def test_block_floating_point(self):
for wl in [5, 3]:
for rounding in ["nearest"]:
for dim in [-1, 0, 1]:
t_max = 1 - (2**(-4))
to_quantize_cuda = torch.linspace(-t_max,
t_max,
steps=20,
device='cuda')
to_quantize_cpu = to_quantize_cuda.clone().to("cpu")
block_quantized_cuda = block_quantize(to_quantize_cuda,
wl=wl,
rounding=rounding)
block_quantized_cpu = block_quantize(to_quantize_cpu,
wl=wl,
rounding=rounding)
mse = self.error(block_quantized_cuda, block_quantized_cpu)
self.assertTrue(mse < 1e-15)
def test_fixed_block_zero_exponent(self):
"""
invariant: when the max exponent of a block is zero, block floating point behaves similar to
a fixed point where fl = wl -1, without the lowest number of the fixed point (-1).
"""
for wl, fl in [(3,2), (5,4)]:
t_max = 1-(2**(-fl))
to_quantize = torch.linspace(-t_max, t_max, steps=1000, device='cuda')
fixed_quantized = fixed_point_quantize(to_quantize, wl=wl, fl=fl, rounding='nearest')
block_quantized = block_quantize(to_quantize, wl=wl, rounding='nearest')
self.assertTrue(torch.eq(fixed_quantized, block_quantized).all().item())
def test_block_floating_point(self):
for wl in [5, 3]:
for rounding in ["nearest"]:
for dim in [-1, 0, 1]:
for device in [("cuda:%d" % d)
for d in range(torch.cuda.device_count())]:
t_max = 1 - (2**(-4))
to_quantize_cuda = torch.linspace(
-t_max,
t_max,
steps=1200,
device=torch.device(device))
to_quantize_cpu = to_quantize_cuda.clone().to("cpu")
block_quantized_cuda = block_quantize(
to_quantize_cuda, wl=wl, rounding=rounding)
block_quantized_cpu = block_quantize(to_quantize_cpu,
wl=wl,
rounding=rounding)
mse = self.error(block_quantized_cuda,
block_quantized_cpu)
self.assertTrue(mse < 1e-15,
msg="%.2e MSE on device '%s'" %
(mse, device))