ReduceMean + resnet

remove unsupported tests

AGENTS.md

@@ -1,92 +1,210 @@
-- Always read the full README.md before doing anything.
-- Build commands:
-    - `cmake --build ./build_release`
-    - `cmake --build ./build_debug`
-- Never use `ninja` directly: it bypasses cmake's configuration and invalidates the build cache.
-- Always tries the release version build first and ask before building with the debug version
+* Always read the full README.md before doing anything
+* Build commands:
+  * `cmake --build ./build_release`
+  * `cmake --build ./build_debug`
+* Never use `ninja` directly: it bypasses cmake's configuration and invalidates the build cache
+* Always try the release build first before building with the debug version
+* Use the debug build only when it is useful to obtain a clear stack trace with symbols, inspect names, place breakpoints, or test a small case interactively
+* The debug build is very slow, so use it only on small fast tests such as operation validations, not on network validations
+
+# Core engineering philosophy
+
+* Clean architecture matters as much as making the immediate test pass
+* Prefer fixes that preserve clear ownership boundaries, explicit invariants, and simple dataflow
+* Do not stack compensating fixes on top of earlier mistakes. If the current approach is becoming messy, stop and explain why
+* A correct fix should usually make the responsible producer, resolver, verifier, or lowering own the behavior directly
+* Avoid late repair passes, defensive cleanup, or broad rewrites when a cleaner owner-side fix is possible
+* Do not hide an upstream modeling bug by normalizing it later in the pipeline. Fix the producer when the producer owns the invariant
+* Prefer patterns/rewrites for local IR canonicalization. Use module walks only when pass-level structural analysis genuinely requires them
+* Prefer compact, structured designs over long case-by-case implementations
+
+# Think before coding
+
+* State assumptions explicitly before implementing when they affect the design
+* If multiple interpretations exist, present them instead of silently choosing one
+* If a simpler approach exists, say so and prefer it unless there is a clear reason not to
+* If something is unclear, stop, name what is confusing, and ask
+* If the requested or obvious approach would make the architecture worse, push back and propose a cleaner alternative
 
 # Code changes
 
-- Keep changes minimal and localized to the relevant parts of the code.
-- Preserve the existing naming conventions and coding style used in the surrounding code.
-- Keep code easy to read, well organized, and suitable for future extensibility. A function must not be longer than
-  200/250 lines for readability and cognitive complexity.
-- Prefer clear naming and structure over comments. Add comments only when they materially improve clarity.
-- Do not rename symbols, move files, or restructure modules unless that is necessary for the requested change.
+* Keep changes minimal and localized to the relevant parts of the code
+* Preserve the existing naming conventions and coding style used in the surrounding code
+* Keep code easy to read, well organized, and suitable for future extensibility
+* A function must not exceed roughly 200/250 lines. If a change pushes a function beyond that, extract focused helpers
+* Prefer clear naming and structure over comments. Add comments only when they materially improve clarity
+* Do not rename symbols, move files, or restructure modules unless that is necessary for the requested change
+* Avoid duplicate ad-hoc logic. If the same concept appears in multiple places, consider whether it deserves a shared helper/API
+* When adding a helper or API, ask:
+  * Could this be useful to another component now
+  * Is another component already implementing the same idea differently
+  * Is this likely to be needed by a future adjacent component
+  * What is the narrowest useful abstraction
+  * What is the correct ownership level for this API
+* If a shared API is justified, place it at the lowest clean layer that can be used by all relevant consumers without creating dependency cycles or leaking policy across layers
+* If an existing component should use a newly introduced shared API, refactor that component in the same patch when doing so is directly related and reduces duplication
+* Do not create broad frameworks just because a helper might someday be useful. Shared APIs should encode a real reusable concept, not speculative generality
+* If the reusable abstraction is plausible but not clearly needed yet, keep the code local and mention the possible future extraction separately
+
+# Avoid case-listing designs
+
+* Avoid solving problems with large chains of `if`/`else`, switches, or repeated special cases that enumerate every possible situation
+* Long case listings tend to overfit the current tests, grow the codebase, and hide the underlying abstraction
+* When you see a growing list of special cases, stop and look for the shared concept, data model, interface, or normalization step that would make the cases collapse
+* Prefer table-driven logic, traits/interfaces, small reusable predicates, structured dispatch, or producer-side normalization when they express the invariant more directly
+* A few explicit cases are fine when the domain is genuinely small and closed
+* If the list is likely to grow, refactor toward a cleaner and more compact design instead of adding another branch
+* When keeping a case list is the pragmatic choice, explain why the domain is closed or why a broader abstraction would be premature
+
+# Ownership and invariants
+
+Before implementing, identify the owner of the behavior:
+
+* A producer should emit IR/data that satisfies the contract of the next stage
+* A lowering should make representation changes explicit and semantically correct
+* A resolver should resolve existing structure without silently changing semantics
+* A verifier should reject invalid states with bounded, actionable diagnostics
+* Codegen should assume verified invariants and fail clearly if they are violated
+
+When fixing a bug:
+
+* State the invariant that was violated
+* State which component should own that invariant
+* Fix that component directly
+* Avoid fixes that merely mask the violation later in the pipeline
+* Add or preserve verification if the invariant is important enough to regress
+
+# Refactor and API policy
+
+You may propose or implement a refactor when:
+
+* the local fix would duplicate logic
+* the local fix would violate a layer boundary
+* the bug exists because responsibility is assigned to the wrong component
+* multiple components already implement ad-hoc variants of the same concept
+* a shared helper/API would make the code smaller, clearer, and easier to maintain
+* existing callers can be migrated cleanly without broad churn
+* the current implementation is turning into a long list of special cases instead of a structured solution
+
+When proposing or implementing a refactor:
+
+* Explain what responsibility is being moved or shared
+* Justify why the new location is the right ownership level
+* Keep the API narrow and named after the concept or invariant it represents
+* Migrate directly related existing users when that improves compactness and consistency
+* Separate changes required for correctness from optional cleanup
+* Avoid unrelated renames, formatting changes, or module moves
+* Do not expand a justified refactor beyond directly related callers
+
+Do not refactor when:
+
+* the issue is truly local and a local fix is clearer
+* the abstraction would have only one user and no clear adjacent use
+* the abstraction would mix policies from different layers
+* the refactor would affect unrelated behavior
+* the refactor is mainly aesthetic
 
 # Working style
 
-- Infer style and conventions from the existing code before introducing new patterns.
-- When several implementation options are possible, prefer the simplest one that fits the current architecture and
-  minimizes churn.
-- Avoid broad refactors unless I explicitly ask for them.
+* Infer style and conventions from the existing code before introducing new patterns
+* When several implementation options are possible, prefer the simplest one that fits the current architecture and minimizes churn
+* Push back when the requested or obvious fix would make the architecture worse
+* If a cleaner fix requires a small refactor or shared helper/API, propose it explicitly and justify it
+* Avoid broad refactors unless explicitly requested or clearly necessary for correctness and maintainability
+* When tests fail, bucket failures by likely root cause and separate patch-related failures from pre-existing or out-of-scope failures
 
-# Responses
+# Simplicity first
 
-- When showing code in chat, make it easy to copy-paste into the codebase.
-- Keep outputs focused on the changed parts.
-- At the end of the response, briefly list any bad practices, mistakes, or cleaner alternatives you noticed, separate
-  from the main solution.
+* Minimum code that solves the problem cleanly. Nothing speculative
+* No features beyond what was asked
+* No error handling for impossible scenarios
+* If you write 200 lines and it could be 50, rewrite it
+* Ask: “Would a senior engineer say this is overcomplicated?” If yes, simplify
+* Prefer direct, explicit code over generic machinery unless the generic machinery clearly reduces duplication and preserves boundaries
 
-# Guidelines
+# Fallbacks and defaults
 
-## 1. Think Before Coding
+* Avoid silent fallback behavior when the semantic category is unknown
+* Do not treat “unknown” as “safe” unless the codebase already defines that convention
+* If a value cannot be classified, either preserve the existing behavior deliberately or fail with a clear diagnostic
+* When adding a fallback, state why it is semantically valid and what invariant makes it safe
 
-**Don't assume. Don't hide confusion. Surface tradeoffs.**
+# Surgical changes
 
-Before implementing:
+* Touch only what you must
+* Clean up only the mess introduced by your own change
+* Do not “improve” adjacent code, comments, or formatting
+* Match existing style, even if you would personally do it differently
+* If you notice unrelated dead code, bad abstractions, or fragile design, mention it separately. Do not delete or rewrite it unless asked
+* When your changes create orphans, remove imports, variables, functions, or files made unused by your change
+* Every changed line should trace directly to the requested fix, a required cleanup, or a justified reuse/refactor decision
 
-- State your assumptions explicitly. If uncertain, ask.
-- If multiple interpretations exist, present them - don't pick silently.
-- If a simpler approach exists, say so. Push back when warranted.
-- If something is unclear, stop. Name what's confusing. Ask.
+# Diagnostics and verification
 
-## 2. Simplicity First
+* Use existing bounded diagnostic mechanisms for pass-level verification or codegen failures
+* Do not emit unbounded repeated diagnostics from loops or parallel workers
+* Diagnostics should identify the violated invariant and the relevant value/op when useful
+* Verifiers should reject invalid states, not repair them
+* Codegen should not compensate for invalid IR/data unless codegen is the owner of that invariant
+* Do not make failing tests pass by weakening verifiers, assertions, or diagnostics unless the check itself is proven wrong
+* If a check is too strict, explain the valid case it rejects and update the invariant accordingly
+* Prefer fixing invalid IR/data producers over relaxing consumers
+* If adding diagnostics only for debugging, remove them or cap them before finalizing
 
-**Minimum code that solves the problem. Nothing speculative.**
+# Temporary debugging code
 
-- No features beyond what was asked.
-- No error handling for impossible scenarios.
-- If you write 200 lines and it could be 50, rewrite it.
+* Temporary diagnostics, dumps, assertions, and debug-only helpers must be removed or intentionally converted into bounded permanent diagnostics before finalizing
+* If debug instrumentation remains, explain why it is useful as permanent infrastructure
+* Do not leave noisy validation output behind
 
-Ask yourself: "Would a senior engineer say this is overcomplicated?" If yes, simplify.
+# Performance awareness
 
-## 3. Surgical Changes
+* Avoid algorithmic regressions in compiler passes, especially repeated full-module walks, repeated expensive analyses, or per-op recomputation inside nested loops
+* If a change adds a walk, cache, analysis, or structural traversal, justify why it is needed
+* For hot paths, prefer preserving existing asymptotic behavior unless a better structure is part of the requested change
+* If performance may change, mention the expected impact and suggest a targeted timing check
 
-**Touch only what you must. Clean up only your own mess.**
-
-When editing existing code:
-
-- Don't "improve" adjacent code, comments, or formatting.
-- Don't refactor things that aren't broken.
-- Match existing style, even if you'd do it differently.
-- If you notice unrelated dead code, mention it - don't delete it.
-
-When your changes create orphans:
-
-- Remove imports/variables/functions that YOUR changes made unused.
-- Don't remove pre-existing dead code unless asked, but mention it.
-
-The test: Every changed line should trace directly to the user's request.
-
-## 4. Goal-Driven Execution
-
-**Define success criteria. Loop until verified.**
-
-Transform tasks into verifiable goals:
-
-- "Add validation" → "Write tests for invalid inputs, then make them pass"
-- "Fix the bug" → "Write a test that reproduces it, then make it pass"
-- "Refactor X" → "Ensure tests pass before and after"
+# Goal-driven execution
 
 For multi-step tasks, state a brief plan:
 
-```
 1. [Step] → verify: [check]
 2. [Step] → verify: [check]
 3. [Step] → verify: [check]
-```
 
-Strong success criteria let you loop independently. Weak criteria ("make it work") require constant clarification.
+Define success criteria before implementing:
 
----
+* For bug fixes, success means reproducing or identifying the failure, fixing the responsible owner, and verifying the targeted case
+* For refactors, success means preserving behavior while making ownership, reuse, or structure cleaner
+* For validation changes, success means checking both valid and invalid cases when applicable
+
+Transform tasks into verifiable goals:
+
+* “Fix the bug” → identify the invariant, reproduce the failure, fix the owner, verify the targeted case
+* “Add validation” → write or identify tests for invalid inputs, then make them pass/fail as expected
+* “Refactor X” → preserve behavior before and after, then run relevant tests
+
+# Final self-review
+
+Before reporting completion, check:
+
+* Did I fix the owner of the invariant rather than masking the issue downstream
+* Did I avoid broad case lists and ad-hoc special handling
+* Did I introduce a helper/API only at the right ownership level
+* Did I migrate directly related duplicate logic when doing so improves compactness
+* Did I avoid weakening verifiers or assertions unnecessarily
+* Did I remove temporary debugging code or make it bounded and intentional
+* Did I avoid unrelated formatting, renames, or cleanup
+* Did I consider performance impact for added walks, analyses, caches, or repeated computations
+* Did I run the required build/test commands
+* Did I clearly report remaining failures or risks
+
+When reporting back:
+
+* Say what changed
+* Say what was verified
+* Say what remains
+* When showing code in chat, make it easy to copy-paste into the codebase
+* Keep outputs focused on the changed parts
+* List bad practices, fragile assumptions, or cleaner alternatives separately
+* If a change is intentionally pragmatic rather than architecturally ideal, say so and explain the tradeoff

README.md

@@ -168,8 +168,8 @@ Each validation run writes artifacts in the model workspace, for example under
 
 The compiler currently dumps dialect snapshots such as `spatial0.mlir`,
 `spatial1_dcp_merged.mlir`, `pim0.mlir`, `pim1_buff.mlir`,
-`pim2_coalesced.mlir`, `pim3_folded.mlir`, and
-`pim4_materialized.mlir` when an output directory is available.
+`pim2_coalesced.mlir`, and `pim3_folded.mlir` when an output directory is
+available.
 
 To rerun the simulator manually with tracing after validation has produced a
 `raptor/pim/` directory:

backend-simulators/pim/pim-simulator/src/lib/memory_manager/mod.rs

@@ -1,3 +1,4 @@
+use std::cmp::min;
 use std::fmt::Debug;
 
 use anyhow::{Context, Result, bail, ensure};
@@ -86,7 +87,7 @@ where {
             size,
         };
         if self.memory.len() < address + size {
-            self.memory.resize((address + size) * 2, 0);
+            self.memory.resize(min((address + size) * 2, u32::MAX as usize), 0);
         }
         self.load_requests.push(load_request);
         Ok(self)

src/PIM/CMakeLists.txt

@@ -121,6 +121,8 @@ add_pim_library(OMPIMAccel
   OMSpatialToPim
   OMPimCommon
   OMPimBufferization
-  OMPimStaticMemoryCoalescing
+  OMPimMemoryCoalescing
+  OMPimHostConstantFolding
+  OMPimVerification
   MLIRTensorInferTypeOpInterfaceImpl
 )

src/PIM/Common/CMakeLists.txt

@@ -1,12 +1,15 @@
 add_pim_library(OMPimCommon
+  IR/AffineUtils.cpp
   IR/AddressAnalysis.cpp
   IR/BatchCoreUtils.cpp
   IR/ConstantUtils.cpp
   IR/CoreBlockUtils.cpp
   IR/EntryPointUtils.cpp
+  IR/LoopUtils.cpp
   IR/ShapeUtils.cpp
   IR/SubviewUtils.cpp
   IR/WeightUtils.cpp
+  Support/CheckedArithmetic.cpp
   Support/DebugDump.cpp
   Support/Diagnostics.cpp
   Support/FileSystemUtils.cpp
@@ -19,6 +22,7 @@ add_pim_library(OMPimCommon
 
   LINK_LIBS PUBLIC
   MLIRLinalgDialect
+  MLIRSCFDialect
   onnx
   SpatialOps
   PimOps

src/PIM/Common/IR/AddressAnalysis.cpp

@@ -69,6 +69,16 @@ mlir::Value resolveLoopCarriedAliasImpl(mlir::Value value, const StaticValueKnow
 llvm::FailureOr<int64_t> resolveOpFoldResult(mlir::OpFoldResult ofr, const StaticValueKnowledge* knowledge);
 llvm::FailureOr<int64_t> resolveIndexValueImpl(mlir::Value value, const StaticValueKnowledge* knowledge);
 
+static llvm::FailureOr<llvm::SmallVector<int64_t>> getStaticMemRefStrides(mlir::MemRefType type) {
+  llvm::SmallVector<int64_t> strides;
+  int64_t offset = 0;
+  if (failed(type.getStridesAndOffset(strides, offset)))
+    return mlir::failure();
+  if (llvm::any_of(strides, mlir::ShapedType::isDynamic))
+    return mlir::failure();
+  return strides;
+}
+
 static llvm::FailureOr<int64_t> resolveConstantGlobalLoad(mlir::memref::LoadOp loadOp,
                                                           const StaticValueKnowledge* knowledge) {
   auto getGlobalOp = loadOp.getMemRef().getDefiningOp<mlir::memref::GetGlobalOp>();
@@ -539,8 +549,10 @@ llvm::FailureOr<ResolvedContiguousAddress> resolveContiguousAddressImpl(mlir::Va
       if (!isMemoryContiguous(sourceType.getShape(), offsets, sizes, strides))
         return mlir::failure();
 
-      auto sourceStrides = computeRowMajorStrides(sourceType.getShape());
-      byteOffset += linearizeIndex(offsets, sourceStrides) * getElementTypeSizeInBytes(subviewType.getElementType());
+      auto sourceStrides = getStaticMemRefStrides(sourceType);
+      if (failed(sourceStrides))
+        return mlir::failure();
+      byteOffset += linearizeIndex(offsets, *sourceStrides) * getElementTypeSizeInBytes(subviewType.getElementType());
       value = resolveAlias(subviewOp.getSource(), knowledge);
       continue;
     }
@@ -651,12 +663,16 @@ llvm::FailureOr<CompiledAddressExpr> compileContiguousAddressExprImpl(mlir::Valu
         if (!isMemoryContiguous(sourceType.getShape(), staticOffsets, staticSizes, staticStrides))
           return mlir::failure();
 
-        auto sourceStrides = computeRowMajorStrides(sourceType.getShape());
+        auto sourceStrides = getStaticMemRefStrides(sourceType);
+        if (failed(sourceStrides))
+          return mlir::failure();
         constantByteOffset +=
-          linearizeIndex(staticOffsets, sourceStrides) * getElementTypeSizeInBytes(subviewType.getElementType());
+          linearizeIndex(staticOffsets, *sourceStrides) * getElementTypeSizeInBytes(subviewType.getElementType());
       }
       else {
-        llvm::SmallVector<int64_t> sourceStrides = computeRowMajorStrides(sourceType.getShape());
+        auto sourceStrides = getStaticMemRefStrides(sourceType);
+        if (failed(sourceStrides))
+          return mlir::failure();
         CompiledIndexExpr offsetExpr;
         {
           CompiledIndexExprNode expr;
@@ -665,7 +681,7 @@ llvm::FailureOr<CompiledAddressExpr> compileContiguousAddressExprImpl(mlir::Valu
           offsetExpr = makeCompiledIndexExpr(std::move(expr));
         }
 
-        for (auto [mixedOffset, sourceStride] : llvm::zip_equal(subviewOp.getMixedOffsets(), sourceStrides)) {
+        for (auto [mixedOffset, sourceStride] : llvm::zip_equal(subviewOp.getMixedOffsets(), *sourceStrides)) {
           CompiledIndexExpr operandExpr;
           if (auto attr = mlir::dyn_cast<mlir::Attribute>(mixedOffset)) {
             CompiledIndexExprNode expr;

src/PIM/Common/IR/AffineUtils.cpp

@@ -0,0 +1,182 @@
+#include "mlir/Dialect/Arith/IR/Arith.h"
+#include "mlir/IR/Matchers.h"
+
+#include "AffineUtils.hpp"
+#include "ConstantUtils.hpp"
+
+using namespace mlir;
+
+namespace onnx_mlir {
+
+static FailureOr<int64_t> floorDivSigned(int64_t lhs, int64_t rhs) {
+  if (rhs <= 0)
+    return failure();
+
+  int64_t quotient = lhs / rhs;
+  int64_t remainder = lhs % rhs;
+  if (remainder != 0 && lhs < 0)
+    --quotient;
+  return quotient;
+}
+
+static FailureOr<int64_t> ceilDivSigned(int64_t lhs, int64_t rhs) {
+  if (rhs <= 0)
+    return failure();
+
+  int64_t quotient = lhs / rhs;
+  int64_t remainder = lhs % rhs;
+  if (remainder != 0 && lhs > 0)
+    ++quotient;
+  return quotient;
+}
+
+Value createOrFoldAffineApply(
+  RewriterBase& rewriter, Location loc, AffineMap map, ValueRange operands, Operation* constantAnchor) {
+  assert(constantAnchor && "expected a valid constant anchor");
+  assert(map.getNumResults() == 1 && "affine.apply expects a single-result affine map");
+
+  SmallVector<Attribute> operandConstants;
+  operandConstants.reserve(operands.size());
+  for (Value operand : operands) {
+    std::optional<int64_t> constantValue = matchConstantIndexValue(operand);
+    if (!constantValue)
+      return affine::AffineApplyOp::create(rewriter, loc, map, operands).getResult();
+    operandConstants.push_back(rewriter.getIndexAttr(*constantValue));
+  }
+
+  SmallVector<Attribute> foldedResults;
+  if (succeeded(map.constantFold(operandConstants, foldedResults)) && foldedResults.size() == 1)
+    if (auto constantResult = dyn_cast<IntegerAttr>(foldedResults.front()))
+      return getOrCreateIndexConstant(rewriter, constantAnchor, constantResult.getInt());
+
+  return affine::AffineApplyOp::create(rewriter, loc, map, operands).getResult();
+}
+
+Value createOrFoldAffineApply(
+  RewriterBase& rewriter, Location loc, AffineExpr expr, ValueRange dims, Operation* constantAnchor) {
+  AffineMap map = AffineMap::get(/*dimCount=*/dims.size(), /*symbolCount=*/0, expr);
+  return createOrFoldAffineApply(rewriter, loc, map, dims, constantAnchor);
+}
+
+Value affineMulConst(RewriterBase& rewriter, Location loc, Value value, int64_t multiplier, Operation* constantAnchor) {
+  assert(constantAnchor && "expected a valid constant anchor");
+  if (multiplier == 0)
+    return getOrCreateIndexConstant(rewriter, constantAnchor, 0);
+  if (multiplier == 1)
+    return value;
+
+  AffineExpr d0 = getAffineDimExpr(0, rewriter.getContext());
+  return createOrFoldAffineApply(rewriter, loc, d0 * multiplier, ValueRange {value}, constantAnchor);
+}
+
+Value affineModConst(RewriterBase& rewriter, Location loc, Value value, int64_t divisor, Operation* constantAnchor) {
+  assert(constantAnchor && "expected a valid constant anchor");
+  assert(divisor > 0 && "expected a positive affine.mod divisor");
+  if (divisor == 1)
+    return getOrCreateIndexConstant(rewriter, constantAnchor, 0);
+
+  AffineExpr d0 = getAffineDimExpr(0, rewriter.getContext());
+  return createOrFoldAffineApply(rewriter, loc, d0 % divisor, ValueRange {value}, constantAnchor);
+}
+
+Value affineFloorDivConst(
+  RewriterBase& rewriter, Location loc, Value value, int64_t divisor, Operation* constantAnchor) {
+  assert(constantAnchor && "expected a valid constant anchor");
+  assert(divisor > 0 && "expected a positive affine.floor_div divisor");
+  if (divisor == 1)
+    return value;
+
+  AffineExpr d0 = getAffineDimExpr(0, rewriter.getContext());
+  return createOrFoldAffineApply(rewriter, loc, d0.floorDiv(divisor), ValueRange {value}, constantAnchor);
+}
+
+FailureOr<int64_t> evaluateAffineExpr(AffineExpr expr, ArrayRef<int64_t> dims, ArrayRef<int64_t> symbols) {
+  if (auto constant = dyn_cast<AffineConstantExpr>(expr))
+    return constant.getValue();
+  if (auto dim = dyn_cast<AffineDimExpr>(expr)) {
+    unsigned position = dim.getPosition();
+    if (position >= dims.size())
+      return failure();
+    return dims[position];
+  }
+  if (auto symbol = dyn_cast<AffineSymbolExpr>(expr)) {
+    unsigned position = symbol.getPosition();
+    if (position >= symbols.size())
+      return failure();
+    return symbols[position];
+  }
+
+  auto binary = dyn_cast<AffineBinaryOpExpr>(expr);
+  if (!binary)
+    return failure();
+
+  FailureOr<int64_t> lhs = evaluateAffineExpr(binary.getLHS(), dims, symbols);
+  FailureOr<int64_t> rhs = evaluateAffineExpr(binary.getRHS(), dims, symbols);
+  if (failed(lhs) || failed(rhs))
+    return failure();
+
+  switch (binary.getKind()) {
+  case AffineExprKind::Add:      return *lhs + *rhs;
+  case AffineExprKind::Mul:      return *lhs * *rhs;
+  case AffineExprKind::FloorDiv: return floorDivSigned(*lhs, *rhs);
+  case AffineExprKind::CeilDiv:  return ceilDivSigned(*lhs, *rhs);
+  case AffineExprKind::Mod:      {
+    FailureOr<int64_t> div = floorDivSigned(*lhs, *rhs);
+    if (failed(div))
+      return failure();
+    return *lhs - *div * *rhs;
+  }
+  default: return failure();
+  }
+}
+
+FailureOr<int64_t> evaluateSingleResultAffineMap(AffineMap map, ArrayRef<int64_t> operands) {
+  if (map.getNumResults() != 1 || operands.size() != map.getNumInputs())
+    return failure();
+
+  ArrayRef<int64_t> dims(operands.data(), map.getNumDims());
+  ArrayRef<int64_t> symbols(operands.data() + map.getNumDims(), map.getNumSymbols());
+  return evaluateAffineExpr(map.getResult(0), dims, symbols);
+}
+
+FailureOr<int64_t> evaluateAffineApply(affine::AffineApplyOp affineApply, IndexValueResolver resolver) {
+  SmallVector<int64_t, 4> operands;
+  operands.reserve(affineApply.getMapOperands().size());
+  for (Value operand : affineApply.getMapOperands()) {
+    FailureOr<int64_t> folded = resolver(operand);
+    if (failed(folded))
+      return failure();
+    operands.push_back(*folded);
+  }
+
+  return evaluateSingleResultAffineMap(affineApply.getAffineMap(), operands);
+}
+
+bool isSingleResultSymbolFreeAffineMap(AffineMap map) { return map.getNumResults() == 1 && map.getNumSymbols() == 0; }
+
+bool isDimAndConstantAffineExpr(AffineExpr expr) {
+  switch (expr.getKind()) {
+  case AffineExprKind::Constant:
+  case AffineExprKind::DimId:    return true;
+  case AffineExprKind::SymbolId: return false;
+  case AffineExprKind::Add:      {
+    auto binaryExpr = cast<AffineBinaryOpExpr>(expr);
+    return isDimAndConstantAffineExpr(binaryExpr.getLHS()) && isDimAndConstantAffineExpr(binaryExpr.getRHS());
+  }
+  case AffineExprKind::Mul: {
+    auto binaryExpr = cast<AffineBinaryOpExpr>(expr);
+    return (isa<AffineConstantExpr>(binaryExpr.getLHS()) && isDimAndConstantAffineExpr(binaryExpr.getRHS()))
+        || (isa<AffineConstantExpr>(binaryExpr.getRHS()) && isDimAndConstantAffineExpr(binaryExpr.getLHS()));
+  }
+  case AffineExprKind::FloorDiv:
+  case AffineExprKind::CeilDiv:
+  case AffineExprKind::Mod:      {
+    auto binaryExpr = cast<AffineBinaryOpExpr>(expr);
+    return isa<AffineConstantExpr>(binaryExpr.getRHS()) && isDimAndConstantAffineExpr(binaryExpr.getLHS());
+  }
+  }
+
+  llvm_unreachable("unexpected affine expression kind");
+}
+
+} // namespace onnx_mlir

src/PIM/Common/IR/AffineUtils.hpp

@@ -0,0 +1,55 @@
+#pragma once
+
+#include "mlir/Dialect/Affine/IR/AffineOps.h"
+#include "mlir/IR/AffineExpr.h"
+#include "mlir/IR/PatternMatch.h"
+#include "mlir/Support/LogicalResult.h"
+
+#include "llvm/ADT/FunctionExtras.h"
+
+namespace onnx_mlir {
+
+using IndexValueResolver = llvm::function_ref<llvm::FailureOr<int64_t>(mlir::Value)>;
+
+mlir::Value createOrFoldAffineApply(mlir::RewriterBase& rewriter,
+                                    mlir::Location loc,
+                                    mlir::AffineMap map,
+                                    mlir::ValueRange operands,
+                                    mlir::Operation* constantAnchor);
+
+mlir::Value createOrFoldAffineApply(mlir::RewriterBase& rewriter,
+                                    mlir::Location loc,
+                                    mlir::AffineExpr expr,
+                                    mlir::ValueRange dims,
+                                    mlir::Operation* constantAnchor);
+
+mlir::Value affineMulConst(mlir::RewriterBase& rewriter,
+                           mlir::Location loc,
+                           mlir::Value value,
+                           int64_t multiplier,
+                           mlir::Operation* constantAnchor);
+
+mlir::Value affineModConst(mlir::RewriterBase& rewriter,
+                           mlir::Location loc,
+                           mlir::Value value,
+                           int64_t divisor,
+                           mlir::Operation* constantAnchor);
+
+mlir::Value affineFloorDivConst(mlir::RewriterBase& rewriter,
+                                mlir::Location loc,
+                                mlir::Value value,
+                                int64_t divisor,
+                                mlir::Operation* constantAnchor);
+
+llvm::FailureOr<int64_t>
+evaluateAffineExpr(mlir::AffineExpr expr, llvm::ArrayRef<int64_t> dims, llvm::ArrayRef<int64_t> symbols = {});
+
+llvm::FailureOr<int64_t> evaluateSingleResultAffineMap(mlir::AffineMap map, llvm::ArrayRef<int64_t> operands);
+
+llvm::FailureOr<int64_t> evaluateAffineApply(mlir::affine::AffineApplyOp affineApply, IndexValueResolver resolver);
+
+bool isSingleResultSymbolFreeAffineMap(mlir::AffineMap map);
+
+bool isDimAndConstantAffineExpr(mlir::AffineExpr expr);
+
+} // namespace onnx_mlir

src/PIM/Common/IR/ConstantUtils.cpp

@@ -1,32 +1,35 @@
-#include "mlir/Dialect/Affine/IR/AffineOps.h"
 #include "mlir/Dialect/Arith/IR/Arith.h"
 #include "mlir/Dialect/Func/IR/FuncOps.h"
 #include "mlir/IR/Builders.h"
 #include "mlir/IR/BuiltinOps.h"
 #include "mlir/IR/Dialect.h"
-#include "mlir/IR/Matchers.h"
 
 #include "ConstantUtils.hpp"
-#include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
-#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 
 using namespace mlir;
 
 namespace onnx_mlir {
 
+static std::optional<int64_t> getIndexConstantValue(arith::ConstantOp constantOp) {
+  if (!constantOp.getType().isIndex())
+    return std::nullopt;
+
+  auto intAttr = dyn_cast<IntegerAttr>(constantOp.getValue());
+  if (!intAttr || !intAttr.getType().isIndex())
+    return std::nullopt;
+
+  return intAttr.getInt();
+}
+
 Block* getConstantInsertionBlock(Operation* anchorOp) {
   assert(anchorOp && "expected a valid anchor operation");
 
-  for (Operation* current = anchorOp; current; current = current->getParentOp())
-    if (isa<spatial::SpatCompute, spatial::SpatComputeBatch, pim::PimCoreOp, pim::PimCoreBatchOp>(current))
-      return current->getBlock();
-
   if (auto funcOp = dyn_cast<func::FuncOp>(anchorOp))
     return &funcOp.getBody().front();
+  if (auto funcOp = anchorOp->getParentOfType<func::FuncOp>())
+    return &funcOp.getBody().front();
   if (auto moduleOp = dyn_cast<ModuleOp>(anchorOp))
     return moduleOp.getBody();
-  if (auto funcOp = anchorOp->getParentOfType<func::FuncOp>())
-    return &funcOp.getBody().front();
   if (auto moduleOp = anchorOp->getParentOfType<ModuleOp>())
     return moduleOp.getBody();
   return anchorOp->getBlock();
@@ -75,24 +78,80 @@ Value getOrCreateIndexConstant(RewriterBase& rewriter, Operation* anchorOp, int6
   return getOrCreateConstant(rewriter, anchorOp, builder.getIndexAttr(value), builder.getIndexType());
 }
 
-Value createAffineApplyOrFoldedConstant(
-  RewriterBase& rewriter, Location loc, AffineMap map, ValueRange operands, Operation* anchorOp) {
-  SmallVector<Attribute> operandConstants;
-  operandConstants.reserve(operands.size());
-  for (Value operand : operands) {
-    APInt constantValue;
-    if (!matchPattern(operand, m_ConstantInt(&constantValue)))
-      return affine::AffineApplyOp::create(rewriter, loc, map, operands).getResult();
-    operandConstants.push_back(rewriter.getIndexAttr(constantValue.getSExtValue()));
+void hoistAndUniquifyIndexConstants(func::FuncOp funcOp, RewriterBase& rewriter) {
+  if (funcOp.getBody().empty())
+    return;
+
+  Block& entryBlock = funcOp.getBody().front();
+  DenseMap<int64_t, Value> canonicalByValue;
+  SmallVector<arith::ConstantOp> constants;
+
+  funcOp.walk([&](arith::ConstantOp constantOp) {
+    if (!getIndexConstantValue(constantOp))
+      return;
+    constants.push_back(constantOp);
+  });
+
+  for (arith::ConstantOp constantOp : constants) {
+    auto value = getIndexConstantValue(constantOp);
+    if (!value || constantOp->getBlock() != &entryBlock)
+      continue;
+    canonicalByValue.try_emplace(*value, constantOp.getResult());
   }
 
-  SmallVector<Attribute> foldedResults;
-  if (succeeded(map.constantFold(operandConstants, foldedResults))) {
-    if (auto constantResult = dyn_cast<IntegerAttr>(foldedResults.front()))
-      return getOrCreateIndexConstant(rewriter, anchorOp, constantResult.getInt());
+  for (arith::ConstantOp constantOp : constants) {
+    auto value = getIndexConstantValue(constantOp);
+    if (!value)
+      continue;
+
+    Value canonical = canonicalByValue.lookup(*value);
+    if (!canonical) {
+      OpBuilder::InsertionGuard guard(rewriter);
+      rewriter.setInsertionPointToStart(&entryBlock);
+      Builder builder(funcOp.getContext());
+      canonical =
+        arith::ConstantOp::create(rewriter, constantOp.getLoc(), builder.getIndexType(), builder.getIndexAttr(*value));
+      canonicalByValue[*value] = canonical;
+    }
+
+    if (constantOp.getResult() == canonical)
+      continue;
+
+    constantOp.getResult().replaceAllUsesWith(canonical);
   }
 
-  return affine::AffineApplyOp::create(rewriter, loc, map, operands).getResult();
+  for (arith::ConstantOp constantOp : llvm::reverse(constants)) {
+    auto value = getIndexConstantValue(constantOp);
+    if (!value)
+      continue;
+    if (constantOp.getResult() == canonicalByValue.lookup(*value))
+      continue;
+    if (constantOp.use_empty())
+      rewriter.eraseOp(constantOp);
+  }
+}
+
+std::optional<int64_t> matchConstantIndexValue(Value value) {
+  if (!value || !value.getType().isIndex())
+    return std::nullopt;
+
+  if (auto constant = value.getDefiningOp<arith::ConstantIndexOp>())
+    return constant.value();
+
+  if (auto constant = value.getDefiningOp<arith::ConstantOp>())
+    if (auto intAttr = dyn_cast<IntegerAttr>(constant.getValue()); intAttr && intAttr.getType().isIndex())
+      return intAttr.getInt();
+
+  return std::nullopt;
+}
+
+std::optional<int64_t> matchConstantIndexValue(OpFoldResult value) {
+  if (auto attr = dyn_cast<Attribute>(value))
+    if (auto intAttr = dyn_cast<IntegerAttr>(attr); intAttr && intAttr.getType().isIndex())
+      return intAttr.getInt();
+  if (auto operand = dyn_cast<Value>(value))
+    return matchConstantIndexValue(operand);
+  return std::nullopt;
 }
 
 } // namespace onnx_mlir

src/PIM/Common/IR/ConstantUtils.hpp

@@ -1,11 +1,13 @@
 #pragma once
 
-#include "mlir/Dialect/Affine/IR/AffineOps.h"
 #include "mlir/Dialect/Arith/IR/Arith.h"
+#include "mlir/Dialect/Func/IR/FuncOps.h"
 #include "mlir/IR/PatternMatch.h"
 #include "mlir/IR/Value.h"
 #include "mlir/Transforms/FoldUtils.h"
 
+#include <optional>
+
 namespace onnx_mlir {
 
 mlir::Block* getConstantInsertionBlock(mlir::Operation* anchorOp);
@@ -22,10 +24,10 @@ mlir::Value getOrCreateIndexConstant(mlir::OperationFolder& folder, mlir::Operat
 
 mlir::Value getOrCreateIndexConstant(mlir::RewriterBase& rewriter, mlir::Operation* anchorOp, int64_t value);
 
-mlir::Value createAffineApplyOrFoldedConstant(mlir::RewriterBase& rewriter,
-                                              mlir::Location loc,
-                                              mlir::AffineMap map,
-                                              mlir::ValueRange operands,
-                                              mlir::Operation* anchorOp);
+void hoistAndUniquifyIndexConstants(mlir::func::FuncOp funcOp, mlir::RewriterBase& rewriter);
+
+std::optional<int64_t> matchConstantIndexValue(mlir::Value value);
+
+std::optional<int64_t> matchConstantIndexValue(mlir::OpFoldResult value);
 
 } // namespace onnx_mlir

src/PIM/Common/IR/LoopUtils.cpp

@@ -0,0 +1,96 @@
+#include "mlir/Dialect/SCF/IR/SCF.h"
+
+#include "llvm/Support/MathExtras.h"
+
+#include <optional>
+
+#include "ConstantUtils.hpp"
+#include "LoopUtils.hpp"
+
+using namespace mlir;
+
+namespace onnx_mlir {
+
+namespace {
+
+static std::optional<int64_t> getStaticTripCount(Value lowerBound, Value upperBound, Value step) {
+  auto lower = matchConstantIndexValue(lowerBound);
+  auto upper = matchConstantIndexValue(upperBound);
+  auto stepValue = matchConstantIndexValue(step);
+  if (!lower || !upper || !stepValue)
+    return std::nullopt;
+  if (*stepValue <= 0)
+    return std::nullopt;
+  if (*upper <= *lower)
+    return int64_t {0};
+  return llvm::divideCeil(*upper - *lower, *stepValue);
+}
+
+} // namespace
+
+static LogicalResult validateNormalizedLoopYields(Location loc, ValueRange initArgs, ArrayRef<Value> yieldedValues) {
+  if (yieldedValues.size() == initArgs.size())
+    return success();
+
+  emitError(loc) << "normalized loop body yielded " << yieldedValues.size() << " values for " << initArgs.size()
+                 << " iter args";
+  return failure();
+}
+
+FailureOr<NormalizedLoopResult> buildNormalizedScfFor(OpBuilder& builder,
+                                                      Location loc,
+                                                      Value lowerBound,
+                                                      Value upperBound,
+                                                      Value step,
+                                                      ValueRange initArgs,
+                                                      NormalizedLoopBodyBuilder bodyBuilder) {
+  NormalizedLoopResult result;
+
+  if (auto stepValue = matchConstantIndexValue(step); stepValue && *stepValue <= 0) {
+    emitError(loc) << "normalized scf.for requires a positive step, got " << *stepValue;
+    return failure();
+  }
+
+  if (auto tripCount = getStaticTripCount(lowerBound, upperBound, step)) {
+    if (*tripCount == 0) {
+      llvm::append_range(result.results, initArgs);
+      return result;
+    }
+
+    if (*tripCount == 1) {
+      result.inductionVar = lowerBound;
+      if (failed(bodyBuilder(builder, loc, lowerBound, initArgs, result.results)))
+        return failure();
+      if (failed(validateNormalizedLoopYields(loc, initArgs, result.results)))
+        return failure();
+      return result;
+    }
+  }
+
+  result.loop = scf::ForOp::create(builder, loc, lowerBound, upperBound, step, initArgs);
+  result.inductionVar = result.loop.getInductionVar();
+
+  {
+    OpBuilder::InsertionGuard guard(builder);
+    Block* body = result.loop.getBody();
+    if (!body->empty())
+      if (auto yieldOp = dyn_cast<scf::YieldOp>(body->back()))
+        yieldOp->erase();
+    builder.setInsertionPointToEnd(body);
+    ValueRange iterArgs = result.loop.getRegionIterArgs();
+    if (failed(bodyBuilder(builder, loc, result.inductionVar, iterArgs, result.results))) {
+      result.loop.erase();
+      return failure();
+    }
+    if (failed(validateNormalizedLoopYields(loc, initArgs, result.results))) {
+      result.loop.erase();
+      return failure();
+    }
+    scf::YieldOp::create(builder, loc, result.results);
+  }
+  builder.setInsertionPointAfter(result.loop);
+  result.results.assign(result.loop.getResults().begin(), result.loop.getResults().end());
+  return result;
+}
+
+} // namespace onnx_mlir

src/PIM/Common/IR/LoopUtils.hpp

@@ -0,0 +1,30 @@
+#pragma once
+
+#include "mlir/Dialect/SCF/IR/SCF.h"
+#include "mlir/IR/Builders.h"
+
+#include "llvm/ADT/STLFunctionalExtras.h"
+#include "llvm/ADT/SmallVector.h"
+
+namespace onnx_mlir {
+
+struct NormalizedLoopResult {
+  mlir::Value inductionVar;
+  llvm::SmallVector<mlir::Value, 4> results;
+  mlir::scf::ForOp loop;
+
+  bool wasInlined() const { return !loop; }
+};
+
+using NormalizedLoopBodyBuilder = llvm::function_ref<mlir::LogicalResult(
+  mlir::OpBuilder&, mlir::Location, mlir::Value, mlir::ValueRange, llvm::SmallVectorImpl<mlir::Value>&)>;
+
+mlir::FailureOr<NormalizedLoopResult> buildNormalizedScfFor(mlir::OpBuilder& builder,
+                                                            mlir::Location loc,
+                                                            mlir::Value lowerBound,
+                                                            mlir::Value upperBound,
+                                                            mlir::Value step,
+                                                            mlir::ValueRange initArgs,
+                                                            NormalizedLoopBodyBuilder bodyBuilder);
+
+} // namespace onnx_mlir

src/PIM/Common/IR/WeightUtils.cpp

@@ -47,6 +47,16 @@ CompiledIndexExpr mulExpr(CompiledIndexExpr lhs, int64_t rhs) {
   return makeBinaryExpr(CompiledIndexExprNode::Kind::Mul, std::move(lhs), makeConstantExpr(rhs));
 }
 
+llvm::FailureOr<llvm::SmallVector<int64_t>> getStaticMemRefTypeStrides(mlir::MemRefType type) {
+  llvm::SmallVector<int64_t> strides;
+  int64_t offset = 0;
+  if (failed(type.getStridesAndOffset(strides, offset)))
+    return mlir::failure();
+  if (llvm::is_contained(strides, mlir::ShapedType::kDynamic))
+    return mlir::failure();
+  return strides;
+}
+
 template <typename VMMOpTy, typename ParentOpTy>
 bool hasVmmWeightUse(ParentOpTy parentOp, unsigned weightIndex) {
   auto weightArg = parentOp.getWeightArgument(weightIndex);
@@ -162,6 +172,11 @@ resolveWeightView(mlir::Operation* weightOwner, mlir::Value weight, const Static
   mlir::Value current = weight;
 
   while (true) {
+    if (mlir::Value directAlias = knowledge.aliases.lookup(current); directAlias && directAlias != current) {
+      current = directAlias;
+      continue;
+    }
+
     if (auto defOp = current.getDefiningOp()) {
       if (auto getGlobalOp = mlir::dyn_cast<mlir::memref::GetGlobalOp>(defOp)) {
         auto moduleOp = weightOwner ? weightOwner->getParentOfType<mlir::ModuleOp>() : mlir::ModuleOp {};
@@ -181,8 +196,6 @@ resolveWeightView(mlir::Operation* weightOwner, mlir::Value weight, const Static
         CompiledIndexExpr offsetExpr = makeConstantExpr(0);
         for (mlir::Operation* viewOp : llvm::reverse(viewOps)) {
           if (auto subview = mlir::dyn_cast<mlir::memref::SubViewOp>(viewOp)) {
-            llvm::SmallVector<int64_t> nextStrides;
-            nextStrides.reserve(subview.getMixedOffsets().size());
             for (auto [offset, stride, sourceStride] :
                  llvm::zip_equal(subview.getMixedOffsets(), subview.getStaticStrides(), view.strides)) {
               CompiledIndexExpr offsetValue = makeConstantExpr(0);
@@ -202,29 +215,47 @@ resolveWeightView(mlir::Operation* weightOwner, mlir::Value weight, const Static
                 return mlir::failure();
               }
               offsetExpr = addExpr(std::move(offsetExpr), mulExpr(std::move(offsetValue), sourceStride));
-              nextStrides.push_back(stride * sourceStride);
             }
-            view.shape.assign(subview.getStaticSizes().begin(), subview.getStaticSizes().end());
-            view.strides = std::move(nextStrides);
+            auto resultType = mlir::cast<mlir::MemRefType>(subview.getResult().getType());
+            auto resultStrides = getStaticMemRefTypeStrides(resultType);
+            if (failed(resultStrides))
+              return mlir::failure();
+            view.shape.assign(resultType.getShape().begin(), resultType.getShape().end());
+            view.strides = std::move(*resultStrides);
             continue;
           }
 
           if (auto collapse = mlir::dyn_cast<mlir::memref::CollapseShapeOp>(viewOp)) {
-            if (view.strides != computeRowMajorStrides(view.shape))
-              return mlir::failure();
             auto resultType = mlir::cast<mlir::MemRefType>(collapse.getResult().getType());
+            auto resultStrides = getStaticMemRefTypeStrides(resultType);
+            if (failed(resultStrides))
+              return mlir::failure();
             view.shape.assign(resultType.getShape().begin(), resultType.getShape().end());
-            view.strides = computeRowMajorStrides(view.shape);
+            view.strides = std::move(*resultStrides);
             continue;
           }
 
           if (auto expand = mlir::dyn_cast<mlir::memref::ExpandShapeOp>(viewOp)) {
-            if (view.strides != computeRowMajorStrides(view.shape))
-              return mlir::failure();
             auto resultType = mlir::cast<mlir::MemRefType>(expand.getResult().getType());
+            auto resultStrides = getStaticMemRefTypeStrides(resultType);
+            if (failed(resultStrides))
+              return mlir::failure();
             view.shape.assign(resultType.getShape().begin(), resultType.getShape().end());
-            view.strides = computeRowMajorStrides(view.shape);
+            view.strides = std::move(*resultStrides);
+            continue;
           }
+
+          if (auto castOp = mlir::dyn_cast<mlir::memref::CastOp>(viewOp)) {
+            auto resultType = mlir::cast<mlir::MemRefType>(castOp.getResult().getType());
+            auto resultStrides = getStaticMemRefTypeStrides(resultType);
+            if (failed(resultStrides))
+              return mlir::failure();
+            view.shape.assign(resultType.getShape().begin(), resultType.getShape().end());
+            view.strides = std::move(*resultStrides);
+            continue;
+          }
+
+          return mlir::failure();
         }
 
         auto resolvedOffset = offsetExpr.evaluate(knowledge);
@@ -234,18 +265,26 @@ resolveWeightView(mlir::Operation* weightOwner, mlir::Value weight, const Static
         return view;
       }
 
-      if (mlir::isa<mlir::memref::SubViewOp, mlir::memref::CollapseShapeOp, mlir::memref::ExpandShapeOp>(defOp)) {
+      if (auto subview = mlir::dyn_cast<mlir::memref::SubViewOp>(defOp)) {
         viewOps.push_back(defOp);
-        if (auto subview = mlir::dyn_cast<mlir::memref::SubViewOp>(defOp))
-          current = subview.getSource();
-        else if (auto collapse = mlir::dyn_cast<mlir::memref::CollapseShapeOp>(defOp))
-          current = collapse.getSrc();
-        else
-          current = mlir::cast<mlir::memref::ExpandShapeOp>(defOp).getSrc();
+        current = subview.getSource();
+        continue;
+      }
+
+      if (auto collapse = mlir::dyn_cast<mlir::memref::CollapseShapeOp>(defOp)) {
+        viewOps.push_back(defOp);
+        current = collapse.getSrc();
+        continue;
+      }
+
+      if (auto expand = mlir::dyn_cast<mlir::memref::ExpandShapeOp>(defOp)) {
+        viewOps.push_back(defOp);
+        current = expand.getSrc();
         continue;
       }
 
       if (auto castOp = mlir::dyn_cast<mlir::memref::CastOp>(defOp)) {
+        viewOps.push_back(defOp);
         current = castOp.getSource();
         continue;
       }
@@ -253,6 +292,11 @@ resolveWeightView(mlir::Operation* weightOwner, mlir::Value weight, const Static
       return mlir::failure();
     }
 
+    if (mlir::Value loopAlias = resolveLoopCarriedAlias(current, knowledge); loopAlias && loopAlias != current) {
+      current = loopAlias;
+      continue;
+    }
+
     auto weightIndex = resolveWeightIndex(weightOwner, current);
     if (!weightIndex)
       return mlir::failure();

src/PIM/Common/Support/CheckedArithmetic.cpp

@@ -0,0 +1,222 @@
+#include "llvm/Support/ErrorHandling.h"
+#include "llvm/Support/raw_ostream.h"
+
+#include "CheckedArithmetic.hpp"
+#include "src/Accelerators/PIM/Common/PimCommon.hpp"
+
+using namespace mlir;
+
+namespace onnx_mlir::pim {
+
+namespace {
+
+static void emitCrashMessage(llvm::StringRef fieldName, llvm::StringRef message) {
+  llvm::errs() << "PIM " << fieldName << " " << message << "\n";
+}
+
+template <typename To, typename From>
+static FailureOr<To> checkedCastAtLocation(From value, Location loc, llvm::StringRef fieldName) {
+  static_assert(std::is_integral_v<To> && std::is_integral_v<From>, "checkedCastAtLocation requires integral types");
+
+  using ToLimits = std::numeric_limits<To>;
+
+  if constexpr (std::is_signed_v<From> == std::is_signed_v<To>) {
+    if (value < static_cast<From>(ToLimits::min()) || value > static_cast<From>(ToLimits::max())) {
+      emitCheckedArithmeticError(loc, fieldName, "is outside representable range");
+      return failure();
+    }
+  }
+  else if constexpr (std::is_signed_v<From>) {
+    using UnsignedFrom = std::make_unsigned_t<From>;
+    using UnsignedTo = std::make_unsigned_t<To>;
+    if (value < 0 || static_cast<UnsignedFrom>(value) > static_cast<UnsignedTo>(ToLimits::max())) {
+      emitCheckedArithmeticError(loc, fieldName, "is outside representable range");
+      return failure();
+    }
+  }
+  else {
+    using UnsignedFrom = std::make_unsigned_t<From>;
+    using UnsignedTo = std::conditional_t<std::is_signed_v<To>, std::make_unsigned_t<To>, To>;
+    if (static_cast<UnsignedFrom>(value) > static_cast<UnsignedTo>(ToLimits::max())) {
+      emitCheckedArithmeticError(loc, fieldName, "is outside representable range");
+      return failure();
+    }
+  }
+
+  return static_cast<To>(value);
+}
+
+template <typename UInt>
+FailureOr<UInt> checkedMulAtLocation(UInt lhs, UInt rhs, Location loc, llvm::StringRef fieldName) {
+  static_assert(std::is_integral_v<UInt> && std::is_unsigned_v<UInt>,
+                "checkedMulAtLocation requires unsigned integral types");
+  if (lhs != 0 && rhs > std::numeric_limits<UInt>::max() / lhs) {
+    emitCheckedArithmeticError(loc, fieldName, "multiplication overflow");
+    return failure();
+  }
+  return lhs * rhs;
+}
+
+} // namespace
+
+InFlightDiagnostic emitCheckedArithmeticError(Operation* anchor, llvm::StringRef fieldName, llvm::StringRef message) {
+  assert(anchor && "expected arithmetic diagnostics to have an anchor op");
+  return anchor->emitOpError() << fieldName << " " << message;
+}
+
+InFlightDiagnostic emitCheckedArithmeticError(Location loc, llvm::StringRef fieldName, llvm::StringRef message) {
+  return emitError(loc) << "PIM " << fieldName << " " << message;
+}
+
+FailureOr<int32_t> checkedI32(int64_t value, Operation* anchor, llvm::StringRef fieldName) {
+  return checkedCast<int32_t>(value, anchor, fieldName);
+}
+
+FailureOr<int32_t> checkedI32(uint64_t value, Operation* anchor, llvm::StringRef fieldName) {
+  return checkedCast<int32_t>(value, anchor, fieldName);
+}
+
+FailureOr<uint8_t> checkedU8(uint64_t value, Operation* anchor, llvm::StringRef fieldName) {
+  return checkedCast<uint8_t>(value, anchor, fieldName);
+}
+
+FailureOr<size_t> checkedSize(int64_t value, Operation* anchor, llvm::StringRef fieldName) {
+  return checkedCast<size_t>(value, anchor, fieldName);
+}
+
+FailureOr<IntegerAttr>
+getCheckedI32Attr(Builder& builder, Operation* anchor, int64_t value, llvm::StringRef fieldName) {
+  assert(anchor && "checked op-based attrs require a non-null diagnostic anchor");
+  auto checkedValue = checkedI32(value, anchor, fieldName);
+  if (failed(checkedValue))
+    return failure();
+  return builder.getI32IntegerAttr(*checkedValue);
+}
+
+FailureOr<IntegerAttr>
+getCheckedI32Attr(Builder& builder, Operation* anchor, uint64_t value, llvm::StringRef fieldName) {
+  assert(anchor && "checked op-based attrs require a non-null diagnostic anchor");
+  auto checkedValue = checkedI32(value, anchor, fieldName);
+  if (failed(checkedValue))
+    return failure();
+  return builder.getI32IntegerAttr(*checkedValue);
+}
+
+FailureOr<IntegerAttr> getCheckedI32Attr(Builder& builder, Location loc, int64_t value, llvm::StringRef fieldName) {
+  auto checkedValue = checkedCastAtLocation<int32_t>(value, loc, fieldName);
+  if (failed(checkedValue))
+    return failure();
+  return builder.getI32IntegerAttr(*checkedValue);
+}
+
+FailureOr<IntegerAttr> getCheckedI32Attr(Builder& builder, Location loc, uint64_t value, llvm::StringRef fieldName) {
+  auto checkedValue = checkedCastAtLocation<int32_t>(value, loc, fieldName);
+  if (failed(checkedValue))
+    return failure();
+  return builder.getI32IntegerAttr(*checkedValue);
+}
+
+FailureOr<uint64_t> getCheckedShapedTypeSizeInBytes(ShapedType type, Operation* anchor, llvm::StringRef fieldName) {
+  assert(anchor && "checked op-based size helpers require a non-null diagnostic anchor");
+  if (!type.hasStaticShape()) {
+    emitCheckedArithmeticError(anchor, fieldName, "requires static shaped type");
+    return failure();
+  }
+  if (!hasByteSizedElementType(type.getElementType())) {
+    emitCheckedArithmeticError(anchor, fieldName, "requires byte-sized element type");
+    return failure();
+  }
+
+  uint64_t elements = 1;
+  for (int64_t dim : type.getShape()) {
+    if (dim < 0) {
+      emitCheckedArithmeticError(anchor, fieldName, "requires nonnegative dimensions");
+      return failure();
+    }
+
+    auto nextElements = checkedMul(elements, static_cast<uint64_t>(dim), anchor, fieldName);
+    if (failed(nextElements))
+      return failure();
+    elements = *nextElements;
+  }
+
+  return checkedMul(
+    elements, static_cast<uint64_t>(getElementTypeSizeInBytes(type.getElementType())), anchor, fieldName);
+}
+
+FailureOr<uint64_t> getCheckedShapedTypeSizeInBytes(ShapedType type, Location loc, llvm::StringRef fieldName) {
+  if (!type.hasStaticShape()) {
+    emitCheckedArithmeticError(loc, fieldName, "requires static shaped type");
+    return failure();
+  }
+  if (!hasByteSizedElementType(type.getElementType())) {
+    emitCheckedArithmeticError(loc, fieldName, "requires byte-sized element type");
+    return failure();
+  }
+
+  uint64_t elements = 1;
+  for (int64_t dim : type.getShape()) {
+    if (dim < 0) {
+      emitCheckedArithmeticError(loc, fieldName, "requires nonnegative dimensions");
+      return failure();
+    }
+
+    auto nextElements = checkedMulAtLocation(elements, static_cast<uint64_t>(dim), loc, fieldName);
+    if (failed(nextElements))
+      return failure();
+    elements = *nextElements;
+  }
+
+  return checkedMulAtLocation(
+    elements, static_cast<uint64_t>(getElementTypeSizeInBytes(type.getElementType())), loc, fieldName);
+}
+
+int32_t checkedI32OrCrash(int64_t value, llvm::StringRef fieldName) {
+  if (value < std::numeric_limits<int32_t>::min() || value > std::numeric_limits<int32_t>::max()) {
+    emitCrashMessage(fieldName, "is outside representable range");
+    llvm_unreachable("PIM checked arithmetic failure");
+  }
+  return static_cast<int32_t>(value);
+}
+
+int32_t checkedI32OrCrash(uint64_t value, llvm::StringRef fieldName) {
+  if (value > static_cast<uint64_t>(std::numeric_limits<int32_t>::max())) {
+    emitCrashMessage(fieldName, "is outside representable range");
+    llvm_unreachable("PIM checked arithmetic failure");
+  }
+  return static_cast<int32_t>(value);
+}
+
+uint8_t checkedU8OrCrash(uint64_t value, llvm::StringRef fieldName) {
+  if (value > static_cast<uint64_t>(std::numeric_limits<uint8_t>::max())) {
+    emitCrashMessage(fieldName, "is outside representable range");
+    llvm_unreachable("PIM checked arithmetic failure");
+  }
+  return static_cast<uint8_t>(value);
+}
+
+size_t checkedSizeOrCrash(int64_t value, llvm::StringRef fieldName) {
+  if (value < 0) {
+    emitCrashMessage(fieldName, "is outside representable range");
+    llvm_unreachable("PIM checked arithmetic failure");
+  }
+  return static_cast<size_t>(value);
+}
+
+size_t checkedAddOrCrash(size_t lhs, size_t rhs, llvm::StringRef fieldName) {
+  if (rhs > std::numeric_limits<size_t>::max() - lhs) {
+    emitCrashMessage(fieldName, "addition overflow");
+    llvm_unreachable("PIM checked arithmetic failure");
+  }
+  return lhs + rhs;
+}
+
+size_t checkedMulOrCrash(size_t lhs, size_t rhs, llvm::StringRef fieldName) {
+  if (lhs != 0 && rhs > std::numeric_limits<size_t>::max() / lhs) {
+    emitCrashMessage(fieldName, "multiplication overflow");
+    llvm_unreachable("PIM checked arithmetic failure");
+  }
+  return lhs * rhs;
+}
+
+} // namespace onnx_mlir::pim

src/PIM/Common/Support/CheckedArithmetic.hpp

@@ -0,0 +1,107 @@
+#pragma once
+
+#include "mlir/IR/Builders.h"
+#include "mlir/IR/BuiltinTypeInterfaces.h"
+#include "mlir/IR/Operation.h"
+#include "mlir/Support/LogicalResult.h"
+
+#include "llvm/ADT/StringRef.h"
+
+#include <cstddef>
+#include <cstdint>
+#include <limits>
+#include <type_traits>
+
+namespace onnx_mlir::pim {
+
+mlir::InFlightDiagnostic
+emitCheckedArithmeticError(mlir::Operation* anchor, llvm::StringRef fieldName, llvm::StringRef message);
+
+mlir::InFlightDiagnostic
+emitCheckedArithmeticError(mlir::Location loc, llvm::StringRef fieldName, llvm::StringRef message);
+
+template <typename To, typename From>
+mlir::FailureOr<To> checkedCast(From value, mlir::Operation* anchor, llvm::StringRef fieldName) {
+  static_assert(std::is_integral_v<To> && std::is_integral_v<From>, "checkedCast requires integral types");
+
+  using ToLimits = std::numeric_limits<To>;
+
+  if constexpr (std::is_signed_v<From> == std::is_signed_v<To>) {
+    if (value < static_cast<From>(ToLimits::min()) || value > static_cast<From>(ToLimits::max())) {
+      emitCheckedArithmeticError(anchor, fieldName, "is outside representable range");
+      return mlir::failure();
+    }
+  }
+  else if constexpr (std::is_signed_v<From>) {
+    using UnsignedFrom = std::make_unsigned_t<From>;
+    using UnsignedTo = std::make_unsigned_t<To>;
+    if (value < 0 || static_cast<UnsignedFrom>(value) > static_cast<UnsignedTo>(ToLimits::max())) {
+      emitCheckedArithmeticError(anchor, fieldName, "is outside representable range");
+      return mlir::failure();
+    }
+  }
+  else {
+    using UnsignedFrom = std::make_unsigned_t<From>;
+    using UnsignedTo = std::conditional_t<std::is_signed_v<To>, std::make_unsigned_t<To>, To>;
+    if (static_cast<UnsignedFrom>(value) > static_cast<UnsignedTo>(ToLimits::max())) {
+      emitCheckedArithmeticError(anchor, fieldName, "is outside representable range");
+      return mlir::failure();
+    }
+  }
+
+  return static_cast<To>(value);
+}
+
+template <typename UInt>
+mlir::FailureOr<UInt> checkedAdd(UInt lhs, UInt rhs, mlir::Operation* anchor, llvm::StringRef fieldName) {
+  static_assert(std::is_integral_v<UInt> && std::is_unsigned_v<UInt>, "checkedAdd requires unsigned integral types");
+  if (rhs > std::numeric_limits<UInt>::max() - lhs) {
+    emitCheckedArithmeticError(anchor, fieldName, "addition overflow");
+    return mlir::failure();
+  }
+  return lhs + rhs;
+}
+
+template <typename UInt>
+mlir::FailureOr<UInt> checkedMul(UInt lhs, UInt rhs, mlir::Operation* anchor, llvm::StringRef fieldName) {
+  static_assert(std::is_integral_v<UInt> && std::is_unsigned_v<UInt>, "checkedMul requires unsigned integral types");
+  if (lhs != 0 && rhs > std::numeric_limits<UInt>::max() / lhs) {
+    emitCheckedArithmeticError(anchor, fieldName, "multiplication overflow");
+    return mlir::failure();
+  }
+  return lhs * rhs;
+}
+
+mlir::FailureOr<int32_t> checkedI32(int64_t value, mlir::Operation* anchor, llvm::StringRef fieldName);
+mlir::FailureOr<int32_t> checkedI32(uint64_t value, mlir::Operation* anchor, llvm::StringRef fieldName);
+
+mlir::FailureOr<uint8_t> checkedU8(uint64_t value, mlir::Operation* anchor, llvm::StringRef fieldName);
+
+mlir::FailureOr<size_t> checkedSize(int64_t value, mlir::Operation* anchor, llvm::StringRef fieldName);
+
+mlir::FailureOr<mlir::IntegerAttr>
+getCheckedI32Attr(mlir::Builder& builder, mlir::Operation* anchor, int64_t value, llvm::StringRef fieldName);
+
+mlir::FailureOr<mlir::IntegerAttr>
+getCheckedI32Attr(mlir::Builder& builder, mlir::Operation* anchor, uint64_t value, llvm::StringRef fieldName);
+
+mlir::FailureOr<mlir::IntegerAttr>
+getCheckedI32Attr(mlir::Builder& builder, mlir::Location loc, int64_t value, llvm::StringRef fieldName);
+
+mlir::FailureOr<mlir::IntegerAttr>
+getCheckedI32Attr(mlir::Builder& builder, mlir::Location loc, uint64_t value, llvm::StringRef fieldName);
+
+mlir::FailureOr<uint64_t>
+getCheckedShapedTypeSizeInBytes(mlir::ShapedType type, mlir::Operation* anchor, llvm::StringRef fieldName);
+
+mlir::FailureOr<uint64_t>
+getCheckedShapedTypeSizeInBytes(mlir::ShapedType type, mlir::Location loc, llvm::StringRef fieldName);
+
+int32_t checkedI32OrCrash(int64_t value, llvm::StringRef fieldName);
+int32_t checkedI32OrCrash(uint64_t value, llvm::StringRef fieldName);
+uint8_t checkedU8OrCrash(uint64_t value, llvm::StringRef fieldName);
+size_t checkedSizeOrCrash(int64_t value, llvm::StringRef fieldName);
+size_t checkedAddOrCrash(size_t lhs, size_t rhs, llvm::StringRef fieldName);
+size_t checkedMulOrCrash(size_t lhs, size_t rhs, llvm::StringRef fieldName);
+
+} // namespace onnx_mlir::pim

src/PIM/Common/Support/Diagnostics.hpp

@@ -28,6 +28,8 @@ struct CappedDiagnosticReporter {
       op->emitError() << "suppressed " << (numFailures - maxReportedFailures) << " additional " << failureDescription;
   }
 
+  void noteFailures(int64_t count) { numFailures += count; }
+
   bool hasFailure() const { return numFailures != 0; }
 
 private:

src/PIM/Common/Support/ReportUtils.cpp

@@ -5,16 +5,18 @@
 
 namespace onnx_mlir {
 
-std::fstream openReportFile(const std::string& name) {
+std::fstream openReportFileWithExtension(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 + ".txt", std::ios::out);
+  return std::fstream(reportsDir + "/" + name + "." + extension.str(), std::ios::out);
 }
 
+std::fstream openReportFile(const std::string& name) { return openReportFileWithExtension(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

@@ -11,6 +11,7 @@
 namespace onnx_mlir {
 
 std::fstream openReportFile(const std::string& name);
+std::fstream openReportFileWithExtension(const std::string& name, llvm::StringRef extension);
 std::string formatReportMemory(uint64_t bytes);
 
 struct ReportField {

src/PIM/Compiler/CMakeLists.txt

@@ -17,6 +17,7 @@ add_pim_library(OMPimCompilerUtils
   PimCompilerUtils.cpp
   PimArtifactWriter.cpp
   PimCodeGen.cpp
+  PimMemoryLiveness.cpp
   PimWeightEmitter.cpp
 
   EXCLUDE_FROM_OM_LIBS
@@ -28,7 +29,9 @@ add_pim_library(OMPimCompilerUtils
   OMPimCompilerOptions
   OMPimCommon
   OMPimBufferization
-  OMPimStaticMemoryCoalescing
+  OMPimMemoryCoalescing
+  OMPimHostConstantFolding
+  OMPimVerification
   OMPimPasses
   OMONNXToSpatial
   OMSpatialToPim

src/PIM/Compiler/PimBinaryFormat.hpp

@@ -6,8 +6,8 @@
 #include "llvm/Support/raw_ostream.h"
 
 #include <array>
-#include <cassert>
-#include <limits>
+
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 
 namespace onnx_mlir::pim_binary {
 
@@ -95,15 +95,10 @@ inline void writeInstructionRecord(llvm::raw_ostream& os, const InstructionRecor
   writeInt32LE(os, record.generic3);
 }
 
-inline int32_t toI32(int64_t value) {
-  assert(value >= std::numeric_limits<int32_t>::min() && value <= std::numeric_limits<int32_t>::max()
-         && "PIM binary field out of int32 range");
-  return static_cast<int32_t>(value);
-}
+inline int32_t toI32(int64_t value) { return onnx_mlir::pim::checkedI32OrCrash(value, "binary field"); }
 
 inline uint8_t toU8(int64_t value) {
-  assert(value >= 0 && value <= std::numeric_limits<uint8_t>::max() && "PIM binary field out of uint8 range");
-  return static_cast<uint8_t>(value);
+  return onnx_mlir::pim::checkedU8OrCrash(static_cast<uint64_t>(value), "binary field");
 }
 
 inline int32_t getOptionalInt(const llvm::json::Object& object, llvm::StringRef key, int32_t defaultValue = 0) {

src/PIM/Compiler/PimCodeGen.cpp

@@ -2,7 +2,6 @@
 #include "mlir/Dialect/Func/IR/FuncOps.h"
 #include "mlir/Dialect/MemRef/IR/MemRef.h"
 #include "mlir/Dialect/SCF/IR/SCF.h"
-#include "mlir/IR/AsmState.h"
 #include "mlir/IR/Attributes.h"
 #include "mlir/IR/BuiltinAttributes.h"
 #include "mlir/IR/BuiltinTypes.h"
@@ -25,20 +24,26 @@
 #include <cassert>
 #include <cstdint>
 #include <fstream>
+#include <limits>
 #include <memory>
+#include <numeric>
 #include <string>
 #include <utility>
 
 #include "Common/IR/CompactAsmUtils.hpp"
 #include "Common/PimCommon.hpp"
+#include "Common/Support/Diagnostics.hpp"
+#include "Common/Support/CheckedArithmetic.hpp"
 #include "Common/Support/ReportUtils.hpp"
 #include "Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "src/Accelerators/PIM/Common/IR/BatchCoreUtils.hpp"
 #include "src/Accelerators/PIM/Common/IR/WeightUtils.hpp"
+#include "src/Accelerators/PIM/Common/Support/FileSystemUtils.hpp"
 #include "src/Accelerators/PIM/Compiler/PimArtifactWriter.hpp"
 #include "src/Accelerators/PIM/Compiler/PimBinaryFormat.hpp"
 #include "src/Accelerators/PIM/Compiler/PimCodeGen.hpp"
 #include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
+#include "src/Accelerators/PIM/Compiler/PimMemoryLiveness.hpp"
 #include "src/Accelerators/PIM/Compiler/PimWeightEmitter.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 
@@ -71,32 +76,159 @@ static MemoryValueKey getMemoryValueKey(mlir::Value value, std::optional<unsigne
   return {value, getLaneForMemoryValue(value, lane)};
 }
 
+static bool isInsidePimCoreLikeOp(memref::AllocOp allocOp) {
+  return allocOp->getParentOfType<pim::PimCoreOp>() || allocOp->getParentOfType<pim::PimCoreBatchOp>();
+}
+
+static MemoryReportKind classifyMemoryReportKind(mlir::Value value) {
+  if (isa<mlir::BlockArgument>(value))
+    return MemoryReportKind::Input;
+  if (auto* op = value.getDefiningOp()) {
+    if (isa<memref::AllocOp>(op))
+      return MemoryReportKind::Alloca;
+    if (isa<memref::GetGlobalOp>(op))
+      return MemoryReportKind::Global;
+  }
+  return MemoryReportKind::None;
+}
+
+static int32_t getVectorByteSizeOrCrash(ShapedType type) {
+  auto byteSize = pim::getCheckedShapedTypeSizeInBytes(type, UnknownLoc::get(type.getContext()), "vector byte size");
+  if (failed(byteSize))
+    llvm_unreachable("Failed to compute checked vector byte size");
+  return pim::checkedI32OrCrash(*byteSize, "vector byte size");
+}
+
+static Operation* getDiagnosticAnchor(mlir::Value value) {
+  if (Operation* definingOp = value.getDefiningOp())
+    return definingOp;
+  if (auto blockArg = dyn_cast<BlockArgument>(value))
+    return blockArg.getOwner()->getParentOp();
+  return nullptr;
+}
+
+// PIM instruction immediates are serialized as signed int32_t fields today
+// (`sldi` goes through checkedI32OrCrash), so local addresses must stay within
+// the non-negative int32_t range.
+static constexpr size_t kPimAddressLimit = static_cast<size_t>(std::numeric_limits<int32_t>::max());
+
+static FailureOr<size_t> checkedAlignTo(size_t value, size_t alignment, Operation* anchor, StringRef fieldName) {
+  if (alignment == 0)
+    return value;
+  size_t remainder = value % alignment;
+  if (remainder == 0)
+    return value;
+  return pim::checkedAdd(value, alignment - remainder, anchor, fieldName);
+}
+
+static void printMemoryOverflowDiagnostic(mlir::Value value,
+                                          const MemoryValueKey& key,
+                                          size_t requestedSize,
+                                          size_t currentFirstAvailableAddress,
+                                          size_t alignedEndAddress) {
+  llvm::errs() << "PIM local memory allocation overflow\n";
+  llvm::errs() << "Requested allocation size: " << requestedSize << " bytes\n";
+  llvm::errs() << "Current firstAvailableAddress: " << currentFirstAvailableAddress << "\n";
+  llvm::errs() << "Aligned end address: " << alignedEndAddress << "\n";
+  llvm::errs() << "Address limit: " << kPimAddressLimit << " (signed int32_t immediate range)\n";
+  if (key.lane)
+    llvm::errs() << "Lane: " << *key.lane << "\n";
+  llvm::errs() << "Value: ";
+  value.print(llvm::errs());
+  llvm::errs() << "\n";
+  llvm::errs() << "Value type: " << value.getType() << "\n";
+  if (Operation* definingOp = value.getDefiningOp()) {
+    llvm::errs() << "Defining op:\n";
+    definingOp->print(llvm::errs());
+    llvm::errs() << "\n";
+  }
+}
+
 } // namespace
 
 MemEntry* PimMemory::gatherMemEntry(mlir::Value value, std::optional<unsigned> lane) {
   auto type = cast<ShapedType>(value.getType());
   assert("Only static shape is supported" && type.hasStaticShape());
-  size_t allocSize = getShapedTypeSizeInBytes(type);
-  MemEntry memEntry = {0, allocSize};
-  return &memEntries.emplace_back(memEntry, getMemoryValueKey(value, lane)).first;
+  auto checkedAllocSize =
+    pim::getCheckedShapedTypeSizeInBytes(type, UnknownLoc::get(type.getContext()), "memory allocation byte size");
+  if (failed(checkedAllocSize))
+    llvm_unreachable("Failed to compute checked allocation byte size");
+  PendingMemEntry pending;
+  pending.memEntry = {0, *checkedAllocSize};
+  pending.key = getMemoryValueKey(value, lane);
+  pending.reportKind = classifyMemoryReportKind(value);
+  return &memEntries.emplace_back(std::move(pending)).memEntry;
 }
 
 void PimMemory::allocateGatheredMemory() {
-  llvm::sort(memEntries, [](auto a, auto b) -> bool { return a.first.size > b.first.size; });
-  for (auto& [memEntry, key] : memEntries)
-    allocateMemoryForValue(key, memEntry);
+  llvm::sort(memEntries, [](const PendingMemEntry& lhs, const PendingMemEntry& rhs) {
+    return lhs.memEntry.size > rhs.memEntry.size;
+  });
+  for (PendingMemEntry& pending : memEntries)
+    allocateMemoryForValue(pending.key, pending.memEntry, pending.reportKind);
   memEntries.clear();
 }
 
-void PimMemory::allocateMemoryForValue(const MemoryValueKey& key, MemEntry& memEntry) {
+void PimMemory::allocateMemoryForValue(const MemoryValueKey& key, MemEntry& memEntry, MemoryReportKind reportKind) {
   memEntry.address = firstAvailableAddress;
-  firstAvailableAddress += memEntry.size;
-  // Alignment
-  if (size_t remainder = firstAvailableAddress % minAlignment)
-    firstAvailableAddress += minAlignment - remainder;
+  Operation* anchor = getDiagnosticAnchor(key.value);
+  auto checkedEnd = pim::checkedAdd(memEntry.address, memEntry.size, anchor, "local memory end");
+  FailureOr<size_t> checkedAlignedEnd = failure();
+  if (succeeded(checkedEnd))
+    checkedAlignedEnd = checkedAlignTo(*checkedEnd, minAlignment, anchor, "local memory alignment");
+  bool startFits = memEntry.address <= kPimAddressLimit;
+  bool endFits = succeeded(checkedEnd) && *checkedEnd <= kPimAddressLimit;
+  bool alignedEndFits = succeeded(checkedAlignedEnd) && *checkedAlignedEnd <= kPimAddressLimit;
+  if (!startFits || !endFits || !alignedEndFits) {
+    printMemoryOverflowDiagnostic(key.value,
+                                  key,
+                                  memEntry.size,
+                                  firstAvailableAddress,
+                                  succeeded(checkedAlignedEnd) ? *checkedAlignedEnd : kPimAddressLimit);
+    llvm_unreachable("PIM local memory allocation overflow");
+  }
+  firstAvailableAddress = *checkedAlignedEnd;
 
   ownedMemEntriesMap[key] = memEntry;
   globalMemEntriesMap[key] = memEntry;
+
+  switch (reportKind) {
+  case MemoryReportKind::Alloca: break;
+  case MemoryReportKind::Global:
+    ++reportRow.numGlobal;
+    reportRow.sizeGlobal += memEntry.size;
+    break;
+  case MemoryReportKind::Input:
+  case MemoryReportKind::None:  break;
+  }
+}
+
+PhysicalSlotInfo PimMemory::allocatePhysicalSlot(size_t slotSize, const MemoryValueKey& key) {
+  PhysicalSlotInfo slot;
+  slot.id = nextPhysicalSlotId++;
+  slot.address = firstAvailableAddress;
+  slot.size = slotSize;
+
+  Operation* anchor = getDiagnosticAnchor(key.value);
+  auto checkedEnd = pim::checkedAdd(slot.address, slot.size, anchor, "local memory end");
+  FailureOr<size_t> checkedAlignedEnd = failure();
+  if (succeeded(checkedEnd))
+    checkedAlignedEnd = checkedAlignTo(*checkedEnd, minAlignment, anchor, "local memory alignment");
+  bool startFits = slot.address <= kPimAddressLimit;
+  bool endFits = succeeded(checkedEnd) && *checkedEnd <= kPimAddressLimit;
+  bool alignedEndFits = succeeded(checkedAlignedEnd) && *checkedAlignedEnd <= kPimAddressLimit;
+  if (!startFits || !endFits || !alignedEndFits) {
+    printMemoryOverflowDiagnostic(key.value,
+                                  key,
+                                  slot.size,
+                                  firstAvailableAddress,
+                                  succeeded(checkedAlignedEnd) ? *checkedAlignedEnd : kPimAddressLimit);
+    llvm_unreachable("PIM local memory allocation overflow");
+  }
+
+  firstAvailableAddress = *checkedAlignedEnd;
+  localPhysicalSlots.push_back(slot);
+  return slot;
 }
 
 void PimMemory::allocateHost(ModuleOp moduleOp, func::FuncOp funcOp) {
@@ -127,7 +259,7 @@ void PimMemory::allocateHost(ModuleOp moduleOp, func::FuncOp funcOp) {
   });
 
   funcOp.walk([&](memref::AllocOp allocOp) {
-    if (!allocOp->getParentOfType<pim::PimCoreOp>())
+    if (!isInsidePimCoreLikeOp(allocOp))
       gatherMemEntry(allocOp.getResult());
   });
 
@@ -138,9 +270,71 @@ void PimMemory::allocateHost(ModuleOp moduleOp, func::FuncOp funcOp) {
 }
 
 void PimMemory::allocateCore(Operation* op, std::optional<unsigned> lane) {
-  op->walk([&](memref::AllocOp allocOp) { gatherMemEntry(allocOp, lane); });
+  auto intervals = buildLocalAllocIntervals(op, lane);
+  SmallVector<PlannedPhysicalSlot> plannedSlots = planPhysicalSlots(intervals);
 
-  allocateGatheredMemory();
+  SmallVector<size_t> slotOrder(plannedSlots.size());
+  std::iota(slotOrder.begin(), slotOrder.end(), 0);
+  llvm::stable_sort(slotOrder, [&](size_t lhsIndex, size_t rhsIndex) {
+    const PlannedPhysicalSlot& lhs = plannedSlots[lhsIndex];
+    const PlannedPhysicalSlot& rhs = plannedSlots[rhsIndex];
+    if (lhs.requiredSize != rhs.requiredSize)
+      return lhs.requiredSize > rhs.requiredSize;
+    return lhs.id < rhs.id;
+  });
+
+  SmallVector<bool, 16> usedExistingSlots(localPhysicalSlots.size(), false);
+  for (size_t slotIndex : slotOrder) {
+    PlannedPhysicalSlot& slot = plannedSlots[slotIndex];
+    size_t bestExistingIndex = std::numeric_limits<size_t>::max();
+    auto bestKey = std::tuple<size_t, size_t, size_t>(
+      std::numeric_limits<size_t>::max(), std::numeric_limits<size_t>::max(), std::numeric_limits<size_t>::max());
+
+    for (size_t existingIndex = 0; existingIndex < localPhysicalSlots.size(); ++existingIndex) {
+      if (usedExistingSlots[existingIndex])
+        continue;
+      const PhysicalSlotInfo& existingSlot = localPhysicalSlots[existingIndex];
+      if (existingSlot.size < slot.requiredSize)
+        continue;
+      auto candidateKey =
+        std::tuple<size_t, size_t, size_t>(existingSlot.size - slot.requiredSize, existingSlot.size, existingSlot.id);
+      if (candidateKey < bestKey) {
+        bestKey = candidateKey;
+        bestExistingIndex = existingIndex;
+      }
+    }
+
+    if (bestExistingIndex != std::numeric_limits<size_t>::max()) {
+      const PhysicalSlotInfo& existingSlot = localPhysicalSlots[bestExistingIndex];
+      slot.id = existingSlot.id;
+      slot.address = existingSlot.address;
+      slot.size = existingSlot.size;
+      usedExistingSlots[bestExistingIndex] = true;
+    }
+    else {
+      PhysicalSlotInfo newSlot = allocatePhysicalSlot(slot.requiredSize, intervals[slot.intervalIndices.front()].key);
+      slot.id = newSlot.id;
+      slot.address = newSlot.address;
+      slot.size = newSlot.size;
+      usedExistingSlots.push_back(true);
+    }
+
+    for (size_t intervalIndex : slot.intervalIndices) {
+      LocalAllocInterval& interval = intervals[intervalIndex];
+      interval.physicalSlotId = slot.id;
+      interval.assignedAddress = slot.address;
+      interval.physicalSlotSize = slot.size;
+      MemEntry memEntry {slot.address, interval.size};
+      ownedMemEntriesMap[interval.key] = memEntry;
+      globalMemEntriesMap[interval.key] = memEntry;
+    }
+  }
+
+  if (pimMemoryReport != PimMemoryReportNone) {
+    MemoryPlanArtifacts artifacts =
+      buildMemoryPlanArtifacts(op, lane, intervals, plannedSlots, kPimAddressLimit, pimMemoryReport);
+    livenessArtifacts.textReport += artifacts.textReport;
+  }
 }
 
 static void printHostMemoryReportRow(raw_ostream& os, const MemoryReportRow& row) {
@@ -181,20 +375,11 @@ static MemoryReportRow addMemoryReportRows(const MemoryReportRow& lhs, const Mem
 }
 
 MemoryReportRow PimMemory::getReportRow() const {
-  MemoryReportRow row;
-  for (auto& [key, memEntry] : ownedMemEntriesMap) {
-    if (auto op = key.value.getDefiningOp()) {
-      if (isa<memref::AllocOp>(op)) {
-        row.numAlloca++;
-        row.sizeAlloca += memEntry.size;
-      }
-
-      if (isa<memref::GetGlobalOp>(op)) {
-        row.numGlobal++;
-        row.sizeGlobal += memEntry.size;
-      }
-    }
-  }
+  MemoryReportRow row = reportRow;
+  row.numAlloca = localPhysicalSlots.size();
+  row.sizeAlloca = 0;
+  for (const PhysicalSlotInfo& slot : localPhysicalSlots)
+    row.sizeAlloca += slot.size;
   return row;
 }
 
@@ -235,6 +420,7 @@ size_t PimAcceleratorMemory::getValueAddress(mlir::Value value,
     if (failed(compiledExpr)) {
       errs() << "Failed to compile contiguous address for value: ";
       value.print(errs());
+      errs() << " : " << value.getType();
       errs() << "\n";
       llvm_unreachable("Failed to compile contiguous address");
     }
@@ -245,6 +431,7 @@ size_t PimAcceleratorMemory::getValueAddress(mlir::Value value,
   if (failed(resolvedAddress)) {
     errs() << "Failed to evaluate contiguous address for value: ";
     value.print(errs());
+    errs() << " : " << value.getType();
     errs() << "\n";
     if (auto* definingOp = value.getDefiningOp()) {
       errs() << "Defining op:\n";
@@ -270,7 +457,8 @@ size_t PimAcceleratorMemory::getValueAddress(mlir::Value value,
     llvm_unreachable("Missing mem entry");
   }
 
-  return iter->second.address + resolvedAddress->byteOffset;
+  size_t byteOffset = pim::checkedSizeOrCrash(resolvedAddress->byteOffset, "resolved PIM byte offset");
+  return pim::checkedAddOrCrash(iter->second.address, byteOffset, "resolved PIM address");
 }
 
 llvm::FailureOr<int64_t> PimAcceleratorMemory::getIndexValue(mlir::Value value,
@@ -289,8 +477,12 @@ llvm::FailureOr<int64_t> PimAcceleratorMemory::getIndexValue(mlir::Value value,
 void PimAcceleratorMemory::reportHost() { hostReportRow = hostMem.getReportRow(); }
 
 void PimAcceleratorMemory::recordCoreReport(size_t coreId, const MemoryReportRow& row) {
-  reportEntries.push_back(
-    {MemoryReportEntry::Kind::Core, coreId, {static_cast<int32_t>(coreId)}, row, row.numAlloca, row.sizeAlloca});
+  reportEntries.push_back({MemoryReportEntry::Kind::Core,
+                           coreId,
+                           {pim::checkedI32OrCrash(coreId, "memory report core id")},
+                           row,
+                           row.numAlloca,
+                           row.sizeAlloca});
 }
 
 void PimAcceleratorMemory::recordBatchReport(uint64_t batchId,
@@ -314,13 +506,15 @@ void PimAcceleratorMemory::flushReport() {
 
   llvm::raw_os_ostream os(fileReport);
   uint64_t totalGlobalMemory = hostReportRow.has_value() ? hostReportRow->sizeGlobal : 0;
+  uint64_t totalWeightsMemory = totalWeightBytes;
   uint64_t totalCoresMemory = 0;
   for (const MemoryReportEntry& entry : reportEntries)
     totalCoresMemory += entry.totalAllocaBytes;
 
-  llvm::SmallVector<ReportField, 2> totalFields = {
-    {"Global memory", formatReportMemory(totalGlobalMemory)},
-    {"Cores memory",  formatReportMemory(totalCoresMemory) }
+  llvm::SmallVector<ReportField, 3> totalFields = {
+    {"Global memory",  formatReportMemory(totalGlobalMemory) },
+    {"Weights memory", formatReportMemory(totalWeightsMemory)},
+    {"Cores memory",   formatReportMemory(totalCoresMemory)  }
   };
   printReportTotalsBlock(os, totalFields);
 
@@ -400,24 +594,24 @@ void PimCodeGen::genSetRegisterImmediateUnsigned(size_t registerNumber, size_t i
   pim_binary::InstructionRecord instruction;
   instruction.opcode = pim_binary::Opcode::sldi;
   instruction.rd = static_cast<uint8_t>(registerNumber);
-  instruction.r2OrImm = static_cast<int32_t>(immediate);
+  instruction.r2OrImm = pim::checkedI32OrCrash(immediate, "register immediate");
   emitInstruction(instruction);
 }
 
 void PimCodeGen::setupRd(size_t rdAddress, size_t rdOffset) const {
-  genSetRegisterImmediateUnsigned(0, rdAddress + rdOffset);
+  genSetRegisterImmediateUnsigned(0, pim::checkedAddOrCrash(rdAddress, rdOffset, "rd address"));
 }
 
 void PimCodeGen::setupRdRs1(size_t rdAddress, size_t rdOffset, size_t rs1Address, size_t rs1Offset) const {
-  genSetRegisterImmediateUnsigned(0, rdAddress + rdOffset);
-  genSetRegisterImmediateUnsigned(1, rs1Address + rs1Offset);
+  genSetRegisterImmediateUnsigned(0, pim::checkedAddOrCrash(rdAddress, rdOffset, "rd address"));
+  genSetRegisterImmediateUnsigned(1, pim::checkedAddOrCrash(rs1Address, rs1Offset, "rs1 address"));
 }
 
 void PimCodeGen::setupRdRs1Rs2(
   size_t rdAddress, size_t rdOffset, size_t rs1Address, size_t rs1Offset, size_t rs2Address, size_t rs2Offset) const {
-  genSetRegisterImmediateUnsigned(0, rdAddress + rdOffset);
-  genSetRegisterImmediateUnsigned(1, rs1Address + rs1Offset);
-  genSetRegisterImmediateUnsigned(2, rs2Address + rs2Offset);
+  genSetRegisterImmediateUnsigned(0, pim::checkedAddOrCrash(rdAddress, rdOffset, "rd address"));
+  genSetRegisterImmediateUnsigned(1, pim::checkedAddOrCrash(rs1Address, rs1Offset, "rs1 address"));
+  genSetRegisterImmediateUnsigned(2, pim::checkedAddOrCrash(rs2Address, rs2Offset, "rs2 address"));
 }
 
 void PimCodeGen::emitMemCopyOp(StringRef opName,
@@ -435,8 +629,7 @@ void PimCodeGen::emitMemCopyOp(StringRef opName,
   instruction.r1 = 1;
   instruction.generic1 = 0;
   instruction.generic2 = 0;
-  instruction.generic3 = static_cast<int32_t>(size);
-  (void) sizeFieldName;
+  instruction.generic3 = pim::checkedI32OrCrash(size, sizeFieldName);
   emitInstruction(instruction);
 }
 
@@ -446,10 +639,10 @@ void PimCodeGen::emitCommunicationOp(StringRef opName, size_t bufferAddr, size_t
   pim_binary::InstructionRecord instruction;
   instruction.opcode = pim_binary::opcodeFromString(opName);
   instruction.rd = 0;
-  instruction.r2OrImm = static_cast<int32_t>(remapCoreId(coreId));
+  instruction.r2OrImm = pim::checkedI32OrCrash(remapCoreId(coreId), "communication core id");
   instruction.generic1 = 0;
   instruction.generic2 = 0;
-  instruction.generic3 = static_cast<int32_t>(size);
+  instruction.generic3 = pim::checkedI32OrCrash(size, "communication byte size");
   emitInstruction(instruction);
 }
 
@@ -462,7 +655,7 @@ void PimCodeGen::emitMvmOp(size_t groupId, size_t rdAddr, size_t rdOffset, size_
   instruction.r1 = 1;
   instruction.r2OrImm = 8;
   instruction.generic1 = 0;
-  instruction.generic2 = static_cast<int32_t>(groupId);
+  instruction.generic2 = pim::checkedI32OrCrash(groupId, "mvm group id");
   emitInstruction(instruction);
 }
 
@@ -493,11 +686,15 @@ void PimCodeGen::codeGenStoreOp(pim::PimMemCopyDevToHostOp storeOp, const Static
 }
 
 void PimCodeGen::codeGenLmvOp(pim::PimMemCopyOp lmvOp, const StaticValueKnowledge& knowledge) const {
+  auto targetOffset = indexOf(lmvOp.getTargetOffset(), knowledge);
+  auto sourceOffset = indexOf(lmvOp.getSourceOffset(), knowledge);
+  assert(succeeded(targetOffset) && succeeded(sourceOffset)
+         && "pim.memcp offsets must be statically resolvable during codegen");
   emitMemCopyOp("lmv",
                 addressOf(lmvOp.getTarget(), knowledge),
-                lmvOp.getTargetOffset(),
+                *targetOffset,
                 addressOf(lmvOp.getSource(), knowledge),
-                lmvOp.getSourceOffset(),
+                *sourceOffset,
                 lmvOp.getSize(),
                 "len");
 }
@@ -572,7 +769,7 @@ void PimCodeGen::codeGenVVAddOp(pim::PimVVAddOp vvaddOp, const StaticValueKnowle
   instruction.rd = 0;
   instruction.r1 = 1;
   instruction.r2OrImm = 2;
-  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vvaddOp.getLhs().getType())));
+  instruction.generic3 = getVectorByteSizeOrCrash(cast<ShapedType>(vvaddOp.getLhs().getType()));
   emitInstruction(instruction);
 }
 
@@ -587,7 +784,7 @@ void PimCodeGen::codeGenVVSubOp(pim::PimVVSubOp vvsubOp, const StaticValueKnowle
   instruction.rd = 0;
   instruction.r1 = 1;
   instruction.r2OrImm = 2;
-  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vvsubOp.getLhs().getType())));
+  instruction.generic3 = getVectorByteSizeOrCrash(cast<ShapedType>(vvsubOp.getLhs().getType()));
   emitInstruction(instruction);
 }
 
@@ -602,7 +799,7 @@ void PimCodeGen::codeGenVVMulOp(pim::PimVVMulOp vvmulOp, const StaticValueKnowle
   instruction.rd = 0;
   instruction.r1 = 1;
   instruction.r2OrImm = 2;
-  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vvmulOp.getLhs().getType())));
+  instruction.generic3 = getVectorByteSizeOrCrash(cast<ShapedType>(vvmulOp.getLhs().getType()));
   emitInstruction(instruction);
 }
 
@@ -617,7 +814,7 @@ void PimCodeGen::codeGenVVMaxOp(pim::PimVVMaxOp vvmaxOp, const StaticValueKnowle
   instruction.rd = 0;
   instruction.r1 = 1;
   instruction.r2OrImm = 2;
-  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vvmaxOp.getLhs().getType())));
+  instruction.generic3 = getVectorByteSizeOrCrash(cast<ShapedType>(vvmaxOp.getLhs().getType()));
   emitInstruction(instruction);
 }
 
@@ -632,7 +829,7 @@ void PimCodeGen::codeGenVVDMulOp(pim::PimVVDMulOp vvdmulOp, const StaticValueKno
   instruction.rd = 0;
   instruction.r1 = 1;
   instruction.r2OrImm = 2;
-  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vvdmulOp.getLhs().getType())));
+  instruction.generic3 = getVectorByteSizeOrCrash(cast<ShapedType>(vvdmulOp.getLhs().getType()));
   emitInstruction(instruction);
 }
 
@@ -647,7 +844,7 @@ void PimCodeGen::codeGenVAvgOp(pim::PimVAvgOp vavgOp, const StaticValueKnowledge
   instruction.r1 = 1;
   instruction.r2OrImm = 1;
   instruction.generic1 = 1;
-  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vavgOp.getInput().getType())));
+  instruction.generic3 = getVectorByteSizeOrCrash(cast<ShapedType>(vavgOp.getInput().getType()));
   emitInstruction(instruction);
 }
 
@@ -660,7 +857,7 @@ void PimCodeGen::codeGenVReluOp(pim::PimVReluOp vreluOp, const StaticValueKnowle
   instruction.opcode = pim_binary::Opcode::vrelu;
   instruction.rd = 0;
   instruction.r1 = 1;
-  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vreluOp.getInput().getType())));
+  instruction.generic3 = getVectorByteSizeOrCrash(cast<ShapedType>(vreluOp.getInput().getType()));
   emitInstruction(instruction);
 }
 
@@ -673,7 +870,7 @@ void PimCodeGen::codeGenVTanhOp(pim::PimVTanhOp vtanhOp, const StaticValueKnowle
   instruction.opcode = pim_binary::Opcode::vtanh;
   instruction.rd = 0;
   instruction.r1 = 1;
-  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vtanhOp.getInput().getType())));
+  instruction.generic3 = getVectorByteSizeOrCrash(cast<ShapedType>(vtanhOp.getInput().getType()));
   emitInstruction(instruction);
 }
 
@@ -686,7 +883,7 @@ void PimCodeGen::codeGenVSigmOp(pim::PimVSigmOp vsigmOp, const StaticValueKnowle
   instruction.opcode = pim_binary::Opcode::vsigm;
   instruction.rd = 0;
   instruction.r1 = 1;
-  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vsigmOp.getInput().getType())));
+  instruction.generic3 = getVectorByteSizeOrCrash(cast<ShapedType>(vsigmOp.getInput().getType()));
   emitInstruction(instruction);
 }
 
@@ -699,8 +896,7 @@ void PimCodeGen::codeGenVSoftmaxOp(pim::PimVSoftmaxOp vsoftmaxOp, const StaticVa
   instruction.opcode = pim_binary::Opcode::vsoftmax;
   instruction.rd = 0;
   instruction.r1 = 1;
-  instruction.generic3 =
-    static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vsoftmaxOp.getInput().getType())));
+  instruction.generic3 = getVectorByteSizeOrCrash(cast<ShapedType>(vsoftmaxOp.getInput().getType()));
   emitInstruction(instruction);
 }
 
@@ -801,11 +997,44 @@ static SmallVector<Operation*> collectTopLevelCoreLikeOps(func::FuncOp funcOp) {
 }
 
 struct CoreEmissionResult {
+  static constexpr size_t kMaxStoredCodegenDiagnostics = 8;
+
+  struct DiagnosticRecord {
+    Operation* op = nullptr;
+    std::string message;
+  };
+
   OnnxMlirCompilerErrorCodes status = CompilerSuccess;
   MemoryReportRow reportRow;
   llvm::SmallVector<ResolvedWeightView, 8> usedWeights;
+  MemoryPlanArtifacts livenessArtifacts;
+  llvm::SmallVector<DiagnosticRecord, kMaxStoredCodegenDiagnostics> diagnostics;
+  size_t diagnosticCount = 0;
+
+  void recordDiagnostic(Operation* op, StringRef message) {
+    ++diagnosticCount;
+    if (diagnostics.size() < kMaxStoredCodegenDiagnostics)
+      diagnostics.push_back({op, message.str()});
+  }
 };
 
+static StaticValueKnowledge seedCoreCodegenKnowledge(pim::PimCoreOp coreOp) {
+  StaticValueKnowledge knowledge;
+  for (auto [index, weight] : llvm::enumerate(coreOp.getWeights()))
+    knowledge.aliases[coreOp.getWeightArgument(index)] = weight;
+  return knowledge;
+}
+
+static StaticValueKnowledge seedCoreBatchCodegenKnowledge(pim::PimCoreBatchOp coreBatchOp, unsigned lane) {
+  StaticValueKnowledge knowledge;
+  knowledge.indexValues[coreBatchOp.getLaneArgument()] = lane;
+  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;
+}
+
 template <typename MapTy>
 class ScopedMapBindings {
   using KeyTy = typename MapTy::key_type;
@@ -1226,7 +1455,20 @@ OnnxMlirCompilerErrorCodes onnx_mlir::compileToPimCode(ModuleOp& moduleOp, std::
                                  const StaticValueKnowledge& knowledge) -> llvm::FailureOr<unsigned> {
       auto weightView = onnx_mlir::resolveWeightView(job.coreLikeOp, vmmOp.getWeight(), knowledge);
       if (failed(weightView)) {
-        vmmOp.emitOpError("requires a statically resolvable dense global weight view during PIM codegen");
+        std::string message;
+        llvm::raw_string_ostream os(message);
+        os << "requires a statically resolvable dense global weight view during PIM codegen; weight="
+           << vmmOp.getWeight() << " type=" << vmmOp.getWeight().getType();
+        result.recordDiagnostic(vmmOp, os.str());
+        return failure();
+      }
+      if (weightView->shape.size() != 2) {
+        std::string message;
+        llvm::raw_string_ostream os(message);
+        os << "requires a rank-2 matrix weight view during PIM codegen; resolved shape=[";
+        llvm::interleaveComma(weightView->shape, os);
+        os << "] weight=" << vmmOp.getWeight() << " type=" << vmmOp.getWeight().getType();
+        result.recordDiagnostic(vmmOp, os.str());
         return failure();
       }
 
@@ -1267,15 +1509,16 @@ OnnxMlirCompilerErrorCodes onnx_mlir::compileToPimCode(ModuleOp& moduleOp, std::
       auto& deviceMemory = jobMemory.getOrCreateDeviceMem(job.emittedCoreId);
       deviceMemory.allocateCore(coreOp);
 
-      int64_t processedOperations = codeGenCoreOps(
-        coreOp.getBody().front(), coreCodeGen, StaticValueKnowledge {}, coreOp.getOperation(), resolveWeightSlot);
+      StaticValueKnowledge knowledge = seedCoreCodegenKnowledge(coreOp);
+      int64_t processedOperations =
+        codeGenCoreOps(coreOp.getBody().front(), coreCodeGen, knowledge, coreOp.getOperation(), resolveWeightSlot);
       if (processedOperations < 0) {
         result.status = CompilerFailure;
         return result;
       }
-      assert(processedOperations > 0);
       result.reportRow = deviceMemory.getReportRow();
       result.usedWeights = std::move(usedWeights);
+      result.livenessArtifacts = deviceMemory.getLivenessArtifacts();
     }
     else {
       auto coreBatchOp = cast<pim::PimCoreBatchOp>(job.coreLikeOp);
@@ -1283,10 +1526,7 @@ OnnxMlirCompilerErrorCodes onnx_mlir::compileToPimCode(ModuleOp& moduleOp, std::
       auto& deviceMemory = jobMemory.getOrCreateDeviceMem(job.emittedCoreId);
 
       for (unsigned lane : job.lanes) {
-        StaticValueKnowledge knowledge;
-        knowledge.indexValues[coreBatchOp.getLaneArgument()] = lane;
-        for (unsigned i = 0; i < coreBatchOp.getInputs().size(); ++i)
-          knowledge.aliases[coreBatchOp.getInputArgument(i)] = coreBatchOp.getInputs()[i];
+        StaticValueKnowledge knowledge = seedCoreBatchCodegenKnowledge(coreBatchOp, lane);
 
         deviceMemory.allocateCore(coreBatchOp, lane);
         coreCodeGen.setBatchLane(lane);
@@ -1301,11 +1541,11 @@ OnnxMlirCompilerErrorCodes onnx_mlir::compileToPimCode(ModuleOp& moduleOp, std::
           result.status = CompilerFailure;
           return result;
         }
-        assert(processedOperations > 0);
       }
 
       result.reportRow = deviceMemory.getReportRow();
       result.usedWeights = std::move(usedWeights);
+      result.livenessArtifacts = deviceMemory.getLivenessArtifacts();
     }
 
     pim_binary::patchInstructionCount(coreBinaryStream, coreCodeGen.getEmittedInstructionCount());
@@ -1324,6 +1564,21 @@ OnnxMlirCompilerErrorCodes onnx_mlir::compileToPimCode(ModuleOp& moduleOp, std::
   mlir::parallelFor(
     moduleOp.getContext(), 0, jobs.size(), [&](size_t index) { jobResults[index] = emitJob(jobs[index]); });
 
+  pim::CappedDiagnosticReporter diagnostics;
+  Operation* summaryAnchor = nullptr;
+  for (const CoreEmissionResult& result : jobResults) {
+    if (!summaryAnchor && !result.diagnostics.empty())
+      summaryAnchor = result.diagnostics.front().op;
+    for (const CoreEmissionResult::DiagnosticRecord& diagnostic : result.diagnostics) {
+      diagnostics.report(diagnostic.op, [&](Operation* op) { op->emitError() << diagnostic.message; });
+    }
+    size_t unreportedCount = result.diagnosticCount - result.diagnostics.size();
+    diagnostics.noteFailures(static_cast<int64_t>(unreportedCount));
+  }
+  if (diagnostics.hasFailure())
+    diagnostics.emitSuppressedSummary(summaryAnchor ? summaryAnchor : moduleOp.getOperation(),
+                                      "PIM codegen diagnostic(s)");
+
   for (size_t jobIndex = 0; jobIndex < jobs.size(); ++jobIndex)
     if (jobResults[jobIndex].status != CompilerSuccess)
       return jobResults[jobIndex].status;
@@ -1336,7 +1591,21 @@ OnnxMlirCompilerErrorCodes onnx_mlir::compileToPimCode(ModuleOp& moduleOp, std::
     request.weights = jobResults[jobIndex].usedWeights;
     weightRequests.push_back(std::move(request));
   }
-  auto mapCoreWeightToFileName = createAndPopulateWeightFolder(weightRequests, outputDirPath);
+  auto weightEmission = createAndPopulateWeightFolder(weightRequests, outputDirPath);
+  memory.setTotalWeightBytes(weightEmission.totalWeightBytes);
+  auto& mapCoreWeightToFileName = weightEmission.mapCoreWeightToFileName;
+  if (std::string reportsRoot = getOutputDir(); !reportsRoot.empty()) {
+    std::string reportsDir = reportsRoot + "/reports";
+    sys::fs::remove(reportsDir + "/pim_memory_liveness_report.txt");
+    sys::fs::remove(reportsDir + "/pim_memory_liveness_report.json");
+    sys::fs::remove(reportsDir + "/pim_memory_liveness_timeline.dot");
+  }
+  std::fstream livenessReportFile;
+  std::unique_ptr<llvm::raw_os_ostream> livenessReportOs;
+  if (pimMemoryReport != PimMemoryReportNone) {
+    livenessReportFile = openReportFileWithExtension("pim_memory_liveness_report", "txt");
+    livenessReportOs = std::make_unique<llvm::raw_os_ostream>(livenessReportFile);
+  }
 
   for (size_t jobIndex = 0; jobIndex < jobs.size(); ++jobIndex) {
     const CoreEmissionJob& job = jobs[jobIndex];
@@ -1348,6 +1617,8 @@ OnnxMlirCompilerErrorCodes onnx_mlir::compileToPimCode(ModuleOp& moduleOp, std::
         return err;
       xbarsPerArrayGroup["core" + std::to_string(job.emittedCoreId)] = std::move(xbarsPerGroup);
       memory.recordCoreReport(job.emittedCoreId, result.reportRow);
+      if (livenessReportFile.is_open())
+        *livenessReportOs << "Core " << job.emittedCoreId << ":\n" << result.livenessArtifacts.textReport;
       continue;
     }
   }
@@ -1364,7 +1635,7 @@ OnnxMlirCompilerErrorCodes onnx_mlir::compileToPimCode(ModuleOp& moduleOp, std::
       if (auto err = linkCoreWeights(job.emittedCoreId, mapCoreWeightToFileName[job.emittedCoreId], xbarsPerGroup))
         return err;
       xbarsPerArrayGroup["core" + std::to_string(job.emittedCoreId)] = std::move(xbarsPerGroup);
-      reportedCoreIds.push_back(static_cast<int32_t>(job.emittedCoreId));
+      reportedCoreIds.push_back(pim::checkedI32OrCrash(job.emittedCoreId, "batch report core id"));
       if (!batchPerCoreRow)
         batchPerCoreRow = result.reportRow;
       batchRow = addMemoryReportRows(batchRow, result.reportRow);
@@ -1376,10 +1647,18 @@ OnnxMlirCompilerErrorCodes onnx_mlir::compileToPimCode(ModuleOp& moduleOp, std::
                              batchPerCoreRow.value_or(MemoryReportRow {}),
                              batchRow.numAlloca,
                              batchRow.sizeAlloca);
+    if (livenessReportFile.is_open())
+      for (size_t jobIndex : group)
+        *livenessReportOs << "Batch " << batchReportId << " core " << jobs[jobIndex].emittedCoreId << ":\n"
+                          << jobResults[jobIndex].livenessArtifacts.textReport;
   }
 
   maxCoreId = nextEmittedCoreId == 0 ? 0 : nextEmittedCoreId - 1;
 
+  if (livenessReportFile.is_open()) {
+    livenessReportOs->flush();
+    livenessReportFile.close();
+  }
   memory.flushReport();
   return writeConfigJson(funcOp, memory, maxCoreId, std::move(xbarsPerArrayGroup), outputDirPath);
 }

src/PIM/Compiler/PimCodeGen.hpp

@@ -5,12 +5,14 @@
 #include "llvm-project/clang/include/clang/Basic/LLVM.h"
 #include "llvm/ADT/DenseMap.h"
 #include "llvm/ADT/Hashing.h"
+#include "llvm/ADT/SmallVector.h"
 #include "llvm/Support/JSON.h"
 #include "llvm/Support/raw_os_ostream.h"
 
 #include <fstream>
 #include <limits>
 #include <optional>
+#include <string>
 
 #include "onnx-mlir/Compiler/OMCompilerTypes.h"
 #include "src/Accelerators/PIM/Common/IR/AddressAnalysis.hpp"
@@ -26,6 +28,16 @@ struct MemEntry {
   size_t size;
 };
 
+struct PhysicalSlotInfo {
+  size_t id = 0;
+  size_t address = 0;
+  size_t size = 0;
+};
+
+struct MemoryPlanArtifacts {
+  std::string textReport;
+};
+
 struct MemoryValueKey {
   mlir::Value value;
   std::optional<unsigned> lane;
@@ -45,6 +57,19 @@ struct MemoryReportRow {
   }
 };
 
+enum class MemoryReportKind {
+  None,
+  Alloca,
+  Global,
+  Input
+};
+
+struct PendingMemEntry {
+  MemEntry memEntry;
+  MemoryValueKey key;
+  MemoryReportKind reportKind = MemoryReportKind::None;
+};
+
 struct MemoryReportEntry {
   enum class Kind {
     Core,
@@ -60,16 +85,21 @@ struct MemoryReportEntry {
 };
 
 class PimMemory {
-  llvm::SmallVector<std::pair<MemEntry, MemoryValueKey>, 32> memEntries;
+  llvm::SmallVector<PendingMemEntry, 32> memEntries;
+  llvm::SmallVector<PhysicalSlotInfo, 32> localPhysicalSlots;
   llvm::SmallDenseMap<MemoryValueKey, MemEntry, 32>& globalMemEntriesMap;
   llvm::SmallDenseMap<MemoryValueKey, MemEntry, 32> ownedMemEntriesMap;
+  MemoryReportRow reportRow;
+  MemoryPlanArtifacts livenessArtifacts;
 
   size_t minAlignment = 4;
   size_t firstAvailableAddress = 0;
+  size_t nextPhysicalSlotId = 0;
 
   MemEntry* gatherMemEntry(mlir::Value value, std::optional<unsigned> lane = std::nullopt);
   void allocateGatheredMemory();
-  void allocateMemoryForValue(const MemoryValueKey& key, MemEntry& memEntry);
+  void allocateMemoryForValue(const MemoryValueKey& key, MemEntry& memEntry, MemoryReportKind reportKind);
+  PhysicalSlotInfo allocatePhysicalSlot(size_t slotSize, const MemoryValueKey& key);
 
 public:
   PimMemory(llvm::SmallDenseMap<MemoryValueKey, MemEntry, 32>& globalMemEntriesMap)
@@ -78,6 +108,7 @@ public:
   void allocateHost(mlir::ModuleOp moduleOp, mlir::func::FuncOp funcOp);
   void allocateCore(mlir::Operation* op, std::optional<unsigned> lane = std::nullopt);
   MemoryReportRow getReportRow() const;
+  const MemoryPlanArtifacts& getLivenessArtifacts() const { return livenessArtifacts; }
   void remove(mlir::Value val);
 
   size_t getFirstAvailableAddress() const { return firstAvailableAddress; }
@@ -94,6 +125,7 @@ private:
   std::fstream fileReport;
   std::optional<MemoryReportRow> hostReportRow;
   llvm::SmallVector<MemoryReportEntry, 32> reportEntries;
+  uint64_t totalWeightBytes = 0;
   mutable llvm::DenseMap<mlir::Value, CompiledIndexExpr> compiledIndexExprs;
   mutable llvm::DenseMap<mlir::Value, CompiledAddressExpr> compiledAddressExprs;
 
@@ -118,6 +150,7 @@ public:
                          const MemoryReportRow& perCoreRow,
                          uint64_t totalAllocaCount,
                          uint64_t totalAllocaBytes);
+  void setTotalWeightBytes(uint64_t bytes) { totalWeightBytes = bytes; }
   void flushReport();
   void clean(mlir::Operation* op);
 };

src/PIM/Compiler/PimCompilerOptions.cpp

@@ -22,12 +22,28 @@ llvm::cl::opt<PimMergeSchedulerType>
                     llvm::cl::init(MergeSchedulerPeft),
                     llvm::cl::cat(OnnxMlirOptions));
 
+llvm::cl::opt<PimMemoryReportLevel> pimMemoryReport(
+  "pim-memory-report",
+  llvm::cl::desc("Emit a human-readable PIM memory planning report"),
+  llvm::cl::values(clEnumValN(PimMemoryReportNone, "none", "Do not emit any PIM memory planning report")),
+  llvm::cl::values(
+    clEnumValN(PimMemoryReportSummary, "summary", "Emit a concise slot reuse report with key offenders")),
+  llvm::cl::values(clEnumValN(PimMemoryReportFull, "full", "Emit the full detailed PIM memory planning report")),
+  llvm::cl::init(PimMemoryReportNone),
+  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)"),
                  llvm::cl::init(false),
                  llvm::cl::cat(OnnxMlirOptions));
 
+llvm::cl::opt<bool>
+  pimDisableMemoryCoalescing("pim-disable-memory-coalescing",
+                             llvm::cl::desc("Skip the PIM memory coalescing pass (developer diagnostic option)"),
+                             llvm::cl::init(false),
+                             llvm::cl::cat(OnnxMlirOptions));
+
 llvm::cl::opt<bool> useExperimentalConvImpl("use-experimental-conv-impl",
                                             llvm::cl::desc("Use experimental implementation for convolution"),
                                             llvm::cl::init(false),

src/PIM/Compiler/PimCompilerOptions.hpp

@@ -24,11 +24,19 @@ typedef enum {
   MergeSchedulerPeft = 0,
 } PimMergeSchedulerType;
 
+typedef enum {
+  PimMemoryReportNone = 0,
+  PimMemoryReportSummary = 1,
+  PimMemoryReportFull = 2,
+} PimMemoryReportLevel;
+
 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<bool> pimOnlyCodegen;
+extern llvm::cl::opt<bool> pimDisableMemoryCoalescing;
 extern llvm::cl::opt<bool> useExperimentalConvImpl;
 extern llvm::cl::opt<bool> pimEmitJson;
 

src/PIM/Compiler/PimCompilerUtils.cpp

@@ -40,14 +40,14 @@ void addPassesPim(OwningOpRef<ModuleOp>& module,
 
   if (pimEmissionTarget >= EmitPimBufferized) {
     pm.addPass(createPimBufferizationPass());
-    pm.addPass(createPimStaticMemoryCoalescingPass());
     pm.addPass(createMessagePass("Pim bufferized"));
   }
 
   if (pimEmissionTarget >= EmitPimCodegen) {
     pm.addPass(createPimHostConstantFoldingPass());
     pm.addPass(createMessagePass("Pim host constants folded"));
-    pm.addPass(createPimMaterializeHostConstantsPass());
+    if (!pimDisableMemoryCoalescing)
+      pm.addPass(createPimMemoryCoalescingPass());
     pm.addPass(createPimVerificationPass());
     pm.addPass(createMessagePass("Pim verified"));
     pm.addPass(createEmitPimCodePass());

src/PIM/Compiler/PimMemoryLiveness.cpp

@@ -0,0 +1,733 @@
+#include "mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h"
+#include "mlir/Dialect/MemRef/IR/MemRef.h"
+#include "mlir/Dialect/SCF/IR/SCF.h"
+#include "mlir/IR/Value.h"
+#include "mlir/Interfaces/DestinationStyleOpInterface.h"
+
+#include "llvm/ADT/DenseMap.h"
+#include "llvm/ADT/STLExtras.h"
+#include "llvm/ADT/SmallPtrSet.h"
+#include "llvm/Support/raw_ostream.h"
+
+#include <numeric>
+#include <string>
+#include <tuple>
+#include <utility>
+
+#include "Common/Support/CheckedArithmetic.hpp"
+#include "Common/Support/ReportUtils.hpp"
+#include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Compiler/PimMemoryLiveness.hpp"
+#include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
+
+using namespace llvm;
+using namespace mlir;
+using namespace onnx_mlir;
+
+namespace {
+
+static std::optional<unsigned> getLaneForMemoryValue(mlir::Value value, std::optional<unsigned> lane) {
+  if (!lane)
+    return std::nullopt;
+  auto allocOp = value.getDefiningOp<memref::AllocOp>();
+  if (!allocOp || !allocOp->getParentOfType<pim::PimCoreBatchOp>())
+    return std::nullopt;
+  return lane;
+}
+
+static MemoryValueKey getMemoryValueKey(mlir::Value value, std::optional<unsigned> lane = std::nullopt) {
+  return {value, getLaneForMemoryValue(value, lane)};
+}
+
+struct MemoryTouchInterval {
+  uint64_t start = 0;
+  uint64_t end = 0;
+  Operation* startOp = nullptr;
+  Operation* endOp = nullptr;
+  Operation* firstTouchOp = nullptr;
+  Operation* lastTouchOp = nullptr;
+  uint64_t firstTouchPosition = 0;
+  uint64_t lastTouchPosition = 0;
+  bool hasRuntimeUse = false;
+  bool startUsedAllocFallback = false;
+  bool endUsedFallback = false;
+  bool escapesLoop = false;
+  std::string fallbackReason;
+  llvm::SmallVector<std::string, 8> aliasesFollowed;
+};
+
+struct OperationOrdering {
+  llvm::DenseMap<Operation*, uint64_t> position;
+  llvm::DenseMap<Operation*, uint64_t> subtreeEnd;
+  uint64_t nextPosition = 0;
+};
+
+static std::string printValueToString(mlir::Value value) {
+  std::string text;
+  llvm::raw_string_ostream os(text);
+  value.print(os);
+  os.flush();
+  return text;
+}
+
+static std::string printOperationToString(Operation* op) {
+  if (!op)
+    return "<none>";
+  std::string text;
+  llvm::raw_string_ostream os(text);
+  op->print(os);
+  os.flush();
+  return text;
+}
+
+static std::string printLocationToString(Location loc) {
+  std::string text;
+  llvm::raw_string_ostream os(text);
+  loc.print(os);
+  os.flush();
+  return text;
+}
+
+static std::string collapseWhitespace(StringRef text) {
+  std::string out;
+  out.reserve(text.size());
+  bool lastWasSpace = false;
+  for (char c : text) {
+    bool isSpace = c == ' ' || c == '\n' || c == '\t' || c == '\r';
+    if (isSpace) {
+      if (!lastWasSpace && !out.empty())
+        out.push_back(' ');
+      lastWasSpace = true;
+      continue;
+    }
+    out.push_back(c);
+    lastWasSpace = false;
+  }
+  return out;
+}
+
+static std::string abbreviate(StringRef text, size_t maxLen) {
+  if (text.size() <= maxLen)
+    return text.str();
+  return (text.take_front(maxLen - 3) + "...").str();
+}
+
+static std::string summarizeValue(mlir::Value value, size_t maxLen = 72) {
+  return abbreviate(collapseWhitespace(printValueToString(value)), maxLen);
+}
+
+static std::string summarizeOperation(Operation* op, size_t maxLen = 96) {
+  if (!op)
+    return "<none>";
+  std::string prefix = op->getName().getStringRef().str();
+  std::string full = collapseWhitespace(printOperationToString(op));
+  if (full == prefix)
+    return prefix;
+  return abbreviate(prefix + " :: " + full, maxLen);
+}
+
+static std::string summarizeLocation(Location loc, size_t maxLen = 88) {
+  return abbreviate(collapseWhitespace(printLocationToString(loc)), maxLen);
+}
+
+static void assignOperationOrdering(Operation* op, OperationOrdering& ordering) {
+  uint64_t position = ordering.nextPosition++;
+  ordering.position[op] = position;
+  uint64_t end = position;
+  for (Region& region : op->getRegions())
+    for (Block& block : region)
+      for (Operation& nestedOp : block) {
+        assignOperationOrdering(&nestedOp, ordering);
+        end = std::max(end, ordering.subtreeEnd.lookup(&nestedOp));
+      }
+  ordering.subtreeEnd[op] = end;
+}
+
+static OperationOrdering buildOperationOrdering(Operation* coreLikeOp) {
+  OperationOrdering ordering;
+  if (!coreLikeOp || coreLikeOp->getNumRegions() != 1 || coreLikeOp->getRegion(0).empty())
+    return ordering;
+
+  for (Operation& op : coreLikeOp->getRegion(0).front())
+    assignOperationOrdering(&op, ordering);
+  return ordering;
+}
+
+static bool isSupportedAliasOp(Operation* op) {
+  return isa<memref::SubViewOp, memref::CastOp, memref::CollapseShapeOp, memref::ExpandShapeOp>(op);
+}
+
+static bool isRuntimeMemoryTouchOp(Operation* op) {
+  return isa<pim::PimMemCopyHostToDevOp,
+             pim::PimMemCopyDevToHostOp,
+             pim::PimMemCopyOp,
+             pim::PimReceiveOp,
+             pim::PimSendOp,
+             pim::PimConcatOp,
+             pim::PimVMMOp,
+             pim::PimTransposeOp,
+             pim::PimVVAddOp,
+             pim::PimVVSubOp,
+             pim::PimVVMulOp,
+             pim::PimVVMaxOp,
+             pim::PimVVDMulOp,
+             pim::PimVAvgOp,
+             pim::PimVReluOp,
+             pim::PimVTanhOp,
+             pim::PimVSigmOp,
+             pim::PimVSoftmaxOp>(op);
+}
+
+static bool isIgnoredLivenessUser(Operation* op) {
+  return isSupportedAliasOp(op) || isa<scf::ForOp, scf::YieldOp, memref::DeallocOp>(op) || isCoreStaticAddressOp(op);
+}
+
+static bool isWithin(mlir::Value value, Region* region) {
+  if (!region)
+    return false;
+  if (auto blockArg = dyn_cast<BlockArgument>(value))
+    return blockArg.getOwner()->getParent() == region;
+  if (Operation* definingOp = value.getDefiningOp())
+    return definingOp->getParentRegion() == region || region->isAncestor(definingOp->getParentRegion());
+  return false;
+}
+
+static bool isNestedAllocation(Operation* coreLikeOp, memref::AllocOp allocOp) {
+  if (!coreLikeOp || coreLikeOp->getNumRegions() != 1 || coreLikeOp->getRegion(0).empty())
+    return false;
+  return allocOp->getBlock() != &coreLikeOp->getRegion(0).front();
+}
+
+static void addFallbackReason(std::string& reason, StringRef newReason) {
+  if (newReason.empty())
+    return;
+  if (!reason.empty())
+    reason += "; ";
+  reason += newReason.str();
+}
+
+static void appendAliasDescription(llvm::SmallVectorImpl<std::string>& aliases, mlir::Value value) {
+  std::string text = printValueToString(value);
+  if (!llvm::is_contained(aliases, text))
+    aliases.push_back(std::move(text));
+}
+
+struct OrderedTouchRange {
+  uint64_t start = 0;
+  uint64_t end = 0;
+  Operation* startOp = nullptr;
+  Operation* endOp = nullptr;
+  bool escapedLoop = false;
+};
+
+static OrderedTouchRange
+getEffectiveTouchRange(mlir::Value definingValue, Operation* user, const OperationOrdering& ordering) {
+  OrderedTouchRange range {ordering.position.lookup(user), ordering.position.lookup(user), user, user, false};
+  for (Operation* current = user; current; current = current->getParentOp()) {
+    auto forOp = dyn_cast<scf::ForOp>(current);
+    if (!forOp || isWithin(definingValue, &forOp.getRegion()))
+      continue;
+    range.start = std::min(range.start, ordering.position.lookup(forOp));
+    range.end = std::max(range.end, ordering.subtreeEnd.lookup(forOp));
+    range.startOp = forOp;
+    range.endOp = forOp;
+    range.escapedLoop = true;
+  }
+  return range;
+}
+
+static MemoryTouchInterval
+computeMemoryTouchInterval(memref::AllocOp allocOp, const OperationOrdering& ordering, uint64_t fallbackEnd) {
+  MemoryTouchInterval interval;
+  interval.start = ordering.position.lookup(allocOp);
+  interval.end = interval.start;
+  interval.startOp = allocOp;
+  interval.endOp = allocOp;
+
+  SmallPtrSet<mlir::Value, 16> visitedValues;
+  SmallPtrSet<Operation*, 32> visitedUsers;
+  SmallVector<mlir::Value> pendingValues;
+  pendingValues.push_back(allocOp.getResult());
+  auto parentLoop = allocOp->getParentOfType<scf::ForOp>();
+
+  while (!pendingValues.empty()) {
+    mlir::Value value = pendingValues.pop_back_val();
+    if (!visitedValues.insert(value).second)
+      continue;
+
+    for (Operation* user : value.getUsers()) {
+      if (!visitedUsers.insert(user).second)
+        continue;
+
+      if (isSupportedAliasOp(user)) {
+        for (mlir::Value result : user->getResults()) {
+          pendingValues.push_back(result);
+          appendAliasDescription(interval.aliasesFollowed, result);
+        }
+      }
+
+      if (auto dpsOp = dyn_cast<DestinationStyleOpInterface>(user)) {
+        for (OpResult result : user->getResults()) {
+          OpOperand* tiedOperand = dpsOp.getTiedOpOperand(result);
+          if (!tiedOperand || tiedOperand->get() != value)
+            continue;
+          pendingValues.push_back(result);
+          appendAliasDescription(interval.aliasesFollowed, result);
+        }
+      }
+
+      if (auto forOp = dyn_cast<scf::ForOp>(user)) {
+        for (auto [index, initArg] : llvm::enumerate(forOp.getInitArgs())) {
+          if (initArg != value)
+            continue;
+          pendingValues.push_back(forOp.getRegionIterArgs()[index]);
+          pendingValues.push_back(forOp.getResult(index));
+          appendAliasDescription(interval.aliasesFollowed, forOp.getRegionIterArgs()[index]);
+          appendAliasDescription(interval.aliasesFollowed, forOp.getResult(index));
+          if (parentLoop && forOp != parentLoop)
+            interval.escapesLoop = true;
+        }
+      }
+
+      if (auto yieldOp = dyn_cast<scf::YieldOp>(user)) {
+        auto forOp = dyn_cast<scf::ForOp>(yieldOp->getParentOp());
+        if (!forOp) {
+          addFallbackReason(interval.fallbackReason, "yield without scf.for parent");
+        }
+        else {
+          for (auto [index, operand] : llvm::enumerate(yieldOp.getOperands())) {
+            if (operand != value)
+              continue;
+            pendingValues.push_back(forOp.getResult(index));
+            appendAliasDescription(interval.aliasesFollowed, forOp.getResult(index));
+            if (parentLoop && forOp == parentLoop)
+              interval.escapesLoop = true;
+          }
+        }
+      }
+
+      if (isRuntimeMemoryTouchOp(user)) {
+        uint64_t touchPosition = ordering.position.lookup(user);
+        if (!interval.hasRuntimeUse || touchPosition < interval.firstTouchPosition) {
+          interval.firstTouchPosition = touchPosition;
+          interval.firstTouchOp = user;
+        }
+        if (!interval.hasRuntimeUse || touchPosition > interval.lastTouchPosition) {
+          interval.lastTouchPosition = touchPosition;
+          interval.lastTouchOp = user;
+        }
+
+        OrderedTouchRange range = getEffectiveTouchRange(allocOp.getResult(), user, ordering);
+        interval.escapesLoop |= range.escapedLoop;
+        if (!interval.hasRuntimeUse) {
+          interval.start = range.start;
+          interval.end = range.end;
+          interval.startOp = range.startOp;
+          interval.endOp = range.endOp;
+          interval.hasRuntimeUse = true;
+        }
+        else {
+          if (range.start < interval.start) {
+            interval.start = range.start;
+            interval.startOp = range.startOp;
+          }
+          if (range.end > interval.end) {
+            interval.end = range.end;
+            interval.endOp = range.endOp;
+          }
+        }
+        continue;
+      }
+
+      if (isIgnoredLivenessUser(user))
+        continue;
+
+      addFallbackReason(interval.fallbackReason, "unhandled user op");
+      interval.endUsedFallback = true;
+    }
+  }
+
+  if (!interval.hasRuntimeUse) {
+    interval.startUsedAllocFallback = true;
+    interval.endUsedFallback = true;
+    interval.start = ordering.position.lookup(allocOp);
+    interval.end = fallbackEnd;
+    interval.startOp = allocOp;
+    interval.endOp = allocOp->getParentOp();
+    interval.firstTouchPosition = interval.start;
+    interval.lastTouchPosition = interval.end;
+    addFallbackReason(interval.fallbackReason, "no runtime memory touch");
+    return interval;
+  }
+
+  if (interval.endUsedFallback) {
+    interval.end = std::max(interval.end, fallbackEnd);
+    interval.endOp = allocOp->getParentOp();
+  }
+
+  return interval;
+}
+
+static FailureOr<size_t> getAllocSizeBytes(memref::AllocOp allocOp) {
+  auto type = dyn_cast<ShapedType>(allocOp.getType());
+  if (!type)
+    return failure();
+  auto checkedBytes = pim::getCheckedShapedTypeSizeInBytes(type, allocOp, "memory allocation byte size");
+  if (failed(checkedBytes))
+    return failure();
+  return pim::checkedSize(*checkedBytes, allocOp, "memory allocation byte size");
+}
+
+static bool intervalsOverlap(const LocalAllocInterval& lhs, const LocalAllocInterval& rhs) {
+  return !(lhs.end < rhs.start || rhs.end < lhs.start);
+}
+
+static uint64_t getSlotLogicalBytes(const PlannedPhysicalSlot& slot, ArrayRef<LocalAllocInterval> intervals) {
+  uint64_t slotLogicalBytes = 0;
+  for (size_t intervalIndex : slot.intervalIndices)
+    slotLogicalBytes += intervals[intervalIndex].size;
+  return slotLogicalBytes;
+}
+
+} // namespace
+
+SmallVector<LocalAllocInterval, 0> onnx_mlir::buildLocalAllocIntervals(Operation* coreLikeOp,
+                                                                       std::optional<unsigned> lane) {
+  SmallVector<LocalAllocInterval, 0> intervals;
+  OperationOrdering ordering = buildOperationOrdering(coreLikeOp);
+  if (ordering.position.empty())
+    return intervals;
+
+  uint64_t fallbackEnd = ordering.nextPosition == 0 ? 0 : ordering.nextPosition - 1;
+  size_t nextIntervalId = 0;
+  coreLikeOp->walk([&](memref::AllocOp allocOp) {
+    auto checkedSize = getAllocSizeBytes(allocOp);
+    if (failed(checkedSize)) {
+      llvm::errs() << "Failed to compute local allocation size for value: ";
+      allocOp.getResult().print(llvm::errs());
+      llvm::errs() << "\n";
+      llvm_unreachable("Failed to compute local allocation size");
+    }
+
+    MemoryTouchInterval touchInterval = computeMemoryTouchInterval(allocOp, ordering, fallbackEnd);
+    LocalAllocInterval interval;
+    interval.id = nextIntervalId++;
+    interval.alloc = allocOp;
+    interval.key = getMemoryValueKey(allocOp.getResult(), lane);
+    interval.start = touchInterval.start;
+    interval.end = touchInterval.end;
+    interval.size = *checkedSize;
+    interval.startOp = touchInterval.startOp;
+    interval.endOp = touchInterval.endOp;
+    interval.firstTouchOp = touchInterval.firstTouchOp;
+    interval.lastTouchOp = touchInterval.lastTouchOp;
+    interval.firstTouchPosition = touchInterval.firstTouchPosition;
+    interval.lastTouchPosition = touchInterval.lastTouchPosition;
+    interval.startUsedAllocFallback = touchInterval.startUsedAllocFallback;
+    interval.endUsedFallback = touchInterval.endUsedFallback;
+    interval.hasRuntimeUse = touchInterval.hasRuntimeUse;
+    interval.insideNestedRegion = isNestedAllocation(coreLikeOp, allocOp);
+    interval.escapesLoop = touchInterval.escapesLoop;
+    interval.fallbackReason = std::move(touchInterval.fallbackReason);
+    interval.aliasesFollowed = std::move(touchInterval.aliasesFollowed);
+    intervals.push_back(std::move(interval));
+  });
+
+  return intervals;
+}
+
+SmallVector<PlannedPhysicalSlot, 0> onnx_mlir::planPhysicalSlots(MutableArrayRef<LocalAllocInterval> intervals) {
+  SmallVector<PlannedPhysicalSlot, 0> slots;
+  SmallVector<size_t> intervalOrder(intervals.size());
+  std::iota(intervalOrder.begin(), intervalOrder.end(), 0);
+  llvm::stable_sort(intervalOrder, [&](size_t lhsIndex, size_t rhsIndex) {
+    const LocalAllocInterval& lhs = intervals[lhsIndex];
+    const LocalAllocInterval& rhs = intervals[rhsIndex];
+    if (lhs.size != rhs.size)
+      return lhs.size > rhs.size;
+    if (lhs.start != rhs.start)
+      return lhs.start < rhs.start;
+    if (lhs.end != rhs.end)
+      return lhs.end < rhs.end;
+    return lhs.id < rhs.id;
+  });
+
+  for (size_t intervalIndex : intervalOrder) {
+    LocalAllocInterval& interval = intervals[intervalIndex];
+    PlannedPhysicalSlot* bestSlot = nullptr;
+    auto bestKey = std::tuple<size_t, size_t, size_t, size_t>(std::numeric_limits<size_t>::max(),
+                                                              std::numeric_limits<size_t>::max(),
+                                                              std::numeric_limits<size_t>::max(),
+                                                              std::numeric_limits<size_t>::max());
+
+    for (size_t slotIndex = 0; slotIndex < slots.size(); ++slotIndex) {
+      PlannedPhysicalSlot& slot = slots[slotIndex];
+      bool compatible = true;
+      for (size_t otherIndex : slot.intervalIndices) {
+        if (intervalsOverlap(interval, intervals[otherIndex])) {
+          compatible = false;
+          break;
+        }
+      }
+      if (!compatible)
+        continue;
+
+      size_t resultingSize = std::max(slot.requiredSize, interval.size);
+      size_t growth = resultingSize - slot.requiredSize;
+      auto candidateKey =
+        std::tuple<size_t, size_t, size_t, size_t>(growth, resultingSize, slot.intervalIndices.size(), slot.id);
+      if (candidateKey < bestKey) {
+        bestKey = candidateKey;
+        bestSlot = &slot;
+      }
+    }
+
+    if (!bestSlot) {
+      slots.push_back({slots.size(), interval.size, interval.size, 0, {intervalIndex}});
+      interval.slotPlanIndex = slots.size() - 1;
+      interval.physicalSlotId = slots.back().id;
+      interval.physicalSlotSize = slots.back().requiredSize;
+      continue;
+    }
+
+    bestSlot->requiredSize = std::max(bestSlot->requiredSize, interval.size);
+    bestSlot->size = bestSlot->requiredSize;
+    bestSlot->intervalIndices.push_back(intervalIndex);
+    interval.slotPlanIndex = static_cast<size_t>(bestSlot - slots.data());
+    interval.physicalSlotId = bestSlot->id;
+    interval.physicalSlotSize = bestSlot->requiredSize;
+  }
+
+  return slots;
+}
+
+MemoryPlanArtifacts onnx_mlir::buildMemoryPlanArtifacts(Operation* coreLikeOp,
+                                                        std::optional<unsigned> lane,
+                                                        ArrayRef<LocalAllocInterval> intervals,
+                                                        ArrayRef<PlannedPhysicalSlot> slots,
+                                                        size_t addressLimit,
+                                                        PimMemoryReportLevel reportLevel) {
+  MemoryPlanArtifacts artifacts;
+
+  uint64_t totalLogicalBytes = 0;
+  uint64_t totalPhysicalBytes = 0;
+  uint64_t fallbackIntervals = 0;
+  uint64_t noRuntimeTouchIntervals = 0;
+  uint64_t reusedAllocations = 0;
+  uint64_t nestedIntervals = 0;
+  uint64_t loopEscapingIntervals = 0;
+  size_t largestLogicalAllocation = 0;
+  size_t largestPhysicalSlot = 0;
+  size_t maximumAssignedAddress = 0;
+
+  for (const LocalAllocInterval& interval : intervals) {
+    totalLogicalBytes += interval.size;
+    largestLogicalAllocation = std::max(largestLogicalAllocation, interval.size);
+    maximumAssignedAddress = std::max(maximumAssignedAddress, interval.assignedAddress + interval.physicalSlotSize);
+    if (interval.startUsedAllocFallback || interval.endUsedFallback)
+      ++fallbackIntervals;
+    if (!interval.hasRuntimeUse)
+      ++noRuntimeTouchIntervals;
+    if (interval.insideNestedRegion)
+      ++nestedIntervals;
+    if (interval.escapesLoop)
+      ++loopEscapingIntervals;
+  }
+  for (const PlannedPhysicalSlot& slot : slots) {
+    totalPhysicalBytes += slot.size;
+    largestPhysicalSlot = std::max(largestPhysicalSlot, slot.size);
+    if (slot.intervalIndices.size() > 1)
+      reusedAllocations += slot.intervalIndices.size() - 1;
+  }
+
+  uint64_t savedBytes = totalLogicalBytes >= totalPhysicalBytes ? totalLogicalBytes - totalPhysicalBytes : 0;
+  double savedPercent =
+    totalLogicalBytes == 0 ? 0.0 : 100.0 * static_cast<double>(savedBytes) / static_cast<double>(totalLogicalBytes);
+
+  raw_string_ostream os(artifacts.textReport);
+  os << "=== PIM Memory Liveness Report ===\n";
+  os << "Op: " << coreLikeOp->getName() << "\n";
+  if (lane)
+    os << "Lane: " << *lane << "\n";
+  os << "Summary:\n";
+  os << "  logical allocation bytes:  " << formatReportMemory(totalLogicalBytes) << " (" << totalLogicalBytes << ")\n";
+  os << "  physical allocation bytes: " << formatReportMemory(totalPhysicalBytes) << " (" << totalPhysicalBytes
+     << ")\n";
+  os << "  saved bytes:               " << formatReportMemory(savedBytes) << " (" << savedBytes << ")\n";
+  os << "  saved percent: " << format("%.2f%%", savedPercent) << "\n";
+  os << "  intervals: " << intervals.size() << "\n";
+  os << "  physical slots: " << slots.size() << "\n";
+  os << "  reused allocations: " << reusedAllocations << "\n";
+  os << "  fallback intervals: " << fallbackIntervals << "\n";
+  os << "  intervals with no runtime memory touch: " << noRuntimeTouchIntervals << "\n";
+  os << "  nested allocations: " << nestedIntervals << "\n";
+  os << "  loop-escaping allocations: " << loopEscapingIntervals << "\n";
+  os << "  largest logical allocation: " << largestLogicalAllocation << "\n";
+  os << "  largest physical slot: " << largestPhysicalSlot << "\n";
+  os << "  address limit: " << addressLimit << "\n";
+  os << "  peak physical memory: " << formatReportMemory(maximumAssignedAddress) << " (" << maximumAssignedAddress
+     << ")\n";
+  os << "  maximum assigned address: " << maximumAssignedAddress << "\n";
+
+  os << "\nHow To Read:\n";
+  os << "  `summary` only shows the strongest reuse cases and the worst offenders.\n";
+  os << "  Use `--pim-memory-report=full` when you need the complete slot-by-slot and interval-by-interval dump.\n";
+  os << "  Large single-use slots, fallback intervals, and nested single-use allocations are the best places\n";
+  os << "  to inspect if allocations should be moved, sunk, or made easier to coalesce earlier in the pipeline.\n";
+
+  SmallVector<const PlannedPhysicalSlot*> reusedSlots;
+  SmallVector<const PlannedPhysicalSlot*> singleUseSlots;
+  for (const PlannedPhysicalSlot& slot : slots)
+    if (slot.intervalIndices.size() > 1)
+      reusedSlots.push_back(&slot);
+    else
+      singleUseSlots.push_back(&slot);
+
+  llvm::stable_sort(reusedSlots, [&](const PlannedPhysicalSlot* lhs, const PlannedPhysicalSlot* rhs) {
+    uint64_t lhsLogicalBytes = getSlotLogicalBytes(*lhs, intervals);
+    uint64_t rhsLogicalBytes = getSlotLogicalBytes(*rhs, intervals);
+    if (lhs->intervalIndices.size() != rhs->intervalIndices.size())
+      return lhs->intervalIndices.size() > rhs->intervalIndices.size();
+    if (lhsLogicalBytes != rhsLogicalBytes)
+      return lhsLogicalBytes > rhsLogicalBytes;
+    if (lhs->size != rhs->size)
+      return lhs->size > rhs->size;
+    return lhs->id < rhs->id;
+  });
+  llvm::stable_sort(singleUseSlots, [&](const PlannedPhysicalSlot* lhs, const PlannedPhysicalSlot* rhs) {
+    if (lhs->size != rhs->size)
+      return lhs->size > rhs->size;
+    return lhs->id < rhs->id;
+  });
+
+  constexpr size_t kSummaryReuseLimit = 6;
+  constexpr size_t kSummaryOffenderLimit = 10;
+
+  os << "\nBest Reuse:\n";
+  if (reusedSlots.empty()) {
+    os << "  no slots were shared by multiple intervals\n";
+  }
+  else {
+    for (const PlannedPhysicalSlot* slot : ArrayRef(reusedSlots).take_front(kSummaryReuseLimit)) {
+      uint64_t slotLogicalBytes = getSlotLogicalBytes(*slot, intervals);
+      os << "  slot #" << slot->id << "  addr=" << slot->address << "  size=" << formatReportMemory(slot->size)
+         << "  intervals=" << slot->intervalIndices.size() << "  logical_sum=" << formatReportMemory(slotLogicalBytes)
+         << "\n";
+      for (size_t intervalIndex : slot->intervalIndices) {
+        const LocalAllocInterval& interval = intervals[intervalIndex];
+        os << "    #" << interval.id << "  [" << interval.start << "," << interval.end << "]"
+           << "  logical=" << formatReportMemory(interval.size)
+           << "  first=" << summarizeOperation(interval.firstTouchOp, 40)
+           << "  last=" << summarizeOperation(interval.lastTouchOp, 40) << "\n";
+      }
+    }
+  }
+
+  os << "\nTop Offenders:\n";
+  bool printedAttention = false;
+  for (const PlannedPhysicalSlot* slot : ArrayRef(singleUseSlots).take_front(kSummaryOffenderLimit)) {
+    const LocalAllocInterval& interval = intervals[slot->intervalIndices.front()];
+    printedAttention = true;
+    os << "  slot #" << slot->id << " is single-use"
+       << "  size=" << formatReportMemory(slot->size) << "  interval=#" << interval.id
+       << "  value=" << summarizeValue(interval.key.value, 56) << "\n";
+    os << "    first=" << summarizeOperation(interval.firstTouchOp, 40)
+       << "  last=" << summarizeOperation(interval.lastTouchOp, 40)
+       << "  nested=" << (interval.insideNestedRegion ? "yes" : "no")
+       << "  escapes_loop=" << (interval.escapesLoop ? "yes" : "no") << "\n";
+  }
+  size_t fallbackPrinted = 0;
+  for (const LocalAllocInterval& interval : intervals) {
+    if (!(interval.startUsedAllocFallback || interval.endUsedFallback) || fallbackPrinted >= kSummaryOffenderLimit)
+      continue;
+    printedAttention = true;
+    ++fallbackPrinted;
+    os << "  fallback interval #" << interval.id << "  size=" << formatReportMemory(interval.size)
+       << "  value=" << summarizeValue(interval.key.value, 56) << "\n";
+    os << "    reason: " << (interval.fallbackReason.empty() ? "<none>" : interval.fallbackReason) << "\n";
+  }
+  size_t nestedPrinted = 0;
+  for (const LocalAllocInterval& interval : intervals) {
+    if (nestedPrinted >= kSummaryOffenderLimit)
+      break;
+    if (!(interval.insideNestedRegion && slots[interval.slotPlanIndex].intervalIndices.size() == 1))
+      continue;
+    printedAttention = true;
+    ++nestedPrinted;
+    os << "  nested single-use interval #" << interval.id << "  slot #" << interval.physicalSlotId
+       << "  size=" << formatReportMemory(interval.size) << "  value=" << summarizeValue(interval.key.value, 56)
+       << "\n";
+    os << "    hint: move or sink this alloc inside the nested region if the IR allows it.\n";
+  }
+  if (!printedAttention)
+    os << "  no obvious blockers detected in this core\n";
+
+  if (reportLevel == PimMemoryReportFull) {
+    os << "\nSlot Reuse:\n";
+    for (const PlannedPhysicalSlot& slot : slots) {
+      uint64_t slotLogicalBytes = getSlotLogicalBytes(slot, intervals);
+      os << "  slot #" << slot.id << "  addr=" << slot.address << "  size=" << formatReportMemory(slot.size) << " ("
+         << slot.size << ")"
+         << "  intervals=" << slot.intervalIndices.size() << "  logical_sum=" << formatReportMemory(slotLogicalBytes)
+         << "\n";
+      for (size_t intervalIndex : slot.intervalIndices) {
+        const LocalAllocInterval& interval = intervals[intervalIndex];
+        mlir::Value allocValue = interval.key.value;
+        os << "    [" << interval.start << "," << interval.end << "]"
+           << "  #" << interval.id << "  logical=" << formatReportMemory(interval.size)
+           << "  nested=" << (interval.insideNestedRegion ? "yes" : "no")
+           << "  escapes_loop=" << (interval.escapesLoop ? "yes" : "no")
+           << "  first=" << summarizeOperation(interval.firstTouchOp, 48)
+           << "  last=" << summarizeOperation(interval.lastTouchOp, 48) << "\n";
+        os << "      value=" << summarizeValue(allocValue) << "\n";
+      }
+    }
+  }
+
+  if (reportLevel == PimMemoryReportFull) {
+    os << "\nInterval Details:\n";
+    for (const LocalAllocInterval& interval : intervals) {
+      const PlannedPhysicalSlot& slot = slots[interval.slotPlanIndex];
+      mlir::Value allocValue = interval.key.value;
+      Operation* definingOp = allocValue.getDefiningOp();
+      os << "  #" << interval.id << "  slot=" << slot.id << "  live=[" << interval.start << "," << interval.end << "]"
+         << "  logical=" << formatReportMemory(interval.size)
+         << "  slot_size=" << formatReportMemory(interval.physicalSlotSize) << "  addr=" << interval.assignedAddress
+         << "\n";
+      os << "    value=" << summarizeValue(allocValue, 88) << "\n";
+      os << "    type=" << allocValue.getType() << "\n";
+      os << "    loc="
+         << summarizeLocation(definingOp ? definingOp->getLoc() : UnknownLoc::get(coreLikeOp->getContext())) << "\n";
+      os << "    nested=" << (interval.insideNestedRegion ? "yes" : "no")
+         << "  escapes_loop=" << (interval.escapesLoop ? "yes" : "no")
+         << "  start_fallback=" << (interval.startUsedAllocFallback ? "yes" : "no")
+         << "  end_fallback=" << (interval.endUsedFallback ? "yes" : "no") << "\n";
+      os << "    first_use=" << summarizeOperation(interval.firstTouchOp) << " @" << interval.firstTouchPosition
+         << "\n";
+      os << "    last_use=" << summarizeOperation(interval.lastTouchOp) << " @" << interval.lastTouchPosition << "\n";
+      os << "    slot_peers=";
+      bool first = true;
+      for (size_t otherIndex : slot.intervalIndices) {
+        if (intervals[otherIndex].id == interval.id)
+          continue;
+        if (!first)
+          os << ", ";
+        os << "#" << intervals[otherIndex].id;
+        first = false;
+      }
+      if (first)
+        os << "<none>";
+      os << "\n";
+      if (!interval.fallbackReason.empty())
+        os << "    fallback_reason=" << interval.fallbackReason << "\n";
+      if (!interval.aliasesFollowed.empty()) {
+        os << "    aliases_followed=" << interval.aliasesFollowed.size() << "\n";
+        for (const std::string& alias : interval.aliasesFollowed)
+          os << "      - " << abbreviate(collapseWhitespace(alias), 108) << "\n";
+      }
+    }
+  }
+  os.flush();
+
+  return artifacts;
+}

src/PIM/Compiler/PimMemoryLiveness.hpp

@@ -0,0 +1,63 @@
+#pragma once
+
+#include "mlir/Dialect/MemRef/IR/MemRef.h"
+
+#include "llvm/ADT/ArrayRef.h"
+#include "llvm/ADT/SmallVector.h"
+
+#include <limits>
+#include <optional>
+#include <string>
+
+#include "src/Accelerators/PIM/Compiler/PimCodeGen.hpp"
+#include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
+
+namespace onnx_mlir {
+
+struct LocalAllocInterval {
+  size_t id = 0;
+  mlir::memref::AllocOp alloc;
+  MemoryValueKey key;
+  uint64_t start = 0;
+  uint64_t end = 0;
+  size_t size = 0;
+  mlir::Operation* startOp = nullptr;
+  mlir::Operation* endOp = nullptr;
+  mlir::Operation* firstTouchOp = nullptr;
+  mlir::Operation* lastTouchOp = nullptr;
+  uint64_t firstTouchPosition = 0;
+  uint64_t lastTouchPosition = 0;
+  bool startUsedAllocFallback = false;
+  bool endUsedFallback = false;
+  bool hasRuntimeUse = false;
+  bool insideNestedRegion = false;
+  bool escapesLoop = false;
+  std::string fallbackReason;
+  llvm::SmallVector<std::string, 8> aliasesFollowed;
+  size_t slotPlanIndex = std::numeric_limits<size_t>::max();
+  size_t physicalSlotId = std::numeric_limits<size_t>::max();
+  size_t assignedAddress = 0;
+  size_t physicalSlotSize = 0;
+};
+
+struct PlannedPhysicalSlot {
+  size_t id = std::numeric_limits<size_t>::max();
+  size_t requiredSize = 0;
+  size_t size = 0;
+  size_t address = 0;
+  llvm::SmallVector<size_t, 8> intervalIndices;
+};
+
+llvm::SmallVector<LocalAllocInterval, 0> buildLocalAllocIntervals(mlir::Operation* coreLikeOp,
+                                                                  std::optional<unsigned> lane);
+
+llvm::SmallVector<PlannedPhysicalSlot, 0> planPhysicalSlots(llvm::MutableArrayRef<LocalAllocInterval> intervals);
+
+MemoryPlanArtifacts buildMemoryPlanArtifacts(mlir::Operation* coreLikeOp,
+                                             std::optional<unsigned> lane,
+                                             llvm::ArrayRef<LocalAllocInterval> intervals,
+                                             llvm::ArrayRef<PlannedPhysicalSlot> slots,
+                                             size_t addressLimit,
+                                             PimMemoryReportLevel reportLevel);
+
+} // namespace onnx_mlir

src/PIM/Compiler/PimWeightEmitter.cpp

@@ -7,6 +7,7 @@
 
 #include <cassert>
 
+#include "Common/Support/CheckedArithmetic.hpp"
 #include "Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "src/Accelerators/PIM/Common/IR/ShapeUtils.hpp"
 #include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
@@ -18,15 +19,14 @@ using namespace mlir;
 namespace onnx_mlir {
 namespace {} // namespace
 
-llvm::DenseMap<size_t, llvm::SmallVector<std::string, 8>>
-createAndPopulateWeightFolder(ArrayRef<WeightFileRequest> requests, StringRef outputDirPath) {
+WeightEmissionResult createAndPopulateWeightFolder(ArrayRef<WeightFileRequest> requests, StringRef outputDirPath) {
   auto coreWeightsDirPath = outputDirPath + "/weights";
   auto error = sys::fs::create_directory(coreWeightsDirPath);
   assert(!error && "Error creating weights directory");
   size_t indexFileName = 0;
 
   int64_t xbarSize = crossbarSize.getValue();
-  llvm::DenseMap<size_t, llvm::SmallVector<std::string, 8>> mapCoreWeightToFileName;
+  WeightEmissionResult result;
   llvm::SmallVector<std::pair<ResolvedWeightView, std::string>, 16> materializedWeights;
 
   auto materializeWeight = [&](const ResolvedWeightView& weightView) -> std::string {
@@ -72,17 +72,22 @@ createAndPopulateWeightFolder(ArrayRef<WeightFileRequest> requests, StringRef ou
 
     weightFileStream.close();
     materializedWeights.push_back({weightView, newFileName});
+    uint64_t weightBytes = pim::checkedMulOrCrash(
+      pim::checkedMulOrCrash(static_cast<size_t>(xbarSize), static_cast<size_t>(xbarSize), "weight element count"),
+      elementByteWidth,
+      "weight byte size");
+    result.totalWeightBytes = pim::checkedAddOrCrash(result.totalWeightBytes, weightBytes, "total weight bytes");
     return newFileName;
   };
 
   for (const WeightFileRequest& request : requests) {
-    auto& coreFiles = mapCoreWeightToFileName[request.coreId];
+    auto& coreFiles = result.mapCoreWeightToFileName[request.coreId];
     coreFiles.reserve(request.weights.size());
     for (const ResolvedWeightView& weight : request.weights)
       coreFiles.push_back(materializeWeight(weight));
   }
 
-  return mapCoreWeightToFileName;
+  return result;
 }
 
 } // namespace onnx_mlir

src/PIM/Compiler/PimWeightEmitter.hpp

@@ -6,6 +6,7 @@
 #include "llvm/ADT/SmallVector.h"
 #include "llvm/ADT/StringRef.h"
 
+#include <cstdint>
 #include <string>
 
 #include "src/Accelerators/PIM/Common/IR/WeightUtils.hpp"
@@ -17,7 +18,12 @@ struct WeightFileRequest {
   llvm::SmallVector<ResolvedWeightView, 8> weights;
 };
 
-llvm::DenseMap<size_t, llvm::SmallVector<std::string, 8>>
-createAndPopulateWeightFolder(llvm::ArrayRef<WeightFileRequest> requests, llvm::StringRef outputDirPath);
+struct WeightEmissionResult {
+  llvm::DenseMap<size_t, llvm::SmallVector<std::string, 8>> mapCoreWeightToFileName;
+  uint64_t totalWeightBytes = 0;
+};
+
+WeightEmissionResult createAndPopulateWeightFolder(llvm::ArrayRef<WeightFileRequest> requests,
+                                                   llvm::StringRef outputDirPath);
 
 } // namespace onnx_mlir

src/PIM/Conversion/ONNXToSpatial/CMakeLists.txt

@@ -22,6 +22,7 @@ add_pim_library(OMONNXToSpatial
   Patterns/Tensor/Gather.cpp
   Patterns/Tensor/Resize.cpp
   Patterns/Tensor/Reshape.cpp
+  Patterns/Tensor/Slice.cpp
   Patterns/Tensor/Split.cpp
   Patterns/Tensor/Transpose.cpp
   ONNXToSpatialPass.cpp

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

@@ -12,6 +12,7 @@
 #include <type_traits>
 #include <utility>
 
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/CompileTime.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 
@@ -180,8 +181,11 @@ auto createSpatComputeBatch(RewriterT& rewriter,
   if (laneCount <= 0 || laneCount > std::numeric_limits<int32_t>::max())
     return mlir::FailureOr<spatial::SpatComputeBatch>(mlir::failure());
 
-  auto batchOp = spatial::SpatComputeBatch::create(
-    rewriter, loc, resultTypes, rewriter.getI32IntegerAttr(static_cast<int32_t>(laneCount)), weights, inputs);
+  auto laneCountAttr = pim::getCheckedI32Attr(rewriter, loc, laneCount, "spatial compute_batch lane count");
+  if (mlir::failed(laneCountAttr))
+    return mlir::FailureOr<spatial::SpatComputeBatch>(mlir::failure());
+
+  auto batchOp = spatial::SpatComputeBatch::create(rewriter, loc, resultTypes, *laneCountAttr, weights, inputs);
 
   mlir::SmallVector<mlir::Type> blockArgTypes {rewriter.getIndexType()};
   mlir::SmallVector<mlir::Location> blockArgLocs {loc};

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

@@ -1,11 +1,6 @@
-#include "mlir/Dialect/Arith/IR/Arith.h"
-
-#include "llvm/ADT/APInt.h"
-
 #include <algorithm>
 
 #include "IndexingUtils.hpp"
-#include "src/Accelerators/PIM/Common/IR/ConstantUtils.hpp"
 
 using namespace mlir;
 
@@ -47,46 +42,4 @@ FailureOr<SmallVector<int64_t>> normalizeAxesChecked(std::optional<ArrayAttr> ax
   return normalizedAxes;
 }
 
-Value createAffineApplyOrFoldedConstant(PatternRewriter& rewriter, Location loc, AffineExpr expr, ValueRange operands) {
-  AffineMap map = AffineMap::get(/*dimCount=*/operands.size(), /*symbolCount=*/0, expr);
-  Operation* anchorOp = rewriter.getInsertionBlock()->getParentOp();
-  return createAffineApplyOrFoldedConstant(rewriter, loc, map, operands, anchorOp);
-}
-
-Value multiplyIndexByConstant(PatternRewriter& rewriter, Operation* anchorOp, Value value, int64_t multiplier) {
-  if (multiplier == 0)
-    return getOrCreateIndexConstant(rewriter, anchorOp, 0);
-  if (multiplier == 1)
-    return value;
-
-  MLIRContext* context = rewriter.getContext();
-  AffineExpr d0 = getAffineDimExpr(0, context);
-  return createAffineApplyOrFoldedConstant(rewriter, anchorOp->getLoc(), d0 * multiplier, ValueRange {value});
-}
-
-Value modIndexByConstant(PatternRewriter& rewriter, Location loc, Value value, int64_t divisor) {
-  if (divisor == 1)
-    return getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
-
-  MLIRContext* context = rewriter.getContext();
-  AffineExpr d0 = getAffineDimExpr(0, context);
-  return createAffineApplyOrFoldedConstant(rewriter, loc, d0 % divisor, ValueRange {value});
-}
-
-Value floorDivIndexByConstant(PatternRewriter& rewriter, Location loc, Value value, int64_t divisor) {
-  if (divisor == 1)
-    return value;
-
-  MLIRContext* context = rewriter.getContext();
-  AffineExpr d0 = getAffineDimExpr(0, context);
-  return createAffineApplyOrFoldedConstant(rewriter, loc, d0.floorDiv(divisor), ValueRange {value});
-}
-
-Value getOrMaterializeIndexValue(PatternRewriter& rewriter, OpFoldResult value) {
-  if (auto attr = dyn_cast<Attribute>(value))
-    return getOrCreateIndexConstant(
-      rewriter, rewriter.getInsertionBlock()->getParentOp(), cast<IntegerAttr>(attr).getInt());
-  return cast<Value>(value);
-}
-
 } // namespace onnx_mlir

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

@@ -1,11 +1,7 @@
 #pragma once
 
-#include "mlir/IR/AffineExpr.h"
 #include "mlir/IR/BuiltinAttributes.h"
-#include "mlir/IR/Value.h"
-#include "mlir/Interfaces/FoldInterfaces.h"
 #include "mlir/Support/LogicalResult.h"
-#include "mlir/Transforms/DialectConversion.h"
 
 #include "llvm/ADT/SmallVector.h"
 
@@ -21,21 +17,4 @@ int64_t normalizeIndex(int64_t index, int64_t dimSize);
 
 mlir::FailureOr<llvm::SmallVector<int64_t>> normalizeAxesChecked(std::optional<mlir::ArrayAttr> axesAttr, int64_t rank);
 
-mlir::Value createAffineApplyOrFoldedConstant(mlir::PatternRewriter& rewriter,
-                                              mlir::Location loc,
-                                              mlir::AffineExpr expr,
-                                              mlir::ValueRange operands);
-
-mlir::Value multiplyIndexByConstant(mlir::PatternRewriter& rewriter,
-                                    mlir::Operation* anchorOp,
-                                    mlir::Value value,
-                                    int64_t multiplier);
-
-mlir::Value modIndexByConstant(mlir::PatternRewriter& rewriter, mlir::Location loc, mlir::Value value, int64_t divisor);
-
-mlir::Value
-floorDivIndexByConstant(mlir::PatternRewriter& rewriter, mlir::Location loc, mlir::Value value, int64_t divisor);
-
-mlir::Value getOrMaterializeIndexValue(mlir::PatternRewriter& rewriter, mlir::OpFoldResult value);
-
 } // namespace onnx_mlir

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

@@ -10,8 +10,6 @@
 #include "src/Accelerators/PIM/Common/IR/ConstantUtils.hpp"
 #include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
-#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/CompileTime.hpp"
-#include "src/Dialect/ONNX/ONNXOps.hpp"
 
 using namespace mlir;
 
@@ -68,14 +66,6 @@ FailureOr<SmallVector<int64_t>> getTransposePermutationChecked(std::optional<Arr
   return permutation;
 }
 
-Value transposeMaybeInCompute(
-  Value value, RankedTensorType resultType, ArrayRef<int64_t> permutation, PatternRewriter& rewriter, Location loc) {
-  auto buildTranspose = [&](Value input) -> Value {
-    return ONNXTransposeOp::create(rewriter, loc, resultType, input, rewriter.getI64ArrayAttr(permutation)).getResult();
-  };
-  return materializeOrComputeUnary(value, resultType, rewriter, loc, buildTranspose);
-}
-
 SmallVector<OpFoldResult> getUnitStrides(PatternRewriter& rewriter, int64_t rank) {
   return SmallVector<OpFoldResult>(rank, rewriter.getIndexAttr(1));
 }

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

@@ -78,12 +78,6 @@ llvm::SmallVector<int64_t> invertPermutation(mlir::ArrayRef<int64_t> permutation
 mlir::FailureOr<llvm::SmallVector<int64_t>> getTransposePermutationChecked(std::optional<mlir::ArrayAttr> permAttr,
                                                                            int64_t rank);
 
-mlir::Value transposeMaybeInCompute(mlir::Value value,
-                                    mlir::RankedTensorType resultType,
-                                    mlir::ArrayRef<int64_t> permutation,
-                                    mlir::PatternRewriter& rewriter,
-                                    mlir::Location loc);
-
 llvm::SmallVector<mlir::OpFoldResult> getUnitStrides(mlir::PatternRewriter& rewriter, int64_t rank);
 
 llvm::SmallVector<mlir::OpFoldResult> getZeroOffsets(mlir::PatternRewriter& rewriter, int64_t rank);

src/PIM/Conversion/ONNXToSpatial/CompileTime.cpp

@@ -11,6 +11,7 @@
 
 #include <utility>
 
+#include "src/Accelerators/PIM/Common/IR/ConstantUtils.hpp"
 #include "src/Accelerators/PIM/Common/IR/ShapeUtils.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/CompileTime.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
@@ -27,8 +28,7 @@ static bool hasStaticUnitStrides(tensor::ExtractSliceOp extractSliceOp) {
 }
 
 static bool hasConstantIndices(tensor::ExtractOp extractOp) {
-  return llvm::all_of(extractOp.getIndices(),
-                      [](Value index) { return isa_and_nonnull<arith::ConstantIndexOp>(index.getDefiningOp()); });
+  return llvm::all_of(extractOp.getIndices(), [](Value index) { return matchConstantIndexValue(index).has_value(); });
 }
 
 static bool isStaticTensorResult(Operation* op) {

src/PIM/Conversion/ONNXToSpatial/ONNXToSpatialPass.cpp

@@ -8,7 +8,6 @@
 #include "mlir/Pass/Pass.h"
 #include "mlir/Pass/PassManager.h"
 #include "mlir/Transforms/Passes.h"
-#include "mlir/Transforms/WalkPatternRewriteDriver.h"
 
 #include "llvm/ADT/SmallVector.h"
 
@@ -114,21 +113,6 @@ void ONNXToSpatialPass::runOnOperation() {
     return;
   }
 
-  RewritePatternSet matmulPatterns(ctx);
-  populateMatMulRewritePatterns(matmulPatterns, ctx);
-  walkAndApplyPatterns(moduleOp, std::move(matmulPatterns));
-
-  bool hasUnloweredMatMul = false;
-  moduleOp.walk([&](ONNXMatMulOp matmulOp) {
-    hasUnloweredMatMul = true;
-    matmulOp.emitOpError("remaining ONNX MatMul before the required ONNX-to-Spatial conversion");
-  });
-  if (hasUnloweredMatMul) {
-    moduleOp.emitError("failed to lower all ONNX MatMul ops before ONNX-to-Spatial conversion");
-    signalPassFailure();
-    return;
-  }
-
   ConversionTarget target(*ctx);
   target.addLegalDialect<spatial::SpatialDialect,
                          ONNXDialect,
@@ -140,6 +124,7 @@ void ONNXToSpatialPass::runOnOperation() {
   target.addIllegalOp<ONNXMatMulOp>();
   target.addIllegalOp<ONNXTransposeOp>();
   target.addIllegalOp<ONNXAddOp>();
+  target.addIllegalOp<ONNXSubOp>();
   target.addIllegalOp<ONNXDivOp>();
   target.addIllegalOp<ONNXMulOp>();
   target.addIllegalOp<ONNXGemmOp>();
@@ -153,7 +138,9 @@ void ONNXToSpatialPass::runOnOperation() {
   target.addIllegalOp<ONNXGatherOp>();
   target.addIllegalOp<ONNXReshapeOp>();
   target.addIllegalOp<ONNXResizeOp>();
+  target.addIllegalOp<ONNXSliceOp>();
   target.addIllegalOp<ONNXLRNOp>();
+  target.addIllegalOp<ONNXReduceMeanOp>();
   target.addIllegalOp<ONNXReduceMeanV13Op>();
   target.addIllegalOp<ONNXSplitOp>();
 
@@ -165,10 +152,6 @@ void ONNXToSpatialPass::runOnOperation() {
     return;
   }
 
-  RewritePatternSet transposePatterns(ctx);
-  populateTransposePatterns(transposePatterns, ctx);
-  walkAndApplyPatterns(moduleOp, std::move(transposePatterns));
-
   ConversionTarget earlyPostTarget(*ctx);
   earlyPostTarget.addLegalDialect<spatial::SpatialDialect,
                                   ONNXDialect,
@@ -206,15 +189,14 @@ void ONNXToSpatialPass::runOnOperation() {
     return;
   }
 
-  if (failed(verifyONNXToSpatial(*entryFunc))) {
-    moduleOp.emitError("ONNX-to-Spatial host legality verification failed");
-    signalPassFailure();
-    return;
-  }
-
   populateEmptyFunction(*entryFunc);
 
   dumpModule(moduleOp, "spatial0");
+
+  if (failed(verifyONNXToSpatial(*entryFunc))) {
+    moduleOp.emitError("ONNX-to-Spatial host legality verification failed");
+    signalPassFailure();
+  }
 }
 
 std::unique_ptr<Pass> createONNXToSpatialPass() { return std::make_unique<ONNXToSpatialPass>(); }

src/PIM/Conversion/ONNXToSpatial/Patterns.cpp

@@ -10,6 +10,7 @@ void populatePrePatterns(RewritePatternSet& patterns, MLIRContext* ctx) { popula
 void populateConversionPatterns(RewritePatternSet& patterns, MLIRContext* ctx) {
   populateGeneratedConversionPatterns(patterns, ctx);
   populateElementwisePatterns(patterns, ctx);
+  populateMatMulRewritePatterns(patterns, ctx);
   populateGemmPatterns(patterns, ctx);
   populateConvPatterns(patterns, ctx);
   populatePoolPatterns(patterns, ctx);
@@ -21,6 +22,7 @@ void populateConversionPatterns(RewritePatternSet& patterns, MLIRContext* ctx) {
   populateGatherPatterns(patterns, ctx);
   populateResizePatterns(patterns, ctx);
   populateReshapePatterns(patterns, ctx);
+  populateSlicePatterns(patterns, ctx);
   populateSplitPatterns(patterns, ctx);
   populateTransposePatterns(patterns, ctx);
 }

src/PIM/Conversion/ONNXToSpatial/Patterns.hpp

@@ -29,6 +29,7 @@ void populateConcatPatterns(mlir::RewritePatternSet& patterns, mlir::MLIRContext
 void populateGatherPatterns(mlir::RewritePatternSet& patterns, mlir::MLIRContext* ctx);
 void populateResizePatterns(mlir::RewritePatternSet& patterns, mlir::MLIRContext* ctx);
 void populateReshapePatterns(mlir::RewritePatternSet& patterns, mlir::MLIRContext* ctx);
+void populateSlicePatterns(mlir::RewritePatternSet& patterns, mlir::MLIRContext* ctx);
 void populateSplitPatterns(mlir::RewritePatternSet& patterns, mlir::MLIRContext* ctx);
 void populateTransposePatterns(mlir::RewritePatternSet& patterns, mlir::MLIRContext* ctx);
 

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

@@ -189,6 +189,7 @@ struct DivToSpatialCompute : OpConversionPattern<ONNXDivOp> {
 
 void populateElementwisePatterns(RewritePatternSet& patterns, MLIRContext* ctx) {
   patterns.add<BinaryElementwiseToSpatialCompute<ONNXAddOp, spatial::SpatVAddOp>>(ctx);
+  patterns.add<BinaryElementwiseToSpatialCompute<ONNXSubOp, spatial::SpatVSubOp>>(ctx);
   patterns.add<BinaryElementwiseToSpatialCompute<ONNXMulOp, spatial::SpatVMulOp>>(ctx);
   patterns.add<DivToSpatialCompute>(ctx);
 }

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

@@ -14,6 +14,8 @@
 #include <utility>
 
 #include "Common/IR/ConstantUtils.hpp"
+#include "src/Accelerators/PIM/Common/IR/AffineUtils.hpp"
+#include "src/Accelerators/PIM/Common/IR/LoopUtils.hpp"
 #include "src/Accelerators/PIM/Common/IR/ShapeUtils.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
 #include "src/Accelerators/PIM/Common/Support/Diagnostics.hpp"
@@ -60,8 +62,11 @@ static Value createGemmBatchKOffset(
 
   MLIRContext* context = rewriter.getContext();
   AffineExpr d0 = getAffineDimExpr(0, context);
-  return createAffineApplyOrFoldedConstant(
-    rewriter, loc, (d0.floorDiv(numOutRows) % numKSlices) * crossbarSize.getValue(), ValueRange {lane});
+  return createOrFoldAffineApply(rewriter,
+                                 loc,
+                                 (d0.floorDiv(numOutRows) % numKSlices) * crossbarSize.getValue(),
+                                 ValueRange {lane},
+                                 rewriter.getInsertionBlock()->getParentOp());
 }
 
 static Value createGemmBatchHOffset(Value lane,
@@ -75,8 +80,11 @@ static Value createGemmBatchHOffset(Value lane,
 
   MLIRContext* context = rewriter.getContext();
   AffineExpr d0 = getAffineDimExpr(0, context);
-  return createAffineApplyOrFoldedConstant(
-    rewriter, loc, d0.floorDiv(numOutRows * numKSlices) * crossbarSize.getValue(), ValueRange {lane});
+  return createOrFoldAffineApply(rewriter,
+                                 loc,
+                                 d0.floorDiv(numOutRows * numKSlices) * crossbarSize.getValue(),
+                                 ValueRange {lane},
+                                 rewriter.getInsertionBlock()->getParentOp());
 }
 
 static Value
@@ -240,16 +248,16 @@ static Value createPaddedInputCompute(Value input,
   return computeOp.getResult(0);
 }
 
-static spatial::SpatComputeBatch createVmmBatch(Value a,
-                                                Value b,
-                                                RankedTensorType aType,
-                                                RankedTensorType paddedBType,
-                                                RankedTensorType partialPiecesType,
-                                                int64_t numOutRows,
-                                                int64_t numKSlices,
-                                                int64_t numOutHSlices,
-                                                ConversionPatternRewriter& rewriter,
-                                                Location loc) {
+static FailureOr<spatial::SpatComputeBatch> createVmmBatch(Value a,
+                                                           Value b,
+                                                           RankedTensorType aType,
+                                                           RankedTensorType paddedBType,
+                                                           RankedTensorType partialPiecesType,
+                                                           int64_t numOutRows,
+                                                           int64_t numKSlices,
+                                                           int64_t numOutHSlices,
+                                                           ConversionPatternRewriter& rewriter,
+                                                           Location loc) {
   const int64_t laneCount = partialPiecesType.getDimSize(0);
   auto batchOp = createSpatComputeBatch(
     rewriter,
@@ -259,7 +267,8 @@ static spatial::SpatComputeBatch createVmmBatch(Value a,
     ValueRange {b},
     ValueRange {a},
     [&](detail::SpatComputeBatchBodyArgs args) {
-      Value row = onnx_mlir::modIndexByConstant(rewriter, loc, args.lane, numOutRows);
+      Value row =
+        onnx_mlir::affineModConst(rewriter, loc, args.lane, numOutRows, rewriter.getInsertionBlock()->getParentOp());
       Value kOffset = createGemmBatchKOffset(args.lane, numOutRows, numKSlices, rewriter, loc);
       Value hOffset = createGemmBatchHOffset(args.lane, numOutRows, numKSlices, numOutHSlices, rewriter, loc);
 
@@ -286,7 +295,8 @@ static spatial::SpatComputeBatch createVmmBatch(Value a,
       createParallelInsertSliceIntoBatchOutput(
         rewriter, loc, piece, args.outputs.front(), pieceOffsets, pieceSizes, unitStrides);
     });
-  assert(succeeded(batchOp) && "expected Gemm VMM batch construction to succeed");
+  if (failed(batchOp))
+    return failure();
   return *batchOp;
 }
 
@@ -297,7 +307,8 @@ createDynamicGemmBatchRow(Value lane, int64_t numOutCols, ConversionPatternRewri
 
   MLIRContext* context = rewriter.getContext();
   AffineExpr d0 = getAffineDimExpr(0, context);
-  return createAffineApplyOrFoldedConstant(rewriter, loc, d0.floorDiv(numOutCols), ValueRange {lane});
+  return createOrFoldAffineApply(
+    rewriter, loc, d0.floorDiv(numOutCols), ValueRange {lane}, rewriter.getInsertionBlock()->getParentOp());
 }
 
 static Value extractDynamicGemmBColumn(
@@ -320,14 +331,6 @@ static Value extractDynamicGemmBColumn(
   return tensor::ExpandShapeOp::create(rewriter, loc, vectorType, collapsed, expandReassociation).getResult();
 }
 
-static Value extractTransposedBRow(
-  Value transposedB, Value row, RankedTensorType vectorType, ConversionPatternRewriter& rewriter, Location loc) {
-  SmallVector<OpFoldResult> offsets {row, rewriter.getIndexAttr(0)};
-  SmallVector<OpFoldResult> sizes {rewriter.getIndexAttr(1), rewriter.getIndexAttr(vectorType.getDimSize(1))};
-  SmallVector<OpFoldResult> strides {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-  return tensor::ExtractSliceOp::create(rewriter, loc, vectorType, transposedB, offsets, sizes, strides).getResult();
-}
-
 static Value extractDynamicGemmRowVector(
   Value matrix, Value row, RankedTensorType vectorType, ConversionPatternRewriter& rewriter, Location loc) {
   SmallVector<OpFoldResult> offsets {row, rewriter.getIndexAttr(0)};
@@ -407,15 +410,14 @@ static Value createBroadcastedBiasScalar(Value bias,
   return tensor::SplatOp::create(rewriter, loc, scalarType, scalar).getResult();
 }
 
-static spatial::SpatComputeBatch createVvdmulBatch(Value a,
-                                                   Value b,
-                                                   RankedTensorType aType,
-                                                   RankedTensorType bType,
-                                                   RankedTensorType scalarPiecesType,
-                                                   RankedTensorType outType,
-                                                   bool bAlreadyTransposed,
-                                                   ConversionPatternRewriter& rewriter,
-                                                   Location loc) {
+static FailureOr<spatial::SpatComputeBatch> createVvdmulBatch(Value a,
+                                                              Value b,
+                                                              RankedTensorType aType,
+                                                              RankedTensorType bType,
+                                                              RankedTensorType scalarPiecesType,
+                                                              RankedTensorType outType,
+                                                              ConversionPatternRewriter& rewriter,
+                                                              Location loc) {
   const int64_t numOutRows = outType.getDimSize(0);
   const int64_t numOutCols = outType.getDimSize(1);
   const int64_t reductionSize = aType.getDimSize(1);
@@ -429,13 +431,13 @@ static spatial::SpatComputeBatch createVvdmulBatch(Value a,
     ValueRange {a, b},
     [&](detail::SpatComputeBatchBodyArgs args) {
       Value row = createDynamicGemmBatchRow(args.lane, numOutCols, rewriter, loc);
-      Value column = onnx_mlir::modIndexByConstant(rewriter, loc, args.lane, numOutCols);
+      Value column =
+        onnx_mlir::affineModConst(rewriter, loc, args.lane, numOutCols, rewriter.getInsertionBlock()->getParentOp());
 
       auto vectorType = RankedTensorType::get({1, reductionSize}, aType.getElementType());
       auto scalarType = RankedTensorType::get({1, 1}, outType.getElementType());
       Value aVector = extractDynamicGemmRowVector(args.inputs[0], row, vectorType, rewriter, loc);
-      Value bVector = bAlreadyTransposed ? extractTransposedBRow(args.inputs[1], column, vectorType, rewriter, loc)
-                                         : extractDynamicGemmBColumn(args.inputs[1], column, vectorType, rewriter, loc);
+      Value bVector = extractDynamicGemmBColumn(args.inputs[1], column, vectorType, rewriter, loc);
       Value scalar = spatial::SpatVVDMulOp::create(rewriter, loc, scalarType, aVector, bVector).getResult();
 
       SmallVector<OpFoldResult> outputOffsets {args.lane, rewriter.getIndexAttr(0)};
@@ -444,26 +446,27 @@ static spatial::SpatComputeBatch createVvdmulBatch(Value a,
       createParallelInsertSliceIntoBatchOutput(
         rewriter, loc, scalar, args.outputs.front(), outputOffsets, scalarSizes, unitStrides);
     });
-  assert(succeeded(batchOp) && "expected Gemm VVDMul batch construction to succeed");
+  if (failed(batchOp))
+    return failure();
   return *batchOp;
 }
 
-static spatial::SpatCompute createDynamicGemmOutputCompute(Value scalarPieces,
-                                                           Value bias,
-                                                           RankedTensorType scalarPiecesType,
-                                                           RankedTensorType biasType,
-                                                           RankedTensorType outType,
-                                                           float alpha,
-                                                           float beta,
-                                                           ConversionPatternRewriter& rewriter,
-                                                           Location loc) {
+static FailureOr<spatial::SpatCompute> createDynamicGemmOutputCompute(Value scalarPieces,
+                                                                      Value bias,
+                                                                      RankedTensorType scalarPiecesType,
+                                                                      RankedTensorType biasType,
+                                                                      RankedTensorType outType,
+                                                                      float alpha,
+                                                                      float beta,
+                                                                      ConversionPatternRewriter& rewriter,
+                                                                      Location loc) {
   const int64_t laneCount = scalarPiecesType.getDimSize(0);
   const int64_t numOutCols = outType.getDimSize(1);
   SmallVector<Value> inputs {scalarPieces};
   if (bias)
     inputs.push_back(bias);
 
-  return createSpatCompute(rewriter, loc, TypeRange {outType}, {}, inputs, [&](ValueRange blockArgs) {
+  return createSpatCompute(rewriter, loc, TypeRange {outType}, {}, inputs, [&](ValueRange blockArgs) -> LogicalResult {
     Value pieces = blockArgs[0];
     Value biasArg = bias ? blockArgs[1] : Value();
     auto scalarType = RankedTensorType::get({1, 1}, outType.getElementType());
@@ -471,39 +474,50 @@ static spatial::SpatCompute createDynamicGemmOutputCompute(Value scalarPieces,
     Value c0 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
     Value c1 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 1);
     Value cLaneCount = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), laneCount);
-    auto loop = scf::ForOp::create(rewriter, loc, c0, cLaneCount, c1, ValueRange {outputInit});
-    rewriter.setInsertionPointToStart(loop.getBody());
+    auto loop = buildNormalizedScfFor(
+      rewriter,
+      loc,
+      c0,
+      cLaneCount,
+      c1,
+      ValueRange {outputInit},
+      [&](OpBuilder&, Location nestedLoc, Value lane, ValueRange iterArgs, SmallVectorImpl<Value>& yielded) {
+        Value outputAcc = iterArgs.front();
+        Value row = createDynamicGemmBatchRow(lane, numOutCols, rewriter, nestedLoc);
+        Value column =
+          onnx_mlir::affineModConst(rewriter, nestedLoc, lane, numOutCols, rewriter.getInsertionBlock()->getParentOp());
+        SmallVector<OpFoldResult> scalarOffsets {lane, rewriter.getIndexAttr(0)};
+        SmallVector<OpFoldResult> scalarSizes {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+        SmallVector<OpFoldResult> unitStrides {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+        Value scalar = tensor::ExtractSliceOp::create(
+                         rewriter, nestedLoc, scalarType, pieces, scalarOffsets, scalarSizes, unitStrides)
+                         .getResult();
+        if (alpha != 1.0f) {
+          Value alphaTensor = createScalarTensorConstant(scalarType, alpha, rewriter, nestedLoc);
+          scalar = spatial::SpatVMulOp::create(rewriter, nestedLoc, scalarType, scalar, alphaTensor).getResult();
+        }
+        if (biasArg) {
+          Value biasScalar =
+            createBroadcastedBiasScalar(biasArg, biasType, row, column, scalarType, rewriter, nestedLoc);
+          if (beta != 1.0f) {
+            Value betaTensor = createScalarTensorConstant(scalarType, beta, rewriter, nestedLoc);
+            biasScalar =
+              spatial::SpatVMulOp::create(rewriter, nestedLoc, scalarType, biasScalar, betaTensor).getResult();
+          }
+          scalar = spatial::SpatVAddOp::create(rewriter, nestedLoc, scalarType, scalar, biasScalar).getResult();
+        }
+        SmallVector<OpFoldResult> outputOffsets {row, column};
+        Value outputNext =
+          tensor::InsertSliceOp::create(rewriter, nestedLoc, scalar, outputAcc, outputOffsets, scalarSizes, unitStrides)
+            .getResult();
+        yielded.push_back(outputNext);
+        return success();
+      });
+    if (failed(loop))
+      return failure();
 
-    Value lane = loop.getInductionVar();
-    Value outputAcc = loop.getRegionIterArgs().front();
-    Value row = createDynamicGemmBatchRow(lane, numOutCols, rewriter, loc);
-    Value column = onnx_mlir::modIndexByConstant(rewriter, loc, lane, numOutCols);
-    SmallVector<OpFoldResult> scalarOffsets {lane, rewriter.getIndexAttr(0)};
-    SmallVector<OpFoldResult> scalarSizes {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-    SmallVector<OpFoldResult> unitStrides {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-    Value scalar =
-      tensor::ExtractSliceOp::create(rewriter, loc, scalarType, pieces, scalarOffsets, scalarSizes, unitStrides)
-        .getResult();
-    if (alpha != 1.0f) {
-      Value alphaTensor = createScalarTensorConstant(scalarType, alpha, rewriter, loc);
-      scalar = spatial::SpatVMulOp::create(rewriter, loc, scalarType, scalar, alphaTensor).getResult();
-    }
-    if (biasArg) {
-      Value biasScalar = createBroadcastedBiasScalar(biasArg, biasType, row, column, scalarType, rewriter, loc);
-      if (beta != 1.0f) {
-        Value betaTensor = createScalarTensorConstant(scalarType, beta, rewriter, loc);
-        biasScalar = spatial::SpatVMulOp::create(rewriter, loc, scalarType, biasScalar, betaTensor).getResult();
-      }
-      scalar = spatial::SpatVAddOp::create(rewriter, loc, scalarType, scalar, biasScalar).getResult();
-    }
-    SmallVector<OpFoldResult> outputOffsets {row, column};
-    Value outputNext =
-      tensor::InsertSliceOp::create(rewriter, loc, scalar, outputAcc, outputOffsets, scalarSizes, unitStrides)
-        .getResult();
-    scf::YieldOp::create(rewriter, loc, outputNext);
-
-    rewriter.setInsertionPointAfter(loop);
-    spatial::SpatYieldOp::create(rewriter, loc, loop.getResult(0));
+    spatial::SpatYieldOp::create(rewriter, loc, loop->results.front());
+    return success();
   });
 }
 
@@ -515,8 +529,11 @@ static Value createPartialGroupOffset(Value hSlice,
                                       Location loc) {
   MLIRContext* context = rewriter.getContext();
   AffineExpr d0 = getAffineDimExpr(0, context);
-  return createAffineApplyOrFoldedConstant(
-    rewriter, loc, d0 * (numKSlices * numOutRows) + kSlice * numOutRows, ValueRange {hSlice});
+  return createOrFoldAffineApply(rewriter,
+                                 loc,
+                                 d0 * (numKSlices * numOutRows) + kSlice * numOutRows,
+                                 ValueRange {hSlice},
+                                 rewriter.getInsertionBlock()->getParentOp());
 }
 
 static Value extractReductionPiece(Value partialPiecesArg,
@@ -565,85 +582,92 @@ static Value reducePartialPiecesForHSlice(Value partialPiecesArg,
   return activePieces.front();
 }
 
-static spatial::SpatCompute createReductionCompute(Value partialPieces,
-                                                   Value bias,
-                                                   RankedTensorType partialPiecesType,
-                                                   RankedTensorType outType,
-                                                   RankedTensorType paddedOutType,
-                                                   int64_t numKSlices,
-                                                   ConversionPatternRewriter& rewriter,
-                                                   Location loc) {
+static FailureOr<spatial::SpatCompute> createReductionCompute(Value partialPieces,
+                                                              Value bias,
+                                                              RankedTensorType partialPiecesType,
+                                                              RankedTensorType outType,
+                                                              RankedTensorType paddedOutType,
+                                                              int64_t numKSlices,
+                                                              ConversionPatternRewriter& rewriter,
+                                                              Location loc) {
   SmallVector<Value> inputs {partialPieces};
   if (bias)
     inputs.push_back(bias);
 
-  auto computeOp = createSpatCompute(rewriter, loc, TypeRange {outType}, {}, inputs, [&](ValueRange blockArgs) {
-    Value partialPiecesArg = blockArgs[0];
-    Value biasArg = bias ? blockArgs[1] : Value();
-    if (biasArg && cast<RankedTensorType>(biasArg.getType()) != paddedOutType)
-      biasArg = createZeroPaddedTensor(biasArg, paddedOutType, rewriter, loc);
+  auto computeOp =
+    createSpatCompute(rewriter, loc, TypeRange {outType}, {}, inputs, [&](ValueRange blockArgs) -> LogicalResult {
+      Value partialPiecesArg = blockArgs[0];
+      Value biasArg = bias ? blockArgs[1] : Value();
+      if (biasArg && cast<RankedTensorType>(biasArg.getType()) != paddedOutType)
+        biasArg = createZeroPaddedTensor(biasArg, paddedOutType, rewriter, loc);
 
-    const int64_t numOutRows = outType.getDimSize(0);
-    const int64_t numOutHSlices = ceilIntegerDivide(outType.getDimSize(1), crossbarSize.getValue());
-    auto pieceType = RankedTensorType::get({numOutRows, static_cast<int64_t>(crossbarSize.getValue())},
-                                           partialPiecesType.getElementType());
+      const int64_t numOutRows = outType.getDimSize(0);
+      const int64_t numOutHSlices = ceilIntegerDivide(outType.getDimSize(1), crossbarSize.getValue());
+      auto pieceType = RankedTensorType::get({numOutRows, static_cast<int64_t>(crossbarSize.getValue())},
+                                             partialPiecesType.getElementType());
 
-    Value outputInit =
-      tensor::EmptyOp::create(rewriter, loc, paddedOutType.getShape(), paddedOutType.getElementType()).getResult();
-    SmallVector<OpFoldResult> unitStrides {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-    SmallVector<OpFoldResult> pieceSizes {rewriter.getIndexAttr(numOutRows),
-                                          rewriter.getIndexAttr(crossbarSize.getValue())};
+      Value outputInit =
+        tensor::EmptyOp::create(rewriter, loc, paddedOutType.getShape(), paddedOutType.getElementType()).getResult();
+      SmallVector<OpFoldResult> unitStrides {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+      SmallVector<OpFoldResult> pieceSizes {rewriter.getIndexAttr(numOutRows),
+                                            rewriter.getIndexAttr(crossbarSize.getValue())};
 
-    auto buildOutputSlice = [&](Value outputAcc, Value hSlice) -> Value {
-      Value reduced =
-        reducePartialPiecesForHSlice(partialPiecesArg, hSlice, pieceType, numKSlices, numOutRows, rewriter, loc);
-      Value hOffset = onnx_mlir::multiplyIndexByConstant(
-        rewriter, rewriter.getInsertionBlock()->getParentOp(), hSlice, crossbarSize.getValue());
-      if (biasArg) {
-        SmallVector<OpFoldResult> biasOffsets {rewriter.getIndexAttr(0), hOffset};
-        Value biasSlice =
-          tensor::ExtractSliceOp::create(rewriter, loc, pieceType, biasArg, biasOffsets, pieceSizes, unitStrides)
-            .getResult();
-        reduced = spatial::SpatVAddOp::create(rewriter, loc, pieceType, reduced, biasSlice).getResult();
+      auto buildOutputSlice = [&](Value outputAcc, Value hSlice) -> Value {
+        Value reduced =
+          reducePartialPiecesForHSlice(partialPiecesArg, hSlice, pieceType, numKSlices, numOutRows, rewriter, loc);
+        Value hOffset = onnx_mlir::affineMulConst(
+          rewriter, loc, hSlice, crossbarSize.getValue(), rewriter.getInsertionBlock()->getParentOp());
+        if (biasArg) {
+          SmallVector<OpFoldResult> biasOffsets {rewriter.getIndexAttr(0), hOffset};
+          Value biasSlice =
+            tensor::ExtractSliceOp::create(rewriter, loc, pieceType, biasArg, biasOffsets, pieceSizes, unitStrides)
+              .getResult();
+          reduced = spatial::SpatVAddOp::create(rewriter, loc, pieceType, reduced, biasSlice).getResult();
+        }
+
+        SmallVector<OpFoldResult> outputOffsets {rewriter.getIndexAttr(0), hOffset};
+        return tensor::InsertSliceOp::create(rewriter, loc, reduced, outputAcc, outputOffsets, pieceSizes, unitStrides)
+          .getResult();
+      };
+
+      Value paddedOutput = outputInit;
+      if (numOutHSlices == 1) {
+        Value hSlice = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
+        paddedOutput = buildOutputSlice(outputInit, hSlice);
+      }
+      else {
+        Value c0 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
+        Value c1 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 1);
+        Value cOutHSlices =
+          getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), numOutHSlices);
+        auto hLoop = buildNormalizedScfFor(
+          rewriter,
+          loc,
+          c0,
+          cOutHSlices,
+          c1,
+          ValueRange {outputInit},
+          [&](OpBuilder&, Location, Value hSlice, ValueRange iterArgs, SmallVectorImpl<Value>& yielded) {
+            yielded.push_back(buildOutputSlice(iterArgs.front(), hSlice));
+            return success();
+          });
+        if (failed(hLoop))
+          return failure();
+        paddedOutput = hLoop->results.front();
       }
 
-      SmallVector<OpFoldResult> outputOffsets {rewriter.getIndexAttr(0), hOffset};
-      return tensor::InsertSliceOp::create(rewriter, loc, reduced, outputAcc, outputOffsets, pieceSizes, unitStrides)
-        .getResult();
-    };
-
-    Value paddedOutput = outputInit;
-    if (numOutHSlices == 1) {
-      Value hSlice = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
-      paddedOutput = buildOutputSlice(outputInit, hSlice);
-    }
-    else {
-      Value c0 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
-      Value c1 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 1);
-      Value cOutHSlices =
-        getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), numOutHSlices);
-      auto hLoop = scf::ForOp::create(rewriter, loc, c0, cOutHSlices, c1, ValueRange {outputInit});
-      rewriter.setInsertionPointToStart(hLoop.getBody());
-
-      Value hSlice = hLoop.getInductionVar();
-      Value outputAcc = hLoop.getRegionIterArgs().front();
-      scf::YieldOp::create(rewriter, loc, buildOutputSlice(outputAcc, hSlice));
-
-      rewriter.setInsertionPointAfter(hLoop);
-      paddedOutput = hLoop.getResult(0);
-    }
-
-    Value result = paddedOutput;
-    if (paddedOutType != outType) {
-      SmallVector<OpFoldResult> outputOffsets {rewriter.getIndexAttr(0), rewriter.getIndexAttr(0)};
-      SmallVector<OpFoldResult> outputSizes {rewriter.getIndexAttr(outType.getDimSize(0)),
-                                             rewriter.getIndexAttr(outType.getDimSize(1))};
-      result =
-        tensor::ExtractSliceOp::create(rewriter, loc, outType, paddedOutput, outputOffsets, outputSizes, unitStrides)
-          .getResult();
-    }
-    spatial::SpatYieldOp::create(rewriter, loc, result);
-  });
+      Value result = paddedOutput;
+      if (paddedOutType != outType) {
+        SmallVector<OpFoldResult> outputOffsets {rewriter.getIndexAttr(0), rewriter.getIndexAttr(0)};
+        SmallVector<OpFoldResult> outputSizes {rewriter.getIndexAttr(outType.getDimSize(0)),
+                                               rewriter.getIndexAttr(outType.getDimSize(1))};
+        result =
+          tensor::ExtractSliceOp::create(rewriter, loc, outType, paddedOutput, outputOffsets, outputSizes, unitStrides)
+            .getResult();
+      }
+      spatial::SpatYieldOp::create(rewriter, loc, result);
+      return success();
+    });
 
   return computeOp;
 }
@@ -666,11 +690,6 @@ LogicalResult GemmToSpatialComputes::matchAndRewrite(ONNXGemmOp gemmOp,
   Value b = gemmOpAdaptor.getB();
   Value c = gemmOpAdaptor.getC();
 
-  if (gemmOpAdaptor.getTransA()) {
-    gemmOp.emitOpError("requires transA=false before tiled Spatial Gemm lowering");
-    return failure();
-  }
-
   auto aType = dyn_cast<RankedTensorType>(a.getType());
   auto bType = dyn_cast<RankedTensorType>(b.getType());
   auto outType = dyn_cast<RankedTensorType>(gemmOp.getY().getType());
@@ -701,6 +720,20 @@ LogicalResult GemmToSpatialComputes::matchAndRewrite(ONNXGemmOp gemmOp,
     return failure();
   }
 
+  if (gemmOpAdaptor.getTransA()) {
+    auto aShape = aType.getShape();
+    auto transposedType = RankedTensorType::get({aShape[1], aShape[0]}, aType.getElementType(), aType.getEncoding());
+    a = ONNXTransposeOp::create(rewriter, loc, transposedType, a, rewriter.getI64ArrayAttr({1, 0})).getResult();
+    aType = transposedType;
+  }
+
+  if (gemmOpAdaptor.getTransB()) {
+    auto bShape = bType.getShape();
+    auto transposedType = RankedTensorType::get({bShape[1], bShape[0]}, bType.getElementType(), bType.getEncoding());
+    b = ONNXTransposeOp::create(rewriter, loc, transposedType, b, rewriter.getI64ArrayAttr({1, 0})).getResult();
+    bType = transposedType;
+  }
+
   const int64_t numOutRows = outType.getDimSize(0);
   const int64_t numOutCols = outType.getDimSize(1);
   const int64_t reductionSize = aType.getDimSize(1);
@@ -724,10 +757,8 @@ LogicalResult GemmToSpatialComputes::matchAndRewrite(ONNXGemmOp gemmOp,
       biasType = *verifiedBiasType;
     }
 
-    const int64_t expectedBRows = gemmOpAdaptor.getTransB() ? numOutCols : reductionSize;
-    const int64_t expectedBCols = gemmOpAdaptor.getTransB() ? reductionSize : numOutCols;
-    if (aType.getDimSize(0) != numOutRows || bType.getDimSize(0) != expectedBRows
-        || bType.getDimSize(1) != expectedBCols) {
+    if (aType.getDimSize(0) != numOutRows || bType.getDimSize(0) != reductionSize
+        || bType.getDimSize(1) != numOutCols) {
       gemmOp.emitOpError("has inconsistent A, B, and output shapes");
       return failure();
     }
@@ -739,11 +770,14 @@ LogicalResult GemmToSpatialComputes::matchAndRewrite(ONNXGemmOp gemmOp,
     }
 
     auto scalarPiecesType = RankedTensorType::get({laneCount64, 1}, outType.getElementType());
-    auto batchOp =
-      createVvdmulBatch(a, b, aType, bType, scalarPiecesType, outType, gemmOpAdaptor.getTransB(), rewriter, loc);
+    auto batchOp = createVvdmulBatch(a, b, aType, bType, scalarPiecesType, outType, rewriter, loc);
+    if (failed(batchOp))
+      return failure();
     auto outputCompute = createDynamicGemmOutputCompute(
-      batchOp.getResult(0), hasC ? c : Value(), scalarPiecesType, biasType, outType, alpha, beta, rewriter, loc);
-    rewriter.replaceOp(gemmOp, outputCompute.getResults());
+      batchOp->getResult(0), hasC ? c : Value(), scalarPiecesType, biasType, outType, alpha, beta, rewriter, loc);
+    if (failed(outputCompute))
+      return failure();
+    rewriter.replaceOp(gemmOp, outputCompute->getResults());
     return success();
   }
 
@@ -755,13 +789,6 @@ LogicalResult GemmToSpatialComputes::matchAndRewrite(ONNXGemmOp gemmOp,
   b = *scaledB;
   bType = cast<RankedTensorType>(b.getType());
 
-  if (gemmOpAdaptor.getTransB()) {
-    auto bShape = bType.getShape();
-    auto transposedType = RankedTensorType::get({bShape[1], bShape[0]}, bType.getElementType());
-    b = transposeMaybeInCompute(b, transposedType, {1, 0}, rewriter, loc);
-    bType = cast<RankedTensorType>(b.getType());
-  }
-
   if (aType.getDimSize(0) != numOutRows || bType.getDimSize(0) != reductionSize || bType.getDimSize(1) != numOutCols) {
     gemmOp.emitOpError("has inconsistent A, B, and output shapes after transpose handling");
     return failure();
@@ -818,10 +845,14 @@ LogicalResult GemmToSpatialComputes::matchAndRewrite(ONNXGemmOp gemmOp,
     RankedTensorType::get({laneCount64, static_cast<int64_t>(crossbarSize.getValue())}, outType.getElementType());
   auto batchOp =
     createVmmBatch(a, b, aType, paddedBType, partialPiecesType, numOutRows, numKSlices, numOutHSlices, rewriter, loc);
+  if (failed(batchOp))
+    return failure();
   auto reductionCompute = createReductionCompute(
-    batchOp.getResult(0), bias, partialPiecesType, outType, paddedOutType, numKSlices, rewriter, loc);
+    batchOp->getResult(0), bias, partialPiecesType, outType, paddedOutType, numKSlices, rewriter, loc);
+  if (failed(reductionCompute))
+    return failure();
 
-  rewriter.replaceOp(gemmOp, reductionCompute.getResults());
+  rewriter.replaceOp(gemmOp, reductionCompute->getResults());
   return success();
 }
 

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

@@ -6,7 +6,10 @@
 
 #include <algorithm>
 #include <numeric>
+#include <optional>
+#include <type_traits>
 
+#include "src/Accelerators/PIM/Common/IR/AffineUtils.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/CompileTime.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
@@ -18,6 +21,85 @@ using namespace mlir;
 namespace onnx_mlir {
 namespace {
 
+struct ReduceMeanSemantics {
+  SmallVector<int64_t> axes;
+  int64_t keepdims = 1;
+  bool isIdentity = false;
+};
+
+static bool isNoneValueLike(Value value) { return isa_and_nonnull<ONNXNoneOp>(value.getDefiningOp()); }
+
+static FailureOr<SmallVector<int64_t>> getConstantIntValues(Value value) {
+  auto denseAttr = dyn_cast_or_null<DenseIntElementsAttr>(getHostConstDenseElementsAttr(value));
+  if (!denseAttr)
+    return failure();
+  return SmallVector<int64_t>(denseAttr.getValues<int64_t>().begin(), denseAttr.getValues<int64_t>().end());
+}
+
+static FailureOr<SmallVector<int64_t>> normalizeAxesChecked(ArrayRef<int64_t> axes, int64_t rank) {
+  SmallVector<int64_t> normalizedAxes;
+  normalizedAxes.reserve(axes.size());
+  for (int64_t axis : axes) {
+    auto normalizedAxis = normalizeAxisChecked(axis, rank);
+    if (failed(normalizedAxis))
+      return failure();
+    normalizedAxes.push_back(*normalizedAxis);
+  }
+  llvm::sort(normalizedAxes);
+  normalizedAxes.erase(std::unique(normalizedAxes.begin(), normalizedAxes.end()), normalizedAxes.end());
+  return normalizedAxes;
+}
+
+template <typename ReduceMeanOp, typename ReduceMeanOpAdaptor>
+static FailureOr<ReduceMeanSemantics>
+getReduceMeanSemantics(ReduceMeanOp reduceMeanOp, ReduceMeanOpAdaptor adaptor, int64_t inputRank) {
+  ReduceMeanSemantics semantics;
+  semantics.keepdims = reduceMeanOp.getKeepdims();
+
+  if constexpr (std::is_same_v<ReduceMeanOp, ONNXReduceMeanV13Op>) {
+    auto axes = onnx_mlir::normalizeAxesChecked(std::optional<ArrayAttr>(reduceMeanOp.getAxesAttr()), inputRank);
+    if (failed(axes))
+      return failure();
+    semantics.axes = std::move(*axes);
+    return semantics;
+  }
+  else {
+    if (isNoneValueLike(adaptor.getAxes())) {
+      if (reduceMeanOp.getNoopWithEmptyAxes() != 0) {
+        semantics.isIdentity = true;
+        return semantics;
+      }
+
+      semantics.axes.reserve(inputRank);
+      for (int64_t axis = 0; axis < inputRank; ++axis)
+        semantics.axes.push_back(axis);
+      return semantics;
+    }
+
+    auto axes = getConstantIntValues(adaptor.getAxes());
+    if (failed(axes))
+      return failure();
+
+    if (axes->empty()) {
+      if (reduceMeanOp.getNoopWithEmptyAxes() != 0) {
+        semantics.isIdentity = true;
+        return semantics;
+      }
+
+      semantics.axes.reserve(inputRank);
+      for (int64_t axis = 0; axis < inputRank; ++axis)
+        semantics.axes.push_back(axis);
+      return semantics;
+    }
+
+    auto normalizedAxes = normalizeAxesChecked(*axes, inputRank);
+    if (failed(normalizedAxes))
+      return failure();
+    semantics.axes = std::move(*normalizedAxes);
+    return semantics;
+  }
+}
+
 static SmallVector<bool> buildReducedAxesMask(ArrayRef<int64_t> axes, int64_t rank) {
   SmallVector<bool> reducedAxes(rank, false);
   for (int64_t axis : axes) {
@@ -82,7 +164,7 @@ computeLaneIndex(Value lane, int64_t stride, int64_t dimSize, ConversionPatternR
     expr = expr.floorDiv(stride);
   if (dimSize != 1)
     expr = expr % dimSize;
-  return createAffineApplyOrFoldedConstant(rewriter, loc, expr, ValueRange {lane});
+  return createOrFoldAffineApply(rewriter, loc, expr, ValueRange {lane}, rewriter.getInsertionBlock()->getParentOp());
 }
 
 static FailureOr<Value> buildReduceMeanKeepdimsBatch(Value input,
@@ -237,14 +319,8 @@ static Value squeezeReducedAxes(Value keepdimsValue,
                                 ArrayRef<bool> reducedAxes,
                                 ConversionPatternRewriter& rewriter,
                                 Location loc) {
-  if (resultType.getRank() == 0) {
-    SmallVector<Value> indices(cast<RankedTensorType>(keepdimsValue.getType()).getRank(),
-                               getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0));
-    Value element = tensor::ExtractOp::create(rewriter, loc, keepdimsValue, indices);
-    return tensor::FromElementsOp::create(rewriter, loc, resultType, ValueRange {element});
-  }
-
-  auto reassociation = buildCollapseReassociation(reducedAxes);
+  SmallVector<ReassociationIndices> reassociation =
+    resultType.getRank() == 0 ? SmallVector<ReassociationIndices> {} : buildCollapseReassociation(reducedAxes);
   if (isCompileTimeComputable(keepdimsValue))
     return tensor::CollapseShapeOp::create(rewriter, loc, resultType, keepdimsValue, reassociation).getResult();
 
@@ -256,11 +332,13 @@ static Value squeezeReducedAxes(Value keepdimsValue,
   return squeezeCompute.getResult(0);
 }
 
-struct ReduceMeanToSpatialCompute : OpConversionPattern<ONNXReduceMeanV13Op> {
-  using OpConversionPattern::OpConversionPattern;
+template <typename ReduceMeanOp>
+struct ReduceMeanToSpatialCompute : OpConversionPattern<ReduceMeanOp> {
+  using OpConversionPattern<ReduceMeanOp>::OpConversionPattern;
+  using Adaptor = typename ReduceMeanOp::Adaptor;
 
-  LogicalResult matchAndRewrite(ONNXReduceMeanV13Op reduceMeanOp,
-                                ONNXReduceMeanV13OpAdaptor adaptor,
+  LogicalResult matchAndRewrite(ReduceMeanOp reduceMeanOp,
+                                Adaptor adaptor,
                                 ConversionPatternRewriter& rewriter) const override {
     auto inputType = dyn_cast<RankedTensorType>(adaptor.getData().getType());
     auto resultType = dyn_cast<RankedTensorType>(reduceMeanOp.getReduced().getType());
@@ -271,10 +349,18 @@ struct ReduceMeanToSpatialCompute : OpConversionPattern<ONNXReduceMeanV13Op> {
       return success();
     }
 
-    auto axes = normalizeAxesChecked(std::optional<ArrayAttr>(reduceMeanOp.getAxesAttr()), inputType.getRank());
-    if (failed(axes))
-      return failure();
-    SmallVector<bool> reducedAxes = buildReducedAxesMask(*axes, inputType.getRank());
+    auto semantics = getReduceMeanSemantics(reduceMeanOp, adaptor, inputType.getRank());
+    if (failed(semantics))
+      return rewriter.notifyMatchFailure(reduceMeanOp, "requires compile-time constant, in-range ReduceMean axes");
+    if (semantics->isIdentity) {
+      if (inputType != resultType)
+        return rewriter.notifyMatchFailure(
+          reduceMeanOp, "noop_with_empty_axes identity requires the result type to match the input type");
+      rewriter.replaceOp(reduceMeanOp, adaptor.getData());
+      return success();
+    }
+
+    SmallVector<bool> reducedAxes = buildReducedAxesMask(semantics->axes, inputType.getRank());
     if (reducedAxes.empty() && inputType.getRank() != 0)
       return failure();
 
@@ -294,7 +380,7 @@ struct ReduceMeanToSpatialCompute : OpConversionPattern<ONNXReduceMeanV13Op> {
     Value reducedKeepdims =
       buildKeepdimsFromLanePackedBatch(*lanePackedKeepdims, keepdimsType, compactKeptType, reducedAxes, rewriter, loc);
 
-    if (reduceMeanOp.getKeepdims() != 0) {
+    if (semantics->keepdims != 0) {
       rewriter.replaceOp(reduceMeanOp, reducedKeepdims);
       return success();
     }
@@ -308,7 +394,7 @@ struct ReduceMeanToSpatialCompute : OpConversionPattern<ONNXReduceMeanV13Op> {
 } // namespace
 
 void populateReduceMeanPatterns(RewritePatternSet& patterns, MLIRContext* ctx) {
-  patterns.add<ReduceMeanToSpatialCompute>(ctx);
+  patterns.add<ReduceMeanToSpatialCompute<ONNXReduceMeanV13Op>, ReduceMeanToSpatialCompute<ONNXReduceMeanOp>>(ctx);
 }
 
 } // namespace onnx_mlir

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

@@ -12,6 +12,7 @@
 #include <optional>
 #include <type_traits>
 
+#include "src/Accelerators/PIM/Common/IR/LoopUtils.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
 #include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
@@ -275,86 +276,102 @@ struct PoolToSpatialComputeBase : public OpConversionPattern<PoolOp> {
         Value cStrideHeight = getOrCreateIndexConstant(rewriter, anchorOp, strideHeight);
         Value cStrideWidth = getOrCreateIndexConstant(rewriter, anchorOp, strideWidth);
 
-        auto outputLoop = scf::ForOp::create(rewriter, loc, c0, cOutputPatchCount, c1, ValueRange {pooledOutputInit});
-        rewriter.setInsertionPointToStart(outputLoop.getBody());
+        auto outputLoop = buildNormalizedScfFor(
+          rewriter,
+          loc,
+          c0,
+          cOutputPatchCount,
+          c1,
+          ValueRange {pooledOutputInit},
+          [&](OpBuilder&,
+              Location nestedLoc,
+              Value outputPatchIndex,
+              ValueRange iterArgs,
+              SmallVectorImpl<Value>& yielded) {
+            Value pooledOutputAcc = iterArgs.front();
+            Value batchIndex = arith::DivUIOp::create(rewriter, nestedLoc, outputPatchIndex, cOutputPixelsPerBatch);
+            Value batchPatchIndex =
+              arith::RemUIOp::create(rewriter, nestedLoc, outputPatchIndex, cOutputPixelsPerBatch);
+            Value outHeightIndex = arith::DivUIOp::create(rewriter, nestedLoc, batchPatchIndex, cOutputWidth);
+            Value outWidthIndex = arith::RemUIOp::create(rewriter, nestedLoc, batchPatchIndex, cOutputWidth);
+            Value windowBaseH = arith::MulIOp::create(rewriter, nestedLoc, outHeightIndex, cStrideHeight);
+            Value windowBaseW = arith::MulIOp::create(rewriter, nestedLoc, outWidthIndex, cStrideWidth);
 
-        Value outputPatchIndex = outputLoop.getInductionVar();
-        Value pooledOutputAcc = outputLoop.getRegionIterArgs().front();
+            Value updatedOutput = pooledOutputAcc;
+            for (int64_t channelTile = 0; channelTile < channelTileCount; ++channelTile) {
+              const int64_t tileChannels = std::min<int64_t>(xbarSize, channels - channelTile * xbarSize);
+              auto tileType = RankedTensorType::get({1, tileChannels, 1, 1}, outType.getElementType());
+              Value reducedWindow =
+                createPoolFillTensor(rewriter, nestedLoc, tileType, std::is_same_v<PoolOp, ONNXMaxPoolSingleOutOp>);
 
-        Value batchIndex = arith::DivUIOp::create(rewriter, loc, outputPatchIndex, cOutputPixelsPerBatch);
-        Value batchPatchIndex = arith::RemUIOp::create(rewriter, loc, outputPatchIndex, cOutputPixelsPerBatch);
-        Value outHeightIndex = arith::DivUIOp::create(rewriter, loc, batchPatchIndex, cOutputWidth);
-        Value outWidthIndex = arith::RemUIOp::create(rewriter, loc, batchPatchIndex, cOutputWidth);
-        Value windowBaseH = arith::MulIOp::create(rewriter, loc, outHeightIndex, cStrideHeight);
-        Value windowBaseW = arith::MulIOp::create(rewriter, loc, outWidthIndex, cStrideWidth);
+              for (int64_t kernelH = 0; kernelH < kernelHeight; ++kernelH) {
+                Value paddedInH = windowBaseH;
+                if (kernelH * dilationHeight != 0) {
+                  Value kernelHOffset = getOrCreateIndexConstant(rewriter, anchorOp, kernelH * dilationHeight);
+                  paddedInH = arith::AddIOp::create(rewriter, nestedLoc, paddedInH, kernelHOffset);
+                }
 
-        Value updatedOutput = pooledOutputAcc;
-        for (int64_t channelTile = 0; channelTile < channelTileCount; ++channelTile) {
-          const int64_t tileChannels = std::min<int64_t>(xbarSize, channels - channelTile * xbarSize);
-          auto tileType = RankedTensorType::get({1, tileChannels, 1, 1}, outType.getElementType());
-          Value reducedWindow =
-            createPoolFillTensor(rewriter, loc, tileType, std::is_same_v<PoolOp, ONNXMaxPoolSingleOutOp>);
+                for (int64_t kernelW = 0; kernelW < kernelWidth; ++kernelW) {
+                  Value paddedInW = windowBaseW;
+                  if (kernelW * dilationWidth != 0) {
+                    Value kernelWOffset = getOrCreateIndexConstant(rewriter, anchorOp, kernelW * dilationWidth);
+                    paddedInW = arith::AddIOp::create(rewriter, nestedLoc, paddedInW, kernelWOffset);
+                  }
 
-          for (int64_t kernelH = 0; kernelH < kernelHeight; ++kernelH) {
-            Value paddedInH = windowBaseH;
-            if (kernelH * dilationHeight != 0) {
-              Value kernelHOffset = getOrCreateIndexConstant(rewriter, anchorOp, kernelH * dilationHeight);
-              paddedInH = arith::AddIOp::create(rewriter, loc, paddedInH, kernelHOffset);
-            }
-
-            for (int64_t kernelW = 0; kernelW < kernelWidth; ++kernelW) {
-              Value paddedInW = windowBaseW;
-              if (kernelW * dilationWidth != 0) {
-                Value kernelWOffset = getOrCreateIndexConstant(rewriter, anchorOp, kernelW * dilationWidth);
-                paddedInW = arith::AddIOp::create(rewriter, loc, paddedInW, kernelWOffset);
+                  SmallVector<OpFoldResult> offsets = {
+                    batchIndex, rewriter.getIndexAttr(channelTile * xbarSize), paddedInH, paddedInW};
+                  SmallVector<OpFoldResult> sizes = {rewriter.getIndexAttr(1),
+                                                     rewriter.getIndexAttr(tileChannels),
+                                                     rewriter.getIndexAttr(1),
+                                                     rewriter.getIndexAttr(1)};
+                  SmallVector<OpFoldResult> strides = {rewriter.getIndexAttr(1),
+                                                       rewriter.getIndexAttr(1),
+                                                       rewriter.getIndexAttr(1),
+                                                       rewriter.getIndexAttr(1)};
+                  Value windowValue =
+                    tensor::ExtractSliceOp::create(rewriter, nestedLoc, tileType, paddedInput, offsets, sizes, strides);
+                  windowValue = materializeTileTensor(rewriter, nestedLoc, windowValue);
+                  reducedWindow = ReduceOp::create(rewriter, nestedLoc, tileType, reducedWindow, windowValue);
+                }
               }
 
-              SmallVector<OpFoldResult> offsets = {
-                batchIndex, rewriter.getIndexAttr(channelTile * xbarSize), paddedInH, paddedInW};
-              SmallVector<OpFoldResult> sizes = {rewriter.getIndexAttr(1),
-                                                 rewriter.getIndexAttr(tileChannels),
-                                                 rewriter.getIndexAttr(1),
-                                                 rewriter.getIndexAttr(1)};
-              SmallVector<OpFoldResult> strides = {
+              if constexpr (std::is_same_v<PoolOp, ONNXAveragePoolOp>) {
+                SmallVector<OpFoldResult> scaleOffsets = {rewriter.getIndexAttr(0),
+                                                          rewriter.getIndexAttr(channelTile * xbarSize),
+                                                          outHeightIndex,
+                                                          outWidthIndex};
+                SmallVector<OpFoldResult> scaleSizes = {rewriter.getIndexAttr(1),
+                                                        rewriter.getIndexAttr(tileChannels),
+                                                        rewriter.getIndexAttr(1),
+                                                        rewriter.getIndexAttr(1)};
+                SmallVector<OpFoldResult> scaleStrides = {rewriter.getIndexAttr(1),
+                                                          rewriter.getIndexAttr(1),
+                                                          rewriter.getIndexAttr(1),
+                                                          rewriter.getIndexAttr(1)};
+                Value scaleSlice = tensor::ExtractSliceOp::create(
+                  rewriter, nestedLoc, tileType, averageScaleTensor, scaleOffsets, scaleSizes, scaleStrides);
+                scaleSlice = materializeTileTensor(rewriter, nestedLoc, scaleSlice);
+                reducedWindow = spatial::SpatVMulOp::create(rewriter, nestedLoc, tileType, reducedWindow, scaleSlice);
+              }
+
+              SmallVector<OpFoldResult> outputOffsets = {
+                batchIndex, rewriter.getIndexAttr(channelTile * xbarSize), outHeightIndex, outWidthIndex};
+              SmallVector<OpFoldResult> outputSizes = {rewriter.getIndexAttr(1),
+                                                       rewriter.getIndexAttr(tileChannels),
+                                                       rewriter.getIndexAttr(1),
+                                                       rewriter.getIndexAttr(1)};
+              SmallVector<OpFoldResult> outputStrides = {
                 rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-              Value windowValue =
-                tensor::ExtractSliceOp::create(rewriter, loc, tileType, paddedInput, offsets, sizes, strides);
-              windowValue = materializeTileTensor(rewriter, loc, windowValue);
-              reducedWindow = ReduceOp::create(rewriter, loc, tileType, reducedWindow, windowValue);
+              updatedOutput = tensor::InsertSliceOp::create(
+                rewriter, nestedLoc, reducedWindow, updatedOutput, outputOffsets, outputSizes, outputStrides);
             }
-          }
+            yielded.push_back(updatedOutput);
+            return success();
+          });
+        if (failed(outputLoop))
+          return failure();
 
-          if constexpr (std::is_same_v<PoolOp, ONNXAveragePoolOp>) {
-            SmallVector<OpFoldResult> scaleOffsets = {
-              rewriter.getIndexAttr(0), rewriter.getIndexAttr(channelTile * xbarSize), outHeightIndex, outWidthIndex};
-            SmallVector<OpFoldResult> scaleSizes = {rewriter.getIndexAttr(1),
-                                                    rewriter.getIndexAttr(tileChannels),
-                                                    rewriter.getIndexAttr(1),
-                                                    rewriter.getIndexAttr(1)};
-            SmallVector<OpFoldResult> scaleStrides = {
-              rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-            Value scaleSlice = tensor::ExtractSliceOp::create(
-              rewriter, loc, tileType, averageScaleTensor, scaleOffsets, scaleSizes, scaleStrides);
-            scaleSlice = materializeTileTensor(rewriter, loc, scaleSlice);
-            reducedWindow = spatial::SpatVMulOp::create(rewriter, loc, tileType, reducedWindow, scaleSlice);
-          }
-
-          SmallVector<OpFoldResult> outputOffsets = {
-            batchIndex, rewriter.getIndexAttr(channelTile * xbarSize), outHeightIndex, outWidthIndex};
-          SmallVector<OpFoldResult> outputSizes = {rewriter.getIndexAttr(1),
-                                                   rewriter.getIndexAttr(tileChannels),
-                                                   rewriter.getIndexAttr(1),
-                                                   rewriter.getIndexAttr(1)};
-          SmallVector<OpFoldResult> outputStrides = {
-            rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-          updatedOutput = tensor::InsertSliceOp::create(
-            rewriter, loc, reducedWindow, updatedOutput, outputOffsets, outputSizes, outputStrides);
-        }
-
-        scf::YieldOp::create(rewriter, loc, updatedOutput);
-
-        rewriter.setInsertionPointAfter(outputLoop);
-        spatial::SpatYieldOp::create(rewriter, loc, outputLoop.getResult(0));
+        spatial::SpatYieldOp::create(rewriter, loc, outputLoop->results.front());
         return success();
       });
     if (failed(computeOp))

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

@@ -3,6 +3,7 @@
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Transforms/DialectConversion.h"
 
+#include "src/Accelerators/PIM/Common/IR/LoopUtils.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
@@ -42,13 +43,13 @@ static Value buildLoopSoftmaxSlice(Value input,
   return tensor::InsertSliceOp::create(rewriter, loc, softmaxSlice, accumulator, offsets, sizes, strides);
 }
 
-static Value buildLoopSoftmaxNest(Value input,
-                                  Value accumulator,
-                                  RankedTensorType inputType,
-                                  int64_t axis,
-                                  SmallVectorImpl<Value>& outerIndices,
-                                  ConversionPatternRewriter& rewriter,
-                                  Location loc) {
+static FailureOr<Value> buildLoopSoftmaxNest(Value input,
+                                             Value accumulator,
+                                             RankedTensorType inputType,
+                                             int64_t axis,
+                                             SmallVectorImpl<Value>& outerIndices,
+                                             ConversionPatternRewriter& rewriter,
+                                             Location loc) {
   if (axis == inputType.getRank() - 1)
     return buildLoopSoftmaxSlice(input, accumulator, inputType, outerIndices, rewriter, loc);
 
@@ -57,38 +58,50 @@ static Value buildLoopSoftmaxNest(Value input,
   Value c1 = getOrCreateIndexConstant(rewriter, anchorOp, 1);
   Value cUpper = getOrCreateIndexConstant(rewriter, anchorOp, inputType.getDimSize(axis));
 
-  auto loop = scf::ForOp::create(rewriter, loc, c0, cUpper, c1, ValueRange {accumulator});
-  rewriter.setInsertionPointToStart(loop.getBody());
-
-  Value loopIndex = loop.getInductionVar();
-  Value loopAccumulator = loop.getRegionIterArgs().front();
-  outerIndices.push_back(loopIndex);
-  Value updatedAccumulator =
-    buildLoopSoftmaxNest(input, loopAccumulator, inputType, axis + 1, outerIndices, rewriter, loc);
-  outerIndices.pop_back();
-
-  scf::YieldOp::create(rewriter, loc, updatedAccumulator);
-  rewriter.setInsertionPointAfter(loop);
-  return loop.getResult(0);
+  auto loop = buildNormalizedScfFor(
+    rewriter,
+    loc,
+    c0,
+    cUpper,
+    c1,
+    ValueRange {accumulator},
+    [&](OpBuilder& builder, Location nestedLoc, Value loopIndex, ValueRange iterArgs, SmallVectorImpl<Value>& yielded) {
+      outerIndices.push_back(loopIndex);
+      auto updatedAccumulator =
+        buildLoopSoftmaxNest(input, iterArgs.front(), inputType, axis + 1, outerIndices, rewriter, nestedLoc);
+      outerIndices.pop_back();
+      if (failed(updatedAccumulator))
+        return failure();
+      yielded.push_back(*updatedAccumulator);
+      return success();
+    });
+  if (failed(loop))
+    return failure();
+  return loop->results.front();
 }
 
-static Value createLoopSoftmaxCompute(Value input, ConversionPatternRewriter& rewriter, Location loc) {
+static FailureOr<Value> createLoopSoftmaxCompute(Value input, ConversionPatternRewriter& rewriter, Location loc) {
   auto inputType = cast<RankedTensorType>(input.getType());
   constexpr size_t numInputs = 1;
-  auto computeOp =
-    createSpatCompute<numInputs>(rewriter, loc, TypeRange {inputType}, {}, ValueRange {input}, [&](Value x) {
+  auto computeOp = createSpatCompute<numInputs>(
+    rewriter, loc, TypeRange {inputType}, {}, ValueRange {input}, [&](Value x) -> LogicalResult {
       if (inputType.getRank() == 1) {
         Value softmax = spatial::SpatSoftmaxOp::create(rewriter, loc, inputType, x).getResult();
         spatial::SpatYieldOp::create(rewriter, loc, softmax);
-        return;
+        return success();
       }
 
       Value outputInit = tensor::EmptyOp::create(rewriter, loc, inputType.getShape(), inputType.getElementType());
       SmallVector<Value> outerIndices;
-      Value result = buildLoopSoftmaxNest(x, outputInit, inputType, /*axis=*/0, outerIndices, rewriter, loc);
-      spatial::SpatYieldOp::create(rewriter, loc, result);
+      auto result = buildLoopSoftmaxNest(x, outputInit, inputType, /*axis=*/0, outerIndices, rewriter, loc);
+      if (failed(result))
+        return failure();
+      spatial::SpatYieldOp::create(rewriter, loc, *result);
+      return success();
     });
-  return computeOp.getResult(0);
+  if (failed(computeOp))
+    return failure();
+  return computeOp->getResult(0);
 }
 
 struct SoftmaxToSpatialCompute : OpConversionPattern<ONNXSoftmaxOp> {
@@ -108,7 +121,10 @@ struct SoftmaxToSpatialCompute : OpConversionPattern<ONNXSoftmaxOp> {
     Value input = adaptor.getInput();
     Value result;
     if (*axis == inputType.getRank() - 1) {
-      result = createLoopSoftmaxCompute(input, rewriter, softmaxOp.getLoc());
+      auto computed = createLoopSoftmaxCompute(input, rewriter, softmaxOp.getLoc());
+      if (failed(computed))
+        return failure();
+      result = *computed;
     }
     else {
       SmallVector<int64_t> permutation;
@@ -121,9 +137,17 @@ struct SoftmaxToSpatialCompute : OpConversionPattern<ONNXSoftmaxOp> {
 
       auto transposedType = RankedTensorType::get(
         permuteShape(inputType.getShape(), permutation), inputType.getElementType(), inputType.getEncoding());
-      Value transposedInput = transposeMaybeInCompute(input, transposedType, permutation, rewriter, softmaxOp.getLoc());
-      Value transposedResult = createLoopSoftmaxCompute(transposedInput, rewriter, softmaxOp.getLoc());
-      result = transposeMaybeInCompute(transposedResult, inputType, inversePermutation, rewriter, softmaxOp.getLoc());
+      Value transposedInput =
+        ONNXTransposeOp::create(
+          rewriter, softmaxOp.getLoc(), transposedType, input, rewriter.getI64ArrayAttr(permutation))
+          .getResult();
+      auto transposedResult = createLoopSoftmaxCompute(transposedInput, rewriter, softmaxOp.getLoc());
+      if (failed(transposedResult))
+        return failure();
+      result =
+        ONNXTransposeOp::create(
+          rewriter, softmaxOp.getLoc(), inputType, *transposedResult, rewriter.getI64ArrayAttr(inversePermutation))
+          .getResult();
     }
 
     rewriter.replaceOp(softmaxOp, result);

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

@@ -9,6 +9,7 @@
 #include "llvm/ADT/SmallVector.h"
 
 #include "src/Accelerators/PIM/Common/IR/WeightUtils.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/WeightMaterialization.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
@@ -192,13 +193,12 @@ struct PromoteWeightLikeComputeBatchInputsPattern : OpRewritePattern<spatial::Sp
 
     rewriter.setInsertionPointAfter(compute);
 
-    auto newCompute =
-      spatial::SpatComputeBatch::create(rewriter,
-                                        compute.getLoc(),
-                                        compute.getResultTypes(),
-                                        rewriter.getI32IntegerAttr(static_cast<int32_t>(compute.getLaneCount())),
-                                        promoted->newWeights,
-                                        promoted->newInputs);
+    auto laneCountAttr = pim::getCheckedI32Attr(
+      rewriter, compute, static_cast<uint64_t>(compute.getLaneCount()), "promoted compute_batch lane count");
+    if (failed(laneCountAttr))
+      return failure();
+    auto newCompute = spatial::SpatComputeBatch::create(
+      rewriter, compute.getLoc(), compute.getResultTypes(), *laneCountAttr, promoted->newWeights, promoted->newInputs);
     auto laneArg = compute.getLaneArgument();
     if (!laneArg)
       return rewriter.notifyMatchFailure(compute, "missing compute_batch lane block argument");

src/PIM/Conversion/ONNXToSpatial/Patterns/Tensor/Resize.cpp

@@ -5,6 +5,7 @@
 
 #include "llvm/ADT/STLExtras.h"
 
+#include "src/Accelerators/PIM/Common/IR/LoopUtils.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
@@ -26,11 +27,11 @@ static Value buildNearestAsymmetricIndex(
   return arith::MinUIOp::create(rewriter, loc, inputIndex, cInputDimLast);
 }
 
-static Value buildNearestResizeLoop(Value input,
-                                    RankedTensorType inputType,
-                                    RankedTensorType resultType,
-                                    ConversionPatternRewriter& rewriter,
-                                    Location loc) {
+static FailureOr<Value> buildNearestResizeLoop(Value input,
+                                               RankedTensorType inputType,
+                                               RankedTensorType resultType,
+                                               ConversionPatternRewriter& rewriter,
+                                               Location loc) {
   auto elemType = resultType.getElementType();
   SmallVector<int64_t> unitShape(resultType.getRank(), 1);
   auto unitTensorType = RankedTensorType::get(unitShape, elemType);
@@ -48,54 +49,94 @@ static Value buildNearestResizeLoop(Value input,
 
   Value outputInit = tensor::EmptyOp::create(rewriter, loc, resultType.getShape(), elemType);
 
-  auto batchLoop = scf::ForOp::create(rewriter, loc, c0, cOutputN, c1, ValueRange {outputInit});
-  rewriter.setInsertionPointToStart(batchLoop.getBody());
+  auto batchLoop = buildNormalizedScfFor(
+    rewriter,
+    loc,
+    c0,
+    cOutputN,
+    c1,
+    ValueRange {outputInit},
+    [&](OpBuilder&, Location nestedLoc, Value outputN, ValueRange batchIterArgs, SmallVectorImpl<Value>& batchYielded) {
+      Value outputBatchAcc = batchIterArgs.front();
+      Value inputN =
+        buildNearestAsymmetricIndex(outputN, inputType.getDimSize(0), resultType.getDimSize(0), rewriter, nestedLoc);
 
-  Value outputN = batchLoop.getInductionVar();
-  Value outputBatchAcc = batchLoop.getRegionIterArgs().front();
-  Value inputN = buildNearestAsymmetricIndex(outputN, inputType.getDimSize(0), resultType.getDimSize(0), rewriter, loc);
+      auto channelLoop = buildNormalizedScfFor(
+        rewriter,
+        nestedLoc,
+        c0,
+        cOutputC,
+        c1,
+        ValueRange {outputBatchAcc},
+        [&](OpBuilder&,
+            Location channelLoc,
+            Value outputC,
+            ValueRange channelIterArgs,
+            SmallVectorImpl<Value>& channelYielded) {
+          Value outputChannelAcc = channelIterArgs.front();
+          Value inputC = buildNearestAsymmetricIndex(
+            outputC, inputType.getDimSize(1), resultType.getDimSize(1), rewriter, channelLoc);
 
-  auto channelLoop = scf::ForOp::create(rewriter, loc, c0, cOutputC, c1, ValueRange {outputBatchAcc});
-  rewriter.setInsertionPointToStart(channelLoop.getBody());
+          auto heightLoop = buildNormalizedScfFor(
+            rewriter,
+            channelLoc,
+            c0,
+            cOutputH,
+            c1,
+            ValueRange {outputChannelAcc},
+            [&](OpBuilder&,
+                Location heightLoc,
+                Value outputH,
+                ValueRange heightIterArgs,
+                SmallVectorImpl<Value>& heightYielded) {
+              Value outputHeightAcc = heightIterArgs.front();
+              Value inputH = buildNearestAsymmetricIndex(
+                outputH, inputType.getDimSize(2), resultType.getDimSize(2), rewriter, heightLoc);
 
-  Value outputC = channelLoop.getInductionVar();
-  Value outputChannelAcc = channelLoop.getRegionIterArgs().front();
-  Value inputC = buildNearestAsymmetricIndex(outputC, inputType.getDimSize(1), resultType.getDimSize(1), rewriter, loc);
+              auto widthLoop = buildNormalizedScfFor(
+                rewriter,
+                heightLoc,
+                c0,
+                cOutputW,
+                c1,
+                ValueRange {outputHeightAcc},
+                [&](OpBuilder&,
+                    Location widthLoc,
+                    Value outputW,
+                    ValueRange widthIterArgs,
+                    SmallVectorImpl<Value>& widthYielded) {
+                  Value outputWidthAcc = widthIterArgs.front();
+                  Value inputW = buildNearestAsymmetricIndex(
+                    outputW, inputType.getDimSize(3), resultType.getDimSize(3), rewriter, widthLoc);
 
-  auto heightLoop = scf::ForOp::create(rewriter, loc, c0, cOutputH, c1, ValueRange {outputChannelAcc});
-  rewriter.setInsertionPointToStart(heightLoop.getBody());
+                  SmallVector<OpFoldResult> inputOffsets = {inputN, inputC, inputH, inputW};
+                  Value inputSlice = tensor::ExtractSliceOp::create(
+                    rewriter, widthLoc, unitTensorType, input, inputOffsets, unitSizes, unitStrides);
 
-  Value outputH = heightLoop.getInductionVar();
-  Value outputHeightAcc = heightLoop.getRegionIterArgs().front();
-  Value inputH = buildNearestAsymmetricIndex(outputH, inputType.getDimSize(2), resultType.getDimSize(2), rewriter, loc);
-
-  auto widthLoop = scf::ForOp::create(rewriter, loc, c0, cOutputW, c1, ValueRange {outputHeightAcc});
-  rewriter.setInsertionPointToStart(widthLoop.getBody());
-
-  Value outputW = widthLoop.getInductionVar();
-  Value outputWidthAcc = widthLoop.getRegionIterArgs().front();
-  Value inputW = buildNearestAsymmetricIndex(outputW, inputType.getDimSize(3), resultType.getDimSize(3), rewriter, loc);
-
-  SmallVector<OpFoldResult> inputOffsets = {inputN, inputC, inputH, inputW};
-  Value inputSlice =
-    tensor::ExtractSliceOp::create(rewriter, loc, unitTensorType, input, inputOffsets, unitSizes, unitStrides);
-
-  SmallVector<OpFoldResult> outputOffsets = {outputN, outputC, outputH, outputW};
-  Value updatedOutput =
-    tensor::InsertSliceOp::create(rewriter, loc, inputSlice, outputWidthAcc, outputOffsets, unitSizes, unitStrides);
-  scf::YieldOp::create(rewriter, loc, updatedOutput);
-
-  rewriter.setInsertionPointAfter(widthLoop);
-  scf::YieldOp::create(rewriter, loc, widthLoop.getResult(0));
-
-  rewriter.setInsertionPointAfter(heightLoop);
-  scf::YieldOp::create(rewriter, loc, heightLoop.getResult(0));
-
-  rewriter.setInsertionPointAfter(channelLoop);
-  scf::YieldOp::create(rewriter, loc, channelLoop.getResult(0));
-
-  rewriter.setInsertionPointAfter(batchLoop);
-  return batchLoop.getResult(0);
+                  SmallVector<OpFoldResult> outputOffsets = {outputN, outputC, outputH, outputW};
+                  Value updatedOutput = tensor::InsertSliceOp::create(
+                    rewriter, widthLoc, inputSlice, outputWidthAcc, outputOffsets, unitSizes, unitStrides);
+                  widthYielded.push_back(updatedOutput);
+                  return success();
+                });
+              if (failed(widthLoop))
+                return failure();
+              heightYielded.push_back(widthLoop->results.front());
+              return success();
+            });
+          if (failed(heightLoop))
+            return failure();
+          channelYielded.push_back(heightLoop->results.front());
+          return success();
+        });
+      if (failed(channelLoop))
+        return failure();
+      batchYielded.push_back(channelLoop->results.front());
+      return success();
+    });
+  if (failed(batchLoop))
+    return failure();
+  return batchLoop->results.front();
 }
 
 struct Resize : OpConversionPattern<ONNXResizeOp> {
@@ -120,12 +161,17 @@ struct Resize : OpConversionPattern<ONNXResizeOp> {
         || llvm::any_of(resultType.getShape(), [](int64_t dim) { return dim <= 0; }))
       return rewriter.notifyMatchFailure(resizeOp, "resize lowering requires positive static dimensions.");
 
-    auto computeOp =
-      createSpatCompute<1>(rewriter, resizeOp.getLoc(), TypeRange {resultType}, {}, adaptor.getX(), [&](Value x) {
-        Value result = buildNearestResizeLoop(x, inputType, resultType, rewriter, resizeOp.getLoc());
-        spatial::SpatYieldOp::create(rewriter, resizeOp.getLoc(), result);
+    auto computeOp = createSpatCompute<1>(
+      rewriter, resizeOp.getLoc(), TypeRange {resultType}, {}, adaptor.getX(), [&](Value x) -> LogicalResult {
+        auto result = buildNearestResizeLoop(x, inputType, resultType, rewriter, resizeOp.getLoc());
+        if (failed(result))
+          return failure();
+        spatial::SpatYieldOp::create(rewriter, resizeOp.getLoc(), *result);
+        return success();
       });
-    rewriter.replaceOp(resizeOp, computeOp.getResults());
+    if (failed(computeOp))
+      return failure();
+    rewriter.replaceOp(resizeOp, computeOp->getResults());
     return success();
   }
 };

src/PIM/Conversion/ONNXToSpatial/Patterns/Tensor/Slice.cpp

@@ -0,0 +1,189 @@
+#include "mlir/Dialect/Tensor/IR/Tensor.h"
+#include "mlir/IR/BuiltinAttributes.h"
+#include "mlir/Transforms/DialectConversion.h"
+
+#include "llvm/ADT/SmallVector.h"
+
+#include <algorithm>
+#include <optional>
+
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/CompileTime.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
+#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
+#include "src/Dialect/ONNX/ONNXOps.hpp"
+
+using namespace mlir;
+
+namespace onnx_mlir {
+namespace {
+
+static FailureOr<SmallVector<int64_t>> getConstantIntValues(Value value) {
+  auto denseAttr = dyn_cast_or_null<DenseIntElementsAttr>(getHostConstDenseElementsAttr(value));
+  if (!denseAttr)
+    return failure();
+  return SmallVector<int64_t>(denseAttr.getValues<int64_t>().begin(), denseAttr.getValues<int64_t>().end());
+}
+
+static bool isNoneValueLike(Value value) { return isa_and_nonnull<ONNXNoneOp>(value.getDefiningOp()); }
+
+static FailureOr<Value> buildSlice(Value data,
+                                   RankedTensorType dataType,
+                                   RankedTensorType resultType,
+                                   ArrayRef<int64_t> starts,
+                                   ArrayRef<int64_t> ends,
+                                   std::optional<ArrayRef<int64_t>> axes,
+                                   std::optional<ArrayRef<int64_t>> steps,
+                                   ConversionPatternRewriter& rewriter,
+                                   Location loc) {
+  int64_t rank = dataType.getRank();
+  if (!dataType.hasStaticShape() || !resultType.hasStaticShape() || resultType.getRank() != rank)
+    return failure();
+
+  if (starts.size() != ends.size())
+    return failure();
+  if (axes && axes->size() != starts.size())
+    return failure();
+  if (steps && steps->size() != starts.size())
+    return failure();
+
+  SmallVector<int64_t> normalizedAxes;
+  if (axes) {
+    SmallVector<bool> seenAxes(rank, false);
+    normalizedAxes.reserve(axes->size());
+    for (int64_t axis : *axes) {
+      auto normalizedAxis = normalizeAxisChecked(axis, rank);
+      if (failed(normalizedAxis))
+        return failure();
+      if (seenAxes[*normalizedAxis])
+        return failure();
+      seenAxes[*normalizedAxis] = true;
+      normalizedAxes.push_back(*normalizedAxis);
+    }
+  }
+  else {
+    if (starts.size() > static_cast<size_t>(rank))
+      return failure();
+    normalizedAxes.reserve(starts.size());
+    for (size_t i = 0; i < starts.size(); ++i)
+      normalizedAxes.push_back(static_cast<int64_t>(i));
+  }
+
+  SmallVector<int64_t> normalizedSteps;
+  if (steps)
+    normalizedSteps.assign(steps->begin(), steps->end());
+  else
+    normalizedSteps.assign(starts.size(), 1);
+
+  SmallVector<int64_t> computedShape(dataType.getShape().begin(), dataType.getShape().end());
+  SmallVector<OpFoldResult> offsets = getZeroOffsets(rewriter, rank);
+  SmallVector<OpFoldResult> sizes = getStaticSizes(rewriter, dataType.getShape());
+  SmallVector<OpFoldResult> strides = getUnitStrides(rewriter, rank);
+
+  for (auto [sliceIndex, axis] : llvm::enumerate(normalizedAxes)) {
+    int64_t step = normalizedSteps[sliceIndex];
+    if (step <= 0)
+      return failure();
+
+    int64_t dimSize = dataType.getShape()[axis];
+    int64_t start = starts[sliceIndex];
+    int64_t end = ends[sliceIndex];
+
+    start = normalizeIndex(start, dimSize);
+    end = normalizeIndex(end, dimSize);
+
+    start = std::clamp(start, int64_t {0}, dimSize);
+    end = std::clamp(end, int64_t {0}, dimSize);
+
+    int64_t extent = std::max(end - start, int64_t {0});
+    int64_t size = (extent + step - 1) / step;
+
+    offsets[axis] = rewriter.getIndexAttr(start);
+    sizes[axis] = rewriter.getIndexAttr(size);
+    strides[axis] = rewriter.getIndexAttr(step);
+    computedShape[axis] = size;
+  }
+
+  if (llvm::ArrayRef(computedShape) != resultType.getShape())
+    return failure();
+
+  return tensor::ExtractSliceOp::create(rewriter, loc, resultType, data, offsets, sizes, strides).getResult();
+}
+
+struct Slice final : OpConversionPattern<ONNXSliceOp> {
+  using OpConversionPattern::OpConversionPattern;
+
+  LogicalResult matchAndRewrite(ONNXSliceOp sliceOp,
+                                ONNXSliceOpAdaptor adaptor,
+                                ConversionPatternRewriter& rewriter) const override {
+    auto dataType = dyn_cast<RankedTensorType>(adaptor.getData().getType());
+    auto resultType = dyn_cast<RankedTensorType>(sliceOp.getResult().getType());
+    if (!dataType || !resultType || !dataType.hasStaticShape() || !resultType.hasStaticShape())
+      return failure();
+
+    auto starts = getConstantIntValues(adaptor.getStarts());
+    auto ends = getConstantIntValues(adaptor.getEnds());
+    if (failed(starts))
+      return rewriter.notifyMatchFailure(sliceOp, "requires compile-time constant starts");
+    if (failed(ends))
+      return rewriter.notifyMatchFailure(sliceOp, "requires compile-time constant ends");
+
+    std::optional<SmallVector<int64_t>> axes;
+    if (!isNoneValueLike(adaptor.getAxes())) {
+      auto parsedAxes = getConstantIntValues(adaptor.getAxes());
+      if (failed(parsedAxes))
+        return rewriter.notifyMatchFailure(sliceOp, "requires compile-time constant axes when present");
+      axes = std::move(*parsedAxes);
+    }
+
+    std::optional<SmallVector<int64_t>> steps;
+    if (!isNoneValueLike(adaptor.getSteps())) {
+      auto parsedSteps = getConstantIntValues(adaptor.getSteps());
+      if (failed(parsedSteps))
+        return rewriter.notifyMatchFailure(sliceOp, "requires compile-time constant steps when present");
+      steps = std::move(*parsedSteps);
+      if (llvm::any_of(*steps, [](int64_t step) { return step <= 0; }))
+        return rewriter.notifyMatchFailure(sliceOp, "supports only positive constant steps");
+    }
+
+    ArrayRef<int64_t> startsRef = *starts;
+    ArrayRef<int64_t> endsRef = *ends;
+    std::optional<ArrayRef<int64_t>> axesRef = axes ? std::optional<ArrayRef<int64_t>>(ArrayRef<int64_t>(*axes))
+                                                    : std::nullopt;
+    std::optional<ArrayRef<int64_t>> stepsRef = steps ? std::optional<ArrayRef<int64_t>>(ArrayRef<int64_t>(*steps))
+                                                       : std::nullopt;
+
+    Location loc = sliceOp.getLoc();
+    auto tryBuildSlice = [&](Value data) {
+      return buildSlice(data, dataType, resultType, startsRef, endsRef, axesRef, stepsRef, rewriter, loc);
+    };
+
+    if (isCompileTimeComputable(adaptor.getData())) {
+      auto sliced = tryBuildSlice(adaptor.getData());
+      if (failed(sliced))
+        return rewriter.notifyMatchFailure(sliceOp, "failed to normalize static slice parameters");
+      rewriter.replaceOp(sliceOp, *sliced);
+      return success();
+    }
+
+    auto computeOp =
+      createSpatCompute<1>(rewriter, loc, TypeRange {resultType}, {}, adaptor.getData(), [&](Value data) {
+        auto sliced = tryBuildSlice(data);
+        if (failed(sliced))
+          return failure();
+        spatial::SpatYieldOp::create(rewriter, loc, *sliced);
+        return success();
+      });
+    if (failed(computeOp))
+      return rewriter.notifyMatchFailure(sliceOp, "failed to build runtime tensor.extract_slice lowering");
+
+    rewriter.replaceOp(sliceOp, computeOp->getResults());
+    return success();
+  }
+};
+
+} // namespace
+
+void populateSlicePatterns(RewritePatternSet& patterns, MLIRContext* ctx) { patterns.add<Slice>(ctx); }
+
+} // namespace onnx_mlir

src/PIM/Conversion/ONNXToSpatial/Patterns/Tensor/Transpose.cpp

@@ -15,6 +15,10 @@ using namespace mlir;
 namespace onnx_mlir {
 namespace {
 
+static bool isInsideSpatialComputeRegion(Operation* op) {
+  return op->getParentOfType<spatial::SpatCompute>() || op->getParentOfType<spatial::SpatComputeBatch>();
+}
+
 static Value createTransposeInit(Value input,
                                  RankedTensorType resultType,
                                  ArrayRef<int64_t> permutation,
@@ -102,10 +106,22 @@ struct TransposeToLinalgTranspose : OpConversionPattern<ONNXTransposeOp> {
         return success();
       }
     }
-    Value init = createTransposeInit(adaptor.getData(), resultType, *permutation, rewriter, transposeOp.getLoc());
-    Value transposed =
-      linalg::TransposeOp::create(rewriter, transposeOp.getLoc(), adaptor.getData(), init, *permutation).getResult()[0];
-    rewriter.replaceOp(transposeOp, transposed);
+
+    auto buildTranspose = [&](Value input) -> Value {
+      Value init = createTransposeInit(input, resultType, *permutation, rewriter, transposeOp.getLoc());
+      return linalg::TransposeOp::create(rewriter, transposeOp.getLoc(), input, init, *permutation).getResult()[0];
+    };
+
+    if (isInsideSpatialComputeRegion(transposeOp.getOperation())) {
+      rewriter.replaceOp(transposeOp, buildTranspose(adaptor.getData()));
+      return success();
+    }
+
+    auto computeOp = createSpatCompute<1>(
+      rewriter, transposeOp.getLoc(), TypeRange {resultType}, {}, ValueRange {adaptor.getData()}, [&](Value input) {
+        spatial::SpatYieldOp::create(rewriter, transposeOp.getLoc(), buildTranspose(input));
+      });
+    rewriter.replaceOp(transposeOp, computeOp.getResult(0));
     return success();
   }
 };

src/PIM/Conversion/SpatialToPim/BatchCoreLoweringPatterns.cpp

@@ -10,6 +10,7 @@
 #include "Conversion/SpatialToPim/SpatialToPimPass.hpp"
 #include "src/Accelerators/PIM/Common/IR/BatchCoreUtils.hpp"
 #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/Dialect/Pim/PimOps.hpp"
 
@@ -25,14 +26,21 @@ static bool isUsedOnlyAsExplicitHostOperand(Value value) {
   });
 }
 
-static SmallVector<int32_t> getPimCoreIdsForBatchOp(spatial::SpatComputeBatch computeBatchOp, size_t& fallbackCoreId) {
+static FailureOr<SmallVector<int32_t>> getPimCoreIdsForBatchOp(spatial::SpatComputeBatch computeBatchOp,
+                                                               size_t& fallbackCoreId) {
   if (auto coreIdsAttr = computeBatchOp->getAttrOfType<DenseI32ArrayAttr>(onnx_mlir::kCoreIdsAttrName))
     return SmallVector<int32_t>(coreIdsAttr.asArrayRef().begin(), coreIdsAttr.asArrayRef().end());
 
   SmallVector<int32_t> coreIds;
   coreIds.reserve(static_cast<size_t>(computeBatchOp.getLaneCount()));
-  for (uint32_t lane = 0; lane < computeBatchOp.getLaneCount(); ++lane)
-    coreIds.push_back(static_cast<int32_t>(fallbackCoreId++));
+  for (uint32_t lane = 0; lane < computeBatchOp.getLaneCount(); ++lane) {
+    auto checkedCoreId =
+      pim::checkedI32(static_cast<uint64_t>(fallbackCoreId), computeBatchOp, "fallback spatial compute_batch core id");
+    if (failed(checkedCoreId))
+      return failure();
+    coreIds.push_back(*checkedCoreId);
+    ++fallbackCoreId;
+  }
   return coreIds;
 }
 
@@ -102,21 +110,24 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeBatchOp(spatial::SpatCompute
       "resultful compute_batch lowering currently requires a spat.in_parallel terminator");
   }
 
-  SmallVector<int32_t> coreIds = getPimCoreIdsForBatchOp(computeBatchOp, coreId);
+  auto coreIds = getPimCoreIdsForBatchOp(computeBatchOp, coreId);
+  if (failed(coreIds))
+    return failure();
   SmallVector<Value> batchWeights(computeBatchOp.getWeights().begin(), computeBatchOp.getWeights().end());
   SmallVector<Value> batchInputs;
   if (!computeBatchOp.getInputs().empty())
     batchInputs.append(computeBatchOp.getInputs().begin(), computeBatchOp.getInputs().end());
 
   rewriter.setInsertionPointAfter(computeBatchOp);
-  auto coreBatchOp = pim::PimCoreBatchOp::create(rewriter,
-                                                 loc,
-                                                 rewriter.getI32IntegerAttr(computeBatchOp.getLaneCount()),
-                                                 ValueRange(batchWeights),
-                                                 ValueRange(batchInputs));
+  auto laneCountAttr = pim::getCheckedI32Attr(
+    rewriter, computeBatchOp, static_cast<uint64_t>(computeBatchOp.getLaneCount()), "pim core_batch lane count");
+  if (failed(laneCountAttr))
+    return failure();
+  auto coreBatchOp =
+    pim::PimCoreBatchOp::create(rewriter, loc, *laneCountAttr, ValueRange(batchWeights), ValueRange(batchInputs));
   coreBatchOp.getProperties().setOperandSegmentSizes(
     {static_cast<int>(batchWeights.size()), static_cast<int>(batchInputs.size())});
-  coreBatchOp->setAttr(onnx_mlir::kCoreIdsAttrName, rewriter.getDenseI32ArrayAttr(coreIds));
+  coreBatchOp->setAttr(onnx_mlir::kCoreIdsAttrName, rewriter.getDenseI32ArrayAttr(*coreIds));
 
   SmallVector<unsigned> returnOperandIndices;
   if (computeBatchOp.getNumResults() != 0) {
@@ -160,14 +171,11 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeBatchOp(spatial::SpatCompute
     auto newArgType = cast<ShapedType>(newArg.getType());
     auto outputBuffer = createEmptyTensorFromShaped(rewriter, loc, newArgType);
     Value zeroOffset = getOrCreateIndexConstant(rewriter, coreBatchOp.getOperation(), 0);
-    auto copied = pim::PimMemCopyHostToDevOp::create(rewriter,
-                                                     loc,
-                                                     outputBuffer.getType(),
-                                                     zeroOffset,
-                                                     zeroOffset,
-                                                     outputBuffer,
-                                                     newArg,
-                                                     getTensorSizeInBytesAttr(rewriter, newArg))
+    auto sizeAttr = getTensorSizeInBytesAttr(rewriter, coreBatchOp.getOperation(), newArg);
+    if (failed(sizeAttr))
+      return failure();
+    auto copied = pim::PimMemCopyHostToDevOp::create(
+                    rewriter, loc, outputBuffer.getType(), zeroOffset, zeroOffset, outputBuffer, newArg, *sizeAttr)
                     .getOutput();
     mapper.map(*oldArg, copied);
   }
@@ -209,6 +217,9 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeBatchOp(spatial::SpatCompute
         auto hostTargetType = cast<ShapedType>(hostTarget.getType());
         Value hostTargetOffset = createHostTargetOffset(rewriter, insertSlice, hostTargetType, mapper);
         Value zeroOffset = getOrCreateIndexConstant(rewriter, coreBatchOp.getOperation(), 0);
+        auto sizeAttr = getTensorSizeInBytesAttr(rewriter, coreBatchOp.getOperation(), mappedSource);
+        if (failed(sizeAttr))
+          return failure();
         pim::PimMemCopyDevToHostOp::create(rewriter,
                                            insertSlice.getLoc(),
                                            hostTarget.getType(),
@@ -216,7 +227,7 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeBatchOp(spatial::SpatCompute
                                            zeroOffset,
                                            hostTarget,
                                            mappedSource,
-                                           getTensorSizeInBytesAttr(rewriter, mappedSource));
+                                           *sizeAttr);
       }
       continue;
     }
@@ -232,15 +243,13 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeBatchOp(spatial::SpatCompute
         auto clonedType = cast<ShapedType>(clonedTensor.getType());
         auto outputBuffer = createEmptyTensorFromShaped(rewriter, loc, clonedType);
         Value zeroOffset = getOrCreateIndexConstant(rewriter, coreBatchOp.getOperation(), 0);
-        auto copied = pim::PimMemCopyHostToDevOp::create(rewriter,
-                                                         loc,
-                                                         outputBuffer.getType(),
-                                                         zeroOffset,
-                                                         zeroOffset,
-                                                         outputBuffer,
-                                                         clonedTensor,
-                                                         getTensorSizeInBytesAttr(rewriter, clonedTensor))
-                        .getOutput();
+        auto sizeAttr = getTensorSizeInBytesAttr(rewriter, coreBatchOp.getOperation(), clonedTensor);
+        if (failed(sizeAttr))
+          return failure();
+        auto copied =
+          pim::PimMemCopyHostToDevOp::create(
+            rewriter, loc, outputBuffer.getType(), zeroOffset, zeroOffset, outputBuffer, clonedTensor, *sizeAttr)
+            .getOutput();
         mapper.map(toTensorOp.getResult(), copied);
         continue;
       }

src/PIM/Conversion/SpatialToPim/Common.cpp

@@ -5,14 +5,18 @@
 #include <cassert>
 
 #include "Common.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 
 using namespace llvm;
 using namespace mlir;
 
 namespace onnx_mlir {
 
-IntegerAttr getTensorSizeInBytesAttr(Builder& builder, mlir::Value value) {
-  return builder.getI32IntegerAttr(static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(value.getType()))));
+FailureOr<IntegerAttr> getTensorSizeInBytesAttr(Builder& builder, Operation* anchor, mlir::Value value) {
+  auto byteSize = pim::getCheckedShapedTypeSizeInBytes(cast<ShapedType>(value.getType()), anchor, "tensor byte size");
+  if (failed(byteSize))
+    return failure();
+  return pim::getCheckedI32Attr(builder, anchor, *byteSize, "tensor byte size");
 }
 
 Operation* getEarliestUserWithinBlock(mlir::Value value) {

src/PIM/Conversion/SpatialToPim/Common.hpp

@@ -1,12 +1,14 @@
 #pragma once
 
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
+#include "mlir/Support/LogicalResult.h"
 
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
 
 namespace onnx_mlir {
 
-mlir::IntegerAttr getTensorSizeInBytesAttr(mlir::Builder& builder, mlir::Value value);
+mlir::FailureOr<mlir::IntegerAttr>
+getTensorSizeInBytesAttr(mlir::Builder& builder, mlir::Operation* anchor, mlir::Value value);
 
 template <class T>
 size_t rangeLength(const mlir::iterator_range<T> range) {

src/PIM/Conversion/SpatialToPim/CoreLoweringPatterns.cpp

@@ -9,6 +9,7 @@
 #include "Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "Conversion/SpatialToPim/SpatialToPimPass.hpp"
 #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/Dialect/Pim/PimOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
@@ -54,10 +55,15 @@ cloneMappedHelperOperands(Operation* op, IRMapping& mapping, IRRewriter& rewrite
   }
 }
 
-static int32_t getPimCoreIdForComputeOp(spatial::SpatCompute computeOp, size_t& fallbackCoreId) {
+static FailureOr<int32_t> getPimCoreIdForComputeOp(spatial::SpatCompute computeOp, size_t& fallbackCoreId) {
   if (auto spatialCoreIdAttr = computeOp->getAttrOfType<IntegerAttr>(onnx_mlir::kCoreIdAttrName))
-    return static_cast<int32_t>(spatialCoreIdAttr.getInt());
-  return static_cast<int32_t>(fallbackCoreId++);
+    return pim::checkedI32(spatialCoreIdAttr.getInt(), computeOp, "spatial compute core id");
+  auto checkedCoreId =
+    pim::checkedI32(static_cast<uint64_t>(fallbackCoreId), computeOp, "fallback spatial compute core id");
+  if (failed(checkedCoreId))
+    return failure();
+  ++fallbackCoreId;
+  return *checkedCoreId;
 }
 
 static LogicalResult collectHelperComputeChain(spatial::SpatCompute computeOp,
@@ -163,10 +169,12 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeOp(spatial::SpatCompute comp
       rewriter.setInsertionPoint(getEarliestUserWithinBlock(*blockArg));
       auto outputType = cast<ShapedType>(blockArg->getType());
       auto outputBuffer = createEmptyTensorFromShaped(rewriter, receiveOp.getLoc(), outputType);
-      auto sizeAttr = getTensorSizeInBytesAttr(rewriter, *blockArg);
+      auto sizeAttr = getTensorSizeInBytesAttr(rewriter, computeOp.getOperation(), *blockArg);
+      if (failed(sizeAttr))
+        return failure();
       Value received =
         PimReceiveOp::create(
-          rewriter, receiveOp.getLoc(), outputBuffer.getType(), outputBuffer, sizeAttr, receiveOp.getSourceCoreId())
+          rewriter, receiveOp.getLoc(), outputBuffer.getType(), outputBuffer, *sizeAttr, receiveOp.getSourceCoreId())
           .getOutput();
       blockArg->replaceAllUsesWith(received);
       markOpToRemove(receiveOp);
@@ -206,8 +214,13 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeOp(spatial::SpatCompute comp
   if (!computeOp.getWeights().empty())
     computeWeights.append(computeOp.getWeights().begin(), computeOp.getWeights().end());
   rewriter.setInsertionPointAfter(computeOp);
-  auto coreOp = PimCoreOp::create(
-    rewriter, loc, ValueRange(computeWeights), rewriter.getI32IntegerAttr(getPimCoreIdForComputeOp(computeOp, coreId)));
+  auto checkedCoreId = getPimCoreIdForComputeOp(computeOp, coreId);
+  if (failed(checkedCoreId))
+    return failure();
+  auto coreIdAttr = pim::getCheckedI32Attr(rewriter, computeOp, static_cast<int64_t>(*checkedCoreId), "pim core id");
+  if (failed(coreIdAttr))
+    return failure();
+  auto coreOp = PimCoreOp::create(rewriter, loc, ValueRange(computeWeights), *coreIdAttr);
   rewriter.setInsertionPointToStart(&block);
   auto& coreOpBlocks = coreOp.getBody().getBlocks();
   for (auto [inputIndex, input] : llvm::enumerate(computeOp.getInputs())) {
@@ -226,6 +239,9 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeOp(spatial::SpatCompute comp
     if (!inputType)
       return computeOp.emitOpError("expected shaped compute input during pim.core lowering");
     auto outputBuffer = createEmptyTensorFromShaped(rewriter, loc, inputType);
+    auto sizeAttr = getTensorSizeInBytesAttr(rewriter, computeOp.getOperation(), input);
+    if (failed(sizeAttr))
+      return failure();
     auto copied =
       PimMemCopyHostToDevOp::create(rewriter,
                                     loc,
@@ -234,7 +250,7 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeOp(spatial::SpatCompute comp
                                     getOrCreateIndexConstant(constantFolder, outputBuffer.getOperation(), 0),
                                     outputBuffer,
                                     input,
-                                    getTensorSizeInBytesAttr(rewriter, input))
+                                    *sizeAttr)
         .getOutput();
     blockArg->replaceAllUsesWith(copied);
   }

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

@@ -14,8 +14,10 @@ struct ChannelSendLowering : OpRewritePattern<spatial::SpatChannelSendOp> {
   using OpRewritePattern::OpRewritePattern;
 
   LogicalResult matchAndRewrite(spatial::SpatChannelSendOp op, PatternRewriter& rewriter) const override {
-    pim::PimSendOp::create(
-      rewriter, op.getLoc(), op.getInput(), getTensorSizeInBytesAttr(rewriter, op.getInput()), op.getTargetCoreId());
+    auto sizeAttr = getTensorSizeInBytesAttr(rewriter, op.getOperation(), op.getInput());
+    if (failed(sizeAttr))
+      return failure();
+    pim::PimSendOp::create(rewriter, op.getLoc(), op.getInput(), *sizeAttr, op.getTargetCoreId());
     rewriter.eraseOp(op);
     return success();
   }
@@ -32,12 +34,11 @@ struct ChannelReceiveLowering : OpRewritePattern<spatial::SpatChannelReceiveOp>
     auto outputType = cast<ShapedType>(op.getResult().getType());
     Value outputBuffer =
       tensor::EmptyOp::create(rewriter, op.getLoc(), outputType.getShape(), outputType.getElementType()).getResult();
-    Value received = pim::PimReceiveOp::create(rewriter,
-                                               op.getLoc(),
-                                               op.getResult().getType(),
-                                               outputBuffer,
-                                               getTensorSizeInBytesAttr(rewriter, op.getResult()),
-                                               op.getSourceCoreId())
+    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();
     rewriter.replaceOp(op, received);
     return success();

src/PIM/Conversion/SpatialToPim/ReturnPathNormalization.cpp

@@ -12,6 +12,7 @@
 #include "Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "Conversion/SpatialToPim/SpatialToPimPass.hpp"
 #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/Dialect/Pim/PimOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
@@ -71,6 +72,20 @@ static SmallVector<int64_t> expandFlatElementIndex(int64_t flatIndex, ArrayRef<i
   return indices;
 }
 
+static FailureOr<int64_t>
+getCheckedByteOffset(int64_t elementOffset, size_t elementSize, Operation* anchor, StringRef fieldName) {
+  if (elementOffset < 0) {
+    anchor->emitOpError() << fieldName << " requires a nonnegative element offset";
+    return failure();
+  }
+
+  auto byteOffset =
+    pim::checkedMul(static_cast<uint64_t>(elementOffset), static_cast<uint64_t>(elementSize), anchor, fieldName);
+  if (failed(byteOffset))
+    return failure();
+  return pim::checkedCast<int64_t>(*byteOffset, anchor, fieldName);
+}
+
 static LogicalResult collectHelperComputeChain(spatial::SpatCompute computeOp,
                                                SmallVectorImpl<Operation*>& helperChain) {
   if (computeOp.getInputs().size() != 1 || computeOp.getNumResults() != 1)
@@ -360,18 +375,72 @@ static void cloneHelperChain(Value sourceValue,
   }
 }
 
-static Value emitHostCopy(IRRewriter& rewriter,
-                          Location loc,
-                          Value outputTensor,
-                          Value sourceValue,
-                          int32_t hostTargetOffset,
-                          int32_t deviceSourceOffset,
-                          int32_t sizeInBytes,
-                          OperationFolder& constantFolder) {
+static bool isHostStaticReturnValue(Value value) {
+  llvm::SmallPtrSet<Operation*, 8> visited;
+  while (Operation* definingOp = value.getDefiningOp()) {
+    if (!visited.insert(definingOp).second)
+      return false;
+    if (isa<arith::ConstantOp>(definingOp) || definingOp->hasTrait<OpTrait::ConstantLike>())
+      return true;
+    if (!isReturnHelperChainOp(definingOp) || definingOp->getNumOperands() != 1)
+      return false;
+    value = definingOp->getOperand(0);
+  }
+  return false;
+}
+
+static FailureOr<Value>
+materializeHostStaticReturnValue(IRRewriter& rewriter, Value value, OperationFolder& constantFolder) {
+  llvm::SmallVector<Operation*> chain;
+  llvm::SmallPtrSet<Operation*, 8> visited;
+  while (Operation* definingOp = value.getDefiningOp()) {
+    if (!visited.insert(definingOp).second)
+      return failure();
+    chain.push_back(definingOp);
+    if (isa<arith::ConstantOp>(definingOp) || definingOp->hasTrait<OpTrait::ConstantLike>())
+      break;
+    if (!isReturnHelperChainOp(definingOp) || definingOp->getNumOperands() != 1)
+      return failure();
+    value = definingOp->getOperand(0);
+  }
+
+  if (chain.empty())
+    return failure();
+
+  IRMapping mapping;
+  Value clonedValue;
+  for (Operation* op : llvm::reverse(chain)) {
+    if (auto constantOp = dyn_cast<arith::ConstantOp>(op)) {
+      clonedValue = getOrCreateConstantLike(constantFolder, constantOp);
+      mapping.map(op->getResult(0), clonedValue);
+      continue;
+    }
+
+    Operation* clonedOp = rewriter.clone(*op, mapping);
+    for (auto [originalResult, newResult] : llvm::zip(op->getResults(), clonedOp->getResults()))
+      mapping.map(originalResult, newResult);
+    clonedValue = clonedOp->getResult(0);
+    rewriter.setInsertionPointAfter(clonedOp);
+  }
+
+  return clonedValue;
+}
+
+static FailureOr<Value> emitHostCopy(IRRewriter& rewriter,
+                                     Location loc,
+                                     Value outputTensor,
+                                     Value sourceValue,
+                                     int64_t hostTargetOffset,
+                                     int64_t deviceSourceOffset,
+                                     uint64_t sizeInBytes,
+                                     OperationFolder& constantFolder) {
   Operation* anchorOp = sourceValue.getDefiningOp() ? sourceValue.getDefiningOp() : outputTensor.getDefiningOp();
   assert(anchorOp && "expected a concrete op anchor for return-path host copy constants");
   Value hostTargetOffsetValue = getOrCreateIndexConstant(constantFolder, anchorOp, hostTargetOffset);
   Value deviceSourceOffsetValue = getOrCreateIndexConstant(constantFolder, anchorOp, deviceSourceOffset);
+  auto sizeAttr = pim::getCheckedI32Attr(rewriter, anchorOp, sizeInBytes, "return-path host copy byte size");
+  if (failed(sizeAttr))
+    return failure();
   return PimMemCopyDevToHostOp::create(rewriter,
                                        loc,
                                        outputTensor.getType(),
@@ -379,7 +448,7 @@ static Value emitHostCopy(IRRewriter& rewriter,
                                        deviceSourceOffsetValue,
                                        outputTensor,
                                        sourceValue,
-                                       rewriter.getI32IntegerAttr(sizeInBytes))
+                                       *sizeAttr)
     .getOutput();
 }
 
@@ -426,25 +495,45 @@ raptor::SpatialToPimPass::ReturnPathLoweringResult raptor::SpatialToPimPass::low
   OperationFolder constantFolder(producerOp->getContext());
   auto storedTensorType = cast<TensorType>(storedValue.getType());
 
+  auto materializeDirectHostReturn = [&](size_t returnIndex,
+                                         Value sourceValue,
+                                         ArrayRef<Operation*> helperChain) -> ReturnPathLoweringResult {
+    rewriter.setInsertionPointAfter(producerOp);
+    auto hostStaticValue = materializeHostStaticReturnValue(rewriter, sourceValue, constantFolder);
+    if (failed(hostStaticValue))
+      return ReturnPathLoweringResult::Failure;
+
+    Value hostReturnValue = *hostStaticValue;
+    if (!helperChain.empty())
+      cloneHelperChain(hostReturnValue, helperChain, rewriter, constantFolder, hostReturnValue);
+
+    outputTensors[returnIndex] =
+      [hostReturnValue](IRRewriter& rewriter, Location loc) -> Value { return hostReturnValue; };
+    return ReturnPathLoweringResult::Handled;
+  };
+
   if (auto returnUse = analyzeReturnUse(producedValue)) {
+    if (isHostStaticReturnValue(storedValue)) {
+      for (Operation* op : returnUse->helperChain)
+        markOpToRemove(op);
+      return materializeDirectHostReturn(returnUse->returnIndex, storedValue, returnUse->helperChain);
+    }
+
     Value currentStoredValue = storedValue;
     cloneHelperChain(storedValue, returnUse->helperChain, rewriter, constantFolder, currentStoredValue);
     for (Operation* op : returnUse->helperChain)
       markOpToRemove(op);
 
     auto storedType = cast<ShapedType>(currentStoredValue.getType());
-    size_t elementSize = getElementTypeSizeInBytes(storedType.getElementType());
+    auto byteSize = pim::getCheckedShapedTypeSizeInBytes(storedType, producerOp, "return-path host copy byte size");
+    if (failed(byteSize))
+      return ReturnPathLoweringResult::Failure;
     if (auto storedOp = currentStoredValue.getDefiningOp())
       rewriter.setInsertionPointAfter(storedOp);
     Value outputTensor = outputTensors[returnUse->returnIndex](rewriter, loc);
-    emitHostCopy(rewriter,
-                 loc,
-                 outputTensor,
-                 currentStoredValue,
-                 0,
-                 0,
-                 static_cast<int32_t>(storedType.getNumElements() * elementSize),
-                 constantFolder);
+    auto copied = emitHostCopy(rewriter, loc, outputTensor, currentStoredValue, 0, 0, *byteSize, constantFolder);
+    if (failed(copied))
+      return ReturnPathLoweringResult::Failure;
     return ReturnPathLoweringResult::Handled;
   }
 
@@ -455,23 +544,27 @@ raptor::SpatialToPimPass::ReturnPathLoweringResult raptor::SpatialToPimPass::low
 
     if (isa<func::ReturnOp>(resultUser)) {
       size_t resultIndexInReturn = resultUse.getOperandNumber();
-      size_t elementSize = getElementTypeSizeInBytes(storedTensorType.getElementType());
+      if (isHostStaticReturnValue(storedValue))
+        return materializeDirectHostReturn(resultIndexInReturn, storedValue, {});
+      auto byteSize =
+        pim::getCheckedShapedTypeSizeInBytes(storedTensorType, producerOp, "return-path host copy byte size");
+      if (failed(byteSize))
+        return ReturnPathLoweringResult::Failure;
       rewriter.setInsertionPointAfterValue(storedValue);
       Value outputTensor = outputTensors[resultIndexInReturn](rewriter, loc);
-      emitHostCopy(rewriter,
-                   loc,
-                   outputTensor,
-                   storedValue,
-                   0,
-                   0,
-                   static_cast<int32_t>(storedTensorType.getNumElements() * elementSize),
-                   constantFolder);
+      auto copied = emitHostCopy(rewriter, loc, outputTensor, storedValue, 0, 0, *byteSize, constantFolder);
+      if (failed(copied))
+        return ReturnPathLoweringResult::Failure;
       return ReturnPathLoweringResult::Handled;
     }
   }
 
   if (auto concatReturnUse = analyzeConcatReturnUse(producedValue)) {
     size_t elementSize = getElementTypeSizeInBytes(storedTensorType.getElementType());
+    auto storedByteSize =
+      pim::getCheckedShapedTypeSizeInBytes(storedTensorType, producerOp, "concat return-path copy byte size");
+    if (failed(storedByteSize))
+      return ReturnPathLoweringResult::Failure;
     for (Operation* concatOp : concatReturnUse->concatChain)
       markOpToRemove(concatOp);
 
@@ -480,14 +573,13 @@ raptor::SpatialToPimPass::ReturnPathLoweringResult raptor::SpatialToPimPass::low
       Value outputTensor = outputTensors[concatReturnUse->returnIndex](rewriter, loc);
       auto outputType = cast<ShapedType>(outputTensor.getType());
       int64_t flatOffset = computeFlatElementIndex(concatReturnUse->sliceOffsets, outputType.getShape());
-      emitHostCopy(rewriter,
-                   loc,
-                   outputTensor,
-                   storedValue,
-                   static_cast<int32_t>(flatOffset * elementSize),
-                   0,
-                   static_cast<int32_t>(storedTensorType.getNumElements() * elementSize),
-                   constantFolder);
+      auto hostOffset = getCheckedByteOffset(flatOffset, elementSize, producerOp, "concat return-path host offset");
+      if (failed(hostOffset))
+        return ReturnPathLoweringResult::Failure;
+      auto copied =
+        emitHostCopy(rewriter, loc, outputTensor, storedValue, *hostOffset, 0, *storedByteSize, constantFolder);
+      if (failed(copied))
+        return ReturnPathLoweringResult::Failure;
       return ReturnPathLoweringResult::Handled;
     }
 
@@ -531,14 +623,18 @@ raptor::SpatialToPimPass::ReturnPathLoweringResult raptor::SpatialToPimPass::low
       rewriter.setInsertionPointAfter(elementSlice);
 
       int64_t destinationFlatOffset = computeFlatElementIndex(destinationIndices, outputType.getShape());
-      outputTensor = emitHostCopy(rewriter,
-                                  loc,
-                                  outputTensor,
-                                  elementSlice.getResult(),
-                                  static_cast<int32_t>(destinationFlatOffset * elementSize),
-                                  0,
-                                  static_cast<int32_t>(elementSize),
-                                  constantFolder);
+      auto hostOffset =
+        getCheckedByteOffset(destinationFlatOffset, elementSize, producerOp, "concat helper return-path host offset");
+      if (failed(hostOffset))
+        return ReturnPathLoweringResult::Failure;
+      auto elementByteSize = pim::checkedCast<uint64_t>(elementSize, producerOp, "return-path scalar copy byte size");
+      if (failed(elementByteSize))
+        return ReturnPathLoweringResult::Failure;
+      auto copied = emitHostCopy(
+        rewriter, loc, outputTensor, elementSlice.getResult(), *hostOffset, 0, *elementByteSize, constantFolder);
+      if (failed(copied))
+        return ReturnPathLoweringResult::Failure;
+      outputTensor = *copied;
     }
     return ReturnPathLoweringResult::Handled;
   }

src/PIM/Conversion/SpatialToPim/SpatialToPim.td

@@ -27,6 +27,12 @@ def spatToPimVVAdd : Pat<
     (NativeCodeCall<"onnx_mlir::getBestOutputTensorFromOperandsOrAllocate($_builder, $0.getDefiningOp())"> $srcOpRes))
 >;
 
+def spatToPimVVSub : Pat<
+  (SpatVSubOp:$srcOpRes $a, $b),
+  (PimVVSubOp $a, $b,
+    (NativeCodeCall<"onnx_mlir::getBestOutputTensorFromOperandsOrAllocate($_builder, $0.getDefiningOp())"> $srcOpRes))
+>;
+
 def spatToPimVVMul : Pat<
   (SpatVMulOp:$srcOpRes $a, $b),
   (PimVVMulOp $a, $b,

src/PIM/Conversion/SpatialToPim/SpatialToPimPass.cpp

@@ -25,7 +25,9 @@
 #include <cassert>
 #include <utility>
 
+#include "Common/IR/ConstantUtils.hpp"
 #include "Common/PimCommon.hpp"
+#include "Common/Support/CheckedArithmetic.hpp"
 #include "Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "Conversion/SpatialToPim/Common.hpp"
 #include "Conversion/SpatialToPim/Patterns.hpp"
@@ -74,21 +76,28 @@ static memref::GlobalOp getOrCreateZeroGlobal(IRRewriter& rewriter, Location loc
                                   IntegerAttr {});
 }
 
-static Value createZeroedDeviceHVector(IRRewriter& rewriter,
-                                       Location loc,
-                                       RankedTensorType tensorType,
-                                       OperationFolder& constantFolder) {
+static FailureOr<Value> createZeroedDeviceHVector(IRRewriter& rewriter,
+                                                  Location loc,
+                                                  RankedTensorType tensorType,
+                                                  OperationFolder& constantFolder) {
   auto outputBuffer = createEmptyTensorFromShaped(rewriter, loc, tensorType);
   auto zeroGlobal = getOrCreateZeroGlobal(rewriter, loc, tensorType);
   auto zeroValue = memref::GetGlobalOp::create(rewriter, loc, zeroGlobal.getType(), zeroGlobal.getName());
   auto zeroIndex = getOrCreateIndexConstant(constantFolder, outputBuffer.getOperation(), 0);
-  auto sizeAttr = rewriter.getI32IntegerAttr(static_cast<int32_t>(getShapedTypeSizeInBytes(tensorType)));
+  auto byteSize =
+    pim::getCheckedShapedTypeSizeInBytes(tensorType, outputBuffer.getOperation(), "host-to-device zero copy byte size");
+  if (failed(byteSize))
+    return failure();
+  auto sizeAttr =
+    pim::getCheckedI32Attr(rewriter, outputBuffer.getOperation(), *byteSize, "host-to-device zero copy byte size");
+  if (failed(sizeAttr))
+    return failure();
   return PimMemCopyHostToDevOp::create(
-           rewriter, loc, tensorType, zeroIndex, zeroIndex, outputBuffer, zeroValue, sizeAttr)
+           rewriter, loc, tensorType, zeroIndex, zeroIndex, outputBuffer, zeroValue, *sizeAttr)
     .getOutput();
 }
 
-static Value
+static FailureOr<Value>
 padHVectorInputToCrossbarSize(IRRewriter& rewriter, Location loc, Value vector, OperationFolder& constantFolder) {
   auto vectorType = cast<RankedTensorType>(vector.getType());
   ArrayRef<int64_t> shape = vectorType.getShape();
@@ -100,10 +109,18 @@ padHVectorInputToCrossbarSize(IRRewriter& rewriter, Location loc, Value vector,
 
   auto paddedType = RankedTensorType::get(
     {shape[0], static_cast<int64_t>(crossbarSize)}, vectorType.getElementType(), vectorType.getEncoding());
-  Value zeroed = createZeroedDeviceHVector(rewriter, loc, paddedType, constantFolder);
-  auto zeroAttr = rewriter.getI32IntegerAttr(0);
-  auto sizeAttr = rewriter.getI32IntegerAttr(static_cast<int32_t>(getShapedTypeSizeInBytes(vectorType)));
-  return PimMemCopyOp::create(rewriter, loc, paddedType, zeroed, vector, zeroAttr, zeroAttr, sizeAttr).getOutput();
+  auto zeroed = createZeroedDeviceHVector(rewriter, loc, paddedType, constantFolder);
+  if (failed(zeroed))
+    return failure();
+  Value zeroIndex = getOrCreateIndexConstant(constantFolder, zeroed->getDefiningOp(), 0);
+  auto byteSize =
+    pim::getCheckedShapedTypeSizeInBytes(vectorType, zeroed->getDefiningOp(), "device padding copy byte size");
+  if (failed(byteSize))
+    return failure();
+  auto sizeAttr = pim::getCheckedI32Attr(rewriter, zeroed->getDefiningOp(), *byteSize, "device padding copy byte size");
+  if (failed(sizeAttr))
+    return failure();
+  return PimMemCopyOp::create(rewriter, loc, paddedType, zeroIndex, zeroIndex, *zeroed, vector, *sizeAttr).getOutput();
 }
 
 void onnx_mlir::raptor::SpatialToPimPass::runOnOperation() {
@@ -233,7 +250,11 @@ void onnx_mlir::raptor::SpatialToPimPass::runOnOperation() {
     }
   }
 
-  enlargeVMMOutTensorsToCrossbarSize(funcOp, rewriter);
+  if (failed(enlargeVMMOutTensorsToCrossbarSize(funcOp, rewriter))) {
+    funcOp.emitOpError("failed to enlarge VMM output tensors to crossbar size");
+    signalPassFailure();
+    return;
+  }
   replaceReturnWithOutputBuffers(returnOp, rewriter);
   eraseOpsToRemove();
 
@@ -264,12 +285,15 @@ void onnx_mlir::raptor::SpatialToPimPass::runOnOperation() {
     signalPassFailure();
     return;
   }
+  hoistAndUniquifyIndexConstants(funcOp, rewriter);
+
   // Dump to file for debug
   dumpModule(moduleOp, "pim0");
 }
 
-void raptor::SpatialToPimPass::enlargeVMMOutTensorsToCrossbarSize(func::FuncOp funcOp, IRRewriter& rewriter) {
+LogicalResult raptor::SpatialToPimPass::enlargeVMMOutTensorsToCrossbarSize(func::FuncOp funcOp, IRRewriter& rewriter) {
   OperationFolder constantFolder(funcOp.getContext());
+  bool hasFailure = false;
   funcOp.walk([&](PimVMMOp vmmOp) {
     auto outputType = cast<RankedTensorType>(vmmOp.getOutput().getType());
     ArrayRef<int64_t> outputShape = outputType.getShape();
@@ -277,19 +301,23 @@ void raptor::SpatialToPimPass::enlargeVMMOutTensorsToCrossbarSize(func::FuncOp f
     assert(outputShape[1] <= static_cast<int64_t>(crossbarSize) && "output width must fit in one crossbar");
 
     rewriter.setInsertionPoint(vmmOp);
-    Value paddedInput = padHVectorInputToCrossbarSize(rewriter, vmmOp.getLoc(), vmmOp.getInput(), constantFolder);
+    auto paddedInput = padHVectorInputToCrossbarSize(rewriter, vmmOp.getLoc(), vmmOp.getInput(), constantFolder);
+    if (failed(paddedInput)) {
+      hasFailure = true;
+      return WalkResult::interrupt();
+    }
     auto paddedOutputType = RankedTensorType::get(
       {outputShape[0], static_cast<int64_t>(crossbarSize)}, outputType.getElementType(), outputType.getEncoding());
     Value paddedOutputBuffer = outputShape[1] == static_cast<int64_t>(crossbarSize)
                                ? vmmOp.getOutputBuffer()
                                : createEmptyTensorFromShaped(rewriter, vmmOp.getLoc(), paddedOutputType).getResult();
-    vmmOp.getInputMutable().assign(paddedInput);
+    vmmOp.getInputMutable().assign(*paddedInput);
     vmmOp.getOutputBufferMutable().assign(paddedOutputBuffer);
 
     vmmOp.getOutput().setType(paddedOutputType);
 
     if (outputShape[1] == static_cast<int64_t>(crossbarSize))
-      return;
+      return WalkResult::advance();
 
     SmallVector<OpFoldResult> offsets = {rewriter.getIndexAttr(0), rewriter.getIndexAttr(0)};
     SmallVector<OpFoldResult> sizes = {rewriter.getIndexAttr(outputShape[0]), rewriter.getIndexAttr(outputShape[1])};
@@ -299,13 +327,16 @@ void raptor::SpatialToPimPass::enlargeVMMOutTensorsToCrossbarSize(func::FuncOp f
       tensor::ExtractSliceOp::create(rewriter, vmmOp.getLoc(), outputType, vmmOp.getOutput(), offsets, sizes, strides);
     SmallPtrSet<Operation*, 2> exceptions = {vmmOp, sliceOp};
     vmmOp.getOutput().replaceAllUsesExcept(sliceOp.getResult(), exceptions);
+    return WalkResult::advance();
   });
+  return success(!hasFailure);
 }
 
 LogicalResult raptor::SpatialToPimPass::allocateAndInitializeCoreLocalVariables(func::FuncOp funcOp,
                                                                                 IRRewriter& rewriter) {
   Location loc = funcOp.getLoc();
   OperationFolder constantFolder(funcOp.getContext());
+  bool hasFailure = false;
 
   auto insertMemCopyHostToDev = [&](Value inputTensor, int64_t elementsOffset) {
     auto tensorType = cast<ShapedType>(inputTensor.getType());
@@ -316,17 +347,28 @@ LogicalResult raptor::SpatialToPimPass::allocateAndInitializeCoreLocalVariables(
     rewriter.setInsertionPointAfter(inputTensor.getDefiningOp());
 
     auto deviceTensor = tensor::EmptyOp::create(rewriter, loc, tensorType.getShape(), elementType);
+    auto offsetBytes = pim::checkedMul(
+      static_cast<size_t>(elementsOffset), elementByteSize, deviceTensor.getOperation(), "host input byte offset");
+    auto byteSize =
+      pim::getCheckedShapedTypeSizeInBytes(tensorType, deviceTensor.getOperation(), "host input copy byte size");
+    auto sizeAttr =
+      succeeded(byteSize)
+        ? pim::getCheckedI32Attr(rewriter, deviceTensor.getOperation(), *byteSize, "host input copy byte size")
+        : FailureOr<IntegerAttr>(failure());
+    if (failed(offsetBytes) || failed(sizeAttr)) {
+      hasFailure = true;
+      return;
+    }
 
     auto memCopyHostToDevOp = PimMemCopyHostToDevOp::create(
       rewriter,
       loc,
       tensorType,
       getOrCreateIndexConstant(constantFolder, deviceTensor.getOperation(), 0),
-      getOrCreateIndexConstant(
-        constantFolder, deviceTensor.getOperation(), static_cast<int64_t>(elementsOffset * elementByteSize)),
+      getOrCreateIndexConstant(constantFolder, deviceTensor.getOperation(), static_cast<int64_t>(*offsetBytes)),
       deviceTensor,
       inputTensor,
-      rewriter.getI32IntegerAttr(static_cast<int32_t>(tensorType.getNumElements() * elementByteSize)));
+      *sizeAttr);
 
     rewriter.replaceAllUsesExcept(inputTensor, memCopyHostToDevOp.getResult(), {memCopyHostToDevOp});
   };
@@ -344,7 +386,7 @@ LogicalResult raptor::SpatialToPimPass::allocateAndInitializeCoreLocalVariables(
       }
     }
 
-  return success();
+  return success(!hasFailure);
 }
 
 void raptor::SpatialToPimPass::markOpToRemove(Operation* op) {

src/PIM/Conversion/SpatialToPim/SpatialToPimPass.hpp

@@ -64,7 +64,7 @@ private:
   void markOpToRemove(mlir::Operation* op);
   void eraseOpsToRemove();
 
-  void enlargeVMMOutTensorsToCrossbarSize(mlir::func::FuncOp funcOp, mlir::IRRewriter& rewriter);
+  mlir::LogicalResult enlargeVMMOutTensorsToCrossbarSize(mlir::func::FuncOp funcOp, mlir::IRRewriter& rewriter);
 };
 
 } // namespace raptor

src/PIM/Dialect/Pim/CMakeLists.txt

@@ -2,7 +2,9 @@ add_onnx_mlir_dialect(Pim pim)
 add_onnx_mlir_dialect_doc(pim Pim.td)
 
 add_subdirectory(Transforms/Bufferization)
-add_subdirectory(Transforms/StaticMemoryCoalescing)
+add_subdirectory(Transforms/MemoryCoalescing)
+add_subdirectory(Transforms/HostConstantFolding)
+add_subdirectory(Transforms/Verification)
 
 add_pim_library(PimOps
   PimOps.hpp

src/PIM/Dialect/Pim/Pim.td

@@ -176,10 +176,10 @@ def PimMemCopyOp : PimOp<"memcp", [DestinationStyleOpInterface]> {
   let summary = "Copy a memory region within the same memory space";
 
   let arguments = (ins
+    Index:$targetOffset,
+    Index:$sourceOffset,
     PimTensor:$target,
     PimTensor:$source,
-    I32Attr:$targetOffset,
-    I32Attr:$sourceOffset,
     I32Attr:$size
   );
 
@@ -194,7 +194,9 @@ def PimMemCopyOp : PimOp<"memcp", [DestinationStyleOpInterface]> {
   }];
 
   let assemblyFormat = [{
-    `(` $target `,` $source `)` attr-dict `:` `(` type($target) `,` type($source) `)` `->` type($output)
+    `[` $targetOffset `,` $sourceOffset `]`
+    `(` $target `,` $source `)` attr-dict
+    `:` type($target) `,` type($source) `->` type($output)
   }];
 }
 

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

@@ -3,36 +3,50 @@
 
 #include "src/Accelerators/PIM/Common/IR/AddressAnalysis.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/Transforms/Bufferization/BufferizationUtils.hpp"
+#include "src/Accelerators/PIM/Dialect/Pim/Transforms/Bufferization/Common.hpp"
 
 using namespace mlir;
 using namespace bufferization;
 
 namespace onnx_mlir::pim {
 
-Value materializeContiguousInputMemRef(Value memrefValue, Location loc, RewriterBase& rewriter) {
-  if (succeeded(resolveContiguousAddress(memrefValue)) || succeeded(compileContiguousAddressExpr(memrefValue)))
+FailureOr<Value> materializeContiguousInputMemRef(Value memrefValue, Location loc, RewriterBase& rewriter) {
+  bool isContiguous =
+    succeeded(resolveContiguousAddress(memrefValue)) || succeeded(compileContiguousAddressExpr(memrefValue));
+  if (isContiguous && isDeviceLocalPimAddress(memrefValue))
     return memrefValue;
 
   auto shapedType = cast<ShapedType>(memrefValue.getType());
   auto contiguousType = MemRefType::get(shapedType.getShape(), shapedType.getElementType());
   Value contiguousBuffer = memref::AllocOp::create(rewriter, loc, contiguousType);
-  auto sizeInBytes = getShapedTypeSizeInBytes(shapedType);
+  auto sizeInBytes =
+    getCheckedShapedTypeSizeInBytes(shapedType, contiguousBuffer.getDefiningOp(), "contiguous copy byte size");
+  if (failed(sizeInBytes))
+    return failure();
+  Value zeroOffset = getOrCreateIndexConstant(rewriter, contiguousBuffer.getDefiningOp(), 0);
+  auto sizeAttr =
+    getCheckedI32Attr(rewriter, contiguousBuffer.getDefiningOp(), *sizeInBytes, "contiguous copy byte size");
+  if (failed(sizeAttr))
+    return failure();
 
-  return PimMemCopyOp::create(rewriter,
-                              loc,
-                              contiguousType,
-                              contiguousBuffer,
-                              memrefValue,
-                              rewriter.getI32IntegerAttr(0),
-                              rewriter.getI32IntegerAttr(0),
-                              rewriter.getI32IntegerAttr(sizeInBytes))
+  if (isHostBackedPimAddress(memrefValue)) {
+    return PimMemCopyHostToDevOp::create(
+             rewriter, loc, contiguousType, zeroOffset, zeroOffset, contiguousBuffer, memrefValue, *sizeAttr)
+      .getOutput();
+  }
+
+  return PimMemCopyOp::create(
+           rewriter, loc, contiguousType, zeroOffset, zeroOffset, contiguousBuffer, memrefValue, *sizeAttr)
     .getOutput();
 }
 
 Value allocateContiguousResultMemRefLike(Value memrefValue, Location loc, RewriterBase& rewriter) {
-  if (succeeded(resolveContiguousAddress(memrefValue)))
+  bool isContiguous =
+    succeeded(resolveContiguousAddress(memrefValue)) || succeeded(compileContiguousAddressExpr(memrefValue));
+  if (isContiguous && isDeviceLocalPimAddress(memrefValue))
     return memrefValue;
 
   auto shapedType = cast<ShapedType>(memrefValue.getType());

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

@@ -5,7 +5,8 @@
 
 namespace onnx_mlir::pim {
 
-mlir::Value materializeContiguousInputMemRef(mlir::Value memrefValue, mlir::Location loc, mlir::RewriterBase& rewriter);
+llvm::FailureOr<mlir::Value>
+materializeContiguousInputMemRef(mlir::Value memrefValue, mlir::Location loc, mlir::RewriterBase& rewriter);
 mlir::Value
 allocateContiguousResultMemRefLike(mlir::Value memrefValue, mlir::Location loc, mlir::RewriterBase& rewriter);
 

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

@@ -1,7 +1,3 @@
-set(LLVM_TARGET_DEFINITIONS PimBufferization.td)
-mlir_tablegen(PimBufferization.hpp.inc -gen-rewriters "-I${ONNX_MLIR_SRC_ROOT}")
-add_public_tablegen_target(PimBufferizationIncGen)
-
 add_pim_library(OMPimBufferization
   PimBufferizationPass.cpp
   BufferizationUtils.hpp
@@ -15,9 +11,6 @@ add_pim_library(OMPimBufferization
 
   EXCLUDE_FROM_OM_LIBS
 
-  DEPENDS
-  PimBufferizationIncGen
-
   LINK_LIBS PUBLIC
   OMPimCommon
   PimOps

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

@@ -1,10 +1,111 @@
 #include "Dialect/Pim/Transforms/Bufferization/Common.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
+#include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 
 using namespace mlir;
 
-IntegerAttr onnx_mlir::pim::getMemRefSizeInBytesAttr(OpBuilder& builder, Value memref) {
-  auto type = mlir::cast<MemRefType>(memref.getType());
-  int32_t sizeInBytes = static_cast<int32_t>(getShapedTypeSizeInBytes(type));
-  return builder.getI32IntegerAttr(sizeInBytes);
+static bool isCoreBatchInputArgument(Value value) {
+  auto blockArg = dyn_cast<BlockArgument>(value);
+  if (!blockArg)
+    return false;
+
+  auto coreBatchOp = dyn_cast_or_null<onnx_mlir::pim::PimCoreBatchOp>(blockArg.getOwner()->getParentOp());
+  if (!coreBatchOp)
+    return false;
+
+  unsigned firstInputArg = 1 + coreBatchOp.getWeights().size();
+  return static_cast<unsigned>(blockArg.getArgNumber()) >= firstInputArg;
+}
+
+static FailureOr<Value> getPimStorageBase(Value value, const onnx_mlir::StaticValueKnowledge& knowledge) {
+  llvm::SmallPtrSet<Value, 8> visited;
+  while (value && visited.insert(value).second) {
+    Value alias = resolveLoopCarriedAlias(value, knowledge);
+    if (alias)
+      value = alias;
+
+    if (auto aliased = knowledge.aliases.lookup(value)) {
+      value = aliased;
+      continue;
+    }
+
+    if (auto base = onnx_mlir::pim::getPimAddressBase(value, knowledge); succeeded(base))
+      return base;
+
+    if (isa<BlockArgument>(value))
+      return value;
+
+    Operation* definingOp = value.getDefiningOp();
+    if (!definingOp)
+      return value;
+
+    if (auto subviewOp = dyn_cast<memref::SubViewOp>(definingOp)) {
+      value = subviewOp.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;
+    }
+    if (auto castOp = dyn_cast<memref::CastOp>(definingOp)) {
+      value = castOp.getSource();
+      continue;
+    }
+
+    return value;
+  }
+
+  if (value)
+    return value;
+  return failure();
+}
+
+FailureOr<IntegerAttr> onnx_mlir::pim::getMemRefSizeInBytesAttr(OpBuilder& builder, Operation* anchor, Value memref) {
+  auto type = mlir::cast<MemRefType>(memref.getType());
+  auto byteSize = getCheckedShapedTypeSizeInBytes(type, anchor, "memref byte size");
+  if (failed(byteSize))
+    return failure();
+  return getCheckedI32Attr(builder, anchor, *byteSize, "memref byte size");
+}
+
+FailureOr<Value> onnx_mlir::pim::getPimAddressBase(Value value, const StaticValueKnowledge& knowledge) {
+  Value alias = resolveLoopCarriedAlias(value, knowledge);
+  if (alias)
+    value = alias;
+
+  auto resolved = resolveContiguousAddress(value, knowledge);
+  if (succeeded(resolved))
+    return resolved->base;
+
+  auto compiled = compileContiguousAddressExpr(value);
+  if (failed(compiled)) {
+    if (isa<BlockArgument>(value))
+      return value;
+    return failure();
+  }
+  return compiled->base;
+}
+
+bool onnx_mlir::pim::isHostBackedPimAddress(Value value, const StaticValueKnowledge& knowledge) {
+  auto base = getPimStorageBase(value, knowledge);
+  if (failed(base))
+    return false;
+
+  if (isCoreBatchInputArgument(*base))
+    return true;
+
+  return isa_and_nonnull<memref::GetGlobalOp>(base->getDefiningOp());
+}
+
+bool onnx_mlir::pim::isDeviceLocalPimAddress(Value value, const StaticValueKnowledge& knowledge) {
+  auto base = getPimStorageBase(value, knowledge);
+  if (failed(base))
+    return false;
+
+  return isa_and_nonnull<memref::AllocOp>(base->getDefiningOp());
 }

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

@@ -2,10 +2,19 @@
 
 #include "mlir/Dialect/MemRef/IR/MemRef.h"
 
+#include "src/Accelerators/PIM/Common/IR/AddressAnalysis.hpp"
+
 namespace onnx_mlir {
 namespace pim {
 
-mlir::IntegerAttr getMemRefSizeInBytesAttr(mlir::OpBuilder& builder, mlir::Value memref);
+mlir::FailureOr<mlir::IntegerAttr>
+getMemRefSizeInBytesAttr(mlir::OpBuilder& builder, mlir::Operation* anchor, mlir::Value memref);
+
+mlir::FailureOr<mlir::Value> getPimAddressBase(mlir::Value value, const StaticValueKnowledge& knowledge = {});
+
+bool isHostBackedPimAddress(mlir::Value value, const StaticValueKnowledge& knowledge = {});
+
+bool isDeviceLocalPimAddress(mlir::Value value, const StaticValueKnowledge& knowledge = {});
 
 } // namespace pim
 } // namespace onnx_mlir

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

@@ -3,220 +3,374 @@
 #include "mlir/Dialect/SCF/IR/SCF.h"
 
 #include "ContiguityPatterns.hpp"
+#include "src/Accelerators/PIM/Common/IR/ConstantUtils.hpp"
+#include "src/Accelerators/PIM/Common/IR/LoopUtils.hpp"
 #include "src/Accelerators/PIM/Common/IR/ShapeUtils.hpp"
-#include "src/Accelerators/PIM/Common/IR/SubviewUtils.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
-#include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 
 using namespace mlir;
 
 namespace onnx_mlir::pim {
 namespace {
 
-static bool isStaticSubviewContiguous(const StaticSubviewInfo& info) {
-  if (llvm::any_of(info.strides, [](int64_t stride) { return stride != 1; }))
-    return false;
+struct ByteOffsetTerm {
+  Value value;
+  int64_t scale = 0;
+};
 
-  return isContiguousSubviewWithDynamicOffsets(info.sourceShape, info.offsets, info.sizes, info.strides);
+struct ByteOffsetExpr {
+  int64_t constant = 0;
+  SmallVector<ByteOffsetTerm> terms;
+};
+
+struct CopyEndpointPlan {
+  Value base;
+  MemRefType originalType;
+  MemRefType baseType;
+  ByteOffsetExpr offset;
+};
+
+struct CopyLoopPlan {
+  SmallVector<int64_t> outerShape;
+  int64_t chunkBytes = 0;
+  ByteOffsetExpr targetBaseOffset;
+  ByteOffsetExpr sourceBaseOffset;
+  SmallVector<int64_t> targetOuterByteStrides;
+  SmallVector<int64_t> sourceOuterByteStrides;
+};
+
+struct CopyRewritePlan {
+  enum class Kind {
+    Direct,
+    Loop
+  } kind = Kind::Direct;
+  CopyEndpointPlan target;
+  CopyEndpointPlan source;
+  int64_t directBytes = 0;
+  CopyLoopPlan loop;
+};
+
+static bool isViewLike(Value value) {
+  Operation* defOp = value.getDefiningOp();
+  return defOp
+      && isa<memref::SubViewOp,
+             memref::ReinterpretCastOp,
+             memref::CollapseShapeOp,
+             memref::ExpandShapeOp,
+             memref::CastOp>(defOp);
 }
 
-static OpFoldResult addConstantOffset(OpFoldResult baseOffset, int64_t extraOffset, PatternRewriter& rewriter) {
-  if (extraOffset == 0)
-    return baseOffset;
+template <typename CopyOp>
+static bool isNormalizedCopyLikeOp(CopyOp copyOp, Value target, Value source, Value targetOffset, Value sourceOffset) {
+  auto targetType = dyn_cast<MemRefType>(target.getType());
+  auto sourceType = dyn_cast<MemRefType>(source.getType());
+  return targetType && sourceType && !isViewLike(target) && !isViewLike(source) && targetOffset.getType().isIndex()
+      && sourceOffset.getType().isIndex() && copyOp.getSize() > 0;
+}
 
-  if (auto attr = dyn_cast<Attribute>(baseOffset)) {
-    auto integerAttr = dyn_cast<IntegerAttr>(attr);
-    assert(integerAttr && "expected integer offset attribute");
-    return rewriter.getIndexAttr(integerAttr.getInt() + extraOffset);
+static void appendTerm(ByteOffsetExpr& expr, Value value, int64_t scale) {
+  if (scale != 0)
+    expr.terms.push_back(ByteOffsetTerm {value, scale});
+}
+
+static FailureOr<SmallVector<int64_t>> getStaticMemRefStrides(MemRefType type) {
+  SmallVector<int64_t> strides;
+  int64_t offset = 0;
+  if (failed(type.getStridesAndOffset(strides, offset)))
+    return failure();
+  if (llvm::any_of(strides, ShapedType::isDynamic))
+    return failure();
+  return strides;
+}
+
+static FailureOr<int64_t> getShapedByteSize(MemRefType type) {
+  if (!type.hasStaticShape() || !hasByteSizedElementType(type.getElementType()))
+    return failure();
+  auto byteSize =
+    pim::getCheckedShapedTypeSizeInBytes(type, UnknownLoc::get(type.getContext()), "normalized copy byte size");
+  if (failed(byteSize))
+    return failure();
+  if (*byteSize > static_cast<uint64_t>(std::numeric_limits<int64_t>::max()))
+    return failure();
+  return static_cast<int64_t>(*byteSize);
+}
+
+static FailureOr<SmallVector<int64_t>>
+inferLogicalCopyShape(MemRefType targetType, MemRefType sourceType, int64_t size) {
+  if (!targetType.hasStaticShape() || !sourceType.hasStaticShape())
+    return failure();
+  if (targetType.getElementType() != sourceType.getElementType() || targetType.getRank() != sourceType.getRank())
+    return failure();
+
+  auto targetBytes = getShapedByteSize(targetType);
+  auto sourceBytes = getShapedByteSize(sourceType);
+  if (failed(targetBytes) || failed(sourceBytes))
+    return failure();
+
+  bool targetMatches = *targetBytes == size;
+  bool sourceMatches = *sourceBytes == size;
+  if (targetMatches && sourceMatches && targetType.getShape() != sourceType.getShape())
+    return failure();
+  if (targetMatches)
+    return SmallVector<int64_t>(targetType.getShape().begin(), targetType.getShape().end());
+  if (sourceMatches)
+    return SmallVector<int64_t>(sourceType.getShape().begin(), sourceType.getShape().end());
+  return failure();
+}
+
+static FailureOr<int64_t> getContiguousSuffixRank(MemRefType type, ArrayRef<int64_t> copyShape) {
+  if (!type.hasStaticShape() || !hasByteSizedElementType(type.getElementType())
+      || type.getRank() != static_cast<int64_t>(copyShape.size()))
+    return failure();
+
+  auto strides = getStaticMemRefStrides(type);
+  if (failed(strides))
+    return failure();
+
+  int64_t expectedStride = 1;
+  int64_t contiguousSuffixRank = 0;
+  for (int64_t dim = type.getRank() - 1; dim >= 0; --dim) {
+    if ((*strides)[dim] != expectedStride)
+      break;
+    ++contiguousSuffixRank;
+    expectedStride *= copyShape[dim];
+  }
+  return contiguousSuffixRank;
+}
+
+static FailureOr<CopyEndpointPlan> analyzeCopyEndpoint(Value value, Value initialByteOffset, MemRefType logicalType) {
+  if (!logicalType.hasStaticShape() || !hasByteSizedElementType(logicalType.getElementType()))
+    return failure();
+
+  CopyEndpointPlan endpoint;
+  endpoint.base = value;
+  endpoint.originalType = logicalType;
+  appendTerm(endpoint.offset, initialByteOffset, 1);
+
+  while (true) {
+    if (auto castOp = endpoint.base.getDefiningOp<memref::CastOp>()) {
+      endpoint.base = castOp.getSource();
+      continue;
+    }
+    if (auto collapseOp = endpoint.base.getDefiningOp<memref::CollapseShapeOp>()) {
+      endpoint.base = collapseOp.getSrc();
+      continue;
+    }
+    if (auto expandOp = endpoint.base.getDefiningOp<memref::ExpandShapeOp>()) {
+      endpoint.base = expandOp.getSrc();
+      continue;
+    }
+    if (auto reinterpretOp = endpoint.base.getDefiningOp<memref::ReinterpretCastOp>()) {
+      endpoint.base = reinterpretOp.getSource();
+      continue;
+    }
+
+    auto subviewOp = endpoint.base.getDefiningOp<memref::SubViewOp>();
+    if (!subviewOp)
+      break;
+
+    auto sourceType = dyn_cast<MemRefType>(subviewOp.getSource().getType());
+    if (!sourceType || !sourceType.hasStaticShape() || !hasByteSizedElementType(sourceType.getElementType()))
+      return failure();
+
+    auto sourceStrides = getStaticMemRefStrides(sourceType);
+    if (failed(sourceStrides))
+      return failure();
+
+    int64_t elementByteWidth = static_cast<int64_t>(getElementTypeSizeInBytes(sourceType.getElementType()));
+    for (auto [offset, stride] : llvm::zip_equal(subviewOp.getMixedOffsets(), *sourceStrides)) {
+      int64_t byteScale = stride * elementByteWidth;
+      if (auto attr = dyn_cast<Attribute>(offset)) {
+        endpoint.offset.constant += cast<IntegerAttr>(attr).getInt() * byteScale;
+        continue;
+      }
+      appendTerm(endpoint.offset, cast<Value>(offset), byteScale);
+    }
+
+    endpoint.base = subviewOp.getSource();
   }
 
-  auto value = cast<Value>(baseOffset);
-  auto cst = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), extraOffset);
-  return arith::AddIOp::create(rewriter, value.getLoc(), value, cst).getResult();
+  endpoint.baseType = dyn_cast<MemRefType>(endpoint.base.getType());
+  if (!endpoint.baseType)
+    return failure();
+  return endpoint;
 }
 
-static Value buildSubviewChunk(const StaticSubviewInfo& info,
-                               ArrayRef<int64_t> outerIndices,
-                               Location loc,
-                               PatternRewriter& rewriter) {
-  SmallVector<OpFoldResult> chunkOffsets;
-  SmallVector<OpFoldResult> chunkSizes;
-  SmallVector<OpFoldResult> chunkStrides;
-  chunkOffsets.reserve(info.offsets.size());
-  chunkSizes.reserve(info.sizes.size());
-  chunkStrides.reserve(info.strides.size());
+static FailureOr<CopyRewritePlan>
+analyzeCopyRewrite(Value target, Value source, Value targetOffset, Value sourceOffset, int64_t size) {
+  auto targetType = dyn_cast<MemRefType>(target.getType());
+  auto sourceType = dyn_cast<MemRefType>(source.getType());
+  if (!targetType || !sourceType || size <= 0)
+    return failure();
 
-  for (size_t dim = 0; dim < info.sizes.size(); ++dim) {
-    int64_t extraOffset = dim + 1 < info.sizes.size() ? outerIndices[dim] * info.strides[dim] : 0;
-    chunkOffsets.push_back(addConstantOffset(info.offsets[dim], extraOffset, rewriter));
-    chunkSizes.push_back(rewriter.getIndexAttr(dim + 1 < info.sizes.size() ? 1 : info.sizes.back()));
-    chunkStrides.push_back(rewriter.getIndexAttr(info.strides[dim]));
+  auto logicalCopyShape = inferLogicalCopyShape(targetType, sourceType, size);
+  if (failed(logicalCopyShape))
+    return failure();
+
+  auto targetPlan = analyzeCopyEndpoint(target, targetOffset, targetType);
+  auto sourcePlan = analyzeCopyEndpoint(source, sourceOffset, sourceType);
+  if (failed(targetPlan) || failed(sourcePlan))
+    return failure();
+
+  auto targetSuffixRank = getContiguousSuffixRank(targetType, *logicalCopyShape);
+  auto sourceSuffixRank = getContiguousSuffixRank(sourceType, *logicalCopyShape);
+  if (failed(targetSuffixRank) || failed(sourceSuffixRank))
+    return failure();
+
+  CopyRewritePlan plan;
+  plan.target = *targetPlan;
+  plan.source = *sourcePlan;
+
+  int64_t contiguousSuffixRank = std::min(*targetSuffixRank, *sourceSuffixRank);
+  if (contiguousSuffixRank == static_cast<int64_t>(logicalCopyShape->size())) {
+    plan.kind = CopyRewritePlan::Kind::Direct;
+    plan.directBytes = size;
+    return plan;
   }
 
-  return memref::SubViewOp::create(rewriter, loc, info.source, chunkOffsets, chunkSizes, chunkStrides);
+  auto targetStrides = getStaticMemRefStrides(targetType);
+  auto sourceStrides = getStaticMemRefStrides(sourceType);
+  if (failed(targetStrides) || failed(sourceStrides))
+    return failure();
+
+  int64_t elementByteWidth = static_cast<int64_t>(getElementTypeSizeInBytes(targetType.getElementType()));
+  plan.kind = CopyRewritePlan::Kind::Loop;
+  plan.loop.targetBaseOffset = plan.target.offset;
+  plan.loop.sourceBaseOffset = plan.source.offset;
+  plan.loop.outerShape.assign(logicalCopyShape->begin(), logicalCopyShape->end() - contiguousSuffixRank);
+  SmallVector<int64_t> chunkShape(logicalCopyShape->end() - contiguousSuffixRank, logicalCopyShape->end());
+  plan.loop.chunkBytes = getNumElements(chunkShape) * elementByteWidth;
+  for (int64_t stride : ArrayRef<int64_t>(*targetStrides).take_front(plan.loop.outerShape.size()))
+    plan.loop.targetOuterByteStrides.push_back(stride * elementByteWidth);
+  for (int64_t stride : ArrayRef<int64_t>(*sourceStrides).take_front(plan.loop.outerShape.size()))
+    plan.loop.sourceOuterByteStrides.push_back(stride * elementByteWidth);
+  if (plan.loop.chunkBytes <= 0)
+    return failure();
+  return plan;
 }
 
-static SmallVector<Value> delinearizeIndexValue(Value linearIndex,
-                                                ArrayRef<int64_t> shape,
-                                                ArrayRef<int64_t> strides,
-                                                PatternRewriter& rewriter) {
+static Value createIndexConstant(PatternRewriter& rewriter, Operation* anchorOp, int64_t value) {
+  return getOrCreateIndexConstant(rewriter, anchorOp, value);
+}
+
+static Value addIndexValues(PatternRewriter& rewriter, Location loc, Value lhs, Value rhs) {
+  if (auto constant = getConstantIntValue(lhs); constant && *constant == 0)
+    return rhs;
+  if (auto constant = getConstantIntValue(rhs); constant && *constant == 0)
+    return lhs;
+  return arith::AddIOp::create(rewriter, loc, lhs, rhs).getResult();
+}
+
+static Value mulIndexValue(PatternRewriter& rewriter, Location loc, Operation* anchorOp, Value value, int64_t scale) {
+  if (scale == 0)
+    return createIndexConstant(rewriter, anchorOp, 0);
+  if (scale == 1)
+    return value;
+  Value scaleValue = createIndexConstant(rewriter, anchorOp, scale);
+  return arith::MulIOp::create(rewriter, loc, value, scaleValue).getResult();
+}
+
+static Value
+materializeByteOffset(PatternRewriter& rewriter, Location loc, Operation* anchorOp, const ByteOffsetExpr& expr) {
+  Value result = createIndexConstant(rewriter, anchorOp, expr.constant);
+  for (const ByteOffsetTerm& term : expr.terms)
+    result = addIndexValues(rewriter, loc, result, mulIndexValue(rewriter, loc, anchorOp, term.value, term.scale));
+  return result;
+}
+
+static SmallVector<Value> materializeDelinearizedIndices(
+  PatternRewriter& rewriter, Location loc, Operation* anchorOp, Value linearIndex, ArrayRef<int64_t> shape) {
   SmallVector<Value> indices;
-  indices.reserve(shape.size());
+  if (shape.empty())
+    return indices;
 
+  auto rowMajorStrides = computeRowMajorStrides(shape);
   Value remaining = linearIndex;
-  for (auto [_dim, stride] : llvm::enumerate(strides)) {
-    auto cStride = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), stride);
-    Value index = arith::DivUIOp::create(rewriter, linearIndex.getLoc(), remaining, cStride);
+  for (auto [dim, stride] : llvm::enumerate(rowMajorStrides)) {
+    if (dim + 1 == rowMajorStrides.size()) {
+      indices.push_back(remaining);
+      break;
+    }
+    Value strideValue = createIndexConstant(rewriter, anchorOp, stride);
+    Value index = arith::DivUIOp::create(rewriter, loc, remaining, strideValue);
     indices.push_back(index);
-    remaining = arith::RemUIOp::create(rewriter, linearIndex.getLoc(), remaining, cStride);
+    remaining = arith::RemUIOp::create(rewriter, loc, remaining, strideValue);
   }
-
   return indices;
 }
 
-static OpFoldResult addDynamicOffset(OpFoldResult baseOffset, Value extraOffset, PatternRewriter& rewriter) {
-  if (auto attr = dyn_cast<Attribute>(baseOffset)) {
-    auto integerAttr = cast<IntegerAttr>(attr);
-    if (integerAttr.getInt() == 0)
-      return extraOffset;
-
-    auto cst = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), integerAttr.getInt());
-    return arith::AddIOp::create(rewriter, extraOffset.getLoc(), cst, extraOffset).getResult();
-  }
-
-  auto value = cast<Value>(baseOffset);
-  return arith::AddIOp::create(rewriter, value.getLoc(), value, extraOffset).getResult();
-}
-
-static Value buildDynamicSubviewChunk(const StaticSubviewInfo& info,
-                                      ArrayRef<Value> outerIndices,
-                                      Location loc,
-                                      PatternRewriter& rewriter) {
-  SmallVector<OpFoldResult> chunkOffsets;
-  SmallVector<OpFoldResult> chunkSizes;
-  SmallVector<OpFoldResult> chunkStrides;
-  chunkOffsets.reserve(info.offsets.size());
-  chunkSizes.reserve(info.sizes.size());
-  chunkStrides.reserve(info.strides.size());
-
-  for (size_t dim = 0; dim < info.sizes.size(); ++dim) {
-    if (dim + 1 < info.sizes.size()) {
-      assert(info.strides[dim] == 1 && "loop-based subview rewrite requires unit strides");
-      chunkOffsets.push_back(addDynamicOffset(info.offsets[dim], outerIndices[dim], rewriter));
-      chunkSizes.push_back(rewriter.getIndexAttr(1));
-    }
-    else {
-      chunkOffsets.push_back(info.offsets[dim]);
-      chunkSizes.push_back(rewriter.getIndexAttr(info.sizes.back()));
-    }
-    chunkStrides.push_back(rewriter.getIndexAttr(info.strides[dim]));
-  }
-
-  return memref::SubViewOp::create(rewriter, loc, info.source, chunkOffsets, chunkSizes, chunkStrides);
-}
-
-static Value buildContiguousChunk(
-  Value source, ArrayRef<int64_t> copyShape, ArrayRef<Value> outerIndices, Location loc, PatternRewriter& rewriter) {
-  SmallVector<OpFoldResult> chunkOffsets;
-  SmallVector<OpFoldResult> chunkSizes;
-  SmallVector<OpFoldResult> chunkStrides;
-  chunkOffsets.reserve(copyShape.size());
-  chunkSizes.reserve(copyShape.size());
-  chunkStrides.reserve(copyShape.size());
-
-  for (size_t dim = 0; dim < copyShape.size(); ++dim) {
-    chunkOffsets.push_back(dim + 1 < copyShape.size() ? OpFoldResult(outerIndices[dim]) : rewriter.getIndexAttr(0));
-    chunkSizes.push_back(rewriter.getIndexAttr(dim + 1 < copyShape.size() ? 1 : copyShape.back()));
-    chunkStrides.push_back(rewriter.getIndexAttr(1));
-  }
-
-  return memref::SubViewOp::create(rewriter, loc, source, chunkOffsets, chunkSizes, chunkStrides);
+static Value materializeOuterByteOffset(PatternRewriter& rewriter,
+                                        Location loc,
+                                        Operation* anchorOp,
+                                        const ByteOffsetExpr& baseOffset,
+                                        ArrayRef<Value> outerIndices,
+                                        ArrayRef<int64_t> outerByteStrides) {
+  Value result = materializeByteOffset(rewriter, loc, anchorOp, baseOffset);
+  for (auto [index, stride] : llvm::zip_equal(outerIndices, outerByteStrides))
+    result = addIndexValues(rewriter, loc, result, mulIndexValue(rewriter, loc, anchorOp, index, stride));
+  return result;
 }
 
 template <typename CopyOp, typename CreateCopyOp>
-static LogicalResult rewriteSubviewCopyLikeOp(CopyOp copyOp,
-                                              Value dst,
-                                              Value src,
-                                              int64_t dstOffset,
-                                              int64_t srcOffset,
-                                              int64_t size,
-                                              bool allowLoopRewrite,
-                                              PatternRewriter& rewriter,
-                                              CreateCopyOp createCopyOp) {
-  auto srcSubview = getStaticSubviewInfo(src);
-  auto dstSubview = getStaticSubviewInfo(dst);
-  const bool splitSrc = succeeded(srcSubview) && !isStaticSubviewContiguous(*srcSubview);
-  const bool splitDst = succeeded(dstSubview) && !isStaticSubviewContiguous(*dstSubview);
-  if (!splitSrc && !splitDst)
+static LogicalResult rewriteCopyLikeOp(CopyOp copyOp,
+                                       Value target,
+                                       Value source,
+                                       Value targetOffset,
+                                       Value sourceOffset,
+                                       Value replacementValue,
+                                       CreateCopyOp createCopyOp,
+                                       PatternRewriter& rewriter) {
+  if (isNormalizedCopyLikeOp(copyOp, target, source, targetOffset, sourceOffset))
     return failure();
 
-  auto sourceType = dyn_cast<MemRefType>(src.getType());
-  auto dstType = dyn_cast<MemRefType>(dst.getType());
-  if (!sourceType || !dstType || !sourceType.hasStaticShape() || !dstType.hasStaticShape())
-    return failure();
-  if (sourceType.getElementType() != dstType.getElementType())
+  auto plan = analyzeCopyRewrite(target, source, targetOffset, sourceOffset, copyOp.getSize());
+  if (failed(plan))
     return failure();
 
-  if (splitSrc && (srcOffset != 0 || llvm::any_of(srcSubview->strides, [](int64_t stride) { return stride != 1; })))
-    return failure();
-  if (splitDst && (dstOffset != 0 || llvm::any_of(dstSubview->strides, [](int64_t stride) { return stride != 1; })))
-    return failure();
-
-  ArrayRef<int64_t> copyShape = splitSrc ? ArrayRef<int64_t>(srcSubview->sizes) : ArrayRef<int64_t>(dstSubview->sizes);
-  if (splitSrc && splitDst && copyShape != ArrayRef<int64_t>(dstSubview->sizes))
-    return failure();
-
-  if (!hasByteSizedElementType(sourceType.getElementType()))
-    return failure();
-  const int64_t elementByteWidth = static_cast<int64_t>(getElementTypeSizeInBytes(sourceType.getElementType()));
-
-  const int64_t totalBytes = getNumElements(copyShape) * elementByteWidth;
-  if (size != totalBytes)
-    return failure();
-
-  const int64_t sliceBytes = copyShape.back() * elementByteWidth;
-  if (sliceBytes <= 0)
-    return failure();
-
-  SmallVector<int64_t> outerShape(copyShape.begin(), copyShape.end() - 1);
-  auto outerStrides = computeRowMajorStrides(outerShape);
-  const int64_t numSlices = outerShape.empty() ? 1 : getNumElements(outerShape);
-  const bool sourceShapeMatchesCopyShape = llvm::equal(sourceType.getShape(), copyShape);
-  const bool dstShapeMatchesCopyShape = llvm::equal(dstType.getShape(), copyShape);
-
-  if (allowLoopRewrite && numSlices > 1 && srcOffset == 0 && dstOffset == 0
-      && sourceType.getRank() == static_cast<int64_t>(copyShape.size())
-      && dstType.getRank() == static_cast<int64_t>(copyShape.size()) && (splitSrc || sourceShapeMatchesCopyShape)
-      && (splitDst || dstShapeMatchesCopyShape)) {
-    auto c0 = getOrCreateIndexConstant(rewriter, copyOp, 0);
-    auto cUpper = getOrCreateIndexConstant(rewriter, copyOp, numSlices);
-    auto cStep = getOrCreateIndexConstant(rewriter, copyOp, 1);
-
-    auto loop = scf::ForOp::create(rewriter, copyOp.getLoc(), c0, cUpper, cStep, ValueRange {});
-    rewriter.setInsertionPointToStart(loop.getBody());
-
-    SmallVector<Value> outerIndices =
-      outerShape.empty() ? SmallVector<Value> {}
-                         : delinearizeIndexValue(loop.getInductionVar(), outerShape, outerStrides, rewriter);
-    Value chunkDst = splitDst ? buildDynamicSubviewChunk(*dstSubview, outerIndices, copyOp.getLoc(), rewriter)
-                              : buildContiguousChunk(dst, copyShape, outerIndices, copyOp.getLoc(), rewriter);
-    Value chunkSrc = splitSrc ? buildDynamicSubviewChunk(*srcSubview, outerIndices, copyOp.getLoc(), rewriter)
-                              : buildContiguousChunk(src, copyShape, outerIndices, copyOp.getLoc(), rewriter);
-    createCopyOp(cast<MemRefType>(chunkDst.getType()), chunkDst, chunkSrc, 0, 0, sliceBytes);
+  Location loc = copyOp.getLoc();
+  Operation* anchorOp = copyOp.getOperation();
+  if (plan->kind == CopyRewritePlan::Kind::Direct) {
+    Value newTargetOffset = materializeByteOffset(rewriter, loc, anchorOp, plan->target.offset);
+    Value newSourceOffset = materializeByteOffset(rewriter, loc, anchorOp, plan->source.offset);
+    auto checkedDirectBytes = pim::checkedI32(plan->directBytes, anchorOp, "normalized direct copy byte size");
+    if (failed(checkedDirectBytes))
+      return failure();
+    auto newCopyOp =
+      createCopyOp(loc, plan->target.base, plan->source.base, newTargetOffset, newSourceOffset, *checkedDirectBytes);
+    assert(isNormalizedCopyOp(newCopyOp) && "copy normalization emitted a non-normalized copy");
+    rewriter.replaceOp(copyOp, replacementValue);
     return success();
   }
 
-  rewriter.setInsertionPoint(copyOp);
-  for (int64_t linearIndex = 0; linearIndex < numSlices; ++linearIndex) {
-    SmallVector<int64_t> outerIndices =
-      outerShape.empty() ? SmallVector<int64_t> {} : delinearizeIndex(linearIndex, outerShape, outerStrides);
-    Value chunkDst = splitDst ? buildSubviewChunk(*dstSubview, outerIndices, copyOp.getLoc(), rewriter) : dst;
-    Value chunkSrc = splitSrc ? buildSubviewChunk(*srcSubview, outerIndices, copyOp.getLoc(), rewriter) : src;
-    const int64_t srcByteOffset = splitSrc ? 0 : srcOffset + linearIndex * sliceBytes;
-    const int64_t dstByteOffset = splitDst ? 0 : dstOffset + linearIndex * sliceBytes;
-    createCopyOp(cast<MemRefType>(chunkDst.getType()), chunkDst, chunkSrc, dstByteOffset, srcByteOffset, sliceBytes);
-  }
-
+  Value c0 = createIndexConstant(rewriter, anchorOp, 0);
+  Value cUpper = createIndexConstant(rewriter, anchorOp, getNumElements(plan->loop.outerShape));
+  Value cStep = createIndexConstant(rewriter, anchorOp, 1);
+  auto loop = buildNormalizedScfFor(
+    rewriter,
+    loc,
+    c0,
+    cUpper,
+    cStep,
+    ValueRange {},
+    [&](OpBuilder&, Location nestedLoc, Value inductionVar, ValueRange iterArgs, SmallVectorImpl<Value>& yielded) {
+      SmallVector<Value> outerIndices =
+        materializeDelinearizedIndices(rewriter, nestedLoc, anchorOp, inductionVar, plan->loop.outerShape);
+      Value loopTargetOffset = materializeOuterByteOffset(
+        rewriter, nestedLoc, anchorOp, plan->loop.targetBaseOffset, outerIndices, plan->loop.targetOuterByteStrides);
+      Value loopSourceOffset = materializeOuterByteOffset(
+        rewriter, nestedLoc, anchorOp, plan->loop.sourceBaseOffset, outerIndices, plan->loop.sourceOuterByteStrides);
+      auto checkedChunkBytes = pim::checkedI32(plan->loop.chunkBytes, anchorOp, "normalized loop copy byte size");
+      if (failed(checkedChunkBytes))
+        return failure();
+      auto newCopyOp = createCopyOp(
+        nestedLoc, plan->target.base, plan->source.base, loopTargetOffset, loopSourceOffset, *checkedChunkBytes);
+      assert(isNormalizedCopyOp(newCopyOp) && "copy normalization emitted a non-normalized copy");
+      return success();
+    });
+  if (failed(loop))
+    return failure();
+  rewriter.replaceOp(copyOp, replacementValue);
   return success();
 }
 
@@ -224,34 +378,24 @@ struct NormalizeCoreSubviewCopyPattern final : OpRewritePattern<pim::PimMemCopyO
   using OpRewritePattern::OpRewritePattern;
 
   LogicalResult matchAndRewrite(pim::PimMemCopyOp copyOp, PatternRewriter& rewriter) const override {
-    if (!copyOp->getParentOfType<pim::PimCoreOp>() && !copyOp->getParentOfType<pim::PimCoreBatchOp>())
-      return failure();
-
-    auto status = rewriteSubviewCopyLikeOp(
+    return rewriteCopyLikeOp(
       copyOp,
       copyOp.getTarget(),
       copyOp.getSource(),
       copyOp.getTargetOffset(),
       copyOp.getSourceOffset(),
-      copyOp.getSize(),
-      /*allowLoopRewrite=*/true,
-      rewriter,
-      [&](
-        MemRefType resultType, Value dst, Value src, int64_t dstByteOffset, int64_t srcByteOffset, int64_t sliceBytes) {
-        pim::PimMemCopyOp::create(rewriter,
-                                  copyOp.getLoc(),
-                                  resultType,
-                                  dst,
-                                  src,
-                                  rewriter.getI32IntegerAttr(static_cast<int32_t>(dstByteOffset)),
-                                  rewriter.getI32IntegerAttr(static_cast<int32_t>(srcByteOffset)),
-                                  rewriter.getI32IntegerAttr(static_cast<int32_t>(sliceBytes)));
-      });
-    if (failed(status))
-      return failure();
-
-    rewriter.replaceOp(copyOp, copyOp.getTarget());
-    return success();
+      copyOp.getTarget(),
+      [&](Location loc, Value target, Value source, Value targetOffset, Value sourceOffset, int32_t size) {
+        return pim::PimMemCopyOp::create(rewriter,
+                                         loc,
+                                         cast<MemRefType>(target.getType()),
+                                         targetOffset,
+                                         sourceOffset,
+                                         target,
+                                         source,
+                                         rewriter.getI32IntegerAttr(size));
+      },
+      rewriter);
   }
 };
 
@@ -259,38 +403,24 @@ struct NormalizeHostSubviewLoadPattern final : OpRewritePattern<pim::PimMemCopyH
   using OpRewritePattern::OpRewritePattern;
 
   LogicalResult matchAndRewrite(pim::PimMemCopyHostToDevOp copyOp, PatternRewriter& rewriter) const override {
-    auto dstOffset = resolveIndexValue(copyOp.getDeviceTargetOffset());
-    auto srcOffset = resolveIndexValue(copyOp.getHostSourceOffset());
-    if (failed(dstOffset) || failed(srcOffset))
-      return failure();
-
-    auto status = rewriteSubviewCopyLikeOp(
+    return rewriteCopyLikeOp(
       copyOp,
       copyOp.getDeviceTarget(),
       copyOp.getHostSource(),
-      *dstOffset,
-      *srcOffset,
-      copyOp.getSize(),
-      /*allowLoopRewrite=*/true,
-      rewriter,
-      [&](
-        MemRefType resultType, Value dst, Value src, int64_t dstByteOffset, int64_t srcByteOffset, int64_t sliceBytes) {
-        Value dstOffsetValue = getOrCreateIndexConstant(rewriter, copyOp, dstByteOffset);
-        Value srcOffsetValue = getOrCreateIndexConstant(rewriter, copyOp, srcByteOffset);
-        pim::PimMemCopyHostToDevOp::create(rewriter,
-                                           copyOp.getLoc(),
-                                           resultType,
-                                           dstOffsetValue,
-                                           srcOffsetValue,
-                                           dst,
-                                           src,
-                                           rewriter.getI32IntegerAttr(static_cast<int32_t>(sliceBytes)));
-      });
-    if (failed(status))
-      return failure();
-
-    rewriter.replaceOp(copyOp, copyOp.getDeviceTarget());
-    return success();
+      copyOp.getDeviceTargetOffset(),
+      copyOp.getHostSourceOffset(),
+      copyOp.getDeviceTarget(),
+      [&](Location loc, Value target, Value source, Value targetOffset, Value sourceOffset, int32_t size) {
+        return pim::PimMemCopyHostToDevOp::create(rewriter,
+                                                  loc,
+                                                  cast<MemRefType>(target.getType()),
+                                                  targetOffset,
+                                                  sourceOffset,
+                                                  target,
+                                                  source,
+                                                  rewriter.getI32IntegerAttr(size));
+      },
+      rewriter);
   }
 };
 
@@ -298,43 +428,43 @@ struct NormalizeHostSubviewStorePattern final : OpRewritePattern<pim::PimMemCopy
   using OpRewritePattern::OpRewritePattern;
 
   LogicalResult matchAndRewrite(pim::PimMemCopyDevToHostOp copyOp, PatternRewriter& rewriter) const override {
-    auto dstOffset = resolveIndexValue(copyOp.getHostTargetOffset());
-    auto srcOffset = resolveIndexValue(copyOp.getDeviceSourceOffset());
-    if (failed(dstOffset) || failed(srcOffset))
-      return failure();
-
-    auto status = rewriteSubviewCopyLikeOp(
+    return rewriteCopyLikeOp(
       copyOp,
       copyOp.getHostTarget(),
       copyOp.getDeviceSource(),
-      *dstOffset,
-      *srcOffset,
-      copyOp.getSize(),
-      /*allowLoopRewrite=*/false,
-      rewriter,
-      [&](
-        MemRefType resultType, Value dst, Value src, int64_t dstByteOffset, int64_t srcByteOffset, int64_t sliceBytes) {
-        Value dstOffsetValue = getOrCreateIndexConstant(rewriter, copyOp, dstByteOffset);
-        Value srcOffsetValue = getOrCreateIndexConstant(rewriter, copyOp, srcByteOffset);
-        pim::PimMemCopyDevToHostOp::create(rewriter,
-                                           copyOp.getLoc(),
-                                           resultType,
-                                           dstOffsetValue,
-                                           srcOffsetValue,
-                                           dst,
-                                           src,
-                                           rewriter.getI32IntegerAttr(static_cast<int32_t>(sliceBytes)));
-      });
-    if (failed(status))
-      return failure();
-
-    rewriter.replaceOp(copyOp, copyOp.getHostTarget());
-    return success();
+      copyOp.getHostTargetOffset(),
+      copyOp.getDeviceSourceOffset(),
+      copyOp.getHostTarget(),
+      [&](Location loc, Value target, Value source, Value targetOffset, Value sourceOffset, int32_t size) {
+        return pim::PimMemCopyDevToHostOp::create(rewriter,
+                                                  loc,
+                                                  cast<MemRefType>(target.getType()),
+                                                  targetOffset,
+                                                  sourceOffset,
+                                                  target,
+                                                  source,
+                                                  rewriter.getI32IntegerAttr(size));
+      },
+      rewriter);
   }
 };
 
 } // namespace
 
+bool isNormalizedCopyOp(pim::PimMemCopyOp op) {
+  return isNormalizedCopyLikeOp(op, op.getTarget(), op.getSource(), op.getTargetOffset(), op.getSourceOffset());
+}
+
+bool isNormalizedCopyOp(pim::PimMemCopyHostToDevOp op) {
+  return isNormalizedCopyLikeOp(
+    op, op.getDeviceTarget(), op.getHostSource(), op.getDeviceTargetOffset(), op.getHostSourceOffset());
+}
+
+bool isNormalizedCopyOp(pim::PimMemCopyDevToHostOp op) {
+  return isNormalizedCopyLikeOp(
+    op, op.getHostTarget(), op.getDeviceSource(), op.getHostTargetOffset(), op.getDeviceSourceOffset());
+}
+
 void populatePimContiguityNormalizationPatterns(RewritePatternSet& patterns) {
   patterns.add<NormalizeCoreSubviewCopyPattern, NormalizeHostSubviewLoadPattern, NormalizeHostSubviewStorePattern>(
     patterns.getContext());

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

@@ -2,8 +2,14 @@
 
 #include "mlir/IR/PatternMatch.h"
 
+#include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
+
 namespace onnx_mlir::pim {
 
+bool isNormalizedCopyOp(pim::PimMemCopyOp op);
+bool isNormalizedCopyOp(pim::PimMemCopyHostToDevOp op);
+bool isNormalizedCopyOp(pim::PimMemCopyDevToHostOp op);
+
 void populatePimContiguityNormalizationPatterns(mlir::RewritePatternSet& patterns);
 
 } // namespace onnx_mlir::pim

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

@@ -101,10 +101,10 @@ struct MemCopyOpInterface : DstBufferizableOpInterfaceExternalModel<MemCopyOpInt
     replaceOpWithNewBufferizedOp<PimMemCopyOp>(rewriter,
                                                memCopyOp,
                                                targetOpt->getType(),
+                                               memCopyOp.getTargetOffset(),
+                                               memCopyOp.getSourceOffset(),
                                                *targetOpt,
                                                *sourceOpt,
-                                               memCopyOp.getTargetOffsetAttr(),
-                                               memCopyOp.getSourceOffsetAttr(),
                                                memCopyOp.getSizeAttr());
     return success();
   }
@@ -148,7 +148,10 @@ struct ConcatOpInterface : DstBufferizableOpInterfaceExternalModel<ConcatOpInter
       auto inputOpt = getBufferOrValue(rewriter, input, options, state);
       if (failed(inputOpt))
         return failure();
-      inputs.push_back(materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter));
+      auto contiguous = materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter);
+      if (failed(contiguous))
+        return failure();
+      inputs.push_back(*contiguous);
     }
 
     auto outputBufferOpt = getBufferOrValue(rewriter, concatOp.getOutputBuffer(), options, state);
@@ -179,12 +182,12 @@ 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);
+    if (failed(contiguousInput))
+      return failure();
 
-    replaceOpWithNewBufferizedOp<PimSendOp>(rewriter,
-                                            op,
-                                            materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter),
-                                            sendOp.getSizeAttr(),
-                                            sendOp.getTargetCoreId());
+    replaceOpWithNewBufferizedOp<PimSendOp>(
+      rewriter, op, *contiguousInput, sendOp.getSizeAttr(), sendOp.getTargetCoreId());
     return success();
   }
 };
@@ -407,11 +410,13 @@ struct TransposeOpInterface : DstBufferizableOpInterfaceExternalModel<TransposeO
     if (failed(outputBufferOpt))
       return failure();
 
-    Value contiguousInput = materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter);
+    auto contiguousInput = materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter);
+    if (failed(contiguousInput))
+      return failure();
     Value contiguousOutput = allocateContiguousResultMemRefLike(*outputBufferOpt, op->getLoc(), rewriter);
 
     replaceOpWithNewBufferizedOp<PimTransposeOp>(
-      rewriter, op, contiguousOutput.getType(), contiguousInput, transposeOp.getPermutation(), contiguousOutput);
+      rewriter, op, contiguousOutput.getType(), *contiguousInput, transposeOp.getPermutation(), contiguousOutput);
     return success();
   }
 };
@@ -451,11 +456,13 @@ struct VMMOpInterface : DstBufferizableOpInterfaceExternalModel<VMMOpInterface,
     if (failed(outputBufferOpt))
       return failure();
 
-    Value contiguousInput = materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter);
+    auto contiguousInput = materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter);
+    if (failed(contiguousInput))
+      return failure();
     Value contiguousOutput = allocateContiguousResultMemRefLike(*outputBufferOpt, op->getLoc(), rewriter);
 
     replaceOpWithNewBufferizedOp<PimVMMOp>(
-      rewriter, op, contiguousOutput.getType(), *weightOpt, contiguousInput, contiguousOutput);
+      rewriter, op, contiguousOutput.getType(), *weightOpt, *contiguousInput, contiguousOutput);
     return success();
   }
 };
@@ -490,12 +497,16 @@ struct BinaryDstOpInterface : DstBufferizableOpInterfaceExternalModel<BinaryDstO
     if (failed(outputBufferOpt))
       return failure();
 
-    Value contiguousLhs = materializeContiguousInputMemRef(*lhsOpt, op->getLoc(), rewriter);
-    Value contiguousRhs = materializeContiguousInputMemRef(*rhsOpt, op->getLoc(), rewriter);
+    auto contiguousLhs = materializeContiguousInputMemRef(*lhsOpt, op->getLoc(), rewriter);
+    if (failed(contiguousLhs))
+      return failure();
+    auto contiguousRhs = materializeContiguousInputMemRef(*rhsOpt, op->getLoc(), rewriter);
+    if (failed(contiguousRhs))
+      return failure();
     Value contiguousOutput = allocateContiguousResultMemRefLike(*outputBufferOpt, op->getLoc(), rewriter);
 
     replaceOpWithNewBufferizedOp<OpTy>(
-      rewriter, op, contiguousOutput.getType(), contiguousLhs, contiguousRhs, contiguousOutput);
+      rewriter, op, contiguousOutput.getType(), *contiguousLhs, *contiguousRhs, contiguousOutput);
     return success();
   }
 };
@@ -523,12 +534,16 @@ struct VVDMulOpInterface : DstBufferizableOpInterfaceExternalModel<VVDMulOpInter
     if (failed(outputBufferOpt))
       return failure();
 
-    Value contiguousLhs = materializeContiguousInputMemRef(*lhsOpt, op->getLoc(), rewriter);
-    Value contiguousRhs = materializeContiguousInputMemRef(*rhsOpt, op->getLoc(), rewriter);
+    auto contiguousLhs = materializeContiguousInputMemRef(*lhsOpt, op->getLoc(), rewriter);
+    if (failed(contiguousLhs))
+      return failure();
+    auto contiguousRhs = materializeContiguousInputMemRef(*rhsOpt, op->getLoc(), rewriter);
+    if (failed(contiguousRhs))
+      return failure();
     Value contiguousOutput = allocateContiguousResultMemRefLike(*outputBufferOpt, op->getLoc(), rewriter);
 
     replaceOpWithNewBufferizedOp<PimVVDMulOp>(
-      rewriter, op, contiguousOutput.getType(), contiguousLhs, contiguousRhs, contiguousOutput);
+      rewriter, op, contiguousOutput.getType(), *contiguousLhs, *contiguousRhs, contiguousOutput);
     return success();
   }
 };
@@ -559,10 +574,12 @@ struct UnaryDstOpInterface : DstBufferizableOpInterfaceExternalModel<UnaryDstOpI
     if (failed(outputBufferOpt))
       return failure();
 
-    Value contiguousInput = materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter);
+    auto contiguousInput = materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter);
+    if (failed(contiguousInput))
+      return failure();
     Value contiguousOutput = allocateContiguousResultMemRefLike(*outputBufferOpt, op->getLoc(), rewriter);
 
-    replaceOpWithNewBufferizedOp<OpTy>(rewriter, op, contiguousOutput.getType(), contiguousInput, contiguousOutput);
+    replaceOpWithNewBufferizedOp<OpTy>(rewriter, op, contiguousOutput.getType(), *contiguousInput, contiguousOutput);
     return success();
   }
 };

src/PIM/Dialect/Pim/Transforms/Bufferization/PimBufferization.td

@@ -1,19 +0,0 @@
-#ifndef PIM_BUFFERIZATION
-#define PIM_BUFFERIZATION
-
-#ifndef OP_BASE
-include "mlir/IR/PatternBase.td"
-include "mlir/Dialect/MemRef/IR/MemRefOps.td"
-include "src/Accelerators/PIM/Dialect/Pim/Pim.td"
-#endif // OP_BASE
-
-def memrefCopyToPimMemCopyOp : Pat<
-  (CopyOp $src, $dst),
-  (PimMemCopyOp $dst, $src,
-    ConstantAttr<I32Attr, "0">,
-    ConstantAttr<I32Attr, "0">,
-    (NativeCodeCall<"pim::getMemRefSizeInBytesAttr($_builder, $0)"> $src),
-    (returnType $dst))
->;
-
-#endif // PIM_BUFFERIZATION

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

@@ -6,14 +6,17 @@
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/IR/PatternMatch.h"
 #include "mlir/Pass/Pass.h"
-#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
+#include "mlir/Rewrite/PatternApplicator.h"
 
+#include "llvm/ADT/SmallPtrSet.h"
 #include "llvm/Support/Casting.h"
 
 #include "Common/PimCommon.hpp"
 #include "Compiler/PimCodeGen.hpp"
 #include "Dialect/Pim/PimOps.hpp"
+#include "Dialect/Pim/Transforms/Bufferization/Common.hpp"
 #include "Dialect/Pim/Transforms/Bufferization/ContiguityPatterns.hpp"
+#include "src/Accelerators/PIM/Common/IR/CoreBlockUtils.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 #include "src/Accelerators/PIM/Pass/PIMPasses.h"
 #include "src/Compiler/CompilerOptions.hpp"
@@ -26,7 +29,92 @@ namespace onnx_mlir {
 
 namespace {
 
-#include "Dialect/Pim/Transforms/Bufferization/PimBufferization.hpp.inc"
+struct MemRefCopyWorkItem {
+  memref::CopyOp copyOp;
+  StaticValueKnowledge knowledge;
+};
+
+static StaticValueKnowledge seedCoreKnowledge(pim::PimCoreOp coreOp) {
+  StaticValueKnowledge knowledge;
+  for (auto [index, weight] : llvm::enumerate(coreOp.getWeights()))
+    knowledge.aliases[coreOp.getWeightArgument(index)] = weight;
+  return knowledge;
+}
+
+static StaticValueKnowledge seedCoreBatchKnowledge(pim::PimCoreBatchOp coreBatchOp, unsigned lane) {
+  StaticValueKnowledge knowledge;
+  knowledge.indexValues[coreBatchOp.getLaneArgument()] = lane;
+  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;
+}
+
+static LogicalResult
+lowerMemRefCopyToPimCopy(memref::CopyOp copyOp, PatternRewriter& rewriter, const StaticValueKnowledge& knowledge) {
+  if (!copyOp->getParentOfType<pim::PimCoreOp>() && !copyOp->getParentOfType<pim::PimCoreBatchOp>())
+    return failure();
+
+  auto sourceType = dyn_cast<MemRefType>(copyOp.getSource().getType());
+  auto targetType = dyn_cast<MemRefType>(copyOp.getTarget().getType());
+  if (!sourceType || !targetType || !sourceType.hasStaticShape() || !targetType.hasStaticShape())
+    return failure();
+  if (sourceType.getElementType() != targetType.getElementType())
+    return failure();
+
+  Value zeroOffset = getOrCreateIndexConstant(rewriter, copyOp, 0);
+  auto sizeAttr = getMemRefSizeInBytesAttr(rewriter, copyOp.getOperation(), copyOp.getSource());
+  if (failed(sizeAttr))
+    return failure();
+
+  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 (targetIsDevice && sourceIsHost) {
+    pim::PimMemCopyHostToDevOp::create(rewriter,
+                                       copyOp.getLoc(),
+                                       copyOp.getTarget().getType(),
+                                       zeroOffset,
+                                       zeroOffset,
+                                       copyOp.getTarget(),
+                                       copyOp.getSource(),
+                                       *sizeAttr);
+  }
+  else if (targetIsHost && sourceIsDevice) {
+    pim::PimMemCopyDevToHostOp::create(rewriter,
+                                       copyOp.getLoc(),
+                                       copyOp.getTarget().getType(),
+                                       zeroOffset,
+                                       zeroOffset,
+                                       copyOp.getTarget(),
+                                       copyOp.getSource(),
+                                       *sizeAttr);
+  }
+  else if (targetIsDevice && sourceIsDevice) {
+    pim::PimMemCopyOp::create(rewriter,
+                              copyOp.getLoc(),
+                              copyOp.getTarget().getType(),
+                              zeroOffset,
+                              zeroOffset,
+                              copyOp.getTarget(),
+                              copyOp.getSource(),
+                              *sizeAttr);
+  }
+  else {
+    copyOp.emitOpError() << "failed to classify memref.copy endpoints: source=" << copyOp.getSource()
+                         << " type=" << copyOp.getSource().getType() << " host=" << sourceIsHost
+                         << " device=" << sourceIsDevice << ", target=" << copyOp.getTarget()
+                         << " type=" << copyOp.getTarget().getType() << " host=" << targetIsHost
+                         << " device=" << targetIsDevice;
+    return failure();
+  }
+
+  rewriter.eraseOp(copyOp);
+  return success();
+}
 
 struct PimBufferizationPass : PassWrapper<PimBufferizationPass, OperationPass<ModuleOp>> {
   MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(PimBufferizationPass)
@@ -43,6 +131,14 @@ private:
   LogicalResult verifyContiguousRuntimeOperands(ModuleOp moduleOp) const;
 };
 
+static LogicalResult applyPatternsOnce(Operation* op, PatternApplicator& applicator, PatternRewriter& rewriter) {
+  if (!op || !op->getBlock())
+    return failure();
+
+  rewriter.setInsertionPoint(op);
+  return applicator.matchAndRewrite(op, rewriter);
+}
+
 } // namespace
 
 void PimBufferizationPass::runOnOperation() {
@@ -62,35 +158,81 @@ void PimBufferizationPass::runOnOperation() {
   }
 
   MLIRContext* ctx = moduleOp.getContext();
-  ConversionTarget target(*ctx);
-  target.addLegalDialect<PimDialect>();
+  PatternRewriter rewriter(ctx);
 
-  RewritePatternSet patterns(ctx);
-  populateWithGenerated(patterns);
+  SmallVector<MemRefCopyWorkItem> copyWorklist;
+  llvm::SmallPtrSet<Operation*, 16> seenCopyOps;
+  auto addCopyOp = [&](memref::CopyOp copyOp, const StaticValueKnowledge& knowledge) {
+    if (seenCopyOps.insert(copyOp.getOperation()).second)
+      copyWorklist.push_back({copyOp, knowledge});
+  };
 
-  // Only convert memref.copy → pim.memcp inside pim.core / pim.core_batch bodies.
-  // Host-level copies (e.g. from split/slice ops) must remain as memref.copy for CPU lowering.
-  FrozenRewritePatternSet frozenPatterns(std::move(patterns));
-  bool hasFailed = false;
-  moduleOp.walk<WalkOrder::PreOrder>([&](Operation* op) {
-    if (!isa<pim::PimCoreOp, pim::PimCoreBatchOp>(op))
-      return WalkResult::advance();
-    if (failed(applyPartialConversion(op, target, frozenPatterns)))
-      hasFailed = true;
-    return WalkResult::skip();
+  moduleOp.walk([&](pim::PimCoreOp coreOp) {
+    StaticValueKnowledge knowledge = seedCoreKnowledge(coreOp);
+    (void) walkPimCoreBlockStructurally(
+      coreOp.getBody().front(), knowledge, [&](Operation& op, const StaticValueKnowledge& opKnowledge) {
+        if (auto copyOp = dyn_cast<memref::CopyOp>(&op))
+          addCopyOp(copyOp, opKnowledge);
+        return success();
+      });
   });
+  moduleOp.walk([&](pim::PimCoreBatchOp coreBatchOp) {
+    llvm::SmallVector<unsigned, 2> lanes;
+    lanes.push_back(0);
+    if (coreBatchOp.getLaneCount() > 1)
+      lanes.push_back(static_cast<unsigned>(coreBatchOp.getLaneCount() - 1));
+    for (unsigned lane : lanes) {
+      StaticValueKnowledge knowledge = seedCoreBatchKnowledge(coreBatchOp, lane);
+      (void) walkPimCoreBlockStructurally(
+        coreBatchOp.getBody().front(), knowledge, [&](Operation& op, const StaticValueKnowledge& opKnowledge) {
+          if (auto copyOp = dyn_cast<memref::CopyOp>(&op))
+            addCopyOp(copyOp, opKnowledge);
+          return success();
+        });
+    }
+  });
+
+  bool hasFailed = false;
+  for (const MemRefCopyWorkItem& workItem : copyWorklist) {
+    memref::CopyOp copyOp = workItem.copyOp;
+    rewriter.setInsertionPoint(copyOp);
+    if (failed(lowerMemRefCopyToPimCopy(copyOp, rewriter, workItem.knowledge)))
+      hasFailed = true;
+  }
   if (hasFailed) {
-    moduleOp.emitError("failed to lower memref.copy-like ops inside PIM core bodies during bufferization");
     signalPassFailure();
     return;
   }
 
-  // After this pass, executable PIM ops must only use contiguous/addressable memrefs.
-  // Later PIM codegen passes may verify this invariant but must not repair it.
   RewritePatternSet contiguityPatterns(ctx);
   populatePimContiguityNormalizationPatterns(contiguityPatterns);
-  if (failed(applyPatternsGreedily(moduleOp, std::move(contiguityPatterns)))) {
-    moduleOp.emitError("failed to normalize PIM runtime operand contiguity during bufferization");
+  FrozenRewritePatternSet frozenContiguityPatterns(std::move(contiguityPatterns));
+  PatternApplicator contiguityApplicator(frozenContiguityPatterns);
+  contiguityApplicator.applyDefaultCostModel();
+
+  SmallVector<Operation*> contiguityWorklist;
+  moduleOp.walk([&](Operation* op) {
+    if (isa<pim::PimMemCopyOp, pim::PimMemCopyHostToDevOp, pim::PimMemCopyDevToHostOp>(op))
+      contiguityWorklist.push_back(op);
+  });
+
+  hasFailed = false;
+  for (Operation* op : contiguityWorklist) {
+    if (auto copyOp = dyn_cast<pim::PimMemCopyOp>(op); copyOp && pim::isNormalizedCopyOp(copyOp))
+      continue;
+    if (auto copyOp = dyn_cast<pim::PimMemCopyHostToDevOp>(op); copyOp && pim::isNormalizedCopyOp(copyOp))
+      continue;
+    if (auto copyOp = dyn_cast<pim::PimMemCopyDevToHostOp>(op); copyOp && pim::isNormalizedCopyOp(copyOp))
+      continue;
+
+    if (failed(applyPatternsOnce(op, contiguityApplicator, rewriter))) {
+      op->emitOpError("failed to normalize PIM copy contiguity");
+      hasFailed = true;
+    }
+  }
+
+  if (hasFailed) {
+    moduleOp.emitError("failed to normalize PIM copy contiguity during bufferization");
     signalPassFailure();
     return;
   }
@@ -137,16 +279,28 @@ LogicalResult PimBufferizationPass::verifyContiguousRuntimeOperands(ModuleOp mod
     };
 
     if (auto memCopyOp = dyn_cast<PimMemCopyOp>(op)) {
+      if (!pim::isNormalizedCopyOp(memCopyOp)) {
+        memCopyOp.emitOpError("must use base memref operands plus explicit byte offsets after bufferization");
+        hasFailure = true;
+      }
       verifyOperand(memCopyOp.getTarget(), 0);
       verifyOperand(memCopyOp.getSource(), 1);
       return;
     }
     if (auto loadOp = dyn_cast<PimMemCopyHostToDevOp>(op)) {
+      if (!pim::isNormalizedCopyOp(loadOp)) {
+        loadOp.emitOpError("must use base memref operands plus explicit byte offsets after bufferization");
+        hasFailure = true;
+      }
       verifyOperand(loadOp.getDeviceTarget(), 2);
       verifyOperand(loadOp.getHostSource(), 3);
       return;
     }
     if (auto storeOp = dyn_cast<PimMemCopyDevToHostOp>(op)) {
+      if (!pim::isNormalizedCopyOp(storeOp)) {
+        storeOp.emitOpError("must use base memref operands plus explicit byte offsets after bufferization");
+        hasFailure = true;
+      }
       verifyOperand(storeOp.getHostTarget(), 2);
       verifyOperand(storeOp.getDeviceSource(), 3);
       return;

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

@@ -0,0 +1,12 @@
+add_pim_library(OMPimHostConstantFolding
+  Common.cpp
+  Patterns/Constant.cpp
+  HostConstantFoldingPass.cpp
+  Patterns/Subview.cpp
+
+  EXCLUDE_FROM_OM_LIBS
+
+  LINK_LIBS PUBLIC
+  MLIRLinalgDialect
+  OMPimCommon
+)

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

@@ -7,6 +7,7 @@
 #include "../Common.hpp"
 #include "../Patterns.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 
 using namespace mlir;
@@ -120,15 +121,15 @@ struct FoldConstantCoreMapPattern final : OpRewritePattern<linalg::MapOp> {
 
     rewriter.setInsertionPoint(mapOp);
     auto getGlobalOp = memref::GetGlobalOp::create(rewriter, mapOp.getLoc(), initType, globalOp.getName());
-    auto sizeInBytes = getShapedTypeSizeInBytes(initType);
-    pim::PimMemCopyOp::create(rewriter,
-                              mapOp.getLoc(),
-                              initType,
-                              mapOp.getInit(),
-                              getGlobalOp.getResult(),
-                              rewriter.getI32IntegerAttr(0),
-                              rewriter.getI32IntegerAttr(0),
-                              rewriter.getI32IntegerAttr(sizeInBytes));
+    auto sizeInBytes = pim::getCheckedShapedTypeSizeInBytes(initType, mapOp, "host constant folding byte size");
+    if (failed(sizeInBytes))
+      return failure();
+    Value zeroOffset = getOrCreateIndexConstant(rewriter, mapOp, 0);
+    auto sizeAttr = pim::getCheckedI32Attr(rewriter, mapOp, *sizeInBytes, "host constant folding byte size");
+    if (failed(sizeAttr))
+      return failure();
+    pim::PimMemCopyHostToDevOp::create(
+      rewriter, mapOp.getLoc(), initType, zeroOffset, zeroOffset, mapOp.getInit(), getGlobalOp.getResult(), *sizeAttr);
     rewriter.eraseOp(mapOp);
     return success();
   }
@@ -487,7 +488,9 @@ struct FoldConstantMemCpPattern final : OpRewritePattern<pim::PimMemCopyOp> {
     if (!allocType || !allocType.hasStaticShape())
       return failure();
 
-    if (copyOp.getTargetOffset() != 0 || copyOp.getSourceOffset() != 0)
+    auto targetOffset = resolveIndexValue(copyOp.getTargetOffset());
+    auto sourceOffset = resolveIndexValue(copyOp.getSourceOffset());
+    if (failed(targetOffset) || failed(sourceOffset) || *targetOffset != 0 || *sourceOffset != 0)
       return failure();
 
     auto moduleOp = copyOp->getParentOfType<ModuleOp>();

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

@@ -0,0 +1,14 @@
+add_pim_library(OMPimMemoryCoalescing
+  MemoryCoalescing.cpp
+  MemoryCoalescing.hpp
+  MemoryCoalescingPass.cpp
+
+  EXCLUDE_FROM_OM_LIBS
+
+  INCLUDE_DIRS PUBLIC
+  ${PIM_PUBLIC_INCLUDE_DIRS}
+
+  LINK_LIBS PUBLIC
+  OMPimCommon
+  PimOps
+)

src/PIM/Dialect/Pim/Transforms/MemoryCoalescing/MemoryCoalescing.cpp

@@ -0,0 +1,230 @@
+#include "mlir/Dialect/MemRef/IR/MemRef.h"
+#include "mlir/Dialect/SCF/IR/SCF.h"
+#include "mlir/Interfaces/DestinationStyleOpInterface.h"
+
+#include "llvm/ADT/DenseMap.h"
+#include "llvm/ADT/STLExtras.h"
+#include "llvm/ADT/SmallPtrSet.h"
+#include "llvm/ADT/SmallVector.h"
+
+#include <limits>
+
+#include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Dialect/Pim/Transforms/MemoryCoalescing/MemoryCoalescing.hpp"
+
+using namespace mlir;
+
+namespace onnx_mlir {
+namespace pim {
+
+namespace {
+
+static bool isSupportedAliasOp(Operation* op) {
+  return isa<memref::SubViewOp, memref::CastOp, memref::CollapseShapeOp, memref::ExpandShapeOp>(op);
+}
+
+static bool isCandidateAllocType(MemRefType type) {
+  return type && type.hasStaticShape() && type.getLayout().isIdentity()
+      && hasByteSizedElementType(type.getElementType());
+}
+
+static uint64_t getTypeSizeBytes(MemRefType type) {
+  return static_cast<uint64_t>(type.getNumElements() * getElementTypeSizeInBytes(type.getElementType()));
+}
+
+static Operation* getTopLevelAncestorInBlock(Operation* op, Block& block) {
+  Operation* current = op;
+  while (current && current->getBlock() != &block)
+    current = current->getParentOp();
+  return current;
+}
+
+static void analyzeBlock(Block& block, MemoryCoalescingAnalysis& analysis);
+
+static FailureOr<uint64_t>
+getLastUseInstruction(memref::AllocOp allocOp, Block& scopeBlock, const DenseMap<Operation*, uint64_t>& opOrder) {
+  uint64_t endInstruction = opOrder.lookup(allocOp);
+  SmallPtrSet<Value, 16> visitedValues;
+  SmallPtrSet<Operation*, 16> visitedUsers;
+  SmallVector<Value> pendingValues;
+  pendingValues.push_back(allocOp.getResult());
+
+  while (!pendingValues.empty()) {
+    Value value = pendingValues.pop_back_val();
+    if (!visitedValues.insert(value).second)
+      continue;
+
+    for (Operation* user : value.getUsers()) {
+      if (!visitedUsers.insert(user).second)
+        continue;
+
+      if (isSupportedAliasOp(user))
+        llvm::append_range(pendingValues, user->getResults());
+
+      if (auto dpsOp = dyn_cast<DestinationStyleOpInterface>(user)) {
+        for (OpResult result : user->getResults()) {
+          OpOperand* tiedOperand = dpsOp.getTiedOpOperand(result);
+          if (tiedOperand && tiedOperand->get() == value)
+            pendingValues.push_back(result);
+        }
+      }
+
+      if (auto forOp = dyn_cast<scf::ForOp>(user)) {
+        for (auto [index, initArg] : llvm::enumerate(forOp.getInitArgs())) {
+          if (initArg != value)
+            continue;
+          pendingValues.push_back(forOp.getRegionIterArgs()[index]);
+          pendingValues.push_back(forOp.getResult(index));
+        }
+      }
+
+      if (auto yieldOp = dyn_cast<scf::YieldOp>(user)) {
+        auto forOp = dyn_cast<scf::ForOp>(yieldOp->getParentOp());
+        if (!forOp)
+          return failure();
+        for (auto [index, operand] : llvm::enumerate(yieldOp.getOperands()))
+          if (operand == value)
+            pendingValues.push_back(forOp.getResult(index));
+      }
+
+      Operation* orderedUser = getTopLevelAncestorInBlock(user, scopeBlock);
+      if (!orderedUser)
+        return failure();
+
+      auto order = opOrder.find(orderedUser);
+      if (order == opOrder.end())
+        return failure();
+      endInstruction = std::max(endInstruction, order->second);
+    }
+  }
+
+  return endInstruction;
+}
+
+static void analyzeBlock(Block& block, MemoryCoalescingAnalysis& analysis) {
+  for (Operation& op : block)
+    for (Region& region : op.getRegions())
+      for (Block& nestedBlock : region)
+        analyzeBlock(nestedBlock, analysis);
+
+  DenseMap<Operation*, uint64_t> opOrder;
+  uint64_t nextInstruction = 0;
+  for (Operation& op : block)
+    opOrder.try_emplace(&op, nextInstruction++);
+
+  MemoryCoalescingBlockAnalysis blockAnalysis;
+  blockAnalysis.block = &block;
+
+  for (Operation& op : block) {
+    auto allocOp = dyn_cast<memref::AllocOp>(&op);
+    if (!allocOp)
+      continue;
+
+    auto allocType = dyn_cast<MemRefType>(allocOp.getType());
+    if (!isCandidateAllocType(allocType)) {
+      ++blockAnalysis.skippedAllocations;
+      continue;
+    }
+
+    auto endInstruction = getLastUseInstruction(allocOp, block, opOrder);
+    if (failed(endInstruction)) {
+      ++blockAnalysis.skippedAllocations;
+      continue;
+    }
+
+    blockAnalysis.candidates.push_back(
+      AllocationCandidate {allocOp, &block, opOrder.lookup(allocOp), *endInstruction, getTypeSizeBytes(allocType)});
+  }
+
+  analysis.skippedAllocations += blockAnalysis.skippedAllocations;
+  if (!blockAnalysis.candidates.empty() || blockAnalysis.skippedAllocations != 0)
+    analysis.blocks.push_back(std::move(blockAnalysis));
+}
+
+} // namespace
+
+uint64_t MemoryCoalescingAnalysis::getCandidateCount() const {
+  uint64_t total = 0;
+  for (const MemoryCoalescingBlockAnalysis& block : blocks)
+    total += block.candidates.size();
+  return total;
+}
+
+MemoryCoalescingAnalysis analyzeMemoryCoalescingCandidates(Operation* coreLikeOp) {
+  MemoryCoalescingAnalysis analysis;
+  if (!coreLikeOp || coreLikeOp->getNumRegions() != 1 || coreLikeOp->getRegion(0).empty())
+    return analysis;
+
+  analyzeBlock(coreLikeOp->getRegion(0).front(), analysis);
+  return analysis;
+}
+
+MemoryCoalescingStats
+coalesceMemory(Operation* coreLikeOp, const MemoryCoalescingAnalysis& analysis, RewriterBase& rewriter) {
+  (void) coreLikeOp;
+
+  MemoryCoalescingStats stats;
+  stats.skippedAllocations = analysis.skippedAllocations;
+
+  for (const MemoryCoalescingBlockAnalysis& blockAnalysis : analysis.blocks) {
+    auto candidates = blockAnalysis.candidates;
+    llvm::sort(candidates, [](const AllocationCandidate& lhs, const AllocationCandidate& rhs) {
+      if (lhs.startInstruction != rhs.startInstruction)
+        return lhs.startInstruction < rhs.startInstruction;
+      return lhs.endInstruction < rhs.endInstruction;
+    });
+
+    struct ActiveStorage {
+      memref::AllocOp root;
+      uint64_t endInstruction = 0;
+    };
+
+    SmallVector<ActiveStorage> active;
+    SmallVector<memref::AllocOp> freeList;
+
+    for (AllocationCandidate& candidate : candidates) {
+      for (auto it = active.begin(); it != active.end();) {
+        if (it->endInstruction < candidate.startInstruction) {
+          freeList.push_back(it->root);
+          it = active.erase(it);
+          continue;
+        }
+        ++it;
+      }
+
+      auto bestFit = freeList.end();
+      uint64_t bestFitBytes = std::numeric_limits<uint64_t>::max();
+      auto candidateType = cast<MemRefType>(candidate.alloc.getType());
+      for (auto it = freeList.begin(); it != freeList.end(); ++it) {
+        auto freeType = cast<MemRefType>((*it).getType());
+        if (freeType != candidateType)
+          continue;
+
+        uint64_t freeBytes = getTypeSizeBytes(freeType);
+        if (freeBytes < candidate.sizeBytes || freeBytes >= bestFitBytes)
+          continue;
+
+        bestFit = it;
+        bestFitBytes = freeBytes;
+      }
+
+      if (bestFit == freeList.end()) {
+        active.push_back(ActiveStorage {candidate.alloc, candidate.endInstruction});
+        continue;
+      }
+
+      memref::AllocOp root = *bestFit;
+      freeList.erase(bestFit);
+      candidate.alloc.getResult().replaceAllUsesWith(root.getResult());
+      rewriter.eraseOp(candidate.alloc);
+      active.push_back(ActiveStorage {root, candidate.endInstruction});
+      ++stats.removedAllocs;
+      stats.savedBytes += candidate.sizeBytes;
+    }
+  }
+
+  return stats;
+}
+
+} // namespace pim
+} // namespace onnx_mlir

src/PIM/Dialect/Pim/Transforms/MemoryCoalescing/MemoryCoalescing.hpp

@@ -0,0 +1,44 @@
+#pragma once
+
+#include "mlir/Dialect/MemRef/IR/MemRef.h"
+#include "mlir/IR/PatternMatch.h"
+
+#include "llvm/ADT/SmallVector.h"
+
+namespace onnx_mlir {
+namespace pim {
+
+struct AllocationCandidate {
+  mlir::memref::AllocOp alloc;
+  mlir::Block* scopeBlock = nullptr;
+  uint64_t startInstruction = 0;
+  uint64_t endInstruction = 0;
+  uint64_t sizeBytes = 0;
+};
+
+struct MemoryCoalescingBlockAnalysis {
+  mlir::Block* block = nullptr;
+  llvm::SmallVector<AllocationCandidate> candidates;
+  uint64_t skippedAllocations = 0;
+};
+
+struct MemoryCoalescingAnalysis {
+  llvm::SmallVector<MemoryCoalescingBlockAnalysis> blocks;
+  uint64_t skippedAllocations = 0;
+
+  uint64_t getCandidateCount() const;
+};
+
+struct MemoryCoalescingStats {
+  uint64_t removedAllocs = 0;
+  uint64_t savedBytes = 0;
+  uint64_t skippedAllocations = 0;
+};
+
+MemoryCoalescingAnalysis analyzeMemoryCoalescingCandidates(mlir::Operation* coreLikeOp);
+
+MemoryCoalescingStats
+coalesceMemory(mlir::Operation* coreLikeOp, const MemoryCoalescingAnalysis& analysis, mlir::RewriterBase& rewriter);
+
+} // namespace pim
+} // namespace onnx_mlir

src/PIM/Dialect/Pim/Transforms/MemoryCoalescing/MemoryCoalescingPass.cpp

@@ -10,10 +10,11 @@
 #include "Common/IR/CompactAsmUtils.hpp"
 #include "src/Accelerators/PIM/Common/IR/BatchCoreUtils.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 #include "src/Accelerators/PIM/Common/Support/DebugDump.hpp"
 #include "src/Accelerators/PIM/Common/Support/ReportUtils.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
-#include "src/Accelerators/PIM/Dialect/Pim/Transforms/StaticMemoryCoalescing/StaticMemoryCoalescing.hpp"
+#include "src/Accelerators/PIM/Dialect/Pim/Transforms/MemoryCoalescing/MemoryCoalescing.hpp"
 #include "src/Accelerators/PIM/Pass/PIMPasses.h"
 
 using namespace mlir;
@@ -22,9 +23,9 @@ using namespace onnx_mlir::compact_asm;
 namespace onnx_mlir {
 namespace {
 
-// This pass assumes bufferization has already normalized executable PIM
-// operands. It only reuses compatible local allocations with non-overlapping
-// lifetimes; it does not repair memory contiguity.
+// This pass is an IR cleanup step after bufferization. It only rewrites
+// obviously compatible local allocations with non-overlapping lifetimes inside
+// the same block and leaves the final physical memory planning to codegen.
 
 struct CoalescingReportRow {
   uint64_t numCandidates = 0;
@@ -151,34 +152,39 @@ static void emitReport(ArrayRef<CoalescingReportEntry> entries) {
   file.close();
 }
 
-struct StaticMemoryCoalescingPass : PassWrapper<StaticMemoryCoalescingPass, OperationPass<ModuleOp>> {
-  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(StaticMemoryCoalescingPass)
+struct PimMemoryCoalescingPass : PassWrapper<PimMemoryCoalescingPass, OperationPass<ModuleOp>> {
+  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(PimMemoryCoalescingPass)
 
-  StringRef getArgument() const override { return "pim-static-memory-coalescing"; }
-  StringRef getDescription() const override { return "Analyze static local PIM memory reuse opportunities"; }
+  StringRef getArgument() const override { return "pim-memory-coalescing"; }
+  StringRef getDescription() const override { return "Analyze local PIM memory reuse opportunities"; }
 
-  StaticMemoryCoalescingPass() = default;
-  StaticMemoryCoalescingPass(const StaticMemoryCoalescingPass& pass) {}
+  PimMemoryCoalescingPass() = default;
+  PimMemoryCoalescingPass(const PimMemoryCoalescingPass& pass) {}
 
   void runOnOperation() override {
     IRRewriter rewriter(&getContext());
     SmallVector<CoalescingReportEntry, 32> reportEntries;
     uint64_t nextBatchId = 0;
+    bool hasFailure = false;
 
     getOperation().walk([&](Operation* op) {
-      if (!isa<pim::PimCoreOp, pim::PimCoreBatchOp>(op))
+      if (hasFailure || !isa<pim::PimCoreOp, pim::PimCoreBatchOp>(op))
         return;
 
-      auto analysis = pim::analyzeStaticMemoryCoalescingCandidates(op);
-      auto stats = pim::coalesceStaticMemory(op, rewriter);
+      auto analysis = pim::analyzeMemoryCoalescingCandidates(op);
+      auto stats = pim::coalesceMemory(op, analysis, rewriter);
       CoalescingReportRow row {
-        analysis.candidates.size(), stats.skippedAllocations, stats.removedAllocs, stats.savedBytes};
+        analysis.getCandidateCount(), stats.skippedAllocations, stats.removedAllocs, stats.savedBytes};
 
       if (auto coreOp = dyn_cast<pim::PimCoreOp>(op)) {
-        reportEntries.push_back({CoalescingReportEntry::Kind::Core,
-                                 static_cast<uint64_t>(coreOp.getCoreId()),
-                                 {static_cast<int32_t>(coreOp.getCoreId())},
-                                 row});
+        auto checkedCoreId =
+          pim::checkedI32(static_cast<uint64_t>(coreOp.getCoreId()), coreOp, "memory coalescing core id");
+        if (failed(checkedCoreId)) {
+          hasFailure = true;
+          return;
+        }
+        reportEntries.push_back(
+          {CoalescingReportEntry::Kind::Core, static_cast<uint64_t>(coreOp.getCoreId()), {*checkedCoreId}, row});
         return;
       }
 
@@ -191,6 +197,11 @@ struct StaticMemoryCoalescingPass : PassWrapper<StaticMemoryCoalescingPass, Oper
       reportEntries.push_back(std::move(entry));
     });
 
+    if (hasFailure) {
+      signalPassFailure();
+      return;
+    }
+
     emitReport(reportEntries);
     dumpModule(getOperation(), "pim2_coalesced");
   }
@@ -198,6 +209,6 @@ struct StaticMemoryCoalescingPass : PassWrapper<StaticMemoryCoalescingPass, Oper
 
 } // namespace
 
-std::unique_ptr<Pass> createPimStaticMemoryCoalescingPass() { return std::make_unique<StaticMemoryCoalescingPass>(); }
+std::unique_ptr<Pass> createPimMemoryCoalescingPass() { return std::make_unique<PimMemoryCoalescingPass>(); }
 
 } // namespace onnx_mlir

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

@@ -1,14 +0,0 @@
-add_pim_library(OMPimStaticMemoryCoalescing
-  StaticMemoryCoalescing.cpp
-  StaticMemoryCoalescing.hpp
-  StaticMemoryCoalescingPass.cpp
-
-  EXCLUDE_FROM_OM_LIBS
-
-  INCLUDE_DIRS PUBLIC
-  ${PIM_PUBLIC_INCLUDE_DIRS}
-
-  LINK_LIBS PUBLIC
-  OMPimCommon
-  PimOps
-)

src/PIM/Dialect/Pim/Transforms/StaticMemoryCoalescing/StaticMemoryCoalescing.cpp

@@ -1,179 +0,0 @@
-#include "mlir/Dialect/MemRef/IR/MemRef.h"
-#include "mlir/Dialect/SCF/IR/SCF.h"
-#include "mlir/Interfaces/DestinationStyleOpInterface.h"
-
-#include "llvm/ADT/DenseMap.h"
-#include "llvm/ADT/STLExtras.h"
-#include "llvm/ADT/SmallPtrSet.h"
-#include "llvm/ADT/SmallVector.h"
-
-#include <limits>
-
-#include "src/Accelerators/PIM/Common/PimCommon.hpp"
-#include "src/Accelerators/PIM/Dialect/Pim/Transforms/StaticMemoryCoalescing/StaticMemoryCoalescing.hpp"
-
-using namespace mlir;
-
-namespace onnx_mlir {
-namespace pim {
-
-namespace {
-
-static bool isSupportedAliasOp(Operation* op) {
-  return isa<memref::SubViewOp, memref::CastOp, memref::CollapseShapeOp, memref::ExpandShapeOp>(op);
-}
-
-static bool isCandidateAllocType(MemRefType type) {
-  return type && type.hasStaticShape() && type.getLayout().isIdentity()
-      && hasByteSizedElementType(type.getElementType());
-}
-
-static uint64_t getTypeSizeBytes(MemRefType type) {
-  return static_cast<uint64_t>(type.getNumElements() * getElementTypeSizeInBytes(type.getElementType()));
-}
-
-static FailureOr<uint64_t>
-getLastUseInstruction(memref::AllocOp allocOp, Block& body, const DenseMap<Operation*, uint64_t>& opOrder) {
-  uint64_t endInstruction = opOrder.lookup(allocOp);
-  SmallPtrSet<Operation*, 16> visited;
-  SmallVector<Value> pendingValues;
-  pendingValues.push_back(allocOp.getResult());
-
-  while (!pendingValues.empty()) {
-    Value value = pendingValues.pop_back_val();
-    for (Operation* user : value.getUsers()) {
-      if (user->getBlock() != &body)
-        return failure();
-      if (!visited.insert(user).second)
-        continue;
-
-      if (isSupportedAliasOp(user))
-        for (Value result : user->getResults())
-          pendingValues.push_back(result);
-
-      if (auto forOp = dyn_cast<scf::ForOp>(user)) {
-        for (auto [index, initArg] : llvm::enumerate(forOp.getInitArgs()))
-          if (initArg == value)
-            pendingValues.push_back(forOp.getResult(index));
-      }
-
-      if (auto dpsOp = dyn_cast<DestinationStyleOpInterface>(user)) {
-        for (OpResult result : user->getResults()) {
-          OpOperand* tiedOperand = dpsOp.getTiedOpOperand(result);
-          if (!tiedOperand || tiedOperand->get() != value)
-            continue;
-          pendingValues.push_back(result);
-        }
-      }
-
-      auto order = opOrder.find(user);
-      if (order == opOrder.end())
-        return failure();
-      endInstruction = std::max(endInstruction, order->second);
-    }
-  }
-
-  return endInstruction;
-}
-
-} // namespace
-
-StaticMemoryCoalescingAnalysis analyzeStaticMemoryCoalescingCandidates(Operation* coreLikeOp) {
-  StaticMemoryCoalescingAnalysis analysis;
-  if (!coreLikeOp || coreLikeOp->getNumRegions() != 1 || coreLikeOp->getRegion(0).empty())
-    return analysis;
-
-  Block& body = coreLikeOp->getRegion(0).front();
-  DenseMap<Operation*, uint64_t> opOrder;
-  uint64_t nextInstruction = 0;
-  for (Operation& op : body)
-    opOrder.try_emplace(&op, nextInstruction++);
-
-  for (Operation& op : body) {
-    auto allocOp = dyn_cast<memref::AllocOp>(&op);
-    if (!allocOp)
-      continue;
-
-    auto allocType = dyn_cast<MemRefType>(allocOp.getType());
-    if (!isCandidateAllocType(allocType)) {
-      ++analysis.skippedAllocations;
-      continue;
-    }
-
-    auto endInstruction = getLastUseInstruction(allocOp, body, opOrder);
-    if (failed(endInstruction)) {
-      ++analysis.skippedAllocations;
-      continue;
-    }
-
-    analysis.candidates.push_back(
-      StaticAllocationCandidate {allocOp, opOrder.lookup(allocOp), *endInstruction, getTypeSizeBytes(allocType)});
-  }
-
-  return analysis;
-}
-
-StaticMemoryCoalescingStats coalesceStaticMemory(Operation* coreLikeOp, RewriterBase& rewriter) {
-  StaticMemoryCoalescingStats stats;
-  auto analysis = analyzeStaticMemoryCoalescingCandidates(coreLikeOp);
-  stats.skippedAllocations = analysis.skippedAllocations;
-
-  llvm::sort(analysis.candidates, [](const StaticAllocationCandidate& lhs, const StaticAllocationCandidate& rhs) {
-    if (lhs.startInstruction != rhs.startInstruction)
-      return lhs.startInstruction < rhs.startInstruction;
-    return lhs.endInstruction < rhs.endInstruction;
-  });
-
-  struct ActiveStorage {
-    memref::AllocOp root;
-    uint64_t endInstruction = 0;
-  };
-
-  SmallVector<ActiveStorage> active;
-  SmallVector<memref::AllocOp> freeList;
-
-  for (StaticAllocationCandidate& candidate : analysis.candidates) {
-    for (auto it = active.begin(); it != active.end();) {
-      if (it->endInstruction < candidate.startInstruction) {
-        freeList.push_back(it->root);
-        it = active.erase(it);
-        continue;
-      }
-      ++it;
-    }
-
-    auto bestFit = freeList.end();
-    uint64_t bestFitBytes = std::numeric_limits<uint64_t>::max();
-    auto candidateType = cast<MemRefType>(candidate.alloc.getType());
-    for (auto it = freeList.begin(); it != freeList.end(); ++it) {
-      auto freeType = cast<MemRefType>((*it).getType());
-      if (freeType != candidateType)
-        continue;
-
-      uint64_t freeBytes = getTypeSizeBytes(freeType);
-      if (freeBytes < candidate.sizeBytes || freeBytes >= bestFitBytes)
-        continue;
-
-      bestFit = it;
-      bestFitBytes = freeBytes;
-    }
-
-    if (bestFit == freeList.end()) {
-      active.push_back(ActiveStorage {candidate.alloc, candidate.endInstruction});
-      continue;
-    }
-
-    memref::AllocOp root = *bestFit;
-    freeList.erase(bestFit);
-    candidate.alloc.getResult().replaceAllUsesWith(root.getResult());
-    rewriter.eraseOp(candidate.alloc);
-    active.push_back(ActiveStorage {root, candidate.endInstruction});
-    ++stats.removedAllocs;
-    stats.savedBytes += candidate.sizeBytes;
-  }
-
-  return stats;
-}
-
-} // namespace pim
-} // namespace onnx_mlir

src/PIM/Dialect/Pim/Transforms/StaticMemoryCoalescing/StaticMemoryCoalescing.hpp

@@ -1,34 +0,0 @@
-#pragma once
-
-#include "mlir/Dialect/MemRef/IR/MemRef.h"
-#include "mlir/IR/PatternMatch.h"
-
-#include "llvm/ADT/SmallVector.h"
-
-namespace onnx_mlir {
-namespace pim {
-
-struct StaticAllocationCandidate {
-  mlir::memref::AllocOp alloc;
-  uint64_t startInstruction = 0;
-  uint64_t endInstruction = 0;
-  uint64_t sizeBytes = 0;
-};
-
-struct StaticMemoryCoalescingAnalysis {
-  llvm::SmallVector<StaticAllocationCandidate> candidates;
-  uint64_t skippedAllocations = 0;
-};
-
-struct StaticMemoryCoalescingStats {
-  uint64_t removedAllocs = 0;
-  uint64_t savedBytes = 0;
-  uint64_t skippedAllocations = 0;
-};
-
-StaticMemoryCoalescingAnalysis analyzeStaticMemoryCoalescingCandidates(mlir::Operation* coreLikeOp);
-
-StaticMemoryCoalescingStats coalesceStaticMemory(mlir::Operation* coreLikeOp, mlir::RewriterBase& rewriter);
-
-} // namespace pim
-} // namespace onnx_mlir

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

@@ -0,0 +1,11 @@
+add_pim_library(OMPimVerification
+  VerificationPass.cpp
+
+  EXCLUDE_FROM_OM_LIBS
+
+  LINK_LIBS PUBLIC
+  OMPimCommon
+  OMPimBufferization
+  PimOps
+  SpatialOps
+)

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

@@ -13,6 +13,7 @@
 #include "src/Accelerators/PIM/Common/PimCommon.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"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 
 using namespace mlir;
@@ -45,19 +46,6 @@ static bool isCodegenAddressableValue(Value value) {
       || isa<memref::AllocOp, memref::GetGlobalOp>(compiledAddress->base.getDefiningOp());
 }
 
-static bool isCodegenAddressableValue(Value value, const StaticValueKnowledge& knowledge) {
-  auto resolvedAddress = resolveContiguousAddress(value, knowledge);
-  if (succeeded(resolvedAddress))
-    return isa<BlockArgument>(resolvedAddress->base)
-        || isa<memref::AllocOp, memref::GetGlobalOp>(resolvedAddress->base.getDefiningOp());
-
-  auto compiledAddress = compileContiguousAddressExpr(value);
-  if (failed(compiledAddress))
-    return false;
-  return isa<BlockArgument>(compiledAddress->base)
-      || isa<memref::AllocOp, memref::GetGlobalOp>(compiledAddress->base.getDefiningOp());
-}
-
 static bool isConstantGlobalView(Value value) {
   while (true) {
     Operation* defOp = value.getDefiningOp();
@@ -137,6 +125,24 @@ static bool isSupportedCoreInstructionOp(Operation* op) {
              memref::GetGlobalOp>(op);
 }
 
+static bool isHostAddressableValue(Value value, const StaticValueKnowledge& knowledge) {
+  auto resolvedAddress = resolveContiguousAddress(value, knowledge);
+  Value base;
+  if (succeeded(resolvedAddress)) {
+    base = resolvedAddress->base;
+  }
+  else {
+    auto compiledAddress = compileContiguousAddressExpr(value);
+    if (failed(compiledAddress))
+      return false;
+    base = compiledAddress->base;
+  }
+
+  if (isa<BlockArgument>(base))
+    return true;
+  return isa_and_nonnull<memref::GetGlobalOp>(base.getDefiningOp());
+}
+
 struct VerificationPass : PassWrapper<VerificationPass, OperationPass<ModuleOp>> {
   MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(VerificationPass)
 
@@ -310,10 +316,10 @@ private:
           }
 
           if (isExplicitHostMemCopyOperand(&op, operandIndex)) {
-            if (!isCodegenAddressableValue(operand, knowledge)) {
+            if (!isHostAddressableValue(operand, knowledge)) {
               diagnostics.report(&op, [&](Operation* illegalOp) {
                 illegalOp->emitOpError() << "host operand #" << operandIndex
-                                         << " is not backed by contiguous addressable storage";
+                                         << " must be backed by host-addressable storage";
               });
               hasFailure = true;
             }
@@ -333,6 +339,12 @@ private:
         }
 
         if (auto storeOp = dyn_cast<pim::PimMemCopyDevToHostOp>(op)) {
+          if (!pim::isNormalizedCopyOp(storeOp)) {
+            diagnostics.report(&op, [](Operation* illegalOp) {
+              illegalOp->emitOpError("must use base memref operands plus explicit byte offsets after bufferization");
+            });
+            hasFailure = true;
+          }
           if (failed(resolveIndexValue(storeOp.getHostTargetOffset(), knowledge))
               || failed(resolveIndexValue(storeOp.getDeviceSourceOffset(), knowledge))) {
             diagnostics.report(&op, [](Operation* illegalOp) {
@@ -343,6 +355,12 @@ private:
         }
 
         if (auto loadOp = dyn_cast<pim::PimMemCopyHostToDevOp>(op)) {
+          if (!pim::isNormalizedCopyOp(loadOp)) {
+            diagnostics.report(&op, [](Operation* illegalOp) {
+              illegalOp->emitOpError("must use base memref operands plus explicit byte offsets after bufferization");
+            });
+            hasFailure = true;
+          }
           if (failed(resolveIndexValue(loadOp.getDeviceTargetOffset(), knowledge))
               || failed(resolveIndexValue(loadOp.getHostSourceOffset(), knowledge))) {
             diagnostics.report(&op, [](Operation* illegalOp) {
@@ -351,6 +369,22 @@ private:
             hasFailure = true;
           }
         }
+
+        if (auto copyOp = dyn_cast<pim::PimMemCopyOp>(op)) {
+          if (!pim::isNormalizedCopyOp(copyOp)) {
+            diagnostics.report(&op, [](Operation* illegalOp) {
+              illegalOp->emitOpError("must use base memref operands plus explicit byte offsets after bufferization");
+            });
+            hasFailure = true;
+          }
+          if (failed(resolveIndexValue(copyOp.getTargetOffset(), knowledge))
+              || failed(resolveIndexValue(copyOp.getSourceOffset(), knowledge))) {
+            diagnostics.report(&op, [](Operation* illegalOp) {
+              illegalOp->emitOpError("offset operands must be statically evaluable for PIM codegen");
+            });
+            hasFailure = true;
+          }
+        }
         return success(!hasFailure);
       });
   }

src/PIM/Dialect/Spatial/Spatial.td

@@ -257,6 +257,25 @@ def SpatVAddOp : SpatOp<"vadd", []> {
   }];
 }
 
+def SpatVSubOp : SpatOp<"vsub", []> {
+  let summary = "Element-wise subtraction between two tensors; rhs must match lhs or be 1x1";
+
+  let arguments = (ins
+    SpatTensor:$lhs,
+    SpatTensor:$rhs
+  );
+
+  let results = (outs
+    SpatTensor:$output
+  );
+
+  let hasVerifier = 1;
+
+  let assemblyFormat = [{
+    $lhs `,` $rhs attr-dict `:` `(` type($lhs) `,` type($rhs) `)` `->` type($output)
+  }];
+}
+
 def SpatVMulOp : SpatOp<"vmul", []> {
   let summary = "Element-wise multiplication between two tensors; rhs must match lhs or be 1x1";
 

src/PIM/Dialect/Spatial/SpatialOpsVerify.cpp

@@ -13,6 +13,8 @@
 #include "llvm/ADT/DenseSet.h"
 #include "llvm/Support/LogicalResult.h"
 
+#include "src/Accelerators/PIM/Common/IR/AffineUtils.hpp"
+#include "src/Accelerators/PIM/Common/IR/ConstantUtils.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
 #include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/CompileTime.hpp"
@@ -56,48 +58,19 @@ static LogicalResult verifyStaticWeights(ComputeOpTy computeOp, StringRef kind)
   return success();
 }
 
-static bool isConstantIndexLike(Value value) {
-  APInt constantValue;
-  return matchPattern(value, m_ConstantInt(&constantValue));
-}
-
-static bool isSupportedLaneAffineExpr(AffineExpr expr) {
-  switch (expr.getKind()) {
-  case AffineExprKind::Constant:
-  case AffineExprKind::DimId:    return true;
-  case AffineExprKind::SymbolId: return false;
-  case AffineExprKind::Add:      {
-    auto binaryExpr = cast<AffineBinaryOpExpr>(expr);
-    return isSupportedLaneAffineExpr(binaryExpr.getLHS()) && isSupportedLaneAffineExpr(binaryExpr.getRHS());
-  }
-  case AffineExprKind::Mul: {
-    auto binaryExpr = cast<AffineBinaryOpExpr>(expr);
-    return (isa<AffineConstantExpr>(binaryExpr.getLHS()) && isSupportedLaneAffineExpr(binaryExpr.getRHS()))
-        || (isa<AffineConstantExpr>(binaryExpr.getRHS()) && isSupportedLaneAffineExpr(binaryExpr.getLHS()));
-  }
-  case AffineExprKind::FloorDiv:
-  case AffineExprKind::CeilDiv:
-  case AffineExprKind::Mod:      {
-    auto binaryExpr = cast<AffineBinaryOpExpr>(expr);
-    return isa<AffineConstantExpr>(binaryExpr.getRHS()) && isSupportedLaneAffineExpr(binaryExpr.getLHS());
-  }
-  }
-  llvm_unreachable("unexpected affine expression kind");
-}
-
 static bool isSupportedLaneOffsetExpr(Value value, BlockArgument laneArg) {
-  if (value == laneArg || isConstantIndexLike(value))
+  if (value == laneArg || matchConstantIndexValue(value))
     return true;
 
   auto affineApply = value.getDefiningOp<affine::AffineApplyOp>();
   if (affineApply) {
-    if (affineApply.getAffineMap().getNumResults() != 1 || affineApply.getAffineMap().getNumSymbols() != 0)
+    if (!isSingleResultSymbolFreeAffineMap(affineApply.getAffineMap()))
       return false;
     if (!llvm::all_of(affineApply.getMapOperands(),
                       [&](Value operand) { return isSupportedLaneOffsetExpr(operand, laneArg); })) {
       return false;
     }
-    return isSupportedLaneAffineExpr(affineApply.getAffineMap().getResult(0));
+    return isDimAndConstantAffineExpr(affineApply.getAffineMap().getResult(0));
   }
 
   auto extractOp = value.getDefiningOp<tensor::ExtractOp>();
@@ -112,8 +85,8 @@ static bool isSupportedLaneOffsetExpr(Value value, BlockArgument laneArg) {
   auto addOp = value.getDefiningOp<arith::AddIOp>();
   if (!addOp)
     return false;
-  return (addOp.getLhs() == laneArg && isConstantIndexLike(addOp.getRhs()))
-      || (addOp.getRhs() == laneArg && isConstantIndexLike(addOp.getLhs()));
+  return (addOp.getLhs() == laneArg && matchConstantIndexValue(addOp.getRhs()))
+      || (addOp.getRhs() == laneArg && matchConstantIndexValue(addOp.getLhs()));
 }
 
 static LogicalResult
@@ -281,6 +254,12 @@ LogicalResult SpatVAddOp::verify() {
   return OpTrait::impl::verifySameOperandsAndResultType(*this);
 }
 
+LogicalResult SpatVSubOp::verify() {
+  if (failed(OpTrait::impl::verifyAtLeastNOperands(*this, 2)))
+    return failure();
+  return OpTrait::impl::verifySameOperandsAndResultType(*this);
+}
+
 LogicalResult SpatVMaxOp::verify() {
   if (failed(OpTrait::impl::verifyAtLeastNOperands(*this, 2)))
     return failure();

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

@@ -1,6 +1,5 @@
 #include "mlir/Analysis/TopologicalSortUtils.h"
 #include "mlir/Dialect/Func/IR/FuncOps.h"
-#include "mlir/Dialect/SCF/IR/SCF.h"
 #include "mlir/IR/IRMapping.h"
 #include "mlir/IR/Location.h"
 #include "mlir/IR/PatternMatch.h"
@@ -14,20 +13,14 @@
 #include "llvm/ADT/STLExtras.h"
 #include "llvm/ADT/SmallSet.h"
 #include "llvm/ADT/SmallVector.h"
-#include "llvm/Support/Debug.h"
-#include "llvm/Support/FormatVariadic.h"
 #include "llvm/Support/raw_os_ostream.h"
-#include "llvm/Support/raw_ostream.h"
 
 #include <algorithm>
-#include <chrono>
 #include <cstddef>
 #include <cstdint>
-#include <cstdlib>
 #include <fstream>
 #include <memory>
 #include <optional>
-#include <tuple>
 #include <utility>
 #include <vector>
 
@@ -37,6 +30,7 @@
 #include "Scheduling/MergeSchedulingAnalysis.hpp"
 #include "src/Accelerators/PIM/Common/IR/CompactAsmUtils.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 #include "src/Accelerators/PIM/Common/Support/ReportUtils.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/ONNXToSpatialVerifier.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
@@ -50,99 +44,16 @@ using SpatCompute = spatial::SpatCompute;
 using SpatComputeBatch = spatial::SpatComputeBatch;
 using spatial::getProducerValueRef;
 
-bool isMergeProfilingEnabled() { return std::getenv("RAPTOR_PROFILE_MERGE") != nullptr; }
-
-class ScopedMergePhaseTimer {
-public:
-  explicit ScopedMergePhaseTimer(StringRef phaseName)
-  : enabled(isMergeProfilingEnabled()), phase(phaseName.str()) {
-    if (enabled)
-      start = std::chrono::steady_clock::now();
-  }
-
-  ~ScopedMergePhaseTimer() {
-    if (!enabled)
-      return;
-    auto elapsed = std::chrono::steady_clock::now() - start;
-    double millis = std::chrono::duration<double, std::milli>(elapsed).count();
-    llvm::errs() << "[merge-profile] " << phase << ": " << llvm::formatv("{0:F3}", millis) << " ms\n";
-  }
-
-private:
-  bool enabled = false;
-  std::string phase;
-  std::chrono::steady_clock::time_point start;
-};
-
-struct MergeIrCounts {
-  uint64_t topLevelComputeCount = 0;
-  uint64_t topLevelComputeBatchCount = 0;
-  uint64_t scalarChannelSendCount = 0;
-  uint64_t scalarChannelReceiveCount = 0;
-  uint64_t wvmmCount = 0;
-  uint64_t vaddCount = 0;
-  uint64_t scfForCount = 0;
-};
-
-MergeIrCounts collectMergeIrCounts(func::FuncOp funcOp) {
-  MergeIrCounts counts;
-
-  auto countComputeBodyOps = [&](Operation* op) {
-    op->walk([&](Operation* nestedOp) {
-      if (isa<spatial::SpatChannelSendOp>(nestedOp))
-        ++counts.scalarChannelSendCount;
-      else if (isa<spatial::SpatChannelReceiveOp>(nestedOp))
-        ++counts.scalarChannelReceiveCount;
-      else if (isa<spatial::SpatVMMOp>(nestedOp))
-        ++counts.wvmmCount;
-      else if (isa<spatial::SpatVAddOp>(nestedOp))
-        ++counts.vaddCount;
-      else if (isa<scf::ForOp>(nestedOp))
-        ++counts.scfForCount;
-    });
-  };
-
-  for (auto compute : funcOp.getOps<SpatCompute>()) {
-    ++counts.topLevelComputeCount;
-    countComputeBodyOps(compute.getOperation());
-  }
-
-  for (auto batch : funcOp.getOps<SpatComputeBatch>()) {
-    ++counts.topLevelComputeBatchCount;
-    countComputeBodyOps(batch.getOperation());
-  }
-
-  return counts;
-}
-
-void emitMergeIrCounts(StringRef phaseName, func::FuncOp funcOp) {
-  if (!isMergeProfilingEnabled())
-    return;
-
-  MergeIrCounts counts = collectMergeIrCounts(funcOp);
-  llvm::errs() << "[merge-profile] " << phaseName << " counts:"
-               << " compute=" << counts.topLevelComputeCount << " compute_batch=" << counts.topLevelComputeBatchCount
-               << " scalar_send=" << counts.scalarChannelSendCount
-               << " scalar_recv=" << counts.scalarChannelReceiveCount << " wvmm=" << counts.wvmmCount
-               << " vadd=" << counts.vaddCount << " scf_for=" << counts.scfForCount << "\n";
-}
-
 static std::optional<int32_t> getComputeCoreId(SpatCompute compute) {
-  if (auto coreIdAttr = compute->getAttrOfType<IntegerAttr>(onnx_mlir::kCoreIdAttrName))
-    return static_cast<int32_t>(coreIdAttr.getInt());
+  if (auto coreIdAttr = compute->getAttrOfType<IntegerAttr>(onnx_mlir::kCoreIdAttrName)) {
+    auto checkedCoreId = pim::checkedI32(coreIdAttr.getInt(), compute, "merge compute core id");
+    if (failed(checkedCoreId))
+      return std::nullopt;
+    return *checkedCoreId;
+  }
   return std::nullopt;
 }
 
-struct ComputeMotifInfo {
-  uint64_t instructionCount = 0;
-  uint64_t weightedVmmCount = 0;
-};
-
-void appendUnique(SmallVector<size_t>& values, size_t value) {
-  if (!llvm::is_contained(values, value))
-    values.push_back(value);
-}
-
 bool isTrivialSerialMergeCandidate(SpatCompute compute) {
   if (!compute->hasOneUse())
     return false;
@@ -261,212 +172,6 @@ void mergeTriviallyConnectedComputes(func::FuncOp funcOp) {
   }
 }
 
-void emitMotifProfile(func::FuncOp funcOp) {
-  if (!std::getenv("DCP_MOTIF_PROFILE"))
-    return;
-
-  SmallVector<SpatCompute> computes(funcOp.getOps<SpatCompute>());
-  DenseMap<SpatCompute, size_t> computeToIndex;
-  computeToIndex.reserve(computes.size());
-  for (auto [index, compute] : llvm::enumerate(computes))
-    computeToIndex[compute] = index;
-
-  SmallVector<ComputeMotifInfo> computeInfos(computes.size());
-  SmallVector<SmallVector<size_t>> parents(computes.size());
-  SmallVector<SmallVector<size_t>> children(computes.size());
-
-  uint64_t weightedVmmNodeCount = 0;
-  uint64_t weightedVmmOpCount = 0;
-  uint64_t edgeCount = 0;
-
-  for (auto [index, compute] : llvm::enumerate(computes)) {
-    ComputeMotifInfo& info = computeInfos[index];
-    info.instructionCount = spatial::countComputeBodyInstructions(compute.getBody());
-    compute.getBody().walk([&](spatial::SpatVMMOp) { info.weightedVmmCount++; });
-    if (info.weightedVmmCount > 0) {
-      weightedVmmNodeCount++;
-      weightedVmmOpCount += info.weightedVmmCount;
-    }
-
-    for (Value input : compute.getInputs()) {
-      auto parent = dyn_cast<SpatCompute>(input.getDefiningOp());
-      if (!parent || parent == compute)
-        continue;
-      auto parentIt = computeToIndex.find(parent);
-      if (parentIt == computeToIndex.end())
-        continue;
-
-      size_t parentIndex = parentIt->second;
-      size_t oldParentCount = parents[index].size();
-      appendUnique(parents[index], parentIndex);
-      if (parents[index].size() != oldParentCount) {
-        appendUnique(children[parentIndex], index);
-        edgeCount++;
-      }
-    }
-  }
-
-  uint64_t maxFanIn = 0;
-  uint64_t maxFanOut = 0;
-  uint64_t fanIn16 = 0;
-  uint64_t fanIn64 = 0;
-  uint64_t fanIn256 = 0;
-  uint64_t fanOut16 = 0;
-  uint64_t fanOut64 = 0;
-  uint64_t fanOut256 = 0;
-  for (size_t index = 0; index < computes.size(); ++index) {
-    uint64_t fanIn = parents[index].size();
-    uint64_t fanOut = children[index].size();
-    maxFanIn = std::max(maxFanIn, fanIn);
-    maxFanOut = std::max(maxFanOut, fanOut);
-    fanIn16 += fanIn >= 16;
-    fanIn64 += fanIn >= 64;
-    fanIn256 += fanIn >= 256;
-    fanOut16 += fanOut >= 16;
-    fanOut64 += fanOut >= 64;
-    fanOut256 += fanOut >= 256;
-  }
-
-  uint64_t serialChainCount = 0;
-  uint64_t serialChainNodeCount = 0;
-  uint64_t maxSerialChain = 0;
-  for (size_t index = 0; index < computes.size(); ++index) {
-    if (parents[index].size() == 1 && children[parents[index][0]].size() == 1)
-      continue;
-
-    uint64_t chainLength = 1;
-    size_t current = index;
-    while (children[current].size() == 1) {
-      size_t child = children[current][0];
-      if (parents[child].size() != 1)
-        break;
-      chainLength++;
-      current = child;
-    }
-
-    if (chainLength >= 2) {
-      serialChainCount++;
-      serialChainNodeCount += chainLength;
-      maxSerialChain = std::max(maxSerialChain, chainLength);
-    }
-  }
-
-  SmallVector<size_t> incomingEdgeCount;
-  incomingEdgeCount.reserve(parents.size());
-  for (ArrayRef<size_t> parentList : parents)
-    incomingEdgeCount.push_back(parentList.size());
-
-  SmallVector<uint64_t> level(computes.size(), 0);
-  SmallVector<size_t> readyNodes;
-  readyNodes.reserve(computes.size());
-  for (size_t index = 0; index < computes.size(); ++index)
-    if (incomingEdgeCount[index] == 0)
-      readyNodes.push_back(index);
-
-  size_t readyIndex = 0;
-  while (readyIndex != readyNodes.size()) {
-    size_t current = readyNodes[readyIndex++];
-    for (size_t child : children[current]) {
-      level[child] = std::max(level[child], level[current] + 1);
-      assert(incomingEdgeCount[child] > 0 && "incoming edge count underflow");
-      incomingEdgeCount[child]--;
-      if (incomingEdgeCount[child] == 0)
-        readyNodes.push_back(child);
-    }
-  }
-
-  SmallVector<uint64_t> weightedVmmNodesByLevel;
-  for (size_t index = 0; index < computes.size(); ++index) {
-    if (computeInfos[index].weightedVmmCount == 0)
-      continue;
-    if (weightedVmmNodesByLevel.size() <= level[index])
-      weightedVmmNodesByLevel.resize(level[index] + 1, 0);
-    weightedVmmNodesByLevel[level[index]]++;
-  }
-
-  uint64_t maxWeightedVmmLevel = 0;
-  uint64_t wideWeightedVmmLevels64 = 0;
-  uint64_t wideWeightedVmmLevels256 = 0;
-  for (uint64_t count : weightedVmmNodesByLevel) {
-    maxWeightedVmmLevel = std::max(maxWeightedVmmLevel, count);
-    wideWeightedVmmLevels64 += count >= 64;
-    wideWeightedVmmLevels256 += count >= 256;
-  }
-
-  using ShapeKey = std::tuple<uint64_t, uint64_t, uint64_t, uint64_t, uint64_t, uint64_t>;
-  SmallVector<ShapeKey> weightedVmmShapeKeys;
-  for (auto [index, compute] : llvm::enumerate(computes)) {
-    const ComputeMotifInfo& info = computeInfos[index];
-    if (info.weightedVmmCount == 0)
-      continue;
-    weightedVmmShapeKeys.push_back({info.instructionCount,
-                                    info.weightedVmmCount,
-                                    static_cast<uint64_t>(compute.getWeights().size()),
-                                    static_cast<uint64_t>(compute.getInputs().size()),
-                                    static_cast<uint64_t>(parents[index].size()),
-                                    static_cast<uint64_t>(children[index].size())});
-  }
-
-  llvm::sort(weightedVmmShapeKeys);
-  SmallVector<std::pair<uint64_t, ShapeKey>> weightedVmmShapeCounts;
-  for (size_t index = 0; index < weightedVmmShapeKeys.size();) {
-    size_t next = index + 1;
-    while (next < weightedVmmShapeKeys.size() && weightedVmmShapeKeys[next] == weightedVmmShapeKeys[index])
-      next++;
-    weightedVmmShapeCounts.push_back({next - index, weightedVmmShapeKeys[index]});
-    index = next;
-  }
-  llvm::sort(weightedVmmShapeCounts, [](const auto& lhs, const auto& rhs) {
-    if (lhs.first != rhs.first)
-      return lhs.first > rhs.first;
-    return lhs.second < rhs.second;
-  });
-
-  llvm::errs() << llvm::formatv("[DCP-MOTIF] computes={0} edges={1} wvmmNodes={2} wvmmOps={3} "
-                                "serialChains={4} serialChainNodes={5} maxSerialChain={6} "
-                                "maxFanIn={7} maxFanOut={8} fanIn>=16/64/256={9}/{10}/{11} "
-                                "fanOut>=16/64/256={12}/{13}/{14} topoVisited={15}\n",
-                                computes.size(),
-                                edgeCount,
-                                weightedVmmNodeCount,
-                                weightedVmmOpCount,
-                                serialChainCount,
-                                serialChainNodeCount,
-                                maxSerialChain,
-                                maxFanIn,
-                                maxFanOut,
-                                fanIn16,
-                                fanIn64,
-                                fanIn256,
-                                fanOut16,
-                                fanOut64,
-                                fanOut256,
-                                readyNodes.size());
-
-  llvm::errs() << llvm::formatv("[DCP-MOTIF] wvmmLevels={0} maxWvmmLevel={1} wideWvmmLevels>=64/256={2}/{3} "
-                                "shapeGroups={4}\n",
-                                weightedVmmNodesByLevel.size(),
-                                maxWeightedVmmLevel,
-                                wideWeightedVmmLevels64,
-                                wideWeightedVmmLevels256,
-                                weightedVmmShapeCounts.size());
-
-  for (size_t rank = 0, end = std::min<size_t>(weightedVmmShapeCounts.size(), 5); rank < end; ++rank) {
-    auto [count, shape] = weightedVmmShapeCounts[rank];
-    auto [insts, vmmOps, weights, inputs, fanIn, fanOut] = shape;
-    llvm::errs() << llvm::formatv("[DCP-MOTIF] wvmmShape rank={0} count={1} insts={2} vmmOps={3} "
-                                  "weights={4} inputs={5} fanIn={6} fanOut={7}\n",
-                                  rank,
-                                  count,
-                                  insts,
-                                  vmmOps,
-                                  weights,
-                                  inputs,
-                                  fanIn,
-                                  fanOut);
-  }
-}
-
 void generateReport(func::FuncOp funcOp, const std::string& name, size_t usedCpuCount = 0) {
   std::fstream file = openReportFile(name);
   if (!file.is_open())
@@ -623,44 +328,27 @@ public:
 
   void runOnOperation() override {
     func::FuncOp func = getOperation();
-    {
-      ScopedMergePhaseTimer timer("trivial-serial-merge");
-      mergeTriviallyConnectedComputes(func);
-    }
-    if (std::getenv("DCP_MOTIF_PROFILE"))
-      emitMotifProfile(func);
+    mergeTriviallyConnectedComputes(func);
 
     const spatial::MergeScheduleResult* analysisResult = nullptr;
-    {
-      ScopedMergePhaseTimer timer("scheduling-analysis");
-      analysisResult = &getAnalysis<spatial::MergeSchedulingAnalysis>().getResult();
-    }
-    {
-      ScopedMergePhaseTimer timer("schedule-materialization");
-      if (failed(spatial::MergeScheduleMaterializer().run(func, *analysisResult, nextChannelId))) {
-        signalPassFailure();
-        return;
-      }
+    analysisResult = &getAnalysis<spatial::MergeSchedulingAnalysis>().getResult();
+    if (failed(spatial::MergeScheduleMaterializer().run(func, *analysisResult, nextChannelId))) {
+      signalPassFailure();
+      return;
     }
 
-    emitMergeIrCounts("after-materialization", func);
-
-    {
-      ScopedMergePhaseTimer timer("cleanup-topological-sort-report");
-      if (!sortTopologically(&func.getBody().front())) {
-        func.emitOpError("failed to topologically order merged Spatial IR");
-        signalPassFailure();
-        return;
-      }
-      if (failed(verifySpatialCommunicationInvariants(func))) {
-        func.emitOpError("merged Spatial communication invariant verification failed");
-        signalPassFailure();
-        return;
-      }
-      emitMergeIrCounts("final-post-merge", func);
-      dumpModule(cast<ModuleOp>(func->getParentOp()), "spatial1_dcp_merged");
-      generateReport(func, "spatial_merge_report", analysisResult->cpuToLastComputeMap.size());
+    if (!sortTopologically(&func.getBody().front())) {
+      func.emitOpError("failed to topologically order merged Spatial IR");
+      signalPassFailure();
+      return;
     }
+    if (failed(verifySpatialCommunicationInvariants(func))) {
+      func.emitOpError("merged Spatial communication invariant verification failed");
+      signalPassFailure();
+      return;
+    }
+    dumpModule(cast<ModuleOp>(func->getParentOp()), "spatial1_merged");
+    generateReport(func, "spatial_merge_report", analysisResult->cpuToLastComputeMap.size());
   }
 };
 

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

@@ -21,6 +21,8 @@
 
 #include "ComputeGraph.hpp"
 #include "ComputeInstanceUtils.hpp"
+#include "src/Accelerators/PIM/Common/IR/AffineUtils.hpp"
+#include "src/Accelerators/PIM/Common/IR/ConstantUtils.hpp"
 #include "src/Support/TypeUtilities.hpp"
 
 namespace onnx_mlir {
@@ -103,6 +105,28 @@ bool isProjectedBatchOffset(OpFoldResult offset, Value laneArg) {
       && succeeded(evaluateIndexLike(offset, bindings, /*lane=*/1, laneArg));
 }
 
+std::optional<uint32_t> getConstantExtractLane(tensor::ExtractSliceOp extract) {
+  if (extract.getMixedOffsets().empty())
+    return std::nullopt;
+
+  OpFoldResult offset = extract.getMixedOffsets().front();
+  if (auto attr = llvm::dyn_cast<Attribute>(offset)) {
+    auto intAttr = dyn_cast<IntegerAttr>(attr);
+    if (!intAttr || intAttr.getInt() < 0)
+      return std::nullopt;
+    return static_cast<uint32_t>(intAttr.getInt());
+  }
+
+  Value offsetValue = llvm::cast<Value>(offset);
+  if (auto constantIndex = offsetValue.getDefiningOp<arith::ConstantIndexOp>()) {
+    if (constantIndex.value() < 0)
+      return std::nullopt;
+    return static_cast<uint32_t>(constantIndex.value());
+  }
+
+  return std::nullopt;
+}
+
 std::optional<Cost> getBatchProjectedInputTransferCost(SpatComputeBatch batch, Value input) {
   auto inputIt = llvm::find(batch.getInputs(), input);
   if (inputIt == batch.getInputs().end())
@@ -141,6 +165,101 @@ Cost getInputTransferCost(const ComputeInstance& consumerInstance, Value input)
   return static_cast<Cost>(getSizeInBytes(inputType));
 }
 
+uint32_t getLaneOverlapCount(const ComputeInstance& lhs, const ComputeInstance& rhs) {
+  uint32_t lhsEnd = lhs.laneStart + lhs.laneCount;
+  uint32_t rhsEnd = rhs.laneStart + rhs.laneCount;
+  return std::max(lhs.laneStart, rhs.laneStart) < std::min(lhsEnd, rhsEnd)
+         ? std::min(lhsEnd, rhsEnd) - std::max(lhs.laneStart, rhs.laneStart)
+         : 0;
+}
+
+Cost scaleTransferCostByLaneCount(Cost totalCost, uint32_t totalLaneCount, uint32_t fragmentLaneCount) {
+  assert(totalLaneCount > 0 && "laneCount must be positive");
+  assert(fragmentLaneCount > 0 && "fragmentLaneCount must be positive");
+  if (fragmentLaneCount >= totalLaneCount)
+    return totalCost;
+  return checkedMultiply(totalCost, static_cast<Cost>(fragmentLaneCount)) / static_cast<Cost>(totalLaneCount);
+}
+
+SmallVector<ProducerValueRef, 4> collectProducerValueRefs(Value value, const ComputeInstance& consumerInstance) {
+  SmallVector<ProducerValueRef, 4> producers;
+  Operation* op = value.getDefiningOp();
+  if (!op)
+    return producers;
+
+  while (auto extract = dyn_cast<tensor::ExtractSliceOp>(op)) {
+    Value source = extract.getSource();
+    auto batch = dyn_cast_or_null<SpatComputeBatch>(source.getDefiningOp());
+    if (batch && batch.getNumResults() != 0) {
+      if (std::optional<uint32_t> lane = getConstantExtractLane(extract)) {
+        ComputeInstance instance = getBatchChunkForLane(batch, *lane);
+        producers.push_back({instance, 0});
+        return producers;
+      }
+
+      if (isa<SpatComputeBatch>(consumerInstance.op))
+        for (ComputeInstance instance :
+             getBatchChunksForRange(batch, consumerInstance.laneStart, consumerInstance.laneCount))
+          producers.push_back({instance, 0});
+      else
+        for (ComputeInstance instance : getBatchChunksForRange(batch, 0, static_cast<uint32_t>(batch.getLaneCount())))
+          producers.push_back({instance, 0});
+      return producers;
+    }
+
+    value = source;
+    op = value.getDefiningOp();
+    if (!op)
+      return producers;
+  }
+
+  if (auto compute = dyn_cast<SpatCompute>(op)) {
+    producers.push_back({
+      ComputeInstance {compute.getOperation(), 0, 1},
+       static_cast<size_t>(cast<OpResult>(value).getResultNumber())
+    });
+    return producers;
+  }
+
+  if (auto batch = dyn_cast<SpatComputeBatch>(op)) {
+    if (batch.getNumResults() != 0) {
+      uint32_t laneStart = isa<SpatComputeBatch>(consumerInstance.op) ? consumerInstance.laneStart : 0;
+      uint32_t laneCount = isa<SpatComputeBatch>(consumerInstance.op) ? consumerInstance.laneCount
+                                                                      : static_cast<uint32_t>(batch.getLaneCount());
+      for (ComputeInstance instance : getBatchChunksForRange(batch, laneStart, laneCount))
+        producers.push_back({instance, 0});
+      return producers;
+    }
+
+    uint32_t lane = cast<OpResult>(value).getResultNumber();
+    ComputeInstance instance = getBatchChunkForLane(batch, lane);
+    producers.push_back({instance, lane - instance.laneStart});
+    return producers;
+  }
+
+  return producers;
+}
+
+Cost getProducerTransferCost(Value input,
+                             const ComputeInstance& consumerInstance,
+                             const ProducerValueRef& producerRef) {
+  Cost transferCost = getInputTransferCost(consumerInstance, input);
+  auto producerBatch = dyn_cast<SpatComputeBatch>(producerRef.instance.op);
+  if (!producerBatch || producerBatch.getNumResults() == 0)
+    return transferCost;
+
+  if (auto consumerBatch = dyn_cast<SpatComputeBatch>(consumerInstance.op)) {
+    if (std::optional<Cost> projectedCost = getBatchProjectedInputTransferCost(consumerBatch, input)) {
+      uint32_t overlapLaneCount = getLaneOverlapCount(consumerInstance, producerRef.instance);
+      assert(overlapLaneCount > 0 && "projected batch edge must overlap consumer lanes");
+      return checkedMultiply(*projectedCost, static_cast<Cost>(overlapLaneCount));
+    }
+  }
+
+  return scaleTransferCostByLaneCount(
+    transferCost, static_cast<uint32_t>(producerBatch.getLaneCount()), producerRef.instance.laneCount);
+}
+
 static CrossbarWeight getOpaqueCrossbarWeight(Value value, std::optional<uint32_t> lane) {
   CrossbarWeight weight;
   weight.opaqueValue = value;
@@ -148,57 +267,6 @@ static CrossbarWeight getOpaqueCrossbarWeight(Value value, std::optional<uint32_
   return weight;
 }
 
-static FailureOr<int64_t> evaluateAffineExpr(AffineExpr expr, ArrayRef<int64_t> dims, ArrayRef<int64_t> symbols) {
-  if (auto constant = dyn_cast<AffineConstantExpr>(expr))
-    return constant.getValue();
-  if (auto dim = dyn_cast<AffineDimExpr>(expr)) {
-    unsigned position = dim.getPosition();
-    if (position >= dims.size())
-      return failure();
-    return dims[position];
-  }
-  if (auto symbol = dyn_cast<AffineSymbolExpr>(expr)) {
-    unsigned position = symbol.getPosition();
-    if (position >= symbols.size())
-      return failure();
-    return symbols[position];
-  }
-
-  auto binary = dyn_cast<AffineBinaryOpExpr>(expr);
-  if (!binary)
-    return failure();
-
-  FailureOr<int64_t> lhs = evaluateAffineExpr(binary.getLHS(), dims, symbols);
-  FailureOr<int64_t> rhs = evaluateAffineExpr(binary.getRHS(), dims, symbols);
-  if (failed(lhs) || failed(rhs))
-    return failure();
-
-  auto floorDiv = [](int64_t value, int64_t divisor) -> FailureOr<int64_t> {
-    if (divisor <= 0)
-      return failure();
-    if (value >= 0)
-      return value / divisor;
-    return -((-value + divisor - 1) / divisor);
-  };
-
-  switch (binary.getKind()) {
-  case AffineExprKind::Add:      return *lhs + *rhs;
-  case AffineExprKind::Mul:      return *lhs * *rhs;
-  case AffineExprKind::FloorDiv: return floorDiv(*lhs, *rhs);
-  case AffineExprKind::CeilDiv:
-    if (*rhs <= 0)
-      return failure();
-    return (*lhs + *rhs - 1) / *rhs;
-  case AffineExprKind::Mod: {
-    FailureOr<int64_t> div = floorDiv(*lhs, *rhs);
-    if (failed(div))
-      return failure();
-    return *lhs - *div * *rhs;
-  }
-  default: return failure();
-  }
-}
-
 static FailureOr<int64_t>
 evaluateIndexLike(Value value, const DenseMap<Value, int64_t>& bindings, std::optional<uint32_t> lane, Value laneArg);
 
@@ -222,12 +290,8 @@ evaluateIndexLike(Value value, const DenseMap<Value, int64_t>& bindings, std::op
   if (auto it = bindings.find(value); it != bindings.end())
     return it->second;
 
-  if (auto constant = value.getDefiningOp<arith::ConstantIndexOp>())
-    return constant.value();
-
-  if (auto constant = value.getDefiningOp<arith::ConstantOp>())
-    if (auto intAttr = dyn_cast<IntegerAttr>(constant.getValue()))
-      return intAttr.getInt();
+  if (std::optional<int64_t> constant = matchConstantIndexValue(value))
+    return *constant;
 
   if (auto extract = value.getDefiningOp<tensor::ExtractOp>()) {
     auto constant = extract.getTensor().getDefiningOp<arith::ConstantOp>();
@@ -245,26 +309,11 @@ evaluateIndexLike(Value value, const DenseMap<Value, int64_t>& bindings, std::op
     return failure();
   }
 
-  auto affineApply = value.getDefiningOp<affine::AffineApplyOp>();
-  if (!affineApply)
-    return failure();
+  if (auto affineApply = value.getDefiningOp<affine::AffineApplyOp>())
+    return evaluateAffineApply(affineApply,
+                               [&](Value operand) { return evaluateIndexLike(operand, bindings, lane, laneArg); });
 
-  AffineMap map = affineApply.getAffineMap();
-  if (map.getNumResults() != 1)
-    return failure();
-
-  SmallVector<int64_t, 4> operands;
-  operands.reserve(affineApply.getMapOperands().size());
-  for (Value operand : affineApply.getMapOperands()) {
-    FailureOr<int64_t> folded = evaluateIndexLike(operand, bindings, lane, laneArg);
-    if (failed(folded))
-      return failure();
-    operands.push_back(*folded);
-  }
-
-  ArrayRef<int64_t> dims(operands.data(), map.getNumDims());
-  ArrayRef<int64_t> symbols(operands.data() + map.getNumDims(), map.getNumSymbols());
-  return evaluateAffineExpr(map.getResult(0), dims, symbols);
+  return failure();
 }
 
 static FailureOr<SmallVector<int64_t, 4>> evaluateIndexList(ArrayRef<OpFoldResult> values,
@@ -526,25 +575,13 @@ ComputeGraph buildComputeGraph(Operation* entryOp) {
   for (const auto& [targetIndex, node] : llvm::enumerate(graph.nodes)) {
     llvm::SmallVector<Value, 4> inputs = getComputeInstanceInputs(node.instance);
     for (Value input : inputs) {
-      Cost transferCost = getInputTransferCost(node.instance, input);
-      if (auto producerBatch = dyn_cast_or_null<SpatComputeBatch>(input.getDefiningOp());
-          producerBatch && producerBatch.getNumResults() != 0 && !isa<SpatComputeBatch>(node.instance.op)) {
-        for (uint32_t lane = 0; lane < static_cast<uint32_t>(producerBatch.getLaneCount()); ++lane) {
-          auto producerIt = graph.instanceToIndex.find(getBatchChunkForLane(producerBatch, lane));
-          if (producerIt == graph.instanceToIndex.end())
-            continue;
-          rawEdges.push_back({producerIt->second, targetIndex, transferCost});
-        }
-        continue;
+      for (const ProducerValueRef& producerRef : collectProducerValueRefs(input, node.instance)) {
+        auto producerIt = graph.instanceToIndex.find(producerRef.instance);
+        if (producerIt == graph.instanceToIndex.end())
+          continue;
+        rawEdges.push_back(
+          {producerIt->second, targetIndex, getProducerTransferCost(input, node.instance, producerRef)});
       }
-
-      auto producerInstance = getComputeProducerInstance(input, &node.instance);
-      if (!producerInstance)
-        continue;
-      auto producerIt = graph.instanceToIndex.find(*producerInstance);
-      if (producerIt == graph.instanceToIndex.end())
-        continue;
-      rawEdges.push_back({producerIt->second, targetIndex, transferCost});
     }
   }
 

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

@@ -20,17 +20,66 @@ size_t getSchedulingCpuBudget() {
 
 size_t getBatchChunkTargetCount(int32_t laneCount) {
   assert(laneCount > 0 && "laneCount must be positive");
-  return static_cast<size_t>(laneCount);
+  return std::min(static_cast<size_t>(laneCount), getSchedulingCpuBudget());
+}
+
+BatchChunkRange getBatchChunkRange(int32_t laneCount, size_t chunkIndex) {
+  assert(laneCount > 0 && "laneCount must be positive");
+  size_t chunkCount = getBatchChunkTargetCount(laneCount);
+  assert(chunkIndex < chunkCount && "chunkIndex out of range");
+
+  size_t laneCountSize = static_cast<size_t>(laneCount);
+  size_t baseChunkSize = laneCountSize / chunkCount;
+  size_t remainder = laneCountSize % chunkCount;
+  size_t extraBefore = std::min(chunkIndex, remainder);
+  size_t start = chunkIndex * baseChunkSize + extraBefore;
+  size_t count = baseChunkSize + (chunkIndex < remainder ? 1 : 0);
+  assert(count > 0 && "chunk size must be positive");
+  return {static_cast<uint32_t>(start), static_cast<uint32_t>(count)};
+}
+
+size_t getBatchChunkIndexForLane(int32_t laneCount, uint32_t lane) {
+  assert(laneCount > 0 && "laneCount must be positive");
+  assert(lane < static_cast<uint32_t>(laneCount) && "lane out of range");
+
+  size_t chunkCount = getBatchChunkTargetCount(laneCount);
+  size_t laneCountSize = static_cast<size_t>(laneCount);
+  size_t baseChunkSize = laneCountSize / chunkCount;
+  size_t remainder = laneCountSize % chunkCount;
+  size_t largeChunkSize = baseChunkSize + 1;
+  size_t laneIndex = static_cast<size_t>(lane);
+  size_t largerChunkLanes = remainder * largeChunkSize;
+
+  if (laneIndex < largerChunkLanes)
+    return laneIndex / largeChunkSize;
+  return remainder + ((laneIndex - largerChunkLanes) / baseChunkSize);
 }
 
 ComputeInstance getBatchChunkForIndex(SpatComputeBatch batch, size_t chunkIndex) {
-  assert(chunkIndex < static_cast<size_t>(batch.getLaneCount()) && "chunkIndex out of range");
-  return {batch.getOperation(), static_cast<uint32_t>(chunkIndex), 1};
+  BatchChunkRange chunk = getBatchChunkRange(batch.getLaneCount(), chunkIndex);
+  return {batch.getOperation(), chunk.laneStart, chunk.laneCount};
 }
 
 ComputeInstance getBatchChunkForLane(SpatComputeBatch batch, uint32_t lane) {
-  assert(lane < static_cast<uint32_t>(batch.getLaneCount()) && "lane out of range");
-  return {batch.getOperation(), lane, 1};
+  return getBatchChunkForIndex(batch, getBatchChunkIndexForLane(batch.getLaneCount(), lane));
+}
+
+llvm::SmallVector<ComputeInstance, 4>
+getBatchChunksForRange(SpatComputeBatch batch, uint32_t laneStart, uint32_t laneCount) {
+  llvm::SmallVector<ComputeInstance, 4> chunks;
+  if (laneCount == 0)
+    return chunks;
+
+  uint32_t laneEnd = laneStart + laneCount;
+  assert(laneEnd >= laneStart && "lane range overflow");
+  assert(laneEnd <= static_cast<uint32_t>(batch.getLaneCount()) && "lane range out of bounds");
+
+  size_t firstChunk = getBatchChunkIndexForLane(batch.getLaneCount(), laneStart);
+  size_t lastChunk = getBatchChunkIndexForLane(batch.getLaneCount(), laneEnd - 1);
+  chunks.reserve(lastChunk - firstChunk + 1);
+  for (size_t chunkIndex = firstChunk; chunkIndex <= lastChunk; ++chunkIndex)
+    chunks.push_back(getBatchChunkForIndex(batch, chunkIndex));
+  return chunks;
 }
 
 static std::optional<uint32_t> getConstantExtractLane(tensor::ExtractSliceOp extract) {

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

@@ -21,10 +21,19 @@ struct ProducerValueRef {
   size_t resultIndex = 0;
 };
 
+struct BatchChunkRange {
+  uint32_t laneStart = 0;
+  uint32_t laneCount = 0;
+};
+
 size_t getSchedulingCpuBudget();
 size_t getBatchChunkTargetCount(int32_t laneCount);
+BatchChunkRange getBatchChunkRange(int32_t laneCount, size_t chunkIndex);
+size_t getBatchChunkIndexForLane(int32_t laneCount, uint32_t lane);
 ComputeInstance getBatchChunkForIndex(SpatComputeBatch batch, size_t chunkIndex);
 ComputeInstance getBatchChunkForLane(SpatComputeBatch batch, uint32_t lane);
+llvm::SmallVector<ComputeInstance, 4>
+getBatchChunksForRange(SpatComputeBatch batch, uint32_t laneStart, uint32_t laneCount);
 
 std::optional<ProducerValueRef> getProducerValueRef(mlir::Value value,
                                                     const ComputeInstance* consumerInstance = nullptr);

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

@@ -1,11 +1,9 @@
 #include "mlir/IR/Threading.h"
 
-#include "llvm/ADT/STLExtras.h"
 #include "llvm/ADT/SmallSet.h"
 #include "llvm/Support/ErrorHandling.h"
 #include "llvm/Support/FormatVariadic.h"
 
-#include <functional>
 #include <limits>
 #include <queue>
 #include <vector>

src/PIM/Pass/CMakeLists.txt

@@ -1,11 +1,5 @@
 add_pim_library(OMPimPasses
   MessagePass.cpp
-  PimCodegen/HostConstantFolding/Common.cpp
-  PimCodegen/HostConstantFolding/Patterns/Constant.cpp
-  PimCodegen/HostConstantFolding/HostConstantFoldingPass.cpp
-  PimCodegen/HostConstantFolding/Patterns/Subview.cpp
-  PimCodegen/MaterializeHostConstantsPass.cpp
-  PimCodegen/VerificationPass.cpp
   PimCodegen/EmitPimCodePass.cpp
 
   EXCLUDE_FROM_OM_LIBS

src/PIM/Pass/PIMPasses.h

@@ -15,14 +15,12 @@ std::unique_ptr<mlir::Pass> createSpatialToPimPass();
 
 std::unique_ptr<mlir::Pass> createPimBufferizationPass();
 
-std::unique_ptr<mlir::Pass> createPimStaticMemoryCoalescingPass();
+std::unique_ptr<mlir::Pass> createPimMemoryCoalescingPass();
 
 std::unique_ptr<mlir::Pass> createMergeComputeNodesPass();
 
 std::unique_ptr<mlir::Pass> createPimHostConstantFoldingPass();
 
-std::unique_ptr<mlir::Pass> createPimMaterializeHostConstantsPass();
-
 std::unique_ptr<mlir::Pass> createPimVerificationPass();
 
 std::unique_ptr<mlir::Pass> createEmitPimCodePass();

src/PIM/Pass/PimCodegen/MaterializeHostConstantsPass.cpp

@@ -1,157 +0,0 @@
-#include "mlir/Dialect/Func/IR/FuncOps.h"
-#include "mlir/Dialect/MemRef/IR/MemRef.h"
-#include "mlir/Dialect/SCF/IR/SCF.h"
-#include "mlir/IR/Builders.h"
-#include "mlir/IR/Dominance.h"
-#include "mlir/IR/PatternMatch.h"
-#include "mlir/Pass/Pass.h"
-
-#include "llvm/ADT/DenseMap.h"
-#include "llvm/ADT/SmallVector.h"
-#include "llvm/Support/MathExtras.h"
-
-#include "src/Accelerators/PIM/Common/IR/BatchCoreUtils.hpp"
-#include "src/Accelerators/PIM/Common/PimCommon.hpp"
-#include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
-
-using namespace mlir;
-
-namespace onnx_mlir {
-
-namespace {
-
-template <typename CoreOpTy>
-static void materializeHostConstantsInCore(CoreOpTy coreOp,
-                                           IRRewriter& rewriter,
-                                           OperationFolder& constantFolder,
-                                           bool& hasFailure) {
-  DenseMap<Value, DenseMap<int64_t, DenseMap<Type, Value>>> materializedValues;
-  DominanceInfo dominance(coreOp);
-  SmallVector<Operation*> ops;
-  coreOp.getBody().front().walk([&](Operation* op) {
-    if (!isa<pim::PimHaltOp, scf::YieldOp>(op))
-      ops.push_back(op);
-  });
-
-  for (Operation* op : ops) {
-    if (auto loadOp = dyn_cast<memref::LoadOp>(op); loadOp && loadOp.getType().isIndex())
-      continue;
-
-    for (OpOperand& operand : op->getOpOperands()) {
-      Value originalValue = operand.get();
-      if (!isa<BaseMemRefType>(originalValue.getType()) || isExplicitHostMemCopyOperand(op, operand.getOperandNumber()))
-        continue;
-
-      auto resolvedAddress = resolveContiguousAddress(originalValue);
-      if (failed(resolvedAddress))
-        continue;
-
-      auto getGlobalOp = dyn_cast_or_null<memref::GetGlobalOp>(resolvedAddress->base.getDefiningOp());
-      if (!getGlobalOp)
-        continue;
-
-      auto originalType = dyn_cast<MemRefType>(originalValue.getType());
-      if (!originalType || !originalType.hasStaticShape()) {
-        op->emitOpError("host constant materialization requires a static memref operand");
-        hasFailure = true;
-        continue;
-      }
-
-      auto& cachedByOffset = materializedValues[resolvedAddress->base];
-      auto& cachedByType = cachedByOffset[resolvedAddress->byteOffset];
-      auto cachedValue = cachedByType.find(originalType);
-      if (cachedValue != cachedByType.end() && dominance.properlyDominates(cachedValue->second, op)) {
-        operand.set(cachedValue->second);
-        continue;
-      }
-
-      int64_t totalBytes = -1;
-      if (auto type = dyn_cast<ShapedType>(originalValue.getType()); type && type.hasStaticShape())
-        totalBytes = static_cast<int64_t>(getShapedTypeSizeInBytes(type));
-      if (totalBytes < 0 || !llvm::isInt<32>(totalBytes) || !llvm::isInt<32>(resolvedAddress->byteOffset)) {
-        op->emitOpError("host constant materialization requires 32-bit copy sizes and offsets");
-        hasFailure = true;
-        continue;
-      }
-
-      auto contiguousType = MemRefType::get(originalType.getShape(), originalType.getElementType());
-
-      rewriter.setInsertionPoint(op);
-      Value localAlloc = memref::AllocOp::create(rewriter, op->getLoc(), contiguousType);
-      Value deviceDst = localAlloc;
-      if (contiguousType != originalType)
-        deviceDst = memref::CastOp::create(rewriter, op->getLoc(), originalType, localAlloc);
-
-      Value zeroOffset = getOrCreateIndexConstant(constantFolder, op, 0);
-      Value hostOffset = getOrCreateIndexConstant(constantFolder, op, resolvedAddress->byteOffset);
-      Value copiedValue =
-        pim::PimMemCopyHostToDevOp::create(rewriter,
-                                           op->getLoc(),
-                                           originalType,
-                                           zeroOffset,
-                                           hostOffset,
-                                           deviceDst,
-                                           getGlobalOp.getResult(),
-                                           rewriter.getI32IntegerAttr(static_cast<int32_t>(totalBytes)))
-          .getOutput();
-
-      cachedByType[originalType] = copiedValue;
-      operand.set(copiedValue);
-    }
-  }
-}
-
-struct MaterializeHostConstantsPass : PassWrapper<MaterializeHostConstantsPass, OperationPass<ModuleOp>> {
-  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(MaterializeHostConstantsPass)
-
-  StringRef getArgument() const override { return "materialize-pim-host-constants"; }
-  StringRef getDescription() const override {
-    return "Materialize explicit host-to-device copies for constant globals used by PIM runtime ops";
-  }
-
-  void runOnOperation() override {
-    ModuleOp moduleOp = getOperation();
-    IRRewriter rewriter(moduleOp.getContext());
-    OperationFolder constantFolder(moduleOp.getContext());
-    bool hasFailure = false;
-
-    for (func::FuncOp funcOp : moduleOp.getOps<func::FuncOp>()) {
-      if (funcOp.isExternal())
-        continue;
-
-      for (pim::PimCoreOp coreOp : funcOp.getOps<pim::PimCoreOp>())
-        materializeHostConstantsInCore(coreOp, rewriter, constantFolder, hasFailure);
-
-      for (pim::PimCoreBatchOp coreBatchOp : funcOp.getOps<pim::PimCoreBatchOp>())
-        materializeHostConstantsInCore(coreBatchOp, rewriter, constantFolder, hasFailure);
-
-      SmallVector<Operation*> hostCompactOps;
-      for (Operation& op : funcOp.getBody().front())
-        if (isa<pim::PimConcatOp>(op))
-          hostCompactOps.push_back(&op);
-
-      for (Operation* op : hostCompactOps) {
-        rewriter.setInsertionPoint(op);
-        auto concatOp = cast<pim::PimConcatOp>(op);
-        concatOp.emitOpError("host-side concat must be folded away or lowered into pim.core before materialization");
-        hasFailure = true;
-      }
-    }
-
-    if (hasFailure) {
-      moduleOp.emitError("PIM host-constant materialization failed; see diagnostics above");
-      signalPassFailure();
-      return;
-    }
-
-    dumpModule(moduleOp, "pim4_materialized");
-  }
-};
-
-} // namespace
-
-std::unique_ptr<Pass> createPimMaterializeHostConstantsPass() {
-  return std::make_unique<MaterializeHostConstantsPass>();
-}
-
-} // namespace onnx_mlir

src/PIM/PimAccelerator.cpp

@@ -75,10 +75,9 @@ void PimAccelerator::registerPasses(int optLevel) const {
   registerPass(createSpatialToGraphvizPass);
   registerPass(createSpatialToPimPass);
   registerPass(createPimBufferizationPass);
-  registerPass(createPimStaticMemoryCoalescingPass);
+  registerPass(createPimMemoryCoalescingPass);
   registerPass(createMergeComputeNodesPass);
   registerPass(createPimHostConstantFoldingPass);
-  registerPass(createPimMaterializeHostConstantsPass);
   registerPass(createPimVerificationPass);
   registerPass(createEmitPimCodePass);
 }

test/PIM/CMakeLists.txt

@@ -30,3 +30,9 @@ add_pim_unittest(LabeledListTest
   OMPimCommon
 )
 
+add_pim_unittest(PimMemoryLivenessPlannerTest
+  PimMemoryLivenessPlannerTest.cpp
+
+  LINK_LIBS PRIVATE
+  OMPimCompilerUtils
+)