Add llama and resnet emitc tests

include/ttmlir/Dialect/TTNN/Utils/TransformUtils.h

@@ -13,7 +13,7 @@
 
 namespace mlir::tt::ttnn::utils {
 // Get or insert device for the given operation.
-GetDeviceOp getOrInsertDevice(mlir::PatternRewriter &rewriter,
+GetDeviceOp getOrInsertDevice(mlir::RewriterBase &rewriter,
                               mlir::Operation *op);
 
 // Helper method to insert a ToLayoutOp to convert the input operand to the

lib/Conversion/TTNNToEmitC/TTNNToEmitC.cpp

@@ -604,6 +604,49 @@ class RepeatOpConversionPattern
   }
 };
 
+// RepeatInterleave op conversion pattern
+//
+class RepeatInterleaveOpConversionPattern
+    : public TTNNToEmitCBaseOpConversionPattern<ttnn::RepeatInterleaveOp> {
+public:
+  using TTNNToEmitCBaseOpConversionPattern<
+      ttnn::RepeatInterleaveOp>::TTNNToEmitCBaseOpConversionPattern;
+
+  LogicalResult
+  matchAndRewrite(ttnn::RepeatInterleaveOp repeatInterleaveOp,
+                  ttnn::RepeatInterleaveOp::Adaptor adaptor,
+                  ConversionPatternRewriter &rewriter) const override {
+    // Create operands vector
+    //
+    llvm::SmallVector<Value, 2> operands{
+        adaptor.getOperands()[0],
+    };
+
+    // Create ArrayAttr object holding attributes and pointers to operands
+    //
+    ArrayAttr arrayAttrs = rewriter.getArrayAttr({
+        rewriter.getIndexAttr(0), // input tensor
+        repeatInterleaveOp.getRepeatsAttr(), repeatInterleaveOp.getDimAttr(),
+        repeatInterleaveOp.getMemoryConfig().has_value()
+            ? (operands.push_back(ttnn_to_emitc::utils::createMemoryConfigOp(
+                                      rewriter,
+                                      repeatInterleaveOp.getMemoryConfigAttr(),
+                                      repeatInterleaveOp.getLoc())
+                                      ->getResult(0)),
+               mlir::cast<Attribute>(rewriter.getIndexAttr(1)))
+            : ttnn_to_emitc::utils::createStdNullopt(
+                  rewriter), // ttnn::MemoryConfig
+    });
+
+    rewriter.replaceOpWithNewOp<emitc::CallOpaqueOp>(
+        repeatInterleaveOp,
+        this->getTypeConverter()->convertType(repeatInterleaveOp.getType()),
+        this->convertOpName(repeatInterleaveOp), arrayAttrs, nullptr, operands);
+
+    return success();
+  }
+};
+
 // GetDeviceOp conversion pattern
 //
 namespace {
@@ -1320,7 +1363,7 @@ void populateTTNNToEmitCPatterns(mlir::MLIRContext *ctx,
   //
   patterns.add<TransposeOpConversionPattern, ConcatOpConversionPattern,
                ReshapeOpConversionPattern, RepeatOpConversionPattern,
-               DefaultOpConversionPattern<ttnn::RepeatInterleaveOp>,
+               RepeatInterleaveOpConversionPattern,
                DefaultOpConversionPattern<ttnn::SliceOp>,
                DefaultOpConversionPattern<ttnn::PermuteOp>,
                DefaultOpConversionPattern<ttnn::PadOp>>(typeConverter, ctx);

lib/Dialect/TTNN/Transforms/Passes.cpp

@@ -9,6 +9,7 @@
 #include "ttmlir/Dialect/TTNN/IR/TTNNOps.h"
 #include "ttmlir/Dialect/TTNN/IR/TTNNOpsAttrs.h"
 #include "ttmlir/Dialect/TTNN/IR/TTNNOpsTypes.h"
+#include "ttmlir/Dialect/TTNN/Utils/TransformUtils.h"
 #include "ttmlir/Dialect/TTNN/Utils/Utils.h"
 
 #include "mlir/Analysis/Liveness.h"
@@ -218,11 +219,24 @@ class TTNNCreateInputGenerators
 
         // Create a new tensor
         //
-        mlir::Value tensorValue = rewriter.create<ttnn::OnesOp>(
+        ttnn::OnesOp onesOp = rewriter.create<ttnn::OnesOp>(
             forwardFuncOp->getLoc(), tensorType, shapeAttr, dTypeAttr,
             tensorLayoutAttr, nullptr, nullptr);
 
-        generatedTensors.push_back(tensorValue);
+        // If tensor is meant to be on device, add ToDevice op
+        //
+        if (layoutAttr.isDeviceBufferType()) {
+          ttnn::GetDeviceOp device =
+              ttnn::utils::getOrInsertDevice(rewriter, onesOp);
+
+          mlir::Value tensorOnDevice = rewriter.create<ttnn::ToDeviceOp>(
+              forwardFuncOp->getLoc(), tensorType, onesOp.getResult(),
+              device.getResult(), nullptr);
+
+          generatedTensors.push_back(tensorOnDevice);
+        } else {
+          generatedTensors.push_back(onesOp.getResult());
+        }
       }
 
       // Return the generated tensors

lib/Dialect/TTNN/Utils/TransformUtils.cpp

@@ -11,7 +11,7 @@
 namespace mlir::tt::ttnn::utils {
 // Gets or inserts a GetDeviceOp at the top of the current block of the given
 // operation.
-GetDeviceOp getOrInsertDevice(PatternRewriter &rewriter, Operation *op) {
+GetDeviceOp getOrInsertDevice(RewriterBase &rewriter, Operation *op) {
   Block *block = op->getBlock();
   for (auto &op : block->getOperations()) {
     if (auto deviceOp = dyn_cast<ttnn::GetDeviceOp>(op)) {

test/ttmlir/EmitC/TTNN/tensor/repeat_interleave.mlir

@@ -0,0 +1,10 @@
+// RUN: ttmlir-opt --ttir-to-ttnn-backend-pipeline="system-desc-path=%system_desc_path%" %s > %t.mlir
+// RUN: ttmlir-translate --ttnn-to-flatbuffer %t.mlir > %basename_t.ttnn
+// RUN: ttmlir-opt --ttnn-modify-signatures-for-dylib --convert-ttnn-to-emitc %t.mlir > %t2.mlir
+// RUN: ttmlir-translate --mlir-to-cpp %t2.mlir > %basename_t.cpp
+
+func.func @repeat_interleave(%arg0: tensor<4x6xf32>) -> tensor<4x24xf32> {
+  %0 = tensor.empty() : tensor<4x24xf32>
+  %1 = "ttir.repeat_interleave"(%arg0, %0) {repeats = 4 : ui32, dim = 1 : si32} : (tensor<4x6xf32>, tensor<4x24xf32>) -> tensor<4x24xf32>
+  return %1 : tensor<4x24xf32>
+}

tools/ttnn-standalone/ci_compile_dylib.py

@@ -145,7 +145,6 @@ def compile_shared_object(cpp_file_path, output_dir):
         destination_path = os.path.join(output_dir, output_file_name)
         shutil.copy2(compiled_so_path, destination_path)
         print(f"  Successfully copied compiled file to {destination_path}.")
-        os.remove(source_cpp_path)
     except subprocess.CalledProcessError as e:
         print(f"  Error during build process: {e}")
         print(e.stderr)

tools/ttnn-standalone/ttnn-precompiled.hpp

@@ -13,6 +13,7 @@
 #include "operations/creation.hpp"
 #include "operations/data_movement/concat/concat.hpp"
 #include "operations/data_movement/repeat/repeat.hpp"
+#include "operations/data_movement/repeat_interleave/repeat_interleave.hpp"
 #include "operations/data_movement/transpose/transpose.hpp"
 #include "operations/eltwise/binary/binary.hpp"
 #include "operations/eltwise/binary/binary_composite.hpp"