File size: 17,695 Bytes
61e6a6c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 |
# Copyright 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of NVIDIA CORPORATION nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from collections.abc import Callable
from typing import Dict, Optional
import numpy as np
import triton_python_backend_utils as pb_utils
from lib.decode import *
from typing_extensions import override
class TritonDecoder(Decoder):
def __init__(self,
streaming=False,
accumulate=False,
preproc_model_name="preprocessing",
postproc_model_name="postprocessing",
llm_model_name="tensorrt_llm",
draft_llm_model_name: Optional[str] = None):
super().__init__(streaming=streaming, accumulate=accumulate)
self.preproc_model_name = preproc_model_name
self.postproc_model_name = postproc_model_name
self.llm_model_name = llm_model_name
self.draft_llm_model_name = draft_llm_model_name
self._preproc_outputs = [
"INPUT_ID",
"DECODER_INPUT_ID",
"REQUEST_INPUT_LEN",
"REQUEST_DECODER_INPUT_LEN",
"BAD_WORDS_IDS",
"STOP_WORDS_IDS",
"EMBEDDING_BIAS",
"OUT_PAD_ID",
"OUT_END_ID",
]
self._llm_outputs = [
"output_ids",
"sequence_length",
"cum_log_probs",
"output_log_probs",
"context_logits",
"generation_logits",
]
self._postproc_outputs = [
"OUTPUT",
]
self.input_names = [
"text_input",
"decoder_text_input",
"max_tokens",
"bad_words",
"stop_words",
"end_id",
"pad_id",
"top_k",
"top_p",
"temperature",
"length_penalty",
"repetition_penalty",
"min_length",
"presence_penalty",
"frequency_penalty",
"random_seed",
"return_log_probs",
"return_context_logits",
"return_generation_logits",
"beam_width",
"stream",
"prompt_embedding_table",
"prompt_vocab_size",
"embedding_bias_words",
"embedding_bias_weights",
"num_draft_tokens",
"use_draft_logits",
]
self.__undo_reshape_whitelist = {
"max_tokens",
"end_id",
"pad_id",
"top_k",
"top_p",
"temperature",
"length_penalty",
"repetition_penalty",
"min_length",
"presence_penalty",
"frequency_penalty",
"random_seed",
"return_log_probs",
"return_context_logits",
"return_generation_logits",
"beam_width",
"stream",
"prompt_vocab_size",
"num_draft_tokens",
"use_draft_logits",
}
def _exec_triton_request(self, request):
responses = request.exec(decoupled=True)
for r in responses:
if r.has_error():
raise pb_utils.TritonModelException(r.error().message())
yield r
def _exec_triton_request_single(self, request):
responses = request.exec(decoupled=False)
if responses.has_error():
raise pb_utils.TritonModelException(responses.error().message())
return responses
def create_triton_response(self, response: Response):
name_map = {
"text_output": "text_output",
"cum_log_probs": "cum_log_probs",
"output_log_probs": "output_log_probs",
"context_logits": "context_logits",
"generation_logits": "generation_logits"
}
tensors = self.create_triton_tensors(response, name_map)
return pb_utils.InferenceResponse(output_tensors=tensors)
def convert_triton_request(self, triton_request) -> Request:
request = Request()
for triton_name in self.input_names:
tensor = pb_utils.get_input_tensor_by_name(triton_request,
triton_name)
target_name = triton_name
if tensor is None:
continue
if not hasattr(request, target_name):
raise AttributeError(
f"Request has no attribute '{target_name}'")
setattr(request, target_name, tensor.as_numpy())
return request
def convert_triton_response(self,
triton_response,
response_factory: Callable,
name_map=None):
response = response_factory()
for tensor in triton_response.output_tensors():
if tensor is None:
continue
triton_name = tensor.name()
value = tensor.as_numpy()
target_name = triton_name
if name_map and triton_name in name_map:
target_name = name_map[triton_name]
if name_map and not triton_name in name_map:
continue
if target_name is None:
# explicitly ignore this triton input
continue
if not hasattr(response, target_name):
raise AttributeError(
f"response object has not attribute '{target_name}'")
setattr(response, target_name, value)
return response
def __undo_reshape(self, x, name):
if name in self.__undo_reshape_whitelist and len(x.shape) == 1:
# handle reshapes
return np.expand_dims(x, 0)
else:
return x
def create_triton_tensors(self, obj, name_map: dict):
tensors = []
for name, triton_name in name_map.items():
if triton_name is None:
continue
value = getattr(obj, name)
if value is None:
continue
t = pb_utils.Tensor(triton_name, self.__undo_reshape(value, name))
tensors.append(t)
return tensors
@override
def preprocess(self, request: Request) -> PreprocResponse:
input_tensors = self._get_preproc_tensors(request)
triton_req = pb_utils.InferenceRequest(
model_name=self.preproc_model_name,
inputs=input_tensors,
requested_output_names=self._preproc_outputs)
triton_output = self._exec_triton_request_single(triton_req)
return self._get_preproc_response(triton_output)
def _get_preproc_tensors(self, request: Request):
name_map = {
"text_input": "QUERY",
"decoder_text_input": "DECODER_QUERY",
"max_tokens": "REQUEST_OUTPUT_LEN",
"bad_words": "BAD_WORDS_DICT",
"stop_words": "STOP_WORDS_DICT",
"embedding_bias_words": "EMBEDDING_BIAS_WORDS",
"embedding_bias_weights": "EMBEDDING_BIAS_WEIGHTS",
"pad_id": "PAD_ID",
"end_id": "END_ID",
}
return self.create_triton_tensors(request, name_map)
def _get_preproc_response(self, triton_output):
name_map = {
"INPUT_ID": "input_ids",
"DECODER_INPUT_ID": "decoder_input_ids",
"REQUEST_INPUT_LEN": "input_lengths",
"REQUEST_DECODER_INPUT_LEN": "decoder_input_lengths",
"BAD_WORDS_IDS": "bad_words_list",
"STOP_WORDS_IDS": "stop_words_list",
"EMBEDDING_BIAS": "embedding_bias",
"OUT_PAD_ID": "pad_id",
"OUT_END_ID": "end_id",
}
return self.convert_triton_response(triton_output, PreprocResponse,
name_map)
@override
def _draft_generate_non_streaming(
self, preproc: PreprocResponse, request: Request,
num_draft_tokens: int) -> GenerationResponse:
input_tensors = self._get_llm_tensors(preproc, request,
num_draft_tokens, None, True)
triton_req = pb_utils.InferenceRequest(
model_name=self.draft_llm_model_name,
inputs=input_tensors,
requested_output_names=self._llm_outputs)
triton_response = self._exec_triton_request_single(triton_req)
llm_response = self._get_llm_response(triton_response)
return llm_response
@override
def _generate(
self,
preproc: PreprocResponse,
request: Request,
draft_request: Optional[DraftRequest] = None
) -> Generator[GenerationResponse, None, None]:
input_tensors = self._get_llm_tensors(preproc, request, None,
draft_request)
triton_req = pb_utils.InferenceRequest(
model_name=self.llm_model_name,
inputs=input_tensors,
requested_output_names=self._llm_outputs)
for r in self._exec_triton_request(triton_req):
yield self._get_llm_response(r)
@override
def _generate_non_streaming(
self,
preproc: PreprocResponse,
request: Request,
draft_request: Optional[DraftRequest] = None
) -> GenerationResponse:
input_tensors = self._get_llm_tensors(preproc, request, None,
draft_request)
triton_req = pb_utils.InferenceRequest(
model_name=self.llm_model_name,
inputs=input_tensors,
requested_output_names=self._llm_outputs)
r = self._exec_triton_request_single(triton_req)
return self._get_llm_response(r)
def _get_llm_tensors(self,
preproc: PreprocResponse,
request: Request,
num_output_tokens: Optional[int] = None,
draft_request: Optional[DraftRequest] = None,
is_draft_model_request: bool = False):
tensors = []
tensors.extend(self._get_tensors_from_preproc(preproc))
tensors.extend(
self._get_llm_tensors_from_request(request, num_output_tokens,
draft_request,
is_draft_model_request))
return tensors
def _get_tensors_from_preproc(self, preproc: PreprocResponse):
name_map = {
"input_ids": "input_ids",
"decoder_input_ids": "decoder_input_ids",
"input_lengths": "input_lengths",
"bad_words_list": "bad_words_list",
"stop_words_list": "stop_words_list",
"embedding_bias": "embedding_bias",
"pad_id": "pad_id",
"end_id": "end_id",
}
return self.create_triton_tensors(preproc, name_map)
def _get_llm_tensors_from_request(
self,
request: Request,
num_output_tokens: Optional[int] = None,
draft_request: Optional[DraftRequest] = None,
is_draft_model_request: bool = False):
name_map: Dict[str, Optional[str]] = {
"beam_width": "beam_width",
"top_k": "runtime_top_k",
"top_p": "runtime_top_p",
"length_penalty": "len_penalty",
"repetition_penalty": "repetition_penalty",
"min_length": "min_length",
"presence_penalty": "presence_penalty",
"frequency_penalty": "frequency_penalty",
"random_seed": "random_seed",
"return_log_probs": "return_log_probs",
"stream": "streaming",
"prompt_embedding_table": "prompt_embedding_table",
"prompt_vocab_size": "prompt_vocab_size",
}
tensors = self.create_triton_tensors(request, name_map)
out_len = request.max_tokens[0][0] if request.max_tokens else None
if num_output_tokens is not None:
out_len = num_output_tokens
elif draft_request:
if draft_request.draft_input_ids is not None:
out_len = len(draft_request.draft_input_ids[0]) + 1
else:
out_len = 1
if out_len is None:
raise Exception("Could not determine request_output_len")
else:
tensors.append(
pb_utils.Tensor("request_output_len",
np.array([[out_len]], dtype=np.int32)))
if draft_request:
if draft_request.draft_input_ids is not None:
tensors.append(
pb_utils.Tensor("draft_input_ids",
draft_request.draft_input_ids))
if draft_request.draft_logits is not None and request.use_draft_logits is not None and request.use_draft_logits[
0]:
tensors.append(
pb_utils.Tensor("draft_logits",
draft_request.draft_logits))
return_context_logits = False
return_generation_logits = False
if draft_request is None:
if is_draft_model_request:
return_generation_logits = request.use_draft_logits[
0] if request.use_draft_logits is not None else False
else:
return_context_logits = request.return_context_logits[
0] if request.return_context_logits is not None else False
return_generation_logits = request.return_generation_logits[
0] if request.return_generation_logits is not None else False
tensors.append(
pb_utils.Tensor("return_context_logits",
np.array([[return_context_logits]])))
tensors.append(
pb_utils.Tensor("return_generation_logits",
np.array([[return_generation_logits]])))
return tensors
def _get_llm_response(self, triton_output):
name_map = {
"output_ids": "output_ids",
"sequence_length": "sequence_length",
"cum_log_probs": "cum_log_probs",
"output_log_probs": "output_log_probs",
"context_logits": "context_logits",
"generation_logits": "generation_logits",
}
return self.convert_triton_response(triton_output, GenerationResponse,
name_map)
def _postprocess(self, tokens: np.ndarray,
sequence_lengths: Optional[np.ndarray],
gen_response: GenerationResponse) -> Response:
input_tensors = self._get_postproc_tensors(tokens, sequence_lengths,
gen_response)
triton_req = pb_utils.InferenceRequest(
model_name=self.postproc_model_name,
inputs=input_tensors,
requested_output_names=self._postproc_outputs)
r = self._exec_triton_request_single(triton_req)
response = self._get_response(r, gen_response)
return response
def _get_postproc_tensors(self, tokens: np.ndarray,
sequence_lengths: Optional[np.ndarray],
gen_response: GenerationResponse):
tensors = [
pb_utils.Tensor("TOKENS_BATCH", tokens),
pb_utils.Tensor(
"SEQUENCE_LENGTH", sequence_lengths
if sequence_lengths else gen_response.sequence_length)
]
return tensors
def _get_response(self, triton_output, gen_res: GenerationResponse):
tensors = triton_output.output_tensors()
t_map = {}
for named_t in tensors:
name = named_t.name()
t = named_t.as_numpy()
t_map[name] = t
response = Response(text_output=t_map["OUTPUT"],
cum_log_probs=gen_res.cum_log_probs,
output_log_probs=gen_res.output_log_probs,
context_logits=gen_res.context_logits,
generation_logits=gen_res.generation_logits)
return response
|