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* test:remove opt/mpt/gptj/gptneox/bloom/falcon/baichuan/internlm/deep_seek_v2 test cases. Signed-off-by: nv-guomingz <37257613+nv-guomingz@users.noreply.github.com> * updatet test case per review comments Signed-off-by: nv-guomingz <37257613+nv-guomingz@users.noreply.github.com> --------- Signed-off-by: nv-guomingz <37257613+nv-guomingz@users.noreply.github.com> Co-authored-by: nv-guomingz <37257613+nv-guomingz@users.noreply.github.com>
69 lines
3.0 KiB
Python
69 lines
3.0 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 venv_check_call
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from defs.conftest import get_gpu_device_list
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from defs.trt_test_alternative import check_call
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INPUT_TEXT = """
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Write a Python function `find_max(words)` to solve the following problem:\nWrite a function that accepts a list of strings.\nThe list contains different words. Return the word with maximum number\nof unique characters. If multiple strings have maximum number of unique\ncharacters, return the one which comes first in lexicographical order.\nfind_max(["name", "of", "string"]) == "string"\nfind_max(["name", "enam", "game"]) == "enam"\nfind_max(["aaaaaaa", "bb" ,"cc"]) == ""aaaaaaa"
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"""
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def test_llm_gptj_fp8_manage_weights_summary(gptj_example_root,
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llm_gptj_model_root,
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llm_datasets_root, llm_rouge_root,
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llm_venv, engine_dir):
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gpus = ["L40S", "H20", "H100"]
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if all(x not in get_gpu_device_list()[0] for x in gpus):
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pytest.skip("FP8 cannot be enabled on Pre-Ada Arch.")
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print("Quantizing model...")
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qcache_dir = "/tmp/cache"
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ckpt_dir = f"{qcache_dir}/quantized_model_cache"
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quantize_cmd = [
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f"{gptj_example_root}/../quantization/quantize.py",
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f"--model_dir={llm_gptj_model_root}",
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f"--calib_dataset={llm_datasets_root}/cnn_dailymail",
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f"--output_dir={ckpt_dir}",
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"--dtype=float16",
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"--calib_size=16",
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"--qformat=fp8",
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]
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venv_check_call(llm_venv, quantize_cmd)
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print("Building engines...")
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build_cmd = [
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"trtllm-build", f"--checkpoint_dir={ckpt_dir}",
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f"--output_dir={engine_dir}", "--max_batch_size=4",
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"--max_input_len=1024", "--max_seq_len=1152", "--max_beam_width=5",
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"--fast_build"
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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 summary...")
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summary_cmd = [
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f"{gptj_example_root}/../summarize.py", "--engine_dir", engine_dir,
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"--hf_model_dir", llm_gptj_model_root, "--batch_size", "1",
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"--test_trt_llm", "--tensorrt_llm_rouge1_threshold", "14",
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"--data_type", "fp16", "--check_accuracy",
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f"--dataset_dir={llm_datasets_root}", f"--rouge_dir={llm_rouge_root}"
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]
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venv_check_call(llm_venv, summary_cmd)
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