mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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b57221b764
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
1097 lines
45 KiB
Python
1097 lines
45 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 argparse
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import json
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import math
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import os
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import time
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from pathlib import Path
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# isort: off
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import torch
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import torch.multiprocessing as mp
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import tensorrt as trt
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# isort: on
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from transformers import LlamaConfig, LlamaForCausalLM
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try:
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from transformers import MixtralForCausalLM
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except ImportError:
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MixtralForCausalLM = None
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try:
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from transformers import LlavaConfig, LlavaForConditionalGeneration
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except ImportError:
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pass
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import tensorrt_llm
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from tensorrt_llm import profiler
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from tensorrt_llm._common import check_max_num_tokens
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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 import MoeConfig
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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 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 tensorrt_llm.runtime.lora_manager import LoraConfig
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from tensorrt_llm.models.llama.weight import ( # isort:skip
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get_scaling_factors, load_from_awq_llama, load_from_binary,
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load_from_gptq_llama, load_from_hf_checkpoint, load_from_hf_llama,
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load_from_meta_llama, parse_bin_config)
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MODEL_NAME = "llama"
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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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import onnx
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from onnx import TensorProto, helper
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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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if dtype == trt.bfloat16:
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return TensorProto.DataType.BFLOAT16
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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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elif dtype == trt.int64:
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return TensorProto.DataType.INT64
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elif dtype == trt.bool:
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return TensorProto.DataType.BOOL
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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, pp_size, rank):
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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 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(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(cmd_args=None):
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parser = argparse.ArgumentParser()
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parser.add_argument('--world_size', type=int, default=1)
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parser.add_argument('--tp_size', type=int, default=1)
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parser.add_argument('--pp_size', type=int, default=1)
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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('--meta_ckpt_dir', type=str, default=None)
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parser.add_argument('--quant_ckpt_path', type=str, default=None)
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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(
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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(
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'--profiling_verbosity',
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type=str,
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default='layer_names_only',
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choices=['layer_names_only', 'detailed', 'none'],
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help=
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'The profiling verbosity for the generated TRT engine. Set to detailed can inspect tactic choices and kernel parameters.'
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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=32000)
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parser.add_argument('--n_layer', type=int, default=32)
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parser.add_argument('--n_positions', type=int, default=2048)
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parser.add_argument('--n_embd', type=int, default=4096)
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parser.add_argument('--n_head', type=int, default=32)
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parser.add_argument('--n_kv_head', type=int, default=None)
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parser.add_argument('--multiple_of', type=int, default=256)
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parser.add_argument('--ffn_dim_multiplier', type=float, default=1.0)
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parser.add_argument('--inter_size', type=int, default=None)
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parser.add_argument('--hidden_act', type=str, default='silu')
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parser.add_argument('--rms_norm_eps', type=float, default=1e-06)
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parser.add_argument('--max_batch_size', type=int, default=8)
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parser.add_argument('--max_input_len', type=int, default=2048)
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parser.add_argument('--max_output_len', type=int, default=512)
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parser.add_argument('--max_beam_width', type=int, default=1)
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parser.add_argument('--rotary_base', type=float, default=10000.0)
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parser.add_argument('--rotary_scaling', nargs=2, type=str, default=None)
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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('--use_rmsnorm_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', 'float32', 'bfloat16'])
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parser.add_argument('--parallel_build', default=False, action='store_true')
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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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'--use_paged_context_fmha',
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action='store_true',
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help=
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'Activates paged context FMHA. This mode of the context FMHA is required for chunked context, speculative decoding and reuse of KV cache blocks. Context FMHA performance is worse when this mode is on.'
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)
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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 beneficial when batch x num_heads cannot fully utilize GPU.'
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)
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parser.add_argument(
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'--disable_xqa',
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default=False,
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action='store_true',
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help=
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'Disable XQA optimization for the generation MHA. See more details in docs/gpt_attention.'
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)
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parser.add_argument('--visualize', default=False, action='store_true')
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parser.add_argument('--load_by_shard',
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action='store_true',
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help='Load a pretrained model shard-by-shard.')
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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('--builder_opt', type=int, default=None)
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parser.add_argument(
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'--output_dir',
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type=str,
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default='engine_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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parser.add_argument(
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'--use_fused_mlp',
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default=False,
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action='store_true',
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help=
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'Enable horizontal fusion in GatedMLP, reduces layer input traffic and potentially improves performance. '
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'For FP8 PTQ, the downside is slight reduction of accuracy because one of the quantization scaling factors are discarded '
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'(0.45734 vs 0.45755 for LLaMA-v2 7B using ammo/examples/hf/instruct_eval/mmlu.py).'
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)
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parser.add_argument('--enable_pos_shift',
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default=False,
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action='store_true',
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help='Enable position shift for streamingllm method')
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parser.add_argument(
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'--dense_context_fmha',
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default=False,
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action='store_true',
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help=
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'Enable dense fmha in context phase, otherwise sliding window attention.'
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'If dense_context_fmha=False, the sliding window size is the max attention window size.'
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)
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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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'--per_group',
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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 weights in the int4 range. '
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'per_group chooses at run time, and for each group, a custom scaling factor. '
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'The flag is built for GPTQ/AWQ quantization.')
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parser.add_argument('--group_size',
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type=int,
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default=128,
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help='Group size used in GPTQ/AWQ quantization.')
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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_parallel_embedding',
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action="store_true",
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default=False,
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help=
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'By default embedding parallelism is disabled. By setting this flag, embedding parallelism is enabled'
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)
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parser.add_argument(
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'--embedding_sharding_dim',
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type=int,
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default=1, # Meta does TP on hidden dim
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choices=[0, 1],
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help=
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'By default the embedding lookup table is sharded along vocab dimension (embedding_sharding_dim=0). '
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'To shard it along hidden dimension, set embedding_sharding_dim=1'
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'Note: embedding sharing is only enabled when embedding_sharding_dim = 0'
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)
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parser.add_argument(
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'--enable_fp8',
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default=False,
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action='store_true',
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help='Use FP8 Linear layer for Attention QKV/Dense and MLP.')
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parser.add_argument(
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'--fp8_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. fp8_kv_cache chooses int8 quantization for KV'
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)
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parser.add_argument(
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'--quantized_fp8_model_path',
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type=str,
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default=None,
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help='Path of a quantized model checkpoint in .npz format')
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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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'--disable_weight_only_quant_plugin',
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default=False,
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action="store_true",
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help=
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'By default, using plugin implementation for weight quantization. Enabling disable_weight_only_quant_plugin flag will use ootb implementation instead of plugin.'
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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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'--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', 'int4_awq', 'int4_gptq'],
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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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'--quantize_lm_head',
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default=False,
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action="store_true",
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help='Quantize lm_head weights as well when using int4_awq.')
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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=128,
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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=
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'Define the max number of tokens supported by the engine, note that it takes no effect if --remove_input_padding is not set'
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)
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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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parser.add_argument(
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'--use_custom_all_reduce',
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|
action='store_true',
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help=
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'Activates latency-optimized algorithm for all-reduce instead of NCCL.')
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|
parser.add_argument(
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'--max_prompt_embedding_table_size',
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type=int,
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default=0,
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help='Setting to a value > 0 enables support for prompt tuning.')
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|
parser.add_argument(
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'--gather_all_token_logits',
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|
action='store_true',
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|
default=False,
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|
help='Enable both gather_context_logits and gather_generation_logits')
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|
parser.add_argument('--gather_context_logits',
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|
action='store_true',
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|
default=False,
|
|
help='Gather context logits')
|
|
parser.add_argument('--gather_generation_logits',
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|
action='store_true',
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default=False,
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|
help='Gather generation logits')
|
|
parser.add_argument(
|
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'--use_lora_plugin',
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|
nargs='?',
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const=None,
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default=False,
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choices=['float16', 'float32', 'bfloat16'],
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help="Activates the lora plugin which enables embedding sharing.")
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|
parser.add_argument(
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'--lora_target_modules',
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nargs='+',
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default=None,
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|
choices=[
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"attn_qkv",
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"attn_q",
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"attn_k",
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"attn_v",
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"attn_dense",
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"mlp_h_to_4h",
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"mlp_gate",
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"mlp_4h_to_h",
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],
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|
help=
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"Add lora in which modules. Only be activated when use_lora_plugin is enabled."
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)
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|
parser.add_argument('--hf_lora_dir', type=str, default=None)
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|
parser.add_argument(
|
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'--max_lora_rank',
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type=int,
|
|
default=64,
|
|
help='maximum lora rank for different lora modules. '
|
|
'It is used to compute the workspace size of lora plugin.')
|
|
parser.add_argument(
|
|
'--moe_num_experts',
|
|
default=0,
|
|
type=int,
|
|
help='Specify the number of experts to use for MOE layers')
|
|
parser.add_argument(
|
|
'--moe_top_k',
|
|
default=0,
|
|
type=int,
|
|
help=
|
|
'Specify the top_k value to use for MOE layers. Default to 1 if --moe_num_experts is set'
|
|
)
|
|
parser.add_argument(
|
|
'--moe_tp_mode',
|
|
default=MoeConfig.ParallelismMode.TENSOR_PARALLEL,
|
|
type=int,
|
|
help=
|
|
'Controls how to distribute experts in TP. Check layers/moe.py for accepted values',
|
|
)
|
|
parser.add_argument(
|
|
'--moe_renorm_mode',
|
|
default=MoeConfig.ExpertScaleNormalizationMode.RENORMALIZE,
|
|
type=int,
|
|
help=
|
|
'Controls renormalization after gate logits. Check layers/moe.py for accepted values',
|
|
)
|
|
|
|
args = parser.parse_args(cmd_args)
|
|
logger.set_level(args.log_level)
|
|
|
|
assert not (
|
|
args.use_smooth_quant and args.use_weight_only
|
|
), "You cannot enable both SmoothQuant and INT8 weight-only together."
|
|
|
|
if not args.remove_input_padding:
|
|
if args.use_gpt_attention_plugin:
|
|
logger.warning(
|
|
f"It is recommended to specify --remove_input_padding when using GPT attention plugin"
|
|
)
|
|
|
|
if args.use_inflight_batching:
|
|
if not args.use_gpt_attention_plugin:
|
|
args.use_gpt_attention_plugin = 'float16'
|
|
logger.info(
|
|
f"Using GPT attention plugin for inflight batching mode. Setting to default '{args.use_gpt_attention_plugin}'"
|
|
)
|
|
if not args.remove_input_padding:
|
|
args.remove_input_padding = True
|
|
logger.info(
|
|
"Using remove input padding for inflight batching mode.")
|
|
if not args.paged_kv_cache:
|
|
args.paged_kv_cache = True
|
|
logger.info("Using paged KV cache for inflight batching mode.")
|
|
|
|
if args.use_smooth_quant:
|
|
args.quant_mode = QuantMode.use_smooth_quant(args.per_token,
|
|
args.per_channel)
|
|
elif args.use_weight_only:
|
|
args.quant_mode = QuantMode.from_description(
|
|
quantize_weights=True,
|
|
quantize_activations=False,
|
|
per_token=False,
|
|
per_channel=False,
|
|
per_group=args.per_group,
|
|
use_int4_weights="int4" in args.weight_only_precision)
|
|
else:
|
|
args.quant_mode = QuantMode(0)
|
|
|
|
if args.int8_kv_cache:
|
|
args.quant_mode = args.quant_mode.set_int8_kv_cache()
|
|
elif args.fp8_kv_cache:
|
|
args.quant_mode = args.quant_mode.set_fp8_kv_cache()
|
|
if args.enable_fp8:
|
|
args.quant_mode = args.quant_mode.set_fp8_qdq()
|
|
|
|
if args.rotary_scaling is not None:
|
|
assert args.use_gpt_attention_plugin, "RoPE scaling is only supported through GPT attention plugin."
|
|
rotary_scaling = {
|
|
"type": args.rotary_scaling[0],
|
|
"factor": float(args.rotary_scaling[1])
|
|
}
|
|
assert rotary_scaling["type"] in ["linear", "dynamic"]
|
|
assert rotary_scaling["factor"] > 1.0
|
|
args.rotary_scaling = rotary_scaling
|
|
|
|
if args.model_dir is not None:
|
|
hf_config = LlamaConfig.from_pretrained(args.model_dir)
|
|
if hf_config.model_type == "llava":
|
|
# LLaVA = Vision model + Llama LLM
|
|
# We load a llava config and use its' text config as llama config
|
|
hf_config = LlavaConfig.from_pretrained(args.model_dir).text_config
|
|
hf_config.model_type = "llava" # Replace llama with llava
|
|
|
|
args.inter_size = hf_config.intermediate_size # override the inter_size for LLaMA
|
|
args.n_embd = hf_config.hidden_size
|
|
args.n_head = hf_config.num_attention_heads
|
|
if hasattr(hf_config, "num_key_value_heads"):
|
|
args.n_kv_head = hf_config.num_key_value_heads
|
|
args.n_layer = hf_config.num_hidden_layers
|
|
args.n_positions = hf_config.max_position_embeddings
|
|
args.vocab_size = hf_config.vocab_size if hf_config.vocab_size is not None else args.vocab_size
|
|
args.hidden_act = hf_config.hidden_act
|
|
args.rms_norm_eps = hf_config.rms_norm_eps
|
|
# These attributes only exists with Mixtral, for the moment
|
|
args.moe_num_experts = getattr(hf_config, "num_local_experts",
|
|
args.moe_num_experts)
|
|
args.moe_top_k = getattr(hf_config, "num_experts_per_tok",
|
|
args.moe_top_k)
|
|
args.rotary_base = getattr(hf_config, "rope_theta", args.rotary_base)
|
|
args.model_type = hf_config.model_type
|
|
if hf_config.model_type == "mixtral":
|
|
# HF LLaMA-type models are implicitly using gated activation.
|
|
# With our MoE implementation, we must make it explicit
|
|
args.hidden_act = "swiglu"
|
|
|
|
elif args.meta_ckpt_dir is not None:
|
|
with open(Path(args.meta_ckpt_dir, "params.json")) as fp:
|
|
meta_config: dict = json.load(fp)
|
|
args.n_embd = meta_config["dim"]
|
|
args.n_head = meta_config["n_heads"]
|
|
args.n_layer = meta_config["n_layers"]
|
|
args.n_kv_head = meta_config.get("n_kv_heads", args.n_head)
|
|
if "hidden_dim" in meta_config:
|
|
args.inter_size = meta_config["hidden_dim"]
|
|
else:
|
|
args.multiple_of = meta_config.get("multiple_of", 1)
|
|
n_embd = int(4 * args.n_embd * 2 / 3)
|
|
args.ffn_dim_multiplier = meta_config.get("ffn_dim_multiplier", 1)
|
|
args.inter_size = args.multiple_of * (
|
|
(int(n_embd * args.ffn_dim_multiplier) + args.multiple_of - 1)
|
|
// args.multiple_of)
|
|
args.rms_norm_eps = meta_config["norm_eps"]
|
|
args.moe_num_experts = meta_config.get("moe", {}).get("num_experts", 0)
|
|
args.moe_top_k = meta_config.get("moe", {}).get("num_experts_per_tok",
|
|
0)
|
|
elif args.bin_model_dir is not None:
|
|
n_embd, n_head, n_layer, n_positions, vocab_size, hidden_act, inter_size, n_kv_head = parse_bin_config(
|
|
Path(args.bin_model_dir) / "config.ini")
|
|
args.inter_size = inter_size # override the inter_size for LLaMA
|
|
args.n_kv_head = n_kv_head
|
|
args.n_embd = n_embd
|
|
args.n_head = n_head
|
|
args.n_layer = n_layer
|
|
args.n_positions = n_positions
|
|
args.vocab_size = vocab_size if args.vocab_size is None else args.vocab_size
|
|
args.hidden_act = hidden_act
|
|
args.rms_norm_eps = 1e-06
|
|
logger.warning("Set rms_norm_eps to 1e-06 directly.")
|
|
if args.n_kv_head is None:
|
|
args.n_kv_head = args.n_head
|
|
elif args.n_kv_head != args.n_head:
|
|
assert (args.n_head % args.n_kv_head) == 0, \
|
|
"MQA/GQA requires the number of heads to be divisible by the number of K/V heads."
|
|
assert (args.n_kv_head % args.tp_size) == 0 or (args.tp_size % args.n_kv_head) == 0, \
|
|
"MQA/GQA requires either the number of K/V heads to be divisible by the tensor parallelism size OR " \
|
|
"the tensor parallelism size to be divisible by the number of K/V heads."
|
|
|
|
hf_modules_to_trtllm_modules = {
|
|
"q_proj": "attn_q",
|
|
"k_proj": "attn_k",
|
|
"v_proj": "attn_v",
|
|
"o_proj": "attn_dense",
|
|
"gate_proj": "mlp_h_to_4h",
|
|
"down_proj": "mlp_4h_to_h",
|
|
"up_proj": "mlp_gate"
|
|
} # lora modules on llama
|
|
|
|
trtllm_modules_to_hf_modules = {
|
|
"attn_q": "q_proj",
|
|
"attn_k": "k_proj",
|
|
"attn_v": "v_proj",
|
|
"attn_dense": "o_proj",
|
|
"mlp_h_to_4h": "gate_proj",
|
|
"mlp_4h_to_h": "down_proj",
|
|
"mlp_gate": "up_proj",
|
|
}
|
|
|
|
lora_config = LoraConfig.from_hf(args.hf_lora_dir,
|
|
hf_modules_to_trtllm_modules,
|
|
trtllm_modules_to_hf_modules)
|
|
|
|
if lora_config.is_valid:
|
|
if args.lora_target_modules is None:
|
|
args.lora_target_modules = lora_config.lora_target_modules
|
|
# the lora checkpoint might finetune the embedding
|
|
if lora_config.vocab_size != 0:
|
|
args.vocab_size = lora_config.vocab_size
|
|
|
|
args.lora_config = lora_config
|
|
|
|
if args.weight_only_precision == 'int4_awq':
|
|
inter_alignment = args.tp_size * 128
|
|
if args.inter_size % inter_alignment != 0:
|
|
args.inter_size = int((args.inter_size + inter_alignment - 1) /
|
|
inter_alignment) * inter_alignment
|
|
logger.info("To use awq we pad intermediate_size to {}.".format(
|
|
args.inter_size))
|
|
|
|
if args.quantize_lm_head:
|
|
vocab_alignment = args.tp_size * 64
|
|
if args.vocab_size % vocab_alignment != 0:
|
|
args.vocab_size = int((args.vocab_size + vocab_alignment - 1) /
|
|
vocab_alignment) * vocab_alignment
|
|
logger.info("To use awq we pad vocab_size to {}.".format(
|
|
args.vocab_size))
|
|
|
|
assert args.pp_size * args.tp_size == args.world_size
|
|
|
|
args.max_num_tokens = check_max_num_tokens(
|
|
max_num_tokens=args.max_num_tokens,
|
|
max_batch_size=args.max_batch_size,
|
|
max_input_len=args.max_input_len,
|
|
remove_input_padding=args.remove_input_padding)
|
|
|
|
assert (math.log2(args.tokens_per_block).is_integer()
|
|
), "tokens_per_block must be power of 2"
|
|
if args.enable_context_fmha or args.enable_context_fmha_fp32_acc:
|
|
assert (args.tokens_per_block >=
|
|
128), "Context fMHA requires >= 128 tokens per block"
|
|
|
|
if args.inter_size is None:
|
|
# this should not be need when loading a real model
|
|
# but it is helpful when creating a dummy model without loading any real weights
|
|
n_embd = int(4 * args.n_embd * 2 / 3)
|
|
args.inter_size = args.multiple_of * (
|
|
(int(n_embd * args.ffn_dim_multiplier) + args.multiple_of - 1) //
|
|
args.multiple_of)
|
|
logger.info(f"Setting inter_size to {args.inter_size}.")
|
|
|
|
if args.enable_pos_shift:
|
|
assert args.use_gpt_attention_plugin, "Position shift is only support in the gpt attention plugin."
|
|
assert args.enable_context_fmha or args.enable_context_fmha_fp32_acc
|
|
|
|
if args.moe_num_experts and args.moe_top_k == 0:
|
|
args.moe_top_k = 1
|
|
args.moe_config = MoeConfig(args.moe_num_experts, args.moe_top_k,
|
|
args.moe_tp_mode,
|
|
args.moe_renorm_mode).validate()
|
|
|
|
if args.gather_all_token_logits:
|
|
args.gather_context_logits = True
|
|
args.gather_generation_logits = True
|
|
|
|
return args
|
|
|
|
|
|
def get_model_object(args, mapping, trt_dtype=None):
|
|
if trt_dtype is None:
|
|
trt_dtype = str_dtype_to_trt(args.dtype)
|
|
# Initialize Module
|
|
tensorrt_llm_llama = tensorrt_llm.models.LLaMAForCausalLM(
|
|
num_layers=args.n_layer,
|
|
num_heads=args.n_head,
|
|
num_kv_heads=args.n_kv_head,
|
|
hidden_size=args.n_embd,
|
|
vocab_size=args.vocab_size,
|
|
hidden_act=args.hidden_act,
|
|
max_position_embeddings=args.n_positions,
|
|
dtype=trt_dtype,
|
|
mlp_hidden_size=args.inter_size,
|
|
position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
|
|
mapping=mapping,
|
|
rotary_base=args.rotary_base,
|
|
rotary_scaling=args.rotary_scaling,
|
|
use_parallel_embedding=args.use_parallel_embedding,
|
|
embedding_sharding_dim=args.embedding_sharding_dim,
|
|
quant_mode=args.quant_mode,
|
|
rms_norm_eps=args.rms_norm_eps,
|
|
use_fused_mlp=args.use_fused_mlp,
|
|
use_prompt_tuning=args.max_prompt_embedding_table_size > 0,
|
|
enable_pos_shift=args.enable_pos_shift,
|
|
dense_context_fmha=args.dense_context_fmha,
|
|
moe_config=args.moe_config,
|
|
max_lora_rank=args.max_lora_rank)
|
|
quantize_kwargs = {}
|
|
if args.use_smooth_quant or args.use_weight_only:
|
|
if args.weight_only_precision == 'int4_awq':
|
|
exclude_modules = ['lm_head'] if not args.quantize_lm_head else []
|
|
quantize_kwargs = {
|
|
"group_size": args.group_size,
|
|
"zero": False,
|
|
"pre_quant_scale": True,
|
|
"exclude_modules": exclude_modules,
|
|
}
|
|
elif args.weight_only_precision == 'int4_gptq':
|
|
quantize_kwargs = {
|
|
"group_size": args.group_size,
|
|
"zero": True,
|
|
"pre_quant_scale": False,
|
|
}
|
|
elif args.enable_fp8 or args.fp8_kv_cache:
|
|
logger.info(f'Loading scaling factors from '
|
|
f'{args.quantized_fp8_model_path}')
|
|
quant_scales = get_scaling_factors(args.quantized_fp8_model_path,
|
|
num_layers=args.n_layer,
|
|
quant_mode=args.quant_mode)
|
|
quantize_kwargs = {"quant_scales": quant_scales}
|
|
|
|
if args.use_weight_only and args.moe_config.has_moe():
|
|
if 'exclude_modules' in quantize_kwargs:
|
|
quantize_kwargs['exclude_modules'].append('router')
|
|
else:
|
|
quantize_kwargs['exclude_modules'] = ['lm_head', 'router']
|
|
|
|
tensorrt_llm_llama = quantize_model(tensorrt_llm_llama, args.quant_mode,
|
|
**quantize_kwargs)
|
|
if args.per_group:
|
|
if args.weight_only_precision == 'int4_awq':
|
|
load_from_awq_llama(tensorrt_llm_llama=tensorrt_llm_llama,
|
|
quant_ckpt_path=args.quant_ckpt_path,
|
|
quantize_lm_head=args.quantize_lm_head,
|
|
mapping=mapping,
|
|
dtype=args.dtype,
|
|
bin_model_dir=args.bin_model_dir)
|
|
else:
|
|
load_from_gptq_llama(tensorrt_llm_llama=tensorrt_llm_llama,
|
|
quant_ckpt_path=args.quant_ckpt_path,
|
|
mapping=mapping,
|
|
dtype=args.dtype,
|
|
bin_model_dir=args.bin_model_dir)
|
|
elif args.meta_ckpt_dir is not None:
|
|
load_from_meta_llama(tensorrt_llm_llama, args.meta_ckpt_dir, mapping,
|
|
args.dtype)
|
|
elif args.model_dir is not None:
|
|
logger.info(f'Loading HF LLaMA ... from {args.model_dir}')
|
|
tik = time.time()
|
|
if not args.load_by_shard:
|
|
if args.model_type == "llava":
|
|
hf_llava = LlavaForConditionalGeneration.from_pretrained(
|
|
args.model_dir, torch_dtype="auto")
|
|
hf_llama = hf_llava.language_model
|
|
else:
|
|
hf_model = LlamaForCausalLM if args.model_type != "mixtral" else MixtralForCausalLM
|
|
hf_llama = hf_model.from_pretrained(
|
|
args.model_dir,
|
|
device_map={
|
|
"model": "cpu",
|
|
"lm_head": "cpu",
|
|
"embed_tokens": "cpu",
|
|
"layers": "cpu",
|
|
"norm": "cpu",
|
|
}, # Load to CPU memory
|
|
torch_dtype='auto',
|
|
)
|
|
use_gemm_woq_plugin = not args.disable_weight_only_quant_plugin
|
|
load_from_hf_llama(tensorrt_llm_llama,
|
|
hf_llama,
|
|
mapping=mapping,
|
|
dtype=args.dtype,
|
|
use_gemm_woq_plugin=use_gemm_woq_plugin,
|
|
lora_config=args.lora_config)
|
|
del hf_llama
|
|
else:
|
|
load_from_hf_checkpoint(tensorrt_llm_llama,
|
|
args.model_dir,
|
|
mapping,
|
|
dtype=args.dtype,
|
|
lora_config=args.lora_config)
|
|
tok = time.time()
|
|
t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
|
|
logger.info(f'HF LLaMA loaded. Total time: {t}')
|
|
|
|
elif args.bin_model_dir is not None:
|
|
load_from_binary(tensorrt_llm_llama,
|
|
args.bin_model_dir,
|
|
mapping,
|
|
fp16=(args.dtype == 'float16'),
|
|
multi_query_mode=(args.n_kv_head != args.n_head))
|
|
|
|
return tensorrt_llm_llama
|
|
|
|
|
|
def update_plugin_configs(args, network):
|
|
if args.use_gpt_attention_plugin:
|
|
network.plugin_config.set_gpt_attention_plugin(
|
|
dtype=args.use_gpt_attention_plugin)
|
|
if args.use_gemm_plugin:
|
|
if not args.enable_fp8:
|
|
network.plugin_config.set_gemm_plugin(dtype=args.use_gemm_plugin)
|
|
else:
|
|
logger.info(
|
|
"Gemm plugin does not support FP8. Disabled Gemm plugin.")
|
|
if args.use_rmsnorm_plugin:
|
|
network.plugin_config.set_rmsnorm_plugin(dtype=args.use_rmsnorm_plugin)
|
|
if args.use_lora_plugin:
|
|
network.plugin_config.set_lora_plugin(dtype=args.use_lora_plugin)
|
|
|
|
# Quantization plugins.
|
|
if args.use_smooth_quant:
|
|
network.plugin_config.set_smooth_quant_gemm_plugin(dtype=args.dtype)
|
|
network.plugin_config.set_rmsnorm_quantization_plugin(dtype=args.dtype)
|
|
network.plugin_config.set_quantize_tensor_plugin()
|
|
network.plugin_config.set_quantize_per_token_plugin()
|
|
assert not (args.enable_context_fmha and args.enable_context_fmha_fp32_acc)
|
|
if args.enable_context_fmha:
|
|
network.plugin_config.set_context_fmha(ContextFMHAType.enabled)
|
|
if args.enable_context_fmha_fp32_acc:
|
|
network.plugin_config.set_context_fmha(
|
|
ContextFMHAType.enabled_with_fp32_acc)
|
|
if args.multi_block_mode:
|
|
network.plugin_config.enable_mmha_multi_block_mode()
|
|
if not args.disable_xqa:
|
|
network.plugin_config.enable_xqa_optimization()
|
|
|
|
if args.use_weight_only and not args.disable_weight_only_quant_plugin:
|
|
if args.per_group:
|
|
network.plugin_config.set_weight_only_groupwise_quant_matmul_plugin(
|
|
dtype=args.dtype)
|
|
else:
|
|
network.plugin_config.set_weight_only_quant_matmul_plugin(
|
|
dtype=args.dtype)
|
|
if args.world_size > 1:
|
|
network.plugin_config.set_nccl_plugin(args.dtype,
|
|
args.use_custom_all_reduce)
|
|
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)
|
|
return
|
|
|
|
|
|
def build_rank_engine(builder: Builder,
|
|
builder_config: tensorrt_llm.builder.BuilderConfig,
|
|
engine_name, rank, args):
|
|
'''
|
|
@brief: Build the engine on the given rank.
|
|
@param rank: The rank to build the engine.
|
|
@param args: The cmd line arguments.
|
|
@return: The built engine.
|
|
'''
|
|
dtype = str_dtype_to_trt(args.dtype)
|
|
mapping = Mapping(world_size=args.world_size,
|
|
rank=rank,
|
|
tp_size=args.tp_size,
|
|
pp_size=args.pp_size)
|
|
|
|
assert args.n_layer % args.pp_size == 0, \
|
|
f"num_layers {args.n_layer} must be a multiple of pipeline parallelism size {args.pp_size}"
|
|
|
|
profiler.print_memory_usage(f'Rank {rank} Engine build starts')
|
|
# Initialize Module
|
|
tensorrt_llm_llama = get_model_object(args,
|
|
mapping=mapping,
|
|
trt_dtype=dtype)
|
|
profiler.print_memory_usage(f'Rank {rank} model weight loaded.')
|
|
|
|
# Module -> Network
|
|
network = builder.create_network()
|
|
network.trt_network.name = engine_name
|
|
update_plugin_configs(args, network)
|
|
|
|
if args.use_paged_context_fmha:
|
|
assert args.enable_context_fmha or args.enable_context_fmha_fp32_acc, "context fmha must be enabled"
|
|
network.plugin_config.set_paged_context_fmha()
|
|
|
|
with net_guard(network):
|
|
# Prepare
|
|
network.set_named_parameters(tensorrt_llm_llama.named_parameters())
|
|
|
|
# Forward
|
|
inputs = tensorrt_llm_llama.prepare_inputs(
|
|
max_batch_size=args.max_batch_size,
|
|
max_input_len=args.max_input_len,
|
|
max_seq_len=args.max_input_len + args.max_output_len,
|
|
use_cache=True,
|
|
max_beam_width=args.max_beam_width,
|
|
max_num_tokens=args.max_num_tokens,
|
|
prompt_embedding_table_size=args.max_prompt_embedding_table_size,
|
|
gather_context_logits=args.gather_context_logits,
|
|
gather_generation_logits=args.gather_generation_logits,
|
|
lora_target_modules=args.lora_target_modules)
|
|
tensorrt_llm_llama(*inputs)
|
|
if args.enable_debug_output:
|
|
# mark intermediate nodes' outputs
|
|
for k, v in tensorrt_llm_llama.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)
|
|
|
|
return engine
|
|
|
|
|
|
def get_builder_config_namespace(args, cache):
|
|
# NOTE: int8 flag is required to be true when INT8 tensors are exposed to TRT
|
|
# TRT-LLM has INT8 I/O when act/weights are quantized without group-scaling (AWQ, GPTQ)
|
|
# OR INT8 KV cache is set to contiguous (without paged KV cache enabled).
|
|
int8_trt_flag = (args.quant_mode.has_act_or_weight_quant()
|
|
and not args.quant_mode.has_per_group_scaling()) or (
|
|
not args.paged_kv_cache
|
|
and args.quant_mode.has_int8_kv_cache())
|
|
config = argparse.Namespace(
|
|
name=MODEL_NAME,
|
|
precision=args.dtype,
|
|
timing_cache=args.timing_cache if cache is None else cache,
|
|
profiling_verbosity=args.profiling_verbosity,
|
|
tensor_parallel=args.tp_size,
|
|
pipeline_parallel=args.pp_size,
|
|
parallel_build=args.parallel_build,
|
|
num_layers=args.n_layer,
|
|
num_heads=args.n_head,
|
|
num_kv_heads=args.n_kv_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_beam_width=args.max_beam_width,
|
|
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,
|
|
opt_level=args.builder_opt,
|
|
max_prompt_embedding_table_size=args.max_prompt_embedding_table_size,
|
|
gather_context_logits=args.gather_context_logits,
|
|
gather_generation_logits=args.gather_generation_logits,
|
|
lora_target_modules=args.lora_target_modules,
|
|
mlp_hidden_size=args.inter_size,
|
|
hf_modules_to_trtllm_modules=args.lora_config.
|
|
hf_modules_to_trtllm_modules,
|
|
trtllm_modules_to_hf_modules=args.lora_config.
|
|
trtllm_modules_to_hf_modules,
|
|
)
|
|
return config
|
|
|
|
|
|
def build(rank, args):
|
|
torch.cuda.set_device(rank % args.gpus_per_node)
|
|
logger.set_level(args.log_level)
|
|
os.makedirs(args.output_dir, exist_ok=True)
|
|
|
|
# when doing serializing build, all ranks share one engine
|
|
builder = Builder()
|
|
cache = None
|
|
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
|
|
tik = time.time()
|
|
|
|
# NOTE: int8 flag is required to be true when INT8 tensors are exposed to TRT
|
|
# TRT-LLM has INT8 I/O when act/weights are quantized without group-scaling (AWQ, GPTQ)
|
|
# OR INT8 KV cache is set to contiguous (without paged KV cache enabled).
|
|
int8_trt_flag = (args.quant_mode.has_act_or_weight_quant()
|
|
and not args.quant_mode.has_per_group_scaling()) or (
|
|
not args.paged_kv_cache
|
|
and args.quant_mode.has_int8_kv_cache())
|
|
builder_config = builder.create_builder_config(
|
|
**vars(get_builder_config_namespace(args, cache)))
|
|
engine_name = get_engine_name(MODEL_NAME, args.dtype, args.tp_size,
|
|
args.pp_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}'
|
|
|
|
local_num_kv_heads = (args.n_kv_head + args.world_size -
|
|
1) // args.world_size
|
|
kv_dtype = str_dtype_to_trt(args.dtype)
|
|
if args.quant_mode.has_int8_kv_cache():
|
|
kv_dtype = str_dtype_to_trt('int8')
|
|
elif args.quant_mode.has_fp8_kv_cache():
|
|
kv_dtype = str_dtype_to_trt('fp8')
|
|
profiler.check_gpt_mem_usage(
|
|
engine=engine,
|
|
kv_dtype=kv_dtype,
|
|
use_gpt_attention_plugin=args.use_gpt_attention_plugin,
|
|
paged_kv_cache=args.paged_kv_cache,
|
|
max_batch_size=args.max_batch_size,
|
|
max_beam_width=args.max_beam_width,
|
|
max_seq_len=args.max_input_len + args.max_output_len,
|
|
local_num_kv_heads=local_num_kv_heads,
|
|
head_size=args.n_embd / args.n_head,
|
|
num_layers=args.n_layer)
|
|
|
|
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
|
|
profiler.print_memory_usage(f'Rank {cur_rank} Engine serialized')
|
|
|
|
tok = time.time()
|
|
t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
|
|
logger.info(
|
|
f'Rank {cur_rank} Engine build time: {t} - {tok - tik} (sec)')
|
|
|
|
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()
|
|
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}')
|