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>
328 lines
14 KiB
Python
328 lines
14 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 csv
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import json
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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 transformers import AutoTokenizer, T5Tokenizer
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import tensorrt_llm
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from tensorrt_llm.quantization import QuantMode
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from tensorrt_llm.runtime import ModelConfig, SamplingConfig
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from build import get_engine_name # isort:skip
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def read_config(config_path: Path):
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with open(config_path, 'r') as f:
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config = json.load(f)
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use_gpt_attention_plugin = config['plugin_config']['gpt_attention_plugin']
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remove_input_padding = config['plugin_config']['remove_input_padding']
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tp_size = config['builder_config']['tensor_parallel']
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pp_size = config['builder_config'].get('pipeline_parallel', 1)
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world_size = tp_size * pp_size
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assert tp_size * pp_size == tensorrt_llm.mpi_world_size(), \
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f'Engine world size ({tp_size} * {pp_size}) != Runtime world size ({tensorrt_llm.mpi_world_size()})'
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assert (config['builder_config']['num_heads'] %
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tp_size) == 0, f"The number of heads must be a multiple of tp_size"
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num_heads = config['builder_config']['num_heads'] // tp_size
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num_kv_heads = (config['builder_config']['num_kv_heads'] + tp_size -
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1) // tp_size
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hidden_size = config['builder_config']['hidden_size'] // tp_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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paged_kv_cache = config['plugin_config']['paged_kv_cache']
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tokens_per_block = config['plugin_config']['tokens_per_block']
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max_prompt_embedding_table_size = config['builder_config'][
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'max_prompt_embedding_table_size']
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dtype = config['builder_config']['precision']
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gather_all_token_logits = config['builder_config'][
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'gather_all_token_logits']
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use_custom_all_reduce = config['plugin_config']['use_custom_all_reduce']
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quant_mode = QuantMode(config['builder_config']['quant_mode'])
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model_config = ModelConfig(
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num_heads=num_heads,
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num_kv_heads=num_kv_heads,
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hidden_size=hidden_size,
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vocab_size=vocab_size,
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num_layers=num_layers,
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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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max_prompt_embedding_table_size=max_prompt_embedding_table_size,
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dtype=dtype,
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quant_mode=quant_mode,
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gather_all_token_logits=gather_all_token_logits,
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use_custom_all_reduce=use_custom_all_reduce)
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dtype = config['builder_config']['precision']
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max_input_len = config['builder_config']['max_input_len']
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return model_config, world_size, dtype, max_input_len
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def parse_input(input_text: str, input_file: str, tokenizer, pad_id: int,
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remove_input_padding: bool):
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input_tokens = []
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if input_file is None:
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input_tokens.append(
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tokenizer.encode(input_text, add_special_tokens=False))
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else:
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if input_file.endswith('.csv'):
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with open(input_file, 'r') as csv_file:
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csv_reader = csv.reader(csv_file, delimiter=',')
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for line in csv_reader:
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input_tokens.append(np.array(line, dtype='int32'))
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elif input_file.endswith('.npy'):
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inputs = np.load(input_file)
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for row in inputs:
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row = row[row != pad_id]
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input_tokens.append(row)
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else:
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print('Input file format not supported.')
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raise SystemExit
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input_ids = None
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input_lengths = torch.tensor([len(x) for x in input_tokens],
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dtype=torch.int32,
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device='cuda')
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if remove_input_padding:
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input_ids = np.concatenate(input_tokens)
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input_ids = torch.tensor(input_ids, dtype=torch.int32,
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device='cuda').unsqueeze(0)
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else:
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input_ids = torch.nested.to_padded_tensor(
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torch.nested.nested_tensor(input_tokens, dtype=torch.int32),
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pad_id).cuda()
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return input_ids, input_lengths
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def ptuning_setup(prompt_table, dtype, hidden_size, tasks, input_ids,
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input_lengths, remove_input_padding):
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if prompt_table is not None:
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prompt_table = torch.from_numpy(np.load(prompt_table))
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task_vocab_size = torch.tensor([prompt_table.shape[1]],
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dtype=torch.int32,
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device="cuda")
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prompt_table = prompt_table.view(
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(prompt_table.shape[0] * prompt_table.shape[1],
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prompt_table.shape[2]))
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prompt_table = prompt_table.cuda().to(
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dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype))
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else:
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prompt_table = torch.empty([1, hidden_size]).cuda()
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task_vocab_size = torch.zeros([1]).cuda()
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num_sequences = input_lengths.size(
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0) if remove_input_padding else input_ids.size(0)
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if tasks is not None:
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tasks = torch.tensor([int(t) for t in tasks.split(',')],
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dtype=torch.int32,
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device="cuda")
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assert tasks.shape[
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0] == num_sequences, "Number of supplied tasks must match input batch size"
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else:
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tasks = torch.zeros([num_sequences]).cuda()
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return [prompt_table, tasks, task_vocab_size]
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def print_output(output_ids, input_lengths, sequence_lengths, tokenizer,
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output_csv, output_npy):
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num_beams = output_ids.size(1)
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if output_csv is None and output_npy is None:
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for batch_idx in range(input_lengths.size(0)):
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inputs = output_ids[batch_idx][0][:input_lengths[batch_idx]].tolist(
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)
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input_text = tokenizer.decode(inputs)
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print(f'Input: \"{input_text}\"')
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for beam in range(num_beams):
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output_begin = input_lengths[batch_idx]
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output_end = sequence_lengths[batch_idx][beam]
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outputs = output_ids[batch_idx][beam][
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output_begin:output_end].tolist()
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output_text = tokenizer.decode(outputs)
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print(f'Output: \"{output_text}\"')
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output_ids = output_ids.reshape((-1, output_ids.size(2)))
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if output_csv is not None:
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output_file = Path(output_csv)
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output_file.parent.mkdir(exist_ok=True, parents=True)
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outputs = output_ids.tolist()
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with open(output_file, 'w') as csv_file:
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writer = csv.writer(csv_file, delimiter=',')
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writer.writerows(outputs)
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if output_npy is not None:
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output_file = Path(output_npy)
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output_file.parent.mkdir(exist_ok=True, parents=True)
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outputs = np.array(output_ids.cpu().contiguous(), dtype='int32')
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np.save(output_file, outputs)
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def parse_arguments():
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parser = argparse.ArgumentParser()
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parser.add_argument('--max_output_len', type=int, required=True)
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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('--log_level', type=str, default='error')
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parser.add_argument('--engine_dir', type=str, default='gpt_outputs')
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parser.add_argument('--input_text',
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type=str,
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default='Born in north-east France, Soyer trained as a')
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parser.add_argument(
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'--input_tokens',
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dest='input_file',
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type=str,
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help=
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'CSV or Numpy file containing tokenized input. Alternative to text input.',
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default=None)
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parser.add_argument('--output_csv',
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type=str,
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help='CSV file where the tokenized output is stored.',
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default=None)
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parser.add_argument('--output_npy',
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type=str,
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help='Numpy file where the tokenized output is stored.',
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default=None)
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parser.add_argument('--tokenizer',
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dest='tokenizer_path',
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help="HF tokenizer config path",
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default='gpt2')
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parser.add_argument('--vocab_file',
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help="Used for sentencepiece tokenizers")
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parser.add_argument('--num_beams',
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type=int,
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help="Use beam search if num_beams >1",
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default=1)
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parser.add_argument(
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'--prompt_table',
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type=Path,
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help="Path to .npy file, exported by nemo_prompt_convert.py")
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parser.add_argument(
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'--tasks',
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help="Comma-separated list of tasks for prompt tuning: ex 0,3,1,0")
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return parser.parse_args()
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def generate(
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max_output_len: int,
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log_level: str = 'error',
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engine_dir: str = 'gpt_outputs',
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input_text: str = 'Born in north-east France, Soyer trained as a',
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input_file: str = None,
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output_csv: str = None,
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output_npy: str = None,
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tokenizer_path: str = 'gpt2',
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vocab_file=None,
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max_kv_cache_len: int = None,
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num_beams: int = 1,
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prompt_table: Path = None,
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tasks: str = None,
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):
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tensorrt_llm.logger.set_level(log_level)
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engine_dir = Path(engine_dir)
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config_path = engine_dir / 'config.json'
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model_config, world_size, dtype, max_input_len = read_config(config_path)
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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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if vocab_file is not None:
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tokenizer = T5Tokenizer(vocab_file=vocab_file)
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EOS_TOKEN = 50256
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else:
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
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EOS_TOKEN = tokenizer.eos_token_id
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# # An example to stop generation when the model generate " London" on first sentence, " eventually became" on second sentence
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# stop_words_list = [[" London"], ["eventually became"]]
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# stop_words_list = tensorrt_llm.runtime.to_word_list_format(stop_words_list, tokenizer)
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# stop_words_list = torch.Tensor(stop_words_list).to(torch.int32).to("cuda").contiguous()
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stop_words_list = None
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# # An example to prevent generating " chef" on first sentence, " eventually" and " chef before" on second sentence
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# bad_words_list = [[" chef"], [" eventually, chef before"]]
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# bad_words_list = tensorrt_llm.runtime.to_word_list_format(bad_words_list, tokenizer)
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# bad_words_list = torch.Tensor(bad_words_list).to(torch.int32).to("cuda").contiguous()
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bad_words_list = None
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sampling_config = SamplingConfig(end_id=EOS_TOKEN,
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pad_id=EOS_TOKEN,
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num_beams=num_beams)
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engine_name = get_engine_name('gpt', dtype, world_size, runtime_rank)
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serialize_path = engine_dir / engine_name
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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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debug_mode=False)
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input_ids, input_lengths = parse_input(input_text, input_file, tokenizer,
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EOS_TOKEN,
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model_config.remove_input_padding)
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max_input_length = torch.max(input_lengths).item()
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decoder.setup(input_lengths.size(0),
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max_input_length,
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max_output_len,
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beam_width=num_beams,
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max_kv_cache_length=max_kv_cache_len)
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ptuning_args = [] if model_config.max_prompt_embedding_table_size == 0 else ptuning_setup(
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prompt_table, dtype, model_config.hidden_size, tasks, input_ids,
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input_lengths, model_config.remove_input_padding)
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outputs = decoder.decode(input_ids,
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input_lengths,
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sampling_config,
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*ptuning_args,
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output_sequence_lengths=True,
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return_dict=True,
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stop_words_list=stop_words_list,
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bad_words_list=bad_words_list)
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output_ids = outputs['output_ids']
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sequence_lengths = outputs['sequence_lengths']
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torch.cuda.synchronize()
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if runtime_rank == 0:
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print_output(output_ids, input_lengths, sequence_lengths, tokenizer,
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output_csv, output_npy)
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if model_config.gather_all_token_logits:
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print(outputs['context_logits'])
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if __name__ == '__main__':
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args = parse_arguments()
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generate(**vars(args))
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