gpt_neox.py 10.7 KB
Newer Older
1
# coding=utf-8
2
3
# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt_neox/modeling_gpt_neox.py
Woosuk Kwon's avatar
Woosuk Kwon committed
4
# Copyright 2023 The vLLM team.
5
6
7
8
9
10
11
12
13
14
15
16
17
# Copyright 2022 EleutherAI 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.
18
19
20
21
22
"""Inference-only GPT-NeoX 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.
"""
23
from typing import List, Optional, Tuple
24
25
26

import torch
from torch import nn
27
28
from transformers import GPTNeoXConfig

Woosuk Kwon's avatar
Woosuk Kwon committed
29
30
31
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 PagedAttentionWithRoPE
32
33
34
35
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               LinearMethodBase,
                                               QKVParallelLinear,
                                               RowParallelLinear)
Woosuk Kwon's avatar
Woosuk Kwon committed
36
from vllm.model_executor.layers.sampler import Sampler
37
38
from vllm.model_executor.layers.vocab_parallel_embedding import (
    VocabParallelEmbedding, ParallelLMHead)
Woosuk Kwon's avatar
Woosuk Kwon committed
39
from vllm.model_executor.parallel_utils.parallel_state import (
40
41
42
    get_tensor_model_parallel_world_size)
from vllm.model_executor.weight_utils import (default_weight_loader,
                                              hf_model_weights_iterator)
43
from vllm.sequence import SamplerOutput
44
45
46
47
48
49

KVCache = Tuple[torch.Tensor, torch.Tensor]


class GPTNeoXAttention(nn.Module):

50
51
52
53
54
    def __init__(
        self,
        config: GPTNeoXConfig,
        linear_method: Optional[LinearMethodBase] = None,
    ):
55
56
57
58
59
        super().__init__()
        self.total_num_heads = config.num_attention_heads
        self.hidden_size = config.hidden_size
        self.head_size = self.hidden_size // self.total_num_heads

60
61
        tensor_model_parallel_world_size = (
            get_tensor_model_parallel_world_size())
62
        assert self.total_num_heads % tensor_model_parallel_world_size == 0
63
64
65
        self.num_heads = (self.total_num_heads //
                          tensor_model_parallel_world_size)

66
        self.query_key_value = QKVParallelLinear(
67
            config.hidden_size,
68
69
70
            self.head_size,
            self.total_num_heads,
            linear_method=linear_method,
71
72
73
74
        )
        self.dense = RowParallelLinear(
            config.hidden_size,
            config.hidden_size,
75
            linear_method=linear_method,
76
        )
77

78
        scaling = self.head_size**-0.5
79
80
        rotary_dim = int(self.head_size * config.rotary_pct)
        assert rotary_dim % 2 == 0
81
82
83
84
85
86
87
88
89
90
        rope_theta = getattr(config, "rope_theta", 10000)
        max_position_embeddings = getattr(config, "max_position_embeddings",
                                          8192)
        self.attn = PagedAttentionWithRoPE(
            self.num_heads,
            self.head_size,
            scaling,
            rotary_dim,
            base=rope_theta,
            max_position=max_position_embeddings)
91
92
93

    def forward(
        self,
94
        position_ids: torch.Tensor,
95
96
97
98
99
100
101
102
        hidden_states: torch.Tensor,
        kv_cache: KVCache,
        input_metadata: InputMetadata,
        cache_event: Optional[torch.cuda.Event],
    ) -> torch.Tensor:
        qkv, _ = self.query_key_value(hidden_states)
        q, k, v = qkv.chunk(chunks=3, dim=-1)
        k_cache, v_cache = kv_cache
103
104
        attn_output = self.attn(position_ids, q, k, v, k_cache, v_cache,
                                input_metadata, cache_event)
105
106
107
108
109
        output, _ = self.dense(attn_output)
        return output


class GPTNeoXMLP(nn.Module):
110

111
112
113
114
115
    def __init__(
        self,
        config: GPTNeoXConfig,
        linear_method: Optional[LinearMethodBase] = None,
    ):
116
        super().__init__()
117
118
119
        self.dense_h_to_4h = ColumnParallelLinear(
            config.hidden_size,
            config.intermediate_size,
120
            linear_method=linear_method,
121
122
123
124
        )
        self.dense_4h_to_h = RowParallelLinear(
            config.intermediate_size,
            config.hidden_size,
125
            linear_method=linear_method,
126
        )
Woosuk Kwon's avatar
Woosuk Kwon committed
127
        self.act = get_act_fn(config.hidden_act)
128
129
130
131
132
133
134
135
136
137

    def forward(self, hidden_states):
        hidden_states, _ = self.dense_h_to_4h(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states, _ = self.dense_4h_to_h(hidden_states)
        return hidden_states


class GPTNeoXLayer(nn.Module):

138
139
140
141
142
    def __init__(
        self,
        config: GPTNeoXConfig,
        linear_method: Optional[LinearMethodBase] = None,
    ):
143
144
        super().__init__()
        self.use_parallel_residual = config.use_parallel_residual
145
146
147
148
        self.input_layernorm = nn.LayerNorm(config.hidden_size,
                                            eps=config.layer_norm_eps)
        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
                                                     eps=config.layer_norm_eps)
149
150
        self.attention = GPTNeoXAttention(config, linear_method)
        self.mlp = GPTNeoXMLP(config, linear_method)
151
152
153

    def forward(
        self,
154
        position_ids: torch.Tensor,
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
        hidden_states: torch.Tensor,
        kv_cache: KVCache,
        input_metadata: InputMetadata,
        cache_event: Optional[torch.cuda.Event],
    ) -> torch.Tensor:
        attn_input = self.input_layernorm(hidden_states)
        attn_output = self.attention(
            position_ids=position_ids,
            hidden_states=attn_input,
            kv_cache=kv_cache,
            input_metadata=input_metadata,
            cache_event=cache_event,
        )

        if self.use_parallel_residual:
            # pseudocode:
            # x = x + attn(ln1(x)) + mlp(ln2(x))
            mlp_input = self.post_attention_layernorm(hidden_states)
            mlp_output = self.mlp(mlp_input)
            hidden_states = mlp_output + attn_output + hidden_states
        else:
            # pseudocode:
            # x = x + attn(ln1(x))
            # x = x + mlp(ln2(x))
            attn_output = attn_output + hidden_states
            mlp_input = self.post_attention_layernorm(attn_output)
            mlp_output = self.mlp(mlp_input)
            hidden_states = mlp_output + attn_output
        return hidden_states


class GPTNeoXModel(nn.Module):
Woosuk Kwon's avatar
Woosuk Kwon committed
187

188
189
190
191
192
    def __init__(
        self,
        config: GPTNeoXConfig,
        linear_method: Optional[LinearMethodBase] = None,
    ):
193
194
195
        super().__init__()
        self.config = config

196
197
198
199
        self.embed_in = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
        )
200
201
202
203
        self.layers = nn.ModuleList([
            GPTNeoXLayer(config, linear_method)
            for _ in range(config.num_hidden_layers)
        ])
204
205
        self.final_layer_norm = nn.LayerNorm(config.hidden_size,
                                             eps=config.layer_norm_eps)
206
207
208

    def forward(
        self,
209
210
        input_ids: torch.Tensor,
        position_ids: torch.Tensor,
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
        kv_caches: List[KVCache],
        input_metadata: InputMetadata,
        cache_events: Optional[List[torch.cuda.Event]],
    ) -> torch.Tensor:
        hidden_states = self.embed_in(input_ids)
        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(
                position_ids,
                hidden_states,
                kv_caches[i],
                input_metadata,
                cache_event,
            )
        hidden_states = self.final_layer_norm(hidden_states)
        return hidden_states


class GPTNeoXForCausalLM(nn.Module):

235
236
237
238
239
    def __init__(
        self,
        config,
        linear_method: Optional[LinearMethodBase] = None,
    ):
240
241
        super().__init__()
        self.config = config
242
243
244
        self.linear_method = linear_method
        self.gpt_neox = GPTNeoXModel(config, linear_method)
        self.embed_out = ParallelLMHead(
245
            config.vocab_size,
246
            config.hidden_size,
247
        )
Woosuk Kwon's avatar
Woosuk Kwon committed
248
        self.sampler = Sampler(config.vocab_size)
249
250
251

    def forward(
        self,
252
253
        input_ids: torch.Tensor,
        positions: torch.Tensor,
254
255
256
        kv_caches: List[KVCache],
        input_metadata: InputMetadata,
        cache_events: Optional[List[torch.cuda.Event]],
257
    ) -> SamplerOutput:
258
259
260
261
        hidden_states = self.gpt_neox(input_ids, positions, kv_caches,
                                      input_metadata, cache_events)
        next_tokens = self.sampler(self.embed_out.weight, hidden_states,
                                   input_metadata)
262
263
        return next_tokens

264
265
    def load_weights(self,
                     model_name_or_path: str,
266
                     cache_dir: Optional[str] = None,
Jasmond L's avatar
Jasmond L committed
267
268
                     load_format: str = "auto",
                     revision: Optional[str] = None):
269
        params_dict = dict(self.named_parameters())
270
        for name, loaded_weight in hf_model_weights_iterator(
Jasmond L's avatar
Jasmond L committed
271
                model_name_or_path, cache_dir, load_format, revision):
272
            if ("attention.bias" in name or "attention.masked_bias" in name
273
                    or "rotary_emb.inv_freq" in name):
274
                continue
275
276
            param = params_dict[name]

277
            if "query_key_value" in name:
278
279
280
                # NOTE: GPT-NeoX's fused QKV's output_dim has the shape of
                # (num_heads * 3 * head_size), while the
                # required shape is (3 * num_heads * head_size).
281
                # Thus, we need weight conversion.
282
                output_dim = getattr(param, "output_dim", None)
283
                num_heads = self.config.num_attention_heads
284
285
286
287
288
289
290
291
292
293
294
295
                if output_dim is not None:
                    loaded_weight_shape = loaded_weight.shape
                    loaded_weight = loaded_weight.view(
                        loaded_weight_shape[:output_dim] + (num_heads, 3, -1) +
                        loaded_weight_shape[output_dim + 1:])
                    loaded_weight = loaded_weight.transpose(
                        output_dim, output_dim + 1)
                    loaded_weight = loaded_weight.reshape(loaded_weight_shape)

            weight_loader = getattr(param, "weight_loader",
                                    default_weight_loader)
            weight_loader(param, loaded_weight)