mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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406 lines
16 KiB
Python
406 lines
16 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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import os
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import random
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import numpy as np
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import torch
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from datasets import load_dataset, load_metric
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from transformers import AutoModelForCausalLM, 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.quantization import QuantMode
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from build import get_engine_name # isort:skip
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def TRTGPTJ(args, config):
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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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num_heads = config['builder_config']['num_heads'] // world_size
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hidden_size = config['builder_config']['hidden_size'] // world_size
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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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use_gpt_attention_plugin = bool(
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config['plugin_config']['gpt_attention_plugin'])
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remove_input_padding = config['plugin_config']['remove_input_padding']
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quant_mode = QuantMode(config['builder_config'].get('quant_mode', 0))
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paged_kv_cache = config['plugin_config']['paged_kv_cache']
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tokens_per_block = config['builder_config']['tokens_per_block']
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model_config = tensorrt_llm.runtime.ModelConfig(
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vocab_size=vocab_size,
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num_layers=num_layers,
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num_heads=num_heads,
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num_kv_heads=num_heads,
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hidden_size=hidden_size,
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gpt_attention_plugin=use_gpt_attention_plugin,
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remove_input_padding=remove_input_padding,
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paged_kv_cache=paged_kv_cache,
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tokens_per_block=tokens_per_block,
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quant_mode=quant_mode)
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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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engine_name = get_engine_name('gptj', dtype, world_size, runtime_rank)
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serialize_path = os.path.join(args.engine_dir, engine_name)
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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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decoder = tensorrt_llm.runtime.GenerationSession(model_config,
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engine_buffer,
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runtime_mapping)
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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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model_dir = args.model_dir
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tokenizer = AutoTokenizer.from_pretrained(model_dir,
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padding_side='left',
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model_max_length=2048,
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truncation=True)
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tokenizer.pad_token = tokenizer.eos_token
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dataset_cnn = load_dataset("ccdv/cnn_dailymail",
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'3.0.0',
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cache_dir=args.dataset_path)
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config_path = os.path.join(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 = 923
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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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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 = TRTGPTJ(args, config)
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if test_hf:
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model = AutoModelForCausalLM.from_pretrained(model_dir)
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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 summarize_tensorrt_llm(datapoint):
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batch_size = len(datapoint['article'])
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line = copy.copy(datapoint['article'])
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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] + ' TL;DR: '
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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(line[i],
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return_tensors='pt').type(torch.int32)
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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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# do padding, should move outside the profiling to prevent the overhead
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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 = [
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torch.tensor(t, dtype=torch.int32).cuda() for t in line_encoded
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]
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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]).type(torch.int32) * pad_id
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line_encoded[i] = torch.cat(
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[torch.tensor(line_encoded[i], dtype=torch.int32), pad],
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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 = tensorrt_llm.runtime.SamplingConfig(
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end_id=end_id, pad_id=pad_id, top_k=top_k, num_beams=num_beams)
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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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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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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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output_ids_list = [
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output_ids[batch_idx, :, input_lengths[batch_idx]:]
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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_list
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def summarize_hf(datapoint):
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batch_size = len(datapoint['article'])
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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. Current batch size is {batch_size}"
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)
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line = copy.copy(datapoint['article'])
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for i in range(batch_size):
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line[i] = line[i] + ' TL;DR: '
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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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line_encoded = tokenizer(line,
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return_tensors='pt',
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padding=True,
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truncation=True)["input_ids"].type(torch.int64)
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line_encoded = line_encoded[:, -test_token_num:]
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line_encoded = line_encoded.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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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_cnn['test'][0:1]
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summary, _ = summarize_tensorrt_llm(datapoint)
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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" Article : {datapoint['article']}")
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logger.info(f"\n Highlights : {datapoint['highlights']}")
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logger.info(f"\n Summary : {summary}")
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logger.info(
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"---------------------------------------------------------")
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if test_hf:
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datapoint = dataset_cnn['test'][0:1]
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summary, _ = summarize_hf(datapoint)
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logger.info("---------------------------------------------------------")
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logger.info("HF Generated : ")
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logger.info(f" Article : {datapoint['article']}")
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logger.info(f"\n Highlights : {datapoint['highlights']}")
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logger.info(f"\n Summary : {summary}")
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logger.info("---------------------------------------------------------")
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tensorrt_llm_result = [[] for _ in range(num_beams)]
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hf_result = [[] for _ in range(num_beams)]
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ite_count = 0
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data_point_idx = 0
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# Support running the set with different order to verify correctness
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test_idx = list(
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range(min(len(dataset_cnn['test']), max_batch_size * args.max_ite)))
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random.seed(args.random_seed)
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random.shuffle(test_idx)
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while (data_point_idx < len(dataset_cnn['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_cnn['test'][test_idx[data_point_idx:(
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data_point_idx + max_batch_size)]]
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if test_trt_llm:
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profiler.start('tensorrt_llm')
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summary_tensorrt_llm, tokens_tensorrt_llm = summarize_tensorrt_llm(
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datapoint)
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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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summary_hf, tokens_hf = summarize_hf(datapoint)
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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(summary_tensorrt_llm)):
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for beam_idx in range(num_beams):
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tensorrt_llm_result[beam_idx].append(
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tuple([
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datapoint['id'][batch_idx],
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summary_tensorrt_llm[batch_idx][beam_idx],
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datapoint['highlights'][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(summary_hf[beam_idx])):
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hf_result[beam_idx].append(
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tuple([
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datapoint['id'][batch_idx],
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summary_hf[beam_idx][batch_idx],
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datapoint['highlights'][batch_idx]
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]))
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logger.debug('-' * 100)
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logger.debug(f"Article : {datapoint['article']}")
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if test_trt_llm:
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logger.debug(f'TensorRT-LLM Summary: {summary_tensorrt_llm}')
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if test_hf:
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logger.debug(f'HF Summary: {summary_hf}')
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logger.debug(f"highlights : {datapoint['highlights']}")
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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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# Because 'rouge' uses sampling to compute the scores, the scores
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# would be different when the results are same with different order.
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# So, sorting them first to prevent this issue.
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metric_tensorrt_llm = load_metric("rouge")
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metric_tensorrt_llm.seed = 0
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beams_results = sorted(tensorrt_llm_result[beam_idx])
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for j in range(len(beams_results)):
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metric_tensorrt_llm.add_batch(
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predictions=[beams_results[j][1]],
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references=[beams_results[j][2]])
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logger.info(f"TensorRT-LLM beam {beam_idx} result")
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computed_metrics_tensorrt_llm = metric_tensorrt_llm.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].mid[2]*100}'
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)
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if args.check_accuracy and beam_idx == 0:
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assert computed_metrics_tensorrt_llm['rouge1'].mid[
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2] * 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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metric_tensorrt_hf = load_metric("rouge")
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metric_tensorrt_hf.seed = 0
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beams_results = sorted(hf_result[beam_idx])
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for j in range(len(beams_results)):
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metric_tensorrt_hf.add_batch(
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predictions=[beams_results[j][1]],
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references=[beams_results[j][2]])
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logger.info(f"HF beam {beam_idx} result")
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computed_metrics_hf = metric_tensorrt_hf.compute()
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for key in computed_metrics_hf.keys():
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logger.info(
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f' {key} : {computed_metrics_hf[key].mid[2]*100}')
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--model_dir', type=str, default='EleutherAI/gpt-j-6B')
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parser.add_argument('--test_hf', action='store_true')
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parser.add_argument('--test_trt_llm', action='store_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='fp32')
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parser.add_argument('--dataset_path', type=str, default='')
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parser.add_argument('--log_level', type=str, default='info')
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parser.add_argument('--engine_dir', type=str, default='gptj_engine')
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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('--check_accuracy', action='store_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('--random_seed', type=int, default=0)
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args = parser.parse_args()
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main(args)
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