mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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b2fd493c16
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
474 lines
18 KiB
Python
474 lines
18 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 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 copy
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import json
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from pathlib import Path
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import evaluate
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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 transformers import AutoModel, AutoTokenizer
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import tensorrt_llm
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import tensorrt_llm.profiler as profiler
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from tensorrt_llm.logger import logger
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from tensorrt_llm.runtime import (ChatGLMGenerationSession, GenerationSession,
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ModelConfig, SamplingConfig)
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from build import find_engines # isort:skip
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model_name = ""
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def TRT(args, config):
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model_name = config['builder_config']['name']
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dtype = config['builder_config']['precision']
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world_size = config['builder_config']['tensor_parallel']
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assert world_size == tensorrt_llm.mpi_world_size(), \
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f'Engine world size ({world_size}) != Runtime world size ({tensorrt_llm.mpi_world_size()})'
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world_size = config['builder_config']['tensor_parallel']
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remove_input_padding = config['plugin_config']['remove_input_padding']
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model_config = ModelConfig(
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model_name=model_name,
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vocab_size=config['builder_config']['vocab_size'],
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num_layers=config['builder_config']['num_layers'],
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num_heads=config['builder_config']['num_heads'] // world_size,
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num_kv_heads=max(config['builder_config']['num_kv_heads'] // world_size,
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1),
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hidden_size=config['builder_config']['hidden_size'] // world_size,
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gpt_attention_plugin=bool(
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config['plugin_config']['gpt_attention_plugin']),
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remove_input_padding=remove_input_padding,
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tokens_per_block=config['plugin_config']['tokens_per_block'],
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paged_kv_cache=config['plugin_config']['paged_kv_cache'],
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dtype=dtype,
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use_custom_all_reduce=config['plugin_config']['use_custom_all_reduce'],
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)
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runtime_rank = tensorrt_llm.mpi_rank()
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runtime_mapping = tensorrt_llm.Mapping(world_size,
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runtime_rank,
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tp_size=world_size)
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torch.cuda.set_device(runtime_rank % runtime_mapping.gpus_per_node)
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serialize_path = find_engines(
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args.engine_dir,
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model_name=model_name,
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dtype=dtype,
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tp_size=world_size,
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rank=runtime_rank,
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)[0]
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tensorrt_llm.logger.set_level(args.log_level)
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with open(serialize_path, 'rb') as f:
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engine_buffer = f.read()
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if model_name == 'chatglm-6b':
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decoder = ChatGLMGenerationSession(
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model_config,
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engine_buffer,
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runtime_mapping,
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)
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else:
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decoder = GenerationSession(
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model_config,
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engine_buffer,
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runtime_mapping,
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)
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return decoder
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def main(args):
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runtime_rank = tensorrt_llm.mpi_rank()
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logger.set_level(args.log_level)
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test_hf = args.test_hf and runtime_rank == 0 # only run hf on rank 0
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test_trt_llm = args.test_trt_llm
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tokenizer = AutoTokenizer.from_pretrained(
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args.tokenizer,
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padding_side='left',
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trust_remote_code=True,
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)
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if args.eval_type == 'code_completion':
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dataset_name = "openai_humaneval"
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dataset_revision = None
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dataset_input_key = 'prompt'
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dataset_output_key = 'canonical_solution'
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elif args.eval_type == 'summarize':
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dataset_name = "ccdv/cnn_dailymail"
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dataset_revision = "3.0.0"
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dataset_input_key = 'article'
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dataset_output_key = 'highlights'
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args.dataset_path.mkdir(parents=True, exist_ok=True)
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dataset = load_dataset(dataset_name,
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dataset_revision,
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cache_dir=args.dataset_path)
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config_path = str(args.engine_dir / '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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max_batch_size = args.batch_size
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# runtime parameters
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# repetition_penalty = 1
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top_k = args.top_k
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output_len = args.output_len
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test_token_num = 800
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# top_p = 0.0
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# random_seed = 5
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temperature = 1
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num_beams = args.num_beams
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length_penalty = args.length_penalty
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pad_id = tokenizer.encode(tokenizer.pad_token, add_special_tokens=False)[0]
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end_id = tokenizer.encode(tokenizer.eos_token, add_special_tokens=False)[0]
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if test_trt_llm:
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tensorrt_llm_gpt = TRT(args, config)
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if test_hf:
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model = AutoModel.from_pretrained(
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args.hf_model_location,
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trust_remote_code=True,
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)
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model.cuda()
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if args.data_type == 'fp16':
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model.half()
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def eval_tensorrt_llm(datapoint, eval_type='summarize'):
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batch_size = len(datapoint)
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append_str = ' TL;DR: ' if eval_type == 'summarize' else ''
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line = copy.copy(datapoint)
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line_encoded = []
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input_lengths = []
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for i in range(batch_size):
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line[i] = line[i] + append_str
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line[i] = line[i].strip()
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line[i] = line[i].replace(" n't", "n't")
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input_id = tokenizer.encode(
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line[i],
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return_tensors='pt',
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).type(torch.int32)
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if model_name == 'chatglm-6b':
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input_id = input_id[:, -test_token_num:]
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else:
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input_id = input_id[:, :test_token_num]
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line_encoded.append(input_id)
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input_lengths.append(input_id.shape[-1])
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max_length = max(input_lengths)
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if tensorrt_llm_gpt.remove_input_padding:
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line_encoded = [t.to(torch.int32).cuda() for t in line_encoded]
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else:
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# do padding, should move outside the profiling to prevent the overhead
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for i in range(batch_size):
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pad_size = max_length - input_lengths[i]
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pad = torch.ones([1, pad_size], dtype=torch.int32) * pad_id
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line_encoded[i] = torch.cat(
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[line_encoded[i].to(torch.int32), pad], axis=-1)
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line_encoded = torch.cat(line_encoded, axis=0).cuda()
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input_lengths = torch.tensor(input_lengths,
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dtype=torch.int32).cuda()
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sampling_config = SamplingConfig(
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end_id=end_id,
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pad_id=pad_id,
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top_k=top_k,
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num_beams=num_beams,
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length_penalty=length_penalty,
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)
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with torch.no_grad():
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tensorrt_llm_gpt.setup(batch_size,
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max_context_length=max_length,
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max_new_tokens=output_len,
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beam_width=num_beams,
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max_kv_cache_length=args.max_kv_cache_len)
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if tensorrt_llm_gpt.remove_input_padding:
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output_ids = tensorrt_llm_gpt.decode_batch(
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line_encoded, sampling_config)
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else:
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output_ids = tensorrt_llm_gpt.decode(
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line_encoded,
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input_lengths,
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sampling_config,
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)
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torch.cuda.synchronize()
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# Extract a list of tensors of shape beam_width x output_ids.
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if tensorrt_llm_gpt.mapping.is_first_pp_rank():
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output_beams_list = [
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tokenizer.batch_decode(output_ids[batch_idx, :,
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input_lengths[batch_idx]:],
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skip_special_tokens=True)
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for batch_idx in range(batch_size)
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]
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return output_beams_list, output_ids[:, :, max_length:].tolist()
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return [], []
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def eval_hf(datapoint, eval_type='summarize'):
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batch_size = len(datapoint)
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append_str = ' TL;DR: ' if eval_type == 'summarize' else ''
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if batch_size > 1:
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logger.warning(
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f"HF does not support batch_size > 1 to verify correctness due to padding and attention mask. Current batch size is {batch_size}"
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)
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line = copy.copy(datapoint)
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line_encoded = []
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input_lengths = []
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for i in range(batch_size):
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line[i] = line[i] + append_str
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line[i] = line[i].strip()
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line[i] = line[i].replace(" n't", "n't")
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input_id = tokenizer.encode(
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line[i],
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return_tensors='pt',
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).type(torch.int64)
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if model_name == 'chatglm-6b':
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input_id = input_id[:, -test_token_num:]
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else:
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input_id = input_id[:, :test_token_num]
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line_encoded.append(input_id)
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input_lengths.append(input_id.shape[-1])
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max_length = max(input_lengths)
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for i in range(batch_size):
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pad_size = max_length - input_lengths[i]
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pad = torch.ones([1, pad_size], dtype=torch.int64) * pad_id
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line_encoded[i] = torch.cat([pad, line_encoded[i].to(torch.int64)],
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axis=-1)
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line_encoded = torch.cat(line_encoded, axis=0).cuda()
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with torch.no_grad():
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output = model.generate(line_encoded,
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max_length=len(line_encoded[0]) +
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output_len,
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top_k=top_k,
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temperature=temperature,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.pad_token_id,
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num_beams=num_beams,
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num_return_sequences=num_beams,
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early_stopping=True,
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length_penalty=length_penalty)
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tokens_list = output[:, len(line_encoded[0]):].tolist()
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output = output.reshape([batch_size, num_beams, -1])
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output_lines_list = [
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tokenizer.batch_decode(output[:, i, len(line_encoded[0]):],
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skip_special_tokens=True)
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for i in range(num_beams)
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]
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return output_lines_list, tokens_list
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if test_trt_llm:
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datapoint = dataset['test'][0:1]
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output, _ = eval_tensorrt_llm(datapoint[dataset_input_key],
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eval_type=args.eval_type)
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if runtime_rank == 0:
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logger.info(
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"---------------------------------------------------------")
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logger.info("TensorRT-LLM Generated : ")
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logger.info(f" Input : {datapoint[dataset_input_key]}")
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logger.info(f"\n Reference : {datapoint[dataset_output_key]}")
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logger.info(f"\n Output : {output}")
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logger.info(
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"---------------------------------------------------------")
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if test_hf:
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datapoint = dataset['test'][0:1]
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output, _ = eval_hf(datapoint[dataset_input_key],
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eval_type=args.eval_type)
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logger.info("---------------------------------------------------------")
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logger.info("HF Generated : ")
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logger.info(f" Input : {datapoint[dataset_input_key]}")
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logger.info(f"\n Reference : {datapoint[dataset_output_key]}")
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logger.info(f"\n Output : {output}")
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logger.info("---------------------------------------------------------")
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metric_tensorrt_llm = [evaluate.load("rouge") for _ in range(num_beams)]
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metric_hf = [evaluate.load("rouge") for _ in range(num_beams)]
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for i in range(num_beams):
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metric_tensorrt_llm[i].seed = 0
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metric_hf[i].seed = 0
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ite_count = 0
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data_point_idx = 0
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while (data_point_idx < len(dataset['test'])) and (ite_count <
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args.max_ite):
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if runtime_rank == 0:
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logger.debug(
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f"run data_point {data_point_idx} ~ {data_point_idx + max_batch_size}"
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)
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datapoint = dataset['test'][data_point_idx:(data_point_idx +
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max_batch_size)]
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if test_trt_llm:
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profiler.start('tensorrt_llm')
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output_tensorrt_llm, _ = eval_tensorrt_llm(
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datapoint[dataset_input_key])
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profiler.stop('tensorrt_llm')
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if test_hf:
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profiler.start('hf')
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output_hf, _ = eval_hf(datapoint[dataset_input_key])
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profiler.stop('hf')
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if runtime_rank == 0:
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if test_trt_llm:
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for batch_idx in range(len(output_tensorrt_llm)):
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for beam_idx in range(num_beams):
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metric_tensorrt_llm[beam_idx].add_batch(
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predictions=[
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output_tensorrt_llm[batch_idx][beam_idx]
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],
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references=[
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datapoint[dataset_output_key][batch_idx]
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])
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if test_hf:
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for beam_idx in range(num_beams):
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for batch_idx in range(len(output_hf[beam_idx])):
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metric_hf[beam_idx].add_batch(
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predictions=[output_hf[beam_idx][batch_idx]],
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references=[
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datapoint[dataset_output_key][batch_idx]
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])
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logger.debug('-' * 100)
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logger.debug(f"Input : {datapoint[dataset_input_key]}")
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if test_trt_llm:
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logger.debug(f'TensorRT-LLM Output: {output_tensorrt_llm}')
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if test_hf:
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logger.debug(f'HF Output: {output_hf}')
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logger.debug(f"highlights : {datapoint[dataset_output_key]}")
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data_point_idx += max_batch_size
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ite_count += 1
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if runtime_rank == 0:
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if test_trt_llm:
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np.random.seed(0) # rouge score use sampling to compute the score
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logger.info(
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f'TensorRT-LLM (total latency: {profiler.elapsed_time_in_sec("tensorrt_llm")} sec)'
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)
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for beam_idx in range(num_beams):
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logger.info(f"TensorRT-LLM beam {beam_idx} result")
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computed_metrics_tensorrt_llm = metric_tensorrt_llm[
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beam_idx].compute()
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for key in computed_metrics_tensorrt_llm.keys():
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logger.info(
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f' {key} : {computed_metrics_tensorrt_llm[key] * 100}')
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if args.check_accuracy and beam_idx == 0:
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assert computed_metrics_tensorrt_llm[
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'rouge1'] * 100 > args.tensorrt_llm_rouge1_threshold
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if test_hf:
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np.random.seed(0) # rouge score use sampling to compute the score
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logger.info(
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f'Hugging Face (total latency: {profiler.elapsed_time_in_sec("hf")} sec)'
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)
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for beam_idx in range(num_beams):
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logger.info(f"HF beam {beam_idx} result")
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computed_metrics_hf = metric_hf[beam_idx].compute()
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for key in computed_metrics_hf.keys():
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logger.info(f' {key} : {computed_metrics_hf[key] * 100}')
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--model_version',
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'-m',
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type=str,
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required=True,
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choices=["1", "2", "3", "2-32k", "3-32k"],
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help=
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'1, 2, 3, 2-32k, 3-32k for ChatGLM-6B, ChatGLM2-6B, ChatGLM3-6B, ChatGLM2-32k and ChatGLM3-32k respectively'
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)
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parser.add_argument('--hf_model_location', type=str, default=None)
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parser.add_argument(
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'--tokenizer',
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default=None,
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help='tokenizer path; defaults to hf_model_location if left unspecified'
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)
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parser.add_argument('--test_hf', action='store_true', default=True)
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parser.add_argument('--test_trt_llm', action='store_true', default=True)
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parser.add_argument('--data_type',
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type=str,
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choices=['fp32', 'fp16'],
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default='fp16')
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parser.add_argument('--dataset_path', type=Path, default='dataset')
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parser.add_argument('--log_level', type=str, default='info')
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parser.add_argument('--engine_dir', type=Path, default='trtModel')
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parser.add_argument('--batch_size', type=int, default=1)
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parser.add_argument('--max_ite', type=int, default=20)
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parser.add_argument('--output_len', type=int, default=100)
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parser.add_argument('--max_kv_cache_len',
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type=int,
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default=None,
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help='The max kv cache length. \
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If the final sequence length exceeds the kv cache length, we will enable cyclic kv cache. \
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If it is set to None, we will use the max sequence length.')
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parser.add_argument('--check_accuracy', action='store_true', default=True)
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parser.add_argument('--tensorrt_llm_rouge1_threshold',
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type=float,
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default=15.0)
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parser.add_argument('--num_beams', type=int, default=1)
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parser.add_argument('--top_k', type=int, default=1)
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parser.add_argument('--eval_type',
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type=str,
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default='summarize',
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choices=['summarize', 'code_completion'])
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parser.add_argument('--length_penalty', type=float, default=1.0)
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args = parser.parse_args()
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if args.model_version == "1":
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args.model_name = "chatglm-6b"
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elif args.model_version in ["2", "3"]:
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args.model_name = "chatglm%s-6b" % args.model_version
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else:
|
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args.model_name = "chatglm%s-6b-32k" % args.model_version.split("-")[0]
|
|
|
|
if args.tokenizer == None:
|
|
args.tokenizer = args.model_name
|
|
|
|
main(args)
|