: kept = keep_float_tensor(name, t, passthrough_orig_dtypes) passthrough[name] = kept stats["int8_payload_bytes"] += tensor_nbytes(kept) @@ -867,12 +865,6 @@ def _init_weights(self) -> None: for module in self.modules(): if isinstance(module, nn.Linear) and getattr(module, "_zero_init", False): nn.init.zeros_(module.weight) - num_layers = len(self.blocks) - for i, block in enumerate(self.blocks): - with torch.no_grad(): - phase = torch.sigmoid(torch.tensor(3.0 * (i / max(num_layers - 1, 1) - 0.5))) - block.resid_mix.data[0] = phase * torch.ones(block.resid_mix.shape[1]) - block.resid_mix.data[1] = (1 - phase) * torch.ones(block.resid_mix.shape[1]) def _run_blocks(self, x: Tensor, x0: Tensor) -> Tensor: if self.encoder_recurrence: From 9f5dea8d2505b0d453458de77a5109c2f7fdb04d Mon Sep 17 00:00:00 2001 From: idan3011 Date: Sat, 21 Mar 2026 01:11:19 -0300 Subject: [PATCH 23/72] feat: EMA replaces SWA + wider pre-enrichment 512-768-512 --- train_gpt.py | 32 +++++++++++++++----------------- 1 file changed, 15 insertions(+), 17 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index da9fc26be..55176fb04 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -86,7 +86,7 @@ class Hyperparameters: grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.0)) muon_wd = float(os.environ.get("MUON_WD", 0.04)) adam_wd = float(os.environ.get("ADAM_WD", 0.04)) - swa_every = int(os.environ.get("SWA_EVERY", 200)) + ema_decay = float(os.environ.get("EMA_DECAY", 0.997)) # ----------------------------- # MUON OPTIMIZER @@ -827,10 +827,11 @@ def __init__( self.logit_softcap = logit_softcap self.encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) self.tok_emb = nn.Embedding(vocab_size, model_dim) + pre_enrich_hidden = model_dim * 3 // 2 self.pre_enrich = nn.Sequential( - CastedLinear(model_dim, model_dim, bias=False), + CastedLinear(model_dim, pre_enrich_hidden, bias=False), nn.GELU(), - CastedLinear(model_dim, model_dim, bias=False), + CastedLinear(pre_enrich_hidden, model_dim, bias=False), ) self.num_encoder_layers = num_layers // 2 self.num_decoder_layers = num_layers - self.num_encoder_layers @@ -1170,7 +1171,7 @@ def lr_mul(step: int, elapsed_ms: float) -> float: training_time_ms = 0.0 stop_after_step: int | None = None - swa_checkpoints: list[dict[str, Tensor]] = [] + ema_state = {k: v.detach().cpu().clone().float() for k, v in base_model.state_dict().items()} torch.cuda.synchronize() t0 = time.perf_counter() @@ -1243,8 +1244,9 @@ def lr_mul(step: int, elapsed_ms: float) -> float: zero_grad_all() step += 1 - if scale < 1.0 and args.swa_every > 0 and step % args.swa_every == 0: - swa_checkpoints.append({k: v.detach().cpu().clone() for k, v in base_model.state_dict().items()}) + with torch.no_grad(): + for k, v in base_model.state_dict().items(): + ema_state[k].mul_(args.ema_decay).add_(v.detach().cpu().float(), alpha=1.0 - args.ema_decay) approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) should_log_train = ( args.train_log_every > 0 @@ -1276,17 +1278,13 @@ def lr_mul(step: int, elapsed_ms: float) -> float: # Save the raw state (useful for debugging/loading in PyTorch directly), then always produce # the compressed int8+zlib artifact and validate the round-tripped weights. - if swa_checkpoints: - log0(f"swa: averaging {len(swa_checkpoints)} checkpoints") - avg_state = {} - for key in swa_checkpoints[0]: - avg_state[key] = torch.stack([ckpt[key].float() for ckpt in swa_checkpoints]).mean(dim=0) - base_model.load_state_dict(avg_state, strict=True) - for module in base_model.modules(): - if isinstance(module, CastedLinear): - module.float() - restore_low_dim_params_to_fp32(base_model) - del swa_checkpoints + log0("ema: loading exponential moving average weights") + base_model.load_state_dict(ema_state, strict=True) + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.float() + restore_low_dim_params_to_fp32(base_model) + del ema_state if master_process: torch.save(base_model.state_dict(), "final_model.pt") From 967b7b4793a8290d649328af9d346cb8e7e28442 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Sat, 21 Mar 2026 02:11:00 -0300 Subject: [PATCH 24/72] feat: SmearGate + BigramHash + EMA + wider pre-enrichment --- train_gpt.py | 35 +++++++++++++++++++++++++++++++++-- 1 file changed, 33 insertions(+), 2 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 55176fb04..a9dd6a20b 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -804,6 +804,31 @@ def forward(self, x: Tensor, x0: Tensor) -> Tensor: return x +class SmearGate(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.gate = nn.Parameter(torch.full((dim,), 3.0, dtype=torch.float32)) + + def forward(self, x: Tensor) -> Tensor: + g = torch.sigmoid(self.gate).to(dtype=x.dtype) + x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + return g * x + (1.0 - g) * x_prev + + +class BigramHash(nn.Module): + def __init__(self, num_buckets: int, hash_dim: int, model_dim: int): + super().__init__() + self.num_buckets = num_buckets + self.table = nn.Embedding(num_buckets, hash_dim) + self.proj = CastedLinear(hash_dim, model_dim, bias=False) + nn.init.normal_(self.table.weight, std=0.01) + + def forward(self, input_ids: Tensor) -> Tensor: + prev_ids = torch.cat([torch.zeros_like(input_ids[:, :1]), input_ids[:, :-1]], dim=1) + h = ((prev_ids.long() * 92821 + input_ids.long()) % self.num_buckets).long() + return self.proj(self.table(h)) + + class GPT(nn.Module): def __init__( self, @@ -827,6 +852,8 @@ def __init__( self.logit_softcap = logit_softcap self.encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram_hash = BigramHash(4096, 64, model_dim) + self.smear_gate = SmearGate(model_dim) pre_enrich_hidden = model_dim * 3 // 2 self.pre_enrich = nn.Sequential( CastedLinear(model_dim, pre_enrich_hidden, bias=False), @@ -902,7 +929,8 @@ def _compute_logits(self, x: Tensor) -> Tensor: return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: - x = self.tok_emb(input_ids) + x = self.tok_emb(input_ids) + self.bigram_hash(input_ids) + x = self.smear_gate(x) x = self.pre_enrich(x) x = F.rms_norm(x, (x.size(-1),)) x0 = x @@ -913,7 +941,8 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: return F.cross_entropy(logits.float(), targets, reduction="mean") def forward_logits(self, input_ids: Tensor) -> Tensor: - x = self.tok_emb(input_ids) + x = self.tok_emb(input_ids) + self.bigram_hash(input_ids) + x = self.smear_gate(x) x = self.pre_enrich(x) x = F.rms_norm(x, (x.size(-1),)) x0 = x @@ -1056,6 +1085,7 @@ def log0(msg: str, console: bool = True) -> None: if p.ndim == 2 and not any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) ] matrix_params.extend(p for p in base_model.pre_enrich.parameters() if p.ndim == 2) + matrix_params.extend(p for p in base_model.bigram_hash.parameters() if p.ndim == 2) scalar_params = [ p for name, p in block_named_params @@ -1063,6 +1093,7 @@ def log0(msg: str, console: bool = True) -> None: ] if base_model.skip_weights.numel() > 0: scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear_gate.gate) token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr optimizer_tok = torch.optim.AdamW( [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}], From 6fb6486d8219bb787d2bf2d3266d8a4da9ef679d Mon Sep 17 00:00:00 2001 From: idan3011 Date: Sat, 21 Mar 2026 02:46:48 -0300 Subject: [PATCH 25/72] Record: val_bpb=1.1668 with SmearGate + BigramHash + EMA --- .../README.md | 96 ++++----- .../submission.json | 26 +-- .../train.log | 145 ++++++------- .../train_gpt.py | 202 +++++++++++++++--- 4 files changed, 296 insertions(+), 173 deletions(-) diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md index b2f33c139..703f1b359 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md @@ -1,100 +1,100 @@ -## Pre-Enrichment + Encoder Recurrence +## Pre-Enrichment + Encoder Recurrence + SmearGate + BigramHash -Two architectural modifications to the baseline transformer: (1) a GELU pre-enrichment block that transforms raw embeddings before they enter the residual stream, and (2) 2x encoder recurrence that runs the encoder blocks twice with RMS norm stabilization between passes. Combined with sliding window evaluation (stride=64), overtone embedding initialization, and decoupled Muon weight decay, this achieves **val_bpb 1.1855** in a 15.75MB artifact trained in 10 minutes on 8xH100. +Architectural modifications to the baseline transformer achieving **val_bpb 1.1668** in a 15.02MB artifact trained in 10 minutes on 8xH100. Key techniques: GELU pre-enrichment (512→768→512), 2x encoder recurrence with RMS norm stabilization, SmearGate for lightweight bigram context, BigramHash for explicit bigram embeddings, and EMA weight averaging for quantization-friendly weights. --- ### Key Contributions -#### GELU Pre-Enrichment +#### GELU Pre-Enrichment (512→768→512) -Raw token embeddings are a poor starting point for the residual stream. A 1024-token vocabulary maps each token to a 512-dimensional vector initialized from a normal distribution — these vectors carry no relational structure and every transformer layer must compensate for this weak initialization. - -I add two `CastedLinear(512→512)` projections with a GELU activation between them, applied after the embedding lookup and before the first transformer block: +Two `CastedLinear` projections with a GELU activation between them, applied after the embedding lookup and before the first transformer block. The wider hidden dimension (768 vs baseline 512) gives the model a richer nonlinear transformation before the residual stream begins. ``` -embedding → Linear(512→512) → GELU → Linear(512→512) → RMS Norm → transformer blocks +embedding → BigramHash add → SmearGate → Linear(512→768) → GELU → Linear(768→512) → RMS Norm → transformer blocks ``` -This gives the model a learned nonlinear transformation to produce richer representations before the residual stream begins. Cost: 0.5M extra parameters (~3% of total), negligible step time overhead. - #### 2x Encoder Recurrence -Depth recurrence is a known technique (ALBERT, Universal Transformers). My contribution is applying it to only the encoder half of a U-Net transformer architecture, with RMS norm stabilization between passes, and providing A/B data showing it beats additional training steps. +I reuse the encoder blocks for a second pass before running the decoder, with RMS norm stabilization between passes. With 10 layers (5 encoder + 5 decoder), this produces **15 effective layers from 10 physical blocks** with zero extra parameters. -The baseline uses a U-Net architecture with encoder and decoder halves connected by skip connections. I reuse the encoder blocks for a second pass before running the decoder. +**A/B Comparison — MLP 3x, seq 2048, int6 QAT (8xH100, 10 minutes):** -With 10 layers (5 encoder + 5 decoder), the forward pass becomes: -1. Run encoder blocks 0-4 (first pass, build initial features) -2. RMS norm (stabilize between passes) -3. Run encoder blocks 0-4 again (second pass, refine features) -4. Run decoder blocks 5-9 with skip connections from the refined second encoder pass +| Metric | With recurrence | Without recurrence | +|---------------------|--------------------|-----------------------| +| Steps completed | 6,423 | 8,950 | +| Step time | 93ms | 67ms | +| Sliding window BPB | **1.1709** | 1.1740 | -This produces **15 effective layers from 10 physical blocks** with zero extra parameters. The only cost is step time: ~75ms vs ~50ms without recurrence (~50% overhead from running 5 extra blocks). +Encoder recurrence consistently wins — deeper processing per step beats more gradient updates. -The critical question: does the architectural depth advantage justify 50% fewer training steps? +#### SmearGate -**A/B Comparison (8xH100, 10 minutes, identical config except recurrence):** +Learned per-dimension gate (512 params) that blends each token's embedding with the previous token's embedding. Provides lightweight bigram context at the embedding layer. Initialized with gate bias 3.0 (sigmoid(3.0)≈0.95, near-identity at init). -| Metric | With recurrence | Without recurrence | -|---------------------|--------------------|-----------------------| -| Steps completed | 8,004 | 11,955 | -| Step time | 75ms | 50ms | -| Standard BPB | 1.2211 | 1.2299 | -| Sliding window BPB | **1.1855** | 1.1947 | -| Submission size | 15.75MB | 15.82MB | +#### BigramHash -50% more training steps could not overcome the depth advantage of encoder recurrence. At step 8000 (where the recurrence run stopped), the pre-quant val_bpb was 1.2065 vs 1.3020 for the no-recurrence run — a 0.0955 gap that the extra 4,000 steps narrowed but never closed. +Hash-table embedding mapping token bigrams to learned vectors. Hash formula: `(prev_token * 92821 + curr_token) % 4096`. Lookup table 4096×64, projected to model_dim via Linear(64, 512). Adds explicit bigram context to the token embedding. -I find encoder recurrence to be a parameter-efficient alternative to adding physical layers: it doubles the effective encoder depth with zero parameters and predictable step time overhead. +#### EMA Weight Averaging + +Exponential moving average (decay=0.997) updated every step, replacing SWA. EMA weights are loaded before quantization. Produces smoother weights that quantize significantly better — quant gap dropped from 0.020 (SWA) to **0.004** (EMA). --- ### Additional Techniques -Overtone embedding init, decoupled Muon weight decay (0.02), batched sliding window eval (stride=64), 10 layers, MATRIX_LR=0.06, TIED_EMBED_LR=0.1, WARMDOWN_ITERS=2500. +Int6 quantization-aware training (fake quant with STE in CastedLinear), lzma compression, MLP 3x expansion, overtone embedding init, decoupled Muon weight decay (0.04), AdamW weight decay (0.04), batched sliding window eval (stride=64), fp16 embedding passthrough in quantization. + +Hyperparameters: NUM_LAYERS=10, TRAIN_SEQ_LEN=2048, TRAIN_BATCH_TOKENS=393216, MATRIX_LR=0.028, SCALAR_LR=0.025, TIED_EMBED_LR=0.035, MUON_MOMENTUM=0.99, WARMDOWN_ITERS=3300. --- ### What Didn't Work -- **FP16 embedding passthrough**: Keeping the tied embedding in fp16 instead of int8 reduced quantization error by ~0.006 BPB (the tied embedding is used twice — input and output — so int8 errors compound). However, the extra ~520KB pushed the artifact over the 16MB cap. I had to revert to int8. +- **Phase-transition resid_mix init**: Sigmoid-scheduled initialization of resid_mix. Slowed convergence at our step count, hurt final score. + +- **Late-K passthrough**: Keeping last 2 layers' c_k.weight in fp16 during quantization. Added artifact size without enough BPB improvement. -- **3x encoder recurrence**: The tripled computation graph exceeded Triton's per-SM shared memory limit on both A100 (168,096 > 166,912 bytes) and RTX 4050. A compiler limitation, not an architectural one. +- **Gradient clipping (GRAD_CLIP_NORM=1.0)**: Constrained the optimizer, slower per-step learning. -- **Warmdown scheduler on A100**: The wallclock-aware warmdown schedule (`WARMDOWN_ITERS=1200`) estimates remaining time as `warmdown_iters × avg_step_time`. On A100 (~1100ms/step), this exceeds the total 600-second budget from step 0, causing the learning rate to decay throughout the entire run. Not relevant to 8xH100 submissions but was a significant debugging finding. +- **12 layers + MLP 2x**: 18 effective layers with recurrence but MLP 2x bottleneck was too narrow. 10L MLP 3x wins. -- Also tried: full U-Net recurrence (too slow), reverse encoder pass order (worse), auxiliary encoder prediction loss (hurt performance). +- **Full dataset (80 shards) with WD=0.04**: More diverse data didn't improve pre-quant BPB. Only helped quant gap when combined with higher WD. + +- **3x encoder recurrence**: Exceeded Triton's per-SM shared memory limit. Compiler limitation. + +- Also tried: full U-Net recurrence (too slow), reverse encoder pass order (worse), auxiliary encoder prediction loss (hurt performance), 6+3 encoder/decoder split (worse than 5+5). --- ### Configuration ``` -VOCAB_SIZE=1024 NUM_LAYERS=10 MODEL_DIM=512 NUM_HEADS=8 NUM_KV_HEADS=4 MLP_MULT=2 -TIE_EMBEDDINGS=1 TIED_EMBED_LR=0.1 MATRIX_LR=0.06 SCALAR_LR=0.04 -WARMDOWN_ITERS=2500 WARMUP_STEPS=20 TRAIN_BATCH_TOKENS=524288 TRAIN_SEQ_LEN=1024 -ENCODER_RECURRENCE=1 +RUN_CONFIG=A +VOCAB_SIZE=1024 NUM_LAYERS=10 MODEL_DIM=512 NUM_HEADS=8 NUM_KV_HEADS=4 MLP_MULT=3 +TIE_EMBEDDINGS=1 TIED_EMBED_LR=0.035 MATRIX_LR=0.028 SCALAR_LR=0.025 +MUON_MOMENTUM=0.99 MUON_MOMENTUM_WARMUP_START=0.92 MUON_MOMENTUM_WARMUP_STEPS=1500 +WARMDOWN_ITERS=3300 WARMUP_STEPS=20 TRAIN_BATCH_TOKENS=393216 TRAIN_SEQ_LEN=2048 +ENCODER_RECURRENCE=1 MUON_WD=0.04 ADAM_WD=0.04 EMA_DECAY=0.997 ``` -Model parameters: 19,421,776 -Submission size (int8+zlib): 15,753,781 bytes (code: 53,089 bytes) - ### Reproduction All defaults are baked into the script: ```bash -torchrun --standalone --nproc_per_node=8 train_gpt.py +RUN_CONFIG=A torchrun --standalone --nproc_per_node=8 train_gpt.py ``` ### Key Metrics | Metric | Value | |---|---| -| Pre-quant val_bpb | 1.2065 | -| Post-quant val_bpb (standard) | 1.2211 | -| Post-quant val_bpb (sliding window) | **1.1855** | -| Training time | 599,979ms (8,004 steps at ~75ms) | -| Peak memory | 16,592 MiB | -| Submission size (int8+zlib) | 15,753,781 bytes | -| Model parameters | 19,421,776 | +| Pre-quant val_bpb | 1.1848 | +| Post-quant val_bpb (standard) | 1.1889 | +| Post-quant val_bpb (sliding window) | **1.1668** | +| Quant gap (standard - pre-quant) | 0.004 | +| Training time | 600,011ms (5,373 steps at ~112ms) | +| Peak memory | 14,124 MiB | +| Submission size (int6+lzma) | 15,022,232 bytes | +| Model parameters | 25,222,224 | diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json index 5abb44808..2e7d8cf9f 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json @@ -1,17 +1,17 @@ { "author": "Idanr", "github_id": "idan3011", - "name": "Pre-Enrichment + Encoder Recurrence", - "blurb": "GELU pre-enrichment + 2x encoder recurrence + sliding window eval (stride=64) + overtone init + Muon WD, 10L 512d. 15 effective layers from 10 physical blocks via encoder-only depth recurrence.", - "date": "2026-03-20T06:00:00Z", - "val_loss": 2.00170864, - "val_bpb": 1.18552460, - "pre_quant_val_loss": 2.0372, - "pre_quant_val_bpb": 1.2065, - "step_stop": 8004, - "wallclock_seconds": 599.979, - "eval_time_seconds": 105.017, - "bytes_total": 15753781, - "bytes_model_int8_zlib": 15700692, - "bytes_code": 53089 + "name": "Pre-Enrichment + Encoder Recurrence + SmearGate + BigramHash", + "blurb": "GELU pre-enrichment (512-768-512) + 2x encoder recurrence + SmearGate + BigramHash + EMA + int6 QAT + lzma + MLP 3x + sliding window eval (stride=64), 10L 512d seq2048.", + "date": "2026-03-21T05:23:00Z", + "val_loss": 1.97015808, + "val_bpb": 1.16683859, + "pre_quant_val_loss": 2.0005, + "pre_quant_val_bpb": 1.1848, + "step_stop": 5373, + "wallclock_seconds": 600.011, + "eval_time_seconds": 233.562, + "bytes_total": 15022232, + "bytes_model_int6_lzma": 14963256, + "bytes_code": 58976 } diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log index bdf7907b1..51623c57b 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log @@ -1,17 +1,18 @@ -W0320 05:59:38.326000 938 torch/distributed/run.py:803] -W0320 05:59:38.326000 938 torch/distributed/run.py:803] ***************************************** -W0320 05:59:38.326000 938 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. -W0320 05:59:38.326000 938 torch/distributed/run.py:803] ***************************************** -logs/3d960f9f-a8f8-4aad-95cf-9c64179f6c7b.txt +W0321 05:23:38.712000 1529 torch/distributed/run.py:803] +W0321 05:23:38.712000 1529 torch/distributed/run.py:803] ***************************************** +W0321 05:23:38.712000 1529 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +W0321 05:23:38.712000 1529 torch/distributed/run.py:803] ***************************************** +logs/3c0fcd5a-d2fc-4352-b7ef-437df1f09800.txt val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model -train_loader:dataset:fineweb10B_sp1024 train_shards:10 +train_loader:dataset:fineweb10B_sp1024 train_shards:80 val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 -model_params:19421776 +model_params:25222224 +encoder_recurrence:ON world_size:8 grad_accum_steps:1 sdp_backends:cudnn=False flash=True mem_efficient=False math=False attention_mode:gqa num_heads:8 num_kv_heads:4 -tie_embeddings:True embed_lr:0.1 head_lr:0.0 matrix_lr:0.06 scalar_lr:0.04 -train_batch_tokens:524288 train_seq_len:1024 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 +tie_embeddings:True embed_lr:0.035 head_lr:0.0 matrix_lr:0.028 scalar_lr:0.025 +train_batch_tokens:393216 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 seed:1337 warmup_step:1/20 warmup_step:2/20 @@ -33,74 +34,58 @@ warmup_step:17/20 warmup_step:18/20 warmup_step:19/20 warmup_step:20/20 -step:0/20000 val_loss:6.9313 val_bpb:4.1051 train_time:0ms step_avg:0.02ms -step:1/20000 train_loss:6.9314 train_time:60ms step_avg:59.71ms -step:2/20000 train_loss:9.7327 train_time:131ms step_avg:65.57ms -step:3/20000 train_loss:9.6676 train_time:209ms step_avg:69.66ms -step:4/20000 train_loss:10.0966 train_time:287ms step_avg:71.70ms -step:5/20000 train_loss:9.0540 train_time:365ms step_avg:72.95ms -step:6/20000 train_loss:8.2739 train_time:443ms step_avg:73.80ms -step:7/20000 train_loss:6.8085 train_time:519ms step_avg:74.12ms -step:8/20000 train_loss:6.3637 train_time:596ms step_avg:74.52ms -step:9/20000 train_loss:5.8905 train_time:674ms step_avg:74.84ms -step:10/20000 train_loss:5.5385 train_time:751ms step_avg:75.06ms -step:200/20000 train_loss:2.7947 train_time:16880ms step_avg:84.40ms -step:400/20000 train_loss:2.3387 train_time:33694ms step_avg:84.23ms -step:600/20000 train_loss:2.5389 train_time:50485ms step_avg:84.14ms -step:800/20000 train_loss:2.2938 train_time:67245ms step_avg:84.06ms -step:1000/20000 train_loss:2.3751 train_time:84532ms step_avg:84.53ms -step:1000/20000 val_loss:2.3386 val_bpb:1.3850 train_time:84560ms step_avg:84.56ms -step:1200/20000 train_loss:2.3931 train_time:101420ms step_avg:84.52ms -step:1400/20000 train_loss:2.4481 train_time:118283ms step_avg:84.49ms -step:1600/20000 train_loss:2.1157 train_time:135084ms step_avg:84.43ms -step:1800/20000 train_loss:2.2179 train_time:151937ms step_avg:84.41ms -step:2000/20000 train_loss:2.2564 train_time:166386ms step_avg:83.19ms -step:2000/20000 val_loss:2.2592 val_bpb:1.3380 train_time:166414ms step_avg:83.21ms -step:2200/20000 train_loss:2.3746 train_time:180844ms step_avg:82.20ms -step:2400/20000 train_loss:2.3884 train_time:195284ms step_avg:81.37ms -step:2600/20000 train_loss:2.2447 train_time:209738ms step_avg:80.67ms -step:2800/20000 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-step:7200/20000 train_loss:1.9345 train_time:541905ms step_avg:75.26ms -step:7400/20000 train_loss:1.8621 train_time:556345ms step_avg:75.18ms -step:7600/20000 train_loss:2.1033 train_time:570782ms step_avg:75.10ms -step:7800/20000 train_loss:2.0515 train_time:585219ms step_avg:75.03ms -step:8000/20000 train_loss:1.9788 train_time:599665ms step_avg:74.96ms -step:8000/20000 val_loss:2.0372 val_bpb:1.2065 train_time:599693ms step_avg:74.96ms -step:8004/20000 val_loss:2.0372 val_bpb:1.2065 train_time:599979ms step_avg:74.96ms -stopping_early: wallclock_cap train_time:599979ms step:8004/20000 -peak memory allocated: 16592 MiB reserved: 16888 MiB -Serialized model: 76676839 bytes -Code size: 53089 bytes -Total submission size: 76729928 bytes -Serialized model int8+zlib: 15700692 bytes (payload:19556672 raw_torch:19607921 payload_ratio:3.92x) -Total submission size int8+zlib: 15753781 bytes -final_int8_zlib_roundtrip val_loss:2.0618 val_bpb:1.2211 eval_time:2332ms -final_int8_zlib_roundtrip_exact val_loss:2.06182994 val_bpb:1.22113183 -final_sliding_window val_loss:2.0017 val_bpb:1.1855 eval_time:105017ms -final_sliding_window_exact val_loss:2.00170864 val_bpb:1.18552460 +step:0/20000 val_loss:6.9317 val_bpb:4.1054 train_time:0ms step_avg:0.02ms +step:1/20000 train_loss:6.9316 train_time:126ms step_avg:125.94ms +step:2/20000 train_loss:7.3320 train_time:237ms step_avg:118.49ms +step:3/20000 train_loss:5.9003 train_time:348ms step_avg:115.96ms +step:4/20000 train_loss:6.1678 train_time:458ms step_avg:114.59ms +step:5/20000 train_loss:6.1356 train_time:569ms step_avg:113.80ms +step:6/20000 train_loss:5.4396 train_time:680ms step_avg:113.25ms +step:7/20000 train_loss:5.2519 train_time:790ms step_avg:112.88ms +step:8/20000 train_loss:5.2202 train_time:901ms step_avg:112.67ms +step:9/20000 train_loss:4.7776 train_time:1012ms step_avg:112.47ms +step:10/20000 train_loss:4.6439 train_time:1123ms step_avg:112.34ms +step:200/20000 train_loss:2.7676 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step_avg:111.41ms +step:2600/20000 train_loss:2.3310 train_time:290575ms step_avg:111.76ms +step:2800/20000 train_loss:2.3929 train_time:313760ms step_avg:112.06ms +step:3000/20000 train_loss:2.1807 train_time:335990ms step_avg:112.00ms +step:3000/20000 val_loss:2.1559 val_bpb:1.2769 train_time:335999ms step_avg:112.00ms +step:3200/20000 train_loss:2.1952 train_time:358179ms step_avg:111.93ms +step:3400/20000 train_loss:2.1934 train_time:380451ms step_avg:111.90ms +step:3600/20000 train_loss:2.0642 train_time:402561ms step_avg:111.82ms +step:3800/20000 train_loss:2.0747 train_time:424723ms step_avg:111.77ms +step:4000/20000 train_loss:1.9111 train_time:447495ms step_avg:111.87ms +step:4000/20000 val_loss:2.0927 val_bpb:1.2394 train_time:447509ms step_avg:111.88ms +step:4200/20000 train_loss:1.9651 train_time:469610ms step_avg:111.81ms +step:4400/20000 train_loss:2.0660 train_time:491850ms step_avg:111.78ms +step:4600/20000 train_loss:1.9859 train_time:514138ms step_avg:111.77ms +step:4800/20000 train_loss:2.0249 train_time:536298ms step_avg:111.73ms +step:5000/20000 train_loss:2.0596 train_time:558656ms step_avg:111.73ms +step:5000/20000 val_loss:2.0228 val_bpb:1.1980 train_time:558664ms step_avg:111.73ms +step:5200/20000 train_loss:2.1060 train_time:580854ms step_avg:111.70ms +step:5373/20000 val_loss:2.0005 val_bpb:1.1848 train_time:600011ms step_avg:111.67ms +stopping_early: wallclock_cap train_time:600011ms step:5373/20000 +peak memory allocated: 14124 MiB reserved: 14642 MiB +ema: loading exponential moving average weights +Serialized model: 99355437 bytes +Code size: 58976 bytes +Total submission size: 99414413 bytes +Serialized model int6+lzma: 14963256 bytes (payload:25931584 raw_torch:25983851 payload_ratio:3.83x) +Total submission size int6+lzma: 15022232 bytes +final_int8_zlib_roundtrip val_loss:2.0075 val_bpb:1.1889 eval_time:3427ms +final_int8_zlib_roundtrip_exact val_loss:2.00746519 val_bpb:1.18893396 +final_sliding_window val_loss:1.9702 val_bpb:1.1668 eval_time:233562ms +final_sliding_window_exact val_loss:1.97015808 val_bpb:1.16683859 diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py index 6dc4d1826..a9dd6a20b 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py @@ -16,6 +16,7 @@ import sys import time import uuid +import lzma import zlib from pathlib import Path @@ -36,8 +37,9 @@ # - vocab size 1024, sequence length 1024, tied embeddings # - 524,288 train tokens per step for 20,000 iterations with a ~10 minute cap +_RUN_CONFIG = os.environ.get("RUN_CONFIG", "A") + class Hyperparameters: - # Data paths are shard globs produced by the existing preprocessing pipeline. data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") train_files = os.path.join(data_path, "fineweb_train_*.bin") val_files = os.path.join(data_path, "fineweb_val_*.bin") @@ -45,47 +47,46 @@ class Hyperparameters: run_id = os.environ.get("RUN_ID", str(uuid.uuid4())) seed = int(os.environ.get("SEED", 1337)) - # Validation cadence and batch size. Validation always uses the full fineweb_val split. val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 1000)) train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 200)) - # Training length. iterations = int(os.environ.get("ITERATIONS", 20000)) - warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 2500)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 2100 if _RUN_CONFIG == "A" else 2600)) warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 524_288)) - train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 1024)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048 if _RUN_CONFIG == "A" else 1024)) max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) - # Model shape. vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) - num_layers = int(os.environ.get("NUM_LAYERS", 10)) + num_layers = int(os.environ.get("NUM_LAYERS", 12 if _RUN_CONFIG == "C" else 10)) num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) model_dim = int(os.environ.get("MODEL_DIM", 512)) num_heads = int(os.environ.get("NUM_HEADS", 8)) - mlp_mult = int(os.environ.get("MLP_MULT", 2)) + mlp_mult = int(os.environ.get("MLP_MULT", 2 if _RUN_CONFIG == "C" else 3)) tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) - # Optimizer hyperparameters. embed_lr = float(os.environ.get("EMBED_LR", 0.6)) head_lr = float(os.environ.get("HEAD_LR", 0.008)) - tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.1)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.035)) tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) - matrix_lr = float(os.environ.get("MATRIX_LR", 0.06)) - scalar_lr = float(os.environ.get("SCALAR_LR", 0.04)) - muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.95)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.035 if _RUN_CONFIG == "A" else 0.025)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.025)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) - muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.85)) - muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 500)) + muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92)) + muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500)) beta1 = float(os.environ.get("BETA1", 0.9)) beta2 = float(os.environ.get("BETA2", 0.95)) adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.0)) + muon_wd = float(os.environ.get("MUON_WD", 0.04)) + adam_wd = float(os.environ.get("ADAM_WD", 0.04)) + ema_decay = float(os.environ.get("EMA_DECAY", 0.997)) # ----------------------------- # MUON OPTIMIZER @@ -410,6 +411,70 @@ def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() return q, scale +def quantize_float_tensor_int6(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + clip_abs = ( + torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) + if t32.numel() + else torch.empty((t32.shape[0],), dtype=torch.float32) + ) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + scale = (clip_abs / 31.0).clamp_min(1.0 / 31.0) + q = torch.clamp(torch.round(clipped / scale[:, None]), -31, 31).to(torch.int8).contiguous() + return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 31.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -31, 31).to(torch.int8).contiguous() + return q, scale + +def quantize_state_dict_int6(state_dict: dict[str, Tensor]): + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL or "tok_emb.weight" in name: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor_int6(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + obj: dict[str, object] = { + "__quant_format__": "int6_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats + def quantize_state_dict_int8(state_dict: dict[str, Tensor]): # Single supported clean-script export format: # - per-row int8 for 2D float tensors @@ -577,11 +642,34 @@ def forward(self, x: Tensor) -> Tensor: return F.rms_norm(x, (x.size(-1),), eps=self.eps) +class _FakeQuantInt6(torch.autograd.Function): + @staticmethod + def forward(ctx, w: Tensor) -> Tensor: + if w.ndim != 2: + return w + row_max = w.abs().amax(dim=1, keepdim=True).clamp_min(1e-12) + scale = row_max / 31.0 + q = (w / scale).round().clamp(-31, 31) + return q * scale + + @staticmethod + def backward(ctx, grad: Tensor) -> Tensor: + return grad + +def fake_quant_int6(w: Tensor) -> Tensor: + return _FakeQuantInt6.apply(w) + class CastedLinear(nn.Linear): - # Keep weights in fp32 for optimizer/state quality, cast at matmul time for bf16 compute. + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.use_qat = False + def forward(self, x: Tensor) -> Tensor: + w = self.weight + if self.use_qat and self.training: + w = fake_quant_int6(w) bias = self.bias.to(x.dtype) if self.bias is not None else None - return F.linear(x, self.weight.to(x.dtype), bias) + return F.linear(x, w.to(x.dtype), bias) def restore_low_dim_params_to_fp32(module: nn.Module) -> None: @@ -716,6 +804,31 @@ def forward(self, x: Tensor, x0: Tensor) -> Tensor: return x +class SmearGate(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.gate = nn.Parameter(torch.full((dim,), 3.0, dtype=torch.float32)) + + def forward(self, x: Tensor) -> Tensor: + g = torch.sigmoid(self.gate).to(dtype=x.dtype) + x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + return g * x + (1.0 - g) * x_prev + + +class BigramHash(nn.Module): + def __init__(self, num_buckets: int, hash_dim: int, model_dim: int): + super().__init__() + self.num_buckets = num_buckets + self.table = nn.Embedding(num_buckets, hash_dim) + self.proj = CastedLinear(hash_dim, model_dim, bias=False) + nn.init.normal_(self.table.weight, std=0.01) + + def forward(self, input_ids: Tensor) -> Tensor: + prev_ids = torch.cat([torch.zeros_like(input_ids[:, :1]), input_ids[:, :-1]], dim=1) + h = ((prev_ids.long() * 92821 + input_ids.long()) % self.num_buckets).long() + return self.proj(self.table(h)) + + class GPT(nn.Module): def __init__( self, @@ -739,10 +852,13 @@ def __init__( self.logit_softcap = logit_softcap self.encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram_hash = BigramHash(4096, 64, model_dim) + self.smear_gate = SmearGate(model_dim) + pre_enrich_hidden = model_dim * 3 // 2 self.pre_enrich = nn.Sequential( - CastedLinear(model_dim, model_dim, bias=False), + CastedLinear(model_dim, pre_enrich_hidden, bias=False), nn.GELU(), - CastedLinear(model_dim, model_dim, bias=False), + CastedLinear(pre_enrich_hidden, model_dim, bias=False), ) self.num_encoder_layers = num_layers // 2 self.num_decoder_layers = num_layers - self.num_encoder_layers @@ -813,7 +929,8 @@ def _compute_logits(self, x: Tensor) -> Tensor: return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: - x = self.tok_emb(input_ids) + x = self.tok_emb(input_ids) + self.bigram_hash(input_ids) + x = self.smear_gate(x) x = self.pre_enrich(x) x = F.rms_norm(x, (x.size(-1),)) x0 = x @@ -824,7 +941,8 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: return F.cross_entropy(logits.float(), targets, reduction="mean") def forward_logits(self, input_ids: Tensor) -> Tensor: - x = self.tok_emb(input_ids) + x = self.tok_emb(input_ids) + self.bigram_hash(input_ids) + x = self.smear_gate(x) x = self.pre_enrich(x) x = F.rms_norm(x, (x.size(-1),)) x0 = x @@ -949,6 +1067,9 @@ def log0(msg: str, console: bool = True) -> None: if isinstance(module, CastedLinear): module.float() restore_low_dim_params_to_fp32(base_model) + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.use_qat = True compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) model: nn.Module = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False) if distributed else compiled_model @@ -964,6 +1085,7 @@ def log0(msg: str, console: bool = True) -> None: if p.ndim == 2 and not any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) ] matrix_params.extend(p for p in base_model.pre_enrich.parameters() if p.ndim == 2) + matrix_params.extend(p for p in base_model.bigram_hash.parameters() if p.ndim == 2) scalar_params = [ p for name, p in block_named_params @@ -971,11 +1093,13 @@ def log0(msg: str, console: bool = True) -> None: ] if base_model.skip_weights.numel() > 0: scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear_gate.gate) token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr - optimizer_tok = torch.optim.Adam( + optimizer_tok = torch.optim.AdamW( [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}], betas=(args.beta1, args.beta2), eps=args.adam_eps, + weight_decay=args.adam_wd, fused=True, ) optimizer_muon = Muon( @@ -986,10 +1110,11 @@ def log0(msg: str, console: bool = True) -> None: ) for group in optimizer_muon.param_groups: group["base_lr"] = args.matrix_lr - optimizer_scalar = torch.optim.Adam( + optimizer_scalar = torch.optim.AdamW( [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], betas=(args.beta1, args.beta2), eps=args.adam_eps, + weight_decay=args.adam_wd, fused=True, ) optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] @@ -1004,6 +1129,7 @@ def log0(msg: str, console: bool = True) -> None: n_params = sum(p.numel() for p in base_model.parameters()) log0(f"model_params:{n_params}") + log0(f"encoder_recurrence:{'ON' if base_model.encoder_recurrence else 'OFF'}") log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") log0("sdp_backends:cudnn=False flash=True mem_efficient=False math=False") log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}") @@ -1076,6 +1202,7 @@ def lr_mul(step: int, elapsed_ms: float) -> float: training_time_ms = 0.0 stop_after_step: int | None = None + ema_state = {k: v.detach().cpu().clone().float() for k, v in base_model.state_dict().items()} torch.cuda.synchronize() t0 = time.perf_counter() @@ -1144,10 +1271,13 @@ def lr_mul(step: int, elapsed_ms: float) -> float: with torch.no_grad(): muon_lr = optimizer_muon.param_groups[0]["lr"] for p in matrix_params: - p.mul_(1.0 - 0.02 * muon_lr) + p.mul_(1.0 - args.muon_wd * muon_lr) zero_grad_all() step += 1 + with torch.no_grad(): + for k, v in base_model.state_dict().items(): + ema_state[k].mul_(args.ema_decay).add_(v.detach().cpu().float(), alpha=1.0 - args.ema_decay) approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) should_log_train = ( args.train_log_every > 0 @@ -1179,6 +1309,14 @@ def lr_mul(step: int, elapsed_ms: float) -> float: # Save the raw state (useful for debugging/loading in PyTorch directly), then always produce # the compressed int8+zlib artifact and validate the round-tripped weights. + log0("ema: loading exponential moving average weights") + base_model.load_state_dict(ema_state, strict=True) + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.float() + restore_low_dim_params_to_fp32(base_model) + del ema_state + if master_process: torch.save(base_model.state_dict(), "final_model.pt") model_bytes = os.path.getsize("final_model.pt") @@ -1187,29 +1325,29 @@ def lr_mul(step: int, elapsed_ms: float) -> float: log0(f"Code size: {code_bytes} bytes") log0(f"Total submission size: {model_bytes + code_bytes} bytes") - quant_obj, quant_stats = quantize_state_dict_int8(base_model.state_dict()) + quant_obj, quant_stats = quantize_state_dict_int6(base_model.state_dict()) quant_buf = io.BytesIO() torch.save(quant_obj, quant_buf) quant_raw = quant_buf.getvalue() - quant_blob = zlib.compress(quant_raw, level=9) + quant_blob = lzma.compress(quant_raw, preset=6) quant_raw_bytes = len(quant_raw) if master_process: - with open("final_model.int8.ptz", "wb") as f: + with open("final_model.int6.ptz", "wb") as f: f.write(quant_blob) - quant_file_bytes = os.path.getsize("final_model.int8.ptz") + quant_file_bytes = os.path.getsize("final_model.int6.ptz") code_bytes = len(code.encode("utf-8")) ratio = quant_stats["baseline_tensor_bytes"] / max(quant_stats["int8_payload_bytes"], 1) log0( - f"Serialized model int8+zlib: {quant_file_bytes} bytes " + f"Serialized model int6+lzma: {quant_file_bytes} bytes " f"(payload:{quant_stats['int8_payload_bytes']} raw_torch:{quant_raw_bytes} payload_ratio:{ratio:.2f}x)" ) - log0(f"Total submission size int8+zlib: {quant_file_bytes + code_bytes} bytes") + log0(f"Total submission size int6+lzma: {quant_file_bytes + code_bytes} bytes") if distributed: dist.barrier() - with open("final_model.int8.ptz", "rb") as f: + with open("final_model.int6.ptz", "rb") as f: quant_blob_disk = f.read() - quant_state = torch.load(io.BytesIO(zlib.decompress(quant_blob_disk)), map_location="cpu") + quant_state = torch.load(io.BytesIO(lzma.decompress(quant_blob_disk)), map_location="cpu") base_model.load_state_dict(dequantize_state_dict_int8(quant_state), strict=True) torch.cuda.synchronize() t_qeval = time.perf_counter() From 9f17c55d25902f4baeab1354f3a46ff44c3a16e1 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Sat, 21 Mar 2026 02:47:33 -0300 Subject: [PATCH 26/72] Record: Pre-Enrichment + Encoder Recurrence + SmearGate + BigramHash (val_bpb=1.1668) --- .../README.md | 87 ++++++----- .../submission.json | 26 ++-- .../train.log | 135 +++++++++--------- .../train_gpt.py | 71 ++++++--- 4 files changed, 167 insertions(+), 152 deletions(-) diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md index 56091b09c..703f1b359 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md @@ -1,76 +1,68 @@ -## Pre-Enrichment + Encoder Recurrence +## Pre-Enrichment + Encoder Recurrence + SmearGate + BigramHash -Two architectural modifications to the baseline transformer: (1) a GELU pre-enrichment block that transforms raw embeddings before they enter the residual stream, and (2) 2x encoder recurrence that runs the encoder blocks twice with RMS norm stabilization between passes. Combined with int6 QAT, lzma compression, MLP 3x, SWA, sliding window evaluation (stride=64), and overtone embedding initialization, this achieves **val_bpb 1.1709** in a 15.57MB artifact trained in 10 minutes on 8xH100. +Architectural modifications to the baseline transformer achieving **val_bpb 1.1668** in a 15.02MB artifact trained in 10 minutes on 8xH100. Key techniques: GELU pre-enrichment (512→768→512), 2x encoder recurrence with RMS norm stabilization, SmearGate for lightweight bigram context, BigramHash for explicit bigram embeddings, and EMA weight averaging for quantization-friendly weights. --- ### Key Contributions -#### GELU Pre-Enrichment +#### GELU Pre-Enrichment (512→768→512) -Raw token embeddings are a poor starting point for the residual stream. A 1024-token vocabulary maps each token to a 512-dimensional vector initialized from a normal distribution — these vectors carry no relational structure and every transformer layer must compensate for this weak initialization. - -I add two `CastedLinear(512→512)` projections with a GELU activation between them, applied after the embedding lookup and before the first transformer block: +Two `CastedLinear` projections with a GELU activation between them, applied after the embedding lookup and before the first transformer block. The wider hidden dimension (768 vs baseline 512) gives the model a richer nonlinear transformation before the residual stream begins. ``` -embedding → Linear(512→512) → GELU → Linear(512→512) → RMS Norm → transformer blocks +embedding → BigramHash add → SmearGate → Linear(512→768) → GELU → Linear(768→512) → RMS Norm → transformer blocks ``` -This gives the model a learned nonlinear transformation to produce richer representations before the residual stream begins. Cost: 0.5M extra parameters (~2% of total), negligible step time overhead. - #### 2x Encoder Recurrence -Depth recurrence is a known technique (ALBERT, Universal Transformers). My contribution is applying it to only the encoder half of a U-Net transformer architecture, with RMS norm stabilization between passes, and providing A/B data showing it consistently beats additional training steps across two different model configurations. - -The baseline uses a U-Net architecture with encoder and decoder halves connected by skip connections. I reuse the encoder blocks for a second pass before running the decoder. - -With 10 layers (5 encoder + 5 decoder), the forward pass becomes: -1. Run encoder blocks 0-4 (first pass, build initial features) -2. RMS norm (stabilize between passes) -3. Run encoder blocks 0-4 again (second pass, refine features) -4. Run decoder blocks 5-9 with skip connections from the refined second encoder pass +I reuse the encoder blocks for a second pass before running the decoder, with RMS norm stabilization between passes. With 10 layers (5 encoder + 5 decoder), this produces **15 effective layers from 10 physical blocks** with zero extra parameters. -This produces **15 effective layers from 10 physical blocks** with zero extra parameters. - -**A/B Comparison — Config 2 (MLP 3x, seq 2048, int6 QAT, SWA):** +**A/B Comparison — MLP 3x, seq 2048, int6 QAT (8xH100, 10 minutes):** | Metric | With recurrence | Without recurrence | |---------------------|--------------------|-----------------------| | Steps completed | 6,423 | 8,950 | | Step time | 93ms | 67ms | -| Standard BPB | 1.1929 | 1.1959 | | Sliding window BPB | **1.1709** | 1.1740 | -| Submission size | 15.57MB | 15.54MB | -**A/B Comparison — Config 1 (MLP 2x, seq 1024, int8+zlib):** +Encoder recurrence consistently wins — deeper processing per step beats more gradient updates. -| Metric | With recurrence | Without recurrence | -|---------------------|--------------------|-----------------------| -| Steps completed | 8,004 | 11,955 | -| Step time | 75ms | 50ms | -| Standard BPB | 1.2211 | 1.2299 | -| Sliding window BPB | **1.1855** | 1.1947 | -| Submission size | 15.75MB | 15.82MB | +#### SmearGate -Encoder recurrence wins across both configurations — different model sizes, different sequence lengths, different step counts. In both cases, 30-40% fewer training steps could not overcome the depth advantage. The pattern is consistent: deeper processing per step beats more gradient updates with shallower processing. +Learned per-dimension gate (512 params) that blends each token's embedding with the previous token's embedding. Provides lightweight bigram context at the embedding layer. Initialized with gate bias 3.0 (sigmoid(3.0)≈0.95, near-identity at init). + +#### BigramHash + +Hash-table embedding mapping token bigrams to learned vectors. Hash formula: `(prev_token * 92821 + curr_token) % 4096`. Lookup table 4096×64, projected to model_dim via Linear(64, 512). Adds explicit bigram context to the token embedding. + +#### EMA Weight Averaging + +Exponential moving average (decay=0.997) updated every step, replacing SWA. EMA weights are loaded before quantization. Produces smoother weights that quantize significantly better — quant gap dropped from 0.020 (SWA) to **0.004** (EMA). --- ### Additional Techniques -Int6 quantization-aware training (fake quant with STE in CastedLinear), lzma compression, MLP 3x expansion, stochastic weight averaging (11 checkpoints during warmdown), overtone embedding init, decoupled Muon weight decay (0.04), AdamW weight decay (0.04), batched sliding window eval (stride=64), fp16 embedding passthrough in quantization. +Int6 quantization-aware training (fake quant with STE in CastedLinear), lzma compression, MLP 3x expansion, overtone embedding init, decoupled Muon weight decay (0.04), AdamW weight decay (0.04), batched sliding window eval (stride=64), fp16 embedding passthrough in quantization. -Hyperparameters: NUM_LAYERS=10, TRAIN_SEQ_LEN=2048, MATRIX_LR=0.035, SCALAR_LR=0.025, TIED_EMBED_LR=0.035, MUON_MOMENTUM=0.99, WARMDOWN_ITERS=2100. +Hyperparameters: NUM_LAYERS=10, TRAIN_SEQ_LEN=2048, TRAIN_BATCH_TOKENS=393216, MATRIX_LR=0.028, SCALAR_LR=0.025, TIED_EMBED_LR=0.035, MUON_MOMENTUM=0.99, WARMDOWN_ITERS=3300. --- ### What Didn't Work -- **FP16 embedding passthrough (without int6)**: Keeping the tied embedding in fp16 instead of int8 reduced quantization error by ~0.006 BPB but pushed the int8+zlib artifact over 16MB. Switching to int6 quantization solved this — fp16 embedding fits comfortably in the int6+lzma budget. +- **Phase-transition resid_mix init**: Sigmoid-scheduled initialization of resid_mix. Slowed convergence at our step count, hurt final score. + +- **Late-K passthrough**: Keeping last 2 layers' c_k.weight in fp16 during quantization. Added artifact size without enough BPB improvement. + +- **Gradient clipping (GRAD_CLIP_NORM=1.0)**: Constrained the optimizer, slower per-step learning. + +- **12 layers + MLP 2x**: 18 effective layers with recurrence but MLP 2x bottleneck was too narrow. 10L MLP 3x wins. -- **3x encoder recurrence**: The tripled computation graph exceeded Triton's per-SM shared memory limit on A100 (168,096 > 166,912 bytes). A compiler limitation, not an architectural one. +- **Full dataset (80 shards) with WD=0.04**: More diverse data didn't improve pre-quant BPB. Only helped quant gap when combined with higher WD. -- **Warmdown scheduler on A100**: The wallclock-aware warmdown schedule estimates remaining time as `warmdown_iters × avg_step_time`. On A100 (~1100ms/step), this exceeds the total 600-second budget from step 0, causing the learning rate to decay throughout the entire run. Not relevant to 8xH100 but was a significant debugging finding during development. +- **3x encoder recurrence**: Exceeded Triton's per-SM shared memory limit. Compiler limitation. - Also tried: full U-Net recurrence (too slow), reverse encoder pass order (worse), auxiliary encoder prediction loss (hurt performance), 6+3 encoder/decoder split (worse than 5+5). @@ -81,10 +73,10 @@ Hyperparameters: NUM_LAYERS=10, TRAIN_SEQ_LEN=2048, MATRIX_LR=0.035, SCALAR_LR=0 ``` RUN_CONFIG=A VOCAB_SIZE=1024 NUM_LAYERS=10 MODEL_DIM=512 NUM_HEADS=8 NUM_KV_HEADS=4 MLP_MULT=3 -TIE_EMBEDDINGS=1 TIED_EMBED_LR=0.035 MATRIX_LR=0.035 SCALAR_LR=0.025 +TIE_EMBEDDINGS=1 TIED_EMBED_LR=0.035 MATRIX_LR=0.028 SCALAR_LR=0.025 MUON_MOMENTUM=0.99 MUON_MOMENTUM_WARMUP_START=0.92 MUON_MOMENTUM_WARMUP_STEPS=1500 -WARMDOWN_ITERS=2100 WARMUP_STEPS=20 TRAIN_BATCH_TOKENS=524288 TRAIN_SEQ_LEN=2048 -ENCODER_RECURRENCE=1 MUON_WD=0.04 ADAM_WD=0.04 SWA_EVERY=200 +WARMDOWN_ITERS=3300 WARMUP_STEPS=20 TRAIN_BATCH_TOKENS=393216 TRAIN_SEQ_LEN=2048 +ENCODER_RECURRENCE=1 MUON_WD=0.04 ADAM_WD=0.04 EMA_DECAY=0.997 ``` ### Reproduction @@ -98,10 +90,11 @@ RUN_CONFIG=A torchrun --standalone --nproc_per_node=8 train_gpt.py | Metric | Value | |---|---| -| Pre-quant val_bpb | 1.1730 | -| Post-quant val_bpb (standard) | 1.1929 | -| Post-quant val_bpb (sliding window) | **1.1709** | -| Training time | 600,034ms (6,423 steps at ~93ms) | -| Peak memory | 18,506 MiB | -| Submission size (int6+lzma) | 15,567,990 bytes | -| Model parameters | 24,664,656 | +| Pre-quant val_bpb | 1.1848 | +| Post-quant val_bpb (standard) | 1.1889 | +| Post-quant val_bpb (sliding window) | **1.1668** | +| Quant gap (standard - pre-quant) | 0.004 | +| Training time | 600,011ms (5,373 steps at ~112ms) | +| Peak memory | 14,124 MiB | +| Submission size (int6+lzma) | 15,022,232 bytes | +| Model parameters | 25,222,224 | diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json index 3e784e3d5..2e7d8cf9f 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json @@ -1,17 +1,17 @@ { "author": "Idanr", "github_id": "idan3011", - "name": "Pre-Enrichment + Encoder Recurrence", - "blurb": "GELU pre-enrichment + 2x encoder recurrence + int6 QAT + lzma + MLP 3x + SWA + sliding window eval (stride=64), 10L 512d seq2048. 15 effective layers from 10 physical blocks via encoder-only depth recurrence.", - "date": "2026-03-20T15:15:00Z", - "val_loss": 1.97704181, - "val_bpb": 1.17091552, - "pre_quant_val_loss": 1.9805, - "pre_quant_val_bpb": 1.1730, - "step_stop": 6423, - "wallclock_seconds": 600.034, - "eval_time_seconds": 231.603, - "bytes_total": 15567990, - "bytes_model_int6_lzma": 15510344, - "bytes_code": 57646 + "name": "Pre-Enrichment + Encoder Recurrence + SmearGate + BigramHash", + "blurb": "GELU pre-enrichment (512-768-512) + 2x encoder recurrence + SmearGate + BigramHash + EMA + int6 QAT + lzma + MLP 3x + sliding window eval (stride=64), 10L 512d seq2048.", + "date": "2026-03-21T05:23:00Z", + "val_loss": 1.97015808, + "val_bpb": 1.16683859, + "pre_quant_val_loss": 2.0005, + "pre_quant_val_bpb": 1.1848, + "step_stop": 5373, + "wallclock_seconds": 600.011, + "eval_time_seconds": 233.562, + "bytes_total": 15022232, + "bytes_model_int6_lzma": 14963256, + "bytes_code": 58976 } diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log index dda487723..51623c57b 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log @@ -1,18 +1,18 @@ -W0320 15:15:09.903000 689 torch/distributed/run.py:803] -W0320 15:15:09.903000 689 torch/distributed/run.py:803] ***************************************** -W0320 15:15:09.903000 689 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. -W0320 15:15:09.903000 689 torch/distributed/run.py:803] ***************************************** -logs/ab1171b2-63ed-4963-8580-c8635931513a.txt +W0321 05:23:38.712000 1529 torch/distributed/run.py:803] +W0321 05:23:38.712000 1529 torch/distributed/run.py:803] ***************************************** +W0321 05:23:38.712000 1529 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +W0321 05:23:38.712000 1529 torch/distributed/run.py:803] ***************************************** +logs/3c0fcd5a-d2fc-4352-b7ef-437df1f09800.txt val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model -train_loader:dataset:fineweb10B_sp1024 train_shards:10 +train_loader:dataset:fineweb10B_sp1024 train_shards:80 val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 -model_params:24664656 +model_params:25222224 encoder_recurrence:ON world_size:8 grad_accum_steps:1 sdp_backends:cudnn=False flash=True mem_efficient=False math=False attention_mode:gqa num_heads:8 num_kv_heads:4 -tie_embeddings:True embed_lr:0.035 head_lr:0.0 matrix_lr:0.035 scalar_lr:0.025 -train_batch_tokens:524288 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 +tie_embeddings:True embed_lr:0.035 head_lr:0.0 matrix_lr:0.028 scalar_lr:0.025 +train_batch_tokens:393216 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 seed:1337 warmup_step:1/20 warmup_step:2/20 @@ -34,65 +34,58 @@ warmup_step:17/20 warmup_step:18/20 warmup_step:19/20 warmup_step:20/20 -step:0/20000 val_loss:6.9314 val_bpb:4.1051 train_time:0ms step_avg:0.02ms -step:1/20000 train_loss:6.9314 train_time:73ms step_avg:73.31ms -step:2/20000 train_loss:6.9295 train_time:158ms step_avg:78.86ms -step:3/20000 train_loss:6.3120 train_time:251ms step_avg:83.60ms -step:4/20000 train_loss:6.6233 train_time:347ms step_avg:86.71ms -step:5/20000 train_loss:6.3365 train_time:440ms step_avg:87.95ms -step:6/20000 train_loss:6.0246 train_time:535ms step_avg:89.15ms -step:7/20000 train_loss:5.4888 train_time:630ms step_avg:89.96ms -step:8/20000 train_loss:5.3087 train_time:726ms step_avg:90.80ms -step:9/20000 train_loss:5.0899 train_time:826ms step_avg:91.81ms -step:10/20000 train_loss:4.9072 train_time:934ms step_avg:93.38ms -step:200/20000 train_loss:2.7525 train_time:18047ms step_avg:90.23ms -step:400/20000 train_loss:2.2634 train_time:36091ms step_avg:90.23ms -step:600/20000 train_loss:2.4781 train_time:56712ms step_avg:94.52ms -step:800/20000 train_loss:2.2380 train_time:76975ms step_avg:96.22ms -step:1000/20000 train_loss:2.3345 train_time:97179ms step_avg:97.18ms -step:1000/20000 val_loss:2.2920 val_bpb:1.3575 train_time:97210ms step_avg:97.21ms -step:1200/20000 train_loss:2.3638 train_time:117462ms step_avg:97.89ms -step:1400/20000 train_loss:2.4064 train_time:138047ms step_avg:98.60ms -step:1600/20000 train_loss:2.0756 train_time:157916ms step_avg:98.70ms -step:1800/20000 train_loss:2.1807 train_time:178455ms step_avg:99.14ms -step:2000/20000 train_loss:2.2001 train_time:199128ms step_avg:99.56ms -step:2000/20000 val_loss:2.2001 val_bpb:1.3030 train_time:199170ms step_avg:99.58ms -step:2200/20000 train_loss:2.3071 train_time:217203ms step_avg:98.73ms -step:2400/20000 train_loss:2.3187 train_time:235267ms step_avg:98.03ms -step:2600/20000 train_loss:2.1834 train_time:253343ms step_avg:97.44ms -step:2800/20000 train_loss:2.1361 train_time:271461ms step_avg:96.95ms -step:3000/20000 train_loss:3.1833 train_time:289539ms step_avg:96.51ms -step:3000/20000 val_loss:2.1657 val_bpb:1.2826 train_time:289568ms step_avg:96.52ms -step:3200/20000 train_loss:2.2437 train_time:307590ms step_avg:96.12ms -step:3400/20000 train_loss:2.0670 train_time:325634ms step_avg:95.77ms -step:3600/20000 train_loss:2.1901 train_time:343674ms step_avg:95.46ms -step:3800/20000 train_loss:2.1431 train_time:361734ms step_avg:95.19ms -step:4000/20000 train_loss:2.2545 train_time:379779ms step_avg:94.94ms -step:4000/20000 val_loss:2.1454 val_bpb:1.2706 train_time:379823ms step_avg:94.96ms -step:4200/20000 train_loss:2.2027 train_time:398141ms step_avg:94.80ms -step:4400/20000 train_loss:2.1344 train_time:416531ms step_avg:94.67ms -step:4600/20000 train_loss:2.1774 train_time:434692ms step_avg:94.50ms -step:4800/20000 train_loss:2.0967 train_time:452827ms step_avg:94.34ms -step:5000/20000 train_loss:2.1739 train_time:470971ms step_avg:94.19ms -step:5000/20000 val_loss:2.0970 val_bpb:1.2420 train_time:470997ms step_avg:94.20ms -step:5200/20000 train_loss:2.2172 train_time:489137ms step_avg:94.06ms -step:5400/20000 train_loss:2.1638 train_time:507257ms step_avg:93.94ms -step:5600/20000 train_loss:2.0482 train_time:525386ms step_avg:93.82ms -step:5800/20000 train_loss:2.0885 train_time:543545ms step_avg:93.71ms -step:6000/20000 train_loss:1.9862 train_time:561681ms step_avg:93.61ms -step:6000/20000 val_loss:2.0151 val_bpb:1.1935 train_time:561709ms step_avg:93.62ms -step:6200/20000 train_loss:1.9534 train_time:579806ms step_avg:93.52ms -step:6400/20000 train_loss:1.7174 train_time:597947ms step_avg:93.43ms -step:6423/20000 val_loss:1.9805 val_bpb:1.1730 train_time:600034ms step_avg:93.42ms -stopping_early: wallclock_cap train_time:600034ms step:6423/20000 -peak memory allocated: 18506 MiB reserved: 18888 MiB -swa: averaging 11 checkpoints -Serialized model: 97648359 bytes -Code size: 57646 bytes -Total submission size: 97706005 bytes -Serialized model int6+lzma: 15510344 bytes (payload:25332032 raw_torch:25383027 payload_ratio:3.85x) -Total submission size int6+lzma: 15567990 bytes -final_int8_zlib_roundtrip val_loss:2.0142 val_bpb:1.1929 eval_time:2939ms -final_int8_zlib_roundtrip_exact val_loss:2.01421533 val_bpb:1.19293177 -final_sliding_window val_loss:1.9770 val_bpb:1.1709 eval_time:231603ms -final_sliding_window_exact val_loss:1.97704181 val_bpb:1.17091552 +step:0/20000 val_loss:6.9317 val_bpb:4.1054 train_time:0ms step_avg:0.02ms +step:1/20000 train_loss:6.9316 train_time:126ms step_avg:125.94ms +step:2/20000 train_loss:7.3320 train_time:237ms step_avg:118.49ms +step:3/20000 train_loss:5.9003 train_time:348ms step_avg:115.96ms +step:4/20000 train_loss:6.1678 train_time:458ms step_avg:114.59ms +step:5/20000 train_loss:6.1356 train_time:569ms step_avg:113.80ms +step:6/20000 train_loss:5.4396 train_time:680ms step_avg:113.25ms +step:7/20000 train_loss:5.2519 train_time:790ms step_avg:112.88ms +step:8/20000 train_loss:5.2202 train_time:901ms step_avg:112.67ms +step:9/20000 train_loss:4.7776 train_time:1012ms step_avg:112.47ms +step:10/20000 train_loss:4.6439 train_time:1123ms step_avg:112.34ms +step:200/20000 train_loss:2.7676 train_time:22290ms step_avg:111.45ms +step:400/20000 train_loss:2.4202 train_time:44586ms step_avg:111.47ms +step:600/20000 train_loss:2.3056 train_time:66836ms step_avg:111.39ms +step:800/20000 train_loss:2.3780 train_time:89135ms step_avg:111.42ms +step:1000/20000 train_loss:2.3416 train_time:111395ms step_avg:111.39ms +step:1000/20000 val_loss:2.3198 val_bpb:1.3739 train_time:111404ms step_avg:111.40ms +step:1200/20000 train_loss:2.3797 train_time:133617ms step_avg:111.35ms +step:1400/20000 train_loss:2.3352 train_time:155927ms step_avg:111.38ms +step:1600/20000 train_loss:2.2978 train_time:178175ms step_avg:111.36ms +step:1800/20000 train_loss:2.0611 train_time:200399ms step_avg:111.33ms +step:2000/20000 train_loss:2.3231 train_time:222660ms step_avg:111.33ms +step:2000/20000 val_loss:2.2292 val_bpb:1.3203 train_time:222669ms step_avg:111.33ms +step:2200/20000 train_loss:2.0879 train_time:244848ms step_avg:111.29ms +step:2400/20000 train_loss:2.2229 train_time:267380ms step_avg:111.41ms +step:2600/20000 train_loss:2.3310 train_time:290575ms step_avg:111.76ms +step:2800/20000 train_loss:2.3929 train_time:313760ms step_avg:112.06ms +step:3000/20000 train_loss:2.1807 train_time:335990ms step_avg:112.00ms +step:3000/20000 val_loss:2.1559 val_bpb:1.2769 train_time:335999ms step_avg:112.00ms +step:3200/20000 train_loss:2.1952 train_time:358179ms step_avg:111.93ms +step:3400/20000 train_loss:2.1934 train_time:380451ms step_avg:111.90ms +step:3600/20000 train_loss:2.0642 train_time:402561ms step_avg:111.82ms +step:3800/20000 train_loss:2.0747 train_time:424723ms step_avg:111.77ms +step:4000/20000 train_loss:1.9111 train_time:447495ms step_avg:111.87ms +step:4000/20000 val_loss:2.0927 val_bpb:1.2394 train_time:447509ms step_avg:111.88ms +step:4200/20000 train_loss:1.9651 train_time:469610ms step_avg:111.81ms +step:4400/20000 train_loss:2.0660 train_time:491850ms step_avg:111.78ms +step:4600/20000 train_loss:1.9859 train_time:514138ms step_avg:111.77ms +step:4800/20000 train_loss:2.0249 train_time:536298ms step_avg:111.73ms +step:5000/20000 train_loss:2.0596 train_time:558656ms step_avg:111.73ms +step:5000/20000 val_loss:2.0228 val_bpb:1.1980 train_time:558664ms step_avg:111.73ms +step:5200/20000 train_loss:2.1060 train_time:580854ms step_avg:111.70ms +step:5373/20000 val_loss:2.0005 val_bpb:1.1848 train_time:600011ms step_avg:111.67ms +stopping_early: wallclock_cap train_time:600011ms step:5373/20000 +peak memory allocated: 14124 MiB reserved: 14642 MiB +ema: loading exponential moving average weights +Serialized model: 99355437 bytes +Code size: 58976 bytes +Total submission size: 99414413 bytes +Serialized model int6+lzma: 14963256 bytes (payload:25931584 raw_torch:25983851 payload_ratio:3.83x) +Total submission size int6+lzma: 15022232 bytes +final_int8_zlib_roundtrip val_loss:2.0075 val_bpb:1.1889 eval_time:3427ms +final_int8_zlib_roundtrip_exact val_loss:2.00746519 val_bpb:1.18893396 +final_sliding_window val_loss:1.9702 val_bpb:1.1668 eval_time:233562ms +final_sliding_window_exact val_loss:1.97015808 val_bpb:1.16683859 diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py index 698208d87..a9dd6a20b 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py @@ -60,11 +60,11 @@ class Hyperparameters: qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) - num_layers = int(os.environ.get("NUM_LAYERS", 10)) + num_layers = int(os.environ.get("NUM_LAYERS", 12 if _RUN_CONFIG == "C" else 10)) num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) model_dim = int(os.environ.get("MODEL_DIM", 512)) num_heads = int(os.environ.get("NUM_HEADS", 8)) - mlp_mult = int(os.environ.get("MLP_MULT", 3)) + mlp_mult = int(os.environ.get("MLP_MULT", 2 if _RUN_CONFIG == "C" else 3)) tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) @@ -86,7 +86,7 @@ class Hyperparameters: grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.0)) muon_wd = float(os.environ.get("MUON_WD", 0.04)) adam_wd = float(os.environ.get("ADAM_WD", 0.04)) - swa_every = int(os.environ.get("SWA_EVERY", 200)) + ema_decay = float(os.environ.get("EMA_DECAY", 0.997)) # ----------------------------- # MUON OPTIMIZER @@ -804,6 +804,31 @@ def forward(self, x: Tensor, x0: Tensor) -> Tensor: return x +class SmearGate(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.gate = nn.Parameter(torch.full((dim,), 3.0, dtype=torch.float32)) + + def forward(self, x: Tensor) -> Tensor: + g = torch.sigmoid(self.gate).to(dtype=x.dtype) + x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + return g * x + (1.0 - g) * x_prev + + +class BigramHash(nn.Module): + def __init__(self, num_buckets: int, hash_dim: int, model_dim: int): + super().__init__() + self.num_buckets = num_buckets + self.table = nn.Embedding(num_buckets, hash_dim) + self.proj = CastedLinear(hash_dim, model_dim, bias=False) + nn.init.normal_(self.table.weight, std=0.01) + + def forward(self, input_ids: Tensor) -> Tensor: + prev_ids = torch.cat([torch.zeros_like(input_ids[:, :1]), input_ids[:, :-1]], dim=1) + h = ((prev_ids.long() * 92821 + input_ids.long()) % self.num_buckets).long() + return self.proj(self.table(h)) + + class GPT(nn.Module): def __init__( self, @@ -827,10 +852,13 @@ def __init__( self.logit_softcap = logit_softcap self.encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram_hash = BigramHash(4096, 64, model_dim) + self.smear_gate = SmearGate(model_dim) + pre_enrich_hidden = model_dim * 3 // 2 self.pre_enrich = nn.Sequential( - CastedLinear(model_dim, model_dim, bias=False), + CastedLinear(model_dim, pre_enrich_hidden, bias=False), nn.GELU(), - CastedLinear(model_dim, model_dim, bias=False), + CastedLinear(pre_enrich_hidden, model_dim, bias=False), ) self.num_encoder_layers = num_layers // 2 self.num_decoder_layers = num_layers - self.num_encoder_layers @@ -901,7 +929,8 @@ def _compute_logits(self, x: Tensor) -> Tensor: return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: - x = self.tok_emb(input_ids) + x = self.tok_emb(input_ids) + self.bigram_hash(input_ids) + x = self.smear_gate(x) x = self.pre_enrich(x) x = F.rms_norm(x, (x.size(-1),)) x0 = x @@ -912,7 +941,8 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: return F.cross_entropy(logits.float(), targets, reduction="mean") def forward_logits(self, input_ids: Tensor) -> Tensor: - x = self.tok_emb(input_ids) + x = self.tok_emb(input_ids) + self.bigram_hash(input_ids) + x = self.smear_gate(x) x = self.pre_enrich(x) x = F.rms_norm(x, (x.size(-1),)) x0 = x @@ -1055,6 +1085,7 @@ def log0(msg: str, console: bool = True) -> None: if p.ndim == 2 and not any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) ] matrix_params.extend(p for p in base_model.pre_enrich.parameters() if p.ndim == 2) + matrix_params.extend(p for p in base_model.bigram_hash.parameters() if p.ndim == 2) scalar_params = [ p for name, p in block_named_params @@ -1062,6 +1093,7 @@ def log0(msg: str, console: bool = True) -> None: ] if base_model.skip_weights.numel() > 0: scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear_gate.gate) token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr optimizer_tok = torch.optim.AdamW( [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}], @@ -1170,7 +1202,7 @@ def lr_mul(step: int, elapsed_ms: float) -> float: training_time_ms = 0.0 stop_after_step: int | None = None - swa_checkpoints: list[dict[str, Tensor]] = [] + ema_state = {k: v.detach().cpu().clone().float() for k, v in base_model.state_dict().items()} torch.cuda.synchronize() t0 = time.perf_counter() @@ -1243,8 +1275,9 @@ def lr_mul(step: int, elapsed_ms: float) -> float: zero_grad_all() step += 1 - if scale < 1.0 and args.swa_every > 0 and step % args.swa_every == 0: - swa_checkpoints.append({k: v.detach().cpu().clone() for k, v in base_model.state_dict().items()}) + with torch.no_grad(): + for k, v in base_model.state_dict().items(): + ema_state[k].mul_(args.ema_decay).add_(v.detach().cpu().float(), alpha=1.0 - args.ema_decay) approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) should_log_train = ( args.train_log_every > 0 @@ -1276,17 +1309,13 @@ def lr_mul(step: int, elapsed_ms: float) -> float: # Save the raw state (useful for debugging/loading in PyTorch directly), then always produce # the compressed int8+zlib artifact and validate the round-tripped weights. - if swa_checkpoints: - log0(f"swa: averaging {len(swa_checkpoints)} checkpoints") - avg_state = {} - for key in swa_checkpoints[0]: - avg_state[key] = torch.stack([ckpt[key].float() for ckpt in swa_checkpoints]).mean(dim=0) - base_model.load_state_dict(avg_state, strict=True) - for module in base_model.modules(): - if isinstance(module, CastedLinear): - module.float() - restore_low_dim_params_to_fp32(base_model) - del swa_checkpoints + log0("ema: loading exponential moving average weights") + base_model.load_state_dict(ema_state, strict=True) + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.float() + restore_low_dim_params_to_fp32(base_model) + del ema_state if master_process: torch.save(base_model.state_dict(), "final_model.pt") From 07f52c2347ba17cecd58cd913a0ee0ddbef2ad6e Mon Sep 17 00:00:00 2001 From: idan3011 Date: Sat, 21 Mar 2026 03:07:50 -0300 Subject: [PATCH 27/72] feat: XSA on last 4 layers --- train_gpt.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/train_gpt.py b/train_gpt.py index a9dd6a20b..46564ca31 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -719,6 +719,7 @@ def __init__( num_kv_heads: int, rope_base: float, qk_gain_init: float, + use_xsa: bool = False, ): super().__init__() if dim % num_heads != 0: @@ -738,6 +739,7 @@ def __init__( self.proj._zero_init = True self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) self.rotary = Rotary(self.head_dim, base=rope_base) + self.use_xsa = use_xsa def forward(self, x: Tensor) -> Tensor: bsz, seqlen, dim = x.shape @@ -758,6 +760,9 @@ def forward(self, x: Tensor) -> Tensor: is_causal=True, enable_gqa=(self.num_kv_heads != self.num_heads), ) + if self.use_xsa: + vn = F.normalize(v, dim=-1) + y = y - (y * vn).sum(dim=-1, keepdim=True) * vn y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) return self.proj(y) @@ -785,11 +790,12 @@ def __init__( mlp_mult: int, rope_base: float, qk_gain_init: float, + use_xsa: bool = False, ): super().__init__() self.attn_norm = RMSNorm() self.mlp_norm = RMSNorm() - self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, use_xsa=use_xsa) self.mlp = MLP(dim, mlp_mult) self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) @@ -864,6 +870,7 @@ def __init__( self.num_decoder_layers = num_layers - self.num_encoder_layers self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + xsa_last_n = int(os.environ.get("XSA_LAST_N", 4)) self.blocks = nn.ModuleList( [ Block( @@ -873,6 +880,7 @@ def __init__( mlp_mult, rope_base, qk_gain_init, + use_xsa=(i >= num_layers - xsa_last_n), ) for i in range(num_layers) ] From 92e1681d2e44e7c822ccd954ead561b6f6d8f697 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Sat, 21 Mar 2026 03:23:06 -0300 Subject: [PATCH 28/72] fix: XSA GQA shape mismatch - expand v to match num_heads --- train_gpt.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/train_gpt.py b/train_gpt.py index 46564ca31..1447588cf 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -761,7 +761,8 @@ def forward(self, x: Tensor) -> Tensor: enable_gqa=(self.num_kv_heads != self.num_heads), ) if self.use_xsa: - vn = F.normalize(v, dim=-1) + v_expanded = v.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1) + vn = F.normalize(v_expanded, dim=-1) y = y - (y * vn).sum(dim=-1, keepdim=True) * vn y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) return self.proj(y) From ec8babe3f7de5c6a087ab09a8d88c92d7d22ce37 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Sat, 21 Mar 2026 03:45:53 -0300 Subject: [PATCH 29/72] Record: +XSA last 4 layers (val_bpb=1.1629) --- .../submission.json | 26 ++-- .../train.log | 116 +++++++++--------- .../train_gpt.py | 11 +- 3 files changed, 82 insertions(+), 71 deletions(-) diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json index 2e7d8cf9f..2b040c91d 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json @@ -1,17 +1,17 @@ { "author": "Idanr", "github_id": "idan3011", - "name": "Pre-Enrichment + Encoder Recurrence + SmearGate + BigramHash", - "blurb": "GELU pre-enrichment (512-768-512) + 2x encoder recurrence + SmearGate + BigramHash + EMA + int6 QAT + lzma + MLP 3x + sliding window eval (stride=64), 10L 512d seq2048.", - "date": "2026-03-21T05:23:00Z", - "val_loss": 1.97015808, - "val_bpb": 1.16683859, - "pre_quant_val_loss": 2.0005, - "pre_quant_val_bpb": 1.1848, - "step_stop": 5373, - "wallclock_seconds": 600.011, - "eval_time_seconds": 233.562, - "bytes_total": 15022232, - "bytes_model_int6_lzma": 14963256, - "bytes_code": 58976 + "name": "Pre-Enrichment + Encoder Recurrence + XSA + SmearGate + BigramHash", + "blurb": "GELU pre-enrichment (512-768-512) + 2x encoder recurrence + XSA last 4 layers + SmearGate + BigramHash + EMA + int6 QAT + lzma + MLP 3x + sliding window eval (stride=64), 10L 512d seq2048.", + "date": "2026-03-21T06:25:00Z", + "val_loss": 1.96347005, + "val_bpb": 1.16287756, + "pre_quant_val_loss": 1.9940, + "pre_quant_val_bpb": 1.1809, + "step_stop": 5636, + "wallclock_seconds": 599.886, + "eval_time_seconds": 246.128, + "bytes_total": 15051927, + "bytes_model_int6_lzma": 14992500, + "bytes_code": 59427 } diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log index 51623c57b..c07c84223 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log @@ -1,8 +1,8 @@ -W0321 05:23:38.712000 1529 torch/distributed/run.py:803] -W0321 05:23:38.712000 1529 torch/distributed/run.py:803] ***************************************** -W0321 05:23:38.712000 1529 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. -W0321 05:23:38.712000 1529 torch/distributed/run.py:803] ***************************************** -logs/3c0fcd5a-d2fc-4352-b7ef-437df1f09800.txt +W0321 06:25:07.491000 851 torch/distributed/run.py:803] +W0321 06:25:07.491000 851 torch/distributed/run.py:803] ***************************************** +W0321 06:25:07.491000 851 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +W0321 06:25:07.491000 851 torch/distributed/run.py:803] ***************************************** +logs/dbb3f63a-cd40-41e5-aa32-4d819311430f.txt val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model train_loader:dataset:fineweb10B_sp1024 train_shards:80 val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 @@ -35,57 +35,59 @@ warmup_step:18/20 warmup_step:19/20 warmup_step:20/20 step:0/20000 val_loss:6.9317 val_bpb:4.1054 train_time:0ms step_avg:0.02ms -step:1/20000 train_loss:6.9316 train_time:126ms step_avg:125.94ms -step:2/20000 train_loss:7.3320 train_time:237ms step_avg:118.49ms -step:3/20000 train_loss:5.9003 train_time:348ms step_avg:115.96ms -step:4/20000 train_loss:6.1678 train_time:458ms step_avg:114.59ms -step:5/20000 train_loss:6.1356 train_time:569ms step_avg:113.80ms -step:6/20000 train_loss:5.4396 train_time:680ms step_avg:113.25ms -step:7/20000 train_loss:5.2519 train_time:790ms step_avg:112.88ms -step:8/20000 train_loss:5.2202 train_time:901ms step_avg:112.67ms -step:9/20000 train_loss:4.7776 train_time:1012ms step_avg:112.47ms -step:10/20000 train_loss:4.6439 train_time:1123ms step_avg:112.34ms -step:200/20000 train_loss:2.7676 train_time:22290ms step_avg:111.45ms -step:400/20000 train_loss:2.4202 train_time:44586ms step_avg:111.47ms -step:600/20000 train_loss:2.3056 train_time:66836ms step_avg:111.39ms -step:800/20000 train_loss:2.3780 train_time:89135ms step_avg:111.42ms -step:1000/20000 train_loss:2.3416 train_time:111395ms step_avg:111.39ms -step:1000/20000 val_loss:2.3198 val_bpb:1.3739 train_time:111404ms step_avg:111.40ms -step:1200/20000 train_loss:2.3797 train_time:133617ms step_avg:111.35ms -step:1400/20000 train_loss:2.3352 train_time:155927ms step_avg:111.38ms -step:1600/20000 train_loss:2.2978 train_time:178175ms step_avg:111.36ms -step:1800/20000 train_loss:2.0611 train_time:200399ms step_avg:111.33ms -step:2000/20000 train_loss:2.3231 train_time:222660ms step_avg:111.33ms -step:2000/20000 val_loss:2.2292 val_bpb:1.3203 train_time:222669ms step_avg:111.33ms -step:2200/20000 train_loss:2.0879 train_time:244848ms step_avg:111.29ms -step:2400/20000 train_loss:2.2229 train_time:267380ms step_avg:111.41ms -step:2600/20000 train_loss:2.3310 train_time:290575ms step_avg:111.76ms -step:2800/20000 train_loss:2.3929 train_time:313760ms step_avg:112.06ms -step:3000/20000 train_loss:2.1807 train_time:335990ms step_avg:112.00ms -step:3000/20000 val_loss:2.1559 val_bpb:1.2769 train_time:335999ms step_avg:112.00ms -step:3200/20000 train_loss:2.1952 train_time:358179ms step_avg:111.93ms -step:3400/20000 train_loss:2.1934 train_time:380451ms step_avg:111.90ms -step:3600/20000 train_loss:2.0642 train_time:402561ms step_avg:111.82ms -step:3800/20000 train_loss:2.0747 train_time:424723ms step_avg:111.77ms -step:4000/20000 train_loss:1.9111 train_time:447495ms step_avg:111.87ms -step:4000/20000 val_loss:2.0927 val_bpb:1.2394 train_time:447509ms step_avg:111.88ms -step:4200/20000 train_loss:1.9651 train_time:469610ms step_avg:111.81ms -step:4400/20000 train_loss:2.0660 train_time:491850ms step_avg:111.78ms -step:4600/20000 train_loss:1.9859 train_time:514138ms step_avg:111.77ms -step:4800/20000 train_loss:2.0249 train_time:536298ms step_avg:111.73ms -step:5000/20000 train_loss:2.0596 train_time:558656ms step_avg:111.73ms -step:5000/20000 val_loss:2.0228 val_bpb:1.1980 train_time:558664ms step_avg:111.73ms -step:5200/20000 train_loss:2.1060 train_time:580854ms step_avg:111.70ms -step:5373/20000 val_loss:2.0005 val_bpb:1.1848 train_time:600011ms step_avg:111.67ms -stopping_early: wallclock_cap train_time:600011ms step:5373/20000 -peak memory allocated: 14124 MiB reserved: 14642 MiB +step:1/20000 train_loss:6.9316 train_time:126ms step_avg:125.58ms +step:2/20000 train_loss:7.3329 train_time:273ms step_avg:136.58ms +step:3/20000 train_loss:5.8995 train_time:419ms step_avg:139.59ms +step:4/20000 train_loss:6.1572 train_time:549ms step_avg:137.20ms +step:5/20000 train_loss:6.1052 train_time:680ms step_avg:136.04ms +step:6/20000 train_loss:5.4252 train_time:1034ms step_avg:172.31ms +step:7/20000 train_loss:5.2387 train_time:1166ms step_avg:166.61ms +step:8/20000 train_loss:5.2325 train_time:1309ms step_avg:163.56ms +step:9/20000 train_loss:4.8017 train_time:1500ms step_avg:166.62ms +step:10/20000 train_loss:4.6419 train_time:1921ms step_avg:192.15ms +step:200/20000 train_loss:2.7593 train_time:22317ms step_avg:111.59ms +step:400/20000 train_loss:2.4099 train_time:43617ms step_avg:109.04ms +step:600/20000 train_loss:2.2983 train_time:64949ms step_avg:108.25ms +step:800/20000 train_loss:2.3723 train_time:86282ms step_avg:107.85ms +step:1000/20000 train_loss:2.3456 train_time:107467ms step_avg:107.47ms +step:1000/20000 val_loss:2.3152 val_bpb:1.3712 train_time:107481ms step_avg:107.48ms +step:1200/20000 train_loss:2.3702 train_time:128686ms step_avg:107.24ms +step:1400/20000 train_loss:2.3280 train_time:150061ms step_avg:107.19ms +step:1600/20000 train_loss:2.2929 train_time:171275ms step_avg:107.05ms +step:1800/20000 train_loss:2.0655 train_time:192473ms step_avg:106.93ms +step:2000/20000 train_loss:2.3196 train_time:213644ms step_avg:106.82ms +step:2000/20000 val_loss:2.2267 val_bpb:1.3188 train_time:213677ms step_avg:106.84ms +step:2200/20000 train_loss:2.0749 train_time:234859ms step_avg:106.75ms +step:2400/20000 train_loss:2.2259 train_time:256063ms step_avg:106.69ms +step:2600/20000 train_loss:2.3451 train_time:277328ms step_avg:106.66ms +step:2800/20000 train_loss:2.4005 train_time:298556ms step_avg:106.63ms +step:3000/20000 train_loss:2.1834 train_time:319701ms step_avg:106.57ms +step:3000/20000 val_loss:2.1620 val_bpb:1.2805 train_time:319720ms step_avg:106.57ms +step:3200/20000 train_loss:2.2050 train_time:340954ms step_avg:106.55ms +step:3400/20000 train_loss:2.2010 train_time:362147ms step_avg:106.51ms +step:3600/20000 train_loss:2.0771 train_time:383413ms step_avg:106.50ms +step:3800/20000 train_loss:2.0850 train_time:404583ms step_avg:106.47ms +step:4000/20000 train_loss:1.9226 train_time:425896ms step_avg:106.47ms +step:4000/20000 val_loss:2.1012 val_bpb:1.2444 train_time:425912ms step_avg:106.48ms +step:4200/20000 train_loss:1.9741 train_time:447139ms step_avg:106.46ms +step:4400/20000 train_loss:2.0774 train_time:468364ms step_avg:106.45ms +step:4600/20000 train_loss:1.9929 train_time:489668ms step_avg:106.45ms +step:4800/20000 train_loss:2.0345 train_time:510844ms step_avg:106.43ms +step:5000/20000 train_loss:2.0716 train_time:532219ms step_avg:106.44ms +step:5000/20000 val_loss:2.0359 val_bpb:1.2058 train_time:532261ms step_avg:106.45ms +step:5200/20000 train_loss:2.1192 train_time:553451ms step_avg:106.43ms +step:5400/20000 train_loss:1.8328 train_time:574769ms step_avg:106.44ms +step:5600/20000 train_loss:2.1500 train_time:596037ms step_avg:106.44ms +step:5636/20000 val_loss:1.9940 val_bpb:1.1809 train_time:599886ms step_avg:106.44ms +stopping_early: wallclock_cap train_time:599886ms step:5636/20000 +peak memory allocated: 14147 MiB reserved: 14652 MiB ema: loading exponential moving average weights Serialized model: 99355437 bytes -Code size: 58976 bytes -Total submission size: 99414413 bytes -Serialized model int6+lzma: 14963256 bytes (payload:25931584 raw_torch:25983851 payload_ratio:3.83x) -Total submission size int6+lzma: 15022232 bytes -final_int8_zlib_roundtrip val_loss:2.0075 val_bpb:1.1889 eval_time:3427ms -final_int8_zlib_roundtrip_exact val_loss:2.00746519 val_bpb:1.18893396 -final_sliding_window val_loss:1.9702 val_bpb:1.1668 eval_time:233562ms -final_sliding_window_exact val_loss:1.97015808 val_bpb:1.16683859 +Code size: 59427 bytes +Total submission size: 99414864 bytes +Serialized model int6+lzma: 14992500 bytes (payload:25931584 raw_torch:25983851 payload_ratio:3.83x) +Total submission size int6+lzma: 15051927 bytes +final_int8_zlib_roundtrip val_loss:2.0005 val_bpb:1.1848 eval_time:2960ms +final_int8_zlib_roundtrip_exact val_loss:2.00047915 val_bpb:1.18479644 +final_sliding_window val_loss:1.9635 val_bpb:1.1629 eval_time:246128ms +final_sliding_window_exact val_loss:1.96347005 val_bpb:1.16287756 diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py index a9dd6a20b..1447588cf 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py @@ -719,6 +719,7 @@ def __init__( num_kv_heads: int, rope_base: float, qk_gain_init: float, + use_xsa: bool = False, ): super().__init__() if dim % num_heads != 0: @@ -738,6 +739,7 @@ def __init__( self.proj._zero_init = True self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) self.rotary = Rotary(self.head_dim, base=rope_base) + self.use_xsa = use_xsa def forward(self, x: Tensor) -> Tensor: bsz, seqlen, dim = x.shape @@ -758,6 +760,10 @@ def forward(self, x: Tensor) -> Tensor: is_causal=True, enable_gqa=(self.num_kv_heads != self.num_heads), ) + if self.use_xsa: + v_expanded = v.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1) + vn = F.normalize(v_expanded, dim=-1) + y = y - (y * vn).sum(dim=-1, keepdim=True) * vn y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) return self.proj(y) @@ -785,11 +791,12 @@ def __init__( mlp_mult: int, rope_base: float, qk_gain_init: float, + use_xsa: bool = False, ): super().__init__() self.attn_norm = RMSNorm() self.mlp_norm = RMSNorm() - self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, use_xsa=use_xsa) self.mlp = MLP(dim, mlp_mult) self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) @@ -864,6 +871,7 @@ def __init__( self.num_decoder_layers = num_layers - self.num_encoder_layers self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + xsa_last_n = int(os.environ.get("XSA_LAST_N", 4)) self.blocks = nn.ModuleList( [ Block( @@ -873,6 +881,7 @@ def __init__( mlp_mult, rope_base, qk_gain_init, + use_xsa=(i >= num_layers - xsa_last_n), ) for i in range(num_layers) ] From 75eb80f7927ff48b3386147fe632957cb9888baa Mon Sep 17 00:00:00 2001 From: idan3011 Date: Sat, 21 Mar 2026 03:46:04 -0300 Subject: [PATCH 30/72] Record: Pre-Enrichment + Encoder Recurrence + XSA (val_bpb=1.1629) --- .../submission.json | 26 ++-- .../train.log | 116 +++++++++--------- .../train_gpt.py | 11 +- 3 files changed, 82 insertions(+), 71 deletions(-) diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json index 2e7d8cf9f..2b040c91d 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json @@ -1,17 +1,17 @@ { "author": "Idanr", "github_id": "idan3011", - "name": "Pre-Enrichment + Encoder Recurrence + SmearGate + BigramHash", - "blurb": "GELU pre-enrichment (512-768-512) + 2x encoder recurrence + SmearGate + BigramHash + EMA + int6 QAT + lzma + MLP 3x + sliding window eval (stride=64), 10L 512d seq2048.", - "date": "2026-03-21T05:23:00Z", - "val_loss": 1.97015808, - "val_bpb": 1.16683859, - "pre_quant_val_loss": 2.0005, - "pre_quant_val_bpb": 1.1848, - "step_stop": 5373, - "wallclock_seconds": 600.011, - "eval_time_seconds": 233.562, - "bytes_total": 15022232, - "bytes_model_int6_lzma": 14963256, - "bytes_code": 58976 + "name": "Pre-Enrichment + Encoder Recurrence + XSA + SmearGate + BigramHash", + "blurb": "GELU pre-enrichment (512-768-512) + 2x encoder recurrence + XSA last 4 layers + SmearGate + BigramHash + EMA + int6 QAT + lzma + MLP 3x + sliding window eval (stride=64), 10L 512d seq2048.", + "date": "2026-03-21T06:25:00Z", + "val_loss": 1.96347005, + "val_bpb": 1.16287756, + "pre_quant_val_loss": 1.9940, + "pre_quant_val_bpb": 1.1809, + "step_stop": 5636, + "wallclock_seconds": 599.886, + "eval_time_seconds": 246.128, + "bytes_total": 15051927, + "bytes_model_int6_lzma": 14992500, + "bytes_code": 59427 } diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log index 51623c57b..c07c84223 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log @@ -1,8 +1,8 @@ -W0321 05:23:38.712000 1529 torch/distributed/run.py:803] -W0321 05:23:38.712000 1529 torch/distributed/run.py:803] ***************************************** -W0321 05:23:38.712000 1529 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. -W0321 05:23:38.712000 1529 torch/distributed/run.py:803] ***************************************** -logs/3c0fcd5a-d2fc-4352-b7ef-437df1f09800.txt +W0321 06:25:07.491000 851 torch/distributed/run.py:803] +W0321 06:25:07.491000 851 torch/distributed/run.py:803] ***************************************** +W0321 06:25:07.491000 851 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +W0321 06:25:07.491000 851 torch/distributed/run.py:803] ***************************************** +logs/dbb3f63a-cd40-41e5-aa32-4d819311430f.txt val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model train_loader:dataset:fineweb10B_sp1024 train_shards:80 val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 @@ -35,57 +35,59 @@ warmup_step:18/20 warmup_step:19/20 warmup_step:20/20 step:0/20000 val_loss:6.9317 val_bpb:4.1054 train_time:0ms step_avg:0.02ms -step:1/20000 train_loss:6.9316 train_time:126ms step_avg:125.94ms -step:2/20000 train_loss:7.3320 train_time:237ms step_avg:118.49ms -step:3/20000 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train_time:532261ms step_avg:106.45ms +step:5200/20000 train_loss:2.1192 train_time:553451ms step_avg:106.43ms +step:5400/20000 train_loss:1.8328 train_time:574769ms step_avg:106.44ms +step:5600/20000 train_loss:2.1500 train_time:596037ms step_avg:106.44ms +step:5636/20000 val_loss:1.9940 val_bpb:1.1809 train_time:599886ms step_avg:106.44ms +stopping_early: wallclock_cap train_time:599886ms step:5636/20000 +peak memory allocated: 14147 MiB reserved: 14652 MiB ema: loading exponential moving average weights Serialized model: 99355437 bytes -Code size: 58976 bytes -Total submission size: 99414413 bytes -Serialized model int6+lzma: 14963256 bytes (payload:25931584 raw_torch:25983851 payload_ratio:3.83x) -Total submission size int6+lzma: 15022232 bytes -final_int8_zlib_roundtrip val_loss:2.0075 val_bpb:1.1889 eval_time:3427ms -final_int8_zlib_roundtrip_exact val_loss:2.00746519 val_bpb:1.18893396 -final_sliding_window val_loss:1.9702 val_bpb:1.1668 eval_time:233562ms -final_sliding_window_exact val_loss:1.97015808 val_bpb:1.16683859 +Code size: 59427 bytes +Total submission size: 99414864 bytes +Serialized model int6+lzma: 14992500 bytes (payload:25931584 raw_torch:25983851 payload_ratio:3.83x) +Total submission size int6+lzma: 15051927 bytes +final_int8_zlib_roundtrip val_loss:2.0005 val_bpb:1.1848 eval_time:2960ms +final_int8_zlib_roundtrip_exact val_loss:2.00047915 val_bpb:1.18479644 +final_sliding_window val_loss:1.9635 val_bpb:1.1629 eval_time:246128ms +final_sliding_window_exact val_loss:1.96347005 val_bpb:1.16287756 diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py index a9dd6a20b..1447588cf 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py @@ -719,6 +719,7 @@ def __init__( num_kv_heads: int, rope_base: float, qk_gain_init: float, + use_xsa: bool = False, ): super().__init__() if dim % num_heads != 0: @@ -738,6 +739,7 @@ def __init__( self.proj._zero_init = True self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) self.rotary = Rotary(self.head_dim, base=rope_base) + self.use_xsa = use_xsa def forward(self, x: Tensor) -> Tensor: bsz, seqlen, dim = x.shape @@ -758,6 +760,10 @@ def forward(self, x: Tensor) -> Tensor: is_causal=True, enable_gqa=(self.num_kv_heads != self.num_heads), ) + if self.use_xsa: + v_expanded = v.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1) + vn = F.normalize(v_expanded, dim=-1) + y = y - (y * vn).sum(dim=-1, keepdim=True) * vn y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) return self.proj(y) @@ -785,11 +791,12 @@ def __init__( mlp_mult: int, rope_base: float, qk_gain_init: float, + use_xsa: bool = False, ): super().__init__() self.attn_norm = RMSNorm() self.mlp_norm = RMSNorm() - self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, use_xsa=use_xsa) self.mlp = MLP(dim, mlp_mult) self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) @@ -864,6 +871,7 @@ def __init__( self.num_decoder_layers = num_layers - self.num_encoder_layers self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + xsa_last_n = int(os.environ.get("XSA_LAST_N", 4)) self.blocks = nn.ModuleList( [ Block( @@ -873,6 +881,7 @@ def __init__( mlp_mult, rope_base, qk_gain_init, + use_xsa=(i >= num_layers - xsa_last_n), ) for i in range(num_layers) ] From 8bf2406fb5e04455135d5a9351a08644a9459221 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Sat, 21 Mar 2026 03:58:00 -0300 Subject: [PATCH 31/72] Record: bake defaults + XSA (val_bpb=1.1629) --- .../README.md | 135 +++++++++++------- .../train_gpt.py | 14 +- 2 files changed, 91 insertions(+), 58 deletions(-) diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md index 703f1b359..446ffa534 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md @@ -1,6 +1,19 @@ -## Pre-Enrichment + Encoder Recurrence + SmearGate + BigramHash +## Pre-Enrichment + Encoder Recurrence + XSA + SmearGate + BigramHash -Architectural modifications to the baseline transformer achieving **val_bpb 1.1668** in a 15.02MB artifact trained in 10 minutes on 8xH100. Key techniques: GELU pre-enrichment (512→768→512), 2x encoder recurrence with RMS norm stabilization, SmearGate for lightweight bigram context, BigramHash for explicit bigram embeddings, and EMA weight averaging for quantization-friendly weights. +**val_bpb: 1.1629** (sliding window, stride=64) | 15.05 MB | 8xH100 SXM, 600s + +--- + +### Progress + +| | v1 | v2 | v3 | v4 (this) | +|---|---|---|---|---| +| val_bpb (sliding) | 1.1855 | 1.1709 | 1.1668 | **1.1629** | +| Params | 19.4M | 24.7M | 25.2M | 25.2M | +| Artifact | 15.75 MB | 15.57 MB | 15.02 MB | 15.05 MB | +| Steps (600s) | 8,004 | 6,423 | 5,373 | 5,636 | +| Step time | 75ms | 93ms | 112ms | 106ms | +| Quant gap | 0.020 | 0.020 | 0.004 | 0.004 | --- @@ -8,93 +21,115 @@ Architectural modifications to the baseline transformer achieving **val_bpb 1.16 #### GELU Pre-Enrichment (512→768→512) -Two `CastedLinear` projections with a GELU activation between them, applied after the embedding lookup and before the first transformer block. The wider hidden dimension (768 vs baseline 512) gives the model a richer nonlinear transformation before the residual stream begins. - -``` +Raw token embeddings carry no relational structure. I add a wider nonlinear transformation before the residual stream: embedding → BigramHash add → SmearGate → Linear(512→768) → GELU → Linear(768→512) → RMS Norm → transformer blocks -``` -#### 2x Encoder Recurrence +The wider bottleneck (768) gives the embedding transformation more capacity than the original 512→512. Cost: ~0.8M params, negligible step time. -I reuse the encoder blocks for a second pass before running the decoder, with RMS norm stabilization between passes. With 10 layers (5 encoder + 5 decoder), this produces **15 effective layers from 10 physical blocks** with zero extra parameters. +#### 2x Encoder Recurrence -**A/B Comparison — MLP 3x, seq 2048, int6 QAT (8xH100, 10 minutes):** +Depth recurrence is a known technique (ALBERT, Universal Transformers). My contribution is applying it to only the encoder half of a U-Net transformer architecture, with RMS norm stabilization between passes. -| Metric | With recurrence | Without recurrence | -|---------------------|--------------------|-----------------------| -| Steps completed | 6,423 | 8,950 | -| Step time | 93ms | 67ms | -| Sliding window BPB | **1.1709** | 1.1740 | +With 10 layers (5 encoder + 5 decoder), the forward pass becomes: +1. Run encoder blocks 0-4 (first pass) +2. RMS norm (stabilize between passes) +3. Run encoder blocks 0-4 again (second pass, refine) +4. Run decoder blocks 5-9 with skip connections from second encoder pass -Encoder recurrence consistently wins — deeper processing per step beats more gradient updates. +**15 effective layers from 10 physical blocks**, zero extra parameters. -#### SmearGate +**A/B Comparison — MLP 3x + seq 2048 config (8xH100, 10 minutes):** -Learned per-dimension gate (512 params) that blends each token's embedding with the previous token's embedding. Provides lightweight bigram context at the embedding layer. Initialized with gate bias 3.0 (sigmoid(3.0)≈0.95, near-identity at init). +| Metric | With recurrence | Without recurrence | +|---|---|---| +| Steps completed | 6,423 | 8,950 | +| Step time | 93ms | 67ms | +| Sliding window BPB | **1.1709** | 1.1740 | -#### BigramHash +**A/B Comparison — MLP 2x + seq 1024 config (8xH100, 10 minutes):** -Hash-table embedding mapping token bigrams to learned vectors. Hash formula: `(prev_token * 92821 + curr_token) % 4096`. Lookup table 4096×64, projected to model_dim via Linear(64, 512). Adds explicit bigram context to the token embedding. +| Metric | With recurrence | Without recurrence | +|---|---|---| +| Steps completed | 8,004 | 11,955 | +| Step time | 75ms | 50ms | +| Sliding window BPB | **1.1855** | 1.1947 | -#### EMA Weight Averaging +Recurrence wins across both configs despite 28-40% fewer gradient updates. -Exponential moving average (decay=0.997) updated every step, replacing SWA. EMA weights are loaded before quantization. Produces smoother weights that quantize significantly better — quant gap dropped from 0.020 (SWA) to **0.004** (EMA). +#### XSA (Exclusive Self Attention) on Last 4 Layers ---- +Removes self-value bias from attention output via orthogonal projection (arXiv:2603.09078). After computing attention output Y, XSA subtracts the component aligned with each token's own value vector: -### Additional Techniques +``` +Vn = normalize(V, dim=-1) +Y = Y - (Y · Vn).sum(dim=-1, keepdim=True) * Vn +``` -Int6 quantization-aware training (fake quant with STE in CastedLinear), lzma compression, MLP 3x expansion, overtone embedding init, decoupled Muon weight decay (0.04), AdamW weight decay (0.04), batched sliding window eval (stride=64), fp16 embedding passthrough in quantization. +Forces attention layers to capture purely contextual information from other tokens. Zero new parameters. Applied to last 4 layers only — early layers retain self-attention for basic feature building. Requires GQA-aware expansion of V to match Q head count before projection. -Hyperparameters: NUM_LAYERS=10, TRAIN_SEQ_LEN=2048, TRAIN_BATCH_TOKENS=393216, MATRIX_LR=0.028, SCALAR_LR=0.025, TIED_EMBED_LR=0.035, MUON_MOMENTUM=0.99, WARMDOWN_ITERS=3300. +v3 → v4 improvement: 1.1668 → 1.1629 (-0.004 BPB). --- -### What Didn't Work - -- **Phase-transition resid_mix init**: Sigmoid-scheduled initialization of resid_mix. Slowed convergence at our step count, hurt final score. +### Additional Techniques -- **Late-K passthrough**: Keeping last 2 layers' c_k.weight in fp16 during quantization. Added artifact size without enough BPB improvement. +- **SmearGate**: Per-dim learnable gate blending each token with previous token's embedding. 512 params. +- **BigramHash** (4096×64): Hash-table embedding for token bigrams, projected to model dim. ~590K params. +- **EMA** (decay=0.997): Exponential moving average replacing SWA. Quant gap reduced from 0.020 to 0.004 across versions. +- **Int6 QAT**: Fake quantization with straight-through estimator during training. Model learns int6-friendly weights. +- **lzma compression**: Stdlib replacement for zlib. Zero dependency risk. -- **Gradient clipping (GRAD_CLIP_NORM=1.0)**: Constrained the optimizer, slower per-step learning. +Also: MLP 3x, seq 2048, overtone init, Muon+AdamW WD=0.04, sliding window eval stride=64. -- **12 layers + MLP 2x**: 18 effective layers with recurrence but MLP 2x bottleneck was too narrow. 10L MLP 3x wins. +Overtone init, Muon weight decay, and sliding window eval adapted from notapplica and Matthew Li's work. -- **Full dataset (80 shards) with WD=0.04**: More diverse data didn't improve pre-quant BPB. Only helped quant gap when combined with higher WD. +--- -- **3x encoder recurrence**: Exceeded Triton's per-SM shared memory limit. Compiler limitation. +### What Didn't Work -- Also tried: full U-Net recurrence (too slow), reverse encoder pass order (worse), auxiliary encoder prediction loss (hurt performance), 6+3 encoder/decoder split (worse than 5+5). +- **FP16 embedding passthrough**: Reduced quant error by ~0.006 BPB but added ~520KB, pushing artifact over 16MB. +- **3x encoder recurrence**: Exceeded Triton's per-SM shared memory limit on A100 and RTX 4050. +- **Reverse encoder recurrence** (second pass in reverse order): Worse than forward-only (1.4140 vs 1.4077 on A100). +- **Auxiliary encoder loss**: Hurt performance. Encoder works better optimized purely for decoder consumption. +- **Phase-transition resid_mix + gradient clipping**: Borrowed from top submissions, hurt our config. Techniques tuned for non-recurrence setups don't always transfer. +- **12L MLP 2x with recurrence (18 effective layers)**: Numbers were significantly worse than 10L MLP 3x. Width beats depth at this scale. +- **Warmdown scheduler on A100**: Wallclock-aware warmdown decayed LR from step 0 on A100 (~1100ms/step). Override to WARMDOWN_ITERS=120 required for local development. --- ### Configuration +TRAIN_BATCH_TOKENS=393216 MATRIX_LR=0.028 MUON_WD=0.04 ADAM_WD=0.04 +WARMDOWN_ITERS=3300 NUM_LAYERS=10 MLP_MULT=3 TRAIN_SEQ_LEN=2048 +ENCODER_RECURRENCE=1 EMA_DECAY=0.997 XSA_LAST_N=4 -``` -RUN_CONFIG=A -VOCAB_SIZE=1024 NUM_LAYERS=10 MODEL_DIM=512 NUM_HEADS=8 NUM_KV_HEADS=4 MLP_MULT=3 -TIE_EMBEDDINGS=1 TIED_EMBED_LR=0.035 MATRIX_LR=0.028 SCALAR_LR=0.025 -MUON_MOMENTUM=0.99 MUON_MOMENTUM_WARMUP_START=0.92 MUON_MOMENTUM_WARMUP_STEPS=1500 -WARMDOWN_ITERS=3300 WARMUP_STEPS=20 TRAIN_BATCH_TOKENS=393216 TRAIN_SEQ_LEN=2048 -ENCODER_RECURRENCE=1 MUON_WD=0.04 ADAM_WD=0.04 EMA_DECAY=0.997 -``` +Model parameters: 25,222,224 +Submission size (int6+lzma): 15,051,927 bytes (code: 59,427 bytes) ### Reproduction -All defaults are baked into the script: +All defaults are baked into the script — no env vars needed. + ```bash -RUN_CONFIG=A torchrun --standalone --nproc_per_node=8 train_gpt.py +python3 data/cached_challenge_fineweb.py --variant sp1024 --train-shards 80 +torchrun --standalone --nproc_per_node=8 train_gpt.py ``` ### Key Metrics | Metric | Value | |---|---| -| Pre-quant val_bpb | 1.1848 | -| Post-quant val_bpb (standard) | 1.1889 | -| Post-quant val_bpb (sliding window) | **1.1668** | +| Pre-quant val_bpb | 1.1809 | +| Post-quant val_bpb (standard) | 1.1848 | +| Post-quant val_bpb (sliding window) | **1.1629** | | Quant gap (standard - pre-quant) | 0.004 | -| Training time | 600,011ms (5,373 steps at ~112ms) | -| Peak memory | 14,124 MiB | -| Submission size (int6+lzma) | 15,022,232 bytes | +| Training time | 599,886ms (5,636 steps at ~106ms) | +| Peak memory | 14,147 MiB | +| Submission size (int6+lzma) | 15,051,927 bytes | | Model parameters | 25,222,224 | + +### Included Files + +- `train_gpt.py` — standalone training script with all modifications +- `train.log` — full 8xH100 training log (seed 1337) +- `submission.json` — leaderboard metadata +- `README.md` — this file diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py index 1447588cf..80ef75210 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py @@ -37,8 +37,6 @@ # - vocab size 1024, sequence length 1024, tied embeddings # - 524,288 train tokens per step for 20,000 iterations with a ~10 minute cap -_RUN_CONFIG = os.environ.get("RUN_CONFIG", "A") - class Hyperparameters: data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") train_files = os.path.join(data_path, "fineweb_train_*.bin") @@ -52,19 +50,19 @@ class Hyperparameters: train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 200)) iterations = int(os.environ.get("ITERATIONS", 20000)) - warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 2100 if _RUN_CONFIG == "A" else 2600)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3300)) warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) - train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 524_288)) - train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048 if _RUN_CONFIG == "A" else 1024)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 393_216)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048)) max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) - num_layers = int(os.environ.get("NUM_LAYERS", 12 if _RUN_CONFIG == "C" else 10)) + num_layers = int(os.environ.get("NUM_LAYERS", 10)) num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) model_dim = int(os.environ.get("MODEL_DIM", 512)) num_heads = int(os.environ.get("NUM_HEADS", 8)) - mlp_mult = int(os.environ.get("MLP_MULT", 2 if _RUN_CONFIG == "C" else 3)) + mlp_mult = int(os.environ.get("MLP_MULT", 3)) tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) @@ -74,7 +72,7 @@ class Hyperparameters: head_lr = float(os.environ.get("HEAD_LR", 0.008)) tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.035)) tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) - matrix_lr = float(os.environ.get("MATRIX_LR", 0.035 if _RUN_CONFIG == "A" else 0.025)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.028)) scalar_lr = float(os.environ.get("SCALAR_LR", 0.025)) muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) From 309411420609d9aa1aa9e7a3ffdf37abaf8c8522 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Sat, 21 Mar 2026 03:58:11 -0300 Subject: [PATCH 32/72] Record: Pre-Enrichment + Encoder Recurrence + XSA + SmearGate + BigramHash (val_bpb=1.1629) --- .../README.md | 135 +++++++++++------- .../train_gpt.py | 14 +- 2 files changed, 91 insertions(+), 58 deletions(-) diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md index 703f1b359..446ffa534 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md @@ -1,6 +1,19 @@ -## Pre-Enrichment + Encoder Recurrence + SmearGate + BigramHash +## Pre-Enrichment + Encoder Recurrence + XSA + SmearGate + BigramHash -Architectural modifications to the baseline transformer achieving **val_bpb 1.1668** in a 15.02MB artifact trained in 10 minutes on 8xH100. Key techniques: GELU pre-enrichment (512→768→512), 2x encoder recurrence with RMS norm stabilization, SmearGate for lightweight bigram context, BigramHash for explicit bigram embeddings, and EMA weight averaging for quantization-friendly weights. +**val_bpb: 1.1629** (sliding window, stride=64) | 15.05 MB | 8xH100 SXM, 600s + +--- + +### Progress + +| | v1 | v2 | v3 | v4 (this) | +|---|---|---|---|---| +| val_bpb (sliding) | 1.1855 | 1.1709 | 1.1668 | **1.1629** | +| Params | 19.4M | 24.7M | 25.2M | 25.2M | +| Artifact | 15.75 MB | 15.57 MB | 15.02 MB | 15.05 MB | +| Steps (600s) | 8,004 | 6,423 | 5,373 | 5,636 | +| Step time | 75ms | 93ms | 112ms | 106ms | +| Quant gap | 0.020 | 0.020 | 0.004 | 0.004 | --- @@ -8,93 +21,115 @@ Architectural modifications to the baseline transformer achieving **val_bpb 1.16 #### GELU Pre-Enrichment (512→768→512) -Two `CastedLinear` projections with a GELU activation between them, applied after the embedding lookup and before the first transformer block. The wider hidden dimension (768 vs baseline 512) gives the model a richer nonlinear transformation before the residual stream begins. - -``` +Raw token embeddings carry no relational structure. I add a wider nonlinear transformation before the residual stream: embedding → BigramHash add → SmearGate → Linear(512→768) → GELU → Linear(768→512) → RMS Norm → transformer blocks -``` -#### 2x Encoder Recurrence +The wider bottleneck (768) gives the embedding transformation more capacity than the original 512→512. Cost: ~0.8M params, negligible step time. -I reuse the encoder blocks for a second pass before running the decoder, with RMS norm stabilization between passes. With 10 layers (5 encoder + 5 decoder), this produces **15 effective layers from 10 physical blocks** with zero extra parameters. +#### 2x Encoder Recurrence -**A/B Comparison — MLP 3x, seq 2048, int6 QAT (8xH100, 10 minutes):** +Depth recurrence is a known technique (ALBERT, Universal Transformers). My contribution is applying it to only the encoder half of a U-Net transformer architecture, with RMS norm stabilization between passes. -| Metric | With recurrence | Without recurrence | -|---------------------|--------------------|-----------------------| -| Steps completed | 6,423 | 8,950 | -| Step time | 93ms | 67ms | -| Sliding window BPB | **1.1709** | 1.1740 | +With 10 layers (5 encoder + 5 decoder), the forward pass becomes: +1. Run encoder blocks 0-4 (first pass) +2. RMS norm (stabilize between passes) +3. Run encoder blocks 0-4 again (second pass, refine) +4. Run decoder blocks 5-9 with skip connections from second encoder pass -Encoder recurrence consistently wins — deeper processing per step beats more gradient updates. +**15 effective layers from 10 physical blocks**, zero extra parameters. -#### SmearGate +**A/B Comparison — MLP 3x + seq 2048 config (8xH100, 10 minutes):** -Learned per-dimension gate (512 params) that blends each token's embedding with the previous token's embedding. Provides lightweight bigram context at the embedding layer. Initialized with gate bias 3.0 (sigmoid(3.0)≈0.95, near-identity at init). +| Metric | With recurrence | Without recurrence | +|---|---|---| +| Steps completed | 6,423 | 8,950 | +| Step time | 93ms | 67ms | +| Sliding window BPB | **1.1709** | 1.1740 | -#### BigramHash +**A/B Comparison — MLP 2x + seq 1024 config (8xH100, 10 minutes):** -Hash-table embedding mapping token bigrams to learned vectors. Hash formula: `(prev_token * 92821 + curr_token) % 4096`. Lookup table 4096×64, projected to model_dim via Linear(64, 512). Adds explicit bigram context to the token embedding. +| Metric | With recurrence | Without recurrence | +|---|---|---| +| Steps completed | 8,004 | 11,955 | +| Step time | 75ms | 50ms | +| Sliding window BPB | **1.1855** | 1.1947 | -#### EMA Weight Averaging +Recurrence wins across both configs despite 28-40% fewer gradient updates. -Exponential moving average (decay=0.997) updated every step, replacing SWA. EMA weights are loaded before quantization. Produces smoother weights that quantize significantly better — quant gap dropped from 0.020 (SWA) to **0.004** (EMA). +#### XSA (Exclusive Self Attention) on Last 4 Layers ---- +Removes self-value bias from attention output via orthogonal projection (arXiv:2603.09078). After computing attention output Y, XSA subtracts the component aligned with each token's own value vector: -### Additional Techniques +``` +Vn = normalize(V, dim=-1) +Y = Y - (Y · Vn).sum(dim=-1, keepdim=True) * Vn +``` -Int6 quantization-aware training (fake quant with STE in CastedLinear), lzma compression, MLP 3x expansion, overtone embedding init, decoupled Muon weight decay (0.04), AdamW weight decay (0.04), batched sliding window eval (stride=64), fp16 embedding passthrough in quantization. +Forces attention layers to capture purely contextual information from other tokens. Zero new parameters. Applied to last 4 layers only — early layers retain self-attention for basic feature building. Requires GQA-aware expansion of V to match Q head count before projection. -Hyperparameters: NUM_LAYERS=10, TRAIN_SEQ_LEN=2048, TRAIN_BATCH_TOKENS=393216, MATRIX_LR=0.028, SCALAR_LR=0.025, TIED_EMBED_LR=0.035, MUON_MOMENTUM=0.99, WARMDOWN_ITERS=3300. +v3 → v4 improvement: 1.1668 → 1.1629 (-0.004 BPB). --- -### What Didn't Work - -- **Phase-transition resid_mix init**: Sigmoid-scheduled initialization of resid_mix. Slowed convergence at our step count, hurt final score. +### Additional Techniques -- **Late-K passthrough**: Keeping last 2 layers' c_k.weight in fp16 during quantization. Added artifact size without enough BPB improvement. +- **SmearGate**: Per-dim learnable gate blending each token with previous token's embedding. 512 params. +- **BigramHash** (4096×64): Hash-table embedding for token bigrams, projected to model dim. ~590K params. +- **EMA** (decay=0.997): Exponential moving average replacing SWA. Quant gap reduced from 0.020 to 0.004 across versions. +- **Int6 QAT**: Fake quantization with straight-through estimator during training. Model learns int6-friendly weights. +- **lzma compression**: Stdlib replacement for zlib. Zero dependency risk. -- **Gradient clipping (GRAD_CLIP_NORM=1.0)**: Constrained the optimizer, slower per-step learning. +Also: MLP 3x, seq 2048, overtone init, Muon+AdamW WD=0.04, sliding window eval stride=64. -- **12 layers + MLP 2x**: 18 effective layers with recurrence but MLP 2x bottleneck was too narrow. 10L MLP 3x wins. +Overtone init, Muon weight decay, and sliding window eval adapted from notapplica and Matthew Li's work. -- **Full dataset (80 shards) with WD=0.04**: More diverse data didn't improve pre-quant BPB. Only helped quant gap when combined with higher WD. +--- -- **3x encoder recurrence**: Exceeded Triton's per-SM shared memory limit. Compiler limitation. +### What Didn't Work -- Also tried: full U-Net recurrence (too slow), reverse encoder pass order (worse), auxiliary encoder prediction loss (hurt performance), 6+3 encoder/decoder split (worse than 5+5). +- **FP16 embedding passthrough**: Reduced quant error by ~0.006 BPB but added ~520KB, pushing artifact over 16MB. +- **3x encoder recurrence**: Exceeded Triton's per-SM shared memory limit on A100 and RTX 4050. +- **Reverse encoder recurrence** (second pass in reverse order): Worse than forward-only (1.4140 vs 1.4077 on A100). +- **Auxiliary encoder loss**: Hurt performance. Encoder works better optimized purely for decoder consumption. +- **Phase-transition resid_mix + gradient clipping**: Borrowed from top submissions, hurt our config. Techniques tuned for non-recurrence setups don't always transfer. +- **12L MLP 2x with recurrence (18 effective layers)**: Numbers were significantly worse than 10L MLP 3x. Width beats depth at this scale. +- **Warmdown scheduler on A100**: Wallclock-aware warmdown decayed LR from step 0 on A100 (~1100ms/step). Override to WARMDOWN_ITERS=120 required for local development. --- ### Configuration +TRAIN_BATCH_TOKENS=393216 MATRIX_LR=0.028 MUON_WD=0.04 ADAM_WD=0.04 +WARMDOWN_ITERS=3300 NUM_LAYERS=10 MLP_MULT=3 TRAIN_SEQ_LEN=2048 +ENCODER_RECURRENCE=1 EMA_DECAY=0.997 XSA_LAST_N=4 -``` -RUN_CONFIG=A -VOCAB_SIZE=1024 NUM_LAYERS=10 MODEL_DIM=512 NUM_HEADS=8 NUM_KV_HEADS=4 MLP_MULT=3 -TIE_EMBEDDINGS=1 TIED_EMBED_LR=0.035 MATRIX_LR=0.028 SCALAR_LR=0.025 -MUON_MOMENTUM=0.99 MUON_MOMENTUM_WARMUP_START=0.92 MUON_MOMENTUM_WARMUP_STEPS=1500 -WARMDOWN_ITERS=3300 WARMUP_STEPS=20 TRAIN_BATCH_TOKENS=393216 TRAIN_SEQ_LEN=2048 -ENCODER_RECURRENCE=1 MUON_WD=0.04 ADAM_WD=0.04 EMA_DECAY=0.997 -``` +Model parameters: 25,222,224 +Submission size (int6+lzma): 15,051,927 bytes (code: 59,427 bytes) ### Reproduction -All defaults are baked into the script: +All defaults are baked into the script — no env vars needed. + ```bash -RUN_CONFIG=A torchrun --standalone --nproc_per_node=8 train_gpt.py +python3 data/cached_challenge_fineweb.py --variant sp1024 --train-shards 80 +torchrun --standalone --nproc_per_node=8 train_gpt.py ``` ### Key Metrics | Metric | Value | |---|---| -| Pre-quant val_bpb | 1.1848 | -| Post-quant val_bpb (standard) | 1.1889 | -| Post-quant val_bpb (sliding window) | **1.1668** | +| Pre-quant val_bpb | 1.1809 | +| Post-quant val_bpb (standard) | 1.1848 | +| Post-quant val_bpb (sliding window) | **1.1629** | | Quant gap (standard - pre-quant) | 0.004 | -| Training time | 600,011ms (5,373 steps at ~112ms) | -| Peak memory | 14,124 MiB | -| Submission size (int6+lzma) | 15,022,232 bytes | +| Training time | 599,886ms (5,636 steps at ~106ms) | +| Peak memory | 14,147 MiB | +| Submission size (int6+lzma) | 15,051,927 bytes | | Model parameters | 25,222,224 | + +### Included Files + +- `train_gpt.py` — standalone training script with all modifications +- `train.log` — full 8xH100 training log (seed 1337) +- `submission.json` — leaderboard metadata +- `README.md` — this file diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py index 1447588cf..80ef75210 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py @@ -37,8 +37,6 @@ # - vocab size 1024, sequence length 1024, tied embeddings # - 524,288 train tokens per step for 20,000 iterations with a ~10 minute cap -_RUN_CONFIG = os.environ.get("RUN_CONFIG", "A") - class Hyperparameters: data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") train_files = os.path.join(data_path, "fineweb_train_*.bin") @@ -52,19 +50,19 @@ class Hyperparameters: train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 200)) iterations = int(os.environ.get("ITERATIONS", 20000)) - warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 2100 if _RUN_CONFIG == "A" else 2600)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3300)) warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) - train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 524_288)) - train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048 if _RUN_CONFIG == "A" else 1024)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 393_216)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048)) max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) - num_layers = int(os.environ.get("NUM_LAYERS", 12 if _RUN_CONFIG == "C" else 10)) + num_layers = int(os.environ.get("NUM_LAYERS", 10)) num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) model_dim = int(os.environ.get("MODEL_DIM", 512)) num_heads = int(os.environ.get("NUM_HEADS", 8)) - mlp_mult = int(os.environ.get("MLP_MULT", 2 if _RUN_CONFIG == "C" else 3)) + mlp_mult = int(os.environ.get("MLP_MULT", 3)) tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) @@ -74,7 +72,7 @@ class Hyperparameters: head_lr = float(os.environ.get("HEAD_LR", 0.008)) tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.035)) tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) - matrix_lr = float(os.environ.get("MATRIX_LR", 0.035 if _RUN_CONFIG == "A" else 0.025)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.028)) scalar_lr = float(os.environ.get("SCALAR_LR", 0.025)) muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) From cda125328bc6923bf75059d40fa2eb425ea062da Mon Sep 17 00:00:00 2001 From: idan3011 Date: Sat, 21 Mar 2026 14:08:38 -0300 Subject: [PATCH 33/72] feat: asymmetric MLP widths (encoder 2x, decoder 3x) --- train_gpt.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/train_gpt.py b/train_gpt.py index 1447588cf..9eaeef4c1 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -872,13 +872,15 @@ def __init__( self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) xsa_last_n = int(os.environ.get("XSA_LAST_N", 4)) + mlp_mult_enc = int(os.environ.get("MLP_MULT_ENCODER", 2)) + mlp_mult_dec = int(os.environ.get("MLP_MULT_DECODER", mlp_mult)) self.blocks = nn.ModuleList( [ Block( model_dim, num_heads, num_kv_heads, - mlp_mult, + mlp_mult_enc if i < self.num_encoder_layers else mlp_mult_dec, rope_base, qk_gain_init, use_xsa=(i >= num_layers - xsa_last_n), From 198a30b2281005086243a60d2707d85301f1d72d Mon Sep 17 00:00:00 2001 From: idan3011 Date: Sat, 21 Mar 2026 15:06:54 -0300 Subject: [PATCH 34/72] feat: delayed recurrence + asymmetric MLP + partial recurrence + KV cache + ceiling split Bug fixes: forward passes recurrence_enabled as arg (not attribute), ceiling division for encoder split, KV cache read once at init. New: RECURRENCE_START_STEP, RECURRENCE_ENCODER_START, MLP_MULT_ENCODER=2, MLP_MULT_DECODER, KV_CACHE_REUSE. All toggleable via env vars. --- train_gpt.py | 84 ++++++++++++++++++++++++++++++++-------------------- 1 file changed, 52 insertions(+), 32 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 9eaeef4c1..c0f576312 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -87,6 +87,8 @@ class Hyperparameters: muon_wd = float(os.environ.get("MUON_WD", 0.04)) adam_wd = float(os.environ.get("ADAM_WD", 0.04)) ema_decay = float(os.environ.get("EMA_DECAY", 0.997)) + recurrence_start_step = int(os.environ.get("RECURRENCE_START_STEP", 0)) + recurrence_encoder_start = int(os.environ.get("RECURRENCE_ENCODER_START", 0)) # ----------------------------- # MUON OPTIMIZER @@ -741,16 +743,21 @@ def __init__( self.rotary = Rotary(self.head_dim, base=rope_base) self.use_xsa = use_xsa - def forward(self, x: Tensor) -> Tensor: + def forward(self, x: Tensor, cached_kv: tuple[Tensor, Tensor] | None = None) -> tuple[Tensor, tuple[Tensor, Tensor] | None]: bsz, seqlen, dim = x.shape q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) - k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) - v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + if cached_kv is not None: + k, v = cached_kv + else: + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) q = F.rms_norm(q, (q.size(-1),)) - k = F.rms_norm(k, (k.size(-1),)) + if cached_kv is None: + k = F.rms_norm(k, (k.size(-1),)) cos, sin = self.rotary(seqlen, x.device, q.dtype) q = apply_rotary_emb(q, cos, sin) - k = apply_rotary_emb(k, cos, sin) + if cached_kv is None: + k = apply_rotary_emb(k, cos, sin) q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None] y = F.scaled_dot_product_attention( q, @@ -765,7 +772,8 @@ def forward(self, x: Tensor) -> Tensor: vn = F.normalize(v_expanded, dim=-1) y = y - (y * vn).sum(dim=-1, keepdim=True) * vn y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) - return self.proj(y) + kv_out = (k.detach(), v.detach()) if cached_kv is None else None + return self.proj(y), kv_out class MLP(nn.Module): @@ -802,13 +810,13 @@ def __init__( self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) - def forward(self, x: Tensor, x0: Tensor) -> Tensor: + def forward(self, x: Tensor, x0: Tensor, cached_kv: tuple[Tensor, Tensor] | None = None) -> tuple[Tensor, tuple[Tensor, Tensor] | None]: mix = self.resid_mix.to(dtype=x.dtype) x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 - attn_out = self.attn(self.attn_norm(x)) + attn_out, kv_out = self.attn(self.attn_norm(x), cached_kv=cached_kv) x = x + self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out x = x + self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x)) - return x + return x, kv_out class SmearGate(nn.Module): @@ -858,6 +866,8 @@ def __init__( self.tied_embed_init_std = tied_embed_init_std self.logit_softcap = logit_softcap self.encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) + self._kv_cache_reuse = bool(int(os.environ.get("KV_CACHE_REUSE", "0"))) + self._recurrence_encoder_start = int(os.environ.get("RECURRENCE_ENCODER_START", 0)) self.tok_emb = nn.Embedding(vocab_size, model_dim) self.bigram_hash = BigramHash(4096, 64, model_dim) self.smear_gate = SmearGate(model_dim) @@ -867,7 +877,7 @@ def __init__( nn.GELU(), CastedLinear(pre_enrich_hidden, model_dim, bias=False), ) - self.num_encoder_layers = num_layers // 2 + self.num_encoder_layers = (num_layers + 1) // 2 self.num_decoder_layers = num_layers - self.num_encoder_layers self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) @@ -905,29 +915,38 @@ def _init_weights(self) -> None: if isinstance(module, nn.Linear) and getattr(module, "_zero_init", False): nn.init.zeros_(module.weight) - def _run_blocks(self, x: Tensor, x0: Tensor) -> Tensor: - if self.encoder_recurrence: - for _pass in range(2): - skips: list[Tensor] = [] - for i in range(self.num_encoder_layers): - x = self.blocks[i](x, x0) - skips.append(x) - if _pass == 0: - x = F.rms_norm(x, (x.size(-1),)) - continue - for i in range(self.num_decoder_layers): - if skips: - x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() - x = self.blocks[self.num_encoder_layers + i](x, x0) + def _run_blocks(self, x: Tensor, x0: Tensor, use_recurrence: bool = True) -> Tensor: + rec_start = self._recurrence_encoder_start + if use_recurrence: + kv_caches: list[tuple[Tensor, Tensor]] = [] + skips: list[Tensor] = [] + for i in range(self.num_encoder_layers): + x, kv_out = self.blocks[i](x, x0) + skips.append(x) + if self._kv_cache_reuse and kv_out is not None and i >= rec_start: + kv_caches.append(kv_out) + x = F.rms_norm(x, (x.size(-1),)) + skips2: list[Tensor] = [] + kv_idx = 0 + for i in range(self.num_encoder_layers): + if i >= rec_start: + cached = kv_caches[kv_idx] if self._kv_cache_reuse and kv_idx < len(kv_caches) else None + x, _ = self.blocks[i](x, x0, cached_kv=cached) + if cached is not None: + kv_idx += 1 + else: + x, _ = self.blocks[i](x, x0) + skips2.append(x) + skips = skips2 else: skips: list[Tensor] = [] for i in range(self.num_encoder_layers): - x = self.blocks[i](x, x0) + x, _ = self.blocks[i](x, x0) skips.append(x) - for i in range(self.num_decoder_layers): - if skips: - x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() - x = self.blocks[self.num_encoder_layers + i](x, x0) + for i in range(self.num_decoder_layers): + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + x, _ = self.blocks[self.num_encoder_layers + i](x, x0) return x def _compute_logits(self, x: Tensor) -> Tensor: @@ -939,13 +958,13 @@ def _compute_logits(self, x: Tensor) -> Tensor: logits_proj = self.lm_head(x) return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) - def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: + def forward(self, input_ids: Tensor, target_ids: Tensor, recurrence_enabled: bool = True) -> Tensor: x = self.tok_emb(input_ids) + self.bigram_hash(input_ids) x = self.smear_gate(x) x = self.pre_enrich(x) x = F.rms_norm(x, (x.size(-1),)) x0 = x - x = self._run_blocks(x, x0) + x = self._run_blocks(x, x0, use_recurrence=recurrence_enabled and self.encoder_recurrence) x = self.final_norm(x).reshape(-1, x.size(-1)) targets = target_ids.reshape(-1) logits = self._compute_logits(x) @@ -1254,6 +1273,7 @@ def lr_mul(step: int, elapsed_ms: float) -> float: elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) scale = lr_mul(step, elapsed_ms) + recurrence_on = step >= args.recurrence_start_step zero_grad_all() train_loss = torch.zeros((), device=device) for micro_step in range(grad_accum_steps): @@ -1261,7 +1281,7 @@ def lr_mul(step: int, elapsed_ms: float) -> float: model.require_backward_grad_sync = micro_step == grad_accum_steps - 1 x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): - loss = model(x, y) + loss = model(x, y, recurrence_enabled=recurrence_on) train_loss += loss.detach() (loss * grad_scale).backward() train_loss /= grad_accum_steps From 7f1a0fd011c0d653d4e480f7f8109915c2a4d467 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Mon, 23 Mar 2026 16:07:19 -0300 Subject: [PATCH 35/72] feat: TTT (test-time training) eval with SGD, freeze first 2 blocks --- train_gpt.py | 77 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 77 insertions(+) diff --git a/train_gpt.py b/train_gpt.py index c0f576312..d89800dcb 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -89,6 +89,11 @@ class Hyperparameters: ema_decay = float(os.environ.get("EMA_DECAY", 0.997)) recurrence_start_step = int(os.environ.get("RECURRENCE_START_STEP", 0)) recurrence_encoder_start = int(os.environ.get("RECURRENCE_ENCODER_START", 0)) + ttt_enabled = bool(int(os.environ.get("TTT_ENABLED", "0"))) + ttt_epochs = int(os.environ.get("TTT_EPOCHS", 3)) + ttt_lr = float(os.environ.get("TTT_LR", 0.002)) + ttt_chunk_size = int(os.environ.get("TTT_CHUNK_SIZE", 32768)) + ttt_freeze_blocks = int(os.environ.get("TTT_FREEZE_BLOCKS", 2)) # ----------------------------- # MUON OPTIMIZER @@ -351,6 +356,64 @@ def eval_val_sliding( return float(val_loss), float(bpb) +def eval_val_ttt( + args: Hyperparameters, base_model: nn.Module, rank: int, world_size: int, + device: torch.device, val_tokens: Tensor, base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, +) -> tuple[float, float]: + seq_len, chunk_size = args.train_seq_len, args.ttt_chunk_size + num_chunks = (val_tokens.numel() - 1) // chunk_size + my_chunks = list(range(rank, num_chunks, world_size)) + original_state = {k: v.detach().cpu().clone() for k, v in base_model.state_dict().items()} + frozen_names = set() + for i in range(args.ttt_freeze_blocks): + for name, _ in base_model.blocks[i].named_parameters(): + frozen_names.add(f"blocks.{i}.{name}") + total_loss = torch.zeros((), device=device, dtype=torch.float64) + total_tokens_counted = torch.zeros((), device=device, dtype=torch.float64) + total_bytes = torch.zeros((), device=device, dtype=torch.float64) + for ci in my_chunks: + base_model.load_state_dict(original_state, strict=True) + start = ci * chunk_size + chunk = val_tokens[start:min(start + chunk_size + 1, val_tokens.numel())].to(device=device, dtype=torch.int64) + if chunk.numel() < 2: + continue + x_chunk, y_chunk = chunk[:-1].unsqueeze(0), chunk[1:].unsqueeze(0) + actual_len = x_chunk.shape[1] + base_model.eval() + with torch.inference_mode(): + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + logits = base_model.forward_logits(x_chunk) + ptl = F.cross_entropy(logits.float().reshape(-1, logits.size(-1)), y_chunk.reshape(-1), reduction="none") + total_loss += ptl.to(torch.float64).sum() + total_tokens_counted += float(actual_len) + tb = base_bytes_lut[y_chunk[0]].to(dtype=torch.int16) + tb += (has_leading_space_lut[y_chunk[0]] & ~is_boundary_token_lut[x_chunk[0]]).to(dtype=torch.int16) + total_bytes += tb.to(torch.float64).sum() + for name, param in base_model.named_parameters(): + param.requires_grad_(name not in frozen_names) + ttt_opt = torch.optim.SGD([p for p in base_model.parameters() if p.requires_grad], lr=args.ttt_lr, momentum=0.9) + base_model.train() + for _ in range(args.ttt_epochs): + for s in range(0, actual_len - seq_len + 1, seq_len): + ttt_opt.zero_grad() + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = base_model(x_chunk[:, s:s + seq_len], y_chunk[:, s:s + seq_len]) + loss.backward() + ttt_opt.step() + for param in base_model.parameters(): + param.requires_grad_(True) + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(total_loss, op=dist.ReduceOp.SUM) + dist.all_reduce(total_tokens_counted, op=dist.ReduceOp.SUM) + dist.all_reduce(total_bytes, op=dist.ReduceOp.SUM) + val_loss = (total_loss / total_tokens_counted).item() + bpb = (total_loss / (total_bytes * math.log(2.0))).item() + base_model.load_state_dict(original_state, strict=True) + base_model.train() + return float(val_loss), float(bpb) + + # ----------------------------- # POST-TRAINING QUANTIZATION # ----------------------------- @@ -1406,6 +1469,20 @@ def lr_mul(step: int, elapsed_ms: float) -> float: ) log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f}") + if args.ttt_enabled: + torch.cuda.synchronize() + t_ttt = time.perf_counter() + ttt_val_loss, ttt_val_bpb = eval_val_ttt( + args, base_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + ) + torch.cuda.synchronize() + log0( + f"final_ttt val_loss:{ttt_val_loss:.4f} val_bpb:{ttt_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_ttt):.0f}ms" + ) + log0(f"final_ttt_exact val_loss:{ttt_val_loss:.8f} val_bpb:{ttt_val_bpb:.8f}") + if distributed: dist.destroy_process_group() From 76508bbb2f1f8945afb6a1dd8835e20efa844015 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 01:40:07 -0300 Subject: [PATCH 36/72] =?UTF-8?q?feat:=20LeakyReLU=C2=B2=20+=20GPTQ-lite?= =?UTF-8?q?=20+=20remove=20dead=20code=20(KV=20cache,=20=20=20delayed/part?= =?UTF-8?q?ial=20recurrence)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- train_gpt.py | 129 ++++++++++++++++++--------------------------------- 1 file changed, 46 insertions(+), 83 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index d89800dcb..1b6c06ab1 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -87,8 +87,8 @@ class Hyperparameters: muon_wd = float(os.environ.get("MUON_WD", 0.04)) adam_wd = float(os.environ.get("ADAM_WD", 0.04)) ema_decay = float(os.environ.get("EMA_DECAY", 0.997)) - recurrence_start_step = int(os.environ.get("RECURRENCE_START_STEP", 0)) - recurrence_encoder_start = int(os.environ.get("RECURRENCE_ENCODER_START", 0)) + leaky_relu = bool(int(os.environ.get("LEAKY_RELU", "0"))) + qat_start_frac = float(os.environ.get("QAT_START_FRAC", 0.0)) ttt_enabled = bool(int(os.environ.get("TTT_ENABLED", "0"))) ttt_epochs = int(os.environ.get("TTT_EPOCHS", 3)) ttt_lr = float(os.environ.get("TTT_LR", 0.002)) @@ -479,15 +479,20 @@ def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: def quantize_float_tensor_int6(t: Tensor) -> tuple[Tensor, Tensor]: t32 = t.float() if t32.ndim == 2: - clip_abs = ( - torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) - if t32.numel() - else torch.empty((t32.shape[0],), dtype=torch.float32) - ) - clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) - scale = (clip_abs / 31.0).clamp_min(1.0 / 31.0) - q = torch.clamp(torch.round(clipped / scale[:, None]), -31, 31).to(torch.int8).contiguous() - return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + best_q = None + best_scale = None + best_mse = float("inf") + for pct in [0.999, 0.9999, 0.99999, 0.999999, 0.9999999]: + clip_abs = torch.quantile(t32.abs(), pct, dim=1) if t32.numel() else torch.empty((t32.shape[0],), dtype=torch.float32) + s = (clip_abs / 31.0).clamp_min(1.0 / 31.0) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + q = torch.clamp(torch.round(clipped / s[:, None]), -31, 31) + mse = ((q * s[:, None] - t32) ** 2).mean().item() + if mse < best_mse: + best_mse = mse + best_q = q.to(torch.int8).contiguous() + best_scale = s.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + return best_q, best_scale clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 scale = torch.tensor(clip_abs / 31.0 if clip_abs > 0 else 1.0, dtype=torch.float32) q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -31, 31).to(torch.int8).contiguous() @@ -806,80 +811,57 @@ def __init__( self.rotary = Rotary(self.head_dim, base=rope_base) self.use_xsa = use_xsa - def forward(self, x: Tensor, cached_kv: tuple[Tensor, Tensor] | None = None) -> tuple[Tensor, tuple[Tensor, Tensor] | None]: + def forward(self, x: Tensor) -> Tensor: bsz, seqlen, dim = x.shape q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) - if cached_kv is not None: - k, v = cached_kv - else: - k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) - v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) q = F.rms_norm(q, (q.size(-1),)) - if cached_kv is None: - k = F.rms_norm(k, (k.size(-1),)) + k = F.rms_norm(k, (k.size(-1),)) cos, sin = self.rotary(seqlen, x.device, q.dtype) q = apply_rotary_emb(q, cos, sin) - if cached_kv is None: - k = apply_rotary_emb(k, cos, sin) + k = apply_rotary_emb(k, cos, sin) q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None] - y = F.scaled_dot_product_attention( - q, - k, - v, - attn_mask=None, - is_causal=True, - enable_gqa=(self.num_kv_heads != self.num_heads), - ) + y = F.scaled_dot_product_attention(q, k, v, attn_mask=None, is_causal=True, enable_gqa=(self.num_kv_heads != self.num_heads)) if self.use_xsa: - v_expanded = v.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1) - vn = F.normalize(v_expanded, dim=-1) + vn = F.normalize(v.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1), dim=-1) y = y - (y * vn).sum(dim=-1, keepdim=True) * vn y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) - kv_out = (k.detach(), v.detach()) if cached_kv is None else None - return self.proj(y), kv_out + return self.proj(y) class MLP(nn.Module): - # relu^2 MLP from the original modded-nanogpt setup - def __init__(self, dim: int, mlp_mult: int): + def __init__(self, dim: int, mlp_mult: int, leaky: bool = False): super().__init__() hidden = mlp_mult * dim self.fc = CastedLinear(dim, hidden, bias=False) self.proj = CastedLinear(hidden, dim, bias=False) self.proj._zero_init = True + self._leaky = leaky def forward(self, x: Tensor) -> Tensor: - x = torch.relu(self.fc(x)) + x = F.leaky_relu(self.fc(x), 0.5) if self._leaky else torch.relu(self.fc(x)) return self.proj(x.square()) class Block(nn.Module): - def __init__( - self, - dim: int, - num_heads: int, - num_kv_heads: int, - mlp_mult: int, - rope_base: float, - qk_gain_init: float, - use_xsa: bool = False, - ): + def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: int, + rope_base: float, qk_gain_init: float, use_xsa: bool = False, leaky: bool = False): super().__init__() self.attn_norm = RMSNorm() self.mlp_norm = RMSNorm() self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, use_xsa=use_xsa) - self.mlp = MLP(dim, mlp_mult) + self.mlp = MLP(dim, mlp_mult, leaky=leaky) self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) - def forward(self, x: Tensor, x0: Tensor, cached_kv: tuple[Tensor, Tensor] | None = None) -> tuple[Tensor, tuple[Tensor, Tensor] | None]: + def forward(self, x: Tensor, x0: Tensor) -> Tensor: mix = self.resid_mix.to(dtype=x.dtype) x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 - attn_out, kv_out = self.attn(self.attn_norm(x), cached_kv=cached_kv) - x = x + self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out + x = x + self.attn_scale.to(dtype=x.dtype)[None, None, :] * self.attn(self.attn_norm(x)) x = x + self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x)) - return x, kv_out + return x class SmearGate(nn.Module): @@ -929,8 +911,6 @@ def __init__( self.tied_embed_init_std = tied_embed_init_std self.logit_softcap = logit_softcap self.encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) - self._kv_cache_reuse = bool(int(os.environ.get("KV_CACHE_REUSE", "0"))) - self._recurrence_encoder_start = int(os.environ.get("RECURRENCE_ENCODER_START", 0)) self.tok_emb = nn.Embedding(vocab_size, model_dim) self.bigram_hash = BigramHash(4096, 64, model_dim) self.smear_gate = SmearGate(model_dim) @@ -947,17 +927,12 @@ def __init__( xsa_last_n = int(os.environ.get("XSA_LAST_N", 4)) mlp_mult_enc = int(os.environ.get("MLP_MULT_ENCODER", 2)) mlp_mult_dec = int(os.environ.get("MLP_MULT_DECODER", mlp_mult)) + leaky = bool(int(os.environ.get("LEAKY_RELU", "0"))) self.blocks = nn.ModuleList( [ - Block( - model_dim, - num_heads, - num_kv_heads, - mlp_mult_enc if i < self.num_encoder_layers else mlp_mult_dec, - rope_base, - qk_gain_init, - use_xsa=(i >= num_layers - xsa_last_n), - ) + Block(model_dim, num_heads, num_kv_heads, + mlp_mult_enc if i < self.num_encoder_layers else mlp_mult_dec, + rope_base, qk_gain_init, use_xsa=(i >= num_layers - xsa_last_n), leaky=leaky) for i in range(num_layers) ] ) @@ -978,38 +953,27 @@ def _init_weights(self) -> None: if isinstance(module, nn.Linear) and getattr(module, "_zero_init", False): nn.init.zeros_(module.weight) - def _run_blocks(self, x: Tensor, x0: Tensor, use_recurrence: bool = True) -> Tensor: - rec_start = self._recurrence_encoder_start - if use_recurrence: - kv_caches: list[tuple[Tensor, Tensor]] = [] + def _run_blocks(self, x: Tensor, x0: Tensor) -> Tensor: + if self.encoder_recurrence: skips: list[Tensor] = [] for i in range(self.num_encoder_layers): - x, kv_out = self.blocks[i](x, x0) + x = self.blocks[i](x, x0) skips.append(x) - if self._kv_cache_reuse and kv_out is not None and i >= rec_start: - kv_caches.append(kv_out) x = F.rms_norm(x, (x.size(-1),)) skips2: list[Tensor] = [] - kv_idx = 0 for i in range(self.num_encoder_layers): - if i >= rec_start: - cached = kv_caches[kv_idx] if self._kv_cache_reuse and kv_idx < len(kv_caches) else None - x, _ = self.blocks[i](x, x0, cached_kv=cached) - if cached is not None: - kv_idx += 1 - else: - x, _ = self.blocks[i](x, x0) + x = self.blocks[i](x, x0) skips2.append(x) skips = skips2 else: skips: list[Tensor] = [] for i in range(self.num_encoder_layers): - x, _ = self.blocks[i](x, x0) + x = self.blocks[i](x, x0) skips.append(x) for i in range(self.num_decoder_layers): if skips: x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() - x, _ = self.blocks[self.num_encoder_layers + i](x, x0) + x = self.blocks[self.num_encoder_layers + i](x, x0) return x def _compute_logits(self, x: Tensor) -> Tensor: @@ -1021,13 +985,13 @@ def _compute_logits(self, x: Tensor) -> Tensor: logits_proj = self.lm_head(x) return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) - def forward(self, input_ids: Tensor, target_ids: Tensor, recurrence_enabled: bool = True) -> Tensor: + def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: x = self.tok_emb(input_ids) + self.bigram_hash(input_ids) x = self.smear_gate(x) x = self.pre_enrich(x) x = F.rms_norm(x, (x.size(-1),)) x0 = x - x = self._run_blocks(x, x0, use_recurrence=recurrence_enabled and self.encoder_recurrence) + x = self._run_blocks(x, x0) x = self.final_norm(x).reshape(-1, x.size(-1)) targets = target_ids.reshape(-1) logits = self._compute_logits(x) @@ -1336,7 +1300,6 @@ def lr_mul(step: int, elapsed_ms: float) -> float: elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) scale = lr_mul(step, elapsed_ms) - recurrence_on = step >= args.recurrence_start_step zero_grad_all() train_loss = torch.zeros((), device=device) for micro_step in range(grad_accum_steps): @@ -1344,7 +1307,7 @@ def lr_mul(step: int, elapsed_ms: float) -> float: model.require_backward_grad_sync = micro_step == grad_accum_steps - 1 x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): - loss = model(x, y, recurrence_enabled=recurrence_on) + loss = model(x, y) train_loss += loss.detach() (loss * grad_scale).backward() train_loss /= grad_accum_steps From 24e3683d319eea628e648d36b983ed6139876d0d Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 01:50:26 -0300 Subject: [PATCH 37/72] fix: TTT scoring in seq_len segments, not full 32K chunk RoPE only covers 2048 positions. Scoring 32K at once gave garbage. Now scores in 2048-token windows matching training seq_len. --- train_gpt.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 1b6c06ab1..688e3b717 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -382,14 +382,16 @@ def eval_val_ttt( actual_len = x_chunk.shape[1] base_model.eval() with torch.inference_mode(): - with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): - logits = base_model.forward_logits(x_chunk) - ptl = F.cross_entropy(logits.float().reshape(-1, logits.size(-1)), y_chunk.reshape(-1), reduction="none") - total_loss += ptl.to(torch.float64).sum() - total_tokens_counted += float(actual_len) - tb = base_bytes_lut[y_chunk[0]].to(dtype=torch.int16) - tb += (has_leading_space_lut[y_chunk[0]] & ~is_boundary_token_lut[x_chunk[0]]).to(dtype=torch.int16) - total_bytes += tb.to(torch.float64).sum() + for s in range(0, actual_len - seq_len + 1, seq_len): + sx, sy = x_chunk[:, s:s + seq_len], y_chunk[:, s:s + seq_len] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + logits = base_model.forward_logits(sx) + ptl = F.cross_entropy(logits.float().reshape(-1, logits.size(-1)), sy.reshape(-1), reduction="none") + total_loss += ptl.to(torch.float64).sum() + total_tokens_counted += float(seq_len) + tb = base_bytes_lut[sy[0]].to(dtype=torch.int16) + tb += (has_leading_space_lut[sy[0]] & ~is_boundary_token_lut[sx[0]]).to(dtype=torch.int16) + total_bytes += tb.to(torch.float64).sum() for name, param in base_model.named_parameters(): param.requires_grad_(name not in frozen_names) ttt_opt = torch.optim.SGD([p for p in base_model.parameters() if p.requires_grad], lr=args.ttt_lr, momentum=0.9) From 8903ccb4e323e785337c3ae6614d03af686fb0aa Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 01:58:47 -0300 Subject: [PATCH 38/72] feat: Flash Attention 3 support + TTT scoring fix --- train_gpt.py | 29 ++++++++++++++++++++--------- 1 file changed, 20 insertions(+), 9 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 688e3b717..bed9a45cc 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -20,6 +20,12 @@ import zlib from pathlib import Path +try: + from flash_attn_3 import flash_attn_func as fa3_func + HAS_FA3 = True +except ImportError: + HAS_FA3 = False + import numpy as np import sentencepiece as spm import torch @@ -815,20 +821,25 @@ def __init__( def forward(self, x: Tensor) -> Tensor: bsz, seqlen, dim = x.shape - q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) - k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) - v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) q = F.rms_norm(q, (q.size(-1),)) k = F.rms_norm(k, (k.size(-1),)) cos, sin = self.rotary(seqlen, x.device, q.dtype) - q = apply_rotary_emb(q, cos, sin) - k = apply_rotary_emb(k, cos, sin) - q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None] - y = F.scaled_dot_product_attention(q, k, v, attn_mask=None, is_causal=True, enable_gqa=(self.num_kv_heads != self.num_heads)) + q = apply_rotary_emb(q.transpose(1, 2), cos, sin).transpose(1, 2) + k = apply_rotary_emb(k.transpose(1, 2), cos, sin).transpose(1, 2) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + use_fa3 = HAS_FA3 and bool(int(os.environ.get("USE_FA3", "1"))) + if use_fa3: + y = fa3_func(q, k, v, causal=True) + else: + y = F.scaled_dot_product_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), attn_mask=None, is_causal=True, enable_gqa=(self.num_kv_heads != self.num_heads)).transpose(1, 2) if self.use_xsa: - vn = F.normalize(v.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1), dim=-1) + v_exp = v.repeat_interleave(self.num_heads // self.num_kv_heads, dim=2) + vn = F.normalize(v_exp, dim=-1) y = y - (y * vn).sum(dim=-1, keepdim=True) * vn - y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) + y = y.contiguous().reshape(bsz, seqlen, dim) return self.proj(y) From a0d2aaad52fe8238e53ac45bf5d2097f86b5df5f Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 02:14:19 -0300 Subject: [PATCH 39/72] fix: revert attention to original SDPA layout, remove FA3 transposes --- train_gpt.py | 23 +++++++++-------------- 1 file changed, 9 insertions(+), 14 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index bed9a45cc..e9db1a468 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -821,25 +821,20 @@ def __init__( def forward(self, x: Tensor) -> Tensor: bsz, seqlen, dim = x.shape - q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim) - k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) - v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) q = F.rms_norm(q, (q.size(-1),)) k = F.rms_norm(k, (k.size(-1),)) cos, sin = self.rotary(seqlen, x.device, q.dtype) - q = apply_rotary_emb(q.transpose(1, 2), cos, sin).transpose(1, 2) - k = apply_rotary_emb(k.transpose(1, 2), cos, sin).transpose(1, 2) - q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] - use_fa3 = HAS_FA3 and bool(int(os.environ.get("USE_FA3", "1"))) - if use_fa3: - y = fa3_func(q, k, v, causal=True) - else: - y = F.scaled_dot_product_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), attn_mask=None, is_causal=True, enable_gqa=(self.num_kv_heads != self.num_heads)).transpose(1, 2) + q = apply_rotary_emb(q, cos, sin) + k = apply_rotary_emb(k, cos, sin) + q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None] + y = F.scaled_dot_product_attention(q, k, v, attn_mask=None, is_causal=True, enable_gqa=(self.num_kv_heads != self.num_heads)) if self.use_xsa: - v_exp = v.repeat_interleave(self.num_heads // self.num_kv_heads, dim=2) - vn = F.normalize(v_exp, dim=-1) + vn = F.normalize(v.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1), dim=-1) y = y - (y * vn).sum(dim=-1, keepdim=True) * vn - y = y.contiguous().reshape(bsz, seqlen, dim) + y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) return self.proj(y) From ebeb541adc29ea48d1f9304e2149918b721ec0b5 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 02:28:18 -0300 Subject: [PATCH 40/72] feat: Partial RoPE + LN Scale + Value Embedding (from top PR audit) --- train_gpt.py | 42 ++++++++++++++++++++++++++++++++++++------ 1 file changed, 36 insertions(+), 6 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index e9db1a468..ed6ac52b1 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -759,10 +759,10 @@ def restore_low_dim_params_to_fp32(module: nn.Module) -> None: class Rotary(nn.Module): - # Caches cos/sin tables per sequence length on the current device. def __init__(self, dim: int, base: float = 10000.0): super().__init__() - inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + rdim = _ROPE_DIMS if _ROPE_DIMS > 0 else dim + inv_freq = 1.0 / (base ** (torch.arange(0, rdim, 2, dtype=torch.float32) / rdim)) self.register_buffer("inv_freq", inv_freq, persistent=False) self._seq_len_cached = 0 self._cos_cached: Tensor | None = None @@ -783,7 +783,16 @@ def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tup return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) +_ROPE_DIMS = int(os.environ.get("ROPE_DIMS", 0)) + def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor: + rd = _ROPE_DIMS + if rd > 0 and rd < x.size(-1): + x_rope, x_pass = x[..., :rd], x[..., rd:] + half = rd // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rope = torch.cat((x1 * cos[..., :half] + x2 * sin[..., :half], x1 * (-sin[..., :half]) + x2 * cos[..., :half]), dim=-1) + return torch.cat((x_rope, x_pass), dim=-1) half = x.size(-1) // 2 x1, x2 = x[..., :half], x[..., half:] return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) @@ -852,9 +861,11 @@ def forward(self, x: Tensor) -> Tensor: return self.proj(x.square()) +_LN_SCALE = bool(int(os.environ.get("LN_SCALE", "0"))) + class Block(nn.Module): def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: int, - rope_base: float, qk_gain_init: float, use_xsa: bool = False, leaky: bool = False): + rope_base: float, qk_gain_init: float, use_xsa: bool = False, leaky: bool = False, layer_idx: int = 0): super().__init__() self.attn_norm = RMSNorm() self.mlp_norm = RMSNorm() @@ -863,15 +874,28 @@ def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: int, self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self._ln_scale = 1.0 / math.sqrt(layer_idx + 1) if _LN_SCALE else 1.0 def forward(self, x: Tensor, x0: Tensor) -> Tensor: mix = self.resid_mix.to(dtype=x.dtype) x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 - x = x + self.attn_scale.to(dtype=x.dtype)[None, None, :] * self.attn(self.attn_norm(x)) - x = x + self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x)) + s = self._ln_scale + x = x + s * self.attn_scale.to(dtype=x.dtype)[None, None, :] * self.attn(self.attn_norm(x)) + x = x + s * self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x)) return x +class ValueEmbedding(nn.Module): + def __init__(self, vocab_size: int, ve_dim: int, model_dim: int): + super().__init__() + self.embed = nn.Embedding(vocab_size, ve_dim) + self.proj = CastedLinear(ve_dim, model_dim, bias=False) + self.scale = nn.Parameter(torch.tensor(0.1, dtype=torch.float32)) + nn.init.normal_(self.embed.weight, std=0.01) + def forward(self, input_ids: Tensor) -> Tensor: + return self.scale * self.proj(self.embed(input_ids)) + + class SmearGate(nn.Module): def __init__(self, dim: int): super().__init__() @@ -920,6 +944,8 @@ def __init__( self.logit_softcap = logit_softcap self.encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) self.tok_emb = nn.Embedding(vocab_size, model_dim) + ve_enabled = bool(int(os.environ.get("VE_ENABLED", "0"))) + self.ve = ValueEmbedding(vocab_size, 128, model_dim) if ve_enabled else None self.bigram_hash = BigramHash(4096, 64, model_dim) self.smear_gate = SmearGate(model_dim) pre_enrich_hidden = model_dim * 3 // 2 @@ -940,7 +966,7 @@ def __init__( [ Block(model_dim, num_heads, num_kv_heads, mlp_mult_enc if i < self.num_encoder_layers else mlp_mult_dec, - rope_base, qk_gain_init, use_xsa=(i >= num_layers - xsa_last_n), leaky=leaky) + rope_base, qk_gain_init, use_xsa=(i >= num_layers - xsa_last_n), leaky=leaky, layer_idx=i) for i in range(num_layers) ] ) @@ -995,6 +1021,8 @@ def _compute_logits(self, x: Tensor) -> Tensor: def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: x = self.tok_emb(input_ids) + self.bigram_hash(input_ids) + if self.ve is not None: + x = x + self.ve(input_ids) x = self.smear_gate(x) x = self.pre_enrich(x) x = F.rms_norm(x, (x.size(-1),)) @@ -1007,6 +1035,8 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: def forward_logits(self, input_ids: Tensor) -> Tensor: x = self.tok_emb(input_ids) + self.bigram_hash(input_ids) + if self.ve is not None: + x = x + self.ve(input_ids) x = self.smear_gate(x) x = self.pre_enrich(x) x = F.rms_norm(x, (x.size(-1),)) From 6e67b57be2f05944679738b09bfec0389171710e Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 02:34:41 -0300 Subject: [PATCH 41/72] feat: remove FA3, BigramHash 2048x128, torch.compile max-autotune --- train_gpt.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index ed6ac52b1..d063fcdcc 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -20,11 +20,6 @@ import zlib from pathlib import Path -try: - from flash_attn_3 import flash_attn_func as fa3_func - HAS_FA3 = True -except ImportError: - HAS_FA3 = False import numpy as np import sentencepiece as spm @@ -946,7 +941,7 @@ def __init__( self.tok_emb = nn.Embedding(vocab_size, model_dim) ve_enabled = bool(int(os.environ.get("VE_ENABLED", "0"))) self.ve = ValueEmbedding(vocab_size, 128, model_dim) if ve_enabled else None - self.bigram_hash = BigramHash(4096, 64, model_dim) + self.bigram_hash = BigramHash(2048, 128, model_dim) self.smear_gate = SmearGate(model_dim) pre_enrich_hidden = model_dim * 3 // 2 self.pre_enrich = nn.Sequential( @@ -1165,7 +1160,7 @@ def log0(msg: str, console: bool = True) -> None: for module in base_model.modules(): if isinstance(module, CastedLinear): module.use_qat = True - compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) + compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True, mode="max-autotune") model: nn.Module = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False) if distributed else compiled_model # Optimizer split: From 755c4c7fb83242e766e10a01f890bd13333f2f4b Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 02:56:48 -0300 Subject: [PATCH 42/72] feat: cuDNN SDP + tight SWA+EMA + max-autotune --- train_gpt.py | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index d063fcdcc..721202fcc 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -1080,7 +1080,7 @@ def main() -> None: torch.backends.cudnn.allow_tf32 = True from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp - enable_cudnn_sdp(False) + enable_cudnn_sdp(True) enable_flash_sdp(True) enable_mem_efficient_sdp(False) enable_math_sdp(False) @@ -1293,6 +1293,8 @@ def lr_mul(step: int, elapsed_ms: float) -> float: training_time_ms = 0.0 stop_after_step: int | None = None ema_state = {k: v.detach().cpu().clone().float() for k, v in base_model.state_dict().items()} + swa_state: dict[str, Tensor] | None = None + swa_count = 0 torch.cuda.synchronize() t0 = time.perf_counter() @@ -1368,6 +1370,12 @@ def lr_mul(step: int, elapsed_ms: float) -> float: with torch.no_grad(): for k, v in base_model.state_dict().items(): ema_state[k].mul_(args.ema_decay).add_(v.detach().cpu().float(), alpha=1.0 - args.ema_decay) + if scale < 0.2 and step % 50 == 0: + sd = {k: v.detach().cpu().float() for k, v in base_model.state_dict().items()} + if swa_state is None: swa_state, swa_count = sd, 1 + else: + for k in swa_state: swa_state[k] += sd[k] + swa_count += 1 approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) should_log_train = ( args.train_log_every > 0 @@ -1399,14 +1407,19 @@ def lr_mul(step: int, elapsed_ms: float) -> float: # Save the raw state (useful for debugging/loading in PyTorch directly), then always produce # the compressed int8+zlib artifact and validate the round-tripped weights. - log0("ema: loading exponential moving average weights") + if swa_state is not None and swa_count > 0: + log0(f"swa: averaging {swa_count} checkpoints on top of EMA") + for k in swa_state: + swa_state[k] /= swa_count + ema_state[k] = 0.5 * ema_state[k] + 0.5 * swa_state[k] + del swa_state + log0("ema: loading weights") base_model.load_state_dict(ema_state, strict=True) for module in base_model.modules(): if isinstance(module, CastedLinear): module.float() restore_low_dim_params_to_fp32(base_model) del ema_state - if master_process: torch.save(base_model.state_dict(), "final_model.pt") model_bytes = os.path.getsize("final_model.pt") From 33b0162a69f7824e4a5f5b3cc478b5cc4af8747b Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 03:11:51 -0300 Subject: [PATCH 43/72] fix: revert max-autotune (crashes), trim TTT diagnostics, condense GPTQ-lite --- train_gpt.py | 38 ++++++++++++++++---------------------- 1 file changed, 16 insertions(+), 22 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 721202fcc..5b7f9043d 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -370,15 +370,17 @@ def eval_val_ttt( for i in range(args.ttt_freeze_blocks): for name, _ in base_model.blocks[i].named_parameters(): frozen_names.add(f"blocks.{i}.{name}") + for name, param in base_model.named_parameters(): + param.requires_grad_(name not in frozen_names) + ttt_params = [p for p in base_model.parameters() if p.requires_grad] + ttt_opt = torch.optim.SGD(ttt_params, lr=args.ttt_lr, momentum=0.9) total_loss = torch.zeros((), device=device, dtype=torch.float64) total_tokens_counted = torch.zeros((), device=device, dtype=torch.float64) total_bytes = torch.zeros((), device=device, dtype=torch.float64) - for ci in my_chunks: - base_model.load_state_dict(original_state, strict=True) + for idx, ci in enumerate(my_chunks): start = ci * chunk_size chunk = val_tokens[start:min(start + chunk_size + 1, val_tokens.numel())].to(device=device, dtype=torch.int64) - if chunk.numel() < 2: - continue + if chunk.numel() < 2: continue x_chunk, y_chunk = chunk[:-1].unsqueeze(0), chunk[1:].unsqueeze(0) actual_len = x_chunk.shape[1] base_model.eval() @@ -393,9 +395,6 @@ def eval_val_ttt( tb = base_bytes_lut[sy[0]].to(dtype=torch.int16) tb += (has_leading_space_lut[sy[0]] & ~is_boundary_token_lut[sx[0]]).to(dtype=torch.int16) total_bytes += tb.to(torch.float64).sum() - for name, param in base_model.named_parameters(): - param.requires_grad_(name not in frozen_names) - ttt_opt = torch.optim.SGD([p for p in base_model.parameters() if p.requires_grad], lr=args.ttt_lr, momentum=0.9) base_model.train() for _ in range(args.ttt_epochs): for s in range(0, actual_len - seq_len + 1, seq_len): @@ -403,9 +402,10 @@ def eval_val_ttt( with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): loss = base_model(x_chunk[:, s:s + seq_len], y_chunk[:, s:s + seq_len]) loss.backward() + torch.nn.utils.clip_grad_norm_(ttt_params, 1.0) ttt_opt.step() - for param in base_model.parameters(): - param.requires_grad_(True) + for param in base_model.parameters(): + param.requires_grad_(True) if dist.is_available() and dist.is_initialized(): dist.all_reduce(total_loss, op=dist.ReduceOp.SUM) dist.all_reduce(total_tokens_counted, op=dist.ReduceOp.SUM) @@ -482,20 +482,14 @@ def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: def quantize_float_tensor_int6(t: Tensor) -> tuple[Tensor, Tensor]: t32 = t.float() if t32.ndim == 2: - best_q = None - best_scale = None - best_mse = float("inf") + best_q, best_s, best_mse = None, None, float("inf") for pct in [0.999, 0.9999, 0.99999, 0.999999, 0.9999999]: - clip_abs = torch.quantile(t32.abs(), pct, dim=1) if t32.numel() else torch.empty((t32.shape[0],), dtype=torch.float32) - s = (clip_abs / 31.0).clamp_min(1.0 / 31.0) - clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) - q = torch.clamp(torch.round(clipped / s[:, None]), -31, 31) + ca = torch.quantile(t32.abs(), pct, dim=1) if t32.numel() else torch.empty((t32.shape[0],), dtype=torch.float32) + s = (ca / 31.0).clamp_min(1.0 / 31.0) + q = torch.clamp(torch.round(torch.clamp(t32, -ca[:, None], ca[:, None]) / s[:, None]), -31, 31) mse = ((q * s[:, None] - t32) ** 2).mean().item() - if mse < best_mse: - best_mse = mse - best_q = q.to(torch.int8).contiguous() - best_scale = s.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() - return best_q, best_scale + if mse < best_mse: best_q, best_s, best_mse = q.to(torch.int8).contiguous(), s.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous(), mse + return best_q, best_s clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 scale = torch.tensor(clip_abs / 31.0 if clip_abs > 0 else 1.0, dtype=torch.float32) q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -31, 31).to(torch.int8).contiguous() @@ -1160,7 +1154,7 @@ def log0(msg: str, console: bool = True) -> None: for module in base_model.modules(): if isinstance(module, CastedLinear): module.use_qat = True - compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True, mode="max-autotune") + compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) model: nn.Module = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False) if distributed else compiled_model # Optimizer split: From bb9a5d65cf2d6ddc72e7fd6ab717ee59a851c3d0 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 04:20:51 -0300 Subject: [PATCH 44/72] feat: TTT sliding window scoring + cleanup dead code TTT now scores with overlapping sliding windows (stride=64, batched) instead of flat seq_len segments, matching eval_val_sliding context. Training still uses flat segments with accumulative SGD. Removed: ValueEmbedding (unused), qat_start_frac (dead), zlib import. Condensed CONTROL_TENSOR patterns. --- train_gpt.py | 63 +++++++++++++++++----------------------------------- 1 file changed, 20 insertions(+), 43 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 5b7f9043d..f3375eaf5 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -17,7 +17,6 @@ import time import uuid import lzma -import zlib from pathlib import Path @@ -69,7 +68,6 @@ class Hyperparameters: tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) - encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) embed_lr = float(os.environ.get("EMBED_LR", 0.6)) head_lr = float(os.environ.get("HEAD_LR", 0.008)) @@ -89,7 +87,6 @@ class Hyperparameters: adam_wd = float(os.environ.get("ADAM_WD", 0.04)) ema_decay = float(os.environ.get("EMA_DECAY", 0.997)) leaky_relu = bool(int(os.environ.get("LEAKY_RELU", "0"))) - qat_start_frac = float(os.environ.get("QAT_START_FRAC", 0.0)) ttt_enabled = bool(int(os.environ.get("TTT_ENABLED", "0"))) ttt_epochs = int(os.environ.get("TTT_EPOCHS", 3)) ttt_lr = float(os.environ.get("TTT_LR", 0.002)) @@ -362,7 +359,7 @@ def eval_val_ttt( device: torch.device, val_tokens: Tensor, base_bytes_lut: Tensor, has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, ) -> tuple[float, float]: - seq_len, chunk_size = args.train_seq_len, args.ttt_chunk_size + seq_len, chunk_size, stride, bsz = args.train_seq_len, args.ttt_chunk_size, 64, 128 num_chunks = (val_tokens.numel() - 1) // chunk_size my_chunks = list(range(rank, num_chunks, world_size)) original_state = {k: v.detach().cpu().clone() for k, v in base_model.state_dict().items()} @@ -385,16 +382,22 @@ def eval_val_ttt( actual_len = x_chunk.shape[1] base_model.eval() with torch.inference_mode(): - for s in range(0, actual_len - seq_len + 1, seq_len): - sx, sy = x_chunk[:, s:s + seq_len], y_chunk[:, s:s + seq_len] + wins, p = [], 0 + while p + seq_len <= actual_len: + wins.append((p, 0 if p == 0 else seq_len - stride)) + p += stride + for bi in range(0, len(wins), bsz): + bw = wins[bi:bi + bsz] + x_b = torch.stack([x_chunk[0, w:w+seq_len] for w, _ in bw]) + y_b = torch.stack([y_chunk[0, w:w+seq_len] for w, _ in bw]) with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): - logits = base_model.forward_logits(sx) - ptl = F.cross_entropy(logits.float().reshape(-1, logits.size(-1)), sy.reshape(-1), reduction="none") - total_loss += ptl.to(torch.float64).sum() - total_tokens_counted += float(seq_len) - tb = base_bytes_lut[sy[0]].to(dtype=torch.int16) - tb += (has_leading_space_lut[sy[0]] & ~is_boundary_token_lut[sx[0]]).to(dtype=torch.int16) - total_bytes += tb.to(torch.float64).sum() + logits = base_model.forward_logits(x_b) + ptl = F.cross_entropy(logits.float().reshape(-1, logits.size(-1)), y_b.reshape(-1), reduction="none").reshape(len(bw), seq_len) + for j, (_, ss) in enumerate(bw): + sl = ptl[j, ss:]; total_loss += sl.to(torch.float64).sum(); total_tokens_counted += float(sl.numel()) + sx, sy = x_b[j, ss:], y_b[j, ss:] + tb = base_bytes_lut[sy].to(dtype=torch.int16) + (has_leading_space_lut[sy] & ~is_boundary_token_lut[sx]).to(dtype=torch.int16) + total_bytes += tb.to(torch.float64).sum() base_model.train() for _ in range(args.ttt_epochs): for s in range(0, actual_len - seq_len + 1, seq_len): @@ -425,22 +428,12 @@ def eval_val_ttt( # Instead, we get approximately the same model (with a small hit) by quantizing the model to int8 & zlib compressing. # We can then decompress the model and run in higher precision for evaluation, after closing in under the size limit. +_ctrl_default = "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights" CONTROL_TENSOR_NAME_PATTERNS = tuple( - pattern - for pattern in os.environ.get( - "CONTROL_TENSOR_NAME_PATTERNS", - "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights", - ).split(",") - if pattern -) + p for p in os.environ.get("CONTROL_TENSOR_NAME_PATTERNS", _ctrl_default).split(",") if p) INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( - pattern - for pattern in os.environ.get( - "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", - ",".join(CONTROL_TENSOR_NAME_PATTERNS), - ).split(",") - if pattern -) + p for p in os.environ.get("INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS)).split(",") if p) INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 INT8_PER_ROW_SCALE_DTYPE = torch.float16 @@ -874,16 +867,6 @@ def forward(self, x: Tensor, x0: Tensor) -> Tensor: return x -class ValueEmbedding(nn.Module): - def __init__(self, vocab_size: int, ve_dim: int, model_dim: int): - super().__init__() - self.embed = nn.Embedding(vocab_size, ve_dim) - self.proj = CastedLinear(ve_dim, model_dim, bias=False) - self.scale = nn.Parameter(torch.tensor(0.1, dtype=torch.float32)) - nn.init.normal_(self.embed.weight, std=0.01) - def forward(self, input_ids: Tensor) -> Tensor: - return self.scale * self.proj(self.embed(input_ids)) - class SmearGate(nn.Module): def __init__(self, dim: int): @@ -933,8 +916,6 @@ def __init__( self.logit_softcap = logit_softcap self.encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) self.tok_emb = nn.Embedding(vocab_size, model_dim) - ve_enabled = bool(int(os.environ.get("VE_ENABLED", "0"))) - self.ve = ValueEmbedding(vocab_size, 128, model_dim) if ve_enabled else None self.bigram_hash = BigramHash(2048, 128, model_dim) self.smear_gate = SmearGate(model_dim) pre_enrich_hidden = model_dim * 3 // 2 @@ -1010,8 +991,6 @@ def _compute_logits(self, x: Tensor) -> Tensor: def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: x = self.tok_emb(input_ids) + self.bigram_hash(input_ids) - if self.ve is not None: - x = x + self.ve(input_ids) x = self.smear_gate(x) x = self.pre_enrich(x) x = F.rms_norm(x, (x.size(-1),)) @@ -1024,8 +1003,6 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: def forward_logits(self, input_ids: Tensor) -> Tensor: x = self.tok_emb(input_ids) + self.bigram_hash(input_ids) - if self.ve is not None: - x = x + self.ve(input_ids) x = self.smear_gate(x) x = self.pre_enrich(x) x = F.rms_norm(x, (x.size(-1),)) From 0d9302c0d522675d6f32d3c827d8adecc2482787 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 12:43:32 -0300 Subject: [PATCH 45/72] fix: TTT GPU sync + Late QAT + cosine LR + defaults --- train_gpt.py | 35 ++++++++++++++++++++++------------- 1 file changed, 22 insertions(+), 13 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index f3375eaf5..3d8c88533 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -91,7 +91,7 @@ class Hyperparameters: ttt_epochs = int(os.environ.get("TTT_EPOCHS", 3)) ttt_lr = float(os.environ.get("TTT_LR", 0.002)) ttt_chunk_size = int(os.environ.get("TTT_CHUNK_SIZE", 32768)) - ttt_freeze_blocks = int(os.environ.get("TTT_FREEZE_BLOCKS", 2)) + ttt_freeze_blocks = int(os.environ.get("TTT_FREEZE_BLOCKS", 0)) # ----------------------------- # MUON OPTIMIZER @@ -361,7 +361,6 @@ def eval_val_ttt( ) -> tuple[float, float]: seq_len, chunk_size, stride, bsz = args.train_seq_len, args.ttt_chunk_size, 64, 128 num_chunks = (val_tokens.numel() - 1) // chunk_size - my_chunks = list(range(rank, num_chunks, world_size)) original_state = {k: v.detach().cpu().clone() for k, v in base_model.state_dict().items()} frozen_names = set() for i in range(args.ttt_freeze_blocks): @@ -374,30 +373,40 @@ def eval_val_ttt( total_loss = torch.zeros((), device=device, dtype=torch.float64) total_tokens_counted = torch.zeros((), device=device, dtype=torch.float64) total_bytes = torch.zeros((), device=device, dtype=torch.float64) - for idx, ci in enumerate(my_chunks): + distributed = dist.is_available() and dist.is_initialized() + for ci in range(num_chunks): start = ci * chunk_size chunk = val_tokens[start:min(start + chunk_size + 1, val_tokens.numel())].to(device=device, dtype=torch.int64) if chunk.numel() < 2: continue x_chunk, y_chunk = chunk[:-1].unsqueeze(0), chunk[1:].unsqueeze(0) actual_len = x_chunk.shape[1] base_model.eval() + chunk_loss = torch.zeros((), device=device, dtype=torch.float64) + chunk_tokens = torch.zeros((), device=device, dtype=torch.float64) + chunk_bytes = torch.zeros((), device=device, dtype=torch.float64) with torch.inference_mode(): wins, p = [], 0 while p + seq_len <= actual_len: wins.append((p, 0 if p == 0 else seq_len - stride)) p += stride - for bi in range(0, len(wins), bsz): - bw = wins[bi:bi + bsz] + my_wins = wins[rank::world_size] + for bi in range(0, len(my_wins), bsz): + bw = my_wins[bi:bi + bsz] x_b = torch.stack([x_chunk[0, w:w+seq_len] for w, _ in bw]) y_b = torch.stack([y_chunk[0, w:w+seq_len] for w, _ in bw]) with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): logits = base_model.forward_logits(x_b) ptl = F.cross_entropy(logits.float().reshape(-1, logits.size(-1)), y_b.reshape(-1), reduction="none").reshape(len(bw), seq_len) for j, (_, ss) in enumerate(bw): - sl = ptl[j, ss:]; total_loss += sl.to(torch.float64).sum(); total_tokens_counted += float(sl.numel()) + sl = ptl[j, ss:]; chunk_loss += sl.to(torch.float64).sum(); chunk_tokens += float(sl.numel()) sx, sy = x_b[j, ss:], y_b[j, ss:] tb = base_bytes_lut[sy].to(dtype=torch.int16) + (has_leading_space_lut[sy] & ~is_boundary_token_lut[sx]).to(dtype=torch.int16) - total_bytes += tb.to(torch.float64).sum() + chunk_bytes += tb.to(torch.float64).sum() + if distributed: + for t in (chunk_loss, chunk_tokens, chunk_bytes): dist.all_reduce(t, op=dist.ReduceOp.SUM) + total_loss += chunk_loss; total_tokens_counted += chunk_tokens; total_bytes += chunk_bytes + lr_t = args.ttt_lr * 0.5 * (1 + math.cos(math.pi * ci / max(num_chunks - 1, 1))) + for pg in ttt_opt.param_groups: pg['lr'] = lr_t base_model.train() for _ in range(args.ttt_epochs): for s in range(0, actual_len - seq_len + 1, seq_len): @@ -409,10 +418,6 @@ def eval_val_ttt( ttt_opt.step() for param in base_model.parameters(): param.requires_grad_(True) - if dist.is_available() and dist.is_initialized(): - dist.all_reduce(total_loss, op=dist.ReduceOp.SUM) - dist.all_reduce(total_tokens_counted, op=dist.ReduceOp.SUM) - dist.all_reduce(total_bytes, op=dist.ReduceOp.SUM) val_loss = (total_loss / total_tokens_counted).item() bpb = (total_loss / (total_bytes * math.log(2.0))).item() base_model.load_state_dict(original_state, strict=True) @@ -723,11 +728,12 @@ class CastedLinear(nn.Linear): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.use_qat = False + self._qat_scale = torch.tensor(0.0) def forward(self, x: Tensor) -> Tensor: w = self.weight if self.use_qat and self.training: - w = fake_quant_int6(w) + w = w + self._qat_scale * (fake_quant_int6(w) - w) bias = self.bias.to(x.dtype) if self.bias is not None else None return F.linear(x, w.to(x.dtype), bias) @@ -929,7 +935,7 @@ def __init__( self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) xsa_last_n = int(os.environ.get("XSA_LAST_N", 4)) - mlp_mult_enc = int(os.environ.get("MLP_MULT_ENCODER", 2)) + mlp_mult_enc = int(os.environ.get("MLP_MULT_ENCODER", mlp_mult)) mlp_mult_dec = int(os.environ.get("MLP_MULT_DECODER", mlp_mult)) leaky = bool(int(os.environ.get("LEAKY_RELU", "0"))) self.blocks = nn.ModuleList( @@ -1306,6 +1312,9 @@ def lr_mul(step: int, elapsed_ms: float) -> float: elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) scale = lr_mul(step, elapsed_ms) + if max_wallclock_ms and elapsed_ms / max_wallclock_ms > 0.85: + for m in base_model.modules(): + if isinstance(m, CastedLinear): m._qat_scale.fill_(1.0) zero_grad_all() train_loss = torch.zeros((), device=device) for micro_step in range(grad_accum_steps): From 8a150c47cbe8ed5d329799b43d62ebff4a4b1bbd Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 14:47:27 -0300 Subject: [PATCH 46/72] feat: n-gram cache + VR + gated attention + EMA on GPU N-gram eval: orders 2-7 backoff, entropy-adaptive alpha, sparse dict. Value Residual: layer-0 V mixed into all layers via learned gates. Gated Attention: per-head sigmoid gates. XSA default all layers. EMA on GPU (5-10ms/step saved). SmearGate F.pad fix. Removed: TTT, encoder recurrence, Late QAT. --- train_gpt.py | 229 +++++++++++++++++++++++++-------------------------- 1 file changed, 114 insertions(+), 115 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 3d8c88533..eef8a2677 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -87,11 +87,6 @@ class Hyperparameters: adam_wd = float(os.environ.get("ADAM_WD", 0.04)) ema_decay = float(os.environ.get("EMA_DECAY", 0.997)) leaky_relu = bool(int(os.environ.get("LEAKY_RELU", "0"))) - ttt_enabled = bool(int(os.environ.get("TTT_ENABLED", "0"))) - ttt_epochs = int(os.environ.get("TTT_EPOCHS", 3)) - ttt_lr = float(os.environ.get("TTT_LR", 0.002)) - ttt_chunk_size = int(os.environ.get("TTT_CHUNK_SIZE", 32768)) - ttt_freeze_blocks = int(os.environ.get("TTT_FREEZE_BLOCKS", 0)) # ----------------------------- # MUON OPTIMIZER @@ -354,73 +349,86 @@ def eval_val_sliding( return float(val_loss), float(bpb) -def eval_val_ttt( +_NGRAM_ORDERS = list(range(7, 1, -1)) +_NGRAM_HASH_MULT = 265443576 +_NGRAM_MOD = (1 << 22) + +def _ngram_hash(tokens: Tensor, end: int, order: int) -> int: + h = 0 + for i in range(end - order + 1, end): + h = (h * _NGRAM_HASH_MULT + tokens[i].item()) % _NGRAM_MOD + return h + +def eval_val_ngram( args: Hyperparameters, base_model: nn.Module, rank: int, world_size: int, device: torch.device, val_tokens: Tensor, base_bytes_lut: Tensor, has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, + stride: int = 64, batch_size: int = 16, ) -> tuple[float, float]: - seq_len, chunk_size, stride, bsz = args.train_seq_len, args.ttt_chunk_size, 64, 128 - num_chunks = (val_tokens.numel() - 1) // chunk_size - original_state = {k: v.detach().cpu().clone() for k, v in base_model.state_dict().items()} - frozen_names = set() - for i in range(args.ttt_freeze_blocks): - for name, _ in base_model.blocks[i].named_parameters(): - frozen_names.add(f"blocks.{i}.{name}") - for name, param in base_model.named_parameters(): - param.requires_grad_(name not in frozen_names) - ttt_params = [p for p in base_model.parameters() if p.requires_grad] - ttt_opt = torch.optim.SGD(ttt_params, lr=args.ttt_lr, momentum=0.9) - total_loss = torch.zeros((), device=device, dtype=torch.float64) - total_tokens_counted = torch.zeros((), device=device, dtype=torch.float64) - total_bytes = torch.zeros((), device=device, dtype=torch.float64) - distributed = dist.is_available() and dist.is_initialized() - for ci in range(num_chunks): - start = ci * chunk_size - chunk = val_tokens[start:min(start + chunk_size + 1, val_tokens.numel())].to(device=device, dtype=torch.int64) - if chunk.numel() < 2: continue - x_chunk, y_chunk = chunk[:-1].unsqueeze(0), chunk[1:].unsqueeze(0) - actual_len = x_chunk.shape[1] - base_model.eval() - chunk_loss = torch.zeros((), device=device, dtype=torch.float64) - chunk_tokens = torch.zeros((), device=device, dtype=torch.float64) - chunk_bytes = torch.zeros((), device=device, dtype=torch.float64) - with torch.inference_mode(): - wins, p = [], 0 - while p + seq_len <= actual_len: - wins.append((p, 0 if p == 0 else seq_len - stride)) - p += stride - my_wins = wins[rank::world_size] - for bi in range(0, len(my_wins), bsz): - bw = my_wins[bi:bi + bsz] - x_b = torch.stack([x_chunk[0, w:w+seq_len] for w, _ in bw]) - y_b = torch.stack([y_chunk[0, w:w+seq_len] for w, _ in bw]) - with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): - logits = base_model.forward_logits(x_b) - ptl = F.cross_entropy(logits.float().reshape(-1, logits.size(-1)), y_b.reshape(-1), reduction="none").reshape(len(bw), seq_len) - for j, (_, ss) in enumerate(bw): - sl = ptl[j, ss:]; chunk_loss += sl.to(torch.float64).sum(); chunk_tokens += float(sl.numel()) - sx, sy = x_b[j, ss:], y_b[j, ss:] - tb = base_bytes_lut[sy].to(dtype=torch.int16) + (has_leading_space_lut[sy] & ~is_boundary_token_lut[sx]).to(dtype=torch.int16) - chunk_bytes += tb.to(torch.float64).sum() - if distributed: - for t in (chunk_loss, chunk_tokens, chunk_bytes): dist.all_reduce(t, op=dist.ReduceOp.SUM) - total_loss += chunk_loss; total_tokens_counted += chunk_tokens; total_bytes += chunk_bytes - lr_t = args.ttt_lr * 0.5 * (1 + math.cos(math.pi * ci / max(num_chunks - 1, 1))) - for pg in ttt_opt.param_groups: pg['lr'] = lr_t - base_model.train() - for _ in range(args.ttt_epochs): - for s in range(0, actual_len - seq_len + 1, seq_len): - ttt_opt.zero_grad() - with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): - loss = base_model(x_chunk[:, s:s + seq_len], y_chunk[:, s:s + seq_len]) - loss.backward() - torch.nn.utils.clip_grad_norm_(ttt_params, 1.0) - ttt_opt.step() - for param in base_model.parameters(): - param.requires_grad_(True) - val_loss = (total_loss / total_tokens_counted).item() - bpb = (total_loss / (total_bytes * math.log(2.0))).item() - base_model.load_state_dict(original_state, strict=True) + seq_len, vocab = args.train_seq_len, args.vocab_size + total_tokens = val_tokens.numel() + windows: list[tuple[int, int]] = [] + pos = 0 + while pos + seq_len < total_tokens: + windows.append((pos, 0 if pos == 0 else seq_len - stride)) + pos += stride + ngram_table: dict[int, dict[int, int]] = {} + total_loss = 0.0 + total_scored = 0.0 + total_bytes = 0.0 + base_model.eval() + with torch.inference_mode(): + for bi in range(0, len(windows), batch_size): + bw = windows[bi:bi + batch_size] + x = torch.stack([val_tokens[w:w+seq_len] for w, _ in bw]).to(device=device, dtype=torch.int64) + y = torch.stack([val_tokens[w+1:w+seq_len+1] for w, _ in bw]).to(device=device, dtype=torch.int64) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + logits = base_model.forward_logits(x) + log_probs = F.log_softmax(logits.float(), dim=-1).cpu() + for j, (wpos, ss) in enumerate(bw): + for t in range(ss, seq_len): + abs_pos = wpos + t + 1 + tgt = val_tokens[abs_pos].item() + model_lp = log_probs[j, t] + ngram_dist = None + for order in _NGRAM_ORDERS: + if abs_pos >= order - 1: + h = _ngram_hash(val_tokens, abs_pos, order) + bucket = ngram_table.get(h) + if bucket is not None: + total_ct = sum(bucket.values()) + if total_ct >= 2: + ngram_dist = bucket + ngram_total = total_ct + break + if ngram_dist is not None: + entropy = -(model_lp.exp() * model_lp).sum().item() + alpha = 0.05 + 0.55 / (1.0 + math.exp(-2.0 * (entropy - 4.0))) + ng_prob = ngram_dist.get(tgt, 0) / ngram_total + model_prob = model_lp[tgt].exp().item() + mixed_prob = (1.0 - alpha) * model_prob + alpha * ng_prob + total_loss -= math.log(max(mixed_prob, 1e-20)) + else: + total_loss -= model_lp[tgt].item() + total_scored += 1.0 + prev_tok = val_tokens[abs_pos - 1].item() if abs_pos > 0 else 0 + tb = base_bytes_lut[tgt].item() + tb += (has_leading_space_lut[tgt].item() & (1 - is_boundary_token_lut[prev_tok].item())) + total_bytes += tb + for order in _NGRAM_ORDERS: + if abs_pos >= order - 1: + h = _ngram_hash(val_tokens, abs_pos, order) + if h not in ngram_table: ngram_table[h] = {} + ngram_table[h][tgt] = ngram_table[h].get(tgt, 0) + 1 + total_loss_t = torch.tensor(total_loss, device=device, dtype=torch.float64) + total_scored_t = torch.tensor(total_scored, device=device, dtype=torch.float64) + total_bytes_t = torch.tensor(total_bytes, device=device, dtype=torch.float64) + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(total_loss_t, op=dist.ReduceOp.SUM) + dist.all_reduce(total_scored_t, op=dist.ReduceOp.SUM) + dist.all_reduce(total_bytes_t, op=dist.ReduceOp.SUM) + val_loss = (total_loss_t / total_scored_t).item() + bpb = (total_loss_t / (total_bytes_t * math.log(2.0))).item() base_model.train() return float(val_loss), float(bpb) @@ -728,12 +736,11 @@ class CastedLinear(nn.Linear): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.use_qat = False - self._qat_scale = torch.tensor(0.0) def forward(self, x: Tensor) -> Tensor: w = self.weight if self.use_qat and self.training: - w = w + self._qat_scale * (fake_quant_int6(w) - w) + w = fake_quant_int6(w) bias = self.bias.to(x.dtype) if self.bias is not None else None return F.linear(x, w.to(x.dtype), bias) @@ -813,14 +820,19 @@ def __init__( self.proj = CastedLinear(dim, dim, bias=False) self.proj._zero_init = True self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.attn_gate = nn.Parameter(torch.ones(num_heads, dtype=torch.float32)) self.rotary = Rotary(self.head_dim, base=rope_base) self.use_xsa = use_xsa + self.vr_lambda = nn.Parameter(torch.tensor([0.5, 0.5], dtype=torch.float32)) - def forward(self, x: Tensor) -> Tensor: + def forward(self, x: Tensor, v0: Tensor | None = None) -> tuple[Tensor, Tensor]: bsz, seqlen, dim = x.shape q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + if v0 is not None: + lam = torch.sigmoid(self.vr_lambda).to(dtype=v.dtype) + v = lam[0] * v0 + lam[1] * v q = F.rms_norm(q, (q.size(-1),)) k = F.rms_norm(k, (k.size(-1),)) cos, sin = self.rotary(seqlen, x.device, q.dtype) @@ -831,8 +843,9 @@ def forward(self, x: Tensor) -> Tensor: if self.use_xsa: vn = F.normalize(v.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1), dim=-1) y = y - (y * vn).sum(dim=-1, keepdim=True) * vn + y = y * torch.sigmoid(self.attn_gate).to(dtype=y.dtype)[None, :, None, None] y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) - return self.proj(y) + return self.proj(y), v class MLP(nn.Module): @@ -864,13 +877,14 @@ def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: int, self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) self._ln_scale = 1.0 / math.sqrt(layer_idx + 1) if _LN_SCALE else 1.0 - def forward(self, x: Tensor, x0: Tensor) -> Tensor: + def forward(self, x: Tensor, x0: Tensor, v0: Tensor | None = None) -> tuple[Tensor, Tensor]: mix = self.resid_mix.to(dtype=x.dtype) x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 s = self._ln_scale - x = x + s * self.attn_scale.to(dtype=x.dtype)[None, None, :] * self.attn(self.attn_norm(x)) + attn_out, v = self.attn(self.attn_norm(x), v0) + x = x + s * self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out x = x + s * self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x)) - return x + return x, v @@ -881,7 +895,7 @@ def __init__(self, dim: int): def forward(self, x: Tensor) -> Tensor: g = torch.sigmoid(self.gate).to(dtype=x.dtype) - x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + x_prev = F.pad(x[:, :-1], (0, 0, 1, 0)) return g * x + (1.0 - g) * x_prev @@ -920,7 +934,6 @@ def __init__( self.tie_embeddings = tie_embeddings self.tied_embed_init_std = tied_embed_init_std self.logit_softcap = logit_softcap - self.encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) self.tok_emb = nn.Embedding(vocab_size, model_dim) self.bigram_hash = BigramHash(2048, 128, model_dim) self.smear_gate = SmearGate(model_dim) @@ -934,7 +947,7 @@ def __init__( self.num_decoder_layers = num_layers - self.num_encoder_layers self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) - xsa_last_n = int(os.environ.get("XSA_LAST_N", 4)) + xsa_last_n = int(os.environ.get("XSA_LAST_N", num_layers)) mlp_mult_enc = int(os.environ.get("MLP_MULT_ENCODER", mlp_mult)) mlp_mult_dec = int(os.environ.get("MLP_MULT_DECODER", mlp_mult)) leaky = bool(int(os.environ.get("LEAKY_RELU", "0"))) @@ -964,26 +977,16 @@ def _init_weights(self) -> None: nn.init.zeros_(module.weight) def _run_blocks(self, x: Tensor, x0: Tensor) -> Tensor: - if self.encoder_recurrence: - skips: list[Tensor] = [] - for i in range(self.num_encoder_layers): - x = self.blocks[i](x, x0) - skips.append(x) - x = F.rms_norm(x, (x.size(-1),)) - skips2: list[Tensor] = [] - for i in range(self.num_encoder_layers): - x = self.blocks[i](x, x0) - skips2.append(x) - skips = skips2 - else: - skips: list[Tensor] = [] - for i in range(self.num_encoder_layers): - x = self.blocks[i](x, x0) - skips.append(x) + v0 = None + skips: list[Tensor] = [] + for i in range(self.num_encoder_layers): + x, v = self.blocks[i](x, x0, v0) + if v0 is None: v0 = v + skips.append(x) for i in range(self.num_decoder_layers): if skips: x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() - x = self.blocks[self.num_encoder_layers + i](x, x0) + x, v = self.blocks[self.num_encoder_layers + i](x, x0, v0) return x def _compute_logits(self, x: Tensor) -> Tensor: @@ -1196,9 +1199,8 @@ def log0(msg: str, console: bool = True) -> None: n_params = sum(p.numel() for p in base_model.parameters()) log0(f"model_params:{n_params}") - log0(f"encoder_recurrence:{'ON' if base_model.encoder_recurrence else 'OFF'}") log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") - log0("sdp_backends:cudnn=False flash=True mem_efficient=False math=False") + log0("sdp_backends:cudnn=True flash=True mem_efficient=False math=False") log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}") log0( f"tie_embeddings:{args.tie_embeddings} embed_lr:{token_lr} " @@ -1269,7 +1271,7 @@ def lr_mul(step: int, elapsed_ms: float) -> float: training_time_ms = 0.0 stop_after_step: int | None = None - ema_state = {k: v.detach().cpu().clone().float() for k, v in base_model.state_dict().items()} + ema_state = {k: v.detach().clone().float() for k, v in base_model.state_dict().items()} swa_state: dict[str, Tensor] | None = None swa_count = 0 torch.cuda.synchronize() @@ -1312,9 +1314,6 @@ def lr_mul(step: int, elapsed_ms: float) -> float: elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) scale = lr_mul(step, elapsed_ms) - if max_wallclock_ms and elapsed_ms / max_wallclock_ms > 0.85: - for m in base_model.modules(): - if isinstance(m, CastedLinear): m._qat_scale.fill_(1.0) zero_grad_all() train_loss = torch.zeros((), device=device) for micro_step in range(grad_accum_steps): @@ -1349,7 +1348,7 @@ def lr_mul(step: int, elapsed_ms: float) -> float: step += 1 with torch.no_grad(): for k, v in base_model.state_dict().items(): - ema_state[k].mul_(args.ema_decay).add_(v.detach().cpu().float(), alpha=1.0 - args.ema_decay) + ema_state[k].mul_(args.ema_decay).add_(v.detach().float(), alpha=1.0 - args.ema_decay) if scale < 0.2 and step % 50 == 0: sd = {k: v.detach().cpu().float() for k, v in base_model.state_dict().items()} if swa_state is None: swa_state, swa_count = sd, 1 @@ -1387,6 +1386,7 @@ def lr_mul(step: int, elapsed_ms: float) -> float: # Save the raw state (useful for debugging/loading in PyTorch directly), then always produce # the compressed int8+zlib artifact and validate the round-tripped weights. + ema_state = {k: v.cpu() for k, v in ema_state.items()} if swa_state is not None and swa_count > 0: log0(f"swa: averaging {swa_count} checkpoints on top of EMA") for k in swa_state: @@ -1458,19 +1458,18 @@ def lr_mul(step: int, elapsed_ms: float) -> float: ) log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f}") - if args.ttt_enabled: - torch.cuda.synchronize() - t_ttt = time.perf_counter() - ttt_val_loss, ttt_val_bpb = eval_val_ttt( - args, base_model, rank, world_size, device, - val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, - ) - torch.cuda.synchronize() - log0( - f"final_ttt val_loss:{ttt_val_loss:.4f} val_bpb:{ttt_val_bpb:.4f} " - f"eval_time:{1000.0 * (time.perf_counter() - t_ttt):.0f}ms" - ) - log0(f"final_ttt_exact val_loss:{ttt_val_loss:.8f} val_bpb:{ttt_val_bpb:.8f}") + torch.cuda.synchronize() + t_ngram = time.perf_counter() + ng_val_loss, ng_val_bpb = eval_val_ngram( + args, base_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + ) + torch.cuda.synchronize() + log0( + f"final_ngram val_loss:{ng_val_loss:.4f} val_bpb:{ng_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_ngram):.0f}ms" + ) + log0(f"final_ngram_exact val_loss:{ng_val_loss:.8f} val_bpb:{ng_val_bpb:.8f}") if distributed: dist.destroy_process_group() From 342685f39729d1d24fa1fa76c19ff339f94cd829 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 15:17:31 -0300 Subject: [PATCH 47/72] fix: make VR/GA toggleable (default OFF), XSA back to 4 GATED_ATTN=0 and VALUE_RESIDUAL=0 by default. Model now matches 1.1535 architecture exactly with defaults. XSA_LAST_N back to 4. N-gram cache always runs at eval. --- train_gpt.py | 20 +++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index eef8a2677..339163f9c 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -793,6 +793,9 @@ def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor: return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) +_GATED_ATTN = bool(int(os.environ.get("GATED_ATTN", "0"))) +_VALUE_RESIDUAL = bool(int(os.environ.get("VALUE_RESIDUAL", "0"))) + class CausalSelfAttention(nn.Module): def __init__( self, @@ -820,17 +823,19 @@ def __init__( self.proj = CastedLinear(dim, dim, bias=False) self.proj._zero_init = True self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) - self.attn_gate = nn.Parameter(torch.ones(num_heads, dtype=torch.float32)) + if _GATED_ATTN: + self.attn_gate = nn.Parameter(torch.ones(num_heads, dtype=torch.float32)) self.rotary = Rotary(self.head_dim, base=rope_base) self.use_xsa = use_xsa - self.vr_lambda = nn.Parameter(torch.tensor([0.5, 0.5], dtype=torch.float32)) + if _VALUE_RESIDUAL: + self.vr_lambda = nn.Parameter(torch.tensor([0.5, 0.5], dtype=torch.float32)) - def forward(self, x: Tensor, v0: Tensor | None = None) -> tuple[Tensor, Tensor]: + def forward(self, x: Tensor, v0: Tensor | None = None) -> Tensor: bsz, seqlen, dim = x.shape q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) - if v0 is not None: + if _VALUE_RESIDUAL and v0 is not None: lam = torch.sigmoid(self.vr_lambda).to(dtype=v.dtype) v = lam[0] * v0 + lam[1] * v q = F.rms_norm(q, (q.size(-1),)) @@ -843,7 +848,8 @@ def forward(self, x: Tensor, v0: Tensor | None = None) -> tuple[Tensor, Tensor]: if self.use_xsa: vn = F.normalize(v.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1), dim=-1) y = y - (y * vn).sum(dim=-1, keepdim=True) * vn - y = y * torch.sigmoid(self.attn_gate).to(dtype=y.dtype)[None, :, None, None] + if _GATED_ATTN: + y = y * torch.sigmoid(self.attn_gate).to(dtype=y.dtype)[None, :, None, None] y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) return self.proj(y), v @@ -881,7 +887,7 @@ def forward(self, x: Tensor, x0: Tensor, v0: Tensor | None = None) -> tuple[Tens mix = self.resid_mix.to(dtype=x.dtype) x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 s = self._ln_scale - attn_out, v = self.attn(self.attn_norm(x), v0) + attn_out, v = self.attn(self.attn_norm(x), v0 if _VALUE_RESIDUAL else None) x = x + s * self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out x = x + s * self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x)) return x, v @@ -947,7 +953,7 @@ def __init__( self.num_decoder_layers = num_layers - self.num_encoder_layers self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) - xsa_last_n = int(os.environ.get("XSA_LAST_N", num_layers)) + xsa_last_n = int(os.environ.get("XSA_LAST_N", 4)) mlp_mult_enc = int(os.environ.get("MLP_MULT_ENCODER", mlp_mult)) mlp_mult_dec = int(os.environ.get("MLP_MULT_DECODER", mlp_mult)) leaky = bool(int(os.environ.get("LEAKY_RELU", "0"))) From bab4d516922574855e3f7ef22b256ef6c70e2cb9 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 16:18:59 -0300 Subject: [PATCH 48/72] feat: eval-only mode + faster n-gram eval MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit EVAL_ONLY=1 skips training, loads saved weights, runs evals only. N-gram batch_size 16→256 for faster GPU forward passes. --- train_gpt.py | 122 +++++++++++++++++++++++++-------------------------- 1 file changed, 60 insertions(+), 62 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 339163f9c..dcb2e4b13 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -363,7 +363,7 @@ def eval_val_ngram( args: Hyperparameters, base_model: nn.Module, rank: int, world_size: int, device: torch.device, val_tokens: Tensor, base_bytes_lut: Tensor, has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, - stride: int = 64, batch_size: int = 16, + stride: int = 64, batch_size: int = 256, ) -> tuple[float, float]: seq_len, vocab = args.train_seq_len, args.vocab_size total_tokens = val_tokens.numel() @@ -1035,6 +1035,7 @@ def main() -> None: global zeropower_via_newtonschulz5 code = Path(__file__).read_text(encoding="utf-8") + eval_only = bool(int(os.environ.get("EVAL_ONLY", "0"))) args = Hyperparameters() zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5) @@ -1243,9 +1244,12 @@ def lr_mul(step: int, elapsed_ms: float) -> float: remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0) return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0 - # Warmup primes the compiled forward/backward/optimizer paths, then we restore the - # initial weights/optimizer state so measured training starts from the true init. - if args.warmup_steps > 0: + if eval_only: + log0("eval_only: skipping training, loading final_model.int6.ptz") + with open("final_model.int6.ptz", "rb") as f: + base_model.load_state_dict(dequantize_state_dict_int8( + torch.load(io.BytesIO(lzma.decompress(f.read())), map_location="cpu")), strict=True) + if not eval_only and args.warmup_steps > 0: initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] model.train() @@ -1275,16 +1279,17 @@ def lr_mul(step: int, elapsed_ms: float) -> float: # MAIN TRAINING LOOP # ----------------------------- - training_time_ms = 0.0 - stop_after_step: int | None = None - ema_state = {k: v.detach().clone().float() for k, v in base_model.state_dict().items()} - swa_state: dict[str, Tensor] | None = None - swa_count = 0 - torch.cuda.synchronize() - t0 = time.perf_counter() + if not eval_only: + training_time_ms = 0.0 + stop_after_step: int | None = None + ema_state = {k: v.detach().clone().float() for k, v in base_model.state_dict().items()} + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + torch.cuda.synchronize() + t0 = time.perf_counter() step = 0 - while True: + while not eval_only: last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) @@ -1381,59 +1386,52 @@ def lr_mul(step: int, elapsed_ms: float) -> float: if stop_after_step is None and reached_cap: stop_after_step = step - log0( - f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " - f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" - ) - - # ----------------------------- - # SERIALIZATION + ROUNDTRIP VALIDATION - # ----------------------------- - # Save the raw state (useful for debugging/loading in PyTorch directly), then always produce - # the compressed int8+zlib artifact and validate the round-tripped weights. - - ema_state = {k: v.cpu() for k, v in ema_state.items()} - if swa_state is not None and swa_count > 0: - log0(f"swa: averaging {swa_count} checkpoints on top of EMA") - for k in swa_state: - swa_state[k] /= swa_count - ema_state[k] = 0.5 * ema_state[k] + 0.5 * swa_state[k] - del swa_state - log0("ema: loading weights") - base_model.load_state_dict(ema_state, strict=True) - for module in base_model.modules(): - if isinstance(module, CastedLinear): - module.float() - restore_low_dim_params_to_fp32(base_model) - del ema_state - if master_process: - torch.save(base_model.state_dict(), "final_model.pt") - model_bytes = os.path.getsize("final_model.pt") - code_bytes = len(code.encode("utf-8")) - log0(f"Serialized model: {model_bytes} bytes") - log0(f"Code size: {code_bytes} bytes") - log0(f"Total submission size: {model_bytes + code_bytes} bytes") - - quant_obj, quant_stats = quantize_state_dict_int6(base_model.state_dict()) - quant_buf = io.BytesIO() - torch.save(quant_obj, quant_buf) - quant_raw = quant_buf.getvalue() - quant_blob = lzma.compress(quant_raw, preset=6) - quant_raw_bytes = len(quant_raw) - if master_process: - with open("final_model.int6.ptz", "wb") as f: - f.write(quant_blob) - quant_file_bytes = os.path.getsize("final_model.int6.ptz") - code_bytes = len(code.encode("utf-8")) - ratio = quant_stats["baseline_tensor_bytes"] / max(quant_stats["int8_payload_bytes"], 1) + if not eval_only: log0( - f"Serialized model int6+lzma: {quant_file_bytes} bytes " - f"(payload:{quant_stats['int8_payload_bytes']} raw_torch:{quant_raw_bytes} payload_ratio:{ratio:.2f}x)" + f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" ) - log0(f"Total submission size int6+lzma: {quant_file_bytes + code_bytes} bytes") + ema_state = {k: v.cpu() for k, v in ema_state.items()} + if swa_state is not None and swa_count > 0: + log0(f"swa: averaging {swa_count} checkpoints on top of EMA") + for k in swa_state: + swa_state[k] /= swa_count + ema_state[k] = 0.5 * ema_state[k] + 0.5 * swa_state[k] + del swa_state + log0("ema: loading weights") + base_model.load_state_dict(ema_state, strict=True) + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.float() + restore_low_dim_params_to_fp32(base_model) + del ema_state + if master_process: + torch.save(base_model.state_dict(), "final_model.pt") + model_bytes = os.path.getsize("final_model.pt") + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model: {model_bytes} bytes") + log0(f"Code size: {code_bytes} bytes") + log0(f"Total submission size: {model_bytes + code_bytes} bytes") + quant_obj, quant_stats = quantize_state_dict_int6(base_model.state_dict()) + quant_buf = io.BytesIO() + torch.save(quant_obj, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = lzma.compress(quant_raw, preset=6) + quant_raw_bytes = len(quant_raw) + if master_process: + with open("final_model.int6.ptz", "wb") as f: + f.write(quant_blob) + quant_file_bytes = os.path.getsize("final_model.int6.ptz") + code_bytes = len(code.encode("utf-8")) + ratio = quant_stats["baseline_tensor_bytes"] / max(quant_stats["int8_payload_bytes"], 1) + log0( + f"Serialized model int6+lzma: {quant_file_bytes} bytes " + f"(payload:{quant_stats['int8_payload_bytes']} raw_torch:{quant_raw_bytes} payload_ratio:{ratio:.2f}x)" + ) + log0(f"Total submission size int6+lzma: {quant_file_bytes + code_bytes} bytes") + if distributed: + dist.barrier() - if distributed: - dist.barrier() with open("final_model.int6.ptz", "rb") as f: quant_blob_disk = f.read() quant_state = torch.load(io.BytesIO(lzma.decompress(quant_blob_disk)), map_location="cpu") From c791d0f662571d245864807b0420089f7c313ed4 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 18:31:27 -0300 Subject: [PATCH 49/72] feat: fast integrated n-gram eval + numpy arrays N-gram cache integrated into sliding window eval (single pass). Multi-order backoff 2-7, entropy-adaptive alpha, numpy arrays. Progress logging every 100 batches. --- train_gpt.py | 185 ++++++++++++++++++--------------------------------- 1 file changed, 65 insertions(+), 120 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index dcb2e4b13..eb2c626f3 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -280,6 +280,12 @@ def eval_val( return float(val_loss.item()), float(bits_per_token * tokens_per_byte) +_NG_B = 1 << 22 +_NG_ORDERS = (7, 6, 5, 4, 3, 2) +_NG_MIN = 2 +_NG_MULT = 265443576 +_NG_PAIR_MULT = 1000003 + def eval_val_sliding( args: Hyperparameters, base_model: nn.Module, @@ -292,43 +298,45 @@ def eval_val_sliding( is_boundary_token_lut: Tensor, stride: int = 64, batch_size: int = 256, -) -> tuple[float, float]: +) -> tuple[float, float, float]: seq_len = args.train_seq_len total_tokens = val_tokens.numel() windows: list[tuple[int, int]] = [] pos = 0 while pos + seq_len < total_tokens: - score_start = 0 if pos == 0 else seq_len - stride - windows.append((pos, score_start)) + windows.append((pos, 0 if pos == 0 else seq_len - stride)) pos += stride my_windows = windows[rank::world_size] - total_loss_sum = torch.zeros((), device=device, dtype=torch.float64) total_scored_tokens = torch.zeros((), device=device, dtype=torch.float64) total_byte_count = torch.zeros((), device=device, dtype=torch.float64) - + ng_loss_sum = 0.0 + ng_ctx = np.zeros(_NG_B, dtype=np.int32) + ng_pair = np.zeros(_NG_B, dtype=np.int32) + vt = val_tokens.numpy() base_model.eval() + num_batches = (len(my_windows) + batch_size - 1) // batch_size with torch.inference_mode(): for batch_start in range(0, len(my_windows), batch_size): + bi = batch_start // batch_size + if bi % 100 == 0: + print(f" eval batch {bi}/{num_batches}", flush=True) batch_windows = my_windows[batch_start:batch_start + batch_size] - x_list = [] - y_list = [] + x_list, y_list = [], [] for win_start, _ in batch_windows: chunk = val_tokens[win_start:win_start + seq_len + 1] - x_list.append(chunk[:-1]) - y_list.append(chunk[1:]) + x_list.append(chunk[:-1]); y_list.append(chunk[1:]) x = torch.stack(x_list).to(device=device, dtype=torch.int64) y = torch.stack(y_list).to(device=device, dtype=torch.int64) - with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): logits = base_model.forward_logits(x) per_token_loss = F.cross_entropy( - logits.float().reshape(-1, logits.size(-1)), - y.reshape(-1), - reduction="none", + logits.float().reshape(-1, logits.size(-1)), y.reshape(-1), reduction="none", ).reshape(len(batch_windows), seq_len) - - for idx, (_, score_start) in enumerate(batch_windows): + lp = F.log_softmax(logits.float(), dim=-1) + entropy = -(lp.exp() * lp).sum(dim=-1).cpu().numpy() + tgt_lp = lp.cpu().numpy() + for idx, (win_start, score_start) in enumerate(batch_windows): scored_loss = per_token_loss[idx, score_start:] total_loss_sum += scored_loss.to(torch.float64).sum() total_scored_tokens += float(scored_loss.numel()) @@ -337,100 +345,50 @@ def eval_val_sliding( token_bytes = base_bytes_lut[scored_tgt].to(dtype=torch.int16) token_bytes += (has_leading_space_lut[scored_tgt] & ~is_boundary_token_lut[scored_prev]).to(dtype=torch.int16) total_byte_count += token_bytes.to(torch.float64).sum() - + for t in range(score_start, seq_len): + abs_pos = win_start + t + 1 + tgt = int(vt[abs_pos]) + ng_p = 0.0 + found = False + for order in _NG_ORDERS: + if abs_pos < order: continue + ch = 0 + for k in range(abs_pos - order + 1, abs_pos): + ch = (ch * _NG_MULT + int(vt[k])) % _NG_B + cc = ng_ctx[ch] + if cc >= _NG_MIN: + ph = (ch * _NG_PAIR_MULT + tgt) % _NG_B + ng_p = ng_pair[ph] / cc + found = True + break + model_p = float(np.exp(tgt_lp[idx, t, tgt])) + if found: + H = float(entropy[idx, t]) + alpha = 0.05 + 0.55 / (1.0 + math.exp(-2.0 * (H - 4.0))) + mixed_p = (1.0 - alpha) * model_p + alpha * ng_p + else: + mixed_p = model_p + ng_loss_sum -= math.log(max(mixed_p, 1e-20)) + for order in _NG_ORDERS: + if abs_pos < order: continue + ch = 0 + for k in range(abs_pos - order + 1, abs_pos): + ch = (ch * _NG_MULT + int(vt[k])) % _NG_B + ng_ctx[ch] += 1 + ph = (ch * _NG_PAIR_MULT + tgt) % _NG_B + ng_pair[ph] += 1 + ng_loss_t = torch.tensor(ng_loss_sum, device=device, dtype=torch.float64) if dist.is_available() and dist.is_initialized(): dist.all_reduce(total_loss_sum, op=dist.ReduceOp.SUM) dist.all_reduce(total_scored_tokens, op=dist.ReduceOp.SUM) dist.all_reduce(total_byte_count, op=dist.ReduceOp.SUM) - + dist.all_reduce(ng_loss_t, op=dist.ReduceOp.SUM) val_loss = (total_loss_sum / total_scored_tokens).item() bpb = (total_loss_sum / (total_byte_count * math.log(2.0))).item() + ng_bpb = (ng_loss_t / (total_byte_count * math.log(2.0))).item() base_model.train() - return float(val_loss), float(bpb) - + return float(val_loss), float(bpb), float(ng_bpb) -_NGRAM_ORDERS = list(range(7, 1, -1)) -_NGRAM_HASH_MULT = 265443576 -_NGRAM_MOD = (1 << 22) - -def _ngram_hash(tokens: Tensor, end: int, order: int) -> int: - h = 0 - for i in range(end - order + 1, end): - h = (h * _NGRAM_HASH_MULT + tokens[i].item()) % _NGRAM_MOD - return h - -def eval_val_ngram( - args: Hyperparameters, base_model: nn.Module, rank: int, world_size: int, - device: torch.device, val_tokens: Tensor, base_bytes_lut: Tensor, - has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, - stride: int = 64, batch_size: int = 256, -) -> tuple[float, float]: - seq_len, vocab = args.train_seq_len, args.vocab_size - total_tokens = val_tokens.numel() - windows: list[tuple[int, int]] = [] - pos = 0 - while pos + seq_len < total_tokens: - windows.append((pos, 0 if pos == 0 else seq_len - stride)) - pos += stride - ngram_table: dict[int, dict[int, int]] = {} - total_loss = 0.0 - total_scored = 0.0 - total_bytes = 0.0 - base_model.eval() - with torch.inference_mode(): - for bi in range(0, len(windows), batch_size): - bw = windows[bi:bi + batch_size] - x = torch.stack([val_tokens[w:w+seq_len] for w, _ in bw]).to(device=device, dtype=torch.int64) - y = torch.stack([val_tokens[w+1:w+seq_len+1] for w, _ in bw]).to(device=device, dtype=torch.int64) - with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): - logits = base_model.forward_logits(x) - log_probs = F.log_softmax(logits.float(), dim=-1).cpu() - for j, (wpos, ss) in enumerate(bw): - for t in range(ss, seq_len): - abs_pos = wpos + t + 1 - tgt = val_tokens[abs_pos].item() - model_lp = log_probs[j, t] - ngram_dist = None - for order in _NGRAM_ORDERS: - if abs_pos >= order - 1: - h = _ngram_hash(val_tokens, abs_pos, order) - bucket = ngram_table.get(h) - if bucket is not None: - total_ct = sum(bucket.values()) - if total_ct >= 2: - ngram_dist = bucket - ngram_total = total_ct - break - if ngram_dist is not None: - entropy = -(model_lp.exp() * model_lp).sum().item() - alpha = 0.05 + 0.55 / (1.0 + math.exp(-2.0 * (entropy - 4.0))) - ng_prob = ngram_dist.get(tgt, 0) / ngram_total - model_prob = model_lp[tgt].exp().item() - mixed_prob = (1.0 - alpha) * model_prob + alpha * ng_prob - total_loss -= math.log(max(mixed_prob, 1e-20)) - else: - total_loss -= model_lp[tgt].item() - total_scored += 1.0 - prev_tok = val_tokens[abs_pos - 1].item() if abs_pos > 0 else 0 - tb = base_bytes_lut[tgt].item() - tb += (has_leading_space_lut[tgt].item() & (1 - is_boundary_token_lut[prev_tok].item())) - total_bytes += tb - for order in _NGRAM_ORDERS: - if abs_pos >= order - 1: - h = _ngram_hash(val_tokens, abs_pos, order) - if h not in ngram_table: ngram_table[h] = {} - ngram_table[h][tgt] = ngram_table[h].get(tgt, 0) + 1 - total_loss_t = torch.tensor(total_loss, device=device, dtype=torch.float64) - total_scored_t = torch.tensor(total_scored, device=device, dtype=torch.float64) - total_bytes_t = torch.tensor(total_bytes, device=device, dtype=torch.float64) - if dist.is_available() and dist.is_initialized(): - dist.all_reduce(total_loss_t, op=dist.ReduceOp.SUM) - dist.all_reduce(total_scored_t, op=dist.ReduceOp.SUM) - dist.all_reduce(total_bytes_t, op=dist.ReduceOp.SUM) - val_loss = (total_loss_t / total_scored_t).item() - bpb = (total_loss_t / (total_bytes_t * math.log(2.0))).item() - base_model.train() - return float(val_loss), float(bpb) # ----------------------------- @@ -1245,11 +1203,11 @@ def lr_mul(step: int, elapsed_ms: float) -> float: return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0 if eval_only: - log0("eval_only: skipping training, loading final_model.int6.ptz") + log0("eval_only: loading final_model.int6.ptz") with open("final_model.int6.ptz", "rb") as f: base_model.load_state_dict(dequantize_state_dict_int8( torch.load(io.BytesIO(lzma.decompress(f.read())), map_location="cpu")), strict=True) - if not eval_only and args.warmup_steps > 0: + elif args.warmup_steps > 0: initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] model.train() @@ -1279,8 +1237,8 @@ def lr_mul(step: int, elapsed_ms: float) -> float: # MAIN TRAINING LOOP # ----------------------------- + training_time_ms = 0.0 if not eval_only: - training_time_ms = 0.0 stop_after_step: int | None = None ema_state = {k: v.detach().clone().float() for k, v in base_model.state_dict().items()} swa_state: dict[str, Tensor] | None = None @@ -1451,29 +1409,16 @@ def lr_mul(step: int, elapsed_ms: float) -> float: torch.cuda.synchronize() t_slide = time.perf_counter() - sw_val_loss, sw_val_bpb = eval_val_sliding( + sw_val_loss, sw_val_bpb, ng_bpb = eval_val_sliding( args, base_model, rank, world_size, device, val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, ) torch.cuda.synchronize() log0( f"final_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{sw_val_bpb:.4f} " - f"eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" - ) - log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f}") - - torch.cuda.synchronize() - t_ngram = time.perf_counter() - ng_val_loss, ng_val_bpb = eval_val_ngram( - args, base_model, rank, world_size, device, - val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, - ) - torch.cuda.synchronize() - log0( - f"final_ngram val_loss:{ng_val_loss:.4f} val_bpb:{ng_val_bpb:.4f} " - f"eval_time:{1000.0 * (time.perf_counter() - t_ngram):.0f}ms" + f"ngram_bpb:{ng_bpb:.4f} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" ) - log0(f"final_ngram_exact val_loss:{ng_val_loss:.8f} val_bpb:{ng_val_bpb:.8f}") + log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f} ngram_bpb:{ng_bpb:.8f}") if distributed: dist.destroy_process_group() From c83e027db5226228bdb2e940b97611c592bcf50f Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 18:45:38 -0300 Subject: [PATCH 50/72] feat: vectorized n-gram eval with numpy Hash computation, lookups, scoring, and cache updates all vectorized. No per-token Python loop. Expected ~5 min eval vs 100 min. --- train_gpt.py | 68 +++++++++++++++++++++++++++------------------------- 1 file changed, 36 insertions(+), 32 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index eb2c626f3..b4773d40c 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -345,38 +345,42 @@ def eval_val_sliding( token_bytes = base_bytes_lut[scored_tgt].to(dtype=torch.int16) token_bytes += (has_leading_space_lut[scored_tgt] & ~is_boundary_token_lut[scored_prev]).to(dtype=torch.int16) total_byte_count += token_bytes.to(torch.float64).sum() - for t in range(score_start, seq_len): - abs_pos = win_start + t + 1 - tgt = int(vt[abs_pos]) - ng_p = 0.0 - found = False - for order in _NG_ORDERS: - if abs_pos < order: continue - ch = 0 - for k in range(abs_pos - order + 1, abs_pos): - ch = (ch * _NG_MULT + int(vt[k])) % _NG_B - cc = ng_ctx[ch] - if cc >= _NG_MIN: - ph = (ch * _NG_PAIR_MULT + tgt) % _NG_B - ng_p = ng_pair[ph] / cc - found = True - break - model_p = float(np.exp(tgt_lp[idx, t, tgt])) - if found: - H = float(entropy[idx, t]) - alpha = 0.05 + 0.55 / (1.0 + math.exp(-2.0 * (H - 4.0))) - mixed_p = (1.0 - alpha) * model_p + alpha * ng_p - else: - mixed_p = model_p - ng_loss_sum -= math.log(max(mixed_p, 1e-20)) - for order in _NG_ORDERS: - if abs_pos < order: continue - ch = 0 - for k in range(abs_pos - order + 1, abs_pos): - ch = (ch * _NG_MULT + int(vt[k])) % _NG_B - ng_ctx[ch] += 1 - ph = (ch * _NG_PAIR_MULT + tgt) % _NG_B - ng_pair[ph] += 1 + n_scored = seq_len - score_start + abs_positions = np.arange(score_start, seq_len) + win_start + 1 + targets = vt[abs_positions].astype(np.int64) + model_lp_scored = tgt_lp[idx, score_start:seq_len] + model_p_tgt = np.exp(model_lp_scored[np.arange(n_scored), targets]) + H = entropy[idx, score_start:seq_len] + alpha = 0.05 + 0.55 / (1.0 + np.exp(-2.0 * (H - 4.0))) + best_ng_p = np.zeros(n_scored) + best_found = np.zeros(n_scored, dtype=bool) + for order in _NG_ORDERS: + mask = (abs_positions >= order) & (~best_found) + if not mask.any(): continue + pos_m = abs_positions[mask] + ch = np.zeros(mask.sum(), dtype=np.int64) + for ki in range(order - 1): + ch = (ch * _NG_MULT + vt[pos_m - order + 1 + ki].astype(np.int64)) % _NG_B + cc = ng_ctx[ch] + has_counts = cc >= _NG_MIN + if not has_counts.any(): continue + ph = (ch * _NG_PAIR_MULT + targets[mask]) % _NG_B + ng_p = np.where(has_counts, ng_pair[ph] / np.maximum(cc, 1), 0.0) + idx_m = np.where(mask)[0] + best_ng_p[idx_m[has_counts]] = ng_p[has_counts] + best_found[idx_m[has_counts]] = True + mixed_p = np.where(best_found, (1.0 - alpha) * model_p_tgt + alpha * best_ng_p, model_p_tgt) + ng_loss_sum -= np.log(np.maximum(mixed_p, 1e-20)).sum() + for order in _NG_ORDERS: + valid = abs_positions >= order + if not valid.any(): continue + pos_v = abs_positions[valid] + ch = np.zeros(valid.sum(), dtype=np.int64) + for ki in range(order - 1): + ch = (ch * _NG_MULT + vt[pos_v - order + 1 + ki].astype(np.int64)) % _NG_B + np.add.at(ng_ctx, ch, 1) + ph = (ch * _NG_PAIR_MULT + targets[valid]) % _NG_B + np.add.at(ng_pair, ph, 1) ng_loss_t = torch.tensor(ng_loss_sum, device=device, dtype=torch.float64) if dist.is_available() and dist.is_initialized(): dist.all_reduce(total_loss_sum, op=dist.ReduceOp.SUM) From 20a74fd13931858248b9c7698fab9a73e1972c5d Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 18:50:07 -0300 Subject: [PATCH 51/72] perf: batch-vectorized n-gram across all windows Process 16K tokens per batch with numpy, not 64 per window. --- train_gpt.py | 69 +++++++++++++++++++++++++--------------------------- 1 file changed, 33 insertions(+), 36 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index b4773d40c..121c84b5b 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -336,6 +336,7 @@ def eval_val_sliding( lp = F.log_softmax(logits.float(), dim=-1) entropy = -(lp.exp() * lp).sum(dim=-1).cpu().numpy() tgt_lp = lp.cpu().numpy() + all_pos, all_tgt, all_mp, all_H = [], [], [], [] for idx, (win_start, score_start) in enumerate(batch_windows): scored_loss = per_token_loss[idx, score_start:] total_loss_sum += scored_loss.to(torch.float64).sum() @@ -345,42 +346,38 @@ def eval_val_sliding( token_bytes = base_bytes_lut[scored_tgt].to(dtype=torch.int16) token_bytes += (has_leading_space_lut[scored_tgt] & ~is_boundary_token_lut[scored_prev]).to(dtype=torch.int16) total_byte_count += token_bytes.to(torch.float64).sum() - n_scored = seq_len - score_start - abs_positions = np.arange(score_start, seq_len) + win_start + 1 - targets = vt[abs_positions].astype(np.int64) - model_lp_scored = tgt_lp[idx, score_start:seq_len] - model_p_tgt = np.exp(model_lp_scored[np.arange(n_scored), targets]) - H = entropy[idx, score_start:seq_len] - alpha = 0.05 + 0.55 / (1.0 + np.exp(-2.0 * (H - 4.0))) - best_ng_p = np.zeros(n_scored) - best_found = np.zeros(n_scored, dtype=bool) - for order in _NG_ORDERS: - mask = (abs_positions >= order) & (~best_found) - if not mask.any(): continue - pos_m = abs_positions[mask] - ch = np.zeros(mask.sum(), dtype=np.int64) - for ki in range(order - 1): - ch = (ch * _NG_MULT + vt[pos_m - order + 1 + ki].astype(np.int64)) % _NG_B - cc = ng_ctx[ch] - has_counts = cc >= _NG_MIN - if not has_counts.any(): continue - ph = (ch * _NG_PAIR_MULT + targets[mask]) % _NG_B - ng_p = np.where(has_counts, ng_pair[ph] / np.maximum(cc, 1), 0.0) - idx_m = np.where(mask)[0] - best_ng_p[idx_m[has_counts]] = ng_p[has_counts] - best_found[idx_m[has_counts]] = True - mixed_p = np.where(best_found, (1.0 - alpha) * model_p_tgt + alpha * best_ng_p, model_p_tgt) - ng_loss_sum -= np.log(np.maximum(mixed_p, 1e-20)).sum() - for order in _NG_ORDERS: - valid = abs_positions >= order - if not valid.any(): continue - pos_v = abs_positions[valid] - ch = np.zeros(valid.sum(), dtype=np.int64) - for ki in range(order - 1): - ch = (ch * _NG_MULT + vt[pos_v - order + 1 + ki].astype(np.int64)) % _NG_B - np.add.at(ng_ctx, ch, 1) - ph = (ch * _NG_PAIR_MULT + targets[valid]) % _NG_B - np.add.at(ng_pair, ph, 1) + positions = np.arange(score_start, seq_len, dtype=np.int64) + win_start + 1 + tgts = vt[positions].astype(np.int64) + mp = np.exp(tgt_lp[idx, score_start:seq_len][np.arange(len(positions)), tgts]) + all_pos.append(positions); all_tgt.append(tgts); all_mp.append(mp) + all_H.append(entropy[idx, score_start:seq_len]) + ap = np.concatenate(all_pos); at = np.concatenate(all_tgt) + amp = np.concatenate(all_mp); aH = np.concatenate(all_H) + n = len(ap) + alpha = 0.05 + 0.55 / (1.0 + np.exp(-2.0 * (aH - 4.0))) + best_ng = np.zeros(n); found = np.zeros(n, dtype=bool) + for order in _NG_ORDERS: + m = (ap >= order) & (~found) + if not m.any(): continue + ch = np.zeros(m.sum(), dtype=np.int64) + for ki in range(order - 1): + ch = (ch * _NG_MULT + vt[ap[m] - order + 1 + ki].astype(np.int64)) % _NG_B + cc = ng_ctx[ch]; has = cc >= _NG_MIN + if not has.any(): continue + ph = (ch * _NG_PAIR_MULT + at[m]) % _NG_B + ng_p = np.where(has, ng_pair[ph] / np.maximum(cc, 1), 0.0) + ix = np.where(m)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True + mixed = np.where(found, (1.0 - alpha) * amp + alpha * best_ng, amp) + ng_loss_sum -= np.log(np.maximum(mixed, 1e-20)).sum() + for order in _NG_ORDERS: + v = ap >= order + if not v.any(): continue + ch = np.zeros(v.sum(), dtype=np.int64) + for ki in range(order - 1): + ch = (ch * _NG_MULT + vt[ap[v] - order + 1 + ki].astype(np.int64)) % _NG_B + np.add.at(ng_ctx, ch, 1) + ph = (ch * _NG_PAIR_MULT + at[v]) % _NG_B + np.add.at(ng_pair, ph, 1) ng_loss_t = torch.tensor(ng_loss_sum, device=device, dtype=torch.float64) if dist.is_available() and dist.is_initialized(): dist.all_reduce(total_loss_sum, op=dist.ReduceOp.SUM) From db6b7f78a23621c7b83e985b7674c77a3c2b516a Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 18:53:13 -0300 Subject: [PATCH 52/72] perf: fix 2GB GPU transfer bottleneck in n-gram eval Only transfer target token log probs (2MB) not full vocab (2GB per batch). --- train_gpt.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 121c84b5b..1e5ecb850 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -335,7 +335,7 @@ def eval_val_sliding( ).reshape(len(batch_windows), seq_len) lp = F.log_softmax(logits.float(), dim=-1) entropy = -(lp.exp() * lp).sum(dim=-1).cpu().numpy() - tgt_lp = lp.cpu().numpy() + tgt_lp = lp.gather(-1, y.unsqueeze(-1)).squeeze(-1).cpu().numpy() all_pos, all_tgt, all_mp, all_H = [], [], [], [] for idx, (win_start, score_start) in enumerate(batch_windows): scored_loss = per_token_loss[idx, score_start:] @@ -348,7 +348,7 @@ def eval_val_sliding( total_byte_count += token_bytes.to(torch.float64).sum() positions = np.arange(score_start, seq_len, dtype=np.int64) + win_start + 1 tgts = vt[positions].astype(np.int64) - mp = np.exp(tgt_lp[idx, score_start:seq_len][np.arange(len(positions)), tgts]) + mp = np.exp(tgt_lp[idx, score_start:seq_len]) all_pos.append(positions); all_tgt.append(tgts); all_mp.append(mp) all_H.append(entropy[idx, score_start:seq_len]) ap = np.concatenate(all_pos); at = np.concatenate(all_tgt) From 2964d050b434f0cf9edb6fef2ebecd82fbf9a38d Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 19:23:58 -0300 Subject: [PATCH 53/72] fix: precompute n-gram hashes + clamp probability bug Precompute all hashes upfront (6 numpy passes). Clamp ng_prob to [0,1] to prevent hash collision artifacts. Progress logging. --- train_gpt.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 1e5ecb850..5619d3037 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -314,6 +314,13 @@ def eval_val_sliding( ng_ctx = np.zeros(_NG_B, dtype=np.int32) ng_pair = np.zeros(_NG_B, dtype=np.int32) vt = val_tokens.numpy() + ng_hashes = {} + for order in _NG_ORDERS: + h = np.zeros(total_tokens, dtype=np.int64) + for ki in range(order - 1): + h[order-1:] = (h[order-1:] * _NG_MULT + vt[ki:total_tokens - order + 1 + ki].astype(np.int64)) % _NG_B + ng_hashes[order] = h + print(" n-gram hashes precomputed", flush=True) base_model.eval() num_batches = (len(my_windows) + batch_size - 1) // batch_size with torch.inference_mode(): @@ -359,22 +366,18 @@ def eval_val_sliding( for order in _NG_ORDERS: m = (ap >= order) & (~found) if not m.any(): continue - ch = np.zeros(m.sum(), dtype=np.int64) - for ki in range(order - 1): - ch = (ch * _NG_MULT + vt[ap[m] - order + 1 + ki].astype(np.int64)) % _NG_B + ch = ng_hashes[order][ap[m]] cc = ng_ctx[ch]; has = cc >= _NG_MIN if not has.any(): continue ph = (ch * _NG_PAIR_MULT + at[m]) % _NG_B - ng_p = np.where(has, ng_pair[ph] / np.maximum(cc, 1), 0.0) + ng_p = np.clip(np.where(has, ng_pair[ph] / np.maximum(cc, 1), 0.0), 0.0, 1.0) ix = np.where(m)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True mixed = np.where(found, (1.0 - alpha) * amp + alpha * best_ng, amp) ng_loss_sum -= np.log(np.maximum(mixed, 1e-20)).sum() for order in _NG_ORDERS: v = ap >= order if not v.any(): continue - ch = np.zeros(v.sum(), dtype=np.int64) - for ki in range(order - 1): - ch = (ch * _NG_MULT + vt[ap[v] - order + 1 + ki].astype(np.int64)) % _NG_B + ch = ng_hashes[order][ap[v]] np.add.at(ng_ctx, ch, 1) ph = (ch * _NG_PAIR_MULT + at[v]) % _NG_B np.add.at(ng_pair, ph, 1) From 623f6b6544a69e5a467b0ec50984a68801782933 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 19:32:36 -0300 Subject: [PATCH 54/72] perf: full GPU n-gram eval with torch MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit All n-gram operations on GPU — hash precomputation, lookups, scoring, cache updates via scatter_add_. No numpy bottleneck. --- train_gpt.py | 54 ++++++++++++++++++++++++++-------------------------- 1 file changed, 27 insertions(+), 27 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 5619d3037..ed32f67aa 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -310,23 +310,23 @@ def eval_val_sliding( total_loss_sum = torch.zeros((), device=device, dtype=torch.float64) total_scored_tokens = torch.zeros((), device=device, dtype=torch.float64) total_byte_count = torch.zeros((), device=device, dtype=torch.float64) - ng_loss_sum = 0.0 - ng_ctx = np.zeros(_NG_B, dtype=np.int32) - ng_pair = np.zeros(_NG_B, dtype=np.int32) - vt = val_tokens.numpy() + ng_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + ng_ctx = torch.zeros(_NG_B, dtype=torch.int32, device=device) + ng_pair = torch.zeros(_NG_B, dtype=torch.int32, device=device) + vt_gpu = val_tokens.to(device=device, dtype=torch.int64) ng_hashes = {} for order in _NG_ORDERS: - h = np.zeros(total_tokens, dtype=np.int64) + h = torch.zeros(total_tokens, dtype=torch.int64, device=device) for ki in range(order - 1): - h[order-1:] = (h[order-1:] * _NG_MULT + vt[ki:total_tokens - order + 1 + ki].astype(np.int64)) % _NG_B + h[order-1:] = (h[order-1:] * _NG_MULT + vt_gpu[ki:total_tokens - order + 1 + ki]) % _NG_B ng_hashes[order] = h - print(" n-gram hashes precomputed", flush=True) + print(" n-gram hashes precomputed (GPU)", flush=True) base_model.eval() num_batches = (len(my_windows) + batch_size - 1) // batch_size with torch.inference_mode(): for batch_start in range(0, len(my_windows), batch_size): bi = batch_start // batch_size - if bi % 100 == 0: + if bi % 500 == 0: print(f" eval batch {bi}/{num_batches}", flush=True) batch_windows = my_windows[batch_start:batch_start + batch_size] x_list, y_list = [], [] @@ -341,8 +341,8 @@ def eval_val_sliding( logits.float().reshape(-1, logits.size(-1)), y.reshape(-1), reduction="none", ).reshape(len(batch_windows), seq_len) lp = F.log_softmax(logits.float(), dim=-1) - entropy = -(lp.exp() * lp).sum(dim=-1).cpu().numpy() - tgt_lp = lp.gather(-1, y.unsqueeze(-1)).squeeze(-1).cpu().numpy() + ent = -(lp.exp() * lp).sum(dim=-1) + tgt_lp_val = lp.gather(-1, y.unsqueeze(-1)).squeeze(-1) all_pos, all_tgt, all_mp, all_H = [], [], [], [] for idx, (win_start, score_start) in enumerate(batch_windows): scored_loss = per_token_loss[idx, score_start:] @@ -353,16 +353,16 @@ def eval_val_sliding( token_bytes = base_bytes_lut[scored_tgt].to(dtype=torch.int16) token_bytes += (has_leading_space_lut[scored_tgt] & ~is_boundary_token_lut[scored_prev]).to(dtype=torch.int16) total_byte_count += token_bytes.to(torch.float64).sum() - positions = np.arange(score_start, seq_len, dtype=np.int64) + win_start + 1 - tgts = vt[positions].astype(np.int64) - mp = np.exp(tgt_lp[idx, score_start:seq_len]) - all_pos.append(positions); all_tgt.append(tgts); all_mp.append(mp) - all_H.append(entropy[idx, score_start:seq_len]) - ap = np.concatenate(all_pos); at = np.concatenate(all_tgt) - amp = np.concatenate(all_mp); aH = np.concatenate(all_H) - n = len(ap) - alpha = 0.05 + 0.55 / (1.0 + np.exp(-2.0 * (aH - 4.0))) - best_ng = np.zeros(n); found = np.zeros(n, dtype=bool) + positions = torch.arange(score_start, seq_len, dtype=torch.int64, device=device) + win_start + 1 + all_pos.append(positions) + all_tgt.append(vt_gpu[positions]) + all_mp.append(tgt_lp_val[idx, score_start:seq_len].exp()) + all_H.append(ent[idx, score_start:seq_len]) + ap = torch.cat(all_pos); at = torch.cat(all_tgt) + amp = torch.cat(all_mp); aH = torch.cat(all_H) + n = ap.shape[0] + alpha = 0.05 + 0.55 / (1.0 + torch.exp(-2.0 * (aH - 4.0))) + best_ng = torch.zeros(n, device=device); found = torch.zeros(n, dtype=torch.bool, device=device) for order in _NG_ORDERS: m = (ap >= order) & (~found) if not m.any(): continue @@ -370,18 +370,18 @@ def eval_val_sliding( cc = ng_ctx[ch]; has = cc >= _NG_MIN if not has.any(): continue ph = (ch * _NG_PAIR_MULT + at[m]) % _NG_B - ng_p = np.clip(np.where(has, ng_pair[ph] / np.maximum(cc, 1), 0.0), 0.0, 1.0) - ix = np.where(m)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True - mixed = np.where(found, (1.0 - alpha) * amp + alpha * best_ng, amp) - ng_loss_sum -= np.log(np.maximum(mixed, 1e-20)).sum() + ng_p = (ng_pair[ph].float() / cc.float().clamp(min=1)).clamp(0, 1) + ix = m.nonzero(as_tuple=True)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True + mixed = torch.where(found, (1.0 - alpha) * amp + alpha * best_ng, amp) + ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).sum() for order in _NG_ORDERS: v = ap >= order if not v.any(): continue ch = ng_hashes[order][ap[v]] - np.add.at(ng_ctx, ch, 1) + ng_ctx.scatter_add_(0, ch, torch.ones_like(ch, dtype=torch.int32)) ph = (ch * _NG_PAIR_MULT + at[v]) % _NG_B - np.add.at(ng_pair, ph, 1) - ng_loss_t = torch.tensor(ng_loss_sum, device=device, dtype=torch.float64) + ng_pair.scatter_add_(0, ph, torch.ones_like(ph, dtype=torch.int32)) + ng_loss_t = ng_loss_sum if dist.is_available() and dist.is_initialized(): dist.all_reduce(total_loss_sum, op=dist.ReduceOp.SUM) dist.all_reduce(total_scored_tokens, op=dist.ReduceOp.SUM) From 8f580fd26e4efa5bdfbeddd759019502f39c8033 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 19:37:19 -0300 Subject: [PATCH 55/72] =?UTF-8?q?perf:=20simplified=205-gram=20eval=20?= =?UTF-8?q?=E2=80=94=203=20GPU=20ops=20per=20batch?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Single 5-gram order, fixed alpha=0.20 No backoff loop, no entropy, no log_softmax for n-gram. Three torch ops per batch: lookup, blend, scatter_add. --- train_gpt.py | 70 ++++++++++++++++++++-------------------------------- 1 file changed, 27 insertions(+), 43 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index ed32f67aa..d3a5b5258 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -281,7 +281,8 @@ def eval_val( _NG_B = 1 << 22 -_NG_ORDERS = (7, 6, 5, 4, 3, 2) +_NG_ORDER = 5 +_NG_ALPHA = 0.20 _NG_MIN = 2 _NG_MULT = 265443576 _NG_PAIR_MULT = 1000003 @@ -314,20 +315,16 @@ def eval_val_sliding( ng_ctx = torch.zeros(_NG_B, dtype=torch.int32, device=device) ng_pair = torch.zeros(_NG_B, dtype=torch.int32, device=device) vt_gpu = val_tokens.to(device=device, dtype=torch.int64) - ng_hashes = {} - for order in _NG_ORDERS: - h = torch.zeros(total_tokens, dtype=torch.int64, device=device) - for ki in range(order - 1): - h[order-1:] = (h[order-1:] * _NG_MULT + vt_gpu[ki:total_tokens - order + 1 + ki]) % _NG_B - ng_hashes[order] = h - print(" n-gram hashes precomputed (GPU)", flush=True) + h5 = torch.zeros(total_tokens, dtype=torch.int64, device=device) + for ki in range(_NG_ORDER - 1): + h5[_NG_ORDER-1:] = (h5[_NG_ORDER-1:] * _NG_MULT + vt_gpu[ki:total_tokens - _NG_ORDER + 1 + ki]) % _NG_B + print(" 5-gram hashes precomputed", flush=True) base_model.eval() num_batches = (len(my_windows) + batch_size - 1) // batch_size with torch.inference_mode(): for batch_start in range(0, len(my_windows), batch_size): - bi = batch_start // batch_size - if bi % 500 == 0: - print(f" eval batch {bi}/{num_batches}", flush=True) + if batch_start % (batch_size * 500) == 0: + print(f" eval batch {batch_start // batch_size}/{num_batches}", flush=True) batch_windows = my_windows[batch_start:batch_start + batch_size] x_list, y_list = [], [] for win_start, _ in batch_windows: @@ -340,10 +337,8 @@ def eval_val_sliding( per_token_loss = F.cross_entropy( logits.float().reshape(-1, logits.size(-1)), y.reshape(-1), reduction="none", ).reshape(len(batch_windows), seq_len) - lp = F.log_softmax(logits.float(), dim=-1) - ent = -(lp.exp() * lp).sum(dim=-1) - tgt_lp_val = lp.gather(-1, y.unsqueeze(-1)).squeeze(-1) - all_pos, all_tgt, all_mp, all_H = [], [], [], [] + tgt_p = F.softmax(logits.float(), dim=-1).gather(-1, y.unsqueeze(-1)).squeeze(-1) + all_pos, all_tgt, all_mp = [], [], [] for idx, (win_start, score_start) in enumerate(batch_windows): scored_loss = per_token_loss[idx, score_start:] total_loss_sum += scored_loss.to(torch.float64).sum() @@ -353,34 +348,23 @@ def eval_val_sliding( token_bytes = base_bytes_lut[scored_tgt].to(dtype=torch.int16) token_bytes += (has_leading_space_lut[scored_tgt] & ~is_boundary_token_lut[scored_prev]).to(dtype=torch.int16) total_byte_count += token_bytes.to(torch.float64).sum() - positions = torch.arange(score_start, seq_len, dtype=torch.int64, device=device) + win_start + 1 - all_pos.append(positions) - all_tgt.append(vt_gpu[positions]) - all_mp.append(tgt_lp_val[idx, score_start:seq_len].exp()) - all_H.append(ent[idx, score_start:seq_len]) - ap = torch.cat(all_pos); at = torch.cat(all_tgt) - amp = torch.cat(all_mp); aH = torch.cat(all_H) - n = ap.shape[0] - alpha = 0.05 + 0.55 / (1.0 + torch.exp(-2.0 * (aH - 4.0))) - best_ng = torch.zeros(n, device=device); found = torch.zeros(n, dtype=torch.bool, device=device) - for order in _NG_ORDERS: - m = (ap >= order) & (~found) - if not m.any(): continue - ch = ng_hashes[order][ap[m]] - cc = ng_ctx[ch]; has = cc >= _NG_MIN - if not has.any(): continue - ph = (ch * _NG_PAIR_MULT + at[m]) % _NG_B - ng_p = (ng_pair[ph].float() / cc.float().clamp(min=1)).clamp(0, 1) - ix = m.nonzero(as_tuple=True)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True - mixed = torch.where(found, (1.0 - alpha) * amp + alpha * best_ng, amp) - ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).sum() - for order in _NG_ORDERS: - v = ap >= order - if not v.any(): continue - ch = ng_hashes[order][ap[v]] - ng_ctx.scatter_add_(0, ch, torch.ones_like(ch, dtype=torch.int32)) - ph = (ch * _NG_PAIR_MULT + at[v]) % _NG_B - ng_pair.scatter_add_(0, ph, torch.ones_like(ph, dtype=torch.int32)) + pos = torch.arange(score_start, seq_len, dtype=torch.int64, device=device) + win_start + 1 + all_pos.append(pos); all_tgt.append(vt_gpu[pos]); all_mp.append(tgt_p[idx, score_start:]) + ap = torch.cat(all_pos); at = torch.cat(all_tgt); amp = torch.cat(all_mp) + valid = ap >= _NG_ORDER + ch = h5[ap[valid]] + cc = ng_ctx[ch].float().clamp(min=1) + ph = (ch * _NG_PAIR_MULT + at[valid]) % _NG_B + ng_p = (ng_pair[ph].float() / cc).clamp(0, 1) + has = ng_ctx[ch] >= _NG_MIN + mp_v = amp[valid] + mixed = torch.where(has, (1 - _NG_ALPHA) * mp_v + _NG_ALPHA * ng_p, mp_v) + ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).to(torch.float64).sum() + mp_inv = amp[~valid] + if mp_inv.numel() > 0: + ng_loss_sum -= torch.log(mp_inv.clamp(min=1e-20)).to(torch.float64).sum() + ng_ctx.scatter_add_(0, ch, torch.ones_like(ch, dtype=torch.int32)) + ng_pair.scatter_add_(0, ph, torch.ones_like(ph, dtype=torch.int32)) ng_loss_t = ng_loss_sum if dist.is_available() and dist.is_initialized(): dist.all_reduce(total_loss_sum, op=dist.ReduceOp.SUM) From b53bb3e6dfa9483bad590010f7e13b9adeb7bde5 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 20:12:55 -0300 Subject: [PATCH 56/72] =?UTF-8?q?feat:=201.0689=20BPB=20=E2=80=94=20EMA-GP?= =?UTF-8?q?U=20+=205-gram=20eval=20cache?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../README.md | 146 +++---- .../submission.json | 26 +- .../train.log | 156 ++++---- .../train_gpt.py | 378 ++++++++++-------- train_gpt.py | 4 +- 5 files changed, 358 insertions(+), 352 deletions(-) diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md index 446ffa534..1323ee270 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md @@ -1,135 +1,93 @@ -## Pre-Enrichment + Encoder Recurrence + XSA + SmearGate + BigramHash +## EMA-GPU + 5-gram Eval Cache + Pre-Enrichment + XSA -**val_bpb: 1.1629** (sliding window, stride=64) | 15.05 MB | 8xH100 SXM, 600s +**val_bpb: 1.0689** (5-gram n-gram cache) | 14.95 MB | 8xH100 SXM, 600s --- -### Progress +### Results -| | v1 | v2 | v3 | v4 (this) | -|---|---|---|---|---| -| val_bpb (sliding) | 1.1855 | 1.1709 | 1.1668 | **1.1629** | -| Params | 19.4M | 24.7M | 25.2M | 25.2M | -| Artifact | 15.75 MB | 15.57 MB | 15.02 MB | 15.05 MB | -| Steps (600s) | 8,004 | 6,423 | 5,373 | 5,636 | -| Step time | 75ms | 93ms | 112ms | 106ms | -| Quant gap | 0.020 | 0.020 | 0.004 | 0.004 | +| Metric | Value | +|---|---| +| **N-gram eval val_bpb** | **1.0689** | +| Sliding window val_bpb | 1.1476 | +| Standard eval val_bpb (post-quant) | 1.1688 | +| Pre-quant val_bpb | 1.1643 | +| Quant gap | 0.004 | +| Steps | 9,312 (64.4ms/step) | +| Training time | 600s | +| Peak memory | 13,058 MiB | +| Artifact size | 14,948,991 bytes | +| Model parameters | 25,254,992 | --- -### Key Contributions - -#### GELU Pre-Enrichment (512→768→512) - -Raw token embeddings carry no relational structure. I add a wider nonlinear transformation before the residual stream: -embedding → BigramHash add → SmearGate → Linear(512→768) → GELU → Linear(768→512) → RMS Norm → transformer blocks - -The wider bottleneck (768) gives the embedding transformation more capacity than the original 512→512. Cost: ~0.8M params, negligible step time. - -#### 2x Encoder Recurrence - -Depth recurrence is a known technique (ALBERT, Universal Transformers). My contribution is applying it to only the encoder half of a U-Net transformer architecture, with RMS norm stabilization between passes. - -With 10 layers (5 encoder + 5 decoder), the forward pass becomes: -1. Run encoder blocks 0-4 (first pass) -2. RMS norm (stabilize between passes) -3. Run encoder blocks 0-4 again (second pass, refine) -4. Run decoder blocks 5-9 with skip connections from second encoder pass - -**15 effective layers from 10 physical blocks**, zero extra parameters. - -**A/B Comparison — MLP 3x + seq 2048 config (8xH100, 10 minutes):** - -| Metric | With recurrence | Without recurrence | -|---|---|---| -| Steps completed | 6,423 | 8,950 | -| Step time | 93ms | 67ms | -| Sliding window BPB | **1.1709** | 1.1740 | +### Architecture -**A/B Comparison — MLP 2x + seq 1024 config (8xH100, 10 minutes):** +10L/512d U-Net, 25.25M params. GQA 8H/4KV, MLP 3x (1536 hidden), tied embeddings, logit softcap=30.0. -| Metric | With recurrence | Without recurrence | -|---|---|---| -| Steps completed | 8,004 | 11,955 | -| Step time | 75ms | 50ms | -| Sliding window BPB | **1.1855** | 1.1947 | +- **GELU Pre-Enrichment** (512→768→512): Wider nonlinear transformation before transformer blocks. Embedding → BigramHash add → SmearGate → Linear(512→768) → GELU → Linear(768→512) → RMS Norm → blocks. +- **XSA** (last 4 layers): Exclusive Self Attention removes self-value bias via orthogonal projection (arXiv:2603.09078, GQA-aware implementation from PR #265 @unnir). Zero parameters. +- **SmearGate**: Per-dim gate blending each token with previous token's embedding. F.pad for efficiency. +- **BigramHash** (2048×128): Hash-table embedding for token bigrams, projected to model dim. +- **U-Net skip connections**: Encoder-decoder with learnable skip weights. -Recurrence wins across both configs despite 28-40% fewer gradient updates. +Training: Muon+AdamW, WD=0.04, matrix_lr=0.025, scalar_lr=0.025, warmdown=3500 iters, batch=524K tokens, seq=2048. EMA decay=0.997. Int6 QAT + lzma (preset=6). -#### XSA (Exclusive Self Attention) on Last 4 Layers - -Removes self-value bias from attention output via orthogonal projection (arXiv:2603.09078). After computing attention output Y, XSA subtracts the component aligned with each token's own value vector: +--- -``` -Vn = normalize(V, dim=-1) -Y = Y - (Y · Vn).sum(dim=-1, keepdim=True) * Vn -``` +### EMA on GPU (37% faster training) — novel contribution -Forces attention layers to capture purely contextual information from other tokens. Zero new parameters. Applied to last 4 layers only — early layers retain self-attention for basic feature building. Requires GQA-aware expansion of V to match Q head count before projection. +EMA state kept on GPU during training instead of synchronous GPU→CPU copy every step. Only moved to CPU at the end for serialization. To my knowledge, this optimization is not used in other submissions. -v3 → v4 improvement: 1.1668 → 1.1629 (-0.004 BPB). +Step time: **64.4ms** (vs 101ms before). Enables **9,312 steps** in 600s vs ~5,900 before — 57% more gradient updates from the same training time. --- -### Additional Techniques - -- **SmearGate**: Per-dim learnable gate blending each token with previous token's embedding. 512 params. -- **BigramHash** (4096×64): Hash-table embedding for token bigrams, projected to model dim. ~590K params. -- **EMA** (decay=0.997): Exponential moving average replacing SWA. Quant gap reduced from 0.020 to 0.004 across versions. -- **Int6 QAT**: Fake quantization with straight-through estimator during training. Model learns int6-friendly weights. -- **lzma compression**: Stdlib replacement for zlib. Zero dependency risk. +### 5-gram Eval Cache (score-first, backward-looking) -Also: MLP 3x, seq 2048, overtone init, Muon+AdamW WD=0.04, sliding window eval stride=64. +Fixed-weight hashed n-gram interpolation during sliding window eval. Concept credited to @deanbrr (PR #659), developed by PR #706 (@newjordan) and PR #727 (@Asukabot0). -Overtone init, Muon weight decay, and sliding window eval adapted from notapplica and Matthew Li's work. +**Protocol:** +- Cache built from already-scored tokens only (backward-looking) +- Score-first: cache updated AFTER segment scoring +- Fixed alpha=0.20: `p_final = 0.80 * p_model + 0.20 * p_ngram` +- Single 5-gram order +- Dual-array hash scheme: separate context count and pair count arrays (4M buckets each) +- min_count=2 threshold +- Per-GPU independent cache, no cross-GPU sync +- Hash table precomputed for all positions in single pass +- Integrated into sliding window eval (single pass, ~5s n-gram overhead) ---- +**Compliance:** +- Score-first, backward-looking: n-gram counts built from previously scored tokens only +- No oracle selection: alpha is fixed, independent of ground-truth +- No cross-GPU sync: each GPU maintains its own independent cache -### What Didn't Work - -- **FP16 embedding passthrough**: Reduced quant error by ~0.006 BPB but added ~520KB, pushing artifact over 16MB. -- **3x encoder recurrence**: Exceeded Triton's per-SM shared memory limit on A100 and RTX 4050. -- **Reverse encoder recurrence** (second pass in reverse order): Worse than forward-only (1.4140 vs 1.4077 on A100). -- **Auxiliary encoder loss**: Hurt performance. Encoder works better optimized purely for decoder consumption. -- **Phase-transition resid_mix + gradient clipping**: Borrowed from top submissions, hurt our config. Techniques tuned for non-recurrence setups don't always transfer. -- **12L MLP 2x with recurrence (18 effective layers)**: Numbers were significantly worse than 10L MLP 3x. Width beats depth at this scale. -- **Warmdown scheduler on A100**: Wallclock-aware warmdown decayed LR from step 0 on A100 (~1100ms/step). Override to WARMDOWN_ITERS=120 required for local development. +**Improvement:** 1.1476 → 1.0689 = **-0.079 BPB** --- -### Configuration -TRAIN_BATCH_TOKENS=393216 MATRIX_LR=0.028 MUON_WD=0.04 ADAM_WD=0.04 -WARMDOWN_ITERS=3300 NUM_LAYERS=10 MLP_MULT=3 TRAIN_SEQ_LEN=2048 -ENCODER_RECURRENCE=1 EMA_DECAY=0.997 XSA_LAST_N=4 +### Toggleable Features (default OFF, not used in this submission) -Model parameters: 25,222,224 -Submission size (int6+lzma): 15,051,927 bytes (code: 59,427 bytes) +- `VALUE_RESIDUAL=1` — Layer-0 V mixed into all subsequent layers via learned sigmoid gates +- `GATED_ATTN=1` — Per-head sigmoid gates on attention output -### Reproduction +--- -All defaults are baked into the script — no env vars needed. +### Reproduce ```bash python3 data/cached_challenge_fineweb.py --variant sp1024 --train-shards 80 torchrun --standalone --nproc_per_node=8 train_gpt.py ``` -### Key Metrics +All defaults baked in. No env vars needed. 8xH100 SXM, 600s training + ~182s eval. -| Metric | Value | -|---|---| -| Pre-quant val_bpb | 1.1809 | -| Post-quant val_bpb (standard) | 1.1848 | -| Post-quant val_bpb (sliding window) | **1.1629** | -| Quant gap (standard - pre-quant) | 0.004 | -| Training time | 599,886ms (5,636 steps at ~106ms) | -| Peak memory | 14,147 MiB | -| Submission size (int6+lzma) | 15,051,927 bytes | -| Model parameters | 25,222,224 | +--- ### Included Files - `train_gpt.py` — standalone training script with all modifications -- `train.log` — full 8xH100 training log (seed 1337) +- `train.log` — full 8xH100 training + eval log (seed 1337) - `submission.json` — leaderboard metadata - `README.md` — this file diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json index 2b040c91d..9ecf56993 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json @@ -1,17 +1,17 @@ { "author": "Idanr", "github_id": "idan3011", - "name": "Pre-Enrichment + Encoder Recurrence + XSA + SmearGate + BigramHash", - "blurb": "GELU pre-enrichment (512-768-512) + 2x encoder recurrence + XSA last 4 layers + SmearGate + BigramHash + EMA + int6 QAT + lzma + MLP 3x + sliding window eval (stride=64), 10L 512d seq2048.", - "date": "2026-03-21T06:25:00Z", - "val_loss": 1.96347005, - "val_bpb": 1.16287756, - "pre_quant_val_loss": 1.9940, - "pre_quant_val_bpb": 1.1809, - "step_stop": 5636, - "wallclock_seconds": 599.886, - "eval_time_seconds": 246.128, - "bytes_total": 15051927, - "bytes_model_int6_lzma": 14992500, - "bytes_code": 59427 + "name": "EMA-GPU + 5-gram Cache + Pre-Enrichment + XSA + SmearGate + BigramHash", + "blurb": "EMA on GPU (64ms/step, 9312 steps). 5-gram eval cache with score-first backward-looking n-gram mixing (alpha=0.20). GELU pre-enrichment + XSA-4 + SmearGate + BigramHash + int6 QAT + lzma. 10L 512d.", + "date": "2026-03-25T22:45:00Z", + "val_loss": 1.93771000, + "val_bpb": 1.06885331, + "pre_quant_val_loss": 1.9659, + "pre_quant_val_bpb": 1.1643, + "step_stop": 9312, + "wallclock_seconds": 600.041, + "eval_time_seconds": 182.423, + "bytes_total": 14948991, + "bytes_model_int6_lzma": 14883400, + "bytes_code": 65591 } diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log index c07c84223..94e1307cc 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log @@ -1,18 +1,17 @@ -W0321 06:25:07.491000 851 torch/distributed/run.py:803] -W0321 06:25:07.491000 851 torch/distributed/run.py:803] ***************************************** -W0321 06:25:07.491000 851 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. -W0321 06:25:07.491000 851 torch/distributed/run.py:803] ***************************************** -logs/dbb3f63a-cd40-41e5-aa32-4d819311430f.txt +W0325 19:00:44.702000 1238 torch/distributed/run.py:803] +W0325 19:00:44.702000 1238 torch/distributed/run.py:803] ***************************************** +W0325 19:00:44.702000 1238 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +W0325 19:00:44.702000 1238 torch/distributed/run.py:803] ***************************************** +logs/0bd45560-4576-46e2-a6e1-32b933fe49ba.txt val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model train_loader:dataset:fineweb10B_sp1024 train_shards:80 val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 -model_params:25222224 -encoder_recurrence:ON +model_params:25254992 world_size:8 grad_accum_steps:1 -sdp_backends:cudnn=False flash=True mem_efficient=False math=False +sdp_backends:cudnn=True flash=True mem_efficient=False math=False attention_mode:gqa num_heads:8 num_kv_heads:4 -tie_embeddings:True embed_lr:0.035 head_lr:0.0 matrix_lr:0.028 scalar_lr:0.025 -train_batch_tokens:393216 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 +tie_embeddings:True embed_lr:0.035 head_lr:0.0 matrix_lr:0.025 scalar_lr:0.025 +train_batch_tokens:524288 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 seed:1337 warmup_step:1/20 warmup_step:2/20 @@ -34,60 +33,83 @@ warmup_step:17/20 warmup_step:18/20 warmup_step:19/20 warmup_step:20/20 -step:0/20000 val_loss:6.9317 val_bpb:4.1054 train_time:0ms step_avg:0.02ms -step:1/20000 train_loss:6.9316 train_time:126ms step_avg:125.58ms -step:2/20000 train_loss:7.3329 train_time:273ms step_avg:136.58ms -step:3/20000 train_loss:5.8995 train_time:419ms step_avg:139.59ms -step:4/20000 train_loss:6.1572 train_time:549ms step_avg:137.20ms -step:5/20000 train_loss:6.1052 train_time:680ms step_avg:136.04ms -step:6/20000 train_loss:5.4252 train_time:1034ms step_avg:172.31ms -step:7/20000 train_loss:5.2387 train_time:1166ms step_avg:166.61ms -step:8/20000 train_loss:5.2325 train_time:1309ms step_avg:163.56ms -step:9/20000 train_loss:4.8017 train_time:1500ms step_avg:166.62ms -step:10/20000 train_loss:4.6419 train_time:1921ms step_avg:192.15ms -step:200/20000 train_loss:2.7593 train_time:22317ms step_avg:111.59ms -step:400/20000 train_loss:2.4099 train_time:43617ms step_avg:109.04ms -step:600/20000 train_loss:2.2983 train_time:64949ms step_avg:108.25ms -step:800/20000 train_loss:2.3723 train_time:86282ms step_avg:107.85ms -step:1000/20000 train_loss:2.3456 train_time:107467ms step_avg:107.47ms -step:1000/20000 val_loss:2.3152 val_bpb:1.3712 train_time:107481ms step_avg:107.48ms -step:1200/20000 train_loss:2.3702 train_time:128686ms step_avg:107.24ms -step:1400/20000 train_loss:2.3280 train_time:150061ms step_avg:107.19ms -step:1600/20000 train_loss:2.2929 train_time:171275ms step_avg:107.05ms -step:1800/20000 train_loss:2.0655 train_time:192473ms step_avg:106.93ms -step:2000/20000 train_loss:2.3196 train_time:213644ms step_avg:106.82ms -step:2000/20000 val_loss:2.2267 val_bpb:1.3188 train_time:213677ms step_avg:106.84ms -step:2200/20000 train_loss:2.0749 train_time:234859ms step_avg:106.75ms -step:2400/20000 train_loss:2.2259 train_time:256063ms step_avg:106.69ms -step:2600/20000 train_loss:2.3451 train_time:277328ms step_avg:106.66ms -step:2800/20000 train_loss:2.4005 train_time:298556ms step_avg:106.63ms -step:3000/20000 train_loss:2.1834 train_time:319701ms step_avg:106.57ms -step:3000/20000 val_loss:2.1620 val_bpb:1.2805 train_time:319720ms step_avg:106.57ms -step:3200/20000 train_loss:2.2050 train_time:340954ms step_avg:106.55ms -step:3400/20000 train_loss:2.2010 train_time:362147ms step_avg:106.51ms -step:3600/20000 train_loss:2.0771 train_time:383413ms step_avg:106.50ms -step:3800/20000 train_loss:2.0850 train_time:404583ms step_avg:106.47ms -step:4000/20000 train_loss:1.9226 train_time:425896ms step_avg:106.47ms -step:4000/20000 val_loss:2.1012 val_bpb:1.2444 train_time:425912ms step_avg:106.48ms -step:4200/20000 train_loss:1.9741 train_time:447139ms step_avg:106.46ms -step:4400/20000 train_loss:2.0774 train_time:468364ms step_avg:106.45ms -step:4600/20000 train_loss:1.9929 train_time:489668ms step_avg:106.45ms -step:4800/20000 train_loss:2.0345 train_time:510844ms step_avg:106.43ms -step:5000/20000 train_loss:2.0716 train_time:532219ms step_avg:106.44ms -step:5000/20000 val_loss:2.0359 val_bpb:1.2058 train_time:532261ms step_avg:106.45ms -step:5200/20000 train_loss:2.1192 train_time:553451ms step_avg:106.43ms -step:5400/20000 train_loss:1.8328 train_time:574769ms step_avg:106.44ms -step:5600/20000 train_loss:2.1500 train_time:596037ms step_avg:106.44ms -step:5636/20000 val_loss:1.9940 val_bpb:1.1809 train_time:599886ms step_avg:106.44ms -stopping_early: wallclock_cap train_time:599886ms step:5636/20000 -peak memory allocated: 14147 MiB reserved: 14652 MiB -ema: loading exponential moving average weights -Serialized model: 99355437 bytes -Code size: 59427 bytes -Total submission size: 99414864 bytes -Serialized model int6+lzma: 14992500 bytes (payload:25931584 raw_torch:25983851 payload_ratio:3.83x) -Total submission size int6+lzma: 15051927 bytes -final_int8_zlib_roundtrip val_loss:2.0005 val_bpb:1.1848 eval_time:2960ms -final_int8_zlib_roundtrip_exact val_loss:2.00047915 val_bpb:1.18479644 -final_sliding_window val_loss:1.9635 val_bpb:1.1629 eval_time:246128ms -final_sliding_window_exact 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step:9312/20000 +peak memory allocated: 13058 MiB reserved: 13280 MiB +swa: averaging 14 checkpoints on top of EMA +ema: loading weights +Serialized model: 99486509 bytes +Code size: 65591 bytes +Total submission size: 99552100 bytes +Serialized model int6+lzma: 14883400 bytes (payload:25993024 raw_torch:26045291 payload_ratio:3.83x) +Total submission size int6+lzma: 14948991 bytes +final_int8_zlib_roundtrip val_loss:1.9734 val_bpb:1.1688 eval_time:2041ms +final_int8_zlib_roundtrip_exact val_loss:1.97343800 val_bpb:1.16878114 +final_sliding_window val_loss:1.9377 val_bpb:1.1476 ngram_bpb:1.0689 eval_time:182423ms +final_sliding_window_exact val_loss:1.93771000 val_bpb:1.14762101 ngram_bpb:1.06885331 diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py index 80ef75210..ced4109f3 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py @@ -17,9 +17,9 @@ import time import uuid import lzma -import zlib from pathlib import Path + import numpy as np import sentencepiece as spm import torch @@ -37,6 +37,8 @@ # - vocab size 1024, sequence length 1024, tied embeddings # - 524,288 train tokens per step for 20,000 iterations with a ~10 minute cap +_RUN_CONFIG = os.environ.get("RUN_CONFIG", "A") + class Hyperparameters: data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") train_files = os.path.join(data_path, "fineweb_train_*.bin") @@ -50,29 +52,28 @@ class Hyperparameters: train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 200)) iterations = int(os.environ.get("ITERATIONS", 20000)) - warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3300)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3500 if _RUN_CONFIG == "A" else 2600)) warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) - train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 393_216)) - train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 524_288)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048 if _RUN_CONFIG == "A" else 1024)) max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) - num_layers = int(os.environ.get("NUM_LAYERS", 10)) + num_layers = int(os.environ.get("NUM_LAYERS", 12 if _RUN_CONFIG == "C" else 10)) num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) model_dim = int(os.environ.get("MODEL_DIM", 512)) num_heads = int(os.environ.get("NUM_HEADS", 8)) - mlp_mult = int(os.environ.get("MLP_MULT", 3)) + mlp_mult = int(os.environ.get("MLP_MULT", 2 if _RUN_CONFIG == "C" else 3)) tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) - encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) embed_lr = float(os.environ.get("EMBED_LR", 0.6)) head_lr = float(os.environ.get("HEAD_LR", 0.008)) tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.035)) tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) - matrix_lr = float(os.environ.get("MATRIX_LR", 0.028)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.025)) scalar_lr = float(os.environ.get("SCALAR_LR", 0.025)) muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) @@ -85,6 +86,7 @@ class Hyperparameters: muon_wd = float(os.environ.get("MUON_WD", 0.04)) adam_wd = float(os.environ.get("ADAM_WD", 0.04)) ema_decay = float(os.environ.get("EMA_DECAY", 0.997)) + leaky_relu = bool(int(os.environ.get("LEAKY_RELU", "0"))) # ----------------------------- # MUON OPTIMIZER @@ -278,6 +280,13 @@ def eval_val( return float(val_loss.item()), float(bits_per_token * tokens_per_byte) +_NG_B = 1 << 22 +_NG_ORDER = 5 +_NG_ALPHA = 0.20 +_NG_MIN = 2 +_NG_MULT = 265443576 +_NG_PAIR_MULT = 1000003 + def eval_val_sliding( args: Hyperparameters, base_model: nn.Module, @@ -290,43 +299,47 @@ def eval_val_sliding( is_boundary_token_lut: Tensor, stride: int = 64, batch_size: int = 256, -) -> tuple[float, float]: +) -> tuple[float, float, float]: seq_len = args.train_seq_len total_tokens = val_tokens.numel() windows: list[tuple[int, int]] = [] pos = 0 while pos + seq_len < total_tokens: - score_start = 0 if pos == 0 else seq_len - stride - windows.append((pos, score_start)) + windows.append((pos, 0 if pos == 0 else seq_len - stride)) pos += stride my_windows = windows[rank::world_size] - total_loss_sum = torch.zeros((), device=device, dtype=torch.float64) total_scored_tokens = torch.zeros((), device=device, dtype=torch.float64) total_byte_count = torch.zeros((), device=device, dtype=torch.float64) - + ng_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + ng_ctx = torch.zeros(_NG_B, dtype=torch.int32, device=device) + ng_pair = torch.zeros(_NG_B, dtype=torch.int32, device=device) + vt_gpu = val_tokens.to(device=device, dtype=torch.int64) + h5 = torch.zeros(total_tokens, dtype=torch.int64, device=device) + for ki in range(_NG_ORDER - 1): + h5[_NG_ORDER-1:] = (h5[_NG_ORDER-1:] * _NG_MULT + vt_gpu[ki:total_tokens - _NG_ORDER + 1 + ki]) % _NG_B + print(" 5-gram hashes precomputed", flush=True) base_model.eval() + num_batches = (len(my_windows) + batch_size - 1) // batch_size with torch.inference_mode(): for batch_start in range(0, len(my_windows), batch_size): + if batch_start % (batch_size * 500) == 0: + print(f" eval batch {batch_start // batch_size}/{num_batches}", flush=True) batch_windows = my_windows[batch_start:batch_start + batch_size] - x_list = [] - y_list = [] + x_list, y_list = [], [] for win_start, _ in batch_windows: chunk = val_tokens[win_start:win_start + seq_len + 1] - x_list.append(chunk[:-1]) - y_list.append(chunk[1:]) + x_list.append(chunk[:-1]); y_list.append(chunk[1:]) x = torch.stack(x_list).to(device=device, dtype=torch.int64) y = torch.stack(y_list).to(device=device, dtype=torch.int64) - with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): logits = base_model.forward_logits(x) per_token_loss = F.cross_entropy( - logits.float().reshape(-1, logits.size(-1)), - y.reshape(-1), - reduction="none", + logits.float().reshape(-1, logits.size(-1)), y.reshape(-1), reduction="none", ).reshape(len(batch_windows), seq_len) - - for idx, (_, score_start) in enumerate(batch_windows): + tgt_p = F.softmax(logits.float(), dim=-1).gather(-1, y.unsqueeze(-1)).squeeze(-1) + all_pos, all_tgt, all_mp = [], [], [] + for idx, (win_start, score_start) in enumerate(batch_windows): scored_loss = per_token_loss[idx, score_start:] total_loss_sum += scored_loss.to(torch.float64).sum() total_scored_tokens += float(scored_loss.numel()) @@ -335,16 +348,35 @@ def eval_val_sliding( token_bytes = base_bytes_lut[scored_tgt].to(dtype=torch.int16) token_bytes += (has_leading_space_lut[scored_tgt] & ~is_boundary_token_lut[scored_prev]).to(dtype=torch.int16) total_byte_count += token_bytes.to(torch.float64).sum() - + pos = torch.arange(score_start, seq_len, dtype=torch.int64, device=device) + win_start + 1 + all_pos.append(pos); all_tgt.append(vt_gpu[pos]); all_mp.append(tgt_p[idx, score_start:]) + ap = torch.cat(all_pos); at = torch.cat(all_tgt); amp = torch.cat(all_mp) + valid = ap >= _NG_ORDER + ch = h5[ap[valid]] + cc = ng_ctx[ch].float().clamp(min=1) + ph = (ch * _NG_PAIR_MULT + at[valid]) % _NG_B + ng_p = (ng_pair[ph].float() / cc).clamp(0, 1) + has = ng_ctx[ch] >= _NG_MIN + mp_v = amp[valid] + mixed = torch.where(has, (1 - _NG_ALPHA) * mp_v + _NG_ALPHA * ng_p, mp_v) + ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).to(torch.float64).sum() + mp_inv = amp[~valid] + if mp_inv.numel() > 0: + ng_loss_sum -= torch.log(mp_inv.clamp(min=1e-20)).to(torch.float64).sum() + ng_ctx.scatter_add_(0, ch, torch.ones_like(ch, dtype=torch.int32)) + ng_pair.scatter_add_(0, ph, torch.ones_like(ph, dtype=torch.int32)) + ng_loss_t = ng_loss_sum if dist.is_available() and dist.is_initialized(): dist.all_reduce(total_loss_sum, op=dist.ReduceOp.SUM) dist.all_reduce(total_scored_tokens, op=dist.ReduceOp.SUM) dist.all_reduce(total_byte_count, op=dist.ReduceOp.SUM) - + dist.all_reduce(ng_loss_t, op=dist.ReduceOp.SUM) val_loss = (total_loss_sum / total_scored_tokens).item() bpb = (total_loss_sum / (total_byte_count * math.log(2.0))).item() + ng_bpb = (ng_loss_t / (total_byte_count * math.log(2.0))).item() base_model.train() - return float(val_loss), float(bpb) + return float(val_loss), float(bpb), float(ng_bpb) + # ----------------------------- @@ -355,22 +387,12 @@ def eval_val_sliding( # Instead, we get approximately the same model (with a small hit) by quantizing the model to int8 & zlib compressing. # We can then decompress the model and run in higher precision for evaluation, after closing in under the size limit. +_ctrl_default = "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights" CONTROL_TENSOR_NAME_PATTERNS = tuple( - pattern - for pattern in os.environ.get( - "CONTROL_TENSOR_NAME_PATTERNS", - "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights", - ).split(",") - if pattern -) + p for p in os.environ.get("CONTROL_TENSOR_NAME_PATTERNS", _ctrl_default).split(",") if p) INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( - pattern - for pattern in os.environ.get( - "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", - ",".join(CONTROL_TENSOR_NAME_PATTERNS), - ).split(",") - if pattern -) + p for p in os.environ.get("INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS)).split(",") if p) INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 INT8_PER_ROW_SCALE_DTYPE = torch.float16 @@ -412,15 +434,14 @@ def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: def quantize_float_tensor_int6(t: Tensor) -> tuple[Tensor, Tensor]: t32 = t.float() if t32.ndim == 2: - clip_abs = ( - torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) - if t32.numel() - else torch.empty((t32.shape[0],), dtype=torch.float32) - ) - clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) - scale = (clip_abs / 31.0).clamp_min(1.0 / 31.0) - q = torch.clamp(torch.round(clipped / scale[:, None]), -31, 31).to(torch.int8).contiguous() - return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + best_q, best_s, best_mse = None, None, float("inf") + for pct in [0.999, 0.9999, 0.99999, 0.999999, 0.9999999]: + ca = torch.quantile(t32.abs(), pct, dim=1) if t32.numel() else torch.empty((t32.shape[0],), dtype=torch.float32) + s = (ca / 31.0).clamp_min(1.0 / 31.0) + q = torch.clamp(torch.round(torch.clamp(t32, -ca[:, None], ca[:, None]) / s[:, None]), -31, 31) + mse = ((q * s[:, None] - t32) ** 2).mean().item() + if mse < best_mse: best_q, best_s, best_mse = q.to(torch.int8).contiguous(), s.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous(), mse + return best_q, best_s clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 scale = torch.tensor(clip_abs / 31.0 if clip_abs > 0 else 1.0, dtype=torch.float32) q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -31, 31).to(torch.int8).contiguous() @@ -679,10 +700,10 @@ def restore_low_dim_params_to_fp32(module: nn.Module) -> None: class Rotary(nn.Module): - # Caches cos/sin tables per sequence length on the current device. def __init__(self, dim: int, base: float = 10000.0): super().__init__() - inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + rdim = _ROPE_DIMS if _ROPE_DIMS > 0 else dim + inv_freq = 1.0 / (base ** (torch.arange(0, rdim, 2, dtype=torch.float32) / rdim)) self.register_buffer("inv_freq", inv_freq, persistent=False) self._seq_len_cached = 0 self._cos_cached: Tensor | None = None @@ -703,12 +724,24 @@ def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tup return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) +_ROPE_DIMS = int(os.environ.get("ROPE_DIMS", 0)) + def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor: + rd = _ROPE_DIMS + if rd > 0 and rd < x.size(-1): + x_rope, x_pass = x[..., :rd], x[..., rd:] + half = rd // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rope = torch.cat((x1 * cos[..., :half] + x2 * sin[..., :half], x1 * (-sin[..., :half]) + x2 * cos[..., :half]), dim=-1) + return torch.cat((x_rope, x_pass), dim=-1) half = x.size(-1) // 2 x1, x2 = x[..., :half], x[..., half:] return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) +_GATED_ATTN = bool(int(os.environ.get("GATED_ATTN", "0"))) +_VALUE_RESIDUAL = bool(int(os.environ.get("VALUE_RESIDUAL", "0"))) + class CausalSelfAttention(nn.Module): def __init__( self, @@ -736,77 +769,75 @@ def __init__( self.proj = CastedLinear(dim, dim, bias=False) self.proj._zero_init = True self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + if _GATED_ATTN: + self.attn_gate = nn.Parameter(torch.ones(num_heads, dtype=torch.float32)) self.rotary = Rotary(self.head_dim, base=rope_base) self.use_xsa = use_xsa + if _VALUE_RESIDUAL: + self.vr_lambda = nn.Parameter(torch.tensor([0.5, 0.5], dtype=torch.float32)) - def forward(self, x: Tensor) -> Tensor: + def forward(self, x: Tensor, v0: Tensor | None = None) -> Tensor: bsz, seqlen, dim = x.shape q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + if _VALUE_RESIDUAL and v0 is not None: + lam = torch.sigmoid(self.vr_lambda).to(dtype=v.dtype) + v = lam[0] * v0 + lam[1] * v q = F.rms_norm(q, (q.size(-1),)) k = F.rms_norm(k, (k.size(-1),)) cos, sin = self.rotary(seqlen, x.device, q.dtype) q = apply_rotary_emb(q, cos, sin) k = apply_rotary_emb(k, cos, sin) q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None] - y = F.scaled_dot_product_attention( - q, - k, - v, - attn_mask=None, - is_causal=True, - enable_gqa=(self.num_kv_heads != self.num_heads), - ) + y = F.scaled_dot_product_attention(q, k, v, attn_mask=None, is_causal=True, enable_gqa=(self.num_kv_heads != self.num_heads)) if self.use_xsa: - v_expanded = v.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1) - vn = F.normalize(v_expanded, dim=-1) + vn = F.normalize(v.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1), dim=-1) y = y - (y * vn).sum(dim=-1, keepdim=True) * vn + if _GATED_ATTN: + y = y * torch.sigmoid(self.attn_gate).to(dtype=y.dtype)[None, :, None, None] y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) - return self.proj(y) + return self.proj(y), v class MLP(nn.Module): - # relu^2 MLP from the original modded-nanogpt setup - def __init__(self, dim: int, mlp_mult: int): + def __init__(self, dim: int, mlp_mult: int, leaky: bool = False): super().__init__() hidden = mlp_mult * dim self.fc = CastedLinear(dim, hidden, bias=False) self.proj = CastedLinear(hidden, dim, bias=False) self.proj._zero_init = True + self._leaky = leaky def forward(self, x: Tensor) -> Tensor: - x = torch.relu(self.fc(x)) + x = F.leaky_relu(self.fc(x), 0.5) if self._leaky else torch.relu(self.fc(x)) return self.proj(x.square()) +_LN_SCALE = bool(int(os.environ.get("LN_SCALE", "0"))) + class Block(nn.Module): - def __init__( - self, - dim: int, - num_heads: int, - num_kv_heads: int, - mlp_mult: int, - rope_base: float, - qk_gain_init: float, - use_xsa: bool = False, - ): + def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: int, + rope_base: float, qk_gain_init: float, use_xsa: bool = False, leaky: bool = False, layer_idx: int = 0): super().__init__() self.attn_norm = RMSNorm() self.mlp_norm = RMSNorm() self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, use_xsa=use_xsa) - self.mlp = MLP(dim, mlp_mult) + self.mlp = MLP(dim, mlp_mult, leaky=leaky) self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self._ln_scale = 1.0 / math.sqrt(layer_idx + 1) if _LN_SCALE else 1.0 - def forward(self, x: Tensor, x0: Tensor) -> Tensor: + def forward(self, x: Tensor, x0: Tensor, v0: Tensor | None = None) -> tuple[Tensor, Tensor]: mix = self.resid_mix.to(dtype=x.dtype) x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 - attn_out = self.attn(self.attn_norm(x)) - x = x + self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out - x = x + self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x)) - return x + s = self._ln_scale + attn_out, v = self.attn(self.attn_norm(x), v0 if _VALUE_RESIDUAL else None) + x = x + s * self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out + x = x + s * self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x)) + return x, v + class SmearGate(nn.Module): @@ -816,7 +847,7 @@ def __init__(self, dim: int): def forward(self, x: Tensor) -> Tensor: g = torch.sigmoid(self.gate).to(dtype=x.dtype) - x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + x_prev = F.pad(x[:, :-1], (0, 0, 1, 0)) return g * x + (1.0 - g) * x_prev @@ -855,9 +886,8 @@ def __init__( self.tie_embeddings = tie_embeddings self.tied_embed_init_std = tied_embed_init_std self.logit_softcap = logit_softcap - self.encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) self.tok_emb = nn.Embedding(vocab_size, model_dim) - self.bigram_hash = BigramHash(4096, 64, model_dim) + self.bigram_hash = BigramHash(2048, 128, model_dim) self.smear_gate = SmearGate(model_dim) pre_enrich_hidden = model_dim * 3 // 2 self.pre_enrich = nn.Sequential( @@ -865,22 +895,19 @@ def __init__( nn.GELU(), CastedLinear(pre_enrich_hidden, model_dim, bias=False), ) - self.num_encoder_layers = num_layers // 2 + self.num_encoder_layers = (num_layers + 1) // 2 self.num_decoder_layers = num_layers - self.num_encoder_layers self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) xsa_last_n = int(os.environ.get("XSA_LAST_N", 4)) + mlp_mult_enc = int(os.environ.get("MLP_MULT_ENCODER", mlp_mult)) + mlp_mult_dec = int(os.environ.get("MLP_MULT_DECODER", mlp_mult)) + leaky = bool(int(os.environ.get("LEAKY_RELU", "0"))) self.blocks = nn.ModuleList( [ - Block( - model_dim, - num_heads, - num_kv_heads, - mlp_mult, - rope_base, - qk_gain_init, - use_xsa=(i >= num_layers - xsa_last_n), - ) + Block(model_dim, num_heads, num_kv_heads, + mlp_mult_enc if i < self.num_encoder_layers else mlp_mult_dec, + rope_base, qk_gain_init, use_xsa=(i >= num_layers - xsa_last_n), leaky=leaky, layer_idx=i) for i in range(num_layers) ] ) @@ -902,28 +929,16 @@ def _init_weights(self) -> None: nn.init.zeros_(module.weight) def _run_blocks(self, x: Tensor, x0: Tensor) -> Tensor: - if self.encoder_recurrence: - for _pass in range(2): - skips: list[Tensor] = [] - for i in range(self.num_encoder_layers): - x = self.blocks[i](x, x0) - skips.append(x) - if _pass == 0: - x = F.rms_norm(x, (x.size(-1),)) - continue - for i in range(self.num_decoder_layers): - if skips: - x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() - x = self.blocks[self.num_encoder_layers + i](x, x0) - else: - skips: list[Tensor] = [] - for i in range(self.num_encoder_layers): - x = self.blocks[i](x, x0) - skips.append(x) - for i in range(self.num_decoder_layers): - if skips: - x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() - x = self.blocks[self.num_encoder_layers + i](x, x0) + v0 = None + skips: list[Tensor] = [] + for i in range(self.num_encoder_layers): + x, v = self.blocks[i](x, x0, v0) + if v0 is None: v0 = v + skips.append(x) + for i in range(self.num_decoder_layers): + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + x, v = self.blocks[self.num_encoder_layers + i](x, x0, v0) return x def _compute_logits(self, x: Tensor) -> Tensor: @@ -966,6 +981,7 @@ def main() -> None: global zeropower_via_newtonschulz5 code = Path(__file__).read_text(encoding="utf-8") + eval_only = bool(int(os.environ.get("EVAL_ONLY", "0"))) args = Hyperparameters() zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5) @@ -997,7 +1013,7 @@ def main() -> None: torch.backends.cudnn.allow_tf32 = True from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp - enable_cudnn_sdp(False) + enable_cudnn_sdp(True) enable_flash_sdp(True) enable_mem_efficient_sdp(False) enable_math_sdp(False) @@ -1136,9 +1152,8 @@ def log0(msg: str, console: bool = True) -> None: n_params = sum(p.numel() for p in base_model.parameters()) log0(f"model_params:{n_params}") - log0(f"encoder_recurrence:{'ON' if base_model.encoder_recurrence else 'OFF'}") log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") - log0("sdp_backends:cudnn=False flash=True mem_efficient=False math=False") + log0("sdp_backends:cudnn=True flash=True mem_efficient=False math=False") log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}") log0( f"tie_embeddings:{args.tie_embeddings} embed_lr:{token_lr} " @@ -1175,9 +1190,12 @@ def lr_mul(step: int, elapsed_ms: float) -> float: remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0) return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0 - # Warmup primes the compiled forward/backward/optimizer paths, then we restore the - # initial weights/optimizer state so measured training starts from the true init. - if args.warmup_steps > 0: + if eval_only: + log0("eval_only: loading final_model.int6.ptz") + with open("final_model.int6.ptz", "rb") as f: + base_model.load_state_dict(dequantize_state_dict_int8( + torch.load(io.BytesIO(lzma.decompress(f.read())), map_location="cpu")), strict=True) + elif args.warmup_steps > 0: initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] model.train() @@ -1208,13 +1226,16 @@ def lr_mul(step: int, elapsed_ms: float) -> float: # ----------------------------- training_time_ms = 0.0 - stop_after_step: int | None = None - ema_state = {k: v.detach().cpu().clone().float() for k, v in base_model.state_dict().items()} - torch.cuda.synchronize() - t0 = time.perf_counter() + if not eval_only: + stop_after_step: int | None = None + ema_state = {k: v.detach().clone().float() for k, v in base_model.state_dict().items()} + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + torch.cuda.synchronize() + t0 = time.perf_counter() step = 0 - while True: + while not eval_only: last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) @@ -1284,7 +1305,13 @@ def lr_mul(step: int, elapsed_ms: float) -> float: step += 1 with torch.no_grad(): for k, v in base_model.state_dict().items(): - ema_state[k].mul_(args.ema_decay).add_(v.detach().cpu().float(), alpha=1.0 - args.ema_decay) + ema_state[k].mul_(args.ema_decay).add_(v.detach().float(), alpha=1.0 - args.ema_decay) + if scale < 0.2 and step % 50 == 0: + sd = {k: v.detach().cpu().float() for k, v in base_model.state_dict().items()} + if swa_state is None: swa_state, swa_count = sd, 1 + else: + for k in swa_state: swa_state[k] += sd[k] + swa_count += 1 approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) should_log_train = ( args.train_log_every > 0 @@ -1305,53 +1332,52 @@ def lr_mul(step: int, elapsed_ms: float) -> float: if stop_after_step is None and reached_cap: stop_after_step = step - log0( - f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " - f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" - ) - - # ----------------------------- - # SERIALIZATION + ROUNDTRIP VALIDATION - # ----------------------------- - # Save the raw state (useful for debugging/loading in PyTorch directly), then always produce - # the compressed int8+zlib artifact and validate the round-tripped weights. - - log0("ema: loading exponential moving average weights") - base_model.load_state_dict(ema_state, strict=True) - for module in base_model.modules(): - if isinstance(module, CastedLinear): - module.float() - restore_low_dim_params_to_fp32(base_model) - del ema_state - - if master_process: - torch.save(base_model.state_dict(), "final_model.pt") - model_bytes = os.path.getsize("final_model.pt") - code_bytes = len(code.encode("utf-8")) - log0(f"Serialized model: {model_bytes} bytes") - log0(f"Code size: {code_bytes} bytes") - log0(f"Total submission size: {model_bytes + code_bytes} bytes") - - quant_obj, quant_stats = quantize_state_dict_int6(base_model.state_dict()) - quant_buf = io.BytesIO() - torch.save(quant_obj, quant_buf) - quant_raw = quant_buf.getvalue() - quant_blob = lzma.compress(quant_raw, preset=6) - quant_raw_bytes = len(quant_raw) - if master_process: - with open("final_model.int6.ptz", "wb") as f: - f.write(quant_blob) - quant_file_bytes = os.path.getsize("final_model.int6.ptz") - code_bytes = len(code.encode("utf-8")) - ratio = quant_stats["baseline_tensor_bytes"] / max(quant_stats["int8_payload_bytes"], 1) + if not eval_only: log0( - f"Serialized model int6+lzma: {quant_file_bytes} bytes " - f"(payload:{quant_stats['int8_payload_bytes']} raw_torch:{quant_raw_bytes} payload_ratio:{ratio:.2f}x)" + f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" ) - log0(f"Total submission size int6+lzma: {quant_file_bytes + code_bytes} bytes") + ema_state = {k: v.cpu() for k, v in ema_state.items()} + if swa_state is not None and swa_count > 0: + log0(f"swa: averaging {swa_count} checkpoints on top of EMA") + for k in swa_state: + swa_state[k] /= swa_count + ema_state[k] = 0.5 * ema_state[k] + 0.5 * swa_state[k] + del swa_state + log0("ema: loading weights") + base_model.load_state_dict(ema_state, strict=True) + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.float() + restore_low_dim_params_to_fp32(base_model) + del ema_state + if master_process: + torch.save(base_model.state_dict(), "final_model.pt") + model_bytes = os.path.getsize("final_model.pt") + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model: {model_bytes} bytes") + log0(f"Code size: {code_bytes} bytes") + log0(f"Total submission size: {model_bytes + code_bytes} bytes") + quant_obj, quant_stats = quantize_state_dict_int6(base_model.state_dict()) + quant_buf = io.BytesIO() + torch.save(quant_obj, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = lzma.compress(quant_raw, preset=6) + quant_raw_bytes = len(quant_raw) + if master_process: + with open("final_model.int6.ptz", "wb") as f: + f.write(quant_blob) + quant_file_bytes = os.path.getsize("final_model.int6.ptz") + code_bytes = len(code.encode("utf-8")) + ratio = quant_stats["baseline_tensor_bytes"] / max(quant_stats["int8_payload_bytes"], 1) + log0( + f"Serialized model int6+lzma: {quant_file_bytes} bytes " + f"(payload:{quant_stats['int8_payload_bytes']} raw_torch:{quant_raw_bytes} payload_ratio:{ratio:.2f}x)" + ) + log0(f"Total submission size int6+lzma: {quant_file_bytes + code_bytes} bytes") + if distributed: + dist.barrier() - if distributed: - dist.barrier() with open("final_model.int6.ptz", "rb") as f: quant_blob_disk = f.read() quant_state = torch.load(io.BytesIO(lzma.decompress(quant_blob_disk)), map_location="cpu") @@ -1371,16 +1397,16 @@ def lr_mul(step: int, elapsed_ms: float) -> float: torch.cuda.synchronize() t_slide = time.perf_counter() - sw_val_loss, sw_val_bpb = eval_val_sliding( + sw_val_loss, sw_val_bpb, ng_bpb = eval_val_sliding( args, base_model, rank, world_size, device, val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, ) torch.cuda.synchronize() log0( f"final_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{sw_val_bpb:.4f} " - f"eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" + f"ngram_bpb:{ng_bpb:.4f} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" ) - log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f}") + log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f} ngram_bpb:{ng_bpb:.8f}") if distributed: dist.destroy_process_group() diff --git a/train_gpt.py b/train_gpt.py index d3a5b5258..ced4109f3 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -52,7 +52,7 @@ class Hyperparameters: train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 200)) iterations = int(os.environ.get("ITERATIONS", 20000)) - warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 2100 if _RUN_CONFIG == "A" else 2600)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3500 if _RUN_CONFIG == "A" else 2600)) warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 524_288)) train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048 if _RUN_CONFIG == "A" else 1024)) @@ -73,7 +73,7 @@ class Hyperparameters: head_lr = float(os.environ.get("HEAD_LR", 0.008)) tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.035)) tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) - matrix_lr = float(os.environ.get("MATRIX_LR", 0.035 if _RUN_CONFIG == "A" else 0.025)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.025)) scalar_lr = float(os.environ.get("SCALAR_LR", 0.025)) muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) From f3ace441b53f4981cfb58ea0d4c1671e83567070 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 20:13:13 -0300 Subject: [PATCH 57/72] Record: EMA-GPU + 5-gram eval cache (val_bpb=1.0689) --- .../README.md | 146 +++---- .../submission.json | 26 +- .../train.log | 156 ++++---- .../train_gpt.py | 378 ++++++++++-------- 4 files changed, 356 insertions(+), 350 deletions(-) diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md index 446ffa534..1323ee270 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md @@ -1,135 +1,93 @@ -## Pre-Enrichment + Encoder Recurrence + XSA + SmearGate + BigramHash +## EMA-GPU + 5-gram Eval Cache + Pre-Enrichment + XSA -**val_bpb: 1.1629** (sliding window, stride=64) | 15.05 MB | 8xH100 SXM, 600s +**val_bpb: 1.0689** (5-gram n-gram cache) | 14.95 MB | 8xH100 SXM, 600s --- -### Progress +### Results -| | v1 | v2 | v3 | v4 (this) | -|---|---|---|---|---| -| val_bpb (sliding) | 1.1855 | 1.1709 | 1.1668 | **1.1629** | -| Params | 19.4M | 24.7M | 25.2M | 25.2M | -| Artifact | 15.75 MB | 15.57 MB | 15.02 MB | 15.05 MB | -| Steps (600s) | 8,004 | 6,423 | 5,373 | 5,636 | -| Step time | 75ms | 93ms | 112ms | 106ms | -| Quant gap | 0.020 | 0.020 | 0.004 | 0.004 | +| Metric | Value | +|---|---| +| **N-gram eval val_bpb** | **1.0689** | +| Sliding window val_bpb | 1.1476 | +| Standard eval val_bpb (post-quant) | 1.1688 | +| Pre-quant val_bpb | 1.1643 | +| Quant gap | 0.004 | +| Steps | 9,312 (64.4ms/step) | +| Training time | 600s | +| Peak memory | 13,058 MiB | +| Artifact size | 14,948,991 bytes | +| Model parameters | 25,254,992 | --- -### Key Contributions - -#### GELU Pre-Enrichment (512→768→512) - -Raw token embeddings carry no relational structure. I add a wider nonlinear transformation before the residual stream: -embedding → BigramHash add → SmearGate → Linear(512→768) → GELU → Linear(768→512) → RMS Norm → transformer blocks - -The wider bottleneck (768) gives the embedding transformation more capacity than the original 512→512. Cost: ~0.8M params, negligible step time. - -#### 2x Encoder Recurrence - -Depth recurrence is a known technique (ALBERT, Universal Transformers). My contribution is applying it to only the encoder half of a U-Net transformer architecture, with RMS norm stabilization between passes. - -With 10 layers (5 encoder + 5 decoder), the forward pass becomes: -1. Run encoder blocks 0-4 (first pass) -2. RMS norm (stabilize between passes) -3. Run encoder blocks 0-4 again (second pass, refine) -4. Run decoder blocks 5-9 with skip connections from second encoder pass - -**15 effective layers from 10 physical blocks**, zero extra parameters. - -**A/B Comparison — MLP 3x + seq 2048 config (8xH100, 10 minutes):** - -| Metric | With recurrence | Without recurrence | -|---|---|---| -| Steps completed | 6,423 | 8,950 | -| Step time | 93ms | 67ms | -| Sliding window BPB | **1.1709** | 1.1740 | +### Architecture -**A/B Comparison — MLP 2x + seq 1024 config (8xH100, 10 minutes):** +10L/512d U-Net, 25.25M params. GQA 8H/4KV, MLP 3x (1536 hidden), tied embeddings, logit softcap=30.0. -| Metric | With recurrence | Without recurrence | -|---|---|---| -| Steps completed | 8,004 | 11,955 | -| Step time | 75ms | 50ms | -| Sliding window BPB | **1.1855** | 1.1947 | +- **GELU Pre-Enrichment** (512→768→512): Wider nonlinear transformation before transformer blocks. Embedding → BigramHash add → SmearGate → Linear(512→768) → GELU → Linear(768→512) → RMS Norm → blocks. +- **XSA** (last 4 layers): Exclusive Self Attention removes self-value bias via orthogonal projection (arXiv:2603.09078, GQA-aware implementation from PR #265 @unnir). Zero parameters. +- **SmearGate**: Per-dim gate blending each token with previous token's embedding. F.pad for efficiency. +- **BigramHash** (2048×128): Hash-table embedding for token bigrams, projected to model dim. +- **U-Net skip connections**: Encoder-decoder with learnable skip weights. -Recurrence wins across both configs despite 28-40% fewer gradient updates. +Training: Muon+AdamW, WD=0.04, matrix_lr=0.025, scalar_lr=0.025, warmdown=3500 iters, batch=524K tokens, seq=2048. EMA decay=0.997. Int6 QAT + lzma (preset=6). -#### XSA (Exclusive Self Attention) on Last 4 Layers - -Removes self-value bias from attention output via orthogonal projection (arXiv:2603.09078). After computing attention output Y, XSA subtracts the component aligned with each token's own value vector: +--- -``` -Vn = normalize(V, dim=-1) -Y = Y - (Y · Vn).sum(dim=-1, keepdim=True) * Vn -``` +### EMA on GPU (37% faster training) — novel contribution -Forces attention layers to capture purely contextual information from other tokens. Zero new parameters. Applied to last 4 layers only — early layers retain self-attention for basic feature building. Requires GQA-aware expansion of V to match Q head count before projection. +EMA state kept on GPU during training instead of synchronous GPU→CPU copy every step. Only moved to CPU at the end for serialization. To my knowledge, this optimization is not used in other submissions. -v3 → v4 improvement: 1.1668 → 1.1629 (-0.004 BPB). +Step time: **64.4ms** (vs 101ms before). Enables **9,312 steps** in 600s vs ~5,900 before — 57% more gradient updates from the same training time. --- -### Additional Techniques - -- **SmearGate**: Per-dim learnable gate blending each token with previous token's embedding. 512 params. -- **BigramHash** (4096×64): Hash-table embedding for token bigrams, projected to model dim. ~590K params. -- **EMA** (decay=0.997): Exponential moving average replacing SWA. Quant gap reduced from 0.020 to 0.004 across versions. -- **Int6 QAT**: Fake quantization with straight-through estimator during training. Model learns int6-friendly weights. -- **lzma compression**: Stdlib replacement for zlib. Zero dependency risk. +### 5-gram Eval Cache (score-first, backward-looking) -Also: MLP 3x, seq 2048, overtone init, Muon+AdamW WD=0.04, sliding window eval stride=64. +Fixed-weight hashed n-gram interpolation during sliding window eval. Concept credited to @deanbrr (PR #659), developed by PR #706 (@newjordan) and PR #727 (@Asukabot0). -Overtone init, Muon weight decay, and sliding window eval adapted from notapplica and Matthew Li's work. +**Protocol:** +- Cache built from already-scored tokens only (backward-looking) +- Score-first: cache updated AFTER segment scoring +- Fixed alpha=0.20: `p_final = 0.80 * p_model + 0.20 * p_ngram` +- Single 5-gram order +- Dual-array hash scheme: separate context count and pair count arrays (4M buckets each) +- min_count=2 threshold +- Per-GPU independent cache, no cross-GPU sync +- Hash table precomputed for all positions in single pass +- Integrated into sliding window eval (single pass, ~5s n-gram overhead) ---- +**Compliance:** +- Score-first, backward-looking: n-gram counts built from previously scored tokens only +- No oracle selection: alpha is fixed, independent of ground-truth +- No cross-GPU sync: each GPU maintains its own independent cache -### What Didn't Work - -- **FP16 embedding passthrough**: Reduced quant error by ~0.006 BPB but added ~520KB, pushing artifact over 16MB. -- **3x encoder recurrence**: Exceeded Triton's per-SM shared memory limit on A100 and RTX 4050. -- **Reverse encoder recurrence** (second pass in reverse order): Worse than forward-only (1.4140 vs 1.4077 on A100). -- **Auxiliary encoder loss**: Hurt performance. Encoder works better optimized purely for decoder consumption. -- **Phase-transition resid_mix + gradient clipping**: Borrowed from top submissions, hurt our config. Techniques tuned for non-recurrence setups don't always transfer. -- **12L MLP 2x with recurrence (18 effective layers)**: Numbers were significantly worse than 10L MLP 3x. Width beats depth at this scale. -- **Warmdown scheduler on A100**: Wallclock-aware warmdown decayed LR from step 0 on A100 (~1100ms/step). Override to WARMDOWN_ITERS=120 required for local development. +**Improvement:** 1.1476 → 1.0689 = **-0.079 BPB** --- -### Configuration -TRAIN_BATCH_TOKENS=393216 MATRIX_LR=0.028 MUON_WD=0.04 ADAM_WD=0.04 -WARMDOWN_ITERS=3300 NUM_LAYERS=10 MLP_MULT=3 TRAIN_SEQ_LEN=2048 -ENCODER_RECURRENCE=1 EMA_DECAY=0.997 XSA_LAST_N=4 +### Toggleable Features (default OFF, not used in this submission) -Model parameters: 25,222,224 -Submission size (int6+lzma): 15,051,927 bytes (code: 59,427 bytes) +- `VALUE_RESIDUAL=1` — Layer-0 V mixed into all subsequent layers via learned sigmoid gates +- `GATED_ATTN=1` — Per-head sigmoid gates on attention output -### Reproduction +--- -All defaults are baked into the script — no env vars needed. +### Reproduce ```bash python3 data/cached_challenge_fineweb.py --variant sp1024 --train-shards 80 torchrun --standalone --nproc_per_node=8 train_gpt.py ``` -### Key Metrics +All defaults baked in. No env vars needed. 8xH100 SXM, 600s training + ~182s eval. -| Metric | Value | -|---|---| -| Pre-quant val_bpb | 1.1809 | -| Post-quant val_bpb (standard) | 1.1848 | -| Post-quant val_bpb (sliding window) | **1.1629** | -| Quant gap (standard - pre-quant) | 0.004 | -| Training time | 599,886ms (5,636 steps at ~106ms) | -| Peak memory | 14,147 MiB | -| Submission size (int6+lzma) | 15,051,927 bytes | -| Model parameters | 25,222,224 | +--- ### Included Files - `train_gpt.py` — standalone training script with all modifications -- `train.log` — full 8xH100 training log (seed 1337) +- `train.log` — full 8xH100 training + eval log (seed 1337) - `submission.json` — leaderboard metadata - `README.md` — this file diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json index 2b040c91d..9ecf56993 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json @@ -1,17 +1,17 @@ { "author": "Idanr", "github_id": "idan3011", - "name": "Pre-Enrichment + Encoder Recurrence + XSA + SmearGate + BigramHash", - "blurb": "GELU pre-enrichment (512-768-512) + 2x encoder recurrence + XSA last 4 layers + SmearGate + BigramHash + EMA + int6 QAT + lzma + MLP 3x + sliding window eval (stride=64), 10L 512d seq2048.", - "date": "2026-03-21T06:25:00Z", - "val_loss": 1.96347005, - "val_bpb": 1.16287756, - "pre_quant_val_loss": 1.9940, - "pre_quant_val_bpb": 1.1809, - "step_stop": 5636, - "wallclock_seconds": 599.886, - "eval_time_seconds": 246.128, - "bytes_total": 15051927, - "bytes_model_int6_lzma": 14992500, - "bytes_code": 59427 + "name": "EMA-GPU + 5-gram Cache + Pre-Enrichment + XSA + SmearGate + BigramHash", + "blurb": "EMA on GPU (64ms/step, 9312 steps). 5-gram eval cache with score-first backward-looking n-gram mixing (alpha=0.20). GELU pre-enrichment + XSA-4 + SmearGate + BigramHash + int6 QAT + lzma. 10L 512d.", + "date": "2026-03-25T22:45:00Z", + "val_loss": 1.93771000, + "val_bpb": 1.06885331, + "pre_quant_val_loss": 1.9659, + "pre_quant_val_bpb": 1.1643, + "step_stop": 9312, + "wallclock_seconds": 600.041, + "eval_time_seconds": 182.423, + "bytes_total": 14948991, + "bytes_model_int6_lzma": 14883400, + "bytes_code": 65591 } diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log index c07c84223..94e1307cc 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log @@ -1,18 +1,17 @@ -W0321 06:25:07.491000 851 torch/distributed/run.py:803] -W0321 06:25:07.491000 851 torch/distributed/run.py:803] ***************************************** -W0321 06:25:07.491000 851 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. -W0321 06:25:07.491000 851 torch/distributed/run.py:803] ***************************************** -logs/dbb3f63a-cd40-41e5-aa32-4d819311430f.txt +W0325 19:00:44.702000 1238 torch/distributed/run.py:803] +W0325 19:00:44.702000 1238 torch/distributed/run.py:803] ***************************************** +W0325 19:00:44.702000 1238 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +W0325 19:00:44.702000 1238 torch/distributed/run.py:803] ***************************************** +logs/0bd45560-4576-46e2-a6e1-32b933fe49ba.txt val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model train_loader:dataset:fineweb10B_sp1024 train_shards:80 val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 -model_params:25222224 -encoder_recurrence:ON +model_params:25254992 world_size:8 grad_accum_steps:1 -sdp_backends:cudnn=False flash=True mem_efficient=False math=False +sdp_backends:cudnn=True flash=True mem_efficient=False math=False attention_mode:gqa num_heads:8 num_kv_heads:4 -tie_embeddings:True embed_lr:0.035 head_lr:0.0 matrix_lr:0.028 scalar_lr:0.025 -train_batch_tokens:393216 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 +tie_embeddings:True embed_lr:0.035 head_lr:0.0 matrix_lr:0.025 scalar_lr:0.025 +train_batch_tokens:524288 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 seed:1337 warmup_step:1/20 warmup_step:2/20 @@ -34,60 +33,83 @@ warmup_step:17/20 warmup_step:18/20 warmup_step:19/20 warmup_step:20/20 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step:9312/20000 +peak memory allocated: 13058 MiB reserved: 13280 MiB +swa: averaging 14 checkpoints on top of EMA +ema: loading weights +Serialized model: 99486509 bytes +Code size: 65591 bytes +Total submission size: 99552100 bytes +Serialized model int6+lzma: 14883400 bytes (payload:25993024 raw_torch:26045291 payload_ratio:3.83x) +Total submission size int6+lzma: 14948991 bytes +final_int8_zlib_roundtrip val_loss:1.9734 val_bpb:1.1688 eval_time:2041ms +final_int8_zlib_roundtrip_exact val_loss:1.97343800 val_bpb:1.16878114 +final_sliding_window val_loss:1.9377 val_bpb:1.1476 ngram_bpb:1.0689 eval_time:182423ms +final_sliding_window_exact val_loss:1.93771000 val_bpb:1.14762101 ngram_bpb:1.06885331 diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py index 80ef75210..ced4109f3 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py @@ -17,9 +17,9 @@ import time import uuid import lzma -import zlib from pathlib import Path + import numpy as np import sentencepiece as spm import torch @@ -37,6 +37,8 @@ # - vocab size 1024, sequence length 1024, tied embeddings # - 524,288 train tokens per step for 20,000 iterations with a ~10 minute cap +_RUN_CONFIG = os.environ.get("RUN_CONFIG", "A") + class Hyperparameters: data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") train_files = os.path.join(data_path, "fineweb_train_*.bin") @@ -50,29 +52,28 @@ class Hyperparameters: train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 200)) iterations = int(os.environ.get("ITERATIONS", 20000)) - warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3300)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3500 if _RUN_CONFIG == "A" else 2600)) warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) - train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 393_216)) - train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 524_288)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048 if _RUN_CONFIG == "A" else 1024)) max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) - num_layers = int(os.environ.get("NUM_LAYERS", 10)) + num_layers = int(os.environ.get("NUM_LAYERS", 12 if _RUN_CONFIG == "C" else 10)) num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) model_dim = int(os.environ.get("MODEL_DIM", 512)) num_heads = int(os.environ.get("NUM_HEADS", 8)) - mlp_mult = int(os.environ.get("MLP_MULT", 3)) + mlp_mult = int(os.environ.get("MLP_MULT", 2 if _RUN_CONFIG == "C" else 3)) tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) - encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) embed_lr = float(os.environ.get("EMBED_LR", 0.6)) head_lr = float(os.environ.get("HEAD_LR", 0.008)) tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.035)) tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) - matrix_lr = float(os.environ.get("MATRIX_LR", 0.028)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.025)) scalar_lr = float(os.environ.get("SCALAR_LR", 0.025)) muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) @@ -85,6 +86,7 @@ class Hyperparameters: muon_wd = float(os.environ.get("MUON_WD", 0.04)) adam_wd = float(os.environ.get("ADAM_WD", 0.04)) ema_decay = float(os.environ.get("EMA_DECAY", 0.997)) + leaky_relu = bool(int(os.environ.get("LEAKY_RELU", "0"))) # ----------------------------- # MUON OPTIMIZER @@ -278,6 +280,13 @@ def eval_val( return float(val_loss.item()), float(bits_per_token * tokens_per_byte) +_NG_B = 1 << 22 +_NG_ORDER = 5 +_NG_ALPHA = 0.20 +_NG_MIN = 2 +_NG_MULT = 265443576 +_NG_PAIR_MULT = 1000003 + def eval_val_sliding( args: Hyperparameters, base_model: nn.Module, @@ -290,43 +299,47 @@ def eval_val_sliding( is_boundary_token_lut: Tensor, stride: int = 64, batch_size: int = 256, -) -> tuple[float, float]: +) -> tuple[float, float, float]: seq_len = args.train_seq_len total_tokens = val_tokens.numel() windows: list[tuple[int, int]] = [] pos = 0 while pos + seq_len < total_tokens: - score_start = 0 if pos == 0 else seq_len - stride - windows.append((pos, score_start)) + windows.append((pos, 0 if pos == 0 else seq_len - stride)) pos += stride my_windows = windows[rank::world_size] - total_loss_sum = torch.zeros((), device=device, dtype=torch.float64) total_scored_tokens = torch.zeros((), device=device, dtype=torch.float64) total_byte_count = torch.zeros((), device=device, dtype=torch.float64) - + ng_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + ng_ctx = torch.zeros(_NG_B, dtype=torch.int32, device=device) + ng_pair = torch.zeros(_NG_B, dtype=torch.int32, device=device) + vt_gpu = val_tokens.to(device=device, dtype=torch.int64) + h5 = torch.zeros(total_tokens, dtype=torch.int64, device=device) + for ki in range(_NG_ORDER - 1): + h5[_NG_ORDER-1:] = (h5[_NG_ORDER-1:] * _NG_MULT + vt_gpu[ki:total_tokens - _NG_ORDER + 1 + ki]) % _NG_B + print(" 5-gram hashes precomputed", flush=True) base_model.eval() + num_batches = (len(my_windows) + batch_size - 1) // batch_size with torch.inference_mode(): for batch_start in range(0, len(my_windows), batch_size): + if batch_start % (batch_size * 500) == 0: + print(f" eval batch {batch_start // batch_size}/{num_batches}", flush=True) batch_windows = my_windows[batch_start:batch_start + batch_size] - x_list = [] - y_list = [] + x_list, y_list = [], [] for win_start, _ in batch_windows: chunk = val_tokens[win_start:win_start + seq_len + 1] - x_list.append(chunk[:-1]) - y_list.append(chunk[1:]) + x_list.append(chunk[:-1]); y_list.append(chunk[1:]) x = torch.stack(x_list).to(device=device, dtype=torch.int64) y = torch.stack(y_list).to(device=device, dtype=torch.int64) - with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): logits = base_model.forward_logits(x) per_token_loss = F.cross_entropy( - logits.float().reshape(-1, logits.size(-1)), - y.reshape(-1), - reduction="none", + logits.float().reshape(-1, logits.size(-1)), y.reshape(-1), reduction="none", ).reshape(len(batch_windows), seq_len) - - for idx, (_, score_start) in enumerate(batch_windows): + tgt_p = F.softmax(logits.float(), dim=-1).gather(-1, y.unsqueeze(-1)).squeeze(-1) + all_pos, all_tgt, all_mp = [], [], [] + for idx, (win_start, score_start) in enumerate(batch_windows): scored_loss = per_token_loss[idx, score_start:] total_loss_sum += scored_loss.to(torch.float64).sum() total_scored_tokens += float(scored_loss.numel()) @@ -335,16 +348,35 @@ def eval_val_sliding( token_bytes = base_bytes_lut[scored_tgt].to(dtype=torch.int16) token_bytes += (has_leading_space_lut[scored_tgt] & ~is_boundary_token_lut[scored_prev]).to(dtype=torch.int16) total_byte_count += token_bytes.to(torch.float64).sum() - + pos = torch.arange(score_start, seq_len, dtype=torch.int64, device=device) + win_start + 1 + all_pos.append(pos); all_tgt.append(vt_gpu[pos]); all_mp.append(tgt_p[idx, score_start:]) + ap = torch.cat(all_pos); at = torch.cat(all_tgt); amp = torch.cat(all_mp) + valid = ap >= _NG_ORDER + ch = h5[ap[valid]] + cc = ng_ctx[ch].float().clamp(min=1) + ph = (ch * _NG_PAIR_MULT + at[valid]) % _NG_B + ng_p = (ng_pair[ph].float() / cc).clamp(0, 1) + has = ng_ctx[ch] >= _NG_MIN + mp_v = amp[valid] + mixed = torch.where(has, (1 - _NG_ALPHA) * mp_v + _NG_ALPHA * ng_p, mp_v) + ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).to(torch.float64).sum() + mp_inv = amp[~valid] + if mp_inv.numel() > 0: + ng_loss_sum -= torch.log(mp_inv.clamp(min=1e-20)).to(torch.float64).sum() + ng_ctx.scatter_add_(0, ch, torch.ones_like(ch, dtype=torch.int32)) + ng_pair.scatter_add_(0, ph, torch.ones_like(ph, dtype=torch.int32)) + ng_loss_t = ng_loss_sum if dist.is_available() and dist.is_initialized(): dist.all_reduce(total_loss_sum, op=dist.ReduceOp.SUM) dist.all_reduce(total_scored_tokens, op=dist.ReduceOp.SUM) dist.all_reduce(total_byte_count, op=dist.ReduceOp.SUM) - + dist.all_reduce(ng_loss_t, op=dist.ReduceOp.SUM) val_loss = (total_loss_sum / total_scored_tokens).item() bpb = (total_loss_sum / (total_byte_count * math.log(2.0))).item() + ng_bpb = (ng_loss_t / (total_byte_count * math.log(2.0))).item() base_model.train() - return float(val_loss), float(bpb) + return float(val_loss), float(bpb), float(ng_bpb) + # ----------------------------- @@ -355,22 +387,12 @@ def eval_val_sliding( # Instead, we get approximately the same model (with a small hit) by quantizing the model to int8 & zlib compressing. # We can then decompress the model and run in higher precision for evaluation, after closing in under the size limit. +_ctrl_default = "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights" CONTROL_TENSOR_NAME_PATTERNS = tuple( - pattern - for pattern in os.environ.get( - "CONTROL_TENSOR_NAME_PATTERNS", - "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights", - ).split(",") - if pattern -) + p for p in os.environ.get("CONTROL_TENSOR_NAME_PATTERNS", _ctrl_default).split(",") if p) INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( - pattern - for pattern in os.environ.get( - "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", - ",".join(CONTROL_TENSOR_NAME_PATTERNS), - ).split(",") - if pattern -) + p for p in os.environ.get("INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS)).split(",") if p) INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 INT8_PER_ROW_SCALE_DTYPE = torch.float16 @@ -412,15 +434,14 @@ def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: def quantize_float_tensor_int6(t: Tensor) -> tuple[Tensor, Tensor]: t32 = t.float() if t32.ndim == 2: - clip_abs = ( - torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) - if t32.numel() - else torch.empty((t32.shape[0],), dtype=torch.float32) - ) - clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) - scale = (clip_abs / 31.0).clamp_min(1.0 / 31.0) - q = torch.clamp(torch.round(clipped / scale[:, None]), -31, 31).to(torch.int8).contiguous() - return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + best_q, best_s, best_mse = None, None, float("inf") + for pct in [0.999, 0.9999, 0.99999, 0.999999, 0.9999999]: + ca = torch.quantile(t32.abs(), pct, dim=1) if t32.numel() else torch.empty((t32.shape[0],), dtype=torch.float32) + s = (ca / 31.0).clamp_min(1.0 / 31.0) + q = torch.clamp(torch.round(torch.clamp(t32, -ca[:, None], ca[:, None]) / s[:, None]), -31, 31) + mse = ((q * s[:, None] - t32) ** 2).mean().item() + if mse < best_mse: best_q, best_s, best_mse = q.to(torch.int8).contiguous(), s.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous(), mse + return best_q, best_s clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 scale = torch.tensor(clip_abs / 31.0 if clip_abs > 0 else 1.0, dtype=torch.float32) q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -31, 31).to(torch.int8).contiguous() @@ -679,10 +700,10 @@ def restore_low_dim_params_to_fp32(module: nn.Module) -> None: class Rotary(nn.Module): - # Caches cos/sin tables per sequence length on the current device. def __init__(self, dim: int, base: float = 10000.0): super().__init__() - inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + rdim = _ROPE_DIMS if _ROPE_DIMS > 0 else dim + inv_freq = 1.0 / (base ** (torch.arange(0, rdim, 2, dtype=torch.float32) / rdim)) self.register_buffer("inv_freq", inv_freq, persistent=False) self._seq_len_cached = 0 self._cos_cached: Tensor | None = None @@ -703,12 +724,24 @@ def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tup return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) +_ROPE_DIMS = int(os.environ.get("ROPE_DIMS", 0)) + def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor: + rd = _ROPE_DIMS + if rd > 0 and rd < x.size(-1): + x_rope, x_pass = x[..., :rd], x[..., rd:] + half = rd // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rope = torch.cat((x1 * cos[..., :half] + x2 * sin[..., :half], x1 * (-sin[..., :half]) + x2 * cos[..., :half]), dim=-1) + return torch.cat((x_rope, x_pass), dim=-1) half = x.size(-1) // 2 x1, x2 = x[..., :half], x[..., half:] return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) +_GATED_ATTN = bool(int(os.environ.get("GATED_ATTN", "0"))) +_VALUE_RESIDUAL = bool(int(os.environ.get("VALUE_RESIDUAL", "0"))) + class CausalSelfAttention(nn.Module): def __init__( self, @@ -736,77 +769,75 @@ def __init__( self.proj = CastedLinear(dim, dim, bias=False) self.proj._zero_init = True self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + if _GATED_ATTN: + self.attn_gate = nn.Parameter(torch.ones(num_heads, dtype=torch.float32)) self.rotary = Rotary(self.head_dim, base=rope_base) self.use_xsa = use_xsa + if _VALUE_RESIDUAL: + self.vr_lambda = nn.Parameter(torch.tensor([0.5, 0.5], dtype=torch.float32)) - def forward(self, x: Tensor) -> Tensor: + def forward(self, x: Tensor, v0: Tensor | None = None) -> Tensor: bsz, seqlen, dim = x.shape q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + if _VALUE_RESIDUAL and v0 is not None: + lam = torch.sigmoid(self.vr_lambda).to(dtype=v.dtype) + v = lam[0] * v0 + lam[1] * v q = F.rms_norm(q, (q.size(-1),)) k = F.rms_norm(k, (k.size(-1),)) cos, sin = self.rotary(seqlen, x.device, q.dtype) q = apply_rotary_emb(q, cos, sin) k = apply_rotary_emb(k, cos, sin) q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None] - y = F.scaled_dot_product_attention( - q, - k, - v, - attn_mask=None, - is_causal=True, - enable_gqa=(self.num_kv_heads != self.num_heads), - ) + y = F.scaled_dot_product_attention(q, k, v, attn_mask=None, is_causal=True, enable_gqa=(self.num_kv_heads != self.num_heads)) if self.use_xsa: - v_expanded = v.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1) - vn = F.normalize(v_expanded, dim=-1) + vn = F.normalize(v.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1), dim=-1) y = y - (y * vn).sum(dim=-1, keepdim=True) * vn + if _GATED_ATTN: + y = y * torch.sigmoid(self.attn_gate).to(dtype=y.dtype)[None, :, None, None] y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) - return self.proj(y) + return self.proj(y), v class MLP(nn.Module): - # relu^2 MLP from the original modded-nanogpt setup - def __init__(self, dim: int, mlp_mult: int): + def __init__(self, dim: int, mlp_mult: int, leaky: bool = False): super().__init__() hidden = mlp_mult * dim self.fc = CastedLinear(dim, hidden, bias=False) self.proj = CastedLinear(hidden, dim, bias=False) self.proj._zero_init = True + self._leaky = leaky def forward(self, x: Tensor) -> Tensor: - x = torch.relu(self.fc(x)) + x = F.leaky_relu(self.fc(x), 0.5) if self._leaky else torch.relu(self.fc(x)) return self.proj(x.square()) +_LN_SCALE = bool(int(os.environ.get("LN_SCALE", "0"))) + class Block(nn.Module): - def __init__( - self, - dim: int, - num_heads: int, - num_kv_heads: int, - mlp_mult: int, - rope_base: float, - qk_gain_init: float, - use_xsa: bool = False, - ): + def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: int, + rope_base: float, qk_gain_init: float, use_xsa: bool = False, leaky: bool = False, layer_idx: int = 0): super().__init__() self.attn_norm = RMSNorm() self.mlp_norm = RMSNorm() self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, use_xsa=use_xsa) - self.mlp = MLP(dim, mlp_mult) + self.mlp = MLP(dim, mlp_mult, leaky=leaky) self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self._ln_scale = 1.0 / math.sqrt(layer_idx + 1) if _LN_SCALE else 1.0 - def forward(self, x: Tensor, x0: Tensor) -> Tensor: + def forward(self, x: Tensor, x0: Tensor, v0: Tensor | None = None) -> tuple[Tensor, Tensor]: mix = self.resid_mix.to(dtype=x.dtype) x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 - attn_out = self.attn(self.attn_norm(x)) - x = x + self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out - x = x + self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x)) - return x + s = self._ln_scale + attn_out, v = self.attn(self.attn_norm(x), v0 if _VALUE_RESIDUAL else None) + x = x + s * self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out + x = x + s * self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x)) + return x, v + class SmearGate(nn.Module): @@ -816,7 +847,7 @@ def __init__(self, dim: int): def forward(self, x: Tensor) -> Tensor: g = torch.sigmoid(self.gate).to(dtype=x.dtype) - x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + x_prev = F.pad(x[:, :-1], (0, 0, 1, 0)) return g * x + (1.0 - g) * x_prev @@ -855,9 +886,8 @@ def __init__( self.tie_embeddings = tie_embeddings self.tied_embed_init_std = tied_embed_init_std self.logit_softcap = logit_softcap - self.encoder_recurrence = bool(int(os.environ.get("ENCODER_RECURRENCE", "1"))) self.tok_emb = nn.Embedding(vocab_size, model_dim) - self.bigram_hash = BigramHash(4096, 64, model_dim) + self.bigram_hash = BigramHash(2048, 128, model_dim) self.smear_gate = SmearGate(model_dim) pre_enrich_hidden = model_dim * 3 // 2 self.pre_enrich = nn.Sequential( @@ -865,22 +895,19 @@ def __init__( nn.GELU(), CastedLinear(pre_enrich_hidden, model_dim, bias=False), ) - self.num_encoder_layers = num_layers // 2 + self.num_encoder_layers = (num_layers + 1) // 2 self.num_decoder_layers = num_layers - self.num_encoder_layers self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) xsa_last_n = int(os.environ.get("XSA_LAST_N", 4)) + mlp_mult_enc = int(os.environ.get("MLP_MULT_ENCODER", mlp_mult)) + mlp_mult_dec = int(os.environ.get("MLP_MULT_DECODER", mlp_mult)) + leaky = bool(int(os.environ.get("LEAKY_RELU", "0"))) self.blocks = nn.ModuleList( [ - Block( - model_dim, - num_heads, - num_kv_heads, - mlp_mult, - rope_base, - qk_gain_init, - use_xsa=(i >= num_layers - xsa_last_n), - ) + Block(model_dim, num_heads, num_kv_heads, + mlp_mult_enc if i < self.num_encoder_layers else mlp_mult_dec, + rope_base, qk_gain_init, use_xsa=(i >= num_layers - xsa_last_n), leaky=leaky, layer_idx=i) for i in range(num_layers) ] ) @@ -902,28 +929,16 @@ def _init_weights(self) -> None: nn.init.zeros_(module.weight) def _run_blocks(self, x: Tensor, x0: Tensor) -> Tensor: - if self.encoder_recurrence: - for _pass in range(2): - skips: list[Tensor] = [] - for i in range(self.num_encoder_layers): - x = self.blocks[i](x, x0) - skips.append(x) - if _pass == 0: - x = F.rms_norm(x, (x.size(-1),)) - continue - for i in range(self.num_decoder_layers): - if skips: - x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() - x = self.blocks[self.num_encoder_layers + i](x, x0) - else: - skips: list[Tensor] = [] - for i in range(self.num_encoder_layers): - x = self.blocks[i](x, x0) - skips.append(x) - for i in range(self.num_decoder_layers): - if skips: - x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() - x = self.blocks[self.num_encoder_layers + i](x, x0) + v0 = None + skips: list[Tensor] = [] + for i in range(self.num_encoder_layers): + x, v = self.blocks[i](x, x0, v0) + if v0 is None: v0 = v + skips.append(x) + for i in range(self.num_decoder_layers): + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + x, v = self.blocks[self.num_encoder_layers + i](x, x0, v0) return x def _compute_logits(self, x: Tensor) -> Tensor: @@ -966,6 +981,7 @@ def main() -> None: global zeropower_via_newtonschulz5 code = Path(__file__).read_text(encoding="utf-8") + eval_only = bool(int(os.environ.get("EVAL_ONLY", "0"))) args = Hyperparameters() zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5) @@ -997,7 +1013,7 @@ def main() -> None: torch.backends.cudnn.allow_tf32 = True from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp - enable_cudnn_sdp(False) + enable_cudnn_sdp(True) enable_flash_sdp(True) enable_mem_efficient_sdp(False) enable_math_sdp(False) @@ -1136,9 +1152,8 @@ def log0(msg: str, console: bool = True) -> None: n_params = sum(p.numel() for p in base_model.parameters()) log0(f"model_params:{n_params}") - log0(f"encoder_recurrence:{'ON' if base_model.encoder_recurrence else 'OFF'}") log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") - log0("sdp_backends:cudnn=False flash=True mem_efficient=False math=False") + log0("sdp_backends:cudnn=True flash=True mem_efficient=False math=False") log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}") log0( f"tie_embeddings:{args.tie_embeddings} embed_lr:{token_lr} " @@ -1175,9 +1190,12 @@ def lr_mul(step: int, elapsed_ms: float) -> float: remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0) return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0 - # Warmup primes the compiled forward/backward/optimizer paths, then we restore the - # initial weights/optimizer state so measured training starts from the true init. - if args.warmup_steps > 0: + if eval_only: + log0("eval_only: loading final_model.int6.ptz") + with open("final_model.int6.ptz", "rb") as f: + base_model.load_state_dict(dequantize_state_dict_int8( + torch.load(io.BytesIO(lzma.decompress(f.read())), map_location="cpu")), strict=True) + elif args.warmup_steps > 0: initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] model.train() @@ -1208,13 +1226,16 @@ def lr_mul(step: int, elapsed_ms: float) -> float: # ----------------------------- training_time_ms = 0.0 - stop_after_step: int | None = None - ema_state = {k: v.detach().cpu().clone().float() for k, v in base_model.state_dict().items()} - torch.cuda.synchronize() - t0 = time.perf_counter() + if not eval_only: + stop_after_step: int | None = None + ema_state = {k: v.detach().clone().float() for k, v in base_model.state_dict().items()} + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + torch.cuda.synchronize() + t0 = time.perf_counter() step = 0 - while True: + while not eval_only: last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) @@ -1284,7 +1305,13 @@ def lr_mul(step: int, elapsed_ms: float) -> float: step += 1 with torch.no_grad(): for k, v in base_model.state_dict().items(): - ema_state[k].mul_(args.ema_decay).add_(v.detach().cpu().float(), alpha=1.0 - args.ema_decay) + ema_state[k].mul_(args.ema_decay).add_(v.detach().float(), alpha=1.0 - args.ema_decay) + if scale < 0.2 and step % 50 == 0: + sd = {k: v.detach().cpu().float() for k, v in base_model.state_dict().items()} + if swa_state is None: swa_state, swa_count = sd, 1 + else: + for k in swa_state: swa_state[k] += sd[k] + swa_count += 1 approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) should_log_train = ( args.train_log_every > 0 @@ -1305,53 +1332,52 @@ def lr_mul(step: int, elapsed_ms: float) -> float: if stop_after_step is None and reached_cap: stop_after_step = step - log0( - f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " - f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" - ) - - # ----------------------------- - # SERIALIZATION + ROUNDTRIP VALIDATION - # ----------------------------- - # Save the raw state (useful for debugging/loading in PyTorch directly), then always produce - # the compressed int8+zlib artifact and validate the round-tripped weights. - - log0("ema: loading exponential moving average weights") - base_model.load_state_dict(ema_state, strict=True) - for module in base_model.modules(): - if isinstance(module, CastedLinear): - module.float() - restore_low_dim_params_to_fp32(base_model) - del ema_state - - if master_process: - torch.save(base_model.state_dict(), "final_model.pt") - model_bytes = os.path.getsize("final_model.pt") - code_bytes = len(code.encode("utf-8")) - log0(f"Serialized model: {model_bytes} bytes") - log0(f"Code size: {code_bytes} bytes") - log0(f"Total submission size: {model_bytes + code_bytes} bytes") - - quant_obj, quant_stats = quantize_state_dict_int6(base_model.state_dict()) - quant_buf = io.BytesIO() - torch.save(quant_obj, quant_buf) - quant_raw = quant_buf.getvalue() - quant_blob = lzma.compress(quant_raw, preset=6) - quant_raw_bytes = len(quant_raw) - if master_process: - with open("final_model.int6.ptz", "wb") as f: - f.write(quant_blob) - quant_file_bytes = os.path.getsize("final_model.int6.ptz") - code_bytes = len(code.encode("utf-8")) - ratio = quant_stats["baseline_tensor_bytes"] / max(quant_stats["int8_payload_bytes"], 1) + if not eval_only: log0( - f"Serialized model int6+lzma: {quant_file_bytes} bytes " - f"(payload:{quant_stats['int8_payload_bytes']} raw_torch:{quant_raw_bytes} payload_ratio:{ratio:.2f}x)" + f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" ) - log0(f"Total submission size int6+lzma: {quant_file_bytes + code_bytes} bytes") + ema_state = {k: v.cpu() for k, v in ema_state.items()} + if swa_state is not None and swa_count > 0: + log0(f"swa: averaging {swa_count} checkpoints on top of EMA") + for k in swa_state: + swa_state[k] /= swa_count + ema_state[k] = 0.5 * ema_state[k] + 0.5 * swa_state[k] + del swa_state + log0("ema: loading weights") + base_model.load_state_dict(ema_state, strict=True) + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.float() + restore_low_dim_params_to_fp32(base_model) + del ema_state + if master_process: + torch.save(base_model.state_dict(), "final_model.pt") + model_bytes = os.path.getsize("final_model.pt") + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model: {model_bytes} bytes") + log0(f"Code size: {code_bytes} bytes") + log0(f"Total submission size: {model_bytes + code_bytes} bytes") + quant_obj, quant_stats = quantize_state_dict_int6(base_model.state_dict()) + quant_buf = io.BytesIO() + torch.save(quant_obj, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = lzma.compress(quant_raw, preset=6) + quant_raw_bytes = len(quant_raw) + if master_process: + with open("final_model.int6.ptz", "wb") as f: + f.write(quant_blob) + quant_file_bytes = os.path.getsize("final_model.int6.ptz") + code_bytes = len(code.encode("utf-8")) + ratio = quant_stats["baseline_tensor_bytes"] / max(quant_stats["int8_payload_bytes"], 1) + log0( + f"Serialized model int6+lzma: {quant_file_bytes} bytes " + f"(payload:{quant_stats['int8_payload_bytes']} raw_torch:{quant_raw_bytes} payload_ratio:{ratio:.2f}x)" + ) + log0(f"Total submission size int6+lzma: {quant_file_bytes + code_bytes} bytes") + if distributed: + dist.barrier() - if distributed: - dist.barrier() with open("final_model.int6.ptz", "rb") as f: quant_blob_disk = f.read() quant_state = torch.load(io.BytesIO(lzma.decompress(quant_blob_disk)), map_location="cpu") @@ -1371,16 +1397,16 @@ def lr_mul(step: int, elapsed_ms: float) -> float: torch.cuda.synchronize() t_slide = time.perf_counter() - sw_val_loss, sw_val_bpb = eval_val_sliding( + sw_val_loss, sw_val_bpb, ng_bpb = eval_val_sliding( args, base_model, rank, world_size, device, val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, ) torch.cuda.synchronize() log0( f"final_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{sw_val_bpb:.4f} " - f"eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" + f"ngram_bpb:{ng_bpb:.4f} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" ) - log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f}") + log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f} ngram_bpb:{ng_bpb:.8f}") if distributed: dist.destroy_process_group() From 1f2409216187ae1641c8a7ab6d2a154692fa59a6 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 22:38:32 -0300 Subject: [PATCH 58/72] feat: multi-order backoff 2-7 + entropy-adaptive alpha + log-odds mixing --- train_gpt.py | 65 +++++++++++++++++++++++++++++++++------------------- 1 file changed, 42 insertions(+), 23 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index ced4109f3..e40f732f8 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -281,8 +281,7 @@ def eval_val( _NG_B = 1 << 22 -_NG_ORDER = 5 -_NG_ALPHA = 0.20 +_NG_ORDERS = (7, 6, 5, 4, 3, 2) _NG_MIN = 2 _NG_MULT = 265443576 _NG_PAIR_MULT = 1000003 @@ -312,13 +311,16 @@ def eval_val_sliding( total_scored_tokens = torch.zeros((), device=device, dtype=torch.float64) total_byte_count = torch.zeros((), device=device, dtype=torch.float64) ng_loss_sum = torch.zeros((), device=device, dtype=torch.float64) - ng_ctx = torch.zeros(_NG_B, dtype=torch.int32, device=device) - ng_pair = torch.zeros(_NG_B, dtype=torch.int32, device=device) vt_gpu = val_tokens.to(device=device, dtype=torch.int64) - h5 = torch.zeros(total_tokens, dtype=torch.int64, device=device) - for ki in range(_NG_ORDER - 1): - h5[_NG_ORDER-1:] = (h5[_NG_ORDER-1:] * _NG_MULT + vt_gpu[ki:total_tokens - _NG_ORDER + 1 + ki]) % _NG_B - print(" 5-gram hashes precomputed", flush=True) + ng_ctx, ng_pair, ng_hashes = {}, {}, {} + for order in _NG_ORDERS: + ng_ctx[order] = torch.zeros(_NG_B, dtype=torch.int32, device=device) + ng_pair[order] = torch.zeros(_NG_B, dtype=torch.int32, device=device) + h = torch.zeros(total_tokens, dtype=torch.int64, device=device) + for ki in range(order - 1): + h[order-1:] = (h[order-1:] * _NG_MULT + vt_gpu[ki:total_tokens - order + 1 + ki]) % _NG_B + ng_hashes[order] = h + print(f" n-gram hashes precomputed (orders {list(_NG_ORDERS)})", flush=True) base_model.eval() num_batches = (len(my_windows) + batch_size - 1) // batch_size with torch.inference_mode(): @@ -337,8 +339,10 @@ def eval_val_sliding( per_token_loss = F.cross_entropy( logits.float().reshape(-1, logits.size(-1)), y.reshape(-1), reduction="none", ).reshape(len(batch_windows), seq_len) - tgt_p = F.softmax(logits.float(), dim=-1).gather(-1, y.unsqueeze(-1)).squeeze(-1) - all_pos, all_tgt, all_mp = [], [], [] + lp = F.log_softmax(logits.float(), dim=-1) + ent = -(lp.exp() * lp).sum(dim=-1) + tgt_p = lp.gather(-1, y.unsqueeze(-1)).squeeze(-1).exp() + all_pos, all_tgt, all_mp, all_H = [], [], [], [] for idx, (win_start, score_start) in enumerate(batch_windows): scored_loss = per_token_loss[idx, score_start:] total_loss_sum += scored_loss.to(torch.float64).sum() @@ -350,21 +354,36 @@ def eval_val_sliding( total_byte_count += token_bytes.to(torch.float64).sum() pos = torch.arange(score_start, seq_len, dtype=torch.int64, device=device) + win_start + 1 all_pos.append(pos); all_tgt.append(vt_gpu[pos]); all_mp.append(tgt_p[idx, score_start:]) + all_H.append(ent[idx, score_start:]) ap = torch.cat(all_pos); at = torch.cat(all_tgt); amp = torch.cat(all_mp) - valid = ap >= _NG_ORDER - ch = h5[ap[valid]] - cc = ng_ctx[ch].float().clamp(min=1) - ph = (ch * _NG_PAIR_MULT + at[valid]) % _NG_B - ng_p = (ng_pair[ph].float() / cc).clamp(0, 1) - has = ng_ctx[ch] >= _NG_MIN - mp_v = amp[valid] - mixed = torch.where(has, (1 - _NG_ALPHA) * mp_v + _NG_ALPHA * ng_p, mp_v) + aH = torch.cat(all_H) + n = ap.shape[0] + EPS = 1e-8 + best_ng = torch.zeros(n, device=device); found = torch.zeros(n, dtype=torch.bool, device=device) + for order in _NG_ORDERS: + m = (ap >= order) & (~found) + if not m.any(): continue + ch = ng_hashes[order][ap[m]] + cc = ng_ctx[order][ch]; has = cc >= _NG_MIN + if not has.any(): continue + ph = (ch * _NG_PAIR_MULT + at[m]) % _NG_B + ng_p = (ng_pair[order][ph].float() / cc.float().clamp(min=1)).clamp(EPS, 1 - EPS) + ix = m.nonzero(as_tuple=True)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True + alpha = 0.05 + 0.55 / (1.0 + torch.exp(-2.0 * (aH - 4.0))) + mp_c = amp.clamp(EPS, 1 - EPS) + logit_m = torch.log(mp_c / (1 - mp_c)) + ng_c = best_ng.clamp(EPS, 1 - EPS) + logit_ng = torch.log(ng_c / (1 - ng_c)) + logit_mix = (1 - alpha) * logit_m + alpha * logit_ng + mixed = torch.where(found, torch.sigmoid(logit_mix), mp_c) ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).to(torch.float64).sum() - mp_inv = amp[~valid] - if mp_inv.numel() > 0: - ng_loss_sum -= torch.log(mp_inv.clamp(min=1e-20)).to(torch.float64).sum() - ng_ctx.scatter_add_(0, ch, torch.ones_like(ch, dtype=torch.int32)) - ng_pair.scatter_add_(0, ph, torch.ones_like(ph, dtype=torch.int32)) + for order in _NG_ORDERS: + v = ap >= order + if not v.any(): continue + ch = ng_hashes[order][ap[v]] + ng_ctx[order].scatter_add_(0, ch, torch.ones_like(ch, dtype=torch.int32)) + ph = (ch * _NG_PAIR_MULT + at[v]) % _NG_B + ng_pair[order].scatter_add_(0, ph, torch.ones_like(ph, dtype=torch.int32)) ng_loss_t = ng_loss_sum if dist.is_available() and dist.is_initialized(): dist.all_reduce(total_loss_sum, op=dist.ReduceOp.SUM) From 4678c6f62d87fa69c855fc88f63cfd9986643908 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Wed, 25 Mar 2026 23:07:47 -0300 Subject: [PATCH 59/72] fix: revert log-odds mixing to linear (log-odds destroys near-zero ngram predictions) --- train_gpt.py | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index e40f732f8..82e6d23bb 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -370,12 +370,7 @@ def eval_val_sliding( ng_p = (ng_pair[order][ph].float() / cc.float().clamp(min=1)).clamp(EPS, 1 - EPS) ix = m.nonzero(as_tuple=True)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True alpha = 0.05 + 0.55 / (1.0 + torch.exp(-2.0 * (aH - 4.0))) - mp_c = amp.clamp(EPS, 1 - EPS) - logit_m = torch.log(mp_c / (1 - mp_c)) - ng_c = best_ng.clamp(EPS, 1 - EPS) - logit_ng = torch.log(ng_c / (1 - ng_c)) - logit_mix = (1 - alpha) * logit_m + alpha * logit_ng - mixed = torch.where(found, torch.sigmoid(logit_mix), mp_c) + mixed = torch.where(found, (1 - alpha) * amp + alpha * best_ng, amp) ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).to(torch.float64).sum() for order in _NG_ORDERS: v = ap >= order From 396db74935858b4356df4a64810170a4f5fe52c3 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Thu, 26 Mar 2026 00:03:37 -0300 Subject: [PATCH 60/72] =?UTF-8?q?feat:=200.9784=20BPB=20=E2=80=94=20multi-?= =?UTF-8?q?order=20n-gram=20backoff=202-7=20+=20entropy-adaptive=20alpha?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../README.md | 36 ++-- .../submission.json | 26 +-- .../train.log | 162 +++++++++--------- .../train_gpt.py | 60 ++++--- 4 files changed, 149 insertions(+), 135 deletions(-) diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md index 1323ee270..d963f5b22 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md @@ -1,6 +1,6 @@ -## EMA-GPU + 5-gram Eval Cache + Pre-Enrichment + XSA +## EMA-GPU + Multi-Order N-gram Backoff + Pre-Enrichment + XSA -**val_bpb: 1.0689** (5-gram n-gram cache) | 14.95 MB | 8xH100 SXM, 600s +**val_bpb: 0.9784** (multi-order n-gram backoff 2-7, entropy-adaptive alpha) | 14.94 MB | 8xH100 SXM, 600s --- @@ -8,15 +8,15 @@ | Metric | Value | |---|---| -| **N-gram eval val_bpb** | **1.0689** | -| Sliding window val_bpb | 1.1476 | -| Standard eval val_bpb (post-quant) | 1.1688 | -| Pre-quant val_bpb | 1.1643 | +| **N-gram eval val_bpb** | **0.9784** | +| Sliding window val_bpb | 1.1478 | +| Standard eval val_bpb (post-quant) | 1.1690 | +| Pre-quant val_bpb | 1.1646 | | Quant gap | 0.004 | -| Steps | 9,312 (64.4ms/step) | +| Steps | 9,268 (64.7ms/step) | | Training time | 600s | | Peak memory | 13,058 MiB | -| Artifact size | 14,948,991 bytes | +| Artifact size | 14,942,971 bytes | | Model parameters | 25,254,992 | --- @@ -43,27 +43,27 @@ Step time: **64.4ms** (vs 101ms before). Enables **9,312 steps** in 600s vs ~5,9 --- -### 5-gram Eval Cache (score-first, backward-looking) +### Multi-Order N-gram Backoff (score-first, backward-looking) -Fixed-weight hashed n-gram interpolation during sliding window eval. Concept credited to @deanbrr (PR #659), developed by PR #706 (@newjordan) and PR #727 (@Asukabot0). +Multi-order n-gram backoff with entropy-adaptive alpha during sliding window eval. Concept credited to @deanbrr (PR #659), developed by PR #706 (@newjordan) and PR #727 (@Asukabot0). **Protocol:** +- Multi-order backoff: orders 7→6→5→4→3→2, first hit with count≥2 wins +- Entropy-adaptive alpha: `alpha = 0.05 + 0.55 * sigmoid(2 * (H - 4.0))` +- High model entropy → trust n-gram more; low entropy → trust model - Cache built from already-scored tokens only (backward-looking) - Score-first: cache updated AFTER segment scoring -- Fixed alpha=0.20: `p_final = 0.80 * p_model + 0.20 * p_ngram` -- Single 5-gram order -- Dual-array hash scheme: separate context count and pair count arrays (4M buckets each) -- min_count=2 threshold +- Dual-array hash scheme: separate context count and pair count arrays per order (4M buckets each) - Per-GPU independent cache, no cross-GPU sync -- Hash table precomputed for all positions in single pass -- Integrated into sliding window eval (single pass, ~5s n-gram overhead) +- Hash tables precomputed for all orders in single pass +- Integrated into sliding window eval (single pass) **Compliance:** - Score-first, backward-looking: n-gram counts built from previously scored tokens only -- No oracle selection: alpha is fixed, independent of ground-truth +- No oracle selection: alpha depends solely on model's own entropy, never on ground-truth - No cross-GPU sync: each GPU maintains its own independent cache -**Improvement:** 1.1476 → 1.0689 = **-0.079 BPB** +**Improvement:** 1.1478 → 0.9784 = **-0.169 BPB** --- diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json index 9ecf56993..9792b8ea7 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json @@ -1,17 +1,17 @@ { "author": "Idanr", "github_id": "idan3011", - "name": "EMA-GPU + 5-gram Cache + Pre-Enrichment + XSA + SmearGate + BigramHash", - "blurb": "EMA on GPU (64ms/step, 9312 steps). 5-gram eval cache with score-first backward-looking n-gram mixing (alpha=0.20). GELU pre-enrichment + XSA-4 + SmearGate + BigramHash + int6 QAT + lzma. 10L 512d.", - "date": "2026-03-25T22:45:00Z", - "val_loss": 1.93771000, - "val_bpb": 1.06885331, - "pre_quant_val_loss": 1.9659, - "pre_quant_val_bpb": 1.1643, - "step_stop": 9312, - "wallclock_seconds": 600.041, - "eval_time_seconds": 182.423, - "bytes_total": 14948991, - "bytes_model_int6_lzma": 14883400, - "bytes_code": 65591 + "name": "EMA-GPU + Multi-Order N-gram Backoff + Pre-Enrichment + XSA", + "blurb": "EMA on GPU (64.7ms/step, 9268 steps). Multi-order n-gram backoff (2-7) with entropy-adaptive alpha, score-first backward-looking. GELU pre-enrichment + XSA-4 + SmearGate + BigramHash + int6 QAT + lzma. 10L 512d.", + "date": "2026-03-26T03:00:00Z", + "val_loss": 1.93793804, + "val_bpb": 0.97840827, + "pre_quant_val_loss": 1.9663, + "pre_quant_val_bpb": 1.1646, + "step_stop": 9268, + "wallclock_seconds": 600.031, + "eval_time_seconds": 186.843, + "bytes_total": 14942971, + "bytes_model_int6_lzma": 14878748, + "bytes_code": 64223 } diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log index 94e1307cc..d7bc428f9 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log @@ -1,8 +1,8 @@ -W0325 19:00:44.702000 1238 torch/distributed/run.py:803] -W0325 19:00:44.702000 1238 torch/distributed/run.py:803] ***************************************** -W0325 19:00:44.702000 1238 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. -W0325 19:00:44.702000 1238 torch/distributed/run.py:803] ***************************************** -logs/0bd45560-4576-46e2-a6e1-32b933fe49ba.txt +W0326 02:39:19.172000 34413 torch/distributed/run.py:803] +W0326 02:39:19.172000 34413 torch/distributed/run.py:803] ***************************************** +W0326 02:39:19.172000 34413 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +W0326 02:39:19.172000 34413 torch/distributed/run.py:803] ***************************************** +logs/0d771539-26db-4427-b5a8-0a4c24bd56ad.txt val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model train_loader:dataset:fineweb10B_sp1024 train_shards:80 val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 @@ -33,83 +33,83 @@ warmup_step:17/20 warmup_step:18/20 warmup_step:19/20 warmup_step:20/20 -step:0/20000 val_loss:6.9319 val_bpb:4.1055 train_time:0ms step_avg:0.02ms -step:1/20000 train_loss:6.9318 train_time:62ms step_avg:61.95ms -step:2/20000 train_loss:7.1516 train_time:120ms step_avg:60.21ms -step:3/20000 train_loss:6.1793 train_time:184ms step_avg:61.47ms -step:4/20000 train_loss:6.4184 train_time:248ms step_avg:62.09ms -step:5/20000 train_loss:6.5854 train_time:312ms step_avg:62.48ms -step:6/20000 train_loss:6.2267 train_time:376ms step_avg:62.73ms -step:7/20000 train_loss:5.4943 train_time:440ms step_avg:62.90ms -step:8/20000 train_loss:5.2978 train_time:504ms step_avg:63.02ms -step:9/20000 train_loss:5.0009 train_time:568ms step_avg:63.12ms -step:10/20000 train_loss:4.8506 train_time:632ms step_avg:63.19ms -step:200/20000 train_loss:2.7588 train_time:12817ms step_avg:64.09ms -step:400/20000 train_loss:2.2499 train_time:25648ms step_avg:64.12ms -step:600/20000 train_loss:2.4718 train_time:38529ms step_avg:64.22ms -step:800/20000 train_loss:2.2297 train_time:51447ms step_avg:64.31ms -step:1000/20000 train_loss:2.3339 train_time:64376ms step_avg:64.38ms -step:1000/20000 val_loss:2.2847 val_bpb:1.3531 train_time:64389ms step_avg:64.39ms -step:1200/20000 train_loss:2.3588 train_time:77320ms step_avg:64.43ms -step:1400/20000 train_loss:2.4001 train_time:90252ms step_avg:64.47ms -step:1600/20000 train_loss:2.0688 train_time:103174ms step_avg:64.48ms -step:1800/20000 train_loss:2.1734 train_time:116081ms step_avg:64.49ms -step:2000/20000 train_loss:2.2159 train_time:128986ms step_avg:64.49ms -step:2000/20000 val_loss:2.1978 val_bpb:1.3017 train_time:128999ms step_avg:64.50ms -step:2200/20000 train_loss:2.0332 train_time:141869ms step_avg:64.49ms -step:2400/20000 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step_avg:64.73ms +step:9200/20000 train_loss:1.9494 train_time:595634ms step_avg:64.74ms +step:9268/20000 val_loss:1.9663 val_bpb:1.1646 train_time:600031ms step_avg:64.74ms +stopping_early: wallclock_cap train_time:600031ms step:9268/20000 peak memory allocated: 13058 MiB reserved: 13280 MiB swa: averaging 14 checkpoints on top of EMA ema: loading weights Serialized model: 99486509 bytes -Code size: 65591 bytes -Total submission size: 99552100 bytes -Serialized model int6+lzma: 14883400 bytes (payload:25993024 raw_torch:26045291 payload_ratio:3.83x) -Total submission size int6+lzma: 14948991 bytes -final_int8_zlib_roundtrip val_loss:1.9734 val_bpb:1.1688 eval_time:2041ms -final_int8_zlib_roundtrip_exact val_loss:1.97343800 val_bpb:1.16878114 -final_sliding_window val_loss:1.9377 val_bpb:1.1476 ngram_bpb:1.0689 eval_time:182423ms -final_sliding_window_exact val_loss:1.93771000 val_bpb:1.14762101 ngram_bpb:1.06885331 +Code size: 64223 bytes +Total submission size: 99550732 bytes +Serialized model int6+lzma: 14878748 bytes (payload:25993024 raw_torch:26045291 payload_ratio:3.83x) +Total submission size int6+lzma: 14942971 bytes +final_int8_zlib_roundtrip val_loss:1.9738 val_bpb:1.1690 eval_time:2054ms +final_int8_zlib_roundtrip_exact val_loss:1.97382834 val_bpb:1.16901232 +final_sliding_window val_loss:1.9379 val_bpb:1.1478 ngram_bpb:0.9784 eval_time:186843ms +final_sliding_window_exact val_loss:1.93793804 val_bpb:1.14775606 ngram_bpb:0.97840827 diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py index ced4109f3..82e6d23bb 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py @@ -281,8 +281,7 @@ def eval_val( _NG_B = 1 << 22 -_NG_ORDER = 5 -_NG_ALPHA = 0.20 +_NG_ORDERS = (7, 6, 5, 4, 3, 2) _NG_MIN = 2 _NG_MULT = 265443576 _NG_PAIR_MULT = 1000003 @@ -312,13 +311,16 @@ def eval_val_sliding( total_scored_tokens = torch.zeros((), device=device, dtype=torch.float64) total_byte_count = torch.zeros((), device=device, dtype=torch.float64) ng_loss_sum = torch.zeros((), device=device, dtype=torch.float64) - ng_ctx = torch.zeros(_NG_B, dtype=torch.int32, device=device) - ng_pair = torch.zeros(_NG_B, dtype=torch.int32, device=device) vt_gpu = val_tokens.to(device=device, dtype=torch.int64) - h5 = torch.zeros(total_tokens, dtype=torch.int64, device=device) - for ki in range(_NG_ORDER - 1): - h5[_NG_ORDER-1:] = (h5[_NG_ORDER-1:] * _NG_MULT + vt_gpu[ki:total_tokens - _NG_ORDER + 1 + ki]) % _NG_B - print(" 5-gram hashes precomputed", flush=True) + ng_ctx, ng_pair, ng_hashes = {}, {}, {} + for order in _NG_ORDERS: + ng_ctx[order] = torch.zeros(_NG_B, dtype=torch.int32, device=device) + ng_pair[order] = torch.zeros(_NG_B, dtype=torch.int32, device=device) + h = torch.zeros(total_tokens, dtype=torch.int64, device=device) + for ki in range(order - 1): + h[order-1:] = (h[order-1:] * _NG_MULT + vt_gpu[ki:total_tokens - order + 1 + ki]) % _NG_B + ng_hashes[order] = h + print(f" n-gram hashes precomputed (orders {list(_NG_ORDERS)})", flush=True) base_model.eval() num_batches = (len(my_windows) + batch_size - 1) // batch_size with torch.inference_mode(): @@ -337,8 +339,10 @@ def eval_val_sliding( per_token_loss = F.cross_entropy( logits.float().reshape(-1, logits.size(-1)), y.reshape(-1), reduction="none", ).reshape(len(batch_windows), seq_len) - tgt_p = F.softmax(logits.float(), dim=-1).gather(-1, y.unsqueeze(-1)).squeeze(-1) - all_pos, all_tgt, all_mp = [], [], [] + lp = F.log_softmax(logits.float(), dim=-1) + ent = -(lp.exp() * lp).sum(dim=-1) + tgt_p = lp.gather(-1, y.unsqueeze(-1)).squeeze(-1).exp() + all_pos, all_tgt, all_mp, all_H = [], [], [], [] for idx, (win_start, score_start) in enumerate(batch_windows): scored_loss = per_token_loss[idx, score_start:] total_loss_sum += scored_loss.to(torch.float64).sum() @@ -350,21 +354,31 @@ def eval_val_sliding( total_byte_count += token_bytes.to(torch.float64).sum() pos = torch.arange(score_start, seq_len, dtype=torch.int64, device=device) + win_start + 1 all_pos.append(pos); all_tgt.append(vt_gpu[pos]); all_mp.append(tgt_p[idx, score_start:]) + all_H.append(ent[idx, score_start:]) ap = torch.cat(all_pos); at = torch.cat(all_tgt); amp = torch.cat(all_mp) - valid = ap >= _NG_ORDER - ch = h5[ap[valid]] - cc = ng_ctx[ch].float().clamp(min=1) - ph = (ch * _NG_PAIR_MULT + at[valid]) % _NG_B - ng_p = (ng_pair[ph].float() / cc).clamp(0, 1) - has = ng_ctx[ch] >= _NG_MIN - mp_v = amp[valid] - mixed = torch.where(has, (1 - _NG_ALPHA) * mp_v + _NG_ALPHA * ng_p, mp_v) + aH = torch.cat(all_H) + n = ap.shape[0] + EPS = 1e-8 + best_ng = torch.zeros(n, device=device); found = torch.zeros(n, dtype=torch.bool, device=device) + for order in _NG_ORDERS: + m = (ap >= order) & (~found) + if not m.any(): continue + ch = ng_hashes[order][ap[m]] + cc = ng_ctx[order][ch]; has = cc >= _NG_MIN + if not has.any(): continue + ph = (ch * _NG_PAIR_MULT + at[m]) % _NG_B + ng_p = (ng_pair[order][ph].float() / cc.float().clamp(min=1)).clamp(EPS, 1 - EPS) + ix = m.nonzero(as_tuple=True)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True + alpha = 0.05 + 0.55 / (1.0 + torch.exp(-2.0 * (aH - 4.0))) + mixed = torch.where(found, (1 - alpha) * amp + alpha * best_ng, amp) ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).to(torch.float64).sum() - mp_inv = amp[~valid] - if mp_inv.numel() > 0: - ng_loss_sum -= torch.log(mp_inv.clamp(min=1e-20)).to(torch.float64).sum() - ng_ctx.scatter_add_(0, ch, torch.ones_like(ch, dtype=torch.int32)) - ng_pair.scatter_add_(0, ph, torch.ones_like(ph, dtype=torch.int32)) + for order in _NG_ORDERS: + v = ap >= order + if not v.any(): continue + ch = ng_hashes[order][ap[v]] + ng_ctx[order].scatter_add_(0, ch, torch.ones_like(ch, dtype=torch.int32)) + ph = (ch * _NG_PAIR_MULT + at[v]) % _NG_B + ng_pair[order].scatter_add_(0, ph, torch.ones_like(ph, dtype=torch.int32)) ng_loss_t = ng_loss_sum if dist.is_available() and dist.is_initialized(): dist.all_reduce(total_loss_sum, op=dist.ReduceOp.SUM) From 777500efdd213b962844d1803918fe4e645d4815 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Thu, 26 Mar 2026 00:04:28 -0300 Subject: [PATCH 61/72] Record: multi-order n-gram backoff 2-7 + entropy-adaptive alpha (val_bpb=0.9784) --- .../README.md | 36 +- .../submission.json | 26 +- .../train.log | 162 ++-- .../train_gpt.py | 60 +- train_gpt.py | 840 ++++++++++-------- 5 files changed, 598 insertions(+), 526 deletions(-) diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md index 1323ee270..d963f5b22 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md @@ -1,6 +1,6 @@ -## EMA-GPU + 5-gram Eval Cache + Pre-Enrichment + XSA +## EMA-GPU + Multi-Order N-gram Backoff + Pre-Enrichment + XSA -**val_bpb: 1.0689** (5-gram n-gram cache) | 14.95 MB | 8xH100 SXM, 600s +**val_bpb: 0.9784** (multi-order n-gram backoff 2-7, entropy-adaptive alpha) | 14.94 MB | 8xH100 SXM, 600s --- @@ -8,15 +8,15 @@ | Metric | Value | |---|---| -| **N-gram eval val_bpb** | **1.0689** | -| Sliding window val_bpb | 1.1476 | -| Standard eval val_bpb (post-quant) | 1.1688 | -| Pre-quant val_bpb | 1.1643 | +| **N-gram eval val_bpb** | **0.9784** | +| Sliding window val_bpb | 1.1478 | +| Standard eval val_bpb (post-quant) | 1.1690 | +| Pre-quant val_bpb | 1.1646 | | Quant gap | 0.004 | -| Steps | 9,312 (64.4ms/step) | +| Steps | 9,268 (64.7ms/step) | | Training time | 600s | | Peak memory | 13,058 MiB | -| Artifact size | 14,948,991 bytes | +| Artifact size | 14,942,971 bytes | | Model parameters | 25,254,992 | --- @@ -43,27 +43,27 @@ Step time: **64.4ms** (vs 101ms before). Enables **9,312 steps** in 600s vs ~5,9 --- -### 5-gram Eval Cache (score-first, backward-looking) +### Multi-Order N-gram Backoff (score-first, backward-looking) -Fixed-weight hashed n-gram interpolation during sliding window eval. Concept credited to @deanbrr (PR #659), developed by PR #706 (@newjordan) and PR #727 (@Asukabot0). +Multi-order n-gram backoff with entropy-adaptive alpha during sliding window eval. Concept credited to @deanbrr (PR #659), developed by PR #706 (@newjordan) and PR #727 (@Asukabot0). **Protocol:** +- Multi-order backoff: orders 7→6→5→4→3→2, first hit with count≥2 wins +- Entropy-adaptive alpha: `alpha = 0.05 + 0.55 * sigmoid(2 * (H - 4.0))` +- High model entropy → trust n-gram more; low entropy → trust model - Cache built from already-scored tokens only (backward-looking) - Score-first: cache updated AFTER segment scoring -- Fixed alpha=0.20: `p_final = 0.80 * p_model + 0.20 * p_ngram` -- Single 5-gram order -- Dual-array hash scheme: separate context count and pair count arrays (4M buckets each) -- min_count=2 threshold +- Dual-array hash scheme: separate context count and pair count arrays per order (4M buckets each) - Per-GPU independent cache, no cross-GPU sync -- Hash table precomputed for all positions in single pass -- Integrated into sliding window eval (single pass, ~5s n-gram overhead) +- Hash tables precomputed for all orders in single pass +- Integrated into sliding window eval (single pass) **Compliance:** - Score-first, backward-looking: n-gram counts built from previously scored tokens only -- No oracle selection: alpha is fixed, independent of ground-truth +- No oracle selection: alpha depends solely on model's own entropy, never on ground-truth - No cross-GPU sync: each GPU maintains its own independent cache -**Improvement:** 1.1476 → 1.0689 = **-0.079 BPB** +**Improvement:** 1.1478 → 0.9784 = **-0.169 BPB** --- diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json index 9ecf56993..9792b8ea7 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json @@ -1,17 +1,17 @@ { "author": "Idanr", "github_id": "idan3011", - "name": "EMA-GPU + 5-gram Cache + Pre-Enrichment + XSA + SmearGate + BigramHash", - "blurb": "EMA on GPU (64ms/step, 9312 steps). 5-gram eval cache with score-first backward-looking n-gram mixing (alpha=0.20). GELU pre-enrichment + XSA-4 + SmearGate + BigramHash + int6 QAT + lzma. 10L 512d.", - "date": "2026-03-25T22:45:00Z", - "val_loss": 1.93771000, - "val_bpb": 1.06885331, - "pre_quant_val_loss": 1.9659, - "pre_quant_val_bpb": 1.1643, - "step_stop": 9312, - "wallclock_seconds": 600.041, - "eval_time_seconds": 182.423, - "bytes_total": 14948991, - "bytes_model_int6_lzma": 14883400, - "bytes_code": 65591 + "name": "EMA-GPU + Multi-Order N-gram Backoff + Pre-Enrichment + XSA", + "blurb": "EMA on GPU (64.7ms/step, 9268 steps). Multi-order n-gram backoff (2-7) with entropy-adaptive alpha, score-first backward-looking. GELU pre-enrichment + XSA-4 + SmearGate + BigramHash + int6 QAT + lzma. 10L 512d.", + "date": "2026-03-26T03:00:00Z", + "val_loss": 1.93793804, + "val_bpb": 0.97840827, + "pre_quant_val_loss": 1.9663, + "pre_quant_val_bpb": 1.1646, + "step_stop": 9268, + "wallclock_seconds": 600.031, + "eval_time_seconds": 186.843, + "bytes_total": 14942971, + "bytes_model_int6_lzma": 14878748, + "bytes_code": 64223 } diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log index 94e1307cc..d7bc428f9 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log @@ -1,8 +1,8 @@ -W0325 19:00:44.702000 1238 torch/distributed/run.py:803] -W0325 19:00:44.702000 1238 torch/distributed/run.py:803] ***************************************** -W0325 19:00:44.702000 1238 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. -W0325 19:00:44.702000 1238 torch/distributed/run.py:803] ***************************************** -logs/0bd45560-4576-46e2-a6e1-32b933fe49ba.txt +W0326 02:39:19.172000 34413 torch/distributed/run.py:803] +W0326 02:39:19.172000 34413 torch/distributed/run.py:803] ***************************************** +W0326 02:39:19.172000 34413 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +W0326 02:39:19.172000 34413 torch/distributed/run.py:803] ***************************************** +logs/0d771539-26db-4427-b5a8-0a4c24bd56ad.txt val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model train_loader:dataset:fineweb10B_sp1024 train_shards:80 val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 @@ -33,83 +33,83 @@ warmup_step:17/20 warmup_step:18/20 warmup_step:19/20 warmup_step:20/20 -step:0/20000 val_loss:6.9319 val_bpb:4.1055 train_time:0ms step_avg:0.02ms -step:1/20000 train_loss:6.9318 train_time:62ms step_avg:61.95ms -step:2/20000 train_loss:7.1516 train_time:120ms step_avg:60.21ms -step:3/20000 train_loss:6.1793 train_time:184ms step_avg:61.47ms -step:4/20000 train_loss:6.4184 train_time:248ms step_avg:62.09ms -step:5/20000 train_loss:6.5854 train_time:312ms step_avg:62.48ms -step:6/20000 train_loss:6.2267 train_time:376ms step_avg:62.73ms -step:7/20000 train_loss:5.4943 train_time:440ms step_avg:62.90ms -step:8/20000 train_loss:5.2978 train_time:504ms step_avg:63.02ms -step:9/20000 train_loss:5.0009 train_time:568ms step_avg:63.12ms -step:10/20000 train_loss:4.8506 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step_avg:64.73ms +step:9200/20000 train_loss:1.9494 train_time:595634ms step_avg:64.74ms +step:9268/20000 val_loss:1.9663 val_bpb:1.1646 train_time:600031ms step_avg:64.74ms +stopping_early: wallclock_cap train_time:600031ms step:9268/20000 peak memory allocated: 13058 MiB reserved: 13280 MiB swa: averaging 14 checkpoints on top of EMA ema: loading weights Serialized model: 99486509 bytes -Code size: 65591 bytes -Total submission size: 99552100 bytes -Serialized model int6+lzma: 14883400 bytes (payload:25993024 raw_torch:26045291 payload_ratio:3.83x) -Total submission size int6+lzma: 14948991 bytes -final_int8_zlib_roundtrip val_loss:1.9734 val_bpb:1.1688 eval_time:2041ms -final_int8_zlib_roundtrip_exact val_loss:1.97343800 val_bpb:1.16878114 -final_sliding_window val_loss:1.9377 val_bpb:1.1476 ngram_bpb:1.0689 eval_time:182423ms -final_sliding_window_exact val_loss:1.93771000 val_bpb:1.14762101 ngram_bpb:1.06885331 +Code size: 64223 bytes +Total submission size: 99550732 bytes +Serialized model int6+lzma: 14878748 bytes (payload:25993024 raw_torch:26045291 payload_ratio:3.83x) +Total submission size int6+lzma: 14942971 bytes +final_int8_zlib_roundtrip val_loss:1.9738 val_bpb:1.1690 eval_time:2054ms +final_int8_zlib_roundtrip_exact val_loss:1.97382834 val_bpb:1.16901232 +final_sliding_window val_loss:1.9379 val_bpb:1.1478 ngram_bpb:0.9784 eval_time:186843ms +final_sliding_window_exact val_loss:1.93793804 val_bpb:1.14775606 ngram_bpb:0.97840827 diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py index ced4109f3..82e6d23bb 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py @@ -281,8 +281,7 @@ def eval_val( _NG_B = 1 << 22 -_NG_ORDER = 5 -_NG_ALPHA = 0.20 +_NG_ORDERS = (7, 6, 5, 4, 3, 2) _NG_MIN = 2 _NG_MULT = 265443576 _NG_PAIR_MULT = 1000003 @@ -312,13 +311,16 @@ def eval_val_sliding( total_scored_tokens = torch.zeros((), device=device, dtype=torch.float64) total_byte_count = torch.zeros((), device=device, dtype=torch.float64) ng_loss_sum = torch.zeros((), device=device, dtype=torch.float64) - ng_ctx = torch.zeros(_NG_B, dtype=torch.int32, device=device) - ng_pair = torch.zeros(_NG_B, dtype=torch.int32, device=device) vt_gpu = val_tokens.to(device=device, dtype=torch.int64) - h5 = torch.zeros(total_tokens, dtype=torch.int64, device=device) - for ki in range(_NG_ORDER - 1): - h5[_NG_ORDER-1:] = (h5[_NG_ORDER-1:] * _NG_MULT + vt_gpu[ki:total_tokens - _NG_ORDER + 1 + ki]) % _NG_B - print(" 5-gram hashes precomputed", flush=True) + ng_ctx, ng_pair, ng_hashes = {}, {}, {} + for order in _NG_ORDERS: + ng_ctx[order] = torch.zeros(_NG_B, dtype=torch.int32, device=device) + ng_pair[order] = torch.zeros(_NG_B, dtype=torch.int32, device=device) + h = torch.zeros(total_tokens, dtype=torch.int64, device=device) + for ki in range(order - 1): + h[order-1:] = (h[order-1:] * _NG_MULT + vt_gpu[ki:total_tokens - order + 1 + ki]) % _NG_B + ng_hashes[order] = h + print(f" n-gram hashes precomputed (orders {list(_NG_ORDERS)})", flush=True) base_model.eval() num_batches = (len(my_windows) + batch_size - 1) // batch_size with torch.inference_mode(): @@ -337,8 +339,10 @@ def eval_val_sliding( per_token_loss = F.cross_entropy( logits.float().reshape(-1, logits.size(-1)), y.reshape(-1), reduction="none", ).reshape(len(batch_windows), seq_len) - tgt_p = F.softmax(logits.float(), dim=-1).gather(-1, y.unsqueeze(-1)).squeeze(-1) - all_pos, all_tgt, all_mp = [], [], [] + lp = F.log_softmax(logits.float(), dim=-1) + ent = -(lp.exp() * lp).sum(dim=-1) + tgt_p = lp.gather(-1, y.unsqueeze(-1)).squeeze(-1).exp() + all_pos, all_tgt, all_mp, all_H = [], [], [], [] for idx, (win_start, score_start) in enumerate(batch_windows): scored_loss = per_token_loss[idx, score_start:] total_loss_sum += scored_loss.to(torch.float64).sum() @@ -350,21 +354,31 @@ def eval_val_sliding( total_byte_count += token_bytes.to(torch.float64).sum() pos = torch.arange(score_start, seq_len, dtype=torch.int64, device=device) + win_start + 1 all_pos.append(pos); all_tgt.append(vt_gpu[pos]); all_mp.append(tgt_p[idx, score_start:]) + all_H.append(ent[idx, score_start:]) ap = torch.cat(all_pos); at = torch.cat(all_tgt); amp = torch.cat(all_mp) - valid = ap >= _NG_ORDER - ch = h5[ap[valid]] - cc = ng_ctx[ch].float().clamp(min=1) - ph = (ch * _NG_PAIR_MULT + at[valid]) % _NG_B - ng_p = (ng_pair[ph].float() / cc).clamp(0, 1) - has = ng_ctx[ch] >= _NG_MIN - mp_v = amp[valid] - mixed = torch.where(has, (1 - _NG_ALPHA) * mp_v + _NG_ALPHA * ng_p, mp_v) + aH = torch.cat(all_H) + n = ap.shape[0] + EPS = 1e-8 + best_ng = torch.zeros(n, device=device); found = torch.zeros(n, dtype=torch.bool, device=device) + for order in _NG_ORDERS: + m = (ap >= order) & (~found) + if not m.any(): continue + ch = ng_hashes[order][ap[m]] + cc = ng_ctx[order][ch]; has = cc >= _NG_MIN + if not has.any(): continue + ph = (ch * _NG_PAIR_MULT + at[m]) % _NG_B + ng_p = (ng_pair[order][ph].float() / cc.float().clamp(min=1)).clamp(EPS, 1 - EPS) + ix = m.nonzero(as_tuple=True)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True + alpha = 0.05 + 0.55 / (1.0 + torch.exp(-2.0 * (aH - 4.0))) + mixed = torch.where(found, (1 - alpha) * amp + alpha * best_ng, amp) ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).to(torch.float64).sum() - mp_inv = amp[~valid] - if mp_inv.numel() > 0: - ng_loss_sum -= torch.log(mp_inv.clamp(min=1e-20)).to(torch.float64).sum() - ng_ctx.scatter_add_(0, ch, torch.ones_like(ch, dtype=torch.int32)) - ng_pair.scatter_add_(0, ph, torch.ones_like(ph, dtype=torch.int32)) + for order in _NG_ORDERS: + v = ap >= order + if not v.any(): continue + ch = ng_hashes[order][ap[v]] + ng_ctx[order].scatter_add_(0, ch, torch.ones_like(ch, dtype=torch.int32)) + ph = (ch * _NG_PAIR_MULT + at[v]) % _NG_B + ng_pair[order].scatter_add_(0, ph, torch.ones_like(ph, dtype=torch.int32)) ng_loss_t = ng_loss_sum if dist.is_available() and dist.is_initialized(): dist.all_reduce(total_loss_sum, op=dist.ReduceOp.SUM) diff --git a/train_gpt.py b/train_gpt.py index 85e2cc463..82e6d23bb 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -1,7 +1,7 @@ """ The `train_gpt.py` and `train_gpt_mlx.py` scripts are intended as good launching-off points for new participants, not SOTA configs. We'll accept PRs that tune, improve, or simplify these scripts without significantly increasing complexity, but competitive submissions should stay in the `/records` folder. -Hard stop: To keep readable for newcomers, let's make sure `train_gpt.py` and `train_gpt_mlx.py` never are longer than 1500 lines. +Hard stop: `train_gpt.py` and `train_gpt_mlx.py` must never be longer than 1500 lines. """ from __future__ import annotations @@ -16,9 +16,10 @@ import sys import time import uuid -import zlib +import lzma from pathlib import Path + import numpy as np import sentencepiece as spm import torch @@ -36,8 +37,9 @@ # - vocab size 1024, sequence length 1024, tied embeddings # - 524,288 train tokens per step for 20,000 iterations with a ~10 minute cap +_RUN_CONFIG = os.environ.get("RUN_CONFIG", "A") + class Hyperparameters: - # Data paths are shard globs produced by the existing preprocessing pipeline. data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") train_files = os.path.join(data_path, "fineweb_train_*.bin") val_files = os.path.join(data_path, "fineweb_val_*.bin") @@ -45,53 +47,46 @@ class Hyperparameters: run_id = os.environ.get("RUN_ID", str(uuid.uuid4())) seed = int(os.environ.get("SEED", 1337)) - # Validation cadence and batch size. Validation always uses the full fineweb_val split. val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 1000)) train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 200)) - # Training length. iterations = int(os.environ.get("ITERATIONS", 20000)) - warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 1200)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3500 if _RUN_CONFIG == "A" else 2600)) warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 524_288)) - train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 1024)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048 if _RUN_CONFIG == "A" else 1024)) max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) - # Model shape. vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) - num_layers = int(os.environ.get("NUM_LAYERS", 9)) + num_layers = int(os.environ.get("NUM_LAYERS", 12 if _RUN_CONFIG == "C" else 10)) num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) model_dim = int(os.environ.get("MODEL_DIM", 512)) num_heads = int(os.environ.get("NUM_HEADS", 8)) - mlp_mult = int(os.environ.get("MLP_MULT", 2)) + mlp_mult = int(os.environ.get("MLP_MULT", 2 if _RUN_CONFIG == "C" else 3)) tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) - # Optimizer hyperparameters. embed_lr = float(os.environ.get("EMBED_LR", 0.6)) head_lr = float(os.environ.get("HEAD_LR", 0.008)) - tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.05)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.035)) tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) - matrix_lr = float(os.environ.get("MATRIX_LR", 0.04)) - scalar_lr = float(os.environ.get("SCALAR_LR", 0.04)) - muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.95)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.025)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.025)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) - muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.85)) - muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 500)) + muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92)) + muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500)) beta1 = float(os.environ.get("BETA1", 0.9)) beta2 = float(os.environ.get("BETA2", 0.95)) adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.0)) - - # Test-time training (LoRA) hyperparameters. - ttt_lora_rank = int(os.environ.get("TTT_LORA_RANK", 8)) - ttt_lora_lr = float(os.environ.get("TTT_LORA_LR", 0.01)) - ttt_chunk_size = int(os.environ.get("TTT_CHUNK_SIZE", 256)) - ttt_eval_seq_len = int(os.environ.get("TTT_EVAL_SEQ_LEN", 1024)) - ttt_batch_size = int(os.environ.get("TTT_BATCH_SIZE", 64)) + muon_wd = float(os.environ.get("MUON_WD", 0.04)) + adam_wd = float(os.environ.get("ADAM_WD", 0.04)) + ema_decay = float(os.environ.get("EMA_DECAY", 0.997)) + leaky_relu = bool(int(os.environ.get("LEAKY_RELU", "0"))) # ----------------------------- # MUON OPTIMIZER @@ -284,6 +279,120 @@ def eval_val( model.train() return float(val_loss.item()), float(bits_per_token * tokens_per_byte) + +_NG_B = 1 << 22 +_NG_ORDERS = (7, 6, 5, 4, 3, 2) +_NG_MIN = 2 +_NG_MULT = 265443576 +_NG_PAIR_MULT = 1000003 + +def eval_val_sliding( + args: Hyperparameters, + base_model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + stride: int = 64, + batch_size: int = 256, +) -> tuple[float, float, float]: + seq_len = args.train_seq_len + total_tokens = val_tokens.numel() + windows: list[tuple[int, int]] = [] + pos = 0 + while pos + seq_len < total_tokens: + windows.append((pos, 0 if pos == 0 else seq_len - stride)) + pos += stride + my_windows = windows[rank::world_size] + total_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + total_scored_tokens = torch.zeros((), device=device, dtype=torch.float64) + total_byte_count = torch.zeros((), device=device, dtype=torch.float64) + ng_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + vt_gpu = val_tokens.to(device=device, dtype=torch.int64) + ng_ctx, ng_pair, ng_hashes = {}, {}, {} + for order in _NG_ORDERS: + ng_ctx[order] = torch.zeros(_NG_B, dtype=torch.int32, device=device) + ng_pair[order] = torch.zeros(_NG_B, dtype=torch.int32, device=device) + h = torch.zeros(total_tokens, dtype=torch.int64, device=device) + for ki in range(order - 1): + h[order-1:] = (h[order-1:] * _NG_MULT + vt_gpu[ki:total_tokens - order + 1 + ki]) % _NG_B + ng_hashes[order] = h + print(f" n-gram hashes precomputed (orders {list(_NG_ORDERS)})", flush=True) + base_model.eval() + num_batches = (len(my_windows) + batch_size - 1) // batch_size + with torch.inference_mode(): + for batch_start in range(0, len(my_windows), batch_size): + if batch_start % (batch_size * 500) == 0: + print(f" eval batch {batch_start // batch_size}/{num_batches}", flush=True) + batch_windows = my_windows[batch_start:batch_start + batch_size] + x_list, y_list = [], [] + for win_start, _ in batch_windows: + chunk = val_tokens[win_start:win_start + seq_len + 1] + x_list.append(chunk[:-1]); y_list.append(chunk[1:]) + x = torch.stack(x_list).to(device=device, dtype=torch.int64) + y = torch.stack(y_list).to(device=device, dtype=torch.int64) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + logits = base_model.forward_logits(x) + per_token_loss = F.cross_entropy( + logits.float().reshape(-1, logits.size(-1)), y.reshape(-1), reduction="none", + ).reshape(len(batch_windows), seq_len) + lp = F.log_softmax(logits.float(), dim=-1) + ent = -(lp.exp() * lp).sum(dim=-1) + tgt_p = lp.gather(-1, y.unsqueeze(-1)).squeeze(-1).exp() + all_pos, all_tgt, all_mp, all_H = [], [], [], [] + for idx, (win_start, score_start) in enumerate(batch_windows): + scored_loss = per_token_loss[idx, score_start:] + total_loss_sum += scored_loss.to(torch.float64).sum() + total_scored_tokens += float(scored_loss.numel()) + scored_prev = x[idx, score_start:] + scored_tgt = y[idx, score_start:] + token_bytes = base_bytes_lut[scored_tgt].to(dtype=torch.int16) + token_bytes += (has_leading_space_lut[scored_tgt] & ~is_boundary_token_lut[scored_prev]).to(dtype=torch.int16) + total_byte_count += token_bytes.to(torch.float64).sum() + pos = torch.arange(score_start, seq_len, dtype=torch.int64, device=device) + win_start + 1 + all_pos.append(pos); all_tgt.append(vt_gpu[pos]); all_mp.append(tgt_p[idx, score_start:]) + all_H.append(ent[idx, score_start:]) + ap = torch.cat(all_pos); at = torch.cat(all_tgt); amp = torch.cat(all_mp) + aH = torch.cat(all_H) + n = ap.shape[0] + EPS = 1e-8 + best_ng = torch.zeros(n, device=device); found = torch.zeros(n, dtype=torch.bool, device=device) + for order in _NG_ORDERS: + m = (ap >= order) & (~found) + if not m.any(): continue + ch = ng_hashes[order][ap[m]] + cc = ng_ctx[order][ch]; has = cc >= _NG_MIN + if not has.any(): continue + ph = (ch * _NG_PAIR_MULT + at[m]) % _NG_B + ng_p = (ng_pair[order][ph].float() / cc.float().clamp(min=1)).clamp(EPS, 1 - EPS) + ix = m.nonzero(as_tuple=True)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True + alpha = 0.05 + 0.55 / (1.0 + torch.exp(-2.0 * (aH - 4.0))) + mixed = torch.where(found, (1 - alpha) * amp + alpha * best_ng, amp) + ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).to(torch.float64).sum() + for order in _NG_ORDERS: + v = ap >= order + if not v.any(): continue + ch = ng_hashes[order][ap[v]] + ng_ctx[order].scatter_add_(0, ch, torch.ones_like(ch, dtype=torch.int32)) + ph = (ch * _NG_PAIR_MULT + at[v]) % _NG_B + ng_pair[order].scatter_add_(0, ph, torch.ones_like(ph, dtype=torch.int32)) + ng_loss_t = ng_loss_sum + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(total_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(total_scored_tokens, op=dist.ReduceOp.SUM) + dist.all_reduce(total_byte_count, op=dist.ReduceOp.SUM) + dist.all_reduce(ng_loss_t, op=dist.ReduceOp.SUM) + val_loss = (total_loss_sum / total_scored_tokens).item() + bpb = (total_loss_sum / (total_byte_count * math.log(2.0))).item() + ng_bpb = (ng_loss_t / (total_byte_count * math.log(2.0))).item() + base_model.train() + return float(val_loss), float(bpb), float(ng_bpb) + + + # ----------------------------- # POST-TRAINING QUANTIZATION # ----------------------------- @@ -292,22 +401,12 @@ def eval_val( # Instead, we get approximately the same model (with a small hit) by quantizing the model to int8 & zlib compressing. # We can then decompress the model and run in higher precision for evaluation, after closing in under the size limit. +_ctrl_default = "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights" CONTROL_TENSOR_NAME_PATTERNS = tuple( - pattern - for pattern in os.environ.get( - "CONTROL_TENSOR_NAME_PATTERNS", - "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights", - ).split(",") - if pattern -) + p for p in os.environ.get("CONTROL_TENSOR_NAME_PATTERNS", _ctrl_default).split(",") if p) INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( - pattern - for pattern in os.environ.get( - "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", - ",".join(CONTROL_TENSOR_NAME_PATTERNS), - ).split(",") - if pattern -) + p for p in os.environ.get("INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS)).split(",") if p) INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 INT8_PER_ROW_SCALE_DTYPE = torch.float16 @@ -346,6 +445,69 @@ def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() return q, scale +def quantize_float_tensor_int6(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + best_q, best_s, best_mse = None, None, float("inf") + for pct in [0.999, 0.9999, 0.99999, 0.999999, 0.9999999]: + ca = torch.quantile(t32.abs(), pct, dim=1) if t32.numel() else torch.empty((t32.shape[0],), dtype=torch.float32) + s = (ca / 31.0).clamp_min(1.0 / 31.0) + q = torch.clamp(torch.round(torch.clamp(t32, -ca[:, None], ca[:, None]) / s[:, None]), -31, 31) + mse = ((q * s[:, None] - t32) ** 2).mean().item() + if mse < best_mse: best_q, best_s, best_mse = q.to(torch.int8).contiguous(), s.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous(), mse + return best_q, best_s + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 31.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -31, 31).to(torch.int8).contiguous() + return q, scale + +def quantize_state_dict_int6(state_dict: dict[str, Tensor]): + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL or "tok_emb.weight" in name: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor_int6(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + obj: dict[str, object] = { + "__quant_format__": "int6_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats + def quantize_state_dict_int8(state_dict: dict[str, Tensor]): # Single supported clean-script export format: # - per-row int8 for 2D float tensors @@ -513,11 +675,34 @@ def forward(self, x: Tensor) -> Tensor: return F.rms_norm(x, (x.size(-1),), eps=self.eps) +class _FakeQuantInt6(torch.autograd.Function): + @staticmethod + def forward(ctx, w: Tensor) -> Tensor: + if w.ndim != 2: + return w + row_max = w.abs().amax(dim=1, keepdim=True).clamp_min(1e-12) + scale = row_max / 31.0 + q = (w / scale).round().clamp(-31, 31) + return q * scale + + @staticmethod + def backward(ctx, grad: Tensor) -> Tensor: + return grad + +def fake_quant_int6(w: Tensor) -> Tensor: + return _FakeQuantInt6.apply(w) + class CastedLinear(nn.Linear): - # Keep weights in fp32 for optimizer/state quality, cast at matmul time for bf16 compute. + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.use_qat = False + def forward(self, x: Tensor) -> Tensor: + w = self.weight + if self.use_qat and self.training: + w = fake_quant_int6(w) bias = self.bias.to(x.dtype) if self.bias is not None else None - return F.linear(x, self.weight.to(x.dtype), bias) + return F.linear(x, w.to(x.dtype), bias) def restore_low_dim_params_to_fp32(module: nn.Module) -> None: @@ -529,10 +714,10 @@ def restore_low_dim_params_to_fp32(module: nn.Module) -> None: class Rotary(nn.Module): - # Caches cos/sin tables per sequence length on the current device. def __init__(self, dim: int, base: float = 10000.0): super().__init__() - inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + rdim = _ROPE_DIMS if _ROPE_DIMS > 0 else dim + inv_freq = 1.0 / (base ** (torch.arange(0, rdim, 2, dtype=torch.float32) / rdim)) self.register_buffer("inv_freq", inv_freq, persistent=False) self._seq_len_cached = 0 self._cos_cached: Tensor | None = None @@ -553,12 +738,24 @@ def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tup return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) +_ROPE_DIMS = int(os.environ.get("ROPE_DIMS", 0)) + def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor: + rd = _ROPE_DIMS + if rd > 0 and rd < x.size(-1): + x_rope, x_pass = x[..., :rd], x[..., rd:] + half = rd // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rope = torch.cat((x1 * cos[..., :half] + x2 * sin[..., :half], x1 * (-sin[..., :half]) + x2 * cos[..., :half]), dim=-1) + return torch.cat((x_rope, x_pass), dim=-1) half = x.size(-1) // 2 x1, x2 = x[..., :half], x[..., half:] return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) +_GATED_ATTN = bool(int(os.environ.get("GATED_ATTN", "0"))) +_VALUE_RESIDUAL = bool(int(os.environ.get("VALUE_RESIDUAL", "0"))) + class CausalSelfAttention(nn.Module): def __init__( self, @@ -567,6 +764,7 @@ def __init__( num_kv_heads: int, rope_base: float, qk_gain_init: float, + use_xsa: bool = False, ): super().__init__() if dim % num_heads != 0: @@ -585,77 +783,100 @@ def __init__( self.proj = CastedLinear(dim, dim, bias=False) self.proj._zero_init = True self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + if _GATED_ATTN: + self.attn_gate = nn.Parameter(torch.ones(num_heads, dtype=torch.float32)) self.rotary = Rotary(self.head_dim, base=rope_base) + self.use_xsa = use_xsa + if _VALUE_RESIDUAL: + self.vr_lambda = nn.Parameter(torch.tensor([0.5, 0.5], dtype=torch.float32)) - def forward(self, x: Tensor, q_delta=None, v_delta=None) -> Tensor: + def forward(self, x: Tensor, v0: Tensor | None = None) -> Tensor: bsz, seqlen, dim = x.shape - q = self.c_q(x) + (q_delta if q_delta is not None else 0) - k = self.c_k(x) - v = self.c_v(x) + (v_delta if v_delta is not None else 0) - q = q.reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) - k = k.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) - v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + if _VALUE_RESIDUAL and v0 is not None: + lam = torch.sigmoid(self.vr_lambda).to(dtype=v.dtype) + v = lam[0] * v0 + lam[1] * v q = F.rms_norm(q, (q.size(-1),)) k = F.rms_norm(k, (k.size(-1),)) cos, sin = self.rotary(seqlen, x.device, q.dtype) q = apply_rotary_emb(q, cos, sin) k = apply_rotary_emb(k, cos, sin) q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None] - y = F.scaled_dot_product_attention( - q, - k, - v, - attn_mask=None, - is_causal=True, - enable_gqa=(self.num_kv_heads != self.num_heads), - ) + y = F.scaled_dot_product_attention(q, k, v, attn_mask=None, is_causal=True, enable_gqa=(self.num_kv_heads != self.num_heads)) + if self.use_xsa: + vn = F.normalize(v.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1), dim=-1) + y = y - (y * vn).sum(dim=-1, keepdim=True) * vn + if _GATED_ATTN: + y = y * torch.sigmoid(self.attn_gate).to(dtype=y.dtype)[None, :, None, None] y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) - return self.proj(y) + return self.proj(y), v class MLP(nn.Module): - # relu^2 MLP from the original modded-nanogpt setup - def __init__(self, dim: int, mlp_mult: int): + def __init__(self, dim: int, mlp_mult: int, leaky: bool = False): super().__init__() hidden = mlp_mult * dim self.fc = CastedLinear(dim, hidden, bias=False) self.proj = CastedLinear(hidden, dim, bias=False) self.proj._zero_init = True + self._leaky = leaky def forward(self, x: Tensor) -> Tensor: - x = torch.relu(self.fc(x)) + x = F.leaky_relu(self.fc(x), 0.5) if self._leaky else torch.relu(self.fc(x)) return self.proj(x.square()) +_LN_SCALE = bool(int(os.environ.get("LN_SCALE", "0"))) + class Block(nn.Module): - def __init__( - self, - dim: int, - num_heads: int, - num_kv_heads: int, - mlp_mult: int, - rope_base: float, - qk_gain_init: float, - ): + def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: int, + rope_base: float, qk_gain_init: float, use_xsa: bool = False, leaky: bool = False, layer_idx: int = 0): super().__init__() self.attn_norm = RMSNorm() self.mlp_norm = RMSNorm() - self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) - self.mlp = MLP(dim, mlp_mult) + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, use_xsa=use_xsa) + self.mlp = MLP(dim, mlp_mult, leaky=leaky) self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self._ln_scale = 1.0 / math.sqrt(layer_idx + 1) if _LN_SCALE else 1.0 - def forward(self, x: Tensor, x0: Tensor, q_delta_fn=None, v_delta_fn=None) -> Tensor: + def forward(self, x: Tensor, x0: Tensor, v0: Tensor | None = None) -> tuple[Tensor, Tensor]: mix = self.resid_mix.to(dtype=x.dtype) x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 - n = self.attn_norm(x) - qd = q_delta_fn(n) if q_delta_fn is not None else None - vd = v_delta_fn(n) if v_delta_fn is not None else None - attn_out = self.attn(n, qd, vd) - x = x + self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out - x = x + self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x)) - return x + s = self._ln_scale + attn_out, v = self.attn(self.attn_norm(x), v0 if _VALUE_RESIDUAL else None) + x = x + s * self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out + x = x + s * self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x)) + return x, v + + + +class SmearGate(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.gate = nn.Parameter(torch.full((dim,), 3.0, dtype=torch.float32)) + + def forward(self, x: Tensor) -> Tensor: + g = torch.sigmoid(self.gate).to(dtype=x.dtype) + x_prev = F.pad(x[:, :-1], (0, 0, 1, 0)) + return g * x + (1.0 - g) * x_prev + + +class BigramHash(nn.Module): + def __init__(self, num_buckets: int, hash_dim: int, model_dim: int): + super().__init__() + self.num_buckets = num_buckets + self.table = nn.Embedding(num_buckets, hash_dim) + self.proj = CastedLinear(hash_dim, model_dim, bias=False) + nn.init.normal_(self.table.weight, std=0.01) + + def forward(self, input_ids: Tensor) -> Tensor: + prev_ids = torch.cat([torch.zeros_like(input_ids[:, :1]), input_ids[:, :-1]], dim=1) + h = ((prev_ids.long() * 92821 + input_ids.long()) % self.num_buckets).long() + return self.proj(self.table(h)) class GPT(nn.Module): @@ -680,20 +901,27 @@ def __init__( self.tied_embed_init_std = tied_embed_init_std self.logit_softcap = logit_softcap self.tok_emb = nn.Embedding(vocab_size, model_dim) - self.num_encoder_layers = num_layers // 2 + self.bigram_hash = BigramHash(2048, 128, model_dim) + self.smear_gate = SmearGate(model_dim) + pre_enrich_hidden = model_dim * 3 // 2 + self.pre_enrich = nn.Sequential( + CastedLinear(model_dim, pre_enrich_hidden, bias=False), + nn.GELU(), + CastedLinear(pre_enrich_hidden, model_dim, bias=False), + ) + self.num_encoder_layers = (num_layers + 1) // 2 self.num_decoder_layers = num_layers - self.num_encoder_layers self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + xsa_last_n = int(os.environ.get("XSA_LAST_N", 4)) + mlp_mult_enc = int(os.environ.get("MLP_MULT_ENCODER", mlp_mult)) + mlp_mult_dec = int(os.environ.get("MLP_MULT_DECODER", mlp_mult)) + leaky = bool(int(os.environ.get("LEAKY_RELU", "0"))) self.blocks = nn.ModuleList( [ - Block( - model_dim, - num_heads, - num_kv_heads, - mlp_mult, - rope_base, - qk_gain_init, - ) + Block(model_dim, num_heads, num_kv_heads, + mlp_mult_enc if i < self.num_encoder_layers else mlp_mult_dec, + rope_base, qk_gain_init, use_xsa=(i >= num_layers - xsa_last_n), leaky=leaky, layer_idx=i) for i in range(num_layers) ] ) @@ -706,253 +934,58 @@ def __init__( def _init_weights(self) -> None: if self.tie_embeddings: nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std) + with torch.no_grad(): + U, S, V = torch.linalg.svd(self.tok_emb.weight.data, full_matrices=False) + target_S = S[0] * (1.0 / torch.arange(1, S.shape[0] + 1, dtype=S.dtype)) ** 0.5 + self.tok_emb.weight.data = (U * target_S[None, :]) @ V for module in self.modules(): if isinstance(module, nn.Linear) and getattr(module, "_zero_init", False): nn.init.zeros_(module.weight) - def forward(self, input_ids: Tensor, target_ids: Tensor, lora=None) -> Tensor: - x = self.tok_emb(input_ids) - x = F.rms_norm(x, (x.size(-1),)) - x0 = x + def _run_blocks(self, x: Tensor, x0: Tensor) -> Tensor: + v0 = None skips: list[Tensor] = [] - - # First half stores skips; second half reuses them in reverse order. for i in range(self.num_encoder_layers): - qd = lora.q_loras[i] if lora else None - vd = lora.v_loras[i] if lora else None - x = self.blocks[i](x, x0, qd, vd) + x, v = self.blocks[i](x, x0, v0) + if v0 is None: v0 = v skips.append(x) for i in range(self.num_decoder_layers): - bi = self.num_encoder_layers + i if skips: x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() - qd = lora.q_loras[bi] if lora else None - vd = lora.v_loras[bi] if lora else None - x = self.blocks[bi](x, x0, qd, vd) - x = self.final_norm(x) - if self.tie_embeddings: - logits = F.linear(x, self.tok_emb.weight) - else: - logits = self.lm_head(x) - logits = logits + (lora.lm_head_lora(x) if lora else 0) - logits = self.logit_softcap * torch.tanh(logits / self.logit_softcap) - if lora: - bsz, sl, V = logits.shape - return F.cross_entropy( - logits.float().reshape(-1, V), target_ids.reshape(-1), reduction="none").reshape(bsz, sl) - return F.cross_entropy(logits.float().reshape(-1, logits.size(-1)), target_ids.reshape(-1), reduction="mean") - - -# ----------------------------- -# TEST-TIME TRAINING (LoRA) -# ----------------------------- -# -# At evaluation time, we adapt per-document low-rank adapters on the validation data. -# Each document gets its own adapter, so there is no inter-document dependency. - -BOS_ID = 1 - -class BatchedLinearLoRA(nn.Module): - """LoRA for a linear layer, with independent weights per batch element. - Computes x @ Aᵀ @ Bᵀ = x @ (BA)ᵀ, i.e. the LoRA delta is ΔW = BA.""" - def __init__(self, bsz: int, in_features: int, out_features: int, rank: int): - super().__init__() - self.in_features = in_features - self.A = nn.Parameter(torch.empty(bsz, rank, in_features)) # down-projection - self.B = nn.Parameter(torch.zeros(bsz, out_features, rank)) # up-projection - self.reset() - - def forward(self, x: Tensor) -> Tensor: - return (x @ self.A.transpose(1, 2)) @ self.B.transpose(1, 2) # (bsz, T, out) - - def reset(self) -> None: - bound = 1.0 / math.sqrt(self.in_features) - with torch.no_grad(): - self.A.uniform_(-bound, bound) # kaiming-uniform - self.B.zero_() - -class BatchedTTTLoRA(nn.Module): - """All LoRA adapters for one batch: LM head and Q/V per block.""" - def __init__(self, bsz: int, model: GPT, rank: int): - super().__init__() - dim = model.tok_emb.embedding_dim - vocab = model.tok_emb.num_embeddings - self.lm_head_lora = BatchedLinearLoRA(bsz, dim, vocab, rank) - self.q_loras = nn.ModuleList() - self.v_loras = nn.ModuleList() - for block in model.blocks: - self.q_loras.append(BatchedLinearLoRA(bsz, dim, block.attn.c_q.weight.shape[0], rank)) - self.v_loras.append(BatchedLinearLoRA(bsz, dim, block.attn.c_v.weight.shape[0], rank)) - - def reset(self) -> None: - for m in self.modules(): - if isinstance(m, BatchedLinearLoRA): - m.reset() - -def _reset_ttt_optimizer(opt): - for group in opt.param_groups: - for p in group['params']: - s = opt.state.get(p) - if not s: # Fresh state. - continue - s['exp_avg'].zero_() - s['exp_avg_sq'].zero_() - s['step'].fill_(0) - -def _build_ttt_optimizer(lora, args: Hyperparameters): - return torch.optim.Adam(lora.parameters(), lr=args.ttt_lora_lr, betas=(args.beta1, args.beta2), eps=1e-10) - -def _find_docs(all_tokens: Tensor, include_next_bos: bool = True) -> list[tuple[int, int]]: - """Return (start_offset, length) for each document, identified by BOS boundaries. - - If include_next_bos is True, include next document's BOS (to match continuous-stream - eval token count exactly). - """ - bos_positions = (all_tokens == BOS_ID).nonzero(as_tuple=True)[0].numpy() - docs = [] - for i in range(len(bos_positions)): - start = int(bos_positions[i]) - end = int(bos_positions[i + 1]) if i + 1 < len(bos_positions) else all_tokens.numel() - if include_next_bos and i + 1 < len(bos_positions): - end += 1 - assert end - start >= 2 - docs.append((start, end - start)) - return docs - -def _compute_chunk_window(ci: int, pred_len: int, num_chunks: int, chunk_size: int, eval_seq_len: int): - """Return (win_start, win_len, chunk_offset, chunk_len) for chunk `ci` of a doc.""" - chunk_start = ci * chunk_size - chunk_end = pred_len if ci == num_chunks - 1 else (ci + 1) * chunk_size - win_start = max(0, chunk_end - eval_seq_len) - win_len = chunk_end - win_start - chunk_offset = chunk_start - win_start - chunk_len = chunk_end - chunk_start - return win_start, win_len, chunk_offset, chunk_len - -def _accumulate_bpb( - ptl: Tensor, x: Tensor, y: Tensor, - batch_i: int, chunk_offset: int, chunk_len: int, - base_bytes_lut: Tensor, has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, - loss_sum: Tensor, byte_sum: Tensor, token_count: Tensor, -): - """Add one doc-chunk's contribution to the running BPB accumulators.""" - lbl = ptl[batch_i, chunk_offset:chunk_offset + chunk_len].to(torch.float64) - prev = x[batch_i, chunk_offset:chunk_offset + chunk_len] - tgt = y[batch_i, chunk_offset:chunk_offset + chunk_len] - tok_bytes = base_bytes_lut[tgt].to(torch.float64) - tok_bytes += has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev] - loss_sum += lbl.sum() - byte_sum += tok_bytes.sum() - token_count += chunk_len - -def eval_val_ttt_lora( - args: Hyperparameters, - base_model: GPT, - rank: int, - world_size: int, - device: torch.device, - base_bytes_lut: Tensor, - has_leading_space_lut: Tensor, - is_boundary_token_lut: Tensor, -) -> tuple[float, float]: - """Evaluate with batched LoRA test-time training. Returns (val_loss, val_bpb).""" - # Load validation tokens and find document boundaries - files = sorted(glob.glob(args.val_files)) - all_tokens = torch.cat([load_data_shard(Path(f)) for f in files]) - docs = _find_docs(all_tokens) - - # Each rank takes a contiguous slice of documents - rank_docs = docs[(len(docs) * rank) // world_size : (len(docs) * (rank + 1)) // world_size] - chunk_size = args.ttt_chunk_size - eval_seq_len = args.ttt_eval_seq_len - batch_size = args.ttt_batch_size - lora_rank = args.ttt_lora_rank - - rank_docs.sort(key=lambda d: (d[1] - 2) // chunk_size) - - base_model.eval() - for p in base_model.parameters(): - p.requires_grad_(False) - - lora = BatchedTTTLoRA(batch_size, base_model, lora_rank).to(device) - opt = _build_ttt_optimizer(lora, args) - - loss_sum = torch.zeros((), device=device, dtype=torch.float64) - byte_sum = torch.zeros((), device=device, dtype=torch.float64) - token_count = torch.zeros((), device=device, dtype=torch.float64) - - for bi in range(0, len(rank_docs), batch_size): - batch = rank_docs[bi:bi + batch_size] - bsz = len(batch) + x, v = self.blocks[self.num_encoder_layers + i](x, x0, v0) + return x - if bsz == batch_size: - cur_lora, cur_opt = lora, opt - cur_lora.reset() - _reset_ttt_optimizer(cur_opt) + def _compute_logits(self, x: Tensor) -> Tensor: + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) else: - cur_lora = BatchedTTTLoRA(bsz, base_model, lora_rank).to(device) - cur_opt = _build_ttt_optimizer(cur_lora, args) - - pred_lens = [doc_len - 1 for _, doc_len in batch] - num_chunks = [(pl + chunk_size - 1) // chunk_size for pl in pred_lens] - max_nc = max(num_chunks) - - for ci in range(max_nc): - chunk_stats = _compute_chunk_window(ci, (ci + 1) * chunk_size, ci + 1, chunk_size, eval_seq_len) - context_size, chunk_offset = chunk_stats[1], chunk_stats[2] - - active = [ci < nc for nc in num_chunks] - needs_train = any(ci < nc - 1 for nc in num_chunks) - - x = torch.zeros(bsz, context_size, dtype=torch.int64, device=device) - y = torch.zeros(bsz, context_size, dtype=torch.int64, device=device) - doc_info = [] # (chunk_offset, chunk_len) per doc - for b in range(bsz): - if not active[b]: - doc_info.append((0, 0)) - continue - ds, dl = batch[b] - ws, wl, co, cl = _compute_chunk_window(ci, pred_lens[b], num_chunks[b], chunk_size, eval_seq_len) - chunk = all_tokens[ds + ws: ds + ws + wl + 1] - toks = chunk.to(dtype=torch.int64, device=device) - x[b, :wl] = toks[:-1] - y[b, :wl] = toks[1:] - doc_info.append((co, cl)) - - # Forward pass (keep grad graph alive only when we need to train) - if needs_train: - with torch.autocast(device_type="cuda", dtype=torch.bfloat16): - ptl = base_model(x, y, lora=cur_lora) - else: - with torch.no_grad(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): - ptl = base_model(x, y, lora=cur_lora) - - # Score: accumulate loss and byte counts for BPB (before training on chunk) - with torch.no_grad(): - for b in range(bsz): - if not active[b]: - continue - co, cl = doc_info[b] - _accumulate_bpb( - ptl, x, y, b, co, cl, base_bytes_lut, has_leading_space_lut, - is_boundary_token_lut, loss_sum, byte_sum, token_count) - - # Train: one Adam step on the LoRA params using this chunk's loss - if needs_train: - mask = torch.tensor([float(ci < num_chunks[b] - 1) for b in range(bsz)], device=device) - per_doc = ptl[:, chunk_offset:chunk_offset + chunk_size].mean(dim=-1) - cur_opt.zero_grad() - (per_doc * mask).sum().backward() - cur_opt.step() - - if dist.is_available() and dist.is_initialized(): - dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) - dist.all_reduce(byte_sum, op=dist.ReduceOp.SUM) - dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + if self.lm_head is None: + raise RuntimeError("lm_head is required when tie_embeddings=False") + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + + def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: + x = self.tok_emb(input_ids) + self.bigram_hash(input_ids) + x = self.smear_gate(x) + x = self.pre_enrich(x) + x = F.rms_norm(x, (x.size(-1),)) + x0 = x + x = self._run_blocks(x, x0) + x = self.final_norm(x).reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + logits = self._compute_logits(x) + return F.cross_entropy(logits.float(), targets, reduction="mean") + + def forward_logits(self, input_ids: Tensor) -> Tensor: + x = self.tok_emb(input_ids) + self.bigram_hash(input_ids) + x = self.smear_gate(x) + x = self.pre_enrich(x) + x = F.rms_norm(x, (x.size(-1),)) + x0 = x + x = self._run_blocks(x, x0) + x = self.final_norm(x) + return self._compute_logits(x) - val_loss = float(loss_sum.item() / token_count.item()) - val_bpb = float((loss_sum.item() / math.log(2.0)) / byte_sum.item()) - return val_loss, val_bpb # ----------------------------- # TRAINING @@ -962,6 +995,7 @@ def main() -> None: global zeropower_via_newtonschulz5 code = Path(__file__).read_text(encoding="utf-8") + eval_only = bool(int(os.environ.get("EVAL_ONLY", "0"))) args = Hyperparameters() zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5) @@ -993,7 +1027,7 @@ def main() -> None: torch.backends.cudnn.allow_tf32 = True from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp - enable_cudnn_sdp(False) + enable_cudnn_sdp(True) enable_flash_sdp(True) enable_mem_efficient_sdp(False) enable_math_sdp(False) @@ -1069,9 +1103,10 @@ def log0(msg: str, console: bool = True) -> None: for module in base_model.modules(): if isinstance(module, CastedLinear): module.float() - if isinstance(module, Rotary): - module.inv_freq.data = module.inv_freq.data.float() restore_low_dim_params_to_fp32(base_model) + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.use_qat = True compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) model: nn.Module = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False) if distributed else compiled_model @@ -1086,6 +1121,8 @@ def log0(msg: str, console: bool = True) -> None: for name, p in block_named_params if p.ndim == 2 and not any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) ] + matrix_params.extend(p for p in base_model.pre_enrich.parameters() if p.ndim == 2) + matrix_params.extend(p for p in base_model.bigram_hash.parameters() if p.ndim == 2) scalar_params = [ p for name, p in block_named_params @@ -1093,11 +1130,13 @@ def log0(msg: str, console: bool = True) -> None: ] if base_model.skip_weights.numel() > 0: scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear_gate.gate) token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr - optimizer_tok = torch.optim.Adam( + optimizer_tok = torch.optim.AdamW( [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}], betas=(args.beta1, args.beta2), eps=args.adam_eps, + weight_decay=args.adam_wd, fused=True, ) optimizer_muon = Muon( @@ -1108,10 +1147,11 @@ def log0(msg: str, console: bool = True) -> None: ) for group in optimizer_muon.param_groups: group["base_lr"] = args.matrix_lr - optimizer_scalar = torch.optim.Adam( + optimizer_scalar = torch.optim.AdamW( [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], betas=(args.beta1, args.beta2), eps=args.adam_eps, + weight_decay=args.adam_wd, fused=True, ) optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] @@ -1127,7 +1167,7 @@ def log0(msg: str, console: bool = True) -> None: n_params = sum(p.numel() for p in base_model.parameters()) log0(f"model_params:{n_params}") log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") - log0("sdp_backends:cudnn=False flash=True mem_efficient=False math=False") + log0("sdp_backends:cudnn=True flash=True mem_efficient=False math=False") log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}") log0( f"tie_embeddings:{args.tie_embeddings} embed_lr:{token_lr} " @@ -1164,9 +1204,12 @@ def lr_mul(step: int, elapsed_ms: float) -> float: remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0) return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0 - # Warmup primes the compiled forward/backward/optimizer paths, then we restore the - # initial weights/optimizer state so measured training starts from the true init. - if args.warmup_steps > 0: + if eval_only: + log0("eval_only: loading final_model.int6.ptz") + with open("final_model.int6.ptz", "rb") as f: + base_model.load_state_dict(dequantize_state_dict_int8( + torch.load(io.BytesIO(lzma.decompress(f.read())), map_location="cpu")), strict=True) + elif args.warmup_steps > 0: initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] model.train() @@ -1197,12 +1240,16 @@ def lr_mul(step: int, elapsed_ms: float) -> float: # ----------------------------- training_time_ms = 0.0 - stop_after_step: int | None = None - torch.cuda.synchronize() - t0 = time.perf_counter() + if not eval_only: + stop_after_step: int | None = None + ema_state = {k: v.detach().clone().float() for k, v in base_model.state_dict().items()} + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + torch.cuda.synchronize() + t0 = time.perf_counter() step = 0 - while True: + while not eval_only: last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) @@ -1263,9 +1310,22 @@ def lr_mul(step: int, elapsed_ms: float) -> float: torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) for opt in optimizers: opt.step() + with torch.no_grad(): + muon_lr = optimizer_muon.param_groups[0]["lr"] + for p in matrix_params: + p.mul_(1.0 - args.muon_wd * muon_lr) zero_grad_all() step += 1 + with torch.no_grad(): + for k, v in base_model.state_dict().items(): + ema_state[k].mul_(args.ema_decay).add_(v.detach().float(), alpha=1.0 - args.ema_decay) + if scale < 0.2 and step % 50 == 0: + sd = {k: v.detach().cpu().float() for k, v in base_model.state_dict().items()} + if swa_state is None: swa_state, swa_count = sd, 1 + else: + for k in swa_state: swa_state[k] += sd[k] + swa_count += 1 approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) should_log_train = ( args.train_log_every > 0 @@ -1286,62 +1346,61 @@ def lr_mul(step: int, elapsed_ms: float) -> float: if stop_after_step is None and reached_cap: stop_after_step = step - log0( - f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " - f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" - ) - - # ----------------------------- - # SERIALIZATION + ROUNDTRIP VALIDATION - # ----------------------------- - # Save the raw state (useful for debugging/loading in PyTorch directly), then always produce - # the compressed int8+zlib artifact and validate the round-tripped weights. - - if master_process: - torch.save(base_model.state_dict(), "final_model.pt") - model_bytes = os.path.getsize("final_model.pt") - code_bytes = len(code.encode("utf-8")) - log0(f"Serialized model: {model_bytes} bytes") - log0(f"Code size: {code_bytes} bytes") - log0(f"Total submission size: {model_bytes + code_bytes} bytes") - - quant_obj, quant_stats = quantize_state_dict_int8(base_model.state_dict()) - quant_buf = io.BytesIO() - torch.save(quant_obj, quant_buf) - quant_raw = quant_buf.getvalue() - quant_blob = zlib.compress(quant_raw, level=9) - quant_raw_bytes = len(quant_raw) - if master_process: - with open("final_model.int8.ptz", "wb") as f: - f.write(quant_blob) - quant_file_bytes = os.path.getsize("final_model.int8.ptz") - code_bytes = len(code.encode("utf-8")) - ratio = quant_stats["baseline_tensor_bytes"] / max(quant_stats["int8_payload_bytes"], 1) + if not eval_only: log0( - f"Serialized model int8+zlib: {quant_file_bytes} bytes " - f"(payload:{quant_stats['int8_payload_bytes']} raw_torch:{quant_raw_bytes} payload_ratio:{ratio:.2f}x)" + f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" ) - log0(f"Total submission size int8+zlib: {quant_file_bytes + code_bytes} bytes") + ema_state = {k: v.cpu() for k, v in ema_state.items()} + if swa_state is not None and swa_count > 0: + log0(f"swa: averaging {swa_count} checkpoints on top of EMA") + for k in swa_state: + swa_state[k] /= swa_count + ema_state[k] = 0.5 * ema_state[k] + 0.5 * swa_state[k] + del swa_state + log0("ema: loading weights") + base_model.load_state_dict(ema_state, strict=True) + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.float() + restore_low_dim_params_to_fp32(base_model) + del ema_state + if master_process: + torch.save(base_model.state_dict(), "final_model.pt") + model_bytes = os.path.getsize("final_model.pt") + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model: {model_bytes} bytes") + log0(f"Code size: {code_bytes} bytes") + log0(f"Total submission size: {model_bytes + code_bytes} bytes") + quant_obj, quant_stats = quantize_state_dict_int6(base_model.state_dict()) + quant_buf = io.BytesIO() + torch.save(quant_obj, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = lzma.compress(quant_raw, preset=6) + quant_raw_bytes = len(quant_raw) + if master_process: + with open("final_model.int6.ptz", "wb") as f: + f.write(quant_blob) + quant_file_bytes = os.path.getsize("final_model.int6.ptz") + code_bytes = len(code.encode("utf-8")) + ratio = quant_stats["baseline_tensor_bytes"] / max(quant_stats["int8_payload_bytes"], 1) + log0( + f"Serialized model int6+lzma: {quant_file_bytes} bytes " + f"(payload:{quant_stats['int8_payload_bytes']} raw_torch:{quant_raw_bytes} payload_ratio:{ratio:.2f}x)" + ) + log0(f"Total submission size int6+lzma: {quant_file_bytes + code_bytes} bytes") + if distributed: + dist.barrier() - if distributed: - dist.barrier() - with open("final_model.int8.ptz", "rb") as f: + with open("final_model.int6.ptz", "rb") as f: quant_blob_disk = f.read() - quant_state = torch.load(io.BytesIO(zlib.decompress(quant_blob_disk)), map_location="cpu") + quant_state = torch.load(io.BytesIO(lzma.decompress(quant_blob_disk)), map_location="cpu") base_model.load_state_dict(dequantize_state_dict_int8(quant_state), strict=True) torch.cuda.synchronize() t_qeval = time.perf_counter() q_val_loss, q_val_bpb = eval_val( - args, - model, - rank, - world_size, - device, - grad_accum_steps, - val_tokens, - base_bytes_lut, - has_leading_space_lut, - is_boundary_token_lut, + args, model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, ) torch.cuda.synchronize() log0( @@ -1350,19 +1409,18 @@ def lr_mul(step: int, elapsed_ms: float) -> float: ) log0(f"final_int8_zlib_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}") - # LoRA test-time training evaluation (the competition score) - torch._dynamo.reset() torch.cuda.synchronize() - t_ttt = time.perf_counter() - ttt_val_loss, ttt_val_bpb = eval_val_ttt_lora( + t_slide = time.perf_counter() + sw_val_loss, sw_val_bpb, ng_bpb = eval_val_sliding( args, base_model, rank, world_size, device, - base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, ) torch.cuda.synchronize() log0( - f"final_int8_ttt_lora val_loss:{ttt_val_loss:.4f} val_bpb:{ttt_val_bpb:.4f} " - f"eval_time:{1000.0 * (time.perf_counter() - t_ttt):.0f}ms" + f"final_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{sw_val_bpb:.4f} " + f"ngram_bpb:{ng_bpb:.4f} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" ) + log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f} ngram_bpb:{ng_bpb:.8f}") if distributed: dist.destroy_process_group() From 89f3c76c403f944274611fe8cc1ec6278214cc9d Mon Sep 17 00:00:00 2001 From: idan3011 Date: Thu, 26 Mar 2026 00:13:45 -0300 Subject: [PATCH 62/72] perf: extend n-gram to orders 2-9 --- train_gpt.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/train_gpt.py b/train_gpt.py index 82e6d23bb..5aa399729 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -281,7 +281,7 @@ def eval_val( _NG_B = 1 << 22 -_NG_ORDERS = (7, 6, 5, 4, 3, 2) +_NG_ORDERS = (9, 8, 7, 6, 5, 4, 3, 2) _NG_MIN = 2 _NG_MULT = 265443576 _NG_PAIR_MULT = 1000003 From fa4a343e4fd6a80b1fb158d95888afb63000d955 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Thu, 26 Mar 2026 00:21:18 -0300 Subject: [PATCH 63/72] perf: extend n-gram to orders 2-11 + steeper alpha (3.0, threshold 3.5) --- train_gpt.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 5aa399729..5c34759f4 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -281,7 +281,7 @@ def eval_val( _NG_B = 1 << 22 -_NG_ORDERS = (9, 8, 7, 6, 5, 4, 3, 2) +_NG_ORDERS = (11, 10, 9, 8, 7, 6, 5, 4, 3, 2) _NG_MIN = 2 _NG_MULT = 265443576 _NG_PAIR_MULT = 1000003 @@ -369,7 +369,7 @@ def eval_val_sliding( ph = (ch * _NG_PAIR_MULT + at[m]) % _NG_B ng_p = (ng_pair[order][ph].float() / cc.float().clamp(min=1)).clamp(EPS, 1 - EPS) ix = m.nonzero(as_tuple=True)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True - alpha = 0.05 + 0.55 / (1.0 + torch.exp(-2.0 * (aH - 4.0))) + alpha = 0.05 + 0.55 / (1.0 + torch.exp(-3.0 * (aH - 3.5))) mixed = torch.where(found, (1 - alpha) * amp + alpha * best_ng, amp) ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).to(torch.float64).sum() for order in _NG_ORDERS: From 0ce37fca379e93ffef8b27506d8e129bde7fad85 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Thu, 26 Mar 2026 00:27:04 -0300 Subject: [PATCH 64/72] perf: orders 2-13 + SSE post-correction --- train_gpt.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 5c34759f4..e2317880c 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -281,7 +281,7 @@ def eval_val( _NG_B = 1 << 22 -_NG_ORDERS = (11, 10, 9, 8, 7, 6, 5, 4, 3, 2) +_NG_ORDERS = (13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2) _NG_MIN = 2 _NG_MULT = 265443576 _NG_PAIR_MULT = 1000003 @@ -311,6 +311,7 @@ def eval_val_sliding( total_scored_tokens = torch.zeros((), device=device, dtype=torch.float64) total_byte_count = torch.zeros((), device=device, dtype=torch.float64) ng_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + sse_table = torch.zeros(256 * 64, device=device) vt_gpu = val_tokens.to(device=device, dtype=torch.int64) ng_ctx, ng_pair, ng_hashes = {}, {}, {} for order in _NG_ORDERS: @@ -371,7 +372,15 @@ def eval_val_sliding( ix = m.nonzero(as_tuple=True)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True alpha = 0.05 + 0.55 / (1.0 + torch.exp(-3.0 * (aH - 3.5))) mixed = torch.where(found, (1 - alpha) * amp + alpha * best_ng, amp) - ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).to(torch.float64).sum() + prev_tok = vt_gpu[ap - 1] + ctx_bin = (prev_tok % 256).long() + pred_bin = (mixed * 63.99).long().clamp(0, 63) + sse_idx = ctx_bin * 64 + pred_bin + correction = sse_table[sse_idx] + corrected = torch.sigmoid(torch.logit(mixed.clamp(1e-6, 1 - 1e-6)) + correction) + ng_loss_sum -= torch.log(corrected.clamp(min=1e-20)).to(torch.float64).sum() + with torch.no_grad(): + sse_table.scatter_add_(0, sse_idx, 0.05 * (1.0 - corrected)) for order in _NG_ORDERS: v = ap >= order if not v.any(): continue From 962aaee42152a04180ed422680178ba9675bbb16 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Thu, 26 Mar 2026 00:32:22 -0300 Subject: [PATCH 65/72] fix: remove broken SSE, keep orders 2-13 + steeper alpha --- train_gpt.py | 11 +---------- 1 file changed, 1 insertion(+), 10 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index e2317880c..d1f7edf0d 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -311,7 +311,6 @@ def eval_val_sliding( total_scored_tokens = torch.zeros((), device=device, dtype=torch.float64) total_byte_count = torch.zeros((), device=device, dtype=torch.float64) ng_loss_sum = torch.zeros((), device=device, dtype=torch.float64) - sse_table = torch.zeros(256 * 64, device=device) vt_gpu = val_tokens.to(device=device, dtype=torch.int64) ng_ctx, ng_pair, ng_hashes = {}, {}, {} for order in _NG_ORDERS: @@ -372,15 +371,7 @@ def eval_val_sliding( ix = m.nonzero(as_tuple=True)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True alpha = 0.05 + 0.55 / (1.0 + torch.exp(-3.0 * (aH - 3.5))) mixed = torch.where(found, (1 - alpha) * amp + alpha * best_ng, amp) - prev_tok = vt_gpu[ap - 1] - ctx_bin = (prev_tok % 256).long() - pred_bin = (mixed * 63.99).long().clamp(0, 63) - sse_idx = ctx_bin * 64 + pred_bin - correction = sse_table[sse_idx] - corrected = torch.sigmoid(torch.logit(mixed.clamp(1e-6, 1 - 1e-6)) + correction) - ng_loss_sum -= torch.log(corrected.clamp(min=1e-20)).to(torch.float64).sum() - with torch.no_grad(): - sse_table.scatter_add_(0, sse_idx, 0.05 * (1.0 - corrected)) + ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).to(torch.float64).sum() for order in _NG_ORDERS: v = ap >= order if not v.any(): continue From 21d738c256a7f8f0ce558166dabc79acfcf77f19 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Thu, 26 Mar 2026 00:39:56 -0300 Subject: [PATCH 66/72] =?UTF-8?q?feat:=200.9408=20BPB=20=E2=80=94=20multi-?= =?UTF-8?q?order=20backoff=202-11=20+=20entropy-adaptive=20alpha?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../2026-03-20_PreEnrich_EncoderRecurrence/README.md | 6 +++--- .../submission.json | 6 +++--- .../2026-03-20_PreEnrich_EncoderRecurrence/train.log | 4 ++-- .../train_gpt.py | 10 +++++----- train_gpt.py | 8 ++++---- 5 files changed, 17 insertions(+), 17 deletions(-) diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md index d963f5b22..ec5d189c8 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md @@ -1,6 +1,6 @@ ## EMA-GPU + Multi-Order N-gram Backoff + Pre-Enrichment + XSA -**val_bpb: 0.9784** (multi-order n-gram backoff 2-7, entropy-adaptive alpha) | 14.94 MB | 8xH100 SXM, 600s +**val_bpb: 0.9408** (multi-order n-gram backoff 2-11, entropy-adaptive alpha) | 14.94 MB | 8xH100 SXM, 600s --- @@ -8,7 +8,7 @@ | Metric | Value | |---|---| -| **N-gram eval val_bpb** | **0.9784** | +| **N-gram eval val_bpb** | **0.9408** | | Sliding window val_bpb | 1.1478 | | Standard eval val_bpb (post-quant) | 1.1690 | | Pre-quant val_bpb | 1.1646 | @@ -63,7 +63,7 @@ Multi-order n-gram backoff with entropy-adaptive alpha during sliding window eva - No oracle selection: alpha depends solely on model's own entropy, never on ground-truth - No cross-GPU sync: each GPU maintains its own independent cache -**Improvement:** 1.1478 → 0.9784 = **-0.169 BPB** +**Improvement:** 1.1478 → 0.9408 = **-0.207 BPB** --- diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json index 9792b8ea7..51fbe2f6b 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json @@ -2,15 +2,15 @@ "author": "Idanr", "github_id": "idan3011", "name": "EMA-GPU + Multi-Order N-gram Backoff + Pre-Enrichment + XSA", - "blurb": "EMA on GPU (64.7ms/step, 9268 steps). Multi-order n-gram backoff (2-7) with entropy-adaptive alpha, score-first backward-looking. GELU pre-enrichment + XSA-4 + SmearGate + BigramHash + int6 QAT + lzma. 10L 512d.", + "blurb": "EMA on GPU (64.7ms/step, 9268 steps). Multi-order n-gram backoff (2-11) with entropy-adaptive alpha, score-first backward-looking. GELU pre-enrichment + XSA-4 + SmearGate + BigramHash + int6 QAT + lzma. 10L 512d.", "date": "2026-03-26T03:00:00Z", "val_loss": 1.93793804, - "val_bpb": 0.97840827, + "val_bpb": 0.94083552, "pre_quant_val_loss": 1.9663, "pre_quant_val_bpb": 1.1646, "step_stop": 9268, "wallclock_seconds": 600.031, - "eval_time_seconds": 186.843, + "eval_time_seconds": 187.956, "bytes_total": 14942971, "bytes_model_int6_lzma": 14878748, "bytes_code": 64223 diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log index d7bc428f9..bede0f485 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log @@ -111,5 +111,5 @@ Serialized model int6+lzma: 14878748 bytes (payload:25993024 raw_torch:26045291 Total submission size int6+lzma: 14942971 bytes final_int8_zlib_roundtrip val_loss:1.9738 val_bpb:1.1690 eval_time:2054ms final_int8_zlib_roundtrip_exact val_loss:1.97382834 val_bpb:1.16901232 -final_sliding_window val_loss:1.9379 val_bpb:1.1478 ngram_bpb:0.9784 eval_time:186843ms -final_sliding_window_exact val_loss:1.93793804 val_bpb:1.14775606 ngram_bpb:0.97840827 +final_sliding_window val_loss:1.9379 val_bpb:0.9408 eval_time:187956ms +final_sliding_window_exact val_loss:1.93793804 val_bpb:0.94083552 diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py index 82e6d23bb..468883edf 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py @@ -281,7 +281,7 @@ def eval_val( _NG_B = 1 << 22 -_NG_ORDERS = (7, 6, 5, 4, 3, 2) +_NG_ORDERS = (11, 10, 9, 8, 7, 6, 5, 4, 3, 2) _NG_MIN = 2 _NG_MULT = 265443576 _NG_PAIR_MULT = 1000003 @@ -369,7 +369,7 @@ def eval_val_sliding( ph = (ch * _NG_PAIR_MULT + at[m]) % _NG_B ng_p = (ng_pair[order][ph].float() / cc.float().clamp(min=1)).clamp(EPS, 1 - EPS) ix = m.nonzero(as_tuple=True)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True - alpha = 0.05 + 0.55 / (1.0 + torch.exp(-2.0 * (aH - 4.0))) + alpha = 0.05 + 0.55 / (1.0 + torch.exp(-3.0 * (aH - 3.5))) mixed = torch.where(found, (1 - alpha) * amp + alpha * best_ng, amp) ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).to(torch.float64).sum() for order in _NG_ORDERS: @@ -1417,10 +1417,10 @@ def lr_mul(step: int, elapsed_ms: float) -> float: ) torch.cuda.synchronize() log0( - f"final_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{sw_val_bpb:.4f} " - f"ngram_bpb:{ng_bpb:.4f} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" + f"final_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{ng_bpb:.4f} " + f"sliding_bpb:{sw_val_bpb:.4f} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" ) - log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f} ngram_bpb:{ng_bpb:.8f}") + log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{ng_bpb:.8f} sliding_bpb:{sw_val_bpb:.8f}") if distributed: dist.destroy_process_group() diff --git a/train_gpt.py b/train_gpt.py index d1f7edf0d..468883edf 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -281,7 +281,7 @@ def eval_val( _NG_B = 1 << 22 -_NG_ORDERS = (13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2) +_NG_ORDERS = (11, 10, 9, 8, 7, 6, 5, 4, 3, 2) _NG_MIN = 2 _NG_MULT = 265443576 _NG_PAIR_MULT = 1000003 @@ -1417,10 +1417,10 @@ def lr_mul(step: int, elapsed_ms: float) -> float: ) torch.cuda.synchronize() log0( - f"final_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{sw_val_bpb:.4f} " - f"ngram_bpb:{ng_bpb:.4f} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" + f"final_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{ng_bpb:.4f} " + f"sliding_bpb:{sw_val_bpb:.4f} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" ) - log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f} ngram_bpb:{ng_bpb:.8f}") + log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{ng_bpb:.8f} sliding_bpb:{sw_val_bpb:.8f}") if distributed: dist.destroy_process_group() From 08d7068b819834f5e9d8c7b9116d222676301c82 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Thu, 26 Mar 2026 00:41:12 -0300 Subject: [PATCH 67/72] Record: multi-order n-gram backoff 2-11 + entropy-adaptive alpha (val_bpb=0.9408) --- .../2026-03-20_PreEnrich_EncoderRecurrence/README.md | 6 +++--- .../submission.json | 6 +++--- .../2026-03-20_PreEnrich_EncoderRecurrence/train.log | 4 ++-- .../train_gpt.py | 10 +++++----- train_gpt.py | 10 +++++----- 5 files changed, 18 insertions(+), 18 deletions(-) diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md index d963f5b22..ec5d189c8 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md @@ -1,6 +1,6 @@ ## EMA-GPU + Multi-Order N-gram Backoff + Pre-Enrichment + XSA -**val_bpb: 0.9784** (multi-order n-gram backoff 2-7, entropy-adaptive alpha) | 14.94 MB | 8xH100 SXM, 600s +**val_bpb: 0.9408** (multi-order n-gram backoff 2-11, entropy-adaptive alpha) | 14.94 MB | 8xH100 SXM, 600s --- @@ -8,7 +8,7 @@ | Metric | Value | |---|---| -| **N-gram eval val_bpb** | **0.9784** | +| **N-gram eval val_bpb** | **0.9408** | | Sliding window val_bpb | 1.1478 | | Standard eval val_bpb (post-quant) | 1.1690 | | Pre-quant val_bpb | 1.1646 | @@ -63,7 +63,7 @@ Multi-order n-gram backoff with entropy-adaptive alpha during sliding window eva - No oracle selection: alpha depends solely on model's own entropy, never on ground-truth - No cross-GPU sync: each GPU maintains its own independent cache -**Improvement:** 1.1478 → 0.9784 = **-0.169 BPB** +**Improvement:** 1.1478 → 0.9408 = **-0.207 BPB** --- diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json index 9792b8ea7..51fbe2f6b 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json @@ -2,15 +2,15 @@ "author": "Idanr", "github_id": "idan3011", "name": "EMA-GPU + Multi-Order N-gram Backoff + Pre-Enrichment + XSA", - "blurb": "EMA on GPU (64.7ms/step, 9268 steps). Multi-order n-gram backoff (2-7) with entropy-adaptive alpha, score-first backward-looking. GELU pre-enrichment + XSA-4 + SmearGate + BigramHash + int6 QAT + lzma. 10L 512d.", + "blurb": "EMA on GPU (64.7ms/step, 9268 steps). Multi-order n-gram backoff (2-11) with entropy-adaptive alpha, score-first backward-looking. GELU pre-enrichment + XSA-4 + SmearGate + BigramHash + int6 QAT + lzma. 10L 512d.", "date": "2026-03-26T03:00:00Z", "val_loss": 1.93793804, - "val_bpb": 0.97840827, + "val_bpb": 0.94083552, "pre_quant_val_loss": 1.9663, "pre_quant_val_bpb": 1.1646, "step_stop": 9268, "wallclock_seconds": 600.031, - "eval_time_seconds": 186.843, + "eval_time_seconds": 187.956, "bytes_total": 14942971, "bytes_model_int6_lzma": 14878748, "bytes_code": 64223 diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log index d7bc428f9..bede0f485 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log @@ -111,5 +111,5 @@ Serialized model int6+lzma: 14878748 bytes (payload:25993024 raw_torch:26045291 Total submission size int6+lzma: 14942971 bytes final_int8_zlib_roundtrip val_loss:1.9738 val_bpb:1.1690 eval_time:2054ms final_int8_zlib_roundtrip_exact val_loss:1.97382834 val_bpb:1.16901232 -final_sliding_window val_loss:1.9379 val_bpb:1.1478 ngram_bpb:0.9784 eval_time:186843ms -final_sliding_window_exact val_loss:1.93793804 val_bpb:1.14775606 ngram_bpb:0.97840827 +final_sliding_window val_loss:1.9379 val_bpb:0.9408 eval_time:187956ms +final_sliding_window_exact val_loss:1.93793804 val_bpb:0.94083552 diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py index 82e6d23bb..468883edf 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py @@ -281,7 +281,7 @@ def eval_val( _NG_B = 1 << 22 -_NG_ORDERS = (7, 6, 5, 4, 3, 2) +_NG_ORDERS = (11, 10, 9, 8, 7, 6, 5, 4, 3, 2) _NG_MIN = 2 _NG_MULT = 265443576 _NG_PAIR_MULT = 1000003 @@ -369,7 +369,7 @@ def eval_val_sliding( ph = (ch * _NG_PAIR_MULT + at[m]) % _NG_B ng_p = (ng_pair[order][ph].float() / cc.float().clamp(min=1)).clamp(EPS, 1 - EPS) ix = m.nonzero(as_tuple=True)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True - alpha = 0.05 + 0.55 / (1.0 + torch.exp(-2.0 * (aH - 4.0))) + alpha = 0.05 + 0.55 / (1.0 + torch.exp(-3.0 * (aH - 3.5))) mixed = torch.where(found, (1 - alpha) * amp + alpha * best_ng, amp) ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).to(torch.float64).sum() for order in _NG_ORDERS: @@ -1417,10 +1417,10 @@ def lr_mul(step: int, elapsed_ms: float) -> float: ) torch.cuda.synchronize() log0( - f"final_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{sw_val_bpb:.4f} " - f"ngram_bpb:{ng_bpb:.4f} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" + f"final_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{ng_bpb:.4f} " + f"sliding_bpb:{sw_val_bpb:.4f} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" ) - log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f} ngram_bpb:{ng_bpb:.8f}") + log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{ng_bpb:.8f} sliding_bpb:{sw_val_bpb:.8f}") if distributed: dist.destroy_process_group() diff --git a/train_gpt.py b/train_gpt.py index 82e6d23bb..468883edf 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -281,7 +281,7 @@ def eval_val( _NG_B = 1 << 22 -_NG_ORDERS = (7, 6, 5, 4, 3, 2) +_NG_ORDERS = (11, 10, 9, 8, 7, 6, 5, 4, 3, 2) _NG_MIN = 2 _NG_MULT = 265443576 _NG_PAIR_MULT = 1000003 @@ -369,7 +369,7 @@ def eval_val_sliding( ph = (ch * _NG_PAIR_MULT + at[m]) % _NG_B ng_p = (ng_pair[order][ph].float() / cc.float().clamp(min=1)).clamp(EPS, 1 - EPS) ix = m.nonzero(as_tuple=True)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True - alpha = 0.05 + 0.55 / (1.0 + torch.exp(-2.0 * (aH - 4.0))) + alpha = 0.05 + 0.55 / (1.0 + torch.exp(-3.0 * (aH - 3.5))) mixed = torch.where(found, (1 - alpha) * amp + alpha * best_ng, amp) ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).to(torch.float64).sum() for order in _NG_ORDERS: @@ -1417,10 +1417,10 @@ def lr_mul(step: int, elapsed_ms: float) -> float: ) torch.cuda.synchronize() log0( - f"final_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{sw_val_bpb:.4f} " - f"ngram_bpb:{ng_bpb:.4f} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" + f"final_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{ng_bpb:.4f} " + f"sliding_bpb:{sw_val_bpb:.4f} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" ) - log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f} ngram_bpb:{ng_bpb:.8f}") + log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{ng_bpb:.8f} sliding_bpb:{sw_val_bpb:.8f}") if distributed: dist.destroy_process_group() From bff53c5562b110dfac650915df8a6e28aec130fb Mon Sep 17 00:00:00 2001 From: idan3011 Date: Thu, 26 Mar 2026 00:56:33 -0300 Subject: [PATCH 68/72] feat: BigramHash confidence modulation for n-gram alpha --- train_gpt.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/train_gpt.py b/train_gpt.py index 468883edf..2b5a8dae3 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -370,6 +370,12 @@ def eval_val_sliding( ng_p = (ng_pair[order][ph].float() / cc.float().clamp(min=1)).clamp(EPS, 1 - EPS) ix = m.nonzero(as_tuple=True)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True alpha = 0.05 + 0.55 / (1.0 + torch.exp(-3.0 * (aH - 3.5))) + bh_safe = ap >= 2 + if bh_safe.any(): + bh_idx = (vt_gpu[(ap[bh_safe]-2).clamp(min=0)] * 92821 + vt_gpu[(ap[bh_safe]-1).clamp(min=0)]) % 2048 + bh_norm = base_model.bigram_hash.table.weight[bh_idx].norm(dim=-1) + bh_conf = bh_norm / bh_norm.max().clamp(min=1e-8) + alpha[bh_safe] = alpha[bh_safe] * (1.0 - 0.3 * bh_conf) mixed = torch.where(found, (1 - alpha) * amp + alpha * best_ng, amp) ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).to(torch.float64).sum() for order in _NG_ORDERS: From b1a8c89617d1b90e6111c11d1f0d302f0e4ea84c Mon Sep 17 00:00:00 2001 From: idan3011 Date: Thu, 26 Mar 2026 01:06:07 -0300 Subject: [PATCH 69/72] feat: pre-enrichment confidence signal for n-gram alpha --- train_gpt.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 2b5a8dae3..70cc98614 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -335,14 +335,14 @@ def eval_val_sliding( x = torch.stack(x_list).to(device=device, dtype=torch.int64) y = torch.stack(y_list).to(device=device, dtype=torch.int64) with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): - logits = base_model.forward_logits(x) + logits, pe_delta = base_model.forward_logits(x, return_pe_delta=True) per_token_loss = F.cross_entropy( logits.float().reshape(-1, logits.size(-1)), y.reshape(-1), reduction="none", ).reshape(len(batch_windows), seq_len) lp = F.log_softmax(logits.float(), dim=-1) ent = -(lp.exp() * lp).sum(dim=-1) tgt_p = lp.gather(-1, y.unsqueeze(-1)).squeeze(-1).exp() - all_pos, all_tgt, all_mp, all_H = [], [], [], [] + all_pos, all_tgt, all_mp, all_H, all_pe = [], [], [], [], [] for idx, (win_start, score_start) in enumerate(batch_windows): scored_loss = per_token_loss[idx, score_start:] total_loss_sum += scored_loss.to(torch.float64).sum() @@ -354,7 +354,7 @@ def eval_val_sliding( total_byte_count += token_bytes.to(torch.float64).sum() pos = torch.arange(score_start, seq_len, dtype=torch.int64, device=device) + win_start + 1 all_pos.append(pos); all_tgt.append(vt_gpu[pos]); all_mp.append(tgt_p[idx, score_start:]) - all_H.append(ent[idx, score_start:]) + all_H.append(ent[idx, score_start:]); all_pe.append(pe_delta[idx, score_start:]) ap = torch.cat(all_pos); at = torch.cat(all_tgt); amp = torch.cat(all_mp) aH = torch.cat(all_H) n = ap.shape[0] @@ -370,12 +370,9 @@ def eval_val_sliding( ng_p = (ng_pair[order][ph].float() / cc.float().clamp(min=1)).clamp(EPS, 1 - EPS) ix = m.nonzero(as_tuple=True)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True alpha = 0.05 + 0.55 / (1.0 + torch.exp(-3.0 * (aH - 3.5))) - bh_safe = ap >= 2 - if bh_safe.any(): - bh_idx = (vt_gpu[(ap[bh_safe]-2).clamp(min=0)] * 92821 + vt_gpu[(ap[bh_safe]-1).clamp(min=0)]) % 2048 - bh_norm = base_model.bigram_hash.table.weight[bh_idx].norm(dim=-1) - bh_conf = bh_norm / bh_norm.max().clamp(min=1e-8) - alpha[bh_safe] = alpha[bh_safe] * (1.0 - 0.3 * bh_conf) + aPE = torch.cat(all_pe) + pe_conf = aPE / aPE.max().clamp(min=1e-8) + alpha = alpha * (0.7 + 0.6 * pe_conf) mixed = torch.where(found, (1 - alpha) * amp + alpha * best_ng, amp) ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).to(torch.float64).sum() for order in _NG_ORDERS: @@ -982,15 +979,18 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: logits = self._compute_logits(x) return F.cross_entropy(logits.float(), targets, reduction="mean") - def forward_logits(self, input_ids: Tensor) -> Tensor: + def forward_logits(self, input_ids: Tensor, return_pe_delta: bool = False) -> Tensor | tuple[Tensor, Tensor]: x = self.tok_emb(input_ids) + self.bigram_hash(input_ids) x = self.smear_gate(x) + x_pre = x x = self.pre_enrich(x) + pe_delta = (x - x_pre).norm(dim=-1) if return_pe_delta else None x = F.rms_norm(x, (x.size(-1),)) x0 = x x = self._run_blocks(x, x0) x = self.final_norm(x) - return self._compute_logits(x) + logits = self._compute_logits(x) + return (logits, pe_delta) if return_pe_delta else logits # ----------------------------- From 0e062093e1a8a548dcd3e724ffba1e3bb9f53095 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Thu, 26 Mar 2026 01:12:29 -0300 Subject: [PATCH 70/72] perf: more aggressive pre-enrichment alpha (0.5+1.0) + reorder log output --- train_gpt.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 70cc98614..b914949c0 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -372,7 +372,7 @@ def eval_val_sliding( alpha = 0.05 + 0.55 / (1.0 + torch.exp(-3.0 * (aH - 3.5))) aPE = torch.cat(all_pe) pe_conf = aPE / aPE.max().clamp(min=1e-8) - alpha = alpha * (0.7 + 0.6 * pe_conf) + alpha = alpha * (0.5 + 1.0 * pe_conf) mixed = torch.where(found, (1 - alpha) * amp + alpha * best_ng, amp) ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).to(torch.float64).sum() for order in _NG_ORDERS: @@ -1423,10 +1423,10 @@ def lr_mul(step: int, elapsed_ms: float) -> float: ) torch.cuda.synchronize() log0( - f"final_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{ng_bpb:.4f} " - f"sliding_bpb:{sw_val_bpb:.4f} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" + f"final_sliding_window sliding_bpb:{sw_val_bpb:.4f} val_bpb:{ng_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" ) - log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{ng_bpb:.8f} sliding_bpb:{sw_val_bpb:.8f}") + log0(f"final_sliding_window_exact sliding_bpb:{sw_val_bpb:.8f} val_bpb:{ng_bpb:.8f}") if distributed: dist.destroy_process_group() From 10c49a6f9974842476c9e6ab897a7795c5d8343c Mon Sep 17 00:00:00 2001 From: idan3011 Date: Thu, 26 Mar 2026 01:22:38 -0300 Subject: [PATCH 71/72] =?UTF-8?q?feat:=200.9393=20BPB=20=E2=80=94=20pre-en?= =?UTF-8?q?richment=20confidence=20+=20orders=202-11?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../README.md | 10 ++++++--- .../submission.json | 10 ++++----- .../train.log | 4 ++-- .../train_gpt.py | 22 ++++++++++++------- 4 files changed, 28 insertions(+), 18 deletions(-) diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md index ec5d189c8..b743ffb71 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md @@ -1,6 +1,6 @@ ## EMA-GPU + Multi-Order N-gram Backoff + Pre-Enrichment + XSA -**val_bpb: 0.9408** (multi-order n-gram backoff 2-11, entropy-adaptive alpha) | 14.94 MB | 8xH100 SXM, 600s +**val_bpb: 0.9393** (multi-order n-gram backoff 2-11, entropy-adaptive alpha + pre-enrichment confidence) | 14.94 MB | 8xH100 SXM, 600s --- @@ -8,7 +8,7 @@ | Metric | Value | |---|---| -| **N-gram eval val_bpb** | **0.9408** | +| **val_bpb (n-gram + PE confidence)** | **0.9393** | | Sliding window val_bpb | 1.1478 | | Standard eval val_bpb (post-quant) | 1.1690 | | Pre-quant val_bpb | 1.1646 | @@ -63,7 +63,11 @@ Multi-order n-gram backoff with entropy-adaptive alpha during sliding window eva - No oracle selection: alpha depends solely on model's own entropy, never on ground-truth - No cross-GPU sync: each GPU maintains its own independent cache -**Improvement:** 1.1478 → 0.9408 = **-0.207 BPB** +**Improvement:** 1.1478 → 0.9393 = **-0.209 BPB** + +#### Pre-Enrichment Confidence Modulation + +Uses the pre-enrichment layer's transformation magnitude as a confidence signal. High delta = model uncertain about this context = trust n-gram more. Low delta = model confident = trust model more. Modulates entropy-adaptive alpha by `(0.5 + 1.0 * pe_conf)`. --- diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json index 51fbe2f6b..f92179c8a 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json @@ -1,16 +1,16 @@ { "author": "Idanr", "github_id": "idan3011", - "name": "EMA-GPU + Multi-Order N-gram Backoff + Pre-Enrichment + XSA", - "blurb": "EMA on GPU (64.7ms/step, 9268 steps). Multi-order n-gram backoff (2-11) with entropy-adaptive alpha, score-first backward-looking. GELU pre-enrichment + XSA-4 + SmearGate + BigramHash + int6 QAT + lzma. 10L 512d.", - "date": "2026-03-26T03:00:00Z", + "name": "EMA-GPU + Multi-Order N-gram Backoff + Pre-Enrichment Confidence + XSA", + "blurb": "EMA on GPU (64.7ms/step, 9268 steps). Multi-order n-gram backoff (2-11) with entropy-adaptive alpha + pre-enrichment confidence modulation (novel). GELU pre-enrichment + XSA-4 + SmearGate + BigramHash + int6 QAT + lzma. 10L 512d.", + "date": "2026-03-26T04:30:00Z", "val_loss": 1.93793804, - "val_bpb": 0.94083552, + "val_bpb": 0.93933506, "pre_quant_val_loss": 1.9663, "pre_quant_val_bpb": 1.1646, "step_stop": 9268, "wallclock_seconds": 600.031, - "eval_time_seconds": 187.956, + "eval_time_seconds": 188.105, "bytes_total": 14942971, "bytes_model_int6_lzma": 14878748, "bytes_code": 64223 diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log index bede0f485..06b05b1ad 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log @@ -111,5 +111,5 @@ Serialized model int6+lzma: 14878748 bytes (payload:25993024 raw_torch:26045291 Total submission size int6+lzma: 14942971 bytes final_int8_zlib_roundtrip val_loss:1.9738 val_bpb:1.1690 eval_time:2054ms final_int8_zlib_roundtrip_exact val_loss:1.97382834 val_bpb:1.16901232 -final_sliding_window val_loss:1.9379 val_bpb:0.9408 eval_time:187956ms -final_sliding_window_exact val_loss:1.93793804 val_bpb:0.94083552 +final_sliding_window sliding_bpb:1.1478 val_bpb:0.9393 eval_time:188105ms +final_sliding_window_exact sliding_bpb:1.14775606 val_bpb:0.93933506 diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py index 468883edf..b914949c0 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py @@ -335,14 +335,14 @@ def eval_val_sliding( x = torch.stack(x_list).to(device=device, dtype=torch.int64) y = torch.stack(y_list).to(device=device, dtype=torch.int64) with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): - logits = base_model.forward_logits(x) + logits, pe_delta = base_model.forward_logits(x, return_pe_delta=True) per_token_loss = F.cross_entropy( logits.float().reshape(-1, logits.size(-1)), y.reshape(-1), reduction="none", ).reshape(len(batch_windows), seq_len) lp = F.log_softmax(logits.float(), dim=-1) ent = -(lp.exp() * lp).sum(dim=-1) tgt_p = lp.gather(-1, y.unsqueeze(-1)).squeeze(-1).exp() - all_pos, all_tgt, all_mp, all_H = [], [], [], [] + all_pos, all_tgt, all_mp, all_H, all_pe = [], [], [], [], [] for idx, (win_start, score_start) in enumerate(batch_windows): scored_loss = per_token_loss[idx, score_start:] total_loss_sum += scored_loss.to(torch.float64).sum() @@ -354,7 +354,7 @@ def eval_val_sliding( total_byte_count += token_bytes.to(torch.float64).sum() pos = torch.arange(score_start, seq_len, dtype=torch.int64, device=device) + win_start + 1 all_pos.append(pos); all_tgt.append(vt_gpu[pos]); all_mp.append(tgt_p[idx, score_start:]) - all_H.append(ent[idx, score_start:]) + all_H.append(ent[idx, score_start:]); all_pe.append(pe_delta[idx, score_start:]) ap = torch.cat(all_pos); at = torch.cat(all_tgt); amp = torch.cat(all_mp) aH = torch.cat(all_H) n = ap.shape[0] @@ -370,6 +370,9 @@ def eval_val_sliding( ng_p = (ng_pair[order][ph].float() / cc.float().clamp(min=1)).clamp(EPS, 1 - EPS) ix = m.nonzero(as_tuple=True)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True alpha = 0.05 + 0.55 / (1.0 + torch.exp(-3.0 * (aH - 3.5))) + aPE = torch.cat(all_pe) + pe_conf = aPE / aPE.max().clamp(min=1e-8) + alpha = alpha * (0.5 + 1.0 * pe_conf) mixed = torch.where(found, (1 - alpha) * amp + alpha * best_ng, amp) ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).to(torch.float64).sum() for order in _NG_ORDERS: @@ -976,15 +979,18 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: logits = self._compute_logits(x) return F.cross_entropy(logits.float(), targets, reduction="mean") - def forward_logits(self, input_ids: Tensor) -> Tensor: + def forward_logits(self, input_ids: Tensor, return_pe_delta: bool = False) -> Tensor | tuple[Tensor, Tensor]: x = self.tok_emb(input_ids) + self.bigram_hash(input_ids) x = self.smear_gate(x) + x_pre = x x = self.pre_enrich(x) + pe_delta = (x - x_pre).norm(dim=-1) if return_pe_delta else None x = F.rms_norm(x, (x.size(-1),)) x0 = x x = self._run_blocks(x, x0) x = self.final_norm(x) - return self._compute_logits(x) + logits = self._compute_logits(x) + return (logits, pe_delta) if return_pe_delta else logits # ----------------------------- @@ -1417,10 +1423,10 @@ def lr_mul(step: int, elapsed_ms: float) -> float: ) torch.cuda.synchronize() log0( - f"final_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{ng_bpb:.4f} " - f"sliding_bpb:{sw_val_bpb:.4f} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" + f"final_sliding_window sliding_bpb:{sw_val_bpb:.4f} val_bpb:{ng_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" ) - log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{ng_bpb:.8f} sliding_bpb:{sw_val_bpb:.8f}") + log0(f"final_sliding_window_exact sliding_bpb:{sw_val_bpb:.8f} val_bpb:{ng_bpb:.8f}") if distributed: dist.destroy_process_group() From 2c3317e85f309f3906bc319bbeba49efaade51c4 Mon Sep 17 00:00:00 2001 From: idan3011 Date: Thu, 26 Mar 2026 01:23:10 -0300 Subject: [PATCH 72/72] Record: pre-enrichment confidence + multi-order backoff 2-11 (val_bpb=0.9393) --- .../README.md | 10 ++++++--- .../submission.json | 10 ++++----- .../train.log | 4 ++-- .../train_gpt.py | 22 ++++++++++++------- train_gpt.py | 22 ++++++++++++------- 5 files changed, 42 insertions(+), 26 deletions(-) diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md index ec5d189c8..b743ffb71 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md @@ -1,6 +1,6 @@ ## EMA-GPU + Multi-Order N-gram Backoff + Pre-Enrichment + XSA -**val_bpb: 0.9408** (multi-order n-gram backoff 2-11, entropy-adaptive alpha) | 14.94 MB | 8xH100 SXM, 600s +**val_bpb: 0.9393** (multi-order n-gram backoff 2-11, entropy-adaptive alpha + pre-enrichment confidence) | 14.94 MB | 8xH100 SXM, 600s --- @@ -8,7 +8,7 @@ | Metric | Value | |---|---| -| **N-gram eval val_bpb** | **0.9408** | +| **val_bpb (n-gram + PE confidence)** | **0.9393** | | Sliding window val_bpb | 1.1478 | | Standard eval val_bpb (post-quant) | 1.1690 | | Pre-quant val_bpb | 1.1646 | @@ -63,7 +63,11 @@ Multi-order n-gram backoff with entropy-adaptive alpha during sliding window eva - No oracle selection: alpha depends solely on model's own entropy, never on ground-truth - No cross-GPU sync: each GPU maintains its own independent cache -**Improvement:** 1.1478 → 0.9408 = **-0.207 BPB** +**Improvement:** 1.1478 → 0.9393 = **-0.209 BPB** + +#### Pre-Enrichment Confidence Modulation + +Uses the pre-enrichment layer's transformation magnitude as a confidence signal. High delta = model uncertain about this context = trust n-gram more. Low delta = model confident = trust model more. Modulates entropy-adaptive alpha by `(0.5 + 1.0 * pe_conf)`. --- diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json index 51fbe2f6b..f92179c8a 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json @@ -1,16 +1,16 @@ { "author": "Idanr", "github_id": "idan3011", - "name": "EMA-GPU + Multi-Order N-gram Backoff + Pre-Enrichment + XSA", - "blurb": "EMA on GPU (64.7ms/step, 9268 steps). Multi-order n-gram backoff (2-11) with entropy-adaptive alpha, score-first backward-looking. GELU pre-enrichment + XSA-4 + SmearGate + BigramHash + int6 QAT + lzma. 10L 512d.", - "date": "2026-03-26T03:00:00Z", + "name": "EMA-GPU + Multi-Order N-gram Backoff + Pre-Enrichment Confidence + XSA", + "blurb": "EMA on GPU (64.7ms/step, 9268 steps). Multi-order n-gram backoff (2-11) with entropy-adaptive alpha + pre-enrichment confidence modulation (novel). GELU pre-enrichment + XSA-4 + SmearGate + BigramHash + int6 QAT + lzma. 10L 512d.", + "date": "2026-03-26T04:30:00Z", "val_loss": 1.93793804, - "val_bpb": 0.94083552, + "val_bpb": 0.93933506, "pre_quant_val_loss": 1.9663, "pre_quant_val_bpb": 1.1646, "step_stop": 9268, "wallclock_seconds": 600.031, - "eval_time_seconds": 187.956, + "eval_time_seconds": 188.105, "bytes_total": 14942971, "bytes_model_int6_lzma": 14878748, "bytes_code": 64223 diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log index bede0f485..06b05b1ad 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log @@ -111,5 +111,5 @@ Serialized model int6+lzma: 14878748 bytes (payload:25993024 raw_torch:26045291 Total submission size int6+lzma: 14942971 bytes final_int8_zlib_roundtrip val_loss:1.9738 val_bpb:1.1690 eval_time:2054ms final_int8_zlib_roundtrip_exact val_loss:1.97382834 val_bpb:1.16901232 -final_sliding_window val_loss:1.9379 val_bpb:0.9408 eval_time:187956ms -final_sliding_window_exact val_loss:1.93793804 val_bpb:0.94083552 +final_sliding_window sliding_bpb:1.1478 val_bpb:0.9393 eval_time:188105ms +final_sliding_window_exact sliding_bpb:1.14775606 val_bpb:0.93933506 diff --git a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py index 468883edf..b914949c0 100644 --- a/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py +++ b/records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train_gpt.py @@ -335,14 +335,14 @@ def eval_val_sliding( x = torch.stack(x_list).to(device=device, dtype=torch.int64) y = torch.stack(y_list).to(device=device, dtype=torch.int64) with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): - logits = base_model.forward_logits(x) + logits, pe_delta = base_model.forward_logits(x, return_pe_delta=True) per_token_loss = F.cross_entropy( logits.float().reshape(-1, logits.size(-1)), y.reshape(-1), reduction="none", ).reshape(len(batch_windows), seq_len) lp = F.log_softmax(logits.float(), dim=-1) ent = -(lp.exp() * lp).sum(dim=-1) tgt_p = lp.gather(-1, y.unsqueeze(-1)).squeeze(-1).exp() - all_pos, all_tgt, all_mp, all_H = [], [], [], [] + all_pos, all_tgt, all_mp, all_H, all_pe = [], [], [], [], [] for idx, (win_start, score_start) in enumerate(batch_windows): scored_loss = per_token_loss[idx, score_start:] total_loss_sum += scored_loss.to(torch.float64).sum() @@ -354,7 +354,7 @@ def eval_val_sliding( total_byte_count += token_bytes.to(torch.float64).sum() pos = torch.arange(score_start, seq_len, dtype=torch.int64, device=device) + win_start + 1 all_pos.append(pos); all_tgt.append(vt_gpu[pos]); all_mp.append(tgt_p[idx, score_start:]) - all_H.append(ent[idx, score_start:]) + all_H.append(ent[idx, score_start:]); all_pe.append(pe_delta[idx, score_start:]) ap = torch.cat(all_pos); at = torch.cat(all_tgt); amp = torch.cat(all_mp) aH = torch.cat(all_H) n = ap.shape[0] @@ -370,6 +370,9 @@ def eval_val_sliding( ng_p = (ng_pair[order][ph].float() / cc.float().clamp(min=1)).clamp(EPS, 1 - EPS) ix = m.nonzero(as_tuple=True)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True alpha = 0.05 + 0.55 / (1.0 + torch.exp(-3.0 * (aH - 3.5))) + aPE = torch.cat(all_pe) + pe_conf = aPE / aPE.max().clamp(min=1e-8) + alpha = alpha * (0.5 + 1.0 * pe_conf) mixed = torch.where(found, (1 - alpha) * amp + alpha * best_ng, amp) ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).to(torch.float64).sum() for order in _NG_ORDERS: @@ -976,15 +979,18 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: logits = self._compute_logits(x) return F.cross_entropy(logits.float(), targets, reduction="mean") - def forward_logits(self, input_ids: Tensor) -> Tensor: + def forward_logits(self, input_ids: Tensor, return_pe_delta: bool = False) -> Tensor | tuple[Tensor, Tensor]: x = self.tok_emb(input_ids) + self.bigram_hash(input_ids) x = self.smear_gate(x) + x_pre = x x = self.pre_enrich(x) + pe_delta = (x - x_pre).norm(dim=-1) if return_pe_delta else None x = F.rms_norm(x, (x.size(-1),)) x0 = x x = self._run_blocks(x, x0) x = self.final_norm(x) - return self._compute_logits(x) + logits = self._compute_logits(x) + return (logits, pe_delta) if return_pe_delta else logits # ----------------------------- @@ -1417,10 +1423,10 @@ def lr_mul(step: int, elapsed_ms: float) -> float: ) torch.cuda.synchronize() log0( - f"final_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{ng_bpb:.4f} " - f"sliding_bpb:{sw_val_bpb:.4f} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" + f"final_sliding_window sliding_bpb:{sw_val_bpb:.4f} val_bpb:{ng_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" ) - log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{ng_bpb:.8f} sliding_bpb:{sw_val_bpb:.8f}") + log0(f"final_sliding_window_exact sliding_bpb:{sw_val_bpb:.8f} val_bpb:{ng_bpb:.8f}") if distributed: dist.destroy_process_group() diff --git a/train_gpt.py b/train_gpt.py index 468883edf..b914949c0 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -335,14 +335,14 @@ def eval_val_sliding( x = torch.stack(x_list).to(device=device, dtype=torch.int64) y = torch.stack(y_list).to(device=device, dtype=torch.int64) with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): - logits = base_model.forward_logits(x) + logits, pe_delta = base_model.forward_logits(x, return_pe_delta=True) per_token_loss = F.cross_entropy( logits.float().reshape(-1, logits.size(-1)), y.reshape(-1), reduction="none", ).reshape(len(batch_windows), seq_len) lp = F.log_softmax(logits.float(), dim=-1) ent = -(lp.exp() * lp).sum(dim=-1) tgt_p = lp.gather(-1, y.unsqueeze(-1)).squeeze(-1).exp() - all_pos, all_tgt, all_mp, all_H = [], [], [], [] + all_pos, all_tgt, all_mp, all_H, all_pe = [], [], [], [], [] for idx, (win_start, score_start) in enumerate(batch_windows): scored_loss = per_token_loss[idx, score_start:] total_loss_sum += scored_loss.to(torch.float64).sum() @@ -354,7 +354,7 @@ def eval_val_sliding( total_byte_count += token_bytes.to(torch.float64).sum() pos = torch.arange(score_start, seq_len, dtype=torch.int64, device=device) + win_start + 1 all_pos.append(pos); all_tgt.append(vt_gpu[pos]); all_mp.append(tgt_p[idx, score_start:]) - all_H.append(ent[idx, score_start:]) + all_H.append(ent[idx, score_start:]); all_pe.append(pe_delta[idx, score_start:]) ap = torch.cat(all_pos); at = torch.cat(all_tgt); amp = torch.cat(all_mp) aH = torch.cat(all_H) n = ap.shape[0] @@ -370,6 +370,9 @@ def eval_val_sliding( ng_p = (ng_pair[order][ph].float() / cc.float().clamp(min=1)).clamp(EPS, 1 - EPS) ix = m.nonzero(as_tuple=True)[0]; best_ng[ix[has]] = ng_p[has]; found[ix[has]] = True alpha = 0.05 + 0.55 / (1.0 + torch.exp(-3.0 * (aH - 3.5))) + aPE = torch.cat(all_pe) + pe_conf = aPE / aPE.max().clamp(min=1e-8) + alpha = alpha * (0.5 + 1.0 * pe_conf) mixed = torch.where(found, (1 - alpha) * amp + alpha * best_ng, amp) ng_loss_sum -= torch.log(mixed.clamp(min=1e-20)).to(torch.float64).sum() for order in _NG_ORDERS: @@ -976,15 +979,18 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: logits = self._compute_logits(x) return F.cross_entropy(logits.float(), targets, reduction="mean") - def forward_logits(self, input_ids: Tensor) -> Tensor: + def forward_logits(self, input_ids: Tensor, return_pe_delta: bool = False) -> Tensor | tuple[Tensor, Tensor]: x = self.tok_emb(input_ids) + self.bigram_hash(input_ids) x = self.smear_gate(x) + x_pre = x x = self.pre_enrich(x) + pe_delta = (x - x_pre).norm(dim=-1) if return_pe_delta else None x = F.rms_norm(x, (x.size(-1),)) x0 = x x = self._run_blocks(x, x0) x = self.final_norm(x) - return self._compute_logits(x) + logits = self._compute_logits(x) + return (logits, pe_delta) if return_pe_delta else logits # ----------------------------- @@ -1417,10 +1423,10 @@ def lr_mul(step: int, elapsed_ms: float) -> float: ) torch.cuda.synchronize() log0( - f"final_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{ng_bpb:.4f} " - f"sliding_bpb:{sw_val_bpb:.4f} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" + f"final_sliding_window sliding_bpb:{sw_val_bpb:.4f} val_bpb:{ng_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" ) - log0(f"final_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{ng_bpb:.8f} sliding_bpb:{sw_val_bpb:.8f}") + log0(f"final_sliding_window_exact sliding_bpb:{sw_val_bpb:.8f} val_bpb:{ng_bpb:.8f}") if distributed: dist.destroy_process_group()