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Add empty_quantized (#18960)

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Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18960

empty_affine_quantized creates an empty affine quantized Tensor from scratch.
We might need this when we implement quantized operators.

Differential Revision: D14810261

fbshipit-source-id: f07d8bf89822d02a202ee81c78a17aa4b3e571cc
This commit is contained in:
Jerry Zhang
2019-04-17 16:10:05 -07:00
committed by Facebook Github Bot
parent 4371cb5e01
commit ad8f34fcca
7 changed files with 88 additions and 12 deletions
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13
test/test_torch.py
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@ -2695,6 +2695,19 @@ class _TestTorchMixin(object):
print(rqr.numpy())
self.assertTrue(np.allclose(r.numpy(), rqr.numpy(), atol=2 / scale))
def test_qtensor_creation(self):
scale = 0.5
zero_point = 10
val = 100
numel = 10
q = torch.empty_affine_quantized(numel, dtype=torch.qint8, scale=scale, zero_point=zero_point)
# for i in range(numel):
# # wait for th_fill
# q[i] = val
# r = q.dequantize()
# for i in range(numel):
# self.assertEqual(r[i], (val - zero_point) * scale)
@unittest.skipIf(torch.cuda.device_count() < 2, 'fewer than 2 GPUs detected')
def test_device_guard(self):
# verify that all operators with `device_guard: False` behave properly with multiple devices.