Move torch ops error message tests into a new file. (#9622)

In summary, this PR:

- Moves tests that checked error message of PyTorch operations into
`test_ops_error_message.py`
- Introduces `expecttest` as a dependency in `requirements.in` file
- Introduces `expecttest` to those tests, so as to avoid
copy-and-pasting error messages

The introduction of `expecttest` Python package was, in fact, implicit
to our tests because of the following PyTorch testing library import:


https://github.com/pytorch/xla/blob/f6ff30d3c2cd837e940aaa70b61faf948aa805f7/test/test_operations.py#L33

Author: ysiraichi

requirements.in

@@ -1,3 +1,4 @@
+expecttest
 filelock
 fsspec
 jinja2

requirements_lock_3_10.txt

@@ -4,6 +4,10 @@
 #
 #    bazel run //:requirements.update
 #
+expecttest==0.3.0 \
+    --hash=sha256:60f88103086e1754240b42175f622be83b6ffeac419434691ee5a5be819d0544 \
+    --hash=sha256:6e8512fb86523ada1f94fd1b14e280f924e379064bb8a29ee399950e513eeccd
+    # via -r requirements.in
 filelock==3.14.0 \
     --hash=sha256:43339835842f110ca7ae60f1e1c160714c5a6afd15a2873419ab185334975c0f \
     --hash=sha256:6ea72da3be9b8c82afd3edcf99f2fffbb5076335a5ae4d03248bb5b6c3eae78a

requirements_lock_3_11.txt

@@ -4,6 +4,10 @@
 #
 #    bazel run //:requirements.update
 #
+expecttest==0.3.0 \
+    --hash=sha256:60f88103086e1754240b42175f622be83b6ffeac419434691ee5a5be819d0544 \
+    --hash=sha256:6e8512fb86523ada1f94fd1b14e280f924e379064bb8a29ee399950e513eeccd
+    # via -r requirements.in
 filelock==3.14.0 \
     --hash=sha256:43339835842f110ca7ae60f1e1c160714c5a6afd15a2873419ab185334975c0f \
     --hash=sha256:6ea72da3be9b8c82afd3edcf99f2fffbb5076335a5ae4d03248bb5b6c3eae78a

requirements_lock_3_12.txt

@@ -4,6 +4,10 @@
 #
 #    bazel run //:requirements.update
 #
+expecttest==0.3.0 \
+    --hash=sha256:60f88103086e1754240b42175f622be83b6ffeac419434691ee5a5be819d0544 \
+    --hash=sha256:6e8512fb86523ada1f94fd1b14e280f924e379064bb8a29ee399950e513eeccd
+    # via -r requirements.in
 filelock==3.18.0 \
     --hash=sha256:adbc88eabb99d2fec8c9c1b229b171f18afa655400173ddc653d5d01501fb9f2 \
     --hash=sha256:c401f4f8377c4464e6db25fff06205fd89bdd83b65eb0488ed1b160f780e21de

requirements_lock_3_13.txt

@@ -4,6 +4,10 @@
 #
 #    bazel run //:requirements.update
 #
+expecttest==0.3.0 \
+    --hash=sha256:60f88103086e1754240b42175f622be83b6ffeac419434691ee5a5be819d0544 \
+    --hash=sha256:6e8512fb86523ada1f94fd1b14e280f924e379064bb8a29ee399950e513eeccd
+    # via -r requirements.in
 filelock==3.18.0 \
     --hash=sha256:adbc88eabb99d2fec8c9c1b229b171f18afa655400173ddc653d5d01501fb9f2 \
     --hash=sha256:c401f4f8377c4464e6db25fff06205fd89bdd83b65eb0488ed1b160f780e21de

requirements_lock_3_8.txt

@@ -4,6 +4,10 @@
 #
 #    bazel run //:requirements.update
 #
+expecttest==0.3.0 \
+    --hash=sha256:60f88103086e1754240b42175f622be83b6ffeac419434691ee5a5be819d0544 \
+    --hash=sha256:6e8512fb86523ada1f94fd1b14e280f924e379064bb8a29ee399950e513eeccd
+    # via -r requirements.in
 filelock==3.14.0 \
     --hash=sha256:43339835842f110ca7ae60f1e1c160714c5a6afd15a2873419ab185334975c0f \
     --hash=sha256:6ea72da3be9b8c82afd3edcf99f2fffbb5076335a5ae4d03248bb5b6c3eae78a

requirements_lock_3_9.txt

@@ -4,6 +4,10 @@
 #
 #    bazel run //:requirements.update
 #
+expecttest==0.3.0 \
+    --hash=sha256:60f88103086e1754240b42175f622be83b6ffeac419434691ee5a5be819d0544 \
+    --hash=sha256:6e8512fb86523ada1f94fd1b14e280f924e379064bb8a29ee399950e513eeccd
+    # via -r requirements.in
 filelock==3.14.0 \
     --hash=sha256:43339835842f110ca7ae60f1e1c160714c5a6afd15a2873419ab185334975c0f \
     --hash=sha256:6ea72da3be9b8c82afd3edcf99f2fffbb5076335a5ae4d03248bb5b6c3eae78a

test/test_operations.py

@@ -88,11 +88,6 @@ def skipIfFunctionalizationDisabled(reason):
   return _skipIfFunctionalization(value=True, reason=reason)
 
 
-def onlyOnCPU(fn):
-  accelerator = os.environ.get("PJRT_DEVICE").lower()
-  return unittest.skipIf(accelerator != "cpu", "PJRT_DEVICE=CPU required")(fn)
-
-
 def onlyIfXLAExperimentalContains(feat):
   experimental = os.environ.get("XLA_EXPERIMENTAL", "").split(":")
   return unittest.skipIf(feat not in experimental,
@@ -2372,165 +2367,6 @@ def test_isneginf_no_fallback(self):
     t = t.to(torch.float16)
     self._test_no_fallback(torch.isneginf, (t,))
 
-  def test_add_broadcast_error(self):
-    a = torch.rand(2, 2, 4, 4, device="xla")
-    b = torch.rand(2, 2, device="xla")
-
-    expected_regex = (
-        r"Shapes are not compatible for broadcasting: f32\[2,2,4,4\] vs. f32\[2,2\]. "
-        r"Expected dimension 2 of shape f32\[2,2,4,4\] \(4\) to match dimension "
-        r"0 of shape f32\[2,2\] \(2\). .*")
-
-    with self.assertRaisesRegex(RuntimeError, expected_regex):
-      torch.add(a, b)
-      torch_xla.sync()
-
-  @onlyOnCPU
-  def test_construct_large_tensor_raises_error(self):
-    with self.assertRaisesRegex(RuntimeError,
-                                r"Out of memory allocating \d+ bytes"):
-      # When eager-mode is enabled, OOM is triggered here.
-      a = torch.rand(1024, 1024, 1024, 1024, 1024, device=torch_xla.device())
-      b = a.sum()
-      # OOM is raised when we try to bring data from the device.
-      b.cpu()
-
-  def test_cat_raises_error_on_incompatible_shapes(self):
-    a = torch.rand(2, 2, device=torch_xla.device())
-    b = torch.rand(5, 1, device=torch_xla.device())
-
-    try:
-      torch.cat([a, b])
-    except RuntimeError as e:
-      expected_error = (
-          "cat(): cannot concatenate tensors of shape f32[2,2] with f32[5,1] "
-          "at dimension 0. Expected shapes to be equal (except at dimension 0) "
-          "or that either of them was a 1D empty tensor of size (0,).")
-      self.assertEqual(str(e), expected_error)
-
-  def test_div_raises_error_on_invalid_rounding_mode(self):
-    a = torch.rand(2, 2, device=torch_xla.device())
-
-    try:
-      torch.div(a, 2, rounding_mode="bad")
-    except RuntimeError as e:
-      expected_error = (
-          "div(): invalid rounding mode `bad`. Expected it to be either "
-          "'trunc', 'floor', or be left unspecified.")
-      self.assertEqual(str(e), expected_error)
-
-  def test_flip_raises_error_on_duplicated_dims(self):
-    a = torch.rand(2, 2, 2, 2, device=torch_xla.device())
-    dims = [0, 0, 0, 1, 2, 3, -1]
-    dims_suggestion = [0, 1, 2, 3]
-
-    try:
-      torch.flip(a, dims=dims)
-    except RuntimeError as e:
-      expected_error = (
-          "flip(): expected each dimension to appear at most once. Found "
-          "dimensions: 0 (3 times), 3 (2 times). Consider changing dims "
-          f"from {dims} to {dims_suggestion}.")
-      self.assertEqual(str(e), expected_error)
-
-  def test_full_raises_error_on_negative_size(self):
-    shape = [2, -2, 2]
-    try:
-      torch.full(shape, 1.5, device="xla")
-    except RuntimeError as e:
-      expected_error = (
-          "full(): expected concrete sizes (i.e. non-symbolic) to be "
-          f"positive values. However found negative ones: {shape}.")
-      self.assertEqual(str(e), expected_error)
-
-  def test_gather_raises_error_on_rank_mismatch(self):
-    S = 2
-
-    input = torch.arange(4, device=torch_xla.device()).view(S, S)
-    index = torch.randint(0, S, (S, S, S), device=torch_xla.device())
-    dim = 1
-
-    try:
-      torch.gather(input, dim, index)
-    except RuntimeError as e:
-      expected_error = (
-          "gather(): expected rank of input (2) and index (3) tensors "
-          "to be the same.")
-      self.assertEqual(str(e), expected_error)
-
-  def test_gather_raises_error_on_invalid_index_size(self):
-    S = 2
-    X = S + 2
-
-    input = torch.arange(16, device=torch_xla.device()).view(S, S, S, S)
-    index = torch.randint(0, S, (X, S, X, S), device=torch_xla.device())
-    dim = 1
-
-    try:
-      torch.gather(input, dim, index)
-    except RuntimeError as e:
-      expected_error = (
-          f"gather(): expected sizes of index [{X}, {S}, {X}, {S}] to be "
-          f"smaller or equal those of input [{S}, {S}, {S}, {S}] on all "
-          f"dimensions, except on dimension {dim}. "
-          "However, that's not true on dimensions [0, 2].")
-      self.assertEqual(str(e), expected_error)
-
-  def test_random__raises_error_on_empty_interval(self):
-    a = torch.empty(10, device=torch_xla.device())
-    from_ = 3
-    to_ = 1
-
-    try:
-      a.random_(from_, to_)
-    except RuntimeError as e:
-      expected_error = (
-          f"random_(): expected `from` ({from_}) to be smaller than "
-          f"`to` ({to_}).")
-      self.assertEqual(str(e), expected_error)
-
-  def test_random__raises_error_on_value_out_of_type_value_range(self):
-    a = torch.empty(10, device=torch_xla.device(), dtype=torch.float16)
-    from_ = 3
-    to_ = 65504 + 1
-
-    try:
-      a.random_(from_, to_)
-    except RuntimeError as e:
-      expected_error = (
-          f"random_(): expected `to` to be within the range "
-          f"[-65504, 65504]. However got value {to_}, which is greater "
-          "than the upper bound.")
-      self.assertEqual(str(e), expected_error)
-
-  def test_mm_raises_error_on_non_matrix_input(self):
-    device = torch_xla.device()
-    a = torch.rand(2, 2, 2, device=device)
-    b = torch.rand(2, 2, device=device)
-
-    try:
-      torch.mm(a, b)
-    except RuntimeError as e:
-      expected_error = (
-          "mm(): expected the first input tensor f32[2,2,2] to be a "
-          "matrix (i.e. a 2D tensor).")
-      self.assertEqual(str(e), expected_error)
-
-  def test_mm_raises_error_on_incompatible_shapes(self):
-    device = torch_xla.device()
-    a = torch.rand(2, 5, device=device)
-    b = torch.rand(8, 2, device=device)
-
-    try:
-      torch.mm(a, b)
-    except RuntimeError as e:
-      expected_error = (
-          "mm(): cannot matrix-multiply tensors f32[2,5] and f32[8,2]. "
-          "Expected the size of dimension 1 of the first input tensor (5) "
-          "to be equal the size of dimension 0 of the second input "
-          "tensor (8).")
-      self.assertEqual(str(e), expected_error)
-
 
 class MNISTComparator(nn.Module):