Add new model: Gpt2 (#1833)
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DanielC12321
286
python/sglang/srt/models/gpt2.py
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286
python/sglang/srt/models/gpt2.py
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# coding=utf-8
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# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt2/modeling_gpt2.py
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# Copyright 2023 The vLLM 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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from typing import Iterable, 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 vllm.config import CacheConfig
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from vllm.distributed.parallel_state import get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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#from sglang.srt.layers.activation import get_act_fn
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from sglang.srt.layers.linear import (
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ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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class GPT2Attention(nn.Module):
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def __init__(
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self,
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layer_id: int,
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config: GPT2Config,
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cache_config = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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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 = (
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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 = QKVParallelLinear(
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self.hidden_size,
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self.head_dim,
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total_num_heads,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.c_attn",
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)
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self.c_proj = RowParallelLinear(
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self.hidden_size,
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self.hidden_size,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.c_proj",
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)
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self.attn = RadixAttention(self.num_heads,
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self.head_dim,
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scaling=self.scale,
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num_kv_heads=total_num_heads,
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layer_id=layer_id)
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def forward(
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self,
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hidden_states: torch.Tensor,
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forward_batch: ForwardBatch,
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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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attn_output = self.attn(q, k, v, forward_batch)
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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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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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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(
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hidden_size,
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intermediate_size,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.c_fc",
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)
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self.c_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.c_proj",
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)
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self.act = get_act_fn(config.activation_function, quant_config,
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intermediate_size)
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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__(
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self,
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layer_id: int,
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config: GPT2Config,
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cache_config = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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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 *
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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(layer_id,
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config,
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cache_config,
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quant_config,
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prefix=f"{prefix}.attn")
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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,
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config,
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quant_config,
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prefix=f"{prefix}.mlp")
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def forward(
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self,
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hidden_states: torch.Tensor,
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forward_batch: ForwardBatch,
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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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forward_batch=forward_batch,
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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__(
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self,
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config: GPT2Config,
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cache_config = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.config = config
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assert not config.add_cross_attention
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assert not config.scale_attn_by_inverse_layer_idx
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assert not config.reorder_and_upcast_attn
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self.embed_dim = config.hidden_size
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self.wte = VocabParallelEmbedding(config.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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[
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GPT2Block(i, config, cache_config, quant_config)
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for i in range(config.num_hidden_layers)
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]
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)
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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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forward_batch: ForwardBatch,
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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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layer = self.h[i]
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hidden_states = layer(hidden_states, forward_batch)
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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__(
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self,
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config: GPT2Config,
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cache_config = None,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.config = config
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self.quant_config = quant_config
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self.transformer = GPT2Model(config,
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cache_config,
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quant_config,
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prefix="transformer")
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self.lm_head = self.transformer.wte
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self.logits_processor = LogitsProcessor(config)
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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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forward_batch: ForwardBatch,
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) -> torch.Tensor:
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hidden_states = self.transformer(input_ids, positions, forward_batch)
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return self.logits_processor(
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input_ids, hidden_states, self.lm_head.weight, forward_batch
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)
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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params_dict = dict(self.named_parameters(remove_duplicate=False))
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for name, loaded_weight in weights:
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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 or ".attn.masked_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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if not name.startswith("transformer."):
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name = "transformer." + name
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param = params_dict[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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# Note(zhuohan): the logic below might break quantized models.
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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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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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EntryClass = GPT2LMHeadModel
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