huge refactor for high RewritePatterns usage and less ad-hoc cpp code

remove Spatial many ops in favor of tensor ops like in pim

src/PIM/Dialect/Pim/PimOpsVerify.cpp

@@ -46,12 +46,47 @@ static LogicalResult verifyTensorCommunication(Operation* op, Type type, ArrayRe
   return success();
 }
 
+static LogicalResult
+verifyTensorBatchCommunication(Operation* op, Type type, ArrayRef<int32_t> coreIds, StringRef kind) {
+  if (coreIds.empty())
+    return op->emitError() << kind << " must carry at least one chunk";
+
+  auto coreBatchOp = op->getParentOfType<PimCoreBatchOp>();
+  if (!coreBatchOp)
+    return op->emitError() << kind << " must be nested inside pim.core_batch";
+
+  int32_t laneCount = coreBatchOp.getLaneCount();
+  if (laneCount <= 0)
+    return op->emitError() << kind << " requires a positive parent laneCount";
+  if (coreIds.size() % static_cast<size_t>(laneCount) != 0)
+    return op->emitError() << kind << " core id count must be divisible by the parent laneCount";
+
+  auto shapedType = dyn_cast<ShapedType>(type);
+  if (!shapedType || !shapedType.hasStaticShape())
+    return op->emitError() << kind << " requires a static shaped tensor or memref";
+
+  int64_t elementBits = shapedType.getElementTypeBitWidth();
+  if (elementBits <= 0 || elementBits % 8 != 0)
+    return op->emitError() << kind << " requires byte-sized elements";
+
+  int64_t chunkCount = static_cast<int64_t>(coreIds.size()) / laneCount;
+  int64_t totalBytes = shapedType.getNumElements() * elementBits / 8;
+  if (totalBytes % chunkCount != 0)
+    return op->emitError() << kind << " tensor byte size must be divisible by the chunk count per lane";
+
+  return success();
+}
+
 } // namespace
 
 LogicalResult PimSendTensorOp::verify() {
   return verifyTensorCommunication(getOperation(), getInput().getType(), getTargetCoreIds(), "send_tensor");
 }
 
+LogicalResult PimSendTensorBatchOp::verify() {
+  return verifyTensorBatchCommunication(getOperation(), getInput().getType(), getTargetCoreIds(), "send_tensor_batch");
+}
+
 LogicalResult PimReceiveTensorOp::verify() {
   if (failed(verifyCompatibleShapedTypes(
         getOperation(), getOutputBuffer().getType(), getOutput().getType(), "output buffer and output must match")))
@@ -60,6 +95,15 @@ LogicalResult PimReceiveTensorOp::verify() {
   return verifyTensorCommunication(getOperation(), getOutput().getType(), getSourceCoreIds(), "receive_tensor");
 }
 
+LogicalResult PimReceiveTensorBatchOp::verify() {
+  if (failed(verifyCompatibleShapedTypes(
+        getOperation(), getOutputBuffer().getType(), getOutput().getType(), "output buffer and output must match")))
+    return failure();
+
+  return verifyTensorBatchCommunication(
+    getOperation(), getOutput().getType(), getSourceCoreIds(), "receive_tensor_batch");
+}
+
 LogicalResult PimConcatOp::verify() {
   if (getInputs().empty())
     return emitError("requires at least one input");