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281 lines
9.9 KiB
Python
281 lines
9.9 KiB
Python
# 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/gptj/modeling_gptj.py
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# Copyright 2023 The vLLM team.
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# Copyright 2021 The EleutherAI and HuggingFace Teams. 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-J 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 List, Optional, Tuple
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import torch
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from torch import nn
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from transformers import GPTJConfig
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from vllm.model_executor.input_metadata import InputMetadata
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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LinearMethodBase,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.sampler import Sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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VocabParallelEmbedding, ParallelLMHead)
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from vllm.model_executor.parallel_utils.parallel_state import (
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get_tensor_model_parallel_world_size)
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from vllm.model_executor.weight_utils import (default_weight_loader,
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hf_model_weights_iterator)
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from vllm.sequence import SamplerOutput
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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class GPTJAttention(nn.Module):
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def __init__(
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self,
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config: GPTJConfig,
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linear_method: Optional[LinearMethodBase] = None,
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):
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super().__init__()
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self.total_num_heads = config.num_attention_heads
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self.hidden_size = config.hidden_size
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self.head_size = self.hidden_size // self.total_num_heads
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self.qkv_proj = QKVParallelLinear(
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config.hidden_size,
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self.head_size,
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self.total_num_heads,
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bias=False,
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linear_method=linear_method,
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)
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self.out_proj = RowParallelLinear(
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config.hidden_size,
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config.hidden_size,
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bias=False,
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linear_method=linear_method,
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)
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tp_world_size = get_tensor_model_parallel_world_size()
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assert self.total_num_heads % tp_world_size == 0
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self.num_heads = self.total_num_heads // tp_world_size
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scaling = self.head_size**-0.5
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assert getattr(config, "rotary", True)
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assert config.rotary_dim % 2 == 0
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rope_theta = getattr(config, "rope_theta", 10000)
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max_position_embeddings = getattr(config, "max_position_embeddings",
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8192)
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self.attn = PagedAttentionWithRoPE(
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self.num_heads,
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self.head_size,
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scaling,
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config.rotary_dim,
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base=rope_theta,
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max_position=max_position_embeddings,
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is_neox_style=False)
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self.warmup = False
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def forward(
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self,
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position_ids: torch.Tensor,
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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.qkv_proj(hidden_states)
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q, k, v = qkv.chunk(chunks=3, dim=-1)
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k_cache, v_cache = kv_cache
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attn_output = self.attn(position_ids, q, k, v, k_cache, v_cache,
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input_metadata, cache_event)
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attn_output, _ = self.out_proj(attn_output)
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return attn_output
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class GPTJMLP(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: GPTJConfig,
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linear_method: Optional[LinearMethodBase] = None,
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):
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super().__init__()
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hidden_size = config.n_embd
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self.fc_in = ColumnParallelLinear(
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hidden_size,
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intermediate_size,
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linear_method=linear_method,
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)
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self.fc_out = RowParallelLinear(
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intermediate_size,
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hidden_size,
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linear_method=linear_method,
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)
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quant_config = getattr(linear_method, "quant_config", None)
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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.fc_in(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states, _ = self.fc_out(hidden_states)
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return hidden_states
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class GPTJBlock(nn.Module):
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def __init__(
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self,
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config: GPTJConfig,
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linear_method: Optional[LinearMethodBase] = None,
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):
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super().__init__()
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inner_dim = 4 * config.n_embd if config.n_inner is None else config.n_inner
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self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
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self.attn = GPTJAttention(config, linear_method)
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self.mlp = GPTJMLP(inner_dim, config, linear_method)
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def forward(
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self,
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position_ids: torch.Tensor,
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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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position_ids=position_ids,
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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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mlp_output = self.mlp(hidden_states)
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hidden_states = attn_output + mlp_output + residual
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return hidden_states
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class GPTJModel(nn.Module):
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def __init__(
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self,
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config: GPTJConfig,
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linear_method: Optional[LinearMethodBase] = None,
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):
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super().__init__()
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self.config = config
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self.embed_dim = config.n_embd
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self.wte = VocabParallelEmbedding(
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config.vocab_size,
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self.embed_dim,
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)
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self.h = nn.ModuleList(
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[GPTJBlock(config, linear_method) for _ in range(config.n_layer)])
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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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hidden_states = self.wte(input_ids)
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for i in range(len(self.h)):
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cache_event = None if cache_events is None else cache_events[i]
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layer = self.h[i]
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hidden_states = layer(
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position_ids,
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hidden_states,
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kv_caches[i],
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input_metadata,
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cache_event,
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)
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hidden_states = self.ln_f(hidden_states)
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return hidden_states
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class GPTJForCausalLM(nn.Module):
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def __init__(
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self,
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config: GPTJConfig,
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linear_method: Optional[LinearMethodBase] = None,
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):
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super().__init__()
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self.config = config
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self.linear_method = linear_method
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assert not config.tie_word_embeddings
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self.transformer = GPTJModel(config, linear_method)
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self.lm_head = ParallelLMHead(
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config.vocab_size,
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config.n_embd,
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bias=True,
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)
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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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) -> SamplerOutput:
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hidden_states = self.transformer(input_ids, positions, kv_caches,
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input_metadata, cache_events)
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next_tokens = self.sampler(self.lm_head.weight, hidden_states,
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input_metadata, self.lm_head.bias)
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return next_tokens
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def load_weights(self,
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model_name_or_path: str,
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cache_dir: Optional[str] = None,
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load_format: str = "auto",
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revision: Optional[str] = None):
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in hf_model_weights_iterator(
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model_name_or_path, cache_dir, load_format, revision):
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if "attn.bias" in name or "attn.masked_bias" in name:
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continue
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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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if weight_name not in name:
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continue
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param = params_dict[name.replace(weight_name, param_name)]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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param = params_dict[name]
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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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