| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| from __future__ import unicode_literals |
| |
| import torch |
| import torch.nn.quantized as nnq |
| import torch.nn.quantized.functional as qF |
| from torch.nn.quantized.modules import Conv2d |
| from common_utils import TestCase, run_tests, tempfile |
| from hypothesis import given |
| from hypothesis import strategies as st |
| |
| |
| ''' |
| Note that tests in this file are just API test, to make sure we wrapped the |
| quantized operator implementations correctly in the user facing APIs, these are |
| not correctness test for the underlying quantized operators. For correctness |
| test please see `caffe2/test/test_quantized.py`. |
| ''' |
| |
| |
| class FunctionalAPITest(TestCase): |
| def test_relu_api(self): |
| X = torch.arange(-5, 5, dtype=torch.float) |
| scale = 2.0 |
| zero_point = 1 |
| qX = torch.quantize_linear(X, scale=scale, zero_point=zero_point, dtype=torch.quint8) |
| qY = torch.ops.quantized.relu(qX) |
| qY_hat = qF.relu(qX) |
| self.assertEqual(qY, qY_hat) |
| |
| |
| class ModuleAPITest(TestCase): |
| @given( |
| batch_size=st.integers(1, 5), |
| in_features=st.integers(16, 32), |
| out_features=st.integers(4, 8), |
| use_bias=st.booleans(), |
| ) |
| def test_linear_api(self, batch_size, in_features, out_features, use_bias): |
| """test API functionality for nn.quantized.linear""" |
| W = torch.rand(out_features, in_features).float() |
| W_q = torch.quantize_linear(W, 0.1, 4, torch.qint8) |
| W_pack = torch.ops.quantized.fbgemm_linear_prepack(W_q) |
| X = torch.rand(batch_size, in_features).float() |
| X_q = torch.quantize_linear(X, 0.2, 10, torch.quint8) |
| B = torch.rand(out_features).float() if use_bias else None |
| B_q = torch.quantize_linear(B, W_q.q_scale() * X_q.q_scale(), 0, torch.qint32) if use_bias else None |
| out_scale = 0.5 |
| out_zero_point = 3 |
| qlinear = nnq.Linear(in_features, out_features) |
| qlinear._packed_weight = W_pack |
| qlinear.bias = B_q if use_bias else None |
| qlinear.out_scale = torch.tensor([out_scale]) |
| qlinear.out_zero_point = torch.tensor([out_zero_point]) |
| Z_q = qlinear(X_q) |
| # Check if the module implementation matches calling the |
| # ops directly |
| Z_ref = torch.ops.quantized.fbgemm_linear(X_q, W_pack, B_q, out_scale, out_zero_point) |
| self.assertEqual(Z_ref, Z_q) |
| |
| # Test serialization of quantized Linear Module using state_dict |
| model_dict = qlinear.state_dict() |
| self.assertEqual(model_dict['weight'], W_q) |
| if use_bias: |
| self.assertEqual(model_dict['bias'], B_q) |
| with tempfile.NamedTemporaryFile() as f: |
| torch.save(model_dict, f) |
| f.seek(0) |
| loaded_dict = torch.load(f) |
| for key in model_dict: |
| self.assertEqual(model_dict[key], loaded_dict[key]) |
| loaded_qlinear = nnq.Linear(in_features, out_features) |
| loaded_qlinear.load_state_dict(loaded_dict) |
| |
| linear_unpack = torch.ops.quantized.fbgemm_linear_unpack |
| self.assertEqual(linear_unpack(qlinear._packed_weight), |
| linear_unpack(loaded_qlinear._packed_weight)) |
| if use_bias: |
| self.assertEqual(qlinear.bias, loaded_qlinear.bias) |
| self.assertEqual(qlinear.out_scale, loaded_qlinear.out_scale) |
| self.assertEqual(qlinear.out_zero_point, loaded_qlinear.out_zero_point) |
| self.assertTrue(dir(qlinear) == dir(loaded_qlinear)) |
| self.assertTrue(hasattr(qlinear, '_packed_weight')) |
| self.assertTrue(hasattr(loaded_qlinear, '_packed_weight')) |
| self.assertTrue(hasattr(qlinear, 'weight')) |
| self.assertTrue(hasattr(loaded_qlinear, 'weight')) |
| self.assertEqual(qlinear.weight, loaded_qlinear.weight) |
| self.assertEqual(qlinear.weight, torch.ops.quantized.fbgemm_linear_unpack(qlinear._packed_weight)) |
| Z_q2 = qlinear(X_q) |
| self.assertEqual(Z_q, Z_q2) |
| |
| # test serialization of module directly - will add this later |
| # with tempfile.NamedTemporaryFile() as f: |
| # torch.save(qLinear, f) |
| # f.seek(0) |
| # loaded = torch.load(f) |
| # state = qLinear.__getstate__() |
| # compareUnpackedWeight(qLinear._packed_weight, loaded._packed_weight) |
| # self.assertEqual(qLinear.bias, loaded.bias) |
| # self.assertEqual(qLinear.out_scale, loaded.out_scale) |
| # self.assertEqual(qLinear.out_zero_point, loaded.out_zero_point) |
| |
| def test_quant_dequant_api(self): |
| r = torch.tensor([[1., -1.], [1., -1.]], dtype=torch.float) |
| scale, zero_point, dtype = 1.0, 2, torch.qint8 |
| # testing Quantize API |
| qr = torch.quantize_linear(r, scale, zero_point, dtype) |
| quant_m = nnq.Quantize(scale, zero_point, dtype) |
| qr2 = quant_m(r) |
| self.assertEqual(qr, qr2) |
| # testing Dequantize API |
| rqr = qr.dequantize() |
| dequant_m = nnq.DeQuantize() |
| rqr2 = dequant_m(qr2) |
| self.assertEqual(rqr, rqr2) |
| |
| def test_conv_api(self): |
| """Tests the correctness of the conv module. |
| |
| The correctness is defined against the functional implementation. |
| """ |
| |
| N, iC, H, W = 10, 10, 10, 3 |
| oC, g, kH, kW = 16, 1, 3, 3 |
| scale, zero_point = 1.0 / 255, 128 |
| |
| X = torch.randn(N, iC, H, W, dtype=torch.float32) |
| X = X.permute([0, 2, 3, 1]).contiguous() |
| qX = torch.quantize_linear(X, scale=scale, zero_point=128, dtype=torch.quint8) |
| |
| w = torch.randn(oC, iC // g, kH, kW, dtype=torch.float32) |
| w = w.permute([0, 2, 3, 1]).contiguous() |
| qw = torch.quantize_linear(w, scale=scale, zero_point=0, dtype=torch.qint8) |
| |
| b = torch.randn(oC, dtype=torch.float32) |
| qb = torch.quantize_linear(b, scale=1.0 / 1024, zero_point=0, dtype=torch.qint32) |
| |
| conv_under_test = Conv2d(in_channels=iC, |
| out_channels=oC, |
| kernel_size=(kH, kW), |
| stride=1, |
| padding=0, |
| dilation=1, |
| groups=g, |
| bias=True, |
| padding_mode='zeros') |
| conv_under_test.weight = qw |
| conv_under_test.bias = qb |
| conv_under_test.scale = scale |
| conv_under_test.zero_point = zero_point |
| |
| # Test members |
| self.assertTrue(hasattr(conv_under_test, '_packed_weight')) |
| self.assertTrue(hasattr(conv_under_test, '_scale')) |
| self.assertTrue(hasattr(conv_under_test, '_zero_point')) |
| |
| # Test properties |
| # self.assertEqual(qw, conv_under_test.weight) |
| self.assertEqual(qb, conv_under_test.bias) |
| self.assertEqual(scale, conv_under_test.scale) |
| self.assertEqual(zero_point, conv_under_test.zero_point) |
| |
| # Test forward |
| result_under_test = conv_under_test(qX) |
| result_reference = qF.conv2d(qX, qw, bias=qb, |
| scale=scale, zero_point=zero_point, |
| stride=1, padding=0, |
| dilation=1, groups=g, |
| prepacked=False, dtype=torch.quint8) |
| |
| self.assertEqual(result_reference, result_under_test, |
| message="Tensors are not equal.") |
| |
| |
| if __name__ == '__main__': |
| run_tests() |
