test/test_quantized.py

import torch
import torch.jit
import numpy as np
import unittest
from caffe2.python import core
from common_utils import TestCase, run_tests


def canonical(graph):
    return str(torch._C._jit_pass_canonicalize(graph))


@unittest.skipIf("Relu_ENGINE_DNNLOWP" not in core._REGISTERED_OPERATORS, "fbgemm-based Caffe2 ops are not linked")
class TestQuantized(TestCase):
    def test_relu(self):
        a = (torch.tensor([4, 6, 1, 10], dtype=torch.uint8), 0.01, 5)
        r = torch.ops.c10.quantized_relu(a)
        np.testing.assert_equal(r[0].numpy(), torch.tensor([5, 6, 5, 10], dtype=torch.uint8).numpy())
        np.testing.assert_almost_equal(0.01, r[1])
        self.assertEqual(5, r[2])

    def test_quantize(self):
        a = (torch.tensor([4, 6, 1, 10], dtype=torch.uint8), 0.01, 5)
        r = torch.ops.c10.dequantize(a)
        np.testing.assert_almost_equal(r.numpy(), [-0.01, 0.01, -0.04, 0.05])
        # default args
        q_def = torch.ops.c10.quantize(r)
        # specified
        q = torch.ops.c10.quantize(r, scale=0.01, zero_point=5)
        np.testing.assert_equal(q[0].numpy(), a[0].numpy())
        np.testing.assert_almost_equal(q[1], a[1])
        self.assertEqual(q[2], a[2])

    def test_script(self):
        @torch.jit.script
        def foo(x):
            # type: (Tuple[Tensor, float, int]) -> Tuple[Tensor, float, int]
            return torch.ops.c10.quantized_relu(x)
        self.assertExpectedInline(canonical(foo.graph), '''\
graph(%x : (Tensor, float, int)):
  %1 : (Tensor, float, int) = c10::quantized_relu(%x)
  return (%1)
''')


if __name__ == '__main__':
    run_tests()