mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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5437075def
Signed-off-by: darraghdog <darragh.hanley@gmail.com> Signed-off-by: Darragh Hanley <darragh.hanley@gmail.com> Co-authored-by: rakib-hasan <rhasan@nvidia.com>
92 lines
3.8 KiB
Python
92 lines
3.8 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 pytest
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from defs.common import convert_weights, venv_check_call
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from defs.trt_test_alternative import check_call
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@pytest.mark.parametrize("batch_size", [8], ids=['bs8'])
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@pytest.mark.parametrize("redrafter_num_beams", [5, 8], ids=['nb5', 'nb8'])
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@pytest.mark.parametrize("redrafter_draft_len_per_beam", [5], ids=['dl5'])
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@pytest.mark.parametrize("data_type", ['bfloat16'])
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@pytest.mark.parametrize("redrafter_model_roots", ["redrafter-vicuna-7b-v1.3"],
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indirect=True)
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@pytest.mark.parametrize("use_py_session", [False, True],
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ids=["use_cpp_session", "use_py_session"])
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def test_llm_redrafter_1gpu(batch_size, data_type, redrafter_model_roots,
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redrafter_num_beams, redrafter_draft_len_per_beam,
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redrafter_example_root, llama_example_root,
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llm_datasets_root, llm_rouge_root, llm_venv,
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cmodel_dir, cmodel_base_dir, engine_dir,
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use_py_session):
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print("Build engines...")
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model_name = "redrafter"
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base_model_name = "llama"
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base_example_root = llama_example_root
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base_model_dir = convert_weights(llm_venv=llm_venv,
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example_root=base_example_root,
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cmodel_dir=cmodel_base_dir,
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model=base_model_name,
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model_path=redrafter_model_roots[0],
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data_type=data_type)
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redrafter_convert_roots = (base_model_dir, redrafter_model_roots[1])
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model_dir = convert_weights(
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llm_venv=llm_venv,
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example_root=redrafter_example_root,
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cmodel_dir=cmodel_dir,
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model=model_name,
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model_path=redrafter_convert_roots,
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data_type=data_type,
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redrafter_num_beams=redrafter_num_beams,
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redrafter_draft_len_per_beam=redrafter_draft_len_per_beam)
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build_cmd = [
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"trtllm-build",
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f"--checkpoint_dir={model_dir}",
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f"--output_dir={engine_dir}",
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f"--gpt_attention_plugin={data_type}",
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f"--gemm_plugin={data_type}",
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f"--max_beam_width=1",
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"--remove_input_padding=enable",
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"--context_fmha=enable",
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"--max_input_len=1024",
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"--max_seq_len=1536",
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f"--max_batch_size={batch_size}",
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"--kv_cache_type=paged",
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'--speculative_decoding_mode=explicit_draft_tokens',
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]
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check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
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print("Run summarize...")
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summary_cmd = [
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f"{redrafter_example_root}/../summarize.py", "--test_trt_llm",
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"--hf_model_dir", f"{redrafter_model_roots[0]}", "--tokenizer_dir",
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f"{redrafter_model_roots[0]}", f"--engine_dir={engine_dir}",
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"--check_accuracy", "--tensorrt_llm_rouge1_threshold=24",
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f"--temperature=1.0", f"--max_ite=40", f"--batch_size={batch_size}",
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f"--dataset_dir={llm_datasets_root}", f"--rouge_dir={llm_rouge_root}"
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]
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if use_py_session:
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summary_cmd.append("--use_py_session")
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venv_check_call(llm_venv, summary_cmd)
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