build: manually update PyTorch version (#3863)

This commit sets the PyTorch and TorchVision version to nightly release
2024-11-07.

This commit also updates the dtype check for the
`aten.fake_quantize_per_tensor_affine` and
`aten.fake_quantize_per_tensor_affine_cachemask` op since the op now
supports bfloat16 input.

Signed-Off By: Vivek Khandelwal <[email protected]>

Author: vivekkhandelwal1

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -11247,7 +11247,6 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    return %0#1 : !torch.int\n"
 "  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.fake_quantize_per_tensor_affine\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.float, %arg2: !torch.int, %arg3: !torch.int, %arg4: !torch.int) -> !torch.int {\n"
-"    %int15 = torch.constant.int 15\n"
 "    %none = torch.constant.none\n"
 "    %str = torch.constant.str \"AssertionError: \"\n"
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
@@ -11258,13 +11257,6 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
 "      torch.prim.If.yield\n"
 "    }\n"
-"    %2 = torch.aten.ne.int %0#1, %int15 : !torch.int, !torch.int -> !torch.bool\n"
-"    torch.prim.If %2 -> () {\n"
-"      torch.prim.If.yield\n"
-"    } else {\n"
-"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
-"      torch.prim.If.yield\n"
-"    }\n"
 "    return %0#1 : !torch.int\n"
 "  }\n"
 "  func.func @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.is_float_dtype(%arg0: !torch.int) -> !torch.bool {\n"
@@ -11282,7 +11274,6 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.fake_quantize_per_tensor_affine_cachemask\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.float, %arg2: !torch.int, %arg3: !torch.int, %arg4: !torch.int) -> !torch.tuple<int, int> {\n"
 "    %int11 = torch.constant.int 11\n"
-"    %int15 = torch.constant.int 15\n"
 "    %none = torch.constant.none\n"
 "    %str = torch.constant.str \"AssertionError: \"\n"
 "    %int1 = torch.constant.int 1\n"
@@ -11294,16 +11285,9 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
 "      torch.prim.If.yield\n"
 "    }\n"
-"    %2 = torch.aten.ne.int %0#1, %int15 : !torch.int, !torch.int -> !torch.bool\n"
-"    torch.prim.If %2 -> () {\n"
-"      torch.prim.If.yield\n"
-"    } else {\n"
-"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
-"      torch.prim.If.yield\n"
-"    }\n"
-"    %3 = torch.prim.TupleIndex %arg0, %int1 : !torch.tuple<int, int>, !torch.int -> !torch.int\n"
-"    %4 = torch.prim.TupleConstruct %3, %int11 : !torch.int, !torch.int -> !torch.tuple<int, int>\n"
-"    return %4 : !torch.tuple<int, int>\n"
+"    %2 = torch.prim.TupleIndex %arg0, %int1 : !torch.tuple<int, int>, !torch.int -> !torch.int\n"
+"    %3 = torch.prim.TupleConstruct %2, %int11 : !torch.int, !torch.int -> !torch.tuple<int, int>\n"
+"    return %3 : !torch.tuple<int, int>\n"
 "  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.fake_quantize_per_tensor_affine.tensor_qparams\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>, %arg2: !torch.tuple<int, int>, %arg3: !torch.int, %arg4: !torch.int) -> !torch.int {\n"
 "    %int15 = torch.constant.int 15\n"

projects/pt1/e2e_testing/xfail_sets.py

@@ -403,7 +403,6 @@
     "QuantizedReluInt32_basic",
     "QuantizedReluInt8_basic",
     "QuantizedReluUint8_basic",
-    "AtenSubFloatModule_basic",
     "BincountMinlengthModule_basic",
     "BincountModule_basic",
     "BincountStaticSizeModule_basic",
@@ -431,20 +430,16 @@
     "ElementwiseQuantizePerTensorModule_basic",
     "ElementwiseQuantizePerTensorUIntModule_basic",
     "ElementwiseToDtypeI64ToUI8Module_basic",
-    "EqIntModule_basic",
     "FloatImplicitModule_basic",
     "GeFloatIntModule_basic",
-    "GeFloatModule_basic",
     "GeIntModule_basic",
     "GtFloatIntModule_basic",
-    "GtIntModule_basic",
     "IntFloatModule_basic",
     "IntImplicitModule_basic",
     "LenStrModule_basic",
     "MulFloatModule_basic",
     "NativeGroupNormBackwardModule_basic",
     "NeFloatIntModule_basic",
-    "NeIntModule_basic",
     "NllLossModuleBackward1DMeanWeight_basic",
     "NllLossModuleBackward1DMean_basic",
     "NllLossModuleBackward1DSumWeight_basic",
@@ -472,7 +467,6 @@
     "SortIntList_basic",
     "SplitDimDynamicModule_basic",
     "SplitDimStaticModule_basic",
-    "SqrtIntConstantModule_basic",
     "SqrtIntModule_basic",
     "SubFloatModule_basic",
     "TensorToBoolZeroRank_basic",
@@ -653,7 +647,6 @@
     "AtenMmQuint8_basic",
     "AtenRealView128Module_basic",
     "AtenRealView64Module_basic",
-    "AtenSubFloatModule_basic",
     "AtenTopKModule_basic",
     "AtenTopKSmallestModule_basic",
     "Aten_EmbeddingBagExample_basic",
@@ -878,7 +871,6 @@
     "SortTensor_basic",
     "SplitDimDynamicModule_basic",
     "SplitDimStaticModule_basic",
-    "SqrtIntConstantModule_basic",
     "SqrtIntModule_basic",
     "SubFloatModule_basic",
     "TModuleRank0_basic",

projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/abstract_interp_lib_gen.py

@@ -2580,19 +2580,17 @@ def prims〇split_dim〡dtype(a_rank_dtype: Tuple[int, int], dim: int, outer_len
     return a_dtype
 
 # note: fake_quantize_per_tensor_affine doesn't support "meta" device, use "cpu" instead.
-@check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1, tensor_device="cpu", scale=0.1, zero_point=0, quant_min=0, quant_max=255, error_types={torch.complex128, torch.complex64, torch.bfloat16, torch.int64, torch.int32, torch.int16, torch.int8, torch.uint8, torch.bool}))
+@check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1, tensor_device="cpu", scale=0.1, zero_point=0, quant_min=0, quant_max=255, error_types={torch.complex128, torch.complex64, torch.int64, torch.int32, torch.int16, torch.int8, torch.uint8, torch.bool}))
 def aten〇fake_quantize_per_tensor_affine〡dtype(self_rank_dtype: Tuple[int, int], scale: float, zero_point: int, quant_min: int, quant_max: int) -> int:
     self_rank, self_dtype = self_rank_dtype
     assert is_float_dtype(self_dtype)
-    assert self_dtype != torch.bfloat16
     return self_dtype
 
 # note: fake_quantize_per_tensor_affine doesn't support "meta" device, use "cpu" instead.
-@check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1, tensor_device="cpu", scale=0.1, zero_point=0, quant_min=0, quant_max=255, error_types={torch.complex128, torch.complex64, torch.bfloat16, torch.int64, torch.int32, torch.int16, torch.int8, torch.uint8, torch.bool}))
+@check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1, tensor_device="cpu", scale=0.1, zero_point=0, quant_min=0, quant_max=255, error_types={torch.complex128, torch.complex64, torch.int64, torch.int32, torch.int16, torch.int8, torch.uint8, torch.bool}))
 def aten〇fake_quantize_per_tensor_affine_cachemask〡dtype(self_rank_dtype: Tuple[int, int], scale: float, zero_point: int, quant_min: int, quant_max: int) -> Tuple[int, int]:
     self_rank, self_dtype = self_rank_dtype
     assert is_float_dtype(self_dtype)
-    assert self_dtype != torch.bfloat16
     return (self_rank_dtype[1], torch.bool)
 
 # note: fake_quantize_per_tensor_affine.tensor_qparams doesn't support "meta" device, use "cpu" instead.

pytorch-hash.txt

@@ -1 +1 @@
-c787213d413e85c66bdad0d8c9cde1c5ced34b1b
+0d5247caf3ffd618d31cf4cf880c47b7dbd323a7

pytorch-requirements.txt

@@ -1,3 +1,3 @@
 -f https://download.pytorch.org/whl/nightly/cpu/torch/
 --pre
-torch==2.6.0.dev20241029
+torch==2.6.0.dev20241107

torchvision-requirements.txt

@@ -1,3 +1,3 @@
 -f https://download.pytorch.org/whl/nightly/cpu/torchvision/
 --pre
-torchvision==0.20.0.dev20241029
+torchvision==0.20.0.dev20241107