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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19984 Add qint8 for QTensor, with underlying type of int8_t Reviewed By: jianyuh Differential Revision: D15150715 fbshipit-source-id: 57580f599d46f9323af5ce462dbbc464b25e40d7
27 lines
791 B
Python
27 lines
791 B
Python
import torch
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import torch.nn.quantized.functional as F
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import numpy as np
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from common_utils import TestCase, run_tests
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def _quantize(x, scale, zero_point, qmin=0, qmax=255):
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"""Quantizes a numpy array."""
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qx = np.round(x / scale + zero_point)
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qx = np.clip(qx, qmin, qmax).astype(np.uint8)
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return qx
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class FunctionalAPITest(TestCase):
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def test_functional_api(self):
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X = torch.arange(-5, 5, dtype=torch.float)
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scale = 2.0
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zero_point = 1
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Y = X.numpy().copy()
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Y[Y < 0] = 0
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qY = _quantize(Y, scale, zero_point)
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qX = X.quantize_linear(scale=scale, zero_point=zero_point, dtype=torch.quint8)
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qY_hat = F.relu(qX)
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np.testing.assert_equal(qY, qY_hat.int_repr())
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if __name__ == '__main__':
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run_tests()
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