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[Model] Add support for GPT-J (#226)
Co-authored-by: woWoosuk Kwon <[email protected]>
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| Original file line number | Diff line number | Diff line change |
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| # coding=utf-8 | ||
| # Adapted from | ||
| # https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gptj/modeling_gptj.py | ||
| # Copyright 2023 The vLLM team. | ||
| # Copyright 2021 The EleutherAI and HuggingFace Teams. 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. | ||
| """Inference-only GPT-J model compatible with HuggingFace weights. | ||
| The input of the model is flattened to a 1D tensor of tokens. The model uses | ||
| InputMetadata to extract the original 2D shape of the input. | ||
| """ | ||
| from typing import Dict, List, Optional, Tuple | ||
|
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||
| import torch | ||
| from torch import nn | ||
| from transformers import GPTJConfig | ||
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||
| from vllm.model_executor.input_metadata import InputMetadata | ||
| from vllm.model_executor.layers.activation import get_act_fn | ||
| from vllm.model_executor.layers.attention import PagedAttentionWithRoPE | ||
| from vllm.model_executor.layers.sampler import Sampler | ||
| from vllm.model_executor.weight_utils import (hf_model_weights_iterator, | ||
| load_tensor_parallel_weights) | ||
| from vllm.model_executor.parallel_utils.parallel_state import ( | ||
| get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size) | ||
| from vllm.model_executor.parallel_utils.tensor_parallel import ( | ||
| VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear) | ||
| from vllm.sequence import SequenceOutputs | ||
|
|
||
| KVCache = Tuple[torch.Tensor, torch.Tensor] | ||
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|
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| class GPTJAttention(nn.Module): | ||
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| def __init__(self, config: GPTJConfig): | ||
| super().__init__() | ||
| self.total_num_heads = config.num_attention_heads | ||
| self.hidden_size = config.hidden_size | ||
| self.head_size = self.hidden_size // self.total_num_heads | ||
|
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| self.qkv_proj = ColumnParallelLinear(config.hidden_size, | ||
| 3 * config.hidden_size, | ||
| bias=False, | ||
| gather_output=False, | ||
| perform_initialization=False) | ||
| self.out_proj = RowParallelLinear(config.hidden_size, | ||
| config.hidden_size, | ||
| bias=False, | ||
| input_is_parallel=True, | ||
| perform_initialization=False) | ||
|
|
||
| tp_world_size = get_tensor_model_parallel_world_size() | ||
| assert self.total_num_heads % tp_world_size == 0 | ||
| self.num_heads = self.total_num_heads // tp_world_size | ||
|
|
||
| scaling = self.head_size**-0.5 | ||
| assert config.rotary | ||
| assert config.rotary_dim % 2 == 0 | ||
| self.attn = PagedAttentionWithRoPE(self.num_heads, self.head_size, | ||
| scaling, config.rotary_dim) | ||
| self.warmup = False | ||
|
|
||
| def forward( | ||
| self, | ||
| position_ids: torch.Tensor, | ||
| hidden_states: torch.Tensor, | ||
| kv_cache: KVCache, | ||
| input_metadata: InputMetadata, | ||
| cache_event: Optional[torch.cuda.Event], | ||
| ) -> torch.Tensor: | ||
| qkv, _ = self.qkv_proj(hidden_states) | ||
| q, k, v = qkv.chunk(chunks=3, dim=-1) | ||
| k_cache, v_cache = kv_cache | ||
| attn_output = self.attn(position_ids, q, k, v, k_cache, v_cache, | ||
| input_metadata, cache_event) | ||
| attn_output, _ = self.out_proj(attn_output) | ||
| return attn_output | ||
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| class GPTJMLP(nn.Module): | ||
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| def __init__(self, intermediate_size: int, config: GPTJConfig): | ||
| super().__init__() | ||
| hidden_size = config.n_embd | ||
| self.fc_in = ColumnParallelLinear(hidden_size, | ||
| intermediate_size, | ||
| gather_output=False, | ||
| perform_initialization=False) | ||
| self.fc_out = RowParallelLinear(intermediate_size, | ||
| hidden_size, | ||
| input_is_parallel=True, | ||
| perform_initialization=False) | ||
| self.act = get_act_fn(config.activation_function) | ||
|
|
||
| def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: | ||
| hidden_states, _ = self.fc_in(hidden_states) | ||
| hidden_states = self.act(hidden_states) | ||
| hidden_states, _ = self.fc_out(hidden_states) | ||
| return hidden_states | ||
|
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| class GPTJBlock(nn.Module): | ||
|
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| def __init__(self, config: GPTJConfig): | ||
| super().__init__() | ||
| if config.n_inner is None: | ||
| inner_dim = 4 * config.n_embd | ||
| else: | ||
| inner_dim = config.n_inner | ||
| self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) | ||
| self.attn = GPTJAttention(config) | ||
| self.mlp = GPTJMLP(inner_dim, config) | ||
|
|
||
| def forward( | ||
| self, | ||
| position_ids: torch.Tensor, | ||
| hidden_states: torch.Tensor, | ||
| kv_cache: KVCache, | ||
| input_metadata: InputMetadata, | ||
| cache_event: Optional[torch.cuda.Event], | ||
| ) -> torch.Tensor: | ||
| residual = hidden_states | ||
| hidden_states = self.ln_1(hidden_states) | ||
| attn_output = self.attn( | ||
| position_ids=position_ids, | ||
| hidden_states=hidden_states, | ||
| kv_cache=kv_cache, | ||
| input_metadata=input_metadata, | ||
| cache_event=cache_event, | ||
| ) | ||
| mlp_output = self.mlp(hidden_states) | ||
| hidden_states = attn_output + mlp_output + residual | ||
| return hidden_states | ||
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| class GPTJModel(nn.Module): | ||
|
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| def __init__(self, config: GPTJConfig): | ||
| super().__init__() | ||
| self.config = config | ||
| self.embed_dim = config.n_embd | ||
| self.wte = VocabParallelEmbedding(config.vocab_size, | ||
| self.embed_dim, | ||
| perform_initialization=False) | ||
| self.h = nn.ModuleList( | ||
| [GPTJBlock(config) for _ in range(config.n_layer)]) | ||
| self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) | ||
|
|
||
| def forward( | ||
| self, | ||
| input_ids: torch.Tensor, | ||
| position_ids: torch.Tensor, | ||
| kv_caches: List[KVCache], | ||
| input_metadata: InputMetadata, | ||
| cache_events: Optional[List[torch.cuda.Event]], | ||
| ) -> torch.Tensor: | ||
| hidden_states = self.wte(input_ids) | ||
| for i in range(len(self.h)): | ||
| if cache_events is None: | ||
| cache_event = None | ||
| else: | ||
| cache_event = cache_events[i] | ||
| layer = self.h[i] | ||
| hidden_states = layer( | ||
| position_ids, | ||
| hidden_states, | ||
| kv_caches[i], | ||
| input_metadata, | ||
| cache_event, | ||
| ) | ||
| hidden_states = self.ln_f(hidden_states) | ||
| return hidden_states | ||
|
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|
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| class GPTJForCausalLM(nn.Module): | ||
|
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| def __init__(self, config: GPTJConfig): | ||
| super().__init__() | ||
| self.config = config | ||
| assert not config.tie_word_embeddings | ||
| self.transformer = GPTJModel(config) | ||
| self.lm_head = ColumnParallelLinear(config.n_embd, | ||
| config.vocab_size, | ||
| gather_output=False, | ||
| perform_initialization=False) | ||
| self.sampler = Sampler(config.vocab_size) | ||
|
|
||
| def forward( | ||
| self, | ||
| input_ids: torch.Tensor, | ||
| positions: torch.Tensor, | ||
| kv_caches: List[KVCache], | ||
| input_metadata: InputMetadata, | ||
| cache_events: Optional[List[torch.cuda.Event]], | ||
| ) -> Dict[int, SequenceOutputs]: | ||
| hidden_states = self.transformer(input_ids, positions, kv_caches, | ||
| input_metadata, cache_events) | ||
| next_tokens = self.sampler(self.lm_head.weight, hidden_states, | ||
| input_metadata, self.lm_head.bias) | ||
| return next_tokens | ||
|
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||
| _column_parallel_weights = [ | ||
| "wte.weight", "fc_in.weight", "fc_in.bias", "lm_head.weight", | ||
| "lm_head.bias" | ||
| ] | ||
| _row_parallel_weights = ["out_proj.weight", "fc_out.weight"] | ||
|
|
||
| def load_weights(self, | ||
| model_name_or_path: str, | ||
| cache_dir: Optional[str] = None, | ||
| use_np_cache: bool = False): | ||
| tp_rank = get_tensor_model_parallel_rank() | ||
| state_dict = self.state_dict() | ||
| for name, loaded_weight in hf_model_weights_iterator( | ||
| model_name_or_path, cache_dir, use_np_cache): | ||
| if "attn.bias" in name or "attn.masked_bias" in name: | ||
| continue | ||
|
|
||
| is_attention_weight = False | ||
| for stride_id, att_weight_name in enumerate( | ||
| ["q_proj", "k_proj", "v_proj"]): | ||
| if att_weight_name not in name: | ||
| continue | ||
| param = state_dict[name.replace(att_weight_name, "qkv_proj")] | ||
| shard_size = param.shape[1] | ||
| loaded_weight = loaded_weight[shard_size * tp_rank:shard_size * | ||
| (tp_rank + 1)] | ||
| param_slice = param.data[shard_size * stride_id:shard_size * | ||
| (stride_id + 1)] | ||
| assert param_slice.shape == loaded_weight.shape | ||
| param_slice.copy_(loaded_weight) | ||
| is_attention_weight = True | ||
| break | ||
| if is_attention_weight: | ||
| continue | ||
|
|
||
| param = state_dict[name] | ||
| load_tensor_parallel_weights(param, loaded_weight, name, | ||
| self._column_parallel_weights, | ||
| self._row_parallel_weights, tp_rank) |
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