Piotr Krzemiński
e77070890a
* [PT FE] Fix sporadics with round & 0 zero_pt, 1.0 scale * [PT FE] Change scale and round input in quantized cat tests * [PT FE] Add rounding to conv & linear tests * Update test_quantized_cat.py * Update test_quantized_cat.py * [PT FE] Replace randn with rand for consistency in convnd
45 lines
1.7 KiB
Python
45 lines
1.7 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import numpy as np
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import pytest
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import torch
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from pytorch_layer_test_class import PytorchLayerTest
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class quantized_mul(torch.nn.Module):
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def __init__(self, scale, zero_point, dtype) -> None:
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torch.nn.Module.__init__(self)
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self.scale = scale
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self.zero_point = zero_point
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self.dtype = dtype
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def forward(self, input_tensor1, input_tensor2):
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quantized_tensor1 = torch.quantize_per_tensor(input_tensor1, 1.0, 0, self.dtype)
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quantized_tensor2 = torch.quantize_per_tensor(input_tensor2, 1.0, 0, self.dtype)
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quantized_mul = torch.ops.quantized.mul(quantized_tensor1, quantized_tensor2, self.scale, self.zero_point)
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dequantized_tensor = torch.dequantize(quantized_mul)
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return dequantized_tensor
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class TestQuantizedMul(PytorchLayerTest):
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def _prepare_input(self):
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return (np.round(np.array(5.00 * np.random.rand(10, 10) - 2.50, dtype=np.float32), 4),
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np.round(np.array(5.00 * np.random.rand(10, 10) - 2.50, dtype=np.float32), 4))
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@pytest.mark.parametrize("scale", [
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1.0, 0.21, 0.62, 0.9999
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])
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@pytest.mark.parametrize("zero_point", [
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0, 4, -7
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])
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@pytest.mark.parametrize("dtype", [
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torch.quint8,
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torch.qint8
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])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_quantized_mul(self, scale, zero_point, dtype, ie_device, precision, ir_version):
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if dtype == torch.quint8: zero_point = abs(zero_point)
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self._test(quantized_mul(scale, zero_point, dtype), None, ["quantized::mul"],
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ie_device, precision, ir_version, quantized_ops=True, quant_size=scale)
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