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[Model] Support for fairseq2 Llama (vllm-project#11442)
Signed-off-by: Martin Gleize <[email protected]> Co-authored-by: mgleize user <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| # Copyright 2024 The vLLM team. | ||
| # Copyright 2024 Meta Platforms, Inc. and affiliates. All rights reserved. | ||
| # | ||
| # 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 model for fairseq2 weights.""" | ||
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| from typing import Iterable, Set, Tuple | ||
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| import torch | ||
| from torch.nn import Parameter | ||
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| from vllm.config import VllmConfig | ||
| from vllm.distributed import (get_tensor_model_parallel_rank, | ||
| get_tensor_model_parallel_world_size) | ||
| from vllm.model_executor.layers.linear import set_weight_attrs | ||
| from vllm.model_executor.models.llama import LlamaForCausalLM | ||
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| from .utils import AutoWeightsLoader, WeightsMapper | ||
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| class Fairseq2LlamaForCausalLM(LlamaForCausalLM): | ||
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| def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): | ||
| super().__init__(vllm_config=vllm_config, prefix=prefix) | ||
| self.tp_rank = get_tensor_model_parallel_rank() | ||
| self.tp_size = get_tensor_model_parallel_world_size() | ||
| # For the model loader to read only the relevant checkpoint files | ||
| self.allow_patterns_overrides = [ | ||
| # either the full checkpoint | ||
| "model.pt", | ||
| # or the tp-sharded checkpoint of the current rank | ||
| f"model.{self.tp_rank}.pt", | ||
| ] | ||
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| def load_weights(self, weights: Iterable[Tuple[str, | ||
| torch.Tensor]]) -> Set[str]: | ||
| # fairseq2's serialization adds a wrapper to usual .pt state_dict's: | ||
| # { "model_key": my_model_name, "my_model_name": state_dict } | ||
| # which we first need to unpack | ||
| weights_wrapped = dict(weights) | ||
| weights = weights_wrapped[ | ||
| weights_wrapped["model_key"]].items() # type: ignore | ||
|
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||
| # remap keys | ||
| fs2_to_vllm_mapper = WeightsMapper( | ||
| orig_to_new_prefix={ | ||
| "decoder_frontend.embed.": "model.embed_tokens.", | ||
| "decoder.": "model.", | ||
| "final_proj.": "lm_head.", | ||
| }, | ||
| orig_to_new_substr={ | ||
| ".self_attn_layer_norm.": ".input_layernorm.", | ||
| ".ffn_layer_norm.": ".post_attention_layernorm.", | ||
| ".self_attn.output_proj.": ".self_attn.o_proj.", | ||
| ".ffn.gate_proj.": ".mlp.gate_proj.", | ||
| ".ffn.inner_proj.": ".mlp.up_proj.", | ||
| ".ffn.output_proj.": ".mlp.down_proj.", | ||
| ".layer_norm.": ".norm.", | ||
| }, | ||
| ) | ||
| weights = fs2_to_vllm_mapper.apply(weights) | ||
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| params = dict(self.named_parameters()) | ||
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| loader = AutoWeightsLoader( | ||
| self, | ||
| skip_prefixes=(["lm_head."] | ||
| if self.config.tie_word_embeddings else None), | ||
| ) | ||
| return loader.load_weights( | ||
| (self.reshape_fairseq2_weights(name, loaded_weight, params) | ||
| for name, loaded_weight in weights)) | ||
|
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| def flag_sharded_weights(self, params: dict[str, Parameter]): | ||
| """Sets the `is_sharded_weight` flag to True for all sharded weights""" | ||
| for name, param in params.items(): | ||
| modules = name.split(".") | ||
| if "norm" in name and len(param.size()) < 2: | ||
| # layer norms are not sharded | ||
| continue | ||
| elif any(emb in modules for emb in ["embed_tokens", "lm_head"]): | ||
| # for now we repeat embedding layers for compatibility | ||
| continue | ||
| else: | ||
| # all other layers are sharded | ||
| set_weight_attrs(param, {"is_sharded_weight": True}) | ||
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| def reshape_fairseq2_weights( | ||
| self, | ||
| name: str, | ||
| loaded_weight: torch.Tensor, | ||
| params: dict[str, Parameter], | ||
| ) -> Tuple[str, torch.Tensor]: | ||
| """Reshape fairseq2's weights.""" | ||
|
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| def permute(w: torch.Tensor, n_heads: int) -> torch.Tensor: | ||
| attn_in = self.config.head_dim * n_heads | ||
| # check for a sharded weight on dim 0 | ||
| if attn_in // self.tp_size == w.size()[0]: | ||
| attn_in //= self.tp_size | ||
| n_heads //= self.tp_size | ||
| attn_out = self.config.hidden_size | ||
| return (w.view(n_heads, attn_in // n_heads // 2, 2, | ||
| attn_out).transpose(1, | ||
| 2).reshape(attn_in, attn_out)) | ||
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| modules = name.split(".") | ||
|
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| # rotary embeds should be sliced | ||
| if "k_proj" in modules: | ||
| loaded_weight = permute(loaded_weight, | ||
| self.config.num_key_value_heads) | ||
|
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| elif "q_proj" in modules: | ||
| loaded_weight = permute(loaded_weight, | ||
| self.config.num_attention_heads) | ||
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| # We make the loaded weights compatible with both | ||
| # full checkpoints and tp sharded checkpoints. | ||
| # Embeddings are repeated to fit the vocab size. | ||
| # Other weights are flagged for the weight_loader calls. | ||
| if any(emb in modules for emb in ["embed_tokens", "lm_head"]): | ||
| # Embeddings are sharded on dim 0 | ||
| dim = 0 | ||
| # In fairseq2, vocab size has to be divisible by tp_size | ||
| # so we don't worry about padding | ||
| if self.tp_size > 1 and loaded_weight.shape[ | ||
| dim] < self.config.vocab_size: | ||
| assert loaded_weight.shape[ | ||
| dim] * self.tp_size == self.config.vocab_size, \ | ||
| "vocab_size should be divisible by tp_size." | ||
| repeats = [1] * len(loaded_weight.size()) | ||
| repeats[dim] = self.tp_size | ||
| # repeat to match vocab size and to be easily 'narrow'able | ||
| loaded_weight = loaded_weight.repeat(repeats) | ||
| set_weight_attrs(params[name], {"is_sharded_weight": False}) | ||
| # if embeddings are sharded, the rest is too | ||
| if "embed_tokens" in modules: | ||
| self.flag_sharded_weights(params) | ||
|
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| return name, loaded_weight |
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