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[MLIR] Lower ONNX element-wise binary ops: Mul, Div, Sub, And, Or, Xor (#388) 

* Lower ONNX element-wise binary ops: Mul, Div, Sub, And, Or, Xor

* Edit gen_doc.py to avoid changes about AnyTypeOf<[AnyMemRef, AnyTensor]>

* Miss a space

* Add tests

* Shorten ONNXElementWiseBinaryOpLowering into ONNXEWBinaryOpLowering

* Move lowering patterns into runOnModule()

* Redundant space
This commit is contained in:
TUNG LEDUC 2019-12-04 01:17:21 +09:00 committed by Tian Jin
parent 05e16dafae
commit c3ef1d93ae
7 changed files with 507 additions and 83 deletions
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6
src/compiler/dialect/onnx/gen_doc.py
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@ -263,7 +263,7 @@ def collect_types(schema, input) :
return allowedTypeStr return allowedTypeStr
def gen_schema(schema) : def gen_schema(schema) :
ShapeInferenceList=['Add', 'MatMul', 'Gemm'] ShapeInferenceList=['Add', 'Mul', 'Div', 'Sub', 'And', 'Or', 'Xor', 'MatMul', 'Gemm']
CanonicalList=['Add', 'Identity'] CanonicalList=['Add', 'Identity']
line_indent = ' ' line_indent = ' '
@ -314,7 +314,7 @@ def gen_schema(schema) :
#TODO handle (variadic, heterogeneous)" #TODO handle (variadic, heterogeneous)"
print('variadic, heterogeneous', input.name) print('variadic, heterogeneous', input.name)
if etypes == '': if etypes == '':
s+= 'AnyTensor' s+= 'AnyTypeOf<[AnyMemRef, AnyTensor]>'
else: else:
s+= 'TensorOf<['+etypes+']>' s+= 'TensorOf<['+etypes+']>'
@ -339,7 +339,7 @@ def gen_schema(schema) :
#need to interpret output.typeStr #need to interpret output.typeStr
etypes=collect_types(schema, output) etypes=collect_types(schema, output)
if etypes == '': if etypes == '':
s+= 'AnyTensor' s+= 'AnyTypeOf<[AnyMemRef, AnyTensor]>'
else: else:
s+= 'TensorOf<['+etypes+']>' s+= 'TensorOf<['+etypes+']>'
s+= ');' s+= ');'

48
src/compiler/dialect/onnx/onnx_ops.cpp
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@ -46,6 +46,54 @@ void ONNXAddOp::inferShapes() {
getResult()->setType(getOperand(0)->getType()); getResult()->setType(getOperand(0)->getType());
} }
//===----------------------------------------------------------------------===//
// Mul
/// Infer the output shape of the ONNXMulOp. This method is required by the
/// shape inference interface.
void ONNXMulOp::inferShapes() {
getResult()->setType(getOperand(0)->getType());
}
//===----------------------------------------------------------------------===//
// Div
/// Infer the output shape of the ONNXDivOp. This method is required by the
/// shape inference interface.
void ONNXDivOp::inferShapes() {
getResult()->setType(getOperand(0)->getType());
}
//===----------------------------------------------------------------------===//
// Sub
/// Infer the output shape of the ONNXSubOp. This method is required by the
/// shape inference interface.
void ONNXSubOp::inferShapes() {
getResult()->setType(getOperand(0)->getType());
}
//===----------------------------------------------------------------------===//
// And
/// Infer the output shape of the ONNXAndOp. This method is required by the
/// shape inference interface.
void ONNXAndOp::inferShapes() {
getResult()->setType(getOperand(0)->getType());
}
//===----------------------------------------------------------------------===//
// Or
/// Infer the output shape of the ONNXOrOp. This method is required by the
/// shape inference interface.
void ONNXOrOp::inferShapes() {
getResult()->setType(getOperand(0)->getType());
}
//===----------------------------------------------------------------------===//
// Xor
/// Infer the output shape of the ONNXXorOp. This method is required by the
/// shape inference interface.
void ONNXXorOp::inferShapes() {
getResult()->setType(getOperand(0)->getType());
}
//===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//
// MatMul // MatMul

26
src/compiler/dialect/onnx/onnxop.inc
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@ -44,7 +44,7 @@ def ONNXAddOp:ONNX_Op<"Add",
} }
def ONNXAndOp:ONNX_Op<"And", def ONNXAndOp:ONNX_Op<"And",
[NoSideEffect]> { [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
let summary = "ONNX And operation"; let summary = "ONNX And operation";
let description = [{ let description = [{
"Returns the tensor resulted from performing the `and` logical operation" "Returns the tensor resulted from performing the `and` logical operation"
@ -61,8 +61,11 @@ def ONNXArgMaxOp:ONNX_Op<"ArgMax",
let summary = "ONNX ArgMax operation"; let summary = "ONNX ArgMax operation";
let description = [{ let description = [{
"Computes the indices of the max elements of the input tensor's element along the " "Computes the indices of the max elements of the input tensor's element along the "
"provided axis. The resulted tensor has the same rank as the input if keepdims equal 1." "provided axis. The resulting tensor has the same rank as the input if keepdims equal 1. "
"If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. " "If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. "
"If select_last_index is True (default False), the index of the last occurence of the max "
"is selected if the max appears more than once in the input. Otherwise the index of the "
"first occurence is selected."
"The type of the output tensor is integer." "The type of the output tensor is integer."
}]; }];
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data); let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
@ -74,8 +77,11 @@ def ONNXArgMinOp:ONNX_Op<"ArgMin",
let summary = "ONNX ArgMin operation"; let summary = "ONNX ArgMin operation";
let description = [{ let description = [{
"Computes the indices of the min elements of the input tensor's element along the " "Computes the indices of the min elements of the input tensor's element along the "
"provided axis. The resulted tensor has the same rank as the input if keepdims equal 1." "provided axis. The resulting tensor has the same rank as the input if keepdims equal 1. "
"If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. " "If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. "
"If select_last_index is True (default False), the index of the last occurence of the min "
"is selected if the min appears more than once in the input. Otherwise the index of the "
"first occurence is selected."
"The type of the output tensor is integer." "The type of the output tensor is integer."
}]; }];
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data); let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
@ -467,7 +473,7 @@ def ONNXDetOp:ONNX_Op<"Det",
} }
def ONNXDivOp:ONNX_Op<"Div", def ONNXDivOp:ONNX_Op<"Div",
[NoSideEffect]> { [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
let summary = "ONNX Div operation"; let summary = "ONNX Div operation";
let description = [{ let description = [{
"Performs element-wise binary division (with Numpy-style broadcasting support)." "Performs element-wise binary division (with Numpy-style broadcasting support)."
@ -1576,7 +1582,7 @@ def ONNXModOp:ONNX_Op<"Mod",
} }
def ONNXMulOp:ONNX_Op<"Mul", def ONNXMulOp:ONNX_Op<"Mul",
[NoSideEffect]> { [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
let summary = "ONNX Mul operation"; let summary = "ONNX Mul operation";
let description = [{ let description = [{
"Performs element-wise binary multiplication (with Numpy-style broadcasting support)." "Performs element-wise binary multiplication (with Numpy-style broadcasting support)."
@ -1678,7 +1684,7 @@ def ONNXOneHotOp:ONNX_Op<"OneHot",
} }
def ONNXOrOp:ONNX_Op<"Or", def ONNXOrOp:ONNX_Op<"Or",
[NoSideEffect]> { [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
let summary = "ONNX Or operation"; let summary = "ONNX Or operation";
let description = [{ let description = [{
"Returns the tensor resulted from performing the `or` logical operation" "Returns the tensor resulted from performing the `or` logical operation"
@ -2954,7 +2960,7 @@ def ONNXStringNormalizerOp:ONNX_Op<"StringNormalizer",
} }
def ONNXSubOp:ONNX_Op<"Sub", def ONNXSubOp:ONNX_Op<"Sub",
[NoSideEffect]> { [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
let summary = "ONNX Sub operation"; let summary = "ONNX Sub operation";
let description = [{ let description = [{
"Performs element-wise binary subtraction (with Numpy-style broadcasting support)." "Performs element-wise binary subtraction (with Numpy-style broadcasting support)."
@ -3223,7 +3229,7 @@ def ONNXWhereOp:ONNX_Op<"Where",
} }
def ONNXXorOp:ONNX_Op<"Xor", def ONNXXorOp:ONNX_Op<"Xor",
[NoSideEffect]> { [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
let summary = "ONNX Xor operation"; let summary = "ONNX Xor operation";
let description = [{ let description = [{
"Returns the tensor resulted from performing the `xor` logical operation" "Returns the tensor resulted from performing the `xor` logical operation"

30
src/compiler/pass/lower_frontend_to_krnl.cpp
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@ -99,17 +99,17 @@ static bool checkInsertDealloc(Operation *currentOp) {
namespace { namespace {
//===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//
// Binary ops lowering to Krnl dialect. // Element-wise binary ops lowering to Krnl dialect.
//===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//
template <typename BinaryOp, typename LoweredBinaryOp> template <typename BinaryOp, typename LoweredBinaryOp>
struct ONNXBinaryOpLowering : public ConversionPattern { struct ONNXEWBinaryOpLowering : public ConversionPattern {
ONNXBinaryOpLowering(MLIRContext* ctx) ONNXEWBinaryOpLowering(MLIRContext* ctx)
: ConversionPattern(BinaryOp::getOperationName(), 1, ctx) {} : ConversionPattern(BinaryOp::getOperationName(), 1, ctx) {}
PatternMatchResult matchAndRewrite(Operation* op, ArrayRef<Value*> operands, PatternMatchResult matchAndRewrite(Operation* op, ArrayRef<Value*> operands,
ConversionPatternRewriter& rewriter) const final { ConversionPatternRewriter& rewriter) const final {
// TODO: Check that the types are valid. // TODO: Check that the types are valid.
// Add is an operation that must have all operands and the result of // An element-wise binary operation must have all operands and the result of
// the same type. This should have been verified by the verifier. // the same type. This should have been verified by the verifier.
auto tensorType = (*op->result_type_begin()).cast<TensorType>(); auto tensorType = (*op->result_type_begin()).cast<TensorType>();
auto loc = op->getLoc(); auto loc = op->getLoc();
@ -118,8 +118,8 @@ struct ONNXBinaryOpLowering : public ConversionPattern {
auto memRefType = convertTensorToMemRef(tensorType); auto memRefType = convertTensorToMemRef(tensorType);
// If the output has a dynamic dimension, pass the operands required for // If the output has a dynamic dimension, pass the operands required for
// each dynamic dimension to the AllocOp. The first operand of the Add // each dynamic dimension to the AllocOp. The first operand of the binary
// operation is used. The operands of the Add need to match in terms of // operation is used. The operands of the op need to match in terms of
// dimensions with the result at this pre-optimization phase. // dimensions with the result at this pre-optimization phase.
// TODO: verify that dimensions match. // TODO: verify that dimensions match.
// TODO: can the dimension of the result differ after optimizations? // TODO: can the dimension of the result differ after optimizations?
@ -186,14 +186,13 @@ struct ONNXBinaryOpLowering : public ConversionPattern {
// 2. Insert instructions inside the KernelIterateOp body. // 2. Insert instructions inside the KernelIterateOp body.
rewriter.setInsertionPointToStart(&iterationBlock); rewriter.setInsertionPointToStart(&iterationBlock);
// Handle AddOp: // Handle the operation:
SmallVector<Value*, 4> loopIVs; SmallVector<Value*, 4> loopIVs;
for (auto arg : iterationBlock.getArguments()) for (auto arg : iterationBlock.getArguments())
loopIVs.push_back(arg); loopIVs.push_back(arg);
auto loadedFirstVal = rewriter.create<LoadOp>(loc, operands[0], loopIVs); auto loadedFirstVal = rewriter.create<LoadOp>(loc, operands[0], loopIVs);
auto loadedSecondVal = rewriter.create<LoadOp>(loc, operands[1], loopIVs); auto loadedSecondVal = rewriter.create<LoadOp>(loc, operands[1], loopIVs);
// TODO: Choose type of the Add for now use the Float Add.
auto loweredOpResult = auto loweredOpResult =
rewriter.create<LoweredBinaryOp>(loc, loadedFirstVal, loadedSecondVal); rewriter.create<LoweredBinaryOp>(loc, loadedFirstVal, loadedSecondVal);
@ -206,11 +205,6 @@ struct ONNXBinaryOpLowering : public ConversionPattern {
} }
}; };
//===----------------------------------------------------------------------===//
// AddOp lowering to Krnl dialect.
//===----------------------------------------------------------------------===//
using ONNXAddOpLowering = ONNXBinaryOpLowering<mlir::ONNXAddOp, AddFOp>;
//===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//
// Conversion from Tensor type to the Standard dialect MemRef type. // Conversion from Tensor type to the Standard dialect MemRef type.
//===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//
@ -291,7 +285,15 @@ void FrontendToKrnlLoweringPass::runOnModule() {
patterns, &getContext(), tensor_to_memref_converter); patterns, &getContext(), tensor_to_memref_converter);
// Frontent operation lowering. // Frontent operation lowering.
patterns.insert<ONNXAddOpLowering>(&getContext()); // TODO: Support 1-N mapping (e.g. different types of the lowered op)
patterns.insert<ONNXEWBinaryOpLowering<mlir::ONNXAddOp, AddFOp>,
ONNXEWBinaryOpLowering<mlir::ONNXMulOp, MulFOp>,
ONNXEWBinaryOpLowering<mlir::ONNXDivOp, DivFOp>,
ONNXEWBinaryOpLowering<mlir::ONNXSubOp, SubFOp>,
ONNXEWBinaryOpLowering<mlir::ONNXAndOp, AndOp>,
ONNXEWBinaryOpLowering<mlir::ONNXOrOp, OrOp>,
ONNXEWBinaryOpLowering<mlir::ONNXXorOp, XOrOp>>
(&getContext());
// With the target and rewrite patterns defined, we can now attempt the // With the target and rewrite patterns defined, we can now attempt the
// conversion. The conversion will signal failure if any of our `illegal` // conversion. The conversion will signal failure if any of our `illegal`

6
src/compiler/pass/shape_inference_pass.cpp
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@ -89,6 +89,12 @@ class ShapeInferencePass : public mlir::FunctionPass<ShapeInferencePass> {
// shaped outputs. All those operation need to implement the inferShape() // shaped outputs. All those operation need to implement the inferShape()
// method. // method.
if (op->getName().getStringRef() != "onnx.Add" && if (op->getName().getStringRef() != "onnx.Add" &&
op->getName().getStringRef() != "onnx.Mul" &&
op->getName().getStringRef() != "onnx.Div" &&
op->getName().getStringRef() != "onnx.Sub" &&
op->getName().getStringRef() != "onnx.And" &&
op->getName().getStringRef() != "onnx.Or" &&
op->getName().getStringRef() != "onnx.Xor" &&
op->getName().getStringRef() != "onnx.MatMul" && op->getName().getStringRef() != "onnx.MatMul" &&
op->getName().getStringRef() != "onnx.Gemm" && op->getName().getStringRef() != "onnx.Gemm" &&
op->getName().getStringRef() != "onnx.FullGemm") op->getName().getStringRef() != "onnx.FullGemm")

156
test/mlir/onnx/onnx_lowering.mlir
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@ -1,23 +1,141 @@
// RUN: onnf-opt --shape-inference --lower-frontend %s -split-input-file | FileCheck %s // RUN: onnf-opt --shape-inference --lower-frontend %s -split-input-file | FileCheck %s
module { func @test_add(%arg0 : tensor<?x10xf32>, %arg1 : tensor<?x10xf32>) -> tensor<*xf32> {
func @test_sigmoid(%a1 : tensor<?x10xf32>, %a2 : tensor<?x10xf32>) -> tensor<*xf32> { %0 = "onnx.Add"(%arg0, %arg1) : (tensor<?x10xf32>, tensor<?x10xf32>) -> tensor<*xf32>
%0 = "onnx.Add"(%a1, %a2) : (tensor<?x10xf32>, tensor<?x10xf32>) -> tensor<*xf32> "std.return"(%0) : (tensor<*xf32>) -> ()
"std.return"(%0) : (tensor<*xf32>) -> ()
} // CHECK-LABEL: test_add
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[ADDF:%.+]] = addf [[LOAD1]], [[LOAD2]] : f32
// CHECK: store [[ADDF]], [[RES]][%arg2, %arg3] : memref<?x10xf32>
// CHECK: return [[RES]] : memref<?x10xf32>
} }
// CHECK: func @test_sigmoid([[ARG0:%.+]]: memref<?x10xf32>, [[ARG1:%.+]]: memref<?x10xf32>) -> memref<?x10xf32> { func @test_mul(%arg0 : tensor<?x10xf32>, %arg1 : tensor<?x10xf32>) -> tensor<*xf32> {
// CHECK: [[DIM_0:%.+]] = dim [[ARG0]], 0 : memref<?x10xf32> %0 = "onnx.Mul"(%arg0, %arg1) : (tensor<?x10xf32>, tensor<?x10xf32>) -> tensor<*xf32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32> "std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops { // CHECK-LABEL: test_mul
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1 // CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: } : () -> (!krnl.loop, !krnl.loop) // CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[DIM_2:%.+]] = dim [[ARG0]], 0 : memref<?x10xf32> // CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) { // CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: [[LOAD1:%.+]] = load [[ARG0]][%arg2, %arg3] : memref<?x10xf32> // CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: [[LOAD2:%.+]] = load [[ARG1]][%arg2, %arg3] : memref<?x10xf32> // CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[ADDF:%.+]] = addf [[LOAD1]], [[LOAD2]] : f32 // CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: store [[ADDF]], [[RES]][%arg2, %arg3] : memref<?x10xf32> // CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: return [[RES]] : memref<?x10xf32> // CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[MULF:%.+]] = mulf [[LOAD1]], [[LOAD2]] : f32
// CHECK: store [[MULF]], [[RES]][%arg2, %arg3] : memref<?x10xf32>
// CHECK: return [[RES]] : memref<?x10xf32>
}
func @test_div(%arg0 : tensor<?x10xf32>, %arg1 : tensor<?x10xf32>) -> tensor<*xf32> {
%0 = "onnx.Div"(%arg0, %arg1) : (tensor<?x10xf32>, tensor<?x10xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_div
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[DIVF:%.+]] = divf [[LOAD1]], [[LOAD2]] : f32
// CHECK: store [[DIVF]], [[RES]][%arg2, %arg3] : memref<?x10xf32>
// CHECK: return [[RES]] : memref<?x10xf32>
}
func @test_sub(%arg0 : tensor<?x10xf32>, %arg1 : tensor<?x10xf32>) -> tensor<*xf32> {
%0 = "onnx.Sub"(%arg0, %arg1) : (tensor<?x10xf32>, tensor<?x10xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_sub
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[SUBF:%.+]] = subf [[LOAD1]], [[LOAD2]] : f32
// CHECK: store [[SUBF]], [[RES]][%arg2, %arg3] : memref<?x10xf32>
// CHECK: return [[RES]] : memref<?x10xf32>
}
func @test_and(%arg0 : tensor<?x10xi32>, %arg1 : tensor<?x10xi32>) -> tensor<*xi32> {
%0 = "onnx.And"(%arg0, %arg1) : (tensor<?x10xi32>, tensor<?x10xi32>) -> tensor<*xi32>
"std.return"(%0) : (tensor<*xi32>) -> ()
// CHECK-LABEL: test_and
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xi32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xi32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xi32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[AND:%.+]] = and [[LOAD1]], [[LOAD2]] : i32
// CHECK: store [[AND]], [[RES]][%arg2, %arg3] : memref<?x10xi32>
// CHECK: return [[RES]] : memref<?x10xi32>
}
func @test_or(%arg0 : tensor<?x10xi32>, %arg1 : tensor<?x10xi32>) -> tensor<*xi32> {
%0 = "onnx.Or"(%arg0, %arg1) : (tensor<?x10xi32>, tensor<?x10xi32>) -> tensor<*xi32>
"std.return"(%0) : (tensor<*xi32>) -> ()
// CHECK-LABEL: test_or
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xi32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xi32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xi32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[OR:%.+]] = or [[LOAD1]], [[LOAD2]] : i32
// CHECK: store [[OR]], [[RES]][%arg2, %arg3] : memref<?x10xi32>
// CHECK: return [[RES]] : memref<?x10xi32>
}
func @test_xor(%arg0 : tensor<?x10xi32>, %arg1 : tensor<?x10xi32>) -> tensor<*xi32> {
%0 = "onnx.Xor"(%arg0, %arg1) : (tensor<?x10xi32>, tensor<?x10xi32>) -> tensor<*xi32>
"std.return"(%0) : (tensor<*xi32>) -> ()
// CHECK-LABEL: test_xor
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xi32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xi32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xi32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[XOR:%.+]] = xor [[LOAD1]], [[LOAD2]] : i32
// CHECK: store [[XOR]], [[RES]][%arg2, %arg3] : memref<?x10xi32>
// CHECK: return [[RES]] : memref<?x10xi32>
}

318
test/mlir/onnx/onnx_lowering_with_dealloc.mlir
View File

@ -1,45 +1,289 @@
// RUN: dlc-opt --shape-inference --lower-frontend %s -split-input-file | FileCheck %s // RUN: dlc-opt --shape-inference --lower-frontend %s -split-input-file | FileCheck %s
module { func @test_add_add(%arg0 : tensor<?x10xf32>, %arg1 : tensor<?x10xf32>) -> tensor<*xf32> {
func @test_sigmoid(%a1 : tensor<?x10xf32>, %a2 : tensor<?x10xf32>) -> tensor<*xf32> { %0 = "onnx.Add"(%arg0, %arg1) : (tensor<?x10xf32>, tensor<?x10xf32>) -> tensor<*xf32>
%0 = "onnx.Add"(%a1, %a2) : (tensor<?x10xf32>, tensor<?x10xf32>) -> tensor<*xf32> %1 = "onnx.Add"(%0, %arg1) : (tensor<*xf32>, tensor<?x10xf32>) -> tensor<*xf32>
%1 = "onnx.Add"(%0, %a2) : (tensor<*xf32>, tensor<?x10xf32>) -> tensor<*xf32> "std.return"(%1) : (tensor<*xf32>) -> ()
"std.return"(%1) : (tensor<*xf32>) -> ()
} // CHECK-LABEL: test_add_add
/// First Add
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[ADDF:%.+]] = addf [[LOAD1]], [[LOAD2]] : f32
// CHECK: store [[ADDF]], [[RES]][%arg2, %arg3] : memref<?x10xf32>
/// Second Add
// CHECK: [[DIM_0:%.+]] = dim [[RES]], 0 : memref<?x10xf32>
// CHECK: [[RET_RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim [[RES]], 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load [[RES]][%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[ADDF:%.+]] = addf [[LOAD1]], [[LOAD2]] : f32
// CHECK: store [[ADDF]], [[RET_RES]][%arg2, %arg3] : memref<?x10xf32>
/// Dealloc of first result.
// CHECK: dealloc [[RES]] : memref<?x10xf32>
// CHECK-NOT: dealloc [[RET_RES]] : memref<?x10xf32>
// CHECK: return [[RET_RES]] : memref<?x10xf32>
} }
// CHECK: func @test_sigmoid([[ARG0:%.+]]: memref<?x10xf32>, [[ARG1:%.+]]: memref<?x10xf32>) -> memref<?x10xf32> {
/// First Add func @test_mul_mul(%arg0 : tensor<?x10xf32>, %arg1 : tensor<?x10xf32>) -> tensor<*xf32> {
// CHECK: [[DIM_0:%.+]] = dim [[ARG0]], 0 : memref<?x10xf32> %0 = "onnx.Mul"(%arg0, %arg1) : (tensor<?x10xf32>, tensor<?x10xf32>) -> tensor<*xf32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32> %1 = "onnx.Mul"(%0, %arg1) : (tensor<*xf32>, tensor<?x10xf32>) -> tensor<*xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2 "std.return"(%1) : (tensor<*xf32>) -> ()
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim [[ARG0]], 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load [[ARG0]][%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[LOAD2:%.+]] = load [[ARG1]][%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[ADDF:%.+]] = addf [[LOAD1]], [[LOAD2]] : f32
// CHECK: store [[ADDF]], [[RES]][%arg2, %arg3] : memref<?x10xf32>
/// Second Add // CHECK-LABEL: test_mul_mul
// CHECK: [[DIM_0:%.+]] = dim [[RES]], 0 : memref<?x10xf32> /// First Mul
// CHECK: [[RET_RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32> // CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2 // CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops { // CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1 // CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: } : () -> (!krnl.loop, !krnl.loop) // CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: [[DIM_2:%.+]] = dim [[RES]], 0 : memref<?x10xf32> // CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) { // CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: [[LOAD1:%.+]] = load [[RES]][%arg2, %arg3] : memref<?x10xf32> // CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD2:%.+]] = load [[ARG1]][%arg2, %arg3] : memref<?x10xf32> // CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[ADDF:%.+]] = addf [[LOAD1]], [[LOAD2]] : f32 // CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xf32>
// CHECK: store [[ADDF]], [[RET_RES]][%arg2, %arg3] : memref<?x10xf32> // CHECK: [[MULF:%.+]] = mulf [[LOAD1]], [[LOAD2]] : f32
// CHECK: store [[MULF]], [[RES]][%arg2, %arg3] : memref<?x10xf32>
/// Dealloc of first result. /// Second Mul
// CHECK: dealloc [[RES]] : memref<?x10xf32> // CHECK: [[DIM_0:%.+]] = dim [[RES]], 0 : memref<?x10xf32>
// CHECK-NOT: dealloc [[RET_RES]] : memref<?x10xf32> // CHECK: [[RET_RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim [[RES]], 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load [[RES]][%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[MULF:%.+]] = mulf [[LOAD1]], [[LOAD2]] : f32
// CHECK: store [[MULF]], [[RET_RES]][%arg2, %arg3] : memref<?x10xf32>
// CHECK: return [[RET_RES]] : memref<?x10xf32> /// Dealloc of first result.
// CHECK: dealloc [[RES]] : memref<?x10xf32>
// CHECK-NOT: dealloc [[RET_RES]] : memref<?x10xf32>
// CHECK: return [[RET_RES]] : memref<?x10xf32>
}
func @test_div_div(%arg0 : tensor<?x10xf32>, %arg1 : tensor<?x10xf32>) -> tensor<*xf32> {
%0 = "onnx.Div"(%arg0, %arg1) : (tensor<?x10xf32>, tensor<?x10xf32>) -> tensor<*xf32>
%1 = "onnx.Div"(%0, %arg1) : (tensor<*xf32>, tensor<?x10xf32>) -> tensor<*xf32>
"std.return"(%1) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_div_div
/// First Div
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[DIVF:%.+]] = divf [[LOAD1]], [[LOAD2]] : f32
// CHECK: store [[DIVF]], [[RES]][%arg2, %arg3] : memref<?x10xf32>
/// Second Div
// CHECK: [[DIM_0:%.+]] = dim [[RES]], 0 : memref<?x10xf32>
// CHECK: [[RET_RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim [[RES]], 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load [[RES]][%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[DIVF:%.+]] = divf [[LOAD1]], [[LOAD2]] : f32
// CHECK: store [[DIVF]], [[RET_RES]][%arg2, %arg3] : memref<?x10xf32>
/// Dealloc of first result.
// CHECK: dealloc [[RES]] : memref<?x10xf32>
// CHECK-NOT: dealloc [[RET_RES]] : memref<?x10xf32>
// CHECK: return [[RET_RES]] : memref<?x10xf32>
}
func @test_sub_sub(%arg0 : tensor<?x10xf32>, %arg1 : tensor<?x10xf32>) -> tensor<*xf32> {
%0 = "onnx.Sub"(%arg0, %arg1) : (tensor<?x10xf32>, tensor<?x10xf32>) -> tensor<*xf32>
%1 = "onnx.Sub"(%0, %arg1) : (tensor<*xf32>, tensor<?x10xf32>) -> tensor<*xf32>
"std.return"(%1) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_sub_sub
/// First Sub
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[SUBF:%.+]] = subf [[LOAD1]], [[LOAD2]] : f32
// CHECK: store [[SUBF]], [[RES]][%arg2, %arg3] : memref<?x10xf32>
/// Second Sub
// CHECK: [[DIM_0:%.+]] = dim [[RES]], 0 : memref<?x10xf32>
// CHECK: [[RET_RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim [[RES]], 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load [[RES]][%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[SUBF:%.+]] = subf [[LOAD1]], [[LOAD2]] : f32
// CHECK: store [[SUBF]], [[RET_RES]][%arg2, %arg3] : memref<?x10xf32>
/// Dealloc of first result.
// CHECK: dealloc [[RES]] : memref<?x10xf32>
// CHECK-NOT: dealloc [[RET_RES]] : memref<?x10xf32>
// CHECK: return [[RET_RES]] : memref<?x10xf32>
}
func @test_and_and(%arg0 : tensor<?x10xi32>, %arg1 : tensor<?x10xi32>) -> tensor<*xi32> {
%0 = "onnx.And"(%arg0, %arg1) : (tensor<?x10xi32>, tensor<?x10xi32>) -> tensor<*xi32>
%1 = "onnx.And"(%0, %arg1) : (tensor<*xi32>, tensor<?x10xi32>) -> tensor<*xi32>
"std.return"(%1) : (tensor<*xi32>) -> ()
// CHECK-LABEL: test_and_and
/// First And
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xi32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xi32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xi32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[AND:%.+]] = and [[LOAD1]], [[LOAD2]] : i32
// CHECK: store [[AND]], [[RES]][%arg2, %arg3] : memref<?x10xi32>
/// Second And
// CHECK: [[DIM_0:%.+]] = dim [[RES]], 0 : memref<?x10xi32>
// CHECK: [[RET_RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xi32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim [[RES]], 0 : memref<?x10xi32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load [[RES]][%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[AND:%.+]] = and [[LOAD1]], [[LOAD2]] : i32
// CHECK: store [[AND]], [[RET_RES]][%arg2, %arg3] : memref<?x10xi32>
/// Dealloc of first result.
// CHECK: dealloc [[RES]] : memref<?x10xi32>
// CHECK-NOT: dealloc [[RET_RES]] : memref<?x10xi32>
// CHECK: return [[RET_RES]] : memref<?x10xi32>
}
func @test_or_or(%arg0 : tensor<?x10xi32>, %arg1 : tensor<?x10xi32>) -> tensor<*xi32> {
%0 = "onnx.Or"(%arg0, %arg1) : (tensor<?x10xi32>, tensor<?x10xi32>) -> tensor<*xi32>
%1 = "onnx.Or"(%0, %arg1) : (tensor<*xi32>, tensor<?x10xi32>) -> tensor<*xi32>
"std.return"(%1) : (tensor<*xi32>) -> ()
// CHECK-LABEL: test_or_or
/// First Or
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xi32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xi32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xi32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[OR:%.+]] = or [[LOAD1]], [[LOAD2]] : i32
// CHECK: store [[OR]], [[RES]][%arg2, %arg3] : memref<?x10xi32>
/// Second Or
// CHECK: [[DIM_0:%.+]] = dim [[RES]], 0 : memref<?x10xi32>
// CHECK: [[RET_RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xi32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim [[RES]], 0 : memref<?x10xi32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load [[RES]][%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[OR:%.+]] = or [[LOAD1]], [[LOAD2]] : i32
// CHECK: store [[OR]], [[RET_RES]][%arg2, %arg3] : memref<?x10xi32>
/// Dealloc of first result.
// CHECK: dealloc [[RES]] : memref<?x10xi32>
// CHECK-NOT: dealloc [[RET_RES]] : memref<?x10xi32>
// CHECK: return [[RET_RES]] : memref<?x10xi32>
}
func @test_xor_xor(%arg0 : tensor<?x10xi32>, %arg1 : tensor<?x10xi32>) -> tensor<*xi32> {
%0 = "onnx.Xor"(%arg0, %arg1) : (tensor<?x10xi32>, tensor<?x10xi32>) -> tensor<*xi32>
%1 = "onnx.Xor"(%0, %arg1) : (tensor<*xi32>, tensor<?x10xi32>) -> tensor<*xi32>
"std.return"(%1) : (tensor<*xi32>) -> ()
// CHECK-LABEL: test_xor_xor
/// First Xor
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xi32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xi32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xi32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[XOR:%.+]] = xor [[LOAD1]], [[LOAD2]] : i32
// CHECK: store [[XOR]], [[RES]][%arg2, %arg3] : memref<?x10xi32>
/// Second Xor
// CHECK: [[DIM_0:%.+]] = dim [[RES]], 0 : memref<?x10xi32>
// CHECK: [[RET_RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xi32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim [[RES]], 0 : memref<?x10xi32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load [[RES]][%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[XOR:%.+]] = xor [[LOAD1]], [[LOAD2]] : i32
// CHECK: store [[XOR]], [[RET_RES]][%arg2, %arg3] : memref<?x10xi32>
/// Dealloc of first result.
// CHECK: dealloc [[RES]] : memref<?x10xi32>
// CHECK-NOT: dealloc [[RET_RES]] : memref<?x10xi32>
// CHECK: return [[RET_RES]] : memref<?x10xi32>
}