build: manually update PyTorch version

Set PyTorch and TorchVision version to nightly release 2023-09-26. aten._convolution.deprecated changes done because upstream PyTorch has now added support for fp16 native convolution on CPU. Refer: pytorch/pytorch@7c90521 Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
llvm · Sep 27, 2023 · 7760bda · 7760bda
1 parent ff7f8b2
commit 7760bda
Show file tree

Hide file tree

Showing 5 changed files with 61 additions and 55 deletions.
diff --git a/lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp b/lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp
@@ -9547,94 +9547,98 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    return %8 : !torch.int\n"
 "  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten._convolution\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>, %arg2: !torch.optional<tuple<int, int>>, %arg3: !torch.list<int>, %arg4: !torch.list<int>, %arg5: !torch.list<int>, %arg6: !torch.bool, %arg7: !torch.list<int>, %arg8: !torch.int, %arg9: !torch.bool, %arg10: !torch.bool, %arg11: !torch.bool, %arg12: !torch.bool) -> !torch.int {\n"
-"    %none = torch.constant.none\n"
-"    %str = torch.constant.str \"AssertionError: \"\n"
 "    %false = torch.constant.bool false\n"
-"    %int5 = torch.constant.int 5\n"
 "    %int11 = torch.constant.int 11\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"
 "    %1:2 = torch.prim.TupleUnpack %arg1 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
-"    %2 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.is_complex_dtype(%0#1) : (!torch.int) -> !torch.bool\n"
-"    %3 = torch.aten.__not__ %2 : !torch.bool -> !torch.bool\n"
-"    %4 = torch.prim.If %3 -> (!torch.bool) {\n"
-"      %11 = torch.prim.ListConstruct %int11, %int5 : (!torch.int, !torch.int) -> !torch.list<int>\n"
-"      %12 = torch.aten.__contains__.int_list %11, %0#1 : !torch.list<int>, !torch.int -> !torch.bool\n"
-"      %13 = torch.aten.__not__ %12 : !torch.bool -> !torch.bool\n"
-"      torch.prim.If.yield %13 : !torch.bool\n"
+"    %2 = torch.aten.eq.int %0#1, %1#1 : !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 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.is_complex_dtype(%0#1) : (!torch.int) -> !torch.bool\n"
+"    %4 = torch.aten.__not__ %3 : !torch.bool -> !torch.bool\n"
+"    %5 = torch.prim.If %4 -> (!torch.bool) {\n"
+"      %12 = torch.aten.__isnot__ %0#1, %int11 : !torch.int, !torch.int -> !torch.bool\n"
+"      torch.prim.If.yield %12 : !torch.bool\n"
 "    } else {\n"
 "      torch.prim.If.yield %false : !torch.bool\n"
 "    }\n"
-"    torch.prim.If %4 -> () {\n"
+"    torch.prim.If %5 -> () {\n"
 "      torch.prim.If.yield\n"
 "    } else {\n"
 "      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
 "      torch.prim.If.yield\n"
 "    }\n"
-"    %5 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.is_complex_dtype(%1#1) : (!torch.int) -> !torch.bool\n"
-"    %6 = torch.aten.__not__ %5 : !torch.bool -> !torch.bool\n"
-"    %7 = torch.prim.If %6 -> (!torch.bool) {\n"
-"      %11 = torch.prim.ListConstruct %int11, %int5 : (!torch.int, !torch.int) -> !torch.list<int>\n"
-"      %12 = torch.aten.__contains__.int_list %11, %1#1 : !torch.list<int>, !torch.int -> !torch.bool\n"
-"      %13 = torch.aten.__not__ %12 : !torch.bool -> !torch.bool\n"
-"      torch.prim.If.yield %13 : !torch.bool\n"
+"    %6 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.is_complex_dtype(%1#1) : (!torch.int) -> !torch.bool\n"
+"    %7 = torch.aten.__not__ %6 : !torch.bool -> !torch.bool\n"
+"    %8 = torch.prim.If %7 -> (!torch.bool) {\n"
+"      %12 = torch.aten.__isnot__ %1#1, %int11 : !torch.int, !torch.int -> !torch.bool\n"
+"      torch.prim.If.yield %12 : !torch.bool\n"
 "    } else {\n"
 "      torch.prim.If.yield %false : !torch.bool\n"
 "    }\n"
-"    torch.prim.If %7 -> () {\n"
+"    torch.prim.If %8 -> () {\n"
 "      torch.prim.If.yield\n"
 "    } else {\n"
 "      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
 "      torch.prim.If.yield\n"
 "    }\n"
-"    %8 = torch.prim.ListConstruct %0#0, %1#0 : (!torch.int, !torch.int) -> !torch.list<optional<int>>\n"
-"    %9 = torch.prim.ListConstruct %0#1, %1#1 : (!torch.int, !torch.int) -> !torch.list<int>\n"
-"    %10 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.promote_dtypes(%8, %9) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
-"    return %10 : !torch.int\n"
+"    %9 = torch.prim.ListConstruct %0#0, %1#0 : (!torch.int, !torch.int) -> !torch.list<optional<int>>\n"
+"    %10 = torch.prim.ListConstruct %0#1, %1#1 : (!torch.int, !torch.int) -> !torch.list<int>\n"
+"    %11 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.promote_dtypes(%9, %10) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
+"    return %11 : !torch.int\n"
 "  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten._convolution.deprecated\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>, %arg2: !torch.optional<tuple<int, int>>, %arg3: !torch.list<int>, %arg4: !torch.list<int>, %arg5: !torch.list<int>, %arg6: !torch.bool, %arg7: !torch.list<int>, %arg8: !torch.int, %arg9: !torch.bool, %arg10: !torch.bool, %arg11: !torch.bool) -> !torch.int {\n"
-"    %none = torch.constant.none\n"
-"    %str = torch.constant.str \"AssertionError: \"\n"
 "    %false = torch.constant.bool false\n"
-"    %int5 = torch.constant.int 5\n"
 "    %int11 = torch.constant.int 11\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"
 "    %1:2 = torch.prim.TupleUnpack %arg1 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
-"    %2 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.is_complex_dtype(%0#1) : (!torch.int) -> !torch.bool\n"
-"    %3 = torch.aten.__not__ %2 : !torch.bool -> !torch.bool\n"
-"    %4 = torch.prim.If %3 -> (!torch.bool) {\n"
-"      %11 = torch.prim.ListConstruct %int11, %int5 : (!torch.int, !torch.int) -> !torch.list<int>\n"
-"      %12 = torch.aten.__contains__.int_list %11, %0#1 : !torch.list<int>, !torch.int -> !torch.bool\n"
-"      %13 = torch.aten.__not__ %12 : !torch.bool -> !torch.bool\n"
-"      torch.prim.If.yield %13 : !torch.bool\n"
+"    %2 = torch.aten.eq.int %0#1, %1#1 : !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 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.is_complex_dtype(%0#1) : (!torch.int) -> !torch.bool\n"
+"    %4 = torch.aten.__not__ %3 : !torch.bool -> !torch.bool\n"
+"    %5 = torch.prim.If %4 -> (!torch.bool) {\n"
+"      %12 = torch.aten.__isnot__ %0#1, %int11 : !torch.int, !torch.int -> !torch.bool\n"
+"      torch.prim.If.yield %12 : !torch.bool\n"
 "    } else {\n"
 "      torch.prim.If.yield %false : !torch.bool\n"
 "    }\n"
-"    torch.prim.If %4 -> () {\n"
+"    torch.prim.If %5 -> () {\n"
 "      torch.prim.If.yield\n"
 "    } else {\n"
 "      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
 "      torch.prim.If.yield\n"
 "    }\n"
-"    %5 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.is_complex_dtype(%1#1) : (!torch.int) -> !torch.bool\n"
-"    %6 = torch.aten.__not__ %5 : !torch.bool -> !torch.bool\n"
-"    %7 = torch.prim.If %6 -> (!torch.bool) {\n"
-"      %11 = torch.prim.ListConstruct %int11, %int5 : (!torch.int, !torch.int) -> !torch.list<int>\n"
-"      %12 = torch.aten.__contains__.int_list %11, %1#1 : !torch.list<int>, !torch.int -> !torch.bool\n"
-"      %13 = torch.aten.__not__ %12 : !torch.bool -> !torch.bool\n"
-"      torch.prim.If.yield %13 : !torch.bool\n"
+"    %6 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.is_complex_dtype(%1#1) : (!torch.int) -> !torch.bool\n"
+"    %7 = torch.aten.__not__ %6 : !torch.bool -> !torch.bool\n"
+"    %8 = torch.prim.If %7 -> (!torch.bool) {\n"
+"      %12 = torch.aten.__isnot__ %1#1, %int11 : !torch.int, !torch.int -> !torch.bool\n"
+"      torch.prim.If.yield %12 : !torch.bool\n"
 "    } else {\n"
 "      torch.prim.If.yield %false : !torch.bool\n"
 "    }\n"
-"    torch.prim.If %7 -> () {\n"
+"    torch.prim.If %8 -> () {\n"
 "      torch.prim.If.yield\n"
 "    } else {\n"
 "      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
 "      torch.prim.If.yield\n"
 "    }\n"
-"    %8 = torch.prim.ListConstruct %0#0, %1#0 : (!torch.int, !torch.int) -> !torch.list<optional<int>>\n"
-"    %9 = torch.prim.ListConstruct %0#1, %1#1 : (!torch.int, !torch.int) -> !torch.list<int>\n"
-"    %10 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.promote_dtypes(%8, %9) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
-"    return %10 : !torch.int\n"
+"    %9 = torch.prim.ListConstruct %0#0, %1#0 : (!torch.int, !torch.int) -> !torch.list<optional<int>>\n"
+"    %10 = torch.prim.ListConstruct %0#1, %1#1 : (!torch.int, !torch.int) -> !torch.list<int>\n"
+"    %11 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.promote_dtypes(%9, %10) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
+"    return %11 : !torch.int\n"
 "  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.conv2d\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>, %arg2: !torch.optional<tuple<int, int>>, %arg3: !torch.list<int>, %arg4: !torch.list<int>, %arg5: !torch.list<int>, %arg6: !torch.int) -> !torch.int {\n"
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"

diff --git a/python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/abstract_interp_lib_gen.py b/python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/abstract_interp_lib_gen.py
@@ -2461,7 +2461,7 @@ def aten〇threshold_backward〡dtype(grad_output_rank_dtype: Tuple[int, int], s
     _check_tensors_with_the_same_dtype(
         tensor_shapes=[(1, 1, 1, 1), (1, 1, 1, 1), (1,)],
         tensor_device="cpu",
-        error_types={torch.bool, torch.float16, torch.complex64, torch.complex128}, **_convolution_kwargs) +
+        error_types={torch.bool, torch.complex64, torch.complex128}, **_convolution_kwargs) +
     [ErrorInvocation(TensorOfShape(1, 1, 1, 1, dtype=torch.bool, device="cpu"), TensorOfShape(1, 1, 1, 1, dtype=torch.float32, device="cpu"),
                      TensorOfShape(1, dtype=torch.float32, device="cpu"), **_convolution_kwargs),
      ErrorInvocation(TensorOfShape(1, 1, 1, 1, dtype=torch.float32, device="cpu"), TensorOfShape(1, 1, 1, 1, dtype=torch.bool, device="cpu"),
@@ -2473,8 +2473,9 @@ def aten〇threshold_backward〡dtype(grad_output_rank_dtype: Tuple[int, int], s
 def aten〇_convolution〡dtype(input_rank_dtype: Tuple[int, int], weight_rank_dtype: Tuple[int, int], bias_rank_dtype: Optional[Tuple[int, int]], stride: List[int], padding: List[int], dilation: List[int], transposed: bool, output_padding: List[int], groups: int, benchmark: bool, deterministic: bool, cudnn_enabled: bool, allow_tf32: bool) -> int:
     input_rank, input_dtype = input_rank_dtype
     weight_rank, weight_dtype = weight_rank_dtype
-    assert not is_complex_dtype(input_dtype) and input_dtype not in [torch.bool, torch.float16]
-    assert not is_complex_dtype(weight_dtype) and weight_dtype not in [torch.bool, torch.float16]
+    assert input_dtype == weight_dtype
+    assert not is_complex_dtype(input_dtype) and input_dtype is not torch.bool
+    assert not is_complex_dtype(weight_dtype) and weight_dtype is not torch.bool
     ranks: List[Optional[int]] = [input_rank, weight_rank]
     dtypes = [input_dtype, weight_dtype]
     return promote_dtypes(ranks, dtypes)
@@ -2494,7 +2495,7 @@ def aten〇_convolution〡dtype(input_rank_dtype: Tuple[int, int], weight_rank_d
     _check_tensors_with_the_same_dtype(
         tensor_shapes=[(1, 1, 1, 1), (1, 1, 1, 1), (1,)],
         tensor_device="cpu",
-        error_types={torch.bool, torch.float16, torch.complex64, torch.complex128}, **_convolution_deprecated_kwargs) +
+        error_types={torch.bool, torch.complex64, torch.complex128}, **_convolution_deprecated_kwargs) +
     [ErrorInvocation(TensorOfShape(1, 1, 1, 1, dtype=torch.bool, device="cpu"), TensorOfShape(1, 1, 1, 1, dtype=torch.float32, device="cpu"),
                      TensorOfShape(1, dtype=torch.float32, device="cpu"), **_convolution_deprecated_kwargs),
      ErrorInvocation(TensorOfShape(1, 1, 1, 1, dtype=torch.float32, device="cpu"), TensorOfShape(1, 1, 1, 1, dtype=torch.bool, device="cpu"),
@@ -2507,8 +2508,9 @@ def aten〇_convolution〡dtype(input_rank_dtype: Tuple[int, int], weight_rank_d
 def aten〇_convolution〇deprecated〡dtype(input_rank_dtype: Tuple[int, int], weight_rank_dtype: Tuple[int, int], bias_rank_dtype: Optional[Tuple[int, int]], stride: List[int], padding: List[int], dilation: List[int], transposed: bool, output_padding: List[int], groups: int, benchmark: bool, deterministic: bool, cudnn_enabled: bool) -> int:
     input_rank, input_dtype = input_rank_dtype
     weight_rank, weight_dtype = weight_rank_dtype
-    assert not is_complex_dtype(input_dtype) and input_dtype not in [torch.bool, torch.float16]
-    assert not is_complex_dtype(weight_dtype) and weight_dtype not in [torch.bool, torch.float16]
+    assert input_dtype == weight_dtype
+    assert not is_complex_dtype(input_dtype) and input_dtype is not torch.bool
+    assert not is_complex_dtype(weight_dtype) and weight_dtype is not torch.bool
     ranks: List[Optional[int]] = [input_rank, weight_rank]
     dtypes = [input_dtype, weight_dtype]
     return promote_dtypes(ranks, dtypes)

diff --git a/pytorch-hash.txt b/pytorch-hash.txt
@@ -1 +1 @@
-90c406a3a198b8f45682a9979b4c091ec5dc647e
+ab61acc20ccd35835b9cd7f587f6a909839cf57f
diff --git a/pytorch-requirements.txt b/pytorch-requirements.txt
@@ -1,3 +1,3 @@
 -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
 --pre
-torch==2.2.0.dev20230922
+torch==2.2.0.dev20230926
diff --git a/torchvision-requirements.txt b/torchvision-requirements.txt
@@ -1,3 +1,3 @@
 -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
 --pre
-torchvision==0.17.0.dev20230922
+torchvision==0.17.0.dev20230926