mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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16d2467ea8
* Update TensorRT-LLM --------- Co-authored-by: Denis Kayshev <topenkoff@gmail.com> Co-authored-by: akhoroshev <arthoroshev@gmail.com> Co-authored-by: Patrick Reiter Horn <patrick.horn@gmail.com> Update
630 lines
26 KiB
Python
Executable File
630 lines
26 KiB
Python
Executable File
# 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 math
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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 (log_mel_spectrogram, store_transcripts,
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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.bindings import GptJsonConfig, KVCacheType
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from tensorrt_llm.runtime import PYTHON_BINDINGS, ModelConfig, SamplingConfig
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from tensorrt_llm.runtime.session import Session, TensorInfo
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if PYTHON_BINDINGS:
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from tensorrt_llm.runtime import ModelRunnerCpp
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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='warning')
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parser.add_argument('--engine_dir', type=str, default='whisper_large_v3')
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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='./assets')
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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(
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'--dataset_name',
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type=str,
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default="clean",
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help=
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"dataset configuration name in the dataset, see https://huggingface.co/docs/datasets/v3.0.0/en/package_reference/loading_methods#datasets.load_dataset"
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)
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parser.add_argument('--dataset_split', type=str, default="validation")
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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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parser.add_argument('--accuracy_check',
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action='store_true',
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help="only for CI test")
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parser.add_argument('--use_py_session',
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action='store_true',
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help="use python session or cpp session")
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parser.add_argument(
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"--compute_cer",
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action="store_true",
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default=False,
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help="""True to compute character error rate (CER), e.g., for Chinese.
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False to compute word error rate (WER), e.g., for English words.
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""",
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)
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parser.add_argument(
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"--text_prefix",
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default="<|startoftranscript|><|en|><|transcribe|><|notimestamps|>",
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help="""Text prefix to be used for decoding. Default is for English ASR.
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""",
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)
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parser.add_argument(
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"--padding_strategy",
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default="max",
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help=
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"""1. max: pad to the 30s, using the option if the model is trained with max padding e.g. openai official models,
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2. longest: pad to the longest sequence in the batch,
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3. nopad: no padding, only works with cpp session,
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""",
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)
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return parser.parse_args()
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def remove_tensor_padding(input_tensor,
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input_tensor_lengths=None,
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pad_value=None):
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if pad_value:
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assert input_tensor_lengths is None, "input_tensor_lengths should be None when pad_value is provided"
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# Text tensor case: batch, seq_len
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assert torch.all(
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input_tensor[:, 0] !=
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pad_value), "First token in each sequence should not be pad_value"
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assert input_tensor_lengths is None
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# Create a mask for all non-pad tokens
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mask = input_tensor != pad_value
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# Apply the mask to input_tensor to remove pad tokens
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output_tensor = input_tensor[mask].view(1, -1)
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else:
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# Audio tensor case: batch, seq_len, feature_len
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# position_ids case: batch, seq_len
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assert input_tensor_lengths is not None, "input_tensor_lengths must be provided for 3D input_tensor"
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# Initialize a list to collect valid sequences
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valid_sequences = []
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for i in range(input_tensor.shape[0]):
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valid_length = input_tensor_lengths[i]
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valid_sequences.append(input_tensor[i, :valid_length])
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# Concatenate all valid sequences along the batch dimension
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output_tensor = torch.cat(valid_sequences, dim=0)
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return output_tensor
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def read_config(component, engine_dir):
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config_path = engine_dir / component / '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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model_config = OrderedDict()
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model_config.update(config['pretrained_config'])
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model_config.update(config['build_config'])
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return model_config
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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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config = read_config('encoder', engine_dir)
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self.n_mels = config['n_mels']
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self.dtype = config['dtype']
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self.num_languages = config['num_languages']
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self.encoder_config = config
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def get_session(self, engine_dir):
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serialize_path = engine_dir / 'encoder' / '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,
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mel,
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mel_input_lengths,
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encoder_downsampling_factor=2):
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if isinstance(mel, list):
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longest_mel = max([f.shape[-1] for f in mel])
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mel = [
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torch.nn.functional.pad(f, (0, longest_mel - f.shape[-1]),
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mode='constant') for f in mel
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]
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mel = torch.cat(mel, dim=0).type(
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str_dtype_to_torch("float16")).contiguous()
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bsz, seq_len = mel.shape[0], mel.shape[2]
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position_ids = torch.arange(
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math.ceil(seq_len / encoder_downsampling_factor),
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dtype=torch.int32,
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device=mel.device).expand(bsz, -1).contiguous()
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if self.encoder_config['plugin_config']['remove_input_padding']:
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# mel B,D,T -> B,T,D -> BxT, D
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mel = mel.transpose(1, 2)
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mel = remove_tensor_padding(mel, mel_input_lengths)
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position_ids = remove_tensor_padding(
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position_ids, mel_input_lengths // encoder_downsampling_factor)
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inputs = OrderedDict()
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inputs['input_features'] = mel
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inputs['input_lengths'] = mel_input_lengths
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inputs['position_ids'] = position_ids
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output_list = [
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TensorInfo('input_features', str_dtype_to_trt(self.dtype),
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mel.shape),
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TensorInfo('input_lengths', str_dtype_to_trt('int32'),
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mel_input_lengths.shape),
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TensorInfo('position_ids', str_dtype_to_trt('int32'),
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inputs['position_ids'].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:
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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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encoder_output = outputs['encoder_output']
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encoder_output_lengths = mel_input_lengths // encoder_downsampling_factor
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return encoder_output, encoder_output_lengths
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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 = read_config('decoder', 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_session(self, engine_dir, runtime_mapping, debug_mode=False):
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serialize_path = engine_dir / 'decoder' / '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_attention_heads'],
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num_kv_heads=self.decoder_config['num_attention_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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cross_attention=True,
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num_layers=self.decoder_config['num_hidden_layers'],
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gpt_attention_plugin=self.decoder_config['plugin_config']
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['gpt_attention_plugin'],
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remove_input_padding=self.decoder_config['plugin_config']
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['remove_input_padding'],
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kv_cache_type=KVCacheType.PAGED
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if self.decoder_config['plugin_config']['paged_kv_cache'] == True
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else KVCacheType.CONTINUOUS,
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has_position_embedding=self.
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decoder_config['has_position_embedding'],
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dtype=self.decoder_config['dtype'],
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has_token_type_embedding=False,
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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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encoder_max_input_length,
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encoder_input_lengths,
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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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batch_size = decoder_input_ids.shape[0]
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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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cross_attention_mask = torch.ones([
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batch_size, decoder_max_input_length + max_new_tokens,
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encoder_max_input_length
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]).int().cuda()
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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_max_input_length)
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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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if self.decoder_config['plugin_config']['remove_input_padding']:
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# 50256 is the index of <pad> for all whisper models' decoder
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WHISPER_PAD_TOKEN_ID = 50256
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decoder_input_ids = remove_tensor_padding(
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decoder_input_ids, pad_value=WHISPER_PAD_TOKEN_ID)
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if encoder_outputs.dim() == 3:
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encoder_output_lens = torch.full((encoder_outputs.shape[0], ),
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encoder_outputs.shape[1],
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dtype=torch.int32,
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device='cuda')
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encoder_outputs = remove_tensor_padding(encoder_outputs,
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encoder_output_lens)
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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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cross_attention_mask=cross_attention_mask,
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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,
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engine_dir,
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debug_mode=False,
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assets_dir=None,
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batch_size=64,
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use_py_session=False,
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num_beams=1):
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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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encoder_config = read_config('encoder', engine_dir)
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decoder_config = read_config('decoder', engine_dir)
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self.n_mels = encoder_config['n_mels']
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self.num_languages = encoder_config['num_languages']
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is_multilingual = (decoder_config['vocab_size'] >= 51865)
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if is_multilingual:
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tokenizer_name = "multilingual"
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assert (Path(assets_dir) / "multilingual.tiktoken").exists(
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), "multilingual.tiktoken file is not existed in assets_dir"
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else:
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tokenizer_name = "gpt2"
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assert (Path(assets_dir) / "gpt2.tiktoken").exists(
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), "gpt2.tiktoken file is not existed in assets_dir"
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self.tokenizer = get_tokenizer(name=tokenizer_name,
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num_languages=self.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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if use_py_session:
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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=debug_mode)
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else:
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json_config = GptJsonConfig.parse_file(engine_dir / 'decoder' /
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'config.json')
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assert json_config.model_config.supports_inflight_batching
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runner_kwargs = dict(engine_dir=engine_dir,
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is_enc_dec=True,
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max_batch_size=batch_size,
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max_input_len=3000,
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max_output_len=96,
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max_beam_width=num_beams,
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debug_mode=debug_mode,
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kv_cache_free_gpu_memory_fraction=0.9,
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cross_kv_cache_fraction=0.5)
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self.model_runner_cpp = ModelRunnerCpp.from_dir(**runner_kwargs)
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self.use_py_session = use_py_session
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def process_batch(
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self,
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mel,
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mel_input_lengths,
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text_prefix="<|startoftranscript|><|en|><|transcribe|><|notimestamps|>",
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num_beams=1,
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max_new_tokens=96):
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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 = len(mel)
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decoder_input_ids = prompt_id.repeat(batch_size, 1)
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if self.use_py_session:
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encoder_output, encoder_output_lengths = self.encoder.get_audio_features(
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mel, mel_input_lengths)
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encoder_max_input_length = torch.max(encoder_output_lengths).item()
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output_ids = self.decoder.generate(decoder_input_ids,
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encoder_output,
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encoder_max_input_length,
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encoder_output_lengths,
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self.eot_id,
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max_new_tokens=max_new_tokens,
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num_beams=num_beams)
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else:
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with torch.no_grad():
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if isinstance(mel, list):
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mel = [
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m.transpose(1, 2).type(
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str_dtype_to_torch("float16")).squeeze(0)
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for m in mel
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]
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else:
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mel = mel.transpose(1, 2)
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outputs = self.model_runner_cpp.generate(
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batch_input_ids=decoder_input_ids,
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encoder_input_features=mel,
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encoder_output_lengths=mel_input_lengths // 2,
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max_new_tokens=max_new_tokens,
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end_id=self.eot_id,
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pad_id=self.eot_id,
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num_beams=num_beams,
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output_sequence_lengths=True,
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return_dict=True)
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torch.cuda.synchronize()
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output_ids = outputs['output_ids'].cpu().numpy().tolist()
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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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padding_strategy="longest"):
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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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if padding_strategy == "longest":
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pass
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else:
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mel = torch.nn.functional.pad(mel, (0, 3000 - mel.shape[2]))
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features_input_lengths = torch.full((mel.shape[0], ),
|
|
mel.shape[2],
|
|
dtype=torch.int32,
|
|
device=mel.device)
|
|
predictions = model.process_batch(mel, features_input_lengths, text_prefix,
|
|
num_beams)
|
|
prediction = predictions[0]
|
|
|
|
# remove all special tokens in the prediction
|
|
prediction = re.sub(r'<\|.*?\|>', '', prediction)
|
|
if normalizer:
|
|
prediction = normalizer(prediction)
|
|
print(f"prediction: {prediction}")
|
|
results = [(0, [""], prediction.split())]
|
|
return results, total_duration
|
|
|
|
|
|
def collate_wrapper(batch):
|
|
speeches, durations, labels, ids = [], [], [], []
|
|
for item in batch:
|
|
speech = item["audio"]["array"]
|
|
duration = speech.shape[-1]
|
|
speech = speech.astype(np.float32)
|
|
speech = torch.from_numpy(speech)
|
|
speeches.append(speech)
|
|
durations.append(duration)
|
|
labels.append(item["text"])
|
|
if 'id' in item:
|
|
ids.append(item["id"])
|
|
else:
|
|
ids.append(item["segment_id"])
|
|
return speeches, durations, labels, ids
|
|
|
|
|
|
def decode_dataset(
|
|
model,
|
|
dataset,
|
|
text_prefix="<|startoftranscript|><|en|><|transcribe|><|notimestamps|>",
|
|
dtype='float16',
|
|
batch_size=1,
|
|
num_beams=1,
|
|
normalizer=None,
|
|
sample_rate=16000,
|
|
mel_filters_dir=None,
|
|
compute_cer=False,
|
|
padding_strategy="longest"):
|
|
data_loader = DataLoader(dataset,
|
|
batch_size=batch_size,
|
|
num_workers=0,
|
|
pin_memory=True,
|
|
collate_fn=collate_wrapper)
|
|
results = []
|
|
total_duration = 0
|
|
for batch in data_loader:
|
|
waveforms, durations, texts, ids = batch
|
|
total_duration += sum(durations) / sample_rate
|
|
|
|
for wave in waveforms:
|
|
assert wave.is_pinned()
|
|
|
|
if padding_strategy == "longest":
|
|
longest_duration = max(durations)
|
|
elif padding_strategy == "nopad":
|
|
longest_duration = 0
|
|
else:
|
|
longest_duration = int(16000 * 30)
|
|
|
|
features = [
|
|
log_mel_spectrogram(wave,
|
|
model.n_mels,
|
|
padding=longest_duration - wave.shape[-1],
|
|
device='cuda',
|
|
mel_filters_dir=mel_filters_dir).unsqueeze(0)
|
|
for wave in waveforms
|
|
]
|
|
|
|
# pad to the even number of features, for remove_padding option, conv layer padding corner case
|
|
for i, feature in enumerate(features):
|
|
if feature.shape[2] % 2:
|
|
features[i] = torch.nn.functional.pad(feature, (0, 1))
|
|
|
|
features_input_lengths = torch.tensor([f.shape[2] for f in features],
|
|
dtype=torch.int32,
|
|
device='cuda')
|
|
|
|
predictions = model.process_batch(features, features_input_lengths,
|
|
text_prefix, num_beams)
|
|
for wav_id, label, prediction in zip(ids, texts, predictions):
|
|
# remove all special tokens in the prediction
|
|
prediction = re.sub(r'<\|.*?\|>', '', prediction)
|
|
if normalizer:
|
|
prediction, label = normalizer(prediction), normalizer(label)
|
|
label = label.split()
|
|
prediction = prediction.split()
|
|
if compute_cer:
|
|
label = list("".join(label))
|
|
prediction = list("".join(prediction))
|
|
print(f"wav_id: {wav_id}, label: {label}, prediction: {prediction}")
|
|
results.append((wav_id, label, prediction))
|
|
return results, total_duration
|
|
|
|
|
|
if __name__ == '__main__':
|
|
args = parse_arguments()
|
|
tensorrt_llm.logger.set_level(args.log_level)
|
|
model = WhisperTRTLLM(args.engine_dir, args.debug, args.assets_dir,
|
|
args.batch_size, args.use_py_session, args.num_beams)
|
|
normalizer = EnglishTextNormalizer()
|
|
dataset = load_dataset(args.dataset,
|
|
args.dataset_name,
|
|
split=args.dataset_split)
|
|
if args.enable_warmup:
|
|
results, total_duration = decode_dataset(
|
|
model,
|
|
dataset,
|
|
batch_size=args.batch_size,
|
|
num_beams=args.num_beams,
|
|
normalizer=normalizer,
|
|
mel_filters_dir=args.assets_dir,
|
|
padding_strategy=args.padding_strategy)
|
|
|
|
start_time = time.time()
|
|
if args.input_file:
|
|
results, total_duration = decode_wav_file(
|
|
args.input_file,
|
|
model,
|
|
text_prefix=args.text_prefix,
|
|
dtype=args.dtype,
|
|
batch_size=args.batch_size,
|
|
num_beams=args.num_beams,
|
|
mel_filters_dir=args.assets_dir,
|
|
padding_strategy=args.padding_strategy)
|
|
else:
|
|
results, total_duration = decode_dataset(
|
|
model,
|
|
dataset,
|
|
text_prefix=args.text_prefix,
|
|
dtype=args.dtype,
|
|
batch_size=args.batch_size,
|
|
num_beams=args.num_beams,
|
|
normalizer=normalizer,
|
|
mel_filters_dir=args.assets_dir,
|
|
compute_cer=args.compute_cer,
|
|
padding_strategy=args.padding_strategy)
|
|
elapsed = time.time() - start_time
|
|
results = sorted(results)
|
|
|
|
Path(args.results_dir).mkdir(parents=True, exist_ok=True)
|
|
store_transcripts(filename=f"{args.results_dir}/recogs-{args.name}.txt",
|
|
texts=results)
|
|
|
|
with open(f"{args.results_dir}/errs-{args.name}.txt", "w") as f:
|
|
total_error_rate = write_error_stats(f,
|
|
"test-set",
|
|
results,
|
|
enable_log=True)
|
|
if args.accuracy_check and args.dataset == "hf-internal-testing/librispeech_asr_dummy" and not args.input_file:
|
|
assert total_error_rate <= 2.8, f"Word Error rate using whisper large-v3 model should be 2.40%, but got {total_error_rate}"
|
|
|
|
rtf = elapsed / total_duration
|
|
s = f"RTF: {rtf:.4f}\n"
|
|
s += f"total_duration: {total_duration:.3f} seconds\n"
|
|
s += f"({total_duration/3600:.2f} hours)\n"
|
|
s += f"processing time: {elapsed:.3f} seconds " f"({elapsed/3600:.2f} hours)\n"
|
|
s += f"batch size: {args.batch_size}\n"
|
|
s += f"num_beams: {args.num_beams}\n"
|
|
s += f"total error rate: {total_error_rate:.2f}%\n"
|
|
print(s)
|
|
|
|
with open(f"{args.results_dir}/rtf-{args.name}.txt", "w") as f:
|
|
f.write(s)
|
|
|
|
del model
|