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https://github.com/NVIDIA/TensorRT-LLM.git
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b777bd6475
* Update TensorRT-LLM --------- Co-authored-by: RunningLeon <mnsheng@yeah.net> Co-authored-by: Tlntin <TlntinDeng01@Gmail.com> Co-authored-by: ZHENG, Zhen <zhengzhen.z@qq.com> Co-authored-by: Pham Van Ngoan <ngoanpham1196@gmail.com> Co-authored-by: Nathan Price <nathan@abridge.com> Co-authored-by: Tushar Goel <tushar.goel.ml@gmail.com> Co-authored-by: Mati <132419219+matichon-vultureprime@users.noreply.github.com>
124 lines
4.5 KiB
Python
124 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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import unittest
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from itertools import product
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import pytest
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# isort: off
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import torch
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# isort: on
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import os
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import sys
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from cuda import cudart
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from parameterized import parameterized
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import tensorrt_llm
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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 (AllReduceConfig, AllReduceStrategy,
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allreduce)
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from tensorrt_llm.plugin.plugin import current_all_reduce_helper
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sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
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from utils.util import (create_session, run_session, skip_bf16_pre_ampere,
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unittest_name_func)
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class TestCommunicationPlugin(unittest.TestCase):
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def setUp(self):
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tensorrt_llm.logger.set_level('error')
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self.world_size = tensorrt_llm.mpi_world_size()
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self.rank = tensorrt_llm.mpi_rank()
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torch.cuda.set_device(self.rank)
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cudart.cudaSetDevice(self.rank)
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self.reference_tensors = [
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torch.full([10000000], i + 1, dtype=torch.float32, device="cuda")
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for i in range(self.world_size)
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]
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self.mapping = Mapping(self.world_size, self.rank, self.world_size,
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self.world_size)
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@parameterized.expand(list(
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product(["bfloat16", 'float16', "float32"], [
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AllReduceStrategy.NCCL, AllReduceStrategy.ONESHOT,
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AllReduceStrategy.TWOSHOT
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], [
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AllReduceConfig(0),
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AllReduceConfig.PUSH_MODE,
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AllReduceConfig.USE_MEMCPY,
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], [64 * 70000, 64 * 70, 64])),
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name_func=unittest_name_func)
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def test_allreduce(self, dtype: str, strategy: AllReduceStrategy,
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config: AllReduceConfig, size: int):
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skip_bf16_pre_ampere(dtype)
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if self.world_size == 1:
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pytest.skip("Skip single GPU NCCL")
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if strategy == AllReduceStrategy.NCCL and config != AllReduceConfig(0):
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pytest.skip("NCCL with specific config discarded")
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workspace = None
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torch_dtype = tensorrt_llm._utils.str_dtype_to_torch(dtype)
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dtype_size = torch.finfo(torch_dtype).bits // 8
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allreduce_ref = torch.zeros(self.reference_tensors[0][:size].shape,
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dtype=torch_dtype,
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device="cuda")
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for i in range(self.world_size):
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allreduce_ref = allreduce_ref + self.reference_tensors[i][:size].to(
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torch_dtype)
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# construct trt network
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builder = tensorrt_llm.Builder()
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network = builder.create_network()
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network.plugin_config.set_nccl_plugin(dtype, use_custom_all_reduce=True)
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_, workspace = current_all_reduce_helper().allocate_workspace(
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self.mapping, size * dtype_size)
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input = self.reference_tensors[self.rank][:size].to(torch_dtype)
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inner_loop = 5
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with peer_access(self.mapping):
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with tensorrt_llm.net_guard(network):
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x = Tensor(name='x',
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shape=input.shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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current_all_reduce_helper().set_workspace_tensor(self.mapping)
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current = x
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for i in range(inner_loop):
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current = allreduce(current, self.mapping.tp_group,
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strategy, config)
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current.mark_output('output', dtype)
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# trt run
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session = create_session(builder, network, precision=dtype)
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inputs = {'x': input, 'all_reduce_workspace': workspace}
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outputs = run_session(session, inputs)
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# compare diff
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torch.testing.assert_close(outputs['output'],
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(self.mapping.tp_size**(inner_loop - 1)) *
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allreduce_ref)
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