mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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81 lines
3.0 KiB
Python
81 lines
3.0 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 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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import numpy as np
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import torch
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from polygraphy.backend.trt import EngineFromNetwork, TrtRunner
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import tensorrt_llm
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from tensorrt_llm import Tensor
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class TestFunctional(unittest.TestCase):
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def setUp(self):
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tensorrt_llm.logger.set_level('error')
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def test_gather(self):
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dtype = 'float32'
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x_data = torch.randn(2, 128, 768)
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y_data = torch.tensor([101, 127]).int()
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builder = tensorrt_llm.Builder()
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net = builder.create_network()
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with tensorrt_llm.net_guard(net):
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network = tensorrt_llm.default_trtnet()
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x = Tensor(name='x',
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shape=x_data.shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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y = Tensor(name='y',
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shape=y_data.shape,
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dtype=tensorrt_llm.str_dtype_to_trt('int32'))
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y = y.view(
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tensorrt_llm.functional.concat(
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[tensorrt_llm.functional.shape(y, 0), 1, 1]))
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y = tensorrt_llm.functional.expand(
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y,
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tensorrt_llm.functional.concat([
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tensorrt_llm.functional.shape(y, 0), 1,
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tensorrt_llm.functional.shape(x, 2)
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]))
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output = tensorrt_llm.functional.gather(x, dim=1, indices=y).view(
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tensorrt_llm.functional.concat([
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tensorrt_llm.functional.shape(x, 0),
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tensorrt_llm.functional.shape(x, 2)
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]))
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output = output.trt_tensor
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output.name = 'output'
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network.mark_output(output)
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build_engine = EngineFromNetwork((builder.trt_builder, net.trt_network))
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with TrtRunner(build_engine) as runner:
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outputs = runner.infer(feed_dict={
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'x': x_data.numpy(),
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'y': y_data.numpy(),
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})
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y_data = y_data.reshape(y_data.size(0), 1, 1)
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y_data = y_data.expand(y_data.size(0), 1, x_data.size(-1))
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ref = torch.gather(x_data, dim=1,
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index=y_data.to(dtype=torch.int64)).view(
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x_data.size(0), x_data.size(2))
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np.testing.assert_allclose(ref.cpu().numpy(),
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outputs['output'],
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atol=1e-5)
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