cose

backend-simulators/pim/pim-simulator/src/lib/instruction_set/isa.rs

@@ -258,24 +258,23 @@ where
 
     let (memory, crossbars) = core.get_memory_crossbar();
     let crossbar = crossbars.get_mut(group).unwrap();
-    let crossbar_stored_bytes = crossbar.stored_bytes();
-    let crossbar_byte_width = crossbar.width();
-
-    let crossbar_elem_width = crossbar_byte_width / size_of::<M>();
-    ensure!(
-        crossbar_byte_width % size_of::<M>() == 0,
-        "M not divisor of the crosbbar size"
-    );
-
     let crossbar_height = crossbar.height();
-    let crossbar_byte_size = crossbar_byte_width * crossbar_height;
+    let crossbar_stored_bytes = crossbar.stored_bytes();
+    let bytes_per_column = crossbar_height * size_of::<M>();
+    ensure!(bytes_per_column != 0, "crossbar height can not be zero");
+    ensure!(
+        crossbar_stored_bytes % bytes_per_column == 0,
+        "Stored crossbar bytes do not describe an integral number of columns"
+    );
+    let crossbar_elem_width = crossbar_stored_bytes / bytes_per_column;
+    ensure!(crossbar_elem_width != 0, "Crossbar contains no stored columns");
 
     let loads = memory
         .reserve_load(r1_val, crossbar_height * size_of::<F>())?
         .execute_load::<F>()?;
     let load = loads[0];
     let vec: Cow<[M]> = load.up();
-    let matrix = crossbar.load::<M>(crossbar_byte_size)?[0];
+    let matrix = crossbar.load::<M>(crossbar_stored_bytes)?[0];
 
     // --- FAER IMPLEMENTATION ---
 

src/PIM/Common/IR/AddressAnalysis.cpp

@@ -56,6 +56,22 @@ mlir::Value resolveLoopCarriedAliasImpl(mlir::Value value, const StaticValueKnow
         return resolveLoopCarriedAliasImpl(tiedOperand->get(), knowledge);
   }
 
+  if (auto forOp = mlir::dyn_cast<mlir::scf::ForOp>(definingOp)) {
+    auto result = mlir::dyn_cast<mlir::OpResult>(value);
+    if (result) {
+      auto yieldOp = mlir::dyn_cast<mlir::scf::YieldOp>(forOp.getBody()->getTerminator());
+      if (yieldOp && result.getResultNumber() < yieldOp.getNumOperands()) {
+        mlir::Value yieldedValue = resolveLoopCarriedAliasImpl(yieldOp.getOperand(result.getResultNumber()), knowledge);
+        if (auto blockArgument = mlir::dyn_cast<mlir::BlockArgument>(yieldedValue)) {
+          if (blockArgument.getOwner() == forOp.getBody() && blockArgument.getArgNumber() > 0
+              && static_cast<unsigned>(blockArgument.getArgNumber() - 1) < forOp.getInitArgs().size())
+            return resolveLoopCarriedAliasImpl(forOp.getInitArgs()[blockArgument.getArgNumber() - 1], knowledge);
+        }
+        return yieldedValue;
+      }
+    }
+  }
+
   if (auto castOp = mlir::dyn_cast<mlir::memref::CastOp>(definingOp))
     return resolveLoopCarriedAliasImpl(castOp.getSource(), knowledge);
   if (auto collapseOp = mlir::dyn_cast<mlir::memref::CollapseShapeOp>(definingOp))
@@ -512,6 +528,24 @@ llvm::FailureOr<ResolvedContiguousAddress> resolveContiguousAddressImpl(mlir::Va
       continue;
     }
 
+    if (auto ifOp = mlir::dyn_cast<mlir::scf::IfOp>(definingOp)) {
+      auto result = mlir::dyn_cast<mlir::OpResult>(value);
+      if (!result)
+        return mlir::failure();
+
+      auto condition = resolveIndexValueImpl(ifOp.getCondition(), knowledge);
+      if (failed(condition))
+        return mlir::failure();
+
+      mlir::Region& selectedRegion = *condition != 0 ? ifOp.getThenRegion() : ifOp.getElseRegion();
+      auto yieldOp = mlir::dyn_cast<mlir::scf::YieldOp>(selectedRegion.front().getTerminator());
+      if (!yieldOp || result.getResultNumber() >= yieldOp.getNumOperands())
+        return mlir::failure();
+
+      value = resolveLoopCarriedAliasImpl(yieldOp.getOperand(result.getResultNumber()), knowledge);
+      continue;
+    }
+
     if (auto subviewOp = mlir::dyn_cast<mlir::memref::SubViewOp>(definingOp)) {
       auto sourceType = mlir::dyn_cast<mlir::MemRefType>(subviewOp.getSource().getType());
       auto subviewType = mlir::dyn_cast<mlir::MemRefType>(subviewOp.getType());
@@ -622,6 +656,33 @@ llvm::FailureOr<CompiledAddressExpr> compileContiguousAddressExprImpl(mlir::Valu
       continue;
     }
 
+    if (auto ifOp = mlir::dyn_cast<mlir::scf::IfOp>(definingOp)) {
+      auto result = mlir::dyn_cast<mlir::OpResult>(value);
+      if (!result)
+        return mlir::failure();
+
+      auto thenYield = mlir::dyn_cast<mlir::scf::YieldOp>(ifOp.getThenRegion().front().getTerminator());
+      auto elseYield = mlir::dyn_cast<mlir::scf::YieldOp>(ifOp.getElseRegion().front().getTerminator());
+      if (!thenYield || !elseYield || result.getResultNumber() >= thenYield.getNumOperands()
+          || result.getResultNumber() >= elseYield.getNumOperands()) {
+        return mlir::failure();
+      }
+
+      auto thenAddress = compileContiguousAddressExprImpl(thenYield.getOperand(result.getResultNumber()));
+      auto elseAddress = compileContiguousAddressExprImpl(elseYield.getOperand(result.getResultNumber()));
+      if (failed(thenAddress) || failed(elseAddress) || thenAddress->base != elseAddress->base)
+        return mlir::failure();
+
+      auto condition = compileIndexValueImpl(ifOp.getCondition());
+      if (failed(condition))
+        return mlir::failure();
+
+      CompiledIndexExprNode selectExpr;
+      selectExpr.kind = CompiledIndexExprNode::Kind::Select;
+      selectExpr.operands = {*condition, thenAddress->byteOffset, elseAddress->byteOffset};
+      return CompiledAddressExpr {thenAddress->base, makeCompiledIndexExpr(std::move(selectExpr))};
+    }
+
     if (auto subviewOp = mlir::dyn_cast<mlir::memref::SubViewOp>(definingOp)) {
       auto sourceType = mlir::dyn_cast<mlir::MemRefType>(subviewOp.getSource().getType());
       auto subviewType = mlir::dyn_cast<mlir::MemRefType>(subviewOp.getType());

src/PIM/Common/Support/ReportUtils.cpp

@@ -17,6 +17,20 @@ std::fstream openReportFileWithExtension(const std::string& name, llvm::StringRe
 
 std::fstream openReportFile(const std::string& name) { return openReportFileWithExtension(name, "txt"); }
 
+std::fstream openAppendedReportFileWithExtension(const std::string& name, llvm::StringRef extension) {
+  std::string outputDir = getOutputDir();
+  if (outputDir.empty())
+    return {};
+
+  std::string reportsDir = outputDir + "/reports";
+  createDirectory(reportsDir);
+  return std::fstream(reportsDir + "/" + name + "." + extension.str(), std::ios::out | std::ios::app);
+}
+
+std::fstream openAppendedReportFile(const std::string& name) {
+  return openAppendedReportFileWithExtension(name, "txt");
+}
+
 std::string formatReportMemory(uint64_t bytes) {
   const char* units[] = {"B", "KB", "MB", "GB", "TB", "PB", "EB"};
   int i = 0;

src/PIM/Common/Support/ReportUtils.hpp

@@ -12,6 +12,8 @@ namespace onnx_mlir {
 
 std::fstream openReportFile(const std::string& name);
 std::fstream openReportFileWithExtension(const std::string& name, llvm::StringRef extension);
+std::fstream openAppendedReportFile(const std::string& name);
+std::fstream openAppendedReportFileWithExtension(const std::string& name, llvm::StringRef extension);
 std::string formatReportMemory(uint64_t bytes);
 
 struct ReportField {

src/PIM/Compiler/PimCodeGen.cpp

@@ -588,13 +588,37 @@ void PimCodeGen::emitInstruction(const pim_binary::InstructionRecord& instructio
   ++emittedInstructionCount;
   if (coreJsonStream)
     *coreJsonStream << json::Value(pim_binary::makeInstructionJson(instruction)) << ',';
+  updateScalarRegisterCache(instruction);
+}
+
+void PimCodeGen::updateScalarRegisterCache(const pim_binary::InstructionRecord& instruction) const {
+  switch (instruction.opcode) {
+  case pim_binary::Opcode::sldi:
+    scalarRegisterValues[instruction.rd] = instruction.r2OrImm;
+    break;
+  case pim_binary::Opcode::sld:
+  case pim_binary::Opcode::sadd:
+  case pim_binary::Opcode::ssub:
+  case pim_binary::Opcode::smul:
+  case pim_binary::Opcode::saddi:
+  case pim_binary::Opcode::smuli:
+    scalarRegisterValues[instruction.rd].reset();
+    break;
+  default:
+    break;
+  }
 }
 
 void PimCodeGen::genSetRegisterImmediateUnsigned(size_t registerNumber, size_t immediate) const {
+  auto registerIndex = pim::checkedU8OrCrash(registerNumber, "register number");
+  auto immediateValue = pim::checkedI32OrCrash(immediate, "register immediate");
+  if (scalarRegisterValues[registerIndex] == immediateValue)
+    return;
+
   pim_binary::InstructionRecord instruction;
   instruction.opcode = pim_binary::Opcode::sldi;
-  instruction.rd = static_cast<uint8_t>(registerNumber);
-  instruction.r2OrImm = pim::checkedI32OrCrash(immediate, "register immediate");
+  instruction.rd = registerIndex;
+  instruction.r2OrImm = immediateValue;
   emitInstruction(instruction);
 }
 

src/PIM/Compiler/PimCodeGen.hpp

@@ -9,6 +9,7 @@
 #include "llvm/Support/JSON.h"
 #include "llvm/Support/raw_os_ostream.h"
 
+#include <array>
 #include <fstream>
 #include <limits>
 #include <optional>
@@ -170,6 +171,7 @@ class PimCodeGen {
   const llvm::DenseMap<size_t, size_t>& emittedCoreIds;
   std::optional<unsigned> batchLane;
   mutable uint32_t emittedInstructionCount = 0;
+  mutable std::array<std::optional<int32_t>, 256> scalarRegisterValues = {};
 
   size_t addressOf(mlir::Value value, const StaticValueKnowledge& knowledge) const {
     return memory.getValueAddress(value, knowledge, batchLane);
@@ -177,6 +179,7 @@ class PimCodeGen {
   size_t remapCoreId(size_t coreId) const;
 
   void emitInstruction(const pim_binary::InstructionRecord& instruction) const;
+  void updateScalarRegisterCache(const pim_binary::InstructionRecord& instruction) const;
 
   void genSetRegisterImmediateUnsigned(size_t registerNumber, size_t immediate) const;
   void setupRd(size_t rdAddress, size_t rdOffset) const;

src/PIM/Compiler/PimCompilerOptions.cpp

@@ -32,6 +32,31 @@ llvm::cl::opt<PimMemoryReportLevel> pimMemoryReport(
   llvm::cl::init(PimMemoryReportNone),
   llvm::cl::cat(OnnxMlirOptions));
 
+llvm::cl::opt<PimConvLoweringType> pimConvLowering(
+  "pim-conv-lowering",
+  llvm::cl::desc("Convolution lowering strategy for PIM"),
+  llvm::cl::values(clEnumValN(PimConvLoweringAuto, "auto", "Select the Conv lowering strategy automatically")),
+  llvm::cl::values(clEnumValN(PimConvLoweringLegacy, "legacy", "Use the legacy explicit-im2col Conv lowering")),
+  llvm::cl::values(clEnumValN(PimConvLoweringDepthwise, "depthwise", "Force the depthwise-specialized Conv lowering")),
+  llvm::cl::values(
+    clEnumValN(PimConvLoweringPackedIm2Col, "packed-im2col", "Use explicit im2col with packed multi-position GEMM")),
+  llvm::cl::values(clEnumValN(PimConvLoweringStreamedPatch,
+                              "streamed-patch",
+                              "Use streamed/chunked im2col rows without multi-position packing")),
+  llvm::cl::values(clEnumValN(PimConvLoweringStreamedPacked,
+                              "streamed-packed",
+                              "Use streamed/chunked im2col rows with packed multi-position GEMM")),
+  llvm::cl::values(clEnumValN(PimConvLoweringOutputChannelTiled,
+                              "output-channel-tiled",
+                              "Force Conv lowering that relies on Gemm output-channel tiling")),
+  llvm::cl::values(
+    clEnumValN(PimConvLoweringInputKTiled, "input-k-tiled", "Force Conv lowering that relies on Gemm K tiling")),
+  llvm::cl::values(clEnumValN(PimConvLoweringTiled2D,
+                              "tiled-2d",
+                              "Force Conv lowering that relies on Gemm 2D K/C tiling")),
+  llvm::cl::init(PimConvLoweringAuto),
+  llvm::cl::cat(OnnxMlirOptions));
+
 llvm::cl::opt<bool>
   pimOnlyCodegen("pim-only-codegen",
                  llvm::cl::desc("Only generate code for PIM (assume input is already in bufferized PIM IR)"),
@@ -49,11 +74,46 @@ llvm::cl::opt<bool> useExperimentalConvImpl("use-experimental-conv-impl",
                                             llvm::cl::init(false),
                                             llvm::cl::cat(OnnxMlirOptions));
 
+llvm::cl::opt<uint64_t> pimConvIm2colMaxElements(
+  "pim-conv-im2col-max-elements",
+  llvm::cl::desc("Maximum number of im2col elements to materialize globally for one Conv before streaming/chunking"),
+  llvm::cl::init(1ull << 20),
+  llvm::cl::cat(OnnxMlirOptions));
+
+llvm::cl::opt<uint64_t> pimConvStreamChunkPositions(
+  "pim-conv-stream-chunk-positions",
+  llvm::cl::desc("Maximum number of Conv output positions to materialize in one streamed chunk"),
+  llvm::cl::init(1024),
+  llvm::cl::cat(OnnxMlirOptions));
+
+llvm::cl::opt<bool> pimReportConvLowering("pim-report-conv-lowering",
+                                          llvm::cl::desc("Emit a bounded Conv lowering report"),
+                                          llvm::cl::init(true),
+                                          llvm::cl::cat(OnnxMlirOptions));
+
 llvm::cl::opt<bool> pimEmitJson("pim-emit-json",
                                 llvm::cl::desc("Also emit per-core JSON instruction files alongside binary .pim files"),
                                 llvm::cl::init(false),
                                 llvm::cl::cat(OnnxMlirOptions));
 
+llvm::cl::opt<bool> pimDetectCommunicationDeadlock(
+  "pim-detect-communication-deadlock",
+  llvm::cl::desc("Expensively simulate the statically expanded PIM send/receive order at verification time and fail if a blocking communication deadlock is found"),
+  llvm::cl::init(false),
+  llvm::cl::cat(OnnxMlirOptions));
+
+llvm::cl::opt<bool> pimMaterializeScalarFanoutGlobalOrder(
+  "pim-materialize-scalar-fanout-global-order",
+  llvm::cl::desc("Experimental expensive materializer mode: emit scalar-source fanout as globally ordered communication events instead of all-send fanout loops"),
+  llvm::cl::init(false),
+  llvm::cl::cat(OnnxMlirOptions));
+
+llvm::cl::opt<bool> pimTraceCommunicationMaterialization(
+  "pim-trace-communication-materialization",
+  llvm::cl::desc("Emit verbose materializer-time diagnostics and provenance attributes for every Spatial communication op"),
+  llvm::cl::init(false),
+  llvm::cl::cat(OnnxMlirOptions));
+
 llvm::cl::opt<size_t>
   crossbarSize("crossbar-size", llvm::cl::desc("Width and height of a single crossbar"), llvm::cl::init(2));
 

src/PIM/Compiler/PimCompilerOptions.hpp

@@ -30,19 +30,38 @@ typedef enum {
   PimMemoryReportFull = 2,
 } PimMemoryReportLevel;
 
+typedef enum {
+  PimConvLoweringAuto = 0,
+  PimConvLoweringLegacy = 1,
+  PimConvLoweringDepthwise = 2,
+  PimConvLoweringPackedIm2Col = 3,
+  PimConvLoweringStreamedPatch = 4,
+  PimConvLoweringStreamedPacked = 5,
+  PimConvLoweringOutputChannelTiled = 6,
+  PimConvLoweringInputKTiled = 7,
+  PimConvLoweringTiled2D = 8,
+} PimConvLoweringType;
+
 extern llvm::cl::OptionCategory OnnxMlirOptions;
 extern llvm::cl::opt<PimEmissionTargetType> pimEmissionTarget;
 extern llvm::cl::opt<PimMergeSchedulerType> pimMergeScheduler;
 extern llvm::cl::opt<PimMemoryReportLevel> pimMemoryReport;
+extern llvm::cl::opt<PimConvLoweringType> pimConvLowering;
 
 extern llvm::cl::opt<bool> pimOnlyCodegen;
 extern llvm::cl::opt<bool> pimDisableMemoryCoalescing;
 extern llvm::cl::opt<bool> useExperimentalConvImpl;
 extern llvm::cl::opt<bool> pimEmitJson;
+extern llvm::cl::opt<bool> pimReportConvLowering;
+extern llvm::cl::opt<bool> pimDetectCommunicationDeadlock;
+extern llvm::cl::opt<bool> pimMaterializeScalarFanoutGlobalOrder;
+extern llvm::cl::opt<bool> pimTraceCommunicationMaterialization;
 
 extern llvm::cl::opt<size_t> crossbarSize;
 extern llvm::cl::opt<size_t> crossbarCountInCore;
 extern llvm::cl::opt<long> coresCount;
+extern llvm::cl::opt<uint64_t> pimConvIm2colMaxElements;
+extern llvm::cl::opt<uint64_t> pimConvStreamChunkPositions;
 
 bool hasExplicitPimCoreCount();
 void verifyExplicitPimCoreCount();

src/PIM/Compiler/PimCompilerUtils.cpp

@@ -29,6 +29,8 @@ void addPassesPim(OwningOpRef<ModuleOp>& module,
 
   if (pimEmissionTarget >= EmitSpatial) {
     pm.addPass(createONNXToSpatialPass());
+    pm.addPass(createSpatialLayoutPlanningPass());
+    pm.addPass(createLowerSpatialPlansPass());
     pm.addPass(createMergeComputeNodesPass());
     pm.addPass(createMessagePass("Onnx lowered to Spatial"));
   }

src/PIM/Conversion/ONNXToSpatial/CMakeLists.txt

@@ -26,6 +26,8 @@ add_pim_library(OMONNXToSpatial
   Patterns/Tensor/Split.cpp
   Patterns/Tensor/Transpose.cpp
   ONNXToSpatialPass.cpp
+  SpatialLayoutPlanningPass.cpp
+  LowerSpatialPlansPass.cpp
   Common/AttributeUtils.cpp
   Common/ComputeRegionBuilder.cpp
   Common/IndexingUtils.cpp

src/PIM/Conversion/ONNXToSpatial/Common/ComputeRegionBuilder.cpp

@@ -9,7 +9,7 @@ using namespace mlir;
 
 namespace onnx_mlir {
 
-Value sumTensors(ArrayRef<Value> tensors, ConversionPatternRewriter& rewriter) {
+Value sumTensors(ArrayRef<Value> tensors, PatternRewriter& rewriter) {
   if (tensors.size() == 1)
     return tensors[0];
 

src/PIM/Conversion/ONNXToSpatial/Common/ComputeRegionBuilder.hpp

@@ -87,17 +87,17 @@ inline mlir::Value createSpatConcat(RewriterT& rewriter, mlir::Location loc, int
   return spatial::SpatConcatOp::create(rewriter, loc, outputType, rewriter.getI64IntegerAttr(axis), inputs).getOutput();
 }
 
-/// Builds a `spat.compute` with a fixed number of SSA inputs and erases it if
+/// Builds a `spat.graph_compute` with a fixed number of SSA inputs and erases it if
 /// the body callback reports failure.
 template <size_t NumInputs, typename RewriterT, typename BodyFn>
-auto createSpatCompute(RewriterT& rewriter,
-                       mlir::Location loc,
-                       mlir::TypeRange resultTypes,
-                       mlir::ValueRange weights,
-                       mlir::ValueRange inputs,
-                       BodyFn&& body) {
+auto createSpatGraphCompute(RewriterT& rewriter,
+                            mlir::Location loc,
+                            mlir::TypeRange resultTypes,
+                            mlir::ValueRange weights,
+                            mlir::ValueRange inputs,
+                            BodyFn&& body) {
   assert(inputs.size() == NumInputs && "NumInputs must match the number of input values");
-  auto computeOp = spatial::SpatCompute::create(rewriter, loc, resultTypes, weights, inputs);
+  auto computeOp = spatial::SpatGraphCompute::create(rewriter, loc, resultTypes, weights, inputs);
 
   auto* block = new mlir::Block();
   for (mlir::Value weight : weights)
@@ -124,23 +124,23 @@ auto createSpatCompute(RewriterT& rewriter,
     if (mlir::failed(bodyResult)) {
       rewriter.setInsertionPointAfter(computeOp);
       rewriter.eraseOp(computeOp);
-      return mlir::FailureOr<spatial::SpatCompute>(mlir::failure());
+      return mlir::FailureOr<spatial::SpatGraphCompute>(mlir::failure());
     }
     rewriter.setInsertionPointAfter(computeOp);
-    return mlir::FailureOr<spatial::SpatCompute>(computeOp);
+    return mlir::FailureOr<spatial::SpatGraphCompute>(computeOp);
   }
 }
 
-/// Builds a `spat.compute` whose body consumes the block arguments as a single
+/// Builds a `spat.graph_compute` whose body consumes the block arguments as a single
 /// `ValueRange`, which is convenient for variadic reductions/concats.
 template <typename RewriterT, typename BodyFn>
-auto createSpatCompute(RewriterT& rewriter,
-                       mlir::Location loc,
-                       mlir::TypeRange resultTypes,
-                       mlir::ValueRange weights,
-                       mlir::ValueRange inputs,
-                       BodyFn&& body) {
-  auto computeOp = spatial::SpatCompute::create(rewriter, loc, resultTypes, weights, inputs);
+auto createSpatGraphCompute(RewriterT& rewriter,
+                            mlir::Location loc,
+                            mlir::TypeRange resultTypes,
+                            mlir::ValueRange weights,
+                            mlir::ValueRange inputs,
+                            BodyFn&& body) {
+  auto computeOp = spatial::SpatGraphCompute::create(rewriter, loc, resultTypes, weights, inputs);
 
   auto* block = new mlir::Block();
   for (mlir::Value weight : weights)
@@ -163,29 +163,29 @@ auto createSpatCompute(RewriterT& rewriter,
     if (mlir::failed(bodyResult)) {
       rewriter.setInsertionPointAfter(computeOp);
       rewriter.eraseOp(computeOp);
-      return mlir::FailureOr<spatial::SpatCompute>(mlir::failure());
+      return mlir::FailureOr<spatial::SpatGraphCompute>(mlir::failure());
     }
     rewriter.setInsertionPointAfter(computeOp);
-    return mlir::FailureOr<spatial::SpatCompute>(computeOp);
+    return mlir::FailureOr<spatial::SpatGraphCompute>(computeOp);
   }
 }
 
 template <typename RewriterT, typename BodyFn>
-auto createSpatComputeBatch(RewriterT& rewriter,
-                            mlir::Location loc,
-                            mlir::TypeRange resultTypes,
-                            int64_t laneCount,
-                            mlir::ValueRange weights,
-                            mlir::ValueRange inputs,
-                            BodyFn&& body) {
+auto createSpatGraphComputeBatch(RewriterT& rewriter,
+                                 mlir::Location loc,
+                                 mlir::TypeRange resultTypes,
+                                 int64_t laneCount,
+                                 mlir::ValueRange weights,
+                                 mlir::ValueRange inputs,
+                                 BodyFn&& body) {
   if (laneCount <= 0 || laneCount > std::numeric_limits<int32_t>::max())
-    return mlir::FailureOr<spatial::SpatComputeBatch>(mlir::failure());
+    return mlir::FailureOr<spatial::SpatGraphComputeBatch>(mlir::failure());
 
   auto laneCountAttr = pim::getCheckedI32Attr(rewriter, loc, laneCount, "spatial compute_batch lane count");
   if (mlir::failed(laneCountAttr))
-    return mlir::FailureOr<spatial::SpatComputeBatch>(mlir::failure());
+    return mlir::FailureOr<spatial::SpatGraphComputeBatch>(mlir::failure());
 
-  auto batchOp = spatial::SpatComputeBatch::create(rewriter, loc, resultTypes, *laneCountAttr, weights, inputs);
+  auto batchOp = spatial::SpatGraphComputeBatch::create(rewriter, loc, resultTypes, *laneCountAttr, weights, inputs);
 
   mlir::SmallVector<mlir::Type> blockArgTypes {rewriter.getIndexType()};
   mlir::SmallVector<mlir::Location> blockArgLocs {loc};
@@ -218,20 +218,53 @@ auto createSpatComputeBatch(RewriterT& rewriter,
   if constexpr (std::is_same_v<BodyResult, void>) {
     std::forward<BodyFn>(body)(args);
     rewriter.setInsertionPointAfter(batchOp);
-    return mlir::FailureOr<spatial::SpatComputeBatch>(batchOp);
+    return mlir::FailureOr<spatial::SpatGraphComputeBatch>(batchOp);
   }
   else {
     auto bodyResult = std::forward<BodyFn>(body)(args);
     if (mlir::failed(bodyResult)) {
       rewriter.setInsertionPointAfter(batchOp);
       rewriter.eraseOp(batchOp);
-      return mlir::FailureOr<spatial::SpatComputeBatch>(mlir::failure());
+      return mlir::FailureOr<spatial::SpatGraphComputeBatch>(mlir::failure());
     }
     rewriter.setInsertionPointAfter(batchOp);
-    return mlir::FailureOr<spatial::SpatComputeBatch>(batchOp);
+    return mlir::FailureOr<spatial::SpatGraphComputeBatch>(batchOp);
   }
 }
 
+template <size_t NumInputs, typename RewriterT, typename BodyFn>
+auto createSpatCompute(RewriterT& rewriter,
+                       mlir::Location loc,
+                       mlir::TypeRange resultTypes,
+                       mlir::ValueRange weights,
+                       mlir::ValueRange inputs,
+                       BodyFn&& body) {
+  return createSpatGraphCompute<NumInputs>(
+    rewriter, loc, resultTypes, weights, inputs, std::forward<BodyFn>(body));
+}
+
+template <typename RewriterT, typename BodyFn>
+auto createSpatCompute(RewriterT& rewriter,
+                       mlir::Location loc,
+                       mlir::TypeRange resultTypes,
+                       mlir::ValueRange weights,
+                       mlir::ValueRange inputs,
+                       BodyFn&& body) {
+  return createSpatGraphCompute(rewriter, loc, resultTypes, weights, inputs, std::forward<BodyFn>(body));
+}
+
+template <typename RewriterT, typename BodyFn>
+auto createSpatComputeBatch(RewriterT& rewriter,
+                            mlir::Location loc,
+                            mlir::TypeRange resultTypes,
+                            int64_t laneCount,
+                            mlir::ValueRange weights,
+                            mlir::ValueRange inputs,
+                            BodyFn&& body) {
+  return createSpatGraphComputeBatch(
+    rewriter, loc, resultTypes, laneCount, weights, inputs, std::forward<BodyFn>(body));
+}
+
 inline void createParallelInsertSliceIntoBatchOutput(mlir::PatternRewriter& rewriter,
                                                      mlir::Location loc,
                                                      mlir::Value source,
@@ -262,6 +295,6 @@ mlir::Value materializeOrComputeUnary(mlir::Value input,
   return computeOp.getResult(0);
 }
 
-mlir::Value sumTensors(mlir::ArrayRef<mlir::Value> tensors, mlir::ConversionPatternRewriter& rewriter);
+mlir::Value sumTensors(mlir::ArrayRef<mlir::Value> tensors, mlir::PatternRewriter& rewriter);
 
 } // namespace onnx_mlir

src/PIM/Conversion/ONNXToSpatial/Common/ShapeTilingUtils.cpp

@@ -83,7 +83,7 @@ SmallVector<OpFoldResult> getStaticSizes(PatternRewriter& rewriter, ArrayRef<int
 }
 
 SmallVector<Value> sliceTensor(
-  const Value& tensorToSlice, size_t axis, int64_t sliceSize, ConversionPatternRewriter& rewriter, Location loc) {
+  const Value& tensorToSlice, size_t axis, int64_t sliceSize, PatternRewriter& rewriter, Location loc) {
   ArrayRef<long> shape = getTensorShape(tensorToSlice);
   assert("Invalid axis" && axis < shape.size());
 
@@ -129,7 +129,7 @@ SmallVector<Value> sliceTensor(
 }
 
 SmallVector<Value>
-sliceVector(const Value& vectorToSlice, int64_t sliceSize, ConversionPatternRewriter& rewriter, Location loc) {
+sliceVector(const Value& vectorToSlice, int64_t sliceSize, PatternRewriter& rewriter, Location loc) {
   ArrayRef<long> shape = getTensorShape(vectorToSlice);
   assert("Not a vector" && isVectorShape(shape));
   size_t axis = shape[0] != 1 ? 0 : 1;
@@ -137,7 +137,7 @@ sliceVector(const Value& vectorToSlice, int64_t sliceSize, ConversionPatternRewr
 }
 
 DenseMap<CoreId, SmallVector<Value>>
-sliceVectorPerCrossbarPerCore(const Value& vectorToSlice, ConversionPatternRewriter& rewriter, Location loc) {
+sliceVectorPerCrossbarPerCore(const Value& vectorToSlice, PatternRewriter& rewriter, Location loc) {
   SmallVector<Value> slices = sliceVector(vectorToSlice, crossbarSize, rewriter, loc);
   DenseMap<CoreId, SmallVector<Value>> slicesPerCore;
   for (size_t sliceId = 0; sliceId < slices.size(); sliceId++) {

src/PIM/Conversion/ONNXToSpatial/Common/ShapeTilingUtils.hpp

@@ -89,18 +89,18 @@ llvm::SmallVector<mlir::OpFoldResult> getStaticSizes(mlir::PatternRewriter& rewr
 llvm::SmallVector<mlir::Value> sliceTensor(const mlir::Value& tensorToSlice,
                                            size_t axis,
                                            int64_t sliceSize,
-                                           mlir::ConversionPatternRewriter& rewriter,
+                                           mlir::PatternRewriter& rewriter,
                                            mlir::Location loc);
 
 llvm::SmallVector<mlir::Value> sliceVector(const mlir::Value& vectorToSlice,
                                            int64_t sliceSize,
-                                           mlir::ConversionPatternRewriter& rewriter,
+                                           mlir::PatternRewriter& rewriter,
                                            mlir::Location loc);
 
 /// Partitions one logical vector into per-core crossbar-sized slices using the
 /// current PIM target geometry.
 llvm::DenseMap<CoreId, llvm::SmallVector<mlir::Value>> sliceVectorPerCrossbarPerCore(
-  const mlir::Value& vectorToSlice, mlir::ConversionPatternRewriter& rewriter, mlir::Location loc);
+  const mlir::Value& vectorToSlice, mlir::PatternRewriter& rewriter, mlir::Location loc);
 
 mlir::Value extractAxisSlice(
   mlir::PatternRewriter& rewriter, mlir::Location loc, mlir::Value source, int64_t axis, int64_t offset, int64_t size);

src/PIM/Conversion/ONNXToSpatial/Common/WeightMaterialization.cpp

@@ -19,9 +19,11 @@ using namespace mlir;
 
 namespace onnx_mlir {
 
-bool isWeightLikeComputeOperand(Value value) {
+static bool isWeightMaterializationValue(Value value, bool requireMatrixShape) {
   auto rankedType = dyn_cast<RankedTensorType>(value.getType());
-  if (!rankedType || !isMatrixShape(rankedType.getShape()))
+  if (!rankedType)
+    return false;
+  if (requireMatrixShape && !isMatrixShape(rankedType.getShape()))
     return false;
 
   llvm::SmallPtrSet<Operation*, 8> visited;
@@ -29,8 +31,14 @@ bool isWeightLikeComputeOperand(Value value) {
   while (auto* definingOp = value.getDefiningOp()) {
     if (!visited.insert(definingOp).second)
       return false;
-    if (isa<arith::ConstantOp, ONNXConstantOp>(definingOp) || hasWeightAlways(definingOp))
+    if (isa<arith::ConstantOp, ONNXConstantOp>(definingOp) || hasWeightAlways(definingOp)) {
+      auto sourceType = dyn_cast<RankedTensorType>(value.getType());
+      if (!sourceType)
+        return false;
+      if (requireMatrixShape && !isMatrixShape(sourceType.getShape()))
+        return false;
       return true;
+    }
 
     if (auto extractSliceOp = dyn_cast<tensor::ExtractSliceOp>(definingOp)) {
       value = extractSliceOp.getSource();
@@ -55,6 +63,8 @@ bool isWeightLikeComputeOperand(Value value) {
   return false;
 }
 
+bool isWeightLikeComputeOperand(Value value) { return isWeightMaterializationValue(value, /*requireMatrixShape=*/true); }
+
 FailureOr<Value> materializeWeightLikeValueInBlock(Value value, IRRewriter& rewriter, IRMapping& mapper) {
   if (auto mapped = mapper.lookupOrNull(value))
     return cast<Value>(mapped);
@@ -91,7 +101,7 @@ FailureOr<Value> materializeWeightLikeValueInBlock(Value value, IRRewriter& rewr
       continue;
     }
 
-    if (isWeightLikeComputeOperand(operand)) {
+    if (isWeightMaterializationValue(operand, /*requireMatrixShape=*/false)) {
       auto clonedOperand = materializeWeightLikeValueInBlock(operand, rewriter, mapper);
       if (failed(clonedOperand))
         return failure();

src/PIM/Conversion/ONNXToSpatial/LowerSpatialPlansPass.cpp

@@ -0,0 +1,409 @@
+#include "mlir/Dialect/Affine/IR/AffineOps.h"
+#include "mlir/Dialect/Arith/IR/Arith.h"
+#include "mlir/Dialect/Func/IR/FuncOps.h"
+#include "mlir/Dialect/Linalg/IR/Linalg.h"
+#include "mlir/Dialect/SCF/IR/SCF.h"
+#include "mlir/Dialect/Tensor/IR/Tensor.h"
+#include "mlir/Pass/Pass.h"
+#include "mlir/Transforms/DialectConversion.h"
+
+#include "llvm/ADT/DenseMap.h"
+#include "llvm/ADT/SmallPtrSet.h"
+
+#include "Conversion/ONNXToSpatial/ONNXToSpatialVerifier.hpp"
+#include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Common/Support/DebugDump.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/PlanLowering.hpp"
+#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
+#include "src/Accelerators/PIM/Pass/PIMPasses.h"
+#include "src/Dialect/ONNX/ONNXOps.hpp"
+
+using namespace mlir;
+
+namespace onnx_mlir {
+namespace {
+
+static constexpr StringLiteral kDenseLayout = "dense_nchw";
+static constexpr StringLiteral kRowStripLayout = "nchw_row_strip";
+
+struct RowStripPhysicalValue {
+  Value physicalValue;
+  RankedTensorType logicalType;
+  SmallVector<int64_t, 16> fragmentOffsets;
+  SmallVector<int64_t, 16> fragmentSizes;
+  std::string indexMap;
+};
+
+static FailureOr<RowStripPhysicalValue> getRowStripValue(llvm::DenseMap<Value, RowStripPhysicalValue>& rowStripValues,
+                                                         Value value) {
+  auto it = rowStripValues.find(value);
+  if (it == rowStripValues.end())
+    return failure();
+  return it->second;
+}
+
+static FailureOr<RowStripPhysicalValue> buildRowStripValue(spatial::SpatReconciliatorOp reconciliator,
+                                                           Value physicalValue) {
+  auto logicalType = dyn_cast<RankedTensorType>(reconciliator.getOutput().getType());
+  if (!logicalType)
+    return reconciliator.emitOpError("requires ranked logical output type"), failure();
+  RowStripPhysicalValue value;
+  value.physicalValue = physicalValue;
+  value.logicalType = logicalType;
+  value.fragmentOffsets.append(reconciliator.getFragmentOffsets().begin(), reconciliator.getFragmentOffsets().end());
+  value.fragmentSizes.append(reconciliator.getFragmentSizes().begin(), reconciliator.getFragmentSizes().end());
+  value.indexMap = reconciliator.getIndexMap().str();
+  return value;
+}
+
+static FailureOr<Value>
+lowerRowStripRelu(const RowStripPhysicalValue& input, spatial::SpatReluPlanOp planOp, PatternRewriter& rewriter) {
+  auto packedType = cast<RankedTensorType>(input.physicalValue.getType());
+  auto computeOp =
+    createSpatCompute<1>(rewriter, planOp.getLoc(), TypeRange {packedType}, {}, input.physicalValue, [&](Value x) {
+      auto relu = spatial::SpatReluOp::create(rewriter, planOp.getLoc(), packedType, x);
+      spatial::SpatYieldOp::create(rewriter, planOp.getLoc(), relu.getResult());
+    });
+  return computeOp.getResult(0);
+}
+
+static FailureOr<Value>
+materializeRowStripToDense(const RowStripPhysicalValue& rowStripValue, Location loc, PatternRewriter& rewriter) {
+  auto packedType = dyn_cast<RankedTensorType>(rowStripValue.physicalValue.getType());
+  if (!packedType || packedType.getRank() != 3 || !packedType.hasStaticShape())
+    return failure();
+  if (rowStripValue.logicalType.getRank() != 4 || !rowStripValue.logicalType.hasStaticShape())
+    return failure();
+  if (rowStripValue.indexMap != "packed_hwc_rows_to_nchw")
+    return failure();
+
+  const int64_t rank = rowStripValue.logicalType.getRank();
+  const int64_t fragmentCount = rowStripValue.fragmentOffsets.size() / rank;
+  const int64_t packedWidth = packedType.getDimSize(1);
+  const int64_t packedChannels = packedType.getDimSize(2);
+  if (fragmentCount != packedType.getDimSize(0))
+    return failure();
+  for (int64_t fragmentIndex = 0; fragmentIndex < fragmentCount; ++fragmentIndex) {
+    if (rowStripValue.fragmentOffsets[fragmentIndex * rank + 0] != 0
+        || rowStripValue.fragmentOffsets[fragmentIndex * rank + 1] != 0
+        || rowStripValue.fragmentOffsets[fragmentIndex * rank + 2] != fragmentIndex
+        || rowStripValue.fragmentOffsets[fragmentIndex * rank + 3] != 0)
+      return failure();
+    if (rowStripValue.fragmentSizes[fragmentIndex * rank + 0] != 1
+        || rowStripValue.fragmentSizes[fragmentIndex * rank + 1] != packedChannels
+        || rowStripValue.fragmentSizes[fragmentIndex * rank + 2] != 1
+        || rowStripValue.fragmentSizes[fragmentIndex * rank + 3] != packedWidth)
+      return failure();
+  }
+
+  auto packedSliceType =
+    RankedTensorType::get({1, packedWidth, packedChannels}, packedType.getElementType(), packedType.getEncoding());
+  auto expandedType =
+    RankedTensorType::get({1, 1, packedWidth, packedChannels}, packedType.getElementType(), packedType.getEncoding());
+  auto logicalFragmentType =
+    RankedTensorType::get({1, packedChannels, 1, packedWidth}, packedType.getElementType(), packedType.getEncoding());
+  auto batchOp = createSpatComputeBatch(
+    rewriter,
+    loc,
+    TypeRange {rowStripValue.logicalType},
+    fragmentCount,
+    {},
+    ValueRange {rowStripValue.physicalValue},
+    [&](detail::SpatComputeBatchBodyArgs args) {
+      SmallVector<OpFoldResult> packedOffsets {args.lane, rewriter.getIndexAttr(0), rewriter.getIndexAttr(0)};
+      SmallVector<OpFoldResult> packedSizes {
+        rewriter.getIndexAttr(1), rewriter.getIndexAttr(packedWidth), rewriter.getIndexAttr(packedChannels)};
+      Value packedSlice = tensor::ExtractSliceOp::create(
+        rewriter, loc, packedSliceType, args.inputs.front(), packedOffsets, packedSizes, getUnitStrides(rewriter, 3));
+
+      Value expanded = tensor::ExpandShapeOp::create(rewriter,
+                                                     loc,
+                                                     expandedType,
+                                                     packedSlice,
+                                                     SmallVector<ReassociationIndices> {
+                                                       {0, 1},
+                                                       {2},
+                                                       {3}
+      });
+      Value transposeInit =
+        tensor::EmptyOp::create(rewriter, loc, logicalFragmentType.getShape(), logicalFragmentType.getElementType());
+      Value logicalFragment =
+        linalg::TransposeOp::create(rewriter, loc, expanded, transposeInit, SmallVector<int64_t> {0, 3, 1, 2})
+          .getResult()[0];
+
+      SmallVector<OpFoldResult> logicalOffsets {
+        rewriter.getIndexAttr(0), rewriter.getIndexAttr(0), args.lane, rewriter.getIndexAttr(0)};
+      SmallVector<OpFoldResult> logicalSizes {rewriter.getIndexAttr(1),
+                                              rewriter.getIndexAttr(packedChannels),
+                                              rewriter.getIndexAttr(1),
+                                              rewriter.getIndexAttr(packedWidth)};
+      createParallelInsertSliceIntoBatchOutput(rewriter,
+                                               loc,
+                                               logicalFragment,
+                                               args.outputs.front(),
+                                               logicalOffsets,
+                                               logicalSizes,
+                                               getUnitStrides(rewriter, 4));
+      return success();
+    });
+  if (failed(batchOp))
+    return failure();
+  return batchOp->getResult(0);
+}
+
+struct LowerSpatialPlansPass final : PassWrapper<LowerSpatialPlansPass, OperationPass<ModuleOp>> {
+  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(LowerSpatialPlansPass)
+
+  StringRef getArgument() const override { return "lower-spatial-plans"; }
+  StringRef getDescription() const override { return "Lower selected Spatial planning ops to low-level Spatial IR."; }
+
+  void runOnOperation() override {
+    ModuleOp moduleOp = getOperation();
+    MLIRContext* ctx = moduleOp.getContext();
+    auto entryFunc = getPimEntryFunc(moduleOp);
+    if (failed(entryFunc)) {
+      moduleOp.emitError("failed to locate the PIM entry function during LowerSpatialPlans");
+      signalPassFailure();
+      return;
+    }
+    func::FuncOp funcOp = *entryFunc;
+    PatternRewriter rewriter(ctx);
+    llvm::DenseMap<Value, RowStripPhysicalValue> rowStripValues;
+    llvm::SmallPtrSet<Operation*, 16> eraseAfterLowering;
+    auto verifyLogicalPhase = [&](StringRef stage) -> bool {
+      if (succeeded(verifyLogicalSpatialGraphInvariants(*entryFunc)))
+        return true;
+      moduleOp.emitError() << "RAPTOR_PHASE_CHECK logical Spatial graph verification failed " << stage;
+      signalPassFailure();
+      return false;
+    };
+
+    if (!verifyLogicalPhase("at the start of LowerSpatialPlans"))
+      return;
+    for (Operation& op : llvm::make_early_inc_range(funcOp.getBody().front())) {
+      if (auto planOp = dyn_cast<spatial::SpatConv2DPlanOp>(&op)) {
+        FailureOr<RowStripPhysicalValue> rowStripInput = getRowStripValue(rowStripValues, planOp.getInput());
+        auto rowStripReconciliator = llvm::find_if(planOp.getResult().getUsers(), [](Operation* user) {
+          auto reconciliator = dyn_cast<spatial::SpatReconciliatorOp>(user);
+          return reconciliator && reconciliator.getPhysicalLayout() == kRowStripLayout;
+        });
+        if (rowStripReconciliator != planOp.getResult().getUsers().end()) {
+          rewriter.setInsertionPoint(planOp);
+          FailureOr<Value> lowered = lowerSelectedConv2DPlan(
+            planOp,
+            succeeded(rowStripInput) ? std::optional<Value> {rowStripInput->physicalValue} : std::nullopt,
+            /*emitRowStripLayout=*/true,
+            rewriter);
+          if (failed(lowered)) {
+            planOp.emitOpError("failed to lower selected row-strip Spatial Conv plan");
+            signalPassFailure();
+            return;
+          }
+          auto reconciliator = cast<spatial::SpatReconciliatorOp>(*rowStripReconciliator);
+          FailureOr<RowStripPhysicalValue> rowStripValue = buildRowStripValue(reconciliator, *lowered);
+          if (failed(rowStripValue)) {
+            signalPassFailure();
+            return;
+          }
+          rowStripValues[reconciliator.getResult()] = *rowStripValue;
+          eraseAfterLowering.insert(planOp);
+          eraseAfterLowering.insert(reconciliator);
+          continue;
+        }
+        rewriter.setInsertionPoint(planOp);
+        FailureOr<Value> lowered =
+          lowerSelectedConv2DPlan(planOp, std::nullopt, /*emitRowStripLayout=*/false, rewriter);
+        if (failed(lowered)) {
+          planOp.emitOpError("failed to lower selected Spatial Conv plan");
+          signalPassFailure();
+          return;
+        }
+        rewriter.replaceOp(planOp, *lowered);
+        continue;
+      }
+
+      if (auto planOp = dyn_cast<spatial::SpatReluPlanOp>(&op)) {
+        if (succeeded(getRowStripValue(rowStripValues, planOp.getInput()))) {
+          auto outputReconciliator = llvm::find_if(planOp.getResult().getUsers(), [](Operation* user) {
+            auto reconciliator = dyn_cast<spatial::SpatReconciliatorOp>(user);
+            return reconciliator && reconciliator.getPhysicalLayout() == kRowStripLayout;
+          });
+          if (outputReconciliator == planOp.getResult().getUsers().end()) {
+            planOp.emitOpError("row-strip Relu plan requires a row-strip reconciliator result");
+            signalPassFailure();
+            return;
+          }
+
+          FailureOr<RowStripPhysicalValue> input = getRowStripValue(rowStripValues, planOp.getInput());
+          rewriter.setInsertionPoint(planOp);
+          FailureOr<Value> lowered = lowerRowStripRelu(*input, planOp, rewriter);
+          if (failed(lowered)) {
+            planOp.emitOpError("failed to lower selected row-strip Spatial Relu plan");
+            signalPassFailure();
+            return;
+          }
+          auto reconciliator = cast<spatial::SpatReconciliatorOp>(*outputReconciliator);
+          FailureOr<RowStripPhysicalValue> output = buildRowStripValue(reconciliator, *lowered);
+          if (failed(output)) {
+            signalPassFailure();
+            return;
+          }
+          rowStripValues[reconciliator.getResult()] = *output;
+          eraseAfterLowering.insert(planOp);
+          eraseAfterLowering.insert(reconciliator);
+          continue;
+        }
+
+        rewriter.setInsertionPoint(planOp);
+        auto computeOp = createSpatCompute<1>(
+          rewriter, planOp.getLoc(), planOp.getOutput().getType(), {}, planOp.getInput(), [&](Value x) {
+            auto relu = spatial::SpatReluOp::create(rewriter, planOp.getLoc(), planOp.getOutput().getType(), x);
+            spatial::SpatYieldOp::create(rewriter, planOp.getLoc(), relu.getResult());
+          });
+        rewriter.replaceOp(planOp, computeOp.getResults());
+        continue;
+      }
+      if (auto materializeOp = dyn_cast<spatial::SpatMaterializeLayoutOp>(&op)) {
+        if (materializeOp.getSourcePhysicalLayout() == kDenseLayout
+            && materializeOp.getTargetPhysicalLayout() == kDenseLayout) {
+          rewriter.replaceOp(materializeOp, materializeOp.getInput());
+          continue;
+        }
+        if (materializeOp.getSourcePhysicalLayout() != kRowStripLayout
+            || materializeOp.getTargetPhysicalLayout() != kDenseLayout) {
+          materializeOp.emitOpError("non-dense materialize_layout lowering is not supported yet");
+          signalPassFailure();
+          return;
+        }
+        FailureOr<RowStripPhysicalValue> rowStripValue = getRowStripValue(rowStripValues, materializeOp.getInput());
+        if (failed(rowStripValue)) {
+          materializeOp.emitOpError("expected a row-strip reconciliator input during row-strip materialization");
+          signalPassFailure();
+          return;
+        }
+        rewriter.setInsertionPoint(materializeOp);
+        FailureOr<Value> dense = materializeRowStripToDense(*rowStripValue, materializeOp.getLoc(), rewriter);
+        if (failed(dense)) {
+          materializeOp.emitOpError("failed to materialize selected row-strip layout back to dense NCHW");
+          signalPassFailure();
+          return;
+        }
+        rewriter.replaceOp(materializeOp, *dense);
+        continue;
+      }
+      if (auto reconciliatorOp = dyn_cast<spatial::SpatReconciliatorOp>(&op)) {
+        if (reconciliatorOp.getPhysicalLayout() == kDenseLayout) {
+          rewriter.replaceOp(reconciliatorOp, reconciliatorOp.getInput());
+          continue;
+        }
+        if (reconciliatorOp.getPhysicalLayout() != kRowStripLayout) {
+          reconciliatorOp.emitOpError("non-dense reconciliator lowering is not supported yet");
+          signalPassFailure();
+          return;
+        }
+        if (!eraseAfterLowering.contains(reconciliatorOp)) {
+          reconciliatorOp.emitOpError("unhandled row-strip reconciliator remained during LowerSpatialPlans");
+          signalPassFailure();
+          return;
+        }
+      }
+    }
+    bool erasedAny = true;
+    while (erasedAny) {
+      erasedAny = false;
+      for (Operation& op : llvm::make_early_inc_range(funcOp.getBody().front())) {
+        if (!eraseAfterLowering.contains(&op))
+          continue;
+        if (!op.use_empty())
+          continue;
+        eraseAfterLowering.erase(&op);
+        rewriter.eraseOp(&op);
+        erasedAny = true;
+      }
+    }
+    if (!eraseAfterLowering.empty()) {
+      for (Operation& op : funcOp.getBody().front())
+        if (eraseAfterLowering.contains(&op))
+          op.emitOpError("selected row-strip planning op could not be fully eliminated during LowerSpatialPlans");
+      signalPassFailure();
+      return;
+    }
+    ConversionTarget helperTarget(*ctx);
+    helperTarget.addLegalDialect<spatial::SpatialDialect,
+                                 tensor::TensorDialect,
+                                 linalg::LinalgDialect,
+                                 affine::AffineDialect,
+                                 arith::ArithDialect,
+                                 scf::SCFDialect,
+                                 func::FuncDialect>();
+    helperTarget.addLegalOp<spatial::SpatGraphCompute, spatial::SpatGraphComputeBatch>();
+    helperTarget.addIllegalOp<ONNXGemmOp, ONNXTransposeOp>();
+    helperTarget.markOpRecursivelyLegal<spatial::SpatGraphCompute, spatial::SpatGraphComputeBatch>();
+
+    RewritePatternSet helperPatterns(ctx);
+    populateGemmPatterns(helperPatterns, ctx);
+    populateTransposePatterns(helperPatterns, ctx);
+    if (failed(applyPartialConversion(moduleOp, helperTarget, std::move(helperPatterns)))) {
+      moduleOp.emitError("failed to lower helper ONNX ops emitted by selected Spatial plan lowering");
+      signalPassFailure();
+      return;
+    }
+    FrozenRewritePatternSet nestedHelperPatterns([&] {
+      RewritePatternSet patterns(ctx);
+      populateGemmPatterns(patterns, ctx);
+      populateTransposePatterns(patterns, ctx);
+      return patterns;
+    }());
+    ConversionTarget nestedHelperTarget(*ctx);
+    nestedHelperTarget.addLegalDialect<spatial::SpatialDialect,
+                                       tensor::TensorDialect,
+                                       linalg::LinalgDialect,
+                                       affine::AffineDialect,
+                                       arith::ArithDialect,
+                                       scf::SCFDialect,
+                                       func::FuncDialect>();
+    nestedHelperTarget.addIllegalOp<ONNXGemmOp, ONNXTransposeOp>();
+    SmallVector<Operation*> computeLikeOps;
+    funcOp.walk([&](Operation* op) {
+      if (isa<spatial::SpatGraphCompute, spatial::SpatGraphComputeBatch>(op))
+        computeLikeOps.push_back(op);
+    });
+    for (Operation* op : computeLikeOps) {
+      if (failed(applyFullConversion(op, nestedHelperTarget, nestedHelperPatterns))) {
+        op->emitOpError("failed to lower nested helper ONNX ops emitted by selected Spatial plan lowering");
+        signalPassFailure();
+        return;
+      }
+    }
+    if (!verifyLogicalPhase("after nested helper conversions"))
+      return;
+    bool hasIllegalOps = false;
+    moduleOp.walk([&](Operation* op) {
+      if (isa<ONNXEntryPointOp>(op))
+        return;
+      if (isa<spatial::SpatConv2DPlanOp,
+              spatial::SpatReluPlanOp,
+              spatial::SpatReconciliatorOp,
+              spatial::SpatMaterializeLayoutOp>(op)
+          || op->getDialect()->getNamespace() == "onnx") {
+        op->emitOpError("operation must not remain after LowerSpatialPlans");
+        hasIllegalOps = true;
+      }
+    });
+    if (hasIllegalOps)
+      signalPassFailure();
+    else
+      dumpModule(moduleOp, "spatial1_premerge");
+
+    if (!verifyLogicalPhase("at the end of LowerSpatialPlans"))
+      return;
+  }
+};
+
+} // namespace
+
+std::unique_ptr<Pass> createLowerSpatialPlansPass() { return std::make_unique<LowerSpatialPlansPass>(); }
+
+} // namespace onnx_mlir

src/PIM/Conversion/ONNXToSpatial/ONNXToSpatialPass.cpp

@@ -18,6 +18,7 @@
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
+#include "ONNXToSpatialVerifier.hpp"
 
 using namespace mlir;
 
@@ -41,10 +42,16 @@ struct ONNXToSpatialPass : PassWrapper<ONNXToSpatialPass, OperationPass<ModuleOp
 static void populateEmptyFunction(func::FuncOp funcOp) {
   IRRewriter rewriter(funcOp.getContext());
   IRMapping mapper;
-  SmallVector<spatial::SpatCompute> computes(funcOp.getOps<spatial::SpatCompute>());
-  SmallVector<spatial::SpatComputeBatch> computeBatches(funcOp.getOps<spatial::SpatComputeBatch>());
-  if (!computes.empty() || !computeBatches.empty())
+  SmallVector<spatial::SpatGraphCompute> computes(funcOp.getOps<spatial::SpatGraphCompute>());
+  SmallVector<spatial::SpatGraphComputeBatch> computeBatches(funcOp.getOps<spatial::SpatGraphComputeBatch>());
+  SmallVector<spatial::SpatConv2DPlanOp> convPlans(funcOp.getOps<spatial::SpatConv2DPlanOp>());
+  SmallVector<spatial::SpatReluPlanOp> reluPlans(funcOp.getOps<spatial::SpatReluPlanOp>());
+  SmallVector<spatial::SpatReconciliatorOp> reconciliators(funcOp.getOps<spatial::SpatReconciliatorOp>());
+  SmallVector<spatial::SpatMaterializeLayoutOp> materializers(funcOp.getOps<spatial::SpatMaterializeLayoutOp>());
+  if (!computes.empty() || !computeBatches.empty() || !convPlans.empty() || !reluPlans.empty() || !reconciliators.empty()
+      || !materializers.empty()) {
     return;
+  }
 
   auto returnOp = cast<func::ReturnOp>(funcOp.getFunctionBody().front().getTerminator());
   rewriter.setInsertionPoint(returnOp);
@@ -58,7 +65,7 @@ static void populateEmptyFunction(func::FuncOp funcOp) {
     sourceLocs.push_back(source.getLoc());
   }
 
-  auto newCompute = spatial::SpatCompute::create(
+  auto newCompute = spatial::SpatGraphCompute::create(
     rewriter, returnOp.getLoc(), returnOp.getOperandTypes(), funcOp.getArguments(), {}, {});
   auto* newBlock = rewriter.createBlock(&newCompute.getBody(), newCompute.getBody().end(), sourceTypes, sourceLocs);
   for (auto [blockArg, computeArg] : llvm::zip(newBlock->getArguments(), newCompute.getOperands()))
@@ -67,7 +74,7 @@ static void populateEmptyFunction(func::FuncOp funcOp) {
 
   rewriter.setInsertionPointToEnd(newBlock);
   for (Operation& op : funcOp.getOps())
-    if (!isa<spatial::SpatCompute, func::ReturnOp>(&op))
+    if (!isa<spatial::SpatGraphCompute, func::ReturnOp>(&op))
       rewriter.clone(op, mapper);
 
   auto yield = spatial::SpatYieldOp::create(rewriter, funcOp.getLoc(), returnOp.getOperands());
@@ -75,7 +82,7 @@ static void populateEmptyFunction(func::FuncOp funcOp) {
     yield.setOperand(i, mapper.lookupOrDefault(yield.getOperand(i)));
 
   for (Operation& op : llvm::make_early_inc_range(funcOp.getOps()))
-    if (!isa<spatial::SpatCompute, func::ReturnOp>(&op)) {
+    if (!isa<spatial::SpatGraphCompute, func::ReturnOp>(&op)) {
       op.dropAllUses();
       rewriter.eraseOp(&op);
     }
@@ -152,6 +159,11 @@ void ONNXToSpatialPass::runOnOperation() {
     return;
   }
 
+    if (failed(verifyLogicalSpatialGraphInvariants(*entryFunc))) {
+      moduleOp.emitError("RAPTOR_PHASE_CHECK logical Spatial graph verification failed after ONNX conversion");
+      signalPassFailure();
+      return;
+    }
   ConversionTarget earlyPostTarget(*ctx);
   earlyPostTarget.addLegalDialect<spatial::SpatialDialect,
                                   ONNXDialect,
@@ -168,6 +180,11 @@ void ONNXToSpatialPass::runOnOperation() {
 
   annotateWeightsConstants(*entryFunc);
 
+    if (failed(verifyLogicalSpatialGraphInvariants(*entryFunc))) {
+      moduleOp.emitError("RAPTOR_PHASE_CHECK logical Spatial graph verification failed after weight annotation");
+      signalPassFailure();
+      return;
+    }
   ConversionTarget postTarget(*ctx);
   postTarget.addLegalDialect<spatial::SpatialDialect,
                              ONNXDialect,
@@ -176,11 +193,16 @@ void ONNXToSpatialPass::runOnOperation() {
                              affine::AffineDialect,
                              arith::ArithDialect,
                              scf::SCFDialect>();
-  postTarget.addDynamicallyLegalOp<spatial::SpatCompute>(
-    [](spatial::SpatCompute computeOp) { return !requiresPostRewrite(computeOp); });
-  postTarget.addDynamicallyLegalOp<spatial::SpatComputeBatch>(
-    [](spatial::SpatComputeBatch computeOp) { return !requiresPostRewrite(computeOp); });
+  postTarget.addDynamicallyLegalOp<spatial::SpatGraphCompute>(
+    [](spatial::SpatGraphCompute computeOp) { return !requiresPostRewrite(computeOp); });
+  postTarget.addDynamicallyLegalOp<spatial::SpatGraphComputeBatch>(
+    [](spatial::SpatGraphComputeBatch computeOp) { return !requiresPostRewrite(computeOp); });
 
+    if (failed(verifyLogicalSpatialGraphInvariants(*entryFunc))) {
+      moduleOp.emitError("RAPTOR_PHASE_CHECK logical Spatial graph verification failed before post rewrites");
+      signalPassFailure();
+      return;
+    }
   RewritePatternSet postPatterns(ctx);
   populatePostPatterns(postPatterns, ctx);
   if (failed(applyPartialConversion(*entryFunc, postTarget, std::move(postPatterns)))) {
@@ -191,6 +213,11 @@ void ONNXToSpatialPass::runOnOperation() {
 
   populateEmptyFunction(*entryFunc);
 
+    if (failed(verifyLogicalSpatialGraphInvariants(*entryFunc))) {
+      moduleOp.emitError("RAPTOR_PHASE_CHECK logical Spatial graph verification failed after ONNX-to-Spatial");
+      signalPassFailure();
+      return;
+    }
   dumpModule(moduleOp, "spatial0");
 
   if (failed(verifyONNXToSpatial(*entryFunc))) {

src/PIM/Conversion/ONNXToSpatial/ONNXToSpatialVerifier.cpp

@@ -1,4 +1,6 @@
 #include "mlir/Dialect/Func/IR/FuncOps.h"
+#include "mlir/IR/BuiltinAttributes.h"
+#include "mlir/IR/Diagnostics.h"
 #include "mlir/Support/LLVM.h"
 
 #include "Common/IR/WeightUtils.hpp"
@@ -13,6 +15,8 @@ namespace onnx_mlir {
 
 namespace {
 
+constexpr StringLiteral kPhaseMarker = "RAPTOR_PHASE_CHECK";
+
 void checkWeightUseChains(func::FuncOp func, pim::CappedDiagnosticReporter& diagnostics) {
   func.walk([&](Operation* op) {
     if (!hasWeightAlways(op))
@@ -23,134 +27,174 @@ void checkWeightUseChains(func::FuncOp func, pim::CappedDiagnosticReporter& diag
         continue;
 
       diagnostics.report(op, [&](Operation* illegalOp) {
-        illegalOp->emitOpError(
-          "weight-marked values may only flow through static view/slice helper chains into Spatial VMM weights");
+        illegalOp->emitOpError()
+          << kPhaseMarker
+          << " weight-marked values may only flow through static view/slice helper chains into Spatial VMM weights";
       });
       return;
     }
   });
 }
 
-Region* getParentRegion(Value value) {
-  if (auto blockArg = dyn_cast<BlockArgument>(value))
-    return blockArg.getOwner()->getParent();
-  if (Operation* definingOp = value.getDefiningOp())
-    return definingOp->getParentRegion();
-  return nullptr;
+bool isRegionOrAncestorOf(Region& region, Region* candidate) {
+  return candidate && (&region == candidate || region.isAncestor(candidate));
 }
 
-bool isDefinedInsideRegion(Value value, Region& region) {
-  Region* parentRegion = getParentRegion(value);
-  return parentRegion && (&region == parentRegion || region.isAncestor(parentRegion));
+bool isValueDefinedInsideRegion(Value value, Region& region) {
+  if (auto blockArg = dyn_cast<BlockArgument>(value))
+    return isRegionOrAncestorOf(region, blockArg.getOwner()->getParent());
+  if (Operation* definingOp = value.getDefiningOp())
+    return isRegionOrAncestorOf(region, definingOp->getParentRegion());
+  return false;
+}
+
+bool isLegalExternalCapture(Value value, Region& region) {
+  if (isValueDefinedInsideRegion(value, region))
+    return true;
+
+  Operation* definingOp = value.getDefiningOp();
+  return definingOp && definingOp->hasTrait<OpTrait::ConstantLike>();
+}
+
+template <typename ComputeOpTy>
+void verifyComputeBodyCaptures(ComputeOpTy compute, StringRef kind, pim::CappedDiagnosticReporter& diagnostics) {
+  Region& body = compute.getBody();
+  body.walk([&](Operation* nestedOp) {
+    for (OpOperand& operand : nestedOp->getOpOperands()) {
+      Value value = operand.get();
+      if (isLegalExternalCapture(value, body))
+        continue;
+
+      Operation* definingOp = value.getDefiningOp();
+      diagnostics.report(compute.getOperation(), [&](Operation* illegalOp) {
+        InFlightDiagnostic diag =
+          illegalOp->emitOpError() << kPhaseMarker << " " << kind << " body captures non-constant external operand #"
+                                   << operand.getOperandNumber() << " used by " << nestedOp->getName().getStringRef();
+        diag << " (type " << value.getType() << ")";
+        if (definingOp)
+          diag.attachNote(definingOp->getLoc()) << "defining op is " << definingOp->getName().getStringRef();
+        else if (auto blockArg = dyn_cast<BlockArgument>(value)) {
+          if (Operation* owner = blockArg.getOwner()->getParentOp())
+            diag.attachNote(owner->getLoc())
+              << "external block argument belongs to " << owner->getName().getStringRef();
+        }
+      });
+    }
+  });
 }
 
 bool isLegalHostBackedValue(Value value) {
   Operation* definingOp = value.getDefiningOp();
   if (!definingOp)
     return isa<BlockArgument>(value);
-
-  if (isa<spatial::SpatChannelReceiveOp>(definingOp))
-    return false;
-
   return definingOp->getDialect()->getNamespace() != "spat";
 }
 
-LogicalResult verifyComputeLikeInputs(Operation* computeLikeOp,
-                                      ValueRange inputs,
-                                      bool allowChannelReceiveInputs,
-                                      StringRef kind,
-                                      pim::CappedDiagnosticReporter& diagnostics) {
-  for (auto [inputIndex, input] : llvm::enumerate(inputs)) {
-    unsigned currentInputIndex = inputIndex;
+template <typename ComputeOpTy>
+void verifyScheduledInputs(ComputeOpTy compute,
+                           bool allowChannelReceiveInputs,
+                           StringRef kind,
+                           pim::CappedDiagnosticReporter& diagnostics) {
+  for (auto [inputIndex, input] : llvm::enumerate(compute.getInputs())) {
     Operation* definingOp = input.getDefiningOp();
     if (allowChannelReceiveInputs && isa_and_nonnull<spatial::SpatChannelReceiveOp>(definingOp))
       continue;
     if (isLegalHostBackedValue(input))
       continue;
 
-    diagnostics.report(computeLikeOp, [&](Operation* illegalOp) {
-      InFlightDiagnostic diagnostic = illegalOp->emitOpError()
-                                   << kind << " input #" << currentInputIndex
-                                   << (allowChannelReceiveInputs ? " must come from the host or an explicit "
-                                                                   "spat.channel_receive"
-                                                                 : " must come from the host");
+    diagnostics.report(compute.getOperation(), [&](Operation* illegalOp) {
+      InFlightDiagnostic diag = illegalOp->emitOpError()
+                                << kPhaseMarker << " " << kind << " input #" << inputIndex
+                                << (allowChannelReceiveInputs ? " must come from the host or explicit spat.channel_receive"
+                                                              : " must come from the host");
       if (definingOp)
-        diagnostic.attachNote(definingOp->getLoc()) << "illegal Spatial producer is " << definingOp->getName();
+        diag.attachNote(definingOp->getLoc()) << "illegal producer is " << definingOp->getName().getStringRef();
     });
-    return failure();
   }
-  return success();
 }
 
-void verifyNoExternalTensorCaptures(Operation* ownerOp,
-                                    Region& region,
-                                    StringRef kind,
-                                    pim::CappedDiagnosticReporter& diagnostics) {
-  region.walk([&](Operation* op) {
-    for (OpOperand& operand : op->getOpOperands()) {
-      Value value = operand.get();
-      if (!isa<TensorType>(value.getType()))
-        continue;
-      if (isDefinedInsideRegion(value, region) || isa<BlockArgument>(value))
-        continue;
+void verifyLogicalTopLevelOps(func::FuncOp funcOp, pim::CappedDiagnosticReporter& diagnostics) {
+  for (Operation& op : funcOp.getOps()) {
+    if (isa<func::ReturnOp,
+            spatial::SpatGraphCompute,
+            spatial::SpatGraphComputeBatch,
+            spatial::SpatConv2DPlanOp,
+            spatial::SpatReluPlanOp,
+            spatial::SpatReconciliatorOp,
+            spatial::SpatMaterializeLayoutOp>(&op)) {
+      continue;
+    }
+    if (isa<spatial::SpatScheduledCompute, spatial::SpatScheduledComputeBatch>(&op)) {
+      diagnostics.report(&op, [&](Operation* illegalOp) {
+        illegalOp->emitOpError() << kPhaseMarker << " scheduled Spatial compute op is not allowed in logical graph phase";
+      });
+      continue;
+    }
+    if (isa<spatial::SpatChannelReceiveOp, spatial::SpatChannelSendOp>(&op)) {
+      diagnostics.report(&op, [&](Operation* illegalOp) {
+        illegalOp->emitOpError() << kPhaseMarker
+                                 << " explicit channel communication is not expected before merge materialization";
+      });
+      continue;
+    }
+    if (isCompileTimeOp(&op))
+      continue;
 
-      Operation* definingOp = value.getDefiningOp();
-      if (definingOp && definingOp->hasTrait<OpTrait::ConstantLike>())
-        continue;
+    diagnostics.report(&op, [&](Operation* illegalOp) {
+      illegalOp->emitOpError()
+        << kPhaseMarker << " non-foldable top-level runtime op remains in logical Spatial graph; lower it inside spat.graph_compute";
+    });
+  }
+}
 
-      diagnostics.report(ownerOp, [&](Operation* illegalOp) {
-        InFlightDiagnostic diagnostic = illegalOp->emitOpError() << kind << " body may not capture external tensor "
-                                                                 << "values";
-        diagnostic.attachNote(op->getLoc())
-          << "tensor operand #" << operand.getOperandNumber() << " is defined outside the compute body by "
-          << (definingOp ? definingOp->getName().getStringRef() : StringRef("<block argument>"));
+void verifyScheduledTopLevelOps(func::FuncOp funcOp, pim::CappedDiagnosticReporter& diagnostics) {
+  for (Operation& op : funcOp.getOps()) {
+    if (isa<spatial::SpatGraphCompute, spatial::SpatGraphComputeBatch>(&op)) {
+      diagnostics.report(&op, [&](Operation* illegalOp) {
+        illegalOp->emitOpError() << kPhaseMarker << " graph Spatial compute op remained after merge materialization";
       });
     }
-  });
+  }
 }
 
 } // namespace
 
-LogicalResult verifyONNXToSpatial(func::FuncOp funcOp) {
+LogicalResult verifyNoComputeBodyCaptures(func::FuncOp funcOp) {
   pim::CappedDiagnosticReporter diagnostics;
-
-  for (Operation& op : funcOp.getOps()) {
-    if (isa<func::ReturnOp, spatial::SpatCompute, spatial::SpatComputeBatch>(&op))
-      continue;
-    if (isCompileTimeOp(&op))
-      continue;
-
-    diagnostics.report(&op, [](Operation* illegalOp) {
-      illegalOp->emitOpError(
-        "non-foldable top-level runtime op remains after ONNX-to-Spatial; lower it inside spat.compute");
-    });
-  }
-  checkWeightUseChains(funcOp, diagnostics);
-  diagnostics.emitSuppressedSummary(funcOp, "ONNX-to-Spatial verification failed");
-
+  for (auto compute : funcOp.getOps<spatial::SpatGraphCompute>())
+    verifyComputeBodyCaptures(compute, "graph_compute", diagnostics);
+  for (auto batch : funcOp.getOps<spatial::SpatGraphComputeBatch>())
+    verifyComputeBodyCaptures(batch, "graph_compute_batch", diagnostics);
+  for (auto compute : funcOp.getOps<spatial::SpatScheduledCompute>())
+    verifyComputeBodyCaptures(compute, "scheduled_compute", diagnostics);
+  for (auto batch : funcOp.getOps<spatial::SpatScheduledComputeBatch>())
+    verifyComputeBodyCaptures(batch, "scheduled_compute_batch", diagnostics);
+  diagnostics.emitSuppressedSummary(funcOp, "compute body capture verification failed");
   return success(!diagnostics.hasFailure());
 }
 
-LogicalResult verifySpatialCommunicationInvariants(func::FuncOp funcOp) {
+LogicalResult verifyONNXToSpatial(func::FuncOp funcOp) { return verifyLogicalSpatialGraphInvariants(funcOp); }
+
+LogicalResult verifyLogicalSpatialGraphInvariants(func::FuncOp funcOp) {
   pim::CappedDiagnosticReporter diagnostics;
+  verifyLogicalTopLevelOps(funcOp, diagnostics);
+  checkWeightUseChains(funcOp, diagnostics);
+  if (failed(verifyNoComputeBodyCaptures(funcOp)))
+    return failure();
+  diagnostics.emitSuppressedSummary(funcOp, "logical Spatial graph verification failed");
+  return success(!diagnostics.hasFailure());
+}
 
-  for (auto computeOp : funcOp.getOps<spatial::SpatCompute>()) {
-    (void) verifyComputeLikeInputs(
-      computeOp.getOperation(), computeOp.getInputs(), /*allowChannelReceiveInputs=*/true, "spat.compute", diagnostics);
-    verifyNoExternalTensorCaptures(computeOp.getOperation(), computeOp.getBody(), "spat.compute", diagnostics);
-  }
-
-  for (auto computeBatchOp : funcOp.getOps<spatial::SpatComputeBatch>()) {
-    (void) verifyComputeLikeInputs(computeBatchOp.getOperation(),
-                                   computeBatchOp.getInputs(),
-                                   /*allowChannelReceiveInputs=*/false,
-                                   "spat.compute_batch",
-                                   diagnostics);
-    verifyNoExternalTensorCaptures(
-      computeBatchOp.getOperation(), computeBatchOp.getBody(), "spat.compute_batch", diagnostics);
-  }
-
-  diagnostics.emitSuppressedSummary(funcOp, "Spatial communication invariant verification failed");
+LogicalResult verifyScheduledSpatialInvariants(func::FuncOp funcOp) {
+  pim::CappedDiagnosticReporter diagnostics;
+  verifyScheduledTopLevelOps(funcOp, diagnostics);
+  for (auto compute : funcOp.getOps<spatial::SpatScheduledCompute>())
+    verifyScheduledInputs(compute, /*allowChannelReceiveInputs=*/true, "spat.scheduled_compute", diagnostics);
+  for (auto batch : funcOp.getOps<spatial::SpatScheduledComputeBatch>())
+    verifyScheduledInputs(batch, /*allowChannelReceiveInputs=*/false, "spat.scheduled_compute_batch", diagnostics);
+  if (failed(verifyNoComputeBodyCaptures(funcOp)))
+    return failure();
+  diagnostics.emitSuppressedSummary(funcOp, "scheduled Spatial verification failed");
   return success(!diagnostics.hasFailure());
 }
 

src/PIM/Conversion/ONNXToSpatial/ONNXToSpatialVerifier.hpp

@@ -6,6 +6,8 @@
 namespace onnx_mlir {
 
 mlir::LogicalResult verifyONNXToSpatial(mlir::func::FuncOp funcOp);
-mlir::LogicalResult verifySpatialCommunicationInvariants(mlir::func::FuncOp funcOp);
+mlir::LogicalResult verifyNoComputeBodyCaptures(mlir::func::FuncOp funcOp);
+mlir::LogicalResult verifyLogicalSpatialGraphInvariants(mlir::func::FuncOp funcOp);
+mlir::LogicalResult verifyScheduledSpatialInvariants(mlir::func::FuncOp funcOp);
 
 } // namespace onnx_mlir

src/PIM/Conversion/ONNXToSpatial/Patterns.hpp

@@ -33,8 +33,8 @@ void populateSlicePatterns(mlir::RewritePatternSet& patterns, mlir::MLIRContext*
 void populateSplitPatterns(mlir::RewritePatternSet& patterns, mlir::MLIRContext* ctx);
 void populateTransposePatterns(mlir::RewritePatternSet& patterns, mlir::MLIRContext* ctx);
 
-bool requiresPostRewrite(spatial::SpatCompute computeOp);
-bool requiresPostRewrite(spatial::SpatComputeBatch computeOp);
+bool requiresPostRewrite(spatial::SpatGraphCompute computeOp);
+bool requiresPostRewrite(spatial::SpatGraphComputeBatch computeOp);
 void annotateWeightsConstants(mlir::func::FuncOp funcOp);
 
 } // namespace onnx_mlir

src/PIM/Conversion/ONNXToSpatial/Patterns/Math/MatMul.cpp

@@ -950,7 +950,12 @@ struct MatMulToGemm : OpRewritePattern<ONNXMatMulOp> {
 
   LogicalResult matchAndRewrite(ONNXMatMulOp matmulOp, PatternRewriter& rewriter) const override {
     auto shapeInfo = analyzeMatMulShape(matmulOp);
-    if (failed(shapeInfo) || shapeInfo->lhsWasVector || shapeInfo->rhsWasVector || !shapeInfo->outputBatchShape.empty())
+    if (failed(shapeInfo) || shapeInfo->lhsWasVector || shapeInfo->rhsWasVector)
+      return failure();
+
+    const bool hasNonSingletonOutputBatch =
+      !shapeInfo->outputBatchShape.empty() && getStaticShapeElementCount(shapeInfo->outputBatchShape) != 1;
+    if (hasNonSingletonOutputBatch)
       return failure();
 
     Location loc = matmulOp.getLoc();
@@ -991,7 +996,17 @@ struct MatMulToGemm : OpRewritePattern<ONNXMatMulOp> {
       gemmResult =
         ONNXTransposeOp::create(rewriter, loc, shapeInfo->outType, gemmResult, rewriter.getI64ArrayAttr({1, 0}))
           .getResult();
-    rewriter.replaceOp(matmulOp, gemmResult);
+
+    if (shapeInfo->outputBatchShape.empty()) {
+      rewriter.replaceOp(matmulOp, gemmResult);
+      return success();
+    }
+
+    auto directOutType =
+      RankedTensorType::get({1, shapeInfo->m, shapeInfo->n}, shapeInfo->outType.getElementType(), shapeInfo->outType.getEncoding());
+    Value batchedResult = ensureBatchedTensor(gemmResult, /*batchSize=*/1, shapeInfo->m, shapeInfo->n, rewriter, loc);
+    Value finalResult = finalizeNormalizedMatMulResult(batchedResult, directOutType, *shapeInfo, rewriter, loc);
+    rewriter.replaceOp(matmulOp, finalResult);
     return success();
   }
 };

src/PIM/Conversion/ONNXToSpatial/Patterns/NN/Relu.cpp

@@ -16,12 +16,9 @@ struct ReluToSpatialCompute : OpConversionPattern<ONNXReluOp> {
   matchAndRewrite(ONNXReluOp reluOp, ONNXReluOpAdaptor adaptor, ConversionPatternRewriter& rewriter) const override {
     Location loc = reluOp.getLoc();
     Type resultType = reluOp.getResult().getType();
-    constexpr size_t numInputs = 1;
-    auto computeOp = createSpatCompute<numInputs>(rewriter, loc, resultType, {}, adaptor.getX(), [&](Value x) {
-      auto spatReluOp = spatial::SpatReluOp::create(rewriter, loc, resultType, x);
-      spatial::SpatYieldOp::create(rewriter, loc, spatReluOp.getResult());
-    });
-    rewriter.replaceOp(reluOp, computeOp);
+    auto reluPlan = spatial::SpatReluPlanOp::create(
+      rewriter, loc, resultType, adaptor.getX(), rewriter.getStringAttr("nchw"));
+    rewriter.replaceOp(reluOp, reluPlan.getResult());
     return success();
   }
 };

src/PIM/Conversion/ONNXToSpatial/Patterns/Post.cpp

@@ -118,17 +118,17 @@ static LogicalResult mapPromotedInputArguments(ComputeOpTy compute,
 }
 
 // Promotes foldable helper chains from runtime inputs to weights to avoid artificial compute inputs.
-struct PromoteWeightLikeComputeInputsPattern : OpRewritePattern<spatial::SpatCompute> {
-  using OpRewritePattern<spatial::SpatCompute>::OpRewritePattern;
+struct PromoteWeightLikeComputeInputsPattern : OpRewritePattern<spatial::SpatGraphCompute> {
+  using OpRewritePattern<spatial::SpatGraphCompute>::OpRewritePattern;
 
-  LogicalResult matchAndRewrite(spatial::SpatCompute compute, PatternRewriter& rewriter) const override {
+  LogicalResult matchAndRewrite(spatial::SpatGraphCompute compute, PatternRewriter& rewriter) const override {
     auto promoted = computePromotedOperands(compute);
     if (failed(promoted))
       return rewriter.notifyMatchFailure(compute, "no weight-like inputs to promote");
     Block& oldBlock = compute.getBody().front();
 
     rewriter.setInsertionPointAfter(compute);
-    auto newCompute = spatial::SpatCompute::create(
+    auto newCompute = spatial::SpatGraphCompute::create(
       rewriter, compute.getLoc(), compute.getResultTypes(), promoted->newWeights, promoted->newInputs);
     SmallVector<Type> newBlockArgTypes;
     SmallVector<Location> newBlockArgLocs;
@@ -182,10 +182,10 @@ struct PromoteWeightLikeComputeInputsPattern : OpRewritePattern<spatial::SpatCom
 };
 
 // Promotes foldable batch helper chains to weights while preserving compact compute_batch IR.
-struct PromoteWeightLikeComputeBatchInputsPattern : OpRewritePattern<spatial::SpatComputeBatch> {
-  using OpRewritePattern<spatial::SpatComputeBatch>::OpRewritePattern;
+struct PromoteWeightLikeComputeBatchInputsPattern : OpRewritePattern<spatial::SpatGraphComputeBatch> {
+  using OpRewritePattern<spatial::SpatGraphComputeBatch>::OpRewritePattern;
 
-  LogicalResult matchAndRewrite(spatial::SpatComputeBatch compute, PatternRewriter& rewriter) const override {
+  LogicalResult matchAndRewrite(spatial::SpatGraphComputeBatch compute, PatternRewriter& rewriter) const override {
     auto promoted = computePromotedOperands(compute);
     if (failed(promoted))
       return rewriter.notifyMatchFailure(compute, "no weight-like batch inputs to promote");
@@ -197,7 +197,7 @@ struct PromoteWeightLikeComputeBatchInputsPattern : OpRewritePattern<spatial::Sp
       rewriter, compute, static_cast<uint64_t>(compute.getLaneCount()), "promoted compute_batch lane count");
     if (failed(laneCountAttr))
       return failure();
-    auto newCompute = spatial::SpatComputeBatch::create(
+    auto newCompute = spatial::SpatGraphComputeBatch::create(
       rewriter, compute.getLoc(), compute.getResultTypes(), *laneCountAttr, promoted->newWeights, promoted->newInputs);
     auto laneArg = compute.getLaneArgument();
     if (!laneArg)
@@ -281,8 +281,8 @@ void annotateWeightsConstants(func::FuncOp funcOp) {
   });
 }
 
-bool requiresPostRewrite(spatial::SpatCompute computeOp) { return hasPromotableWeightLikeInputs(computeOp); }
+bool requiresPostRewrite(spatial::SpatGraphCompute computeOp) { return hasPromotableWeightLikeInputs(computeOp); }
 
-bool requiresPostRewrite(spatial::SpatComputeBatch computeOp) { return hasPromotableWeightLikeInputs(computeOp); }
+bool requiresPostRewrite(spatial::SpatGraphComputeBatch computeOp) { return hasPromotableWeightLikeInputs(computeOp); }
 
 } // namespace onnx_mlir

src/PIM/Conversion/ONNXToSpatial/PlanLowering.hpp

@@ -0,0 +1,21 @@
+#pragma once
+
+#include <optional>
+
+#include "mlir/IR/PatternMatch.h"
+#include "mlir/Support/LogicalResult.h"
+
+#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
+
+namespace onnx_mlir {
+
+mlir::FailureOr<mlir::Value>
+lowerSelectedConv2DPlan(spatial::SpatConv2DPlanOp planOp,
+                        std::optional<mlir::Value> rowStripInput,
+                        bool emitRowStripLayout,
+                        mlir::PatternRewriter& rewriter);
+
+mlir::LogicalResult canLowerConvPlanToRowStrip(spatial::SpatConv2DPlanOp planOp);
+mlir::LogicalResult canConsumeAndProduceRowStrip(spatial::SpatConv2DPlanOp planOp);
+
+} // namespace onnx_mlir

src/PIM/Conversion/ONNXToSpatial/SpatialLayoutPlanningPass.cpp

@@ -0,0 +1,206 @@
+#include "mlir/Dialect/Func/IR/FuncOps.h"
+#include "mlir/IR/PatternMatch.h"
+#include "mlir/Pass/Pass.h"
+
+#include "llvm/ADT/DenseMap.h"
+
+#include "Conversion/ONNXToSpatial/ONNXToSpatialVerifier.hpp"
+#include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/PlanLowering.hpp"
+#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
+#include "src/Accelerators/PIM/Pass/PIMPasses.h"
+#include "src/Accelerators/PIM/Pass/PIMPasses.h"
+
+using namespace mlir;
+
+namespace onnx_mlir {
+namespace {
+
+static constexpr StringLiteral kLogicalLayout = "nchw";
+static constexpr StringLiteral kDenseLayout = "dense_nchw";
+static constexpr StringLiteral kRowStripLayout = "nchw_row_strip";
+static constexpr StringLiteral kRowStripIndexMap = "packed_hwc_rows_to_nchw";
+
+enum class SelectedLayout {
+  DenseNchw,
+  NchwRowStrip,
+};
+
+static SelectedLayout getSelectedLayout(llvm::DenseMap<Value, SelectedLayout>& layouts, Value value) {
+  auto it = layouts.find(value);
+  return it == layouts.end() ? SelectedLayout::DenseNchw : it->second;
+}
+
+static bool usesSelectedRowStrip(Operation* user, llvm::DenseMap<Value, SelectedLayout>& layouts) {
+  if (auto reluPlan = dyn_cast<spatial::SpatReluPlanOp>(user))
+    return getSelectedLayout(layouts, reluPlan.getResult()) == SelectedLayout::NchwRowStrip;
+  if (auto convPlan = dyn_cast<spatial::SpatConv2DPlanOp>(user))
+    return getSelectedLayout(layouts, convPlan.getResult()) == SelectedLayout::NchwRowStrip;
+  return false;
+}
+
+static bool allUsersCanHandleRowStrip(Value value, llvm::DenseMap<Value, SelectedLayout>& layouts) {
+  for (Operation* user : value.getUsers()) {
+    if (usesSelectedRowStrip(user, layouts))
+      continue;
+    // Dense-only users must be materialized explicitly.
+    continue;
+  }
+  return true;
+}
+
+static std::pair<SmallVector<int64_t>, SmallVector<int64_t>> buildRowStripMetadata(RankedTensorType type) {
+  SmallVector<int64_t> offsets;
+  SmallVector<int64_t> sizes;
+  const int64_t channels = type.getDimSize(1);
+  const int64_t height = type.getDimSize(2);
+  const int64_t width = type.getDimSize(3);
+  offsets.reserve(height * 4);
+  sizes.reserve(height * 4);
+  for (int64_t row = 0; row < height; ++row) {
+    offsets.append({0, 0, row, 0});
+    sizes.append({1, channels, 1, width});
+  }
+  return {offsets, sizes};
+}
+
+static bool canSelectConvRowStrip(spatial::SpatConv2DPlanOp convPlan,
+                                  llvm::DenseMap<Value, SelectedLayout>& layouts) {
+  SelectedLayout inputLayout = getSelectedLayout(layouts, convPlan.getInput());
+  if (inputLayout == SelectedLayout::NchwRowStrip)
+    return succeeded(canConsumeAndProduceRowStrip(convPlan));
+  return succeeded(canLowerConvPlanToRowStrip(convPlan));
+}
+
+static SelectedLayout chooseConvLayout(spatial::SpatConv2DPlanOp convPlan,
+                                       llvm::DenseMap<Value, SelectedLayout>& layouts) {
+  if (!canSelectConvRowStrip(convPlan, layouts))
+    return SelectedLayout::DenseNchw;
+  if (!allUsersCanHandleRowStrip(convPlan.getResult(), layouts))
+    return SelectedLayout::DenseNchw;
+  return SelectedLayout::NchwRowStrip;
+}
+
+static SelectedLayout chooseReluLayout(spatial::SpatReluPlanOp reluPlan,
+                                       llvm::DenseMap<Value, SelectedLayout>& layouts) {
+  if (getSelectedLayout(layouts, reluPlan.getInput()) != SelectedLayout::NchwRowStrip)
+    return SelectedLayout::DenseNchw;
+  if (!allUsersCanHandleRowStrip(reluPlan.getResult(), layouts))
+    return SelectedLayout::DenseNchw;
+  return SelectedLayout::NchwRowStrip;
+}
+
+static spatial::SpatReconciliatorOp insertRowStripReconciliator(IRRewriter& rewriter, Value value) {
+  auto outputType = cast<RankedTensorType>(value.getType());
+  auto [offsets, sizes] = buildRowStripMetadata(outputType);
+  return spatial::SpatReconciliatorOp::create(rewriter,
+                                              value.getLoc(),
+                                              outputType,
+                                              value,
+                                              ValueRange {},
+                                              rewriter.getStringAttr(kLogicalLayout),
+                                              rewriter.getStringAttr(kRowStripLayout),
+                                              rewriter.getDenseI64ArrayAttr(offsets),
+                                              rewriter.getDenseI64ArrayAttr(sizes),
+                                              rewriter.getStringAttr(kRowStripIndexMap),
+                                              nullptr,
+                                              nullptr,
+                                              nullptr,
+                                              nullptr,
+                                              nullptr);
+}
+
+static void materializeDenseUses(IRRewriter& rewriter,
+                                 Value layoutValue,
+                                 llvm::DenseMap<Value, SelectedLayout>& layouts) {
+  SmallVector<OpOperand*> denseUses;
+  for (OpOperand& use : layoutValue.getUses()) {
+    if (usesSelectedRowStrip(use.getOwner(), layouts))
+      continue;
+    denseUses.push_back(&use);
+  }
+
+  for (OpOperand* use : denseUses) {
+    Operation* owner = use->getOwner();
+    rewriter.setInsertionPoint(owner);
+    auto materialized = spatial::SpatMaterializeLayoutOp::create(rewriter,
+                                                                 owner->getLoc(),
+                                                                 use->get().getType(),
+                                                                 use->get(),
+                                                                 rewriter.getStringAttr(kLogicalLayout),
+                                                                 rewriter.getStringAttr(kRowStripLayout),
+                                                                 rewriter.getStringAttr(kDenseLayout));
+    use->set(materialized.getResult());
+  }
+}
+
+struct SpatialLayoutPlanningPass final : PassWrapper<SpatialLayoutPlanningPass, OperationPass<ModuleOp>> {
+  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(SpatialLayoutPlanningPass)
+
+  StringRef getArgument() const override { return "spatial-layout-planning"; }
+  StringRef getDescription() const override { return "Select conservative Spatial layouts and insert reconciliation barriers."; }
+
+  void runOnOperation() override {
+    auto entryFunc = getPimEntryFunc(getOperation());
+    if (failed(entryFunc)) {
+      getOperation().emitError("failed to locate the PIM entry function during Spatial layout planning");
+      signalPassFailure();
+      return;
+    }
+
+    func::FuncOp funcOp = *entryFunc;
+    IRRewriter rewriter(&getContext());
+    llvm::DenseMap<Value, SelectedLayout> layouts;
+
+    bool changed = true;
+    while (changed) {
+      changed = false;
+      for (Operation& op : llvm::make_early_inc_range(funcOp.getBody().front())) {
+        if (auto convPlan = dyn_cast<spatial::SpatConv2DPlanOp>(&op)) {
+          SelectedLayout selected = chooseConvLayout(convPlan, layouts);
+          if (layouts[convPlan.getResult()] != selected) {
+            layouts[convPlan.getResult()] = selected;
+            changed = true;
+          }
+          continue;
+        }
+        if (auto reluPlan = dyn_cast<spatial::SpatReluPlanOp>(&op)) {
+          SelectedLayout selected = chooseReluLayout(reluPlan, layouts);
+          if (layouts[reluPlan.getResult()] != selected) {
+            layouts[reluPlan.getResult()] = selected;
+            changed = true;
+          }
+          continue;
+        }
+      }
+    }
+
+    for (Operation& op : llvm::make_early_inc_range(funcOp.getBody().front())) {
+      Value producedValue;
+      if (auto convPlan = dyn_cast<spatial::SpatConv2DPlanOp>(&op))
+        producedValue = convPlan.getResult();
+      else if (auto reluPlan = dyn_cast<spatial::SpatReluPlanOp>(&op))
+        producedValue = reluPlan.getResult();
+      else
+        continue;
+
+      if (getSelectedLayout(layouts, producedValue) != SelectedLayout::NchwRowStrip)
+        continue;
+
+      rewriter.setInsertionPointAfter(&op);
+      auto reconciliator = insertRowStripReconciliator(rewriter, producedValue);
+      rewriter.replaceAllUsesExcept(producedValue, reconciliator.getResult(), reconciliator);
+      materializeDenseUses(rewriter, reconciliator.getResult(), layouts);
+    }
+    if (failed(verifyLogicalSpatialGraphInvariants(*entryFunc))) {
+      getOperation().emitError("RAPTOR_PHASE_CHECK logical Spatial graph verification failed after SpatialLayoutPlanning");
+      signalPassFailure();
+    }
+  }
+};
+
+} // namespace
+
+std::unique_ptr<Pass> createSpatialLayoutPlanningPass() { return std::make_unique<SpatialLayoutPlanningPass>(); }
+
+} // namespace onnx_mlir

src/PIM/Conversion/SpatialToPim/BatchCoreLoweringPatterns.cpp

@@ -5,6 +5,7 @@
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/IR/IRMapping.h"
 #include "mlir/IR/Matchers.h"
+#include <limits>
 
 #include "Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "Conversion/SpatialToPim/SpatialToPimPass.hpp"
@@ -26,7 +27,83 @@ static bool isUsedOnlyAsExplicitHostOperand(Value value) {
   });
 }
 
-static FailureOr<SmallVector<int32_t>> getPimCoreIdsForBatchOp(spatial::SpatComputeBatch computeBatchOp,
+static bool isMaterializableExternalTensorOp(Operation* op) {
+  return isa<spatial::SpatChannelReceiveOp,
+             spatial::SpatExtractRowsOp,
+             tensor::ExtractSliceOp,
+             tensor::ExpandShapeOp,
+             tensor::CollapseShapeOp>(op);
+}
+
+//TODO REMOVE THIS UGLY FIX 
+//TODO: Remove this helper once compute_batch external tensor captures are
+// fixed at the producer side.
+//
+// This function is a temporary SpatialToPim repair path. It clones selected
+// external tensor producers, such as channel_receive and tensor view/slice ops,
+// into the new pim.core_batch body when the old spat.compute_batch body refers
+// to tensor values defined outside the batch.
+//
+// The real invariant should be stronger:
+//
+//   A spat.compute_batch body must not capture external tensor values.
+//   Every tensor used inside the body must be either:
+//     - a compute_batch block argument,
+//     - defined inside the compute_batch body,
+//     - or a legal constant-like value.
+//
+// If this invariant is violated, the responsible producer, most likely merge
+// schedule materialization, should emit verifier-clean Spatial IR instead of
+// relying on SpatialToPim to clone external producer chains later.
+//
+// After that producer-side fix:
+//   1. remove isMaterializableExternalTensorOp,
+//   2. remove materializeExternalTensorValue,
+//   3. make lowerComputeBatchOp emit a hard diagnostic for any unmapped external
+//      tensor operand,
+//   4. keep/strengthen the Spatial verifier so the invalid capture is rejected
+//      before SpatialToPim.
+//
+// Be careful not to replace every external tensor capture with a normal
+// compute_batch input blindly: host-backed tensors and explicit inter-core
+// communication have different semantics. In particular, channel_receive-like
+// values should be materialized through the communication model, not silently
+// treated as host inputs.
+static FailureOr<Value> materializeExternalTensorValue(IRRewriter& rewriter,
+                                                       Location loc,
+                                                       Block& oldBlock,
+                                                       Value value,
+                                                       IRMapping& mapper) {
+  if (mapper.contains(value))
+    return mapper.lookup(value);
+
+  if (!isa<TensorType>(value.getType()))
+    return value;
+
+  Operation* definingOp = value.getDefiningOp();
+  if (!definingOp || definingOp->hasTrait<OpTrait::ConstantLike>())
+    return failure();
+
+  if (definingOp->getBlock() == &oldBlock)
+    return failure();
+
+  if (!isMaterializableExternalTensorOp(definingOp))
+    return failure();
+
+  for (Value operand : definingOp->getOperands()) {
+    FailureOr<Value> materializedOperand = materializeExternalTensorValue(rewriter, loc, oldBlock, operand, mapper);
+    if (succeeded(materializedOperand))
+      mapper.map(operand, *materializedOperand);
+  }
+
+  Operation* cloned = rewriter.clone(*definingOp, mapper);
+  for (auto [originalResult, clonedResult] : llvm::zip(definingOp->getResults(), cloned->getResults()))
+    mapper.map(originalResult, clonedResult);
+
+  return mapper.lookup(value);
+}
+
+static FailureOr<SmallVector<int32_t>> getPimCoreIdsForBatchOp(spatial::SpatScheduledComputeBatch computeBatchOp,
                                                                size_t& fallbackCoreId) {
   if (auto coreIdsAttr = computeBatchOp->getAttrOfType<DenseI32ArrayAttr>(onnx_mlir::kCoreIdsAttrName))
     return SmallVector<int32_t>(coreIdsAttr.asArrayRef().begin(), coreIdsAttr.asArrayRef().end());
@@ -62,26 +139,49 @@ static Value createScaledIndexValue(IRRewriter& rewriter, Location loc, Value ba
   return arith::MulIOp::create(rewriter, loc, base, scaleValue).getResult();
 }
 
+static SmallVector<OpFoldResult, 4> getStaticIndexAttrs(Builder& builder, ArrayRef<int64_t> values) {
+  SmallVector<OpFoldResult, 4> attrs;
+  attrs.reserve(values.size());
+  for (int64_t value : values)
+    attrs.push_back(builder.getIndexAttr(value));
+  return attrs;
+}
+
+static SmallVector<OpFoldResult, 4> getUnitStrides(Builder& builder, int64_t rank) {
+  SmallVector<OpFoldResult, 4> strides;
+  strides.reserve(rank);
+  for (int64_t dim = 0; dim < rank; ++dim)
+    strides.push_back(builder.getIndexAttr(1));
+  return strides;
+}
+
 static Value createHostTargetOffset(IRRewriter& rewriter,
-                                    tensor::ParallelInsertSliceOp insertSlice,
+                                    Location loc,
                                     ShapedType destinationType,
+                                    ArrayRef<OpFoldResult> mixedOffsets,
+                                    ArrayRef<int64_t> additionalOffsets,
                                     IRMapping& mapper) {
   int64_t elementBytes = static_cast<int64_t>(getElementTypeSizeInBytes(destinationType.getElementType()));
   SmallVector<int64_t> strides = computeRowMajorStrides(destinationType.getShape());
 
   Value totalOffset;
-  Location loc = insertSlice.getLoc();
-  for (auto [dim, offset] : llvm::enumerate(insertSlice.getMixedOffsets())) {
+  for (auto [dim, offset] : llvm::enumerate(mixedOffsets)) {
     int64_t scale = strides[dim] * elementBytes;
     Value scaledOffset;
     if (auto attr = dyn_cast<Attribute>(offset)) {
       auto intAttr = dyn_cast<IntegerAttr>(attr);
       assert(intAttr && "expected integer offset attribute");
-      scaledOffset =
-        getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), intAttr.getInt() * scale);
-    }
-    else {
+      scaledOffset = getOrCreateIndexConstant(rewriter,
+                                              rewriter.getInsertionBlock()->getParentOp(),
+                                              (intAttr.getInt() + additionalOffsets[dim]) * scale);
+    } else {
       scaledOffset = createScaledIndexValue(rewriter, loc, mapper.lookupOrDefault(cast<Value>(offset)), scale);
+      if (additionalOffsets[dim] != 0) {
+        Value staticOffset = getOrCreateIndexConstant(rewriter,
+                                                      rewriter.getInsertionBlock()->getParentOp(),
+                                                      additionalOffsets[dim] * scale);
+        scaledOffset = arith::AddIOp::create(rewriter, loc, scaledOffset, staticOffset).getResult();
+      }
     }
 
     totalOffset =
@@ -93,9 +193,130 @@ static Value createHostTargetOffset(IRRewriter& rewriter,
   return totalOffset;
 }
 
+static Value createHostTargetOffset(IRRewriter& rewriter,
+                                    tensor::ParallelInsertSliceOp insertSlice,
+                                    ShapedType destinationType,
+                                    IRMapping& mapper) {
+  SmallVector<int64_t> zeroOffsets(destinationType.getRank(), 0);
+  return createHostTargetOffset(rewriter,
+                                insertSlice.getLoc(),
+                                destinationType,
+                                insertSlice.getMixedOffsets(),
+                                zeroOffsets,
+                                mapper);
+}
+
+static SmallVector<OpFoldResult, 4> buildFragmentOffsets(IRRewriter& rewriter,
+                                                         Location loc,
+                                                         ArrayRef<OpFoldResult> baseOffsets,
+                                                         ArrayRef<int64_t> fragmentOffsets,
+                                                         IRMapping& mapper) {
+  SmallVector<OpFoldResult, 4> combined;
+  combined.reserve(fragmentOffsets.size());
+  for (auto [dim, baseOffset] : llvm::enumerate(baseOffsets)) {
+    if (auto attr = dyn_cast<Attribute>(baseOffset)) {
+      int64_t base = cast<IntegerAttr>(attr).getInt();
+      combined.push_back(rewriter.getIndexAttr(base + fragmentOffsets[dim]));
+      continue;
+    }
+
+    Value dynamicBase = mapper.lookupOrDefault(cast<Value>(baseOffset));
+    if (fragmentOffsets[dim] == 0) {
+      combined.push_back(dynamicBase);
+      continue;
+    }
+
+    Value staticOffset =
+      getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), fragmentOffsets[dim]);
+    combined.push_back(arith::AddIOp::create(rewriter, loc, dynamicBase, staticOffset).getResult());
+  }
+  return combined;
+}
+
+static FailureOr<Value> lowerFragmentAssemblyHostCopies(IRRewriter& rewriter,
+                                                        spatial::SpatReconciliatorOp reconciliator,
+                                                        Value hostTarget,
+                                                        ArrayRef<OpFoldResult> baseOffsets,
+                                                        IRMapping& mapper) {
+  auto hostTargetType = dyn_cast<RankedTensorType>(hostTarget.getType());
+  auto resultType = dyn_cast<RankedTensorType>(reconciliator.getOutput().getType());
+  if (!hostTargetType || !resultType || !resultType.hasStaticShape())
+    return reconciliator.emitOpError("fragment assembly lowering requires static ranked tensor results");
+
+  std::optional<ArrayRef<int64_t>> operandIndicesAttr = reconciliator.getFragmentOperandIndices();
+  std::optional<ArrayRef<int64_t>> fragmentStridesAttr = reconciliator.getFragmentStrides();
+  if (!operandIndicesAttr || !fragmentStridesAttr)
+    return reconciliator.emitOpError(
+      "fragment assembly lowering requires explicit operand indices and unit strides");
+
+  ArrayRef<int64_t> operandIndices = *operandIndicesAttr;
+  ArrayRef<int64_t> flatOffsets = reconciliator.getFragmentOffsets();
+  ArrayRef<int64_t> flatSizes = reconciliator.getFragmentSizes();
+  ArrayRef<int64_t> flatStrides = *fragmentStridesAttr;
+  int64_t rank = resultType.getRank();
+
+  SmallVector<Value> fragmentOperands {reconciliator.getInput()};
+  llvm::append_range(fragmentOperands, reconciliator.getFragments());
+
+  DenseMap<int64_t, int64_t> packedFragmentOrdinals;
+  for (int64_t fragmentIndex = 0; fragmentIndex < static_cast<int64_t>(operandIndices.size()); ++fragmentIndex) {
+    int64_t operandIndex = operandIndices[fragmentIndex];
+    if (operandIndex < 0 || operandIndex >= static_cast<int64_t>(fragmentOperands.size()))
+      return reconciliator.emitOpError("fragment assembly operand index is out of range");
+
+    SmallVector<int64_t, 4> fragmentOffsets;
+    int64_t fragmentElements = 1;
+    for (int64_t dim = 0; dim < rank; ++dim) {
+      int64_t flatIndex = fragmentIndex * rank + dim;
+      if (flatStrides[flatIndex] != 1)
+        return reconciliator.emitOpError("fragment assembly lowering only supports unit strides");
+      fragmentOffsets.push_back(flatOffsets[flatIndex]);
+      fragmentElements *= flatSizes[flatIndex];
+    }
+
+    Value source = mapper.lookupOrDefault(fragmentOperands[operandIndex]);
+    auto sourceType = dyn_cast<ShapedType>(source.getType());
+    if (!sourceType || !sourceType.hasStaticShape())
+      return reconciliator.emitOpError("fragment assembly lowering requires static ranked tensor operands");
+
+    int64_t packedFragmentOrdinal = packedFragmentOrdinals[operandIndex]++;
+    SmallVector<int64_t, 4> fragmentShape;
+    fragmentShape.reserve(rank);
+    for (int64_t dim = 0; dim < rank; ++dim)
+      fragmentShape.push_back(flatSizes[fragmentIndex * rank + dim]);
+
+    Value fragment = source;
+      if (llvm::to_vector(sourceType.getShape()) != fragmentShape) {
+        SmallVector<int64_t, 4> extractOffsets(rank, 0);
+        extractOffsets[0] = packedFragmentOrdinal * fragmentShape[0];
+        fragment = tensor::ExtractSliceOp::create(rewriter,
+                                                  reconciliator.getLoc(),
+                                                source,
+                                                getStaticIndexAttrs(rewriter, extractOffsets),
+                                                getStaticIndexAttrs(rewriter, fragmentShape),
+                                                getUnitStrides(rewriter, rank));
+    }
+
+    hostTarget = tensor::InsertSliceOp::create(rewriter,
+                                               reconciliator.getLoc(),
+                                               fragment,
+                                               hostTarget,
+                                               buildFragmentOffsets(rewriter,
+                                                                    reconciliator.getLoc(),
+                                                                    baseOffsets,
+                                                                    fragmentOffsets,
+                                                                    mapper),
+                                               getStaticIndexAttrs(rewriter, fragmentShape),
+                                               getUnitStrides(rewriter, rank))
+                   .getResult();
+  }
+
+  return hostTarget;
+}
+
 } // namespace
 
-LogicalResult raptor::SpatialToPimPass::lowerComputeBatchOp(spatial::SpatComputeBatch computeBatchOp,
+LogicalResult raptor::SpatialToPimPass::lowerComputeBatchOp(spatial::SpatScheduledComputeBatch computeBatchOp,
                                                             IRRewriter& rewriter) {
   Location loc = computeBatchOp.getLoc();
   Block& oldBlock = computeBatchOp.getBody().front();
@@ -131,8 +352,10 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeBatchOp(spatial::SpatCompute
 
   SmallVector<unsigned> returnOperandIndices;
   if (computeBatchOp.getNumResults() != 0) {
-    returnOperandIndices.resize(computeBatchOp.getNumResults());
+    returnOperandIndices.resize(computeBatchOp.getNumResults(), std::numeric_limits<unsigned>::max());
     for (auto [resultIndex, result] : llvm::enumerate(computeBatchOp.getResults())) {
+      if (result.use_empty())
+        continue;
       FailureOr<unsigned> returnOperandIndex = getDirectReturnOperandIndex(cast<OpResult>(result));
       if (failed(returnOperandIndex))
         return computeBatchOp.emitOpError(
@@ -195,6 +418,18 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeBatchOp(spatial::SpatCompute
     if (isa<spatial::SpatYieldOp>(op))
       continue;
 
+    if (auto reconciliator = dyn_cast<spatial::SpatReconciliatorOp>(op)) {
+      std::optional<StringRef> modeAttr = reconciliator.getMode();
+      if (modeAttr && *modeAttr == "fragment_assembly") {
+        for (Operation* user : reconciliator.getOutput().getUsers()) {
+          if (!isa<tensor::ParallelInsertSliceOp>(user))
+            return reconciliator.emitOpError(
+              "fragment assembly reconciliator lowering expects only tensor.parallel_insert_slice users");
+        }
+        continue;
+      }
+    }
+
     if (auto parallelOp = dyn_cast<spatial::SpatInParallelOp>(op)) {
       auto firstOutputArg = computeBatchOp.getOutputArgument(0);
       if (!firstOutputArg)
@@ -211,10 +446,28 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeBatchOp(spatial::SpatCompute
         unsigned resultIndex = outputArg.getArgNumber() - firstOutputArg->getArgNumber();
         if (resultIndex >= returnOperandIndices.size())
           return insertSlice.emitOpError("result index out of range while lowering host batch output");
+        if (returnOperandIndices[resultIndex] == std::numeric_limits<unsigned>::max())
+          continue;
 
-        Value mappedSource = mapper.lookup(insertSlice.getSource());
         Value hostTarget = getOrCreateHostOutputTensor(resultIndex, insertSlice.getLoc());
         auto hostTargetType = cast<ShapedType>(hostTarget.getType());
+        if (auto reconciliator =
+              insertSlice.getSource().getDefiningOp<spatial::SpatReconciliatorOp>()) {
+          std::optional<StringRef> modeAttr = reconciliator.getMode();
+          if (modeAttr && *modeAttr == "fragment_assembly") {
+            FailureOr<Value> updatedHostTarget = lowerFragmentAssemblyHostCopies(rewriter,
+                                                                                 reconciliator,
+                                                                                 hostTarget,
+                                                                                 insertSlice.getMixedOffsets(),
+                                                                                 mapper);
+            if (failed(updatedHostTarget))
+              return failure();
+            hostOutputTensors[resultIndex] = *updatedHostTarget;
+            continue;
+          }
+        }
+
+        Value mappedSource = mapper.lookup(insertSlice.getSource());
         Value hostTargetOffset = createHostTargetOffset(rewriter, insertSlice, hostTargetType, mapper);
         Value zeroOffset = getOrCreateIndexConstant(rewriter, coreBatchOp.getOperation(), 0);
         auto sizeAttr = getTensorSizeInBytesAttr(rewriter, coreBatchOp.getOperation(), mappedSource);
@@ -264,9 +517,18 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeBatchOp(spatial::SpatCompute
       Operation* definingOp = operand.getDefiningOp();
       if (definingOp && definingOp->getBlock() == &oldBlock)
         continue;
+      if (definingOp && definingOp->hasTrait<OpTrait::ConstantLike>())
+        continue;
 
-      return computeBatchOp.emitOpError(
-        "expected external tensor communication to be materialized in Spatial before batch lowering");
+      if (succeeded(materializeExternalTensorValue(rewriter, loc, oldBlock, operand, mapper)))
+        continue;
+
+      InFlightDiagnostic diagnostic =
+        computeBatchOp.emitOpError("expected external tensor communication to be materialized in Spatial before batch lowering");
+      diagnostic << " while cloning nested op '" << op.getName() << "' tensor operand #" << operandIndex;
+      if (definingOp)
+        diagnostic << " from external producer '" << definingOp->getName() << "'";
+      return diagnostic;
     }
 
     Operation* cloned = rewriter.clone(op, mapper);

src/PIM/Conversion/SpatialToPim/ComputeLikeRegionUtils.cpp

@@ -17,10 +17,10 @@ std::optional<unsigned> getDirectComputeLikeInputIndex(Operation* owner, unsigne
     return operandNumber - inputBegin;
   };
 
-  if (auto compute = dyn_cast<spatial::SpatCompute>(owner))
+  if (auto compute = dyn_cast<spatial::SpatScheduledCompute>(owner))
     return getInputIndex(owner, compute.getInputs().size());
 
-  if (auto computeBatch = dyn_cast<spatial::SpatComputeBatch>(owner))
+  if (auto computeBatch = dyn_cast<spatial::SpatScheduledComputeBatch>(owner))
     return getInputIndex(owner, computeBatch.getInputs().size());
 
   return std::nullopt;
@@ -32,13 +32,13 @@ void replaceAndEraseDirectComputeLikeInput(PatternRewriter& rewriter,
                                            Value replacement) {
   Block& body = owner->getRegion(0).front();
   BlockArgument bodyArgument;
-  if (auto compute = dyn_cast<spatial::SpatCompute>(owner)) {
+  if (auto compute = dyn_cast<spatial::SpatScheduledCompute>(owner)) {
     auto computeArg = compute.getInputArgument(inputIndex);
     assert(computeArg && "expected compute input block argument");
     bodyArgument = *computeArg;
   }
   else {
-    auto batchArg = cast<spatial::SpatComputeBatch>(owner).getInputArgument(inputIndex);
+    auto batchArg = cast<spatial::SpatScheduledComputeBatch>(owner).getInputArgument(inputIndex);
     assert(batchArg && "expected compute_batch input block argument");
     bodyArgument = *batchArg;
   }
@@ -46,10 +46,10 @@ void replaceAndEraseDirectComputeLikeInput(PatternRewriter& rewriter,
 
   rewriter.startOpModification(owner);
   bodyArgument.replaceAllUsesWith(replacement);
-  if (auto compute = dyn_cast<spatial::SpatCompute>(owner))
+  if (auto compute = dyn_cast<spatial::SpatScheduledCompute>(owner))
     compute.getInputsMutable().erase(inputIndex);
   else
-    cast<spatial::SpatComputeBatch>(owner).getInputsMutable().erase(inputIndex);
+    cast<spatial::SpatScheduledComputeBatch>(owner).getInputsMutable().erase(inputIndex);
   body.eraseArgument(bodyArgIndex);
   rewriter.finalizeOpModification(owner);
 }

src/PIM/Conversion/SpatialToPim/CoreLoweringPatterns.cpp

@@ -30,6 +30,91 @@ static bool isChannelUseChainOp(Operation* op) {
              pim::PimTransposeOp>(op);
 }
 
+static Value createStaticHostTargetOffset(IRRewriter& rewriter,
+                                          Location loc,
+                                          ShapedType destinationType,
+                                          ArrayRef<int64_t> fragmentOffsets) {
+  int64_t elementBytes = static_cast<int64_t>(getElementTypeSizeInBytes(destinationType.getElementType()));
+  SmallVector<int64_t> strides = computeRowMajorStrides(destinationType.getShape());
+
+  int64_t byteOffset = 0;
+  for (auto [dim, offset] : llvm::enumerate(fragmentOffsets))
+    byteOffset += offset * strides[dim] * elementBytes;
+  return getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), byteOffset);
+}
+
+static FailureOr<Value> lowerFragmentAssemblyReconciliator(IRRewriter& rewriter,
+                                                           spatial::SpatReconciliatorOp reconciliator,
+                                                           IRMapping& mapping) {
+  auto resultType = dyn_cast<ShapedType>(reconciliator.getOutput().getType());
+  if (!resultType || !resultType.hasStaticShape())
+    return reconciliator.emitOpError("fragment assembly lowering requires a static ranked tensor result");
+
+  std::optional<StringRef> modeAttr = reconciliator.getMode();
+  std::optional<ArrayRef<int64_t>> operandIndicesAttr = reconciliator.getFragmentOperandIndices();
+  std::optional<ArrayRef<int64_t>> fragmentStridesAttr = reconciliator.getFragmentStrides();
+  if (!modeAttr || *modeAttr != "fragment_assembly" || !operandIndicesAttr || !fragmentStridesAttr)
+    return reconciliator.emitOpError("fragment assembly lowering requires explicit fragment metadata");
+
+  ArrayRef<int64_t> operandIndices = *operandIndicesAttr;
+  ArrayRef<int64_t> flatOffsets = reconciliator.getFragmentOffsets();
+  ArrayRef<int64_t> flatSizes = reconciliator.getFragmentSizes();
+  ArrayRef<int64_t> flatStrides = *fragmentStridesAttr;
+  int64_t rank = resultType.getRank();
+
+  SmallVector<Value> fragmentOperands {reconciliator.getInput()};
+  llvm::append_range(fragmentOperands, reconciliator.getFragments());
+
+  Value currentOutput = createEmptyTensorFromShaped(rewriter, reconciliator.getLoc(), resultType);
+  DenseMap<int64_t, int64_t> packedFragmentOrdinals;
+  for (int64_t fragmentIndex = 0; fragmentIndex < static_cast<int64_t>(operandIndices.size()); ++fragmentIndex) {
+    int64_t operandIndex = operandIndices[fragmentIndex];
+    if (operandIndex < 0 || operandIndex >= static_cast<int64_t>(fragmentOperands.size()))
+      return reconciliator.emitOpError("fragment assembly operand index is out of range");
+
+    SmallVector<int64_t, 4> fragmentOffsets;
+    int64_t fragmentElements = 1;
+    for (int64_t dim = 0; dim < rank; ++dim) {
+      int64_t flatIndex = fragmentIndex * rank + dim;
+      if (flatStrides[flatIndex] != 1)
+        return reconciliator.emitOpError("fragment assembly lowering only supports unit strides");
+      fragmentOffsets.push_back(flatOffsets[flatIndex]);
+      fragmentElements *= flatSizes[flatIndex];
+    }
+
+    Value source = mapping.lookupOrDefault(fragmentOperands[operandIndex]);
+    auto sourceType = dyn_cast<ShapedType>(source.getType());
+    if (!sourceType || !sourceType.hasStaticShape())
+      return reconciliator.emitOpError("fragment assembly lowering requires static ranked tensor operands");
+
+    int64_t fragmentBytes =
+      fragmentElements * static_cast<int64_t>(getElementTypeSizeInBytes(sourceType.getElementType()));
+    auto sizeAttr = pim::getCheckedI32Attr(rewriter,
+                                           reconciliator.getOperation(),
+                                           fragmentBytes,
+                                           "fragment assembly host copy size");
+    if (failed(sizeAttr))
+      return failure();
+
+    int64_t packedFragmentOrdinal = packedFragmentOrdinals[operandIndex]++;
+    Value hostTargetOffset = createStaticHostTargetOffset(rewriter, reconciliator.getLoc(), resultType, fragmentOffsets);
+    Value deviceSourceOffset = getOrCreateIndexConstant(rewriter,
+                                                        rewriter.getInsertionBlock()->getParentOp(),
+                                                        packedFragmentOrdinal * fragmentBytes);
+    currentOutput = pim::PimMemCopyDevToHostOp::create(rewriter,
+                                                       reconciliator.getLoc(),
+                                                       currentOutput.getType(),
+                                                       hostTargetOffset,
+                                                       deviceSourceOffset,
+                                                       currentOutput,
+                                                       source,
+                                                       *sizeAttr)
+                      .getOutput();
+  }
+
+  return currentOutput;
+}
+
 static void
 cloneMappedHelperOperands(Operation* op, IRMapping& mapping, IRRewriter& rewriter, OperationFolder& constantFolder) {
   for (Value operand : op->getOperands()) {
@@ -55,7 +140,7 @@ cloneMappedHelperOperands(Operation* op, IRMapping& mapping, IRRewriter& rewrite
   }
 }
 
-static FailureOr<int32_t> getPimCoreIdForComputeOp(spatial::SpatCompute computeOp, size_t& fallbackCoreId) {
+static FailureOr<int32_t> getPimCoreIdForComputeOp(spatial::SpatScheduledCompute computeOp, size_t& fallbackCoreId) {
   if (auto spatialCoreIdAttr = computeOp->getAttrOfType<IntegerAttr>(onnx_mlir::kCoreIdAttrName))
     return pim::checkedI32(spatialCoreIdAttr.getInt(), computeOp, "spatial compute core id");
   auto checkedCoreId =
@@ -66,7 +151,7 @@ static FailureOr<int32_t> getPimCoreIdForComputeOp(spatial::SpatCompute computeO
   return *checkedCoreId;
 }
 
-static LogicalResult collectHelperComputeChain(spatial::SpatCompute computeOp,
+static LogicalResult collectHelperComputeChain(spatial::SpatScheduledCompute computeOp,
                                                SmallVectorImpl<Operation*>& helperChain,
                                                bool requireReturnUse = true) {
   if (computeOp.getInputs().size() != 1 || computeOp.getNumResults() != 1)
@@ -104,13 +189,13 @@ static LogicalResult collectHelperComputeChain(spatial::SpatCompute computeOp,
   return success();
 }
 
-static bool inlineInputlessHelperComputeForWeightLikeUsers(spatial::SpatCompute computeOp,
+static bool inlineInputlessHelperComputeForWeightLikeUsers(spatial::SpatScheduledCompute computeOp,
                                                            IRRewriter& rewriter,
                                                            OperationFolder& constantFolder) {
   if (!computeOp.getInputs().empty() || computeOp.getNumResults() != 1)
     return false;
   if (!llvm::all_of(computeOp.getResult(0).getUsers(), [](Operation* user) {
-        return isa<spatial::SpatCompute, spatial::SpatComputeBatch, pim::PimCoreOp, pim::PimCoreBatchOp>(user);
+        return isa<spatial::SpatScheduledCompute, spatial::SpatScheduledComputeBatch, pim::PimCoreOp, pim::PimCoreBatchOp>(user);
       }))
     return false;
 
@@ -131,6 +216,17 @@ static bool inlineInputlessHelperComputeForWeightLikeUsers(spatial::SpatCompute
     mapping.map(*weightArg, weight);
   }
   for (Operation& op : block.without_terminator()) {
+    if (auto reconciliator = dyn_cast<spatial::SpatReconciliatorOp>(op)) {
+      std::optional<StringRef> modeAttr = reconciliator.getMode();
+      if (modeAttr && *modeAttr == "fragment_assembly") {
+        auto lowered = lowerFragmentAssemblyReconciliator(rewriter, reconciliator, mapping);
+        if (failed(lowered))
+          return false;
+        mapping.map(reconciliator.getOutput(), *lowered);
+        continue;
+      }
+    }
+
     cloneMappedHelperOperands(&op, mapping, rewriter, constantFolder);
     Operation* clonedOp = rewriter.clone(op, mapping);
     for (auto [originalResult, newResult] : llvm::zip(op.getResults(), clonedOp->getResults()))
@@ -145,7 +241,7 @@ static bool inlineInputlessHelperComputeForWeightLikeUsers(spatial::SpatCompute
 
 } // namespace
 
-LogicalResult raptor::SpatialToPimPass::lowerComputeOp(spatial::SpatCompute computeOp,
+LogicalResult raptor::SpatialToPimPass::lowerComputeOp(spatial::SpatScheduledCompute computeOp,
                                                        IRRewriter& rewriter,
                                                        OperationFolder& constantFolder) {
   Location loc = computeOp->getLoc();

src/PIM/Conversion/SpatialToPim/Patterns.cpp

@@ -1,6 +1,10 @@
+#include "mlir/Transforms/DialectConversion.h"
+#include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 #include "src/Accelerators/PIM/Conversion/SpatialToPim/Common.hpp"
 #include "src/Accelerators/PIM/Conversion/SpatialToPim/Patterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
+#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 
 using namespace mlir;
 
@@ -11,6 +15,107 @@ namespace raptor {
 
 } // namespace raptor
 
+static SmallVector<OpFoldResult, 4> getStaticIndexAttrs(Builder& builder, ArrayRef<int64_t> values) {
+  SmallVector<OpFoldResult, 4> attrs;
+  attrs.reserve(values.size());
+  for (int64_t value : values)
+    attrs.push_back(builder.getIndexAttr(value));
+  return attrs;
+}
+
+static SmallVector<OpFoldResult, 4> getUnitStrides(Builder& builder, int64_t rank) {
+  SmallVector<OpFoldResult, 4> strides;
+  strides.reserve(rank);
+  for (int64_t dim = 0; dim < rank; ++dim)
+    strides.push_back(builder.getIndexAttr(1));
+  return strides;
+}
+
+struct LowerFragmentAssemblyReconciliatorPattern
+  : OpConversionPattern<spatial::SpatReconciliatorOp> {
+  using OpConversionPattern::OpConversionPattern;
+
+  LogicalResult matchAndRewrite(spatial::SpatReconciliatorOp op,
+                                OpAdaptor adaptor,
+                                ConversionPatternRewriter& rewriter) const override {
+    std::optional<StringRef> modeAttr = op.getMode();
+    if (!modeAttr || *modeAttr != "fragment_assembly")
+      return failure();
+
+    auto resultType = dyn_cast<ShapedType>(op.getOutput().getType());
+    if (!resultType || !resultType.hasStaticShape())
+      return op.emitOpError("fragment assembly lowering requires a static ranked tensor result");
+
+    std::optional<ArrayRef<int64_t>> operandIndicesAttr = op.getFragmentOperandIndices();
+    std::optional<ArrayRef<int64_t>> fragmentStridesAttr = op.getFragmentStrides();
+    if (!operandIndicesAttr || !fragmentStridesAttr)
+      return op.emitOpError("fragment assembly lowering requires explicit fragment metadata");
+
+    ArrayRef<int64_t> operandIndices = *operandIndicesAttr;
+    ArrayRef<int64_t> flatOffsets = op.getFragmentOffsets();
+    ArrayRef<int64_t> flatSizes = op.getFragmentSizes();
+    ArrayRef<int64_t> flatStrides = *fragmentStridesAttr;
+    int64_t rank = resultType.getRank();
+
+    SmallVector<Value> fragmentOperands {adaptor.getInput()};
+    llvm::append_range(fragmentOperands, adaptor.getFragments());
+
+    Value currentOutput =
+      tensor::EmptyOp::create(rewriter, op.getLoc(), resultType.getShape(), resultType.getElementType()).getResult();
+    DenseMap<int64_t, int64_t> packedFragmentOrdinals;
+    for (int64_t fragmentIndex = 0; fragmentIndex < static_cast<int64_t>(operandIndices.size()); ++fragmentIndex) {
+      int64_t operandIndex = operandIndices[fragmentIndex];
+      if (operandIndex < 0 || operandIndex >= static_cast<int64_t>(fragmentOperands.size()))
+        return op.emitOpError("fragment assembly operand index is out of range");
+
+      SmallVector<int64_t, 4> fragmentOffsets;
+      int64_t fragmentElements = 1;
+      for (int64_t dim = 0; dim < rank; ++dim) {
+        int64_t flatIndex = fragmentIndex * rank + dim;
+        if (flatStrides[flatIndex] != 1)
+          return op.emitOpError("fragment assembly lowering only supports unit strides");
+        fragmentOffsets.push_back(flatOffsets[flatIndex]);
+        fragmentElements *= flatSizes[flatIndex];
+      }
+
+      Value source = fragmentOperands[operandIndex];
+      auto sourceType = dyn_cast<RankedTensorType>(source.getType());
+      if (!sourceType || !sourceType.hasStaticShape())
+        return op.emitOpError("fragment assembly lowering requires static ranked tensor operands");
+
+      int64_t packedFragmentOrdinal = packedFragmentOrdinals[operandIndex]++;
+      SmallVector<int64_t, 4> fragmentShape;
+      fragmentShape.reserve(rank);
+      for (int64_t dim = 0; dim < rank; ++dim)
+        fragmentShape.push_back(flatSizes[fragmentIndex * rank + dim]);
+
+      Value fragment = source;
+      if (llvm::to_vector(sourceType.getShape()) != fragmentShape) {
+        SmallVector<int64_t, 4> extractOffsets(rank, 0);
+        extractOffsets[0] = packedFragmentOrdinal * fragmentShape[0];
+        fragment = tensor::ExtractSliceOp::create(rewriter,
+                                                  op.getLoc(),
+                                                  source,
+                                                  getStaticIndexAttrs(rewriter, extractOffsets),
+                                                  getStaticIndexAttrs(rewriter, fragmentShape),
+                                                  getUnitStrides(rewriter, rank));
+      }
+
+      currentOutput = tensor::InsertSliceOp::create(rewriter,
+                                                    op.getLoc(),
+                                                    fragment,
+                                                    currentOutput,
+                                                    getStaticIndexAttrs(rewriter, fragmentOffsets),
+                                                    getStaticIndexAttrs(rewriter, fragmentShape),
+                                                    getUnitStrides(rewriter, rank))
+                        .getResult();
+    }
+
+    rewriter.replaceOp(op, currentOutput);
+    return success();
+  }
+};
+
 void populateInitialPatterns(RewritePatternSet& patterns) {
   raptor::populateWithGenerated(patterns);
   populateTransposeLoweringPatterns(patterns);
@@ -19,6 +124,7 @@ void populateInitialPatterns(RewritePatternSet& patterns) {
 void populateCoreBodyPatterns(RewritePatternSet& patterns) {
   raptor::populateWithGenerated(patterns);
   populateTransposeLoweringPatterns(patterns);
+  patterns.add<LowerFragmentAssemblyReconciliatorPattern>(patterns.getContext());
 }
 
 } // namespace onnx_mlir

src/PIM/Conversion/SpatialToPim/Patterns/ChannelLowering.cpp

@@ -10,6 +10,14 @@ using namespace mlir;
 namespace onnx_mlir {
 namespace {
 
+static void copyRaptorDebugAttrs(Operation* source, Operation* target) {
+  for (NamedAttribute attr : source->getAttrs()) {
+    StringRef name = attr.getName().strref();
+    if (name.starts_with("raptor."))
+      target->setAttr(attr.getName(), attr.getValue());
+  }
+}
+
 struct ChannelSendLowering : OpRewritePattern<spatial::SpatChannelSendOp> {
   using OpRewritePattern::OpRewritePattern;
 
@@ -17,7 +25,8 @@ struct ChannelSendLowering : OpRewritePattern<spatial::SpatChannelSendOp> {
     auto sizeAttr = getTensorSizeInBytesAttr(rewriter, op.getOperation(), op.getInput());
     if (failed(sizeAttr))
       return failure();
-    pim::PimSendOp::create(rewriter, op.getLoc(), op.getInput(), *sizeAttr, op.getTargetCoreId());
+    auto send = pim::PimSendOp::create(rewriter, op.getLoc(), op.getInput(), *sizeAttr, op.getTargetCoreId());
+    copyRaptorDebugAttrs(op.getOperation(), send.getOperation());
     rewriter.eraseOp(op);
     return success();
   }
@@ -37,9 +46,10 @@ struct ChannelReceiveLowering : OpRewritePattern<spatial::SpatChannelReceiveOp>
     auto sizeAttr = getTensorSizeInBytesAttr(rewriter, op.getOperation(), op.getResult());
     if (failed(sizeAttr))
       return failure();
-    Value received = pim::PimReceiveOp::create(
-                       rewriter, op.getLoc(), op.getResult().getType(), outputBuffer, *sizeAttr, op.getSourceCoreId())
-                       .getOutput();
+    auto receive = pim::PimReceiveOp::create(
+      rewriter, op.getLoc(), op.getResult().getType(), outputBuffer, *sizeAttr, op.getSourceCoreId());
+    copyRaptorDebugAttrs(op.getOperation(), receive.getOperation());
+    Value received = receive.getOutput();
     rewriter.replaceOp(op, received);
     return success();
   }

src/PIM/Conversion/SpatialToPim/Patterns/GlobalTensorMaterialization.cpp

@@ -59,7 +59,7 @@ struct MoveExtractSliceIntoCompute final : OpRewritePattern<mlir::tensor::Extrac
       return failure();
 
     for (auto& uses : extractSliceOp->getUses()) {
-      if (isa<spatial::SpatCompute>(uses.getOwner())) {
+      if (isa<spatial::SpatScheduledCompute>(uses.getOwner())) {
         if (!getDirectComputeLikeInputIndex(uses.getOwner(), uses.getOperandNumber()))
           return failure();
       }
@@ -72,7 +72,7 @@ struct MoveExtractSliceIntoCompute final : OpRewritePattern<mlir::tensor::Extrac
 
     for (auto& uses : llvm::make_early_inc_range(extractSliceOp->getUses())) {
 
-      if (auto spatCompute = dyn_cast<spatial::SpatCompute>(uses.getOwner())) {
+      if (auto spatCompute = dyn_cast<spatial::SpatScheduledCompute>(uses.getOwner())) {
         auto inputIndex = getDirectComputeLikeInputIndex(spatCompute, uses.getOperandNumber());
         if (!inputIndex)
           return failure();
@@ -92,7 +92,7 @@ struct MoveExtractSliceIntoCompute final : OpRewritePattern<mlir::tensor::Extrac
         replaceAndEraseDirectComputeLikeInput(
           rewriter, spatCompute.getOperation(), *inputIndex, mapSpatToExtract[spatCompute.getOperation()]);
       }
-      else if (auto spatComputeBatch = dyn_cast<spatial::SpatComputeBatch>(uses.getOwner())) {
+      else if (auto spatComputeBatch = dyn_cast<spatial::SpatScheduledComputeBatch>(uses.getOwner())) {
         auto inputIndex = getDirectComputeLikeInputIndex(spatComputeBatch, uses.getOperandNumber());
         if (!inputIndex)
           return failure();
@@ -114,7 +114,7 @@ struct MoveExtractSliceIntoCompute final : OpRewritePattern<mlir::tensor::Extrac
       }
       else {
         {
-          if (auto spatCompute = uses.getOwner()->getParentOfType<spatial::SpatCompute>()) {
+          if (auto spatCompute = uses.getOwner()->getParentOfType<spatial::SpatScheduledCompute>()) {
             rewriter.setInsertionPoint(&spatCompute.getBody().front().front());
             if (!mapSpatToExtract.contains(spatCompute.getOperation())) {
               auto newExtractSlice = rewriter.clone(*extractSliceOp.getOperation());
@@ -125,7 +125,7 @@ struct MoveExtractSliceIntoCompute final : OpRewritePattern<mlir::tensor::Extrac
             uses.set(mapSpatToExtract[spatCompute.getOperation()]);
             rewriter.finalizeOpModification(spatCompute.getOperation());
           }
-          else if (auto spatComputeBatch = uses.getOwner()->getParentOfType<spatial::SpatComputeBatch>()) {
+          else if (auto spatComputeBatch = uses.getOwner()->getParentOfType<spatial::SpatScheduledComputeBatch>()) {
             rewriter.setInsertionPoint(&spatComputeBatch.getBody().front().front());
             if (!mapSpatToExtract.contains(spatComputeBatch.getOperation())) {
               auto newExtractSlice = rewriter.clone(*extractSliceOp.getOperation());
@@ -179,7 +179,7 @@ struct FuncOpArgToGlobalMemoryPattern final : OpRewritePattern<mlir::func::FuncO
 
       for (auto& argUses : llvm::make_early_inc_range(arg.getUses())) {
         auto argUser = argUses.getOwner();
-        if (auto spatCompute = dyn_cast<spatial::SpatCompute>(argUser)) {
+        if (auto spatCompute = dyn_cast<spatial::SpatScheduledCompute>(argUser)) {
           auto inputIndex = getDirectComputeLikeInputIndex(spatCompute, argUses.getOperandNumber());
           if (!inputIndex)
             return failure();
@@ -191,7 +191,7 @@ struct FuncOpArgToGlobalMemoryPattern final : OpRewritePattern<mlir::func::FuncO
 
           replaceAndEraseDirectComputeLikeInput(rewriter, spatCompute.getOperation(), BBArgIndex, toTensor);
         }
-        else if (auto spatComputeBatch = dyn_cast<spatial::SpatComputeBatch>(argUser)) {
+        else if (auto spatComputeBatch = dyn_cast<spatial::SpatScheduledComputeBatch>(argUser)) {
           auto inputIndex = getDirectComputeLikeInputIndex(spatComputeBatch, argUses.getOperandNumber());
           if (!inputIndex)
             return failure();

src/PIM/Conversion/SpatialToPim/ReturnPathNormalization.cpp

@@ -86,7 +86,7 @@ getCheckedByteOffset(int64_t elementOffset, size_t elementSize, Operation* ancho
   return pim::checkedCast<int64_t>(*byteOffset, anchor, fieldName);
 }
 
-static LogicalResult collectHelperComputeChain(spatial::SpatCompute computeOp,
+static LogicalResult collectHelperComputeChain(spatial::SpatScheduledCompute computeOp,
                                                SmallVectorImpl<Operation*>& helperChain) {
   if (computeOp.getInputs().size() != 1 || computeOp.getNumResults() != 1)
     return failure();
@@ -212,7 +212,7 @@ static std::optional<ConcatReturnUseInfo> analyzeConcatReturnUse(Value value) {
   }
 
   SmallVector<Operation*> helperChain;
-  if (auto helperCompute = dyn_cast<spatial::SpatCompute>(currentUser)) {
+  if (auto helperCompute = dyn_cast<spatial::SpatScheduledCompute>(currentUser)) {
     if (helperCompute.getInputs().size() != 1 || helperCompute.getInputs().front() != currentValue)
       return std::nullopt;
 
@@ -643,7 +643,7 @@ raptor::SpatialToPimPass::ReturnPathLoweringResult raptor::SpatialToPimPass::low
 }
 
 raptor::SpatialToPimPass::ReturnPathLoweringResult raptor::SpatialToPimPass::lowerComputeResultReturnPath(
-  spatial::SpatCompute computeOp, OpResult result, Value yieldValue, IRRewriter& rewriter) {
+  spatial::SpatScheduledCompute computeOp, OpResult result, Value yieldValue, IRRewriter& rewriter) {
   return lowerProducedValueReturnPath(computeOp.getOperation(), result, yieldValue, rewriter);
 }
 
@@ -656,7 +656,7 @@ void raptor::SpatialToPimPass::replaceReturnWithOutputBuffers(func::ReturnOp ret
     if (!isExclusivelyOwnedByReturnChain && op->hasOneUse()) {
       Operation* onlyUser = *op->getUsers().begin();
       isExclusivelyOwnedByReturnChain =
-        isa<func::ReturnOp, tensor::ConcatOp, spatial::SpatConcatOp, pim::PimConcatOp, spatial::SpatCompute>(onlyUser)
+        isa<func::ReturnOp, tensor::ConcatOp, spatial::SpatConcatOp, pim::PimConcatOp, spatial::SpatScheduledCompute>(onlyUser)
         || isReturnHelperChainOp(onlyUser);
     }
     if (!isExclusivelyOwnedByReturnChain)
@@ -669,7 +669,7 @@ void raptor::SpatialToPimPass::replaceReturnWithOutputBuffers(func::ReturnOp ret
       return;
     }
 
-    if (auto computeOp = dyn_cast<spatial::SpatCompute>(op)) {
+    if (auto computeOp = dyn_cast<spatial::SpatScheduledCompute>(op)) {
       markOpToRemove(computeOp);
       if (!computeOp.getInputs().empty())
         for (Value input : computeOp.getInputs())

src/PIM/Conversion/SpatialToPim/SpatialToPimPass.cpp

@@ -25,9 +25,11 @@
 #include <cassert>
 #include <utility>
 
+#include "Common/IR/ShapeUtils.hpp"
 #include "Common/IR/ConstantUtils.hpp"
 #include "Common/PimCommon.hpp"
 #include "Common/Support/CheckedArithmetic.hpp"
+#include "Conversion/ONNXToSpatial/ONNXToSpatialVerifier.hpp"
 #include "Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "Conversion/SpatialToPim/Common.hpp"
 #include "Conversion/SpatialToPim/Patterns.hpp"
@@ -97,6 +99,64 @@ static FailureOr<Value> createZeroedDeviceHVector(IRRewriter& rewriter,
     .getOutput();
 }
 
+static bool isHostBackedMemRefValue(Value value) {
+  while (Operation* definingOp = value.getDefiningOp()) {
+    if (auto subviewOp = dyn_cast<memref::SubViewOp>(definingOp)) {
+      value = subviewOp.getSource();
+      continue;
+    }
+    if (auto castOp = dyn_cast<memref::CastOp>(definingOp)) {
+      value = castOp.getSource();
+      continue;
+    }
+    if (auto collapseOp = dyn_cast<memref::CollapseShapeOp>(definingOp)) {
+      value = collapseOp.getSrc();
+      continue;
+    }
+    if (auto expandOp = dyn_cast<memref::ExpandShapeOp>(definingOp)) {
+      value = expandOp.getSrc();
+      continue;
+    }
+    return isa<memref::GetGlobalOp>(definingOp);
+  }
+  return false;
+}
+
+static bool isHostBackedTensorValue(Value value) {
+  while (Operation* definingOp = value.getDefiningOp()) {
+    if (auto extractSliceOp = dyn_cast<tensor::ExtractSliceOp>(definingOp)) {
+      auto sourceType = dyn_cast<RankedTensorType>(extractSliceOp.getSource().getType());
+      auto resultType = dyn_cast<RankedTensorType>(extractSliceOp.getResult().getType());
+      if (!sourceType || !resultType || !sourceType.hasStaticShape() || !resultType.hasStaticShape())
+        return false;
+      if (!onnx_mlir::isContiguousSubviewWithDynamicOffsets(sourceType.getShape(),
+                                                            extractSliceOp.getMixedOffsets(),
+                                                            extractSliceOp.getStaticSizes(),
+                                                            extractSliceOp.getStaticStrides())) {
+        return false;
+      }
+      value = extractSliceOp.getSource();
+      continue;
+    }
+    if (auto collapseOp = dyn_cast<tensor::CollapseShapeOp>(definingOp)) {
+      value = collapseOp.getSrc();
+      continue;
+    }
+    if (auto expandOp = dyn_cast<tensor::ExpandShapeOp>(definingOp)) {
+      value = expandOp.getSrc();
+      continue;
+    }
+    if (auto castOp = dyn_cast<tensor::CastOp>(definingOp)) {
+      value = castOp.getSource();
+      continue;
+    }
+    if (auto toTensorOp = dyn_cast<bufferization::ToTensorOp>(definingOp))
+      return isHostBackedMemRefValue(toTensorOp.getBuffer());
+    return false;
+  }
+  return false;
+}
+
 static FailureOr<Value>
 padHVectorInputToCrossbarSize(IRRewriter& rewriter, Location loc, Value vector, OperationFolder& constantFolder) {
   auto vectorType = cast<RankedTensorType>(vector.getType());
@@ -120,6 +180,10 @@ padHVectorInputToCrossbarSize(IRRewriter& rewriter, Location loc, Value vector,
   auto sizeAttr = pim::getCheckedI32Attr(rewriter, zeroed->getDefiningOp(), *byteSize, "device padding copy byte size");
   if (failed(sizeAttr))
     return failure();
+  if (isHostBackedTensorValue(vector)) {
+    return PimMemCopyHostToDevOp::create(rewriter, loc, paddedType, zeroIndex, zeroIndex, *zeroed, vector, *sizeAttr)
+      .getOutput();
+  }
   return PimMemCopyOp::create(rewriter, loc, paddedType, zeroIndex, zeroIndex, *zeroed, vector, *sizeAttr).getOutput();
 }
 
@@ -137,6 +201,12 @@ void onnx_mlir::raptor::SpatialToPimPass::runOnOperation() {
     return;
   }
   func::FuncOp funcOp = *entryFunc;
+  if (failed(verifyScheduledSpatialInvariants(funcOp))) {
+    funcOp.emitOpError(
+      "RAPTOR_PHASE_CHECK scheduled Spatial verification failed at the start of SpatialToPim");
+    signalPassFailure();
+    return;
+  }
 
   IRRewriter rewriter(&getContext());
   OperationFolder constantFolder(&getContext());
@@ -176,19 +246,19 @@ void onnx_mlir::raptor::SpatialToPimPass::runOnOperation() {
     return;
   }
 
-  for (auto computeOp : funcOp.getOps<spatial::SpatCompute>()) {
+  for (auto computeOp : funcOp.getOps<spatial::SpatScheduledCompute>()) {
     markOpToRemove(computeOp);
     if (failed(lowerComputeOp(computeOp, rewriter, constantFolder))) {
-      computeOp.emitOpError("failed to lower spat.compute to pim.core");
+      computeOp.emitOpError("failed to lower spat.scheduled_compute to pim.core");
       signalPassFailure();
       return;
     }
   }
 
-  for (auto computeBatchOp : funcOp.getOps<spatial::SpatComputeBatch>()) {
+  for (auto computeBatchOp : funcOp.getOps<spatial::SpatScheduledComputeBatch>()) {
     markOpToRemove(computeBatchOp);
     if (failed(lowerComputeBatchOp(computeBatchOp, rewriter))) {
-      computeBatchOp.emitOpError("failed to lower spat.compute_batch to pim.core_batch");
+      computeBatchOp.emitOpError("failed to lower spat.scheduled_compute_batch to pim.core_batch");
       signalPassFailure();
       return;
     }
@@ -374,7 +444,7 @@ LogicalResult raptor::SpatialToPimPass::allocateAndInitializeCoreLocalVariables(
   };
 
   for (auto& op : funcOp.getBody().getOps())
-    if (auto computeOp = dyn_cast<spatial::SpatCompute>(op)) {
+    if (auto computeOp = dyn_cast<spatial::SpatScheduledCompute>(op)) {
       if (!computeOp.getInputs().empty() || computeOp.getBody().front().getNumArguments() != 0)
         continue;
       for (auto getGlobal : computeOp.getOps<memref::GetGlobalOp>()) {

src/PIM/Conversion/SpatialToPim/SpatialToPimPass.hpp

@@ -41,8 +41,11 @@ private:
 
   mlir::LogicalResult allocateAndInitializeCoreLocalVariables(mlir::func::FuncOp funcOp, mlir::IRRewriter& rewriter);
   mlir::LogicalResult
-  lowerComputeOp(spatial::SpatCompute computeOp, mlir::IRRewriter& rewriter, mlir::OperationFolder& constantFolder);
-  mlir::LogicalResult lowerComputeBatchOp(spatial::SpatComputeBatch computeBatchOp, mlir::IRRewriter& rewriter);
+  lowerComputeOp(spatial::SpatScheduledCompute computeOp,
+                 mlir::IRRewriter& rewriter,
+                 mlir::OperationFolder& constantFolder);
+  mlir::LogicalResult lowerComputeBatchOp(spatial::SpatScheduledComputeBatch computeBatchOp,
+                                          mlir::IRRewriter& rewriter);
 
   enum class ReturnPathLoweringResult {
     Handled,
@@ -51,7 +54,7 @@ private:
   };
 
   void addReturnOutputBuffers(mlir::func::ReturnOp returnOp, mlir::IRRewriter& rewriter);
-  ReturnPathLoweringResult lowerComputeResultReturnPath(spatial::SpatCompute computeOp,
+  ReturnPathLoweringResult lowerComputeResultReturnPath(spatial::SpatScheduledCompute computeOp,
                                                         mlir::OpResult result,
                                                         mlir::Value yieldValue,
                                                         mlir::IRRewriter& rewriter);

src/PIM/Dialect/Pim/Transforms/Bufferization/BufferizationUtils.cpp

@@ -13,10 +13,13 @@ using namespace bufferization;
 
 namespace onnx_mlir::pim {
 
-FailureOr<Value> materializeContiguousInputMemRef(Value memrefValue, Location loc, RewriterBase& rewriter) {
+FailureOr<Value> materializeContiguousInputMemRef(Value memrefValue,
+                                                  Location loc,
+                                                  RewriterBase& rewriter,
+                                                  const StaticValueKnowledge& knowledge) {
   bool isContiguous =
-    succeeded(resolveContiguousAddress(memrefValue)) || succeeded(compileContiguousAddressExpr(memrefValue));
-  if (isContiguous && isDeviceLocalPimAddress(memrefValue))
+    succeeded(resolveContiguousAddress(memrefValue, knowledge)) || succeeded(compileContiguousAddressExpr(memrefValue));
+  if (isContiguous && isDeviceLocalPimAddress(memrefValue, knowledge))
     return memrefValue;
 
   auto shapedType = cast<ShapedType>(memrefValue.getType());
@@ -32,7 +35,7 @@ FailureOr<Value> materializeContiguousInputMemRef(Value memrefValue, Location lo
   if (failed(sizeAttr))
     return failure();
 
-  if (isHostBackedPimAddress(memrefValue)) {
+  if (isHostBackedPimAddress(memrefValue, knowledge)) {
     return PimMemCopyHostToDevOp::create(
              rewriter, loc, contiguousType, zeroOffset, zeroOffset, contiguousBuffer, memrefValue, *sizeAttr)
       .getOutput();

src/PIM/Dialect/Pim/Transforms/Bufferization/BufferizationUtils.hpp

@@ -3,10 +3,15 @@
 #include "mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h"
 #include "mlir/IR/PatternMatch.h"
 
+#include "src/Accelerators/PIM/Common/IR/AddressAnalysis.hpp"
+
 namespace onnx_mlir::pim {
 
 llvm::FailureOr<mlir::Value>
-materializeContiguousInputMemRef(mlir::Value memrefValue, mlir::Location loc, mlir::RewriterBase& rewriter);
+materializeContiguousInputMemRef(mlir::Value memrefValue,
+                                 mlir::Location loc,
+                                 mlir::RewriterBase& rewriter,
+                                 const onnx_mlir::StaticValueKnowledge& knowledge = {});
 mlir::Value
 allocateContiguousResultMemRefLike(mlir::Value memrefValue, mlir::Location loc, mlir::RewriterBase& rewriter);
 

src/PIM/Dialect/Pim/Transforms/Bufferization/OpBufferizationInterfaces.cpp

@@ -15,6 +15,26 @@ using namespace bufferization;
 namespace onnx_mlir {
 namespace pim {
 
+static StaticValueKnowledge getEnclosingBufferizationKnowledge(Operation* op) {
+  StaticValueKnowledge knowledge;
+
+  if (auto coreBatchOp = op->getParentOfType<PimCoreBatchOp>()) {
+    knowledge.indexValues[coreBatchOp.getLaneArgument()] = 0;
+    for (auto [index, weight] : llvm::enumerate(coreBatchOp.getWeights()))
+      knowledge.aliases[coreBatchOp.getWeightArgument(index)] = weight;
+    for (auto [index, input] : llvm::enumerate(coreBatchOp.getInputs()))
+      knowledge.aliases[coreBatchOp.getInputArgument(index)] = input;
+    return knowledge;
+  }
+
+  if (auto coreOp = op->getParentOfType<PimCoreOp>()) {
+    for (auto [index, weight] : llvm::enumerate(coreOp.getWeights()))
+      knowledge.aliases[coreOp.getWeightArgument(index)] = weight;
+  }
+
+  return knowledge;
+}
+
 struct MemCopyHostToDevOpInterface
 : DstBufferizableOpInterfaceExternalModel<MemCopyHostToDevOpInterface, PimMemCopyHostToDevOp> {
   LogicalResult bufferize(Operation* op,
@@ -148,7 +168,8 @@ struct ConcatOpInterface : DstBufferizableOpInterfaceExternalModel<ConcatOpInter
       auto inputOpt = getBufferOrValue(rewriter, input, options, state);
       if (failed(inputOpt))
         return failure();
-      auto contiguous = materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter);
+      auto contiguous =
+        materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter, getEnclosingBufferizationKnowledge(op));
       if (failed(contiguous))
         return failure();
       inputs.push_back(*contiguous);
@@ -182,7 +203,8 @@ struct SendOpInterface : BufferizableOpInterface::ExternalModel<SendOpInterface,
     auto inputOpt = getBufferOrValue(rewriter, sendOp.getInput(), options, state);
     if (failed(inputOpt))
       return failure();
-    auto contiguousInput = materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter);
+    auto contiguousInput =
+      materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter, getEnclosingBufferizationKnowledge(op));
     if (failed(contiguousInput))
       return failure();
 
@@ -410,7 +432,8 @@ struct TransposeOpInterface : DstBufferizableOpInterfaceExternalModel<TransposeO
     if (failed(outputBufferOpt))
       return failure();
 
-    auto contiguousInput = materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter);
+    auto contiguousInput =
+      materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter, getEnclosingBufferizationKnowledge(op));
     if (failed(contiguousInput))
       return failure();
     Value contiguousOutput = allocateContiguousResultMemRefLike(*outputBufferOpt, op->getLoc(), rewriter);
@@ -456,7 +479,8 @@ struct VMMOpInterface : DstBufferizableOpInterfaceExternalModel<VMMOpInterface,
     if (failed(outputBufferOpt))
       return failure();
 
-    auto contiguousInput = materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter);
+    auto contiguousInput =
+      materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter, getEnclosingBufferizationKnowledge(op));
     if (failed(contiguousInput))
       return failure();
     Value contiguousOutput = allocateContiguousResultMemRefLike(*outputBufferOpt, op->getLoc(), rewriter);
@@ -497,10 +521,12 @@ struct BinaryDstOpInterface : DstBufferizableOpInterfaceExternalModel<BinaryDstO
     if (failed(outputBufferOpt))
       return failure();
 
-    auto contiguousLhs = materializeContiguousInputMemRef(*lhsOpt, op->getLoc(), rewriter);
+    auto contiguousLhs =
+      materializeContiguousInputMemRef(*lhsOpt, op->getLoc(), rewriter, getEnclosingBufferizationKnowledge(op));
     if (failed(contiguousLhs))
       return failure();
-    auto contiguousRhs = materializeContiguousInputMemRef(*rhsOpt, op->getLoc(), rewriter);
+    auto contiguousRhs =
+      materializeContiguousInputMemRef(*rhsOpt, op->getLoc(), rewriter, getEnclosingBufferizationKnowledge(op));
     if (failed(contiguousRhs))
       return failure();
     Value contiguousOutput = allocateContiguousResultMemRefLike(*outputBufferOpt, op->getLoc(), rewriter);
@@ -534,10 +560,12 @@ struct VVDMulOpInterface : DstBufferizableOpInterfaceExternalModel<VVDMulOpInter
     if (failed(outputBufferOpt))
       return failure();
 
-    auto contiguousLhs = materializeContiguousInputMemRef(*lhsOpt, op->getLoc(), rewriter);
+    auto contiguousLhs =
+      materializeContiguousInputMemRef(*lhsOpt, op->getLoc(), rewriter, getEnclosingBufferizationKnowledge(op));
     if (failed(contiguousLhs))
       return failure();
-    auto contiguousRhs = materializeContiguousInputMemRef(*rhsOpt, op->getLoc(), rewriter);
+    auto contiguousRhs =
+      materializeContiguousInputMemRef(*rhsOpt, op->getLoc(), rewriter, getEnclosingBufferizationKnowledge(op));
     if (failed(contiguousRhs))
       return failure();
     Value contiguousOutput = allocateContiguousResultMemRefLike(*outputBufferOpt, op->getLoc(), rewriter);
@@ -574,7 +602,8 @@ struct UnaryDstOpInterface : DstBufferizableOpInterfaceExternalModel<UnaryDstOpI
     if (failed(outputBufferOpt))
       return failure();
 
-    auto contiguousInput = materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter);
+    auto contiguousInput =
+      materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter, getEnclosingBufferizationKnowledge(op));
     if (failed(contiguousInput))
       return failure();
     Value contiguousOutput = allocateContiguousResultMemRefLike(*outputBufferOpt, op->getLoc(), rewriter);

src/PIM/Dialect/Pim/Transforms/Bufferization/PimBufferizationPass.cpp

@@ -116,6 +116,36 @@ lowerMemRefCopyToPimCopy(memref::CopyOp copyOp, PatternRewriter& rewriter, const
   return success();
 }
 
+static LogicalResult verifyLoweredPimCopy(pim::PimMemCopyHostToDevOp copyOp, const StaticValueKnowledge& knowledge) {
+  bool sourceIsHost = isHostBackedPimAddress(copyOp.getHostSource(), knowledge);
+  bool targetIsHost = isHostBackedPimAddress(copyOp.getDeviceTarget(), knowledge);
+  bool sourceIsDevice = isDeviceLocalPimAddress(copyOp.getHostSource(), knowledge);
+  bool targetIsDevice = isDeviceLocalPimAddress(copyOp.getDeviceTarget(), knowledge);
+  if (!sourceIsHost || !targetIsDevice || targetIsHost || sourceIsDevice)
+    return copyOp.emitOpError("pim.memcp_hd requires a host-backed source and a device-local target");
+  return success();
+}
+
+static LogicalResult verifyLoweredPimCopy(pim::PimMemCopyDevToHostOp copyOp, const StaticValueKnowledge& knowledge) {
+  bool sourceIsHost = isHostBackedPimAddress(copyOp.getDeviceSource(), knowledge);
+  bool targetIsHost = isHostBackedPimAddress(copyOp.getHostTarget(), knowledge);
+  bool sourceIsDevice = isDeviceLocalPimAddress(copyOp.getDeviceSource(), knowledge);
+  bool targetIsDevice = isDeviceLocalPimAddress(copyOp.getHostTarget(), knowledge);
+  if (!targetIsHost || !sourceIsDevice || sourceIsHost || targetIsDevice)
+    return copyOp.emitOpError("pim.memcp_dh requires a device-local source and a host-backed target");
+  return success();
+}
+
+static LogicalResult verifyLoweredPimCopy(pim::PimMemCopyOp copyOp, const StaticValueKnowledge& knowledge) {
+  bool sourceIsHost = isHostBackedPimAddress(copyOp.getSource(), knowledge);
+  bool targetIsHost = isHostBackedPimAddress(copyOp.getTarget(), knowledge);
+  bool sourceIsDevice = isDeviceLocalPimAddress(copyOp.getSource(), knowledge);
+  bool targetIsDevice = isDeviceLocalPimAddress(copyOp.getTarget(), knowledge);
+  if (!sourceIsDevice || !targetIsDevice || sourceIsHost || targetIsHost)
+    return copyOp.emitOpError("pim.memcp requires device-local source and target operands");
+  return success();
+}
+
 struct PimBufferizationPass : PassWrapper<PimBufferizationPass, OperationPass<ModuleOp>> {
   MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(PimBufferizationPass)
   StringRef getArgument() const override { return "bufferize-pim"; }
@@ -129,6 +159,7 @@ struct PimBufferizationPass : PassWrapper<PimBufferizationPass, OperationPass<Mo
 private:
   void annotateWeightsMemrefs(ModuleOp moduleOp, func::FuncOp funcOp) const;
   LogicalResult verifyContiguousRuntimeOperands(ModuleOp moduleOp) const;
+  LogicalResult verifyPimCopyAddressSpaces(ModuleOp moduleOp) const;
 };
 
 static LogicalResult applyPatternsOnce(Operation* op, PatternApplicator& applicator, PatternRewriter& rewriter) {
@@ -240,6 +271,10 @@ void PimBufferizationPass::runOnOperation() {
     signalPassFailure();
     return;
   }
+  if (failed(verifyPimCopyAddressSpaces(moduleOp))) {
+    signalPassFailure();
+    return;
+  }
 
   annotateWeightsMemrefs(moduleOp, funcOp);
 
@@ -346,6 +381,31 @@ LogicalResult PimBufferizationPass::verifyContiguousRuntimeOperands(ModuleOp mod
   return success();
 }
 
+LogicalResult PimBufferizationPass::verifyPimCopyAddressSpaces(ModuleOp moduleOp) const {
+  bool hasFailure = false;
+  auto verifyWithKnowledge = [&](auto coreLikeOp, const StaticValueKnowledge& initialKnowledge) {
+    (void) walkPimCoreBlockStructurally(
+      coreLikeOp.getBody().front(), initialKnowledge, [&](Operation& op, const StaticValueKnowledge& knowledge) {
+        if (auto copyOp = dyn_cast<pim::PimMemCopyOp>(&op); copyOp && failed(verifyLoweredPimCopy(copyOp, knowledge)))
+          hasFailure = true;
+        if (auto copyOp = dyn_cast<pim::PimMemCopyHostToDevOp>(&op);
+            copyOp && failed(verifyLoweredPimCopy(copyOp, knowledge)))
+          hasFailure = true;
+        if (auto copyOp = dyn_cast<pim::PimMemCopyDevToHostOp>(&op);
+            copyOp && failed(verifyLoweredPimCopy(copyOp, knowledge)))
+          hasFailure = true;
+        return success();
+      });
+  };
+
+  moduleOp.walk([&](pim::PimCoreOp coreOp) { verifyWithKnowledge(coreOp, seedCoreKnowledge(coreOp)); });
+  moduleOp.walk([&](pim::PimCoreBatchOp coreBatchOp) {
+    StaticValueKnowledge knowledge = seedCoreBatchKnowledge(coreBatchOp, 0);
+    verifyWithKnowledge(coreBatchOp, knowledge);
+  });
+  return success(!hasFailure);
+}
+
 std::unique_ptr<Pass> createPimBufferizationPass() { return std::make_unique<PimBufferizationPass>(); }
 
 } // namespace onnx_mlir

src/PIM/Dialect/Pim/Transforms/HostConstantFolding/Patterns/Constant.cpp

@@ -96,8 +96,7 @@ struct FoldConstantCoreMapPattern final : OpRewritePattern<linalg::MapOp> {
   using OpRewritePattern::OpRewritePattern;
 
   LogicalResult matchAndRewrite(linalg::MapOp mapOp, PatternRewriter& rewriter) const override {
-    auto coreOp = mapOp->getParentOfType<pim::PimCoreOp>();
-    if (!coreOp)
+    if (!mapOp->getParentOfType<pim::PimCoreOp>() && !mapOp->getParentOfType<pim::PimCoreBatchOp>())
       return failure();
 
     auto initType = dyn_cast<MemRefType>(mapOp.getInit().getType());

src/PIM/Dialect/Pim/Transforms/Verification/CMakeLists.txt

@@ -5,6 +5,7 @@ add_pim_library(OMPimVerification
 
   LINK_LIBS PUBLIC
   OMPimCommon
+  OMPimCompilerOptions
   OMPimBufferization
   PimOps
   SpatialOps

src/PIM/Dialect/Pim/Transforms/Verification/VerificationPass.cpp

@@ -5,12 +5,17 @@
 #include "mlir/Pass/Pass.h"
 
 #include "llvm/ADT/STLExtras.h"
+#include "llvm/Support/FormatVariadic.h"
+#include "llvm/Support/raw_ostream.h"
+
+#include <string>
 
 #include "src/Accelerators/PIM/Common/IR/BatchCoreUtils.hpp"
 #include "src/Accelerators/PIM/Common/IR/CoreBlockUtils.hpp"
 #include "src/Accelerators/PIM/Common/IR/SubviewUtils.hpp"
 #include "src/Accelerators/PIM/Common/IR/WeightUtils.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
 #include "src/Accelerators/PIM/Common/Support/Diagnostics.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/Transforms/Bufferization/ContiguityPatterns.hpp"
@@ -143,6 +148,479 @@ static bool isHostAddressableValue(Value value, const StaticValueKnowledge& know
   return isa_and_nonnull<memref::GetGlobalOp>(base.getDefiningOp());
 }
 
+
+enum class CommunicationEventKind { Send, Receive };
+
+struct CommunicationEvent {
+  CommunicationEventKind kind = CommunicationEventKind::Send;
+  int64_t coreId = 0;
+  int64_t peerCoreId = 0;
+  int64_t size = 0;
+  uint64_t ordinal = 0;
+  std::optional<int64_t> minChannelId;
+  std::string materializer;
+  std::optional<int64_t> traceId;
+  std::optional<int64_t> commOrder;
+  std::optional<int64_t> traceClassId;
+  std::optional<int64_t> traceBlockOrdinal;
+  std::string traceKind;
+  std::string tracePhase;
+  std::string traceClassKind;
+  std::string tracePayload;
+  std::string traceMessages;
+  std::string tracePrevOp;
+  std::string traceNextOp;
+  Operation* op = nullptr;
+};
+
+using CommunicationEventVector = SmallVector<CommunicationEvent, 0>;
+
+static StringRef getCommunicationEventKindName(CommunicationEventKind kind) {
+  return kind == CommunicationEventKind::Send ? "send" : "receive";
+}
+
+constexpr StringLiteral kRaptorMinChannelIdAttr = "raptor.min_channel_id";
+constexpr StringLiteral kRaptorMaterializerAttr = "raptor.materializer";
+constexpr StringLiteral kRaptorCommOrderAttr = "raptor.comm_order";
+constexpr StringLiteral kRaptorCommTraceIdAttr = "raptor.comm_trace_id";
+constexpr StringLiteral kRaptorCommTraceKindAttr = "raptor.comm_trace_kind";
+constexpr StringLiteral kRaptorCommTracePhaseAttr = "raptor.comm_trace_phase";
+constexpr StringLiteral kRaptorCommTraceClassIdAttr = "raptor.comm_trace_class_id";
+constexpr StringLiteral kRaptorCommTraceClassKindAttr = "raptor.comm_trace_class_kind";
+constexpr StringLiteral kRaptorCommTraceBlockOrdinalAttr = "raptor.comm_trace_block_ordinal";
+constexpr StringLiteral kRaptorCommTracePayloadAttr = "raptor.comm_trace_payload";
+constexpr StringLiteral kRaptorCommTraceMessagesAttr = "raptor.comm_trace_messages";
+constexpr StringLiteral kRaptorCommTracePrevOpAttr = "raptor.comm_trace_prev_op";
+constexpr StringLiteral kRaptorCommTraceNextOpAttr = "raptor.comm_trace_next_op";
+
+static std::optional<int64_t> getNearestIntegerAttr(Operation* op, StringRef name) {
+  for (Operation* current = op; current; current = current->getParentOp())
+    if (auto attr = current->getAttrOfType<IntegerAttr>(name))
+      return attr.getInt();
+  return std::nullopt;
+}
+
+static std::string getNearestStringAttr(Operation* op, StringRef name) {
+  for (Operation* current = op; current; current = current->getParentOp())
+    if (auto attr = current->getAttrOfType<StringAttr>(name))
+      return attr.getValue().str();
+  return "";
+}
+
+static std::string formatLocation(Location loc) {
+  std::string text;
+  llvm::raw_string_ostream os(text);
+  loc.print(os);
+  return os.str();
+}
+
+static std::string formatOperationSummary(Operation* op) {
+  std::string text;
+  llvm::raw_string_ostream os(text);
+  OpPrintingFlags flags;
+  flags.skipRegions();
+  flags.elideLargeElementsAttrs();
+  op->print(os, flags);
+  return os.str();
+}
+
+static std::string formatCommunicationEvent(const CommunicationEvent& event) {
+  std::string text;
+  llvm::raw_string_ostream os(text);
+  os << "core " << event.coreId << " " << getCommunicationEventKindName(event.kind) << " "
+     << (event.kind == CommunicationEventKind::Send ? "to" : "from") << " " << event.peerCoreId
+     << " size " << event.size << "B ordinal " << event.ordinal;
+  if (event.minChannelId)
+    os << " min_channel " << *event.minChannelId;
+  if (event.commOrder)
+    os << " comm_order " << *event.commOrder;
+  if (!event.materializer.empty())
+    os << " materializer " << event.materializer;
+  if (event.traceId)
+    os << " trace#" << *event.traceId;
+  if (!event.tracePhase.empty())
+    os << " phase " << event.tracePhase;
+  if (event.traceClassId)
+    os << " class " << event.traceClassKind << "#" << *event.traceClassId;
+  if (event.traceBlockOrdinal)
+    os << " block_ordinal " << *event.traceBlockOrdinal;
+  if (!event.tracePayload.empty())
+    os << " payload " << event.tracePayload;
+  if (!event.traceMessages.empty())
+    os << " messages {" << event.traceMessages << "}";
+  if (!event.tracePrevOp.empty() || !event.traceNextOp.empty())
+    os << " inserted_between [" << event.tracePrevOp << " | " << event.traceNextOp << "]";
+  return os.str();
+}
+
+static bool areMatchedCommunicationEvents(const CommunicationEvent& lhs, const CommunicationEvent& rhs) {
+  if (lhs.coreId != rhs.peerCoreId || lhs.peerCoreId != rhs.coreId || lhs.size != rhs.size)
+    return false;
+
+  return (lhs.kind == CommunicationEventKind::Send && rhs.kind == CommunicationEventKind::Receive)
+      || (lhs.kind == CommunicationEventKind::Receive && rhs.kind == CommunicationEventKind::Send);
+}
+
+
+static std::optional<size_t> findMatchingCounterpartIndex(const CommunicationEventVector& events,
+                                                           const CommunicationEvent& event,
+                                                           size_t begin) {
+  for (size_t index = begin; index < events.size(); ++index)
+    if (areMatchedCommunicationEvents(event, events[index]))
+      return index;
+  return std::nullopt;
+}
+
+static void printCounterpartProbe(llvm::raw_ostream& os,
+                                  const DenseMap<int64_t, CommunicationEventVector>& coreEvents,
+                                  const DenseMap<int64_t, size_t>& programCounters,
+                                  const CommunicationEvent& blockedEvent) {
+  auto peerEventsIt = coreEvents.find(blockedEvent.peerCoreId);
+  if (peerEventsIt == coreEvents.end()) {
+    os << "  no local stream was collected for peer core " << blockedEvent.peerCoreId << "\n";
+    return;
+  }
+
+  const CommunicationEventVector& peerEvents = peerEventsIt->second;
+  size_t peerPc = 0;
+  auto peerPcIt = programCounters.find(blockedEvent.peerCoreId);
+  if (peerPcIt != programCounters.end())
+    peerPc = peerPcIt->second;
+
+  os << "  counterpart probe for " << formatCommunicationEvent(blockedEvent) << "\n";
+  os << "    peer core " << blockedEvent.peerCoreId << " current pc " << peerPc << " of " << peerEvents.size()
+     << "\n";
+
+  std::optional<size_t> nextMatch = findMatchingCounterpartIndex(peerEvents, blockedEvent, peerPc);
+  std::optional<size_t> anyMatch = findMatchingCounterpartIndex(peerEvents, blockedEvent, 0);
+
+  if (!nextMatch && !anyMatch) {
+    os << "    no matching counterpart exists anywhere in the peer stream\n";
+    return;
+  }
+
+  if (!nextMatch && anyMatch) {
+    os << "    matching counterpart exists only before the peer pc at ordinal " << *anyMatch
+       << "; this usually means the static stream expansion or ordering metadata is inconsistent\n";
+    os << "      " << formatCommunicationEvent(peerEvents[*anyMatch]) << "\n";
+    os << "      op: " << formatOperationSummary(peerEvents[*anyMatch].op) << "\n";
+    return;
+  }
+
+  const CommunicationEvent& match = peerEvents[*nextMatch];
+  os << "    next matching counterpart is at peer ordinal " << *nextMatch << " (distance +"
+     << (*nextMatch >= peerPc ? *nextMatch - peerPc : 0) << ")\n";
+  os << "      " << formatCommunicationEvent(match) << "\n";
+  os << "      op: " << formatOperationSummary(match.op) << "\n";
+
+  if (*nextMatch == peerPc)
+    return;
+
+  os << "    peer operations blocking before that counterpart:\n";
+  size_t end = std::min(peerEvents.size(), std::min(*nextMatch + static_cast<size_t>(1), peerPc + static_cast<size_t>(12)));
+  for (size_t index = peerPc; index < end; ++index) {
+    os << (index == peerPc ? "      pc => " : "           ") << "#" << index << " "
+       << formatCommunicationEvent(peerEvents[index]) << "\n";
+    os << "             op: " << formatOperationSummary(peerEvents[index].op) << "\n";
+  }
+  if (end <= *nextMatch)
+    os << "      ... " << (*nextMatch - end + 1) << " more peer communication event(s) before the counterpart\n";
+}
+
+static CommunicationEvent makeCommunicationEvent(CommunicationEventKind kind,
+                                                 int64_t coreId,
+                                                 int64_t peerCoreId,
+                                                 int64_t size,
+                                                 uint64_t ordinal,
+                                                 Operation* op) {
+  return CommunicationEvent {kind,
+                             coreId,
+                             peerCoreId,
+                             size,
+                             ordinal,
+                             getNearestIntegerAttr(op, kRaptorMinChannelIdAttr),
+                             getNearestStringAttr(op, kRaptorMaterializerAttr),
+                             getNearestIntegerAttr(op, kRaptorCommTraceIdAttr),
+                             getNearestIntegerAttr(op, kRaptorCommOrderAttr),
+                             getNearestIntegerAttr(op, kRaptorCommTraceClassIdAttr),
+                             getNearestIntegerAttr(op, kRaptorCommTraceBlockOrdinalAttr),
+                             getNearestStringAttr(op, kRaptorCommTraceKindAttr),
+                             getNearestStringAttr(op, kRaptorCommTracePhaseAttr),
+                             getNearestStringAttr(op, kRaptorCommTraceClassKindAttr),
+                             getNearestStringAttr(op, kRaptorCommTracePayloadAttr),
+                             getNearestStringAttr(op, kRaptorCommTraceMessagesAttr),
+                             getNearestStringAttr(op, kRaptorCommTracePrevOpAttr),
+                             getNearestStringAttr(op, kRaptorCommTraceNextOpAttr),
+                             op};
+}
+
+static LogicalResult appendCoreCommunicationEvents(Block& block,
+                                                   int64_t coreId,
+                                                   const StaticValueKnowledge& initialKnowledge,
+                                                   SmallVectorImpl<CommunicationEvent>& events,
+                                                   pim::CappedDiagnosticReporter& diagnostics) {
+  return walkPimCoreBlock(block, initialKnowledge, [&](Operation& op, const StaticValueKnowledge& knowledge) {
+    if (auto sendOp = dyn_cast<pim::PimSendOp>(&op)) {
+      auto targetCoreId = resolveIndexValue(sendOp.getTargetCoreId(), knowledge);
+      if (failed(targetCoreId)) {
+        diagnostics.report(&op, [](Operation* illegalOp) {
+          illegalOp->emitOpError("cannot statically resolve send target core for PIM communication deadlock check");
+        });
+        return failure();
+      }
+
+      events.push_back(makeCommunicationEvent(CommunicationEventKind::Send,
+                                              coreId,
+                                              *targetCoreId,
+                                              sendOp.getSize(),
+                                              static_cast<uint64_t>(events.size()),
+                                              &op));
+      return success();
+    }
+
+    if (auto receiveOp = dyn_cast<pim::PimReceiveOp>(&op)) {
+      auto sourceCoreId = resolveIndexValue(receiveOp.getSourceCoreId(), knowledge);
+      if (failed(sourceCoreId)) {
+        diagnostics.report(&op, [](Operation* illegalOp) {
+          illegalOp->emitOpError("cannot statically resolve receive source core for PIM communication deadlock check");
+        });
+        return failure();
+      }
+
+      events.push_back(makeCommunicationEvent(CommunicationEventKind::Receive,
+                                              coreId,
+                                              *sourceCoreId,
+                                              receiveOp.getSize(),
+                                              static_cast<uint64_t>(events.size()),
+                                              &op));
+      return success();
+    }
+
+    return success();
+  });
+}
+
+static void printCommunicationWindow(llvm::raw_ostream& os,
+                                     const DenseMap<int64_t, CommunicationEventVector>& coreEvents,
+                                     int64_t coreId,
+                                     size_t pc,
+                                     unsigned radius = 4) {
+  auto eventsIt = coreEvents.find(coreId);
+  if (eventsIt == coreEvents.end())
+    return;
+
+  const CommunicationEventVector& events = eventsIt->second;
+  size_t begin = pc > radius ? pc - radius : 0;
+  size_t end = std::min(events.size(), pc + static_cast<size_t>(radius) + 1);
+  os << "  local stream for core " << coreId << " around pc " << pc << " of " << events.size() << ":\n";
+  for (size_t index = begin; index < end; ++index) {
+    os << (index == pc ? "    => " : "       ") << formatCommunicationEvent(events[index]) << "\n";
+    os << "          op: " << formatOperationSummary(events[index].op) << "\n";
+  }
+}
+
+static void printCommunicationDeadlockReport(const DenseMap<int64_t, CommunicationEventVector>& coreEvents,
+                                             const DenseMap<int64_t, size_t>& programCounters,
+                                             ArrayRef<int64_t> cycle) {
+  llvm::errs() << "\n=== PIM static communication deadlock report ===\n";
+  llvm::errs() << "wait cycle:";
+  for (int64_t coreId : cycle)
+    llvm::errs() << " " << coreId;
+  if (!cycle.empty())
+    llvm::errs() << " -> " << cycle.front();
+  llvm::errs() << "\n\nblocked heads:\n";
+
+  for (int64_t coreId : cycle) {
+    auto eventsIt = coreEvents.find(coreId);
+    auto pcIt = programCounters.find(coreId);
+    if (eventsIt == coreEvents.end() || pcIt == programCounters.end() || pcIt->second >= eventsIt->second.size())
+      continue;
+    const CommunicationEvent& event = eventsIt->second[pcIt->second];
+    llvm::errs() << "  " << formatCommunicationEvent(event) << "\n";
+    llvm::errs() << "    loc: " << formatLocation(event.op->getLoc()) << "\n";
+    llvm::errs() << "    op : " << formatOperationSummary(event.op) << "\n";
+  }
+
+  llvm::errs() << "\npeer counterpart probes:\n";
+  for (int64_t coreId : cycle) {
+    auto eventsIt = coreEvents.find(coreId);
+    auto pcIt = programCounters.find(coreId);
+    if (eventsIt == coreEvents.end() || pcIt == programCounters.end() || pcIt->second >= eventsIt->second.size())
+      continue;
+    printCounterpartProbe(llvm::errs(), coreEvents, programCounters, eventsIt->second[pcIt->second]);
+  }
+
+  llvm::errs() << "\nlocal communication streams:\n";
+  for (int64_t coreId : cycle) {
+    auto pcIt = programCounters.find(coreId);
+    if (pcIt == programCounters.end())
+      continue;
+    printCommunicationWindow(llvm::errs(), coreEvents, coreId, pcIt->second);
+  }
+  llvm::errs() << "=== end PIM static communication deadlock report ===\n\n";
+}
+
+static void emitCommunicationDeadlockCycle(ModuleOp moduleOp,
+                                           const DenseMap<int64_t, CommunicationEventVector>& coreEvents,
+                                           const DenseMap<int64_t, size_t>& programCounters,
+                                           ArrayRef<int64_t> cycle) {
+  printCommunicationDeadlockReport(coreEvents, programCounters, cycle);
+
+  auto diagnostic = moduleOp.emitError()
+      << "PIM communication deadlock check found a blocking send/receive cycle while statically simulating the "
+         "expanded per-core communication streams; see the PIM static communication deadlock report above";
+
+  for (int64_t coreId : cycle) {
+    auto eventsIt = coreEvents.find(coreId);
+    auto pcIt = programCounters.find(coreId);
+    if (eventsIt == coreEvents.end() || pcIt == programCounters.end() || pcIt->second >= eventsIt->second.size())
+      continue;
+
+    const CommunicationEvent& event = eventsIt->second[pcIt->second];
+    Diagnostic& note = diagnostic.attachNote(event.op->getLoc());
+    note << formatCommunicationEvent(event);
+    if (!event.materializer.empty())
+      note << " emitted by " << event.materializer;
+  }
+}
+
+static FailureOr<SmallVector<int64_t>> findCommunicationWaitCycle(
+  const DenseMap<int64_t, CommunicationEventVector>& coreEvents,
+  const DenseMap<int64_t, size_t>& programCounters) {
+  for (const auto& [startCoreId, events] : coreEvents) {
+    auto startPcIt = programCounters.find(startCoreId);
+    if (startPcIt == programCounters.end() || startPcIt->second >= events.size())
+      continue;
+
+    DenseMap<int64_t, size_t> positionInPath;
+    SmallVector<int64_t, 8> path;
+    int64_t currentCoreId = startCoreId;
+    while (true) {
+      auto eventsIt = coreEvents.find(currentCoreId);
+      auto pcIt = programCounters.find(currentCoreId);
+      if (eventsIt == coreEvents.end() || pcIt == programCounters.end() || pcIt->second >= eventsIt->second.size())
+        break;
+
+      auto positionIt = positionInPath.find(currentCoreId);
+      if (positionIt != positionInPath.end()) {
+        SmallVector<int64_t> cycle;
+        for (size_t index = positionIt->second; index < path.size(); ++index)
+          cycle.push_back(path[index]);
+        return cycle;
+      }
+
+      positionInPath[currentCoreId] = path.size();
+      path.push_back(currentCoreId);
+      currentCoreId = eventsIt->second[pcIt->second].peerCoreId;
+    }
+  }
+
+  return failure();
+}
+
+static LogicalResult verifyNoStaticCommunicationDeadlock(ModuleOp moduleOp,
+                                                          pim::CappedDiagnosticReporter& diagnostics) {
+  DenseMap<int64_t, CommunicationEventVector> coreEvents;
+  bool hasFailure = false;
+
+  for (func::FuncOp funcOp : moduleOp.getOps<func::FuncOp>()) {
+    if (funcOp.isExternal())
+      continue;
+
+    for (Operation& op : funcOp.getBody().front().getOperations()) {
+      if (auto coreOp = dyn_cast<pim::PimCoreOp>(&op)) {
+        int64_t coreId = coreOp.getCoreId();
+        if (failed(appendCoreCommunicationEvents(
+              coreOp.getBody().front(), coreId, StaticValueKnowledge {}, coreEvents[coreId], diagnostics)))
+          hasFailure = true;
+        continue;
+      }
+
+      if (auto coreBatchOp = dyn_cast<pim::PimCoreBatchOp>(&op)) {
+        SmallVector<int32_t> coreIds = getBatchCoreIds(coreBatchOp);
+        size_t laneCount = static_cast<size_t>(coreBatchOp.getLaneCount());
+        for (size_t lane = 0; lane < laneCount; ++lane) {
+          StaticValueKnowledge laneKnowledge;
+          laneKnowledge.indexValues[coreBatchOp.getLaneArgument()] = static_cast<int64_t>(lane);
+          for (unsigned inputIndex = 0; inputIndex < coreBatchOp.getInputs().size(); ++inputIndex)
+            laneKnowledge.aliases[coreBatchOp.getInputArgument(inputIndex)] = coreBatchOp.getInputs()[inputIndex];
+
+          SmallVector<int32_t> laneCoreIds = getLaneChunkCoreIds(coreIds, laneCount, static_cast<unsigned>(lane));
+          for (int32_t coreId : laneCoreIds) {
+            if (failed(appendCoreCommunicationEvents(
+                  coreBatchOp.getBody().front(), coreId, laneKnowledge, coreEvents[coreId], diagnostics)))
+              hasFailure = true;
+          }
+        }
+      }
+    }
+  }
+
+  if (hasFailure)
+    return failure();
+
+  DenseMap<int64_t, size_t> programCounters;
+  for (const auto& [coreId, events] : coreEvents)
+    programCounters[coreId] = 0;
+
+  while (true) {
+    bool madeProgress = false;
+    for (const auto& [coreId, events] : coreEvents) {
+      size_t pc = programCounters[coreId];
+      if (pc >= events.size())
+        continue;
+
+      const CommunicationEvent& event = events[pc];
+      auto peerEventsIt = coreEvents.find(event.peerCoreId);
+      if (peerEventsIt == coreEvents.end())
+        continue;
+
+      size_t peerPc = programCounters[event.peerCoreId];
+      if (peerPc >= peerEventsIt->second.size())
+        continue;
+
+      const CommunicationEvent& peerEvent = peerEventsIt->second[peerPc];
+      if (!areMatchedCommunicationEvents(event, peerEvent))
+        continue;
+
+      ++programCounters[coreId];
+      ++programCounters[event.peerCoreId];
+      madeProgress = true;
+    }
+
+    if (madeProgress)
+      continue;
+
+    bool allDone = true;
+    for (const auto& [coreId, events] : coreEvents) {
+      if (programCounters[coreId] < events.size()) {
+        allDone = false;
+        break;
+      }
+    }
+    if (allDone)
+      return success();
+
+    auto cycle = findCommunicationWaitCycle(coreEvents, programCounters);
+    if (succeeded(cycle)) {
+      emitCommunicationDeadlockCycle(moduleOp, coreEvents, programCounters, *cycle);
+      return failure();
+    }
+
+    auto diagnostic = moduleOp.emitError()
+        << "PIM communication deadlock check stalled without finding a closed wait cycle; this usually means a "
+           "send/receive peer is missing or ordered after a finished core";
+    for (const auto& [coreId, events] : coreEvents) {
+      size_t pc = programCounters[coreId];
+      if (pc >= events.size())
+        continue;
+      const CommunicationEvent& event = events[pc];
+      diagnostic.attachNote(event.op->getLoc()) << formatCommunicationEvent(event);
+    }
+    return failure();
+  }
+}
+
 struct VerificationPass : PassWrapper<VerificationPass, OperationPass<ModuleOp>> {
   MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(VerificationPass)
 
@@ -212,11 +690,18 @@ struct VerificationPass : PassWrapper<VerificationPass, OperationPass<ModuleOp>>
       }
     }
 
+    bool hasFailure = false;
+    if (pimDetectCommunicationDeadlock && failed(verifyNoStaticCommunicationDeadlock(moduleOp, diagnostics)))
+      hasFailure = true;
+
     if (diagnostics.hasFailure()) {
       diagnostics.emitSuppressedSummary(moduleOp, "verification failures");
       moduleOp.emitError("PIM codegen verification failed; see diagnostics above");
-      signalPassFailure();
+      hasFailure = true;
     }
+
+    if (hasFailure)
+      signalPassFailure();
   }
 
 private:

src/PIM/Dialect/Spatial/Spatial.td

@@ -26,7 +26,7 @@ def SpatTensor :
 // Execution
 //===----------------------------------------------------------------------===//
 
-def SpatCompute : SpatOp<"compute",
+class SpatComputeLikeBase<string mnemonic> : SpatOp<mnemonic,
     [SingleBlock, AttrSizedOperandSegments,
      DeclareOpInterfaceMethods<OpAsmOpInterface, ["getAsmBlockArgumentNames"]>]> {
   let summary = "Compute region with attached constant weights";
@@ -42,6 +42,12 @@ def SpatCompute : SpatOp<"compute",
 
   let regions = (region SizedRegion<1>:$body);
 
+  let hasVerifier = 1;
+  let hasFolder = 1;
+  let hasCustomAssemblyFormat = 1;
+}
+
+def SpatGraphCompute : SpatComputeLikeBase<"graph_compute"> {
   let extraClassDeclaration = [{
     std::optional<::mlir::BlockArgument> getWeightArgument(unsigned idx);
     std::optional<::mlir::BlockArgument> getInputArgument(unsigned idx);
@@ -50,16 +56,26 @@ def SpatCompute : SpatOp<"compute",
     std::optional<std::tuple<::mlir::Value, ::mlir::BlockArgument>>
       insertInput(unsigned idx, ::mlir::Value input, ::mlir::Location loc);
     ::llvm::SetVector<::mlir::Value, ::llvm::SmallVector<::mlir::Value, 4>, ::llvm::SmallDenseSet<::mlir::Value, 4>> getCrossbarWeights();
-    ::mlir::FailureOr<std::tuple<::mlir::OpResult, SpatCompute>>
+    ::mlir::FailureOr<std::tuple<::mlir::OpResult, SpatGraphCompute>>
       insertOutput(::mlir::RewriterBase &rewriter, unsigned idx, ::mlir::Type type, ::mlir::Location loc);
   }];
-
-  let hasVerifier = 1;
-  let hasFolder = 1;
-  let hasCustomAssemblyFormat = 1;
 }
 
-def SpatComputeBatch : SpatOp<"compute_batch",
+def SpatScheduledCompute : SpatComputeLikeBase<"scheduled_compute"> {
+  let extraClassDeclaration = [{
+    std::optional<::mlir::BlockArgument> getWeightArgument(unsigned idx);
+    std::optional<::mlir::BlockArgument> getInputArgument(unsigned idx);
+    std::optional<std::tuple<::mlir::Value, ::mlir::BlockArgument>>
+      insertWeight(unsigned idx, ::mlir::Value weight, ::mlir::Location loc);
+    std::optional<std::tuple<::mlir::Value, ::mlir::BlockArgument>>
+      insertInput(unsigned idx, ::mlir::Value input, ::mlir::Location loc);
+    ::llvm::SetVector<::mlir::Value, ::llvm::SmallVector<::mlir::Value, 4>, ::llvm::SmallDenseSet<::mlir::Value, 4>> getCrossbarWeights();
+    ::mlir::FailureOr<std::tuple<::mlir::OpResult, SpatScheduledCompute>>
+      insertOutput(::mlir::RewriterBase &rewriter, unsigned idx, ::mlir::Type type, ::mlir::Location loc);
+  }];
+}
+
+class SpatComputeBatchLikeBase<string mnemonic> : SpatOp<mnemonic,
     [SingleBlock, AttrSizedOperandSegments,
      DeclareOpInterfaceMethods<OpAsmOpInterface, ["getAsmBlockArgumentNames"]>]> {
   let summary = "Tensor-native batch of equivalent compute lanes with shared weights and packed inputs";
@@ -76,6 +92,11 @@ def SpatComputeBatch : SpatOp<"compute_batch",
 
   let regions = (region SizedRegion<1>:$body);
 
+  let hasVerifier = 1;
+  let hasCustomAssemblyFormat = 1;
+}
+
+def SpatGraphComputeBatch : SpatComputeBatchLikeBase<"graph_compute_batch"> {
   let extraClassDeclaration = [{
     std::optional<::mlir::BlockArgument> getLaneArgument();
     std::optional<::mlir::BlockArgument> getWeightArgument(unsigned idx);
@@ -86,21 +107,33 @@ def SpatComputeBatch : SpatOp<"compute_batch",
     std::optional<std::tuple<::mlir::Value, ::mlir::BlockArgument>>
       insertInput(unsigned idx, ::mlir::Value input, ::mlir::Location loc);
     ::llvm::SetVector<::mlir::Value, ::llvm::SmallVector<::mlir::Value, 4>, ::llvm::SmallDenseSet<::mlir::Value, 4>> getCrossbarWeights();
-    ::mlir::FailureOr<std::tuple<::mlir::OpResult, ::mlir::BlockArgument, SpatComputeBatch>>
+    ::mlir::FailureOr<std::tuple<::mlir::OpResult, ::mlir::BlockArgument, SpatGraphComputeBatch>>
       insertOutput(::mlir::RewriterBase &rewriter, unsigned idx, ::mlir::Type type, ::mlir::Location loc);
   }];
+}
 
-  let hasVerifier = 1;
-  let hasCustomAssemblyFormat = 1;
+def SpatScheduledComputeBatch : SpatComputeBatchLikeBase<"scheduled_compute_batch"> {
+  let extraClassDeclaration = [{
+    std::optional<::mlir::BlockArgument> getLaneArgument();
+    std::optional<::mlir::BlockArgument> getWeightArgument(unsigned idx);
+    std::optional<::mlir::BlockArgument> getInputArgument(unsigned idx);
+    std::optional<::mlir::BlockArgument> getOutputArgument(unsigned idx);
+    std::optional<std::tuple<::mlir::Value, ::mlir::BlockArgument>>
+      insertWeight(unsigned idx, ::mlir::Value weight, ::mlir::Location loc);
+    std::optional<std::tuple<::mlir::Value, ::mlir::BlockArgument>>
+      insertInput(unsigned idx, ::mlir::Value input, ::mlir::Location loc);
+    ::llvm::SetVector<::mlir::Value, ::llvm::SmallVector<::mlir::Value, 4>, ::llvm::SmallDenseSet<::mlir::Value, 4>> getCrossbarWeights();
+    ::mlir::FailureOr<std::tuple<::mlir::OpResult, ::mlir::BlockArgument, SpatScheduledComputeBatch>>
+      insertOutput(::mlir::RewriterBase &rewriter, unsigned idx, ::mlir::Type type, ::mlir::Location loc);
+  }];
 }
 
 def SpatInParallelOp : SpatOp<"in_parallel", [
        Pure,
        Terminator,
        DeclareOpInterfaceMethods<InParallelOpInterface>,
-       HasParent<"SpatComputeBatch">,
       ] # GraphRegionNoTerminator.traits> {
-  let summary = "Parallel combining terminator for resultful spat.compute_batch";
+  let summary = "Parallel combining terminator for resultful Spatial compute batches";
 
   let regions = (region SizedRegion<1>:$region);
 
@@ -159,6 +192,88 @@ def SpatConcatOp : SpatOp<"concat", []> {
   let hasCustomAssemblyFormat = 1;
 }
 
+//===----------------------------------------------------------------------===//
+// Planning
+//===----------------------------------------------------------------------===//
+
+def SpatConv2DPlanOp : SpatOp<"conv2d_plan", []> {
+  let summary = "Structured Conv2D planning op that preserves logical ONNX geometry";
+
+  let arguments = (ins
+    SpatTensor:$input,
+    SpatTensor:$weight,
+    Optional<SpatTensor>:$bias,
+    DenseI64ArrayAttr:$pads,
+    DenseI64ArrayAttr:$strides,
+    DenseI64ArrayAttr:$dilations,
+    I64Attr:$group,
+    StrAttr:$logicalLayout
+  );
+
+  let results = (outs
+    SpatTensor:$output
+  );
+
+  let hasVerifier = 1;
+}
+
+def SpatReluPlanOp : SpatOp<"relu_plan", []> {
+  let summary = "Layout-aware ReLU planning op";
+
+  let arguments = (ins
+    SpatTensor:$input,
+    StrAttr:$logicalLayout
+  );
+
+  let results = (outs
+    SpatTensor:$output
+  );
+
+  let hasVerifier = 1;
+}
+
+def SpatReconciliatorOp : SpatOp<"reconciliator", []> {
+  let summary = "Logical-to-physical layout record or explicit fragment assembly";
+
+  let arguments = (ins
+    SpatTensor:$input,
+    Variadic<SpatTensor>:$fragments,
+    StrAttr:$logicalLayout,
+    StrAttr:$physicalLayout,
+    DenseI64ArrayAttr:$fragmentOffsets,
+    DenseI64ArrayAttr:$fragmentSizes,
+    StrAttr:$indexMap,
+    OptionalAttr<StrAttr>:$mode,
+    OptionalAttr<DenseI64ArrayAttr>:$fragmentOperandIndices,
+    OptionalAttr<DenseI64ArrayAttr>:$fragmentStrides,
+    OptionalAttr<StrAttr>:$conflictPolicy,
+    OptionalAttr<StrAttr>:$coveragePolicy
+  );
+
+  let results = (outs
+    SpatTensor:$output
+  );
+
+  let hasVerifier = 1;
+}
+
+def SpatMaterializeLayoutOp : SpatOp<"materialize_layout", []> {
+  let summary = "Explicit layout conversion or materialization barrier";
+
+  let arguments = (ins
+    SpatTensor:$input,
+    StrAttr:$logicalLayout,
+    StrAttr:$sourcePhysicalLayout,
+    StrAttr:$targetPhysicalLayout
+  );
+
+  let results = (outs
+    SpatTensor:$output
+  );
+
+  let hasVerifier = 1;
+}
+
 //===----------------------------------------------------------------------===//
 // Communication
 //===----------------------------------------------------------------------===//

src/PIM/Dialect/Spatial/SpatialOps.cpp

@@ -29,11 +29,19 @@ std::optional<BlockArgument> insertBlockArgument(Region& body, unsigned argIdx,
 }
 
 void setComputeOperandSegmentSizes(Operation* op, int32_t weightCount, int32_t inputCount) {
-  if (auto compute = dyn_cast<SpatCompute>(op)) {
+  if (auto compute = dyn_cast<SpatGraphCompute>(op)) {
     compute.getProperties().setOperandSegmentSizes({weightCount, inputCount});
     return;
   }
-  cast<SpatComputeBatch>(op).getProperties().setOperandSegmentSizes({weightCount, inputCount});
+  if (auto compute = dyn_cast<SpatScheduledCompute>(op)) {
+    compute.getProperties().setOperandSegmentSizes({weightCount, inputCount});
+    return;
+  }
+  if (auto batch = dyn_cast<SpatGraphComputeBatch>(op)) {
+    batch.getProperties().setOperandSegmentSizes({weightCount, inputCount});
+    return;
+  }
+  cast<SpatScheduledComputeBatch>(op).getProperties().setOperandSegmentSizes({weightCount, inputCount});
 }
 
 using CrossbarWeightSet = llvm::SetVector<Value, llvm::SmallVector<Value, 4>, llvm::SmallDenseSet<Value, 4>>;
@@ -47,116 +55,205 @@ CrossbarWeightSet collectCrossbarWeights(Region& body) {
   return weights;
 }
 
-} // namespace
-
-std::optional<BlockArgument> SpatCompute::getWeightArgument(unsigned idx) { return getBlockArgument(getBody(), idx); }
-
-std::optional<BlockArgument> SpatCompute::getInputArgument(unsigned idx) {
-  return getBlockArgument(getBody(), getWeights().size() + idx);
+template <typename ComputeOpTy>
+std::optional<BlockArgument> getComputeWeightArgument(ComputeOpTy compute, unsigned idx) {
+  return getBlockArgument(compute.getBody(), idx);
 }
 
-std::optional<std::tuple<Value, BlockArgument>> SpatCompute::insertWeight(unsigned idx, Value weight, Location loc) {
-  if (auto existing = llvm::find(getWeights(), weight); existing != getWeights().end()) {
-    auto index = std::distance(getWeights().begin(), existing);
-    return {
-      {*existing, *getWeightArgument(index)}
-    };
+template <typename ComputeOpTy>
+std::optional<BlockArgument> getComputeInputArgument(ComputeOpTy compute, unsigned idx) {
+  return getBlockArgument(compute.getBody(), compute.getWeights().size() + idx);
+}
+
+template <typename ComputeOpTy>
+std::optional<std::tuple<Value, BlockArgument>>
+insertComputeWeight(ComputeOpTy compute, unsigned idx, Value weight, Location loc) {
+  if (auto existing = llvm::find(compute.getWeights(), weight); existing != compute.getWeights().end()) {
+    auto index = std::distance(compute.getWeights().begin(), existing);
+    return {{*existing, *getComputeWeightArgument(compute, index)}};
   }
 
-  unsigned weightCount = getWeights().size();
-  unsigned inputCount = getInputs().size();
-  getOperation()->insertOperands(idx, ValueRange {weight});
+  unsigned weightCount = compute.getWeights().size();
+  unsigned inputCount = compute.getInputs().size();
+  compute.getOperation()->insertOperands(idx, ValueRange {weight});
   setComputeOperandSegmentSizes(
-    getOperation(), static_cast<int32_t>(weightCount + 1), static_cast<int32_t>(inputCount));
-  auto blockArg = insertBlockArgument(getBody(), idx, weight.getType(), loc);
+    compute.getOperation(), static_cast<int32_t>(weightCount + 1), static_cast<int32_t>(inputCount));
+  auto blockArg = insertBlockArgument(compute.getBody(), idx, weight.getType(), loc);
   if (!blockArg)
     return std::nullopt;
-  return std::make_tuple(getOperation()->getOperand(idx), *blockArg);
+  return std::make_tuple(compute.getOperation()->getOperand(idx), *blockArg);
 }
 
-std::optional<std::tuple<Value, BlockArgument>> SpatCompute::insertInput(unsigned idx, Value input, Location loc) {
-  unsigned weightCount = getWeights().size();
-  unsigned inputCount = getInputs().size();
-  getOperation()->insertOperands(weightCount + idx, ValueRange {input});
+template <typename ComputeBatchOpTy>
+std::optional<std::tuple<Value, BlockArgument>>
+insertComputeBatchWeight(ComputeBatchOpTy batch, unsigned idx, Value weight, Location loc) {
+  if (auto existing = llvm::find(batch.getWeights(), weight); existing != batch.getWeights().end()) {
+    auto index = std::distance(batch.getWeights().begin(), existing);
+    return {{*existing, *batch.getWeightArgument(index)}};
+  }
+
+  unsigned weightCount = batch.getWeights().size();
+  unsigned inputCount = batch.getInputs().size();
+  batch.getOperation()->insertOperands(idx, ValueRange {weight});
   setComputeOperandSegmentSizes(
-    getOperation(), static_cast<int32_t>(weightCount), static_cast<int32_t>(inputCount + 1));
-  auto blockArg = insertBlockArgument(getBody(), weightCount + idx, input.getType(), loc);
+    batch.getOperation(), static_cast<int32_t>(weightCount + 1), static_cast<int32_t>(inputCount));
+
+  auto blockArg = insertBlockArgument(batch.getBody(), 1 + idx, weight.getType(), loc);
   if (!blockArg)
     return std::nullopt;
-  return std::make_tuple(getOperation()->getOperand(weightCount + idx), *blockArg);
+  return std::make_tuple(batch.getOperation()->getOperand(idx), *blockArg);
 }
 
-CrossbarWeightSet SpatCompute::getCrossbarWeights() { return collectCrossbarWeights(getBody()); }
-
-FailureOr<std::tuple<OpResult, SpatCompute>>
-SpatCompute::insertOutput(RewriterBase& rewriter, unsigned idx, Type type, Location loc) {
-  if (idx > getNumResults())
-    return failure();
-
-  rewriter.setInsertionPoint(getOperation());
-  SmallVector<Type> resultTypes(getResultTypes().begin(), getResultTypes().end());
-  resultTypes.insert(resultTypes.begin() + idx, type);
-  auto newCompute = SpatCompute::create(rewriter, getLoc(), TypeRange(resultTypes), getWeights(), getInputs());
-  newCompute->setAttrs((*this)->getAttrs());
-  setComputeOperandSegmentSizes(newCompute.getOperation(),
-                                static_cast<int32_t>(newCompute.getWeights().size()),
-                                static_cast<int32_t>(newCompute.getInputs().size()));
-  rewriter.inlineRegionBefore(getBody(), newCompute.getBody(), newCompute.getBody().end());
-  for (unsigned oldResultIdx = 0; oldResultIdx < getNumResults(); ++oldResultIdx)
-    getResult(oldResultIdx)
-      .replaceAllUsesWith(newCompute.getResult(oldResultIdx < idx ? oldResultIdx : oldResultIdx + 1));
-  rewriter.eraseOp(getOperation());
-  return std::make_tuple(cast<OpResult>(newCompute.getResult(idx)), newCompute);
+template <typename ComputeOpTy>
+std::optional<std::tuple<Value, BlockArgument>>
+insertComputeInput(ComputeOpTy compute, unsigned idx, Value input, Location loc) {
+  unsigned weightCount = compute.getWeights().size();
+  unsigned inputCount = compute.getInputs().size();
+  compute.getOperation()->insertOperands(weightCount + idx, ValueRange {input});
+  setComputeOperandSegmentSizes(
+    compute.getOperation(), static_cast<int32_t>(weightCount), static_cast<int32_t>(inputCount + 1));
+  auto blockArg = insertBlockArgument(compute.getBody(), weightCount + idx, input.getType(), loc);
+  if (!blockArg)
+    return std::nullopt;
+  return std::make_tuple(compute.getOperation()->getOperand(weightCount + idx), *blockArg);
 }
 
-void SpatCompute::getAsmBlockArgumentNames(Region& region, OpAsmSetValueNameFn setNameFn) {
+template <typename ComputeOpTy>
+void setComputeAsmBlockArgumentNames(ComputeOpTy compute, Region& region, OpAsmSetValueNameFn setNameFn) {
   if (region.empty())
     return;
 
-  for (unsigned index = 0; index < getWeights().size(); ++index)
-    if (auto weightArg = getWeightArgument(index))
+  for (unsigned index = 0; index < compute.getWeights().size(); ++index)
+    if (auto weightArg = compute.getWeightArgument(index))
       setNameFn(*weightArg, ("w" + std::to_string(index)).c_str());
 
-  for (unsigned index = 0; index < getInputs().size(); ++index)
-    if (auto inputArg = getInputArgument(index))
+  for (unsigned index = 0; index < compute.getInputs().size(); ++index)
+    if (auto inputArg = compute.getInputArgument(index))
       setNameFn(*inputArg, ("in" + std::to_string(index)).c_str());
 }
 
-std::optional<BlockArgument> SpatComputeBatch::getLaneArgument() { return getBlockArgument(getBody(), 0); }
+template <typename ComputeOpTy>
+FailureOr<std::tuple<OpResult, ComputeOpTy>>
+insertComputeOutput(ComputeOpTy compute, RewriterBase& rewriter, unsigned idx, Type type, Location loc) {
+  if (idx > compute.getNumResults())
+    return failure();
 
-std::optional<BlockArgument> SpatComputeBatch::getWeightArgument(unsigned idx) {
+  rewriter.setInsertionPoint(compute.getOperation());
+  SmallVector<Type> resultTypes(compute.getResultTypes().begin(), compute.getResultTypes().end());
+  resultTypes.insert(resultTypes.begin() + idx, type);
+  auto newCompute =
+    ComputeOpTy::create(rewriter, compute.getLoc(), TypeRange(resultTypes), compute.getWeights(), compute.getInputs());
+  newCompute->setAttrs(compute->getAttrs());
+  setComputeOperandSegmentSizes(newCompute.getOperation(),
+                                static_cast<int32_t>(newCompute.getWeights().size()),
+                                static_cast<int32_t>(newCompute.getInputs().size()));
+  rewriter.inlineRegionBefore(compute.getBody(), newCompute.getBody(), newCompute.getBody().end());
+  for (unsigned oldResultIdx = 0; oldResultIdx < compute.getNumResults(); ++oldResultIdx)
+    compute.getResult(oldResultIdx)
+      .replaceAllUsesWith(newCompute.getResult(oldResultIdx < idx ? oldResultIdx : oldResultIdx + 1));
+  rewriter.eraseOp(compute.getOperation());
+  return std::make_tuple(cast<OpResult>(newCompute.getResult(idx)), newCompute);
+}
+
+template <typename ComputeBatchOpTy>
+FailureOr<std::tuple<OpResult, BlockArgument, ComputeBatchOpTy>>
+insertComputeBatchOutput(ComputeBatchOpTy batch, RewriterBase& rewriter, unsigned idx, Type type, Location loc) {
+  if (idx > batch.getNumResults())
+    return failure();
+
+  rewriter.setInsertionPoint(batch.getOperation());
+  SmallVector<Type> resultTypes(batch.getResultTypes().begin(), batch.getResultTypes().end());
+  resultTypes.insert(resultTypes.begin() + idx, type);
+  auto newBatch =
+    ComputeBatchOpTy::create(rewriter, batch.getLoc(), TypeRange(resultTypes), batch.getLaneCountAttr(), batch.getWeights(), batch.getInputs());
+  newBatch->setAttrs(batch->getAttrs());
+  setComputeOperandSegmentSizes(newBatch.getOperation(),
+                                static_cast<int32_t>(newBatch.getWeights().size()),
+                                static_cast<int32_t>(newBatch.getInputs().size()));
+  rewriter.inlineRegionBefore(batch.getBody(), newBatch.getBody(), newBatch.getBody().end());
+  if (newBatch.getBody().empty()) {
+    rewriter.eraseOp(newBatch);
+    return failure();
+  }
+  auto blockArg = newBatch.getBody().front().insertArgument(
+    1 + newBatch.getWeights().size() + newBatch.getInputs().size() + idx, type, loc);
+  for (unsigned oldResultIdx = 0; oldResultIdx < batch.getNumResults(); ++oldResultIdx)
+    batch.getResult(oldResultIdx)
+      .replaceAllUsesWith(newBatch.getResult(oldResultIdx < idx ? oldResultIdx : oldResultIdx + 1));
+  rewriter.eraseOp(batch.getOperation());
+  return std::make_tuple(cast<OpResult>(newBatch.getResult(idx)), blockArg, newBatch);
+}
+
+} // namespace
+
+bool isGraphComputeLike(Operation* op) { return isa<SpatGraphCompute, SpatGraphComputeBatch>(op); }
+
+bool isGraphBatchComputeLike(Operation* op) { return isa<SpatGraphComputeBatch>(op); }
+
+bool isScheduledComputeLike(Operation* op) { return isa<SpatScheduledCompute, SpatScheduledComputeBatch>(op); }
+
+bool isScheduledBatchComputeLike(Operation* op) { return isa<SpatScheduledComputeBatch>(op); }
+
+bool isAnySpatialComputeLike(Operation* op) {
+  return isa<SpatGraphCompute, SpatGraphComputeBatch, SpatScheduledCompute, SpatScheduledComputeBatch>(op);
+}
+
+bool isAnySpatialComputeBatchLike(Operation* op) { return isa<SpatGraphComputeBatch, SpatScheduledComputeBatch>(op); }
+
+std::optional<BlockArgument> SpatGraphCompute::getWeightArgument(unsigned idx) { return getComputeWeightArgument(*this, idx); }
+std::optional<BlockArgument> SpatGraphCompute::getInputArgument(unsigned idx) { return getComputeInputArgument(*this, idx); }
+std::optional<std::tuple<Value, BlockArgument>> SpatGraphCompute::insertWeight(unsigned idx, Value weight, Location loc) {
+  return insertComputeWeight(*this, idx, weight, loc);
+}
+std::optional<std::tuple<Value, BlockArgument>> SpatGraphCompute::insertInput(unsigned idx, Value input, Location loc) {
+  return insertComputeInput(*this, idx, input, loc);
+}
+CrossbarWeightSet SpatGraphCompute::getCrossbarWeights() { return collectCrossbarWeights(getBody()); }
+FailureOr<std::tuple<OpResult, SpatGraphCompute>>
+SpatGraphCompute::insertOutput(RewriterBase& rewriter, unsigned idx, Type type, Location loc) {
+  return insertComputeOutput(*this, rewriter, idx, type, loc);
+}
+void SpatGraphCompute::getAsmBlockArgumentNames(Region& region, OpAsmSetValueNameFn setNameFn) {
+  setComputeAsmBlockArgumentNames(*this, region, setNameFn);
+}
+
+std::optional<BlockArgument> SpatScheduledCompute::getWeightArgument(unsigned idx) {
+  return getComputeWeightArgument(*this, idx);
+}
+std::optional<BlockArgument> SpatScheduledCompute::getInputArgument(unsigned idx) { return getComputeInputArgument(*this, idx); }
+std::optional<std::tuple<Value, BlockArgument>>
+SpatScheduledCompute::insertWeight(unsigned idx, Value weight, Location loc) {
+  return insertComputeWeight(*this, idx, weight, loc);
+}
+std::optional<std::tuple<Value, BlockArgument>>
+SpatScheduledCompute::insertInput(unsigned idx, Value input, Location loc) {
+  return insertComputeInput(*this, idx, input, loc);
+}
+CrossbarWeightSet SpatScheduledCompute::getCrossbarWeights() { return collectCrossbarWeights(getBody()); }
+FailureOr<std::tuple<OpResult, SpatScheduledCompute>>
+SpatScheduledCompute::insertOutput(RewriterBase& rewriter, unsigned idx, Type type, Location loc) {
+  return insertComputeOutput(*this, rewriter, idx, type, loc);
+}
+void SpatScheduledCompute::getAsmBlockArgumentNames(Region& region, OpAsmSetValueNameFn setNameFn) {
+  setComputeAsmBlockArgumentNames(*this, region, setNameFn);
+}
+
+std::optional<BlockArgument> SpatGraphComputeBatch::getLaneArgument() { return getBlockArgument(getBody(), 0); }
+std::optional<BlockArgument> SpatGraphComputeBatch::getWeightArgument(unsigned idx) {
   return getBlockArgument(getBody(), 1 + idx);
 }
-
-std::optional<BlockArgument> SpatComputeBatch::getInputArgument(unsigned idx) {
+std::optional<BlockArgument> SpatGraphComputeBatch::getInputArgument(unsigned idx) {
   return getBlockArgument(getBody(), 1 + getWeights().size() + idx);
 }
-
-std::optional<BlockArgument> SpatComputeBatch::getOutputArgument(unsigned idx) {
+std::optional<BlockArgument> SpatGraphComputeBatch::getOutputArgument(unsigned idx) {
   return getBlockArgument(getBody(), 1 + getWeights().size() + getInputs().size() + idx);
 }
-
 std::optional<std::tuple<Value, BlockArgument>>
-SpatComputeBatch::insertWeight(unsigned idx, Value weight, Location loc) {
-  if (auto existing = llvm::find(getWeights(), weight); existing != getWeights().end()) {
-    auto index = std::distance(getWeights().begin(), existing);
-    return {
-      {*existing, *getWeightArgument(index)}
-    };
-  }
-
-  unsigned weightCount = getWeights().size();
-  unsigned inputCount = getInputs().size();
-  getOperation()->insertOperands(idx, ValueRange {weight});
-  setComputeOperandSegmentSizes(
-    getOperation(), static_cast<int32_t>(weightCount + 1), static_cast<int32_t>(inputCount));
-  auto blockArg = insertBlockArgument(getBody(), 1 + idx, weight.getType(), loc);
-  if (!blockArg)
-    return std::nullopt;
-  return std::make_tuple(getOperation()->getOperand(idx), *blockArg);
+SpatGraphComputeBatch::insertWeight(unsigned idx, Value weight, Location loc) {
+  return insertComputeBatchWeight(*this, idx, weight, loc);
 }
-
-std::optional<std::tuple<Value, BlockArgument>> SpatComputeBatch::insertInput(unsigned idx, Value input, Location loc) {
+std::optional<std::tuple<Value, BlockArgument>>
+SpatGraphComputeBatch::insertInput(unsigned idx, Value input, Location loc) {
   unsigned weightCount = getWeights().size();
   unsigned inputCount = getInputs().size();
   getOperation()->insertOperands(weightCount + idx, ValueRange {input});
@@ -167,52 +264,68 @@ std::optional<std::tuple<Value, BlockArgument>> SpatComputeBatch::insertInput(un
     return std::nullopt;
   return std::make_tuple(getOperation()->getOperand(weightCount + idx), *blockArg);
 }
-
-CrossbarWeightSet SpatComputeBatch::getCrossbarWeights() { return collectCrossbarWeights(getBody()); }
-
-FailureOr<std::tuple<OpResult, BlockArgument, SpatComputeBatch>>
-SpatComputeBatch::insertOutput(RewriterBase& rewriter, unsigned idx, Type type, Location loc) {
-  if (idx > getNumResults())
-    return failure();
-
-  rewriter.setInsertionPoint(getOperation());
-  SmallVector<Type> resultTypes(getResultTypes().begin(), getResultTypes().end());
-  resultTypes.insert(resultTypes.begin() + idx, type);
-  auto newBatch =
-    SpatComputeBatch::create(rewriter, getLoc(), TypeRange(resultTypes), getLaneCountAttr(), getWeights(), getInputs());
-  newBatch->setAttrs((*this)->getAttrs());
-  setComputeOperandSegmentSizes(newBatch.getOperation(),
-                                static_cast<int32_t>(newBatch.getWeights().size()),
-                                static_cast<int32_t>(newBatch.getInputs().size()));
-  rewriter.inlineRegionBefore(getBody(), newBatch.getBody(), newBatch.getBody().end());
-  if (newBatch.getBody().empty()) {
-    rewriter.eraseOp(newBatch);
-    return failure();
-  }
-  auto blockArg = newBatch.getBody().front().insertArgument(
-    1 + newBatch.getWeights().size() + newBatch.getInputs().size() + idx, type, loc);
-  for (unsigned oldResultIdx = 0; oldResultIdx < getNumResults(); ++oldResultIdx)
-    getResult(oldResultIdx)
-      .replaceAllUsesWith(newBatch.getResult(oldResultIdx < idx ? oldResultIdx : oldResultIdx + 1));
-  rewriter.eraseOp(getOperation());
-  return std::make_tuple(cast<OpResult>(newBatch.getResult(idx)), blockArg, newBatch);
+CrossbarWeightSet SpatGraphComputeBatch::getCrossbarWeights() { return collectCrossbarWeights(getBody()); }
+FailureOr<std::tuple<OpResult, BlockArgument, SpatGraphComputeBatch>>
+SpatGraphComputeBatch::insertOutput(RewriterBase& rewriter, unsigned idx, Type type, Location loc) {
+  return insertComputeBatchOutput(*this, rewriter, idx, type, loc);
 }
-
-void SpatComputeBatch::getAsmBlockArgumentNames(Region& region, OpAsmSetValueNameFn setNameFn) {
+void SpatGraphComputeBatch::getAsmBlockArgumentNames(Region& region, OpAsmSetValueNameFn setNameFn) {
   if (region.empty())
     return;
 
   if (auto laneArg = getLaneArgument())
     setNameFn(*laneArg, "lane");
+  setComputeAsmBlockArgumentNames(*this, region, setNameFn);
+  for (unsigned index = 0; index < getNumResults(); ++index) {
+    auto outputArg = getOutputArgument(index);
+    if (!outputArg)
+      continue;
+    if (index == 0) {
+      setNameFn(*outputArg, "out");
+      continue;
+    }
+    setNameFn(*outputArg, ("out" + std::to_string(index)).c_str());
+  }
+}
 
-  for (unsigned index = 0; index < getWeights().size(); ++index)
-    if (auto weightArg = getWeightArgument(index))
-      setNameFn(*weightArg, ("w" + std::to_string(index)).c_str());
-
-  for (unsigned index = 0; index < getInputs().size(); ++index)
-    if (auto inputArg = getInputArgument(index))
-      setNameFn(*inputArg, ("in" + std::to_string(index)).c_str());
+std::optional<BlockArgument> SpatScheduledComputeBatch::getLaneArgument() { return getBlockArgument(getBody(), 0); }
+std::optional<BlockArgument> SpatScheduledComputeBatch::getWeightArgument(unsigned idx) {
+  return getBlockArgument(getBody(), 1 + idx);
+}
+std::optional<BlockArgument> SpatScheduledComputeBatch::getInputArgument(unsigned idx) {
+  return getBlockArgument(getBody(), 1 + getWeights().size() + idx);
+}
+std::optional<BlockArgument> SpatScheduledComputeBatch::getOutputArgument(unsigned idx) {
+  return getBlockArgument(getBody(), 1 + getWeights().size() + getInputs().size() + idx);
+}
+std::optional<std::tuple<Value, BlockArgument>>
+SpatScheduledComputeBatch::insertWeight(unsigned idx, Value weight, Location loc) {
+  return insertComputeBatchWeight(*this, idx, weight, loc);
+}
+std::optional<std::tuple<Value, BlockArgument>>
+SpatScheduledComputeBatch::insertInput(unsigned idx, Value input, Location loc) {
+  unsigned weightCount = getWeights().size();
+  unsigned inputCount = getInputs().size();
+  getOperation()->insertOperands(weightCount + idx, ValueRange {input});
+  setComputeOperandSegmentSizes(
+    getOperation(), static_cast<int32_t>(weightCount), static_cast<int32_t>(inputCount + 1));
+  auto blockArg = insertBlockArgument(getBody(), 1 + weightCount + idx, input.getType(), loc);
+  if (!blockArg)
+    return std::nullopt;
+  return std::make_tuple(getOperation()->getOperand(weightCount + idx), *blockArg);
+}
+CrossbarWeightSet SpatScheduledComputeBatch::getCrossbarWeights() { return collectCrossbarWeights(getBody()); }
+FailureOr<std::tuple<OpResult, BlockArgument, SpatScheduledComputeBatch>>
+SpatScheduledComputeBatch::insertOutput(RewriterBase& rewriter, unsigned idx, Type type, Location loc) {
+  return insertComputeBatchOutput(*this, rewriter, idx, type, loc);
+}
+void SpatScheduledComputeBatch::getAsmBlockArgumentNames(Region& region, OpAsmSetValueNameFn setNameFn) {
+  if (region.empty())
+    return;
 
+  if (auto laneArg = getLaneArgument())
+    setNameFn(*laneArg, "lane");
+  setComputeAsmBlockArgumentNames(*this, region, setNameFn);
   for (unsigned index = 0; index < getNumResults(); ++index) {
     auto outputArg = getOutputArgument(index);
     if (!outputArg)
@@ -231,7 +344,11 @@ void SpatInParallelOp::build(OpBuilder& builder, OperationState& result) {
   builder.createBlock(bodyRegion);
 }
 
-OpResult SpatInParallelOp::getParentResult(int64_t idx) { return getOperation()->getParentOp()->getResult(idx); }
+OpResult SpatInParallelOp::getParentResult(int64_t idx) {
+  Operation* parent = getOperation()->getParentOp();
+  assert(isAnySpatialComputeBatchLike(parent) && "expected Spatial compute batch parent");
+  return parent->getResult(idx);
+}
 
 llvm::iterator_range<Block::iterator> SpatInParallelOp::getYieldingOps() { return getRegion().front().getOperations(); }
 

src/PIM/Dialect/Spatial/SpatialOps.hpp

@@ -26,3 +26,19 @@
 
 #define GET_OP_CLASSES
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp.inc"
+
+namespace onnx_mlir {
+namespace spatial {
+
+bool isGraphComputeLike(mlir::Operation* op);
+bool isGraphBatchComputeLike(mlir::Operation* op);
+bool isScheduledComputeLike(mlir::Operation* op);
+bool isScheduledBatchComputeLike(mlir::Operation* op);
+bool isAnySpatialComputeLike(mlir::Operation* op);
+bool isAnySpatialComputeBatchLike(mlir::Operation* op);
+
+using SpatCompute = SpatGraphCompute;
+using SpatComputeBatch = SpatGraphComputeBatch;
+
+} // namespace spatial
+} // namespace onnx_mlir

src/PIM/Dialect/Spatial/SpatialOpsAsm.cpp

@@ -115,6 +115,254 @@ static ParseResult parseBoundValueList(OpAsmParser& parser,
   return success();
 }
 
+template <typename ComputeOpTy>
+void printComputeLikeOp(ComputeOpTy op, OpAsmPrinter& printer) {
+  SmallVector<Value> weightArgs;
+  weightArgs.reserve(op.getWeights().size());
+  for (unsigned index = 0; index < op.getWeights().size(); ++index) {
+    auto weightArg = op.getWeightArgument(index);
+    if (!weightArg)
+      return printer.printGenericOp(op.getOperation(), /*printOpName=*/false);
+    weightArgs.push_back(*weightArg);
+  }
+  SmallVector<Value> inputArgs;
+  inputArgs.reserve(op.getInputs().size());
+  for (unsigned index = 0; index < op.getInputs().size(); ++index) {
+    auto inputArg = op.getInputArgument(index);
+    if (!inputArg)
+      return printer.printGenericOp(op.getOperation(), /*printOpName=*/false);
+    inputArgs.push_back(*inputArg);
+  }
+
+  printer << " ";
+  printBoundValueList(printer, weightArgs, op.getWeights(), ListDelimiter::Square);
+  printer << " ";
+  printBoundValueList(printer, inputArgs, op.getInputs(), ListDelimiter::Paren);
+
+  if (auto coreIdAttr = op->template getAttrOfType<IntegerAttr>(onnx_mlir::kCoreIdAttrName))
+    printer << " coreId " << coreIdAttr.getInt();
+  printer << " crossbarWeights " << collectDistinctCrossbarWeights(op.getOperation()).size();
+
+  printer.printOptionalAttrDict(op->getAttrs(), {op.getOperandSegmentSizesAttrName().getValue(), onnx_mlir::kCoreIdAttrName});
+
+  printer << " : ";
+  printCompressedTypeList(printer, TypeRange(op.getWeights()), ListDelimiter::Square);
+  printer << " ";
+  printCompressedTypeList(printer, TypeRange(op.getInputs()), ListDelimiter::Paren);
+  printer << " -> ";
+  printCompressedTypeSequence(printer, op.getResultTypes());
+  printer << " ";
+  printer.printRegion(op.getBody(), /*printEntryBlockArgs=*/false);
+}
+
+template <typename ComputeOpTy>
+ParseResult parseComputeLikeOp(OpAsmParser& parser, OperationState& result) {
+  SmallVector<OpAsmParser::Argument> weightArgs;
+  SmallVector<OpAsmParser::Argument> regionArgs;
+  SmallVector<OpAsmParser::UnresolvedOperand> weights;
+  SmallVector<OpAsmParser::UnresolvedOperand> inputs;
+  SmallVector<Type> weightTypes;
+  SmallVector<Type> inputTypes;
+  SmallVector<Type> outputTypes;
+  int32_t crossbarWeightCount = 0;
+  int32_t coreId = 0;
+
+  if (parseBoundValueList(parser, ListDelimiter::Square, weightArgs, weights))
+    return failure();
+
+  SmallVector<OpAsmParser::Argument> inputArgs;
+  if (parseBoundValueList(parser, ListDelimiter::Paren, inputArgs, inputs))
+    return failure();
+
+  bool hasCoreId = parseOptionalKeywordAlias(parser, "coreId", "core_id");
+  if (hasCoreId && parser.parseInteger(coreId))
+    return failure();
+
+  bool hasCrossbarWeightCount = parseOptionalKeywordAlias(parser, "crossbarWeights", "crossbar_weights");
+  if (hasCrossbarWeightCount && parser.parseInteger(crossbarWeightCount))
+    return failure();
+  (void) crossbarWeightCount;
+
+  if (parser.parseOptionalAttrDict(result.attributes) || parser.parseColon()
+      || parseCompressedRepeatedList(
+        parser, ListDelimiter::Square, weightTypes, [&](Type& type) { return parser.parseType(type); })
+      || parseCompressedRepeatedList(
+        parser, ListDelimiter::Paren, inputTypes, [&](Type& type) { return parser.parseType(type); })
+      || parser.parseArrow() || parseCompressedTypeSequence(parser, outputTypes, /*allowEmpty=*/true))
+    return failure();
+
+  if (weights.size() != weightTypes.size())
+    return parser.emitError(parser.getCurrentLocation(), "number of weights and weight types must match");
+  if (weightArgs.size() != weights.size())
+    return parser.emitError(parser.getCurrentLocation(), "number of weight bindings and weight operands must match");
+  if (inputs.size() != inputTypes.size())
+    return parser.emitError(parser.getCurrentLocation(), "number of inputs and input types must match");
+  if (inputArgs.size() != inputs.size())
+    return parser.emitError(parser.getCurrentLocation(), "number of argument bindings and input operands must match");
+  if (hasCoreId && result.attributes.get(onnx_mlir::kCoreIdAttrName))
+    return parser.emitError(parser.getCurrentLocation(),
+                            "coreId cannot be specified both positionally and in attr-dict");
+
+  auto& builder = parser.getBuilder();
+  result.addAttribute(
+    "operandSegmentSizes",
+    builder.getDenseI32ArrayAttr({static_cast<int32_t>(weights.size()), static_cast<int32_t>(inputs.size())}));
+  if (hasCoreId)
+    result.addAttribute(onnx_mlir::kCoreIdAttrName, getI32Attr(parser, coreId));
+
+  if (parser.resolveOperands(weights, weightTypes, parser.getCurrentLocation(), result.operands)
+      || parser.resolveOperands(inputs, inputTypes, parser.getCurrentLocation(), result.operands))
+    return failure();
+  result.addTypes(outputTypes);
+
+  Region* body = result.addRegion();
+  applyArgumentTypes(weightTypes, weightArgs);
+  applyArgumentTypes(inputTypes, inputArgs);
+  llvm::append_range(regionArgs, weightArgs);
+  llvm::append_range(regionArgs, inputArgs);
+  return parser.parseRegion(*body, regionArgs);
+}
+
+template <typename ComputeBatchOpTy>
+void printComputeBatchLikeOp(ComputeBatchOpTy op, OpAsmPrinter& printer) {
+  auto laneArg = op.getLaneArgument();
+  SmallVector<Value> weightArgs;
+  weightArgs.reserve(op.getWeights().size());
+  for (unsigned index = 0; index < op.getWeights().size(); ++index) {
+    auto weightArg = op.getWeightArgument(index);
+    if (!weightArg)
+      return printer.printGenericOp(op.getOperation(), /*printOpName=*/false);
+    weightArgs.push_back(*weightArg);
+  }
+  SmallVector<Value> inputArgs;
+  inputArgs.reserve(op.getInputs().size());
+  for (unsigned index = 0; index < op.getInputs().size(); ++index) {
+    auto inputArg = op.getInputArgument(index);
+    if (!inputArg)
+      return printer.printGenericOp(op.getOperation(), /*printOpName=*/false);
+    inputArgs.push_back(*inputArg);
+  }
+
+  SmallVector<BlockArgument> outputArgs;
+  if (!laneArg)
+    return printer.printGenericOp(op.getOperation(), /*printOpName=*/false);
+  if (op.getNumResults() != 0) {
+    outputArgs.reserve(op.getNumResults());
+    for (unsigned index = 0; index < op.getNumResults(); ++index) {
+      auto outputArg = op.getOutputArgument(index);
+      if (!outputArg)
+        return printer.printGenericOp(op.getOperation(), /*printOpName=*/false);
+      outputArgs.push_back(*outputArg);
+    }
+  }
+
+  printer << " ";
+  printer.printOperand(*laneArg);
+  printer << " = 0 to " << op.getLaneCount();
+  printer << " ";
+  printBoundValueList(printer, weightArgs, op.getWeights(), ListDelimiter::Square);
+  printer << " ";
+  printBoundValueList(printer, inputArgs, op.getInputs(), ListDelimiter::Paren);
+  if (op.getNumResults() != 0) {
+    printer << " shared_outs";
+    printBlockArgumentList(printer, outputArgs);
+  }
+  printer << " crossbarWeights " << getComputeInstanceCrossbarUsage({op.getOperation(), 0, op.getLaneCount()}).size();
+  if (auto coreIdsAttr = op->template getAttrOfType<DenseI32ArrayAttr>(onnx_mlir::kCoreIdsAttrName)) {
+    printer << " coreIds ";
+    printCompressedIntegerList(printer, coreIdsAttr.asArrayRef());
+  }
+  printer.printOptionalAttrDict(
+    op->getAttrs(),
+    {op.getLaneCountAttrName().getValue(), op.getOperandSegmentSizesAttrName().getValue(), onnx_mlir::kCoreIdsAttrName});
+  printer << " : ";
+  printCompressedTypeList(printer, TypeRange(op.getWeights()), ListDelimiter::Square);
+  printer << " ";
+  printCompressedTypeList(printer, TypeRange(op.getInputs()), ListDelimiter::Paren);
+  printer << " -> ";
+  printCompressedTypeSequence(printer, op.getResultTypes());
+  printer << " ";
+  printer.printRegion(op.getBody(), /*printEntryBlockArgs=*/false);
+}
+
+template <typename ComputeBatchOpTy>
+ParseResult parseComputeBatchLikeOp(OpAsmParser& parser, OperationState& result) {
+  int64_t lowerBound = 0;
+  int32_t laneCount = 0;
+  OpAsmParser::Argument laneArg;
+  SmallVector<OpAsmParser::Argument> weightArgs;
+  SmallVector<OpAsmParser::Argument> inputArgs;
+  SmallVector<OpAsmParser::Argument> outputArgs;
+  SmallVector<OpAsmParser::Argument> regionArgs;
+  SmallVector<OpAsmParser::UnresolvedOperand> weights;
+  SmallVector<OpAsmParser::UnresolvedOperand> inputs;
+  SmallVector<Type> weightTypes;
+  SmallVector<Type> inputTypes;
+  SmallVector<Type> outputTypes;
+  int32_t crossbarWeightCount = 0;
+  SmallVector<int32_t> coreIds;
+
+  if (parser.parseArgument(laneArg) || parser.parseEqual() || parser.parseInteger(lowerBound)
+      || parser.parseKeyword("to") || parser.parseInteger(laneCount))
+    return failure();
+  if (lowerBound != 0)
+    return parser.emitError(parser.getCurrentLocation(), "compute_batch currently requires a zero lower bound");
+  if (parseBoundValueList(parser, ListDelimiter::Square, weightArgs, weights))
+    return failure();
+  if (parseBoundValueList(parser, ListDelimiter::Paren, inputArgs, inputs))
+    return failure();
+  if (succeeded(parser.parseOptionalKeyword("shared_outs")))
+    if (parseBlockArgumentList(parser, outputArgs))
+      return failure();
+  bool hasCoreIds = parseOptionalKeywordAlias(parser, "coreIds", "core_ids");
+  if (hasCoreIds && parseCompressedIntegerList(parser, coreIds))
+    return failure();
+  bool hasCrossbarWeightCount = parseOptionalKeywordAlias(parser, "crossbarWeights", "crossbar_weights");
+  if (hasCrossbarWeightCount && parser.parseInteger(crossbarWeightCount))
+    return failure();
+  (void) crossbarWeightCount;
+
+  if (parser.parseOptionalAttrDict(result.attributes) || parser.parseColon()
+      || parseCompressedOrTupleTypeList(parser, ListDelimiter::Square, weightTypes)
+      || parseCompressedRepeatedList(
+        parser, ListDelimiter::Paren, inputTypes, [&](Type& type) { return parser.parseType(type); })
+      || parser.parseArrow() || parseCompressedTypeSequence(parser, outputTypes, /*allowEmpty=*/true))
+    return failure();
+
+  if (weights.size() != weightTypes.size())
+    return parser.emitError(parser.getCurrentLocation(), "number of weights and weight types must match");
+  if (weightArgs.size() != weights.size())
+    return parser.emitError(parser.getCurrentLocation(), "number of weight bindings and weight operands must match");
+  if (inputs.size() != inputTypes.size())
+    return parser.emitError(parser.getCurrentLocation(), "number of inputs and input types must match");
+  if (inputArgs.size() != inputs.size())
+    return parser.emitError(parser.getCurrentLocation(), "number of argument bindings and input operands must match");
+  if (outputArgs.size() != outputTypes.size())
+    return parser.emitError(parser.getCurrentLocation(),
+                            "number of shared output bindings and result types must match");
+  if (hasCoreIds && result.attributes.get(onnx_mlir::kCoreIdsAttrName))
+    return parser.emitError(parser.getCurrentLocation(),
+                            "coreIds cannot be specified both positionally and in attr-dict");
+
+  auto& builder = parser.getBuilder();
+  result.addAttribute("laneCount", builder.getI32IntegerAttr(laneCount));
+  result.addAttribute(
+    "operandSegmentSizes",
+    builder.getDenseI32ArrayAttr({static_cast<int32_t>(weights.size()), static_cast<int32_t>(inputs.size())}));
+  if (hasCoreIds)
+    result.addAttribute(onnx_mlir::kCoreIdsAttrName, getDenseI32ArrayAttr(parser, coreIds));
+
+  if (parser.resolveOperands(weights, weightTypes, parser.getCurrentLocation(), result.operands)
+      || parser.resolveOperands(inputs, inputTypes, parser.getCurrentLocation(), result.operands))
+    return failure();
+  result.addTypes(outputTypes);
+
+  Region* body = result.addRegion();
+  applyBatchRegionArgumentTypes(
+    inputTypes, weightTypes, outputTypes, laneArg, weightArgs, inputArgs, outputArgs, regionArgs, parser.getBuilder());
+  return parser.parseRegion(*body, regionArgs);
+}
+
 } // namespace
 
 void SpatYieldOp::print(OpAsmPrinter& printer) {
@@ -218,260 +466,21 @@ ParseResult SpatConcatOp::parse(OpAsmParser& parser, OperationState& result) {
   return success();
 }
 
-void SpatCompute::print(OpAsmPrinter& printer) {
-  SmallVector<Value> weightArgs;
-  weightArgs.reserve(getWeights().size());
-  for (unsigned index = 0; index < getWeights().size(); ++index) {
-    auto weightArg = getWeightArgument(index);
-    if (!weightArg)
-      return printer.printGenericOp(getOperation(), /*printOpName=*/false);
-    weightArgs.push_back(*weightArg);
-  }
-  SmallVector<Value> inputArgs;
-  inputArgs.reserve(getInputs().size());
-  for (unsigned index = 0; index < getInputs().size(); ++index) {
-    auto inputArg = getInputArgument(index);
-    if (!inputArg)
-      return printer.printGenericOp(getOperation(), /*printOpName=*/false);
-    inputArgs.push_back(*inputArg);
-  }
-
-  printer << " ";
-  printBoundValueList(printer, weightArgs, getWeights(), ListDelimiter::Square);
-  printer << " ";
-  printBoundValueList(printer, inputArgs, getInputs(), ListDelimiter::Paren);
-
-  if (auto coreIdAttr = (*this)->getAttrOfType<IntegerAttr>(onnx_mlir::kCoreIdAttrName))
-    printer << " coreId " << coreIdAttr.getInt();
-  printer << " crossbarWeights " << collectDistinctCrossbarWeights(getOperation()).size();
-
-  printer.printOptionalAttrDict((*this)->getAttrs(),
-                                {getOperandSegmentSizesAttrName().getValue(), onnx_mlir::kCoreIdAttrName});
-
-  printer << " : ";
-  printCompressedTypeList(printer, TypeRange(getWeights()), ListDelimiter::Square);
-  printer << " ";
-  printCompressedTypeList(printer, TypeRange(getInputs()), ListDelimiter::Paren);
-  printer << " -> ";
-  printCompressedTypeSequence(printer, getResultTypes());
-  printer << " ";
-  printer.printRegion(getBody(), /*printEntryBlockArgs=*/false);
+void SpatGraphCompute::print(OpAsmPrinter& printer) { printComputeLikeOp(*this, printer); }
+ParseResult SpatGraphCompute::parse(OpAsmParser& parser, OperationState& result) {
+  return parseComputeLikeOp<SpatGraphCompute>(parser, result);
 }
-
-ParseResult SpatCompute::parse(OpAsmParser& parser, OperationState& result) {
-  SmallVector<OpAsmParser::Argument> weightArgs;
-  SmallVector<OpAsmParser::Argument> regionArgs;
-  SmallVector<OpAsmParser::UnresolvedOperand> weights;
-  SmallVector<OpAsmParser::UnresolvedOperand> inputs;
-  SmallVector<Type> weightTypes;
-  SmallVector<Type> inputTypes;
-  SmallVector<Type> outputTypes;
-  int32_t crossbarWeightCount = 0;
-  int32_t coreId = 0;
-
-  if (parseBoundValueList(parser, ListDelimiter::Square, weightArgs, weights))
-    return failure();
-
-  SmallVector<OpAsmParser::Argument> inputArgs;
-  if (parseBoundValueList(parser, ListDelimiter::Paren, inputArgs, inputs))
-    return failure();
-
-  bool hasCoreId = parseOptionalKeywordAlias(parser, "coreId", "core_id");
-  if (hasCoreId && parser.parseInteger(coreId))
-    return failure();
-
-  bool hasCrossbarWeightCount = parseOptionalKeywordAlias(parser, "crossbarWeights", "crossbar_weights");
-  if (hasCrossbarWeightCount && parser.parseInteger(crossbarWeightCount))
-    return failure();
-  (void) crossbarWeightCount;
-
-  if (parser.parseOptionalAttrDict(result.attributes) || parser.parseColon()
-      || parseCompressedRepeatedList(
-        parser, ListDelimiter::Square, weightTypes, [&](Type& type) { return parser.parseType(type); })
-      || parseCompressedRepeatedList(
-        parser, ListDelimiter::Paren, inputTypes, [&](Type& type) { return parser.parseType(type); })
-      || parser.parseArrow() || parseCompressedTypeSequence(parser, outputTypes, /*allowEmpty=*/true))
-    return failure();
-
-  if (weights.size() != weightTypes.size())
-    return parser.emitError(parser.getCurrentLocation(), "number of weights and weight types must match");
-  if (weightArgs.size() != weights.size())
-    return parser.emitError(parser.getCurrentLocation(), "number of weight bindings and weight operands must match");
-  if (inputs.size() != inputTypes.size())
-    return parser.emitError(parser.getCurrentLocation(), "number of inputs and input types must match");
-  if (inputArgs.size() != inputs.size())
-    return parser.emitError(parser.getCurrentLocation(), "number of argument bindings and input operands must match");
-  if (hasCoreId && result.attributes.get(onnx_mlir::kCoreIdAttrName))
-    return parser.emitError(parser.getCurrentLocation(),
-                            "coreId cannot be specified both positionally and in attr-dict");
-
-  auto& builder = parser.getBuilder();
-  result.addAttribute(
-    "operandSegmentSizes",
-    builder.getDenseI32ArrayAttr({static_cast<int32_t>(weights.size()), static_cast<int32_t>(inputs.size())}));
-  if (hasCoreId)
-    result.addAttribute(onnx_mlir::kCoreIdAttrName, getI32Attr(parser, coreId));
-
-  if (parser.resolveOperands(weights, weightTypes, parser.getCurrentLocation(), result.operands)
-      || parser.resolveOperands(inputs, inputTypes, parser.getCurrentLocation(), result.operands))
-    return failure();
-  result.addTypes(outputTypes);
-
-  Region* body = result.addRegion();
-  applyArgumentTypes(weightTypes, weightArgs);
-  applyArgumentTypes(inputTypes, inputArgs);
-  llvm::append_range(regionArgs, weightArgs);
-  llvm::append_range(regionArgs, inputArgs);
-  return parser.parseRegion(*body, regionArgs);
+void SpatScheduledCompute::print(OpAsmPrinter& printer) { printComputeLikeOp(*this, printer); }
+ParseResult SpatScheduledCompute::parse(OpAsmParser& parser, OperationState& result) {
+  return parseComputeLikeOp<SpatScheduledCompute>(parser, result);
 }
-
-void SpatComputeBatch::print(OpAsmPrinter& printer) {
-  auto laneArg = getLaneArgument();
-  SmallVector<Value> weightArgs;
-  weightArgs.reserve(getWeights().size());
-  for (unsigned index = 0; index < getWeights().size(); ++index) {
-    auto weightArg = getWeightArgument(index);
-    if (!weightArg)
-      return printer.printGenericOp(getOperation(), /*printOpName=*/false);
-    weightArgs.push_back(*weightArg);
-  }
-  SmallVector<Value> inputArgs;
-  inputArgs.reserve(getInputs().size());
-  for (unsigned index = 0; index < getInputs().size(); ++index) {
-    auto inputArg = getInputArgument(index);
-    if (!inputArg)
-      return printer.printGenericOp(getOperation(), /*printOpName=*/false);
-    inputArgs.push_back(*inputArg);
-  }
-
-  SmallVector<BlockArgument> outputArgs;
-  if (!laneArg)
-    return printer.printGenericOp(getOperation(), /*printOpName=*/false);
-  if (getNumResults() != 0) {
-    outputArgs.reserve(getNumResults());
-    for (unsigned index = 0; index < getNumResults(); ++index) {
-      auto outputArg = getOutputArgument(index);
-      if (!outputArg)
-        return printer.printGenericOp(getOperation(), /*printOpName=*/false);
-      outputArgs.push_back(*outputArg);
-    }
-  }
-
-  printer << " ";
-  printer.printOperand(*laneArg);
-  printer << " = 0 to " << getLaneCount();
-
-  printer << " ";
-  printBoundValueList(printer, weightArgs, getWeights(), ListDelimiter::Square);
-  printer << " ";
-  printBoundValueList(printer, inputArgs, getInputs(), ListDelimiter::Paren);
-
-  if (getNumResults() != 0) {
-    printer << " shared_outs";
-    printBlockArgumentList(printer, outputArgs);
-  }
-
-  printer << " crossbarWeights " << getComputeInstanceCrossbarUsage({getOperation(), 0, getLaneCount()}).size();
-
-  if (auto coreIdsAttr = (*this)->getAttrOfType<DenseI32ArrayAttr>(onnx_mlir::kCoreIdsAttrName)) {
-    printer << " coreIds ";
-    printCompressedIntegerList(printer, coreIdsAttr.asArrayRef());
-  }
-
-  printer.printOptionalAttrDict(
-    (*this)->getAttrs(),
-    {getLaneCountAttrName().getValue(), getOperandSegmentSizesAttrName().getValue(), onnx_mlir::kCoreIdsAttrName});
-
-  printer << " : ";
-  printCompressedTypeList(printer, TypeRange(getWeights()), ListDelimiter::Square);
-  printer << " ";
-  printCompressedTypeList(printer, TypeRange(getInputs()), ListDelimiter::Paren);
-  printer << " -> ";
-  printCompressedTypeSequence(printer, getResultTypes());
-  printer << " ";
-  printer.printRegion(getBody(), /*printEntryBlockArgs=*/false);
+void SpatGraphComputeBatch::print(OpAsmPrinter& printer) { printComputeBatchLikeOp(*this, printer); }
+ParseResult SpatGraphComputeBatch::parse(OpAsmParser& parser, OperationState& result) {
+  return parseComputeBatchLikeOp<SpatGraphComputeBatch>(parser, result);
 }
-
-ParseResult SpatComputeBatch::parse(OpAsmParser& parser, OperationState& result) {
-  int64_t lowerBound = 0;
-  int32_t laneCount = 0;
-  OpAsmParser::Argument laneArg;
-  SmallVector<OpAsmParser::Argument> weightArgs;
-  SmallVector<OpAsmParser::Argument> inputArgs;
-  SmallVector<OpAsmParser::Argument> outputArgs;
-  SmallVector<OpAsmParser::Argument> regionArgs;
-  SmallVector<OpAsmParser::UnresolvedOperand> weights;
-  SmallVector<OpAsmParser::UnresolvedOperand> inputs;
-  SmallVector<Type> weightTypes;
-  SmallVector<Type> inputTypes;
-  SmallVector<Type> outputTypes;
-  int32_t crossbarWeightCount = 0;
-  SmallVector<int32_t> coreIds;
-
-  if (parser.parseArgument(laneArg) || parser.parseEqual() || parser.parseInteger(lowerBound)
-      || parser.parseKeyword("to") || parser.parseInteger(laneCount))
-    return failure();
-  if (lowerBound != 0)
-    return parser.emitError(parser.getCurrentLocation(), "compute_batch currently requires a zero lower bound");
-
-  if (parseBoundValueList(parser, ListDelimiter::Square, weightArgs, weights))
-    return failure();
-
-  if (parseBoundValueList(parser, ListDelimiter::Paren, inputArgs, inputs))
-    return failure();
-
-  if (succeeded(parser.parseOptionalKeyword("shared_outs")))
-    if (parseBlockArgumentList(parser, outputArgs))
-      return failure();
-
-  bool hasCoreIds = parseOptionalKeywordAlias(parser, "coreIds", "core_ids");
-  if (hasCoreIds && parseCompressedIntegerList(parser, coreIds))
-    return failure();
-
-  bool hasCrossbarWeightCount = parseOptionalKeywordAlias(parser, "crossbarWeights", "crossbar_weights");
-  if (hasCrossbarWeightCount && parser.parseInteger(crossbarWeightCount))
-    return failure();
-  (void) crossbarWeightCount;
-
-  if (parser.parseOptionalAttrDict(result.attributes) || parser.parseColon()
-      || parseCompressedOrTupleTypeList(parser, ListDelimiter::Square, weightTypes)
-      || parseCompressedRepeatedList(
-        parser, ListDelimiter::Paren, inputTypes, [&](Type& type) { return parser.parseType(type); })
-      || parser.parseArrow() || parseCompressedTypeSequence(parser, outputTypes, /*allowEmpty=*/true))
-    return failure();
-
-  if (weights.size() != weightTypes.size())
-    return parser.emitError(parser.getCurrentLocation(), "number of weights and weight types must match");
-  if (weightArgs.size() != weights.size())
-    return parser.emitError(parser.getCurrentLocation(), "number of weight bindings and weight operands must match");
-  if (inputs.size() != inputTypes.size())
-    return parser.emitError(parser.getCurrentLocation(), "number of inputs and input types must match");
-  if (inputArgs.size() != inputs.size())
-    return parser.emitError(parser.getCurrentLocation(), "number of argument bindings and input operands must match");
-  if (outputArgs.size() != outputTypes.size())
-    return parser.emitError(parser.getCurrentLocation(),
-                            "number of shared output bindings and result types must match");
-  if (hasCoreIds && result.attributes.get(onnx_mlir::kCoreIdsAttrName))
-    return parser.emitError(parser.getCurrentLocation(),
-                            "coreIds cannot be specified both positionally and in attr-dict");
-
-  auto& builder = parser.getBuilder();
-  result.addAttribute("laneCount", builder.getI32IntegerAttr(laneCount));
-  result.addAttribute(
-    "operandSegmentSizes",
-    builder.getDenseI32ArrayAttr({static_cast<int32_t>(weights.size()), static_cast<int32_t>(inputs.size())}));
-  if (hasCoreIds)
-    result.addAttribute(onnx_mlir::kCoreIdsAttrName, getDenseI32ArrayAttr(parser, coreIds));
-
-  if (parser.resolveOperands(weights, weightTypes, parser.getCurrentLocation(), result.operands)
-      || parser.resolveOperands(inputs, inputTypes, parser.getCurrentLocation(), result.operands))
-    return failure();
-  result.addTypes(outputTypes);
-
-  Region* body = result.addRegion();
-  applyBatchRegionArgumentTypes(
-    inputTypes, weightTypes, outputTypes, laneArg, weightArgs, inputArgs, outputArgs, regionArgs, parser.getBuilder());
-  return parser.parseRegion(*body, regionArgs);
+void SpatScheduledComputeBatch::print(OpAsmPrinter& printer) { printComputeBatchLikeOp(*this, printer); }
+ParseResult SpatScheduledComputeBatch::parse(OpAsmParser& parser, OperationState& result) {
+  return parseComputeBatchLikeOp<SpatScheduledComputeBatch>(parser, result);
 }
 
 void SpatInParallelOp::print(OpAsmPrinter& printer) {

src/PIM/Dialect/Spatial/SpatialOpsCanonicalization.cpp

@@ -10,8 +10,9 @@ using namespace mlir;
 namespace onnx_mlir {
 namespace spatial {
 
-LogicalResult SpatCompute::fold(FoldAdaptor adaptor, ::llvm::SmallVectorImpl<::mlir::OpFoldResult>& results) {
-  Block& block = getBody().front();
+template <typename ComputeOpTy>
+LogicalResult foldComputeLike(ComputeOpTy compute, ::llvm::SmallVectorImpl<::mlir::OpFoldResult>& results) {
+  Block& block = compute.getBody().front();
   if (!llvm::hasSingleElement(block))
     return failure();
 
@@ -22,7 +23,7 @@ LogicalResult SpatCompute::fold(FoldAdaptor adaptor, ::llvm::SmallVectorImpl<::m
   for (Value yieldedValue : yieldOp.getOperands()) {
     if (auto blockArg = dyn_cast<BlockArgument>(yieldedValue)) {
       if (blockArg.getOwner() == &block) {
-        results.push_back(getOperand(blockArg.getArgNumber()));
+        results.push_back(compute.getOperand(blockArg.getArgNumber()));
         continue;
       }
     }
@@ -31,5 +32,13 @@ LogicalResult SpatCompute::fold(FoldAdaptor adaptor, ::llvm::SmallVectorImpl<::m
   return success();
 }
 
+LogicalResult SpatGraphCompute::fold(FoldAdaptor adaptor, ::llvm::SmallVectorImpl<::mlir::OpFoldResult>& results) {
+  return foldComputeLike(*this, results);
+}
+
+LogicalResult SpatScheduledCompute::fold(FoldAdaptor adaptor, ::llvm::SmallVectorImpl<::mlir::OpFoldResult>& results) {
+  return foldComputeLike(*this, results);
+}
+
 } // namespace spatial
 } // namespace onnx_mlir

src/PIM/Dialect/Spatial/SpatialOpsVerify.cpp

@@ -35,7 +35,8 @@ static FailureOr<ArrayRef<int64_t>> getWeightShapeForWeightedOp(Value weight) {
   return shapedType.getShape();
 }
 
-static bool isBatchOutputArgument(SpatComputeBatch batchOp, Value value) {
+template <typename ComputeBatchOpTy>
+static bool isBatchOutputArgument(ComputeBatchOpTy batchOp, Value value) {
   if (batchOp.getNumResults() == 0)
     return false;
   auto blockArg = dyn_cast<BlockArgument>(value);
@@ -58,8 +59,28 @@ static LogicalResult verifyStaticWeights(ComputeOpTy computeOp, StringRef kind)
   return success();
 }
 
+static bool isStaticIndexExpr(Value value) {
+  if (matchConstantIndexValue(value))
+    return true;
+
+  auto affineApply = value.getDefiningOp<affine::AffineApplyOp>();
+  if (affineApply) {
+    if (!isSingleResultSymbolFreeAffineMap(affineApply.getAffineMap()))
+      return false;
+    return llvm::all_of(affineApply.getMapOperands(), isStaticIndexExpr);
+  }
+
+  if (auto addOp = value.getDefiningOp<arith::AddIOp>())
+    return isStaticIndexExpr(addOp.getLhs()) && isStaticIndexExpr(addOp.getRhs());
+
+  if (auto mulOp = value.getDefiningOp<arith::MulIOp>())
+    return isStaticIndexExpr(mulOp.getLhs()) && isStaticIndexExpr(mulOp.getRhs());
+
+  return false;
+}
+
 static bool isSupportedLaneOffsetExpr(Value value, BlockArgument laneArg) {
-  if (value == laneArg || matchConstantIndexValue(value))
+  if (value == laneArg || isStaticIndexExpr(value))
     return true;
 
   auto affineApply = value.getDefiningOp<affine::AffineApplyOp>();
@@ -83,10 +104,15 @@ static bool isSupportedLaneOffsetExpr(Value value, BlockArgument laneArg) {
   }
 
   auto addOp = value.getDefiningOp<arith::AddIOp>();
-  if (!addOp)
+  if (addOp)
+    return (isSupportedLaneOffsetExpr(addOp.getLhs(), laneArg) && isStaticIndexExpr(addOp.getRhs()))
+        || (isSupportedLaneOffsetExpr(addOp.getRhs(), laneArg) && isStaticIndexExpr(addOp.getLhs()));
+
+  auto mulOp = value.getDefiningOp<arith::MulIOp>();
+  if (!mulOp)
     return false;
-  return (addOp.getLhs() == laneArg && matchConstantIndexValue(addOp.getRhs()))
-      || (addOp.getRhs() == laneArg && matchConstantIndexValue(addOp.getLhs()));
+  return (isSupportedLaneOffsetExpr(mulOp.getLhs(), laneArg) && isStaticIndexExpr(mulOp.getRhs()))
+      || (isSupportedLaneOffsetExpr(mulOp.getRhs(), laneArg) && isStaticIndexExpr(mulOp.getLhs()));
 }
 
 static LogicalResult
@@ -158,17 +184,27 @@ static LogicalResult verifyOnlyConstantExternalValues(Operation* ownerOp, Region
       if (isDefinedInsideRegion(value, region) || isConstantExternalValue(value))
         continue;
 
-      InFlightDiagnostic diagnostic = ownerOp->emitOpError()
-                                   << kind << " body may only directly reference external constants";
+      InFlightDiagnostic diagnostic =
+        ownerOp->emitOpError() << kind << " body may not capture external values";
       diagnostic.attachNote(op->getLoc())
-        << "non-constant external operand #" << operand.getOperandNumber() << " is used by " << op->getName();
+        << "owner='" << ownerOp->getName() << "' nestedOp='" << op->getName() << "' operand#"
+        << operand.getOperandNumber() << " type=" << value.getType()
+        << " category=" << (isa<TensorType>(value.getType()) ? "tensor" : (value.getType().isIndex() ? "index"
+                                                                                                        : "scalar"));
+      if (Operation* definingOp = value.getDefiningOp())
+        diagnostic.attachNote(definingOp->getLoc()) << "defining op is '" << definingOp->getName() << "'";
+      else if (auto blockArg = dyn_cast<BlockArgument>(value))
+        diagnostic.attachNote(blockArg.getOwner()->getParentOp()->getLoc())
+          << "value is block argument #" << blockArg.getArgNumber() << " of '"
+          << blockArg.getOwner()->getParentOp()->getName() << "'";
       hasFailure = true;
     }
   });
   return success(!hasFailure);
 }
 
-static LogicalResult verifyBatchBody(SpatComputeBatch batchOp, Block& block) {
+template <typename ComputeBatchOpTy>
+static LogicalResult verifyBatchBody(ComputeBatchOpTy batchOp, Block& block) {
   if (batchOp.getNumResults() == 0) {
     auto yieldOp = dyn_cast_or_null<SpatYieldOp>(block.getTerminator());
     if (!yieldOp)
@@ -344,144 +380,399 @@ LogicalResult SpatConcatOp::verify() {
   return success();
 }
 
-LogicalResult verifyComputeResultsUses(Operation* op) {
-  if (!isa<SpatCompute, SpatComputeBatch>(op))
-    return op->emitError("verifyComputeResultUses: Op is not a SpatCompute/SpatComputeBatch operation");
-  if (!llvm::all_of(op->getResults(), [](Value result) {
-        return llvm::all_of(result.getUsers(), [](Operation* op) {
-          return !(op->getParentOfType<SpatCompute>() || op->getParentOfType<SpatComputeBatch>());
-        });
-      })) {
-    return op->emitError("ComputeResult used directly inside another Compute");
+static bool isKnownLogicalLayout(StringRef layout) { return layout == "nchw"; }
+
+static bool isKnownPhysicalLayout(StringRef layout) {
+  return layout == "dense_nchw" || layout == "nchw_row_strip" || layout == "fragmented";
+}
+
+static LogicalResult verifyPlanTensorTypes(Operation* op, Value input, Value output, StringRef kind) {
+  auto inputType = dyn_cast<RankedTensorType>(input.getType());
+  auto outputType = dyn_cast<RankedTensorType>(output.getType());
+  if (!inputType || !outputType)
+    return op->emitOpError() << kind << " requires ranked tensor input and output types";
+  if (inputType.getElementType() != outputType.getElementType())
+    return op->emitOpError() << kind << " requires matching input/output element types";
+  return success();
+}
+
+LogicalResult SpatConv2DPlanOp::verify() {
+  auto inputType = dyn_cast<RankedTensorType>(getInput().getType());
+  auto weightType = dyn_cast<RankedTensorType>(getWeight().getType());
+  auto outputType = dyn_cast<RankedTensorType>(getOutput().getType());
+  if (!inputType || !weightType || !outputType)
+    return emitError("requires ranked tensor input, weight, and output");
+  if (inputType.getRank() != 4 || weightType.getRank() != 4 || outputType.getRank() != 4)
+    return emitError("requires rank-4 input, weight, and output tensors");
+  if (!isKnownLogicalLayout(getLogicalLayout()))
+    return emitError("requires a known logical layout");
+  if (getPads().size() != 4)
+    return emitError("requires exactly four pad values");
+  if (getStrides().size() != 2)
+    return emitError("requires exactly two stride values");
+  if (getDilations().size() != 2)
+    return emitError("requires exactly two dilation values");
+  if (getGroup() < 1)
+    return emitError("requires group >= 1");
+  if (inputType.getElementType() != weightType.getElementType()
+      || inputType.getElementType() != outputType.getElementType()) {
+    return emitError("requires matching input, weight, and output element types");
+  }
+  if (getBias()) {
+    auto biasType = dyn_cast<RankedTensorType>(getBias().getType());
+    if (!biasType)
+      return emitError("requires ranked tensor bias type");
+    if (biasType.getElementType() != outputType.getElementType())
+      return emitError("requires bias element type to match output element type");
   }
   return success();
 }
 
-LogicalResult SpatCompute::verify() {
-  auto& block = getBody().front();
-  unsigned expectedArgCount = getWeights().size() + getInputs().size();
-  if (block.getNumArguments() != expectedArgCount)
-    return emitError("compute body must have weight and input block arguments");
+LogicalResult SpatReluPlanOp::verify() {
+  if (failed(verifyPlanTensorTypes(getOperation(), getInput(), getOutput(), "spat.relu_plan")))
+    return failure();
+  if (!isKnownLogicalLayout(getLogicalLayout()))
+    return emitError("requires a known logical layout");
+  return success();
+}
 
-  for (auto [weightIndex, weight] : llvm::enumerate(getWeights())) {
-    auto blockArg = getWeightArgument(weightIndex);
-    if (!blockArg || blockArg->getType() != weight.getType())
-      return emitError("compute weight block argument types must match weight operand types exactly");
+LogicalResult SpatReconciliatorOp::verify() {
+  auto modeAttr = getModeAttr();
+  bool isFragmentAssembly = modeAttr && modeAttr.getValue() == "fragment_assembly";
+  if (!isFragmentAssembly && failed(verifyPlanTensorTypes(getOperation(), getInput(), getOutput(), "spat.reconciliator")))
+    return failure();
+  if (!isKnownLogicalLayout(getLogicalLayout()))
+    return emitError("requires a known logical layout");
+  if (!isKnownPhysicalLayout(getPhysicalLayout()))
+    return emitError("requires a known physical layout");
+
+  auto logicalType = dyn_cast<RankedTensorType>(getOutput().getType());
+  if (!logicalType)
+    return emitError("requires ranked tensor output");
+
+  auto offsets = getFragmentOffsets();
+  auto sizes = getFragmentSizes();
+  if (offsets.size() != sizes.size())
+    return emitError("fragment offset and size arrays must have the same length");
+  int64_t rank = logicalType.getRank();
+  if (offsets.empty())
+    return success();
+  if (rank <= 0 || offsets.size() % rank != 0)
+    return emitError("fragment metadata must be a whole number of rank-sized fragments");
+
+  auto verifyBoundsOnly = [&](ArrayRef<int64_t> strideValues) -> LogicalResult {
+    ArrayRef<int64_t> shape = logicalType.getShape();
+    for (int64_t index = 0; index < static_cast<int64_t>(offsets.size()); ++index) {
+      int64_t dim = index % rank;
+      int64_t offset = offsets[index];
+      int64_t size = sizes[index];
+      int64_t stride = strideValues.empty() ? 1 : strideValues[index];
+      if (offset < 0 || size < 0 || stride < 0)
+        return emitError("fragment offsets, sizes, and strides must be non-negative");
+      int64_t logicalDim = shape[dim];
+      if (!ShapedType::isDynamic(logicalDim) && offset + size > logicalDim)
+        return emitError("fragment bounds must stay within the logical tensor shape");
+      if (stride != 1)
+        return emitError("fragment assembly currently requires unit strides");
+    }
+    return success();
+  };
+
+  if (!isFragmentAssembly) {
+    if (failed(verifyBoundsOnly({})))
+      return failure();
+    if (!getFragments().empty())
+      return emitError("legacy reconciliator does not accept extra fragment operands");
+    if (getFragmentStridesAttr() || getConflictPolicyAttr() || getCoveragePolicyAttr())
+      return emitError("legacy reconciliator does not accept fragment assembly attributes");
+    return success();
   }
-  for (auto [inputIndex, input] : llvm::enumerate(getInputs())) {
-    auto blockArg = getInputArgument(inputIndex);
+
+  auto stridesAttr = getFragmentStridesAttr();
+  auto operandIndicesAttr = getFragmentOperandIndicesAttr();
+  if (!operandIndicesAttr)
+    return emitError("fragment assembly reconciliator requires fragment operand indices");
+  if (!stridesAttr)
+    return emitError("fragment assembly reconciliator requires fragment strides");
+  ArrayRef<int64_t> operandIndices = operandIndicesAttr.asArrayRef();
+  ArrayRef<int64_t> strides = stridesAttr.asArrayRef();
+  if (strides.size() != offsets.size())
+    return emitError("fragment stride and offset arrays must have the same length");
+  if (!getConflictPolicyAttr() || !getCoveragePolicyAttr())
+    return emitError("fragment assembly reconciliator requires conflict and coverage policies");
+  if (getConflictPolicy() != "disjoint")
+    return emitError("fragment assembly reconciliator currently supports only conflict_policy=\"disjoint\"");
+  if (getCoveragePolicy() != "complete" && getCoveragePolicy() != "partial")
+    return emitError("fragment assembly reconciliator coverage_policy must be \"complete\" or \"partial\"");
+
+  SmallVector<Value> operands;
+  operands.push_back(getInput());
+  llvm::append_range(operands, getFragments());
+  int64_t operandCount = static_cast<int64_t>(operands.size());
+  int64_t fragmentCount = static_cast<int64_t>(operandIndices.size());
+  if (operandCount == 0)
+    return emitError("fragment assembly reconciliator requires at least one operand");
+  if (static_cast<int64_t>(offsets.size()) != fragmentCount * rank)
+    return emitError("fragment assembly metadata count must match operand count * result rank");
+  if (failed(verifyBoundsOnly(strides)))
+    return failure();
+
+  SmallVector<std::pair<SmallVector<int64_t, 4>, SmallVector<int64_t, 4>>, 8> slices;
+  slices.reserve(static_cast<size_t>(fragmentCount));
+  SmallVector<SmallVector<SmallVector<int64_t, 4>, 4>, 8> sizesByOperand(static_cast<size_t>(operandCount));
+  for (int64_t fragmentIndex = 0; fragmentIndex < fragmentCount; ++fragmentIndex) {
+    int64_t operandIndex = operandIndices[fragmentIndex];
+    if (operandIndex < 0 || operandIndex >= operandCount)
+      return emitError("fragment assembly operand index is out of range");
+
+    auto operandType = dyn_cast<RankedTensorType>(operands[operandIndex].getType());
+    if (!operandType || !operandType.hasStaticShape())
+      return emitError("fragment assembly reconciliator requires static ranked tensor operands");
+    if (operandType.getRank() != rank)
+      return emitError("fragment assembly reconciliator requires operand/result rank match");
+
+    SmallVector<int64_t, 4> fragmentOffsets;
+    SmallVector<int64_t, 4> fragmentSizes;
+    fragmentOffsets.reserve(rank);
+    fragmentSizes.reserve(rank);
+    for (int64_t dim = 0; dim < rank; ++dim) {
+      int64_t flatIndex = fragmentIndex * rank + dim;
+      fragmentOffsets.push_back(offsets[flatIndex]);
+      fragmentSizes.push_back(sizes[flatIndex]);
+    }
+
+    sizesByOperand[static_cast<size_t>(operandIndex)].push_back(fragmentSizes);
+
+    for (const auto& [existingOffsets, existingSizes] : slices) {
+      bool overlaps = true;
+      for (int64_t dim = 0; dim < rank; ++dim) {
+        int64_t begin = fragmentOffsets[dim];
+        int64_t end = begin + fragmentSizes[dim];
+        int64_t existingBegin = existingOffsets[dim];
+        int64_t existingEnd = existingBegin + existingSizes[dim];
+        if (end <= existingBegin || existingEnd <= begin) {
+          overlaps = false;
+          break;
+        }
+      }
+      if (overlaps)
+        return emitError("fragment assembly reconciliator requires disjoint static slices");
+    }
+    slices.push_back({std::move(fragmentOffsets), std::move(fragmentSizes)});
+  }
+
+  for (int64_t operandIndex = 0; operandIndex < operandCount; ++operandIndex) {
+    if (sizesByOperand[static_cast<size_t>(operandIndex)].empty())
+      return emitError("fragment assembly reconciliator requires every operand to contribute at least one fragment");
+
+    auto operandType = cast<RankedTensorType>(operands[operandIndex].getType());
+    ArrayRef<int64_t> operandShape = operandType.getShape();
+    auto& fragmentShapes = sizesByOperand[static_cast<size_t>(operandIndex)];
+    if (fragmentShapes.size() == 1) {
+      if (!llvm::equal(operandShape, fragmentShapes.front()))
+        return emitError("single-fragment reconciliator operand shape must match declared fragment size");
+      continue;
+    }
+
+    ArrayRef<int64_t> fragmentShape = fragmentShapes.front();
+    for (ArrayRef<int64_t> otherShape : fragmentShapes)
+      if (!llvm::equal(fragmentShape, otherShape))
+        return emitError("packed reconciliator operand requires equal fragment sizes per operand");
+    if (llvm::equal(operandShape, fragmentShape))
+      continue;
+    if (!llvm::equal(operandShape.drop_front(), fragmentShape.drop_front()))
+      return emitError("packed reconciliator operand must match fragment shape on non-packed dimensions");
+    if (operandShape.front() != static_cast<int64_t>(fragmentShapes.size()) * fragmentShape.front())
+      return emitError("packed reconciliator operand first dimension must equal fragment_count * fragment_size");
+  }
+
+  if (getCoveragePolicy() == "complete") {
+    int64_t covered = 0;
+    int64_t logicalElements = 1;
+    for (int64_t dimSize : logicalType.getShape()) {
+      if (ShapedType::isDynamic(dimSize))
+        return emitError("fragment assembly complete coverage requires static result shape");
+      logicalElements *= dimSize;
+    }
+    for (const auto& [ignoredOffsets, fragmentSizes] : slices) {
+      int64_t fragmentElements = 1;
+      for (int64_t dimSize : fragmentSizes)
+        fragmentElements *= dimSize;
+      covered += fragmentElements;
+    }
+    if (covered != logicalElements)
+      return emitError("fragment assembly complete coverage must cover the whole result exactly");
+  }
+
+  return success();
+}
+
+LogicalResult SpatMaterializeLayoutOp::verify() {
+  if (failed(verifyPlanTensorTypes(getOperation(), getInput(), getOutput(), "spat.materialize_layout")))
+    return failure();
+  if (!isKnownLogicalLayout(getLogicalLayout()))
+    return emitError("requires a known logical layout");
+  if (!isKnownPhysicalLayout(getSourcePhysicalLayout()))
+    return emitError("requires a known source physical layout");
+  if (!isKnownPhysicalLayout(getTargetPhysicalLayout()))
+    return emitError("requires a known target physical layout");
+  return success();
+}
+
+LogicalResult verifyComputeResultsUses(Operation* op) {
+  if (!isAnySpatialComputeLike(op))
+    return op->emitError("verifyComputeResultUses: op is not a Spatial compute-like operation");
+  if (!llvm::all_of(op->getResults(), [](Value result) {
+        return llvm::all_of(result.getUsers(), [](Operation* op) {
+          return !isAnySpatialComputeLike(op->getParentOp());
+        });
+      })) {
+    return op->emitError("compute result used directly inside another Spatial compute body");
+  }
+  return success();
+}
+
+template <typename ComputeOpTy>
+LogicalResult verifyComputeLikeOp(ComputeOpTy compute, StringRef opName) {
+  auto& block = compute.getBody().front();
+  unsigned expectedArgCount = compute.getWeights().size() + compute.getInputs().size();
+  if (block.getNumArguments() != expectedArgCount)
+    return compute.emitOpError("compute body must have weight and input block arguments");
+
+  for (auto [weightIndex, weight] : llvm::enumerate(compute.getWeights())) {
+    auto blockArg = compute.getWeightArgument(weightIndex);
+    if (!blockArg || blockArg->getType() != weight.getType())
+      return compute.emitOpError("compute weight block argument types must match weight operand types exactly");
+  }
+  for (auto [inputIndex, input] : llvm::enumerate(compute.getInputs())) {
+    auto blockArg = compute.getInputArgument(inputIndex);
     if (!blockArg || blockArg->getType() != input.getType())
-      return emitError("compute input block argument types must match input operand types exactly");
+      return compute.emitOpError("compute input block argument types must match input operand types exactly");
   }
 
   if (block.mightHaveTerminator()) {
     auto yieldOp = dyn_cast_or_null<SpatYieldOp>(block.getTerminator());
     if (!yieldOp)
-      return emitError("ComputeOp must have a single yield operation");
+      return compute.emitOpError("ComputeOp must have a single yield operation");
 
-    auto resultTypes = getResultTypes();
+    auto resultTypes = compute.getResultTypes();
     auto yieldTypes = yieldOp->getOperandTypes();
     if (resultTypes.size() != yieldTypes.size())
-      return emitError("ComputeOp must have same number of results as yieldOp operands");
+      return compute.emitOpError("ComputeOp must have same number of results as yieldOp operands");
 
     for (auto it : llvm::reverse(llvm::zip(resultTypes, yieldTypes))) {
       auto resultType = std::get<0>(it);
       auto yieldType = std::get<1>(it);
 
       if (resultType != yieldType || failed(verifyCompatibleShape(resultType, yieldType)))
-        return emitError("ComputeOp output must be of the same type as yieldOp operand");
+        return compute.emitOpError("ComputeOp output must be of the same type as yieldOp operand");
 
       if (auto resultRankedType = dyn_cast<RankedTensorType>(resultType)) {
         if (auto yieldRankedType = dyn_cast<RankedTensorType>(yieldType)) {
           if (resultRankedType.getEncoding() != yieldRankedType.getEncoding())
-            return emitError("ComputeOp output must have the same encoding as yieldOp operand");
+            return compute.emitOpError("ComputeOp output must have the same encoding as yieldOp operand");
         }
         else {
-          return emitError("ComputeOp output has an encoding while yieldOp operand does not have one");
+          return compute.emitOpError("ComputeOp output has an encoding while yieldOp operand does not have one");
         }
       }
       else if (dyn_cast<RankedTensorType>(yieldType)) {
-        return emitError("ComputeOp output must not have an encoding if yieldOp operand has one");
+        return compute.emitOpError("ComputeOp output must not have an encoding if yieldOp operand has one");
       }
     }
   }
 
-  for (unsigned inputIndex = 0; inputIndex < getInputs().size(); ++inputIndex)
-    if (auto inputArg = getInputArgument(inputIndex); !inputArg || inputArg->use_empty())
-      return emitError("ComputeOp block argument is not used");
-  if (failed(verifyStaticWeights(*this, "compute")))
+  for (unsigned inputIndex = 0; inputIndex < compute.getInputs().size(); ++inputIndex)
+    if (auto inputArg = compute.getInputArgument(inputIndex); !inputArg || inputArg->use_empty())
+      return compute.emitOpError("ComputeOp block argument is not used");
+  if (failed(verifyStaticWeights(compute, opName)))
     return failure();
-  if (failed(verifyOnlyConstantExternalValues(this->getOperation(), getBody(), "spat.compute")))
+  if (failed(verifyOnlyConstantExternalValues(compute.getOperation(), compute.getBody(), opName)))
     return failure();
-  if (failed(verifyComputeResultsUses(this->getOperation())))
+  if (failed(verifyComputeResultsUses(compute.getOperation())))
     return failure();
   return success();
 }
 
-LogicalResult SpatComputeBatch::verify() {
-  int32_t count = getLaneCount();
+LogicalResult SpatGraphCompute::verify() { return verifyComputeLikeOp(*this, "spat.graph_compute"); }
+
+LogicalResult SpatScheduledCompute::verify() { return verifyComputeLikeOp(*this, "spat.scheduled_compute"); }
+
+template <typename ComputeBatchOpTy>
+LogicalResult verifyComputeBatchLikeOp(ComputeBatchOpTy batch, StringRef opName) {
+  int32_t count = batch.getLaneCount();
   if (count <= 0)
-    return emitError("laneCount must be positive");
+    return batch.emitOpError("laneCount must be positive");
 
   auto laneCountSz = static_cast<size_t>(count);
 
-  if (auto coreIdAttr = (*this)->getAttr(kCoreIdsAttrName)) {
+  if (auto coreIdAttr = batch->getAttr(kCoreIdsAttrName)) {
     auto coreIdsAttr = dyn_cast<DenseI32ArrayAttr>(coreIdAttr);
     if (!coreIdsAttr)
-      return emitError("compute_batch coreIds attribute must be a dense i32 array");
+      return batch.emitOpError("compute_batch coreIds attribute must be a dense i32 array");
     if (coreIdsAttr.size() != static_cast<int64_t>(laneCountSz))
-      return emitError("compute_batch coreIds array length must match laneCount");
+      return batch.emitOpError("compute_batch coreIds array length must match laneCount");
     if (llvm::any_of(coreIdsAttr.asArrayRef(), [](int32_t coreId) { return coreId < 0; }))
-      return emitError("compute_batch coreIds values must be non-negative");
+      return batch.emitOpError("compute_batch coreIds values must be non-negative");
     DenseSet<int32_t> seenCoreIds;
     for (int32_t coreId : coreIdsAttr.asArrayRef())
       if (!seenCoreIds.insert(coreId).second)
-        return emitError("compute_batch coreIds values must be unique");
+        return batch.emitOpError("compute_batch coreIds values must be unique");
   }
 
-  Block& block = getBody().front();
+  Block& block = batch.getBody().front();
   if (block.getNumArguments() == 0)
-    return emitError("compute_batch body must have exactly one lane block argument");
-  unsigned expectedArgCount = 1 + getWeights().size() + getInputs().size() + getNumResults();
+    return batch.emitOpError("compute_batch body must have exactly one lane block argument");
+  unsigned expectedArgCount = 1 + batch.getWeights().size() + batch.getInputs().size() + batch.getNumResults();
   if (block.getNumArguments() != expectedArgCount)
-    return emitError("compute_batch body block arguments must match lane, weight, input, and output operands/results");
-  auto laneArg = getLaneArgument();
+    return batch.emitOpError("compute_batch body block arguments must match lane, weight, input, and output operands/results");
+  auto laneArg = batch.getLaneArgument();
   if (!laneArg || !laneArg->getType().isIndex())
-    return emitError("compute_batch first block argument must have index type");
+    return batch.emitOpError("compute_batch first block argument must have index type");
 
-  for (auto [weightIndex, weight] : llvm::enumerate(getWeights())) {
-    auto blockArg = getWeightArgument(weightIndex);
+  for (auto [weightIndex, weight] : llvm::enumerate(batch.getWeights())) {
+    auto blockArg = batch.getWeightArgument(weightIndex);
     if (!blockArg || blockArg->getType() != weight.getType())
-      return emitError("compute_batch weight block argument types must match weight operand types exactly");
+      return batch.emitOpError("compute_batch weight block argument types must match weight operand types exactly");
   }
-  for (auto [inputIndex, input] : llvm::enumerate(getInputs())) {
-    auto blockArg = getInputArgument(inputIndex);
+  for (auto [inputIndex, input] : llvm::enumerate(batch.getInputs())) {
+    auto blockArg = batch.getInputArgument(inputIndex);
     if (!blockArg || blockArg->getType() != input.getType())
-      return emitError("compute_batch input block argument types must match input operand types exactly");
+      return batch.emitOpError("compute_batch input block argument types must match input operand types exactly");
   }
-  for (auto [resultIndex, resultType] : llvm::enumerate(getResultTypes())) {
-    auto blockArg = getOutputArgument(resultIndex);
+  for (auto [resultIndex, resultType] : llvm::enumerate(batch.getResultTypes())) {
+    auto blockArg = batch.getOutputArgument(resultIndex);
     if (!blockArg || blockArg->getType() != resultType)
-      return emitError("compute_batch output block argument types must match result types exactly");
+      return batch.emitOpError("compute_batch output block argument types must match result types exactly");
   }
 
-  if (failed(verifyComputeResultsUses(this->getOperation())))
+  if (failed(verifyComputeResultsUses(batch.getOperation())))
     return failure();
-  if (failed(verifyStaticWeights(*this, "compute_batch")))
+  if (failed(verifyStaticWeights(batch, opName)))
     return failure();
-  if (failed(verifyOnlyConstantExternalValues(this->getOperation(), getBody(), "spat.compute_batch")))
+  if (failed(verifyOnlyConstantExternalValues(batch.getOperation(), batch.getBody(), opName)))
     return failure();
-  return verifyBatchBody(*this, block);
+  return verifyBatchBody(batch, block);
+}
+
+LogicalResult SpatGraphComputeBatch::verify() { return verifyComputeBatchLikeOp(*this, "spat.graph_compute_batch"); }
+
+LogicalResult SpatScheduledComputeBatch::verify() {
+  return verifyComputeBatchLikeOp(*this, "spat.scheduled_compute_batch");
 }
 
 LogicalResult SpatInParallelOp::verify() {
-  auto batchOp = getOperation()->getParentOfType<SpatComputeBatch>();
-  if (!batchOp)
-    return emitOpError("expected spat.compute_batch parent");
-  if (batchOp.getNumResults() == 0)
+  Operation* parent = getOperation()->getParentOp();
+  if (!isAnySpatialComputeBatchLike(parent))
+    return emitOpError("expected spat.graph_compute_batch or spat.scheduled_compute_batch parent");
+  if (parent->getNumResults() == 0)
     return emitOpError("requires a resultful spat.compute_batch parent");
 
-  auto laneArg = batchOp.getLaneArgument();
+  std::optional<BlockArgument> laneArg;
+  if (auto graphBatch = dyn_cast<SpatGraphComputeBatch>(parent))
+    laneArg = graphBatch.getLaneArgument();
+  else
+    laneArg = cast<SpatScheduledComputeBatch>(parent).getLaneArgument();
   if (!laneArg)
     return emitOpError("expected compute_batch lane block argument");
   for (Operation& op : getRegion().front().getOperations()) {
@@ -494,7 +785,10 @@ LogicalResult SpatInParallelOp::verify() {
 
     MutableOperandRange destinations = insertSliceOp.getUpdatedDestinations();
     for (OpOperand& destination : destinations)
-      if (!isBatchOutputArgument(batchOp, destination.get()))
+      if ((isa<SpatGraphComputeBatch>(parent)
+             && !isBatchOutputArgument(cast<SpatGraphComputeBatch>(parent), destination.get()))
+          || (isa<SpatScheduledComputeBatch>(parent)
+              && !isBatchOutputArgument(cast<SpatScheduledComputeBatch>(parent), destination.get())))
         return op.emitOpError("may only insert into a compute_batch output block argument");
   }
 

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/MaterializeMergeSchedule.cpp.rej

@@ -0,0 +1,128 @@
+--- src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/MaterializeMergeSchedule.cpp	2026-06-24 18:51:29.043731129 +0000
++++ src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/MaterializeMergeSchedule.cpp	2026-06-24 18:51:29.026726895 +0000
+@@ -4112,104 +4112,8 @@
+                                                                 Value originalOutput,
+                                                                 Location loc);
+ 
+-FailureOr<SmallVector<OpFoldResult, 4>> rematerializeProjectionIndexListForBatchHostOutput(
+-  MaterializerState& state,
+-  MaterializedClass& sourceClass,
+-  ArrayRef<OpFoldResult> values,
+-  IRMapping& mapper,
+-  Location loc) {
+-  SmallVector<OpFoldResult, 4> localized;
+-  localized.reserve(values.size());
+-  for (OpFoldResult value : values) {
+-    FailureOr<OpFoldResult> remapped =
+-      rematerializeIndexOpFoldResultInClass(state, sourceClass, value, loc, &mapper);
+-    if (failed(remapped))
+-      return failure();
+-    localized.push_back(*remapped);
+-  }
+-  return localized;
+-}
+-
+-LogicalResult createProjectionAwareBatchHostInsert(MaterializerState& state,
+-                                                  MaterializedClass& sourceClass,
+-                                                  Value originalOutput,
+-                                                  Value payload,
+-                                                  Value destination,
+-                                                  ArrayRef<ProducerKey> keys,
+-                                                  Location loc) {
+-  auto originalResult = dyn_cast<OpResult>(originalOutput);
+-  if (!originalResult)
+-    return failure();
+-
+-  auto sourceBatch = dyn_cast_or_null<SpatComputeBatch>(originalResult.getOwner());
+-  if (!sourceBatch || sourceBatch.getNumResults() == 0)
+-    return failure();
+-
+-  FailureOr<tensor::ParallelInsertSliceOp> projection =
+-    getBatchResultProjectionInsert(sourceBatch, originalResult.getResultNumber());
+-  if (failed(projection))
+-    return failure();
+-
+-  auto sourceLaneArg = sourceBatch.getLaneArgument();
+-  if (!sourceLaneArg)
+-    return failure();
+-
+-  auto materializedBatch = dyn_cast<SpatScheduledComputeBatch>(sourceClass.op);
+-  if (!materializedBatch)
+-    return failure();
+-
+-  auto materializedLaneArg = materializedBatch.getLaneArgument();
+-  if (!materializedLaneArg)
+-    return failure();
+-
+-  if (keys.size() != sourceClass.cpus.size())
+-    return failure();
+-
+-  SmallVector<int64_t, 8> logicalLanes;
+-  logicalLanes.reserve(keys.size());
+-  for (ProducerKey key : keys) {
+-    if (key.instance.op != sourceBatch.getOperation() || key.resultIndex != originalResult.getResultNumber())
+-      return failure();
+-    logicalLanes.push_back(key.instance.laneStart);
+-  }
+-
+-  IRMapping mapper;
+-  Value logicalLane = createIndexedIndexValue(state,
+-                                             sourceClass.op,
+-                                             ArrayRef<int64_t>(logicalLanes),
+-                                             *materializedLaneArg,
+-                                             loc,
+-                                             static_cast<int64_t>(sourceClass.cpus.size()),
+-                                             /*allowExhaustiveTiledSearch=*/false);
+-  mapper.map(*sourceLaneArg, logicalLane);
+-
+-  FailureOr<SmallVector<OpFoldResult, 4>> offsets =
+-    rematerializeProjectionIndexListForBatchHostOutput(
+-      state, sourceClass, projection->getMixedOffsets(), mapper, loc);
+-  if (failed(offsets))
+-    return failure();
+-  FailureOr<SmallVector<OpFoldResult, 4>> sizes =
+-    rematerializeProjectionIndexListForBatchHostOutput(
+-      state, sourceClass, projection->getMixedSizes(), mapper, loc);
+-  if (failed(sizes))
+-    return failure();
+-  FailureOr<SmallVector<OpFoldResult, 4>> strides =
+-    rematerializeProjectionIndexListForBatchHostOutput(
+-      state, sourceClass, projection->getMixedStrides(), mapper, loc);
+-  if (failed(strides))
+-    return failure();
+-
+-  tensor::ParallelInsertSliceOp::create(
+-    state.rewriter, loc, payload, destination, *offsets, *sizes, *strides);
+-  return success();
+-}
+-
+ LogicalResult
+-setHostOutputValue(MaterializerState& state,
+-                   MaterializedClass& sourceClass,
+-                   Value originalOutput,
+-                   Value payload,
+-                   ArrayRef<ProducerKey> keys = {}) {
++setHostOutputValue(MaterializerState& state, MaterializedClass& sourceClass, Value originalOutput, Value payload) {
+   auto resultIt = sourceClass.hostOutputToResultIndex.find(originalOutput);
+   if (resultIt == sourceClass.hostOutputToResultIndex.end())
+     return sourceClass.op->emitError("missing host result slot for materialized output")
+@@ -4253,10 +4157,6 @@
+     return batch.emitOpError("expected compute_batch output block argument while materializing batch output");
+ 
+   state.rewriter.setInsertionPointToStart(&inParallelOp.getRegion().front());
+-  if (succeeded(createProjectionAwareBatchHostInsert(
+-        state, sourceClass, originalOutput, payload, *outputArg, keys, payload.getLoc())))
+-    return success();
+-
+   createDim0ParallelInsertSlice(state, payload.getLoc(), payload, *outputArg, *laneArg);
+   return success();
+ }
+@@ -4276,7 +4176,7 @@
+ 
+   MaterializedClass& ownerClass = state.classes[ownerIt->second];
+   if (sourceClass.id == ownerClass.id)
+-    return setHostOutputValue(state, ownerClass, originalOutput, payload, keys);
++    return setHostOutputValue(state, ownerClass, originalOutput, payload);
+ 
+   // Keep the old deadlock-free communication discipline: only scalar-to-scalar
+   // host-owner forwarding is introduced here. Batch host publication remains on

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/MergeComputeNodesPass.cpp

@@ -40,11 +40,10 @@ using namespace mlir;
 namespace onnx_mlir {
 namespace {
 using namespace onnx_mlir::compact_asm;
-using SpatCompute = spatial::SpatCompute;
-using SpatComputeBatch = spatial::SpatComputeBatch;
-using spatial::getProducerValueRef;
+using SpatCompute = spatial::SpatGraphCompute;
+using SpatComputeBatch = spatial::SpatGraphComputeBatch;
 
-static std::optional<int32_t> getComputeCoreId(SpatCompute compute) {
+static std::optional<int32_t> getComputeCoreId(spatial::SpatScheduledCompute compute) {
   if (auto coreIdAttr = compute->getAttrOfType<IntegerAttr>(onnx_mlir::kCoreIdAttrName)) {
     auto checkedCoreId = pim::checkedI32(coreIdAttr.getInt(), compute, "merge compute core id");
     if (failed(checkedCoreId))
@@ -187,32 +186,50 @@ void generateReport(func::FuncOp funcOp, const std::string& name, size_t usedCpu
     SmallVector<int32_t> coreIds;
   };
 
+  //TODO Used for report refactor
+  struct CollectorConcatRow {
+    uint64_t computeId = 0;
+    int32_t coreId = -1;
+    uint64_t operandCount = 0;
+  };
+
   uint64_t totalComputeOps = 0;
   uint64_t totalLogicalComputes = 0;
   uint64_t totalBatchComputeOps = 0;
   uint64_t totalInstructionCount = 0;
   uint64_t totalCrossbarCount = 0;
   uint64_t nextBatchId = 0;
+  //TODO Used for report refactor
   std::vector<ReportRow> collectedData;
+  //TODO Used for report refactor
+  std::vector<CollectorConcatRow> collectorConcatRows;
 
   auto getPerInstanceCrossbarCount = [&](Operation* op) -> uint64_t {
     return static_cast<uint64_t>(spatial::collectDistinctCrossbarWeights(op).size());
   };
 
   for (Operation& op : funcOp.getBody().front()) {
-    if (auto spatCompute = dyn_cast<SpatCompute>(&op)) {
+    if (auto spatCompute = dyn_cast<spatial::SpatScheduledCompute>(&op)) {
       uint64_t numInst = spatial::countComputeBodyInstructions(spatCompute.getBody());
       uint64_t perInstanceCrossbarCount = getPerInstanceCrossbarCount(spatCompute.getOperation());
       SmallVector<int32_t> coreIds;
       if (auto coreId = getComputeCoreId(spatCompute))
         coreIds.push_back(*coreId);
-      collectedData.push_back({totalComputeOps++, 1, perInstanceCrossbarCount, numInst, false, coreIds});
+      uint64_t computeId = totalComputeOps++;
+      collectedData.push_back({computeId, 1, perInstanceCrossbarCount, numInst, false, coreIds});
+      uint64_t maxConcatOperands = 0;
+      spatCompute.getBody().walk([&](spatial::SpatConcatOp concatOp) {
+        maxConcatOperands = std::max<uint64_t>(maxConcatOperands, concatOp.getInputs().size());
+      });
+      //TODO 128 is a magic number
+      if (maxConcatOperands >= 128 && !coreIds.empty())
+        collectorConcatRows.push_back({computeId, coreIds.front(), maxConcatOperands});
       totalLogicalComputes += 1;
       totalInstructionCount += numInst;
       totalCrossbarCount += perInstanceCrossbarCount;
       continue;
     }
-    if (auto batch = dyn_cast<SpatComputeBatch>(&op)) {
+    if (auto batch = dyn_cast<spatial::SpatScheduledComputeBatch>(&op)) {
       uint64_t numInst = spatial::countComputeBodyInstructions(batch.getBody());
       uint64_t logicalCount = static_cast<uint64_t>(batch.getLaneCount());
       uint64_t perInstanceCrossbarCount = getPerInstanceCrossbarCount(batch.getOperation());
@@ -238,9 +255,17 @@ void generateReport(func::FuncOp funcOp, const std::string& name, size_t usedCpu
     {"Number of used crossbars",              std::to_string(totalCrossbarCount)   }
   };
   printReportTotalsBlock(os, totalFields);
-  if (!collectedData.empty())
+  if (!collectedData.empty() || !collectorConcatRows.empty())
     os << "\n";
 
+  if (!collectorConcatRows.empty()) {
+    os << "Collector concat materialization:\n";
+    for (const CollectorConcatRow& row : collectorConcatRows)
+      os << "\tmaterialization_kind = single_collector_concat, compute = " << row.computeId
+         << ", concat_operand_count = " << row.operandCount << ", collector_core = " << row.coreId << "\n";
+    os << "\n";
+  }
+
   sortReportEntriesByFirstCore(collectedData);
 
   for (uint64_t cI = 0; cI < totalComputeOps; ++cI) {
@@ -328,7 +353,17 @@ public:
 
   void runOnOperation() override {
     func::FuncOp func = getOperation();
+    if (failed(verifyLogicalSpatialGraphInvariants(func))) {
+      func.emitOpError("RAPTOR_PHASE_CHECK logical Spatial graph verification failed at the start of MergeComputeNodes");
+      signalPassFailure();
+      return;
+    }
     mergeTriviallyConnectedComputes(func);
+    if (failed(verifyLogicalSpatialGraphInvariants(func))) {
+      func.emitOpError("RAPTOR_PHASE_CHECK logical Spatial graph verification failed after trivial merge simplification");
+      signalPassFailure();
+      return;
+    }
 
     const spatial::MergeScheduleResult* analysisResult = nullptr;
     analysisResult = &getAnalysis<spatial::MergeSchedulingAnalysis>().getResult();
@@ -342,8 +377,8 @@ public:
       signalPassFailure();
       return;
     }
-    if (failed(verifySpatialCommunicationInvariants(func))) {
-      func.emitOpError("merged Spatial communication invariant verification failed");
+    if (failed(verifyScheduledSpatialInvariants(func))) {
+      func.emitOpError("RAPTOR_PHASE_CHECK scheduled Spatial verification failed after merge materialization");
       signalPassFailure();
       return;
     }

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/Scheduling/ComputeGraph.cpp

@@ -12,6 +12,7 @@
 #include "llvm/Support/Casting.h"
 
 #include <algorithm>
+#include <cmath>
 #include <iterator>
 #include <limits>
 #include <optional>
@@ -21,6 +22,7 @@
 
 #include "ComputeGraph.hpp"
 #include "ComputeInstanceUtils.hpp"
+#include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
 #include "src/Accelerators/PIM/Common/IR/AffineUtils.hpp"
 #include "src/Accelerators/PIM/Common/IR/ConstantUtils.hpp"
 #include "src/Support/TypeUtilities.hpp"
@@ -35,9 +37,223 @@ uint64_t countComputeBodyOperationInstances(Region& body);
 
 namespace {
 
-Cost getComputeBodyCost(Region& body) {
-  constexpr Cost kOperationCost = 100;
-  return checkedMultiply(static_cast<Cost>(countComputeBodyOperationInstances(body)), kOperationCost);
+struct PimsimSchedulerCostModel {
+  static constexpr Cost kDefaultBitwidth = 8;
+  static constexpr Cost kCorePeriodNs = 1;
+  static constexpr Cost kLocalMemoryWidthBytes = 64;
+  static constexpr Cost kLocalMemoryLatencyCycles = 1;
+  static constexpr Cost kNetworkBusWidthBytes = 8;
+  static constexpr Cost kNetworkBaseLatencyNs = 2;
+  static constexpr Cost kNetworkPerHopLatencyNs = 1;
+  static constexpr Cost kVectorWidth = 16;
+  static constexpr Cost kVectorLatencyCycles = 4;
+  static constexpr Cost kDacResolutionBits = 1;
+  static constexpr Cost kDacLatencyCycles = 1;
+  static constexpr Cost kDacCount = 128;
+  static constexpr Cost kXbarReadLatencyNs = 30;
+  static constexpr Cost kSampleHoldLatencyCycles = 1;
+  static constexpr Cost kAdcLatencyCycles = 10;
+  static constexpr Cost kAdcCount = 2;
+  static constexpr Cost kShiftAdderLatencyCycles = 1;
+  static constexpr Cost kOutputBufferLatencyCycles = 1;
+  static constexpr Cost kInputBufferLatencyCycles = 0;
+  static constexpr Cost kFallbackOperationCost = 1;
+
+  static Cost ceilDiv(Cost numerator, Cost denominator) {
+    assert(denominator > 0 && "denominator must be positive");
+    return (numerator + denominator - 1) / denominator;
+  }
+
+  static std::optional<Cost> getStaticElementCount(Type type) {
+    auto shaped = dyn_cast<ShapedType>(type);
+    if (!shaped || !shaped.hasStaticShape())
+      return std::nullopt;
+    return static_cast<Cost>(shaped.getNumElements());
+  }
+
+  static Cost getBitwidthOrDefault(Type type) {
+    if (auto shaped = dyn_cast<ShapedType>(type))
+      type = shaped.getElementType();
+    if (auto intType = dyn_cast<IntegerType>(type))
+      return intType.getWidth();
+    if (auto floatType = dyn_cast<FloatType>(type))
+      return floatType.getWidth();
+    if (isa<IndexType>(type))
+      return 64;
+    return kDefaultBitwidth;
+  }
+
+  static Cost getComputeBitwidth(Type type) {
+    return std::min(getBitwidthOrDefault(type), kDefaultBitwidth);
+  }
+
+  static Cost getByteSize(Type type, Cost fallbackBitwidth = kDefaultBitwidth) {
+    auto elementCount = getStaticElementCount(type);
+    if (!elementCount)
+      return kFallbackOperationCost;
+    Cost bitwidth = fallbackBitwidth;
+    if (bitwidth <= 0)
+      bitwidth = getBitwidthOrDefault(type);
+    if (bitwidth <= 0)
+      bitwidth = kDefaultBitwidth;
+    return ceilDiv(checkedMultiply(*elementCount, bitwidth), static_cast<Cost>(8));
+  }
+
+  static Cost getVectorReadWriteCost(Cost readBytes, Cost writeBytes) {
+    Cost totalBytes = checkedAdd(readBytes, writeBytes);
+    return checkedMultiply(ceilDiv(totalBytes, kLocalMemoryWidthBytes),
+                           checkedMultiply(kLocalMemoryLatencyCycles, kCorePeriodNs));
+  }
+
+  static Cost getVectorComputeCost(Cost elementCount) {
+    return checkedMultiply(ceilDiv(elementCount, kVectorWidth),
+                           checkedMultiply(kVectorLatencyCycles, kCorePeriodNs));
+  }
+
+  static Cost getTensorMoveCost(Type type) {
+    return getVectorReadWriteCost(getByteSize(type), 0);
+  }
+
+  static std::pair<Cost, Cost> estimateMeshShape() {
+    Cost coreCount = static_cast<Cost>(std::max<long>(1, coresCount.getValue()));
+    Cost rows = static_cast<Cost>(std::sqrt(static_cast<long double>(coreCount)));
+    if (rows == 0)
+      rows = 1;
+    while (rows > 1 && coreCount % rows != 0)
+      --rows;
+    Cost cols = ceilDiv(coreCount, rows);
+    return {rows, cols};
+  }
+
+  static Cost getAverageInterCoreLatencyNs() {
+    auto [rows, cols] = estimateMeshShape();
+    auto averageAxisDistance = [](Cost size) -> Cost {
+      if (size <= 1)
+        return 0;
+      return checkedMultiply(size, size) - 1;
+    };
+    Cost avgRow = averageAxisDistance(rows) / (static_cast<Cost>(3) * rows);
+    Cost avgCol = averageAxisDistance(cols) / (static_cast<Cost>(3) * cols);
+    return checkedAdd(kNetworkBaseLatencyNs, checkedMultiply(kNetworkPerHopLatencyNs, checkedAdd(avgRow, avgCol)));
+  }
+
+  static Cost getInterCoreTransferCostFromBytes(Cost bytes) {
+    Cost localRead = checkedMultiply(ceilDiv(bytes, kLocalMemoryWidthBytes),
+                                     checkedMultiply(kLocalMemoryLatencyCycles, kCorePeriodNs));
+    Cost localWrite = checkedMultiply(ceilDiv(bytes, kLocalMemoryWidthBytes),
+                                      checkedMultiply(kLocalMemoryLatencyCycles, kCorePeriodNs));
+    Cost payloadFlits = ceilDiv(bytes, kNetworkBusWidthBytes);
+    Cost averageNoCLatency = getAverageInterCoreLatencyNs();
+    Cost network = checkedMultiply(checkedAdd(static_cast<Cost>(2), payloadFlits), averageNoCLatency);
+    return checkedAdd(checkedAdd(localRead, localWrite), network);
+  }
+
+  static Cost getUnaryVectorCost(Type inputType, Type outputType, bool scalarOutput = false) {
+    auto maybeElements = getStaticElementCount(inputType);
+    if (!maybeElements)
+      return kFallbackOperationCost;
+    Cost inputBytes = getByteSize(inputType, getComputeBitwidth(inputType));
+    Cost outputBytes = scalarOutput ? ceilDiv(getComputeBitwidth(outputType), static_cast<Cost>(8))
+                                    : getByteSize(outputType, getComputeBitwidth(outputType));
+    return checkedAdd(getVectorReadWriteCost(inputBytes, outputBytes), getVectorComputeCost(*maybeElements));
+  }
+
+  static Cost getBinaryVectorCost(Type lhsType, Type rhsType, Type outputType, bool scalarOutput = false) {
+    auto maybeElements = getStaticElementCount(lhsType);
+    if (!maybeElements)
+      return kFallbackOperationCost;
+    Cost readBytes = checkedAdd(getByteSize(lhsType, getComputeBitwidth(lhsType)),
+                                getByteSize(rhsType, getComputeBitwidth(rhsType)));
+    Cost outputBytes = scalarOutput ? ceilDiv(getComputeBitwidth(outputType), static_cast<Cost>(8))
+                                    : getByteSize(outputType, getComputeBitwidth(outputType));
+    return checkedAdd(getVectorReadWriteCost(readBytes, outputBytes), getVectorComputeCost(*maybeElements));
+  }
+
+  static Cost getMatrixComputeLatency(Cost inputBitwidth) {
+    Cost xbarDim = static_cast<Cost>(crossbarSize.getValue());
+    Cost inputTimes = ceilDiv(inputBitwidth, kDacResolutionBits);
+    Cost dacTimes = ceilDiv(xbarDim, kDacCount);
+    Cost adcTimes = ceilDiv(xbarDim, kAdcCount);
+    Cost frontStage = kInputBufferLatencyCycles + kDacLatencyCycles + kXbarReadLatencyNs + kSampleHoldLatencyCycles;
+    Cost backPipe = std::max(kAdcLatencyCycles, checkedAdd(kShiftAdderLatencyCycles, kOutputBufferLatencyCycles));
+    Cost backStage = checkedAdd(checkedAdd(kAdcLatencyCycles, kShiftAdderLatencyCycles), kOutputBufferLatencyCycles);
+    backStage = checkedAdd(backStage, checkedMultiply(adcTimes - 1, backPipe));
+    Cost totalTimes = checkedMultiply(inputTimes, dacTimes);
+    Cost stagePipe = std::max(frontStage, backStage);
+    return checkedAdd(checkedAdd(frontStage, backStage),
+                      checkedMultiply(totalTimes - 1, stagePipe));
+  }
+
+  static Cost getWvmmCost(Type inputType, Type outputType) {
+    Cost inputBitwidth = getComputeBitwidth(inputType);
+    Cost inputBytes = checkedMultiply(static_cast<Cost>(crossbarSize.getValue()),
+                                      ceilDiv(inputBitwidth, static_cast<Cost>(8)));
+    inputBytes = checkedMultiply(inputBytes, static_cast<Cost>(8));
+    Cost outputBytes = getByteSize(outputType, getComputeBitwidth(outputType));
+    return checkedAdd(getVectorReadWriteCost(inputBytes, outputBytes), getMatrixComputeLatency(inputBitwidth));
+  }
+};
+
+std::optional<uint64_t> getStaticTripCount(scf::ForOp loop);
+[[maybe_unused]] Cost getOperationCost(Operation& op);
+
+[[maybe_unused]] Cost getRegionCost(Region& body) {
+  Cost cost = 0;
+  for (Block& block : body)
+    for (Operation& op : block)
+      cost = checkedAdd(cost, getOperationCost(op));
+  return cost;
+}
+
+[[maybe_unused]] Cost getOperationCost(Operation& op) {
+  if (auto loop = dyn_cast<scf::ForOp>(&op)) {
+    std::optional<uint64_t> tripCount = getStaticTripCount(loop);
+    if (!tripCount)
+      return PimsimSchedulerCostModel::kFallbackOperationCost;
+    return checkedMultiply(getRegionCost(loop.getRegion()), static_cast<Cost>(*tripCount));
+  }
+
+  if (isa<SpatYieldOp, SpatInParallelOp, affine::AffineApplyOp, arith::ConstantOp,
+          tensor::EmptyOp, tensor::CollapseShapeOp, tensor::ExpandShapeOp>(&op))
+    return 0;
+
+  if (auto wvmm = dyn_cast<SpatVMMOp>(&op))
+    return PimsimSchedulerCostModel::getWvmmCost(wvmm.getInput().getType(), wvmm.getOutput().getType());
+  if (auto vvdmul = dyn_cast<SpatVVDMulOp>(&op))
+    return PimsimSchedulerCostModel::getBinaryVectorCost(
+      vvdmul.getLhs().getType(), vvdmul.getRhs().getType(), vvdmul.getOutput().getType(), /*scalarOutput=*/true);
+  if (auto vadd = dyn_cast<SpatVAddOp>(&op))
+    return PimsimSchedulerCostModel::getBinaryVectorCost(vadd.getLhs().getType(), vadd.getRhs().getType(),
+                                                         vadd.getOutput().getType());
+  if (auto vsub = dyn_cast<SpatVSubOp>(&op))
+    return PimsimSchedulerCostModel::getBinaryVectorCost(vsub.getLhs().getType(), vsub.getRhs().getType(),
+                                                         vsub.getOutput().getType());
+  if (auto vmul = dyn_cast<SpatVMulOp>(&op))
+    return PimsimSchedulerCostModel::getBinaryVectorCost(vmul.getLhs().getType(), vmul.getRhs().getType(),
+                                                         vmul.getOutput().getType());
+  if (auto vmax = dyn_cast<SpatVMaxOp>(&op))
+    return PimsimSchedulerCostModel::getBinaryVectorCost(vmax.getLhs().getType(), vmax.getRhs().getType(),
+                                                         vmax.getOutput().getType());
+  if (auto vavg = dyn_cast<SpatVAvgOp>(&op))
+    return PimsimSchedulerCostModel::getUnaryVectorCost(vavg.getInput().getType(), vavg.getOutput().getType(),
+                                                        /*scalarOutput=*/true);
+  if (auto relu = dyn_cast<SpatReluOp>(&op))
+    return PimsimSchedulerCostModel::getUnaryVectorCost(relu.getInput().getType(), relu.getOutput().getType());
+  if (auto sigm = dyn_cast<SpatSigmoidOp>(&op))
+    return PimsimSchedulerCostModel::getUnaryVectorCost(sigm.getInput().getType(), sigm.getOutput().getType());
+  if (auto softmax = dyn_cast<SpatSoftmaxOp>(&op)) {
+    Cost unary = PimsimSchedulerCostModel::getUnaryVectorCost(softmax.getInput().getType(), softmax.getOutput().getType());
+    return checkedMultiply(unary, static_cast<Cost>(4));
+  }
+  if (auto extract = dyn_cast<tensor::ExtractSliceOp>(&op))
+    return PimsimSchedulerCostModel::getTensorMoveCost(extract.getResult().getType());
+  if (auto insert = dyn_cast<tensor::InsertSliceOp>(&op))
+    return PimsimSchedulerCostModel::getTensorMoveCost(insert.getSource().getType());
+
+  Cost nestedCost = 0;
+  for (Region& region : op.getRegions())
+    nestedCost = checkedAdd(nestedCost, getRegionCost(region));
+  return checkedAdd(PimsimSchedulerCostModel::kFallbackOperationCost, nestedCost);
 }
 
 std::optional<uint64_t> getStaticTripCount(scf::ForOp loop) {
@@ -54,6 +270,11 @@ std::optional<uint64_t> getStaticTripCount(scf::ForOp loop) {
   return (distance + stride - 1) / stride;
 }
 
+Cost getComputeBodyCost(Region& body) {
+  constexpr Cost kOperationCost = 100;
+  return checkedMultiply(static_cast<Cost>(countComputeBodyOperationInstances(body)), kOperationCost);
+}
+
 uint64_t countOperationInstances(Operation& op) {
   if (auto loop = dyn_cast<scf::ForOp>(&op)) {
     std::optional<uint64_t> tripCount = getStaticTripCount(loop);
@@ -149,7 +370,8 @@ std::optional<Cost> getBatchProjectedInputTransferCost(SpatComputeBatch batch, V
     auto resultType = dyn_cast<ShapedType>(extract.getResult().getType());
     if (!resultType || !resultType.hasStaticShape())
       return std::nullopt;
-    projectedCost = checkedAdd(projectedCost, static_cast<Cost>(getSizeInBytes(resultType)));
+    projectedCost = checkedAdd(
+      projectedCost, PimsimSchedulerCostModel::getInterCoreTransferCostFromBytes(static_cast<Cost>(getSizeInBytes(resultType))));
   }
 
   if (projectedCost == 0)
@@ -162,7 +384,7 @@ Cost getInputTransferCost(const ComputeInstance& consumerInstance, Value input)
   if (auto batch = dyn_cast<SpatComputeBatch>(consumerInstance.op))
     if (std::optional<Cost> projectedCost = getBatchProjectedInputTransferCost(batch, input))
       return *projectedCost;
-  return static_cast<Cost>(getSizeInBytes(inputType));
+  return PimsimSchedulerCostModel::getInterCoreTransferCostFromBytes(static_cast<Cost>(getSizeInBytes(inputType)));
 }
 
 uint32_t getLaneOverlapCount(const ComputeInstance& lhs, const ComputeInstance& rhs) {
@@ -451,7 +673,7 @@ std::vector<ComputeGraphEdge> aggregateEdges(llvm::ArrayRef<ComputeGraphEdge> ed
       continue;
     auto inserted = edgeCosts.try_emplace({edge.source, edge.target}, edge.transferCost);
     if (!inserted.second)
-      inserted.first->second = std::max(inserted.first->second, edge.transferCost);
+      inserted.first->second = checkedAdd(inserted.first->second, edge.transferCost);
   }
 
   std::vector<ComputeGraphEdge> aggregatedEdges;

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/Scheduling/MergeSchedulingAnalysis.cpp

@@ -23,7 +23,10 @@ MergeSchedulerKind getSchedulerKind() {
   llvm_unreachable("unknown merge scheduler kind");
 }
 
-void verifySchedule(const ComputeGraph& graph, const MergeScheduleResult& result, unsigned long crossbarCapacity) {
+void verifySchedule(const ComputeGraph& graph,
+                    const MergeScheduleResult& result,
+                    unsigned long crossbarCapacity,
+                    size_t processorCount) {
   llvm::DenseMap<size_t, std::vector<std::pair<size_t, size_t>>> tasksByCpu;
   tasksByCpu.reserve(result.cpuToLastComputeMap.size());
 
@@ -79,7 +82,8 @@ void verifySchedule(const ComputeGraph& graph, const MergeScheduleResult& result
 
     Time earliestTargetStart = addOrMax(sourceStart, graph.nodes[edge.source].cost);
     if (sourceCpu != targetCpu)
-      earliestTargetStart = addOrMax(earliestTargetStart, edge.transferCost);
+      earliestTargetStart = addOrMax(
+        earliestTargetStart, getPeftTransferTime(edge.transferCost, sourceCpu, targetCpu, processorCount));
     if (targetStart < earliestTargetStart) {
       std::string message = llvm::formatv("merge scheduling: dependency legality failed between tasks {0} and {1}",
                                           graph.nodes[edge.source].originalOrder,
@@ -115,7 +119,10 @@ MergeScheduleResult MergeSchedulingAnalysis::run() {
                                                      static_cast<unsigned long>(crossbarCountInCore.getValue()),
                                                      entryOp->getContext()});
   }
-  verifySchedule(graph, schedule, static_cast<unsigned long>(crossbarCountInCore.getValue()));
+  verifySchedule(graph,
+                 schedule,
+                 static_cast<unsigned long>(crossbarCountInCore.getValue()),
+                 options.processorCount);
   return schedule;
 }
 

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/Scheduling/PeftScheduler.cpp

@@ -4,6 +4,7 @@
 #include "llvm/Support/ErrorHandling.h"
 #include "llvm/Support/FormatVariadic.h"
 
+#include <cmath>
 #include <limits>
 #include <queue>
 #include <vector>
@@ -21,6 +22,63 @@ struct ScheduledTask {
   Time endTime = 0;
 };
 
+struct MeshModel {
+  size_t rows = 1;
+  size_t cols = 1;
+  long double averageDistance = 0.0L;
+
+  static MeshModel infer(size_t processorCount) {
+    MeshModel model;
+    if (processorCount == 0)
+      return model;
+
+    model.rows = static_cast<size_t>(std::sqrt(static_cast<long double>(processorCount)));
+    if (model.rows == 0)
+      model.rows = 1;
+    while (model.rows > 1 && processorCount % model.rows != 0)
+      --model.rows;
+    model.cols = (processorCount + model.rows - 1) / model.rows;
+
+    auto averageAxisDistance = [](size_t size) -> long double {
+      if (size <= 1)
+        return 0.0L;
+      return static_cast<long double>(size * size - 1) / (3.0L * static_cast<long double>(size));
+    };
+    model.averageDistance = averageAxisDistance(model.rows) + averageAxisDistance(model.cols);
+    return model;
+  }
+
+  std::pair<size_t, size_t> getCoord(size_t processor) const {
+    return {processor / cols, processor % cols};
+  }
+
+  size_t getDistance(size_t lhs, size_t rhs) const {
+    auto [lhsRow, lhsCol] = getCoord(lhs);
+    auto [rhsRow, rhsCol] = getCoord(rhs);
+    size_t rowDistance = lhsRow > rhsRow ? lhsRow - rhsRow : rhsRow - lhsRow;
+    size_t colDistance = lhsCol > rhsCol ? lhsCol - rhsCol : rhsCol - lhsCol;
+    return rowDistance + colDistance;
+  }
+
+  Time scaleTransferCost(Time transferCost, size_t sourceProcessor, size_t targetProcessor) const {
+    if (sourceProcessor == targetProcessor || transferCost == 0)
+      return 0;
+    long double distance = static_cast<long double>(getDistance(sourceProcessor, targetProcessor));
+    long double scale = averageDistance > 0.0L ? distance / averageDistance : 1.0L;
+    scale = std::max(0.25L, scale);
+    return static_cast<Time>(std::ceil(static_cast<long double>(transferCost) * scale));
+  }
+
+  size_t getCenterDistance(size_t processor) const {
+    auto [row, col] = getCoord(processor);
+    size_t centerRow = rows / 2;
+    size_t centerCol = cols / 2;
+    size_t rowDistance = row > centerRow ? row - centerRow : centerRow - row;
+    size_t colDistance = col > centerCol ? col - centerCol : centerCol - col;
+    return rowDistance + colDistance;
+  }
+};
+
 std::vector<std::vector<size_t>> buildReverseLevels(const ComputeGraph& graph) {
   std::vector<size_t> remainingSuccessors(graph.nodes.size(), 0);
   std::queue<size_t> readySinks;
@@ -77,11 +135,16 @@ void verifyOctTableSize(size_t nodeCount, size_t processorCount) {
 
 } // namespace
 
+Time getPeftTransferTime(Time transferCost, size_t sourceProcessor, size_t targetProcessor, size_t processorCount) {
+  return MeshModel::infer(processorCount).scaleTransferCost(transferCost, sourceProcessor, targetProcessor);
+}
+
 MergeScheduleResult runPeftScheduler(const ComputeGraph& graph, const PeftScheduleOptions& options) {
   const size_t nodeCount = graph.nodes.size();
   const size_t processorCount = options.processorCount;
   if (processorCount == 0)
     llvm::report_fatal_error("PEFT scheduler: processor count must be positive");
+  MeshModel mesh = MeshModel::infer(processorCount);
 
   verifyOctTableSize(nodeCount, processorCount);
   std::vector<std::vector<size_t>> reverseLevels = buildReverseLevels(graph);
@@ -89,7 +152,6 @@ MergeScheduleResult runPeftScheduler(const ComputeGraph& graph, const PeftSchedu
   // MOCK: Replace this with your actual heterogeneous cost lookup.
   // If graph.nodes[task] is modified to hold a vector of costs per processor, access it here.
   auto getComputeCost = [&](size_t task, size_t processor) -> Time { return graph.nodes[task].cost; };
-
   std::vector<Time> oct(nodeCount * processorCount, 0);
   std::vector<Time> minOctPlusComp(nodeCount, 0);
 
@@ -177,6 +239,7 @@ MergeScheduleResult runPeftScheduler(const ComputeGraph& graph, const PeftSchedu
     Time bestEft = 0;
     Time bestOeft = std::numeric_limits<Time>::max();
     unsigned int bestOverlapCount = 0;
+    size_t bestCenterDistance = std::numeric_limits<size_t>::max();
     bool crossbarRejected = false;
 
     for (size_t processor = 0; processor < processorCount; ++processor) {
@@ -191,7 +254,7 @@ MergeScheduleResult runPeftScheduler(const ComputeGraph& graph, const PeftSchedu
       Time dataReady = 0;
       for (const auto& [pred, comm] : graph.predecessors[task]) {
         const ScheduledTask& predSchedule = schedules[pred];
-        Time commPenalty = predSchedule.processor == processor ? 0 : comm;
+        Time commPenalty = getPeftTransferTime(comm, predSchedule.processor, processor, processorCount);
         dataReady = std::max(dataReady, addOrMax(predSchedule.endTime, commPenalty));
       }
 
@@ -218,6 +281,7 @@ MergeScheduleResult runPeftScheduler(const ComputeGraph& graph, const PeftSchedu
 
       Time eft = addOrMax(est, computeCost);
       Time oeft = addOrMax(eft, oct[task * processorCount + processor]);
+      size_t centerDistance = mesh.getCenterDistance(processor);
 
       if (oeft < bestOeft || (oeft == bestOeft && eft < bestEft)
           || (oeft == bestOeft && eft == bestEft && est < bestEst)) {
@@ -226,13 +290,25 @@ MergeScheduleResult runPeftScheduler(const ComputeGraph& graph, const PeftSchedu
         bestEft = eft;
         bestOeft = oeft;
         bestOverlapCount = overlapCount;
+        bestCenterDistance = centerDistance;
       }
-      else if (oeft == bestOeft && eft == bestEft && est < bestEst && overlapCount < bestOverlapCount) {
+      else if (oeft == bestOeft && eft == bestEft && est == bestEst
+               && centerDistance < bestCenterDistance) {
         bestProcessor = processor;
         bestEst = est;
         bestEft = eft;
         bestOeft = oeft;
         bestOverlapCount = overlapCount;
+        bestCenterDistance = centerDistance;
+      }
+      else if (oeft == bestOeft && eft == bestEft && est == bestEst
+               && centerDistance == bestCenterDistance && overlapCount < bestOverlapCount) {
+        bestProcessor = processor;
+        bestEst = est;
+        bestEft = eft;
+        bestOeft = oeft;
+        bestOverlapCount = overlapCount;
+        bestCenterDistance = centerDistance;
       }
     }
 

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/Scheduling/PeftScheduler.hpp

@@ -14,6 +14,7 @@ struct PeftScheduleOptions {
   mlir::MLIRContext* context = nullptr;
 };
 
+Time getPeftTransferTime(Time transferCost, size_t sourceProcessor, size_t targetProcessor, size_t processorCount);
 MergeScheduleResult runPeftScheduler(const ComputeGraph& graph, const PeftScheduleOptions& options);
 
 } // namespace spatial

src/PIM/Pass/PIMPasses.h

@@ -8,6 +8,8 @@
 namespace onnx_mlir {
 
 std::unique_ptr<mlir::Pass> createONNXToSpatialPass();
+std::unique_ptr<mlir::Pass> createSpatialLayoutPlanningPass();
+std::unique_ptr<mlir::Pass> createLowerSpatialPlansPass();
 
 std::unique_ptr<mlir::Pass> createSpatialToGraphvizPass();
 

src/PIM/PimAccelerator.cpp

@@ -72,6 +72,8 @@ void PimAccelerator::registerDialects(mlir::DialectRegistry& registry) const {
 void PimAccelerator::registerPasses(int optLevel) const {
   LLVM_DEBUG(llvm::dbgs() << "Registering passes for PIM accelerator\n");
   registerPass(createONNXToSpatialPass);
+  registerPass(createSpatialLayoutPlanningPass);
+  registerPass(createLowerSpatialPlansPass);
   registerPass(createSpatialToGraphvizPass);
   registerPass(createSpatialToPimPass);
   registerPass(createPimBufferizationPass);

validation/gen_network_runner.py

@@ -6,15 +6,15 @@ from onnx import TensorProto
 
 # ONNX dtype -> (ctype, printf, ONNX_TYPE_*)
 DTYPES = {
-    TensorProto.FLOAT:   ("float",     "%g",  "ONNX_TYPE_FLOAT"),
-    TensorProto.DOUBLE:  ("double",    "%g",  "ONNX_TYPE_DOUBLE"),
-    TensorProto.INT64:   ("int64_t",   "%lld","ONNX_TYPE_INT64"),
-    TensorProto.INT32:   ("int32_t",   "%d",  "ONNX_TYPE_INT32"),
-    TensorProto.UINT8:   ("uint8_t",   "%u",  "ONNX_TYPE_UINT8"),
-    TensorProto.INT8:    ("int8_t",    "%d",  "ONNX_TYPE_INT8"),
-    TensorProto.BOOL:    ("uint8_t",   "%u",  "ONNX_TYPE_BOOL"),      # stored as byte
-    TensorProto.FLOAT16: ("uint16_t",  "%u",  "ONNX_TYPE_FLOAT16"),   # raw 16-bit
-    TensorProto.BFLOAT16:("uint16_t",  "%u",  "ONNX_TYPE_BFLOAT16"),
+    TensorProto.FLOAT:   ("float",     "%.9g",  "ONNX_TYPE_FLOAT"),
+    TensorProto.DOUBLE:  ("double",    "%.17g", "ONNX_TYPE_DOUBLE"),
+    TensorProto.INT64:   ("int64_t",   "%lld",  "ONNX_TYPE_INT64"),
+    TensorProto.INT32:   ("int32_t",   "%d",    "ONNX_TYPE_INT32"),
+    TensorProto.UINT8:   ("uint8_t",   "%u",    "ONNX_TYPE_UINT8"),
+    TensorProto.INT8:    ("int8_t",    "%d",    "ONNX_TYPE_INT8"),
+    TensorProto.BOOL:    ("uint8_t",   "%u",    "ONNX_TYPE_BOOL"),
+    TensorProto.FLOAT16: ("uint16_t",  "%u",    "ONNX_TYPE_FLOAT16"),
+    TensorProto.BFLOAT16:("uint16_t",  "%u",    "ONNX_TYPE_BFLOAT16"),
 }
 
 def esc(s): return s.replace("\\","\\\\").replace('"','\\"')

validation/tools/classification_local_image_validation.py

@@ -0,0 +1,295 @@
+#!/usr/bin/env python3.13
+
+import argparse
+import math
+import subprocess
+import sys
+from pathlib import Path
+
+import numpy as np
+from PIL import Image, ImageDraw
+
+SCRIPT_DIR = Path(__file__).resolve().parent
+VALIDATION_DIR = SCRIPT_DIR.parent
+REPO_ROOT = VALIDATION_DIR.parent
+if str(VALIDATION_DIR) not in sys.path:
+    sys.path.insert(0, str(VALIDATION_DIR))
+
+from onnx_utils import _ONNX_TO_NP, onnx_io, write_inputs_to_memory_bin
+from validate_one import (
+    MODE_COMPILE_ONLY,
+    build_dump_ranges,
+    parse_pim_simulator_outputs,
+    run_pim_simulator,
+    sanitize_output_name,
+    validate_network,
+)
+from yolo_real_image_validation import save_tensor_csv
+
+IMAGENET_MEAN = np.asarray([0.485, 0.456, 0.406], dtype=np.float32)
+IMAGENET_STD = np.asarray([0.229, 0.224, 0.225], dtype=np.float32)
+DEFAULT_VGG_MODEL = VALIDATION_DIR / "networks" / "vgg16" / "depth_35" / "vgg16_depth_35.onnx"
+DEFAULT_RESNET_MODEL = VALIDATION_DIR / "networks" / "resnet" / "resnet18_torchvision.onnx"
+
+
+def resolve_default_paths():
+    return {
+        "raptor_path": REPO_ROOT / "build_release" / "Release" / "bin" / "onnx-mlir",
+        "onnx_include_dir": REPO_ROOT / "onnx-mlir" / "include",
+        "simulator_dir": REPO_ROOT / "backend-simulators" / "pim" / "pim-simulator",
+    }
+
+
+def resolve_model_path(network: str | None, model: Path | None) -> Path:
+    if model is not None:
+        return model.resolve()
+    if network == "resnet":
+        return DEFAULT_RESNET_MODEL.resolve()
+    if network == "vgg":
+        return DEFAULT_VGG_MODEL.resolve()
+    raise SystemExit("Pass --model or select a default with --network {resnet,vgg}.")
+
+
+def ensure_local_artifacts(args, model_path: Path):
+    validate_network(
+        network_onnx_path=model_path,
+        raptor_path=args.raptor_path,
+        onnx_include_dir=args.onnx_include_dir,
+        simulator_dir=args.simulator_dir,
+        crossbar_size=args.crossbar_size,
+        crossbar_count=args.crossbar_count,
+        core_count=args.core_count,
+        command_timeout_seconds=args.command_timeout_seconds,
+        mode=MODE_COMPILE_ONLY,
+        verbose=args.verbose,
+    )
+
+
+def ensure_existing_artifacts(model_dir: Path):
+    required_paths = [
+        model_dir / "runner" / "build" / "runner",
+        model_dir / "raptor" / "pim" / "config.json",
+        model_dir / "raptor" / "pim" / "memory.bin",
+    ]
+    missing = [str(path) for path in required_paths if not path.exists()]
+    if missing:
+        raise FileNotFoundError(
+            "Missing compiled local artifacts. Re-run without --skip-compile or restore these paths:\n  "
+            + "\n  ".join(missing)
+        )
+
+
+def preprocess_classification_image(image_path: Path) -> tuple[Image.Image, np.ndarray]:
+    image = Image.open(image_path).convert("RGB")
+    width, height = image.size
+    scale = 256.0 / min(width, height)
+    resized_size = (
+        max(1, int(round(width * scale))),
+        max(1, int(round(height * scale))),
+    )
+    resized = image.resize(resized_size, Image.Resampling.BILINEAR)
+
+    left = (resized.width - 224) // 2
+    top = (resized.height - 224) // 2
+    cropped = resized.crop((left, top, left + 224, top + 224))
+
+    array = np.asarray(cropped, dtype=np.float32) / 255.0
+    array = (array - IMAGENET_MEAN) / IMAGENET_STD
+    chw = np.transpose(array, (2, 0, 1))
+    tensor = np.expand_dims(chw.astype(np.float32, copy=False), axis=0)
+    return image, tensor
+
+
+def load_labels(labels_path: Path | None) -> list[str] | None:
+    if labels_path is None:
+        return None
+    labels = [line.strip() for line in labels_path.read_text().splitlines()]
+    return labels or None
+
+
+def softmax(values: np.ndarray) -> np.ndarray:
+    shifted = values - np.max(values)
+    exp = np.exp(shifted)
+    denom = exp.sum()
+    if not math.isfinite(float(denom)) or denom <= 0.0:
+        raise RuntimeError("Softmax received non-finite output scores.")
+    return exp / denom
+
+
+def decode_classification_output(output: np.ndarray, labels: list[str] | None, top_k: int):
+    scores = np.asarray(output, dtype=np.float64).reshape(-1)
+    probabilities = softmax(scores)
+    limit = min(top_k, probabilities.size)
+    top_indices = np.argsort(probabilities)[-limit:][::-1]
+    results = []
+    for index in top_indices:
+        label = None
+        if labels is not None and 0 <= int(index) < len(labels):
+            label = labels[int(index)]
+        results.append(
+            {
+                "index": int(index),
+                "label": label,
+                "probability": float(probabilities[int(index)]),
+            }
+        )
+    return results
+
+
+def render_result_line(result) -> str:
+    name = result["label"] if result["label"] else f'class {result["index"]}'
+    return f'{name}: {result["probability"] * 100.0:.2f}%'
+
+
+def draw_classification_panel(image: Image.Image, results, output_path: Path):
+    annotated = image.copy()
+    draw = ImageDraw.Draw(annotated)
+    lines = [render_result_line(result) for result in results]
+    if not lines:
+        lines = ["No predictions"]
+
+    padding = 10
+    line_gap = 4
+    max_width = 0
+    line_heights = []
+    for line in lines:
+        left, top, right, bottom = draw.textbbox((0, 0), line)
+        max_width = max(max_width, right - left)
+        line_heights.append(bottom - top)
+
+    panel_height = padding * 2 + sum(line_heights) + line_gap * (len(lines) - 1)
+    panel_width = padding * 2 + max_width
+    origin_x = 12
+    origin_y = 12
+    draw.rounded_rectangle(
+        (origin_x, origin_y, origin_x + panel_width, origin_y + panel_height),
+        radius=10,
+        fill=(0, 0, 0),
+    )
+
+    y = origin_y + padding
+    for line, line_height in zip(lines, line_heights):
+        draw.text((origin_x + padding, y), line, fill=(255, 255, 255))
+        y += line_height + line_gap
+
+    annotated.save(output_path)
+
+
+def run_reference_and_simulator(args, model_path: Path, tensor: np.ndarray):
+    model_dir = model_path.parent
+    runner_build_dir = model_dir / "runner" / "build"
+    runner_path = runner_build_dir / "runner"
+    pim_dir = model_dir / "raptor" / "pim"
+    simulation_dir = model_dir / "classification_demo" / "simulation"
+    reference_dir = model_dir / "classification_demo" / "reference"
+    inputs_dir = model_dir / "classification_demo" / "inputs"
+
+    simulation_dir.mkdir(parents=True, exist_ok=True)
+    reference_dir.mkdir(parents=True, exist_ok=True)
+    inputs_dir.mkdir(parents=True, exist_ok=True)
+
+    input_descriptors, output_descriptors = onnx_io(model_path)
+    if len(input_descriptors) != 1:
+        raise RuntimeError(f"Expected one classification input tensor, found {len(input_descriptors)}")
+    if len(output_descriptors) != 1:
+        raise RuntimeError(f"Expected one classification output tensor, found {len(output_descriptors)}")
+
+    input_index, _input_name, _input_dtype, input_shape = input_descriptors[0]
+    if list(tensor.shape) != list(input_shape):
+        raise RuntimeError(f"Preprocessed tensor shape {list(tensor.shape)} does not match model input {input_shape}")
+
+    input_csv = inputs_dir / "in0.csv"
+    save_tensor_csv(tensor, input_csv)
+
+    runner_cmd = [
+        str(runner_path),
+        f"--in{input_index}-csv-file",
+        str(input_csv),
+        f"--in{input_index}-shape",
+        "x".join(str(dim) for dim in tensor.shape),
+        "--save-csv-dir",
+        str(reference_dir),
+    ]
+    subprocess.run(runner_cmd, cwd=runner_build_dir, check=True)
+
+    write_inputs_to_memory_bin(pim_dir / "memory.bin", pim_dir / "config.json", [tensor])
+    dump_ranges = build_dump_ranges(pim_dir / "config.json", output_descriptors)
+    output_bin_path = simulation_dir / "out.bin"
+    run_pim_simulator(
+        args.simulator_dir,
+        pim_dir,
+        output_bin_path,
+        dump_ranges,
+        timeout_sec=args.command_timeout_seconds,
+    )
+
+    output_index, output_name, output_dtype_code, output_shape = output_descriptors[0]
+    output_dtype = np.dtype(_ONNX_TO_NP[output_dtype_code])
+    reference_csv = reference_dir / f"output{output_index}_{sanitize_output_name(output_name)}.csv"
+    reference_output = np.loadtxt(reference_csv, delimiter=",", dtype=output_dtype).reshape(output_shape)
+    simulator_output = parse_pim_simulator_outputs(output_bin_path, output_descriptors)[0]
+    return reference_output, simulator_output
+
+
+def print_topk(title: str, results):
+    print(title)
+    for rank, result in enumerate(results, start=1):
+        label_text = result["label"] if result["label"] else f'class {result["index"]}'
+        print(f'  {rank}. {label_text} ({result["probability"] * 100.0:.2f}%) [index={result["index"]}]')
+
+
+def main():
+    defaults = resolve_default_paths()
+
+    parser = argparse.ArgumentParser(description="Run a VGG or ResNet ONNX model through the Raptor simulator and annotate the image with top classification results.")
+    parser.add_argument("--model", type=Path, default=None)
+    parser.add_argument("--network", choices=("resnet", "vgg"), default=None)
+    parser.add_argument("--image", type=Path, required=True)
+    parser.add_argument("--labels", type=Path, default=None)
+    parser.add_argument("--output", type=Path, required=True)
+    parser.add_argument("--raptor-path", type=Path, default=defaults["raptor_path"])
+    parser.add_argument("--onnx-include-dir", type=Path, default=defaults["onnx_include_dir"])
+    parser.add_argument("--simulator-dir", type=Path, default=defaults["simulator_dir"])
+    parser.add_argument("--crossbar-size", type=int, default=2048)
+    parser.add_argument("--crossbar-count", type=int, default=256)
+    parser.add_argument("--core-count", type=int, default=1000)
+    parser.add_argument("--top-k", type=int, default=5)
+    parser.add_argument("--command-timeout-seconds", type=float, default=7200.0)
+    parser.add_argument("--skip-compile", action="store_true")
+    parser.add_argument("--verbose", action="store_true")
+    args = parser.parse_args()
+
+    args.model = resolve_model_path(args.network, args.model)
+    args.image = args.image.resolve()
+    args.output = args.output.resolve()
+    args.labels = args.labels.resolve() if args.labels else None
+    args.raptor_path = args.raptor_path.resolve()
+    args.onnx_include_dir = args.onnx_include_dir.resolve()
+    args.simulator_dir = args.simulator_dir.resolve()
+
+    if not args.skip_compile:
+        ensure_local_artifacts(args, args.model)
+    else:
+        ensure_existing_artifacts(args.model.parent)
+
+    original_image, tensor = preprocess_classification_image(args.image)
+    labels = load_labels(args.labels)
+    reference_output, simulator_output = run_reference_and_simulator(args, args.model, tensor)
+    reference_results = decode_classification_output(reference_output, labels, args.top_k)
+    simulator_results = decode_classification_output(simulator_output, labels, args.top_k)
+
+    print_topk("Reference top-k:", reference_results)
+    print_topk("Simulator top-k:", simulator_results)
+
+    reference_scores = np.asarray(reference_output, dtype=np.float64).reshape(-1)
+    simulator_scores = np.asarray(simulator_output, dtype=np.float64).reshape(-1)
+    max_abs_diff = float(np.max(np.abs(reference_scores - simulator_scores)))
+    print(f"Max absolute score diff: {max_abs_diff:.6e}")
+
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    draw_classification_panel(original_image, simulator_results, args.output)
+    print(f"Annotated image saved to {args.output}")
+
+
+if __name__ == "__main__":
+    main()