mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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802 lines
35 KiB
Python
802 lines
35 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import copy
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import json
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import math
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from pathlib import Path
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from typing import List, Optional, Tuple, Union
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import numpy as np
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import tensorrt as trt
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import torch
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from .. import profiler
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from .._utils import mpi_comm, mpi_world_size
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from ..bindings import GptSession
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from ..builder import Engine, get_engine_version
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from ..logger import logger
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from ..mapping import Mapping
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from ..quantization import QuantMode
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from .generation import (ChatGLMGenerationSession, GenerationSession,
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LogitsProcessor, LoraManager,
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MambaLMHeadModelGenerationSession, ModelConfig,
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QWenForCausalLMGenerationSession, SamplingConfig,
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StoppingCriteria)
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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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return _builder_to_model_config(config)
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def _builder_to_model_config(config: dict) -> Tuple[ModelConfig, dict]:
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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 == mpi_world_size(), \
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f'Engine world size ({tp_size} * {pp_size}) != Runtime world size ({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, if exists in config, should override multi_query_mode
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if multi_query_mode and ('num_kv_heads' not in builder_config):
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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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head_size = builder_config.get('head_size', None)
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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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max_batch_size = builder_config['max_batch_size']
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max_beam_width = builder_config['max_beam_width']
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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_context_logits = builder_config.get('gather_context_logits', False)
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gather_generation_logits = builder_config.get('gather_generation_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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lora_target_modules = builder_config.get('lora_target_modules')
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lora_trtllm_modules_to_hf_modules = builder_config.get(
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'trtllm_modules_to_hf_modules')
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max_medusa_token_len = builder_config.get('max_draft_len', 0)
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num_medusa_heads = builder_config.get('num_medusa_heads', 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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mamba_conv1d_plugin = bool(plugin_config['mamba_conv1d_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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paged_state = plugin_config['paged_state']
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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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lora_plugin = plugin_config.get('lora_plugin')
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use_context_fmha_for_generation = plugin_config.get(
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'use_context_fmha_for_generation')
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model_config = ModelConfig(
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max_batch_size=max_batch_size,
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max_beam_width=max_beam_width,
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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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head_size=head_size,
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gpt_attention_plugin=use_gpt_attention_plugin,
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mamba_conv1d_plugin=mamba_conv1d_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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paged_state=paged_state,
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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_context_logits=gather_context_logits,
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gather_generation_logits=gather_generation_logits,
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dtype=dtype,
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use_custom_all_reduce=use_custom_all_reduce,
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lora_plugin=lora_plugin,
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lora_target_modules=lora_target_modules,
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use_context_fmha_for_generation=use_context_fmha_for_generation,
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trtllm_modules_to_hf_modules=lora_trtllm_modules_to_hf_modules,
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num_medusa_heads=num_medusa_heads,
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max_medusa_tokens=max_medusa_token_len,
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)
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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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'max_output_len': builder_config['max_output_len'],
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'max_beam_width': builder_config['max_beam_width']
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}
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return model_config, other_config
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class ModelRunnerMixin:
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def _check_inputs(self, batch_input_ids: List[torch.Tensor],
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sampling_config: SamplingConfig):
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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 or specified limit ({self.max_batch_size})"
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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 or specified limit ({self.max_input_len})"
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)
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if max_length + sampling_config.max_new_tokens > self.max_seq_len:
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raise RuntimeError(
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f"Maximum input length ({max_length}) + maximum new tokens ({sampling_config.max_new_tokens}) exceeds the engine or specified limit ({self.max_seq_len})"
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)
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if sampling_config.num_beams > self.max_beam_width:
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raise RuntimeError(
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f"Num beams ({sampling_config.num_beams}) exceeds the engine or specified limit ({self.max_beam_width})"
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)
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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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# Cast to int32
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batch_input_ids = [x.type(torch.int32) for x in batch_input_ids]
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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 self.remove_input_padding:
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batch_input_ids = torch.concat(batch_input_ids)
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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: Optional[dict],
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input_lengths: torch.Tensor) -> dict:
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if outputs is not None:
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batch_size = input_lengths.size(0)
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if 'context_logits' in outputs:
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if self.mapping.has_pp():
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# If pp size > 1, the context logits and generation logits are both in last pp
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# Last pp rank send context logits and generation logits to rank 0
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if self.mapping.is_last_pp_rank():
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context_logits = outputs['context_logits']
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context_logits_host = context_logits.cpu()
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mpi_comm().send(context_logits_host, dest=0)
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elif self.mapping.is_first_pp_rank():
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context_logits_host = mpi_comm().recv(
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source=self.mapping.prev_pp_rank()
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) # Prev pp rank of rank=0 is the last pp
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context_logits = context_logits_host.to(
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torch.device('cuda:0'))
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outputs['context_logits'] = context_logits
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context_logits = outputs['context_logits']
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context_logits_output = []
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if self.remove_input_padding:
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if isinstance(self.session, GptSession) and batch_size > 1:
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# The starting position of the context logits buffer of each micro batch is separated
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num_batches = self.mapping.pp_size
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micro_batch_size = math.ceil(batch_size /
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self.mapping.pp_size)
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for i in range(num_batches):
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start_idx = i * micro_batch_size
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end_idx = min(start_idx + micro_batch_size,
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batch_size)
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micro_context_logits = context_logits[
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start_idx:end_idx]
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micro_input_lengths = input_lengths[
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start_idx:end_idx]
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micro_context_logits = micro_context_logits.flatten(
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end_dim=-2)
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seg_points = [0] + micro_input_lengths.cumsum(
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dim=0).tolist()
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context_logits_output += [
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micro_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 = context_logits.flatten(end_dim=-2)
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seg_points = [0] + input_lengths.cumsum(dim=0).tolist()
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context_logits_output = [
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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_output = [
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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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assert len(context_logits_output) == batch_size
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outputs['context_logits'] = context_logits_output
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if 'generation_logits' in outputs:
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if self.mapping.has_pp():
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if self.mapping.is_last_pp_rank():
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generation_logits = outputs['generation_logits']
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if isinstance(generation_logits, list):
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generation_logits_host = [
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logits.cpu() for logits in generation_logits
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]
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else:
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generation_logits_host = generation_logits.cpu()
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mpi_comm().send(generation_logits_host, dest=0)
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elif self.mapping.is_first_pp_rank():
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generation_logits_host = mpi_comm().recv(
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source=self.mapping.prev_pp_rank()
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) # Prev pp rank of rank=0 is the last pp
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if isinstance(generation_logits_host, list):
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generation_logits = [
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logits.to(torch.device('cuda:0'))
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for logits in generation_logits_host
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]
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else:
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generation_logits = generation_logits_host.to(
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torch.device('cuda:0'))
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outputs['generation_logits'] = generation_logits
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if isinstance(self.session, GenerationSession):
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# Convert logits format to be same as GptSession
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generation_logits = torch.stack(
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outputs['generation_logits'], dim=1)
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batch_x_beam, max_gen_len, voc_size = generation_logits.size(
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)
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num_beams = batch_x_beam // batch_size
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generation_logits = generation_logits.view(
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batch_size, num_beams, max_gen_len, voc_size)
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outputs['generation_logits'] = generation_logits
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return outputs
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def _prepare_ptuning(self, prompt_table: Union[str, torch.Tensor],
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tasks: str, batch_size: int):
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if self.max_prompt_embedding_table_size == 0:
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return {}
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if prompt_table is not None:
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if isinstance(prompt_table, str):
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prompt_table_data = torch.from_numpy(
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np.load(prompt_table)).to(dtype=self.dtype)
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else:
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assert isinstance(
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prompt_table,
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torch.Tensor), "Prompt table should be str or torch.Tensor"
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prompt_table_data = prompt_table.to(dtype=self.dtype)
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_, task_vocab_size, hidden_size = prompt_table_data.size()
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task_vocab_size = torch.tensor([task_vocab_size], dtype=torch.int32)
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prompt_table_data = prompt_table_data.view(-1, hidden_size)
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else:
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prompt_table_data = torch.empty([1, self.hidden_size],
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dtype=self.dtype)
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task_vocab_size = torch.zeros([1], dtype=torch.int32)
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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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assert tasks.size(0) == batch_size, \
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f"Number of supplied tasks ({tasks.size(0)}) must match input batch size ({batch_size})"
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else:
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tasks = torch.zeros([batch_size], dtype=torch.int32)
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if isinstance(self.session, GenerationSession):
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return {
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'prompt_embedding_table': prompt_table_data.cuda(),
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'tasks': tasks.cuda(),
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'prompt_vocab_size': task_vocab_size.cuda()
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}
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else:
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return {
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'embedding_table': prompt_table_data.cuda(),
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'tasks': tasks.cuda(),
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'vocab_size': task_vocab_size.cuda()
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}
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class ModelRunner(ModelRunnerMixin):
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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,
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session: GenerationSession,
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max_batch_size: int,
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max_input_len: int,
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max_seq_len: int,
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max_beam_width: int,
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lora_manager: Optional[LoraManager] = None) -> 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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max_seq_len (int):
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The maximum sequence length (input + new tokens).
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max_beam_width (int):
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The maximum beam width.
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lora_manager (LoraManager):
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The LoRA manager to handle LoRA weights.
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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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self.max_seq_len = max_seq_len
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self.max_beam_width = max_beam_width
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self.lora_manager = lora_manager
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@classmethod
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def from_engine(cls,
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engine: Engine,
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lora_dir: Optional[List[str]] = None,
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rank: int = 0,
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debug_mode: bool = False,
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lora_ckpt_source: str = "hf",
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medusa_choices: List[List[int]] = None,
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stream: torch.cuda.Stream = None) -> 'ModelRunner':
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pretrained_config = engine.config.pretrained_config
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build_config = engine.config.build_config
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tp_size = pretrained_config.mapping.tp_size
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num_heads = pretrained_config.num_attention_heads // tp_size
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num_kv_heads = pretrained_config.num_key_value_heads
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num_kv_heads = (num_kv_heads + tp_size - 1) // tp_size
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hidden_size = pretrained_config.hidden_size // tp_size
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head_size = pretrained_config.head_size
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if pretrained_config.architecture == 'MambaLMHeadModel':
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mamba_d_state = pretrained_config.ssm_cfg['d_state']
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mamba_expand = pretrained_config.ssm_cfg['expand']
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mamba_d_conv = pretrained_config.ssm_cfg['d_conv']
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else:
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mamba_d_state, mamba_expand, mamba_d_conv = 0, 0, 0
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model_config = ModelConfig(
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max_batch_size=build_config.max_batch_size,
|
|
max_beam_width=build_config.max_beam_width,
|
|
vocab_size=pretrained_config.vocab_size,
|
|
num_layers=pretrained_config.num_hidden_layers,
|
|
num_heads=num_heads,
|
|
num_kv_heads=num_kv_heads,
|
|
hidden_size=hidden_size,
|
|
head_size=head_size,
|
|
gpt_attention_plugin=bool(
|
|
build_config.plugin_config.gpt_attention_plugin),
|
|
mamba_conv1d_plugin=bool(
|
|
build_config.plugin_config.mamba_conv1d_plugin),
|
|
remove_input_padding=build_config.plugin_config.
|
|
remove_input_padding,
|
|
paged_kv_cache=build_config.plugin_config.paged_kv_cache,
|
|
paged_state=build_config.plugin_config.paged_state,
|
|
tokens_per_block=build_config.plugin_config.tokens_per_block,
|
|
quant_mode=pretrained_config.quant_mode,
|
|
gather_context_logits=build_config.gather_context_logits,
|
|
gather_generation_logits=build_config.gather_generation_logits,
|
|
dtype=pretrained_config.dtype,
|
|
max_prompt_embedding_table_size=build_config.
|
|
max_prompt_embedding_table_size,
|
|
mamba_d_state=mamba_d_state,
|
|
mamba_expand=mamba_expand,
|
|
mamba_d_conv=mamba_d_conv,
|
|
lora_plugin=build_config.plugin_config.lora_plugin,
|
|
lora_target_modules=build_config.lora_config.lora_target_modules,
|
|
trtllm_modules_to_hf_modules=build_config.lora_config.
|
|
trtllm_modules_to_hf_modules,
|
|
max_medusa_tokens=pretrained_config.max_draft_len if hasattr(
|
|
pretrained_config, 'max_draft_len') else 0,
|
|
num_medusa_heads=pretrained_config.num_medusa_heads if hasattr(
|
|
pretrained_config, 'num_medusa_heads') else 0,
|
|
use_custom_all_reduce=build_config.plugin_config.
|
|
use_custom_all_reduce,
|
|
conv_kernel=pretrained_config.conv_kernel if hasattr(
|
|
pretrained_config, 'conv_kernel') else 0,
|
|
layer_types=pretrained_config.layer_types if hasattr(
|
|
pretrained_config, 'layer_types') else [],
|
|
rnn_hidden_size=pretrained_config.rnn_hidden_size if hasattr(
|
|
pretrained_config, 'rnn_hidden_size') else 0,
|
|
)
|
|
max_batch_size = build_config.max_batch_size
|
|
max_input_len = build_config.max_input_len
|
|
max_output_len = build_config.max_output_len
|
|
max_beam_width = build_config.max_beam_width
|
|
if pretrained_config.architecture == 'ChatGLMForCausalLM' and pretrained_config.chatglm_version in [
|
|
'glm', 'chatglm'
|
|
]:
|
|
session_cls = ChatGLMGenerationSession
|
|
elif pretrained_config.architecture == 'MambaLMHeadModel':
|
|
session_cls = MambaLMHeadModelGenerationSession
|
|
else:
|
|
session_cls = GenerationSession
|
|
engine_buffer = engine.engine
|
|
runtime_mapping = pretrained_config.mapping
|
|
|
|
if medusa_choices is not None:
|
|
assert session_cls == GenerationSession, "Medusa is only supported by GenerationSession"
|
|
|
|
assert model_config.max_medusa_tokens > 0, \
|
|
"medusa_chioce is specified but model_config.max_medusa_tokens is 0."
|
|
|
|
torch.cuda.set_device(rank % runtime_mapping.gpus_per_node)
|
|
session = session_cls(model_config,
|
|
engine_buffer,
|
|
runtime_mapping,
|
|
debug_mode=debug_mode,
|
|
stream=stream)
|
|
|
|
if session.use_lora_plugin:
|
|
lora_manager = LoraManager()
|
|
if lora_dir is not None:
|
|
lora_manager.load_from_ckpt(model_dir=lora_dir,
|
|
model_config=model_config,
|
|
runtime_mapping=runtime_mapping,
|
|
ckpt_source=lora_ckpt_source)
|
|
else:
|
|
lora_manager = None
|
|
|
|
return cls(session=session,
|
|
max_batch_size=max_batch_size,
|
|
max_input_len=max_input_len,
|
|
max_seq_len=max_input_len + max_output_len,
|
|
max_beam_width=max_beam_width,
|
|
lora_manager=lora_manager)
|
|
|
|
@classmethod
|
|
def from_dir(cls,
|
|
engine_dir: str,
|
|
lora_dir: Optional[List[str]] = None,
|
|
rank: int = 0,
|
|
debug_mode: bool = False,
|
|
lora_ckpt_source: str = "hf",
|
|
medusa_choices: List[List[int]] = None,
|
|
stream: torch.cuda.Stream = None) -> 'ModelRunner':
|
|
"""
|
|
Create a ModelRunner instance from an engine directory.
|
|
|
|
Args:
|
|
engine_dir (str):
|
|
The directory that contains the serialized engine files and config files.
|
|
lora_dir (Optional[List[str]]):
|
|
The directories that contain LoRA weights.
|
|
rank (int):
|
|
The runtime rank id.
|
|
debug_mode (bool):
|
|
Whether or not to turn on the debug mode.
|
|
medusa_choices (List[List[int]]):
|
|
Medusa choices to use when in Medusa decoding
|
|
stream (torch.cuda.Stream):
|
|
Stream to use.
|
|
Returns:
|
|
ModelRunner: An instance of ModelRunner.
|
|
"""
|
|
engine_version = get_engine_version(engine_dir)
|
|
profiler.start('load tensorrt_llm engine')
|
|
# the old engine format
|
|
if engine_version is None:
|
|
engine_dir = Path(engine_dir)
|
|
config_path = engine_dir / "config.json"
|
|
model_config, other_config = read_config(config_path)
|
|
world_size = other_config.pop('world_size')
|
|
tp_size = other_config.pop('tp_size')
|
|
pp_size = other_config.pop('pp_size')
|
|
max_batch_size = other_config.pop('max_batch_size')
|
|
max_input_len = other_config.pop('max_input_len')
|
|
max_output_len = other_config.pop('max_output_len')
|
|
max_beam_width = other_config.pop('max_beam_width')
|
|
runtime_mapping = Mapping(world_size=world_size,
|
|
rank=rank,
|
|
tp_size=tp_size,
|
|
pp_size=pp_size)
|
|
|
|
engine_name = get_engine_name(model_config.model_name,
|
|
model_config.dtype, tp_size, pp_size,
|
|
rank)
|
|
serialize_path = engine_dir / engine_name
|
|
|
|
with open(serialize_path, 'rb') as f:
|
|
engine_buffer = f.read()
|
|
|
|
if model_config.model_name in ('chatglm_6b', 'glm_10b'):
|
|
session_cls = ChatGLMGenerationSession
|
|
elif model_config.model_name == 'qwen':
|
|
session_cls = QWenForCausalLMGenerationSession
|
|
else:
|
|
session_cls = GenerationSession
|
|
|
|
if medusa_choices is not None:
|
|
assert session_cls == GenerationSession, "Medusa is only supported by GenerationSession"
|
|
|
|
assert model_config.max_medusa_tokens > 0, \
|
|
"medusa_chioce is specified but model_config.max_medusa_tokens is 0."
|
|
|
|
torch.cuda.set_device(rank % runtime_mapping.gpus_per_node)
|
|
session = session_cls(model_config,
|
|
engine_buffer,
|
|
runtime_mapping,
|
|
debug_mode=debug_mode,
|
|
stream=stream)
|
|
if session.use_lora_plugin:
|
|
lora_manager = LoraManager()
|
|
if lora_dir is not None:
|
|
lora_manager.load_from_ckpt(model_dir=lora_dir,
|
|
model_config=model_config,
|
|
runtime_mapping=runtime_mapping,
|
|
ckpt_source=lora_ckpt_source)
|
|
else:
|
|
lora_manager = None
|
|
|
|
profiler.stop('load tensorrt_llm engine')
|
|
loading_time = profiler.elapsed_time_in_sec(
|
|
"load tensorrt_llm engine")
|
|
logger.info(f'Load engine takes: {loading_time} sec')
|
|
|
|
return cls(session=session,
|
|
max_batch_size=max_batch_size,
|
|
max_input_len=max_input_len,
|
|
max_seq_len=max_input_len + max_output_len,
|
|
max_beam_width=max_beam_width,
|
|
lora_manager=lora_manager)
|
|
else:
|
|
# the new engine format
|
|
engine = Engine.from_dir(engine_dir, rank)
|
|
if lora_dir is None:
|
|
config_lora_dir = engine.config.build_config.lora_config.lora_dir
|
|
if len(config_lora_dir) > 0:
|
|
lora_dir = [
|
|
f"{engine_dir}/{dir}" for dir in config_lora_dir
|
|
]
|
|
lora_ckpt_source = engine.config.build_config.lora_config.lora_ckpt_source
|
|
runner = ModelRunner.from_engine(engine, lora_dir, rank, debug_mode,
|
|
lora_ckpt_source, medusa_choices,
|
|
stream)
|
|
profiler.stop('load tensorrt_llm engine')
|
|
loading_time = profiler.elapsed_time_in_sec(
|
|
"load tensorrt_llm engine")
|
|
logger.info(f'Load engine takes: {loading_time} sec')
|
|
return runner
|
|
|
|
@property
|
|
def dtype(self) -> torch.dtype:
|
|
return self.session.dtype
|
|
|
|
@property
|
|
def vocab_size(self) -> int:
|
|
return self.session.vocab_size
|
|
|
|
@property
|
|
def vocab_size_padded(self) -> int:
|
|
return self.session.vocab_size_padded
|
|
|
|
@property
|
|
def hidden_size(self) -> int:
|
|
return self.session.hidden_size
|
|
|
|
@property
|
|
def num_heads(self) -> int:
|
|
return self.session.num_heads
|
|
|
|
@property
|
|
def num_layers(self) -> int:
|
|
return self.session.num_layers
|
|
|
|
@property
|
|
def max_sequence_length(self) -> int:
|
|
return self.max_seq_len
|
|
|
|
@property
|
|
def remove_input_padding(self) -> bool:
|
|
return self.session.remove_input_padding
|
|
|
|
@property
|
|
def use_lora_plugin(self) -> bool:
|
|
return self.session.use_lora_plugin
|
|
|
|
@property
|
|
def max_prompt_embedding_table_size(self) -> int:
|
|
return self.session.max_prompt_embedding_table_size
|
|
|
|
@property
|
|
def mapping(self) -> Mapping:
|
|
return self.session.mapping
|
|
|
|
@property
|
|
def gather_context_logits(self) -> bool:
|
|
return self.session.gather_context_logits
|
|
|
|
@property
|
|
def gather_generation_logits(self) -> bool:
|
|
return self.session.gather_generation_logits
|
|
|
|
def generate(self,
|
|
batch_input_ids: List[torch.Tensor],
|
|
sampling_config: Optional[SamplingConfig] = None,
|
|
prompt_table: Optional[Union[str, torch.Tensor]] = None,
|
|
prompt_tasks: Optional[str] = None,
|
|
lora_uids: Optional[list] = None,
|
|
streaming: bool = False,
|
|
stopping_criteria: Optional[StoppingCriteria] = None,
|
|
logits_processor: Optional[LogitsProcessor] = None,
|
|
medusa_choices: Optional[List[List[int]]] = None,
|
|
**kwargs) -> Union[torch.Tensor, dict]:
|
|
"""
|
|
Generates sequences of token ids.
|
|
The generation-controlling parameters are set in the sampling_config; it will be set to a default one if not passed.
|
|
You can override any sampling_config's attributes by passing corresponding parameters.
|
|
|
|
Args:
|
|
batch_input_ids (List[torch.Tensor]):
|
|
A list of input id tensors. Each tensor is of shape (sequence_length, ).
|
|
sampling_config (SamplingConfig):
|
|
The sampling configuration to be used as base parametrization for the generation call.
|
|
The passed **kwargs matching the sampling_config's attributes will override them.
|
|
If the sampling_config is not provided, a default will be used.
|
|
prompt_table (str or torch.Tensor):
|
|
The file path of prompt table (.npy format, exported by nemo_prompt_convert.py) or the prompt table itself.
|
|
prompt_tasks (str):
|
|
The prompt tuning task ids for the input batch, in format of comma-separated list (e.g., 0,3,1,0).
|
|
lora_uids (list):
|
|
The uids of LoRA weights for the input batch. Use -1 to disable the LoRA module.
|
|
streaming (bool):
|
|
Whether or not to use streaming mode for generation.
|
|
stopping_criteria (StoppingCriteria):
|
|
Custom stopping criteria.
|
|
logits_processor (LogitsProcessor):
|
|
Custom logits processors.
|
|
medusa_choices (List[List[int]]):
|
|
Medusa decoding choices.
|
|
kwargs (Dict[str, Any]:
|
|
Ad hoc parametrization of sampling_config.
|
|
The passed **kwargs matching the sampling_config's attributes will override them.
|
|
Returns:
|
|
torch.Tensor or dict:
|
|
If return_dict=False, the method returns generated output_ids.
|
|
If return_dict=True, the method returns a dict of output_ids,
|
|
sequence_lengths (if sampling_config.output_sequence_lengths=True),
|
|
context_logits and generation_logits (if self.gather_context_logits=True
|
|
and self.gather_generation_logits=True, respectively).
|
|
"""
|
|
# Use sampling_config like HF's generation_config
|
|
if sampling_config is None:
|
|
sampling_config = SamplingConfig(end_id=None, pad_id=None)
|
|
else:
|
|
sampling_config = copy.deepcopy(sampling_config)
|
|
sampling_config.update(**kwargs)
|
|
self._check_inputs(batch_input_ids, sampling_config)
|
|
|
|
batch_size = len(batch_input_ids)
|
|
batch_input_ids, input_lengths = self._prepare_inputs(
|
|
batch_input_ids, sampling_config.pad_id)
|
|
|
|
self.session.setup(
|
|
batch_size=batch_size,
|
|
max_context_length=input_lengths.max().item(),
|
|
max_new_tokens=sampling_config.max_new_tokens,
|
|
beam_width=sampling_config.num_beams,
|
|
max_attention_window_size=sampling_config.max_attention_window_size,
|
|
sink_token_length=sampling_config.sink_token_length,
|
|
lora_manager=self.lora_manager,
|
|
lora_uids=lora_uids,
|
|
medusa_choices=medusa_choices)
|
|
|
|
batch_input_ids = batch_input_ids.cuda()
|
|
input_lengths = input_lengths.cuda()
|
|
ptuning_kwargs = self._prepare_ptuning(prompt_table, prompt_tasks,
|
|
batch_size)
|
|
outputs = self.session.decode(
|
|
batch_input_ids,
|
|
input_lengths,
|
|
sampling_config,
|
|
stop_words_list=sampling_config.stop_words_list,
|
|
bad_words_list=sampling_config.bad_words_list,
|
|
output_sequence_lengths=sampling_config.output_sequence_lengths,
|
|
return_dict=sampling_config.return_dict,
|
|
streaming=streaming,
|
|
stopping_criteria=stopping_criteria,
|
|
logits_processor=logits_processor,
|
|
**ptuning_kwargs)
|
|
if sampling_config.return_dict:
|
|
if streaming:
|
|
outputs = (self._prepare_outputs(curr_outputs, input_lengths)
|
|
for curr_outputs in outputs)
|
|
else:
|
|
outputs = self._prepare_outputs(outputs, input_lengths)
|
|
return outputs
|
|
|
|
def serialize_engine(self) -> trt.IHostMemory:
|
|
"""
|
|
Serialize the engine.
|
|
|
|
Returns:
|
|
bytes: The serialized engine.
|
|
"""
|
|
return self.session.runtime._serialize_engine()
|