mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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eb8f26c7e4
* Update TensorRT-LLM --------- Co-authored-by: Eddie-Wang1120 <wangjinheng1120@163.com> Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
413 lines
16 KiB
Python
413 lines
16 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import json
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import re
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import time
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from collections import OrderedDict
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from pathlib import Path
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import numpy as np
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import torch
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from datasets import load_dataset
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from tokenizer import get_tokenizer
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from torch.utils.data import DataLoader
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from whisper.normalizers import EnglishTextNormalizer
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from whisper_utils import (N_SAMPLES, log_mel_spectrogram, pad_or_trim,
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store_transcripts, write_error_stats)
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import tensorrt_llm
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import tensorrt_llm.logger as logger
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from tensorrt_llm._utils import (str_dtype_to_torch, str_dtype_to_trt,
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trt_dtype_to_torch)
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from tensorrt_llm.runtime import ModelConfig, SamplingConfig
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from tensorrt_llm.runtime.session import Session, TensorInfo
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def parse_arguments():
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parser = argparse.ArgumentParser()
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parser.add_argument('--log_level', type=str, default='error')
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parser.add_argument('--engine_dir', type=str, default='whisper_outputs')
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parser.add_argument('--results_dir', type=str, default='tmp')
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parser.add_argument('--assets_dir', type=str, default=None)
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parser.add_argument('--input_file', type=str, default=None)
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parser.add_argument('--dataset',
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type=str,
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default="hf-internal-testing/librispeech_asr_dummy")
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parser.add_argument('--name',
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type=str,
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default="librispeech_dummy_benchmark")
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parser.add_argument('--batch_size', type=int, default=4)
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parser.add_argument('--num_beams', type=int, default=1)
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parser.add_argument('--debug', action='store_true')
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parser.add_argument('--enable_warmup', action='store_true')
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parser.add_argument('--dtype',
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type=str,
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default='float16',
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choices=['float16'])
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return parser.parse_args()
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class WhisperEncoding:
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def __init__(self, engine_dir):
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self.session = self.get_session(engine_dir)
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def get_session(self, engine_dir):
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config_path = engine_dir / 'encoder_config.json'
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with open(config_path, 'r') as f:
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config = json.load(f)
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use_gpt_attention_plugin = config['plugin_config'][
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'gpt_attention_plugin']
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dtype = config['builder_config']['precision']
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n_mels = config['builder_config']['n_mels']
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num_languages = config['builder_config']['num_languages']
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self.dtype = dtype
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self.n_mels = n_mels
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self.num_languages = num_languages
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serialize_path = engine_dir / f'whisper_encoder_{self.dtype}_tp1_rank0.engine'
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with open(serialize_path, 'rb') as f:
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session = Session.from_serialized_engine(f.read())
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return session
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def get_audio_features(self, mel):
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input_lengths = torch.tensor(
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[mel.shape[2] // 2 for _ in range(mel.shape[0])],
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dtype=torch.int32,
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device=mel.device)
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inputs = OrderedDict()
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inputs['x'] = mel
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inputs['input_lengths'] = input_lengths
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output_list = [
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TensorInfo('x', str_dtype_to_trt(self.dtype), mel.shape),
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TensorInfo('input_lengths', str_dtype_to_trt('int32'),
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input_lengths.shape)
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]
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output_info = (self.session).infer_shapes(output_list)
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logger.debug(f'output info {output_info}')
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outputs = {
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t.name: torch.empty(tuple(t.shape),
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dtype=trt_dtype_to_torch(t.dtype),
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device='cuda')
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for t in output_info
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}
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stream = torch.cuda.current_stream()
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ok = self.session.run(inputs=inputs,
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outputs=outputs,
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stream=stream.cuda_stream)
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assert ok, 'Engine execution failed'
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stream.synchronize()
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audio_features = outputs['output']
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return audio_features
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class WhisperDecoding:
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def __init__(self, engine_dir, runtime_mapping, debug_mode=False):
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self.decoder_config = self.get_config(engine_dir)
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self.decoder_generation_session = self.get_session(
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engine_dir, runtime_mapping, debug_mode)
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def get_config(self, engine_dir):
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config_path = engine_dir / 'decoder_config.json'
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with open(config_path, 'r') as f:
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config = json.load(f)
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decoder_config = OrderedDict()
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decoder_config.update(config['plugin_config'])
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decoder_config.update(config['builder_config'])
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return decoder_config
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def get_session(self, engine_dir, runtime_mapping, debug_mode=False):
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dtype = self.decoder_config['precision']
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serialize_path = engine_dir / f'whisper_decoder_{dtype}_tp1_rank0.engine'
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with open(serialize_path, "rb") as f:
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decoder_engine_buffer = f.read()
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decoder_model_config = ModelConfig(
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max_batch_size=self.decoder_config['max_batch_size'],
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max_beam_width=self.decoder_config['max_beam_width'],
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num_heads=self.decoder_config['num_heads'],
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num_kv_heads=self.decoder_config['num_heads'],
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hidden_size=self.decoder_config['hidden_size'],
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vocab_size=self.decoder_config['vocab_size'],
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num_layers=self.decoder_config['num_layers'],
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gpt_attention_plugin=self.decoder_config['gpt_attention_plugin'],
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remove_input_padding=self.decoder_config['remove_input_padding'],
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cross_attention=self.decoder_config['cross_attention'],
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has_position_embedding=self.
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decoder_config['has_position_embedding'],
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has_token_type_embedding=self.
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decoder_config['has_token_type_embedding'],
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)
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decoder_generation_session = tensorrt_llm.runtime.GenerationSession(
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decoder_model_config,
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decoder_engine_buffer,
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runtime_mapping,
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debug_mode=debug_mode)
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return decoder_generation_session
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def generate(self,
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decoder_input_ids,
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encoder_outputs,
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eot_id,
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max_new_tokens=40,
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num_beams=1):
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encoder_input_lengths = torch.tensor(
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[encoder_outputs.shape[1] for x in range(encoder_outputs.shape[0])],
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dtype=torch.int32,
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device='cuda')
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decoder_input_lengths = torch.tensor([
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decoder_input_ids.shape[-1]
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for _ in range(decoder_input_ids.shape[0])
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],
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dtype=torch.int32,
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device='cuda')
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decoder_max_input_length = torch.max(decoder_input_lengths).item()
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# generation config
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sampling_config = SamplingConfig(end_id=eot_id,
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pad_id=eot_id,
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num_beams=num_beams)
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self.decoder_generation_session.setup(
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decoder_input_lengths.size(0),
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decoder_max_input_length,
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max_new_tokens,
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beam_width=num_beams,
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encoder_max_input_length=encoder_outputs.shape[1])
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torch.cuda.synchronize()
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decoder_input_ids = decoder_input_ids.type(torch.int32).cuda()
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output_ids = self.decoder_generation_session.decode(
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decoder_input_ids,
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decoder_input_lengths,
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sampling_config,
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encoder_output=encoder_outputs,
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encoder_input_lengths=encoder_input_lengths,
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)
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torch.cuda.synchronize()
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# get the list of int from output_ids tensor
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output_ids = output_ids.cpu().numpy().tolist()
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return output_ids
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class WhisperTRTLLM(object):
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def __init__(self, engine_dir, debug_mode=False, assets_dir=None):
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world_size = 1
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runtime_rank = tensorrt_llm.mpi_rank()
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runtime_mapping = tensorrt_llm.Mapping(world_size, runtime_rank)
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torch.cuda.set_device(runtime_rank % runtime_mapping.gpus_per_node)
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engine_dir = Path(engine_dir)
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self.encoder = WhisperEncoding(engine_dir)
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self.decoder = WhisperDecoding(engine_dir,
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runtime_mapping,
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debug_mode=False)
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self.n_mels = self.encoder.n_mels
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self.tokenizer = get_tokenizer(num_languages=self.encoder.num_languages,
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tokenizer_dir=assets_dir)
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self.eot_id = self.tokenizer.encode(
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"<|endoftext|>",
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allowed_special=self.tokenizer.special_tokens_set)[0]
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def process_batch(
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self,
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mel,
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text_prefix="<|startoftranscript|><|en|><|transcribe|><|notimestamps|>",
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num_beams=1):
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prompt_id = self.tokenizer.encode(
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text_prefix, allowed_special=self.tokenizer.special_tokens_set)
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prompt_id = torch.tensor(prompt_id)
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batch_size = mel.shape[0]
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decoder_input_ids = prompt_id.repeat(batch_size, 1)
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encoder_output = self.encoder.get_audio_features(mel)
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output_ids = self.decoder.generate(decoder_input_ids,
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encoder_output,
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self.eot_id,
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max_new_tokens=96,
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num_beams=num_beams)
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texts = []
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for i in range(len(output_ids)):
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text = self.tokenizer.decode(output_ids[i][0]).strip()
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texts.append(text)
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return texts
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def decode_wav_file(
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input_file_path,
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model,
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text_prefix="<|startoftranscript|><|en|><|transcribe|><|notimestamps|>",
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dtype='float16',
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batch_size=1,
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num_beams=1,
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normalizer=None,
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mel_filters_dir=None):
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mel, total_duration = log_mel_spectrogram(input_file_path,
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model.n_mels,
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device='cuda',
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return_duration=True,
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mel_filters_dir=mel_filters_dir)
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mel = mel.type(str_dtype_to_torch(dtype))
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mel = mel.unsqueeze(0)
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# repeat the mel spectrogram to match the batch size
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mel = mel.repeat(batch_size, 1, 1)
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predictions = model.process_batch(mel, text_prefix, num_beams)
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prediction = predictions[0]
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# remove all special tokens in the prediction
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prediction = re.sub(r'<\|.*?\|>', '', prediction)
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if normalizer:
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prediction = normalizer(prediction)
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print(f"prediction: {prediction}")
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results = [(0, [""], prediction.split())]
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return results, total_duration
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def collate_wrapper(batch):
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speeches, durations, labels, ids = [], [], [], []
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for item in batch:
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speech = item["audio"]["array"]
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duration = speech.shape[-1]
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speech = pad_or_trim(speech, N_SAMPLES)
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speech = speech.astype(np.float32)
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speech = torch.from_numpy(speech)
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speeches.append(speech)
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durations.append(duration)
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labels.append(item["text"])
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ids.append(item["id"])
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return speeches, durations, labels, ids
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def decode_dataset(
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model,
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dataset,
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text_prefix="<|startoftranscript|><|en|><|transcribe|><|notimestamps|>",
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dtype='float16',
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batch_size=1,
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num_beams=1,
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normalizer=None,
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sample_rate=16000,
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mel_filters_dir=None):
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librispeech_dummy = load_dataset(dataset, "clean", split="validation")
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data_loader = DataLoader(librispeech_dummy,
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batch_size=batch_size,
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num_workers=4,
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pin_memory=True,
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collate_fn=collate_wrapper)
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results = []
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total_duration = 0
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for batch in data_loader:
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waveforms, durations, texts, ids = batch
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total_duration += sum(durations) / sample_rate
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for wave in waveforms:
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assert wave.is_pinned()
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features = [
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log_mel_spectrogram(wave,
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model.n_mels,
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device='cuda',
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mel_filters_dir=mel_filters_dir).unsqueeze(0)
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for wave in waveforms
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]
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features = torch.cat(features, dim=0).type(str_dtype_to_torch(dtype))
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predictions = model.process_batch(features, text_prefix, num_beams)
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for wav_id, label, prediction in zip(ids, texts, predictions):
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# remove all special tokens in the prediction
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prediction = re.sub(r'<\|.*?\|>', '', prediction)
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if normalizer:
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prediction, label = normalizer(prediction), normalizer(label)
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print(f"wav_id: {wav_id}, label: {label}, prediction: {prediction}")
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results.append((wav_id, label.split(), prediction.split()))
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return results, total_duration
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if __name__ == '__main__':
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args = parse_arguments()
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tensorrt_llm.logger.set_level(args.log_level)
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model = WhisperTRTLLM(args.engine_dir, args.debug, args.assets_dir)
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normallizer = EnglishTextNormalizer()
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if args.enable_warmup:
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results, total_duration = decode_dataset(
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model,
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"hf-internal-testing/librispeech_asr_dummy",
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batch_size=args.batch_size,
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num_beams=args.num_beams,
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normalizer=normallizer,
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mel_filters_dir=args.assets_dir)
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start_time = time.time()
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if args.input_file:
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results, total_duration = decode_wav_file(
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args.input_file,
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model,
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dtype=args.dtype,
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batch_size=args.batch_size,
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num_beams=args.num_beams,
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mel_filters_dir=args.assets_dir)
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else:
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results, total_duration = decode_dataset(
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model,
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args.dataset,
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dtype=args.dtype,
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batch_size=args.batch_size,
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num_beams=args.num_beams,
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normalizer=normallizer,
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mel_filters_dir=args.assets_dir)
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elapsed = time.time() - start_time
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results = sorted(results)
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Path(args.results_dir).mkdir(parents=True, exist_ok=True)
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store_transcripts(filename=f"{args.results_dir}/recogs-{args.name}.txt",
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texts=results)
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with open(f"{args.results_dir}/errs-{args.name}.txt", "w") as f:
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total_error_rate = write_error_stats(f,
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"test-set",
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results,
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enable_log=True)
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if args.dataset == "hf-internal-testing/librispeech_asr_dummy" and not args.input_file:
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assert total_error_rate <= 3.1, f"Word Error rate using whisper large model should be less than 3.1% but got {total_error_rate}"
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rtf = elapsed / total_duration
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s = f"RTF: {rtf:.4f}\n"
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s += f"total_duration: {total_duration:.3f} seconds\n"
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s += f"({total_duration/3600:.2f} hours)\n"
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s += f"processing time: {elapsed:.3f} seconds " f"({elapsed/3600:.2f} hours)\n"
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s += f"batch size: {args.batch_size}\n"
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s += f"num_beams: {args.num_beams}\n"
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print(s)
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with open(f"{args.results_dir}/rtf-{args.name}.txt", "w") as f:
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f.write(s)
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del model
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