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https://github.com/NVIDIA/TensorRT-LLM.git
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4bb65f216f
* Update TensorRT-LLM --------- Co-authored-by: meghagarwal <16129366+megha95@users.noreply.github.com> Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
79 lines
3.0 KiB
Python
79 lines
3.0 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 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 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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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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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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@parameterized.expand([
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((3, 5), 1, 1, True),
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((3, 4, 6), 2, 0, True),
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((3, 5), 1, 1, False),
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((3, 4, 6), 2, 0, False),
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],
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name_func=unittest_name_func)
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def test_topk(self, input_shape, k, d, largest):
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value_dtype = 'float32'
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indices_dtype = 'int32'
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# construct trt network
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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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input_data = np.random.rand(*input_shape).astype(np.float32)
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m = tensorrt_llm.functional.constant(input_data)
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topk_values, topk_indices = tensorrt_llm.functional.topk(
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m, k, d, largest=largest)
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topk_values = topk_values.trt_tensor
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topk_indices = topk_indices.trt_tensor
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topk_values.name = 'output_values'
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topk_indices.name = 'output_indices'
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network.mark_output(topk_values)
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network.mark_output(topk_indices)
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topk_values.dtype = tensorrt_llm.str_dtype_to_trt(value_dtype)
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topk_indices.dtype = tensorrt_llm.str_dtype_to_trt(indices_dtype)
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# trt run
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build_engine = EngineFromNetwork(
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(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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values, indices = torch.topk(torch.Tensor(input_data),
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k,
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dim=d,
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largest=largest)
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np.testing.assert_allclose(values.cpu().numpy(),
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outputs['output_values'],
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atol=1e-5)
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np.testing.assert_allclose(indices.cpu().numpy(),
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outputs['output_indices'])
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