[MLIR][TORCH] Add decomposition for aten.randn_like op

This commit decomposes aten.randn_like op into aten.randn.generator op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>

e2e_testing/xfail_sets.py

@@ -787,4 +787,6 @@ LTC_XFAIL_SET = {
     "ElementwisePreluModule_basic",
     "VarMeanBiasedModule_basic",
     "VarMeanUnbiasedModule_basic",
+    "RandnLikeModule_basic",
+    "RandnLikeDtypeModule_basic",
 }

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

@@ -3771,6 +3771,34 @@ def Torch_AtenRandnGeneratorOp : Torch_Op<"aten.randn.generator", [
   }];
 }
 
+def Torch_AtenRandnLikeOp : Torch_Op<"aten.randn_like", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::randn_like : (Tensor, int?, int?, Device?, bool?, int?) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    AnyTorchOptionalIntType:$dtype,
+    AnyTorchOptionalIntType:$layout,
+    AnyTorchOptionalDeviceType:$device,
+    AnyTorchOptionalBoolType:$pin_memory,
+    AnyTorchOptionalIntType:$memory_format
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenRandnLikeOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 6, 1);
+    }
+    void AtenRandnLikeOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 6, 1);
+    }
+  }];
+}
+
 def Torch_AtenTriuOp : Torch_Op<"aten.triu", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -6639,6 +6639,9 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "  func.func @\"__torch_mlir_shape_fn.aten.rand_like\"(%arg0: !torch.list<int>, %arg1: !torch.optional<int>, %arg2: !torch.optional<int>, %arg3: !torch.optional<Device>, %arg4: !torch.optional<bool>, %arg5: !torch.optional<int>) -> !torch.list<int> {\n"
 "    return %arg0 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.randn_like\"(%arg0: !torch.list<int>, %arg1: !torch.optional<int>, %arg2: !torch.optional<int>, %arg3: !torch.optional<Device>, %arg4: !torch.optional<bool>, %arg5: !torch.optional<int>) -> !torch.list<int> {\n"
+"    return %arg0 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.randint.low\"(%arg0: !torch.int, %arg1: !torch.int, %arg2: !torch.list<int>, %arg3: !torch.optional<int>, %arg4: !torch.optional<int>, %arg5: !torch.optional<Device>, %arg6: !torch.optional<bool>) -> !torch.list<int> {\n"
 "    return %arg2 : !torch.list<int>\n"
 "  }\n"

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -3535,6 +3535,40 @@ public:
 };
 } // namespace
 
+namespace {
+// Decompose `aten.randn_like` op into `aten.randn.generator` op.
+class DecomposeAtenRandnLikeOp : public OpRewritePattern<AtenRandnLikeOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenRandnLikeOp op,
+                                PatternRewriter &rewriter) const override {
+    // Only `none`, `contiguous` and `preserve` memory_format is supported.
+    if (!op.getMemoryFormat().getType().isa<Torch::NoneType>()) {
+      int64_t memoryFormat;
+      if (!matchPattern(op.getMemoryFormat(),
+                        m_TorchConstantInt(&memoryFormat)))
+        return rewriter.notifyMatchFailure(
+            op, "unimplemented: the memory format should be specified in "
+                "an integer constant");
+      if (memoryFormat != torch_upstream::MemoryFormat::Contiguous &&
+          memoryFormat != torch_upstream::MemoryFormat::Preserve)
+        return rewriter.notifyMatchFailure(
+            op, "unimplemented: only none, contiguous and preserve "
+                "memory_format is supported");
+    }
+    Value none = rewriter.create<Torch::ConstantNoneOp>(op.getLoc());
+    auto sizeListType =
+        Torch::ListType::get(Torch::IntType::get(op.getContext()));
+    Value sizeList =
+        rewriter.create<AtenSizeOp>(op.getLoc(), sizeListType, op.getSelf());
+    rewriter.replaceOpWithNewOp<AtenRandnGeneratorOp>(
+        op, op.getType(), sizeList, /*generator=*/none, op.getDtype(),
+        op.getLayout(), op.getDevice(), op.getPinMemory());
+    return success();
+  }
+};
+} // namespace
+
 namespace {
 class DecomposeAtenVarMeanOp : public OpRewritePattern<AtenVarMeanOp> {
 public:
@@ -3704,6 +3738,7 @@ public:
     addPatternIfTargetOpIsIllegal<DecomposePrimsSqrtOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenRandnOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenRandnGeneratorOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenRandnLikeOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenVarMeanOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenLeakyReluOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenLeakyReluBackwardOp>(patterns);

lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp

@@ -442,6 +442,7 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
   target.addIllegalOp<PrimsSqrtOp>();
   target.addIllegalOp<AtenRandnOp>();
   target.addIllegalOp<AtenRandnGeneratorOp>();
+  target.addIllegalOp<AtenRandnLikeOp>();
   target.addIllegalOp<AtenVarMeanOp>();
   for (std::string opName : backendLegalOps) {
     target.addLegalOp(OperationName(opName, context));

lib/Dialect/Torch/Transforms/RefineTypes.cpp

@@ -1039,6 +1039,9 @@ void TypeAnalysis::visitOperation(Operation *op,
   } else if (auto randLike = dyn_cast<AtenRandLikeOp>(op)) {
     visitConstantTensorAllocLikeOp<AtenRandLikeOp>(randLike, operands);
     return;
+  } else if (auto randLike = dyn_cast<AtenRandnLikeOp>(op)) {
+    visitConstantTensorAllocLikeOp<AtenRandnLikeOp>(randLike, operands);
+    return;
   } else if (auto toCopy = dyn_cast<Aten_ToCopyOp>(op)) {
     visitConstantTensorAllocLikeOp<Aten_ToCopyOp>(toCopy, operands);
     return;

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

@@ -616,6 +616,9 @@ def aten〇cumsum〡shape(self: List[int], dim: int, dtype: Optional[int] = None
 def aten〇rand_like〡shape(self: List[int], dtype: Optional[int] = None, layout: Optional[int] = None, device: Optional[device] = None, pin_memory: Optional[bool] = None, memory_format: Optional[int] = None) -> List[int]:
     return self
 
+def aten〇randn_like〡shape(self: List[int], dtype: Optional[int] = None, layout: Optional[int] = None, device: Optional[device] = None, pin_memory: Optional[bool] = None, memory_format: Optional[int] = None) -> List[int]:
+    return self
+
 def aten〇randint〇low〡shape(low: int, high: int, size: List[int], dtype: Optional[int] = 4, layout: Optional[int] = None, device: Optional[device] = None, pin_memory: Optional[bool] = None) -> List[int]:
     return size
 

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

@@ -335,6 +335,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit_with_mutating_variants("aten::bernoulli.Tensor : (Tensor, Tensor, Generator?) -> (Tensor)")
     emit("aten::randn : (int[], int?, int?, Device?, bool?) -> (Tensor)")
     emit("aten::randn.generator : (int[], Generator?, int?, int?, Device?, bool?) -> (Tensor)")
+    emit("aten::randn_like : (Tensor, int?, int?, Device?, bool?, int?) -> (Tensor)")
 
     emit_with_mutating_variants("aten::triu : (Tensor, int) -> (Tensor)")
     emit_with_mutating_variants("aten::round : (Tensor) -> (Tensor)", has_folder=True)

python/torch_mlir_e2e_test/test_suite/rng.py

@@ -364,3 +364,46 @@ class RandnGeneratorModule(torch.nn.Module):
 @register_test_case(module_factory=lambda: RandnGeneratorModule())
 def RandnGeneratorModule_basic(module, tu: TestUtils):
     module.forward()
+
+
+# ==============================================================================
+
+class RandnLikeModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1, -1], torch.float64, True),
+    ])
+    def forward(self, x):
+        a = torch.ops.aten.randn_like(x)
+        std = torch.std(a)
+        return std
+
+
+@register_test_case(module_factory=lambda: RandnLikeModule())
+def RandnLikeModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(4, 512, 1024).double())
+
+# ==============================================================================
+
+class RandnLikeDtypeModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.float64, True),
+    ])
+    def forward(self, x):
+        a = torch.ops.aten.randn_like(x, dtype=torch.float32)
+        std = torch.std(a)
+        return std
+
+
+@register_test_case(module_factory=lambda: RandnLikeDtypeModule())
+def RandnLikeDtypeModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(256, 1024).double())