8000 Add test_binops_dtype_precedence and todo notes · pytorch/pytorch@9debeaf · GitHub
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Add test_binops_dtype_precedence and todo notes
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‎aten/src/ATen/native/mps/operations/ConstantOps.mm

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@@ -51,6 +51,7 @@
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MPSGraph *mpsGraph = make_mps_graph();
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newCachedGraph = new CachedGraph(mpsGraph);
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// TODO: Does not work for MPSDataTypeBool
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MPSGraphTensor* inputTensor = [mpsGraph constantWithScalar:value.toDouble()
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shape:input_shape
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dataType:getMPSScalarType(self.scalar_type())];

‎test/test_mps.py

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@@ -1300,6 +1300,56 @@ def test_to(self):
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self.assertEqual(torch.tensor(-8.34, device='cpu').to('mps', torch.int),
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torch.tensor(-8.34, device='cpu').to('mps').to(torch.int))
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# Test dtype precedence (casting order) in binary operations by comparing to CPU result
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def test_binops_dtype_precedence(self):
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sample_vals = {
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torch.bool: True,
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torch.int16: 8,
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torch.int32: -376,
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torch.int64: 123898458,
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#torch.float16: -234.5, # TODO: broken
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torch.float32: 111.99
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}
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# Test all combinations of dtypes, operations, dimensionality
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for dtype1, dtype2, binop in itertools.product(
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sample_vals.keys(), sample_vals.keys(), ['add', 'sub', 'mul']): # TODO: 'div' broken
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if binop == 'sub' and (dtype1 == torch.bool or dtype2 == torch.bool):
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# Not supported, so skip
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continue
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self.assertEqual(
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getattr(torch.tensor(sample_vals[dtype1], dtype=dtype1, device='mps'), binop)
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(torch.tensor(sample_vals[dtype2], dtype=dtype2, device='mps')),
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getattr(torch.tensor(sample_vals[dtype1], dtype=dtype1, device='cpu'), binop)
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(torch.tensor(sample_vals[dtype2], dtype=dtype2, device='cpu')))
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self.assertEqual(
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getattr(torch.tensor([sample_vals[dtype1]], dtype=dtype1, device='mps'), binop)
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(torch.tensor([sample_vals[dtype2]], dtype=dtype2, device='mps')),
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getattr(torch.tensor([sample_vals[dtype1]], dtype=dtype1, device='cpu'), binop)
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(torch.tensor([sample_vals[dtype2]], dtype=dtype2, device='cpu')))
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self.assertEqual(
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getattr(torch.tensor(sample_vals[dtype1], dtype=dtype1, device='mps'), binop)
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(torch.tensor([sample_vals[dtype2]], dtype=dtype2, device='mps')),
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getattr(torch.tensor(sample_vals[dtype1], dtype=dtype1, device='cpu'), binop)
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(torch.tensor([sample_vals[dtype2]], dtype=dtype2, device='cpu')))
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self.assertEqual(
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getattr(torch.tensor([sample_vals[dtype1]], dtype=dtype1, device='mps'), binop)
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(torch.tensor(sample_vals[dtype2], dtype=dtype2, device='mps')),
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getattr(torch.tensor([sample_vals[dtype1]], dtype=dtype1, device='cpu'), binop)
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(torch.tensor(sample_vals[dtype2], dtype=dtype2, device='cpu')))
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'''
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# TODO: broken because [MPSGraph constantWithScalar:::] does not support MPSDataTypeBool
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self.assertEqual(
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getattr(torch.full((100,), sample_vals[dtype1], dtype=dtype1, device='mps'), binop)
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(torch.tensor(sample_vals[dtype2], dtype=dtype2, device='mps')),
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getattr(torch.full((100,), sample_vals[dtype1], dtype=dtype1, device='cpu'), binop)
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(torch.tensor(sample_vals[dtype2], dtype=dtype2, device='cpu')))
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self.assertEqual(
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getattr(torch.tensor(sample_vals[dtype1], dtype=dtype1, device='mps'), binop)
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(torch.full((100,), sample_vals[dtype2], dtype=dtype2, device='mps')),
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getattr(torch.tensor(sample_vals[dtype1], dtype=dtype1, device='cpu'), binop)
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(torch.full((100,), sample_vals[dtype2], dtype=dtype2, device='cpu')))
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'''
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class TestSmoothL1Loss(TestCase):
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