mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-21 05:34:18 +08:00
032c27cff7
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/35235 For dynamic quantization in graph mode, we need an operator that returns the qparams of the tensor similar to the linear_dynamic quantized op Test Plan: python test/test_quantized_tensor.py TestQuantizedTensor.test_choose_qparams Imported from OSS Differential Revision: D20608793 fbshipit-source-id: b923b2620421b32d05f4097db0d6153d53198221
430 lines
19 KiB
Python
430 lines
19 KiB
Python
import numpy as np
|
|
import math
|
|
import torch
|
|
import io
|
|
from copy import deepcopy
|
|
from hypothesis import given
|
|
from hypothesis import strategies as st
|
|
|
|
from torch.testing._internal.common_utils import TestCase, run_tests
|
|
import torch.testing._internal.hypothesis_utils as hu
|
|
|
|
hu.assert_deadline_disabled()
|
|
|
|
import tempfile
|
|
|
|
class Foo(torch.nn.Module):
|
|
def __init__(self):
|
|
super(Foo, self).__init__()
|
|
self.qscheme = torch.per_tensor_symmetric
|
|
|
|
def _calculate_dynamic_qparams(X, dtype, reduce_range=False):
|
|
"""Calculate the dynamic quantization parameters (scale, zero_point)
|
|
according to the min and max element of the tensor"""
|
|
if isinstance(X, torch.Tensor):
|
|
X = X.numpy()
|
|
if dtype == torch.qint8:
|
|
if reduce_range:
|
|
qmin, qmax = -64, 63
|
|
else:
|
|
qmin, qmax = -128, 127
|
|
else: # dtype == torch.quint8
|
|
if reduce_range:
|
|
qmin, qmax = 0, 127
|
|
else:
|
|
qmin, qmax = 0, 255
|
|
|
|
min_val = X.min().astype(dtype=np.float32)
|
|
max_val = X.max().astype(dtype=np.float32)
|
|
min_val = min(0.0, min_val)
|
|
max_val = max(0.0, max_val)
|
|
scale = (np.float64(max_val) - min_val) / (qmax - qmin)
|
|
if scale == 0.0 or math.isinf(1.0 / scale):
|
|
scale = np.float64(0.1)
|
|
zero_point = 0
|
|
|
|
zero_point_from_min = qmin - min_val / float(scale)
|
|
zero_point_from_max = qmax - max_val / float(scale)
|
|
zero_point_from_min_error = abs(qmin) - abs(min_val / float(scale))
|
|
zero_point_from_max_error = abs(qmax) - abs(max_val / float(scale))
|
|
if zero_point_from_min_error < zero_point_from_max_error:
|
|
initial_zero_point = zero_point_from_min
|
|
else:
|
|
initial_zero_point = zero_point_from_max
|
|
nudged_zero_point = 0
|
|
|
|
if initial_zero_point < qmin:
|
|
nudged_zero_point = qmin
|
|
elif initial_zero_point > qmax:
|
|
nudged_zero_point = qmax
|
|
else:
|
|
nudged_zero_point = int(round(initial_zero_point))
|
|
|
|
return [scale.astype(np.float32), int(nudged_zero_point)]
|
|
|
|
class TestQuantizedTensor(TestCase):
|
|
def test_qtensor(self):
|
|
num_elements = 10
|
|
r = torch.ones(num_elements, dtype=torch.float)
|
|
scale = 1.0
|
|
zero_point = 2
|
|
qr = torch.quantize_per_tensor(r, scale, zero_point, torch.quint8)
|
|
self.assertEqual(qr.q_scale(), scale)
|
|
self.assertEqual(qr.q_zero_point(), zero_point)
|
|
self.assertTrue(qr.is_quantized)
|
|
self.assertFalse(r.is_quantized)
|
|
self.assertEqual(qr.qscheme(), torch.per_tensor_affine)
|
|
self.assertTrue(isinstance(qr.qscheme(), torch.qscheme))
|
|
# slicing and int_repr
|
|
int_repr = qr.int_repr()
|
|
for num in int_repr:
|
|
self.assertEqual(num, 3)
|
|
for num in qr[2:].int_repr():
|
|
self.assertEqual(num, 3)
|
|
# dequantize
|
|
rqr = qr.dequantize()
|
|
for i in range(num_elements):
|
|
self.assertEqual(r[i], rqr[i])
|
|
# Scalar Tensor
|
|
# item
|
|
r = torch.ones(1, dtype=torch.float)
|
|
qr = torch.quantize_per_tensor(r, scale, zero_point, torch.quint8)
|
|
self.assertEqual(qr.item(), 1)
|
|
self.assertEqual(qr[0].item(), 1)
|
|
# assignment
|
|
self.assertTrue(qr[0].is_quantized)
|
|
qr[0] = 11.3 # float asignment
|
|
self.assertEqual(qr.item(), 11)
|
|
x = torch.ones(1, dtype=torch.float) * 15.3
|
|
# Copying from a float Tensor
|
|
qr[:] = x
|
|
self.assertEqual(qr.item(), 15)
|
|
# we can also print a qtensor
|
|
self.assertEqual(' '.join(str(qr).split()),
|
|
"tensor([15.], size=(1,), dtype=torch.quint8, " +
|
|
"quantization_scheme=torch.per_tensor_affine, " +
|
|
"scale=1.0, zero_point=2)")
|
|
empty_r = torch.ones((0, 1), dtype=torch.float)
|
|
empty_qr = torch.quantize_per_tensor(empty_r, scale, zero_point, torch.quint8)
|
|
self.assertEqual(' '.join(str(empty_qr).split()),
|
|
"tensor([], size=(0, 1), dtype=torch.quint8, " +
|
|
"quantization_scheme=torch.per_tensor_affine, " +
|
|
"scale=1.0, zero_point=2)")
|
|
|
|
def test_qtensor_quant_dequant(self):
|
|
r = torch.rand(3, 2, dtype=torch.float) * 4 - 2
|
|
scale = 0.02
|
|
zero_point = 2
|
|
qr = torch.quantize_per_tensor(r, scale, zero_point, torch.quint8)
|
|
rqr = qr.dequantize()
|
|
self.assertTrue(np.allclose(r.numpy(), rqr.numpy(), atol=2 / scale))
|
|
|
|
# legacy constructor/new doesn't support qtensors
|
|
def test_qtensor_legacy_new_failure(self):
|
|
r = torch.rand(3, 2, dtype=torch.float) * 4 - 2
|
|
scale = 0.02
|
|
zero_point = 2
|
|
qr = torch.quantize_per_tensor(r, scale, zero_point, torch.quint8)
|
|
self.assertRaises(RuntimeError, lambda: qr.new(device='cpu'))
|
|
self.assertRaises(RuntimeError, lambda: qr.new(r.storage()))
|
|
self.assertRaises(RuntimeError, lambda: qr.new(r))
|
|
self.assertRaises(RuntimeError, lambda: qr.new(torch.Size([2, 3])))
|
|
self.assertRaises(RuntimeError, lambda: qr.new([6]))
|
|
|
|
def test_per_channel_qtensor_creation(self):
|
|
numel = 10
|
|
ch_axis = 0
|
|
scales = torch.rand(numel)
|
|
zero_points = torch.randint(0, 10, size=(numel,))
|
|
q = torch._empty_per_channel_affine_quantized(
|
|
[numel], scales=scales, zero_points=zero_points, axis=ch_axis, dtype=torch.quint8)
|
|
self.assertEqual(scales, q.q_per_channel_scales())
|
|
self.assertEqual(zero_points, q.q_per_channel_zero_points())
|
|
self.assertEqual(ch_axis, q.q_per_channel_axis())
|
|
|
|
# create Tensor from uint8_t Tensor, scales and zero_points
|
|
int_tensor = torch.randint(0, 100, size=(numel,), dtype=torch.uint8)
|
|
q = torch._make_per_channel_quantized_tensor(int_tensor, scales, zero_points, ch_axis)
|
|
self.assertEqual(int_tensor, q.int_repr())
|
|
self.assertEqual(scales, q.q_per_channel_scales())
|
|
self.assertEqual(zero_points, q.q_per_channel_zero_points())
|
|
self.assertEqual(ch_axis, q.q_per_channel_axis())
|
|
|
|
def test_qtensor_creation(self):
|
|
scale = 0.5
|
|
zero_point = 10
|
|
val = 100
|
|
numel = 10
|
|
q = torch._empty_affine_quantized([numel], scale=scale, zero_point=zero_point, dtype=torch.quint8)
|
|
self.assertEqual(scale, q.q_scale())
|
|
self.assertEqual(zero_point, q.q_zero_point())
|
|
|
|
# create Tensor from uint8_t Tensor, scale and zero_point
|
|
int_tensor = torch.randint(0, 100, size=(10,), dtype=torch.uint8)
|
|
q = torch._make_per_tensor_quantized_tensor(int_tensor, scale, zero_point)
|
|
self.assertEqual(int_tensor, q.int_repr())
|
|
self.assertEqual(scale, q.q_scale())
|
|
self.assertEqual(zero_point, q.q_zero_point())
|
|
|
|
# create via empty_like
|
|
q = torch._empty_affine_quantized([numel], scale=scale, zero_point=zero_point, dtype=torch.quint8)
|
|
q_el = torch.empty_like(q)
|
|
self.assertEqual(q.q_scale(), q_el.q_scale())
|
|
self.assertEqual(q.q_zero_point(), q_el.q_zero_point())
|
|
self.assertEqual(q.dtype, q_el.dtype)
|
|
|
|
# create via empty_like but change the dtype (currently not supported)
|
|
with self.assertRaises(RuntimeError):
|
|
torch.empty_like(q, dtype=torch.qint8)
|
|
|
|
def test_qtensor_dtypes(self):
|
|
r = torch.rand(3, 2, dtype=torch.float) * 4 - 2
|
|
scale = 0.2
|
|
zero_point = 2
|
|
qr = torch.quantize_per_tensor(r, scale, zero_point, torch.qint8)
|
|
rqr = qr.dequantize()
|
|
self.assertTrue(np.allclose(r.numpy(), rqr.numpy(), atol=2 / scale))
|
|
qr = torch.quantize_per_tensor(r, scale, zero_point, torch.quint8)
|
|
rqr = qr.dequantize()
|
|
self.assertTrue(np.allclose(r.numpy(), rqr.numpy(), atol=2 / scale))
|
|
qr = torch.quantize_per_tensor(r, scale, zero_point, torch.qint32)
|
|
rqr = qr.dequantize()
|
|
self.assertTrue(np.allclose(r.numpy(), rqr.numpy(), atol=2 / scale))
|
|
|
|
def test_qtensor_quantize_per_channel(self):
|
|
r = torch.rand(3, 2, dtype=torch.float) * 4 - 2
|
|
scales = torch.tensor([0.2, 0.03], dtype=torch.double)
|
|
zero_points = torch.tensor([5, 10], dtype=torch.long)
|
|
axis = 1
|
|
|
|
def quantize_c(data, scales, zero_points):
|
|
res = torch.empty((3, 2))
|
|
quant_min, quant_max = 0, 255
|
|
for i in range(3):
|
|
for j in range(2):
|
|
res[i][j] = np.clip(np.round(data[i][j] / scales[j]) + zero_points[j], quant_min, quant_max)
|
|
return res
|
|
qr = torch.quantize_per_channel(r, scales, zero_points, axis, torch.quint8)
|
|
rqr = qr.dequantize()
|
|
self.assertTrue(np.allclose(qr.int_repr(), quantize_c(r, scales, zero_points)))
|
|
self.assertTrue(np.allclose(r.numpy(), rqr.numpy(), atol=2 / np.min(scales.numpy())))
|
|
|
|
def test_qtensor_permute(self):
|
|
r = torch.rand(10, 30, 2, 2, dtype=torch.float) * 4 - 2
|
|
scale = 0.02
|
|
zero_point = 1
|
|
qr = torch.quantize_per_tensor(r, scale, zero_point, torch.qint8)
|
|
qr = qr.transpose(0, 1)
|
|
rqr = qr.dequantize()
|
|
# compare transpose + dequantized result with original transposed result
|
|
self.assertTrue(np.allclose(r.numpy().transpose([1, 0, 2, 3]), rqr.numpy(), atol=2 / scale))
|
|
|
|
qr = torch.quantize_per_tensor(r, scale, zero_point, torch.qint8)
|
|
qr1 = qr.permute([1, 0, 2, 3])
|
|
qr2 = qr.transpose(0, 1)
|
|
# compare int representation after transformations
|
|
self.assertEqual(qr1.int_repr(), qr2.int_repr())
|
|
self.assertEqual(qr1.q_scale(), qr2.q_scale())
|
|
self.assertEqual(qr1.q_zero_point(), qr2.q_zero_point())
|
|
# compare dequantized result
|
|
self.assertEqual(qr1.dequantize(), qr2.dequantize())
|
|
# compare permuted + dequantized result with original transposed result
|
|
self.assertTrue(np.allclose(qr2.dequantize().numpy(), r.numpy().transpose([1, 0, 2, 3]), atol=2 / scale))
|
|
# make permuted result contiguous
|
|
self.assertEqual(qr2.contiguous().int_repr(), qr2.int_repr())
|
|
|
|
# change memory format
|
|
qlast = qr.contiguous(memory_format=torch.channels_last)
|
|
self.assertEqual(qr.stride(), list(reversed(sorted(qr.stride()))))
|
|
self.assertNotEqual(qlast.stride(), list(reversed(sorted(qlast.stride()))))
|
|
self.assertEqual(qr.int_repr(), qlast.int_repr())
|
|
self.assertEqual(qr.q_scale(), qlast.q_scale())
|
|
self.assertEqual(qr.q_zero_point(), qlast.q_zero_point())
|
|
self.assertEqual(qlast.dequantize(), qr.dequantize())
|
|
|
|
# permuting larger tensors
|
|
x = torch.randn(64, 64)
|
|
qx = torch.quantize_per_tensor(x, 1.0, 0, torch.qint32)
|
|
# should work
|
|
qx.permute([1, 0])
|
|
|
|
def test_qtensor_per_channel_permute(self):
|
|
r = torch.rand(20, 10, 2, 2, dtype=torch.float) * 4 - 2
|
|
scales = torch.rand(10) * 0.02 + 0.01
|
|
zero_points = torch.round(torch.rand(10) * 2 - 1).to(torch.long)
|
|
qr = torch.quantize_per_channel(r, scales, zero_points, 1, torch.qint8)
|
|
|
|
# we can't reorder the axis
|
|
with self.assertRaises(RuntimeError):
|
|
qr.transpose(0, 1)
|
|
|
|
# but we can change memory format
|
|
qlast = qr.contiguous(memory_format=torch.channels_last)
|
|
self.assertEqual(qr.stride(), list(reversed(sorted(qr.stride()))))
|
|
self.assertNotEqual(qlast.stride(), list(reversed(sorted(qlast.stride()))))
|
|
self.assertEqual(qr.int_repr(), qlast.int_repr())
|
|
self.assertEqual(scales, qlast.q_per_channel_scales())
|
|
self.assertEqual(zero_points, qlast.q_per_channel_zero_points())
|
|
self.assertEqual(1, qlast.q_per_channel_axis())
|
|
self.assertEqual(qlast.dequantize(), qr.dequantize())
|
|
|
|
def test_qtensor_load_save(self):
|
|
scale = 0.2
|
|
zero_point = 10
|
|
r = torch.rand(15, 2, dtype=torch.float32) * 2
|
|
for dtype in [torch.quint8, torch.qint8, torch.qint32]:
|
|
qr = torch.quantize_per_tensor(r, scale, zero_point, dtype)
|
|
qrv = qr[:, 1]
|
|
with tempfile.NamedTemporaryFile() as f:
|
|
# Serializing and Deserializing Tensor
|
|
torch.save((qr, qrv), f)
|
|
f.seek(0)
|
|
qr2, qrv2 = torch.load(f)
|
|
self.assertEqual(qr, qr2)
|
|
self.assertEqual(qrv, qrv2)
|
|
self.assertEqual(qr2.storage().data_ptr(), qrv2.storage().data_ptr())
|
|
|
|
def test_qtensor_per_channel_load_save(self):
|
|
r = torch.rand(20, 10, dtype=torch.float) * 4 - 2
|
|
scales = torch.rand(10, dtype=torch.double) * 0.02 + 0.01
|
|
zero_points = torch.round(torch.rand(10) * 20 + 1).to(torch.long)
|
|
# quint32 is not supported yet
|
|
for dtype in [torch.quint8, torch.qint8]:
|
|
qr = torch.quantize_per_channel(r, scales, zero_points, 1, dtype)
|
|
with tempfile.NamedTemporaryFile() as f:
|
|
# Serializing and Deserializing Tensor
|
|
torch.save(qr, f)
|
|
f.seek(0)
|
|
qr2 = torch.load(f)
|
|
self.assertEqual(qr, qr2)
|
|
|
|
def test_qtensor_copy(self):
|
|
scale = 0.5
|
|
zero_point = 10
|
|
val = 100
|
|
numel = 10
|
|
# copy from same scale and zero_point
|
|
q = torch._empty_affine_quantized([numel], scale=scale, zero_point=zero_point, dtype=torch.quint8)
|
|
q2 = torch._empty_affine_quantized([numel], scale=scale, zero_point=zero_point, dtype=torch.quint8)
|
|
q.copy_(q2)
|
|
self.assertEqual(q.int_repr(), q2.int_repr())
|
|
self.assertEqual(q.q_scale(), q2.q_scale())
|
|
self.assertEqual(q.q_zero_point(), q2.q_zero_point())
|
|
# copying from different scale and zero_point
|
|
scale = 3.2
|
|
zero_point = 5
|
|
q = torch._empty_affine_quantized([numel], scale=scale, zero_point=zero_point, dtype=torch.quint8)
|
|
# check original scale and zero_points are set correctly
|
|
self.assertEqual(q.q_scale(), scale)
|
|
self.assertEqual(q.q_zero_point(), zero_point)
|
|
q.copy_(q2)
|
|
# check scale and zero_points has been copied
|
|
self.assertEqual(q, q2)
|
|
# deep copy
|
|
scale, zero_point, dtype = 1.0, 2, torch.uint8
|
|
q_int = torch.randint(0, 100, [3, 5], dtype=dtype)
|
|
scale, zero_point = 2.0, 3
|
|
q = torch._make_per_tensor_quantized_tensor(q_int, scale=scale, zero_point=zero_point)
|
|
qc = deepcopy(q)
|
|
self.assertEqual(qc, q)
|
|
|
|
# can't copy from quantized tensor to non-quantized tensor
|
|
r = torch.empty([numel], dtype=torch.float)
|
|
q = torch._empty_affine_quantized([numel], scale=scale, zero_point=zero_point, dtype=torch.quint8)
|
|
with self.assertRaisesRegex(RuntimeError, "please use dequantize"):
|
|
r.copy_(q)
|
|
|
|
def test_qtensor_clone(self):
|
|
numel = 10
|
|
scale = 0.5
|
|
zero_point = 10
|
|
q2 = torch._empty_affine_quantized([numel], scale=scale, zero_point=zero_point, dtype=torch.quint8)
|
|
q = q2.clone()
|
|
# Check to make sure the scale and zero_point has been copied.
|
|
self.assertEqual(q, q2)
|
|
|
|
def test_qtensor_view(self):
|
|
scale, zero_point, dtype = 1.0, 2, torch.uint8
|
|
q_int = torch.randint(0, 100, [1, 2, 3], dtype=dtype)
|
|
q = torch._make_per_tensor_quantized_tensor(q_int, scale=scale, zero_point=zero_point)
|
|
q2 = q.view(1, 3, 2)
|
|
self.assertEqual(q.numel(), q2.numel())
|
|
# testing -1
|
|
self.assertEqual(q, q2.view(1, -1, 3))
|
|
|
|
a_int = torch.randint(0, 100, [1, 2, 3, 4], dtype=dtype)
|
|
a = torch._make_per_tensor_quantized_tensor(a_int, scale=scale, zero_point=zero_point)
|
|
b = a.transpose(1, 2) # swaps 2nd and 3rd dimension
|
|
c = a.view(1, 3, 2, 4) # does not change tensor layout in memory
|
|
self.assertEqual(b.size(), c.size())
|
|
self.assertEqual(b.q_scale(), c.q_scale())
|
|
self.assertEqual(b.q_zero_point(), c.q_zero_point())
|
|
self.assertNotEqual(b.stride(), c.stride())
|
|
# size is the same but the underlying data is different
|
|
self.assertNotEqual(b.int_repr(), c.int_repr())
|
|
self.assertFalse(torch.equal(b, c))
|
|
|
|
# a case can't view non-contiguos Tensor
|
|
a_int = torch.randint(0, 100, [1, 2, 3, 4], dtype=dtype)
|
|
a = torch._make_per_tensor_quantized_tensor(a_int, scale=scale, zero_point=zero_point)
|
|
b = a.transpose(1, 2) # swaps 2nd and 3rd dimension
|
|
err_str = "view size is not compatible with input tensor's size and stride*"
|
|
with self.assertRaisesRegex(RuntimeError, err_str):
|
|
b.view(1, 4, 2, 3)
|
|
# view on contiguous tensor is fine
|
|
b.contiguous().view(1, 4, 2, 3)
|
|
|
|
|
|
def test_qtensor_reshape(self):
|
|
scale, zero_point, dtype = 1.0, 2, torch.uint8
|
|
q_int = torch.randint(0, 100, [3, 5], dtype=dtype)
|
|
q = torch._make_per_tensor_quantized_tensor(q_int, scale=scale, zero_point=zero_point)
|
|
q2 = q.reshape([15])
|
|
self.assertEqual(q.numel(), q2.numel())
|
|
self.assertEqual(q2.size(), [15])
|
|
# testing -1
|
|
self.assertEqual(q, q2.reshape([3, -1]))
|
|
|
|
a_int = torch.randint(0, 100, [1, 2, 3, 4], dtype=dtype)
|
|
a = torch._make_per_tensor_quantized_tensor(a_int, scale=scale, zero_point=zero_point)
|
|
b = a.transpose(1, 2) # swaps 2nd and 3rd dimension
|
|
c = a.reshape(1, 3, 2, 4) # does not change tensor layout
|
|
self.assertEqual(b.size(), c.size())
|
|
self.assertEqual(b.q_scale(), c.q_scale())
|
|
self.assertEqual(b.q_zero_point(), c.q_zero_point())
|
|
self.assertNotEqual(b.stride(), c.stride())
|
|
self.assertNotEqual(b.int_repr(), c.int_repr())
|
|
self.assertFalse(torch.equal(b, c))
|
|
|
|
# we can use reshape for non-contiguous Tensor
|
|
a_int = torch.randint(0, 100, [1, 2, 3, 4], dtype=dtype)
|
|
a = torch._make_per_tensor_quantized_tensor(a_int, scale=scale, zero_point=zero_point)
|
|
b = a.transpose(1, 2) # swaps 2nd and 3rd dimension
|
|
c = b.reshape(1, 4, 2, 3)
|
|
|
|
def test_qscheme_pickle(self):
|
|
f = Foo()
|
|
buf = io.BytesIO()
|
|
torch.save(f, buf)
|
|
|
|
buf.seek(0)
|
|
f2 = torch.load(buf)
|
|
|
|
self.assertEqual(f2.qscheme, torch.per_tensor_symmetric)
|
|
|
|
@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_choose_qparams(self, X, reduce_range):
|
|
X, (scale, zero_point, torch_type) = X
|
|
X = torch.from_numpy(X)
|
|
X_scale, X_zp = _calculate_dynamic_qparams(X, torch.quint8, reduce_range=reduce_range)
|
|
qparams = torch._choose_qparams_per_tensor(X, reduce_range)
|
|
np.testing.assert_array_almost_equal(X_scale, qparams[0], decimal=3)
|
|
self.assertEqual(X_zp, qparams[1])
|
|
|
|
if __name__ == "__main__":
|
|
run_tests()
|