284 lines
12 KiB
Python
284 lines
12 KiB
Python
# coding=utf-8
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# Adapted from https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt2/modeling_gpt2.py
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# Copyright 2023 The CacheFlow team.
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# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Inference-only GPT-2 model compatible with HuggingFace weights.
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The input of the model is flattened to a 1D tensor of tokens. The model uses
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InputMetadata to extract the original 2D shape of the input.
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"""
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from typing import Dict, List, Optional, Tuple
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import torch
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from torch import nn
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from transformers import GPT2Config
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from cacheflow.model_executor.input_metadata import InputMetadata
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from cacheflow.model_executor.layers.attention import GPTCacheFlowAttention
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from cacheflow.model_executor.layers.sampler import Sampler
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from cacheflow.model_executor.weight_utils import (hf_model_weights_iterator,
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load_tensor_parallel_weights)
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from cacheflow.model_executor.parallel_utils.parallel_state import (
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get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
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from cacheflow.model_executor.parallel_utils.tensor_parallel import (
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VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
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from cacheflow.sequence import SequenceOutputs
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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class GPT2Attention(nn.Module):
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def __init__(self, config: GPT2Config):
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super().__init__()
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self.hidden_size = config.hidden_size
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total_num_heads = config.num_attention_heads
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tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
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assert total_num_heads % tensor_model_parallel_world_size == 0
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self.num_heads = total_num_heads // tensor_model_parallel_world_size
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self.head_dim = self.hidden_size // total_num_heads
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self.scale = self.head_dim ** -0.5
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self.c_attn = ColumnParallelLinear(self.hidden_size, 3 * self.hidden_size, bias=True,
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gather_output=False,
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perform_initialization=False)
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self.c_proj = RowParallelLinear(self.hidden_size, self.hidden_size, bias=True,
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input_is_parallel=True,
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perform_initialization=False)
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self.attn = GPTCacheFlowAttention(self.num_heads, self.head_dim,
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scale=self.scale)
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def forward(
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self,
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hidden_states: torch.Tensor,
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kv_cache: KVCache,
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input_metadata: InputMetadata,
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cache_event: Optional[torch.cuda.Event],
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) -> torch.Tensor:
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qkv, _ = self.c_attn(hidden_states)
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q, k, v = qkv.chunk(chunks=3, dim=-1)
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key_cache, value_cache = kv_cache
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attn_output = self.attn(
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q, k, v, key_cache, value_cache, input_metadata, cache_event)
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attn_output, _ = self.c_proj(attn_output)
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return attn_output
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class GPT2MLP(nn.Module):
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def __init__(
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self,
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intermediate_size: int,
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config: GPT2Config,
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):
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super().__init__()
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hidden_size = config.hidden_size
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self.c_fc = ColumnParallelLinear(hidden_size, intermediate_size,
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bias=True, gather_output=False,
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perform_initialization=False)
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self.c_proj = RowParallelLinear(intermediate_size, hidden_size,
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bias=True, input_is_parallel=True,
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perform_initialization=False)
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act_fn = config.activation_function
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if act_fn != "gelu_new":
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raise ValueError(f"Unsupported activation: {act_fn}. "
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"GPT-2 only supports gelu_new for now.")
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self.act = torch.nn.GELU(approximate="tanh")
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states, _ = self.c_fc(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states, _ = self.c_proj(hidden_states)
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return hidden_states
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class GPT2Block(nn.Module):
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def __init__(self, config: GPT2Config):
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super().__init__()
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hidden_size = config.hidden_size
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inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
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self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
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self.attn = GPT2Attention(config)
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self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
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self.mlp = GPT2MLP(inner_dim, config)
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def forward(
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self,
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hidden_states: torch.Tensor,
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kv_cache: KVCache,
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input_metadata: InputMetadata,
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cache_event: Optional[torch.cuda.Event],
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) -> torch.Tensor:
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residual = hidden_states
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hidden_states = self.ln_1(hidden_states)
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attn_output = self.attn(
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hidden_states=hidden_states,
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kv_cache=kv_cache,
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input_metadata=input_metadata,
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cache_event=cache_event,
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)
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# residual connection
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hidden_states = attn_output + residual
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residual = hidden_states
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hidden_states = self.ln_2(hidden_states)
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feed_forward_hidden_states = self.mlp(hidden_states)
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# residual connection
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hidden_states = residual + feed_forward_hidden_states
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return hidden_states
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class GPT2Model(nn.Module):
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def __init__(self, config: GPT2Config):
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super().__init__()
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self.config = config
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assert config.add_cross_attention == False
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assert config.scale_attn_by_inverse_layer_idx == False
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assert config.reorder_and_upcast_attn == False
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self.embed_dim = config.hidden_size
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# Optimization: While the vocab size of GPT-2 is 50257, we extend it
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# to 50304 in order to make it divisible by 64.
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# This improves performance since GPUs are faster if the dimension
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# is divisible by 64. In addition, it allows us to shard the embedding
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# layer across 2, 4, 8, or more GPUs.
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vocab_size = ((config.vocab_size + 63) // 64) * 64
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self.wte = VocabParallelEmbedding(vocab_size, self.embed_dim)
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self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
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self.h = nn.ModuleList(
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[GPT2Block(config) for _ in range(config.num_hidden_layers)])
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self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
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def forward(
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self,
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input_ids: torch.Tensor,
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position_ids: torch.Tensor,
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kv_caches: List[KVCache],
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input_metadata: InputMetadata,
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cache_events: Optional[List[torch.cuda.Event]],
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) -> torch.Tensor:
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inputs_embeds = self.wte(input_ids)
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position_embeds = self.wpe(position_ids)
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hidden_states = inputs_embeds + position_embeds
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for i in range(len(self.h)):
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if cache_events is None:
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cache_event = None
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else:
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cache_event = cache_events[i]
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layer = self.h[i]
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hidden_states = layer(
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hidden_states, kv_caches[i], input_metadata, cache_event)
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hidden_states = self.ln_f(hidden_states)
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return hidden_states
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class GPT2LMHeadModel(nn.Module):
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def __init__(self, config: GPT2Config):
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super().__init__()
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self.config = config
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self.transformer = GPT2Model(config)
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# TODO(zhuohan): create a new weight after implementing pipeline
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# parallelism
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self.lm_head_weight = self.transformer.wte.weight
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self.sampler = Sampler(config.vocab_size)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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kv_caches: List[KVCache],
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input_metadata: InputMetadata,
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cache_events: Optional[List[torch.cuda.Event]],
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) -> Dict[int, SequenceOutputs]:
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hidden_states = self.transformer(
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input_ids, positions, kv_caches, input_metadata, cache_events)
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next_tokens = self.sampler(
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self.lm_head_weight, hidden_states, input_metadata)
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return next_tokens
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_column_parallel_weights = ["wte.weight", "c_fc.weight", "c_fc.bias"]
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_row_parallel_weights = ["c_proj.weight"]
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def load_weights(self, model_name_or_path: str,
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cache_dir: Optional[str] = None,
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use_np_cache: bool = False):
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tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
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tensor_model_parallel_rank = get_tensor_model_parallel_rank()
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state_dict = self.state_dict()
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for name, loaded_weight in hf_model_weights_iterator(
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model_name_or_path, cache_dir, use_np_cache):
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if "lm_head.weight" in name:
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# GPT-2 ties the weights of the embedding layer and the final
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# linear layer.
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continue
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if ".attn.bias" in name:
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# Skip attention mask.
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# NOTE: "c_attn.bias" should not be skipped.
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continue
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name = "transformer." + name
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# The HF's GPT-2 implementation uses Conv1D instead of Linear.
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# Because of this, we need to transpose the weights.
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for conv1d_weight_name in ["c_attn", "c_proj", "c_fc"]:
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if conv1d_weight_name not in name:
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continue
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if not name.endswith(".weight"):
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continue
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loaded_weight = loaded_weight.t()
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param = state_dict[name]
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if name == "transformer.wte.weight":
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# Consider padding in the vocab size.
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padded_vocab_size = param.shape[0] * tensor_model_parallel_world_size
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num_extra_rows = padded_vocab_size - self.config.vocab_size
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extra_rows = torch.empty(num_extra_rows, loaded_weight.shape[1])
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extra_rows = extra_rows.to(loaded_weight)
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loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0)
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# For the fused QKV linear layer, manually shard the weights.
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if "c_attn" in name:
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# GPT-2's fused QKV has the shape of [3 * num_heads * head_size, hidden_size].
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# When tensor parallelism is used, we shard the weights along the head dimension.
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total_num_heads = self.config.num_attention_heads
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hidden_size = self.config.hidden_size
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head_size = hidden_size // total_num_heads
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num_heads = total_num_heads // tensor_model_parallel_world_size
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head_start = tensor_model_parallel_rank * num_heads
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head_end = (tensor_model_parallel_rank + 1) * num_heads
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if name.endswith(".weight"):
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loaded_weight = loaded_weight.view(3, total_num_heads, head_size, hidden_size)
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loaded_weight = loaded_weight[:, head_start:head_end, :, :]
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loaded_weight = loaded_weight.reshape(-1, hidden_size)
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elif name.endswith(".bias"):
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loaded_weight = loaded_weight.view(3, total_num_heads, head_size)
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loaded_weight = loaded_weight[:, head_start:head_end, :]
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loaded_weight = loaded_weight.reshape(-1)
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else:
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raise ValueError(f"Unexpected parameter name {name}")
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load_tensor_parallel_weights(param, loaded_weight, name,
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self._column_parallel_weights,
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self._row_parallel_weights,
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tensor_model_parallel_rank)
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