build.py

# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import time

import onnx
import tensorrt as trt
import torch
import torch.multiprocessing as mp
from onnx import TensorProto, helper
from transformers import AutoConfig, AutoModelForCausalLM

import tensorrt_llm
from tensorrt_llm._utils import str_dtype_to_trt
from tensorrt_llm.builder import Builder
from tensorrt_llm.layers.attention import PositionEmbeddingType
from tensorrt_llm.logger import logger
from tensorrt_llm.mapping import Mapping
from tensorrt_llm.models import BaichuanForCausalLM, weight_only_quantize
from tensorrt_llm.network import net_guard
from tensorrt_llm.plugin.plugin import ContextFMHAType
from tensorrt_llm.quantization import QuantMode

from weight import load_from_hf_baichuan  # isort:skip

# 2 routines: get_engine_name, serialize_engine
# are direct copy from gpt example, TODO: put in utils?


def trt_dtype_to_onnx(dtype):
    if dtype == trt.float16:
        return TensorProto.DataType.FLOAT16
    elif dtype == trt.float32:
        return TensorProto.DataType.FLOAT
    elif dtype == trt.int32:
        return TensorProto.DataType.INT32
    else:
        raise TypeError("%s is not supported" % dtype)


def to_onnx(network, path):
    inputs = []
    for i in range(network.num_inputs):
        network_input = network.get_input(i)
        inputs.append(
            helper.make_tensor_value_info(
                network_input.name, trt_dtype_to_onnx(network_input.dtype),
                list(network_input.shape)))

    outputs = []
    for i in range(network.num_outputs):
        network_output = network.get_output(i)
        outputs.append(
            helper.make_tensor_value_info(
                network_output.name, trt_dtype_to_onnx(network_output.dtype),
                list(network_output.shape)))

    nodes = []
    for i in range(network.num_layers):
        layer = network.get_layer(i)
        layer_inputs = []
        for j in range(layer.num_inputs):
            ipt = layer.get_input(j)
            if ipt is not None:
                layer_inputs.append(layer.get_input(j).name)
        layer_outputs = [
            layer.get_output(j).name for j in range(layer.num_outputs)
        ]
        nodes.append(
            helper.make_node(str(layer.type),
                             name=layer.name,
                             inputs=layer_inputs,
                             outputs=layer_outputs,
                             domain="com.nvidia"))

    onnx_model = helper.make_model(helper.make_graph(nodes,
                                                     'attention',
                                                     inputs,
                                                     outputs,
                                                     initializer=None),
                                   producer_name='NVIDIA')
    onnx.save(onnx_model, path)


def get_engine_name(model, dtype, tp_size, rank):
    return '{}_{}_tp{}_rank{}.engine'.format(model, dtype, tp_size, rank)


def serialize_engine(engine, path):
    logger.info(f'Serializing engine to {path}...')
    tik = time.time()
    with open(path, 'wb') as f:
        f.write(bytearray(engine))
    tok = time.time()
    t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
    logger.info(f'Engine serialized. Total time: {t}')


def parse_arguments():
    parser = argparse.ArgumentParser()
    parser.add_argument('--world_size',
                        type=int,
                        default=1,
                        help='world size, only support tensor parallelism now')
    parser.add_argument('--model_dir',
                        type=str,
                        default='baichuan-inc/Baichuan-13B-Chat')
    parser.add_argument('--model_version',
                        type=str,
                        default='v1_13b',
                        choices=['v1_7b', 'v1_13b', 'v2_7b', 'v2_13b'])
    parser.add_argument('--dtype',
                        type=str,
                        default='float16',
                        choices=['float32', 'bfloat16', 'float16'])
    parser.add_argument(
        '--timing_cache',
        type=str,
        default='model.cache',
        help=
        'The path of to read timing cache from, will be ignored if the file does not exist'
    )
    parser.add_argument('--log_level', type=str, default='info')
    parser.add_argument('--vocab_size', type=int, default=64000)
    parser.add_argument('--n_layer', type=int, default=40)
    parser.add_argument('--n_positions', type=int, default=4096)
    parser.add_argument('--n_embd', type=int, default=5120)
    parser.add_argument('--n_head', type=int, default=40)
    parser.add_argument('--inter_size', type=int, default=13696)
    parser.add_argument('--hidden_act', type=str, default='silu')
    parser.add_argument('--max_batch_size', type=int, default=1)
    parser.add_argument('--max_input_len', type=int, default=1024)
    parser.add_argument('--max_output_len', type=int, default=1024)
    parser.add_argument('--max_beam_width', type=int, default=1)
    parser.add_argument('--use_gpt_attention_plugin',
                        nargs='?',
                        const='float16',
                        type=str,
                        default=False,
                        choices=['float16', 'bfloat16', 'float32'])
    parser.add_argument('--use_gemm_plugin',
                        nargs='?',
                        const='float16',
                        type=str,
                        default=False,
                        choices=['float16', 'bfloat16', 'float32'])
    parser.add_argument('--enable_context_fmha',
                        default=False,
                        action='store_true')
    parser.add_argument('--enable_context_fmha_fp32_acc',
                        default=False,
                        action='store_true')
    parser.add_argument('--parallel_build', default=False, action='store_true')
    parser.add_argument('--visualize', default=False, action='store_true')
    parser.add_argument('--enable_debug_output',
                        default=False,
                        action='store_true')
    parser.add_argument('--gpus_per_node', type=int, default=8)
    parser.add_argument(
        '--output_dir',
        type=str,
        default='baichuan_outputs',
        help=
        'The path to save the serialized engine files, timing cache file and model configs'
    )
    parser.add_argument('--remove_input_padding',
                        default=False,
                        action='store_true')

    parser.add_argument(
        '--use_weight_only',
        default=False,
        action="store_true",
        help='Quantize weights for the various GEMMs to INT4/INT8.'
        'See --weight_only_precision to set the precision')

    parser.add_argument(
        '--weight_only_precision',
        const='int8',
        type=str,
        nargs='?',
        default='int8',
        choices=['int8', 'int4'],
        help=
        'Define the precision for the weights when using weight-only quantization.'
        'You must also use --use_weight_only for that argument to have an impact.'
    )
    parser.add_argument(
        '--use_inflight_batching',
        action="store_true",
        default=False,
        help="Activates inflight batching mode of gptAttentionPlugin.")
    parser.add_argument(
        '--paged_kv_cache',
        action="store_true",
        default=False,
        help=
        'By default we use contiguous KV cache. By setting this flag you enable paged KV cache'
    )
    parser.add_argument('--tokens_per_block',
                        type=int,
                        default=64,
                        help='Number of tokens per block in paged KV cache')
    parser.add_argument(
        '--max_num_tokens',
        type=int,
        default=None,
        help='Define the max number of tokens supported by the engine')

    args = parser.parse_args()

    if args.use_weight_only:
        args.quant_mode = QuantMode.use_weight_only(
            args.weight_only_precision == 'int4')
    else:
        args.quant_mode = QuantMode(0)

    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.max_num_tokens is not None:
        assert args.enable_context_fmha

    if args.model_dir is not None:
        hf_config = AutoConfig.from_pretrained(args.model_dir,
                                               trust_remote_code=True)
        # override the inter_size for Baichuan
        args.inter_size = hf_config.intermediate_size
        args.n_embd = hf_config.hidden_size
        args.n_head = hf_config.num_attention_heads
        args.n_layer = hf_config.num_hidden_layers
        if args.model_version == 'v1_7b' or args.model_version == 'v2_7b':
            args.n_positions = hf_config.max_position_embeddings
        else:
            args.n_positions = hf_config.model_max_length
        args.vocab_size = hf_config.vocab_size
        args.hidden_act = hf_config.hidden_act
    else:
        # default values are based on v1_13b, change them based on model_version
        if args.model_version == 'v1_7b':
            args.inter_size = 11008
            args.n_embd = 4096
            args.n_head = 32
            args.n_layer = 32
            args.n_positions = 4096
            args.vocab_size = 64000
            args.hidden_act = 'silu'
        elif args.model_version == 'v2_7b':
            args.inter_size = 11008
            args.n_embd = 4096
            args.n_head = 32
            args.n_layer = 32
            args.n_positions = 4096
            args.vocab_size = 125696
            args.hidden_act = 'silu'
        elif args.model_version == 'v2_13b':
            args.inter_size = 13696
            args.n_embd = 5120
            args.n_head = 40
            args.n_layer = 40
            args.n_positions = 4096
            args.vocab_size = 125696
            args.hidden_act = 'silu'

    if args.dtype == 'bfloat16':
        assert args.use_gemm_plugin, "Please use gemm plugin when dtype is bfloat16"

    return args


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.
    '''
    kv_dtype = str_dtype_to_trt(args.dtype)
    if args.model_version == 'v1_7b' or args.model_version == 'v2_7b':
        position_embedding_type = PositionEmbeddingType.rope_gpt_neox
    else:
        position_embedding_type = PositionEmbeddingType.alibi

    # Initialize Module
    tensorrt_llm_baichuan = BaichuanForCausalLM(
        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,
        position_embedding_type=position_embedding_type,
        dtype=kv_dtype,
        mlp_hidden_size=args.inter_size,
        mapping=Mapping(world_size=args.world_size,
                        rank=rank,
                        tp_size=args.world_size))
    if args.use_weight_only and args.weight_only_precision == 'int8':
        tensorrt_llm_baichuan = weight_only_quantize(
            tensorrt_llm_baichuan, QuantMode.use_weight_only())
    elif args.use_weight_only and args.weight_only_precision == 'int4':
        tensorrt_llm_baichuan = weight_only_quantize(
            tensorrt_llm_baichuan,
            QuantMode.use_weight_only(use_int4_weights=True))
    if args.model_dir is not None:
        logger.info(
            f'Loading HF Baichuan {args.model_version} ... from {args.model_dir}'
        )
        tik = time.time()
        hf_baichuan = AutoModelForCausalLM.from_pretrained(
            args.model_dir,
            device_map={
                "model": "cpu",
                "lm_head": "cpu"
            },  # Load to CPU memory
            torch_dtype="auto",
            trust_remote_code=True)
        tok = time.time()
        t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
        logger.info(f'HF Baichuan {args.model_version} loaded. Total time: {t}')
        load_from_hf_baichuan(tensorrt_llm_baichuan,
                              hf_baichuan,
                              args.model_version,
                              rank,
                              args.world_size,
                              dtype=args.dtype)
        del hf_baichuan

    # Module -> Network
    network = builder.create_network()
    network.trt_network.name = engine_name
    if args.use_gpt_attention_plugin:
        network.plugin_config.set_gpt_attention_plugin(
            dtype=args.use_gpt_attention_plugin)
    if args.use_gemm_plugin:
        network.plugin_config.set_gemm_plugin(dtype=args.use_gemm_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.use_weight_only:
        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 = kv_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 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
        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=args.quant_mode.has_act_and_weight_quant())
        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))

    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}')