[TORCH][MLIR] Add E2E support for aten._softmax operation. (#431)

Signed-Off-By: Prateek Gupta <[email protected]>

Author: gprateek93

e2e_testing/torchscript/basic.py

@@ -457,6 +457,23 @@ def forward(self, tensor):
 def SoftmaxIntModule_basic(module, tu: TestUtils):
     module.forward(torch.randn(3, 2, 4))
 
+class _SoftmaxModule(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._softmax(tensor, 0, False)
+
+
+@register_test_case(module_factory=lambda: _SoftmaxModule())
+def _SoftmaxModule_basic(module, tu: TestUtils):
+    module.forward(torch.randn(3, 2, 4))
+
 
 class SoftmaxIntNegDimModule(torch.nn.Module):
     def __init__(self):

include/torch-mlir/Dialect/Torch/IR/GeneratedAtenOps.td

@@ -1392,6 +1392,22 @@ def Torch_AtenSqrtOp : Torch_Op<"aten.sqrt", [
   let assemblyFormat = "$self attr-dict `:` type($self) `->` type($result)";
 }
 
+def Torch_Aten_SoftmaxOp : Torch_Op<"aten._softmax", [
+    AllowsTypeRefinement,
+    HasValueSemantics
+  ]> {
+  let summary = "Generated op for `aten::_softmax : (Tensor, int, bool) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    Torch_IntType:$dim,
+    Torch_BoolType:$half_to_float
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let assemblyFormat = "$self `,` $dim `,` $half_to_float attr-dict `:` type($self) `,` type($dim) `,` type($half_to_float) `->` type($result)";
+}
+
 def Torch_AtenUnsqueezeOp : Torch_Op<"aten.unsqueeze", [
     AllowsTypeRefinement
   ]> {

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -126,16 +126,34 @@ class DecomposeAtenSizeOp : public OpRewritePattern<AtenSizeOp> {
 };
 } // namespace
 
+// Calculates the softmax function on the given `input` tensor. Softmax(x) =
+// exp(x)/sum(exp(x)).
+template <typename OpTy>
+static Value getSoftmaxResult(OpTy op, Type resultType,
+                              PatternRewriter &rewriter) {
+  Location loc = op.getLoc();
+  Value dim = op.dim();
+  Value self = op.self();
+
+  // exp(x)
+  Value exp = rewriter.create<AtenExpOp>(loc, resultType, self);
+  // sum(exp(x))
+  Value sum =
+      createSumAlongDimension(rewriter, loc, op, exp, dim, /*keepDim=*/true);
+  if (!sum)
+    return nullptr;
+  // exp(x) / sum(exp(x))
+  return rewriter.create<AtenDivTensorOp>(loc, resultType, exp, sum);
+}
+
 // Decompose softmax into: exp(x) / sum(exp(x))
 namespace {
 class DecomposeAtenSoftmaxIntOp : public OpRewritePattern<AtenSoftmaxIntOp> {
 public:
   using OpRewritePattern::OpRewritePattern;
   LogicalResult matchAndRewrite(AtenSoftmaxIntOp op,
                                 PatternRewriter &rewriter) const override {
-    Location loc = op.getLoc();
     Value self = op.self();
-    Value dim = op.dim();
     if (!op.dtype().getType().isa<Torch::NoneType>())
       return rewriter.notifyMatchFailure(
           op, "Unimplemented non-None dtype for softmax");
@@ -144,14 +162,40 @@ class DecomposeAtenSoftmaxIntOp : public OpRewritePattern<AtenSoftmaxIntOp> {
     if (!tensorType.hasDtype() || !tensorType.getDtype().isa<mlir::FloatType>())
       return rewriter.notifyMatchFailure(op, "Only support floating type");
 
-    // exp(x)
-    Value exp = rewriter.create<AtenExpOp>(loc, tensorType, self);
-    // sum(exp(x))
-    Value sum = createSumAlongDimension(rewriter, loc, op, exp, dim, /*keepDim=*/true);
-    if (!sum)
+    Value result = getSoftmaxResult(op, tensorType, rewriter);
+    if (!result)
       return failure();
-    // exp(x) / sum(exp(x))
-    Value result = rewriter.create<AtenDivTensorOp>(loc, tensorType, exp, sum);
+    rewriter.replaceOpWithNewOp<TensorStaticInfoCastOp>(op, op.getType(),
+                                                        result);
+    return success();
+  }
+};
+} // namespace
+
+namespace {
+class DecomposeAten_SoftmaxOp : public OpRewritePattern<Aten_SoftmaxOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(Aten_SoftmaxOp op,
+                                PatternRewriter &rewriter) const override {
+    Value self = op.self();
+    BaseTensorType tensorType = self.getType().cast<BaseTensorType>();
+    if (!tensorType.hasDtype() || !tensorType.getDtype().isa<mlir::FloatType>())
+      return rewriter.notifyMatchFailure(op, "Only support floating type");
+    bool halfToFloat;
+    if (!matchPattern(op.half_to_float(), m_TorchConstantBool(&halfToFloat)))
+      return rewriter.notifyMatchFailure(
+          op, "Expected a boolean value for half_to_float");
+
+    // Currently, setting `halfToFloat` is not supported as the E2E testing for
+    // the same is not present on CPU.
+    if (halfToFloat)
+      return rewriter.notifyMatchFailure(
+          op, "halfToFloat is currently not supported.");
+
+    Value result = getSoftmaxResult(op, tensorType, rewriter);
+    if (!result)
+      return op.emitError("failed to get softmax result");
     rewriter.replaceOpWithNewOp<TensorStaticInfoCastOp>(op, op.getType(),
                                                         result);
     return success();
@@ -406,6 +450,8 @@ class DecomposeComplexOpsPass
 
     patterns.add<DecomposeAtenSoftmaxIntOp>(context);
     target.addIllegalOp<AtenSoftmaxIntOp>();
+    patterns.add<DecomposeAten_SoftmaxOp>(context);
+    target.addIllegalOp<Aten_SoftmaxOp>();
     patterns.add<DecomposeAtenLogSoftmaxIntOp>(context);
     target.addIllegalOp<AtenLogSoftmaxIntOp>();
     patterns.add<DecomposeAtenExpandOp>(context);

lib/Dialect/Torch/Transforms/RefineTypes.cpp

@@ -418,6 +418,8 @@ class TypeAnalyzer : public ForwardDataFlowAnalysis<ValueKnowledge> {
       return visitAtenMatmulOp(matmul, operands);
     } else if (auto softmaxIntOp = dyn_cast<AtenSoftmaxIntOp>(op)) {
       return visitAtenSoftmaxLikeOp(softmaxIntOp, operands);
+    } else if (auto _softmaxOp = dyn_cast<Aten_SoftmaxOp>(op)) {
+      return visitAten_SoftmaxOp(_softmaxOp, operands);
     } else if (auto logSoftmaxIntOp = dyn_cast<AtenLogSoftmaxIntOp>(op)) {
       return visitAtenSoftmaxLikeOp(logSoftmaxIntOp, operands);
     } else if (auto numToTensorOp = dyn_cast<PrimNumToTensorScalarOp>(op)) {
@@ -541,6 +543,10 @@ class TypeAnalyzer : public ForwardDataFlowAnalysis<ValueKnowledge> {
   ChangeResult
   visitAtenAddCLikeOp(Operation *op,
                       ArrayRef<LatticeElement<ValueKnowledge> *> operands);
+
+  ChangeResult
+  visitAten_SoftmaxOp(Aten_SoftmaxOp op,
+                      ArrayRef<LatticeElement<ValueKnowledge> *> operands);
 };
 } // namespace
 
@@ -1332,21 +1338,40 @@ ChangeResult TypeAnalyzer::visitAtenEmbeddingOp(
   return getLatticeElement(op.getResult()).join(knowledge);
 }
 
+static ValueKnowledge
+getSameSizeAsInput(Operation *op,
+                   ArrayRef<LatticeElement<ValueKnowledge> *> operands) {
+  auto input = operands[0]->getValue();
+  auto knowledge =
+      ValueKnowledge::getNotNonePessimisticValueState(op->getContext());
+  knowledge.hasSizes = input.hasSizes;
+  knowledge.sizes = input.sizes;
+  return knowledge;
+}
 
 // Common template for softmax like ops, eg., log_softmax.
 template <typename OpTy>
 ChangeResult TypeAnalyzer::visitAtenSoftmaxLikeOp(
     OpTy op, ArrayRef<LatticeElement<ValueKnowledge> *> operands) {
   auto input = operands[0]->getValue();
   auto dtype = op.dtype();
-  auto knowledge =
-      ValueKnowledge::getNotNonePessimisticValueState(op->getContext());
-  knowledge.hasSizes = input.hasSizes;
-  knowledge.sizes = input.sizes;
+  ValueKnowledge knowledge = getSameSizeAsInput(op, operands);
   fillInDTypeGivenDTypeIntAndInputDType(knowledge, dtype, input.dtype);
   return getLatticeElement(op.getResult()).join(knowledge);
 }
 
+ChangeResult TypeAnalyzer::visitAten_SoftmaxOp(
+    Aten_SoftmaxOp op, ArrayRef<LatticeElement<ValueKnowledge> *> operands) {
+  auto input = operands[0]->getValue();
+  ValueKnowledge knowledge = getSameSizeAsInput(op, operands);
+  bool halfToFloat;
+  if (matchPattern(op.half_to_float(), m_TorchConstantBool(&halfToFloat))) {
+    knowledge.dtype =
+        halfToFloat ? Float32Type::get(op->getContext()) : input.dtype;
+  }
+  return getLatticeElement(op.getResult()).join(knowledge);
+}
+
 ChangeResult TypeAnalyzer::visitAtenBmmOp(
     AtenBmmOp op, ArrayRef<LatticeElement<ValueKnowledge> *> operands) {
   auto knowledge =

python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/torch_ods_gen.py

@@ -516,6 +516,7 @@ def emit_with_mutating_variants(key, **kwargs):
         emit("aten::mean.dim : (Tensor, int[], bool, int?) -> (Tensor)")
         emit("aten::__and__.Tensor : (Tensor, Tensor) -> (Tensor)")
         emit("aten::sqrt : (Tensor) -> (Tensor)")
+        emit("aten::_softmax : (Tensor, int, bool) -> (Tensor)")
 
         # Misc tensor ops.
         emit("aten::unsqueeze : (Tensor, int) -> (Tensor)")