mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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138 lines
6.3 KiB
Python
138 lines
6.3 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.conftest import skip_post_blackwell, skip_pre_ada
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from defs.trt_test_alternative import check_call
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@pytest.mark.parametrize("use_dynamic_tree", [False, True],
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ids=['eagle1', 'eagle2'])
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@pytest.mark.parametrize("batch_size", [1, 8], ids=['bs1', 'bs8'])
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@pytest.mark.parametrize("data_type", ['float16'])
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@pytest.mark.parametrize("eagle_model_roots", ["EAGLE-Vicuna-7B-v1.3"],
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indirect=True)
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def test_llm_eagle_1gpu(batch_size, data_type, use_dynamic_tree,
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eagle_model_roots, eagle_example_root,
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llm_datasets_root, llm_rouge_root, llm_venv, cmodel_dir,
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engine_dir):
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print("Build engines...")
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model_name = "eagle"
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model_dir = convert_weights(llm_venv=llm_venv,
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example_root=eagle_example_root,
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cmodel_dir=cmodel_dir,
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model=model_name,
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model_path=eagle_model_roots,
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data_type=data_type)
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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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"--use_paged_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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"--paged_kv_cache=enable",
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'--speculative_decoding_mode=eagle',
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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 run...")
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run_cmd = [
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f"{eagle_example_root}/../run.py",
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"--max_output_len=100",
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f"--tokenizer_dir={eagle_model_roots[0]}",
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"--log_level=verbose",
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f"--engine_dir={engine_dir}",
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]
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if use_dynamic_tree:
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run_cmd.extend(
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[f"--eagle_dynamic_tree_max_top_k={3}", "--eagle_use_dynamic_tree"])
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venv_check_call(llm_venv, run_cmd)
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print("Run summarize...")
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summary_cmd = [
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f"{eagle_example_root}/../summarize.py", "--test_trt_llm",
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"--hf_model_dir", f"{eagle_model_roots[0]}", "--tokenizer_dir",
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f"{eagle_model_roots[0]}", f"--engine_dir={engine_dir}",
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"--check_accuracy", "--tensorrt_llm_rouge1_threshold=24",
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"--eagle_choices=[[0], [0, 0], [1], [0, 1], [2], [0, 0, 0], [1, 0], [0, 2], [3], [0, 3], [4], [0, 4], [2, 0], [0, 5], [0, 0, 1], [5], [0, 6], [6], [0, 7], [0, 1, 0], [1, 1], [7], [0, 8], [0, 0, 2], [3, 0], [0, 9], [8], [9], [1, 0, 0], [0, 2, 0], [1, 2], [0, 0, 3], [4, 0], [2, 1], [0, 0, 4], [0, 0, 5], [0, 0, 0, 0], [0, 1, 1], [0, 0, 6], [0, 3, 0], [5, 0], [1, 3], [0, 0, 7], [0, 0, 8], [0, 0, 9], [6, 0], [0, 4, 0], [1, 4], [7, 0], [0, 1, 2], [2, 0, 0], [3, 1], [2, 2], [8, 0], [0, 5, 0], [1, 5], [1, 0, 1], [0, 2, 1], [9, 0], [0, 6, 0], [0, 0, 0, 1], [1, 6], [0, 7, 0]]",
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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_dynamic_tree:
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summary_cmd.extend(
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[f"--eagle_dynamic_tree_max_top_k={3}", "--eagle_use_dynamic_tree"])
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venv_check_call(llm_venv, summary_cmd)
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# TODO: remove skip_post_blackwell after Speculative decoding is supported.
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@skip_post_blackwell
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@skip_pre_ada
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@pytest.mark.parametrize("batch_size", [8], ids=['bs8'])
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@pytest.mark.parametrize("data_type", ['float16'])
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@pytest.mark.parametrize("eagle_model_roots", ["llama3.1-eagle-8b-hf_v0.5"],
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indirect=True)
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def test_llm_eagle_1gpu(batch_size, data_type, eagle_model_roots,
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eagle_example_root, llm_datasets_root, llm_rouge_root,
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llm_venv, cmodel_dir, engine_dir):
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print("Build engines...")
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model_name = "eagle"
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# Although the datatype is float16, the actual weights are FP8.
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# The datatype in the convert stage is used for the input and output of the plugin.
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model_dir = convert_weights(llm_venv=llm_venv,
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example_root=eagle_example_root,
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cmodel_dir=cmodel_dir,
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model=model_name,
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model_path=eagle_model_roots,
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data_type=data_type)
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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"--max_beam_width=1",
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"--use_paged_context_fmha=enable",
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f"--max_batch_size={batch_size}",
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"--speculative_decoding_mode=eagle",
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"--multiple_profiles=enable" # also test multiple_profiles
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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 run...")
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run_cmd = [
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f"{eagle_example_root}/../run.py", f"--engine_dir={engine_dir}",
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f"--tokenizer_dir={eagle_model_roots}",
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"--eagle_choices=[[0], [0, 0], [1], [0, 1], [2], [0, 0, 0], [1, 0], [0, 2], [3], [0, 3], [4], [0, 4], [2, 0], [0, 5], [0, 0, 1], [5], [0, 6], [6], [0, 7], [0, 1, 0], [1, 1], [7], [0, 8], [0, 0, 2], [3, 0], [0, 9], [8], [9], [1, 0, 0], [0, 2, 0], [1, 2], [0, 0, 3], [4, 0], [2, 1], [0, 0, 4], [0, 0, 5], [0, 0, 0, 0], [0, 1, 1], [0, 0, 6], [0, 3, 0], [5, 0], [1, 3], [0, 0, 7], [0, 0, 8], [0, 0, 9], [6, 0], [0, 4, 0], [1, 4], [7, 0], [0, 1, 2], [2, 0, 0], [3, 1], [2, 2], [8, 0], [0, 5, 0], [1, 5], [1, 0, 1], [0, 2, 1], [9, 0], [0, 6, 0], [0, 0, 0, 1], [1, 6], [0, 7, 0]]",
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"--max_output_len=100"
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]
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venv_check_call(llm_venv, run_cmd)
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