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https://github.com/NVIDIA/TensorRT-LLM.git
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b57221b764
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
128 lines
4.5 KiB
Python
128 lines
4.5 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from argparse import ArgumentParser
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# isort: off
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import torch
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# isort: on
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from cuda import cuda, cudart
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from mpi4py import MPI
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from polygraphy.backend.trt import CreateConfig, EngineFromNetwork
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import tensorrt_llm as tllm
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from tensorrt_llm import Mapping, Tensor
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from tensorrt_llm._ipc_utils import peer_access
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from tensorrt_llm.functional import AllReduceStrategy, allreduce
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from tensorrt_llm.plugin.plugin import current_all_reduce_helper
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def allreduce_benchmark(dtype: str, test_range: str = "10,10000000,10"):
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tllm.logger.set_level('error')
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world_size = tllm.mpi_world_size()
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rank = tllm.mpi_rank()
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torch.cuda.set_device(rank)
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cudart.cudaSetDevice(rank)
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mapping = Mapping(world_size, rank, world_size, world_size)
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if world_size == 1:
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raise RuntimeError("Benchmark must run with mpi_world_size > 1")
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torch_dtype = tllm._utils.str_dtype_to_torch(dtype)
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min_size, max_size, ratio = [int(i) for i in test_range.split(",")]
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inner_loop = 1000
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size = min_size
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dtype_size = torch.finfo(torch_dtype).bits // 8
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while size < max_size:
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input = torch.ones(size, dtype=torch_dtype, device="cuda")
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for strategy in [
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AllReduceStrategy.RING, AllReduceStrategy.ONESHOT,
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AllReduceStrategy.TWOSHOT
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]:
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builder = tllm.Builder()
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net = builder.create_network()
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net.plugin_config.set_nccl_plugin(dtype, use_custom_all_reduce=True)
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_buffers, workspace = current_all_reduce_helper(
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).allocate_workspace(mapping, size * dtype_size)
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with tllm.net_guard(net):
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network = tllm.default_trtnet()
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x = Tensor(name='x',
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shape=input.shape,
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dtype=tllm.str_dtype_to_trt(dtype))
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current_all_reduce_helper().set_workspace_tensor(mapping)
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current = x
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for _ in range(inner_loop):
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current = allreduce(current, mapping.tp_group, strategy)
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output = current.trt_tensor
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output.name = 'output'
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output.dtype = tllm.str_dtype_to_trt(dtype)
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network.mark_output(output)
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build_engine = EngineFromNetwork(
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(builder.trt_builder, net.trt_network),
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config=CreateConfig(
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fp16=(dtype == 'float16'),
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bf16=(dtype == 'bfloat16'),
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precision_constraints='obey',
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))
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output = torch.zeros_like(input)
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stream = torch.cuda.current_stream()
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feed_dict = {'x': input, 'all_reduce_workspace': workspace}
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session = tllm.runtime.Session.from_engine(build_engine())
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_, start = cuda.cuEventCreate(0)
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_, stop = cuda.cuEventCreate(0)
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with peer_access(mapping):
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MPI.COMM_WORLD.barrier()
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cuda.cuEventRecord(start, stream.cuda_stream)
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session.run(inputs=feed_dict,
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outputs={"output": output},
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stream=stream.cuda_stream)
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cuda.cuEventRecord(stop, stream.cuda_stream)
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torch.cuda.synchronize()
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_, ms = cuda.cuEventElapsedTime(start, stop)
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assert torch.allclose(output, (input * world_size)**inner_loop)
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if mapping.rank == 0:
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print(f"{size=}, {strategy=}, {ms=}")
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size *= ratio
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if mapping.rank == 0:
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print("")
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if __name__ == "__main__":
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parser = ArgumentParser()
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parser.add_argument("--dtype", "-t", default="float16")
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parser.add_argument("--range",
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"-r",
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default="256,25600000,10",
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help="min_size,max_size,multiplicative_ratio")
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args = parser.parse_args()
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allreduce_benchmark(args.dtype, args.range)
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