288 lines
9.6 KiB
Python
288 lines
9.6 KiB
Python
# 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 Iterable, Optional, Tuple, Type
|
|
|
|
import torch
|
|
from torch import nn
|
|
from transformers import GPT2Config
|
|
|
|
from sglang.srt.distributed.parallel_state import get_tensor_model_parallel_world_size
|
|
from sglang.srt.layers.activation import NewGELU
|
|
from sglang.srt.layers.linear import (
|
|
ColumnParallelLinear,
|
|
QKVParallelLinear,
|
|
RowParallelLinear,
|
|
)
|
|
from sglang.srt.layers.logits_processor import LogitsProcessor
|
|
from sglang.srt.layers.quantization.base_config import QuantizationConfig
|
|
from sglang.srt.layers.radix_attention import RadixAttention
|
|
from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
|
|
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
|
|
from sglang.srt.model_loader.weight_utils import default_weight_loader
|
|
from sglang.srt.utils import add_prefix
|
|
|
|
|
|
class GPT2Attention(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
layer_id: int,
|
|
config: GPT2Config,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
):
|
|
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,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("c_attn", prefix),
|
|
)
|
|
self.c_proj = RowParallelLinear(
|
|
self.hidden_size,
|
|
self.hidden_size,
|
|
bias=True,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("c_proj", prefix),
|
|
)
|
|
self.attn = RadixAttention(
|
|
self.num_heads,
|
|
self.head_dim,
|
|
scaling=self.scale,
|
|
num_kv_heads=total_num_heads,
|
|
layer_id=layer_id,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
) -> torch.Tensor:
|
|
qkv, _ = self.c_attn(hidden_states)
|
|
q, k, v = qkv.chunk(chunks=3, dim=-1)
|
|
attn_output = self.attn(q, k, v, forward_batch)
|
|
attn_output, _ = self.c_proj(attn_output)
|
|
return attn_output
|
|
|
|
|
|
class GPT2MLP(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
intermediate_size: int,
|
|
config: GPT2Config,
|
|
act_layer: Type[nn.Module] = NewGELU,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
):
|
|
super().__init__()
|
|
hidden_size = config.hidden_size
|
|
self.c_fc = ColumnParallelLinear(
|
|
hidden_size,
|
|
intermediate_size,
|
|
bias=True,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("c_fc", prefix),
|
|
)
|
|
self.c_proj = RowParallelLinear(
|
|
intermediate_size,
|
|
hidden_size,
|
|
bias=True,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("c_proj", prefix),
|
|
)
|
|
self.act = act_layer()
|
|
|
|
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,
|
|
layer_id: int,
|
|
config: GPT2Config,
|
|
act_layer: Type[nn.Module] = NewGELU,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
):
|
|
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(
|
|
layer_id, config, quant_config, prefix=add_prefix("attn", prefix)
|
|
)
|
|
self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
|
|
self.mlp = GPT2MLP(
|
|
inner_dim,
|
|
config,
|
|
act_layer=act_layer,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("mlp", prefix),
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
) -> torch.Tensor:
|
|
residual = hidden_states
|
|
hidden_states = self.ln_1(hidden_states)
|
|
attn_output = self.attn(
|
|
hidden_states=hidden_states,
|
|
forward_batch=forward_batch,
|
|
)
|
|
# 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,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
):
|
|
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(
|
|
i,
|
|
config,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix(f"h.{i}", prefix),
|
|
)
|
|
for i 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,
|
|
forward_batch: ForwardBatch,
|
|
) -> 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, forward_batch)
|
|
|
|
hidden_states = self.ln_f(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class GPT2LMHeadModel(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: GPT2Config,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
):
|
|
super().__init__()
|
|
self.config = config
|
|
self.quant_config = quant_config
|
|
self.transformer = GPT2Model(
|
|
config, quant_config, prefix=add_prefix("transformer", prefix)
|
|
)
|
|
self.lm_head = self.transformer.wte
|
|
|
|
self.logits_processor = LogitsProcessor(config)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
) -> torch.Tensor:
|
|
hidden_states = self.transformer(input_ids, positions, forward_batch)
|
|
return self.logits_processor(
|
|
input_ids, hidden_states, self.lm_head, forward_batch
|
|
)
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
params_dict = dict(self.named_parameters(remove_duplicate=False))
|
|
for name, loaded_weight in weights:
|
|
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)
|
|
|
|
|
|
EntryClass = GPT2LMHeadModel
|