mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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198 lines
7.4 KiB
Python
198 lines
7.4 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 as tllm
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from tensorrt_llm import Mapping, Tensor
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from tensorrt_llm.functional import (allgather, allreduce, concat, recv,
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reduce_scatter, send)
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from tensorrt_llm.plugin.plugin import (current_all_reduce_helper,
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init_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, unittest_name_func
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sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
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from utils.util import unittest_name_func
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def forward_allreduce(x: Tensor, y: Tensor, mapping: Mapping) -> Tensor:
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current = x
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if mapping.tp_size > 1 and mapping.tp_group is not None:
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current = allreduce(current, mapping.tp_group)
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current = current + y
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return current
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def forward_reduce_scatter(x: Tensor, y: Tensor, mapping: Mapping,
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hidden_size: int) -> Tensor:
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if mapping.tp_rank == 0:
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current = x + y
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else:
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current = x + 0
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# reshape to (-1)
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current = current.view(concat([-1]))
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if mapping.tp_size > 1 and mapping.tp_group is not None:
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current = reduce_scatter(current, mapping.tp_group)
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# reshape to (-1, hidden_size // tp_size)
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current = current.view(concat([-1, hidden_size // mapping.tp_size]))
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return current
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class TestPPReduceScatter(unittest.TestCase):
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def setUp(self):
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torch.manual_seed(20240603)
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torch.cuda.manual_seed(20240603)
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tllm.logger.set_level('error')
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self.world_size = tllm.mpi_world_size()
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self.rank = tllm.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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@parameterized.expand(list(
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product(['bfloat16', 'float16', 'float32'], [1, 4, 16, 64],
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[4096, 8192, 12288], [2, 4, 8])),
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name_func=unittest_name_func)
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def test_pp_reduce_scatter(self, dtype: str, token_num: int,
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hidden_size: int, pp_size: int):
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if self.world_size == 1 or pp_size > self.world_size:
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pytest.skip("Skip single GPU and pp_size > world_size case")
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tp_size = self.world_size // pp_size
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mapping = Mapping(self.world_size,
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self.rank,
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self.world_size,
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tp_size=tp_size,
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pp_size=pp_size)
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size = token_num * hidden_size # tensor size
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torch_dtype = tllm._utils.str_dtype_to_torch(dtype)
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dtype_size = torch.finfo(torch_dtype).bits // 8
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input = self.reference_tensors[self.rank][:size].to(
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torch_dtype).reshape(token_num, hidden_size)
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residual = torch.rand(input.shape, dtype=torch_dtype, device="cuda")
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input_recv = torch.zeros(torch.Size([token_num,
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hidden_size // tp_size]),
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dtype=torch_dtype,
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device="cuda")
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builder = tllm.Builder()
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net_ref = builder.create_network()
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net = builder.create_network()
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init_all_reduce_helper()
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_, workspace = current_all_reduce_helper().allocate_workspace(
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mapping, size * dtype_size)
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with tllm.net_guard(net_ref):
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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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y = Tensor(name='y',
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shape=residual.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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if not mapping.is_first_pp_rank():
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net_ref_input = x
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net_ref_input = recv(net_ref_input, mapping.prev_pp_rank())
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else:
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net_ref_input = x
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if not mapping.is_last_pp_rank():
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output_ref = forward_allreduce(net_ref_input, y, mapping)
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output_ref = send(output_ref, mapping.next_pp_rank())
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else:
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output_ref = forward_allreduce(net_ref_input, y, mapping)
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output_ref.mark_output('output', dtype)
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with tllm.net_guard(net):
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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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y = Tensor(name='y',
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shape=residual.shape,
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dtype=tllm.str_dtype_to_trt(dtype))
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x_recv = Tensor(name='x_recv',
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shape=torch.Size(
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[token_num, hidden_size // mapping.tp_size]),
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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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if not mapping.is_first_pp_rank():
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net_input = x_recv
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net_input = recv(net_input, mapping.prev_pp_rank())
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net_input = allgather(net_input, mapping.tp_group, gather_dim=0)
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# reshape to (-1, hidden_size)
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net_input = net_input.view(concat([-1, hidden_size]))
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else:
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net_input = x
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if not mapping.is_last_pp_rank():
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output = forward_reduce_scatter(net_input, y, mapping,
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hidden_size)
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output = send(output, mapping.next_pp_rank())
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else:
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output = forward_allreduce(net_input, y, mapping)
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output.mark_output('output', dtype)
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feed_dict_ref = {
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'x': input,
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'y': residual,
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'all_reduce_workspace': workspace
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}
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feed_dict = {
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'x': input,
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'y': residual,
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'x_recv': input_recv,
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'all_reduce_workspace': workspace
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}
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# trt run
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session_ref = create_session(builder, net_ref, precision=dtype)
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outputs_ref = run_session(session_ref, feed_dict_ref)
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session = create_session(builder, net, precision=dtype)
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outputs = run_session(session, feed_dict)
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# compare diff
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if mapping.is_last_pp_rank():
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torch.testing.assert_allclose(outputs['output'],
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outputs_ref['output'],
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atol=1e-5,
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rtol=1e-2)
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if __name__ == "__main__":
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unittest.main()
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