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https://github.com/NVIDIA/TensorRT-LLM.git
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250d9c293d
* Update TensorRT-LLM --------- Co-authored-by: Bhuvanesh Sridharan <bhuvan.sridharan@gmail.com> Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com> Co-authored-by: Eddie-Wang1120 <wangjinheng1120@163.com> Co-authored-by: meghagarwal <16129366+megha95@users.noreply.github.com>
49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
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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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 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([[0], [1], [2]], name_func=unittest_name_func)
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def test_repeat_interleave(self, axis):
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dtype = 'float32'
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repeats = 3
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x_data = torch.randn(
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(2, 3, 4), dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype))
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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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output = tensorrt_llm.functional.repeat_interleave(
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x, repeats, axis).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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})
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ref = torch.repeat_interleave(x_data, repeats, axis)
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np.testing.assert_allclose(ref, outputs['output'])
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