mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
8709fe8b53
test: add test cases for 0.19 release (#3608) * fix test name * add quickstart test for nemotron-ultra * add rcca multi-node test case for deepseek-v3 * add rcca info --------- squash (#3642) fix: nvbugs/5187237: fix deterministic mode crash (#3448) * nvbugs/5187237 nvbugs/5112075: fix deterministic mode error * remove waive * Revert "remove waive" This reverts commit 0bf5486d19906d692bfb7a6262333c296b0087ac. * revert ar fusion --------- update fp8 doc (#3647) tests: change qa perf test to trtllm-bench (#3619) fix: FP8 quantized lm_head (NvBug 5214229) (#3567) infra: Add PR approval protection for the release branch (#3634) fix: nvbugs/5231298: pytorch allreduce issue (#3673) Fix: nvbugs/5222698 variable not defined (#3630) * Fix: nvbugs/5222698 variable not defined * Tidy code --------- test:sync waives.txt from main branch by disabling test_perf/gpt_350m-cppmanager case (#3685) test:restore fp8 kv cache testing for L0 (#3671) doc: Update DeepSeek perf docs (#3693) * Update DeepSeek perf docs * update * Apply suggestions from code review --------- tests: waive test_llm_multi_node (#3664) fix: update test_user_buffers_mm_add_prologue atol (#3711) Fix: cherry-pick hmac encryption from main branch (#3635) * security fix cherry-pick changes from main * fix hmac in remote mpi session (#3649) --------- Un-waive DS-V3-Lite tests. (#3621) fix: FP8 kv accuracy (#3675) * fix FP8 kv accuracy * update doc --------- Fix script options for engines. (#3622) unwaive multi-node test (#3721) chore : Split more tests out of gpt tests (#3524) (#3674) doc:add torch examples link into torch backend documentation (#3749) test: Get Eagle tests working (#3593) (#3722) Waive L0 test (#3756) waive failed case in perf test, change default max_batch_size to 512 and write config.json to output log (#3656) Update ds v3 parameters in stress test. (#3676) waive gemma on L20 (#3766) https://nvbugs/5141291: Fix convert.py script for Qwen model. (#3758) Include Qwen2VLDecoderLayer in the smooth_qwen2_model function. fix: PP4 fixes and cleanup (#3688) remove benchmark test list (#3643) skip disagg deepseek test if sm!=90 (#3720) test: skip failed cases on B200 (#3710) * add skip condition to tests * fix error --------- test: [nvbug: 5234494] skip_pre_ada for fp8 cases (#3718) * skip_pre_ada for fp8 cases * update * update after rebase --------- add know issue to deepseek doc. (#3800) Fix ModelOpt Mixtral AWQ OOM (#3714) (#3761) Waive L0 tests (#3826) fix: Reduce memory usage in fused moe op associated with AutoTuning and fix moe fallback issue. (#3793) * Reduce memory usage in fused moe op associated with AutoTuning. * Replace pre-defined bucket size strategy with a generating function based on the tune_max_num_tokens. * Add free_memory logic of workspace in min_latency_mode fused moe path. * Fix fused_moe fallback issue. (#3652) min_latency_mode is only set to False during warmup phase. Thus when it becomes true during inference, all tactics fall back to the default one and thus cause perf regression. --------- [doc] Better document for Draft-Target-Model (DTM) speculative decoding (#3797) Fix pre-commit Fix again Address some review comments for the MI Signed-off-by: Dom Brown <3886319+DomBrown@users.noreply.github.com> Co-authored-by: Zhanrui Sun <184402041+ZhanruiSunCh@users.noreply.github.com>
649 lines
25 KiB
Python
649 lines
25 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 csv
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import os
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import uuid
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import pytest
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from defs.common import (convert_weights, quantize_data,
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test_multi_lora_support, venv_check_call,
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venv_mpi_check_call)
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from defs.conftest import (LLM_GATE_WAY_CLIENT_ID, LLM_GATE_WAY_TOKEN,
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evaltool_mmlu_post_process,
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evaltool_mtbench_post_process,
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evaltool_wikilingua_post_process, get_device_memory,
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get_sm_version, skip_fp8_pre_ada,
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skip_post_blackwell, skip_pre_ada)
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from defs.trt_test_alternative import check_call
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from evaltool.constants import (EVALTOOL_INFERENCE_SERVER_STARTUP_SCRIPT,
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EVALTOOL_INFERENCE_SERVER_STOP_SCRIPT,
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EVALTOOL_MMLU_CONFIG, EVALTOOL_MMLU_RESULT_FILE,
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EVALTOOL_MTBENCH_CONFIG,
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EVALTOOL_MTBENCH_RESULT_FILE,
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EVALTOOL_WIKILINGUA_CONFIG,
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EVALTOOL_WIKILINGUA_RESULT_FILE)
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@pytest.fixture(scope="module")
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def phi_example_root(llm_root, llm_venv):
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"Get phi example root"
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example_root = os.path.join(llm_root, "examples", "models", "core", "phi")
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llm_venv.run_cmd([
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"-m", "pip", "install", "-r",
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os.path.join(example_root, "requirements.txt")
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])
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return example_root
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@pytest.mark.skip_less_device_memory(40000)
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@pytest.mark.parametrize("num_beams", [1, 2, 4],
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ids=lambda num_beams: f'nb:{num_beams}')
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@pytest.mark.parametrize(
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"context_fmha_type",
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["enable_fmha", "enable_fmha_with_fp32_acc", "disable_fmha"])
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@pytest.mark.parametrize(
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"use_attention_plugin", [True, False],
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ids=["enable_attention_plugin", "disable_attention_plugin"])
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@pytest.mark.parametrize("use_gemm_plugin", [True, False],
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ids=["enable_gemm_plugin", "disable_gemm_plugin"])
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@pytest.mark.parametrize("dtype", ["float16", "bfloat16"])
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@pytest.mark.parametrize("llm_phi_model_root", [
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"phi-2", "Phi-3-mini-4k-instruct", "Phi-3-mini-128k-instruct",
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"Phi-3-small-8k-instruct", "Phi-3-small-128k-instruct",
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"Phi-3.5-mini-instruct"
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],
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indirect=True)
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def test_llm_phi_single_gpu_summary(phi_example_root, llm_phi_model_root,
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llm_datasets_root, llm_rouge_root, llm_venv,
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cmodel_dir, engine_dir,
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use_attention_plugin, use_gemm_plugin,
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dtype, context_fmha_type, num_beams):
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"Build & run phi on single gpu."
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if (not use_attention_plugin or not use_gemm_plugin) \
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and get_device_memory() < 80000:
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pytest.skip("device memory is insufficient.")
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if context_fmha_type != "disable_fmha":
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# --enable_context_fmha / --enable_context_fmha_fp32_acc
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# have to be used together with --use_gpt_attention_plugin
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use_attention_plugin = True
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print("Converting checkpoint...")
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model_name = os.path.basename(llm_phi_model_root)
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ckpt_dir = convert_weights(llm_venv=llm_venv,
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example_root=phi_example_root,
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cmodel_dir=cmodel_dir,
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model=model_name,
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model_path=llm_phi_model_root,
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data_type=dtype)
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print("Building engines...")
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build_cmd = [
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"trtllm-build",
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f"--checkpoint_dir={ckpt_dir}",
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f"--output_dir={engine_dir}",
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f"--max_batch_size={16}",
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f"--max_input_len={1024}",
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f"--max_seq_len={2048}",
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f"--max_beam_width={num_beams}",
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]
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if use_attention_plugin:
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build_cmd.append(f"--gpt_attention_plugin={dtype}")
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if context_fmha_type == "enable_fmha":
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build_cmd.append("--context_fmha=enable")
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elif context_fmha_type == "disable_fmha":
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build_cmd.append("--context_fmha=disable")
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else:
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build_cmd.extend([
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"--gpt_attention_plugin=disable",
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"--context_fmha=disable",
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"--paged_kv_cache=disable",
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"--remove_input_padding=disable",
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])
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if use_gemm_plugin:
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build_cmd.append(f"--gemm_plugin={dtype}")
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else:
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build_cmd.append("--gemm_plugin=disable")
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check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
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print('Run phi...')
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run_cmd = [
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f"{phi_example_root}/../../../run.py",
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"--max_output_len=50",
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f"--engine_dir={engine_dir}",
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f"--tokenizer_dir={llm_phi_model_root}",
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]
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venv_check_call(llm_venv, run_cmd)
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rouge1_threshold = 20
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if model_name == 'Phi-3-small-8k-instruct': rouge1_threshold = 18.0
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if model_name == 'Phi-3-small-128k-instruct': rouge1_threshold = 19.0
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summary_cmd = [
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f"{phi_example_root}/../summarize.py", "--test_trt_llm",
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"--hf_model_dir", f"{llm_phi_model_root}", "--data_type", "fp16",
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"--check_accuracy", f"--engine_dir={engine_dir}",
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f"--tensorrt_llm_rouge1_threshold={rouge1_threshold}",
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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 context_fmha_type == "enable_fmha_with_fp32_acc":
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summary_cmd.append("--enable_context_fmha_fp32_acc")
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venv_check_call(llm_venv, summary_cmd)
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@pytest.mark.skip_less_device(2)
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@pytest.mark.skip_less_device_memory(40000)
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@pytest.mark.parametrize("num_beams", [1, 4],
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ids=lambda num_beams: f'nb:{num_beams}')
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@pytest.mark.parametrize("llm_phi_model_root", [
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"phi-2", "Phi-3-mini-4k-instruct", "Phi-3-mini-128k-instruct",
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"Phi-3-small-8k-instruct", "Phi-3-small-128k-instruct",
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'Phi-3.5-MoE-instruct'
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],
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indirect=True)
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def test_llm_phi_1node_2gpus_summary(phi_example_root, llm_phi_model_root,
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llm_datasets_root, llm_rouge_root,
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llm_venv, cmodel_dir, engine_dir,
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num_beams):
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"Build & run phi on 2 gpus."
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print("Converting checkpoint...")
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model_name = os.path.basename(llm_phi_model_root)
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ckpt_dir = convert_weights(llm_venv=llm_venv,
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example_root=phi_example_root,
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cmodel_dir=cmodel_dir,
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model=model_name,
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model_path=llm_phi_model_root,
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data_type="float16",
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tp_size=2,
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pp_size=1)
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print("Building engines...")
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build_cmd = [
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"trtllm-build",
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f"--checkpoint_dir={ckpt_dir}",
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f"--output_dir={engine_dir}",
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f"--max_batch_size={16}",
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f"--max_input_len={1024}",
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f"--max_seq_len={2048}",
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f"--max_beam_width={num_beams}",
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"--gemm_plugin=float16",
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"--gpt_attention_plugin=float16",
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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 phi...')
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rouge1_threshold = 21.2
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if model_name == 'Phi-3.5-MoE-instruct': rouge1_threshold = 24.0
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summary_cmd = [
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f"{phi_example_root}/../../../summarize.py", "--test_trt_llm",
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"--hf_model_dir", f"{llm_phi_model_root}", "--data_type", "fp16",
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"--check_accuracy", f"--engine_dir={engine_dir}",
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f"--tensorrt_llm_rouge1_threshold={rouge1_threshold}",
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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_mpi_check_call(llm_venv, ["mpirun", "-n", "2", "--allow-run-as-root"],
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summary_cmd)
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@pytest.mark.parametrize("llm_phi_model_root",
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["phi-2", "Phi-3-mini-4k-instruct"],
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indirect=True)
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def test_phi_evaltool(phi_example_root, llm_phi_model_root, llm_venv,
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engine_dir, cmodel_dir, evaltool_root):
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print("Build engines...")
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dtype = 'float16'
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model_name = os.path.basename(llm_phi_model_root)
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model_dir = convert_weights(llm_venv=llm_venv,
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example_root=phi_example_root,
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cmodel_dir=cmodel_dir,
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model=model_name,
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model_path=llm_phi_model_root,
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data_type=dtype)
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print("Build engines...")
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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={dtype}",
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f"--gemm_plugin={dtype}",
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"--gather_context_logits",
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"--max_batch_size=8",
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"--max_input_len=5000",
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"--max_seq_len=8192",
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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("Lm evaluation harness")
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# start inference server
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start_inference_server = [
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EVALTOOL_INFERENCE_SERVER_STARTUP_SCRIPT, "-e", engine_dir, "-t",
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llm_phi_model_root, "-d", evaltool_root, "-m", "1024"
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]
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check_call(" ".join(start_inference_server), shell=True)
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task_list = ['mmlu', 'wikilingua']
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try:
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for task in task_list:
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project_id = str(uuid.uuid4())
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if task == "wikilingua":
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config_file = EVALTOOL_WIKILINGUA_CONFIG
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result_file = EVALTOOL_WIKILINGUA_RESULT_FILE
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if task == "mmlu":
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config_file = EVALTOOL_MMLU_CONFIG
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result_file = EVALTOOL_MMLU_RESULT_FILE
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# Update config dynamically
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import yaml
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with open(config_file, 'r') as f:
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lm_eval_config = yaml.safe_load(f)
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lm_eval_config['model']['llm_name'] = model_name
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lm_eval_config['model']['tokenizer_path'] = llm_phi_model_root
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config_file = os.path.join(llm_venv.get_working_directory(),
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"lm_eval_config.yaml")
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with open(config_file, 'w') as f:
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yaml.dump(lm_eval_config, f)
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# launch evaluation
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run_cmd = [
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f"cd {evaltool_root}",
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"&&",
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"source .venv/bin/activate",
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"&&",
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"python3",
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f"evaltool/interfaces/cli/main.py",
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"project",
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"launch",
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f"--eval_project_config_file '{config_file}'",
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"--infra_name local",
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f"--output_dir '{llm_venv.get_working_directory()}'",
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f"--project_id {project_id}",
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]
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check_call(" ".join(run_cmd), shell=True, executable="/bin/bash")
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# process result
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result_path = f"{llm_venv.get_working_directory()}/{project_id}/{result_file}"
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check_call(f"cat {result_path}", shell=True)
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if task == 'mmlu':
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# Phi-2 suffers bad accuracy when no lstrip applied.
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# evaltool_mmlu_post_process(result_path, 0.4949, 0.006)
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evaltool_mmlu_post_process(result_path, 0.567, 0.006)
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if task == 'wikilingua':
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# evaltool_wikilingua_post_process(result_path, 0.1569, 0.003)
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evaltool_wikilingua_post_process(result_path, 0.1827, 0.006)
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finally:
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# stop the server
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check_call(f"{EVALTOOL_INFERENCE_SERVER_STOP_SCRIPT}", shell=True)
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@pytest.mark.parametrize("llm_phi_model_root", ["Phi-3-mini-4k-instruct"],
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indirect=True)
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def test_phi3_mtbench(phi_example_root, llm_phi_model_root, llm_venv,
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engine_dir, cmodel_dir, evaltool_root):
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print("Build engines...")
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data_type = "bfloat16"
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model_name = os.path.basename(llm_phi_model_root)
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model_dir = convert_weights(llm_venv=llm_venv,
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example_root=phi_example_root,
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cmodel_dir=cmodel_dir,
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model=model_name,
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model_path=llm_phi_model_root,
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data_type=data_type)
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print("Build engines...")
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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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"--gather_context_logits",
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"--max_batch_size=8",
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"--max_input_len=5000",
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"--max_seq_len=8192",
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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("MT-Bench evaluation")
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# start inference server
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start_inference_server = [
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EVALTOOL_INFERENCE_SERVER_STARTUP_SCRIPT, "-e", engine_dir, "-t",
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llm_phi_model_root, "-d", evaltool_root, "-m", "1024"
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]
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check_call(" ".join(start_inference_server), shell=True)
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try:
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project_id = str(uuid.uuid4())
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config_file = EVALTOOL_MTBENCH_CONFIG
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result_file = EVALTOOL_MTBENCH_RESULT_FILE
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model_name = os.path.basename(llm_phi_model_root)
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# Update config dynamically
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import yaml
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with open(config_file, 'r') as f:
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mt_bench_config = yaml.safe_load(f)
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mt_bench_config['model']['llm_name'] = model_name
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mt_bench_config['model']['tokenizer_path'] = phi_example_root
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mt_bench_config['evaluations'][0]['judge_model'][
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'client_id'] = LLM_GATE_WAY_CLIENT_ID
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mt_bench_config['evaluations'][0]['judge_model'][
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'client_secret'] = LLM_GATE_WAY_TOKEN
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config_file = os.path.join(llm_venv.get_working_directory(),
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f"{model_name}_mtbench_config.yaml")
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with open(config_file, 'w') as f:
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yaml.dump(mt_bench_config, f)
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# Update resource config
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run_cmd = [
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f"cd {evaltool_root}",
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"&&",
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"source .venv/bin/activate",
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"&&",
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"python3",
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"evaltool/interfaces/cli/main.py",
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"config",
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"resource",
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"--resource_config_file examples/resource_configs/resource_local.yaml",
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]
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check_call(" ".join(run_cmd), shell=True, executable="/bin/bash")
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# launch evaluation
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run_cmd = [
|
|
f"cd {evaltool_root}",
|
|
"&&",
|
|
"source .venv/bin/activate",
|
|
"&&",
|
|
"python3",
|
|
f"evaltool/interfaces/cli/main.py",
|
|
"project",
|
|
"launch",
|
|
f"--eval_project_config_file '{config_file}'",
|
|
"--infra_name local",
|
|
f"--output_dir '{llm_venv.get_working_directory()}'",
|
|
f"--project_id {project_id}",
|
|
]
|
|
check_call(" ".join(run_cmd), shell=True, executable="/bin/bash")
|
|
finally:
|
|
# stop the server
|
|
check_call(f"{EVALTOOL_INFERENCE_SERVER_STOP_SCRIPT}", shell=True)
|
|
|
|
# process result
|
|
result_path = f"{llm_venv.get_working_directory()}/{project_id}/{result_file}/{model_name}.csv"
|
|
check_call(f"cat {result_path}", shell=True)
|
|
|
|
evaltool_mtbench_post_process(result_path, 7.45, 0.2)
|
|
|
|
|
|
@pytest.mark.parametrize("data_type", ["float16", "fp8"],
|
|
ids=["base_fp16", "base_fp8"])
|
|
@pytest.mark.parametrize("lora_data_type", ["float16"], ids=["lora_fp16"])
|
|
@pytest.mark.parametrize("llm_phi_model_root", ["Phi-3-mini-4k-instruct"],
|
|
indirect=True)
|
|
@pytest.mark.parametrize("llm_lora_model_root",
|
|
["Phi-3-mini-4k-instruct-ru-lora"],
|
|
indirect=True)
|
|
def test_llm_phi_lora_1gpu(data_type, lora_data_type, phi_example_root,
|
|
llm_phi_model_root, llm_datasets_root, llm_venv,
|
|
cmodel_dir, engine_dir, llm_lora_model_root,
|
|
qcache_dir_without_install_package):
|
|
"run phi lora test on 1gpu"
|
|
print("Converting checkpoint...")
|
|
model_name = 'phi-3-lora'
|
|
if data_type == 'fp8':
|
|
skip_fp8_pre_ada(use_fp8=True)
|
|
if get_sm_version() >= 100:
|
|
pytest.skip("FP8 is not supported on post-Blackwell architectures")
|
|
model_dir = quantize_data(
|
|
llm_venv,
|
|
phi_example_root,
|
|
model_dir=llm_phi_model_root,
|
|
calib_dataset=f"{llm_datasets_root}/cnn_dailymail",
|
|
dtype="float16",
|
|
qformat="fp8",
|
|
kv_cache_dtype="fp8",
|
|
quantize_dir=qcache_dir_without_install_package,
|
|
calib_size=512)
|
|
else:
|
|
model_dir = convert_weights(llm_venv=llm_venv,
|
|
example_root=phi_example_root,
|
|
cmodel_dir=cmodel_dir,
|
|
model=model_name,
|
|
model_path=llm_phi_model_root)
|
|
|
|
print("Build engines...")
|
|
build_cmd = [
|
|
"trtllm-build",
|
|
f"--checkpoint_dir={model_dir}",
|
|
f"--output_dir={engine_dir}",
|
|
"--lora_plugin=auto",
|
|
"--gemm_plugin=auto",
|
|
"--max_batch_size=8",
|
|
f"--lora_dir={llm_lora_model_root}",
|
|
]
|
|
check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
|
|
|
|
ref_1 = [
|
|
1, 1815, 366, 3867, 5837, 304, 17545, 18240, 310, 9892, 16397, 322,
|
|
8338, 265, 29888, 21211, 29973, 306, 29915, 29885, 3063, 363, 907, 1230,
|
|
322, 9045, 29891, 9522, 5547, 393, 11039, 403, 1716, 285, 21211, 29889,
|
|
29871
|
|
]
|
|
|
|
ref_2 = [
|
|
1815, 366, 3867, 5837, 304, 17545, 18240, 310, 9892, 16397, 322, 8338,
|
|
265, 29888, 21211, 29973, 13, 13, 7900, 22137, 29901, 315, 13946, 368,
|
|
29991, 2266, 526, 777, 907, 1230, 5837, 304, 13389, 9892, 16397, 322
|
|
]
|
|
|
|
input_text = "Can you provide ways to eat combinations of bananas and dragonfruits?"
|
|
|
|
print(f"Run inference with lora id 0...")
|
|
venv_check_call(llm_venv, [
|
|
f"{phi_example_root}/../../../run.py",
|
|
"--max_output_len=20",
|
|
f"--input_text={input_text}",
|
|
"--lora_task_uids=0",
|
|
f"--tokenizer_dir={llm_lora_model_root}",
|
|
f"--engine_dir={engine_dir}",
|
|
f"--output_csv={llm_venv.get_working_directory()}/use_lora.csv",
|
|
"--use_py_session",
|
|
])
|
|
|
|
with open(f"{llm_venv.get_working_directory()}/use_lora.csv") as f:
|
|
predict = csv.reader(f)
|
|
predict = next(predict)
|
|
predict = [int(p) for p in predict]
|
|
assert ref_1 == predict or data_type != "float16"
|
|
|
|
print(f"Run inference with lora id -1...")
|
|
venv_check_call(llm_venv, [
|
|
f"{phi_example_root}/../../../run.py",
|
|
"--max_output_len=20",
|
|
f"--input_text={input_text}",
|
|
"--lora_task_uids=-1",
|
|
f"--tokenizer_dir={llm_phi_model_root}",
|
|
f"--engine_dir={engine_dir}",
|
|
f"--output_csv={llm_venv.get_working_directory()}/no_lora.csv",
|
|
"--use_py_session",
|
|
])
|
|
|
|
with open(f"{llm_venv.get_working_directory()}/no_lora.csv") as f:
|
|
predict = csv.reader(f)
|
|
predict = next(predict)
|
|
predict = [int(p) for p in predict]
|
|
|
|
assert ref_2 == predict or data_type != "float16"
|
|
|
|
|
|
@skip_pre_ada
|
|
@pytest.mark.parametrize("data_type", ['float16', 'bfloat16'])
|
|
@pytest.mark.parametrize("qformat", ['fp8'])
|
|
@pytest.mark.parametrize("llm_phi_model_root", [
|
|
"phi-2", "Phi-3-mini-128k-instruct", "Phi-3-small-128k-instruct",
|
|
"Phi-3.5-mini-instruct", "Phi-3.5-MoE-instruct", "Phi-4-mini-instruct"
|
|
],
|
|
indirect=True)
|
|
def test_llm_phi_quantization_1gpu(data_type, llm_phi_model_root, llm_venv,
|
|
cmodel_dir, engine_dir, phi_example_root,
|
|
llm_datasets_root, llm_rouge_root, qformat):
|
|
"Run phi quantization tests"
|
|
# Workaround for Modelopt can't convert Phi-3 on multi GPUs.
|
|
gpu_constraint = {"CUDA_VISIBLE_DEVICES": "0"}
|
|
|
|
print("Convert checkpoint by modelopt...")
|
|
convert_cmd = [
|
|
f"{phi_example_root}/../../../quantization/quantize.py",
|
|
f"--model_dir={llm_phi_model_root}",
|
|
f"--calib_dataset={llm_datasets_root}/cnn_dailymail",
|
|
f"--dtype={data_type}",
|
|
f"--qformat={qformat}",
|
|
f"--kv_cache_dtype={qformat}",
|
|
f"--output_dir={cmodel_dir}",
|
|
]
|
|
venv_check_call(llm_venv, convert_cmd, env=gpu_constraint)
|
|
|
|
print("Build engines...")
|
|
build_cmd = [
|
|
"trtllm-build",
|
|
f"--checkpoint_dir={cmodel_dir}",
|
|
f"--output_dir={engine_dir}",
|
|
"--max_input_len=3000",
|
|
"--max_seq_len=3100",
|
|
f"--max_batch_size={16}",
|
|
]
|
|
|
|
build_env = {
|
|
**llm_venv._new_env,
|
|
**gpu_constraint
|
|
} if llm_venv._new_env else gpu_constraint
|
|
check_call(" ".join(build_cmd), shell=True, env=build_env)
|
|
|
|
print("Run summarize...")
|
|
threshold_score = 24.0
|
|
model_name = os.path.basename(llm_phi_model_root)
|
|
if model_name == "phi-2":
|
|
threshold_score = 22.0
|
|
|
|
summary_cmd = [
|
|
f"{phi_example_root}/../../../summarize.py",
|
|
"--test_trt_llm",
|
|
f"--hf_model_dir={llm_phi_model_root}",
|
|
f"--tokenizer_dir={llm_phi_model_root}",
|
|
f"--engine_dir={engine_dir}",
|
|
"--check_accuracy",
|
|
f"--tensorrt_llm_rouge1_threshold={threshold_score}",
|
|
"--max_ite=40",
|
|
f"--batch_size={16}",
|
|
f"--dataset_dir={llm_datasets_root}",
|
|
f"--rouge_dir={llm_rouge_root}",
|
|
]
|
|
|
|
venv_check_call(llm_venv, summary_cmd, env=gpu_constraint)
|
|
|
|
|
|
@skip_pre_ada
|
|
@skip_post_blackwell
|
|
@pytest.mark.parametrize("llm_phi_model_root", [
|
|
"phi-2", "Phi-3-mini-128k-instruct", "Phi-3-small-128k-instruct",
|
|
"Phi-3.5-mini-instruct", "Phi-3.5-MoE-instruct", "Phi-4-mini-instruct"
|
|
],
|
|
indirect=True)
|
|
def test_phi_fp8_with_bf16_lora(llm_phi_model_root,
|
|
llm_venv,
|
|
cmodel_dir,
|
|
engine_dir,
|
|
phi_example_root,
|
|
llm_datasets_root,
|
|
llm_rouge_root,
|
|
data_type='bfloat16',
|
|
qformat='fp8'):
|
|
"Run Phi models with multiple pseudo LoRAs."
|
|
|
|
model_name = os.path.basename(llm_phi_model_root)
|
|
if model_name == "Phi-3.5-MoE-instruct" and \
|
|
get_device_memory() < 95000:
|
|
pytest.skip(f"This test is only supported when memory >= 95000")
|
|
|
|
# Quantize the base model to fp8.
|
|
print("Convert checkpoint by modelopt...")
|
|
convert_cmd = [
|
|
f"{phi_example_root}/../../../quantization/quantize.py",
|
|
f"--model_dir={llm_phi_model_root}",
|
|
f"--calib_dataset={llm_datasets_root}/cnn_dailymail",
|
|
f"--dtype={data_type}",
|
|
f"--qformat={qformat}",
|
|
f"--kv_cache_dtype={qformat}",
|
|
f"--output_dir={cmodel_dir}",
|
|
]
|
|
# Workaround for Modelopt can't convert Phi-3-small-128k-instruct on multi GPUs.
|
|
env = None
|
|
if model_name == "Phi-3-small-128k-instruct":
|
|
env = {"CUDA_VISIBLE_DEVICES": "0"}
|
|
venv_check_call(llm_venv, convert_cmd, env=env)
|
|
|
|
print("Creating pseudo LoRAs...")
|
|
hf_target_modules = {
|
|
"phi-2": ["q_proj", "k_proj", "v_proj"],
|
|
"Phi-3-mini-128k-instruct": ["qkv_proj"],
|
|
"Phi-3-small-128k-instruct": ["query_key_value"],
|
|
"Phi-3.5-mini-instruct": ["qkv_proj"],
|
|
"Phi-3.5-MoE-instruct":
|
|
["q_proj", "k_proj", "v_proj", "w1", "w2", "w3"],
|
|
"Phi-4-mini-instruct": ["qkv_proj"],
|
|
}
|
|
trtllm_target_modules = {
|
|
"phi-2": ["attn_q", "attn_k", "attn_v"],
|
|
"Phi-3-mini-128k-instruct": ["attn_qkv"],
|
|
"Phi-3-small-128k-instruct": ["attn_qkv"],
|
|
"Phi-3.5-mini-instruct": ["attn_qkv"],
|
|
"Phi-3.5-MoE-instruct": [
|
|
"attn_q", "attn_k", "attn_v", "moe_h_to_4h", "moe_4h_to_h",
|
|
"moe_gate"
|
|
],
|
|
"Phi-4-mini-instruct": ["attn_qkv"],
|
|
}
|
|
model_name = os.path.basename(llm_phi_model_root)
|
|
test_multi_lora_support(
|
|
hf_model_dir=llm_phi_model_root,
|
|
tllm_ckpt_dir=cmodel_dir,
|
|
engine_dir=engine_dir,
|
|
llm_venv=llm_venv,
|
|
example_root=phi_example_root,
|
|
num_loras=2,
|
|
lora_rank=8,
|
|
target_hf_modules=hf_target_modules[model_name],
|
|
target_trtllm_modules=trtllm_target_modules[model_name],
|
|
zero_lora_weights=True,
|
|
)
|