Add e2e linalg support for aten.atan (#2070)

* new atan op

* update shape

---------

Co-authored-by: Ze Zhang <ze.zhang@getcruise.com>

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

@@ -700,6 +700,51 @@ def Torch_AtenCos_Op : Torch_Op<"aten.cos_", [
   }];
 }
 
+def Torch_AtenAtanOp : Torch_Op<"aten.atan", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::atan : (Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenAtanOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 1);
+    }
+    void AtenAtanOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 1);
+    }
+  }];
+}
+
+def Torch_AtenAtan_Op : Torch_Op<"aten.atan_", [
+    IsTrailingUnderscoreInplaceVariant,
+    AllowsTypeRefinement
+  ]> {
+  let summary = "Generated op for `aten::atan_ : (Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenAtan_Op::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 1);
+    }
+    void AtenAtan_Op::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 1);
+    }
+  }];
+}
+
 def Torch_AtenAtan2Op : Torch_Op<"aten.atan2", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Conversion/TorchToLinalg/Uncategorized.cpp

@@ -229,6 +229,10 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
     return createCalculationForMathOpWithDtypeConversion<math::CosOp>(
         b, converter, payloadArgs[0], op);
   }
+  if (isa<AtenAtanOp>(op)) {
+    return createCalculationForMathOpWithDtypeConversion<math::AtanOp>(
+        b, converter, payloadArgs[0], op);
+  }
   if (auto clone = dyn_cast<AtenCloneOp>(op)) {
     int64_t memoryFormat;
     if (!clone.getMemoryFormat().getType().isa<Torch::NoneType>() &&
@@ -1119,7 +1123,7 @@ public:
              AtenCloneOp, AtenSinOp, AtenCosOp, AtenNeScalarOp, AtenNegOp,
              AtenMaskedFillTensorOp, AtenLogicalOrOp, AtenLogicalAndOp,
              AtenLogicalXorOp, AtenLogicalNotOp, AtenTriuOp, AtenBitwiseNotOp,
-             AtenRoundOp, AtenFillScalarOp, AtenFillTensorOp>(op))
+             AtenRoundOp, AtenFillScalarOp, AtenFillTensorOp, AtenAtanOp>(op))
       return rewriter.notifyMatchFailure(op, "not a supported elementwise op");
 
     if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
@@ -1595,7 +1599,7 @@ void mlir::torch::torch_to_linalg::populateUncategorizedPatternsAndLegality(
       AtenWhereSelfOp, AtenGtTensorOp, AtenGeTensorOp, AtenEqTensorOp,
       AtenLtTensorOp, AtenLeTensorOp, AtenThresholdOp, AtenThresholdBackwardOp,
       AtenHardtanhBackwardOp, AtenCloneOp, AtenSinOp, AtenCosOp, AtenNeScalarOp,
-      AtenMaskedFillTensorOp, AtenLogicalOrOp, AtenLogicalAndOp,
+      AtenMaskedFillTensorOp, AtenLogicalOrOp, AtenLogicalAndOp, AtenAtanOp,
       AtenLogicalXorOp, AtenLogicalNotOp, AtenTriuOp, AtenRemainderScalarOp,
       AtenBitwiseNotOp, AtenRoundOp, AtenFillScalarOp, AtenFillTensorOp>();
   patterns.add<ConvertElementwiseOp>(typeConverter, context);

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -6115,6 +6115,10 @@ 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.atan\"(%arg0: !torch.list<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.tanh\"(%arg0: !torch.list<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"

lib/Dialect/Torch/Transforms/RefineTypes.cpp

@@ -664,10 +664,10 @@ void TypeAnalysis::visitOperation(Operation *op,
   }
 
   // Dtype is always float32, except for bfloat16, float16, float64 and nullptr.
-  if (isa<AtenTanhOp, AtenExpOp, AtenSinOp, AtenCosOp, AtenSigmoidOp,
+  if (isa<AtenAtanOp, AtenTanhOp, AtenExpOp, AtenSinOp, AtenCosOp,
-          AtenReciprocalOp, AtenLogOp, AtenSqrtOp, AtenLog2Op, AtenLog1pOp,
+          AtenSigmoidOp, AtenReciprocalOp, AtenLogOp, AtenSqrtOp, AtenLog2Op,
-          AtenRsqrtOp, AtenErfOp, AtenSoftplusOp, AtenFrobeniusNormDimOp,
+          AtenLog1pOp, AtenRsqrtOp, AtenErfOp, AtenSoftplusOp,
-          PrimsSqrtOp>(op)) {
+          AtenFrobeniusNormDimOp, PrimsSqrtOp>(op)) {
     ValueKnowledge knowledge =
         ValueKnowledge::getTensorPessimisticValueState(op->getContext());
     Type dtype = operands[0]->getValue().dtype;

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

@@ -46,6 +46,9 @@ def _embedding_bag_helper(weight: List[int], indices: List[int], offsets: List[i
 def aten〇triu〡shape(self: List[int], diagonal: int = 0) -> List[int]:
     return upstream_shape_functions.unary(self)
 
+def aten〇atan〡shape(self: List[int]) -> List[int]:
+    return upstream_shape_functions.unary(self)
+
 def aten〇tanh〡shape(self: List[int]) -> List[int]:
     return upstream_shape_functions.unary(self)
 

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

@@ -255,6 +255,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
             "aten::exp : (Tensor) -> (Tensor)",
             "aten::expm1 : (Tensor) -> (Tensor)",
             "aten::cos : (Tensor) -> (Tensor)",
+            "aten::atan : (Tensor) -> (Tensor)",
             "aten::atan2 : (Tensor, Tensor) -> (Tensor)",
             "aten::neg : (Tensor) -> (Tensor)",
             "aten::floor : (Tensor) -> (Tensor)",

python/torch_mlir_e2e_test/test_suite/elementwise.py

@@ -933,6 +933,51 @@ def ElementwiseMishModule_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class ElementwiseAtanTensorFloatModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.float32, True),
+    ])
+    def forward(self, a):
+        return torch.atan(a)
+
+
+@register_test_case(module_factory=lambda: ElementwiseAtanTensorFloatModule())
+def ElementwiseAtanTensorFloatModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(4, 4))
+
+
+# ==============================================================================
+
+
+class ElementwiseAtanTensorIntModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1], torch.int32, True),
+    ])
+    def forward(self, a):
+        return torch.atan(a)
+
+
+@register_test_case(module_factory=lambda: ElementwiseAtanTensorIntModule())
+def ElementwiseAtanTensorIntModule_basic(module, tu: TestUtils):
+    module.forward(
+        tu.randint(4, low=1, high=10).type(torch.int32))
+
+
+# ==============================================================================
+
+
 class ElementwiseAtan2TensorFloatModule(torch.nn.Module):
 
     def __init__(self):