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# 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."""
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.sampling_metadata import SamplingMetadata
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,
) -> 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)
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,
) -> 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)
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,
) -> 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)):
layer = self.layers[i]
hidden_states = layer(hidden_states, kv_caches[i], input_metadata)
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,
) -> torch.Tensor:
return self.decoder(input_ids, positions, kv_caches, input_metadata)
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,
) -> torch.Tensor:
hidden_states = self.model(input_ids, positions, kv_caches,
input_metadata)
return hidden_states
def sample(
self,
hidden_states: torch.Tensor,
sampling_metadata: SamplingMetadata,
) -> Optional[SamplerOutput]:
next_tokens = self.sampler(self.lm_head_weight, hidden_states,
sampling_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
if name.startswith("decoder."):
name = "model." + name
for (param_name, weight_name, shard_id) in stacked_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
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