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[NFC reformat] Run pre-commit on all files and format misc. 

This is part 1 of ~3, formatting all miscellaneous text files and CPP files matched by a first run of pre-commit. These tend to be low change-traffic and are likely not disruptive.

Subsequent patches will format Python files and remaining CPP files.
pull/3244/head
Stella Laurenzo 2024-04-27 14:08:09 -07:00
parent 6679728c56
commit 5d4b803914
40 changed files with 99 additions and 113 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
CMakeLists.txt
View File

@ -247,4 +247,4 @@ add_subdirectory(projects)
# Finish with top-level Python bindings so it can handle additional deps.
if(MLIR_ENABLE_BINDINGS_PYTHON)
add_subdirectory(python)
endif()
endif()

2
build_tools/ci/test_posix.sh
View File

@ -30,7 +30,7 @@ echo "::endgroup::"
case $torch_version in
nightly)
# Failing with: NotImplementedError:
# Failing with: NotImplementedError:
# Could not run 'aten::empty.memory_format' with arguments from the 'Lazy' backend.
# As of 2024-01-07
# echo "::group::Run Lazy Tensor Core e2e integration tests"

2
build_tools/python_deploy/build_linux_packages.sh
View File

@ -282,7 +282,7 @@ function _check_file_not_changed_by() {
function test_in_tree() {
local torch_version="$1"
echo ":::: Test in-tree"
cmake --build /main_checkout/torch-mlir/build --target check-torch-mlir-all

1
docs/importers/onnx_importer.md
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@ -140,4 +140,3 @@ torch-mlir's representation:
* `ConstantOfShape`: Mapped to `torch.vtensor.literal` with
a corresponding `value` attribute.

1
docs/roadmap.md
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@ -277,4 +277,3 @@ directly provided a way to plug into this.
Additionally, we can leverage the [`pytorch-jit-paritybench`](https://github.com/jansel/pytorch-jit-paritybench)
to verify our end-to-end correctness on real models.

2
include/CMakeLists.txt
View File

@ -1,2 +1,2 @@
add_subdirectory(torch-mlir)
add_subdirectory(torch-mlir-dialects)
add_subdirectory(torch-mlir-dialects)

18
include/torch-mlir/Dialect/Torch/IR/TorchOps.td
View File

@ -756,12 +756,12 @@ def Torch_ConstantNumberOp : Torch_Op<"constant.number",
[ConstantLike, Pure]> {
let summary = "Materialize a constant `number` value.";
let description = [{
This op is used as a workaround to the fact that the constant
materialization in MLIR must materialize a constant with a single op.
To materialize ops with a static `!torch.number` type, we must use this op,
This op is used as a workaround to the fact that the constant
materialization in MLIR must materialize a constant with a single op.
To materialize ops with a static `!torch.number` type, we must use this op,
even though we statically know if it is an integer or a float.
Note: This op unconditionally canonicalizes to
Note: This op unconditionally canonicalizes to
`torch.constant.{float,int}` + `torch.derefine`
}];
let arguments = (ins
@ -846,7 +846,7 @@ def Torch_OperatorOp : Torch_Op<"operator", [
let regions = (region VariadicRegion<AnyRegion>:$regions);
let assemblyFormat = [{
$name `(` $operands `)` attr-dict `:` functional-type($operands, $results) $regions
$name `(` $operands `)` attr-dict `:` functional-type($operands, $results) $regions
}];
}
@ -1146,10 +1146,10 @@ def Torch_PromoteDtypesOp: Torch_Op<"promote_dtypes", [
let assemblyFormat = "$ranks `,` $dtypes attr-dict `:` functional-type(operands, results)";
}
// To handle runtime assertions, torchscript provides us `torch._assert` operation.
// But TS compiler introduces control flow for `torch._assert` operation. The
// `torch._assert` would introduce control flow like:
//
// To handle runtime assertions, torchscript provides us `torch._assert` operation.
// But TS compiler introduces control flow for `torch._assert` operation. The
// `torch._assert` would introduce control flow like:
//
// %cond = "torch.aten.Bool.Tensor"(%0) : (!torch.tensor) -> !torch.bool
// "torch.prim.If"(%cond) ({
// "torch.prim.If.yield"() : () -> ()

2
include/torch-mlir/Dialect/Torch/Transforms/Passes.td
View File

@ -369,7 +369,7 @@ def LowerToBackendContract
to the backend contract. This pass does not do any global program
restructuring -- it works entirely within a single semantic model
of a `builtin.module` with `torch.global_slot` ops and `func.func` ops.
This pass runs a set of simplifications within that semantic model until
the backend contract is satisfied, and fails if it cannot be satisfied.
In particular, the backend contract consists of:

65
lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp
View File

@ -628,42 +628,39 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
binder.op, resultType, operand);
return success();
});
patterns.onOp("Not", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType)) {
return failure();
}
patterns.onOp(
"Not", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType)) {
return failure();
}
auto loc = binder.getLoc();
auto operandTy =
cast<Torch::ValueTensorType>(operand.getType());
auto eTy = operandTy.getDtype();
auto loc = binder.getLoc();
auto operandTy = cast<Torch::ValueTensorType>(operand.getType());
auto eTy = operandTy.getDtype();
if (!eTy.isInteger(1)) {
auto i1ty = rewriter.getI1Type();
auto ty = rewriter.getType<Torch::ValueTensorType>(
operandTy.getSizes(), i1ty);
auto torchqTy = Torch::getScalarTypeForType(i1ty);
Value tyConst = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(
rewriter.getIntegerType(64),
static_cast<int64_t>(torchqTy)));
Value none = rewriter.create<Torch::ConstantNoneOp>(loc);
Value cstFalse =
rewriter.create<Torch::ConstantBoolOp>(loc, false);
operand = rewriter.create<Torch::AtenToDtypeOp>(
loc, ty, operand, tyConst,
/*non_blocking=*/cstFalse, /*copy=*/cstFalse,
/*memory_format=*/none);
}
rewriter.replaceOpWithNewOp<Torch::AtenBitwiseNotOp>(
binder.op, resultType, operand);
return success();
});
if (!eTy.isInteger(1)) {
auto i1ty = rewriter.getI1Type();
auto ty = rewriter.getType<Torch::ValueTensorType>(
operandTy.getSizes(), i1ty);
auto torchqTy = Torch::getScalarTypeForType(i1ty);
Value tyConst = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64),
static_cast<int64_t>(torchqTy)));
Value none = rewriter.create<Torch::ConstantNoneOp>(loc);
Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(loc, false);
operand = rewriter.create<Torch::AtenToDtypeOp>(
loc, ty, operand, tyConst,
/*non_blocking=*/cstFalse, /*copy=*/cstFalse,
/*memory_format=*/none);
}
rewriter.replaceOpWithNewOp<Torch::AtenBitwiseNotOp>(
binder.op, resultType, operand);
return success();
});
patterns.onOp("Or", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;

5
lib/Conversion/TorchToStablehlo/StablehloLegalizeUtils.cpp
View File

@ -189,9 +189,8 @@ Value promoteAndBroadcast(ConversionPatternRewriter &rewriter, Value input,
do_bcast = true;
} else {
op->emitError("The size of tensor a (")
<< inDim << ")"
<< "must match the size of tensor b (" << outDim << ")"
<< "at non-singleton dimension " << inPos;
<< inDim << ")" << "must match the size of tensor b (" << outDim
<< ")" << "at non-singleton dimension " << inPos;
}
}
std::reverse(bcastDims.begin(), bcastDims.end());

8
lib/Conversion/TorchToTosa/TosaLegalizeUtils.cpp
View File

@ -287,19 +287,19 @@ static LogicalResult checkValidityOfCast(Type src, Type dest) {
(src.isInteger(1) && dest.isInteger(64)) ||
(src.isInteger(1) && dest.isF32()) ||
// f64 -> *
(src.isF64() && dest.isF32()) ||
(src.isF64() && dest.isF32()) ||
(src.isF64() && dest.isBF16()) ||
// f32 -> *
(src.isF32() && dest.isF64()) ||
(src.isF32() && dest.isF64()) ||
(src.isF32() && dest.isBF16()) ||
(src.isF32() && dest.isF16()) ||
(src.isF32() && dest.isF16()) ||
(src.isF32() && dest.isInteger(8)) ||
(src.isF32() && dest.isInteger(64)) ||
(src.isF32() && dest.isInteger(1)) ||
// bf16 -> *
(src.isBF16() && dest.isInteger(8)) ||
(src.isBF16() && dest.isInteger(16)) ||
(src.isBF16() && dest.isInteger(32)) ||
(src.isBF16() && dest.isInteger(32)) ||
(src.isBF16() && dest.isF32())) {
return success();
}

2
lib/Dialect/TMTensor/Transforms/CMakeLists.txt
View File

@ -22,4 +22,4 @@ add_mlir_library(TorchMLIRTMTensorPasses
MLIRTransforms
)
torch_mlir_target_includes(TorchMLIRTMTensorPasses)
torch_mlir_target_includes(TorchMLIRTMTensorPasses)

3
lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp
View File

@ -305,8 +305,7 @@ public:
return signalPassFailure();
} while (!satisfiesBackendContract(module, target));
LLVM_DEBUG({
llvm::dbgs() << "LowerToBackendContractPass: "
<< "succeeded after " << i
llvm::dbgs() << "LowerToBackendContractPass: " << "succeeded after " << i
<< " iterations of the simplification pipeline\n";
});
}

2
lib/Dialect/TorchConversion/Transforms/CMakeLists.txt
View File

@ -21,7 +21,7 @@ endif()
add_mlir_library(TorchMLIRTorchConversionPasses
BackendTypeConversion.cpp
BackendTypeConversionPasses.cpp
BackendTypeConversionPasses.cpp
Passes.cpp
ConvertCustomQuantOp.cpp
UnpackQuantTensor.cpp

8
projects/CMakeLists.txt
View File

@ -44,16 +44,16 @@ if(TORCH_MLIR_ENABLE_JIT_IR_IMPORTER OR TORCH_MLIR_ENABLE_LTC)
message(FATAL_ERROR "Without TORCH_MLIR_USE_INSTALLED_PYTORCH, expected to find Torch configuration at ${Torch_DIR}, which does not exist")
endif()
endif()
find_package(Torch 1.11 REQUIRED)
set(TORCHGEN_DIR ${Torch_ROOT}/../../../torchgen)
include_directories(BEFORE
${TORCH_INCLUDE_DIRS}
${Python3_INCLUDE_DIRS}
)
link_directories("${TORCH_INSTALL_PREFIX}/lib")
link_directories("${TORCH_INSTALL_PREFIX}/lib")
message(STATUS "TORCH_CXXFLAGS is = ${TORCH_CXXFLAGS}")
if(${CMAKE_SYSTEM_NAME} STREQUAL "Linux" AND NOT TORCH_CXXFLAGS)
message(WARNING

2
projects/ltc/csrc/base_lazy_backend/mlir_native_functions.cpp
View File

@ -713,4 +713,4 @@ at::Tensor &LazyNativeFunctions::logsumexp_out(const at::Tensor &self,
void InitializeAtenBindings() {}
} // namespace lazy
} // namespace torch
} // namespace torch

2
projects/ltc/csrc/base_lazy_backend/ops/unbind_int.h
View File

@ -34,4 +34,4 @@ public:
};
} // namespace lazy
} // namespace torch
} // namespace torch

4
projects/pt1/python/CMakeLists.txt
View File

@ -56,7 +56,7 @@ endif()
# Can we build the JIT IR importer with `declare_mlir_python_extension`?
# Then it would "just work".
if(TORCH_MLIR_ENABLE_JIT_IR_IMPORTER)
add_dependencies(TorchMLIRPythonTorchExtensionsSources
add_dependencies(TorchMLIRPythonTorchExtensionsSources
TorchMLIRJITIRImporter
TorchMLIRJITIRImporterPybind
TorchMLIRE2ETestPythonModules
@ -65,7 +65,7 @@ endif()
if(TORCH_MLIR_ENABLE_LTC)
# Add Torch-MLIR LTC backend as dependency
add_dependencies(TorchMLIRPythonTorchExtensionsSources
add_dependencies(TorchMLIRPythonTorchExtensionsSources
torch_mlir_ltc_backend
reference_lazy_backend
)

1
projects/pt1/python/torch_mlir/_torch_mlir_custom_op_example/CMakeLists.txt
View File

@ -28,4 +28,3 @@ set_target_properties(torch_mlir_custom_op_example PROPERTIES
)
torch_mlir_python_target_compile_options(torch_mlir_custom_op_example)
mlir_check_all_link_libraries(torch_mlir_custom_op_example)

2
projects/pt1/test/CMakeLists.txt
View File

@ -13,7 +13,7 @@ configure_lit_site_cfg(
set(TORCH_MLIR_TEST_DEPENDS
FileCheck count not
TorchMLIRPythonModules
torch-mlir-opt
torch-mlir-opt
torch-mlir-capi-torch-test
)

2
test/CAPI/lit.local.cfg
View File

@ -1 +1 @@
config.suffixes.add('.c')
config.suffixes.add('.c')

14
test/CAPI/torch.c
View File

@ -36,7 +36,7 @@ static void testTensor(MlirContext ctx, intptr_t numSizes, int64_t *sizes,
fprintf(stderr, #TTT "Type %s rank: %zu\n", testName, \
torchMlirTorch##TTT##TypeGetRank(TTT##Type)); \
int64_t *TTT##Sizes = malloc(sizeof(int64_t) * numSizes); \
torchMlirTorch##TTT##TypeGetSizes(TTT##Type, TTT##Sizes); \
torchMlirTorch##TTT##TypeGetSizes(TTT##Type, TTT##Sizes); \
for (int i = 0; i < numSizes; ++i) { \
fprintf(stderr, #TTT "Type %s pos %d size: %ld\n", testName, i, \
TTT##Sizes[i]); \
@ -157,22 +157,26 @@ static void testTypeMetaDataAccessors(MlirContext ctx) {
MlirType dictType1 = torchMlirTorchDictTypeGet(strType, floatType);
fprintf(stderr, "dict keyType: ");
mlirTypePrint(torchMlirTorchDictTypeGetKeyType(dictType1), printToStderr, NULL);
mlirTypePrint(torchMlirTorchDictTypeGetKeyType(dictType1), printToStderr,
NULL);
fprintf(stderr, "\n");
// CHECK: dict keyType: !torch.str
fprintf(stderr, "dict valueType: ");
mlirTypePrint(torchMlirTorchDictTypeGetValueType(dictType1), printToStderr, NULL);
mlirTypePrint(torchMlirTorchDictTypeGetValueType(dictType1), printToStderr,
NULL);
fprintf(stderr, "\n");
// CHECK: dict valueType: !torch.float
MlirType dictType2 = torchMlirTorchDictTypeGet(floatType, strType);
fprintf(stderr, "dict keyType: ");
mlirTypePrint(torchMlirTorchDictTypeGetKeyType(dictType2), printToStderr, NULL);
mlirTypePrint(torchMlirTorchDictTypeGetKeyType(dictType2), printToStderr,
NULL);
fprintf(stderr, "\n");
// CHECK: dict keyType: !torch.float
fprintf(stderr, "dict valueType: ");
mlirTypePrint(torchMlirTorchDictTypeGetValueType(dictType2), printToStderr, NULL);
mlirTypePrint(torchMlirTorchDictTypeGetValueType(dictType2), printToStderr,
NULL);
fprintf(stderr, "\n");
// CHECK: dict valueType: !torch.str
}

2
test/CMakeLists.txt
View File

@ -14,7 +14,7 @@ configure_lit_site_cfg(
set(TORCH_MLIR_TEST_DEPENDS
FileCheck count not
TorchMLIRPythonModules
torch-mlir-opt
torch-mlir-opt
torch-mlir-capi-torch-test
)

18
test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir
View File

@ -86,7 +86,7 @@ func.func @test_argmax_negative_axis_keepdims_random_select_last_index(%arg0: !t
// CHECK: %[[C1:.*]] = torch.constant.int 1
// CHECK: %[[SUB:.*]] = torch.aten.sub.Scalar %[[ARGMAX]], %[[C3]], %[[C1]] : !torch.vtensor<[2,3,1],si64>, !torch.int, !torch.int -> !torch.vtensor<[2,3,1],si64>
// CHECK: %[[ABS:.*]] = torch.aten.abs %[[SUB]] : !torch.vtensor<[2,3,1],si64> -> !torch.vtensor<[2,3,1],si64>
%0 = torch.operator "onnx.ArgMax"(%arg0) {torch.onnx.axis = -1 : si64, torch.onnx.keepdims = 1 : si64, torch.onnx.select_last_index = 1 : si64} : (!torch.vtensor<[2,3,4],f32>) -> !torch.vtensor<[2,3,1],si64>
%0 = torch.operator "onnx.ArgMax"(%arg0) {torch.onnx.axis = -1 : si64, torch.onnx.keepdims = 1 : si64, torch.onnx.select_last_index = 1 : si64} : (!torch.vtensor<[2,3,4],f32>) -> !torch.vtensor<[2,3,1],si64>
return %0 : !torch.vtensor<[2,3,1],si64>
}
@ -115,7 +115,7 @@ func.func @test_argmax_no_keepdims_random_select_last_index(%arg0: !torch.vtenso
// CHECK: %[[C1_1:.*]] = torch.constant.int 1
// CHECK: %[[SUB:.*]] = torch.aten.sub.Scalar %[[ARGMAX]], %[[C2]], %[[C1_1]] : !torch.vtensor<[2,4],si64>, !torch.int, !torch.int -> !torch.vtensor<[2,4],si64>
// CHECK: %[[ABS:.*]] = torch.aten.abs %[[SUB]] : !torch.vtensor<[2,4],si64> -> !torch.vtensor<[2,4],si64>
%0 = torch.operator "onnx.ArgMax"(%arg0) {torch.onnx.axis = 1 : si64, torch.onnx.keepdims = 0 : si64, torch.onnx.select_last_index = 1 : si64} : (!torch.vtensor<[2,3,4],f32>) -> !torch.vtensor<[2,4],si64>
%0 = torch.operator "onnx.ArgMax"(%arg0) {torch.onnx.axis = 1 : si64, torch.onnx.keepdims = 0 : si64, torch.onnx.select_last_index = 1 : si64} : (!torch.vtensor<[2,3,4],f32>) -> !torch.vtensor<[2,4],si64>
return %0 : !torch.vtensor<[2,4],si64>
}
@ -155,7 +155,7 @@ func.func @test_argmin_negative_axis_keepdims_random_select_last_index(%arg0: !t
// CHECK: %[[C1:.*]] = torch.constant.int 1
// CHECK: %[[SUB:.*]] = torch.aten.sub.Scalar %[[ARGMIN]], %[[C3]], %[[C1]] : !torch.vtensor<[2,3,1],si64>, !torch.int, !torch.int -> !torch.vtensor<[2,3,1],si64>
// CHECK: %[[ABS:.*]] = torch.aten.abs %[[SUB]] : !torch.vtensor<[2,3,1],si64> -> !torch.vtensor<[2,3,1],si64>
%0 = torch.operator "onnx.ArgMin"(%arg0) {torch.onnx.axis = -1 : si64, torch.onnx.keepdims = 1 : si64, torch.onnx.select_last_index = 1 : si64} : (!torch.vtensor<[2,3,4],f32>) -> !torch.vtensor<[2,3,1],si64>
%0 = torch.operator "onnx.ArgMin"(%arg0) {torch.onnx.axis = -1 : si64, torch.onnx.keepdims = 1 : si64, torch.onnx.select_last_index = 1 : si64} : (!torch.vtensor<[2,3,4],f32>) -> !torch.vtensor<[2,3,1],si64>
return %0 : !torch.vtensor<[2,3,1],si64>
}
@ -851,7 +851,7 @@ func.func @test_dynamicquantizelinear(%arg0: !torch.vtensor<[3,4,5],f32>) -> (!t
// CHECK: %[[SCALE:.*]] = torch.aten.item %[[SCALE_T]] : !torch.vtensor<[],f32> -> !torch.float
// CHECK: %[[QUANT:.*]] = torch.aten.quantize_per_tensor %arg0, %[[SCALE]], %[[ZP]], %[[CI13]] : !torch.vtensor<[3,4,5],f32>, !torch.float, !torch.int, !torch.int -> !torch.vtensor<[3,4,5],!torch.quint8>
// CHECK: %[[INTQUANT:.*]] = torch.aten.int_repr %[[QUANT]] : !torch.vtensor<[3,4,5],!torch.quint8> -> !torch.vtensor<[3,4,5],ui8>
%0:3 = torch.operator "onnx.DynamicQuantizeLinear"(%arg0) : (!torch.vtensor<[3,4,5],f32>) -> (!torch.vtensor<[3,4,5],ui8>, !torch.vtensor<[],f32>, !torch.vtensor<[],ui8>)
%0:3 = torch.operator "onnx.DynamicQuantizeLinear"(%arg0) : (!torch.vtensor<[3,4,5],f32>) -> (!torch.vtensor<[3,4,5],ui8>, !torch.vtensor<[],f32>, !torch.vtensor<[],ui8>)
// CHECK: return %[[INTQUANT]], %[[SCALE_T]], %[[ZP_T]] : !torch.vtensor<[3,4,5],ui8>, !torch.vtensor<[],f32>, !torch.vtensor<[],ui8>
return %0#0, %0#1, %0#2 : !torch.vtensor<[3,4,5],ui8>, !torch.vtensor<[],f32>, !torch.vtensor<[],ui8>
}
@ -1035,7 +1035,7 @@ func.func @test_convinteger_without_padding(%arg0: !torch.vtensor<[1,1,3,3],ui8>
// CHECK: %[[WEIGHT:.*]] = torch.aten._make_per_tensor_quantized_tensor %arg1, %[[SCALE]], %[[WEIGHT_ZP]] : !torch.vtensor<[1,1,2,2],ui8>, !torch.float, !torch.int -> !torch.vtensor<[1,1,2,2],!torch.quint8>
// CHECK: torch.aten.convolution %[[INPUT]], %[[WEIGHT]], %[[BIAS]], %[[STRIDE]], %[[PADDING]], %[[DILATIONS]], %[[TRANSPOSED]], %[[OUTPUT_PADDING]], %[[GROUPS]] : !torch.vtensor<[1,1,3,3],!torch.quint8>, !torch.vtensor<[1,1,2,2],!torch.quint8>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,1,2,2],si32>
%none = torch.constant.none
%0 = torch.operator "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) : (!torch.vtensor<[1,1,3,3],ui8>, !torch.vtensor<[1,1,2,2],ui8>, !torch.vtensor<[],ui8>, !torch.vtensor<[1],ui8>) -> !torch.vtensor<[1,1,2,2],si32>
%0 = torch.operator "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) : (!torch.vtensor<[1,1,3,3],ui8>, !torch.vtensor<[1,1,2,2],ui8>, !torch.vtensor<[],ui8>, !torch.vtensor<[1],ui8>) -> !torch.vtensor<[1,1,2,2],si32>
return %0 : !torch.vtensor<[1,1,2,2],si32>
}
@ -1066,7 +1066,7 @@ func.func @test_convinteger_with_padding(%arg0: !torch.vtensor<[1,1,3,3],ui8>, %
// CHECK: %[[WEIGHT:.*]] = torch.aten._make_per_tensor_quantized_tensor %arg1, %[[SCALE]], %[[WEIGHT_ZP]] : !torch.vtensor<[1,1,2,2],ui8>, !torch.float, !torch.int -> !torch.vtensor<[1,1,2,2],!torch.quint8>
// CHECK: torch.aten.convolution %[[INPUT]], %[[WEIGHT]], %[[BIAS]], %[[STRIDE]], %[[PADDING]], %[[DILATIONS]], %[[TRANSPOSED]], %[[OUTPUT_PADDING]], %[[GROUPS]] : !torch.vtensor<[1,1,3,3],!torch.quint8>, !torch.vtensor<[1,1,2,2],!torch.quint8>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,1,4,4],si32>
%none = torch.constant.none
%0 = torch.operator "onnx.ConvInteger"(%arg0, %arg1, %arg2) {torch.onnx.pads = [1 : si64, 1 : si64, 1 : si64, 1 : si64]} : (!torch.vtensor<[1,1,3,3],ui8>, !torch.vtensor<[1,1,2,2],ui8>, !torch.vtensor<[],ui8>) -> !torch.vtensor<[1,1,4,4],si32>
%0 = torch.operator "onnx.ConvInteger"(%arg0, %arg1, %arg2) {torch.onnx.pads = [1 : si64, 1 : si64, 1 : si64, 1 : si64]} : (!torch.vtensor<[1,1,3,3],ui8>, !torch.vtensor<[1,1,2,2],ui8>, !torch.vtensor<[],ui8>) -> !torch.vtensor<[1,1,4,4],si32>
return %0 : !torch.vtensor<[1,1,4,4],si32>
}
@ -1597,9 +1597,9 @@ func.func @dense_constant() -> () attributes {torch.onnx_meta.ir_version = 8 : s
// CHECK-LABEL: @dense_constant_i1
func.func @dense_constant_i1() -> !torch.vtensor<[5],i1> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 17 : si64} {
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<[true, false, false, true, true]> : tensor<5xi1>) : !torch.vtensor<[5],i1>
// CHECK: return %[[CST]] : !torch.vtensor<[5],i1>
%0 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<_> : tensor<5xi1>} : () -> !torch.vtensor<[5],i1>
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<[true, false, false, true, true]> : tensor<5xi1>) : !torch.vtensor<[5],i1>
// CHECK: return %[[CST]] : !torch.vtensor<[5],i1>
%0 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<_> : tensor<5xi1>} : () -> !torch.vtensor<[5],i1>
return %0 : !torch.vtensor<[5],i1>
}

8
test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir
View File

@ -782,7 +782,7 @@ func.func @test_mod_int64_no_fmod(%arg0: !torch.vtensor<[6],si64>, %arg1: !torch
// CHECK: %[[NONE:.*]] = torch.constant.none
// CHECK: %[[LSM:.*]] = torch.aten.log_softmax.int %arg0, %[[CIM1]], %[[NONE]] : !torch.vtensor<[1,3],f32>, !torch.int, !torch.none -> !torch.vtensor<[1,3],f32>
// CHECK: return %[[LSM]] : !torch.vtensor<[1,3],f32>
%0 = torch.operator "onnx.LogSoftmax"(%arg0) : (!torch.vtensor<[1,3],f32>) -> !torch.vtensor<[1,3],f32>
%0 = torch.operator "onnx.LogSoftmax"(%arg0) : (!torch.vtensor<[1,3],f32>) -> !torch.vtensor<[1,3],f32>
return %0 : !torch.vtensor<[1,3],f32>
}
@ -794,7 +794,7 @@ func.func @test_mod_int64_no_fmod(%arg0: !torch.vtensor<[6],si64>, %arg1: !torch
// CHECK: %[[NONE:.*]] = torch.constant.none
// CHECK: %[[LSM:.*]] = torch.aten.log_softmax.int %arg0, %[[CI2]], %[[NONE]] : !torch.vtensor<[3,4,5],f32>, !torch.int, !torch.none -> !torch.vtensor<[3,4,5],f32>
// CHECK: return %[[LSM]] : !torch.vtensor<[3,4,5],f32>
%0 = torch.operator "onnx.LogSoftmax"(%arg0) {torch.onnx.axis = 2 : si64} : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
%0 = torch.operator "onnx.LogSoftmax"(%arg0) {torch.onnx.axis = 2 : si64} : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
return %0 : !torch.vtensor<[3,4,5],f32>
}
@ -812,7 +812,7 @@ func.func @test_mod_int64_no_fmod(%arg0: !torch.vtensor<[6],si64>, %arg1: !torch
// CHECK: %[[LSM:.*]] = torch.aten.log_softmax.int %[[FLAT_IN]], %[[CI1]], %[[NONE]] : !torch.vtensor<[3,?],f32>, !torch.int, !torch.none -> !torch.vtensor<[3,?],f32>
// CHECK: %[[UNFLAT:.*]] = torch.aten.unflatten.int %[[LSM]], %[[CI1]], %[[LIST]] : !torch.vtensor<[3,?],f32>, !torch.int, !torch.list<int> -> !torch.vtensor<[3,4,?],f32>
// CHECK: return %[[UNFLAT]] : !torch.vtensor<[3,4,?],f32>
%0 = torch.operator "onnx.LogSoftmax"(%arg0) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[3,4,?],f32>) -> !torch.vtensor<[3,4,?],f32>
%0 = torch.operator "onnx.LogSoftmax"(%arg0) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[3,4,?],f32>) -> !torch.vtensor<[3,4,?],f32>
return %0 : !torch.vtensor<[3,4,?],f32>
}
@ -830,7 +830,7 @@ func.func @test_mod_int64_no_fmod(%arg0: !torch.vtensor<[6],si64>, %arg1: !torch
// CHECK: %[[LSM:.*]] = torch.aten.log_softmax.int %[[FLAT_IN]], %[[CI1]], %[[NONE]] : !torch.vtensor<[3,20],f32>, !torch.int, !torch.none -> !torch.vtensor<[3,20],f32>
// CHECK: %[[UNFLAT:.*]] = torch.aten.unflatten.int %[[LSM]], %[[CI1]], %[[LIST]] : !torch.vtensor<[3,20],f32>, !torch.int, !torch.list<int> -> !torch.vtensor<[3,4,5],f32>
// CHECK: return %[[UNFLAT]] : !torch.vtensor<[3,4,5],f32>
%0 = torch.operator "onnx.LogSoftmax"(%arg0) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
%0 = torch.operator "onnx.LogSoftmax"(%arg0) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
return %0 : !torch.vtensor<[3,4,5],f32>
}

8
test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
View File

@ -1842,7 +1842,7 @@ func.func @test_random_normal() -> !torch.vtensor<[10],f32> attributes {torch.on
// CHECK-DAG: %[[F0:.+]] = torch.constant.float 0.000000e+00
// CHECK-DAG: %[[F1:.+]] = torch.constant.float 1.000000e+00
// CHECK: torch.aten.normal_functional %[[EMPTY_TENSOR]], %[[F0]], %[[F1]], %[[NONE]] : !torch.vtensor<[10],f32>, !torch.float, !torch.float, !torch.none -> !torch.vtensor<[10],f32>
%0 = torch.operator "onnx.RandomNormal"() {torch.onnx.dtype = 1 : si64, torch.onnx.mean = 0.000000e+00 : f32, torch.onnx.scale = 1.000000e+00 : f32, torch.onnx.shape = [10 : si64]} : () -> !torch.vtensor<[10],f32>
%0 = torch.operator "onnx.RandomNormal"() {torch.onnx.dtype = 1 : si64, torch.onnx.mean = 0.000000e+00 : f32, torch.onnx.scale = 1.000000e+00 : f32, torch.onnx.shape = [10 : si64]} : () -> !torch.vtensor<[10],f32>
return %0 : !torch.vtensor<[10],f32>
}
@ -1857,7 +1857,7 @@ func.func @test_random_normal_like(%arg0: !torch.vtensor<[10],f32>) -> !torch.vt
// CHECK-DAG: %[[F0:.+]] = torch.constant.float 0.000000e+00
// CHECK-DAG: %[[F1:.+]] = torch.constant.float 1.000000e+00
// CHECK: torch.aten.normal_functional %[[CAST]], %[[F0]], %[[F1]], %[[NONE]] : !torch.vtensor<[10],f32>, !torch.float, !torch.float, !torch.none -> !torch.vtensor<[10],f32>
%0 = torch.operator "onnx.RandomNormalLike"(%arg0) {torch.onnx.dtype = 1 : si64, torch.onnx.mean = 0.000000e+00 : f32, torch.onnx.scale = 1.000000e+00 : f32} : (!torch.vtensor<[10],f32>) -> !torch.vtensor<[10],f32>
%0 = torch.operator "onnx.RandomNormalLike"(%arg0) {torch.onnx.dtype = 1 : si64, torch.onnx.mean = 0.000000e+00 : f32, torch.onnx.scale = 1.000000e+00 : f32} : (!torch.vtensor<[10],f32>) -> !torch.vtensor<[10],f32>
return %0 : !torch.vtensor<[10],f32>
}
@ -1873,7 +1873,7 @@ func.func @test_random_uniform() -> !torch.vtensor<[10],f32> attributes {torch.o
// CHECK-DAG: %[[F1:.+]] = torch.constant.float 1.000000e+00
// CHECK-DAG: %[[F0:.+]] = torch.constant.float 0.000000e+00
// CHECK: torch.aten.uniform %[[EMPTY_TENSOR]], %[[F0]], %[[F1]], %[[NONE]] : !torch.vtensor<[10],f32>, !torch.float, !torch.float, !torch.none -> !torch.vtensor<[10],f32>
%0 = torch.operator "onnx.RandomUniform"() {torch.onnx.dtype = 1 : si64, torch.onnx.high = 1.000000e+00 : f32, torch.onnx.low = 0.000000e+00 : f32, torch.onnx.shape = [10 : si64]} : () -> !torch.vtensor<[10],f32>
%0 = torch.operator "onnx.RandomUniform"() {torch.onnx.dtype = 1 : si64, torch.onnx.high = 1.000000e+00 : f32, torch.onnx.low = 0.000000e+00 : f32, torch.onnx.shape = [10 : si64]} : () -> !torch.vtensor<[10],f32>
return %0 : !torch.vtensor<[10],f32>
}
@ -1888,6 +1888,6 @@ func.func @test_random_uniform_like(%arg0: !torch.vtensor<[10],f32>) -> !torch.v
// CHECK-DAG: %[[F0:.+]] = torch.constant.float 0.000000e+00
// CHECK-DAG: %[[F1:.+]] = torch.constant.float 1.000000e+00
// CHECK: torch.aten.uniform %[[CAST]], %[[F0]], %[[F1]], %[[NONE]] : !torch.vtensor<[10],f32>, !torch.float, !torch.float, !torch.none -> !torch.vtensor<[10],f32>
%0 = torch.operator "onnx.RandomUniformLike"(%arg0) {torch.onnx.dtype = 1 : si64, torch.onnx.high = 1.000000e+00 : f32, torch.onnx.low = 0.000000e+00 : f32} : (!torch.vtensor<[10],f32>) -> !torch.vtensor<[10],f32>
%0 = torch.operator "onnx.RandomUniformLike"(%arg0) {torch.onnx.dtype = 1 : si64, torch.onnx.high = 1.000000e+00 : f32, torch.onnx.low = 0.000000e+00 : f32} : (!torch.vtensor<[10],f32>) -> !torch.vtensor<[10],f32>
return %0 : !torch.vtensor<[10],f32>
}

2
test/Conversion/TorchOnnxToTorch/unsupported_fb_opt_ops.mlir
View File

@ -45,4 +45,4 @@ func.func @cumsum_operation(%arg0: !torch.vtensor<[2,3],f64>,
-> !torch.vtensor<[2,3],f64> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
%212 = torch.operator "onnx.CumSum"(%arg0, %arg1) : (!torch.vtensor<[2,3],f64>, !torch.vtensor<[],si32>) -> !torch.vtensor<[2,3],f64>
return %212 : !torch.vtensor<[2,3],f64>
}
}

2
test/Conversion/TorchToLinalg/flatten.mlir
View File

@ -82,5 +82,3 @@ func.func @torch.aten.flatten.using_ints$rank0(%arg0: !torch.vtensor<[],f32>) ->
%0 = torch.aten.flatten.using_ints %arg0, %int0, %int0 : !torch.vtensor<[],f32>, !torch.int, !torch.int -> !torch.vtensor<[1],f32>
return %0 : !torch.vtensor<[1],f32>
}

1
test/Conversion/TorchToLinalg/gridsampler.mlir
View File

@ -86,4 +86,3 @@ func.func @grid_sampler3(%arg0: !torch.vtensor<[?,?,?,?],f32>, %arg1: !torch.vte
%4 = torch.aten.grid_sampler %arg0, %arg1, %int0, %int1, %false : !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[?,?,?,?],f32>, !torch.int, !torch.int, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
return %4 : !torch.vtensor<[?,?,?,?],f32>
}

1
test/Conversion/TorchToLinalg/view.mlir
View File

@ -254,4 +254,3 @@ func.func @torch.aten.view$dynamicInferredSame(%arg0: !torch.vtensor<[10,?,2,3],
%1 = torch.aten.view %arg0, %0 : !torch.vtensor<[10,?,2,3],f32>, !torch.list<int> -> !torch.vtensor<[2,5,?,6],f32>
return %1 : !torch.vtensor<[2,5,?,6],f32>
}

2
test/Conversion/TorchToStablehlo/elementwise.mlir
View File

@ -636,4 +636,4 @@ func.func @torch.aten.div.Tensor_mode$floor(%arg0: !torch.vtensor<[?,?,?,?],f32>
func.func @torch.aten.abs(%arg0: !torch.vtensor<[15,15],si64>) -> !torch.vtensor<[15,15],si64>{
%0 = torch.aten.abs %arg0 : !torch.vtensor<[15,15],si64> -> !torch.vtensor<[15,15],si64>
return %0 : !torch.vtensor<[15,15],si64>
}
}

1
test/Conversion/TorchToStablehlo/gather.mlir
View File

@ -63,4 +63,3 @@ func.func @torch.aten.embedding$rank_two_indices(%weight: !torch.vtensor<[?,?],f
%ret = torch.aten.embedding %weight, %indices, %int-1, %false, %false : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,1], si64>, !torch.int, !torch.bool, !torch.bool -> !torch.vtensor<[?,1,?],f32>
return %ret: !torch.vtensor<[?,1,?],f32>
}

2
test/Conversion/TorchToStablehlo/scatter.mlir
View File

@ -32,4 +32,4 @@ func.func @forward(%arg0: !torch.vtensor<[?,?],si64>, %arg1: !torch.vtensor<[?,?
%int0 = torch.constant.int 0
%0 = torch.aten.scatter.src %arg0, %int0, %arg1, %arg2 : !torch.vtensor<[?,?],si64>, !torch.int, !torch.vtensor<[?,?],si64>, !torch.vtensor<[?,?],si64> -> !torch.vtensor<[?,?],si64>
return %0 : !torch.vtensor<[?,?],si64>
}
}

1
test/Conversion/TorchToStablehlo/view_like.mlir
View File

@ -565,4 +565,3 @@ func.func @torch.aten.unsqueeze$from_end(%arg0: !torch.vtensor<[?,?,?,?],f32>) -
%0 = torch.aten.unsqueeze %arg0, %int-2 : !torch.vtensor<[?,?,?,?],f32>, !torch.int -> !torch.vtensor<[?,?,?,1,?],f32>
return %0 : !torch.vtensor<[?,?,?,1,?],f32>
}

4
test/Conversion/TorchToTosa/cast_fp32_to_fp16.mlir
View File

@ -1,6 +1,6 @@
// RUN: torch-mlir-opt <%s -convert-torch-to-tosa -split-input-file
// CHECK: %{{.*}} = tosa.cast %{{.*}} : (tensor<1x32x220x220xf32>) -> tensor<1x32x220x220xf16>
// CHECK: %{{.*}} = tosa.cast %{{.*}} : (tensor<1x32x220x220xf32>) -> tensor<1x32x220x220xf16>
func.func @forward(%arg0: !torch.vtensor<[1,32,220,220],f32>) -> !torch.vtensor<[1,32,220,220],f16> {
%int5 = torch.constant.int 5
%false = torch.constant.bool false
@ -8,5 +8,3 @@ func.func @forward(%arg0: !torch.vtensor<[1,32,220,220],f32>) -> !torch.vtensor<
%out = torch.aten.to.dtype %arg0, %int5, %false, %false, %none : !torch.vtensor<[1,32,220,220],f32>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[1,32,220,220],f16>
return %out : !torch.vtensor<[1,32,220,220],f16>
}

1
test/Conversion/TorchToTosa/conv2d_transpose.mlir
View File

@ -15,4 +15,3 @@ func.func @forward(%input: !torch.vtensor<[1,64,1,100],f32>) -> !torch.vtensor<[
%output = torch.aten.convolution %input, %weight, %bias, %stride, %int1x1, %int1x1, %true, %int1x1, %int1 : !torch.vtensor<[1,64,1,100],f32>, !torch.vtensor<[64,64,3,3],f32>, !torch.vtensor<[64],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,64,2,200],f32>
return %output : !torch.vtensor<[1,64,2,200],f32>
}

4
test/Dialect/Torch/canonicalize.mlir
View File

@ -1524,7 +1524,7 @@ func.func @torch.aten.tensor.float() -> !torch.vtensor<[],f32> {
// CHECK-NEXT: torch.vtensor.literal(dense<45> : tensor<si32>) : !torch.vtensor<[],si32>
func.func @torch.aten.tensor.int() -> !torch.vtensor<[],si32> {
%none = torch.constant.none
%false = torch.constant.bool false
%false = torch.constant.bool false
%int45 = torch.constant.int 45
%67 = torch.aten.tensor.int %int45, %none, %none, %false : !torch.int, !torch.none, !torch.none, !torch.bool -> !torch.vtensor<[],si32>
return %67 : !torch.vtensor<[],si32>
@ -2091,7 +2091,7 @@ func.func @torch.aten.broadcast_to$fold(%arg0: !torch.vtensor<[3,4,2],f32>) -> !
// -----
// CHECK-LABEL: func.func @torch.aten.broadcast_to$fold_splat
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<3.000000e+00> : tensor<3x4x2xf32>) : !torch.vtensor<[3,4,2],f32>
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<3.000000e+00> : tensor<3x4x2xf32>) : !torch.vtensor<[3,4,2],f32>
// CHECK: return %[[CST]]
func.func @torch.aten.broadcast_to$fold_splat() -> !torch.vtensor<[3,4,2],f32> {
%tensor = torch.vtensor.literal(dense<3.0> : tensor<1x4x1xf32>) : !torch.vtensor<[1,4,1],f32>

1
test/Dialect/Torch/ops.mlir
View File

@ -186,4 +186,3 @@ func.func @torch.permute$negative_index_valid (%arg0: !torch.vtensor<[1,2,3],f32
%3 = torch.aten.permute %arg0, %perm : !torch.vtensor<[1,2,3],f32>, !torch.list<int> -> !torch.vtensor<[1,2,3],f32>
return %3 : !torch.vtensor<[1,2,3],f32>
}

2
test/python/fx_importer/v2.3/lit.local.cfg
View File

@ -4,6 +4,6 @@ try:
import torch
if torch.__version__ >= "2.3.0":
print("Enabling Torch v2.3+ tests")
config.unsupported = False
config.unsupported = False
except ModuleNotFoundError:
...

2
utils/bazel/torch-mlir-overlay/.bazelignore
View File

@ -2,6 +2,6 @@
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
# Skip the following directories when overlaying
# Skip the following directories when overlaying
utils/bazel
externals