diff --git a/src/modalities/checkpointing/torch/torch_checkpoint_loading.py b/src/modalities/checkpointing/torch/torch_checkpoint_loading.py index c29cea27c..2837c42a2 100644 --- a/src/modalities/checkpointing/torch/torch_checkpoint_loading.py +++ b/src/modalities/checkpointing/torch/torch_checkpoint_loading.py @@ -51,7 +51,7 @@ def load_model_checkpoint(self, model: nn.Module, file_path: Path) -> nn.Module: if self.precision is not None and self.precision.value != model_state_dtype: warning( f"WARNING: Model checkpoint was stored with precision {model_state_dtype} " - "but is loaded with precision {self.precision.value}." + f"but is loaded with precision {self.precision.value}." ) # assign=True makes sure that the model is loaded with the same precision diff --git a/src/modalities/conversion/__init__.py b/src/modalities/conversion/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/modalities/conversion/gpt2/__init__.py b/src/modalities/conversion/gpt2/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/modalities/conversion/gpt2/configuration_gpt2.py b/src/modalities/conversion/gpt2/configuration_gpt2.py new file mode 100644 index 000000000..7663cd227 --- /dev/null +++ b/src/modalities/conversion/gpt2/configuration_gpt2.py @@ -0,0 +1,226 @@ +# coding=utf-8 +# This code was copied and modified from the Llama implementation of the Hugging Face Transformers library. +# The original code can be found at: +# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/configuration_llama.py +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""LLaMA-like GPT2 model configuration""" + +from transformers.configuration_utils import PretrainedConfig +from transformers.modeling_rope_utils import rope_config_validation + + +class GPT2Config(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`GPT2Model`]. It is used to instantiate an GPT2 + model according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the LLaMA-7B. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 32000): + Vocabulary size of the GPT2 model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`GPT2Model`] + hidden_size (`int`, *optional*, defaults to 4096): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 11008): + Dimension of the MLP representations. + num_hidden_layers (`int`, *optional*, defaults to 32): + Number of hidden layers in the Transformer decoder. + num_attention_heads (`int`, *optional*, defaults to 32): + Number of attention heads for each attention layer in the Transformer decoder. + num_key_value_heads (`int`, *optional*): + This is the number of key_value heads that should be used to implement Grouped Query Attention. If + `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if + `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When + converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed + by meanpooling all the original heads within that group. For more details checkout [this + paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to + `num_attention_heads`. + hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): + The non-linear activation function (function or string) in the decoder. + max_position_embeddings (`int`, *optional*, defaults to 2048): + The maximum sequence length that this model might ever be used with. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + rms_norm_eps (`float`, *optional*, defaults to 1e-06): + The epsilon used by the rms normalization layers. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). Only + relevant if `config.is_decoder=True`. + pad_token_id (`int`, *optional*): + Padding token id. + bos_token_id (`int`, *optional*, defaults to 1): + Beginning of stream token id. + eos_token_id (`int`, *optional*, defaults to 2): + End of stream token id. + pretraining_tp (`int`, *optional*, defaults to 1): + Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this + document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to + understand more about it. This value is necessary to ensure exact reproducibility of the pretraining + results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232). + tie_word_embeddings (`bool`, *optional*, defaults to `False`): + Whether to tie weight embeddings + rope_theta (`float`, *optional*, defaults to 10000.0): + The base period of the RoPE embeddings. + rope_scaling (`Dict`, *optional*): + Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type + and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value + accordingly. + Expected contents: + `rope_type` (`str`): + The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', + 'llama3'], with 'default' being the original RoPE implementation. + `factor` (`float`, *optional*): + Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In + most scaling types, a `factor` of x will enable the model to handle sequences of length x * + original maximum pre-trained length. + `original_max_position_embeddings` (`int`, *optional*): + Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during + pretraining. + `attention_factor` (`float`, *optional*): + Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention + computation. If unspecified, it defaults to value recommended by the implementation, using the + `factor` field to infer the suggested value. + `beta_fast` (`float`, *optional*): + Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear + ramp function. If unspecified, it defaults to 32. + `beta_slow` (`float`, *optional*): + Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear + ramp function. If unspecified, it defaults to 1. + `short_factor` (`List[float]`, *optional*): + Only used with 'longrope'. The scaling factor to be applied to short contexts (< + `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden + size divided by the number of attention heads divided by 2 + `long_factor` (`List[float]`, *optional*): + Only used with 'longrope'. The scaling factor to be applied to long contexts (< + `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden + size divided by the number of attention heads divided by 2 + `low_freq_factor` (`float`, *optional*): + Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE + `high_freq_factor` (`float`, *optional*): + Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE + attention_bias (`bool`, *optional*, defaults to `False`): + Whether to use a bias in the query, key, value and output projection layers during self-attention. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + mlp_bias (`bool`, *optional*, defaults to `False`): + Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers. + head_dim (`int`, *optional*): + The attention head dimension. If None, it will default to hidden_size // num_heads + + ```python + >>> from transformers import GPT2Model, GPT2Config + + >>> # Initializing a GPT2 with a llama-7b style configuration + >>> configuration = GPT2Config() + + >>> # Initializing a model from the llama-7b style configuration + >>> model = GPT2Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "modalities-gpt2" + keys_to_ignore_at_inference = ["past_key_values"] + # Default tensor parallel plan for base model `GPT2Model` + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise", + "layers.*.self_attn.k_proj": "colwise", + "layers.*.self_attn.v_proj": "colwise", + "layers.*.self_attn.o_proj": "rowwise", + "layers.*.mlp.gate_proj": "colwise", + "layers.*.mlp.up_proj": "colwise", + "layers.*.mlp.down_proj": "rowwise", + } + + def __init__( + self, + vocab_size=32000, + hidden_size=4096, + intermediate_size=11008, + num_hidden_layers=32, + num_attention_heads=32, + num_key_value_heads=None, + hidden_act="silu", + max_position_embeddings=2048, + initializer_range=0.02, + rms_norm_eps=None, + layer_norm_eps: float = 1e-06, + layer_norm_bias: bool = True, + layer_norm_elementwise_affine: bool = True, + use_cache=True, + pad_token_id=None, + bos_token_id=1, + eos_token_id=2, + pretraining_tp=1, + tie_word_embeddings=False, + rope_theta=10000.0, + rope_scaling=None, + attention_bias=False, + attention_dropout=0.0, + mlp_bias=False, + head_dim=None, + **kwargs, + ): + if rms_norm_eps is not None: + raise ValueError("RMSNorm is not supported in GPT2 model.") + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + + # for backward compatibility + if num_key_value_heads is None: + num_key_value_heads = num_attention_heads + + self.num_key_value_heads = num_key_value_heads + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.layer_norm_eps = layer_norm_eps + self.layer_norm_bias = layer_norm_bias + self.layer_norm_elementwise_affine = layer_norm_elementwise_affine + self.pretraining_tp = pretraining_tp + self.use_cache = use_cache + self.rope_theta = rope_theta + self.rope_scaling = rope_scaling + self.attention_bias = attention_bias + self.attention_dropout = attention_dropout + self.mlp_bias = mlp_bias + self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads + # Validate the correctness of rotary position embeddings parameters + # BC: if there is a 'type' field, copy it it to 'rope_type'. + if self.rope_scaling is not None and "type" in self.rope_scaling: + self.rope_scaling["rope_type"] = self.rope_scaling["type"] + rope_config_validation(self) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/src/modalities/conversion/gpt2/conversion_code.py b/src/modalities/conversion/gpt2/conversion_code.py new file mode 100644 index 000000000..5f2b36c05 --- /dev/null +++ b/src/modalities/conversion/gpt2/conversion_code.py @@ -0,0 +1,32 @@ +import os +import shutil + + +def _copy_model_files(output_dir: str): + source_dir = os.path.dirname(__file__) + modeling_gpt2_path = os.path.join(source_dir, "modeling_gpt2.py") + configuration_gpt2_path = os.path.join(source_dir, "configuration_gpt2.py") + shutil.copy(modeling_gpt2_path, output_dir) + shutil.copy(configuration_gpt2_path, output_dir) + + +def _change_modalities_import_to_relative_import(output_dir: str): + target_modeling_file = os.path.join(output_dir, "modeling_gpt2.py") + with open(target_modeling_file, "r") as file: + content = file.read() + content = content.replace("modalities.conversion.gpt2.configuration_gpt2", ".configuration_gpt2") + with open(target_modeling_file, "w") as file: + file.write(content) + + +def transfer_model_code(output_dir: str): + """Copies the required model code to the output directory and replaces modalities imports. + This allows the converted model to be used without the modalities package via: + >>> from transformers import AutoModelForCausalLM + >>> model = AutoModelForCausalLM.from_pretrained("path/to/converted/model", trust_remote_code=True) + + Args: + output_dir (str): Directory of the converted model. + """ + _copy_model_files(output_dir) + _change_modalities_import_to_relative_import(output_dir) diff --git a/src/modalities/conversion/gpt2/conversion_model.py b/src/modalities/conversion/gpt2/conversion_model.py new file mode 100644 index 000000000..67e045262 --- /dev/null +++ b/src/modalities/conversion/gpt2/conversion_model.py @@ -0,0 +1,170 @@ +import torch +import torch.nn as nn +from tqdm import tqdm + +from modalities.conversion.gpt2.configuration_gpt2 import GPT2Config +from modalities.conversion.gpt2.modeling_gpt2 import GPT2DecoderLayer, GPT2ForCausalLM +from modalities.models.components.layer_norms import LayerNormConfig +from modalities.models.gpt2.gpt2_model import GPT2LLM, GPT2Block, PositionTypes +from modalities.models.model import SwiGLU +from modalities.models.utils import ModelTypeEnum, get_model_from_config + + +def convert_model_checkpoint(modalities_config: dict) -> tuple[GPT2ForCausalLM, GPT2LLM]: + """Converts the modalities model to a Huggingface transformers model. + Both the loaded modalities model and the converted Huggingface model are returned + so that they can be compared. + + Args: + modalities_config (dict): Modalities config dictionary. + + Returns: + tuple[GPT2ForCausalLM, GPT2LLM]: Converted Hugging Face model and the original modalities model. + """ + gpt2_config = convert_model_config(modalities_config) + hf_model = GPT2ForCausalLM(gpt2_config).to(dtype=torch.bfloat16) + modalities_model = get_model_from_config(modalities_config, model_type=ModelTypeEnum.CHECKPOINTED_MODEL) + _copy_weights_model(hf_model, modalities_model) + return hf_model, modalities_model + + +def convert_model_config(modalities_config: dict) -> GPT2Config: + """Converts the modalities model configuration to a Huggingface transformers configuration. + For this the model_raw or model section of the modalities config is used. + Corresponding entries are mapped to the Huggingface configuration. + + Args: + modalities_config (dict): Modalities config dictionary. + + Returns: + GPT2Config: Converted Huggingface model configuration. + """ + config = modalities_config["model_raw" if "model_raw" in modalities_config else "model"]["config"] + _check_conversion_criteria(config) + + return GPT2Config( + vocab_size=config["vocab_size"], + hidden_size=config["n_embd"], + pad_token_id=None, + num_hidden_layers=config["n_layer"], + num_key_value_heads=config["n_head_kv"], + num_attention_heads=config["n_head_q"], + intermediate_size=SwiGLU._get_hidden_dim(ffn_hidden=config["ffn_hidden"]), + attention_bias=config["bias"], + mlp_bias=config["bias"], + hidden_act="silu", + layer_norm_eps=_get_layer_norm_value(config["ffn_norm"]["config"], "eps"), + layer_norm_elementwise_affine=_get_layer_norm_value(config["ffn_norm"]["config"], "elementwise_affine"), + layer_norm_bias=_get_layer_norm_value(config["ffn_norm"]["config"], "bias"), + max_position_embeddings=config["sequence_length"], + rope_theta=config["attention_config"]["qkv_transforms"][0]["config"]["base_freq"], + _attn_implementation=_map_attention_type(config), + output_attentions=False, + ) + + +def check_converted_model(hf_model: GPT2ForCausalLM, modalities_model: GPT2LLM, num_testruns: int, vocab_size: int): + """Tests the converted model by inputting a random token sequence and comparing the output logits of both models. + + Args: + hf_model (GPT2ForCausalLM): Huggingface transformers model. + modalities_model (GPT2LLM): Modalities model. + num_testruns (int): Number of test runs to perform. + vocab_size (int): Vocabulary size of the model. (Required for generating random input tokens.) + """ + for _ in tqdm(range(num_testruns), desc="Testing converted model"): + input_ids = torch.randint(0, vocab_size, (1, modalities_model.sequence_length), device=hf_model.device) + inputs = {modalities_model.sample_key: input_ids.to(modalities_model.transformer.wte.weight.device)} + + with torch.no_grad(): + llama_logits = hf_model(input_ids=input_ids).logits.to("cpu") + modalities_logits = modalities_model(inputs)[modalities_model.prediction_key].to("cpu") + + assert llama_logits.shape == modalities_logits.shape + assert torch.equal(llama_logits, modalities_logits) + + +def _check_conversion_criteria(model_config: dict) -> None: + """Checks that the modalities config fulfills criteria necessary for conversion + + Args: + model_config (dict): model or model_raw part of the Modalities config dictionary. + + Returns: + None + """ + assert model_config["poe_type"] == PositionTypes.NOPE + assert model_config["activation_type"] == "swiglu" + assert model_config["attention_implementation"] in ["pytorch_flash", "manual"] + + norms = ["attention_norm", "ffn_norm", "lm_head_norm"] + for norm in norms: + assert model_config[norm]["variant_key"] == "layer_norm" + + assert ( + len(set(_get_layer_norm_value(model_config[norm]["config"], "bias") for norm in norms)) == 1 + ), "All norms must have the same bias setting." + assert ( + len(set(_get_layer_norm_value(model_config[norm]["config"], "elementwise_affine") for norm in norms)) == 1 + ), "All norms must have the same elementwise_affine setting." + assert ( + len(set(_get_layer_norm_value(model_config[norm]["config"], "eps") for norm in norms)) == 1 + ), "All norms must have the same eps setting." + + +def _get_layer_norm_value(config: dict, field: str) -> bool | float | int: + default = LayerNormConfig.model_fields[field].default + return config.get(field, default) + + +def _map_attention_type(config: dict): + if config["attention_implementation"] == "pytorch_flash": + attention_impl = "sdpa" + elif config["attention_implementation"] == "manual": + attention_impl = "eager" + else: + raise ValueError(f"Unknown or unsupported attention implementation {config['attention_implementation']}.") + return attention_impl + + +def _copy_weights_model(hf_model: GPT2ForCausalLM, modalities_model: GPT2LLM): + """Copies the weights of the modalities model to the Huggingface transformers model. + + Args: + hf_model (GPT2ForCausalLM): The uninitialized Huggingface transformers model. + The weights will be copied here. + modalities_model (GPT2LLM): The modalities model from which the weights will be copied. + """ + hf_model.model.embed_tokens.weight.data.copy_(modalities_model.transformer.wte.weight.data) + for hf_layer, modalities_layer in zip(hf_model.model.layers, modalities_model.transformer.h): + _copy_weights_attention(hf_layer, modalities_layer) + _copy_weights_mlp(hf_layer, modalities_layer) + _copy_weights_layer_norms(hf_layer, modalities_layer) + _copy_weights_base_modules(hf_model.lm_head, modalities_model.lm_head) + _copy_weights_base_modules(hf_model.model.norm, modalities_model.transformer.lm_head_norm) + + +def _copy_weights_attention(hf_layer: GPT2DecoderLayer, modalities_layer: GPT2Block): + _copy_weights_base_modules(hf_layer.self_attn.q_proj, modalities_layer.attn.q_attn) + _copy_weights_base_modules(hf_layer.self_attn.k_proj, modalities_layer.attn.k_attn) + _copy_weights_base_modules(hf_layer.self_attn.v_proj, modalities_layer.attn.v_attn) + _copy_weights_base_modules(hf_layer.self_attn.o_proj, modalities_layer.attn.c_proj) + + +def _copy_weights_mlp(hf_layer: GPT2DecoderLayer, modalities_layer: GPT2Block): + _copy_weights_base_modules(hf_layer.mlp.down_proj, modalities_layer.mlp.W_2) + _copy_weights_base_modules(hf_layer.mlp.gate_proj, modalities_layer.mlp.W) + _copy_weights_base_modules(hf_layer.mlp.up_proj, modalities_layer.mlp.V) + + +def _copy_weights_layer_norms(hf_layer: GPT2DecoderLayer, modalities_layer: GPT2Block): + _copy_weights_base_modules(hf_layer.input_layernorm, modalities_layer.attention_norm) + _copy_weights_base_modules(hf_layer.post_attention_layernorm, modalities_layer.ffn_norm) + + +def _copy_weights_base_modules(m1: nn.Linear | nn.LayerNorm, m2: nn.Linear | nn.LayerNorm): + assert m1.weight.shape == m2.weight.shape + assert (m1.bias is None and m2.bias is None) or m1.bias.shape == m2.bias.shape + m1.weight.data.copy_(m2.weight.data) + if m1.bias is not None: + m1.bias.data.copy_(m2.bias.data) diff --git a/src/modalities/conversion/gpt2/convert_gpt2.py b/src/modalities/conversion/gpt2/convert_gpt2.py new file mode 100644 index 000000000..bb2bc1d3b --- /dev/null +++ b/src/modalities/conversion/gpt2/convert_gpt2.py @@ -0,0 +1,89 @@ +""" +usage: convert_gpt2.py [-h] [--num_testruns NUM_TESTRUNS] [--device_modalities DEVICE_MODALITIES] + [--device_hf DEVICE_HF] modalities_config output_dir + +Convert GPT-2 model checkpoint to Huggingface transformers format. + +positional arguments: + modalities_config Path to the modalities config file. + output_dir Directory to save the converted model. + +options: + -h, --help show this help message and exit + --num_testruns NUM_TESTRUNS + Number of test runs to perform. + --device_modalities DEVICE_MODALITIES + Device for the modalities model. + --device_hf DEVICE_HF + Device for the Hugging Face model. +""" + +import argparse +import os +from pathlib import Path + +from modalities.config.config import load_app_config_dict +from modalities.conversion.gpt2.conversion_code import transfer_model_code +from modalities.conversion.gpt2.conversion_model import check_converted_model, convert_model_checkpoint + + +def convert_gpt2( + modalities_config_path: str, + output_dir: str, + num_testruns: int = 0, + device_modalities: str = "cpu", + device_hf: str = "cpu", +) -> None: + """Takes a modalities gpt2 model and converts it to a Huggingface transformers model. + The provided config yaml file should contain the model_raw or model section with the model configuration. + Additionally, the checkpointed_model section should be present and contain the path to the model checkpoint. + Optionally, the function can run a number of test runs to compare the converted model with the original one. + + Args: + modalities_config_path (str): Path to the modalities config file. + output_dir (str): Directory to save the converted model. + num_testruns (int, optional): Number of test runs to perform. Defaults to 0. + device_modalities (str, optional): Device for the modalities model. Defaults to "cpu". + device_hf (str, optional): Device for the Hugging Face model. Defaults to "cpu". + """ + modalities_config = load_app_config_dict(Path(modalities_config_path)) + hf_model, modalities_model = convert_model_checkpoint(modalities_config) + + if num_testruns > 0: + check_converted_model( + hf_model.to(device_hf), + modalities_model.to(device_modalities), + num_testruns, + modalities_config["model_raw" if "model_raw" in modalities_config else "model"]["config"]["vocab_size"], + ) + + hf_model.config.auto_map = { + "AutoConfig": "configuration_gpt2.GPT2Config", + "AutoModel": "modeling_gpt2.GPT2Model", + "AutoModelForCausalLM": "modeling_gpt2.GPT2ForCausalLM", + } + hf_model.save_pretrained(output_dir) + transfer_model_code(output_dir) + + +if __name__ == "__main__": + os.environ["LOCAL_RANK"] = "0" + os.environ["WORLD_SIZE"] = "1" + os.environ["RANK"] = "0" + + parser = argparse.ArgumentParser(description="Convert GPT-2 model checkpoint to Huggingface transformers format.") + parser.add_argument("modalities_config", type=str, help="Path to the modalities config file.") + parser.add_argument("output_dir", type=str, help="Directory to save the converted model.") + parser.add_argument("--num_testruns", type=int, default=0, help="Number of test runs to perform.") + parser.add_argument("--device_modalities", type=str, default="cpu", help="Device for the modalities model.") + parser.add_argument("--device_hf", type=str, default="cpu", help="Device for the Hugging Face model.") + + args = parser.parse_args() + + convert_gpt2( + args.modalities_config, + args.output_dir, + args.num_testruns, + args.device_modalities, + args.device_hf, + ) diff --git a/src/modalities/conversion/gpt2/modeling_gpt2.py b/src/modalities/conversion/gpt2/modeling_gpt2.py new file mode 100644 index 000000000..f803d2f07 --- /dev/null +++ b/src/modalities/conversion/gpt2/modeling_gpt2.py @@ -0,0 +1,1479 @@ +# coding=utf-8 +# This code was copied and modified from the Llama implementation of the Hugging Face Transformers library. +# The original code can be found at: +# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py +# Original license information: +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import math +from typing import List, Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn +from transformers.activations import ACT2FN +from transformers.cache_utils import Cache, DynamicCache, StaticCache +from transformers.generation import GenerationMixin +from transformers.modeling_attn_mask_utils import AttentionMaskConverter +from transformers.modeling_flash_attention_utils import FlashAttentionKwargs, _flash_attention_forward +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, + QuestionAnsweringModelOutput, + SequenceClassifierOutputWithPast, + TokenClassifierOutput, +) +from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS +from transformers.modeling_utils import PreTrainedModel +from transformers.processing_utils import Unpack +from transformers.utils import ( + LossKwargs, + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_flash_attn_greater_or_equal_2_10, + logging, + replace_return_docstrings, +) + +from modalities.conversion.gpt2.configuration_gpt2 import GPT2Config + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "meta-llama/Llama-2-7b-hf" # TODO: update to the actual checkpoint +_CONFIG_FOR_DOC = "GPT2Config" + + +class LlamaRotaryEmbedding(nn.Module): + def __init__( + self, + dim=None, + max_position_embeddings=2048, + base=10000, + device=None, + scaling_factor=1.0, + rope_type="default", + config: Optional[GPT2Config] = None, + ): + super().__init__() + # TODO (joao): remove the `if` below, only used for BC + self.rope_kwargs = {} + if config is None: + logger.warning_once( + "`LlamaRotaryEmbedding` can now be fully parameterized by passing the model config through the " + "`config` argument. All other arguments will be removed in v4.46" + ) + self.rope_kwargs = { + "rope_type": rope_type, + "factor": scaling_factor, + "dim": dim, + "base": base, + "max_position_embeddings": max_position_embeddings, + } + self.rope_type = rope_type + self.max_seq_len_cached = max_position_embeddings + self.original_max_seq_len = max_position_embeddings + else: + # BC: "rope_type" was originally "type" + if config.rope_scaling is not None: + self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) + else: + self.rope_type = "default" + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.original_inv_freq = self.inv_freq + + def _dynamic_frequency_update(self, position_ids, device): + """ + dynamic RoPE layers should recompute `inv_freq` in the following situations: + 1 - growing beyond the cached sequence length (allow scaling) + 2 - the current sequence length is in the original scale (avoid losing precision with small sequences) + """ + seq_len = torch.max(position_ids) + 1 + if seq_len > self.max_seq_len_cached: # growth + inv_freq, self.attention_scaling = self.rope_init_fn( + self.config, device, seq_len=seq_len, **self.rope_kwargs + ) + self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: may break with compilation + self.max_seq_len_cached = seq_len + + if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset + self.register_buffer("inv_freq", self.original_inv_freq, persistent=False) + self.max_seq_len_cached = self.original_max_seq_len + + @torch.no_grad() + def forward(self, x, position_ids): + if "dynamic" in self.rope_type: + self._dynamic_frequency_update(position_ids, device=x.device) + + # Core RoPE block + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) + position_ids_expanded = position_ids[:, None, :].float() + # Force float32 (see https://github.com/huggingface/transformers/pull/29285) + device_type = x.device.type + device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() + sin = emb.sin() + + # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention + cos = cos * self.attention_scaling + sin = sin * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding): + """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" + + def __init__(self, *args, **kwargs): + logger.warning_once( + "`LlamaLinearScalingRotaryEmbedding` is deprecated an will be removed in v4.46. Please use " + "`LlamaRotaryEmbedding`, which now also does linear scaling (simply pass the model config to __init__)." + ) + kwargs["rope_type"] = "linear" + super().__init__(*args, **kwargs) + + +class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding): + """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" + + def __init__(self, *args, **kwargs): + logger.warning_once( + "`LlamaDynamicNTKScalingRotaryEmbedding` is deprecated an will be removed in v4.46. Please use " + "`LlamaRotaryEmbedding`, which now also does dynamic ntk scaling (simply pass the model config to " + "__init__)." + ) + kwargs["rope_type"] = "dynamic" + super().__init__(*args, **kwargs) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + position_ids (`torch.Tensor`, *optional*): + Deprecated and unused. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +class LlamaMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +class LlamaAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: GPT2Config, layer_idx: Optional[int] = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " + "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.attention_dropout = config.attention_dropout + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads) + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.max_position_embeddings = config.max_position_embeddings + self.rope_theta = config.rope_theta + self.is_causal = True + + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias) + + # TODO (joao): remove in v4.46 (RoPE is computed in the model, not in the decoder layers) + self.rotary_emb = LlamaRotaryEmbedding(config=self.config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + # use -1 to infer num_heads and num_key_value_heads as they may vary if tensor parallel is used + query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) + + if position_embeddings is None: + logger.warning_once( + "The attention layers in this model are transitioning from computing the RoPE embeddings internally " + "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed " + "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be " + "removed and `position_embeddings` will be mandatory." + ) + cos, sin = self.rotary_emb(value_states, position_ids) + else: + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_value is not None: + # sin and cos are specific to RoPE models; cache_position needed for the static cache + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attention_mask is not None: # no matter the length, we just slice it + causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] + attn_weights = attn_weights + causal_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + + attn_output = attn_output.reshape(bsz, q_len, -1) + + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class LlamaFlashAttention2(LlamaAttention): + """ + Llama flash attention module. This module inherits from `LlamaAttention` as the weights of the module stays + untouched. The only required change would be on the forward pass where it needs to correctly call the public API of + flash attention and deal with padding tokens in case the input contains any of them. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is + # needed here is bottom-right alignement, that was made default for flash_attn>=2.1. + # This attribute is used to handle this difference. Reference: + # https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen + # (except for the case q_seqlen == 1) produces a wrong mask (top-left). + self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 + **kwargs: Unpack[FlashAttentionKwargs], + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if isinstance(past_key_value, StaticCache): + raise ValueError( + "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` " + "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers" + ) + + output_attentions = False + + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + # Flash attention requires the input to have the shape + # batch_size x seq_length x head_dim x hidden_dim + # therefore we just need to keep the original shape + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + if position_embeddings is None: + logger.warning_once( + "The attention layers in this model are transitioning from computing the RoPE embeddings internally " + "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed " + "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be " + "removed and `position_embeddings` will be mandatory." + ) + cos, sin = self.rotary_emb(value_states, position_ids) + else: + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_value is not None: + # sin and cos are specific to RoPE models; cache_position needed for the static cache + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + # TODO: These transpose are quite inefficient but Flash Attention requires the layout + # [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache + # to be able to avoid many of these transpose/reshape/view. + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + dropout_rate = self.attention_dropout if self.training else 0.0 + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in the correct dtype just to be sure everything works as expected. + # This might slowdown training & inference so it is recommended to not cast the LayerNorms + # in fp32. (LlamaRMSNorm handles it correctly) + + input_dtype = query_states.dtype + if input_dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + # Handle the case where the model is quantized + elif hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + else: + target_dtype = self.q_proj.weight.dtype + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + attn_output = _flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + position_ids=position_ids, + dropout=dropout_rate, + sliding_window=getattr(self, "sliding_window", None), + use_top_left_mask=self._flash_attn_uses_top_left_mask, + is_causal=self.is_causal, + **kwargs, + ) + + attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class LlamaSdpaAttention(LlamaAttention): + """ + Llama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from + `LlamaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to + SDPA API. + """ + + # Adapted from LlamaAttention.forward + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if output_attentions: + # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` + # once this is implemented. + logger.warning_once( + "LlamaModel is using LlamaSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does " + "not support `output_attentions=True`. Falling back to the manual attention implementation, " + "but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. " + 'This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' + ) + return super().forward( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + ) + + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + # use -1 to infer num_heads and num_key_value_heads as they may vary if tensor parallel is used + query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) + + if position_embeddings is None: + logger.warning_once( + "The attention layers in this model are transitioning from computing the RoPE embeddings internally " + "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed " + "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be " + "removed and `position_embeddings` will be mandatory." + ) + cos, sin = self.rotary_emb(value_states, position_ids) + else: + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_value is not None: + # sin and cos are specific to RoPE models; cache_position needed for the static cache + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + causal_mask = attention_mask + if attention_mask is not None: + causal_mask = causal_mask[:, :, :, : key_states.shape[-2]] + + # SDPA with memory-efficient backend is currently (torch==2.1.2) + # bugged with non-contiguous inputs with custom attn_mask, + # Reference: https://github.com/pytorch/pytorch/issues/112577. + if query_states.device.type == "cuda" and causal_mask is not None: + query_states = query_states.contiguous() + key_states = key_states.contiguous() + value_states = value_states.contiguous() + + # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` + # if statement instead of an inline conditional assignment + # in SDPA to support both torch.compile's dynamic shapes and full graph options. + # An inline conditional prevents dynamic shapes from compiling. + is_causal = True if causal_mask is None and q_len > 1 else False + + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=causal_mask, + dropout_p=self.attention_dropout if self.training else 0.0, + is_causal=is_causal, + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.view(bsz, q_len, -1) + + attn_output = self.o_proj(attn_output) + + return attn_output, None, past_key_value + + +LLAMA_ATTENTION_CLASSES = { + "eager": LlamaAttention, + "flash_attention_2": LlamaFlashAttention2, + "sdpa": LlamaSdpaAttention, +} + + +class GPT2DecoderLayer(nn.Module): + def __init__(self, config: GPT2Config, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx) + + self.mlp = LlamaMLP(config) + self.input_layernorm = nn.LayerNorm( + config.hidden_size, + eps=config.layer_norm_eps, + elementwise_affine=config.layer_norm_elementwise_affine, + bias=config.layer_norm_bias, + ) + self.post_attention_layernorm = nn.LayerNorm( + config.hidden_size, + eps=config.layer_norm_eps, + elementwise_affine=config.layer_norm_elementwise_affine, + bias=config.layer_norm_bias, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 + **kwargs, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): + attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1, + query_sequence_length, key_sequence_length)` if default attention is used. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): + Indices depicting the position of the input sequence tokens in the sequence + position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*): + Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`, + with `head_dim` being the embedding dimension of each attention head. + kwargs (`dict`, *optional*): + Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code + into the model + """ + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +LLAMA_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`LlamaConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare LLaMA Model outputting raw hidden-states without any specific head on top.", + LLAMA_START_DOCSTRING, +) +class GPT2PreTrainedModel(PreTrainedModel): + config_class = GPT2Config + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["LlamaDecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn_2 = True + _supports_sdpa = True + _supports_cache_class = True + _supports_quantized_cache = True + _supports_static_cache = True + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + +LLAMA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. + + [What are position IDs?](../glossary#position-ids) + past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): + Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` + returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. + + Two formats are allowed: + - a [`~cache_utils.Cache`] instance, see our + [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache); + - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy + cache format. + + The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the + legacy cache format will be returned. + + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't + have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` + of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): + Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, + this tensor is not affected by padding. It is used to update the cache in the correct position and to infer + the complete sequence length. +""" + + +@add_start_docstrings( + "The bare LLaMA Model outputting raw hidden-states without any specific head on top.", + LLAMA_START_DOCSTRING, +) +class GPT2Model(GPT2PreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`] + + Args: + config: LlamaConfig + """ + + def __init__(self, config: GPT2Config): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [GPT2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = nn.LayerNorm( + config.hidden_size, + eps=config.layer_norm_eps, + elementwise_affine=config.layer_norm_elementwise_affine, + bias=config.layer_norm_bias, + ) + self.rotary_emb = LlamaRotaryEmbedding(config=config) + + self.gradient_checkpointing = False + if getattr(config, "pretraining_tp", 1) != 1: + logger.warn("`pretraining_tp` is deprecated, please use `model.tensor_parallel` instead.") + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + **flash_attn_kwargs: Unpack[FlashAttentionKwargs], + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if self.gradient_checkpointing and self.training and use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`." + ) + use_cache = False + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + # kept for BC (non `Cache` `past_key_values` inputs) + return_legacy_cache = False + if use_cache and not isinstance(past_key_values, Cache): + return_legacy_cache = True + if past_key_values is None: + past_key_values = DynamicCache() + else: + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + logger.warning_once( + "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and " + "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class " + "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)" + ) + + if cache_position is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + cache_position = torch.arange( + past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device + ) + if position_ids is None: + position_ids = cache_position.unsqueeze(0) + + causal_mask = self._update_causal_mask( + attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions + ) + hidden_states = inputs_embeds + + # create position embeddings to be shared across the decoder layers + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = None + + for decoder_layer in self.layers[: self.config.num_hidden_layers]: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + causal_mask, + position_ids, + past_key_values, + output_attentions, + use_cache, + cache_position, + position_embeddings, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + **flash_attn_kwargs, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + if return_legacy_cache: + next_cache = next_cache.to_legacy_cache() + + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + def _update_causal_mask( + self, + attention_mask: torch.Tensor, + input_tensor: torch.Tensor, + cache_position: torch.Tensor, + past_key_values: Cache, + output_attentions: bool, + ): + if self.config._attn_implementation == "flash_attention_2": + if attention_mask is not None and 0.0 in attention_mask: + return attention_mask + return None + + # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in + # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail + # to infer the attention mask. + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + using_static_cache = isinstance(past_key_values, StaticCache) + + # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward + if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: + if AttentionMaskConverter._ignore_causal_mask_sdpa( + attention_mask, + inputs_embeds=input_tensor, + past_key_values_length=past_seen_tokens, + is_training=self.training, + ): + return None + + dtype, device = input_tensor.dtype, input_tensor.device + sequence_length = input_tensor.shape[1] + if using_static_cache: + target_length = past_key_values.get_max_cache_shape() + else: + target_length = ( + attention_mask.shape[-1] + if isinstance(attention_mask, torch.Tensor) + else past_seen_tokens + sequence_length + 1 + ) + + # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). + causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( + attention_mask, + sequence_length=sequence_length, + target_length=target_length, + dtype=dtype, + device=device, + cache_position=cache_position, + batch_size=input_tensor.shape[0], + ) + + if ( + self.config._attn_implementation == "sdpa" + and attention_mask is not None + and attention_mask.device.type == "cuda" + and not output_attentions + ): + # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when + # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. + # Details: https://github.com/pytorch/pytorch/issues/110213 + min_dtype = torch.finfo(dtype).min + causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) + + return causal_mask + + @staticmethod + def _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask: torch.Tensor, + sequence_length: int, + target_length: int, + dtype: torch.dtype, + device: torch.device, + cache_position: torch.Tensor, + batch_size: int, + **kwargs, + ): + """ + Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape + `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. + + Args: + attention_mask (`torch.Tensor`): + A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape + `(batch_size, 1, query_length, key_value_length)`. + sequence_length (`int`): + The sequence length being processed. + target_length (`int`): + The target length: when generating with static cache, the mask should be as long as the static cache, + to account for the 0 padding, the part of the cache that is not filled yet. + dtype (`torch.dtype`): + The dtype to use for the 4D attention mask. + device (`torch.device`): + The device to plcae the 4D attention mask on. + cache_position (`torch.Tensor`): + Indices depicting the position of the input sequence tokens in the sequence. + batch_size (`torch.Tensor`): + Batch size. + """ + if attention_mask is not None and attention_mask.dim() == 4: + # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. + causal_mask = attention_mask + else: + min_dtype = torch.finfo(dtype).min + causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device) + if sequence_length != 1: + causal_mask = torch.triu(causal_mask, diagonal=1) + causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) + causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) + if attention_mask is not None: + causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit + mask_length = attention_mask.shape[-1] + padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] + padding_mask = padding_mask == 0 + causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( + padding_mask, min_dtype + ) + + return causal_mask + + +class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): + ... + + +class GPT2ForCausalLM(GPT2PreTrainedModel, GenerationMixin): + _tied_weights_keys = ["lm_head.weight"] + _tp_plan = {"lm_head": "colwise_rep"} + + def __init__(self, config): + super().__init__(config) + self.model = GPT2Model(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + num_logits_to_keep: int = 0, + **kwargs: Unpack[KwargsForCausalLM], + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + num_logits_to_keep (`int`, *optional*): + Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all + `input_ids` (special case). Only last token logits are needed for generation, and calculating + them only for that token can save memory, which becomes pretty significant for long sequences + or large vocabulary size. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, GPT2ForCausalLM + + >>> model = GPT2ForCausalLM.from_pretrained("...") + >>> tokenizer = AutoTokenizer.from_pretrained("...") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + cache_position=cache_position, + **kwargs, + ) + + hidden_states = outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + """ + The LLaMa-like GPT2 Model transformer with a sequence classification head on top (linear layer). + + [`GPT2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + LLAMA_START_DOCSTRING, +) +class GPT2ForSequenceClassification(GPT2PreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = GPT2Model(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility + sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 + sequence_lengths = sequence_lengths % input_ids.shape[-1] + sequence_lengths = sequence_lengths.to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config) + + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + +@add_start_docstrings( + """ +The Llama-like Model transformer with a span classification head on top for extractive question-answering tasks like +SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`). + """, + LLAMA_START_DOCSTRING, +) +class GPT2ForQuestionAnswering(GPT2PreTrainedModel): + base_model_prefix = "transformer" + + # Copied from transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->Llama + def __init__(self, config): + super().__init__(config) + self.transformer = GPT2Model(config) + self.qa_outputs = nn.Linear(config.hidden_size, 2) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.transformer.embed_tokens + + def set_input_embeddings(self, value): + self.transformer.embed_tokens = value + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + start_positions: Optional[torch.LongTensor] = None, + end_positions: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + **kwargs, + ) -> Union[Tuple, QuestionAnsweringModelOutput]: + r""" + start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the start of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the end of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.transformer( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1).contiguous() + end_logits = end_logits.squeeze(-1).contiguous() + + loss = None + if start_positions is not None and end_positions is not None: + loss = self.loss_function(start_logits, end_logits, start_positions, end_positions, **kwargs) + + if not return_dict: + output = (start_logits, end_logits) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return QuestionAnsweringModelOutput( + loss=loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + """ + The Llama-like GPT2 Model transformer with a token classification head on top (a linear layer + on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. + """, + LLAMA_START_DOCSTRING, +) +class GPT2ForTokenClassification(GPT2PreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = GPT2Model(config) + if getattr(config, "classifier_dropout", None) is not None: + classifier_dropout = config.classifier_dropout + elif getattr(config, "hidden_dropout", None) is not None: + classifier_dropout = config.hidden_dropout + else: + classifier_dropout = 0.1 + self.dropout = nn.Dropout(classifier_dropout) + self.score = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=TokenClassifierOutput, + config_class=_CONFIG_FOR_DOC, + ) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, TokenClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = outputs[0] + sequence_output = self.dropout(sequence_output) + logits = self.score(sequence_output) + + loss = None + if labels is not None: + loss = self.loss_function(logits, labels, self.config) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/tests/conversion/__init__.py b/tests/conversion/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/conversion/gpt2/__init__.py b/tests/conversion/gpt2/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/conversion/gpt2/conftest.py b/tests/conversion/gpt2/conftest.py new file mode 100644 index 000000000..36280c92a --- /dev/null +++ b/tests/conversion/gpt2/conftest.py @@ -0,0 +1,56 @@ +import os +import shutil +from pathlib import Path + +import pytest +import torch + +from modalities.config.config import load_app_config_dict +from modalities.models.gpt2.gpt2_model import GPT2LLM +from modalities.models.utils import ModelTypeEnum, get_model_from_config +from tests.conftest import _ROOT_DIR + + +@pytest.fixture +def gpt2_config_path(tmpdir_factory: pytest.TempdirFactory, initialized_model: GPT2LLM, config_file_path: str) -> str: + tmp_path = tmpdir_factory.mktemp("gpt2_model") + new_config_filename = tmp_path / "gpt2_config_test.yaml" + model_path = tmp_path / "model.pth" + shutil.copy(config_file_path, new_config_filename) + torch.save(initialized_model.state_dict(), model_path) + with open(new_config_filename, "r") as file: + content = file.read() + content = content.replace("checkpoint_path: null", f"checkpoint_path: {model_path}") + with open(new_config_filename, "w") as file: + file.write(content) + return str(new_config_filename) + + +@pytest.fixture() +def initialized_model(set_env, modalities_config_dict: dict) -> GPT2LLM: + model = get_model_from_config(config=modalities_config_dict, model_type=ModelTypeEnum.MODEL) + assert isinstance(model, GPT2LLM) + return model + + +@pytest.fixture() +def set_env(): + os.environ["LOCAL_RANK"] = "0" + os.environ["RANK"] = "0" + os.environ["WORLD_SIZE"] = "1" + + +@pytest.fixture() +def modalities_config_dict(config_file_path: Path) -> dict: + return load_app_config_dict(config_file_path=config_file_path) + + +@pytest.fixture() +def config_file_path(config_file_name: str) -> Path: + config_file_path = _ROOT_DIR / Path("tests/conversion/test_configs/" + config_file_name) + return config_file_path + + +@pytest.fixture(params=["gpt2_config_test.yaml"]) +def config_file_name(request) -> str: + return request.param diff --git a/tests/conversion/gpt2/helper.py b/tests/conversion/gpt2/helper.py new file mode 100644 index 000000000..40eb75ba0 --- /dev/null +++ b/tests/conversion/gpt2/helper.py @@ -0,0 +1,41 @@ +import torch +import torch.nn as nn + +from modalities.conversion.gpt2.modeling_gpt2 import GPT2DecoderLayer, GPT2ForCausalLM +from modalities.models.gpt2.gpt2_model import GPT2LLM, GPT2Block + + +def check_same_weight_model(converted_model: GPT2ForCausalLM, modalities_model: GPT2LLM): + converted_model.to(device=modalities_model.transformer.h[0].attn.q_attn.weight.device) + assert torch.equal(converted_model.model.embed_tokens.weight, modalities_model.transformer.wte.weight) + for i, (llama_layer, modalities_layer) in enumerate( + zip(converted_model.model.layers, modalities_model.transformer.h) + ): + check_same_weight_attention(llama_layer, modalities_layer) + check_same_weight_mlp(llama_layer, modalities_layer) + check_same_weight_layer_norms(llama_layer, modalities_layer) + check_same_weight_base_modules(converted_model.lm_head, modalities_model.lm_head) + check_same_weight_base_modules(converted_model.model.norm, modalities_model.transformer.lm_head_norm) + + +def check_same_weight_attention(llama_layer: GPT2DecoderLayer, modalities_layer: GPT2Block): + check_same_weight_base_modules(llama_layer.self_attn.q_proj, modalities_layer.attn.q_attn) + check_same_weight_base_modules(llama_layer.self_attn.k_proj, modalities_layer.attn.k_attn) + check_same_weight_base_modules(llama_layer.self_attn.v_proj, modalities_layer.attn.v_attn) + check_same_weight_base_modules(llama_layer.self_attn.o_proj, modalities_layer.attn.c_proj) + + +def check_same_weight_mlp(llama_layer: GPT2DecoderLayer, modalities_layer: GPT2Block): + check_same_weight_base_modules(llama_layer.mlp.down_proj, modalities_layer.mlp.W_2) + check_same_weight_base_modules(llama_layer.mlp.gate_proj, modalities_layer.mlp.W) + check_same_weight_base_modules(llama_layer.mlp.up_proj, modalities_layer.mlp.V) + + +def check_same_weight_layer_norms(llama_layer: GPT2DecoderLayer, modalities_layer: GPT2Block): + check_same_weight_base_modules(llama_layer.input_layernorm, modalities_layer.attention_norm) + check_same_weight_base_modules(llama_layer.post_attention_layernorm, modalities_layer.ffn_norm) + + +def check_same_weight_base_modules(l1: nn.Linear | nn.LayerNorm, l2: nn.Linear | nn.LayerNorm): + assert torch.equal(l1.weight, l2.weight) + assert (l1.bias is None and l2.bias is None) or torch.equal(l1.bias, l2.bias) diff --git a/tests/conversion/gpt2/test_conversion_code.py b/tests/conversion/gpt2/test_conversion_code.py new file mode 100644 index 000000000..e24dd74fd --- /dev/null +++ b/tests/conversion/gpt2/test_conversion_code.py @@ -0,0 +1,33 @@ +from pathlib import Path + +from modalities.conversion.gpt2.conversion_code import transfer_model_code + + +def test_modeling_gpt2_gets_transferred_with_model_files(tmp_path: Path): + modeling_gpt2_path = tmp_path / "modeling_gpt2.py" + assert not modeling_gpt2_path.exists() + transfer_model_code(str(tmp_path)) + assert modeling_gpt2_path.exists() + + +def test_configuration_gpt2_gets_transferred_with_model_files(tmp_path: Path): + configuration_gpt2_path = tmp_path / "configuration_gpt2.py" + assert not configuration_gpt2_path.exists() + transfer_model_code(str(tmp_path)) + assert configuration_gpt2_path.exists() + + +def test_transferred_modeling_gpt2_does_not_import_from_modalities(tmp_path: Path): + transfer_model_code(str(tmp_path)) + with open(tmp_path / "modeling_gpt2.py") as f: + text = f.read() + assert "from modalities" not in text + assert "import modalities" not in text + + +def test_transferred_configuration_gpt2_does_not_import_from_modalities(tmp_path: Path): + transfer_model_code(str(tmp_path)) + with open(tmp_path / "configuration_gpt2.py") as f: + text = f.read() + assert "from modalities" not in text + assert "import modalities" not in text diff --git a/tests/conversion/gpt2/test_conversion_model.py b/tests/conversion/gpt2/test_conversion_model.py new file mode 100644 index 000000000..b75adaba0 --- /dev/null +++ b/tests/conversion/gpt2/test_conversion_model.py @@ -0,0 +1,61 @@ +import pytest +import torch +import torch.nn as nn + +from modalities.config.config import load_app_config_dict +from modalities.conversion.gpt2.conversion_model import ( + _copy_weights_base_modules, + check_converted_model, + convert_model_checkpoint, +) +from tests.conversion.gpt2.helper import check_same_weight_base_modules, check_same_weight_model + + +def test_convert_model_can_generate(gpt2_config_path: str): + modalities_config = load_app_config_dict(gpt2_config_path) + hf_model, _ = convert_model_checkpoint(modalities_config) + assert hf_model.can_generate() + + +def test_convert_model_checkpoint_does_not_change_weights(gpt2_config_path: str): + modalities_config = load_app_config_dict(gpt2_config_path) + hf_model, modalities_model = convert_model_checkpoint(modalities_config) + check_same_weight_model(hf_model, modalities_model) + + +def test_convert_model_checkpoint_produces_same_logits_as_original(gpt2_config_path: str): + modalities_config = load_app_config_dict(gpt2_config_path) + hf_model, modalities_model = convert_model_checkpoint(modalities_config) + vocab_size = modalities_config["model_raw" if "model_raw" in modalities_config else "model"]["config"]["vocab_size"] + check_converted_model(hf_model, modalities_model, num_testruns=1, vocab_size=vocab_size) + + +def test_copying_base_modules_weights_yields_identical_modules(): + m1 = nn.Linear(10, 10, bias=True) + m2 = nn.Linear(10, 10, bias=True) + m2.weight.data = torch.randn(10, 10) + m2.bias.data = torch.randn(10) + + _copy_weights_base_modules(m1, m2) + + check_same_weight_base_modules(m1, m2) + + +def test_copying_base_modules_works_when_bias_is_false(): + m1 = nn.Linear(10, 10, bias=False) + m2 = nn.Linear(10, 10, bias=False) + m2.weight.data = torch.randn(10, 10) + + _copy_weights_base_modules(m1, m2) + + check_same_weight_base_modules(m1, m2) + + +def test_copying_base_modules_fails_if_bias_settings_mismatch(): + m1 = nn.Linear(10, 10, bias=False) + m2 = nn.Linear(10, 10, bias=True) + m2.weight.data = torch.randn(10, 10) + m2.bias.data = torch.randn(10) + + with pytest.raises(AttributeError): + _copy_weights_base_modules(m1, m2) diff --git a/tests/conversion/gpt2/test_convert_gpt2.py b/tests/conversion/gpt2/test_convert_gpt2.py new file mode 100644 index 000000000..9764c8027 --- /dev/null +++ b/tests/conversion/gpt2/test_convert_gpt2.py @@ -0,0 +1,53 @@ +from pathlib import Path + +import pytest +import torch +from transformers import AutoModelForCausalLM, PreTrainedModel + +from modalities.config.config import load_app_config_dict +from modalities.conversion.gpt2.conversion_model import check_converted_model +from modalities.conversion.gpt2.convert_gpt2 import convert_gpt2 +from modalities.models.gpt2.gpt2_model import GPT2LLM +from modalities.models.utils import ModelTypeEnum, get_model_from_config +from tests.conversion.gpt2.helper import check_same_weight_model + + +def test_converting_gpt2_does_not_change_weights(converted_model: PreTrainedModel, original_model: GPT2LLM): + check_same_weight_model(converted_model, original_model) + + +def test_converting_gpt2_does_not_change_outputs( + converted_model: PreTrainedModel, original_model: GPT2LLM, vocab_size: int +): + check_converted_model( + hf_model=converted_model, modalities_model=original_model, num_testruns=1, vocab_size=vocab_size + ) + + +@pytest.fixture +def converted_model(run_convert_gpt2: None, output_dir: Path) -> PreTrainedModel: + return AutoModelForCausalLM.from_pretrained(output_dir, local_files_only=True, trust_remote_code=True).to( + dtype=torch.bfloat16 + ) + + +@pytest.fixture +def run_convert_gpt2(gpt2_config_path: str, output_dir: Path): + convert_gpt2(gpt2_config_path, output_dir) + + +@pytest.fixture +def original_model(gpt2_config_path: str) -> GPT2LLM: + modalities_config = load_app_config_dict(gpt2_config_path) + return get_model_from_config(modalities_config, model_type=ModelTypeEnum.CHECKPOINTED_MODEL) + + +@pytest.fixture +def vocab_size(gpt2_config_path: str) -> int: + modalities_config = load_app_config_dict(gpt2_config_path) + return modalities_config["model_raw" if "model_raw" in modalities_config else "model"]["config"]["vocab_size"] + + +@pytest.fixture +def output_dir(tmp_path: Path) -> Path: + return tmp_path / "output" diff --git a/tests/conversion/test_configs/gpt2_config_test.yaml b/tests/conversion/test_configs/gpt2_config_test.yaml new file mode 100644 index 000000000..ca98b980d --- /dev/null +++ b/tests/conversion/test_configs/gpt2_config_test.yaml @@ -0,0 +1,62 @@ +model: + component_key: model + variant_key: gpt2 + config: + sample_key: input_ids + poe_type: NOPE + sequence_length: 128 + prediction_key: logits + vocab_size: 50304 # GPT-2 vocab_size of 50257, padded up to nearest multiple of 64 for efficiency + n_layer: 3 + n_head_q: 4 + n_head_kv: 4 + ffn_hidden: 512 + n_embd: 256 + dropout: 0.0 + bias: false # True: bias in Linears, like GPT-2. False: a bit better and faster + attention_config: + qkv_transforms: + - type_hint: RotaryTransform + config: + n_embd: ${model.config.n_embd} + n_head: ${model.config.n_head_q} #it has to be head_q here + seq_length_dim: -2 + base_freq: 500000 + attention_implementation: pytorch_flash # manual + activation_type: swiglu + attention_norm: + component_key: layer_norm + variant_key: layer_norm + config: + normalized_shape: ${model.config.n_embd} + eps: 1e-5 + bias: true + ffn_norm: + component_key: layer_norm + variant_key: layer_norm + config: + normalized_shape: ${model.config.n_embd} + eps: 1e-5 + bias: true + lm_head_norm: + component_key: layer_norm + variant_key: layer_norm + config: + normalized_shape: ${model.config.n_embd} + eps: 1e-5 + bias: true + +checkpointed_model: + component_key: model + variant_key: checkpointed + config: + checkpoint_loading: + component_key: checkpoint_loading + variant_key: torch + config: + device: cpu + precision: BF16 + model: + instance_key: model + pass_type: BY_REFERENCE + checkpoint_path: null \ No newline at end of file diff --git a/tests/tests.py b/tests/tests.py index 2c10e8679..4b387f311 100644 --- a/tests/tests.py +++ b/tests/tests.py @@ -30,6 +30,15 @@ def check_existence_and_clear_getting_started_example_output( except OSError as e: print(f"Error: {e.filename} - {e.strerror}.") + # wandb directory + output_directory_wandb = join(run_getting_started_example_directory, "data", "wandb_storage") + assert isdir(output_directory_wandb), f"ERROR! {output_directory_wandb} does not exist" + try: + shutil.rmtree(output_directory_wandb) + print(f"> removed {output_directory_wandb}") + except OSError as e: + print(f"Error: {e.filename} - {e.strerror}.") + # checkpoint output_directory_checkpoints = join(run_getting_started_example_directory, "checkpoints") checkpoints = [elem for elem in os.listdir(output_directory_checkpoints) if elem.startswith("20")] @@ -48,6 +57,96 @@ def check_existence_and_clear_getting_started_example_output( except OSError as e: print(f"Error: {e.filename} - {e.strerror}.") + # checkpoint converted + checkpoints_converted = [ + join(output_directory_checkpoints, elem) + for elem in os.listdir(output_directory_checkpoints) + if elem.startswith("eid_") + ] + for checkpoint_converted in checkpoints_converted: + assert isdir(checkpoint_converted), f"ERROR! {checkpoint_converted} does not exist" + try: + shutil.rmtree(checkpoint_converted) + print(f"> removed {checkpoint_converted}") + except OSError as e: + print(f"Error: {e.filename} - {e.strerror}.") + + # config converted + config_converted = join(run_getting_started_example_directory, "configs", "example_conversion_config.yaml") + assert isfile(config_converted), f"ERROR! {config_converted} does not exist" + try: + os.remove(config_converted) + print(f"> removed {config_converted}") + except OSError as e: + print(f"Error: {e.filename} - {e.strerror}.") + + +def get_checkpoint_from_getting_started_example(run_getting_started_example_directory: str) -> str: + output_directory_checkpoints = join(run_getting_started_example_directory, "checkpoints") + + checkpoint_directories = [ + join(output_directory_checkpoints, elem) + for elem in os.listdir(output_directory_checkpoints) + if isdir(join(output_directory_checkpoints, elem)) + ] + assert ( + len(checkpoint_directories) == 1 + ), f"ERROR! found {len(checkpoint_directories)} checkpoint directories for getting started example, expected 1." + checkpoint_directory = checkpoint_directories[0] + + checkpoints = [ + join(checkpoint_directory, elem) + for elem in os.listdir(checkpoint_directory) + if isfile(join(checkpoint_directory, elem)) + ] + checkpoints = [elem for elem in checkpoints if "model" in elem and elem.endswith(".bin")] + assert ( + len(checkpoints) == 1 + ), f"ERROR! found {len(checkpoints)} checkpoints for getting started example, expected 1." + checkpoint = checkpoints[0] + + return checkpoint + + +def replace_checkpoint_in_conversion_config( + run_getting_started_example_directory: str, modalities_checkpoint: str +) -> str: + # read example config + example_config = join(run_getting_started_example_directory, "configs", "example_config.yaml") + assert isfile(example_config), f"ERROR! could not find file at {example_config}" + with open(example_config, "r") as f: + lines = f.readlines() + + # read conversion config template + conversion_config_template = join( + run_getting_started_example_directory, "configs", "example_conversion_config_template.yaml" + ) + assert isfile(conversion_config_template), f"ERROR! could not find file at {conversion_config_template}" + with open(conversion_config_template, "r") as f: + lines_additional = f.readlines() + lines += lines_additional + + last_line_start = " checkpoint_path:" + assert lines[-1].startswith( + last_line_start + ), f"ERROR! expected file at {conversion_config_template} to contain 'checkpoint_path' in last line." + lines[-1] = f"{last_line_start} {modalities_checkpoint}" + + # write conversion config + conversion_config = join(run_getting_started_example_directory, "configs", "example_conversion_config.yaml") + with open(conversion_config, "w") as f: + for line in lines: + f.write(line) + return conversion_config + + +def subprocess_run(command: str) -> None: + print(command) + try: + subprocess.run(command, shell=True, capture_output=False, check=True, text=True) + except subprocess.CalledProcessError: + raise Exception("SUBPROCESS RUN FAILED.") + def main(cpu: bool = False, single_gpu: bool = False, multi_gpu: bool = False, devices: str = "0,1"): """ @@ -88,8 +187,7 @@ def main(cpu: bool = False, single_gpu: bool = False, multi_gpu: bool = False, d command_unit_tests = ( f"cd {_ROOT_DIR} && CUDA_VISIBLE_DEVICES={devices[0] if single_gpu else None} python -m pytest" ) - print(command_unit_tests) - subprocess.run(command_unit_tests, shell=True, capture_output=False, text=True) + subprocess_run(command_unit_tests) # run multi-gpu tests if multi_gpu: @@ -101,24 +199,37 @@ def main(cpu: bool = False, single_gpu: bool = False, multi_gpu: bool = False, d command_end_to_end_tests = ( f"cd {run_distributed_tests_directory}; bash run_distributed_tests.sh {devices[0]} {devices[1]} --no-cov" ) - print(command_end_to_end_tests) - subprocess.run(command_end_to_end_tests, shell=True, capture_output=False, text=True) + subprocess_run(command_end_to_end_tests) # getting started example print("\n=== RUN GETTING STARTED EXAMPLE ===") run_getting_started_example_directory = _ROOT_DIR / "tutorials" / "getting_started" run_getting_started_example_script = ( - _ROOT_DIR / "tutorials" / "getting_started" / "run_getting_started_example.sh" + _ROOT_DIR / "tutorials" / "getting_started" / "scripts" / "run_getting_started_example.sh" ) assert isfile( run_getting_started_example_script ), f"ERROR! {run_getting_started_example_script} does not exist." - command_getting_started_example = ( - f"cd {run_getting_started_example_directory}; bash run_getting_started_example.sh {devices[0]} {devices[1]}" - ) - print(command_getting_started_example) + command_getting_started_example = f"cd {run_getting_started_example_directory}; " + command_getting_started_example += f"bash scripts/run_getting_started_example.sh {devices[0]} {devices[1]}" date_of_run = datetime.now().strftime("%Y-%m-%d__%H-%M-%S") - subprocess.run(command_getting_started_example, shell=True, capture_output=False, text=True) + subprocess_run(command_getting_started_example) + + # checkpoint conversion (based on getting started example) + print("\n=== RUN CHECKPOINT CONVERSION (BASED ON GETTING STARTED EXAMPLE) ===") + modalities_checkpoint = get_checkpoint_from_getting_started_example(run_getting_started_example_directory) + conversion_config_path = replace_checkpoint_in_conversion_config( + run_getting_started_example_directory, modalities_checkpoint + ) + + run_conversion_script = _ROOT_DIR / "tutorials" / "getting_started" / "scripts" / "run_checkpoint_conversion.sh" + assert isfile(run_conversion_script), f"ERROR! {run_conversion_script} does not exist." + command_conversion = f"cd {run_getting_started_example_directory}; " + command_conversion += f"sh scripts/run_checkpoint_conversion.sh {conversion_config_path} " + command_conversion += ( + f"{run_getting_started_example_directory}/checkpoints/{modalities_checkpoint.split('/')[-1]}" + ) + subprocess_run(command_conversion) check_existence_and_clear_getting_started_example_output(run_getting_started_example_directory, date_of_run) @@ -130,8 +241,7 @@ def main(cpu: bool = False, single_gpu: bool = False, multi_gpu: bool = False, d command_warmstart_example = ( f"cd {run_warmstart_example_directory}; sh pre_train_and_warmstart.sh {devices[0]} {devices[1]}" ) - print(command_warmstart_example) - subprocess.run(command_warmstart_example, shell=True, capture_output=False, text=True) + subprocess_run(command_warmstart_example) print("\n=== DONE ===") diff --git a/tutorials/getting_started/README.md b/tutorials/getting_started/README.md index 45bb96870..11700863f 100644 --- a/tutorials/getting_started/README.md +++ b/tutorials/getting_started/README.md @@ -7,20 +7,27 @@ As a reference, this example has the following folder structure. Folders in <> w ``` └── getting_started ├── checkpoints - │ └─ - ├── example_config.yaml + │ └── + ├── configs + │ ├── example_config.yaml + │ ├── example_conversion_config_template.yaml + │ ├── example_dataset_config_test.yaml + │ ├── example_dataset_config_train.yaml + │ └── example_text_generation_config.yaml ├── data │ ├── mem_map - │ ├── - │ └── raw - │ ├── redpajama_v2_samples_512_test.jsonl - │ └── redpajama_v2_samples_512_train.jsonl - ├── getting_started_example.md + │ │ └── + │ ├── raw + │ │ ├── redpajama_v2_samples_512_test.jsonl + │ │ └── redpajama_v2_samples_512_train.jsonl + │ └── + ├── scripts + │ ├── run_checkpoint_conversion.sh + │ └── run_getting_started_example.sh ├── tokenizer - │ ├── tokenizer.json - │ └── tokenizer_config.json - └── wandb - └── + │ ├── tokenizer_config.json + │ └── tokenizer.json + └── README.md ``` ## 1. Preprocessing @@ -40,7 +47,7 @@ The two raw dataset splits for training and evaluation can be found in and need to be preprocessed into the [MemMap dataset format](https://github.com/Modalities/modalities/blob/main/src/modalities/dataloader/dataset.py). ### Config File -To do so, we employ the `example_dataset_config_train.yaml` and `example_dataset_config_test.yaml` configuration files, which contain the paths of the input and output files, the path of the tokenizer as well as some configurable parameters: +To do so, we employ the `configs/example_dataset_config_train.yaml` and `configs/example_dataset_config_test.yaml` configuration files, which contain the paths of the input and output files, the path of the tokenizer as well as some configurable parameters: ```yaml # example_dataset_config_train.yaml @@ -88,10 +95,10 @@ After having determined the index, we create the packed dataset as described bel ```sh # train split -modalities data pack_encoded_data example_dataset_config_train.yaml +modalities data pack_encoded_data configs/example_dataset_config_train.yaml # test split -modalities data pack_encoded_data example_dataset_config_test.yaml +modalities data pack_encoded_data configs/example_dataset_config_test.yaml ``` This will create the following file structure which can we can directly load into the [PackedMemMapdataset](https://github.com/Modalities/modalities/blob/main/src/modalities/dataloader/dataset.py#L65). ``` @@ -148,7 +155,7 @@ first and then divides it into chunks of size context-length. ### Config File In Modalities, we describe the entire training and evaluation setup (i.e., components such as model, trainer, evaluator, dataloder etc.) within a single configuration file. Not only does this increase reproducibility but also allows for having the entire training runs under version control. A full list of all the components already available in modalities an be found [here](../../docs/components/components.md). -The example config file for this experiment can be found in `tutorials/getting_started/example_config.yaml`. +The example config file for this experiment can be found in `tutorials/getting_started/configs/example_config.yaml`. ### Training Having created the dataset and defined the experiment in the configuration file, we can already start the training by running the following command. @@ -157,7 +164,7 @@ Having created the dataset and defined the experiment in the configuration file, CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 torchrun --rdzv-endpoint localhost:29505 \ --nnodes 1 \ --nproc_per_node 8 \ - $(which modalities) run --config_file_path example_config.yaml + $(which modalities) run --config_file_path configs/example_config.yaml ``` The command can be broken down into the following parts: @@ -183,15 +190,15 @@ The command can be broken down into the following parts: 7. **`run`**: - Command argument for the `modalities` executable to initiate the training. -8. **`--config_file_path example_config.yaml`**: - - Specifies the path to the configuration file. The file `example_config.yaml` contains the configuration of the components, including dataset and model configurations, training parameters, etc. +8. **`--config_file_path configs/example_config.yaml`**: + - Specifies the path to the configuration file. The file `configs/example_config.yaml` contains the configuration of the components, including dataset and model configurations, training parameters, etc. Already during the training, the checkpoints can be found locally in `checkpoints/` and the loss and metric developments can be inspected online in [Weights&Biases](https://wandb.ai/). ### Evaluation -In order to let the model generate text, we need to specify the last training checkpoint under `model_path` in the config file `example_text_generation_config.yaml`: +In order to let the model generate text, we need to specify the last training checkpoint under `model_path` in the config file `configs/example_text_generation_config.yaml`: ``` # example_text_generation_config.yaml @@ -210,7 +217,7 @@ settings: Subsequently, given the checkpoint and tokenizer, we can load the model for text generation as follows: ```sh -modalities generate_text --config_file_path example_text_generation_config.yaml +modalities generate_text --config_file_path configs/example_text_generation_config.yaml ``` This opens an interactive chatting CMD interface. diff --git a/tutorials/getting_started/example_config.yaml b/tutorials/getting_started/configs/example_config.yaml similarity index 93% rename from tutorials/getting_started/example_config.yaml rename to tutorials/getting_started/configs/example_config.yaml index 4faeec9d6..f1b282101 100644 --- a/tutorials/getting_started/example_config.yaml +++ b/tutorials/getting_started/configs/example_config.yaml @@ -211,7 +211,7 @@ model_raw: ffn_hidden: 128 n_embd: 128 dropout: 0.0 - bias: true # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster + bias: false attention_config: qkv_transforms: - type_hint: RotaryTransform @@ -220,29 +220,26 @@ model_raw: n_head: ${model_raw.config.n_head_q} #it has to be head_q here seq_length_dim: -2 base_freq: 10000 - attention_implementation: manual - activation_type: gelu + attention_implementation: pytorch_flash + activation_type: swiglu attention_norm: component_key: layer_norm - variant_key: rms_norm + variant_key: layer_norm config: - ndim: ${model_raw.config.n_embd} - bias: true - epsilon: 1e-5 + normalized_shape: ${model_raw.config.n_embd} + eps: 1.0e-05 ffn_norm: component_key: layer_norm - variant_key: rms_norm + variant_key: layer_norm config: - ndim: ${model_raw.config.n_embd} - bias: true - epsilon: 1e-5 + normalized_shape: ${model_raw.config.n_embd} + eps: 1.0e-05 lm_head_norm: component_key: layer_norm - variant_key: rms_norm + variant_key: layer_norm config: - ndim: ${model_raw.config.n_embd} - bias: true - epsilon: 1e-5 + normalized_shape: ${model_raw.config.n_embd} + eps: 1.0e-05 scheduler: component_key: scheduler @@ -297,5 +294,5 @@ evaluation_subscriber: project: modalities_getting_started mode: OFFLINE experiment_id: ${settings.experiment_id} - directory: wandb_storage - config_file_path: ${settings.config_file_path} \ No newline at end of file + directory: data/wandb_storage + config_file_path: ${settings.config_file_path} diff --git a/tutorials/getting_started/configs/example_conversion_config_template.yaml b/tutorials/getting_started/configs/example_conversion_config_template.yaml new file mode 100644 index 000000000..92f602996 --- /dev/null +++ b/tutorials/getting_started/configs/example_conversion_config_template.yaml @@ -0,0 +1,22 @@ +tokenizer: + component_key: tokenizer + variant_key: pretrained_hf_tokenizer + config: + pretrained_model_name_or_path: tokenizer + padding: false + truncation: false + +checkpointed_model: + component_key: model + variant_key: checkpointed + config: + checkpoint_loading: + component_key: checkpoint_loading + variant_key: torch + config: + device: cpu + precision: BF16 + model: + instance_key: model + pass_type: BY_REFERENCE + checkpoint_path: \ No newline at end of file diff --git a/tutorials/getting_started/example_dataset_config_test.yaml b/tutorials/getting_started/configs/example_dataset_config_test.yaml similarity index 100% rename from tutorials/getting_started/example_dataset_config_test.yaml rename to tutorials/getting_started/configs/example_dataset_config_test.yaml diff --git a/tutorials/getting_started/example_dataset_config_train.yaml b/tutorials/getting_started/configs/example_dataset_config_train.yaml similarity index 100% rename from tutorials/getting_started/example_dataset_config_train.yaml rename to tutorials/getting_started/configs/example_dataset_config_train.yaml diff --git a/tutorials/getting_started/example_text_generation_config.yaml b/tutorials/getting_started/configs/example_text_generation_config.yaml similarity index 78% rename from tutorials/getting_started/example_text_generation_config.yaml rename to tutorials/getting_started/configs/example_text_generation_config.yaml index 714d05148..3caad5c0a 100644 --- a/tutorials/getting_started/example_text_generation_config.yaml +++ b/tutorials/getting_started/configs/example_text_generation_config.yaml @@ -2,7 +2,7 @@ settings: referencing_keys: sample_key: input_ids prediction_key: logits - model_path: ./checkpoints/2024-06-27__14-17-52/eid_2024-06-27__14-17-52-model-num_steps_48-num_tokens_393216.bin + model_path: ./checkpoints/2025-02-24__08-53-31_5b6cf982/eid_2025-02-24__08-53-31_5b6cf982-model-seen_steps_48-seen_tokens_393216-target_steps_95-target_tokens_778240.bin device: 0 sequence_length: 1024 @@ -53,7 +53,7 @@ model: ffn_hidden: 128 n_embd: 128 dropout: 0.0 - bias: true # True: bias in Linears, like GPT-2. False: a bit better and faster + bias: false attention_config: qkv_transforms: - type_hint: RotaryTransform @@ -62,29 +62,26 @@ model: n_head: ${model.config.n_head_q} #it has to be head_q here seq_length_dim: -2 base_freq: 10000 - attention_implementation: manual - activation_type: gelu + attention_implementation: pytorch_flash + activation_type: swiglu attention_norm: component_key: layer_norm - variant_key: rms_norm + variant_key: layer_norm config: - ndim: ${model.config.n_embd} - bias: true - epsilon: 1e-5 + normalized_shape: ${model.config.n_embd} + eps: 1.0e-05 ffn_norm: component_key: layer_norm - variant_key: rms_norm + variant_key: layer_norm config: - ndim: ${model.config.n_embd} - bias: true - epsilon: 1e-5 + normalized_shape: ${model.config.n_embd} + eps: 1.0e-05 lm_head_norm: component_key: layer_norm - variant_key: rms_norm + variant_key: layer_norm config: - ndim: ${model.config.n_embd} - bias: true - epsilon: 1e-5 + normalized_shape: ${model.config.n_embd} + eps: 1.0e-05 tokenizer: component_key: tokenizer diff --git a/tutorials/getting_started/scripts/run_checkpoint_conversion.sh b/tutorials/getting_started/scripts/run_checkpoint_conversion.sh new file mode 100644 index 000000000..61f0fd5b3 --- /dev/null +++ b/tutorials/getting_started/scripts/run_checkpoint_conversion.sh @@ -0,0 +1,22 @@ +#!/bin/sh +set -e + +# --------------------------------------------- +# bash run_checkpoint_conversion +# --------------------------------------------- + +####################### +### INPUT ARGUMENTS ### +####################### +if [ -z "$1" ] || [ -z "$2" ] # if one of the two input arguments does not exist + then + echo "Need to specify arguments, e.g. bash run_checkpoint_conversion modalities_config output_dir" + exit +fi + +############# +### RUN ##### +############# +echo "> run checkpoint conversion" +echo "python ../../src/modalities/conversion/gpt2/convert_gpt2.py" $1 $2 "--num_testruns 5" +python ../../src/modalities/conversion/gpt2/convert_gpt2.py $1 $2 --num_testruns 5 diff --git a/tutorials/getting_started/run_getting_started_example.sh b/tutorials/getting_started/scripts/run_getting_started_example.sh similarity index 85% rename from tutorials/getting_started/run_getting_started_example.sh rename to tutorials/getting_started/scripts/run_getting_started_example.sh index 30555256a..5e56d5142 100644 --- a/tutorials/getting_started/run_getting_started_example.sh +++ b/tutorials/getting_started/scripts/run_getting_started_example.sh @@ -29,6 +29,6 @@ echo "> run getting_started_examples on CUDA_VISIBLE_DEVICES="$CUDA_VISIBLE_DEVI modalities data create_raw_index --index_path data/mem_map/redpajama_v2_samples_512_train.idx data/raw/redpajama_v2_samples_512_train.jsonl modalities data create_raw_index --index_path data/mem_map/redpajama_v2_samples_512_test.idx data/raw/redpajama_v2_samples_512_test.jsonl -modalities data pack_encoded_data example_dataset_config_train.yaml -modalities data pack_encoded_data example_dataset_config_test.yaml -CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES torchrun --rdzv-endpoint localhost:29505 --nnodes 1 --nproc_per_node 2 $(which modalities) run --config_file_path example_config.yaml +modalities data pack_encoded_data configs/example_dataset_config_train.yaml +modalities data pack_encoded_data configs/example_dataset_config_test.yaml +CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES torchrun --rdzv-endpoint localhost:29505 --nnodes 1 --nproc_per_node 2 $(which modalities) run --config_file_path configs/example_config.yaml