| # Torch |
| import torch |
| from torch.quantization import ( |
| MinMaxObserver, |
| PerChannelMinMaxObserver, |
| MovingAverageMinMaxObserver, |
| MovingAveragePerChannelMinMaxObserver, |
| MinMaxDynamicQuantObserver, |
| HistogramObserver, |
| RecordingObserver, |
| FakeQuantize, |
| default_debug_qconfig, |
| default_observer, |
| default_per_channel_weight_observer, |
| get_observer_dict, |
| prepare, |
| ) |
| import torch.nn as nn |
| |
| # Standard library |
| import copy |
| import io |
| import unittest |
| import math |
| import numpy as np |
| |
| # Testing utils |
| from hypothesis import given |
| from hypothesis import strategies as st |
| import torch.testing._internal.hypothesis_utils as hu |
| hu.assert_deadline_disabled() |
| from torch.testing._internal.common_cuda import TEST_MULTIGPU, TEST_CUDA |
| from torch.testing._internal.common_utils import TestCase |
| from torch.testing._internal.common_quantization import ( |
| QuantizationTestCase, |
| ModelWithNoQconfigPropagation, |
| AnnotatedSingleLayerLinearModel, |
| test_only_eval_fn, |
| ) |
| |
| from torch.testing._internal.common_quantized import ( |
| override_quantized_engine, |
| supported_qengines, |
| ) |
| |
| # Reference method for fake quantize |
| def _fake_quantize_per_tensor_affine_reference(X, scale, zero_point, quant_min, quant_max): |
| res = (torch.clamp(torch.round(X * (1.0 / scale) + zero_point), quant_min, quant_max) - zero_point) * scale |
| return res |
| |
| # Reference method for the gradient of the fake quantize operator |
| def _fake_quantize_per_tensor_affine_grad_reference(dY, X, scale, zero_point, quant_min, quant_max): |
| Xq = torch.round(X * (1.0 / scale) + zero_point) |
| mask = (Xq >= quant_min) * (Xq <= quant_max) |
| res = torch.zeros_like(dY) |
| res[mask] = dY[mask] |
| return res |
| |
| # Helper function used to simulate per-channel fake-quant against any axis |
| def _permute_to_axis_zero(X, axis): |
| new_axis_list = list(range(X.dim())) |
| new_axis_list[axis] = 0 |
| new_axis_list[0] = axis |
| y = X.permute(tuple(new_axis_list)) |
| return y, new_axis_list |
| |
| # Reference method for fake quantize |
| def _fake_quantize_per_channel_affine_reference(X, per_channel_scale, per_channel_zero_point, axis, quant_min, quant_max): |
| X, permute_axis_list = _permute_to_axis_zero(X, axis) |
| res = torch.zeros_like(X) |
| |
| for i in range(X.size()[0]): |
| res[i] = (torch.clamp(torch.round(X[i] * (1.0 / per_channel_scale[i]) + |
| per_channel_zero_point[i]), quant_min, quant_max) - per_channel_zero_point[i]) * per_channel_scale[i] |
| |
| out = res.permute(tuple(permute_axis_list)) |
| return out |
| |
| # Reference method for the gradient of the fake quantize operator |
| def _fake_quantize_per_channel_affine_grad_reference(dY, X, per_channel_scale, per_channel_zero_point, axis, quant_min, quant_max): |
| X, permute_axis_list = _permute_to_axis_zero(X, axis) |
| Xq = torch.zeros_like(X) |
| for i in range(X.size()[0]): |
| Xq[i] = torch.round(X[i] * (1.0 / per_channel_scale[i]) + per_channel_zero_point[i]) |
| Xq = Xq.permute(tuple(permute_axis_list)) |
| mask = (Xq >= quant_min) * (Xq <= quant_max) |
| res = torch.zeros_like(dY) |
| res[mask] = dY[mask] |
| return res |
| |
| def to_tensor(X, device): |
| return torch.tensor(X).to(device=torch.device(device), dtype=torch.float32) |
| |
| NP_RANDOM_SEED = 19 |
| tolerance = 1e-6 |
| |
| |
| class TestObserver(QuantizationTestCase): |
| @given(qdtype=st.sampled_from((torch.qint8, torch.quint8)), |
| qscheme=st.sampled_from((torch.per_tensor_affine, torch.per_tensor_symmetric)), |
| reduce_range=st.booleans()) |
| def test_per_tensor_observers(self, qdtype, qscheme, reduce_range): |
| # reduce_range cannot be true for symmetric quantization with uint8 |
| if qdtype == torch.quint8 and qscheme == torch.per_tensor_symmetric: |
| reduce_range = False |
| ObserverList = [MinMaxObserver(dtype=qdtype, qscheme=qscheme, reduce_range=reduce_range), |
| MovingAverageMinMaxObserver(averaging_constant=0.5, |
| dtype=qdtype, |
| qscheme=qscheme, |
| reduce_range=reduce_range)] |
| for myobs in ObserverList: |
| # Calculate Qparams should return with a warning for observers with no data |
| qparams = myobs.calculate_qparams() |
| if type(myobs) == MinMaxObserver: |
| x = torch.tensor([1.0, 2.0, 2.0, 3.0, 4.0, 5.0, 6.0]) |
| y = torch.tensor([4.0, 5.0, 5.0, 6.0, 7.0, 8.0]) |
| else: |
| # Moving average of min/max for x and y matches that of |
| # extreme values for x/y used for minmax observer |
| x = torch.tensor([0.0, 2.0, 2.0, 3.0, 4.0, 5.0, 6.0]) |
| y = torch.tensor([2.0, 5.0, 5.0, 6.0, 7.0, 10.0]) |
| |
| result = myobs(x) |
| result = myobs(y) |
| self.assertEqual(result, y) |
| self.assertEqual(myobs.min_val, 1.0) |
| self.assertEqual(myobs.max_val, 8.0) |
| qparams = myobs.calculate_qparams() |
| if reduce_range: |
| if qscheme == torch.per_tensor_symmetric: |
| ref_scale = 0.062745 * 255 / 127 |
| ref_zero_point = 0 if qdtype is torch.qint8 else 128 |
| else: |
| ref_scale = 0.0313725 * 255 / 127 |
| ref_zero_point = -64 if qdtype is torch.qint8 else 0 |
| else: |
| if qscheme == torch.per_tensor_symmetric: |
| ref_scale = 0.062745 |
| ref_zero_point = 0 if qdtype is torch.qint8 else 128 |
| else: |
| ref_scale = 0.0313725 |
| ref_zero_point = -128 if qdtype is torch.qint8 else 0 |
| self.assertEqual(qparams[1].item(), ref_zero_point) |
| self.assertAlmostEqual(qparams[0].item(), ref_scale, delta=1e-5) |
| state_dict = myobs.state_dict() |
| b = io.BytesIO() |
| torch.save(state_dict, b) |
| b.seek(0) |
| loaded_dict = torch.load(b) |
| for key in state_dict: |
| self.assertEqual(state_dict[key], loaded_dict[key]) |
| loaded_obs = MinMaxObserver(dtype=qdtype, qscheme=qscheme, reduce_range=reduce_range) |
| loaded_obs.load_state_dict(loaded_dict) |
| loaded_qparams = loaded_obs.calculate_qparams() |
| self.assertEqual(myobs.min_val, loaded_obs.min_val) |
| self.assertEqual(myobs.max_val, loaded_obs.max_val) |
| self.assertEqual(myobs.calculate_qparams(), loaded_obs.calculate_qparams()) |
| |
| |
| @given(X=hu.tensor(shapes=hu.array_shapes(min_dims=2, max_dims=4, |
| min_side=1, max_side=10), |
| qparams=hu.qparams()), |
| reduce_range=st.booleans()) |
| def test_per_tensor_dynamic_quant_observers(self, X, reduce_range): |
| |
| X, (scale, zero_point, torch_type) = X |
| x = torch.from_numpy(X) |
| |
| obs = MinMaxDynamicQuantObserver(dtype=torch.quint8, reduce_range=reduce_range) |
| |
| result = obs(x) |
| qparams = obs.calculate_qparams() |
| ref = torch._choose_qparams_per_tensor(x, reduce_range) |
| |
| self.assertEqual(ref[0], qparams[0]) |
| self.assertEqual(ref[1], qparams[1]) |
| |
| @given(qdtype=st.sampled_from((torch.qint8, torch.quint8)), |
| qscheme=st.sampled_from((torch.per_channel_affine, torch.per_channel_symmetric)), |
| ch_axis=st.sampled_from((0, 1, 2, 3)), reduce_range=st.booleans()) |
| def test_per_channel_observers(self, qdtype, qscheme, ch_axis, reduce_range): |
| # reduce_range cannot be true for symmetric quantization with uint8 |
| if qdtype == torch.quint8 and qscheme == torch.per_channel_symmetric: |
| reduce_range = False |
| ObserverList = [PerChannelMinMaxObserver(reduce_range=reduce_range, |
| ch_axis=ch_axis, |
| dtype=qdtype, |
| qscheme=qscheme), |
| MovingAveragePerChannelMinMaxObserver(averaging_constant=0.5, |
| reduce_range=reduce_range, |
| ch_axis=ch_axis, |
| dtype=qdtype, |
| qscheme=qscheme)] |
| |
| for myobs in ObserverList: |
| # Calculate qparams should work for empty observers |
| qparams = myobs.calculate_qparams() |
| x = torch.tensor( |
| [ |
| [[[1.0, 2.0], [2.0, 2.5]], [[3.0, 4.0], [4.5, 6.0]]], |
| [[[-4.0, -3.0], [5.0, 5.0]], [[6.0, 3.0], [7.0, 8.0]]], |
| ] |
| ) |
| if type(myobs) == MovingAveragePerChannelMinMaxObserver: |
| # Scaling the input tensor to model change in min/max values |
| # across batches |
| result = myobs(0.5 * x) |
| result = myobs(1.5 * x) |
| self.assertEqual(result, 1.5 * x) |
| else: |
| result = myobs(x) |
| self.assertEqual(result, x) |
| |
| qparams = myobs.calculate_qparams() |
| ref_min_vals = [[1.0, -4.0], [-4.0, 3.0], [-4.0, 2.0], [-4.0, -3.0]] |
| ref_max_vals = [[6.0, 8.0], [5.0, 8.0], [6.0, 8.0], [7.0, 8.0]] |
| per_channel_symmetric_ref_scales = [ |
| [0.04705882, 0.06274509], |
| [0.03921569, 0.0627451], |
| [0.04705882, 0.0627451], |
| [0.05490196, 0.0627451], |
| ] |
| per_channel_affine_ref_scales = [ |
| [0.02352941, 0.04705882], |
| [0.03529412, 0.03137255], |
| [0.03921569, 0.03137255], |
| [0.04313726, 0.04313726], |
| ] |
| per_channel_affine_qint8_zp = [ |
| [-128, -43], |
| [-15, -128], |
| [-26, -128], |
| [-35, -58], |
| ] |
| per_channel_affine_quint8_zp = [[0, 85], [113, 0], [102, 0], [93, 70]] |
| |
| self.assertEqual(myobs.min_vals, ref_min_vals[ch_axis]) |
| self.assertEqual(myobs.max_vals, ref_max_vals[ch_axis]) |
| if qscheme == torch.per_channel_symmetric: |
| ref_scales = per_channel_symmetric_ref_scales[ch_axis] |
| ref_zero_points = [0, 0] if qdtype is torch.qint8 else [128, 128] |
| else: |
| ref_scales = per_channel_affine_ref_scales[ch_axis] |
| ref_zero_points = ( |
| per_channel_affine_qint8_zp[ch_axis] |
| if qdtype is torch.qint8 |
| else per_channel_affine_quint8_zp[ch_axis] |
| ) |
| |
| if reduce_range: |
| ref_scales = [s * 255 / 127 for s in ref_scales] |
| ref_zero_points = [math.floor(z / 2) for z in ref_zero_points] |
| |
| self.assertTrue(torch.allclose(qparams[0], torch.tensor(ref_scales, dtype=qparams[0].dtype))) |
| self.assertTrue(torch.allclose(qparams[1], torch.tensor(ref_zero_points, dtype=qparams[1].dtype))) |
| |
| # Test for serializability |
| state_dict = myobs.state_dict() |
| b = io.BytesIO() |
| torch.save(state_dict, b) |
| b.seek(0) |
| loaded_dict = torch.load(b) |
| for key in state_dict: |
| self.assertEqual(state_dict[key], loaded_dict[key]) |
| loaded_obs = PerChannelMinMaxObserver(reduce_range=reduce_range, ch_axis=ch_axis, dtype=qdtype, qscheme=qscheme) |
| loaded_obs.load_state_dict(loaded_dict) |
| loaded_qparams = loaded_obs.calculate_qparams() |
| self.assertEqual(myobs.min_vals, loaded_obs.min_vals) |
| self.assertEqual(myobs.max_vals, loaded_obs.max_vals) |
| self.assertEqual(myobs.calculate_qparams(), loaded_obs.calculate_qparams()) |
| |
| def test_observer_scriptable(self): |
| obs_list = [MinMaxObserver(), MovingAverageMinMaxObserver(), MinMaxDynamicQuantObserver()] |
| for obs in obs_list: |
| scripted = torch.jit.script(obs) |
| |
| x = torch.rand(3, 4) |
| obs(x) |
| scripted(x) |
| self.assertEqual(obs.calculate_qparams(), scripted.calculate_qparams()) |
| |
| buf = io.BytesIO() |
| torch.jit.save(scripted, buf) |
| buf.seek(0) |
| loaded = torch.jit.load(buf) |
| self.assertEqual(obs.calculate_qparams(), loaded.calculate_qparams()) |
| |
| # TODO: move this to test_quantize.py |
| def test_no_qconfig_propagation(self): |
| model = ModelWithNoQconfigPropagation() |
| model.qconfig = torch.quantization.default_qconfig |
| |
| model = prepare(model) |
| self.assertTrue(hasattr(model.fc1, 'qconfig'), |
| "QConfig is expected to propagate") |
| self.assertFalse(hasattr(model.no_quant_module, 'qconfig'), |
| "QConfig is expected to NOT propagate") |
| |
| |
| class TestRecordHistogramObserver(QuantizationTestCase): |
| # TODO: move this to quantize.py |
| def test_record_observer(self): |
| for qengine in supported_qengines: |
| with override_quantized_engine(qengine): |
| model = AnnotatedSingleLayerLinearModel() |
| model.qconfig = default_debug_qconfig |
| model = prepare(model) |
| # run the evaluation and dump all tensors |
| test_only_eval_fn(model, self.calib_data) |
| test_only_eval_fn(model, self.calib_data) |
| observer_dict = {} |
| get_observer_dict(model, observer_dict) |
| |
| self.assertTrue('fc1.module.activation_post_process' in observer_dict.keys(), |
| 'observer is not recorded in the dict') |
| self.assertEqual(len(observer_dict['fc1.module.activation_post_process'].get_tensor_value()), |
| 2 * len(self.calib_data)) |
| self.assertEqual(observer_dict['fc1.module.activation_post_process'].get_tensor_value()[0], |
| model(self.calib_data[0][0])) |
| |
| @given(qdtype=st.sampled_from((torch.qint8, torch.quint8)), |
| qscheme=st.sampled_from((torch.per_tensor_affine, torch.per_tensor_symmetric))) |
| def test_observer_scriptable(self, qdtype, qscheme): |
| obs = RecordingObserver(dtype=qdtype, qscheme=qscheme) |
| scripted = torch.jit.script(obs) |
| |
| x = torch.rand(3, 4) |
| obs(x) |
| scripted(x) |
| self.assertTrue(torch.equal(obs.get_tensor_value()[0], scripted.get_tensor_value()[0])) |
| buf = io.BytesIO() |
| torch.jit.save(scripted, buf) |
| buf.seek(0) |
| loaded = torch.jit.load(buf) |
| self.assertTrue(torch.equal(obs.get_tensor_value()[0], loaded.get_tensor_value()[0])) |
| |
| @given(qdtype=st.sampled_from((torch.qint8, torch.quint8)), |
| qscheme=st.sampled_from((torch.per_tensor_affine, torch.per_tensor_symmetric)), |
| reduce_range=st.booleans()) |
| def test_histogram_observer(self, qdtype, qscheme, reduce_range): |
| myobs = HistogramObserver(bins=3, dtype=qdtype, qscheme=qscheme, reduce_range=reduce_range) |
| # Calculate qparams should work for empty observers |
| qparams = myobs.calculate_qparams() |
| x = torch.tensor([2.0, 3.0, 4.0, 5.0], requires_grad=True) |
| y = torch.tensor([5.0, 6.0, 7.0, 8.0]) |
| out_x = myobs(x) |
| self.assertTrue(out_x.requires_grad) |
| myobs(y) |
| self.assertEqual(myobs.min_val, 2.0) |
| self.assertEqual(myobs.max_val, 8.0) |
| self.assertEqual(myobs.histogram, [2., 3., 3.]) |
| |
| qparams = myobs.calculate_qparams() |
| |
| if reduce_range: |
| if qscheme == torch.per_tensor_symmetric: |
| ref_scale = 0.0470588 * 255 / 127 |
| ref_zero_point = 0 if qdtype is torch.qint8 else 128 |
| else: |
| ref_scale = 0.0235294 * 255 / 127 |
| ref_zero_point = -64 if qdtype is torch.qint8 else 0 |
| else: |
| if qscheme == torch.per_tensor_symmetric: |
| ref_scale = 0.0470588 |
| ref_zero_point = 0 if qdtype is torch.qint8 else 128 |
| else: |
| ref_scale = 0.0235294 |
| ref_zero_point = -128 if qdtype is torch.qint8 else 0 |
| |
| self.assertEqual(qparams[1].item(), ref_zero_point) |
| self.assertAlmostEqual(qparams[0].item(), ref_scale, delta=1e-5) |
| # Test for serializability |
| state_dict = myobs.state_dict() |
| b = io.BytesIO() |
| torch.save(state_dict, b) |
| b.seek(0) |
| loaded_dict = torch.load(b) |
| for key in state_dict: |
| self.assertEqual(state_dict[key], loaded_dict[key]) |
| loaded_obs = HistogramObserver(bins=3, dtype=qdtype, qscheme=qscheme, reduce_range=reduce_range) |
| loaded_obs.load_state_dict(loaded_dict) |
| loaded_qparams = loaded_obs.calculate_qparams() |
| self.assertEqual(myobs.min_val, loaded_obs.min_val) |
| self.assertEqual(myobs.max_val, loaded_obs.max_val) |
| self.assertEqual(myobs.histogram, loaded_obs.histogram) |
| self.assertEqual(myobs.bins, loaded_obs.bins) |
| self.assertEqual(myobs.calculate_qparams(), loaded_obs.calculate_qparams()) |
| |
| def test_histogram_observer_one_sided(self): |
| myobs = HistogramObserver(bins=8, dtype=torch.quint8, qscheme=torch.per_tensor_affine, reduce_range=True) |
| x = torch.tensor([0.0, 0.3, 1.2, 1.7]) |
| y = torch.tensor([0.1, 1.3, 2.0, 2.7]) |
| myobs(x) |
| myobs(y) |
| self.assertEqual(myobs.min_val, 0) |
| qparams = myobs.calculate_qparams() |
| self.assertEqual(qparams[1].item(), 0) |
| |
| class TestFakeQuantizePerTensor(TestCase): |
| @given(device=st.sampled_from(['cpu', 'cuda'] if torch.cuda.is_available() else ['cpu']), |
| X=hu.tensor(shapes=hu.array_shapes(1, 5,), |
| qparams=hu.qparams(dtypes=torch.quint8))) |
| def test_forward_per_tensor(self, device, X): |
| r"""Tests the forward path of the FakeQuantizePerTensorAffine op. |
| """ |
| np.random.seed(NP_RANDOM_SEED) |
| X, (scale, zero_point, torch_type) = X |
| quant_min = torch.iinfo(torch_type).min |
| quant_max = torch.iinfo(torch_type).max |
| |
| X = to_tensor(X, device) |
| Y = _fake_quantize_per_tensor_affine_reference(X.cpu(), scale, zero_point, quant_min, quant_max) |
| Y_prime = torch.fake_quantize_per_tensor_affine( |
| X, scale, zero_point, quant_min, quant_max) |
| np.testing.assert_allclose(Y, Y_prime.cpu(), rtol=tolerance, atol=tolerance) |
| |
| @given(device=st.sampled_from(['cpu', 'cuda'] if torch.cuda.is_available() else ['cpu']), |
| X=hu.tensor(shapes=hu.array_shapes(1, 5,), |
| qparams=hu.qparams(dtypes=torch.quint8))) |
| @unittest.skip("temporarily disable the test") |
| def test_backward_per_tensor(self, device, X): |
| r"""Tests the backward method. |
| """ |
| np.random.seed(NP_RANDOM_SEED) |
| X, (scale, zero_point, torch_type) = X |
| quant_min = torch.iinfo(torch_type).min |
| quant_max = torch.iinfo(torch_type).max |
| |
| X = to_tensor(X, device) |
| X.requires_grad_() |
| Y = _fake_quantize_per_tensor_affine_reference(X.cpu(), scale, zero_point, quant_min, quant_max) |
| Y_prime = torch.fake_quantize_per_tensor_affine( |
| X, scale, zero_point, quant_min, quant_max) |
| dout = torch.rand(X.shape, dtype=torch.float).to(device) |
| dX = _fake_quantize_per_tensor_affine_grad_reference( |
| dout, X, scale, zero_point, quant_min, quant_max) |
| Y_prime.backward(dout) |
| np.testing.assert_allclose(dX.cpu(), X.grad.cpu().detach().numpy(), rtol=tolerance, atol=tolerance) |
| |
| @given(device=st.sampled_from(['cpu', 'cuda'] if torch.cuda.is_available() else ['cpu']), |
| X=hu.tensor(shapes=hu.array_shapes(1, 5,), |
| qparams=hu.qparams(dtypes=torch.quint8))) |
| # https://github.com/pytorch/pytorch/issues/30604 |
| @unittest.skip("temporarily disable the test") |
| def test_numerical_consistency_per_tensor(self, device, X): |
| r"""Comparing numerical consistency between CPU quantize/dequantize op and the CPU fake quantize op |
| """ |
| np.random.seed(NP_RANDOM_SEED) |
| X, (scale, zero_point, torch_type) = X |
| quant_min = torch.iinfo(torch_type).min |
| quant_max = torch.iinfo(torch_type).max |
| |
| X = to_tensor(X, device) |
| # quantize_per_tensor and dequantize are only implemented in CPU |
| Y = torch.dequantize(torch.quantize_per_tensor(X.cpu(), scale, zero_point, torch_type)) |
| Y_prime = torch.fake_quantize_per_tensor_affine( |
| X, scale, zero_point, quant_min, quant_max) |
| np.testing.assert_allclose(Y, Y_prime.cpu(), rtol=tolerance, atol=tolerance) |
| |
| @given(device=st.sampled_from(['cpu', 'cuda'] if torch.cuda.is_available() else ['cpu']), |
| X=hu.tensor(shapes=hu.array_shapes(1, 5,), |
| qparams=hu.qparams(dtypes=[torch.quint8])), |
| ) |
| def test_fq_module(self, device, X): |
| np.random.seed(NP_RANDOM_SEED) |
| X, (scale, zero_point, torch_type) = X |
| quant_min = torch.iinfo(torch_type).min |
| quant_max = torch.iinfo(torch_type).max |
| |
| X = to_tensor(X, device) |
| X.requires_grad_() |
| fq_module = torch.quantization.default_fake_quant().to(device) |
| Y_prime = fq_module(X) |
| assert fq_module.scale is not None |
| assert fq_module.zero_point is not None |
| Y = _fake_quantize_per_tensor_affine_reference(X, fq_module.scale, fq_module.zero_point, quant_min, quant_max) |
| np.testing.assert_allclose(Y.cpu().detach().numpy(), Y_prime.cpu().detach().numpy(), rtol=tolerance, atol=tolerance) |
| |
| # Test backward |
| dout = torch.rand(X.shape, dtype=torch.float, device=device) |
| Y_prime.backward(dout) |
| dX = _fake_quantize_per_tensor_affine_grad_reference(dout, X, fq_module.scale, fq_module.zero_point, quant_min, quant_max) |
| np.testing.assert_allclose(dX.cpu().numpy(), X.grad.cpu().detach().numpy(), rtol=tolerance, atol=tolerance) |
| |
| def test_fq_serializable(self): |
| observer = default_observer |
| quant_min = 0 |
| quant_max = 255 |
| fq_module = FakeQuantize(observer, quant_min, quant_max) |
| X = torch.tensor([-5, -3.5, -2, 0, 3, 5, 7], dtype=torch.float32) |
| y_ref = fq_module(X) |
| state_dict = fq_module.state_dict() |
| self.assertEqual(state_dict['scale'], 0.094488) |
| self.assertEqual(state_dict['zero_point'], 53) |
| b = io.BytesIO() |
| torch.save(state_dict, b) |
| b.seek(0) |
| loaded_dict = torch.load(b) |
| loaded_fq_module = FakeQuantize(observer, quant_min, quant_max) |
| loaded_fq_module.load_state_dict(loaded_dict) |
| for key in state_dict: |
| self.assertEqual(state_dict[key], loaded_fq_module.state_dict()[key]) |
| |
| self.assertEqual(loaded_fq_module.calculate_qparams(), fq_module.calculate_qparams()) |
| |
| def test_fake_quant_control(self): |
| torch.manual_seed(42) |
| X = torch.rand(20, 10, dtype=torch.float32) |
| fq_module = torch.quantization.default_fake_quant() |
| # Output of fake quant is not identical to input |
| Y = fq_module(X) |
| self.assertNotEqual(Y, X) |
| torch.quantization.disable_fake_quant(fq_module) |
| X = torch.rand(20, 10, dtype=torch.float32) |
| Y = fq_module(X) |
| # Fake quant is disabled,output is identical to input |
| self.assertEqual(Y, X) |
| |
| # Explicit copy at this point in time, because FakeQuant keeps internal |
| # state in mutable buffers. |
| scale = fq_module.scale.clone().detach() |
| zero_point = fq_module.zero_point.clone().detach() |
| |
| torch.quantization.disable_observer(fq_module) |
| torch.quantization.enable_fake_quant(fq_module) |
| X = 10.0 * torch.rand(20, 10, dtype=torch.float32) - 5.0 |
| Y = fq_module(X) |
| self.assertNotEqual(Y, X) |
| # Observer is disabled, scale and zero-point do not change |
| self.assertEqual(fq_module.scale, scale) |
| self.assertEqual(fq_module.zero_point, zero_point) |
| torch.quantization.enable_observer(fq_module) |
| Y = fq_module(X) |
| self.assertNotEqual(Y, X) |
| # Observer is enabled, scale and zero-point are different |
| self.assertNotEqual(fq_module.scale, scale) |
| self.assertNotEqual(fq_module.zero_point, zero_point) |
| |
| def test_fake_quant_preserves_qparam_shapes_for_activations(self): |
| class Model(nn.Module): |
| def __init__(self): |
| super(Model, self).__init__() |
| self.linear = nn.Linear(4, 4) |
| |
| def forward(self, x): |
| x = self.linear(x) |
| return x |
| |
| m = Model() |
| |
| m.qconfig = torch.quantization.get_default_qat_qconfig('fbgemm') |
| torch.quantization.prepare_qat(m, inplace=True) |
| |
| scale_shape_before = m.linear.activation_post_process.scale.shape |
| zero_point_shape_before = m.linear.activation_post_process.zero_point.shape |
| |
| x = torch.rand(4, 4, 4, 4) |
| m(x) |
| scale_shape_after = m.linear.activation_post_process.scale.shape |
| zero_point_shape_after = m.linear.activation_post_process.zero_point.shape |
| self.assertEqual( |
| scale_shape_before, scale_shape_after, |
| msg="FakeQuant scale shape must stay consistent") |
| self.assertEqual( |
| zero_point_shape_before, zero_point_shape_after, |
| msg="FakeQuant zero_point shape must stay consistent") |
| |
| |
| class TestFakeQuantizePerChannel(TestCase): |
| |
| @given(device=st.sampled_from(['cpu', 'cuda'] if torch.cuda.is_available() else ['cpu']), |
| X=hu.per_channel_tensor(shapes=hu.array_shapes(1, 5,), |
| qparams=hu.qparams(dtypes=torch.quint8))) |
| def test_forward_per_channel(self, device, X): |
| r"""Tests the forward path of the FakeQuantizePerTensorAffine op. |
| """ |
| np.random.seed(NP_RANDOM_SEED) |
| X, (scale, zero_point, axis, torch_type) = X |
| quant_min = torch.iinfo(torch_type).min |
| quant_max = torch.iinfo(torch_type).max |
| |
| X = to_tensor(X, device) |
| scale = to_tensor(scale, device) |
| zero_point = torch.tensor(zero_point).to(dtype=torch.int64, device=device) |
| Y = _fake_quantize_per_channel_affine_reference(X.cpu(), scale.cpu(), zero_point.cpu(), axis, quant_min, quant_max) |
| Y_prime = torch.fake_quantize_per_channel_affine( |
| X, scale, zero_point, axis, quant_min, quant_max) |
| np.testing.assert_allclose(Y, Y_prime.cpu(), rtol=tolerance, atol=tolerance) |
| |
| @given(device=st.sampled_from(['cpu', 'cuda'] if torch.cuda.is_available() else ['cpu']), |
| X=hu.per_channel_tensor(shapes=hu.array_shapes(1, 5,), |
| qparams=hu.qparams(dtypes=torch.quint8))) |
| def test_backward_per_channel(self, device, X): |
| r"""Tests the backward method. |
| """ |
| np.random.seed(NP_RANDOM_SEED) |
| X, (scale, zero_point, axis, torch_type) = X |
| quant_min = torch.iinfo(torch_type).min |
| quant_max = torch.iinfo(torch_type).max |
| |
| X = to_tensor(X, device) |
| scale = to_tensor(scale, device) |
| zero_point = torch.tensor(zero_point).to(dtype=torch.int64, device=device) |
| X.requires_grad_() |
| Y_prime = torch.fake_quantize_per_channel_affine( |
| X, scale, zero_point, axis, quant_min, quant_max) |
| dout = torch.rand(X.shape, dtype=torch.float).to(device) |
| dX = _fake_quantize_per_channel_affine_grad_reference( |
| dout, X, scale, zero_point, axis, quant_min, quant_max) |
| Y_prime.backward(dout) |
| np.testing.assert_allclose(dX.cpu().detach().numpy(), X.grad.cpu().detach().numpy(), rtol=tolerance, atol=tolerance) |
| |
| @given(device=st.sampled_from(['cpu', 'cuda'] if torch.cuda.is_available() else ['cpu']), |
| X=hu.per_channel_tensor(shapes=hu.array_shapes(1, 5,), |
| qparams=hu.qparams(dtypes=torch.quint8))) |
| @unittest.skip("temporarily disable the test") |
| def test_numerical_consistency_per_channel(self, device, X): |
| r"""Comparing numerical consistency between CPU quantize/dequantize op and the CPU fake quantize op |
| """ |
| np.random.seed(NP_RANDOM_SEED) |
| X, (scale, zero_point, axis, torch_type) = X |
| quant_min = torch.iinfo(torch_type).min |
| quant_max = torch.iinfo(torch_type).max |
| |
| X = to_tensor(X, device) |
| scale = to_tensor(scale, device) |
| zero_point = torch.tensor(zero_point).to(dtype=torch.int64, device=device) |
| # quantize_linear and dequantize are only implemented in CPU |
| Y = torch.dequantize(torch.quantize_per_channel(X.cpu(), scale.cpu(), zero_point.cpu(), axis, torch_type)) |
| Y_prime = torch.fake_quantize_per_channel_affine( |
| X, scale, zero_point, axis, quant_min, quant_max) |
| np.testing.assert_allclose(Y, Y_prime.cpu(), rtol=tolerance, atol=tolerance) |
| |
| @given(device=st.sampled_from(['cpu', 'cuda'] if torch.cuda.is_available() else ['cpu']), |
| X=hu.per_channel_tensor(shapes=hu.array_shapes(2, 5,), |
| qparams=hu.qparams(dtypes=torch.qint8))) |
| def test_fq_module(self, device, X): |
| np.random.seed(NP_RANDOM_SEED) |
| X, (scale, zero_point, axis, torch_type) = X |
| quant_min = torch.iinfo(torch_type).min |
| quant_max = torch.iinfo(torch_type).max |
| |
| X = to_tensor(X, device) |
| X.requires_grad_() |
| fq_module = FakeQuantize(default_per_channel_weight_observer, quant_min, quant_max, ch_axis=axis).to(device) |
| Y_prime = fq_module(X) |
| assert fq_module.scale is not None |
| assert fq_module.zero_point is not None |
| Y = _fake_quantize_per_channel_affine_reference(X, fq_module.scale, |
| fq_module.zero_point, axis, quant_min, quant_max) |
| np.testing.assert_allclose(Y.cpu().detach().numpy(), Y_prime.cpu().detach().numpy(), rtol=tolerance, atol=tolerance) |
| |
| # Test backward |
| dout = torch.rand(X.shape, dtype=torch.float, device=device) |
| Y_prime.backward(dout) |
| dX = _fake_quantize_per_channel_affine_grad_reference(dout, X, fq_module.scale, |
| fq_module.zero_point, axis, quant_min, quant_max) |
| np.testing.assert_allclose(dX.cpu().numpy(), X.grad.cpu().detach().numpy(), rtol=tolerance, atol=tolerance) |
| |
| def test_fq_serializable(self): |
| observer = default_per_channel_weight_observer |
| quant_min = -128 |
| quant_max = 127 |
| fq_module = FakeQuantize(observer, quant_min, quant_max) |
| X = torch.tensor([[-5, -3.5, -2, 0, 3, 5, 7], [1, 3, 2, 5, 6.5, 8, 10]], dtype=torch.float32) |
| y_ref = fq_module(X) |
| state_dict = fq_module.state_dict() |
| self.assertEqual(state_dict['scale'], [0.054902, 0.078431]) |
| self.assertEqual(state_dict['zero_point'], [0, 0]) |
| b = io.BytesIO() |
| torch.save(state_dict, b) |
| b.seek(0) |
| loaded_dict = torch.load(b) |
| for key in state_dict: |
| self.assertEqual(state_dict[key], loaded_dict[key]) |
| |
| def _get_buffer_ids(module): |
| """ |
| Object addresses stay constant if and only if all modifications are in-place |
| """ |
| return [id(v) for k, v in module._buffers.items()] |
| |
| class TestDistributed(QuantizationTestCase): |
| |
| def test_observers_preserve_buffers(self): |
| """ |
| Tests that observers only modify buffers in place. Note: this is important |
| because nn.DataParallel depends on this assumption to work correctly. |
| However, DataParallel does not expose IDs of the replicas, so we test it |
| without DataParallel in order to easily access the object IDs. |
| """ |
| observer_types = [ |
| torch.quantization.MinMaxObserver.with_args(dtype=torch.qint8), |
| torch.quantization.MovingAverageMinMaxObserver.with_args(dtype=torch.qint8), |
| torch.quantization.MinMaxDynamicQuantObserver.with_args(dtype=torch.qint8), |
| torch.quantization.PerChannelMinMaxObserver.with_args(dtype=torch.qint8), |
| torch.quantization.MovingAveragePerChannelMinMaxObserver.with_args(dtype=torch.qint8), |
| torch.quantization.HistogramObserver.with_args(dtype=torch.qint8), |
| torch.quantization.RecordingObserver.with_args(dtype=torch.qint8), |
| torch.quantization.NoopObserver.with_args(dtype=torch.float16), |
| ] |
| |
| for observer_type in observer_types: |
| observer = observer_type() |
| buffer_ids_before = _get_buffer_ids(observer) |
| for _i in range(5): |
| inputs = torch.rand((4, 4, 4)) |
| observer(inputs) |
| buffer_ids_after = _get_buffer_ids(observer) |
| self.assertEqual( |
| buffer_ids_before, |
| buffer_ids_after, |
| msg="{}: Buffers must be modified in place".format(str(observer))) |
| |
| def test_fake_quant_preserves_buffers(self): |
| """ |
| Tests that fake quant only modifies buffers in place. Note: this is important |
| because nn.DataParallel depends on this assumption to work correctly. |
| However, DataParallel does not expose IDs of the replicas, so we test it |
| without DataParallel in order to easily access the object IDs. |
| """ |
| model = torch.quantization.FakeQuantize() |
| buffer_ids_before = _get_buffer_ids(model) |
| for _i in range(5): |
| inputs = torch.rand((4, 4, 4)) |
| model(inputs) |
| model.apply(torch.quantization.enable_fake_quant) |
| model.apply(torch.quantization.disable_fake_quant) |
| model.apply(torch.quantization.enable_observer) |
| model.apply(torch.quantization.disable_observer) |
| buffer_ids_after = _get_buffer_ids(model) |
| self.assertEqual( |
| buffer_ids_before, |
| buffer_ids_after, |
| msg="FakeQuant: Buffers must be modified in place") |
| |
| @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported") |
| @unittest.skipIf(not TEST_CUDA, "CUDA unavailable") |
| def test_qat_data_parallel(self): |
| """ |
| Tests that doing QAT in nn.DataParallel does not crash. |
| """ |
| if 'fbgemm' not in torch.backends.quantized.supported_engines: |
| return |
| with override_quantized_engine('fbgemm'): |
| device = torch.device('cuda') |
| |
| model = nn.Sequential( |
| torch.quantization.QuantStub(), |
| nn.Conv2d(3, 1, 1, bias=False), |
| nn.BatchNorm2d(1), |
| nn.ReLU(), |
| nn.Conv2d(1, 2, 3, stride=2, padding=1, bias=False), |
| nn.BatchNorm2d(2), |
| nn.AvgPool2d(14), |
| nn.Sigmoid(), |
| torch.quantization.DeQuantStub(), |
| ) |
| |
| torch.quantization.fuse_modules(model, [['1', '2', '3'], ['4', '5']], inplace=True) |
| |
| model.qconfig = torch.quantization.get_default_qat_qconfig('fbgemm') |
| torch.quantization.prepare_qat(model, inplace=True) |
| model = nn.DataParallel(model, device_ids=[0, 1]) |
| model.to(device) |
| model.train() |
| |
| for epoch in range(3): |
| inputs = torch.rand(2, 3, 28, 28).to(device) |
| model(inputs) |
| if epoch >= 1: |
| model.apply(torch.quantization.disable_observer) |
| if epoch >= 2: |
| model.apply(torch.nn.intrinsic.qat.freeze_bn_stats) |
| quant_model = copy.deepcopy(model.module) |
| quant_model = torch.quantization.convert(quant_model.eval().cpu(), inplace=False) |
| with torch.no_grad(): |
| out = quant_model(torch.rand(1, 3, 28, 28)) |
| |
| def test_qat_convbn_fused_syncbn_replacement(self): |
| """ |
| Tests that SyncBatchNorm replacement works for fused ConvBN. |
| """ |
| if 'fbgemm' not in torch.backends.quantized.supported_engines: |
| return |
| with override_quantized_engine('fbgemm'): |
| # create conv-bn |
| class Model(nn.Module): |
| def __init__(self): |
| super(Model, self).__init__() |
| self.conv = nn.Conv2d(4, 1, 3, padding=1) |
| self.bn = nn.BatchNorm2d(1) |
| |
| def forward(self, x): |
| x = self.conv(x) |
| x = self.bn(x) |
| return x |
| |
| model = Model() |
| # fuse it |
| fused_model = torch.quantization.fuse_modules( |
| model, |
| [['conv', 'bn']], |
| ) |
| # convert to QAT |
| fused_model.qconfig = torch.quantization.get_default_qconfig('fbgemm') |
| torch.quantization.prepare_qat(fused_model, inplace=True) |
| # replace with DDP |
| fused_model = nn.SyncBatchNorm.convert_sync_batchnorm(fused_model) |
| self.assertTrue( |
| isinstance(fused_model.conv.bn, nn.SyncBatchNorm), |
| "Expected BN to be converted to SyncBN") |
