forked from NVIDIA/TensorRT-LLM
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmodel.py
299 lines (270 loc) · 13.2 KB
/
model.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
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
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
from collections import OrderedDict
import tensorrt as trt
from tensorrt_llm._common import default_net
from tensorrt_llm.bindings import KVCacheType
from tensorrt_llm.functional import Tensor, cast, categorical_sample
from tensorrt_llm.models import LLaMAForCausalLM
from tensorrt_llm.models.generation_mixin import GenerationMixin
from ..._utils import pad_vocab_size, str_dtype_to_trt
from .drafter import Drafter
from .redrafter_helper import (_beam_search_candidates, _beams2tree,
_process_logits_and_hidden_states)
class ReDrafterForCausalLM(LLaMAForCausalLM):
def __init__(self, config):
super().__init__(config)
self.dtype = str_dtype_to_trt(config.dtype)
self.vocab_size = config.vocab_size
vocab_size_padded = pad_vocab_size(self.vocab_size,
config.mapping.tp_size)
self.drafter = Drafter.from_config(config, vocab_size_padded)
self.num_beams = config.redrafter_num_beams
self.beam_candidate_length = config.redrafter_draft_len_per_beam
self.beam_length = self.beam_candidate_length + 1 # including true token
self.greedy_search = config.redrafter_greedy_search
self.is_rnn = config.redrafter_is_rnn
assert self.dtype == self.drafter.dtype, f"{self.dtype} != {self.drafter.dtype}"
def _fwd_helper(self, hidden_states, lm_logits, embedding, drafter,
kwargs: dict):
'''
Must enable remove_input_padding:
hidden_states [total_tokens, H]
lm_logits [total_tokens, V]
1. process_logits: context vs gen
a. Context: just return the last hidden states, and logits/probs
b. Gen:
i. verify: use lm_logits, draft_probs, draft_indices, draft_tokens
ii. select hidden state and update probs
3. Sample token based on probs
4. Generate candidates using hidden_states, sampled token
5. Using beams, generate validation buffers, mark them as output
6. Mark all the outputs
'''
num_beams = self.num_beams
beam_length = self.beam_length
# Get the inputs needed
rand_data_sample = kwargs['rand_data_sample']
position_ids_base = kwargs['position_ids_base']
# Step 1: Process logits and hidden states
# process the base model output (verify for gen-phase)
probs, draft_input, num_accepted_tokens, \
accepted_beam_index = _process_logits_and_hidden_states(
self, lm_logits, hidden_states, kwargs)
# NOTE: num_accepted_tokens doesn't include true token so add 1 here
num_accepted_tokens = num_accepted_tokens + 1
# At this point:
# probs : [bs, V]
# hidden_states : [bs, H]
# Step 2: Sample token
next_token = categorical_sample(probs, rand_data_sample)
# Step 3: beam search
new_draft_tokens, new_draft_logits = _beam_search_candidates(
draft_input, next_token, embedding, drafter, self.num_beams,
self.beam_length, self.is_rnn)
# Step 4: tree processing
active_tokens_flattened, new_draft_token_indices, new_mask, \
new_position_offsets, packed_position_ids, next_num_gen_tokens, max_gen_token, \
total_gen_token = _beams2tree(new_draft_tokens, num_beams, beam_length,
position_ids_base + num_accepted_tokens)
# Step 5: mark all the tensors we need
num_accepted_tokens.mark_output('num_accepted_tokens')
accepted_beam_index.mark_output('accepted_beam_index')
max_gen_token.mark_output('max_gen_token')
total_gen_token.mark_output('total_gen_token')
next_num_gen_tokens.mark_output('next_spec_decoding_generation_lengths')
active_tokens_flattened.mark_output('next_flat_tokens')
new_draft_tokens.mark_output('next_draft_tokens')
new_draft_logits.mark_output('next_draft_probs')
new_draft_token_indices.mark_output('next_draft_indices')
new_mask.mark_output('spec_decoding_mask')
new_position_offsets.mark_output('next_spec_decoding_position_offsets')
packed_position_ids.mark_output('packed_position_ids')
return next_token, probs, draft_input
def forward(self, *args, **kwargs):
"""
0. run base model, get logits, hidden_states
"""
extra_args = [
'draft_tokens',
'draft_indices',
'draft_probs',
'device_request_types',
'redrafter_inverted_temperature',
'rand_data_validation',
'rand_data_sample',
'position_ids_base',
]
use_cache = True
base_kwargs = {k: v for k, v in kwargs.items() if k not in extra_args}
if use_cache and default_net().plugin_config.paged_kv_cache is False:
lm_logits, presents, hidden_states = super().forward(
*args, **base_kwargs)
else:
lm_logits, hidden_states = super().forward(*args, **base_kwargs)
# lm_logits could be in fp32
lm_logits_cast = cast(lm_logits, self.dtype) # no-op if same type
self.register_network_output("hidden_states",
hidden_states) # debugging
new_draft_tokens, new_draft_logits, probs = self._fwd_helper(
hidden_states,
lm_logits_cast,
self.transformer.vocab_embedding,
self.drafter,
kwargs=kwargs)
return new_draft_tokens, new_draft_logits, probs
def prepare_inputs(self, *args, **kwargs):
"""
Inputs needed:
Assuming, max_gen_tokens = 1 + nb*(bl - 1), counting true token
device_request_types: [bs]
draft_tokens: [bs, nb, bl]
draft_indices: [bs, nb, bl]
draft_probs: [bs, nb, bl-1, V]
spec_decoding_generation_lengths: [bs]
spec_decoding_position_offsets: [bs, max_gen_tokens]
spec_decoding_packed_mask: [bs, max_gen_tokens, packed_length] **
redrafter_inverted_temperature: [bs]
rand_data_sample: [bs]
rand_data_validation: [bs, nb, bl-1]
** The mask is tricky since the boolean mask will need to be
packed in runtime. So, the last dim will be:
packed_length = ceil(max_gen_tokens/32)
"""
default_range = GenerationMixin.default_range
remove_input_padding = default_net().plugin_config.remove_input_padding
use_gpt_attention_plugin = default_net(
).plugin_config.gpt_attention_plugin
use_gemm_plugin = default_net().plugin_config.gemm_plugin
paged_kv_cache = default_net().plugin_config.paged_kv_cache
max_batch_size = kwargs['max_batch_size']
assert max_batch_size is not None
bb_range = default_range(max_batch_size)
bb0_range = default_range(max_batch_size, min_range=0, opt_offset=1)
num_beam_tokens = self.num_beams * self.beam_length
max_draft_tokens = num_beam_tokens - self.num_beams # ignore the true token
max_gen_token_len = 1 + max_draft_tokens # for the true token
max_gen_token_len_range = default_range(max_gen_token_len)
bb_max_gen_token_len_range = default_range(max_gen_token_len *
max_batch_size,
min_range=0)
kwargs['speculative_decoding_draft_tokens_external'] = False
kwargs['max_draft_len'] = max_draft_tokens
kwargs['spec_decoding_is_generation_length_variable'] = True
inputs = super().prepare_inputs(*args, **kwargs)
assert inputs['spec_decoding_params'] is not None
enable_two_optimization_profiles = GenerationMixin.has_ctx_gen_opt_profiles(
use_gpt_attention_plugin=use_gpt_attention_plugin,
use_gemm_plugin=use_gemm_plugin,
remove_input_padding=remove_input_padding,
kv_cache_type=KVCacheType.PAGED
if paged_kv_cache else KVCacheType.CONTINUOUS)
if enable_two_optimization_profiles:
bb_range = [bb_range, bb_range]
bb0_range = [bb0_range, bb0_range]
max_gen_token_len_range = [
max_gen_token_len_range, max_gen_token_len_range
]
bb_max_gen_token_len_range = [
bb_max_gen_token_len_range, bb_max_gen_token_len_range
]
num_beams_range = [self.num_beams, self.num_beams]
beam_length_range = [self.beam_length, self.beam_length]
candidate_length_range = [
self.beam_candidate_length, self.beam_candidate_length
]
vocab_size_range = [self.vocab_size, self.vocab_size]
else:
bb_range = [bb_range]
bb0_range = [bb0_range]
max_gen_token_len_range = [max_gen_token_len_range]
bb_max_gen_token_len_range = [bb_max_gen_token_len_range]
num_beams_range = [self.num_beams]
beam_length_range = [self.beam_length]
candidate_length_range = [self.beam_candidate_length]
vocab_size_range = [self.vocab_size]
device_request_types = Tensor(name='device_request_types',
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([
('batch_size', bb_range),
]))
draft_tokens = Tensor(name='draft_tokens',
dtype=trt.int32,
shape=[-1, self.num_beams, self.beam_length],
dim_range=OrderedDict([
('batch_size_wt0', bb0_range),
('num_beams', num_beams_range),
('beam_length', beam_length_range),
]))
draft_indices = Tensor(name='draft_indices',
dtype=trt.int32,
shape=[-1, self.num_beams, self.beam_length],
dim_range=OrderedDict([
('batch_size_wt0', bb0_range),
('num_beams', num_beams_range),
('beam_length', beam_length_range),
]))
draft_probs = Tensor(
name='draft_probs',
dtype=self.dtype,
shape=[-1, self.num_beams, self.beam_length - 1, self.vocab_size],
dim_range=OrderedDict([
('batch_size_wt0', bb0_range),
('num_beams', num_beams_range),
('candidate_length', candidate_length_range),
('vocab_size', vocab_size_range),
]))
redrafter_inverted_temperature = Tensor(
name='redrafter_inverted_temperature',
dtype=self.dtype,
shape=[-1],
dim_range=OrderedDict([
("batch_size", bb_range),
]))
rand_data_validation = Tensor(
name='rand_data_validation',
dtype=self.dtype,
shape=[-1, self.num_beams, self.beam_length - 1],
dim_range=OrderedDict([
('batch_size_wt0', bb0_range),
('num_beams', num_beams_range),
('candidate_length', candidate_length_range),
]))
rand_data_sample = Tensor(name='rand_data_sample',
dtype=self.dtype,
shape=[-1],
dim_range=OrderedDict([
('batch_size', bb_range),
]))
position_ids_base = Tensor(
name="position_ids_base",
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([
("batch_size", bb_range),
]),
)
inputs[
'device_request_types'] = device_request_types # needed by process_logits
inputs['draft_tokens'] = draft_tokens
inputs['draft_indices'] = draft_indices
inputs['draft_probs'] = draft_probs
inputs[
'redrafter_inverted_temperature'] = redrafter_inverted_temperature
inputs['rand_data_validation'] = rand_data_validation
inputs['rand_data_sample'] = rand_data_sample
inputs['position_ids_base'] = position_ids_base
return inputs