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[Quality] Add code formatter and linter (#326)

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This commit is contained in:
Zhuohan Li
2023-07-03 11:31:55 -07:00
committed by GitHub
parent 0ffded812a
commit d6fa1be3a8
47 changed files with 1547 additions and 617 deletions
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115
vllm/model_executor/models/opt.py
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@@ -1,7 +1,9 @@
# coding=utf-8
# Adapted from https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/opt/modeling_opt.py
# 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.
# 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.
@@ -43,8 +45,9 @@ 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
# 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)
@@ -62,20 +65,26 @@ class OPTAttention(nn.Module):
) -> None:
super().__init__()
self.embed_dim = embed_dim
tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
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.scaling = self.head_dim**-0.5
self.qkv_proj = ColumnParallelLinear(embed_dim, 3 * embed_dim, bias=bias,
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,
self.out_proj = RowParallelLinear(embed_dim,
embed_dim,
bias=bias,
input_is_parallel=True,
perform_initialization=False)
self.attn = PagedAttention(self.num_heads, self.head_dim,
self.attn = PagedAttention(self.num_heads,
self.head_dim,
scale=self.scaling)
def forward(
@@ -88,8 +97,8 @@ class OPTAttention(nn.Module):
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)
attn_output = self.attn(q, k, v, key_cache, value_cache,
input_metadata, cache_event)
output, _ = self.out_proj(attn_output)
return output
@@ -109,17 +118,21 @@ class OPTDecoderLayer(nn.Module):
self.activation_fn = get_act_fn(config.activation_function)
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,
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,
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)
self.embed_dim,
elementwise_affine=config.layer_norm_elementwise_affine)
def forward(
self,
@@ -133,11 +146,10 @@ class OPTDecoderLayer(nn.Module):
# 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 = 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:
@@ -167,35 +179,42 @@ class OPTDecoder(nn.Module):
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)
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)
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)
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
# 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
)
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)])
self.layers = nn.ModuleList(
[OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)])
def forward(
self,
@@ -217,8 +236,8 @@ class OPTDecoder(nn.Module):
else:
cache_event = cache_events[i]
layer = self.layers[i]
hidden_states = layer(
hidden_states, kv_caches[i], input_metadata, cache_event)
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)
@@ -241,8 +260,8 @@ class OPTModel(nn.Module):
input_metadata: InputMetadata,
cache_events: Optional[List[torch.cuda.Event]],
) -> torch.Tensor:
return self.decoder(
input_ids, positions, kv_caches, input_metadata, cache_events)
return self.decoder(input_ids, positions, kv_caches, input_metadata,
cache_events)
class OPTForCausalLM(nn.Module):
@@ -264,23 +283,26 @@ class OPTForCausalLM(nn.Module):
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)
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"]
_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,
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):
model_name_or_path, cache_dir, use_np_cache):
if "lm_head.weight" in name:
continue
@@ -288,16 +310,17 @@ class OPTForCausalLM(nn.Module):
name = "model." + name
is_attention_weight = False
for stride_id, att_weight_name in enumerate(["q_proj", "k_proj", "v_proj"]):
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)]
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