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# coding=utf-8
# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt2/modeling_gpt2.py
# Copyright 2023 The vLLM team.
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. 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-2 model compatible with HuggingFace weights."""
from typing import List, Optional, Tuple
import torch
from torch import nn
from transformers import GPT2Config
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 PagedAttention
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
LinearMethodBase,
QKVParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.sampler import Sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
VocabParallelEmbedding)
from vllm.model_executor.parallel_utils.parallel_state import (
get_tensor_model_parallel_world_size)
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.model_executor.weight_utils import (default_weight_loader,
hf_model_weights_iterator)
from vllm.sequence import SamplerOutput
KVCache = Tuple[torch.Tensor, torch.Tensor]
class GPT2Attention(nn.Module):
def __init__(
self,
config: GPT2Config,
linear_method: Optional[LinearMethodBase] = None,
):
super().__init__()
self.hidden_size = config.hidden_size
total_num_heads = config.num_attention_heads
tensor_model_parallel_world_size = (
get_tensor_model_parallel_world_size())
assert total_num_heads % tensor_model_parallel_world_size == 0
self.num_heads = total_num_heads // tensor_model_parallel_world_size
self.head_dim = self.hidden_size // total_num_heads
self.scale = self.head_dim**-0.5
self.c_attn = QKVParallelLinear(
self.hidden_size,
self.head_dim,
total_num_heads,
bias=True,
linear_method=linear_method,
)
self.c_proj = RowParallelLinear(
self.hidden_size,
self.hidden_size,
bias=True,
linear_method=linear_method,
)
self.attn = PagedAttention(self.num_heads,
self.head_dim,
scale=self.scale)
def forward(
self,
hidden_states: torch.Tensor,
kv_cache: KVCache,
input_metadata: InputMetadata,
) -> torch.Tensor:
qkv, _ = self.c_attn(hidden_states)
q, k, v = qkv.chunk(chunks=3, dim=-1)
key_cache, value_cache = kv_cache
attn_output = self.attn(q, k, v, key_cache, value_cache,
input_metadata)
attn_output, _ = self.c_proj(attn_output)
return attn_output
class GPT2MLP(nn.Module):
def __init__(
self,
intermediate_size: int,
config: GPT2Config,
linear_method: Optional[LinearMethodBase] = None,
):
super().__init__()
hidden_size = config.hidden_size
self.c_fc = ColumnParallelLinear(
hidden_size,
intermediate_size,
bias=True,
linear_method=linear_method,
)
self.c_proj = RowParallelLinear(
intermediate_size,
hidden_size,
bias=True,
linear_method=linear_method,
)
quant_config = getattr(linear_method, "quant_config", None)
self.act = get_act_fn(config.activation_function, quant_config,
intermediate_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states, _ = self.c_fc(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states, _ = self.c_proj(hidden_states)
return hidden_states
class GPT2Block(nn.Module):
def __init__(
self,
config: GPT2Config,
linear_method: Optional[LinearMethodBase] = None,
):
super().__init__()
hidden_size = config.hidden_size
inner_dim = (config.n_inner if config.n_inner is not None else 4 *
hidden_size)
self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.attn = GPT2Attention(config, linear_method)
self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.mlp = GPT2MLP(inner_dim, config, linear_method)
def forward(
self,
hidden_states: torch.Tensor,
kv_cache: KVCache,
input_metadata: InputMetadata,
) -> torch.Tensor:
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_output = self.attn(
hidden_states=hidden_states,
kv_cache=kv_cache,
input_metadata=input_metadata,
)
# residual connection
hidden_states = attn_output + residual
residual = hidden_states
hidden_states = self.ln_2(hidden_states)
feed_forward_hidden_states = self.mlp(hidden_states)
# residual connection
hidden_states = residual + feed_forward_hidden_states
return hidden_states
class GPT2Model(nn.Module):
def __init__(
self,
config: GPT2Config,
linear_method: Optional[LinearMethodBase] = None,
):
super().__init__()
self.config = config
assert not config.add_cross_attention
assert not config.scale_attn_by_inverse_layer_idx
assert not config.reorder_and_upcast_attn
self.embed_dim = config.hidden_size
self.wte = VocabParallelEmbedding(config.vocab_size, self.embed_dim)
self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
self.h = nn.ModuleList([
GPT2Block(config, linear_method)
for _ in range(config.num_hidden_layers)
])
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,
) -> torch.Tensor:
inputs_embeds = self.wte(input_ids)
position_embeds = self.wpe(position_ids)
hidden_states = inputs_embeds + position_embeds
for i in range(len(self.h)):
layer = self.h[i]
hidden_states = layer(hidden_states, kv_caches[i], input_metadata)
hidden_states = self.ln_f(hidden_states)
return hidden_states
class GPT2LMHeadModel(nn.Module):
def __init__(
self,
config: GPT2Config,
linear_method: Optional[LinearMethodBase] = None,
):
super().__init__()
self.config = config
self.linear_method = linear_method
self.transformer = GPT2Model(config, linear_method)
self.lm_head_weight = self.transformer.wte.weight
self.sampler = Sampler(config.vocab_size)
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[KVCache],
input_metadata: InputMetadata,
) -> torch.Tensor:
hidden_states = self.transformer(input_ids, positions, kv_caches,
input_metadata)
return hidden_states
def sample(
self,
hidden_states: torch.Tensor,
sampling_metadata: SamplingMetadata,
) -> Optional[SamplerOutput]:
next_tokens = self.sampler(self.lm_head_weight, hidden_states,
sampling_metadata)
return next_tokens
def load_weights(self,
model_name_or_path: str,
cache_dir: Optional[str] = None,
load_format: str = "auto",
revision: Optional[str] = None):
params_dict = dict(self.named_parameters(remove_duplicate=False))
for name, loaded_weight in hf_model_weights_iterator(
model_name_or_path, cache_dir, load_format, revision):
if "lm_head.weight" in name:
# GPT-2 ties the weights of the embedding layer and the final
# linear layer.
continue
if ".attn.bias" in name or ".attn.masked_bias" in name:
# Skip attention mask.
# NOTE: "c_attn.bias" should not be skipped.
continue
if not name.startswith("transformer."):
name = "transformer." + name
param = params_dict[name]
# The HF's GPT-2 implementation uses Conv1D instead of Linear.
# Because of this, we need to transpose the weights.
# Note(zhuohan): the logic below might break quantized models.
for conv1d_weight_name in ["c_attn", "c_proj", "c_fc"]:
if conv1d_weight_name not in name:
continue
if not name.endswith(".weight"):
continue
loaded_weight = loaded_weight.t()
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
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