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https://github.com/NVIDIA/TensorRT-LLM.git
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6755a3f077
* Update TensorRT-LLM --------- Co-authored-by: Tltin <TltinDeng01@gmail.com> Co-authored-by: zhaohb <zhaohbcloud@126.com> Co-authored-by: Bradley Heilbrun <brad@repl.it> Co-authored-by: nqbao11 <nqbao11.01@gmail.com> Co-authored-by: Nikhil Varghese <nikhil@bot-it.ai>
336 lines
14 KiB
Python
336 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 copy
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import json
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from pathlib import Path
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from typing import List, Optional, Tuple, Union
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import torch
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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 tensorrt_llm.runtime import GenerationSession, ModelConfig, SamplingConfig
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def get_engine_name(model: str, dtype: str, tp_size: int, pp_size: int,
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rank: int) -> str:
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"""
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Get the serialized engine file name.
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Args:
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model (str):
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Model name, e.g., bloom, gpt.
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dtype (str):
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Data type, e.g., float32, float16, bfloat16,
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tp_size (int):
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The size of tensor parallel.
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pp_size (int):
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The size of pipeline parallel.
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rank (int):
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The rank id.
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Returns:
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str: The serialized engine file name.
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"""
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if pp_size == 1:
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return '{}_{}_tp{}_rank{}.engine'.format(model, dtype, tp_size, rank)
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return '{}_{}_tp{}_pp{}_rank{}.engine'.format(model, dtype, tp_size,
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pp_size, rank)
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def read_config(config_path: Path) -> Tuple[ModelConfig, dict]:
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"""
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Read the engine config file and create a ModelConfig instance, return the ModelConfig instance
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and other config fields in a dict.
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Args:
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config_path (Path):
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The path of engine config file.
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Returns:
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Tuple[ModelConfig, dict]: A ModelConfig instance and other config fields.
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"""
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with open(config_path, 'r') as f:
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config = json.load(f)
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builder_config = config['builder_config']
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model_name = builder_config['name']
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dtype = builder_config['precision']
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tp_size = builder_config['tensor_parallel']
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pp_size = builder_config.get('pipeline_parallel', 1)
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world_size = tp_size * pp_size
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assert world_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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num_heads = builder_config['num_heads']
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assert num_heads % tp_size == 0, \
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f"The number of heads ({num_heads}) is not a multiple of tp_size ({tp_size})"
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num_kv_heads = builder_config.get('num_kv_heads', num_heads)
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# TODO: multi_query_mode should be removed
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multi_query_mode = builder_config.get('multi_query_mode', False)
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if multi_query_mode:
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logger.warning(
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"`multi_query_mode` config is deprecated. Please rebuild the engine."
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)
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num_kv_heads = 1
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num_heads = num_heads // tp_size
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num_kv_heads = (num_kv_heads + tp_size - 1) // tp_size
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hidden_size = builder_config['hidden_size'] // tp_size
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vocab_size = builder_config['vocab_size']
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num_layers = builder_config['num_layers']
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cross_attention = builder_config.get('cross_attention', False)
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has_position_embedding = builder_config.get('has_position_embedding', True)
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has_token_type_embedding = builder_config.get('has_token_type_embedding',
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False)
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gather_all_token_logits = builder_config.get('gather_all_token_logits',
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False)
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max_prompt_embedding_table_size = builder_config.get(
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'max_prompt_embedding_table_size', 0)
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quant_mode = QuantMode(builder_config.get('quant_mode', 0))
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plugin_config = config['plugin_config']
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use_gpt_attention_plugin = bool(plugin_config['gpt_attention_plugin'])
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remove_input_padding = plugin_config['remove_input_padding']
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paged_kv_cache = plugin_config['paged_kv_cache']
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tokens_per_block = plugin_config['tokens_per_block']
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use_custom_all_reduce = plugin_config.get('use_custom_all_reduce', False)
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model_config = 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_kv_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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model_name=model_name,
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paged_kv_cache=paged_kv_cache,
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cross_attention=cross_attention,
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has_position_embedding=has_position_embedding,
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has_token_type_embedding=has_token_type_embedding,
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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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quant_mode=quant_mode,
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gather_all_token_logits=gather_all_token_logits,
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dtype=dtype,
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use_custom_all_reduce=use_custom_all_reduce)
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other_config = {
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'world_size': world_size,
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'tp_size': tp_size,
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'pp_size': pp_size,
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'max_batch_size': builder_config['max_batch_size'],
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'max_input_len': builder_config['max_input_len']
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}
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return model_config, other_config
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class ModelRunner:
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"""
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An interface class that wraps GenerationSession and provides generation methods.
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"""
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def __init__(self, session: GenerationSession, max_batch_size: int,
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max_input_len: int) -> None:
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"""
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Create a ModelRunner instance.
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You are recommended to use the from_dir method to load the engine and create a ModelRunner instance.
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Args:
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session (GenerationSession):
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The TensorRT session created from an engine.
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max_batch_size (int):
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The maximum batch size allowed for the input.
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max_input_len (int):
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The maximum input length allowed for the input.
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"""
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self.session = session
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self.max_batch_size = max_batch_size
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self.max_input_len = max_input_len
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@classmethod
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def from_dir(cls,
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engine_dir: str,
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rank: int = 0,
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debug_mode: bool = False) -> 'ModelRunner':
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"""
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Create a ModelRunner instance from an engine directory.
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Args:
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engine_dir (str):
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The directory that contains the serialized engine files and config files.
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rank (int):
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The runtime rank id.
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debug_mode (int):
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Whether or not to turn on the debug mode.
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Returns:
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ModelRunner: An instance of ModelRunner.
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"""
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# session setup
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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, other_config = read_config(config_path)
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world_size = other_config.pop('world_size')
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tp_size = other_config.pop('tp_size')
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pp_size = other_config.pop('pp_size')
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runtime_mapping = tensorrt_llm.Mapping(world_size=world_size,
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rank=rank,
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tp_size=tp_size,
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pp_size=pp_size)
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torch.cuda.set_device(rank % runtime_mapping.gpus_per_node)
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engine_name = get_engine_name(model_config.model_name,
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model_config.dtype, tp_size, pp_size,
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rank)
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serialize_path = engine_dir / engine_name
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profiler.start('load tensorrt_llm engine')
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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_config.model_name in ('chatglm_6b', 'glm_10b'):
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session_cls = tensorrt_llm.runtime.ChatGLMGenerationSession
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else:
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session_cls = tensorrt_llm.runtime.GenerationSession
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session = session_cls(model_config,
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engine_buffer,
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runtime_mapping,
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debug_mode=debug_mode)
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profiler.stop('load tensorrt_llm engine')
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loading_time = profiler.elapsed_time_in_sec("load tensorrt_llm engine")
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logger.info(f'Load engine takes: {loading_time} sec')
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return cls(session, **other_config)
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@property
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def remove_input_padding(self) -> bool:
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return self.session.remove_input_padding
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def _prepare_inputs(self, batch_input_ids: List[torch.Tensor],
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pad_id: int) -> Tuple[torch.Tensor]:
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# Remove potential additional dim, cast to int32
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batch_input_ids = [
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x.flatten().type(torch.int32) for x in batch_input_ids
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]
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input_lengths = [x.size(0) for x in batch_input_ids]
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max_length = max(input_lengths)
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if max_length > self.max_input_len:
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raise RuntimeError(
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f"Maximum input length ({max_length}) exceeds the engine limit ({self.max_input_len})"
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)
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batch_size = len(batch_input_ids)
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if batch_size > self.max_batch_size:
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raise RuntimeError(
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f"Input batch size ({batch_size}) exceeds the engine limit ({self.max_batch_size})"
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)
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if self.remove_input_padding:
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batch_input_ids = torch.concat(batch_input_ids).unsqueeze(0)
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else:
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# Right padding for trt-llm
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paddings = [
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torch.ones(max_length - l, dtype=torch.int32) * pad_id
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for l in input_lengths
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]
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batch_input_ids = [
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torch.cat([x, pad]) for x, pad in zip(batch_input_ids, paddings)
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]
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batch_input_ids = torch.stack(batch_input_ids)
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input_lengths = torch.tensor(input_lengths, dtype=torch.int32)
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return batch_input_ids, input_lengths
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def _prepare_outputs(self, outputs: dict,
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input_lengths: torch.Tensor) -> dict:
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if 'context_logits' in outputs:
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batch_size = input_lengths.size(0)
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context_logits = outputs['context_logits']
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if self.remove_input_padding:
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context_logits = context_logits.flatten(end_dim=1)
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seg_points = [0] + input_lengths.cumsum(dim=0).tolist()
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context_logits = [
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context_logits[s:e]
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for s, e in zip(seg_points[:-1], seg_points[1:])
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]
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else:
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context_logits = [
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context_logits[bidx, :input_lengths[bidx]]
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for bidx in range(batch_size)
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]
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outputs['context_logits'] = context_logits
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return outputs
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def generate(self,
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batch_input_ids: List[torch.Tensor],
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sampling_config: Optional[SamplingConfig] = None,
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**kwargs) -> Union[torch.Tensor, dict]:
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"""
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Generates sequences of token ids.
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The generation-controlling parameters are set in the sampling_config; it will be set to a default one if not passed.
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You can override any sampling_config's attributes by passing corresponding parameters.
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Args:
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batch_input_ids (List[torch.Tensor]):
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A list of input id tensors. Each tensor is of shape (sequence_length, ).
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sampling_config (Optional[SamplingConfig]):
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The sampling configuration to be used as base parametrization for the generation call.
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The passed **kwargs matching the sampling_config's attributes will override them.
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If the sampling_config is not provided, a default will be used.
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kwargs (Dict[str, Any]:
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Ad hoc parametrization of sampling_config.
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The passed **kwargs matching the sampling_config's attributes will override them.
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Returns:
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torch.Tensor or dict:
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If return_dict=False, the method returns generated output_ids.
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If return_dict=True, the method returns a dict of output_ids,
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sequence_lengths (if sampling_config.output_sequence_lengths=True),
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context_logits and generation_logits (if self.session.gather_all_token_logits=True).
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"""
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# Use sampling_config like HF's generation_config
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if sampling_config is None:
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sampling_config = SamplingConfig(end_id=None, pad_id=None)
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else:
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sampling_config = copy.deepcopy(sampling_config)
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sampling_config.update(**kwargs)
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batch_size = len(batch_input_ids)
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batch_input_ids, input_lengths = self._prepare_inputs(
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batch_input_ids, sampling_config.pad_id)
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self.session.setup(
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batch_size=batch_size,
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max_context_length=input_lengths.max().item(),
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max_new_tokens=sampling_config.max_new_tokens,
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beam_width=sampling_config.num_beams,
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max_kv_cache_length=sampling_config.max_kv_cache_length)
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batch_input_ids = batch_input_ids.cuda()
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input_lengths = input_lengths.cuda()
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outputs = self.session.decode(
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batch_input_ids,
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input_lengths,
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sampling_config,
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output_sequence_lengths=sampling_config.output_sequence_lengths,
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return_dict=sampling_config.return_dict)
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if sampling_config.return_dict:
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outputs = self._prepare_outputs(outputs, input_lengths)
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return outputs
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