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TensorRT-LLMs/tests/integration/defs/examples/test_qwen.py
xinhe-nv 263c6c0ad0
test: skip post blackwell (#6357) 
Signed-off-by: Xin He (SW-GPU) <200704525+xinhe-nv@users.noreply.github.com>
2025-08-01 13:10:14 -04:00

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# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Module test_qwen test qwen examples."""
import csv
import os
import pytest
from defs.common import (convert_weights, test_multi_lora_support,
venv_check_call, venv_mpi_check_call)
from defs.conftest import (get_device_count, get_device_memory, get_sm_version,
skip_post_blackwell, skip_pre_ada)
from defs.trt_test_alternative import check_call
# skip trt flow cases on post-Blackwell-Ultra
if get_sm_version() >= 103:
pytest.skip(
"TRT workflow tests are not supported on post Blackwell-Ultra architecture",
allow_module_level=True)
@pytest.mark.parametrize(
"context_fmha_type",
['enable_fmha', 'disable_fmha', 'enable_fmha_fp32_acc'])
@pytest.mark.parametrize("use_weight_only",
[pytest.param(True, marks=skip_post_blackwell), False],
ids=["enable_weight_only", "disable_weight_only"])
@pytest.mark.parametrize(
"remove_input_padding", [True, False],
ids=["enable_remove_input_padding", "disable_remove_input_padding"])
@pytest.mark.parametrize(
"paged_kv_cache", [True, False],
ids=["enable_paged_kv_cache", "disable_paged_kv_cache"])
@pytest.mark.parametrize("llm_qwen_model_root", [
"qwen2.5_7b_instruct", "qwen_7b_chat", "qwen1.5_0.5b_chat",
"qwen1.5_7b_chat", "qwen2_7b_instruct", "qwen2_vl_7b_instruct",
"qwen2_0.5b_instruct", "qwen2.5_0.5b_instruct", "qwen2.5_1.5b_instruct"
],
indirect=True)
def test_llm_qwen_single_gpu_summary(
qwen_example_root,
llm_qwen_model_root,
llm_datasets_root,
llm_rouge_root,
llm_venv,
cmodel_dir,
engine_dir,
context_fmha_type,
use_weight_only,
remove_input_padding,
paged_kv_cache,
):
"Build & run Qwen-7B-Chat on single gpu."
print("Converting checkpoint...")
dtype = 'float16'
model_name = os.path.basename(llm_qwen_model_root)
ckpt_dir = convert_weights(llm_venv=llm_venv,
example_root=qwen_example_root,
cmodel_dir=cmodel_dir,
model=model_name,
model_path=llm_qwen_model_root,
data_type=dtype,
use_weight_only=use_weight_only)
print("Building engines...")
build_cmd = [
"trtllm-build",
f"--checkpoint_dir={ckpt_dir}",
f"--output_dir={engine_dir}",
f"--max_batch_size={8}",
f"--gemm_plugin={dtype}",
f"--gpt_attention_plugin={dtype}",
f"--max_seq_len=2048",
]
if context_fmha_type == "enable_fmha":
build_cmd.append("--context_fmha=enable")
elif context_fmha_type == "disable_fmha":
build_cmd.append("--context_fmha=disable")
if remove_input_padding:
build_cmd.append("--remove_input_padding=enable")
else:
build_cmd.append("--remove_input_padding=disable")
if paged_kv_cache:
build_cmd.append("--paged_kv_cache=enable")
else:
build_cmd.append("--paged_kv_cache=disable")
check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
summary_cmd = [
f"{qwen_example_root}/../../../summarize.py", "--test_trt_llm",
"--hf_model_dir", f"{llm_qwen_model_root}", "--data_type", "fp16",
"--check_accuracy", f"--engine_dir={engine_dir}",
f"--tensorrt_llm_rouge1_threshold=22",
f"--dataset_dir={llm_datasets_root}", f"--rouge_dir={llm_rouge_root}"
]
if context_fmha_type == "enable_fmha_fp32_acc":
summary_cmd.append("--enable_context_fmha_fp32_acc")
venv_check_call(llm_venv, summary_cmd)
@pytest.mark.skip_less_device_memory(40000)
@pytest.mark.parametrize(
"context_fmha_type",
['enable_fmha', 'disable_fmha', 'enable_fmha_fp32_acc'])
@pytest.mark.parametrize("use_weight_only", [True, False],
ids=["enable_weight_only", "disable_weight_only"])
@pytest.mark.parametrize(
"remove_input_padding", [True, False],
ids=["enable_remove_input_padding", "disable_remove_input_padding"])
@pytest.mark.parametrize(
"paged_kv_cache", [True, False],
ids=["enable_paged_kv_cache", "disable_paged_kv_cache"])
@pytest.mark.parametrize("llm_qwen_model_root", ["qwen1.5_moe_a2.7b_chat"],
indirect=True)
def test_llm_qwen_moe_single_gpu_summary(
qwen_example_root,
llm_qwen_model_root,
llm_datasets_root,
llm_rouge_root,
llm_venv,
cmodel_dir,
engine_dir,
context_fmha_type,
use_weight_only,
remove_input_padding,
paged_kv_cache,
):
"Build & run Qwen1.5-MoE-A2.7B-Chat on single gpu."
print("Converting checkpoint...")
dtype = 'float16'
model_name = os.path.basename(llm_qwen_model_root)
ckpt_dir = convert_weights(llm_venv=llm_venv,
example_root=qwen_example_root,
cmodel_dir=cmodel_dir,
model=model_name,
model_path=llm_qwen_model_root,
data_type=dtype,
use_weight_only=use_weight_only)
print("Building engines...")
build_cmd = [
"trtllm-build",
f"--checkpoint_dir={ckpt_dir}",
f"--output_dir={engine_dir}",
f"--max_batch_size={8}",
f"--gemm_plugin={dtype}",
f"--gpt_attention_plugin={dtype}",
f"--max_seq_len=2048",
]
if context_fmha_type == "enable_fmha":
build_cmd.append("--context_fmha=enable")
elif context_fmha_type == "disable_fmha":
build_cmd.append("--context_fmha=disable")
if remove_input_padding:
build_cmd.append("--remove_input_padding=enable")
else:
build_cmd.append("--remove_input_padding=disable")
if paged_kv_cache:
build_cmd.append("--paged_kv_cache=enable")
else:
build_cmd.append("--paged_kv_cache=disable")
check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
summary_cmd = [
f"{qwen_example_root}/../../../summarize.py", "--test_trt_llm",
"--hf_model_dir", f"{llm_qwen_model_root}", "--data_type", "fp16",
"--check_accuracy", f"--engine_dir={engine_dir}",
f"--tensorrt_llm_rouge1_threshold=22",
f"--dataset_dir={llm_datasets_root}", f"--rouge_dir={llm_rouge_root}"
]
if context_fmha_type == "enable_fmha_fp32_acc":
summary_cmd.append("--enable_context_fmha_fp32_acc")
venv_check_call(llm_venv, summary_cmd)
# Delete this case refer to https://nvbugs/5072417
# @pytest.mark.parametrize("llm_lora_model_root", ["Ko-QWEN-7B-Chat-LoRA"],
# indirect=True)
# @pytest.mark.parametrize("llm_qwen_model_root", ["qwen_7b_chat"], indirect=True)
# def test_llm_qwen_7b_single_gpu_lora(
# qwen_example_root,
# llm_qwen_model_root,
# llm_venv,
# cmodel_dir,
# engine_dir,
# llm_lora_model_root,
# ):
# "run Qwen lora test on single gpu."
# print("Build engines...")
# dtype = 'float16'
# model_name = os.path.basename(llm_qwen_model_root)
# ckpt_dir = convert_weights(llm_venv=llm_venv,
# example_root=qwen_example_root,
# cmodel_dir=cmodel_dir,
# model=model_name,
# model_path=llm_qwen_model_root,
# data_type=dtype)
# print("Build engines...")
# build_cmd = [
# "trtllm-build",
# f"--checkpoint_dir={ckpt_dir}",
# f"--output_dir={engine_dir}",
# "--lora_plugin=auto",
# "--gemm_plugin=auto",
# f"--lora_dir={llm_lora_model_root}",
# ]
# check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
# ref_1 = [
# 151644, 8948, 198, 2610, 525, 264, 10950, 17847, 13, 151645, 198,
# 151644, 872, 198, 126246, 144370, 91145, 11, 137601, 29326, 86034,
# 12802, 5140, 98734, 19391, 35711, 30, 151645, 198, 151644, 77091, 198,
# 126246, 144370, 91145, 0, 134561, 58677, 78125, 21329, 66019, 124685,
# 134619, 94152, 28626, 17380, 11, 134637, 20401, 138520, 19391, 143603
# ]
# ref_2 = [
# 151644, 8948, 198, 2610, 525, 264, 10950, 17847, 13, 151645, 198,
# 151644, 872, 198, 126246, 144370, 91145, 11, 137601, 29326, 86034,
# 12802, 5140, 98734, 19391, 35711, 30, 151645, 198, 151644, 77091, 198,
# 126246, 144370, 91145, 0, 134561, 330, 48, 1103, 54, 268, 1, 78952, 13,
# 151645, 198, 151643, 151643, 151643, 151643, 151643
# ]
# input_text = "안녕하세요, 혹시 이름이 뭐에요?"
# print("Run inference with lora id 0...")
# venv_check_call(llm_venv, [
# f"{qwen_example_root}/../run.py",
# "--max_output_len=20",
# f"--input_text={input_text}",
# "--lora_task_uids=0",
# f"--tokenizer_dir={llm_qwen_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
# print("Run inference with lora id -1...")
# venv_check_call(llm_venv, [
# f"{qwen_example_root}/../run.py",
# "--max_output_len=20",
# f"--input_text={input_text}",
# "--lora_task_uids=-1",
# f"--tokenizer_dir={llm_qwen_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
@pytest.mark.parametrize("llm_lora_model_root", ["Qwen1.5-7B-Chat-750Mb-lora"],
indirect=True)
@pytest.mark.parametrize("llm_qwen_model_root", ["qwen1.5_7b_chat"],
indirect=True)
def test_llm_qwen1_5_7b_single_gpu_lora(
qwen_example_root,
llm_qwen_model_root,
llm_venv,
cmodel_dir,
engine_dir,
llm_lora_model_root,
):
"run Qwen1.5 lora test on single gpu."
print("Build engines...")
dtype = 'float16'
model_name = os.path.basename(llm_qwen_model_root)
ckpt_dir = convert_weights(llm_venv=llm_venv,
example_root=qwen_example_root,
cmodel_dir=cmodel_dir,
model=model_name,
model_path=llm_qwen_model_root,
data_type=dtype)
print("Build engines...")
build_cmd = [
"trtllm-build",
f"--checkpoint_dir={ckpt_dir}",
f"--output_dir={engine_dir}",
"--lora_plugin=auto",
"--gemm_plugin=auto",
f"--lora_dir={llm_lora_model_root}",
]
check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
ref_1 = [
151644, 8948, 198, 2610, 525, 264, 10950, 17847, 13, 151645, 198,
151644, 872, 198, 3838, 374, 697, 829, 30, 151645, 198, 151644, 77091,
198, 40, 2776, 458, 15235, 7881, 553, 5264, 15469, 11, 773, 358, 1513,
944, 614, 264, 829, 304, 279, 8606, 5530
]
ref_2 = [
151644, 8948, 198, 2610, 525, 264, 10950, 17847, 13, 151645, 198,
151644, 872, 198, 3838, 374, 697, 829, 30, 151645, 198, 151644, 77091,
198, 40, 1079, 1207, 16948, 11, 264, 3460, 4128, 1614, 3465, 553, 54364,
14817, 13, 151645, 151645, 151645, 151645, 151645, 151645
]
input_text = "What is your name?"
print("Run inference with lora id 0...")
venv_check_call(llm_venv, [
f"{qwen_example_root}/../../../run.py",
"--max_output_len=20",
f"--input_text={input_text}",
"--lora_task_uids=0",
f"--tokenizer_dir={llm_qwen_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
print("Run inference with lora id -1...")
venv_check_call(llm_venv, [
f"{qwen_example_root}/../../../run.py",
"--max_output_len=20",
f"--input_text={input_text}",
"--lora_task_uids=-1",
f"--tokenizer_dir={llm_qwen_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
@pytest.mark.skip_less_device_memory(60000)
@pytest.mark.parametrize("llm_lora_model_root",
["Upcycled-Qwen1.5-MoE2.7B-LoRA"],
indirect=True)
@pytest.mark.parametrize("llm_qwen_model_root", ["qwen1.5_moe_a2.7b_chat"],
indirect=True)
def test_llm_qwen1_5_moe_single_gpu_lora(
qwen_example_root,
llm_qwen_model_root,
llm_venv,
cmodel_dir,
engine_dir,
llm_lora_model_root,
):
"run Qwen1.5 MoE lora test on single gpu."
print("Build engines...")
dtype = 'float16'
model_name = os.path.basename(llm_qwen_model_root)
ckpt_dir = convert_weights(llm_venv=llm_venv,
example_root=qwen_example_root,
cmodel_dir=cmodel_dir,
model=model_name,
model_path=llm_qwen_model_root,
data_type=dtype)
print("Build engines...")
build_cmd = [
"trtllm-build",
f"--checkpoint_dir={ckpt_dir}",
f"--output_dir={engine_dir}",
"--lora_plugin=auto",
"--moe_plugin=disable",
f"--lora_dir={llm_lora_model_root}",
]
check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
ref_1 = [
151644, 8948, 198, 2610, 525, 264, 10950, 17847, 13, 151645, 198,
151644, 872, 198, 3838, 374, 697, 829, 30, 151645, 198, 151644, 77091,
198, 151935, 151935, 151935, 151935, 151935, 151935, 151935, 151935,
151935, 151935, 151935, 151935, 151935, 151935, 151935, 151935, 151935,
151935, 151935, 151935
]
ref_2 = [
151644, 8948, 198, 2610, 525, 264, 10950, 17847, 13, 151645, 198,
151644, 872, 198, 3838, 374, 697, 829, 30, 151645, 198, 151644, 77091,
198, 40, 1079, 1207, 16948, 11, 264, 3460, 4128, 1614, 3465, 553, 54364,
14817, 13, 151645, 151645, 151645, 151645, 151645, 151645
]
input_text = "What is your name?"
print("Run inference with lora id 0...")
venv_check_call(llm_venv, [
f"{qwen_example_root}/../../../run.py",
"--max_output_len=20",
f"--input_text={input_text}",
"--lora_task_uids=0",
f"--tokenizer_dir={llm_qwen_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
print("Run inference with lora id -1...")
venv_check_call(llm_venv, [
f"{qwen_example_root}/../../../run.py",
"--max_output_len=20",
f"--input_text={input_text}",
"--lora_task_uids=-1",
f"--tokenizer_dir={llm_qwen_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
@pytest.mark.skip_less_device_memory(60000)
@pytest.mark.parametrize("llm_lora_model_root",
["Upcycled-Qwen1.5-MoE2.7B-LoRA"],
indirect=True)
@pytest.mark.parametrize("llm_qwen_model_root", ["qwen1.5_moe_a2.7b_chat"],
indirect=True)
def test_llm_qwen1_5_moe_plugin_single_gpu_lora(
qwen_example_root,
llm_qwen_model_root,
llm_venv,
cmodel_dir,
engine_dir,
llm_lora_model_root,
):
"run Qwen1.5 MoE lora test on single gpu."
print("Build engines...")
dtype = 'float16'
model_name = os.path.basename(llm_qwen_model_root)
ckpt_dir = convert_weights(llm_venv=llm_venv,
example_root=qwen_example_root,
cmodel_dir=cmodel_dir,
model=model_name,
model_path=llm_qwen_model_root,
data_type=dtype)
print("Build engines...")
build_cmd = [
"trtllm-build",
f"--checkpoint_dir={ckpt_dir}",
f"--output_dir={engine_dir}",
"--lora_plugin=auto",
"--moe_plugin=auto",
f"--lora_dir={llm_lora_model_root}",
]
check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
ref_1 = [
151644, 8948, 198, 2610, 525, 264, 10950, 17847, 13, 151645, 198,
151644, 872, 198, 3838, 374, 697, 829, 30, 151645, 198, 151644, 77091,
198, 151935, 151935, 151935, 151935, 151935, 151935, 151935, 151935,
151935, 151935, 151935, 151935, 151935, 151935, 151935, 151935, 151935,
151935, 151935, 151935
]
ref_2 = [
151644, 8948, 198, 2610, 525, 264, 10950, 17847, 13, 151645, 198,
151644, 872, 198, 3838, 374, 697, 829, 30, 151645, 198, 151644, 77091,
198, 40, 1079, 1207, 16948, 11, 264, 3460, 4128, 1614, 3465, 553, 54364,
14817, 13, 151645, 151645, 151645, 151645, 151645, 151645
]
input_text = "What is your name?"
print("Run inference with lora id 0...")
venv_check_call(llm_venv, [
f"{qwen_example_root}/../../../run.py",
"--max_output_len=20",
f"--input_text={input_text}",
"--lora_task_uids=0",
f"--tokenizer_dir={llm_qwen_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
print("Run inference with lora id -1...")
venv_check_call(llm_venv, [
f"{qwen_example_root}/../../../run.py",
"--max_output_len=20",
f"--input_text={input_text}",
"--lora_task_uids=-1",
f"--tokenizer_dir={llm_qwen_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
@skip_post_blackwell
@pytest.mark.parametrize("use_weight_only", [True, False],
ids=['enable_weight_only', 'disable_weight_only'])
@pytest.mark.parametrize("use_gemm_plugin", [True, False],
ids=["enable_gemm_plugin", "disable_gemm_plugin"])
@pytest.mark.parametrize("llm_qwen_model_root", [
"qwen2.5_7b_chat", "qwen1.5_7b_chat", "qwen2_7b_instruct",
"qwen2_vl_7b_instruct"
],
indirect=True)
def test_llm_qwen_7b_int8_kv_1node_1gpus(qwen_example_root, llm_qwen_model_root,
llm_datasets_root, llm_rouge_root,
llm_venv, cmodel_dir, engine_dir,
use_weight_only, use_gemm_plugin):
"Build & Run Qwen-7B-Chat int8 kv cache"
print("Converting checkpoint...")
dtype = 'float16'
model_name = "Qwen-chat-7b-int8-kv"
ckpt_dir = convert_weights(
llm_venv=llm_venv,
example_root=qwen_example_root,
cmodel_dir=cmodel_dir,
model=model_name,
model_path=llm_qwen_model_root,
data_type=dtype,
int8_kv_cache=True,
use_weight_only=use_weight_only,
weight_only_precision='int8' if use_weight_only else None,
calib_dataset=f"{llm_datasets_root}/ccdv/cnn_dailymail")
print("Building engines...")
build_cmd = [
"trtllm-build", f"--checkpoint_dir={ckpt_dir}",
f"--output_dir={engine_dir}", f"--max_batch_size={8}",
f"--gpt_attention_plugin={dtype}"
]
if use_gemm_plugin:
build_cmd.append(f"--gemm_plugin={dtype}")
else:
build_cmd.append("--gemm_plugin=disable")
check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
summary_cmd = [
f"{qwen_example_root}/../../../summarize.py", "--test_trt_llm",
"--hf_model_dir", f"{llm_qwen_model_root}", "--data_type", "fp16",
"--check_accuracy", f"--engine_dir={engine_dir}",
"--tensorrt_llm_rouge1_threshold=22",
f"--dataset_dir={llm_datasets_root}", f"--rouge_dir={llm_rouge_root}"
]
venv_check_call(llm_venv, summary_cmd)
@pytest.mark.skip_less_device_memory(40000)
@pytest.mark.parametrize("num_beams", [1, 4],
ids=lambda num_beams: f'nb:{num_beams}')
@pytest.mark.parametrize(
"tp_pp_size", [(2, 1), (4, 1), (2, 2)],
ids=lambda tp_pp_size: f'tp{tp_pp_size[0]}pp{tp_pp_size[1]}')
@pytest.mark.parametrize("use_plugin", ["enable_plugin", "disable_plugin"])
@pytest.mark.parametrize(
"context_fmha_type",
['enable_fmha', 'disable_fmha', 'enable_fmha_fp32_acc'])
@pytest.mark.parametrize("llm_qwen_model_root", [
"qwen2.5_7b_chat", "qwen1.5_7b_chat", "qwen2_7b_instruct",
"qwen2_vl_7b_instruct"
],
indirect=True)
def test_llm_qwen_7b_multi_gpus_summary(qwen_example_root, llm_qwen_model_root,
llm_datasets_root, llm_rouge_root,
llm_venv, cmodel_dir, engine_dir,
num_beams, tp_pp_size, use_plugin,
context_fmha_type):
"Run qwen on multi gpus"
tp_size, pp_size = tp_pp_size
world_size = tp_size * pp_size
if get_device_count() < world_size:
pytest.skip(f"devices are less than {world_size}.")
print("Converting checkpoint...")
dtype = 'float16'
model_name = os.path.basename(llm_qwen_model_root)
ckpt_dir = convert_weights(llm_venv=llm_venv,
example_root=qwen_example_root,
cmodel_dir=cmodel_dir,
model=model_name,
model_path=llm_qwen_model_root,
data_type=dtype,
gpus=world_size,
tp_size=tp_size,
pp_size=pp_size,
workers=world_size)
print("Build engines...")
build_cmd = [
"trtllm-build", f"--checkpoint_dir={ckpt_dir}",
f"--output_dir={engine_dir}", f"--max_batch_size={8}",
"--max_seq_len=8192", f"--max_beam_width={num_beams}",
f"--workers={world_size}"
]
if use_plugin:
build_cmd.append(f"--gemm_plugin={dtype}")
build_cmd.append(f"--gpt_attention_plugin={dtype}")
else:
build_cmd.append("--gemm_plugin=disable")
build_cmd.append("--gpt_attention_plugin=disable")
if context_fmha_type == "enable_fmha":
build_cmd.append("--context_fmha=enable")
elif context_fmha_type == "disable_fmha":
build_cmd.append("--context_fmha=disable")
check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
summary_cmd = [
f"{qwen_example_root}/../../../summarize.py", "--test_trt_llm",
"--hf_model_dir", f"{llm_qwen_model_root}", "--data_type", "fp16",
"--check_accuracy", f"--engine_dir={engine_dir}",
f"--num_beams={num_beams}", "--tensorrt_llm_rouge1_threshold=24",
f"--dataset_dir={llm_datasets_root}", f"--rouge_dir={llm_rouge_root}"
]
if context_fmha_type == "enable_fmha_fp32_acc":
summary_cmd.append("--enable_context_fmha_fp32_acc")
venv_mpi_check_call(
llm_venv, ["mpirun", "-n", f"{world_size}", "--allow-run-as-root"],
summary_cmd)
@skip_post_blackwell
@pytest.mark.parametrize("num_beams", [1, 2, 4],
ids=lambda num_beams: f'nb:{num_beams}')
@pytest.mark.parametrize("per_token_channel", [True, False],
ids=["enable_ptpc", "disable_ptpc"])
@pytest.mark.parametrize("llm_qwen_model_root", [
"qwen_7b_chat", "qwen_14b_chat", "qwen1.5_7b_chat", "qwen2_7b_instruct",
"qwen2_vl_7b_instruct", "qwen2.5_7b_instruct"
],
indirect=True)
def test_llm_qwen_smooth_quant_single_gpu_summary(qwen_example_root,
llm_qwen_model_root,
llm_datasets_root,
llm_rouge_root, llm_venv,
engine_dir, cmodel_dir,
per_token_channel, num_beams):
"Run qwen with smooth quant on single gpu"
if "14B" in llm_qwen_model_root and get_device_memory() < 80000:
pytest.skip("GPU memory is insufficient.")
print("Converting checkpoint...")
dtype = 'float16'
model_name = os.path.basename(llm_qwen_model_root)
ckpt_dir = convert_weights(
llm_venv=llm_venv,
example_root=qwen_example_root,
cmodel_dir=cmodel_dir,
model=model_name,
model_path=llm_qwen_model_root,
data_type=dtype,
smoothquant=0.5,
per_token=per_token_channel,
per_channel=per_token_channel,
calib_dataset=f"{llm_datasets_root}/ccdv/cnn_dailymail")
print("Build engines...")
build_cmd = [
"trtllm-build",
f"--checkpoint_dir={ckpt_dir}",
f"--gemm_plugin={dtype}",
f"--gpt_attention_plugin={dtype}",
f"--max_beam_width={num_beams}",
f"--output_dir={engine_dir}",
f"--max_batch_size={8}",
f"--max_seq_len={8192}",
]
check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
summary_cmd = [
f"{qwen_example_root}/../../../summarize.py", "--test_trt_llm",
"--hf_model_dir", f"{llm_qwen_model_root}", "--data_type", "fp16",
"--check_accuracy", f"--engine_dir={engine_dir}",
f"--num_beams={num_beams}", "--tensorrt_llm_rouge1_threshold=24",
f"--dataset_dir={llm_datasets_root}", f"--rouge_dir={llm_rouge_root}"
]
venv_check_call(llm_venv, summary_cmd)
@skip_post_blackwell
@pytest.mark.parametrize("num_beams", [1, 4],
ids=lambda num_beams: f'nb:{num_beams}')
@pytest.mark.parametrize("llm_qwen_model_root", [
"qwen_7b_chat_int4", "qwen1.5_14b_chat_int4", "qwen1.5_7b_chat_awq",
"qwen2_7b_awq", "qwen2.5_14b_instruct_int4"
],
indirect=True)
def test_llm_qwen_int4_single_gpu_summary(qwen_example_root,
llm_qwen_model_root,
llm_datasets_root, llm_rouge_root,
llm_venv, cmodel_dir, engine_dir,
num_beams):
"Run qwen with gptq on single gpu"
print("Converting checkpoint...")
dtype = 'float16'
model_name = os.path.basename(llm_qwen_model_root)
ckpt_dir = convert_weights(llm_venv=llm_venv,
example_root=qwen_example_root,
cmodel_dir=cmodel_dir,
model=model_name,
model_path=llm_qwen_model_root,
data_type=dtype)
print("Build engines...")
build_cmd = [
"trtllm-build",
f"--checkpoint_dir={ckpt_dir}",
f"--gemm_plugin={dtype}",
f"--gpt_attention_plugin={dtype}",
f"--max_beam_width={num_beams}",
f"--output_dir={engine_dir}",
f"--max_batch_size={8}",
f"--max_seq_len={8192}",
]
check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
summary_cmd = [
f"{qwen_example_root}/../../../summarize.py", "--test_trt_llm",
"--hf_model_dir", f"{llm_qwen_model_root}", "--data_type", "fp16",
"--check_accuracy", f"--engine_dir={engine_dir}",
f"--num_beams={num_beams}", "--tensorrt_llm_rouge1_threshold=24",
f"--dataset_dir={llm_datasets_root}", f"--rouge_dir={llm_rouge_root}"
]
venv_check_call(llm_venv, summary_cmd)
@skip_post_blackwell
@pytest.mark.parametrize("num_beams", [1, 4],
ids=lambda num_beams: f'nb:{num_beams}')
@pytest.mark.parametrize("llm_qwen_model_root", [
"qwen_7b_chat", "qwen1.5_7b_chat", "qwen2_7b_instruct",
"qwen2_vl_7b_instruct", "qwen2.5_7b_instruct"
],
indirect=True)
def test_llm_qwen_awq_single_gpu_summary(qwen_example_root, llm_qwen_model_root,
llm_datasets_root, llm_rouge_root,
llm_venv, engine_dir, num_beams,
qcache_dir):
"Build & run int4 awq on 1 gpus."
print("Quantizing model...")
dtype = 'float16'
output_dir = f"{qcache_dir}/quantized_int4-awq"
quantize_cmd = [
f"{qwen_example_root}/../../../quantization/quantize.py",
f"--model_dir={llm_qwen_model_root}",
f"--calib_dataset={llm_datasets_root}/cnn_dailymail",
f"--output_dir={output_dir}",
"--awq_block_size=128",
"--qformat=int4_awq",
"--dtype=float16",
"--calib_size=32",
]
venv_check_call(llm_venv, quantize_cmd)
print("Build engines...")
build_cmd = [
"trtllm-build",
f"--checkpoint_dir={output_dir}",
f"--gemm_plugin={dtype}",
f"--gpt_attention_plugin={dtype}",
f"--max_beam_width={num_beams}",
f"--output_dir={engine_dir}",
"--max_seq_len=8192",
f"--max_batch_size={8}",
]
check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
summary_cmd = [
f"{qwen_example_root}/../../../summarize.py", "--test_trt_llm",
"--hf_model_dir", f"{llm_qwen_model_root}", "--data_type", "fp16",
"--check_accuracy", f"--engine_dir={engine_dir}",
f"--num_beams={num_beams}", "--tensorrt_llm_rouge1_threshold=22.8",
f"--dataset_dir={llm_datasets_root}", f"--rouge_dir={llm_rouge_root}"
]
venv_check_call(llm_venv, summary_cmd)
@pytest.mark.skip_less_device(8)
@pytest.mark.skip_less_device_memory(80000)
@pytest.mark.parametrize(
"context_fmha", ["context_fmha", "context_fmha_fp32_acc", "disable_fmha"])
@pytest.mark.parametrize(
"tp_pp_size", [(8, 1), (4, 2)],
ids=lambda tp_pp_size: f'tp{tp_pp_size[0]}pp{tp_pp_size[1]}')
@pytest.mark.parametrize(
"llm_qwen_model_root",
["qwen2.5_72b_chat", "qwen1.5_72b_chat", "qwen2_72b_instruct"],
indirect=True)
def test_llm_qwen_1node_8gpus_summary(qwen_example_root, llm_qwen_model_root,
llm_datasets_root, llm_rouge_root,
llm_venv, cmodel_dir, engine_dir,
context_fmha, tp_pp_size):
"Run qwen with smooth quant on single gpu"
print("Converting checkpoint...")
dtype = 'float16'
tp_size, pp_size = tp_pp_size
world_size = tp_size * pp_size
ckpt_dir = convert_weights(llm_venv=llm_venv,
example_root=qwen_example_root,
cmodel_dir=cmodel_dir,
model="qwen-72b",
model_path=llm_qwen_model_root,
data_type=dtype,
tp_size=4,
pp_size=2)
print("Build engines...")
build_cmd = [
"trtllm-build",
f"--checkpoint_dir={ckpt_dir}",
f"--gemm_plugin={dtype}",
f"--output_dir={engine_dir}",
f"--max_batch_size={8}",
f"--workers={world_size}",
"--max_beam_width=4",
"--max_seq_len=8192",
"--max_input_len=2048",
]
if context_fmha == "context_fmha":
build_cmd.append("--context_fmha=enable")
elif context_fmha == "disable_fmha":
build_cmd.append("--context_fmha=disable")
check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
summary_cmd = [
f"{qwen_example_root}/../../../summarize.py", "--test_trt_llm",
f"--hf_model_dir={llm_qwen_model_root}", "--data_type=fp16",
"--check_accuracy", f"--engine_dir={engine_dir}", "--num_beams=4",
"--tensorrt_llm_rouge1_threshold=22", "--max_input_length=2048",
"--output_len=2048", f"--dataset_dir={llm_datasets_root}",
f"--rouge_dir={llm_rouge_root}"
]
if context_fmha == "context_fmha_fp32_acc":
summary_cmd.append("--enable_context_fmha_fp32_acc")
venv_mpi_check_call(
llm_venv, ["mpirun", "-n", f"{world_size}", "--allow-run-as-root"],
summary_cmd)
@pytest.mark.skip_less_device(4)
@pytest.mark.skip_less_device_memory(40000)
@pytest.mark.parametrize(
"context_fmha", ["context_fmha", "context_fmha_fp32_acc", "disable_fmha"])
@pytest.mark.parametrize(
"tp_pp_size", [(4, 1), (2, 2)],
ids=lambda tp_pp_size: f'tp{tp_pp_size[0]}pp{tp_pp_size[1]}')
@pytest.mark.parametrize("llm_qwen_model_root", ["qwen2_57b_a14b"],
indirect=True)
def test_llm_qwen_moe_multi_gpu_summary(qwen_example_root, llm_qwen_model_root,
llm_datasets_root, llm_rouge_root,
llm_venv, cmodel_dir, engine_dir,
context_fmha, tp_pp_size):
"Run qwen with smooth quant on single gpu"
print("Converting checkpoint...")
dtype = 'float16'
tp_size, pp_size = tp_pp_size
world_size = tp_size * pp_size
ckpt_dir = convert_weights(llm_venv=llm_venv,
example_root=qwen_example_root,
cmodel_dir=cmodel_dir,
model="qwen-57b",
model_path=llm_qwen_model_root,
data_type=dtype,
tp_size=tp_size,
pp_size=pp_size)
print("Build engines...")
build_cmd = [
"trtllm-build",
f"--checkpoint_dir={ckpt_dir}",
f"--gemm_plugin={dtype}",
f"--output_dir={engine_dir}",
f"--max_batch_size={8}",
f"--workers={world_size}",
"--max_input_len=2048",
"--max_seq_len=8192",
]
if context_fmha == "context_fmha":
build_cmd.append("--context_fmha=enable")
elif context_fmha == "disable_fmha":
build_cmd.append("--context_fmha=disable")
check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
summary_cmd = [
f"{qwen_example_root}/../../../summarize.py", "--test_trt_llm",
f"--hf_model_dir={llm_qwen_model_root}", "--data_type=fp16",
"--check_accuracy", f"--engine_dir={engine_dir}",
"--tensorrt_llm_rouge1_threshold=20", "--max_input_length=2048",
"--output_len=2048", f"--dataset_dir={llm_datasets_root}",
f"--rouge_dir={llm_rouge_root}"
]
if context_fmha == "context_fmha_fp32_acc":
summary_cmd.append("--enable_context_fmha_fp32_acc")
venv_mpi_check_call(
llm_venv, ["mpirun", "-n", f"{world_size}", "--allow-run-as-root"],
summary_cmd)
@skip_pre_ada
@pytest.mark.parametrize("dtype", ['float16', 'bfloat16'])
@pytest.mark.parametrize("qformat", ['fp8'])
@pytest.mark.parametrize("llm_qwen_model_root", [
"qwen2.5_7b_instruct", "qwen2_7b_instruct", "qwen2_vl_7b_instruct",
"qwen2_0.5b_instruct", "qwen2.5_0.5b_instruct", "qwen2.5_1.5b_instruct"
],
indirect=True)
def test_llm_hf_qwen_quantization_1gpu(dtype, llm_qwen_model_root, llm_venv,
cmodel_dir, engine_dir,
qwen_example_root, llm_datasets_root,
llm_rouge_root, qformat):
"Run qwen quantization tests"
print("Convert checkpoint by modelopt...")
convert_cmd = [
f"{qwen_example_root}/../../../quantization/quantize.py",
f"--model_dir={llm_qwen_model_root}",
f"--calib_dataset={llm_datasets_root}/cnn_dailymail",
f"--dtype={dtype}",
f"--qformat={qformat}",
f"--kv_cache_dtype={qformat}",
f"--output_dir={cmodel_dir}",
]
venv_check_call(llm_venv, convert_cmd)
print("Build engines...")
build_cmd = [
"trtllm-build", f"--checkpoint_dir={cmodel_dir}",
f"--output_dir={engine_dir}", f"--gemm_plugin={dtype}",
f"--gpt_attention_plugin={dtype}", "--max_seq_len=4096",
f"--max_batch_size={8}", "--use_fp8_context_fmha=disable",
"--use_paged_context_fmha=disable"
]
check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
print("Run evaluation...")
threshold_score = 21
mmlu_score = None
if "0.5B" in llm_qwen_model_root:
mmlu_score = 46.0
elif "1.5B" in llm_qwen_model_root:
mmlu_score = 58.5
elif "7B" in llm_qwen_model_root:
mmlu_score = 71.5
# Install custom jinja to overcome ImportError from transformers library.
# ImportError: apply_chat_template requires jinja2>=3.1.0 to be installed.
llm_venv.run_cmd(['-m', 'pip', 'install', 'jinja2==3.1.0'])
# Run MMLU for Qwen 2.5 models.
if '2.5' in llm_qwen_model_root:
mmlu_cmd = [
"trtllm-eval", f"--model={engine_dir}",
f"--tokenizer={llm_qwen_model_root}", "--backend=tensorrt", "mmlu",
f"--dataset_path={llm_datasets_root}/mmlu", "--check_accuracy",
f"--accuracy_threshold={mmlu_score}"
]
check_call(" ".join(mmlu_cmd), shell=True, env=llm_venv._new_env)
else:
summary_cmd = [
f"{qwen_example_root}/../../../summarize.py",
"--test_trt_llm",
f"--hf_model_dir={llm_qwen_model_root}",
f"--engine_dir={engine_dir}",
"--check_accuracy",
f"--tensorrt_llm_rouge1_threshold={threshold_score}",
f"--dataset_dir={llm_datasets_root}",
f"--rouge_dir={llm_rouge_root}",
]
venv_check_call(llm_venv, summary_cmd)
@skip_pre_ada
@pytest.mark.parametrize(
"llm_qwen_model_root",
["qwen2_0.5b_instruct", "qwen2.5_0.5b_instruct", "qwen2.5_1.5b_instruct"],
indirect=True)
def test_llm_hf_qwen_multi_lora_1gpu(llm_qwen_model_root,
llm_venv,
cmodel_dir,
engine_dir,
qwen_example_root,
llm_datasets_root,
qformat='fp8',
dtype='bfloat16'):
"Run Qwen models with multiple dummy LoRAs."
print("Convert checkpoint by modelopt...")
convert_cmd = [
f"{qwen_example_root}/../../../quantization/quantize.py",
f"--model_dir={llm_qwen_model_root}",
f"--calib_dataset={llm_datasets_root}/cnn_dailymail",
f"--dtype={dtype}",
f"--qformat={qformat}",
f"--kv_cache_dtype={qformat}",
f"--output_dir={cmodel_dir}",
]
venv_check_call(llm_venv, convert_cmd)
test_multi_lora_support(
hf_model_dir=llm_qwen_model_root,
tllm_ckpt_dir=cmodel_dir,
engine_dir=engine_dir,
llm_venv=llm_venv,
example_root=qwen_example_root,
num_loras=2,
lora_rank=8,
target_hf_modules=["q_proj", "k_proj", "v_proj"],
target_trtllm_modules=["attn_q", "attn_k", "attn_v"],
zero_lora_weights=True,
)
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