TensorRT-LLMs/cpp/tests/resources/scripts/generate_expected_gpt_output.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.
# 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.

from pathlib import Path

import run


def generate_output(engine: str,
                    num_beams: int,
                    input_name: str,
                    output_name: str,
                    max_output_len: int = 8,
                    output_logits: bool = False,
                    output_cum_log_probs: bool = False,
                    output_log_probs: bool = False):
    tp_size = 1
    pp_size = 1
    model = 'gpt2'
    resources_dir = Path(__file__).parent.resolve().parent
    models_dir = resources_dir / 'models'
    tp_pp_dir = 'tp' + str(tp_size) + '-pp' + str(pp_size) + '-gpu/'
    engine_dir = models_dir / 'rt_engine' / model / engine / tp_pp_dir

    data_dir = resources_dir / 'data'
    input_file = data_dir / (input_name + '.npy')
    model_data_dir = data_dir / model
    if num_beams <= 1:
        output_dir = model_data_dir / 'sampling'
    else:
        output_dir = model_data_dir / ('beam_search_' + str(num_beams))

    output_name += '_tp' + str(tp_size) + '_pp' + str(pp_size)

    output_logits_npy = None
    if output_logits:
        output_logits_npy = str(output_dir / (output_name + '_logits' + '.npy'))

    args_list = [
        '--engine_dir',
        str(engine_dir), '--input_file',
        str(input_file), '--tokenizer_dir',
        str(models_dir / model), '--output_npy',
        str(output_dir / (output_name + '.npy')), '--output_csv',
        str(output_dir / (output_name + '.csv')), '--max_output_len',
        str(max_output_len), '--num_beams',
        str(num_beams), '--output_logits_npy',
        str(output_logits_npy), '--use_py_session'
    ]

    output_cum_log_probs_npy = None
    if output_cum_log_probs:
        output_cum_log_probs_npy = str(
            output_dir / (output_name + '_cum_log_probs' + '.npy'))
        args_list.extend(
            ['--output_cum_log_probs_npy',
             str(output_cum_log_probs_npy)])

    output_log_probs_npy = None
    if output_log_probs:
        output_log_probs_npy = str(output_dir /
                                   (output_name + '_log_probs' + '.npy'))
        args_list.extend(['--output_log_probs_npy', str(output_log_probs_npy)])

    args = run.parse_arguments(args_list)

    run.main(args)


def generate_outputs(num_beams):
    print('Generating GPT2 FP32 outputs')
    if num_beams == 1:
        generate_output(engine='fp32-default',
                        num_beams=num_beams,
                        input_name='input_tokens',
                        output_name='output_tokens_fp32')
    generate_output(engine='fp32-plugin',
                    num_beams=num_beams,
                    input_name='input_tokens',
                    output_name='output_tokens_fp32_plugin')

    print('Generating GPT2 FP16 outputs')
    if num_beams == 1:
        generate_output(engine='fp16-default',
                        num_beams=num_beams,
                        input_name='input_tokens',
                        output_name='output_tokens_fp16')
    generate_output(engine='fp16-plugin',
                    num_beams=num_beams,
                    input_name='input_tokens',
                    output_name='output_tokens_fp16_plugin')
    generate_output(engine='fp16-plugin-packed',
                    num_beams=num_beams,
                    input_name='input_tokens',
                    output_name='output_tokens_fp16_plugin_packed')
    generate_output(engine='fp16-plugin-packed-paged-gather',
                    num_beams=num_beams,
                    input_name='input_tokens',
                    output_name='output_tokens_fp16_plugin_packed_paged_gather',
                    output_logits=True,
                    output_log_probs=True,
                    output_cum_log_probs=True)
    generate_output(
        engine='fp16-plugin-packed-paged-context-fmha-for-gen',
        num_beams=num_beams,
        input_name='input_tokens',
        output_name=
        'output_tokens_fp16_plugin_packed_paged_context_fmha_for_gen',
        output_logits=False)
    generate_output(engine='fp16-plugin-packed-paged',
                    num_beams=num_beams,
                    input_name='input_tokens',
                    output_name='output_tokens_fp16_plugin_packed_paged',
                    output_logits=False,
                    output_log_probs=True,
                    output_cum_log_probs=True)
    generate_output(engine='fp16-plugin-packed-paged',
                    num_beams=num_beams,
                    input_name='input_tokens',
                    output_name='output_tokens_long_fp16_plugin_packed_paged',
                    output_logits=False,
                    max_output_len=128)
    generate_output(
        engine='fp16-plugin-packed-paged',
        num_beams=num_beams,
        input_name='input_tokens_long',
        output_name='output_tokens_long_input_fp16_plugin_packed_paged',
        output_logits=False)
    generate_output(engine='fp16-plugin-packed-paged-sq',
                    num_beams=num_beams,
                    input_name='input_tokens',
                    output_name='output_tokens_fp16_plugin_packed_paged_sq',
                    output_logits=False)


if __name__ == '__main__':
    generate_outputs(num_beams=1)
    generate_outputs(num_beams=2)