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Rahuls/ltc debug #3952

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LTC On
rahuls-cerebras Jan 9, 2025
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rahuls-cerebras Jan 10, 2025
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rahuls-cerebras Jan 10, 2025
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rrrelu
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rrrelu 
Signed-off-by: rahul shrivastava <[email protected]>
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@rahuls-cerebras
rahuls-cerebras committed Jan 10, 2025
commit daa7417af3a267ca70490a0c7611a34460073d4b
2 changes: 2 additions & 0 deletions CMakeLists.txt
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Expand Up @@ -24,6 +24,8 @@ endif()
project(torch-mlir LANGUAGES CXX C)
set(CMAKE_C_STANDARD 11)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_VERBOSE_MAKEFILE ON)


include(CMakeDependentOption)

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2 changes: 1 addition & 1 deletion build_tools/python_deploy/build_linux_packages.sh
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Expand Up @@ -223,7 +223,7 @@ function build_in_tree() {
#fi

echo ":::: Build in-tree Torch from binary: $torch_from_bin with Python: $python_version"
cmake -GNinja -B/main_checkout/torch-mlir/build \
cmake -GNinja -B/main_checkout/torch-mlir/build \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_C_COMPILER=clang \
-DCMAKE_CXX_COMPILER=clang++ \
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111 changes: 0 additions & 111 deletions include/torch-mlir/Dialect/Torch/IR/GeneratedTorchOps.td
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Expand Up @@ -309,59 +309,6 @@ def Torch_AtenRrelu_Op : Torch_Op<"aten.rrelu_", [
}];
}

def Torch_AtenRreluWithNoiseOp : Torch_Op<"aten.rrelu_with_noise", [
AllowsTypeRefinement
]> {
let summary = "Generated op for `aten::rrelu_with_noise : (Tensor, Tensor, Scalar, Scalar, bool, Generator?) -> (Tensor)`";
let arguments = (ins
AnyTorchTensorType:$self,
AnyTorchTensorType:$noise,
AnyTorchScalarType:$lower,
AnyTorchScalarType:$upper,
Torch_BoolType:$training,
AnyTorchOptionalGeneratorType:$generator
);
let results = (outs
AnyTorchOptionalTensorType:$result
);
let hasCustomAssemblyFormat = 1;
let extraClassDefinition = [{
ParseResult AtenRreluWithNoiseOp::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 6, 1);
}
void AtenRreluWithNoiseOp::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 6, 1);
}
}];
}

def Torch_AtenRreluWithNoise_Op : Torch_Op<"aten.rrelu_with_noise_", [
IsTrailingUnderscoreInplaceVariant,
AllowsTypeRefinement
]> {
let summary = "Generated op for `aten::rrelu_with_noise_ : (Tensor, Tensor, Scalar, Scalar, bool, Generator?) -> (Tensor)`";
let arguments = (ins
Torch_NonValueTensorType:$self,
Torch_NonValueTensorType:$noise,
AnyTorchScalarType:$lower,
AnyTorchScalarType:$upper,
Torch_BoolType:$training,
AnyTorchOptionalGeneratorType:$generator
);
let results = (outs
AnyTorchOptionalNonValueTensorType:$result
);
let hasCustomAssemblyFormat = 1;
let extraClassDefinition = [{
ParseResult AtenRreluWithNoise_Op::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 6, 1);
}
void AtenRreluWithNoise_Op::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 6, 1);
}
}];
}

def Torch_AtenCeluOp : Torch_Op<"aten.celu", [
AllowsTypeRefinement,
HasValueSemantics,
Expand Down Expand Up @@ -17512,64 +17459,6 @@ def Torch_AtenLeakyReluBackwardOp : Torch_Op<"aten.leaky_relu_backward", [
}];
}

def Torch_AtenRreluWithNoiseBackwardOp : Torch_Op<"aten.rrelu_with_noise_backward", [
AllowsTypeRefinement,
HasValueSemantics,
ReadOnly
]> {
let summary = "Generated op for `aten::rrelu_with_noise_backward : (Tensor, Tensor, Tensor, Scalar, Scalar, bool, bool) -> (Tensor)`";
let arguments = (ins
AnyTorchTensorType:$grad_output,
AnyTorchTensorType:$self,
AnyTorchTensorType:$noise,
AnyTorchScalarType:$lower,
AnyTorchScalarType:$upper,
Torch_BoolType:$training,
Torch_BoolType:$self_is_result
);
let results = (outs
AnyTorchOptionalTensorType:$result
);
let hasCustomAssemblyFormat = 1;
let extraClassDefinition = [{
ParseResult AtenRreluWithNoiseBackwardOp::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 7, 1);
}
void AtenRreluWithNoiseBackwardOp::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 7, 1);
}
}];
}

def Torch_AtenRreluWithNoiseFunctionalOp : Torch_Op<"aten.rrelu_with_noise_functional", [
AllowsTypeRefinement,
HasValueSemantics,
ReadOnly
]> {
let summary = "Generated op for `aten::rrelu_with_noise_functional : (Tensor, Tensor, Scalar, Scalar, bool, Generator?) -> (Tensor, Tensor)`";
let arguments = (ins
AnyTorchTensorType:$self,
AnyTorchTensorType:$noise,
AnyTorchScalarType:$lower,
AnyTorchScalarType:$upper,
Torch_BoolType:$training,
AnyTorchOptionalGeneratorType:$generator
);
let results = (outs
AnyTorchOptionalTensorType:$result0,
AnyTorchOptionalTensorType:$noise_out
);
let hasCustomAssemblyFormat = 1;
let extraClassDefinition = [{
ParseResult AtenRreluWithNoiseFunctionalOp::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 6, 2);
}
void AtenRreluWithNoiseFunctionalOp::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 6, 2);
}
}];
}

def Torch_AtenQuantizePerChannelOp : Torch_Op<"aten.quantize_per_channel", [
AllowsTypeRefinement,
HasValueSemantics,
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51 changes: 0 additions & 51 deletions lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp
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Expand Up @@ -6694,10 +6694,6 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
" %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
" return %0 : !torch.list<int>\n"
" }\n"
" func.func @\"__torch_mlir_shape_fn.aten.rrelu_with_noise_backward\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>, %arg2: !torch.list<int>, %arg3: !torch.float, %arg4: !torch.float, %arg5: !torch.bool, %arg6: !torch.bool) -> !torch.list<int> {\n"
" %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
" return %0 : !torch.list<int>\n"
" }\n"
" func.func @\"__torch_mlir_shape_fn.aten.hardtanh_backward\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>, %arg2: !torch.float, %arg3: !torch.float) -> !torch.list<int> {\n"
" %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
" return %0 : !torch.list<int>\n"
Expand Down Expand Up @@ -7300,16 +7296,6 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
" %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
" return %0 : !torch.list<int>\n"
" }\n"
" func.func @\"__torch_mlir_shape_fn.aten.rrelu_with_noise\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>, %arg2: !torch.float, %arg3: !torch.float, %arg4: !torch.bool, %arg5: !torch.any) -> !torch.list<int> {\n"
" %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
" return %0 : !torch.list<int>\n"
" }\n"
" func.func @\"__torch_mlir_shape_fn.aten.rrelu_with_noise_functional\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>, %arg2: !torch.float, %arg3: !torch.float, %arg4: !torch.bool, %arg5: !torch.any) -> !torch.tuple<list<int>, list<int>> {\n"
" %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
" %1 = call @__torch__.torch.jit._shape_functions.unary(%arg1) : (!torch.list<int>) -> !torch.list<int>\n"
" %2 = torch.prim.TupleConstruct %0, %1 : !torch.list<int>, !torch.list<int> -> !torch.tuple<list<int>, list<int>>\n"
" return %2 : !torch.tuple<list<int>, list<int>>\n"
" }\n"
" func.func @\"__torch_mlir_shape_fn.aten.selu\"(%arg0: !torch.list<int>) -> !torch.list<int> {\n"
" %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
" return %0 : !torch.list<int>\n"
Expand Down Expand Up @@ -12428,14 +12414,6 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
" %4 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.promote_dtypes(%2, %3) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
" return %4 : !torch.int\n"
" }\n"
" func.func @\"__torch_mlir_dtype_fn.aten.rrelu_with_noise_backward\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>, %arg2: !torch.tuple<int, int>, %arg3: !torch.number, %arg4: !torch.number, %arg5: !torch.bool, %arg6: !torch.bool) -> !torch.int {\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 = torch.prim.ListConstruct %0#0, %1#0 : (!torch.int, !torch.int) -> !torch.list<optional<int>>\n"
" %3 = torch.prim.ListConstruct %0#1, %1#1 : (!torch.int, !torch.int) -> !torch.list<int>\n"
" %4 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.promote_dtypes(%2, %3) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
" return %4 : !torch.int\n"
" }\n"
" func.func @\"__torch_mlir_dtype_fn.aten.lift_fresh_copy\"(%arg0: !torch.tuple<int, int>) -> !torch.int {\n"
" %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
" return %0#1 : !torch.int\n"
Expand Down Expand Up @@ -12612,35 +12590,6 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
" %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
" return %0#1 : !torch.int\n"
" }\n"
" func.func @\"__torch_mlir_dtype_fn.aten.rrelu_with_noise\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>, %arg2: !torch.number, %arg3: !torch.number, %arg4: !torch.bool, %arg5: !torch.any) -> !torch.int {\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 = torch.aten.eq.int %0#0, %1#0 : !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_mlir_dtype_fn.aten.rrelu_with_noise_functional\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>, %arg2: !torch.number, %arg3: !torch.number, %arg4: !torch.bool, %arg5: !torch.any) -> !torch.tuple<int, int> {\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 = torch.aten.eq.int %0#0, %1#0 : !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.TupleConstruct %0#1, %1#1 : !torch.int, !torch.int -> !torch.tuple<int, int>\n"
" return %3 : !torch.tuple<int, int>\n"
" }\n"
" func.func @\"__torch_mlir_dtype_fn.aten.relu6\"(%arg0: !torch.tuple<int, int>) -> !torch.int {\n"
" %none = torch.constant.none\n"
" %str = torch.constant.str \"AssertionError: \"\n"
Expand Down
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