def test_setters(self): quantizer = tensor_quantizer.TensorQuantizer() quantizer.num_bits = 7 quantizer.unsigned = True assert quantizer.num_bits == 7 assert quantizer.unsigned
def test_per_channel_scale(self, verbose): """Quantizer performs per channel scaling""" x_np = np.random.rand(15, 15, 64, 128).astype('float32') x_torch = torch.Tensor(x_np).cuda() # Pytorch filter layout seems to be KCRS, reduce max to shape [K, 1, 1, 1] to test per channel scale # Shrink max a little, so that clip behavior is tested amax_x_np = 0.7 * np.max(np.abs(x_np), axis=(1, 2, 3), keepdims=True) quant_x_np = test_utils.quant_np(x_np, amax_x_np) quantizer = tensor_quantizer.TensorQuantizer( tensor_quant.QuantDescriptor(num_bits=8, axis=(0), fake_quant=False, scale_amax=0.7)) quantizer.cuda() module_quant_x = quantizer(x_torch) # np.testing.assert_array_equal(quant_x_torch.cpu().numpy(), quant_x_np) # Pytorch numerics is not the same as numpy, it will be off by 1 error = np.abs(module_quant_x.cpu().numpy() - quant_x_np) np.testing.assert_array_less(error, 2) if verbose: mismatches = np.where(error >= 1) print("Mismatches:") print(" Original: ", x_np[mismatches]) print(" numpy: ", quant_x_np[mismatches]) print(" TensorQuantizer: ", module_quant_x.cpu().numpy()[mismatches])
def test_per_tensor_scale(self): """Quantizer performs expected quantization""" x_np = np.random.rand(1023) x_torch = torch.Tensor(x_np) quant_x_np = test_utils.quant_np(x_np, np.max(np.abs(x_np))) quantizer = tensor_quantizer.TensorQuantizer(tensor_quant.QuantDescriptor(num_bits=8, fake_quant=False)) module_quant_x = quantizer(x_torch) np.testing.assert_array_equal(module_quant_x.cpu().numpy(), quant_x_np)
def test_simple_run(self): """Quantizer calls fake_tensor_quant by default""" x = torch.randn(3, 7).cuda() amax_x = torch.max(torch.abs(x)) fn_quant_x = tensor_quant.fake_tensor_quant(x, amax_x) quantizer = tensor_quantizer.TensorQuantizer() module_quant_x = quantizer(x) np.testing.assert_array_equal(fn_quant_x.cpu().numpy(), module_quant_x.cpu().numpy())
def test_init_calib(self): quant_desc2 = tensor_quant.QuantDescriptor(axis=(0, 1)) quantizer2 = tensor_quantizer.TensorQuantizer(quant_desc2, if_calib=True).cuda() x_2 = torch.rand(127, 63, 7, 7).cuda() quantizer2(x_2) quantizer2.load_calib_amax() assert quantizer2.amax.numel() == 127 * 63
def test_properties(self): quant_desc1 = tensor_quant.QuantDescriptor(amax=3.14) quantizer1 = tensor_quantizer.TensorQuantizer(quant_desc1) quantizer1.amax = 0.577 assert quantizer1.amax.detach().cpu().numpy() == np.float32(0.577) np.testing.assert_array_equal(quantizer1.amax.detach().cpu().numpy(), quantizer1.amax) assert quantizer1.step_size == 0.577 / 127. quant_desc2 = tensor_quant.QuantDescriptor() quantizer2 = tensor_quantizer.TensorQuantizer(quant_desc2) amax_np = np.array([3.142, 2.718], dtype=np.float32) quantizer2.amax = amax_np np.testing.assert_array_equal(quantizer2.amax.detach().cpu().numpy(), amax_np) quant_desc3 = tensor_quant.QuantDescriptor() quantizer3 = tensor_quantizer.TensorQuantizer(quant_desc3) assert quantizer3.amax is None
def test_state_loading(self): """Test quant_desc loading via state_dict""" amax = [3.142, 2.718] quant_desc1 = tensor_quant.QuantDescriptor(amax=amax) quantizer1 = tensor_quantizer.TensorQuantizer(quant_desc1) # copy state quantizer1.load_state_dict(quantizer1.state_dict()) np.testing.assert_array_equal(quantizer1.amax.detach().cpu().numpy(), quant_desc1.amax)
def test_simple_run_no_fake(self): """Quantizer fake_quant=False calls tensor_quant and sets the scale property""" x = torch.randn(3, 7).cuda() amax_x = torch.max(torch.abs(x)) fn_quant_x, fn_scale = tensor_quant.tensor_quant(x, amax_x) quantizer = tensor_quantizer.TensorQuantizer(tensor_quant.QuantDescriptor(num_bits=8, fake_quant=False)) module_quant_x = quantizer(x) module_scale = quantizer.scale np.testing.assert_array_equal(fn_quant_x.cpu().numpy(), module_quant_x.cpu().numpy()) np.testing.assert_array_equal(fn_scale.cpu().numpy(), module_scale.cpu().numpy())
def test_learn_amax(self): """Test the clip implied by learn_amax""" x_np = np.random.rand(1023).astype(np.float32) x_torch = torch.Tensor(x_np) amax = 0.5 quant_x_np = test_utils.quant_np(x_np, 0.5, fake=True) quantizer = tensor_quantizer.TensorQuantizer( tensor_quant.QuantDescriptor(num_bits=8, amax=amax, learn_amax=True)) assert hasattr(quantizer, 'clip') module_quant_x = quantizer(x_torch) np.testing.assert_array_equal(module_quant_x.cpu().detach().numpy(), quant_x_np)
def test_clip_mode(self): """Test the clip stage only""" x_np = np.random.rand(1023).astype(np.float32) x_torch = torch.Tensor(x_np) amax = 0.5 clip_x_np = np.clip(x_np, -amax, amax) quantizer = tensor_quantizer.TensorQuantizer( tensor_quant.QuantDescriptor(amax=amax, learn_amax=True), if_quant=False, if_clip=True) assert hasattr(quantizer, 'clip') module_clip_x = quantizer(x_torch) np.testing.assert_array_equal(module_clip_x.cpu().detach().numpy(), clip_x_np)
def test_max_calib(self): axis = 0 reduce_axis = (1, 2, 3) quant_desc1 = tensor_quant.QuantDescriptor(axis=axis) quantizer1 = tensor_quantizer.TensorQuantizer(quant_desc1).cuda() quantizer1.enable_calib() quant_desc1 = tensor_quant.QuantDescriptor(axis=axis) quantizer1 = tensor_quantizer.TensorQuantizer(quant_desc1).cuda() quantizer1.enable_calib() with pytest.raises(RuntimeError, match="Calibrator returned None"): quantizer1.load_calib_amax() x_1 = torch.rand(127, 63, 7, 7).cuda() x_2 = torch.rand(127, 63, 7, 7).cuda() quantizer1(x_1) quantizer1(x_2) quantizer1.disable_calib() global_amax = torch.max( quant_utils.reduce_amax(x_1, axis=reduce_axis, keepdims=True), quant_utils.reduce_amax(x_2, axis=reduce_axis, keepdims=True)) test_utils.compare(quantizer1._calibrator.compute_amax(), global_amax, atol=0, rtol=0, ctol=0) quantizer1.load_calib_amax() test_utils.compare(quantizer1.amax, global_amax, atol=0, rtol=0, ctol=0) quant_desc2 = tensor_quant.QuantDescriptor(learn_amax=True) quantizer2 = tensor_quantizer.TensorQuantizer(quant_desc2).cuda() quantizer2.enable_calib() quantizer2(x_1) quantizer2(x_2) quantizer2.load_calib_amax() quantizer2.init_learn_amax() test_utils.compare(quantizer2.clip.clip_value_min, -torch.max(global_amax), atol=0, rtol=0, ctol=0) test_utils.compare(quantizer2.clip.clip_value_max, torch.max(global_amax), atol=0, rtol=0, ctol=0)
def test_scale_amax(self): x_np = np.random.rand(1023).astype(np.float32) x_torch = torch.Tensor(x_np) amax = 0.5 scale_amax = 0.9 quant_x_np = test_utils.quant_np(x_np, amax * scale_amax, fake=True) quantizer = tensor_quantizer.TensorQuantizer( tensor_quant.QuantDescriptor(num_bits=8, amax=amax, scale_amax=scale_amax)) module_quant_x = quantizer(x_torch) np.testing.assert_array_equal(module_quant_x.cpu().detach().numpy(), quant_x_np) # Test twice. There was a but in scale amax logic that modify the amax every time module_quant_x = quantizer(x_torch) np.testing.assert_array_equal(module_quant_x.cpu().detach().numpy(), quant_x_np)
def test_entropy_and_percentile_calib(self): """Don't really have a good way to test it.""" quant_desc1 = tensor_quant.QuantDescriptor(calib_method='histogram') quantizer1 = tensor_quantizer.TensorQuantizer(quant_desc1, if_calib=True, if_quant=False).cuda() x_1 = torch.rand(3, 63, 7, 7).cuda() x_2 = torch.rand(3, 63, 7, 7).cuda() quantizer1(x_1) quantizer1(x_2) quantizer1.load_calib_amax("entropy") test_utils.compare(quantizer1._calibrator.compute_amax("entropy"), quantizer1.amax, atol=0, rtol=0, ctol=0) quantizer1._calibrator.reset() quantizer1(x_1) quantizer1(x_2) quantizer1.load_calib_amax("percentile", percentile=99.99) test_utils.compare(quantizer1._calibrator.compute_amax( "percentile", percentile=99.99), quantizer1.amax, atol=0, rtol=0, ctol=0)
def test_disable(self): x = torch.randn(3, 7).cuda() amax_x = torch.max(torch.abs(x)) quantizer = tensor_quantizer.TensorQuantizer(disabled=True) module_quant_x = quantizer(x) np.testing.assert_array_equal(x.cpu().numpy(), module_quant_x.cpu().numpy())