mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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2d234357c6
* Update TensorRT-LLM --------- Co-authored-by: Altair-Alpha <62340011+Altair-Alpha@users.noreply.github.com>
188 lines
7.1 KiB
Python
Executable File
188 lines
7.1 KiB
Python
Executable File
#!/usr/bin/env python3
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# 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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from pathlib import Path
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import run
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from build_engines_utils import init_model_spec_module
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init_model_spec_module()
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import os
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import model_spec
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import tensorrt_llm.bindings as _tb
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def generate_output(engine: str,
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num_beams: int,
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input_name: str,
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model_spec_obj: model_spec.ModelSpec,
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max_output_len: int = 8,
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output_logits: bool = False,
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output_cum_log_probs: bool = False,
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output_log_probs: bool = False):
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tp_size = 1
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pp_size = 1
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model = 'gpt2'
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resources_dir = Path(__file__).parent.resolve().parent
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models_dir = resources_dir / 'models'
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tp_pp_dir = 'tp' + str(tp_size) + '-pp' + str(pp_size) + '-gpu/'
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engine_dir = models_dir / 'rt_engine' / model / engine / tp_pp_dir
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data_dir = resources_dir / 'data'
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input_file = data_dir / input_name
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model_data_dir = data_dir / model
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if num_beams <= 1:
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output_dir = model_data_dir / 'sampling'
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else:
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output_dir = model_data_dir / ('beam_search_' + str(num_beams))
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model_spec_obj.use_tensor_parallelism(tp_size).use_pipeline_parallelism(
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pp_size)
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base_output_name = os.path.splitext(model_spec_obj.get_results_file())[0]
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output_logits_npy = None
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if output_logits:
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logits_file = base_output_name + '_logits.npy'
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output_logits_npy = str(output_dir / logits_file)
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results_file = str(output_dir / (base_output_name + '.npy'))
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results_csv = str(output_dir / (base_output_name + '.csv'))
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args_list = [
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'--engine_dir',
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str(engine_dir), '--input_file',
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str(input_file), '--tokenizer_dir',
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str(models_dir / model), '--output_npy', results_file, '--output_csv',
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results_csv, '--max_output_len',
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str(max_output_len), '--num_beams',
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str(num_beams), '--output_logits_npy',
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str(output_logits_npy), '--use_py_session'
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]
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assert not os.path.exists(results_file) and not os.path.exists(results_csv)
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if output_cum_log_probs:
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args_list.extend([
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'--output_cum_log_probs_npy',
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f'{output_dir / model_spec_obj.get_cum_log_probs_file()}'
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])
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if output_log_probs:
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args_list.extend([
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'--output_log_probs_npy',
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f'{output_dir / model_spec_obj.get_log_probs_file()}'
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])
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args = run.parse_arguments(args_list)
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print(args_list)
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run.main(args)
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def generate_outputs(num_beams):
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print('Generating GPT2 FP32 outputs')
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input_name = 'input_tokens.npy'
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input_name_long = 'input_tokens_long.npy'
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model_spec_obj = model_spec.ModelSpec(input_name, _tb.DataType.FLOAT)
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model_spec_obj.set_kv_cache_type(model_spec.KVCacheType.CONTINUOUS)
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if num_beams == 1:
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generate_output(engine=model_spec_obj.get_model_path(),
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num_beams=num_beams,
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input_name=input_name,
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model_spec_obj=model_spec_obj)
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model_spec_obj.use_gpt_plugin()
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generate_output(engine=model_spec_obj.get_model_path(),
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num_beams=num_beams,
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input_name=input_name,
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model_spec_obj=model_spec_obj)
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print('Generating GPT2 FP16 outputs')
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model_spec_obj = model_spec.ModelSpec(input_name, _tb.DataType.HALF)
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model_spec_obj.set_kv_cache_type(model_spec.KVCacheType.CONTINUOUS)
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if num_beams == 1:
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generate_output(engine=model_spec_obj.get_model_path(),
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num_beams=num_beams,
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input_name=input_name,
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model_spec_obj=model_spec_obj)
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model_spec_obj.use_gpt_plugin()
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generate_output(engine=model_spec_obj.get_model_path(),
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num_beams=num_beams,
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input_name=input_name,
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model_spec_obj=model_spec_obj)
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model_spec_obj.use_packed_input()
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generate_output(engine=model_spec_obj.get_model_path(),
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num_beams=num_beams,
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input_name=input_name,
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model_spec_obj=model_spec_obj)
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model_spec_obj.set_kv_cache_type(model_spec.KVCacheType.PAGED)
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model_spec_obj.gather_logits()
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generate_output(engine=model_spec_obj.get_model_path(),
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num_beams=num_beams,
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input_name=input_name,
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model_spec_obj=model_spec_obj,
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output_logits=True,
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output_log_probs=True,
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output_cum_log_probs=True)
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model_spec_obj = model_spec.ModelSpec(input_name, _tb.DataType.HALF)
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model_spec_obj.use_gpt_plugin()
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model_spec_obj.set_kv_cache_type(model_spec.KVCacheType.PAGED)
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model_spec_obj.use_packed_input()
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generate_output(engine=model_spec_obj.get_model_path(),
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num_beams=num_beams,
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input_name=input_name,
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model_spec_obj=model_spec_obj,
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output_logits=False,
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output_log_probs=True,
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output_cum_log_probs=True)
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model_spec_obj.set_max_output_length(128)
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generate_output(engine=model_spec_obj.get_model_path(),
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num_beams=num_beams,
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input_name=input_name,
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model_spec_obj=model_spec_obj,
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output_logits=False,
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max_output_len=128)
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model_spec_obj = model_spec.ModelSpec(input_name_long, _tb.DataType.HALF)
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model_spec_obj.use_gpt_plugin()
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model_spec_obj.use_packed_input()
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model_spec_obj.set_kv_cache_type(model_spec.KVCacheType.PAGED)
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generate_output(engine=model_spec_obj.get_model_path(),
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num_beams=num_beams,
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input_name=input_name_long,
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model_spec_obj=model_spec_obj,
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output_logits=False)
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model_spec_obj = model_spec.ModelSpec(input_name, _tb.DataType.HALF)
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model_spec_obj.use_gpt_plugin()
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model_spec_obj.use_packed_input()
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model_spec_obj.set_kv_cache_type(model_spec.KVCacheType.PAGED)
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model_spec_obj.set_quant_method(model_spec.QuantMethod.SMOOTH_QUANT)
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generate_output(engine=model_spec_obj.get_model_path(),
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num_beams=num_beams,
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input_name=input_name,
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model_spec_obj=model_spec_obj,
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output_logits=False)
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if __name__ == '__main__':
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generate_outputs(num_beams=1)
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generate_outputs(num_beams=2)
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