mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-26 21:53:30 +08:00
b57221b764
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
136 lines
4.8 KiB
Python
136 lines
4.8 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
|
|
from cuda import cudart
|
|
from parameterized import parameterized
|
|
from polygraphy.backend.trt import CreateConfig, EngineFromNetwork
|
|
|
|
import tensorrt_llm as tllm
|
|
from tensorrt_llm import Mapping, Tensor
|
|
from tensorrt_llm._ipc_utils import peer_access
|
|
from tensorrt_llm.functional import AllReduceStrategy, allreduce
|
|
from tensorrt_llm.plugin.plugin import current_all_reduce_helper
|
|
|
|
|
|
def custom_name_func(testcase_func, param_num, param):
|
|
return "%s_%s" % (
|
|
testcase_func.__name__,
|
|
parameterized.to_safe_name("_".join(str(x) for x in param.args)),
|
|
)
|
|
|
|
|
|
class TestCommunicationPlugin(unittest.TestCase):
|
|
|
|
def setUp(self):
|
|
tllm.logger.set_level('error')
|
|
self.world_size = tllm.mpi_world_size()
|
|
self.rank = tllm.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,
|
|
self.world_size)
|
|
|
|
@parameterized.expand(list(
|
|
product(["bfloat16", 'float16', "float32"], [
|
|
AllReduceStrategy.RING, AllReduceStrategy.ONESHOT,
|
|
AllReduceStrategy.TWOSHOT
|
|
], [64 * 70000, 64 * 70, 64])),
|
|
name_func=custom_name_func)
|
|
def test_nccl_allreduce(self, dtype: str, strategy: AllReduceStrategy,
|
|
size: int):
|
|
if self.world_size == 1:
|
|
pytest.skip()
|
|
|
|
workspace = None
|
|
|
|
torch_dtype = tllm._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)
|
|
|
|
builder = tllm.Builder()
|
|
net = builder.create_network()
|
|
net.plugin_config.set_nccl_plugin(dtype, use_custom_all_reduce=True)
|
|
_, 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 peer_access(self.mapping):
|
|
with tllm.net_guard(net):
|
|
network = tllm.default_trtnet()
|
|
|
|
x = Tensor(name='x',
|
|
shape=input.shape,
|
|
dtype=tllm.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,
|
|
strategy)
|
|
output = current.trt_tensor
|
|
|
|
output.name = 'output'
|
|
output.dtype = tllm.str_dtype_to_trt(dtype)
|
|
network.mark_output(output)
|
|
|
|
build_engine = EngineFromNetwork(
|
|
(builder.trt_builder, net.trt_network),
|
|
config=CreateConfig(
|
|
fp16=(dtype == 'float16'),
|
|
bf16=(dtype == 'bfloat16'),
|
|
precision_constraints='obey',
|
|
))
|
|
|
|
output = torch.zeros_like(input)
|
|
|
|
stream = torch.cuda.current_stream()
|
|
feed_dict = {'x': input, 'all_reduce_workspace': workspace}
|
|
|
|
session = tllm.runtime.Session.from_engine(build_engine())
|
|
session.run(inputs=feed_dict,
|
|
outputs={"output": output},
|
|
stream=stream.cuda_stream)
|
|
torch.cuda.synchronize()
|
|
|
|
self.assertTrue(
|
|
torch.allclose(output.cpu(),
|
|
(self.mapping.tp_size**(inner_loop - 1)) *
|
|
allreduce_ref.cpu()))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|