TensorRT-LLMs/cpp/tests/resources/scripts/build_gptj_engines.py

#!/usr/bin/env python3
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# 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.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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# See the License for the specific language governing permissions and
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import argparse as _arg
import os as _os
import pathlib as _pl
import platform as _pf
import sys as _sys
import typing as _tp

from build_engines_utils import init_model_spec_module, run_command, wincopy

init_model_spec_module()
import model_spec

import tensorrt_llm.bindings as _tb


def get_ckpt_without_quatization(model_dir, output_dir):
    build_args = [
        _sys.executable, "examples/models/contrib/gpt/convert_checkpoint.py"
    ] + [
        '--model_dir={}'.format(model_dir),
        '--output_dir={}'.format(output_dir),
    ]
    run_command(build_args)


def get_ckpt_with_modelopt_quant(model_dir, output_dir, model_cache):
    build_args = [_sys.executable, "examples/quantization/quantize.py"] + [
        '--model_dir={}'.format(model_dir),
        '--output_dir={}'.format(output_dir), '--qformat=fp8',
        '--kv_cache_dtype=fp8',
        f'--calib_dataset={model_cache}/datasets/cnn_dailymail'
    ]
    run_command(build_args)


def build_engine(checkpoint_dir: _pl.Path, engine_dir: _pl.Path, *args):
    build_args = ["trtllm-build"] + (
        ['--checkpoint_dir', str(checkpoint_dir)] if checkpoint_dir else []) + [
            '--output_dir',
            str(engine_dir),
            '--logits_dtype=float16',
            '--gemm_plugin=float16',
            '--max_batch_size=32',
            '--max_input_len=40',
            '--max_seq_len=60',
            '--max_beam_width=2',
            '--log_level=error',
        ] + list(args)
    run_command(build_args)


def build_engines(model_cache: _tp.Optional[str] = None, only_fp8=False):
    resources_dir = _pl.Path(__file__).parent.resolve().parent
    models_dir = resources_dir / 'models'
    model_name = 'gpt-j-6b'

    # Clone or update the model directory without lfs
    hf_dir = models_dir / model_name
    if hf_dir.exists():
        assert hf_dir.is_dir()
        run_command(["git", "pull"], cwd=hf_dir)
    else:
        if _pf.system() == "Windows":
            url_prefix = ""
        else:
            url_prefix = "file://"
        model_url = url_prefix + str(
            _pl.Path(model_cache) / model_name
        ) if model_cache else "https://huggingface.co/EleutherAI/gpt-j-6b"
        run_command([
            "git", "clone", model_url, "--single-branch", "--no-local",
            model_name
        ],
                    cwd=hf_dir.parent,
                    env={
                        **_os.environ, "GIT_LFS_SKIP_SMUDGE": "1"
                    })

    assert (hf_dir.is_dir())

    # Download the model file
    model_file_name = "pytorch_model.bin"
    if model_cache:
        if _pf.system() == "Windows":
            wincopy(source=str(
                _pl.Path(model_cache) / model_name / model_file_name),
                    dest=model_file_name,
                    isdir=False,
                    cwd=hf_dir)
        else:
            run_command([
                "rsync", "-rlptD",
                str(_pl.Path(model_cache) / model_name / model_file_name), "."
            ],
                        cwd=hf_dir)
    else:
        run_command(["git", "lfs", "pull", "--include", model_file_name],
                    cwd=hf_dir)

    assert ((hf_dir / model_file_name).is_file())

    engine_dir = models_dir / 'rt_engine' / model_name

    # TODO add Tensor and Pipeline parallelism to GPT-J
    tp_size = 1
    pp_size = 1
    cp_size = 1
    tp_pp_cp_dir = f"tp{tp_size}-pp{pp_size}-cp{cp_size}-gpu"
    input_file = 'input_tokens.npy'

    if only_fp8:
        # with ifb, new plugin
        print(
            "\nBuilding fp8-plugin engine using gpt_attention_plugin with inflight-batching, packed"
        )
        # TODO: use dummy scales atm; to re-enable when data is uploaded to the model cache
        # quantized_fp8_model_arg = '--quantized_fp8_model_path=' + \
        #     str(_pl.Path(model_cache) / 'fp8-quantized-modelopt' / 'gptj_tp1_rank0.npz')
        fp8_ckpt_path = engine_dir / 'fp8' / tp_pp_cp_dir
        get_ckpt_with_modelopt_quant(hf_dir, fp8_ckpt_path, model_cache)
        model_spec_obj = model_spec.ModelSpec(input_file, _tb.DataType.FP8)
        model_spec_obj.use_gpt_plugin()
        model_spec_obj.set_kv_cache_type(_tb.KVCacheType.PAGED)
        model_spec_obj.use_packed_input()
        build_engine(
            fp8_ckpt_path,
            engine_dir / model_spec_obj.get_model_path() / tp_pp_cp_dir,
            '--gpt_attention_plugin=float16',
            '--paged_kv_cache=enable',
            '--remove_input_padding=enable',
            '--use_paged_context_fmha=enable',
        )
    else:
        fp16_ckpt_path = engine_dir / 'fp16' / tp_pp_cp_dir
        get_ckpt_without_quatization(hf_dir, fp16_ckpt_path)
        print("\nBuilding fp16-plugin engine")
        model_spec_obj = model_spec.ModelSpec(input_file, _tb.DataType.HALF)
        model_spec_obj.use_gpt_plugin()
        model_spec_obj.set_kv_cache_type(_tb.KVCacheType.CONTINUOUS)

        build_engine(
            fp16_ckpt_path,
            engine_dir / model_spec_obj.get_model_path() / tp_pp_cp_dir,
            '--gpt_attention_plugin=float16', '--paged_kv_cache=disable',
            '--remove_input_padding=disable', "--context_fmha=disable")

        print("\nBuilding fp16-plugin-packed engine")
        model_spec_obj.use_packed_input()
        build_engine(
            fp16_ckpt_path,
            engine_dir / model_spec_obj.get_model_path() / tp_pp_cp_dir,
            '--gpt_attention_plugin=float16', '--paged_kv_cache=disable',
            '--remove_input_padding=enable', "--context_fmha=disable")

        print("\nBuilding fp16-plugin-packed-paged engine")
        model_spec_obj.set_kv_cache_type(_tb.KVCacheType.PAGED)
        build_engine(
            fp16_ckpt_path,
            engine_dir / model_spec_obj.get_model_path() / tp_pp_cp_dir,
            '--gpt_attention_plugin=float16', '--paged_kv_cache=enable',
            '--remove_input_padding=enable', "--context_fmha=disable")
        print("Done.")


if __name__ == "__main__":
    parser = _arg.ArgumentParser()
    parser.add_argument("--model_cache",
                        type=str,
                        help="Directory where models are stored")
    parser.add_argument(
        "--only_fp8",
        action="store_true",
        help="Build engines for only FP8 tests. Implemented for H100 runners.")

    build_engines(**vars(parser.parse_args()))