build: manually update PyTorch version (#4102)

This commit sets the PyTorch and TorchVision versions to nightly release
2025-03-25.

This commit also adds the `strict` flag (by default set to False) in
fx's `export_and_import` method in accordance with the changes made here
pytorch/pytorch@ab45aac.
It also updates the `torch.export` call from the `_export_run` method in
fx_importer_backend by adding the `strict` flag.

---------

Signed-off-by: Vivek Khandelwal <[email protected]>

Author: vivekkhandelwal1

projects/pt1/e2e_testing/xfail_sets.py

@@ -520,6 +520,7 @@
     "ReflectionPad3dModuleBack_basic",
     # RuntimeError: Unknown function SliceOutOfLowerBoundEndIndexModule
     "SliceOutOfLowerBoundEndIndexModule_basic",
+    "NativeGroupNormModule_basic",
 }
 
 FX_IMPORTER_CRASHING_SET = LINALG_CRASHING_SET | {
@@ -954,6 +955,7 @@
     "AtenSymConstrainRange_basic",
     "AtenSymConstrainRangeForSize_basic",
     "Aten_AssertScalar_basic",
+    "NativeGroupNormModule_basic",
 }
 
 FX_IMPORTER_STABLEHLO_CRASHING_SET = {

projects/pt1/python/torch_mlir_e2e_test/configs/fx_importer_backend.py

@@ -134,7 +134,9 @@ def invoke_func(*torch_inputs):
     def _export_run(self, artifact: torch.nn.Module, trace: Trace) -> Trace:
         result: Trace = []
         for item in trace:
-            prog: ExportedProgram = torch.export.export(artifact, tuple(item.inputs))
+            prog: ExportedProgram = torch.export.export(
+                artifact, tuple(item.inputs), strict=True
+            )
             module = fx.export_and_import(
                 prog,
                 output_type=self._output_type,

python/torch_mlir/fx.py

@@ -73,6 +73,7 @@ def export_and_import(
     output_type: Union[str, OutputType] = OutputType.RAW,
     fx_importer: Optional[FxImporter] = None,
     dynamic_shapes: Optional[Union[Dict[str, Any], Tuple[Any]]] = None,
+    strict: bool = False,
     experimental_support_mutation: bool = False,
     import_symbolic_shape_expressions: bool = False,
     hooks: Optional[FxImporterHooks] = None,
@@ -94,7 +95,9 @@ def export_and_import(
     else:
         # pytorch 2.1 or lower doesn't have `dyanmic_shapes` keyword argument in torch.export
         if version.Version(torch.__version__) >= version.Version("2.2.0"):
-            prog = torch.export.export(f, args, kwargs, dynamic_shapes=dynamic_shapes)
+            prog = torch.export.export(
+                f, args, kwargs, dynamic_shapes=dynamic_shapes, strict=strict
+            )
         else:
             prog = torch.export.export(f, args, kwargs)
     if decomposition_table is None:

pytorch-hash.txt

@@ -1 +1 @@
-cdb42bd8cc05bef0ec9b682b274c2acb273f2d62
+3794824ceb12a9d4396eaa17795bf2147fd9e1c3

pytorch-requirements.txt

@@ -1,3 +1,3 @@
 -f https://download.pytorch.org/whl/nightly/cpu/torch/
 --pre
-torch==2.7.0.dev20250310
+torch==2.8.0.dev20250325

test/python/fx_importer/basic_test.py

@@ -31,7 +31,7 @@ def run(f):
 @run
 # CHECK-LABEL: test_import_frozen_exported_program
 # CHECK:     func.func @main(%[[ARG0:[a-zA-Z0-9]+]]: !torch.vtensor<[3,4],f32>) -> !torch.vtensor<[3,4],f32>
-# CHECK-DAG: %[[a:.+]] = torch.vtensor.literal(dense_resource<torch_tensor_1_4_torch.float32> : tensor<1x4xf32>) : !torch.vtensor<[1,4],f32>
+# CHECK-DAG: %[[a:.+]] = torch.aten.randn
 # CHECK-DAG: %[[b:.+]] = torch.vtensor.literal(dense_resource<torch_tensor_3_1_torch.float32> : tensor<3x1xf32>) : !torch.vtensor<[3,1],f32>
 # CHECK-DAG: %[[p:.+]] = torch.vtensor.literal(dense<{{.*>+}} : tensor<1x1xf32>) : !torch.vtensor<[1,1],f32>
 # CHECK-DAG: %[[tanh:.+]] = torch.aten.tanh %[[ARG0]]
@@ -42,7 +42,6 @@ def run(f):
 #
 # Validate dialect resources exist.
 # CHECK: dialect_resources:
-# CHECK-DAG: torch_tensor_1_4_torch.float32
 # CHECK-DAG: torch_tensor_3_1_torch.float32
 def test_import_frozen_exported_program():
     # Tests the basic structural premises of import_frozen_exported_program,
@@ -210,11 +209,7 @@ def forward(self):
 @run
 # CHECK-LABEL: test_stack_trace
 # CHECK: #loc[[LOC1:.+]] = loc(
-# CHECK: #loc[[LOC2:.+]] = loc(
-# CHECK: #loc[[LOC3:.+]] = loc(
-# CHECK: #loc[[LOC4:.+]] = loc(callsite(#loc[[LOC2]] at #loc[[LOC3]]))
-# CHECK: #loc[[LOC5:.+]] = loc(callsite(#loc[[LOC1]] at #loc[[LOC4]]))
-# CHECK: %{{.+}} = torch.aten.add.Tensor {{.+}} loc(#loc[[LOC4]])
+# CHECK: %{{.+}} = torch.aten.add.Tensor {{.+}} loc(#loc[[LOC1]])
 def test_stack_trace():
     class Basic(nn.Module):
         def __init__(self):

test/python/fx_importer/symbolic_shape_expr_test.py

@@ -222,6 +222,7 @@ def forward(self, x):
         SliceTensorDynamicOutput(),
         x,
         dynamic_shapes=dynamic_shapes,
+        strict=True,
         import_symbolic_shape_expressions=True,
     )
     print(m)

test/python/fx_importer/v2.3/mutation_import.py

@@ -31,7 +31,7 @@ def run(f):
 # This doesn't do mutation but ensures that the basics remain functional.
 # CHECK-LABEL: test_import_frozen_exported_program
 # CHECK:     func.func @main(%[[ARG0:[a-zA-Z0-9]+]]: !torch.vtensor<[3,4],f32>) -> !torch.vtensor<[3,4],f32>
-# CHECK-DAG: %[[a:.+]] = torch.vtensor.literal(dense_resource<torch_tensor_1_4_torch.float32> : tensor<1x4xf32>) : !torch.vtensor<[1,4],f32>
+# CHECK-DAG: %[[a:.+]] = torch.aten.randn
 # CHECK-DAG: %[[b:.+]] = torch.vtensor.literal(dense_resource<torch_tensor_3_1_torch.float32> : tensor<3x1xf32>) : !torch.vtensor<[3,1],f32>
 # CHECK-DAG: %[[p:.+]] = torch.vtensor.literal(dense<{{.*>+}} : tensor<1x1xf32>) : !torch.vtensor<[1,1],f32>
 # CHECK-DAG: %[[tanh:.+]] = torch.aten.tanh %[[ARG0]]

torchvision-requirements.txt

@@ -1,3 +1,3 @@
 -f https://download.pytorch.org/whl/nightly/cpu/torchvision/
 --pre
-torchvision==0.22.0.dev20250310
+torchvision==0.22.0.dev20250325