kanshan/TensorRT-LLMs
1
0
Fork 0
mirror of https://github.com/NVIDIA/TensorRT-LLM.git synced 2026-01-14 06:27:45 +08:00
Code Issues Actions 1 Packages Projects Releases Wiki Activity
4c498bfe58
TensorRT-LLMs/tests/integration/defs/examples/test_eagle.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

409 lines
18 KiB
Python
Raw Blame History

# 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 os
import pytest
from defs.common import (convert_weights, get_dummy_spec_decoding_heads,
venv_check_call)
from defs.conftest import 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)
@skip_post_blackwell
@pytest.mark.parametrize("use_dynamic_tree", [False, True],
ids=['eagle1', 'eagle2'])
@pytest.mark.parametrize("batch_size", [1, 8], ids=['bs1', 'bs8'])
@pytest.mark.parametrize("data_type", ['float16'])
@pytest.mark.parametrize("eagle_model_roots", ["EAGLE-Vicuna-7B-v1.3"],
indirect=True)
def test_llm_eagle_1gpu(batch_size, data_type, use_dynamic_tree,
eagle_model_roots, eagle_example_root,
llm_datasets_root, llm_rouge_root, llm_venv, cmodel_dir,
engine_dir):
print("Build engines...")
model_name = "eagle"
model_dir = convert_weights(llm_venv=llm_venv,
example_root=eagle_example_root,
cmodel_dir=cmodel_dir,
model=model_name,
model_path=eagle_model_roots,
data_type=data_type)
build_cmd = [
"trtllm-build",
f"--checkpoint_dir={model_dir}",
f"--output_dir={engine_dir}",
f"--gpt_attention_plugin={data_type}",
f"--gemm_plugin={data_type}",
f"--max_beam_width=1",
"--remove_input_padding=enable",
"--context_fmha=enable",
"--use_paged_context_fmha=enable",
"--max_input_len=1024",
"--max_seq_len=1536",
f"--max_batch_size={batch_size}",
"--paged_kv_cache=enable",
'--speculative_decoding_mode=eagle',
]
check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
print("Run run...")
run_cmd = [
f"{eagle_example_root}/../run.py",
"--max_output_len=100",
f"--tokenizer_dir={eagle_model_roots[0]}",
"--log_level=verbose",
f"--engine_dir={engine_dir}",
]
if use_dynamic_tree:
run_cmd.extend(
[f"--eagle_dynamic_tree_max_top_k={3}", "--eagle_use_dynamic_tree"])
venv_check_call(llm_venv, run_cmd)
print("Run summarize...")
summary_cmd = [
f"{eagle_example_root}/../summarize.py", "--test_trt_llm",
"--hf_model_dir", f"{eagle_model_roots[0]}", "--tokenizer_dir",
f"{eagle_model_roots[0]}", f"--engine_dir={engine_dir}",
"--check_accuracy", "--tensorrt_llm_rouge1_threshold=24",
"--eagle_choices=[[0], [0, 0], [1], [0, 1], [2], [0, 0, 0], [1, 0], [0, 2], [3], [0, 3], [4], [0, 4], [2, 0], [0, 5], [0, 0, 1], [5], [0, 6], [6], [0, 7], [0, 1, 0], [1, 1], [7], [0, 8], [0, 0, 2], [3, 0], [0, 9], [8], [9], [1, 0, 0], [0, 2, 0], [1, 2], [0, 0, 3], [4, 0], [2, 1], [0, 0, 4], [0, 0, 5], [0, 0, 0, 0], [0, 1, 1], [0, 0, 6], [0, 3, 0], [5, 0], [1, 3], [0, 0, 7], [0, 0, 8], [0, 0, 9], [6, 0], [0, 4, 0], [1, 4], [7, 0], [0, 1, 2], [2, 0, 0], [3, 1], [2, 2], [8, 0], [0, 5, 0], [1, 5], [1, 0, 1], [0, 2, 1], [9, 0], [0, 6, 0], [0, 0, 0, 1], [1, 6], [0, 7, 0]]",
f"--max_ite=40", f"--batch_size={batch_size}",
f"--dataset_dir={llm_datasets_root}", f"--rouge_dir={llm_rouge_root}"
]
if use_dynamic_tree:
summary_cmd.extend(
[f"--eagle_dynamic_tree_max_top_k={3}", "--eagle_use_dynamic_tree"])
venv_check_call(llm_venv, summary_cmd)
# TODO: remove skip_post_blackwell after Speculative decoding is supported.
@skip_post_blackwell
@skip_pre_ada
@pytest.mark.parametrize("batch_size", [8], ids=['bs8'])
@pytest.mark.parametrize("data_type", ['float16'])
@pytest.mark.parametrize("eagle_model_roots", ["llama3.1-eagle-8b-hf_v0.5"],
indirect=True)
def test_llm_eagle_1gpu_modelopt_ckpt(batch_size, data_type, eagle_model_roots,
eagle_example_root, llm_datasets_root,
llm_rouge_root, llm_venv, cmodel_dir,
engine_dir):
print("Build engines...")
model_name = "eagle"
# Although the datatype is float16, the actual weights are FP8.
# The datatype in the convert stage is used for the input and output of the plugin.
model_dir = convert_weights(llm_venv=llm_venv,
example_root=eagle_example_root,
cmodel_dir=cmodel_dir,
model=model_name,
model_path=eagle_model_roots,
data_type=data_type)
build_cmd = [
"trtllm-build",
f"--checkpoint_dir={model_dir}",
f"--output_dir={engine_dir}",
f"--max_beam_width=1",
"--use_paged_context_fmha=enable",
f"--max_batch_size={batch_size}",
"--speculative_decoding_mode=eagle",
"--multiple_profiles=enable" # also test multiple_profiles
]
check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
print("Run run...")
run_cmd = [
f"{eagle_example_root}/../run.py", f"--engine_dir={engine_dir}",
f"--tokenizer_dir={eagle_model_roots}",
"--eagle_choices=[[0], [0, 0], [1], [0, 1], [2], [0, 0, 0], [1, 0], [0, 2], [3], [0, 3], [4], [0, 4], [2, 0], [0, 5], [0, 0, 1], [5], [0, 6], [6], [0, 7], [0, 1, 0], [1, 1], [7], [0, 8], [0, 0, 2], [3, 0], [0, 9], [8], [9], [1, 0, 0], [0, 2, 0], [1, 2], [0, 0, 3], [4, 0], [2, 1], [0, 0, 4], [0, 0, 5], [0, 0, 0, 0], [0, 1, 1], [0, 0, 6], [0, 3, 0], [5, 0], [1, 3], [0, 0, 7], [0, 0, 8], [0, 0, 9], [6, 0], [0, 4, 0], [1, 4], [7, 0], [0, 1, 2], [2, 0, 0], [3, 1], [2, 2], [8, 0], [0, 5, 0], [1, 5], [1, 0, 1], [0, 2, 1], [9, 0], [0, 6, 0], [0, 0, 0, 1], [1, 6], [0, 7, 0]]",
"--max_output_len=100"
]
venv_check_call(llm_venv, run_cmd)
def test_with_dummy_eagle(hf_model_root,
use_dynamic_tree,
eagle_example_root,
llm_datasets_root,
llm_rouge_root,
llm_venv,
cmodel_dir,
engine_dir,
batch_size=8,
data_type="bfloat16"):
print("Build engines...")
model_name = "eagle"
# We unset WORLD_SIZE while running tests in specific cluster nodes to
# deal with a bug in transformers library. Trainer initialization in
# get_dummy_spec_decoding_heads() function fails if WORLD_SIZE is unset.
# Preemptively skip tests if WORLD_SIZE is unset.
if os.environ.get("WORLD_SIZE") is None:
pytest.skip(
"[test_with_dummy_eagle] Skipping test due to missing WORLD_SIZE env variable."
)
print("Creating dummy Eagle heads...")
get_dummy_spec_decoding_heads(hf_model_dir=hf_model_root,
save_dir=llm_venv.get_working_directory(),
mode='eagle')
eagle_model_root = os.path.join(llm_venv.get_working_directory(), 'fp8')
ckpt_dir = convert_weights(llm_venv=llm_venv,
example_root=eagle_example_root,
cmodel_dir=cmodel_dir,
model=model_name,
model_path=eagle_model_root,
data_type=data_type)
build_cmd = [
"trtllm-build",
f"--checkpoint_dir={ckpt_dir}",
f"--output_dir={engine_dir}",
f"--gpt_attention_plugin={data_type}",
f"--gemm_plugin={data_type}",
f"--max_beam_width=1",
"--remove_input_padding=enable",
"--context_fmha=enable",
"--use_paged_context_fmha=enable",
"--max_input_len=1024",
"--max_seq_len=1536",
f"--max_batch_size={batch_size}",
"--paged_kv_cache=enable",
'--speculative_decoding_mode=eagle',
]
check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
print("Run run...")
run_cmd = [
f"{eagle_example_root}/../run.py",
"--max_output_len=100",
f"--tokenizer_dir={hf_model_root}",
"--log_level=verbose",
f"--engine_dir={engine_dir}",
]
if use_dynamic_tree:
run_cmd.extend(
[f"--eagle_dynamic_tree_max_top_k={3}", "--eagle_use_dynamic_tree"])
venv_check_call(llm_venv, run_cmd)
print("Run summarize...")
summary_cmd = [
f"{eagle_example_root}/../summarize.py", "--test_trt_llm",
"--hf_model_dir", f"{hf_model_root}", "--tokenizer_dir",
f"{hf_model_root}", f"--engine_dir={engine_dir}",
"--eagle_choices=[[0], [0, 0], [1], [0, 1], [2], [0, 0, 0], [1, 0], [0, 2], [3], [0, 3], [4], [0, 4], [2, 0], [0, 5], [0, 0, 1], [5], [0, 6], [6], [0, 7], [0, 1, 0], [1, 1], [7], [0, 8], [0, 0, 2], [3, 0], [0, 9], [8], [9], [1, 0, 0], [0, 2, 0], [1, 2], [0, 0, 3], [4, 0], [2, 1], [0, 0, 4], [0, 0, 5], [0, 0, 0, 0], [0, 1, 1], [0, 0, 6], [0, 3, 0], [5, 0], [1, 3], [0, 0, 7], [0, 0, 8], [0, 0, 9], [6, 0], [0, 4, 0], [1, 4], [7, 0], [0, 1, 2], [2, 0, 0], [3, 1], [2, 2], [8, 0], [0, 5, 0], [1, 5], [1, 0, 1], [0, 2, 1], [9, 0], [0, 6, 0], [0, 0, 0, 1], [1, 6], [0, 7, 0]]",
f"--max_ite=40", f"--batch_size={batch_size}",
f"--dataset_dir={llm_datasets_root}", f"--rouge_dir={llm_rouge_root}"
]
if use_dynamic_tree:
summary_cmd.extend(
[f"--eagle_dynamic_tree_max_top_k={3}", "--eagle_use_dynamic_tree"])
venv_check_call(llm_venv, summary_cmd)
@pytest.mark.parametrize("use_dynamic_tree", [
False,
pytest.param(True, marks=pytest.mark.skip(reason="https://nvbugs/5219534"))
],
ids=['eagle1', 'eagle2'])
@pytest.mark.parametrize("llama_model_root",
['llama-v2-7b-hf', 'llama-3.1-8b', 'llama-3.2-1b'],
indirect=True)
def test_llama_eagle_1gpu(llama_model_root,
eagle_example_root,
llm_datasets_root,
llm_rouge_root,
llm_venv,
cmodel_dir,
engine_dir,
use_dynamic_tree,
batch_size=8,
data_type='bfloat16'):
test_with_dummy_eagle(hf_model_root=llama_model_root,
eagle_example_root=eagle_example_root,
llm_venv=llm_venv,
cmodel_dir=cmodel_dir,
engine_dir=engine_dir,
batch_size=batch_size,
data_type=data_type,
use_dynamic_tree=use_dynamic_tree,
llm_datasets_root=llm_datasets_root,
llm_rouge_root=llm_rouge_root)
@pytest.mark.skip(reason="https://nvbugs/5219534")
@pytest.mark.parametrize("use_dynamic_tree", [False, True],
ids=['eagle1', 'eagle2'])
@pytest.mark.parametrize("code_llama_model_root", ['CodeLlama-7b-Instruct'],
indirect=True)
def test_codellama_eagle_1gpu(code_llama_model_root,
eagle_example_root,
llm_datasets_root,
llm_rouge_root,
llm_venv,
cmodel_dir,
engine_dir,
use_dynamic_tree,
batch_size=8,
data_type='bfloat16'):
test_with_dummy_eagle(hf_model_root=code_llama_model_root,
eagle_example_root=eagle_example_root,
llm_venv=llm_venv,
cmodel_dir=cmodel_dir,
engine_dir=engine_dir,
batch_size=batch_size,
data_type=data_type,
use_dynamic_tree=use_dynamic_tree,
llm_datasets_root=llm_datasets_root,
llm_rouge_root=llm_rouge_root)
@pytest.mark.parametrize("use_dynamic_tree", [False, True],
ids=['eagle1', 'eagle2'])
@pytest.mark.parametrize("llm_mistral_model_root", ['mistral-7b-v0.1'],
indirect=True)
def test_mistral_eagle_1gpu(llm_mistral_model_root,
eagle_example_root,
llm_datasets_root,
llm_rouge_root,
llm_venv,
cmodel_dir,
engine_dir,
use_dynamic_tree,
batch_size=8,
data_type='bfloat16'):
test_with_dummy_eagle(hf_model_root=llm_mistral_model_root,
eagle_example_root=eagle_example_root,
llm_venv=llm_venv,
cmodel_dir=cmodel_dir,
engine_dir=engine_dir,
batch_size=batch_size,
data_type=data_type,
use_dynamic_tree=use_dynamic_tree,
llm_datasets_root=llm_datasets_root,
llm_rouge_root=llm_rouge_root)
@skip_post_blackwell
@skip_pre_ada
@pytest.mark.skip_less_device_memory(80000)
@pytest.mark.parametrize("use_dynamic_tree", [False, True],
ids=['eagle1', 'eagle2'])
@pytest.mark.parametrize("mistral_nemo_model_root", ['Mistral-Nemo-12b-Base'],
indirect=True)
def test_mistral_nemo_eagle_1gpu(mistral_nemo_model_root,
eagle_example_root,
llm_datasets_root,
llm_rouge_root,
llm_venv,
cmodel_dir,
engine_dir,
use_dynamic_tree,
batch_size=8,
data_type='bfloat16'):
test_with_dummy_eagle(hf_model_root=mistral_nemo_model_root,
eagle_example_root=eagle_example_root,
llm_venv=llm_venv,
cmodel_dir=cmodel_dir,
engine_dir=engine_dir,
batch_size=batch_size,
data_type=data_type,
use_dynamic_tree=use_dynamic_tree,
llm_datasets_root=llm_datasets_root,
llm_rouge_root=llm_rouge_root)
@pytest.mark.parametrize("use_dynamic_tree", [False, True],
ids=['eagle1', 'eagle2'])
@pytest.mark.parametrize("llm_qwen_model_root", [
"qwen_7b_chat", "qwen1.5_7b_chat", "qwen2_7b_instruct",
"qwen2_0.5b_instruct", "qwen2.5_1.5b_instruct"
],
indirect=True)
def test_qwen_eagle_1gpu(llm_qwen_model_root,
eagle_example_root,
llm_datasets_root,
llm_rouge_root,
llm_venv,
cmodel_dir,
engine_dir,
use_dynamic_tree,
batch_size=8,
data_type='bfloat16'):
test_with_dummy_eagle(hf_model_root=llm_qwen_model_root,
eagle_example_root=eagle_example_root,
llm_venv=llm_venv,
cmodel_dir=cmodel_dir,
engine_dir=engine_dir,
batch_size=batch_size,
data_type=data_type,
use_dynamic_tree=use_dynamic_tree,
llm_datasets_root=llm_datasets_root,
llm_rouge_root=llm_rouge_root)
@pytest.mark.parametrize("use_dynamic_tree", [False, True],
ids=['eagle1', 'eagle2'])
@pytest.mark.parametrize("llm_phi_model_root", [
"phi-2", "Phi-3-mini-128k-instruct", "Phi-3-small-128k-instruct",
"Phi-3.5-mini-instruct"
],
indirect=True)
def test_phi_eagle_1gpu(llm_phi_model_root,
eagle_example_root,
llm_datasets_root,
llm_rouge_root,
llm_venv,
cmodel_dir,
engine_dir,
use_dynamic_tree,
batch_size=8,
data_type='bfloat16'):
test_with_dummy_eagle(hf_model_root=llm_phi_model_root,
eagle_example_root=eagle_example_root,
llm_venv=llm_venv,
cmodel_dir=cmodel_dir,
engine_dir=engine_dir,
batch_size=batch_size,
data_type=data_type,
use_dynamic_tree=use_dynamic_tree,
llm_datasets_root=llm_datasets_root,
llm_rouge_root=llm_rouge_root)
Reference in New Issue View Git Blame Copy Permalink