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Revert "[MLIR][TORCH] Add E2E support for aten.ceil.float op" 

This reverts commit 78f5747568.
pull/808/head
Sean Silva 2022-04-28 13:36:07 +00:00
parent ab0eafb617
commit 5ef9f501fa
7 changed files with 9 additions and 136 deletions
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24
include/torch-mlir/Dialect/Torch/IR/GeneratedTorchOps.td
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@ -6934,30 +6934,6 @@ def Torch_AtenEqDeviceOp : Torch_Op<"aten.eq.device", [
}];
}
def Torch_AtenCeilFloatOp : Torch_Op<"aten.ceil.float", [
AllowsTypeRefinement,
HasValueSemantics,
ReadOnly
]> {
let summary = "Generated op for `aten::ceil.float : (float) -> (int)`";
let arguments = (ins
Torch_FloatType:$a
);
let results = (outs
Torch_IntType:$result
);
let hasCustomAssemblyFormat = 1;
let extraClassDefinition = [{
ParseResult AtenCeilFloatOp::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 1, 1);
}
void AtenCeilFloatOp::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 1, 1);
}
}];
let hasFolder = 1;
}
def Torch_Aten_SoftmaxBackwardDataOp : Torch_Op<"aten._softmax_backward_data", [
AllowsTypeRefinement,
HasValueSemantics,

26
lib/Conversion/TorchToStd/TorchToStd.cpp
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@ -13,7 +13,6 @@
#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
#include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/Math/IR/Math.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Traits.h"
#include "mlir/Transforms/DialectConversion.h"
@ -78,25 +77,6 @@ public:
};
} // namespace
namespace {
template <typename AtenOp, typename UnaryOp>
class ConvertAtenUnaryOp : public OpConversionPattern<AtenOp> {
public:
using OpConversionPattern<AtenOp>::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenOp op,
typename OpConversionPattern<AtenOp>::OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Type resultType =
this->getTypeConverter()->convertType(op->getResult(0).getType());
Value result = rewriter.create<UnaryOp>(op.getLoc(), adaptor.a());
rewriter.replaceOp(
op, convertScalarToDtype(rewriter, op.getLoc(), result, resultType));
return success();
}
};
} // namespace
namespace {
// Lowers aten integer comparison ops.
template <typename AtenOp, arith::CmpIPredicate Pred>
@ -202,7 +182,6 @@ public:
registry.insert<arith::ArithmeticDialect>();
registry.insert<tensor::TensorDialect>();
registry.insert<cf::ControlFlowDialect>();
registry.insert<math::MathDialect>();
TorchConversion::getBackendTypeConversionDependentDialects(registry);
}
@ -211,7 +190,7 @@ public:
ConversionTarget target(*context);
target.addLegalDialect<Torch::TorchDialect, func::FuncDialect,
arith::ArithmeticDialect, tensor::TensorDialect,
cf::ControlFlowDialect, math::MathDialect>();
cf::ControlFlowDialect>();
TypeConverter typeConverter;
typeConverter.addConversion([](Type type) { return type; });
@ -267,9 +246,6 @@ public:
target.addIllegalOp<AtenDivFloatOp>();
patterns.add<ConvertAtenBinaryOp<AtenDivFloatOp, arith::DivFOp>>(
typeConverter, context);
target.addIllegalOp<AtenCeilFloatOp>();
patterns.add<ConvertAtenUnaryOp<AtenCeilFloatOp, math::CeilOp>>(
typeConverter, context);
if (failed(applyPartialConversion(getOperation(), target,
std::move(patterns))))

10
lib/Dialect/Torch/IR/TorchOps.cpp
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@ -1545,16 +1545,6 @@ OpFoldResult AtenDivFloatOp::fold(ArrayRef<Attribute> operands) {
return nullptr;
}
// AtenCeilFloatOp
//===----------------------------------------------------------------------===//
OpFoldResult AtenCeilFloatOp::fold(ArrayRef<Attribute> operands) {
double c;
if (matchPattern(getOperand(), m_TorchConstantFloat(&c)))
return getI64IntegerAttr(getContext(), std::ceil(c));
return nullptr;
}
//===----------------------------------------------------------------------===//
//===----------------------------------------------------------------------===//

1
python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/torch_ods_gen.py
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@ -511,7 +511,6 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
emit("aten::_set_item.t : (t[], int, t) -> (t[])")
emit("aten::div : (Scalar, Scalar) -> (float)")
emit("aten::eq.device : (Device, Device) -> (bool)")
emit("aten::ceil.float : (float) -> (int)", has_folder=True)
# backprop ops
emit("aten::_softmax_backward_data : (Tensor, Tensor, int, int) -> (Tensor)")

54
python/torch_mlir_e2e_test/test_suite/scalar.py
View File

@ -11,9 +11,7 @@ from torch_mlir_e2e_test.torchscript.annotations import annotate_args, export
# ==============================================================================
class AddIntModule(torch.nn.Module):
def __init__(self):
super().__init__()
@ -24,19 +22,16 @@ class AddIntModule(torch.nn.Module):
([], torch.int64, True),
])
def forward(self, lhs, rhs):
return int(lhs) + int(rhs)
return int(lhs)+int(rhs)
@register_test_case(module_factory=lambda: AddIntModule())
def AddIntModule_basic(module, tu: TestUtils):
module.forward(torch.randint(-100, 100, ()), torch.randint(-100, 100, ()))
module.forward(torch.randint(-100, 100,()), torch.randint(-100, 100,()))
# ==============================================================================
class SubIntModule(torch.nn.Module):
def __init__(self):
super().__init__()
@ -47,19 +42,16 @@ class SubIntModule(torch.nn.Module):
([], torch.int64, True),
])
def forward(self, lhs, rhs):
return int(lhs) - int(rhs)
return int(lhs)-int(rhs)
@register_test_case(module_factory=lambda: SubIntModule())
def SubIntModule_basic(module, tu: TestUtils):
module.forward(torch.randint(-100, 100, ()), torch.randint(-100, 100, ()))
module.forward(torch.randint(-100, 100,()), torch.randint(-100, 100,()))
# ==============================================================================
class SubFloatModule(torch.nn.Module):
def __init__(self):
super().__init__()
@ -70,19 +62,16 @@ class SubFloatModule(torch.nn.Module):
([], torch.float64, True),
])
def forward(self, lhs, rhs):
return float(lhs) - float(rhs)
return float(lhs)-float(rhs)
@register_test_case(module_factory=lambda: SubFloatModule())
def SubFloatModule_basic(module, tu: TestUtils):
module.forward(torch.rand(()).double(), torch.rand(()).double())
# ==============================================================================
class MulIntModule(torch.nn.Module):
def __init__(self):
super().__init__()
@ -93,19 +82,16 @@ class MulIntModule(torch.nn.Module):
([], torch.int64, True),
])
def forward(self, lhs, rhs):
return int(lhs) * int(rhs)
return int(lhs)*int(rhs)
@register_test_case(module_factory=lambda: MulIntModule())
def MulIntModule_basic(module, tu: TestUtils):
module.forward(torch.randint(-100, 100, ()), torch.randint(-100, 100, ()))
module.forward(torch.randint(-100, 100,()), torch.randint(-100, 100,()))
# ==============================================================================
class DivFloatModule(torch.nn.Module):
def __init__(self):
super().__init__()
@ -116,33 +102,9 @@ class DivFloatModule(torch.nn.Module):
([], torch.float64, True),
])
def forward(self, lhs, rhs):
return float(lhs) / float(rhs)
return float(lhs)/float(rhs)
@register_test_case(module_factory=lambda: DivFloatModule())
def DivFloatModule_basic(module, tu: TestUtils):
module.forward(torch.rand(()).double(), torch.rand(()).double())
# ==============================================================================
class CeilFloatModule(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([], torch.float64, True),
([], torch.float64, True),
])
def forward(self, lhs, rhs):
sub = float(lhs) - float(rhs)
return torch.ops.aten.ceil(float(sub))
@register_test_case(module_factory=lambda: CeilFloatModule())
def CeilFloatModule_basic(module, tu: TestUtils):
module.forward(torch.rand(()).double(), torch.rand(()).double())

12
test/Conversion/TorchToStd/basic.mlir
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@ -207,15 +207,3 @@ func @torch.aten.ne.float_int(%arg0: !torch.float, %arg1: !torch.int) -> !torch.
%0 = torch.aten.ne.float_int %arg0, %arg1 : !torch.float, !torch.int -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.ceil.float(
// CHECK-SAME: %[[ARG:.*]]: !torch.float) -> !torch.int {
// CHECK: %[[ARG_F64:.*]] = torch_c.to_f64 %[[ARG]]
// CHECK: %[[CEIL:.*]] = math.ceil %[[ARG_F64]] : f64
// CHECK: %[[CEIL_I64:.*]] = arith.fptosi %[[CEIL]] : f64 to i64
// CHECK: %[[OUT:.*]] = torch_c.from_i64 %[[CEIL_I64]]
// CHECK: return %[[OUT]] : !torch.int
func @torch.aten.ceil.float(%arg0: !torch.float) -> !torch.int {
%0 = torch.aten.ceil.float %arg0 : !torch.float -> !torch.int
return %0 : !torch.int
}

18
test/Dialect/Torch/canonicalize.mlir
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@ -1191,21 +1191,3 @@ func @torch.aten.ge.float$different_value() -> !torch.bool {
%2 = torch.aten.ge.float %float4, %float4_0: !torch.float, !torch.float -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.ceil.float$fold_cst() -> !torch.int {
// CHECK: %[[CST2:.*]] = torch.constant.int 2
// CHECK: return %[[CST2]] : !torch.int
func @torch.aten.ceil.float$fold_cst() -> !torch.int {
%float = torch.constant.float 1.5
%1 = torch.aten.ceil.float %float : !torch.float -> !torch.int
return %1 : !torch.int
}
// CHECK-LABEL: func @torch.aten.ceil.float$no_fold(
// CHECK-SAME: %[[ARG:.*]]: !torch.float) -> !torch.int {
// CHECK: %[[RESULT:.*]] = torch.aten.ceil.float %[[ARG]] : !torch.float -> !torch.int
// CHECK: return %[[RESULT]] : !torch.int
func @torch.aten.ceil.float$no_fold(%arg0 : !torch.float) -> !torch.int {
%1 = torch.aten.ceil.float %arg0 : !torch.float -> !torch.int
return %1 : !torch.int
}