mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
126 lines
4.6 KiB
Python
126 lines
4.6 KiB
Python
# 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 unittest
|
|
from itertools import product
|
|
|
|
import pytest
|
|
|
|
# isort: off
|
|
import torch
|
|
# isort: on
|
|
import os
|
|
import sys
|
|
|
|
from cuda import cudart
|
|
from parameterized import parameterized
|
|
|
|
import tensorrt_llm
|
|
from tensorrt_llm import Mapping, Tensor
|
|
from tensorrt_llm.functional import (AllReduceConfig, AllReduceParams,
|
|
AllReduceStrategy, allreduce)
|
|
from tensorrt_llm.plugin.plugin import current_all_reduce_helper
|
|
|
|
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
|
|
from utils.util import (create_session, run_session, skip_bf16_pre_ampere,
|
|
unittest_name_func)
|
|
|
|
|
|
class TestCommunicationPlugin(unittest.TestCase):
|
|
|
|
def setUp(self):
|
|
tensorrt_llm.logger.set_level('error')
|
|
self.world_size = tensorrt_llm.mpi_world_size()
|
|
self.rank = tensorrt_llm.mpi_rank()
|
|
|
|
torch.cuda.set_device(self.rank)
|
|
cudart.cudaSetDevice(self.rank)
|
|
|
|
self.reference_tensors = [
|
|
torch.full([10000000], i + 1, dtype=torch.float32, device="cuda")
|
|
for i in range(self.world_size)
|
|
]
|
|
self.mapping = Mapping(self.world_size,
|
|
self.rank,
|
|
self.world_size,
|
|
tp_size=self.world_size)
|
|
|
|
@parameterized.expand(list(
|
|
product(["bfloat16", 'float16', "float32"], [
|
|
AllReduceStrategy.NCCL, AllReduceStrategy.ONESHOT,
|
|
AllReduceStrategy.TWOSHOT
|
|
], [
|
|
AllReduceConfig(0),
|
|
AllReduceConfig.PUSH_MODE,
|
|
AllReduceConfig.USE_MEMCPY,
|
|
], [64 * 70000, 64 * 70, 64])),
|
|
name_func=unittest_name_func)
|
|
def test_allreduce(self, dtype: str, strategy: AllReduceStrategy,
|
|
config: AllReduceConfig, size: int):
|
|
|
|
skip_bf16_pre_ampere(dtype)
|
|
if self.world_size == 1:
|
|
pytest.skip("Skip single GPU NCCL")
|
|
|
|
if strategy == AllReduceStrategy.NCCL and config != AllReduceConfig(0):
|
|
pytest.skip("NCCL with specific config discarded")
|
|
|
|
workspace = None
|
|
|
|
torch_dtype = tensorrt_llm._utils.str_dtype_to_torch(dtype)
|
|
dtype_size = torch.finfo(torch_dtype).bits // 8
|
|
|
|
allreduce_ref = torch.zeros(self.reference_tensors[0][:size].shape,
|
|
dtype=torch_dtype,
|
|
device="cuda")
|
|
for i in range(self.world_size):
|
|
allreduce_ref = allreduce_ref + self.reference_tensors[i][:size].to(
|
|
torch_dtype)
|
|
|
|
# construct trt network
|
|
builder = tensorrt_llm.Builder()
|
|
network = builder.create_network()
|
|
network.plugin_config.set_nccl_plugin(dtype)
|
|
_, workspace = current_all_reduce_helper().allocate_workspace(
|
|
self.mapping, size * dtype_size)
|
|
|
|
input = self.reference_tensors[self.rank][:size].to(torch_dtype)
|
|
inner_loop = 5
|
|
|
|
with tensorrt_llm.net_guard(network):
|
|
|
|
x = Tensor(name='x',
|
|
shape=input.shape,
|
|
dtype=tensorrt_llm.str_dtype_to_trt(dtype))
|
|
current_all_reduce_helper().set_workspace_tensor(self.mapping)
|
|
|
|
current = x
|
|
for i in range(inner_loop):
|
|
current = allreduce(current,
|
|
self.mapping.tp_group,
|
|
all_reduce_params=AllReduceParams(
|
|
strategy=strategy, config=config))
|
|
|
|
current.mark_output('output', dtype)
|
|
|
|
# trt run
|
|
session = create_session(builder, network, precision=dtype)
|
|
inputs = {'x': input, 'all_reduce_workspace': workspace}
|
|
outputs = run_session(session, inputs)
|
|
|
|
# compare diff
|
|
torch.testing.assert_close(outputs['output'],
|
|
(self.mapping.tp_size**(inner_loop - 1)) *
|
|
allreduce_ref)
|