vllm/cacheflow/model_executor/models/opt.py

# coding=utf-8
# Copyright 2023 The CacheFlow team.
# Adapted from https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/opt/modeling_opt.py
#
# Copyright 2022 The Fairseq Authors and 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 OPT model compatible with HuggingFace weights."""
from typing import Dict, List, Optional, Tuple

import torch
from torch import nn
from transformers import OPTConfig

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 OPTLearnedPositionalEmbedding(nn.Embedding):

    def __init__(self, num_embeddings: int, embedding_dim: int):
        # OPT is set up so that if padding_idx is specified then offset the embedding ids by 2
        # and adjust num_embeddings appropriately. Other models don't have this hack
        self.offset = 2
        super().__init__(num_embeddings + self.offset, embedding_dim)

    def forward(self, positions: torch.LongTensor):
        return super().forward(positions + self.offset)


class OPTAttention(nn.Module):

    def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        bias: bool = True,
    ) -> None:
        super().__init__()
        self.embed_dim = embed_dim
        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
        total_num_heads = num_heads
        assert num_heads % tensor_model_parallel_world_size == 0
        self.num_heads = total_num_heads // tensor_model_parallel_world_size
        self.head_dim = embed_dim // total_num_heads
        self.scaling = self.head_dim ** -0.5

        self.qkv_proj = ColumnParallelLinear(embed_dim, 3 * embed_dim, bias=bias,
                                             gather_output=False,
                                             perform_initialization=False)
        self.out_proj = RowParallelLinear(embed_dim, embed_dim, bias=bias,
                                          input_is_parallel=True,
                                          perform_initialization=False)
        self.attn = GPTCacheFlowAttention(scale=self.scaling)

    def forward(
        self,
        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)
        key_cache, value_cache = kv_cache
        attn_output = self.attn(
            q, k, v, key_cache, value_cache, input_metadata, cache_event)
        output, _ = self.out_proj(attn_output)
        return output


class OPTDecoderLayer(nn.Module):

    def __init__(self, config: OPTConfig):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.self_attn = OPTAttention(
            embed_dim=self.embed_dim,
            num_heads=config.num_attention_heads,
            bias=config.enable_bias,
        )
        self.do_layer_norm_before = config.do_layer_norm_before
        assert config.activation_function == 'relu'
        self.activation_fn = nn.ReLU()

        self.self_attn_layer_norm = nn.LayerNorm(
            self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine)
        self.fc1 = ColumnParallelLinear(self.embed_dim, config.ffn_dim,
                                        bias=config.enable_bias,
                                        gather_output=False,
                                        perform_initialization=False)
        self.fc2 = RowParallelLinear(config.ffn_dim, self.embed_dim,
                                     bias=config.enable_bias,
                                     input_is_parallel=True,
                                     perform_initialization=False)
        self.final_layer_norm = nn.LayerNorm(
            self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine)

    def forward(
        self,
        hidden_states: torch.Tensor,
        kv_cache: KVCache,
        input_metadata: InputMetadata,
        cache_event: Optional[torch.cuda.Event],
    ) -> torch.Tensor:
        # Self Attention
        residual = hidden_states
        # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
        if self.do_layer_norm_before:
            hidden_states = self.self_attn_layer_norm(hidden_states)
        hidden_states = self.self_attn(
            hidden_states=hidden_states,
            kv_cache=kv_cache,
            input_metadata=input_metadata,
            cache_event=cache_event)
        hidden_states = residual + hidden_states
        # 350m applies layer norm AFTER attention
        if not self.do_layer_norm_before:
            hidden_states = self.self_attn_layer_norm(hidden_states)

        # Fully Connected
        residual = hidden_states
        # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
        if self.do_layer_norm_before:
            hidden_states = self.final_layer_norm(hidden_states)
        hidden_states, _ = self.fc1(hidden_states)
        hidden_states = self.activation_fn(hidden_states)
        hidden_states, _ = self.fc2(hidden_states)
        hidden_states = residual + hidden_states
        # 350m applies layer norm AFTER attention
        if not self.do_layer_norm_before:
            hidden_states = self.final_layer_norm(hidden_states)
        return hidden_states


class OPTDecoder(nn.Module):

    def __init__(self, config: OPTConfig):
        super().__init__()
        self.config = config
        self.padding_idx = config.pad_token_id
        self.max_target_positions = config.max_position_embeddings
        self.vocab_size = config.vocab_size

        self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
                                                   config.word_embed_proj_dim,
                                                   perform_initialization=False)
        # Positional embeddings are replicated (not sharded).
        self.embed_positions = OPTLearnedPositionalEmbedding(
            config.max_position_embeddings, config.hidden_size)

        # Project out & in will be replicated if they exist.
        if config.word_embed_proj_dim != config.hidden_size:
            self.project_out = nn.Linear(config.hidden_size, config.word_embed_proj_dim, bias=False)
        else:
            self.project_out = None

        if config.word_embed_proj_dim != config.hidden_size:
            self.project_in = nn.Linear(config.word_embed_proj_dim, config.hidden_size, bias=False)
        else:
            self.project_in = None

        # Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility
        # with checkpoints that have been fine-tuned before transformers v4.20.1
        # see https://github.com/facebookresearch/metaseq/pull/164
        if config.do_layer_norm_before and not config._remove_final_layer_norm:
            self.final_layer_norm = nn.LayerNorm(
                config.hidden_size, elementwise_affine=config.layer_norm_elementwise_affine
            )
        else:
            self.final_layer_norm = None

        self.layers = nn.ModuleList([OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)])

    def forward(
        self,
        input_ids: torch.LongTensor,
        positions: torch.LongTensor,
        kv_caches: List[KVCache],
        input_metadata: InputMetadata,
        cache_events: Optional[List[torch.cuda.Event]],
    ) -> torch.Tensor:
        inputs_embeds = self.embed_tokens(input_ids)
        pos_embeds = self.embed_positions(positions)
        if self.project_in is not None:
            inputs_embeds = self.project_in(inputs_embeds)
        hidden_states = inputs_embeds + pos_embeds

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

        if self.final_layer_norm is not None:
            hidden_states = self.final_layer_norm(hidden_states)
        if self.project_out is not None:
            hidden_states = self.project_out(hidden_states)
        return hidden_states


class OPTModel(nn.Module):

    def __init__(self, config: OPTConfig):
        super().__init__()
        self.decoder = OPTDecoder(config)

    def forward(
        self,
        input_ids: torch.LongTensor,
        positions: torch.LongTensor,
        kv_caches: List[KVCache],
        input_metadata: InputMetadata,
        cache_events: Optional[List[torch.cuda.Event]],
    ) -> torch.Tensor:
        return self.decoder(
            input_ids, positions, kv_caches, input_metadata, cache_events)


class OPTForCausalLM(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.config = config
        self.model = OPTModel(config)
        # TODO(zhuohan): create a new weight after implementing pipeline
        #                parallelism
        self.lm_head_weight = self.model.decoder.embed_tokens.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.model(
            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 = ["embed_tokens.weight", "fc1.weight", "fc1.bias"]
    _row_parallel_weights = ["out_proj.weight", "fc2.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 "lm_head.weight" in name:
                continue

            if name.startswith("decoder."):
                name = "model." + name

            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[0] // 3
                loaded_weight = loaded_weight[
                    shard_size * tensor_model_parallel_rank
                    :shard_size * (tensor_model_parallel_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,
                                         tensor_model_parallel_rank)