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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>
103 lines
3.8 KiB
Python
103 lines
3.8 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 os
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import sys
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import unittest
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import torch
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from parameterized import parameterized
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import tensorrt_llm
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from tensorrt_llm import Tensor
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from tensorrt_llm._utils import str_dtype_to_torch
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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
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class TestScatter(unittest.TestCase):
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def setUp(self):
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tensorrt_llm.logger.set_level('error')
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@parameterized.expand([
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(
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[
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[0.0, 0.0, 0.0],
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[0.0, 0.0, 0.0],
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[0.0, 0.0, 0.0],
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],
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[
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[1, 0, 2],
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[0, 2, 1],
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],
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[[1.0, 1.1, 1.2], [2.0, 2.1, 2.2]],
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0,
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),
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([[1, 2, 3], [4, 5, 6]], [[1, 2], [0, 1]], [[-1, -2], [-3, -4]], 1),
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(
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[[[-3.0, -2.0, -1.0, 10.0, -25.0]], [[0.0, 1.0, 2.0, -2.0, -1.0]]],
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[[[1, 2, 3, 0, 4]], [[4, 1, 2, 3, 0]]],
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[[[-1.0, 2.4, 3.2, 10.8, 8.9]], [[0, -11.2, 34.2, 223.9, -100]]],
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2,
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),
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])
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def test_scatter(self, input_data, indices, updates, dim):
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dtype = 'float32'
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torch_dtype = str_dtype_to_torch(dtype)
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input_data = input_data.cuda() if isinstance(
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input_data, torch.Tensor) else torch.tensor(input_data).cuda()
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indices = indices.cuda() if isinstance(
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indices, torch.Tensor) else torch.tensor(indices).int().cuda()
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updates = updates.cuda() if isinstance(
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updates, torch.Tensor) else torch.tensor(updates).cuda()
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input_data = input_data.to(torch_dtype)
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updates = updates.to(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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with tensorrt_llm.net_guard(network):
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input_t = Tensor(name='input',
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shape=input_data.shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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indices_t = Tensor(name='indices',
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shape=indices.shape,
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dtype=tensorrt_llm.str_dtype_to_trt('int32'))
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updates_t = Tensor(name='updates',
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shape=updates.shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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output = tensorrt_llm.functional.scatter(input_t,
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dim=dim,
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indices=indices_t,
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updates=updates_t)
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output.mark_output('output')
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# trt run
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session = create_session(builder, network, precision=dtype)
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inputs = {'input': input_data, 'indices': indices, 'updates': updates}
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outputs = run_session(session, inputs)
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# pytorch run
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ref = torch.scatter(input_data,
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dim=dim,
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index=indices.to(dtype=torch.int64),
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src=updates)
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# compare diff
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torch.testing.assert_close(ref, outputs['output'])
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