gpt2.py

# coding=utf-8
# Copyright 2023 The CacheFlow team.
# Adapted from https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt2/modeling_gpt2.py
#
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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-2 model compatible with HuggingFace weights."""
from typing import Dict, List, Optional, Tuple

import torch
from torch import nn
from transformers import GPT2Config

from cacheflow.model_executor.input_metadata import InputMetadata
from cacheflow.model_executor.layers.attention import GPTCacheFlowAttention
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 GPT2Attention(nn.Module):

    def __init__(self, config: GPT2Config):
        super().__init__()
        self.hidden_size = config.hidden_size
        total_num_heads = config.num_attention_heads
        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
        assert total_num_heads % tensor_model_parallel_world_size == 0
        self.num_heads = total_num_heads // tensor_model_parallel_world_size
        self.head_dim = self.hidden_size // total_num_heads
        self.scale = self.head_dim ** -0.5

        self.c_attn = ColumnParallelLinear(self.hidden_size, 3 * self.hidden_size, bias=True,
                                           gather_output=False,
                                           perform_initialization=False)
        self.c_proj = RowParallelLinear(self.hidden_size, self.hidden_size, bias=True,
                                        input_is_parallel=True,
                                        perform_initialization=False)
        self.attn = GPTCacheFlowAttention(scale=self.scale)

    def forward(
        self,
        hidden_states: torch.Tensor,
        kv_cache: KVCache,
        input_metadata: InputMetadata,
        cache_event: Optional[torch.cuda.Event],
    ) -> torch.Tensor:
        qkv, _ = self.c_attn(hidden_states)
        q, k, v = qkv.chunk(chunks=3, dim=-1)
        key_cache, value_cache = kv_cache
        attn_output = self.attn(
            q, k, v, key_cache, value_cache, input_metadata, cache_event)
        attn_output, _ = self.c_proj(attn_output)
        return attn_output


class GPT2MLP(nn.Module):

    def __init__(
        self,
        intermediate_size: int,
        config: GPT2Config,
    ):
        super().__init__()
        hidden_size = config.hidden_size
        self.c_fc = ColumnParallelLinear(hidden_size, intermediate_size,
                                         bias=True, gather_output=False,
                                         perform_initialization=False)
        self.c_proj = RowParallelLinear(intermediate_size, hidden_size,
                                        bias=True, input_is_parallel=True,
                                        perform_initialization=False)

        act_fn = config.activation_function
        if act_fn != "gelu_new":
            raise ValueError(f"Unsupported activation: {act_fn}. "
                             "GPT-2 only supports gelu_new for now.")
        self.act = torch.nn.GELU(approximate="tanh")

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states, _ = self.c_fc(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states, _ = self.c_proj(hidden_states)
        return hidden_states


class GPT2Block(nn.Module):

    def __init__(self, config: GPT2Config):
        super().__init__()
        hidden_size = config.hidden_size
        inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size

        self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        self.attn = GPT2Attention(config)
        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        self.mlp = GPT2MLP(inner_dim, config)

    def forward(
        self,
        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(
            hidden_states=hidden_states,
            kv_cache=kv_cache,
            input_metadata=input_metadata,
            cache_event=cache_event,
        )
        # residual connection
        hidden_states = attn_output + residual

        residual = hidden_states
        hidden_states = self.ln_2(hidden_states)
        feed_forward_hidden_states = self.mlp(hidden_states)
        # residual connection
        hidden_states = residual + feed_forward_hidden_states
        return hidden_states


class GPT2Model(nn.Module):

    def __init__(self, config: GPT2Config):
        super().__init__()
        self.config = config
        assert config.add_cross_attention == False
        assert config.scale_attn_by_inverse_layer_idx == False
        assert config.reorder_and_upcast_attn == False
        self.embed_dim = config.hidden_size

        # Optimization: While the vocab size of GPT-2 is 50257, we extend it
        # to 50304 in order to make it divisible by 64.
        # This improves performance since GPUs are faster if the dimension
        # is divisible by 64. In addition, it allows us to shard the embedding
        # layer across 2, 4, 8, or more GPUs.
        vocab_size = ((config.vocab_size + 63) // 64) * 64
        self.wte = VocabParallelEmbedding(vocab_size, self.embed_dim)
        self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
        self.h = nn.ModuleList(
            [GPT2Block(config) for _ in range(config.num_hidden_layers)])
        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)

    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:
        inputs_embeds = self.wte(input_ids)
        position_embeds = self.wpe(position_ids)
        hidden_states = inputs_embeds + position_embeds

        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(
                hidden_states, kv_caches[i], input_metadata, cache_event)

        hidden_states = self.ln_f(hidden_states)
        return hidden_states


class GPT2LMHeadModel(nn.Module):

    def __init__(self, config: GPT2Config):
        super().__init__()
        self.config = config
        self.transformer = GPT2Model(config)
        # TODO(zhuohan): create a new weight after implementing pipeline
        #                parallelism
        self.lm_head_weight = self.transformer.wte.weight
        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.transformer(
            input_ids, positions, kv_caches, input_metadata, cache_events)
        next_tokens = self.sampler(
            self.lm_head_weight, hidden_states, input_metadata)
        return next_tokens

    _column_parallel_weights = ["wte.weight", "c_fc.weight", "c_fc.bias"]
    _row_parallel_weights = ["c_proj.weight"]

    def load_weights(self, model_name_or_path: str,
                     cache_dir: Optional[str] = None,
                     use_np_cache: bool = False):
        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
        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 "lm_head.weight" in name:
                # GPT-2 ties the weights of the embedding layer and the final
                # linear layer.
                continue
            if ".attn.bias" in name:
                # Skip attention mask.
                # NOTE: "c_attn.bias" should not be skipped.
                continue
            name = "transformer." + name

            # The HF's GPT-2 implementation uses Conv1D instead of Linear.
            # Because of this, we need to transpose the weights.
            for conv1d_weight_name in ["c_attn", "c_proj", "c_fc"]:
                if conv1d_weight_name not in name:
                    continue
                if not name.endswith(".weight"):
                    continue
                loaded_weight = loaded_weight.t()
            param = state_dict[name]

            if name == "transformer.wte.weight":
                # Consider padding in the vocab size.
                padded_vocab_size = param.shape[0] * tensor_model_parallel_world_size
                num_extra_rows = padded_vocab_size - self.config.vocab_size
                extra_rows = torch.empty(num_extra_rows, loaded_weight.shape[1])
                extra_rows = extra_rows.to(loaded_weight)
                loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0)

            # For the fused QKV linear layer, manually shard the weights.
            if "c_attn" in name:
                # GPT-2's fused QKV has the shape of [3 * num_heads * head_size, hidden_size].
                # When tensor parallelism is used, we shard the weights along the head dimension.
                total_num_heads = self.config.num_attention_heads
                hidden_size = self.config.hidden_size
                head_size = hidden_size // total_num_heads
                num_heads = total_num_heads // tensor_model_parallel_world_size
                head_start = tensor_model_parallel_rank * num_heads
                head_end = (tensor_model_parallel_rank + 1) * num_heads

                if name.endswith(".weight"):
                    loaded_weight = loaded_weight.view(3, total_num_heads, head_size, hidden_size)
                    loaded_weight = loaded_weight[:, head_start:head_end, :, :]
                    loaded_weight = loaded_weight.reshape(-1, hidden_size)
                elif name.endswith(".bias"):
                    loaded_weight = loaded_weight.view(3, total_num_heads, head_size)
                    loaded_weight = loaded_weight[:, head_start:head_end, :]
                    loaded_weight = loaded_weight.reshape(-1)
                else:
                    raise ValueError(f"Unexpected parameter name {name}")
            load_tensor_parallel_weights(param, loaded_weight, name,
                                         self._column_parallel_weights,
                                         self._row_parallel_weights,
                                         tensor_model_parallel_rank)