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>
582 lines
23 KiB
Python
582 lines
23 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 os
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import time
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from pathlib import Path
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import onnx
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import tensorrt as trt
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import torch
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import torch.multiprocessing as mp
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from onnx import TensorProto, helper
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from transformers import AutoConfig, AutoModelForCausalLM
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import tensorrt_llm
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from tensorrt_llm._utils import str_dtype_to_trt
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from tensorrt_llm.builder import Builder
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from tensorrt_llm.layers.attention import PositionEmbeddingType
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from tensorrt_llm.logger import logger
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from tensorrt_llm.mapping import Mapping
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from tensorrt_llm.models import BaichuanForCausalLM, quantize_model
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from tensorrt_llm.network import net_guard
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from tensorrt_llm.plugin.plugin import ContextFMHAType
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from tensorrt_llm.quantization import QuantMode
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from weight import load_from_hf_baichuan, load_from_binary, parse_bin_config # isort:skip
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# 2 routines: get_engine_name, serialize_engine
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# are direct copy from gpt example, TODO: put in utils?
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def trt_dtype_to_onnx(dtype):
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if dtype == trt.float16:
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return TensorProto.DataType.FLOAT16
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elif dtype == trt.float32:
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return TensorProto.DataType.FLOAT
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elif dtype == trt.int32:
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return TensorProto.DataType.INT32
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else:
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raise TypeError("%s is not supported" % dtype)
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def to_onnx(network, path):
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inputs = []
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for i in range(network.num_inputs):
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network_input = network.get_input(i)
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inputs.append(
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helper.make_tensor_value_info(
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network_input.name, trt_dtype_to_onnx(network_input.dtype),
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list(network_input.shape)))
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outputs = []
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for i in range(network.num_outputs):
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network_output = network.get_output(i)
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outputs.append(
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helper.make_tensor_value_info(
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network_output.name, trt_dtype_to_onnx(network_output.dtype),
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list(network_output.shape)))
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nodes = []
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for i in range(network.num_layers):
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layer = network.get_layer(i)
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layer_inputs = []
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for j in range(layer.num_inputs):
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ipt = layer.get_input(j)
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if ipt is not None:
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layer_inputs.append(layer.get_input(j).name)
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layer_outputs = [
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layer.get_output(j).name for j in range(layer.num_outputs)
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]
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nodes.append(
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helper.make_node(str(layer.type),
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name=layer.name,
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inputs=layer_inputs,
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outputs=layer_outputs,
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domain="com.nvidia"))
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onnx_model = helper.make_model(helper.make_graph(nodes,
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'attention',
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inputs,
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outputs,
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initializer=None),
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producer_name='NVIDIA')
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onnx.save(onnx_model, path)
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def get_engine_name(model, dtype, tp_size, rank):
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return '{}_{}_tp{}_rank{}.engine'.format(model, dtype, tp_size, rank)
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def serialize_engine(engine, path):
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logger.info(f'Serializing engine to {path}...')
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tik = time.time()
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with open(path, 'wb') as f:
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f.write(bytearray(engine))
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tok = time.time()
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t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
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logger.info(f'Engine serialized. Total time: {t}')
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def parse_arguments():
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parser = argparse.ArgumentParser()
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parser.add_argument('--world_size',
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type=int,
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default=1,
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help='world size, only support tensor parallelism now')
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parser.add_argument('--model_dir', type=str, default=None)
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parser.add_argument('--bin_model_dir', type=str, default=None)
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parser.add_argument('--model_version',
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type=str,
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default='v1_13b',
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choices=['v1_7b', 'v1_13b', 'v2_7b', 'v2_13b'])
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parser.add_argument('--dtype',
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type=str,
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default='float16',
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choices=['float32', 'bfloat16', 'float16'])
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parser.add_argument('--logits_dtype',
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type=str,
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default='float32',
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choices=['float16', 'float32'])
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parser.add_argument(
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'--timing_cache',
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type=str,
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default='model.cache',
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help=
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'The path of to read timing cache from, will be ignored if the file does not exist'
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)
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parser.add_argument('--log_level', type=str, default='info')
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parser.add_argument('--vocab_size', type=int, default=64000)
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parser.add_argument('--n_layer', type=int, default=40)
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parser.add_argument('--n_positions', type=int, default=4096)
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parser.add_argument('--n_embd', type=int, default=5120)
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parser.add_argument('--n_head', type=int, default=40)
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parser.add_argument('--inter_size', type=int, default=13696)
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parser.add_argument('--hidden_act', type=str, default='silu')
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parser.add_argument('--max_batch_size', type=int, default=1)
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parser.add_argument('--max_input_len', type=int, default=1024)
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parser.add_argument('--max_output_len', type=int, default=1024)
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parser.add_argument('--max_beam_width', type=int, default=1)
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parser.add_argument('--use_gpt_attention_plugin',
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nargs='?',
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const='float16',
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type=str,
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default=False,
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choices=['float16', 'bfloat16', 'float32'])
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parser.add_argument('--use_gemm_plugin',
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nargs='?',
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const='float16',
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type=str,
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default=False,
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choices=['float16', 'bfloat16', 'float32'])
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parser.add_argument('--enable_context_fmha',
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default=False,
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action='store_true')
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parser.add_argument('--enable_context_fmha_fp32_acc',
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default=False,
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action='store_true')
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parser.add_argument(
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'--multi_block_mode',
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default=False,
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action='store_true',
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help=
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'Split long kv sequence into multiple blocks (applied to generation MHA kernels). \
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It is beneifical when batchxnum_heads cannot fully utilize GPU.'
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)
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parser.add_argument('--parallel_build', default=False, action='store_true')
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parser.add_argument('--visualize', default=False, action='store_true')
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parser.add_argument('--enable_debug_output',
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default=False,
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action='store_true')
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parser.add_argument('--gpus_per_node', type=int, default=8)
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parser.add_argument(
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'--output_dir',
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type=str,
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default='baichuan_outputs',
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help=
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'The path to save the serialized engine files, timing cache file and model configs'
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)
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parser.add_argument('--remove_input_padding',
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default=False,
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action='store_true')
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# Arguments related to the quantization of the model.
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parser.add_argument(
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'--use_smooth_quant',
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default=False,
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action="store_true",
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help=
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'Use the SmoothQuant method to quantize activations and weights for the various GEMMs.'
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'See --per_channel and --per_token for finer-grained quantization options.'
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)
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parser.add_argument(
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'--per_channel',
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default=False,
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action="store_true",
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help=
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'By default, we use a single static scaling factor for the GEMM\'s result. '
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'per_channel instead uses a different static scaling factor for each channel. '
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'The latter is usually more accurate, but a little slower.')
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parser.add_argument(
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'--per_token',
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default=False,
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action="store_true",
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help=
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'By default, we use a single static scaling factor to scale activations in the int8 range. '
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'per_token chooses at run time, and for each token, a custom scaling factor. '
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'The latter is usually more accurate, but a little slower.')
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parser.add_argument(
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'--int8_kv_cache',
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default=False,
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action="store_true",
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help=
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'By default, we use dtype for KV cache. int8_kv_cache chooses int8 quantization for KV'
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)
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parser.add_argument(
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'--use_weight_only',
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default=False,
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action="store_true",
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help='Quantize weights for the various GEMMs to INT4/INT8.'
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'See --weight_only_precision to set the precision')
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parser.add_argument(
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'--weight_only_precision',
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const='int8',
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type=str,
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nargs='?',
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default='int8',
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choices=['int8', 'int4'],
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help=
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'Define the precision for the weights when using weight-only quantization.'
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'You must also use --use_weight_only for that argument to have an impact.'
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)
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parser.add_argument(
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'--use_inflight_batching',
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action="store_true",
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default=False,
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help="Activates inflight batching mode of gptAttentionPlugin.")
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parser.add_argument(
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'--paged_kv_cache',
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action="store_true",
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default=False,
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help=
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'By default we use contiguous KV cache. By setting this flag you enable paged KV cache'
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)
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parser.add_argument('--tokens_per_block',
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type=int,
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default=64,
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help='Number of tokens per block in paged KV cache')
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parser.add_argument(
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'--max_num_tokens',
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type=int,
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default=None,
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help='Define the max number of tokens supported by the engine')
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parser.add_argument(
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'--strongly_typed',
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default=False,
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action="store_true",
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help=
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'This option is introduced with trt 9.1.0.1+ and will reduce the building time significantly for fp8.'
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)
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args = parser.parse_args()
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assert not (
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args.use_smooth_quant and args.use_weight_only
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), "You cannot enable both SmoothQuant and INT8 weight-only together."
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if not args.remove_input_padding:
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if args.use_gpt_attention_plugin:
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logger.warning(
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f"It is recommended to specify --remove_input_padding when using GPT attention plugin"
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)
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if args.use_inflight_batching:
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if not args.use_gpt_attention_plugin:
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args.use_gpt_attention_plugin = 'float16'
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logger.info(
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f"Using GPT attention plugin for inflight batching mode. Setting to default '{args.use_gpt_attention_plugin}'"
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)
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if not args.remove_input_padding:
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args.remove_input_padding = True
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logger.info(
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"Using remove input padding for inflight batching mode.")
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if not args.paged_kv_cache:
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args.paged_kv_cache = True
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logger.info("Using paged KV cache for inflight batching mode.")
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if args.max_num_tokens is not None:
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assert args.enable_context_fmha
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if args.use_smooth_quant:
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args.quant_mode = QuantMode.use_smooth_quant(args.per_token,
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args.per_channel)
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elif args.use_weight_only:
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args.quant_mode = QuantMode.use_weight_only(
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args.weight_only_precision == 'int4')
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else:
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args.quant_mode = QuantMode(0)
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if args.int8_kv_cache:
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args.quant_mode = args.quant_mode.set_int8_kv_cache()
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if args.model_dir is not None:
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hf_config = AutoConfig.from_pretrained(args.model_dir,
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trust_remote_code=True)
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# override the inter_size for Baichuan
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args.inter_size = hf_config.intermediate_size
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args.n_embd = hf_config.hidden_size
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args.n_head = hf_config.num_attention_heads
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args.n_layer = hf_config.num_hidden_layers
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if args.model_version == 'v1_7b' or args.model_version == 'v2_7b':
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args.n_positions = hf_config.max_position_embeddings
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else:
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args.n_positions = hf_config.model_max_length
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args.vocab_size = hf_config.vocab_size
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args.hidden_act = hf_config.hidden_act
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elif args.bin_model_dir is not None:
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n_embd, n_head, n_layer, n_positions, vocab_size, hidden_act, inter_size, _ = parse_bin_config(
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Path(args.bin_model_dir) / "config.ini")
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args.inter_size = inter_size
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args.n_embd = n_embd
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args.n_head = n_head
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args.n_layer = n_layer
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args.n_positions = n_positions
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args.vocab_size = vocab_size
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args.hidden_act = hidden_act
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else:
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# default values are based on v1_13b, change them based on model_version
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if args.model_version == 'v1_7b':
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args.inter_size = 11008
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args.n_embd = 4096
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args.n_head = 32
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args.n_layer = 32
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args.n_positions = 4096
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args.vocab_size = 64000
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args.hidden_act = 'silu'
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elif args.model_version == 'v2_7b':
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args.inter_size = 11008
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args.n_embd = 4096
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args.n_head = 32
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args.n_layer = 32
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args.n_positions = 4096
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args.vocab_size = 125696
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args.hidden_act = 'silu'
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elif args.model_version == 'v2_13b':
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args.inter_size = 13696
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args.n_embd = 5120
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args.n_head = 40
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args.n_layer = 40
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args.n_positions = 4096
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args.vocab_size = 125696
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args.hidden_act = 'silu'
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return args
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def build_rank_engine(builder: Builder,
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builder_config: tensorrt_llm.builder.BuilderConfig,
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engine_name, rank, args):
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'''
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@brief: Build the engine on the given rank.
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@param rank: The rank to build the engine.
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@param args: The cmd line arguments.
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@return: The built engine.
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'''
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dtype = str_dtype_to_trt(args.dtype)
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mapping = Mapping(world_size=args.world_size,
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rank=rank,
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tp_size=args.world_size)
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if args.model_version == 'v1_7b' or args.model_version == 'v2_7b':
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position_embedding_type = PositionEmbeddingType.rope_gpt_neox
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else:
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position_embedding_type = PositionEmbeddingType.alibi
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# Initialize Module
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tensorrt_llm_baichuan = BaichuanForCausalLM(
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num_layers=args.n_layer,
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num_heads=args.n_head,
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num_kv_heads=None,
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hidden_size=args.n_embd,
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vocab_size=args.vocab_size,
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hidden_act=args.hidden_act,
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max_position_embeddings=args.n_positions,
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position_embedding_type=position_embedding_type,
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dtype=dtype,
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mlp_hidden_size=args.inter_size,
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mapping=mapping,
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quant_mode=args.quant_mode,
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logits_dtype=args.logits_dtype)
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if args.use_smooth_quant or args.use_weight_only:
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tensorrt_llm_baichuan = quantize_model(tensorrt_llm_baichuan,
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args.quant_mode)
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if args.model_dir is not None:
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logger.info(
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f'Loading HF Baichuan {args.model_version} ... from {args.model_dir}'
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)
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tik = time.time()
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hf_baichuan = AutoModelForCausalLM.from_pretrained(
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args.model_dir,
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device_map={
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"model": "cpu",
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"lm_head": "cpu"
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}, # Load to CPU memory
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torch_dtype="auto",
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trust_remote_code=True)
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tok = time.time()
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t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
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logger.info(f'HF Baichuan {args.model_version} loaded. Total time: {t}')
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load_from_hf_baichuan(tensorrt_llm_baichuan,
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hf_baichuan,
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args.model_version,
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rank,
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args.world_size,
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dtype=args.dtype)
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del hf_baichuan
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elif args.bin_model_dir is not None:
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load_from_binary(tensorrt_llm_baichuan,
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args.bin_model_dir,
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args.model_version,
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mapping,
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fp16=(args.dtype == 'float16'),
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multi_query_mode=False)
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# Module -> Network
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network = builder.create_network()
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network.trt_network.name = engine_name
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if args.use_gpt_attention_plugin:
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network.plugin_config.set_gpt_attention_plugin(
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dtype=args.use_gpt_attention_plugin)
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if args.use_gemm_plugin:
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network.plugin_config.set_gemm_plugin(dtype=args.use_gemm_plugin)
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# Quantization plugins.
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if args.use_smooth_quant:
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network.plugin_config.set_smooth_quant_gemm_plugin(dtype=args.dtype)
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network.plugin_config.set_rmsnorm_quantization_plugin(dtype=args.dtype)
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network.plugin_config.set_quantize_tensor_plugin()
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network.plugin_config.set_quantize_per_token_plugin()
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assert not (args.enable_context_fmha and args.enable_context_fmha_fp32_acc)
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if args.enable_context_fmha:
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network.plugin_config.set_context_fmha(ContextFMHAType.enabled)
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if args.enable_context_fmha_fp32_acc:
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network.plugin_config.set_context_fmha(
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ContextFMHAType.enabled_with_fp32_acc)
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if args.multi_block_mode:
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network.plugin_config.enable_mmha_multi_block_mode()
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if args.use_weight_only:
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network.plugin_config.set_weight_only_quant_matmul_plugin(
|
|
dtype='float16')
|
|
if args.world_size > 1:
|
|
network.plugin_config.set_nccl_plugin(args.dtype)
|
|
if args.remove_input_padding:
|
|
network.plugin_config.enable_remove_input_padding()
|
|
if args.paged_kv_cache:
|
|
network.plugin_config.enable_paged_kv_cache(args.tokens_per_block)
|
|
|
|
with net_guard(network):
|
|
# Prepare
|
|
network.set_named_parameters(tensorrt_llm_baichuan.named_parameters())
|
|
|
|
# Forward
|
|
inputs = tensorrt_llm_baichuan.prepare_inputs(args.max_batch_size,
|
|
args.max_input_len,
|
|
args.max_output_len, True,
|
|
args.max_beam_width,
|
|
args.max_num_tokens)
|
|
tensorrt_llm_baichuan(*inputs)
|
|
if args.enable_debug_output:
|
|
# mark intermediate nodes' outputs
|
|
for k, v in tensorrt_llm_baichuan.named_network_outputs():
|
|
v = v.trt_tensor
|
|
v.name = k
|
|
network.trt_network.mark_output(v)
|
|
v.dtype = dtype
|
|
if args.visualize:
|
|
model_path = os.path.join(args.output_dir, 'test.onnx')
|
|
to_onnx(network.trt_network, model_path)
|
|
|
|
tensorrt_llm.graph_rewriting.optimize(network)
|
|
|
|
engine = None
|
|
|
|
# Network -> Engine
|
|
engine = builder.build_engine(network, builder_config)
|
|
if rank == 0:
|
|
config_path = os.path.join(args.output_dir, 'config.json')
|
|
builder.save_config(builder_config, config_path)
|
|
|
|
tensorrt_llm.tools.cleanup(network, tensorrt_llm_baichuan)
|
|
|
|
return engine
|
|
|
|
|
|
def build(rank, args):
|
|
torch.cuda.set_device(rank % args.gpus_per_node)
|
|
tensorrt_llm.logger.set_level(args.log_level)
|
|
if not os.path.exists(args.output_dir):
|
|
os.makedirs(args.output_dir)
|
|
|
|
# when doing serializing build, all ranks share one engine
|
|
builder = Builder()
|
|
|
|
cache = None
|
|
model_name = 'baichuan'
|
|
for cur_rank in range(args.world_size):
|
|
# skip other ranks if parallel_build is enabled
|
|
if args.parallel_build and cur_rank != rank:
|
|
continue
|
|
# NOTE(nkorobov): when only int8 kv cache is used together with paged kv cache no int8 tensors are exposed to TRT
|
|
int8_trt_flag = args.quant_mode.has_act_or_weight_quant() or (
|
|
not args.paged_kv_cache and args.quant_mode.has_int8_kv_cache())
|
|
builder_config = builder.create_builder_config(
|
|
name=model_name,
|
|
precision=args.dtype,
|
|
timing_cache=args.timing_cache if cache is None else cache,
|
|
tensor_parallel=args.world_size, # TP only
|
|
parallel_build=args.parallel_build,
|
|
num_layers=args.n_layer,
|
|
num_heads=args.n_head,
|
|
hidden_size=args.n_embd,
|
|
vocab_size=args.vocab_size,
|
|
hidden_act=args.hidden_act,
|
|
max_position_embeddings=args.n_positions,
|
|
max_batch_size=args.max_batch_size,
|
|
max_input_len=args.max_input_len,
|
|
max_output_len=args.max_output_len,
|
|
max_num_tokens=args.max_num_tokens,
|
|
int8=int8_trt_flag,
|
|
quant_mode=args.quant_mode,
|
|
strongly_typed=args.strongly_typed)
|
|
engine_name = get_engine_name(model_name, args.dtype, args.world_size,
|
|
cur_rank)
|
|
engine = build_rank_engine(builder, builder_config, engine_name,
|
|
cur_rank, args)
|
|
assert engine is not None, f'Failed to build engine for rank {cur_rank}'
|
|
|
|
if cur_rank == 0:
|
|
# Use in-memory timing cache for multiple builder passes.
|
|
if not args.parallel_build:
|
|
cache = builder_config.trt_builder_config.get_timing_cache()
|
|
|
|
serialize_engine(engine, os.path.join(args.output_dir, engine_name))
|
|
del engine
|
|
|
|
if rank == 0:
|
|
ok = builder.save_timing_cache(
|
|
builder_config, os.path.join(args.output_dir, "model.cache"))
|
|
assert ok, "Failed to save timing cache."
|
|
|
|
|
|
if __name__ == '__main__':
|
|
args = parse_arguments()
|
|
logger.set_level(args.log_level)
|
|
tik = time.time()
|
|
if args.parallel_build and args.world_size > 1 and \
|
|
torch.cuda.device_count() >= args.world_size:
|
|
logger.warning(
|
|
f'Parallelly build TensorRT engines. Please make sure that all of the {args.world_size} GPUs are totally free.'
|
|
)
|
|
mp.spawn(build, nprocs=args.world_size, args=(args, ))
|
|
else:
|
|
args.parallel_build = False
|
|
logger.info('Serially build TensorRT engines.')
|
|
build(0, args)
|
|
|
|
tok = time.time()
|
|
t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
|
|
logger.info(f'Total time of building all {args.world_size} engines: {t}')
|