# coding=utf-8 # Adapted from # https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gptj/modeling_gptj.py # Copyright 2023 The PygmalionAI team. # Copyright 2023 The vLLM team. # Copyright 2021 The EleutherAI and HuggingFace Teams. 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-J model compatible with HuggingFace weights. The input of the model is flattened to a 1D tensor of tokens. The model uses InputMetadata to extract the original 2D shape of the input. """ from typing import List, Optional, Tuple import torch from torch import nn from transformers import GPTJConfig from aphrodite.modeling.metadata import InputMetadata from aphrodite.modeling.layers.activation import get_act_fn from aphrodite.modeling.layers.attention import PagedAttentionWithRoPE from aphrodite.modeling.layers.sampler import Sampler from aphrodite.modeling.hf_downloader import (hf_model_weights_iterator, load_tensor_parallel_weights) from aphrodite.modeling.megatron.parallel_state import ( get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size) from aphrodite.modeling.megatron.tensor_parallel import ( VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear) from aphrodite.common.sequence import SamplerOutput KVCache = Tuple[torch.Tensor, torch.Tensor] class GPTJAttention(nn.Module): def __init__(self, config: GPTJConfig): super().__init__() self.total_num_heads = config.num_attention_heads self.hidden_size = config.hidden_size self.head_size = self.hidden_size // self.total_num_heads self.qkv_proj = ColumnParallelLinear(config.hidden_size, 3 * config.hidden_size, bias=False, gather_output=False, perform_initialization=False) self.out_proj = RowParallelLinear(config.hidden_size, config.hidden_size, bias=False, input_is_parallel=True, perform_initialization=False) tp_world_size = get_tensor_model_parallel_world_size() assert self.total_num_heads % tp_world_size == 0 self.num_heads = self.total_num_heads // tp_world_size scaling = self.head_size**-0.5 assert getattr(config, "rotary", True) assert config.rotary_dim % 2 == 0 self.attn = PagedAttentionWithRoPE(self.num_heads, self.head_size, scaling, config.rotary_dim, is_neox_style=False) self.warmup = False def forward( self, position_ids: torch.Tensor, hidden_states: torch.Tensor, kv_cache: KVCache, input_metadata: InputMetadata, cache_event: Optional[torch.cuda.Event], ) -> torch.Tensor: qkv, _ = self.qkv_proj(hidden_states) q, k, v = qkv.chunk(chunks=3, dim=-1) k_cache, v_cache = kv_cache attn_output = self.attn(position_ids, q, k, v, k_cache, v_cache, input_metadata, cache_event) attn_output, _ = self.out_proj(attn_output) return attn_output class GPTJMLP(nn.Module): def __init__(self, intermediate_size: int, config: GPTJConfig): super().__init__() hidden_size = config.n_embd self.fc_in = ColumnParallelLinear(hidden_size, intermediate_size, gather_output=False, perform_initialization=False) self.fc_out = RowParallelLinear(intermediate_size, hidden_size, input_is_parallel=True, perform_initialization=False) self.act = get_act_fn(config.activation_function) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states, _ = self.fc_in(hidden_states) hidden_states = self.act(hidden_states) hidden_states, _ = self.fc_out(hidden_states) return hidden_states class GPTJBlock(nn.Module): def __init__(self, config: GPTJConfig): super().__init__() if config.n_inner is None: inner_dim = 4 * config.n_embd else: inner_dim = config.n_inner self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) self.attn = GPTJAttention(config) self.mlp = GPTJMLP(inner_dim, config) def forward( self, position_ids: torch.Tensor, hidden_states: torch.Tensor, kv_cache: KVCache, input_metadata: InputMetadata, cache_event: Optional[torch.cuda.Event], ) -> torch.Tensor: residual = hidden_states hidden_states = self.ln_1(hidden_states) attn_output = self.attn( position_ids=position_ids, hidden_states=hidden_states, kv_cache=kv_cache, input_metadata=input_metadata, cache_event=cache_event, ) mlp_output = self.mlp(hidden_states) hidden_states = attn_output + mlp_output + residual return hidden_states class GPTJModel(nn.Module): def __init__(self, config: GPTJConfig): super().__init__() self.config = config self.embed_dim = config.n_embd self.wte = VocabParallelEmbedding(config.vocab_size, self.embed_dim, perform_initialization=False) self.h = nn.ModuleList( [GPTJBlock(config) for _ in range(config.n_layer)]) 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, cache_events: Optional[List[torch.cuda.Event]], ) -> torch.Tensor: hidden_states = self.wte(input_ids) for i in range(len(self.h)): if cache_events is None: cache_event = None else: cache_event = cache_events[i] layer = self.h[i] hidden_states = layer( position_ids, hidden_states, kv_caches[i], input_metadata, cache_event, ) hidden_states = self.ln_f(hidden_states) return hidden_states class GPTJForCausalLM(nn.Module): def __init__(self, config: GPTJConfig): super().__init__() self.config = config assert not config.tie_word_embeddings self.transformer = GPTJModel(config) self.lm_head = ColumnParallelLinear(config.n_embd, config.vocab_size, gather_output=False, perform_initialization=False) self.sampler = Sampler(config.vocab_size) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[KVCache], input_metadata: InputMetadata, cache_events: Optional[List[torch.cuda.Event]], ) -> SamplerOutput: hidden_states = self.transformer(input_ids, positions, kv_caches, input_metadata, cache_events) next_tokens = self.sampler(self.lm_head.weight, hidden_states, input_metadata, self.lm_head.bias) return next_tokens _column_parallel_weights = [ "wte.weight", "fc_in.weight", "fc_in.bias", "lm_head.weight", "lm_head.bias" ] _row_parallel_weights = ["out_proj.weight", "fc_out.weight"] def load_weights(self, model_name_or_path: str, cache_dir: Optional[str] = None, load_format: str = "auto", revision: Optional[str] = None): tp_rank = get_tensor_model_parallel_rank() state_dict = self.state_dict() for name, loaded_weight in hf_model_weights_iterator( model_name_or_path, cache_dir, load_format, revision): if "attn.bias" in name or "attn.masked_bias" in name: continue is_attention_weight = False for stride_id, att_weight_name in enumerate( ["q_proj", "k_proj", "v_proj"]): if att_weight_name not in name: continue param = state_dict[name.replace(att_weight_name, "qkv_proj")] shard_size = param.shape[1] loaded_weight = loaded_weight[shard_size * tp_rank:shard_size * (tp_rank + 1)] param_slice = param.data[shard_size * stride_id:shard_size * (stride_id + 1)] assert param_slice.shape == loaded_weight.shape param_slice.copy_(loaded_weight) is_attention_weight = True break if is_attention_weight: continue param = state_dict[name] load_tensor_parallel_weights(param, loaded_weight, name, self._column_parallel_weights, self._row_parallel_weights, tp_rank)