build: manually update PyTorch version (#2992)

Set PyTorch and TorchVision version to nightly release 2024-03-07.
This commit also removes the deprecated constraints API:
342e7929b8

Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>

projects/pt1/e2e_testing/xfail_sets.py

@@ -2167,7 +2167,6 @@ ONNX_XFAIL_SET = {
     "ElementwiseTanIntModule_basic",
     "ElementwiseUnaryIntModule_basic",
     "ElementwiseUnsqueezeNegDimsModule_basic",
-    "ElementwiseWhereScalarModule_basic",
     "EmbeddingModuleF16_basic",
     "EmbeddingModuleI32_basic",
     "EmbeddingModuleI64_basic",
@@ -2192,5 +2191,11 @@ ONNX_XFAIL_SET = {
     "TensorsStackPromoteDTypeModule_basic",
 }
 
+if torch_version_for_comparison() < version.parse("2.3.0.dev"):
+    ONNX_XFAIL_SET = ONNX_XFAIL_SET | {
+        # ERROR: dtype (torch.float64) is not equal to golden dtype (torch.float32)
+        "ElementwiseWhereScalarModule_basic",
+    }
+
 ONNX_CRASHING_SET = { }
 

python/torch_mlir/fx.py

@@ -20,7 +20,6 @@ def export_and_import(
     f,
     *args,
     fx_importer: Optional[FxImporter] = None,
-    constraints: Optional[torch.export.Constraint] = None,
     experimental_support_mutation: bool = False,
     hooks: Optional[FxImporterHooks] = None,
     func_name: str = "main",
@@ -31,7 +30,7 @@ def export_and_import(
 
     if fx_importer is None:
         fx_importer = FxImporter(context=context, hooks=hooks)
-    prog = torch.export.export(f, args, kwargs, constraints=constraints)
+    prog = torch.export.export(f, args, kwargs)
     decomp_table = get_decomposition_table()
     prog = prog.run_decompositions(decomp_table)
     if experimental_support_mutation:

pytorch-hash.txt

@@ -1 +1 @@
-8efa066dc0870521652c1319bd6b5b0f6dc3fe25
+ce013333221ff2d1285a8e8cf7c427584e65fea2

pytorch-requirements.txt

@@ -1,3 +1,3 @@
 -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
 --pre
-torch==2.3.0.dev20240220
+torch==2.3.0.dev20240307

test/python/fx_importer/sparse_test.py

@@ -104,7 +104,7 @@ def sparse_export(
     mask = [a.layout in SPARSE_LAYOUTS for a in args]
     # Build the regular FX traced graph with only dense arguments
     # (the current version would crash otherwise, see issue above).
-    prog = torch.export.export(f, dargs, kwargs, constraints=None)
+    prog = torch.export.export(f, dargs, kwargs)
     # Annotate sparse arguments in the graph. Note that we currently
     # only account for sparsity defined by the user inputs to the model.
     # TODO: support sparsity in model parameters (weights, biases)

torchvision-requirements.txt

@@ -1,3 +1,3 @@
 -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
 --pre
-torchvision==0.18.0.dev20240220
+torchvision==0.18.0.dev20240307