style: apply ruff format

Normalizes formatting (blank lines, wrapping) in 8 files that predated
CI format-checking. No logic changes.

Author: DawerG

coreai_torch/_compression/utils.py

@@ -99,6 +99,7 @@ def repeat_tensor_as(tensor: torch.Tensor, target_shape: torch.Size) -> torch.Te
             )
     return repeated_tensor
 
+
 def wrap_for_parametrization(
     compression_module_class: type[torch.nn.Module],
 ) -> type[torch.nn.Module]:

docs/coreai-core/tutorials/construct-a-graph.ipynb

@@ -174,6 +174,7 @@
     "    ) -> Annotated[Value, output_spec]:\n",
     "        return ops.add(x, x)\n",
     "\n",
+    "\n",
     "module.verify()\n",
     "print(\"Module verified.\")"
    ]

docs/coreai-core/tutorials/run-an-aimodel.ipynb

@@ -125,6 +125,7 @@
     "    ) -> Annotated[Value, TensorSpec(shape=[2, 3], dtype=np.float32, name=\"y\")]:\n",
     "        return ops.add(x, x)\n",
     "\n",
+    "\n",
     "AIProgram(module).save_asset(asset_path)\n",
     "print(f\"created {asset_path}\")"
    ]

docs/getting-started/quickstart.ipynb

@@ -68,7 +68,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
     "example_input = (torch.randn(1, 10),)\n",
     "exported = torch.export.export(model, args=example_input)"
    ]
@@ -239,8 +238,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "async def run():\n",
     "    with tempfile.TemporaryDirectory() as tmpdir:\n",
     "        asset = coreai_program.save_asset(Path(tmpdir) / \"mobilenet_v2_example.aimodel\")\n",

docs/guides/composite-ops.ipynb

@@ -97,8 +97,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "class RMSNorm(nn.Module):\n",
     "    \"\"\"Convenience wrapper that owns the learnable scale parameter.\"\"\"\n",
     "\n",

docs/guides/externalization.ipynb

@@ -99,6 +99,7 @@
     "        inv_rms = torch.rsqrt((x_f32 * x_f32).mean(self.axes, keepdim=True) + self.eps)\n",
     "        return (input * inv_rms).to(input.dtype) * scale\n",
     "\n",
+    "\n",
     "model = RMSNormComposite().eval()\n",
     "sample = (torch.randn(10), torch.randn(10))"
    ]

tests/compression/test_compression.py

@@ -29,6 +29,7 @@
 # We add "./tests/coreai" path, in order to use some existing utils
 sys.path.append(str(Path(__file__).parents[2]))
 
+
 def _scale_shape(
     input_shape: tuple[int, ...],
     axis: int,
@@ -56,6 +57,7 @@ async def lower_to_coreai(
     converter = TorchConverter().add_exported_program(coreaten_program)
     return converter.to_coreai()
 
+
 async def _validate_execution(
     coreai_program: AIProgram,
     torch_out: torch.Tensor,
@@ -81,6 +83,7 @@ async def _validate_execution(
             atol=atol,
         )
 
+
 @pytest.mark.parametrize(
     "nbits",
     [4, 8],
@@ -1055,9 +1058,7 @@ def test_ir(
             "" if granularity == "per_tensor" else f"{activation_shape[axis]}x"
         )
 
-        msg = (
-            "TODO: reshape on consts such as offset and scale is not const eliminated"
-        )
+        msg = "TODO: reshape on consts such as offset and scale is not const eliminated"
         pytest.xfail(reason=msg)
         truth = f"""
         // CHECK-LABEL: coreai.graph @main

tests/test_externalize.py

@@ -3032,10 +3032,12 @@ def forward(
 
 @pytest.mark.ir
 @pytest.mark.flaky(reruns=3)
-@pytest.mark.skip(reason=(
-    "transform_with_custom_compression_ops has been deprecated. Consider removing "
-    "these tests or use an alternative way to generate quantized weights"
-))
+@pytest.mark.skip(
+    reason=(
+        "transform_with_custom_compression_ops has been deprecated. Consider removing "
+        "these tests or use an alternative way to generate quantized weights"
+    )
+)
 def test_externalize_rms_norm_with_quantized_linears_ir() -> None:
     """IR check: Quantized (int4) weights retain si4 dtype when externalization re-exports the model."""
 
@@ -3109,10 +3111,12 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 
 @pytest.mark.flaky(reruns=3)
-@pytest.mark.skip(reason=(
-    "transform_with_custom_compression_ops has been deprecated. Consider removing "
-    "these tests or use an alternative way to generate quantized weights"
-))
+@pytest.mark.skip(
+    reason=(
+        "transform_with_custom_compression_ops has been deprecated. Consider removing "
+        "these tests or use an alternative way to generate quantized weights"
+    )
+)
 async def test_externalize_rms_norm_with_quantized_linears() -> None:
     """Quantized (int4) weights retain si4 dtype when externalization re-exports the model.
 
@@ -3176,10 +3180,12 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 @pytest.mark.ir
 @pytest.mark.flaky(reruns=3)
-@pytest.mark.skip(reason=(
-    "transform_with_custom_compression_ops has been deprecated. Consider removing "
-    "these tests or use an alternative way to generate quantized weights"
-))
+@pytest.mark.skip(
+    reason=(
+        "transform_with_custom_compression_ops has been deprecated. Consider removing "
+        "these tests or use an alternative way to generate quantized weights"
+    )
+)
 def test_externalize_gather_mm_with_quantized_rhs_ir() -> None:
     """IR check: Quantized expert weight flows as rhs into an externalized GatherMM composite."""
     num_experts = 4
@@ -3263,10 +3269,12 @@ def forward(self, x: torch.Tensor, indices: torch.Tensor) -> torch.Tensor:
 
 
 @pytest.mark.flaky(reruns=3)
-@pytest.mark.skip(reason=(
-    "transform_with_custom_compression_ops has been deprecated. Consider removing "
-    "these tests or use an alternative way to generate quantized weights"
-))
+@pytest.mark.skip(
+    reason=(
+        "transform_with_custom_compression_ops has been deprecated. Consider removing "
+        "these tests or use an alternative way to generate quantized weights"
+    )
+)
 async def test_externalize_gather_mm_with_quantized_rhs() -> None:
     """Quantized expert weight flows as rhs into an externalized GatherMM composite.
 
@@ -3345,10 +3353,12 @@ def forward(self, x: torch.Tensor, indices: torch.Tensor) -> torch.Tensor:
 
 @pytest.mark.ir
 @pytest.mark.flaky(reruns=3)
-@pytest.mark.skip(reason=(
-    "transform_with_custom_compression_ops has been deprecated. Consider removing "
-    "these tests or use an alternative way to generate quantized weights"
-))
+@pytest.mark.skip(
+    reason=(
+        "transform_with_custom_compression_ops has been deprecated. Consider removing "
+        "these tests or use an alternative way to generate quantized weights"
+    )
+)
 def test_externalize_multiple_composites_with_quantized_weights_ir() -> None:
     """IR check: Multiple composite ops (RMSNorm + SDPA) externalized with quantized linears."""
     head_dim = 16
@@ -3433,10 +3443,12 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 
 @pytest.mark.flaky(reruns=3)
-@pytest.mark.skip(reason=(
-    "transform_with_custom_compression_ops has been deprecated. Consider removing "
-    "these tests or use an alternative way to generate quantized weights"
-))
+@pytest.mark.skip(
+    reason=(
+        "transform_with_custom_compression_ops has been deprecated. Consider removing "
+        "these tests or use an alternative way to generate quantized weights"
+    )
+)
 async def test_externalize_multiple_composites_with_quantized_weights() -> None:
     """Multiple composite ops (RMSNorm + SDPA) externalized with quantized linears.
 
@@ -3512,10 +3524,12 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 
 @pytest.mark.ir
-@pytest.mark.skip(reason=(
-    "transform_with_custom_compression_ops has been deprecated. Consider removing "
-    "these tests or use an alternative way to generate quantized weights"
-))
+@pytest.mark.skip(
+    reason=(
+        "transform_with_custom_compression_ops has been deprecated. Consider removing "
+        "these tests or use an alternative way to generate quantized weights"
+    )
+)
 def test_externalize_gather_mm_combined_with_rms_norm_ir() -> None:
     """IR check: GatherMM + RMSNorm both externalized alongside quantized weights."""
     num_experts = 4
@@ -3590,10 +3604,13 @@ def forward(self, x: torch.Tensor, indices: torch.Tensor) -> torch.Tensor:
     """
     filecheck_pattern(ir, check_file=pattern)
 
-@pytest.mark.skip(reason=(
-    "transform_with_custom_compression_ops has been deprecated. Consider removing "
-    "these tests or use an alternative way to generate quantized weights"
-))
+
+@pytest.mark.skip(
+    reason=(
+        "transform_with_custom_compression_ops has been deprecated. Consider removing "
+        "these tests or use an alternative way to generate quantized weights"
+    )
+)
 async def test_externalize_gather_mm_combined_with_rms_norm() -> None:
     """GatherMM + RMSNorm both externalized alongside quantized weights.