cose

.serena/project.yml

@@ -1,134 +0,0 @@
-# the name by which the project can be referenced within Serena
-project_name: raptor
-
-# list of languages for which language servers are started; choose from:
-#   al                  angular             ansible             bash                clojure
-#   cpp                 cpp_ccls            crystal             csharp              csharp_omnisharp
-#   dart                elixir              elm                 erlang              fortran
-#   fsharp              go                  groovy              haskell             haxe
-#   hlsl                html                java                json                julia
-#   kotlin              lean4               lua                 luau                markdown
-#   matlab              msl                 nix                 ocaml               pascal
-#   perl                php                 php_phpactor        powershell          python
-#   python_jedi         python_ty           r                   rego                ruby
-#   ruby_solargraph     rust                scala               scss                solidity
-#   svelte              swift               systemverilog       terraform           toml
-#   typescript          typescript_vts      vue                 yaml                zig
-#   (This list may be outdated. For the current list, see values of Language enum here:
-#   https://github.com/oraios/serena/blob/main/src/solidlsp/ls_config.py
-#   For some languages, there are alternative language servers, e.g. csharp_omnisharp, ruby_solargraph.)
-# Note:
-#   - For C, use cpp
-#   - For JavaScript, use typescript
-#   - For Angular projects, use angular (subsumes typescript+html; requires `npm install` in the project root)
-#   - For Svelte projects, use svelte (subsumes typescript/javascript for .svelte projects; requires npm)
-#   - For SCSS / Sass / plain CSS, use scss (some-sass-language-server handles all three)
-#   - For Free Pascal/Lazarus, use pascal
-# Special requirements:
-#   Some languages require additional setup/installations.
-#   See here for details: https://oraios.github.io/serena/01-about/020_programming-languages.html#language-servers
-# When using multiple languages, the first language server that supports a given file will be used for that file.
-# The first language is the default language and the respective language server will be used as a fallback.
-# Note that when using the JetBrains backend, language servers are not used and this list is correspondingly ignored.
-languages:
-- cpp
-- rust
-- python
-
-# the encoding used by text files in the project
-# For a list of possible encodings, see https://docs.python.org/3.11/library/codecs.html#standard-encodings
-encoding: utf-8
-
-# list of additional paths to ignore in this project.
-# Same syntax as gitignore, so you can use * and **.
-# Note: global ignored_paths from serena_config.yml are also applied additively.
-ignored_paths:
-
-# list of mode names that are to be activated by default, overriding the setting in the global configuration.
-# The full set of modes to be activated is base_modes (from global config) + default_modes + added_modes.
-# If the setting is undefined/empty, the default_modes from the global configuration (serena_config.yml) apply.
-# Otherwise, this overrides the setting from the global configuration (serena_config.yml).
-# Therefore, you can set this to [] if you do not want the default modes defined in the global config to apply
-# for this project.
-# This setting can, in turn, be overridden by CLI parameters (--mode).
-# See https://oraios.github.io/serena/02-usage/050_configuration.html#modes
-default_modes:
-
-# list of mode names to be activated additionally for this project, e.g. ["query-projects"]
-# The full set of modes to be activated is base_modes (from global config) + default_modes + added_modes.
-# See https://oraios.github.io/serena/02-usage/050_configuration.html#modes
-added_modes:
-
-# list of tool names to exclude.
-# This extends the existing exclusions (e.g. from the global configuration)
-# Find the list of tools here: https://oraios.github.io/serena/01-about/035_tools.html
-excluded_tools: []
-
-# list of tools to include that would otherwise be disabled (particularly optional tools that are disabled by default).
-# This extends the existing inclusions (e.g. from the global configuration).
-# Find the list of tools here: https://oraios.github.io/serena/01-about/035_tools.html
-included_optional_tools: []
-
-# fixed set of tools to use as the base tool set (if non-empty), replacing Serena's default set of tools.
-# This cannot be combined with non-empty excluded_tools or included_optional_tools.
-# Find the list of tools here: https://oraios.github.io/serena/01-about/035_tools.html
-fixed_tools: []
-
-# time budget (seconds) per tool call for the retrieval of additional symbol information
-# such as docstrings or parameter information.
-# This overrides the corresponding setting in the global configuration; see the documentation there.
-# If null or missing, use the setting from the global configuration.
-symbol_info_budget:
-
-# The language backend to use for this project.
-# If not set, the global setting from serena_config.yml is used.
-# Valid values: LSP, JetBrains
-# Note: the backend is fixed at startup. If a project with a different backend
-# is activated post-init, an error will be returned.
-language_backend:
-
-# line ending convention to use when writing source files.
-# Possible values: unset (use global setting), "lf", "crlf", or "native" (platform default)
-# This does not affect Serena's own files (e.g. memories and configuration files), which always use native line endings.
-line_ending:
-
-# list of regex patterns which, when matched, mark a memory entry as read‑only.
-# Extends the list from the global configuration, merging the two lists.
-read_only_memory_patterns: []
-
-# list of regex patterns for memories to completely ignore.
-# Matching memories will not appear in list_memories or activate_project output
-# and cannot be accessed via read_memory or write_memory.
-# To access ignored memory files, use the read_file tool on the raw file path.
-# Extends the list from the global configuration, merging the two lists.
-# Example: ["_archive/.*", "_episodes/.*"]
-ignored_memory_patterns: []
-
-# advanced configuration option allowing to configure language server-specific options.
-# Maps the language key to the options.
-# Have a look at the docstring of the constructors of the LS implementations within solidlsp (e.g., for C# or PHP) to see which options are available.
-# No documentation on options means no options are available.
-ls_specific_settings: {}
-
-# list of additional workspace folder paths for cross-package reference support (e.g. in monorepos).
-# Paths can be absolute or relative to the project root.
-# Each folder is registered as an LSP workspace folder, enabling language servers to discover
-# symbols and references across package boundaries.
-# Currently supported for: TypeScript.
-# Example:
-#   additional_workspace_folders:
-#     - ../sibling-package
-#     - ../shared-lib
-additional_workspace_folders: []
-
-# whether the project is in read-only mode
-# If set to true, all editing tools will be disabled and attempts to use them will result in an error
-# Added on 2025-04-18
-read_only: false
-
-# whether to use project's .gitignore files to ignore files
-ignore_all_files_in_gitignore: true
-
-# initial prompt for the project. It will always be given to the LLM upon activating the project
-# (contrary to the memories, which are loaded on demand).
-initial_prompt: ''

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"
@@ -138,26 +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 =
@@ -169,6 +193,127 @@ 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::SpatScheduledComputeBatch computeBatchOp,
@@ -207,8 +352,10 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeBatchOp(spatial::SpatSchedul
 
   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(
@@ -271,6 +418,18 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeBatchOp(spatial::SpatSchedul
     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)
@@ -287,10 +446,28 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeBatchOp(spatial::SpatSchedul
         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);

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()) {
@@ -131,6 +216,17 @@ static bool inlineInputlessHelperComputeForWeightLikeUsers(spatial::SpatSchedule
     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()))

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/Dialect/Spatial/Transforms/MergeComputeNodes/MaterializeMergeSchedule.cpp

@@ -306,10 +306,12 @@ struct ProjectedExtractReplacement {
 struct PendingProjectedHostOutputFragment {
   Value originalOutput;
   ClassId sourceClass = 0;
+  ProducerKey producerKey;
   Value operand;
   RankedTensorType operandType;
   RankedTensorType fragmentType;
   int64_t packedFragmentIndex = -1;
+  int64_t currentLane = -1;
   SmallVector<int64_t, 4> offsets;
   SmallVector<int64_t, 4> sizes;
   SmallVector<int64_t, 4> strides;
@@ -1137,6 +1139,59 @@ LogicalResult createEmptyMaterializedOps(MaterializerState& state) {
   return success();
 }
 
+void setInsertionPointForNewMaterializedOp(MaterializerState& state) {
+  Block& funcBlock = state.func.getBody().front();
+  for (Operation& op : funcBlock) {
+    if (state.oldComputeOps.contains(&op)) {
+      state.rewriter.setInsertionPoint(&op);
+      return;
+    }
+  }
+  state.rewriter.setInsertionPointToEnd(&funcBlock);
+}
+
+FailureOr<ClassId> createProjectedHostAssemblyClass(MaterializerState& state, Value originalOutput, Location loc) {
+  DenseSet<CpuId> usedCpus;
+  for (const auto& [cpu, _] : state.cpuToClass)
+    usedCpus.insert(cpu);
+
+  CpuId assemblyCpu = 0;
+  while (usedCpus.contains(assemblyCpu))
+    ++assemblyCpu;
+
+  setInsertionPointForNewMaterializedOp(state);
+
+  auto resultType = dyn_cast<RankedTensorType>(originalOutput.getType());
+  if (!resultType || !resultType.hasStaticShape())
+    return state.func.emitError("projected host assembly class requires a static ranked tensor output");
+
+  auto compute = SpatScheduledCompute::create(state.rewriter, loc, TypeRange {resultType}, ValueRange {}, ValueRange {});
+  compute.getProperties().setOperandSegmentSizes({0, 0});
+  auto coreIdAttr = pim::getCheckedI32Attr(state.rewriter, state.func, assemblyCpu, "projected host assembly core id");
+  if (failed(coreIdAttr))
+    return failure();
+  compute->setAttr(onnx_mlir::kCoreIdAttrName, *coreIdAttr);
+
+  Block* body = state.rewriter.createBlock(&compute.getBody());
+  state.rewriter.setInsertionPointToEnd(body);
+  Value placeholder =
+    tensor::EmptyOp::create(state.rewriter, loc, resultType.getShape(), resultType.getElementType()).getResult();
+  SpatYieldOp::create(state.rewriter, loc, ValueRange {placeholder});
+  state.rewriter.setInsertionPointAfter(compute.getOperation());
+
+  MaterializedClass materializedClass;
+  materializedClass.id = state.classes.size();
+  materializedClass.cpus.push_back(assemblyCpu);
+  materializedClass.op = compute.getOperation();
+  materializedClass.body = body;
+  materializedClass.hostOutputToResultIndex[originalOutput] = 0;
+  materializedClass.hostOutputs.push_back(originalOutput);
+  state.cpuToClass[assemblyCpu] = materializedClass.id;
+  state.hostOutputOwners[originalOutput] = materializedClass.id;
+  state.classes.push_back(std::move(materializedClass));
+  return state.classes.back().id;
+}
+
 BlockArgument appendWeight(MaterializerState& state, MaterializedClass& materializedClass, Value weight) {
   auto it = materializedClass.weightArgs.find(weight);
   if (it != materializedClass.weightArgs.end())
@@ -1897,6 +1952,14 @@ FailureOr<SmallVector<OpFoldResult, 4>> buildProjectedFragmentOffsetsInClass(Mat
   return fragmentOffsets;
 }
 
+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;
+}
+
 Value createDim0InsertSlice(
   MaterializerState& state, Location loc, Value fragment, Value destination, OpFoldResult firstOffset) {
   auto fragmentType = cast<RankedTensorType>(fragment.getType());
@@ -3639,6 +3702,9 @@ LogicalResult appendSend(MaterializerState& state,
 
   if (sourceClass.isBatch) {
     state.rewriter.setInsertionPoint(sourceClass.body->getTerminator());
+    if (messages.size() != sourceClass.cpus.size())
+      return sourceClass.op->emitError("batch send expects exactly one message per materialized lane")
+             << " messageCount=" << messages.size() << " laneCount=" << sourceClass.cpus.size();
 
     Value channelId = createLaneIndexedIndexValue(state, sourceClass, messages.channelIds, loc);
     Value sourceCoreId = createLaneIndexedIndexValue(state, sourceClass, messages.sourceCoreIds, loc);
@@ -3686,6 +3752,11 @@ Value appendReceive(
   state.rewriter.setInsertionPoint(targetClass.body->getTerminator());
 
   if (targetClass.isBatch) {
+    if (messages.size() != targetClass.cpus.size()) {
+      targetClass.op->emitOpError("batch receive expects exactly one message per materialized lane")
+        << " messageCount=" << messages.size() << " laneCount=" << targetClass.cpus.size();
+      return Value();
+    }
     Value channelId = createLaneIndexedIndexValue(state, targetClass, messages.channelIds, loc);
     Value sourceCoreId = createLaneIndexedIndexValue(state, targetClass, messages.sourceCoreIds, loc);
     Value targetCoreId = createLaneIndexedIndexValue(state, targetClass, messages.targetCoreIds, loc);
@@ -5481,10 +5552,12 @@ FailureOr<bool> recordProjectedScalarHostFragmentsFromPackedRun(MaterializerStat
     state.pendingProjectedHostOutputFragments.push_back(PendingProjectedHostOutputFragment {
       originalOutput,
       sourceClass.id,
+      ProducerKey {peer, resultIndex},
       packed,
       cast<RankedTensorType>(packed.getType()),
       fragmentType,
       static_cast<int64_t>(runIndex),
+      static_cast<int64_t>(runIndex),
       SmallVector<int64_t, 4>(*offsets),
       SmallVector<int64_t, 4>(*sizes),
       SmallVector<int64_t, 4>(*strides),
@@ -5572,10 +5645,12 @@ FailureOr<bool> recordProjectedScalarHostFragmentsFromPackedValue(MaterializerSt
     state.pendingProjectedHostOutputFragments.push_back(PendingProjectedHostOutputFragment {
       originalOutput,
       sourceClass.id,
+      key,
       packed,
       packedType,
       fragmentType,
       operandIsDim0Packed ? static_cast<int64_t>(fragmentIndex) : -1,
+      static_cast<int64_t>(fragmentIndex),
       SmallVector<int64_t, 4>(*offsets),
       SmallVector<int64_t, 4>(*sizes),
       SmallVector<int64_t, 4>(*strides),
@@ -5611,16 +5686,6 @@ LogicalResult finalizeProjectedHostOutputFragments(MaterializerState& state) {
     }
 
     MaterializedClass* ownerClass = &state.classes[ownerIt->second];
-    if (ownerClass->isBatch) {
-      auto scalarOwnerIt = llvm::find_if(state.classes, [](const MaterializedClass& candidate) {
-        return !candidate.isBatch;
-      });
-      if (scalarOwnerIt == state.classes.end())
-        return ownerClass->op->emitError(
-          "projected host output finalization requires a scalar assembly class when the preferred host owner is batch");
-      ownerClass = &*scalarOwnerIt;
-      state.hostOutputOwners[originalOutput] = ownerClass->id;
-    }
 
     auto resultType = dyn_cast<RankedTensorType>(originalOutput.getType());
     if (!resultType || !resultType.hasStaticShape())
@@ -5646,6 +5711,119 @@ LogicalResult finalizeProjectedHostOutputFragments(MaterializerState& state) {
     if (allFromSameSourceClass) {
       ownerClass = &state.classes[fragments.front()->sourceClass];
       state.hostOutputOwners[originalOutput] = ownerClass->id;
+    } else {
+      if (!ownerClass->isBatch && ownerClass->hostOutputToResultIndex.contains(originalOutput))
+        goto owner_selected;
+      FailureOr<ClassId> createdOwner =
+        createProjectedHostAssemblyClass(state, originalOutput, fragments.front()->loc);
+      if (failed(createdOwner))
+        return failure();
+      ownerClass = &state.classes[*createdOwner];
+    }
+owner_selected:
+
+    if (ownerClass->isBatch && allFromSameSourceClass && ownerClass->id == fragments.front()->sourceClass) {
+      auto sourceBatch = dyn_cast<SpatComputeBatch>(fragments.front()->producerKey.instance.op);
+      auto batch = dyn_cast<SpatScheduledComputeBatch>(ownerClass->op);
+      auto inParallelOp = dyn_cast_or_null<SpatInParallelOp>(ownerClass->body->getTerminator());
+      auto resultIt = ownerClass->hostOutputToResultIndex.find(originalOutput);
+      if (!sourceBatch || !batch || !inParallelOp || resultIt == ownerClass->hostOutputToResultIndex.end())
+        return ownerClass->op->emitError("missing batch host assembly state for projected host output");
+      FailureOr<tensor::ParallelInsertSliceOp> sourceProjection =
+        getBatchResultProjectionInsert(sourceBatch, fragments.front()->producerKey.resultIndex);
+      std::optional<BlockArgument> sourceLaneArg = sourceBatch.getLaneArgument();
+      if (failed(sourceProjection) || !sourceLaneArg)
+        return ownerClass->op->emitError(
+          "direct batch host output assembly requires the source batch projection metadata");
+
+      auto outputArg = batch.getOutputArgument(resultIt->second);
+      auto laneArg = batch.getLaneArgument();
+      if (!outputArg || !laneArg)
+        return ownerClass->op->emitError("missing compute_batch output block argument for projected host output");
+
+      if (fragments.size() != ownerClass->cpus.size())
+        return ownerClass->op->emitError(
+          "direct batch host output assembly expects exactly one fragment per materialized lane");
+
+      SmallVector<PendingProjectedHostOutputFragment*, 8> fragmentsByLane(ownerClass->cpus.size(), nullptr);
+      for (PendingProjectedHostOutputFragment* fragmentRecord : fragments) {
+        int64_t currentLane = fragmentRecord->currentLane >= 0 ? fragmentRecord->currentLane : fragmentRecord->sourceLane;
+        if (currentLane < 0 || currentLane >= static_cast<int64_t>(fragmentsByLane.size()))
+          return ownerClass->op->emitError("projected batch host output fragment current lane is out of bounds");
+        if (fragmentsByLane[currentLane])
+          return ownerClass->op->emitError("projected batch host output has duplicate fragments for one lane");
+        fragmentsByLane[currentLane] = fragmentRecord;
+      }
+
+      if (llvm::any_of(fragmentsByLane, [](PendingProjectedHostOutputFragment* fragment) { return fragment == nullptr; }))
+        return ownerClass->op->emitError("projected batch host output is missing a fragment for one or more lanes");
+
+      FailureOr<SmallVector<int64_t, 4>> firstSizes =
+        evaluateStaticProjectionIndices(sourceProjection->getMixedSizes(), *sourceLaneArg, fragmentsByLane.front()->sourceLane);
+      FailureOr<SmallVector<int64_t, 4>> firstStrides =
+        evaluateStaticProjectionIndices(sourceProjection->getMixedStrides(), *sourceLaneArg, fragmentsByLane.front()->sourceLane);
+      if (failed(firstSizes) || failed(firstStrides))
+        return ownerClass->op->emitError("failed to evaluate direct batch host output fragment shape");
+      SmallVector<int64_t, 4> referenceSizes(*firstSizes);
+      SmallVector<int64_t, 4> referenceStrides(*firstStrides);
+      Value laneOperand;
+      for (PendingProjectedHostOutputFragment* fragmentRecord : fragmentsByLane) {
+        FailureOr<SmallVector<int64_t, 4>> fragmentSizes =
+          evaluateStaticProjectionIndices(sourceProjection->getMixedSizes(), *sourceLaneArg, fragmentRecord->sourceLane);
+        FailureOr<SmallVector<int64_t, 4>> fragmentStrides =
+          evaluateStaticProjectionIndices(sourceProjection->getMixedStrides(), *sourceLaneArg, fragmentRecord->sourceLane);
+        if (failed(fragmentSizes) || failed(fragmentStrides))
+          return ownerClass->op->emitError("failed to evaluate direct batch host output fragment shape");
+        if (SmallVector<int64_t, 4>(*fragmentSizes) != referenceSizes
+            || SmallVector<int64_t, 4>(*fragmentStrides) != referenceStrides)
+          return ownerClass->op->emitError(
+            "direct batch host output assembly expects a uniform fragment shape and strides");
+
+        MaterializedClass& sourceClass = state.classes[fragmentRecord->sourceClass];
+        Value operand;
+        if (std::optional<Value> availableValue =
+              state.availableValues.lookup(state, fragmentRecord->producerKey, sourceClass.id)) {
+          operand = *availableValue;
+        } else {
+          operand = fragmentRecord->operand;
+        }
+        if (!isValueLegalInMaterializedClassBody(operand, *ownerClass))
+          return ownerClass->op->emitError(
+            "projected batch host output assembly requires source-local fragment operands");
+        if (laneOperand && laneOperand != operand)
+          return ownerClass->op->emitError(
+            "direct batch host output assembly expects one shared lane-local fragment producer");
+        laneOperand = operand;
+      }
+
+      SmallVector<OpFoldResult, 4> mixedOffsets;
+      mixedOffsets.reserve(referenceSizes.size());
+      for (size_t dim = 0; dim < referenceSizes.size(); ++dim) {
+        SmallVector<int64_t, 8> offsetsByLane;
+        offsetsByLane.reserve(fragmentsByLane.size());
+        for (PendingProjectedHostOutputFragment* fragmentRecord : fragmentsByLane) {
+          FailureOr<SmallVector<int64_t, 4>> fragmentOffsets =
+            evaluateStaticProjectionIndices(sourceProjection->getMixedOffsets(), *sourceLaneArg, fragmentRecord->sourceLane);
+          if (failed(fragmentOffsets))
+            return ownerClass->op->emitError("failed to evaluate direct batch host output fragment offsets");
+          offsetsByLane.push_back((*fragmentOffsets)[dim]);
+        }
+        mixedOffsets.push_back(allEqual(offsetsByLane)
+                                 ? OpFoldResult(state.rewriter.getIndexAttr(offsetsByLane.front()))
+                                 : OpFoldResult(createLaneIndexedIndexValue(
+                                     state, *ownerClass, ArrayRef<int64_t>(offsetsByLane), fragments.front()->loc)));
+      }
+
+      state.hostReplacements[originalOutput] = ownerClass->op->getResult(resultIt->second);
+      state.rewriter.setInsertionPointToStart(&inParallelOp.getRegion().front());
+      tensor::ParallelInsertSliceOp::create(state.rewriter,
+                                            fragments.front()->loc,
+                                            laneOperand,
+                                            *outputArg,
+                                            mixedOffsets,
+                                            getStaticIndexAttrs(state.rewriter, referenceSizes),
+                                            getStaticIndexAttrs(state.rewriter, referenceStrides));
+      continue;
     }
 
     state.rewriter.setInsertionPoint(ownerClass->body->getTerminator());
@@ -5656,28 +5834,73 @@ LogicalResult finalizeProjectedHostOutputFragments(MaterializerState& state) {
     SmallVector<int64_t, 64> flatSizes;
     SmallVector<int64_t, 64> flatStrides;
     DenseMap<Value, int64_t> operandIndicesByValue;
+    DenseSet<ClassId> emittedBatchForwarding;
 
     for (PendingProjectedHostOutputFragment* fragmentRecord : fragments) {
-      Value operand = fragmentRecord->operand;
       MaterializedClass& sourceClass = state.classes[fragmentRecord->sourceClass];
+      Value operand;
+
+      if (std::optional<Value> availableValue =
+            state.availableValues.lookup(state, fragmentRecord->producerKey, sourceClass.id)) {
+        operand = *availableValue;
+      } else if (fragmentRecord->sourceClass == sourceClass.id) {
+        operand = fragmentRecord->operand;
+      } else {
+        return sourceClass.op->emitError(
+          "projected host output fragment assembly is missing source-visible fragment operands before finalization");
+      }
 
       if (fragmentRecord->sourceClass != ownerClass->id) {
-        if (sourceClass.isBatch || ownerClass->isBatch)
-          return sourceClass.op->emitError(
-            "projected host output fragment assembly requires scalarized cross-class operands before finalization");
-        MessageVector messages;
-        auto checkedSourceCpu = getCheckedCoreId(sourceClass.op,
-                                                 sourceClass.cpus.front(),
-                                                 "projected host output source core id");
-        auto checkedTargetCpu = getCheckedCoreId(ownerClass->op,
-                                                 ownerClass->cpus.front(),
-                                                 "projected host output target core id");
-        if (failed(checkedSourceCpu) || failed(checkedTargetCpu))
-          return failure();
-        messages.append(state.nextChannelId++, *checkedSourceCpu, *checkedTargetCpu);
-        if (failed(appendSend(state, sourceClass, operand, messages, fragmentRecord->loc)))
-          return failure();
-        operand = appendReceive(state, *ownerClass, fragmentRecord->operandType, messages, fragmentRecord->loc);
+        if (sourceClass.isBatch && !ownerClass->isBatch) {
+          if (!emittedBatchForwarding.insert(sourceClass.id).second) {
+            std::optional<Value> localized = state.availableValues.lookup(state, fragmentRecord->producerKey, ownerClass->id);
+            if (!localized)
+              return ownerClass->op->emitError(
+                "projected host output fragment assembly is missing forwarded batch fragments");
+            operand = *localized;
+          } else {
+            SmallVector<ProducerKey, 8> forwardedKeys;
+            forwardedKeys.reserve(sourceClass.cpus.size());
+            Value forwardedPayload = fragmentRecord->operand;
+            for (PendingProjectedHostOutputFragment* candidate : fragments) {
+              if (candidate->sourceClass != sourceClass.id)
+                continue;
+              if (candidate->operand != forwardedPayload)
+                return ownerClass->op->emitError(
+                  "projected host output batch forwarding expects one shared batch payload per source class");
+              forwardedKeys.push_back(candidate->producerKey);
+            }
+            llvm::sort(forwardedKeys, [](ProducerKey lhs, ProducerKey rhs) {
+              return lhs.instance.laneStart < rhs.instance.laneStart;
+            });
+            if (failed(emitClassToClassCommunication(
+                  state, sourceClass, *ownerClass, forwardedKeys, forwardedPayload, fragmentRecord->loc)))
+              return failure();
+            std::optional<Value> localized = state.availableValues.lookup(state, fragmentRecord->producerKey, ownerClass->id);
+            if (!localized)
+              return ownerClass->op->emitError(
+                "projected host output fragment assembly failed to recover forwarded batch fragment");
+            operand = *localized;
+          }
+        } else {
+          MessageVector messages;
+          auto checkedSourceCpu = getCheckedCoreId(sourceClass.op,
+                                                   sourceClass.cpus.front(),
+                                                   "projected host output source core id");
+          auto checkedTargetCpu = getCheckedCoreId(ownerClass->op,
+                                                   ownerClass->cpus.front(),
+                                                   "projected host output target core id");
+          if (failed(checkedSourceCpu) || failed(checkedTargetCpu))
+            return failure();
+          messages.append(state.nextChannelId++, *checkedSourceCpu, *checkedTargetCpu);
+          if (failed(appendSend(state, sourceClass, operand, messages, fragmentRecord->loc)))
+            return failure();
+          operand = appendReceive(state,
+                                  *ownerClass,
+                                  cast<RankedTensorType>(operand.getType()),
+                                  messages,
+                                  fragmentRecord->loc);
+        }
       } else if (!ownerClass->isBatch) {
         FailureOr<Value> localOperand = materializeTensorValueForMaterializedClassUse(
           state,

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('"','\\"')