gpt_neox.py

# coding=utf-8
# Copyright 2023 The CacheFlow team.
# Adapted from https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt_neox/modeling_gpt_neox.py
#
# 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.
"""1D GPT-NeoX model compatible with HuggingFace weights."""
from typing import Dict, List, Optional, Tuple

import torch
from torch import nn
from transformers import GPTNeoXConfig

from cacheflow.model_executor.input_metadata import InputMetadata
from cacheflow.model_executor.layers.attention import GPTNeoXCacheFlowAttention
from cacheflow.model_executor.layers.sampler import Sampler
from cacheflow.model_executor.weight_utils import (hf_model_weights_iterator,
                                                   load_tensor_parallel_weights)
from cacheflow.model_executor.parallel_utils.parallel_state import (
    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
from cacheflow.model_executor.parallel_utils.tensor_parallel import (
    VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
from cacheflow.sequence import SequenceOutputs

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,
                                                    perform_initialization=False)
        self.dense = RowParallelLinear(config.hidden_size, config.hidden_size,
                                       input_is_parallel=True,
                                       perform_initialization=False)

        scaling = self.head_size ** -0.5
        rotary_dim = int(self.head_size * config.rotary_pct)
        assert rotary_dim % 2 == 0
        self.attn = GPTNeoXCacheFlowAttention(scaling, rotary_dim)

    def forward(
        self,
        position_ids: torch.LongTensor,
        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,
                                                  perform_initialization=False)
        self.dense_4h_to_h = RowParallelLinear(config.intermediate_size, config.hidden_size,
                                               input_is_parallel=True,
                                               perform_initialization=False)
        if config.hidden_act != 'gelu':
            raise ValueError(f'Unsupported activation: {config.hidden_act}. '
                             'Only gelu is supported for now.')
        self.act = torch.nn.GELU()

    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.LongTensor,
        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,
                                               perform_initialization=False)
        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.LongTensor,
        position_ids: torch.LongTensor,
        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,
                                              perform_initialization=False)
        self.sampler = Sampler(config.vocab_size)

    def forward(
        self,
        input_ids: torch.LongTensor,
        positions: torch.LongTensor,
        kv_caches: List[KVCache],
        input_metadata: InputMetadata,
        cache_events: Optional[List[torch.cuda.Event]],
    ) -> Dict[int, SequenceOutputs]:
        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,
                     use_np_cache: bool = False):
        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, use_np_cache):
            if ("attention.bias" in name or "attention.masked_bias" in name
                or "rotary_emb.inv_freq" in name):
                continue
            param = state_dict[name]
            if "query_key_value" in name:
                # NOTE(woosuk): 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)