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https://github.com/NVIDIA/TensorRT-LLM.git
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9bd15f1937
* TensorRT-LLM Release 0.10.0 --------- Co-authored-by: Loki <lokravi@amazon.com> Co-authored-by: meghagarwal <16129366+megha95@users.noreply.github.com>
107 lines
3.8 KiB
Python
107 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 unittest
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import numpy as np
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import torch
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from parameterized import parameterized
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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('warning')
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@parameterized.expand([
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(True, ),
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(False, ),
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])
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def test_where_from_bool(self, condition=True):
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dtype = 'float32'
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t_data = torch.randn(2, 3)
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f_data = torch.randn(2, 3)
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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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t = Tensor(name='t',
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shape=t_data.shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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f = Tensor(name='f',
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shape=f_data.shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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output = tensorrt_llm.functional.where(condition, t, f).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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't': t_data.numpy(),
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'f': f_data.numpy(),
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})
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ref = torch.where(torch.tensor(condition), t_data, f_data)
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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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def test_where_from_tensor(self):
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dtype = 'float32'
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t_data = torch.randn(3, 4)
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f_data = torch.randn(3, 4)
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c_data = torch.randint(2, size=(3, 1), dtype=torch.bool)
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ref = torch.where(c_data, t_data, f_data)
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print(ref)
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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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t = Tensor(name='t',
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shape=t_data.shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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f = Tensor(name='f',
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shape=f_data.shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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c = Tensor(name='c',
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shape=c_data.shape,
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dtype=tensorrt_llm.str_dtype_to_trt('bool'))
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output = tensorrt_llm.functional.where(c, t, f).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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't': t_data.numpy(),
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'f': f_data.numpy(),
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'c': c_data.numpy(),
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})
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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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print(t_data)
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print(f_data)
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print(c_data)
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print(outputs['output'])
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# assert False, "FORCED"
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