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Revert D29312809: add quantized_resize and dequantize for some cuda backends

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Test Plan: revert-hammer

Differential Revision:
D29312809 (c4cc26f26a)

Original commit changeset: c5c5eabb98bc

fbshipit-source-id: 565e215513b68eae0dacdd1660b1a01759215511
This commit is contained in:
Alban Desmaison 2021-06-30 07:35:52 -07:00 committed by Facebook GitHub Bot
parent bfe03120ee
commit 1fd65967e5
2 changed files with 1 additions and 62 deletions
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3
aten/src/ATen/native/native_functions.yaml
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@ -1793,7 +1793,7 @@
dispatch:
CPU, Meta: resize_
CUDA: resize_cuda_
QuantizedCPU, QuantizedCUDA: quantized_resize_cpu_
QuantizedCPU: quantized_resize_cpu_
- func: empty_quantized(int[] size, Tensor qtensor) -> Tensor
variants: function
@ -5161,7 +5161,6 @@
variants: function, method
dispatch:
CPU: dequantize_cpu
CUDA: dequantize_quantized_cpu
QuantizedCPU, QuantizedCUDA: dequantize_quantized_cpu
- func: dequantize.tensors(Tensor[] tensors) -> Tensor[]

60
test/quantization/core/test_quantized_tensor.py
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@ -762,66 +762,6 @@ class TestQuantizedTensor(TestCase):
q3 = q1.resize(*sizes2)
q4 = q1.contiguous().resize(*sizes2)
@unittest.skipIf(not torch.cuda.is_available() or TEST_WITH_ROCM, 'CUDA is not available')
def test_cuda_cpu_qimplementation_consistency(self):
numel, zero_point, scale = 100, 2, 0.02
r = torch.rand(numel, dtype=torch.float32, device='cpu') * 25 - 4
for dtype in [torch.qint8, torch.quint8, torch.qint32]:
qr_cpu = torch.quantize_per_tensor(r, scale, zero_point, dtype=dtype)
qr_cuda = torch.quantize_per_tensor(r.cuda(), scale, zero_point, dtype=dtype)
# intr repr must be the same
np.testing.assert_equal(qr_cpu.int_repr().numpy(), qr_cuda.int_repr().cpu().numpy())
# dequantized values must be the same
r_cpu, r_cuda = qr_cpu.dequantize().numpy(), qr_cuda.dequantize().cpu().numpy()
np.testing.assert_almost_equal(r_cuda, r_cpu, decimal=5)
@unittest.skipIf(not torch.cuda.is_available() or TEST_WITH_ROCM, 'CUDA is not available')
def test_qtensor_resize_cuda(self):
scale, zero_point, dtype = 1.0, 2, torch.uint8
sizes1 = [1, 2, 3, 4]
sizes2 = [1 * 2, 3 * 4]
sizes3 = [1, 2 * 3, 4]
sizes4 = [1 * 2 * 3 * 4]
sizes5 = [1, 2, 1, 3, 1, 4]
q1_int = torch.randint(0, 100, sizes1, dtype=dtype, device='cpu')
q1 = torch._make_per_tensor_quantized_tensor(q1_int.cuda(), scale=scale, zero_point=zero_point)
q2 = q1.resize(*sizes2)
q3 = q2.resize(*sizes3)
q4 = q3.resize(*sizes4)
q5 = q4.resize(*sizes5)
self.assertEqual(q1.numel(), q2.numel())
self.assertEqual(q1.numel(), q3.numel())
self.assertEqual(q1.numel(), q4.numel())
self.assertEqual(q1.numel(), q5.numel())
# Compare original and post-transpose
a_int = torch.randint(0, 100, sizes1, dtype=dtype, device='cpu')
a = torch._make_per_tensor_quantized_tensor(a_int.cuda(), scale=scale, zero_point=zero_point)
b = a.transpose(1, 2) # swaps 2nd and 3rd dimension
c = b.resize(*sizes1) # Change the sizes back to the original
self.assertEqual(a.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))
# Throws an error if numel is wrong
q1_int = torch.randint(0, 100, sizes1, dtype=dtype, device='cpu')
q1 = torch._make_per_tensor_quantized_tensor(a_int.cuda(), scale=scale, zero_point=zero_point)
err_str = "requested resize to*"
with self.assertRaisesRegex(RuntimeError, err_str):
q2 = q1.resize(*sizes1[:-1])
# resize on both contiguous and non-contiguous tensor should be fine
q3 = q1.resize(*sizes2)
q4 = q1.contiguous().resize(*sizes2)
def test_qtensor_reshape(self):
scale, zero_point, dtype = 1.0, 2, torch.uint8
for device in get_supported_device_types():