[MLIR][TORCH] Add e2e support for aten.randint

-- This commit adds e2e support for aten.randint by decomposing it into
   an aten.randint.low by setting low=0.

Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>

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

@@ -3742,6 +3742,34 @@ def Torch_AtenRandintLowOp : Torch_Op<"aten.randint.low", [
   }];
 }
 
+def Torch_AtenRandintOp : Torch_Op<"aten.randint", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::randint : (int, int[], int?, int?, Device?, bool?) -> (Tensor)`";
+  let arguments = (ins
+    Torch_IntType:$high,
+    AnyTorchListOfTorchIntType:$size,
+    AnyTorchOptionalIntType:$dtype,
+    AnyTorchOptionalIntType:$layout,
+    AnyTorchOptionalDeviceType:$device,
+    AnyTorchOptionalBoolType:$pin_memory
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenRandintOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 6, 1);
+    }
+    void AtenRandintOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 6, 1);
+    }
+  }];
+}
+
 def Torch_AtenBernoulliTensorOp : Torch_Op<"aten.bernoulli.Tensor", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -6881,6 +6881,9 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "  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"
+"  func.func @\"__torch_mlir_shape_fn.aten.randint\"(%arg0: !torch.int, %arg1: !torch.list<int>, %arg2: !torch.optional<int>, %arg3: !torch.optional<int>, %arg4: !torch.optional<Device>, %arg5: !torch.optional<bool>) -> !torch.list<int> {\n"
+"    return %arg1 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.randn\"(%arg0: !torch.list<int>, %arg1: !torch.optional<int>, %arg2: !torch.optional<int>, %arg3: !torch.optional<Device>, %arg4: !torch.optional<bool>) -> !torch.list<int> {\n"
 "    return %arg0 : !torch.list<int>\n"
 "  }\n"

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -3814,6 +3814,28 @@ public:
 };
 } // namespace
 
+namespace {
+class DecomposeAtenRandintOp : public OpRewritePattern<AtenRandintOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenRandintOp op,
+                                PatternRewriter &rewriter) const override {
+
+    Location loc = op.getLoc();
+    Type resultType = op.getType();
+
+    Value low = rewriter.create<Torch::ConstantIntOp>(
+        loc, rewriter.getI64IntegerAttr(0));
+    
+    rewriter.replaceOpWithNewOp<AtenRandintLowOp>(
+        op, resultType, low, op.getHigh(), op.getSize(), op.getDtype(), op.getLayout(),
+        op.getDevice(), op.getPinMemory());
+    
+    return success();
+  }
+};
+} // namespace
+
 namespace {
 // Decompose `aten.varMean.correction` op into `aten.var.correction` and
 // `aten.mean.dim` op.
@@ -4287,6 +4309,7 @@ public:
         patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenMseLossOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenNormScalarOptDimOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenRandintOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenRandintLowOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenVarMeanCorrectionOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposePrimsConvertElementTypeOp>(patterns);

lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp

@@ -461,6 +461,7 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
   target.addIllegalOp<AtenIndexTensorHackedTwinOp>();
   target.addIllegalOp<AtenMseLossOp>();
   target.addIllegalOp<AtenRandintLowOp>();
+  target.addIllegalOp<AtenRandintOp>();
   target.addIllegalOp<AtenVarMeanCorrectionOp>();
   target.addIllegalOp<PrimsConvertElementTypeOp>();
   target.addIllegalOp<PrimsVarOp>();

lib/Dialect/Torch/Transforms/RefineTypes.cpp

@@ -1157,6 +1157,17 @@ void TypeAnalysis::visitOperation(Operation *op,
     return;
   }
 
+  if (auto randInt = dyn_cast<AtenRandintOp>(op)) {
+    auto knowledge =
+        ValueKnowledge::getTensorPessimisticValueState(op->getContext());
+    Type defaultDtype =
+        IntegerType::get(op->getContext(), 64, IntegerType::Signed);
+    knowledge.dtype =
+        getDtypeOrDefault(op->getContext(), randInt.getDtype(), defaultDtype);
+    incorporateKnowledge(randInt.getResult(), knowledge);
+    return;
+  }
+
   if (isa<AtenVarMeanCorrectionOp, AtenVarMeanOp>(op)) {
     auto input = operands[0]->getValue();
     auto knowledge =

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

@@ -625,6 +625,9 @@ def aten〇randn_like〡shape(self: List[int], dtype: Optional[int] = None, layo
 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
 
+def aten〇randint〡shape(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
+
 def aten〇randn〡shape(size: List[int], dtype: Optional[int] = None, 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

@@ -334,6 +334,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit("aten::bernoulli_.float : (Tensor, float, Generator?) -> (Tensor)")
     emit("aten::bernoulli.p : (Tensor, float, Generator?) -> (Tensor)")
     emit("aten::randint.low : (int, int, int[], int?, int?, Device?, bool?) -> (Tensor)")
+    emit("aten::randint : (int, int[], int?, int?, Device?, bool?) -> (Tensor)")
     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)")

python/torch_mlir_e2e_test/test_suite/rng.py

@@ -329,6 +329,65 @@ def RandIntLowDtypeModule_basic(module, tu: TestUtils):
 
 # ==============================================================================
 
+class RandIntModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+    ])
+    def forward(self):
+        a = torch.ops.aten.randint(high=1000, size=[1024, 1024])
+        mean = torch.mean(a.to(torch.float32))
+        return mean
+
+
+@register_test_case(module_factory=lambda: RandIntModule())
+def RandIntModule_basic(module, tu: TestUtils):
+    module.forward()
+
+# ==============================================================================
+
+class RandIntDtypeModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+    ])
+    def forward(self):
+        a = torch.ops.aten.randint(high=1000, size=[128, 256, 512], dtype=torch.float64)
+        mean = torch.mean(a.to(torch.float32))
+        return mean
+
+
+@register_test_case(module_factory=lambda: RandIntDtypeModule())
+def RandIntDtypeModule_basic(module, tu: TestUtils):
+    module.forward()
+
+# ==============================================================================
+
+class RandIntPinMemoryModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+    ])
+    def forward(self):
+        a = torch.ops.aten.randint(high=1000, size=[128, 256, 512], pin_memory=False)
+        mean = torch.mean(a.to(torch.float32))
+        return mean
+
+
+@register_test_case(module_factory=lambda: RandIntPinMemoryModule())
+def RandIntPinMemoryModule_basic(module, tu: TestUtils):
+    module.forward()
+
+# ==============================================================================
 
 class RandnModule(torch.nn.Module):