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[mxfp8 moe training] initialize zero tensor differently to avoid d2h sync #3253

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Merged
merged 1 commit into from
Oct 29, 2025
Merged

[mxfp8 moe training] initialize zero tensor differently to avoid d2h sync #3253

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6 changes: 3 additions & 3 deletions test/prototype/moe_training/mxfp8/test_mxfp8_a2a.py
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Original file line number Diff line number Diff line change
Expand Up @@ -73,7 +73,7 @@ def test_a2a_fwd_bwd(self):
tokens_per_ep_rank,
dim,
device=self.device,
dtype=torch.float32,
dtype=torch.bfloat16,
requires_grad=True,
)
ref_input_tensor = input_tensor.detach().clone().requires_grad_(True)
Expand Down Expand Up @@ -107,7 +107,7 @@ def test_a2a_fwd_bwd(self):
total_tokens_on_rank_after_a2a,
dim,
device=self.device,
dtype=torch.float32,
dtype=torch.bfloat16,
)

# Do the actual all_to_all_single
Expand Down Expand Up @@ -188,7 +188,7 @@ def test_a2a_fwd_bwd(self):
tokens_per_ep_rank,
dim,
device=self.device,
dtype=torch.float32,
dtype=torch.bfloat16,
requires_grad=True,
)
ref_input_tensor = input_tensor.detach().clone().requires_grad_(True)
Expand Down
6 changes: 3 additions & 3 deletions torchao/prototype/moe_training/kernels/mxfp8/quant.py
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Original file line number Diff line number Diff line change
Expand Up @@ -175,7 +175,7 @@ def compute_blocked_scale_offsets_for_M_groups(offsets: torch.Tensor):
- starting_row_after_padding: 1D integer tensor representing the starting row after padding each to blocked format.
"""
# Calculate group sizes
zero = torch.tensor([0], dtype=offsets.dtype, device=offsets.device)
zero = torch.zeros(1, dtype=offsets.dtype, device=offsets.device)
group_sizes = torch.diff(offsets, prepend=zero)

# Round each group size up to the nearest multiple of 128
Expand Down Expand Up @@ -203,8 +203,8 @@ def compute_blocked_scale_offsets_for_K_groups(
- starting_col_after_padding: 1D integer tensor representing the starting row after padding each to blocked format.
"""
# Calculate group sizes
zero = torch.tensor(
[0], dtype=scale_group_offsets.dtype, device=scale_group_offsets.device
zero = torch.zeros(
1, dtype=scale_group_offsets.dtype, device=scale_group_offsets.device
)
group_sizes = torch.diff(scale_group_offsets, prepend=zero)

Expand Down
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