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TensorRT-LLMs/tests/integration/defs/examples/test_phi.py
Tracin 446f62bbab
chore: Deprecate evaltool (#4173) 
Signed-off-by: Tracin <10434017+Tracin@users.noreply.github.com>
2025-05-09 20:31:53 +08:00

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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.
import csv
import os
import pytest
from defs.common import (convert_weights, quantize_data,
test_multi_lora_support, venv_check_call,
venv_mpi_check_call)
from defs.conftest import (get_device_memory, get_sm_version, skip_fp8_pre_ada,
skip_post_blackwell, skip_pre_ada)
from defs.trt_test_alternative import check_call
@pytest.fixture(scope="module")
def phi_example_root(llm_root, llm_venv):
"Get phi example root"
example_root = os.path.join(llm_root, "examples", "models", "core", "phi")
llm_venv.run_cmd([
"-m", "pip", "install", "-r",
os.path.join(example_root, "requirements.txt")
])
return example_root
@pytest.mark.skip_less_device_memory(40000)
@pytest.mark.parametrize("num_beams", [1, 2, 4],
ids=lambda num_beams: f'nb:{num_beams}')
@pytest.mark.parametrize(
"context_fmha_type",
["enable_fmha", "enable_fmha_with_fp32_acc", "disable_fmha"])
@pytest.mark.parametrize(
"use_attention_plugin", [True, False],
ids=["enable_attention_plugin", "disable_attention_plugin"])
@pytest.mark.parametrize("use_gemm_plugin", [True, False],
ids=["enable_gemm_plugin", "disable_gemm_plugin"])
@pytest.mark.parametrize("dtype", ["float16", "bfloat16"])
@pytest.mark.parametrize("llm_phi_model_root", [
"phi-2", "Phi-3-mini-4k-instruct", "Phi-3-mini-128k-instruct",
"Phi-3-small-8k-instruct", "Phi-3-small-128k-instruct",
"Phi-3.5-mini-instruct"
],
indirect=True)
def test_llm_phi_single_gpu_summary(phi_example_root, llm_phi_model_root,
llm_datasets_root, llm_rouge_root, llm_venv,
cmodel_dir, engine_dir,
use_attention_plugin, use_gemm_plugin,
dtype, context_fmha_type, num_beams):
"Build & run phi on single gpu."
if (not use_attention_plugin or not use_gemm_plugin) \
and get_device_memory() < 80000:
pytest.skip("device memory is insufficient.")
if context_fmha_type != "disable_fmha":
# --enable_context_fmha / --enable_context_fmha_fp32_acc
# have to be used together with --use_gpt_attention_plugin
use_attention_plugin = True
print("Converting checkpoint...")
model_name = os.path.basename(llm_phi_model_root)
ckpt_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,
data_type=dtype)
print("Building engines...")
build_cmd = [
"trtllm-build",
f"--checkpoint_dir={ckpt_dir}",
f"--output_dir={engine_dir}",
f"--max_batch_size={16}",
f"--max_input_len={1024}",
f"--max_seq_len={2048}",
f"--max_beam_width={num_beams}",
]
if use_attention_plugin:
build_cmd.append(f"--gpt_attention_plugin={dtype}")
if context_fmha_type == "enable_fmha":
build_cmd.append("--context_fmha=enable")
elif context_fmha_type == "disable_fmha":
build_cmd.append("--context_fmha=disable")
else:
build_cmd.extend([
"--gpt_attention_plugin=disable",
"--context_fmha=disable",
"--paged_kv_cache=disable",
"--remove_input_padding=disable",
])
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)
print('Run phi...')
run_cmd = [
f"{phi_example_root}/../../../run.py",
"--max_output_len=50",
f"--engine_dir={engine_dir}",
f"--tokenizer_dir={llm_phi_model_root}",
]
venv_check_call(llm_venv, run_cmd)
rouge1_threshold = 20
if model_name == 'Phi-3-small-8k-instruct': rouge1_threshold = 18.0
if model_name == 'Phi-3-small-128k-instruct': rouge1_threshold = 19.0
summary_cmd = [
f"{phi_example_root}/../summarize.py", "--test_trt_llm",
"--hf_model_dir", f"{llm_phi_model_root}", "--data_type", "fp16",
"--check_accuracy", f"--engine_dir={engine_dir}",
f"--tensorrt_llm_rouge1_threshold={rouge1_threshold}",
f"--dataset_dir={llm_datasets_root}", f"--rouge_dir={llm_rouge_root}"
]
if context_fmha_type == "enable_fmha_with_fp32_acc":
summary_cmd.append("--enable_context_fmha_fp32_acc")
venv_check_call(llm_venv, summary_cmd)
@pytest.mark.skip_less_device(2)
@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("llm_phi_model_root", [
"phi-2", "Phi-3-mini-4k-instruct", "Phi-3-mini-128k-instruct",
"Phi-3-small-8k-instruct", "Phi-3-small-128k-instruct",
'Phi-3.5-MoE-instruct'
],
indirect=True)
def test_llm_phi_1node_2gpus_summary(phi_example_root, llm_phi_model_root,
llm_datasets_root, llm_rouge_root,
llm_venv, cmodel_dir, engine_dir,
num_beams):
"Build & run phi on 2 gpus."
print("Converting checkpoint...")
model_name = os.path.basename(llm_phi_model_root)
ckpt_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,
data_type="float16",
tp_size=2,
pp_size=1)
print("Building engines...")
build_cmd = [
"trtllm-build",
f"--checkpoint_dir={ckpt_dir}",
f"--output_dir={engine_dir}",
f"--max_batch_size={16}",
f"--max_input_len={1024}",
f"--max_seq_len={2048}",
f"--max_beam_width={num_beams}",
"--gemm_plugin=float16",
"--gpt_attention_plugin=float16",
]
check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
print('Run phi...')
rouge1_threshold = 21.2
if model_name == 'Phi-3.5-MoE-instruct': rouge1_threshold = 24.0
summary_cmd = [
f"{phi_example_root}/../../../summarize.py", "--test_trt_llm",
"--hf_model_dir", f"{llm_phi_model_root}", "--data_type", "fp16",
"--check_accuracy", f"--engine_dir={engine_dir}",
f"--tensorrt_llm_rouge1_threshold={rouge1_threshold}",
f"--dataset_dir={llm_datasets_root}", f"--rouge_dir={llm_rouge_root}"
]
venv_mpi_check_call(llm_venv, ["mpirun", "-n", "2", "--allow-run-as-root"],
summary_cmd)
@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", [
pytest.param("phi-2", marks=skip_post_blackwell),
pytest.param("Phi-3-mini-128k-instruct", marks=skip_post_blackwell),
pytest.param("Phi-3-small-128k-instruct", marks=skip_post_blackwell),
pytest.param("Phi-3.5-mini-instruct", marks=skip_post_blackwell),
"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,
)
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