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())
