# coding=utf-8 # Adapted from # https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt_neox/modeling_gpt_neox.py # Copyright 2023 The PygmalionAI team. # Copyright 2023 The vLLM team. # Copyright 2022 EleutherAI The HuggingFace Inc. team. 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-NeoX 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 GPTNeoXConfig 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.layers import (VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear) from aphrodite.common.sequence import SamplerOutput KVCache = Tuple[torch.Tensor, torch.Tensor] class GPTNeoXAttention(nn.Module): def __init__(self, config: GPTNeoXConfig): 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 tensor_model_parallel_world_size = ( get_tensor_model_parallel_world_size()) assert self.total_num_heads % tensor_model_parallel_world_size == 0 self.num_heads = (self.total_num_heads // tensor_model_parallel_world_size) self.query_key_value = ColumnParallelLinear( config.hidden_size, 3 * config.hidden_size, gather_output=False, ) self.dense = RowParallelLinear( config.hidden_size, config.hidden_size, input_is_parallel=True, ) scaling = self.head_size**-0.5 rotary_dim = int(self.head_size * config.rotary_pct) assert rotary_dim % 2 == 0 rope_theta = getattr(config, "rope_theta", 10000) max_position_embeddings = getattr(config, "max_position_embeddings", 8192) self.attn = PagedAttentionWithRoPE( self.num_heads, self.head_size, scaling, rotary_dim, base=rope_theta, max_position=max_position_embeddings) 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.query_key_value(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) output, _ = self.dense(attn_output) return output class GPTNeoXMLP(nn.Module): def __init__(self, config: GPTNeoXConfig): super().__init__() self.dense_h_to_4h = ColumnParallelLinear( config.hidden_size, config.intermediate_size, gather_output=False, ) self.dense_4h_to_h = RowParallelLinear( config.intermediate_size, config.hidden_size, input_is_parallel=True, ) self.act = get_act_fn(config.hidden_act) def forward(self, hidden_states): hidden_states, _ = self.dense_h_to_4h(hidden_states) hidden_states = self.act(hidden_states) hidden_states, _ = self.dense_4h_to_h(hidden_states) return hidden_states class GPTNeoXLayer(nn.Module): def __init__(self, config: GPTNeoXConfig): super().__init__() self.use_parallel_residual = config.use_parallel_residual self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.attention = GPTNeoXAttention(config) self.mlp = GPTNeoXMLP(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: attn_input = self.input_layernorm(hidden_states) attn_output = self.attention( position_ids=position_ids, hidden_states=attn_input, kv_cache=kv_cache, input_metadata=input_metadata, cache_event=cache_event, ) if self.use_parallel_residual: # pseudocode: # x = x + attn(ln1(x)) + mlp(ln2(x)) mlp_input = self.post_attention_layernorm(hidden_states) mlp_output = self.mlp(mlp_input) hidden_states = mlp_output + attn_output + hidden_states else: # pseudocode: # x = x + attn(ln1(x)) # x = x + mlp(ln2(x)) attn_output = attn_output + hidden_states mlp_input = self.post_attention_layernorm(attn_output) mlp_output = self.mlp(mlp_input) hidden_states = mlp_output + attn_output return hidden_states class GPTNeoXModel(nn.Module): def __init__(self, config: GPTNeoXConfig): super().__init__() self.config = config self.embed_in = VocabParallelEmbedding( config.vocab_size, config.hidden_size, ) self.layers = nn.ModuleList( [GPTNeoXLayer(config) for _ in range(config.num_hidden_layers)]) self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) 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.embed_in(input_ids) for i in range(len(self.layers)): if cache_events is None: cache_event = None else: cache_event = cache_events[i] layer = self.layers[i] hidden_states = layer( position_ids, hidden_states, kv_caches[i], input_metadata, cache_event, ) hidden_states = self.final_layer_norm(hidden_states) return hidden_states class GPTNeoXForCausalLM(nn.Module): def __init__(self, config): super().__init__() self.config = config self.gpt_neox = GPTNeoXModel(config) self.embed_out = ColumnParallelLinear( config.hidden_size, config.vocab_size, bias=False, gather_output=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.gpt_neox(input_ids, positions, kv_caches, input_metadata, cache_events) next_tokens = self.sampler(self.embed_out.weight, hidden_states, input_metadata) return next_tokens _column_parallel_weights = [ "embed_in.weight", "embed_out.weight", "dense_h_to_4h.weight", "dense_h_to_4h.bias" ] _row_parallel_weights = ["dense.weight", "dense_4h_to_h.weight"] def load_weights(self, model_name_or_path: str, cache_dir: Optional[str] = None, load_format: str = "auto", revision: Optional[str] = None): tensor_model_parallel_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 ("attention.bias" in name or "attention.masked_bias" in name or "rotary_emb.inv_freq" in name): continue # pylint: disable=unsubscriptable-object param = state_dict[name] if "query_key_value" in name: # NOTE: GPT-NeoX's fused QKV has the shape of # [num_heads * 3 * head_size, hidden_size], while the # required shape is [3 * num_heads * head_size, hidden_size]. # Thus, we need weight conversion. shard_size = param.shape[0] loaded_weight = loaded_weight[ shard_size * tensor_model_parallel_rank:shard_size * (tensor_model_parallel_rank + 1)] num_heads = self.config.num_attention_heads hidden_size = self.config.hidden_size head_size = hidden_size // num_heads if "query_key_value.weight" in name: loaded_weight = loaded_weight.view(-1, 3, head_size, hidden_size) loaded_weight = loaded_weight.transpose(0, 1) loaded_weight = loaded_weight.reshape(-1, hidden_size) elif "query_key_value.bias" in name: loaded_weight = loaded_weight.view(-1, 3, head_size) loaded_weight = loaded_weight.transpose(0, 1) loaded_weight = loaded_weight.reshape(-1) else: raise ValueError(f"Unexpected weight name: {name}") load_tensor_parallel_weights(param, loaded_weight, name, self._column_parallel_weights, self._row_parallel_weights, tensor_model_parallel_rank)