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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>
90 lines
2.9 KiB
Python
90 lines
2.9 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 itertools
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import math
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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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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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sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
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from utils.util import skip_bf16_pre_ampere, 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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@staticmethod
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def gelu(x, dtype):
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if dtype == 'float32':
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res = torch.nn.functional.gelu(x)
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else:
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res = 0.5 * x * (1 + torch.tanh(
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math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
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return res
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@parameterized.expand(itertools.product(
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('float32', 'float16', 'bfloat16'),
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(False, True),
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),
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name_func=unittest_name_func)
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def test_gelu(self, dtype, strongly_typed):
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# Skip tests that are not supported in pre-ampere architecture
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skip_bf16_pre_ampere(dtype)
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torch_dtype = tensorrt_llm._utils.str_dtype_to_torch(dtype)
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x_shape = (12, 12, 96, 96)
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x_data = torch.rand(x_shape, dtype=torch_dtype)
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# construct trt network
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builder = tensorrt_llm.Builder()
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builder.strongly_typed = strongly_typed
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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_shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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output = tensorrt_llm.functional.gelu(x).trt_tensor
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output.name = 'output'
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network.mark_output(output)
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# trt run
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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={'x': x_data})
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out = outputs['output'].to(torch_dtype)
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# Reference
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ref = self.gelu(x_data, dtype)
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if dtype == 'bfloat16':
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atol, rtol = 1e-5, 2e-2
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else:
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atol, rtol = 1e-5, 2e-3
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torch.testing.assert_close(out, ref, atol=atol, rtol=rtol)
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if __name__ == '__main__':
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unittest.main()
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