Add Decompostion for `Aten_SafeSoftmaxOp` (#3708)

Co-authored-by: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
pull/3710/head
zjgarvey 2024-09-12 14:58:10 -07:00 committed by GitHub
parent edf725ef42
commit d61986cfcf
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8 changed files with 160 additions and 8 deletions

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@ -8370,6 +8370,31 @@ def Torch_Aten_SoftmaxOp : Torch_Op<"aten._softmax", [
}];
}
def Torch_Aten_SafeSoftmaxOp : Torch_Op<"aten._safe_softmax", [
AllowsTypeRefinement,
HasValueSemantics,
ReadOnly
]> {
let summary = "Generated op for `aten::_safe_softmax : (Tensor, int, int?) -> (Tensor)`";
let arguments = (ins
AnyTorchTensorType:$self,
Torch_IntType:$dim,
AnyTorchOptionalIntType:$dtype
);
let results = (outs
AnyTorchOptionalTensorType:$result
);
let hasCustomAssemblyFormat = 1;
let extraClassDefinition = [{
ParseResult Aten_SafeSoftmaxOp::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 3, 1);
}
void Aten_SafeSoftmaxOp::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 3, 1);
}
}];
}
def Torch_AtenMeanOp : Torch_Op<"aten.mean", [
AllowsTypeRefinement,
HasValueSemantics,

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@ -6772,6 +6772,10 @@ 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._safe_softmax\"(%arg0: !torch.list<int>, %arg1: !torch.int, %arg2: !torch.optional<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"
" }\n"
" func.func @\"__torch_mlir_shape_fn.aten.softmax.int\"(%arg0: !torch.list<int>, %arg1: !torch.int, %arg2: !torch.optional<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"
@ -15367,6 +15371,18 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
" }\n"
" return %1 : !torch.int\n"
" }\n"
" func.func @\"__torch_mlir_dtype_fn.aten._safe_softmax\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.int, %arg2: !torch.optional<int>) -> !torch.int {\n"
" %none = torch.constant.none\n"
" %0 = torch.aten.__isnot__ %arg2, %none : !torch.optional<int>, !torch.none -> !torch.bool\n"
" %1 = torch.prim.If %0 -> (!torch.int) {\n"
" %2 = torch.prim.unchecked_cast %arg2 : !torch.optional<int> -> !torch.int\n"
" torch.prim.If.yield %2 : !torch.int\n"
" } else {\n"
" %2:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
" torch.prim.If.yield %2#1 : !torch.int\n"
" }\n"
" return %1 : !torch.int\n"
" }\n"
" func.func @\"__torch_mlir_dtype_fn.aten._log_softmax\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.int, %arg2: !torch.bool) -> !torch.int {\n"
" %int6 = torch.constant.int 6\n"
" %none = torch.constant.none\n"

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@ -2148,6 +2148,62 @@ public:
};
} // namespace
// Ref:
// https://github.com/pytorch/pytorch/blob/5314ae2660a778b87987030182f787bb6cb092c0/aten/src/ATen/native/transformers/attention.cpp#L663-L673
namespace {
class DecomposeAten_SafeSoftmaxOp
: public OpRewritePattern<Aten_SafeSoftmaxOp> {
public:
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(Aten_SafeSoftmaxOp op,
PatternRewriter &rewriter) const override {
BaseTensorType resultTensorType = cast<BaseTensorType>(op.getType());
if (!resultTensorType.hasDtype() || !resultTensorType.hasSizes()) {
return rewriter.notifyMatchFailure(
op, "expected result type to have sizes and dtype");
}
SmallVector<int64_t> sizes(resultTensorType.getSizes());
int64_t dimInt;
if (!matchPattern(op.getDim(), m_TorchConstantInt(&dimInt)))
return rewriter.notifyMatchFailure(op, "Unsupported: non-constant dim");
dimInt = toPositiveDim(dimInt, sizes.size());
if (!isValidDim(dimInt, sizes.size()))
return rewriter.notifyMatchFailure(op, "dim int is not valid");
Location loc = op.getLoc();
Value softmax = rewriter.create<AtenSoftmaxIntOp>(
loc, op.getType(), op.getSelf(), op.getDim(), op.getDtype());
Type resultTensorDtype = resultTensorType.getDtype();
Value negInfinity = getConstantWithGivenDtypeAndValue(
rewriter, loc, -std::numeric_limits<double>::infinity(),
resultTensorDtype);
auto boolDtype = rewriter.getI1Type();
auto boolTensorType =
resultTensorType.getWithSizesAndDtype(sizes, boolDtype);
Value masked = rewriter.create<AtenEqScalarOp>(loc, boolTensorType,
op.getSelf(), negInfinity);
sizes[dimInt] = 1;
auto maskedRowsType =
resultTensorType.getWithSizesAndDtype(sizes, boolDtype);
Value cstTrue =
rewriter.create<Torch::ConstantBoolOp>(loc, rewriter.getBoolAttr(true));
Value maskedRows = rewriter.create<AtenAllDimOp>(
loc, maskedRowsType, masked, op.getDim(), cstTrue);
Value cstZero = getConstantWithGivenDtypeAndValue(rewriter, loc, 0.0,
resultTensorDtype);
rewriter.replaceOpWithNewOp<AtenWhereScalarSelfOp>(
op, resultTensorType, maskedRows, cstZero, softmax);
return success();
}
};
} // namespace
// Aten_SoftmaxBackwardDataOp(gradOutput, output, dim) =>
// newGrad = gradOutput * output
// result = newGrad - output * sum(newGrad, dim))
@ -9608,6 +9664,7 @@ public:
patterns);
addPatternIfTargetOpIsIllegal<DecomposeAtenSoftmaxIntOp>(patterns);
addPatternIfTargetOpIsIllegal<DecomposeAten_SoftmaxOp>(patterns);
addPatternIfTargetOpIsIllegal<DecomposeAten_SafeSoftmaxOp>(patterns);
addPatternIfTargetOpIsIllegal<DecomposeAten_LogSoftmaxOp>(patterns);
addPatternIfTargetOpIsIllegal<DecomposeAtenLogSoftmaxIntOp>(patterns);
addPatternIfTargetOpIsIllegal<DecomposeAtenLogSigmoidOp>(patterns);

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@ -371,6 +371,7 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
llvm::StringSet<> backendLegalOpsSet) {
target.addIllegalOp<AtenSoftmaxIntOp>();
target.addIllegalOp<Aten_SoftmaxOp>();
target.addIllegalOp<Aten_SafeSoftmaxOp>();
target.addIllegalOp<Aten_LogSoftmaxOp>();
target.addIllegalOp<AtenLogSoftmaxIntOp>();
target.addIllegalOp<AtenLogSigmoidOp>();

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@ -504,14 +504,6 @@ FX_IMPORTER_XFAIL_SET = {
"ViewCollapseDynamicWithAtenSizeIntModule_basic",
"ViewSizeFromOtherTensor_basic",
"WeightNormInterfaceModule_basic",
# REMOVE WHEN ENABLE_GQA IS ADDED
"ScaledDotProductAttentionBoolMaskModule_basic",
"ScaledDotProductAttentionDifferentCausalModule_basic",
"ScaledDotProductAttentionDifferentModule_basic",
"ScaledDotProductAttentionMaskModule_basic",
"ScaledDotProductAttentionSameCausalModule_basic",
"ScaledDotProductAttentionSameDynamicModule_basic",
"ScaledDotProductAttentionSameModule_basic",
}
FX_IMPORTER_CRASHING_SET = LINALG_CRASHING_SET | {
@ -826,6 +818,9 @@ FX_IMPORTER_STABLEHLO_XFAIL_SET = {
"ReplicationPad2dModule_top0",
"RsubInt0d_NumToTensor_Module_basic",
"ScalarImplicitFloatModule_basic",
# need aten.all.dim lowering to stablehlo
"SafeSoftmaxModule_basic",
"SafeSoftmaxNonNoneDtypeModule_basic",
# REMOVE WHEN ENABLE_GQA IS ADDED
"ScaledDotProductAttentionBoolMaskModule_basic",
"ScaledDotProductAttentionDifferentCausalModule_basic",
@ -2770,6 +2765,8 @@ ONNX_XFAIL_SET = {
"ReshapeAliasExpandModule_basic",
"ReshapeExpandModule_basic",
"Rot90DynamicDimsModule_basic",
"SafeSoftmaxModule_basic",
"SafeSoftmaxNonNoneDtypeModule_basic",
"ScalarConstantTupleModule_basic",
"ScalarImplicitFloatModule_basic",
"ScalarImplicitIntModule_basic",

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@ -348,6 +348,9 @@ def atenglu〡shape(self: List[int], dim: int = -1) -> List[int]:
def aten_softmax〡shape(self: List[int], dim: int, half_to_float: bool) -> List[int]:
return upstream_shape_functions.unary(self)
def aten_safe_softmax〡shape(self: List[int], dim: int, dtype: Optional[int] = None) -> List[int]:
return upstream_shape_functions.unary(self)
def atensoftmaxint〡shape(self: List[int], dim: int, dtype: Optional[int] = None) -> List[int]:
return upstream_shape_functions.unary(self)
@ -5426,6 +5429,12 @@ def aten_softmax〡dtype(self_rank_dtype: Tuple[int, int], dim: int, half_to_
return torch.float32
return self_dtype
def aten_safe_softmax〡dtype(self_rank_dtype: Tuple[int, int], dim: int, dtype: Optional[int] = None) -> int:
if dtype is not None:
return dtype
self_rank, self_dtype = self_rank_dtype
return self_dtype
@check_dtype_function(
# _check_tensors_with_the_same_dtype(num_of_tensors=1, dim=0, half_to_float=False) +
_check_tensors_with_the_same_dtype(

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@ -692,6 +692,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
emit("aten::__lshift__.Scalar : (Tensor, Scalar) -> (Tensor)")
emit("aten::__rshift__.Scalar : (Tensor, Scalar) -> (Tensor)")
emit("aten::_softmax : (Tensor, int, bool) -> (Tensor)")
emit("aten::_safe_softmax : (Tensor, int, int?) -> (Tensor)")
emit("aten::mean : (Tensor, int?) -> (Tensor)")
emit("aten::std : (Tensor, bool) -> (Tensor)")
emit("aten::std.dim : (Tensor, int[]?, bool, bool) -> (Tensor)")

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@ -1907,6 +1907,52 @@ def _LogSoftmaxModuleStable_basic(module, tu: TestUtils):
# ==============================================================================
class SafeSoftmaxModule(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args(
[
None,
([-1, -1, -1], torch.float32, True),
]
)
def forward(self, tensor):
return torch.ops.aten._safe_softmax(tensor, dim=0)
@register_test_case(module_factory=lambda: SafeSoftmaxModule())
def SafeSoftmaxModule_basic(module, tu: TestUtils):
module.forward(tu.rand(3, 2, 4))
# ==============================================================================
class SafeSoftmaxNonNoneDtypeModule(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args(
[
None,
([-1, -1, -1], torch.float32, True),
]
)
def forward(self, tensor):
return torch.ops.aten._safe_softmax(tensor, dim=2, dtype=torch.float64)
@register_test_case(module_factory=lambda: SafeSoftmaxNonNoneDtypeModule())
def SafeSoftmaxNonNoneDtypeModule_basic(module, tu: TestUtils):
module.forward(tu.rand(3, 2, 4))
# ==============================================================================
class SoftplusModule(torch.nn.Module):
def __init__(self):
super().__init__()