opt.py

# coding=utf-8
# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/opt/modeling_opt.py
# Copyright 2023 The vLLM team.
# 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.
"""Inference-only OPT 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 OPTConfig

from vllm.model_executor.input_metadata import InputMetadata
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.attention import PagedAttention
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               LinearMethodBase,
                                               QKVParallelLinear,
                                               ReplicatedLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.sampler import Sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
    VocabParallelEmbedding)
from vllm.model_executor.parallel_utils.parallel_state import (
    get_tensor_model_parallel_world_size)
from vllm.model_executor.weight_utils import (default_weight_loader,
                                              hf_model_weights_iterator)
from vllm.sequence import SamplerOutput

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.Tensor):
        return super().forward(positions + self.offset)


class OPTAttention(nn.Module):

    def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        bias: bool = True,
        linear_method: Optional[LinearMethodBase] = None,
    ) -> 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 = QKVParallelLinear(
            embed_dim,
            self.head_dim,
            total_num_heads,
            bias=bias,
            linear_method=linear_method,
        )
        self.out_proj = RowParallelLinear(
            embed_dim,
            embed_dim,
            bias=bias,
            linear_method=linear_method,
        )
        self.attn = PagedAttention(self.num_heads,
                                   self.head_dim,
                                   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,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        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,
            linear_method=linear_method,
        )
        self.do_layer_norm_before = config.do_layer_norm_before

        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,
            linear_method=linear_method,
        )
        quant_config = getattr(linear_method, "quant_config", None)
        self.activation_fn = get_act_fn(config.activation_function,
                                        quant_config, config.ffn_dim)
        self.fc2 = RowParallelLinear(
            config.ffn_dim,
            self.embed_dim,
            bias=config.enable_bias,
            linear_method=linear_method,
        )
        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,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        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,
        )
        # 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 = ReplicatedLinear(config.hidden_size,
                                                config.word_embed_proj_dim,
                                                bias=False,
                                                linear_method=linear_method)
        else:
            self.project_out = None

        if config.word_embed_proj_dim != config.hidden_size:
            self.project_in = ReplicatedLinear(config.word_embed_proj_dim,
                                               config.hidden_size,
                                               bias=False,
                                               linear_method=linear_method)
        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, linear_method)
            for _ in range(config.num_hidden_layers)
        ])

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        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)):
            cache_event = None if cache_events is None else 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,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()
        self.decoder = OPTDecoder(config, linear_method)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        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,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()
        self.config = config
        self.linear_method = linear_method
        self.model = OPTModel(config, linear_method)
        self.lm_head_weight = self.model.decoder.embed_tokens.weight
        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.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

    def load_weights(self,
                     model_name_or_path: str,
                     cache_dir: Optional[str] = None,
                     load_format: str = "auto",
                     revision: Optional[str] = None):
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
        ]
        params_dict = dict(self.named_parameters(remove_duplicate=False))
        for name, loaded_weight in hf_model_weights_iterator(
                model_name_or_path, cache_dir, load_format, revision):
            if "lm_head.weight" in name:
                continue
            for (param_name, weight_name, shard_id) in stacked_params_mapping:
                if weight_name not in name:
                    continue
                param = params_dict[name.replace(weight_name, param_name)]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                weight_loader(param, loaded_weight)