Dynamic gemm/conv

AGENTS.md

@@ -3,6 +3,7 @@
     - `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
 
 # Code changes
 

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

@@ -1,3 +1,4 @@
+use crate::utility::AddressArg;
 use std::{collections::HashMap, fmt::Debug};
 use anyhow::{Context, Result, ensure};
 
@@ -9,6 +10,7 @@ use crate::{
 
 pub mod crossbar;
 
+
 #[derive(Debug, Clone)]
 pub struct CPU<'a> {
     cores: Box<[Core<'a>]>,

backend-simulators/pim/pim-simulator/src/lib/utility.rs

@@ -1,3 +1,4 @@
+use anyhow::{Result,Context};
 use std::{fmt::Debug, mem::transmute};
 
 

src/PIM/Common/IR/ConstantUtils.cpp

@@ -1,8 +1,10 @@
+#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"
@@ -79,4 +81,24 @@ Value getOrCreateHostI64Constant(Operation* anchorOp, int64_t value, OperationFo
   return getOrCreateHostConstant(anchorOp, builder.getI64IntegerAttr(value), builder.getI64Type(), folder);
 }
 
+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()));
+  }
+
+  SmallVector<Attribute> foldedResults;
+  if (succeeded(map.constantFold(operandConstants, foldedResults))) {
+    if (auto constantResult = dyn_cast<IntegerAttr>(foldedResults.front()))
+      return getOrCreateHostIndexConstant(anchorOp, constantResult.getInt(), rewriter);
+  }
+
+  return affine::AffineApplyOp::create(rewriter, loc, map, operands).getResult();
+}
+
 } // namespace onnx_mlir

src/PIM/Common/IR/ConstantUtils.hpp

@@ -1,5 +1,6 @@
 #pragma once
 
+#include "mlir/Dialect/Affine/IR/AffineOps.h"
 #include "mlir/Dialect/Arith/IR/Arith.h"
 #include "mlir/IR/PatternMatch.h"
 #include "mlir/IR/Value.h"
@@ -29,4 +30,10 @@ mlir::Value getOrCreateHostI32Constant(mlir::Operation* anchorOp, int32_t value,
 
 mlir::Value getOrCreateHostI64Constant(mlir::Operation* anchorOp, int64_t value, mlir::OperationFolder& folder);
 
+mlir::Value createAffineApplyOrFoldedConstant(mlir::RewriterBase& rewriter,
+                                              mlir::Location loc,
+                                              mlir::AffineMap map,
+                                              mlir::ValueRange operands,
+                                              mlir::Operation* anchorOp);
+
 } // namespace onnx_mlir

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

@@ -1,8 +1,12 @@
 #include "mlir/Dialect/Arith/IR/Arith.h"
+#include "mlir/Dialect/SCF/IR/SCF.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
+#include "mlir/IR/Matchers.h"
 
 #include "llvm/ADT/SmallVector.h"
 
+#include <algorithm>
+
 #include "ShapeTilingUtils.hpp"
 #include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
@@ -12,6 +16,72 @@ using namespace mlir;
 
 namespace onnx_mlir {
 
+static Value getIndexValue(OpFoldResult result, ConversionPatternRewriter& rewriter, Location loc) {
+  if (auto attr = dyn_cast<Attribute>(result))
+    return arith::ConstantIndexOp::create(rewriter, loc, cast<IntegerAttr>(attr).getInt()).getResult();
+  return cast<Value>(result);
+}
+
+static Value addIndexValues(Value lhs, Value rhs, ConversionPatternRewriter& rewriter, Location loc) {
+  APInt lhsConst;
+  if (matchPattern(lhs, m_ConstantInt(&lhsConst)) && lhsConst.isZero())
+    return rhs;
+
+  APInt rhsConst;
+  if (matchPattern(rhs, m_ConstantInt(&rhsConst)) && rhsConst.isZero())
+    return lhs;
+
+  return arith::AddIOp::create(rewriter, loc, lhs, rhs).getResult();
+}
+
+static Value multiplyIndexValue(Value value, OpFoldResult factor, ConversionPatternRewriter& rewriter, Location loc) {
+  APInt factorConst;
+  if (auto attr = dyn_cast<Attribute>(factor))
+    factorConst = cast<IntegerAttr>(attr).getValue();
+  else if (!matchPattern(cast<Value>(factor), m_ConstantInt(&factorConst)))
+    return arith::MulIOp::create(rewriter, loc, value, cast<Value>(factor)).getResult();
+
+  if (factorConst.isZero())
+    return arith::ConstantIndexOp::create(rewriter, loc, 0).getResult();
+  if (factorConst.isOne())
+    return value;
+
+  auto factorValue = arith::ConstantIndexOp::create(rewriter, loc, factorConst.getSExtValue()).getResult();
+  return arith::MulIOp::create(rewriter, loc, value, factorValue).getResult();
+}
+
+static bool isContiguousTensorSlice(Value source, RankedTensorType resultType, ArrayRef<OpFoldResult> strides) {
+  auto sourceType = dyn_cast<RankedTensorType>(source.getType());
+  if (!sourceType || !sourceType.hasStaticShape() || !resultType.hasStaticShape() || sourceType.getRank() != resultType.getRank())
+    return false;
+
+  for (OpFoldResult stride : strides) {
+    APInt strideValue;
+    if (auto attr = dyn_cast<Attribute>(stride)) {
+      if (cast<IntegerAttr>(attr).getInt() != 1)
+        return false;
+      continue;
+    }
+    if (!matchPattern(cast<Value>(stride), m_ConstantInt(&strideValue)) || !strideValue.isOne())
+      return false;
+  }
+
+  auto sizesAndShape = llvm::zip_equal(llvm::make_range(resultType.getShape().rbegin(), resultType.getShape().rend()),
+                                       llvm::make_range(sourceType.getShape().rbegin(), sourceType.getShape().rend()));
+  auto firstDifferentSize = std::find_if(sizesAndShape.begin(), sizesAndShape.end(), [&](auto sizeAndShape) -> bool {
+    auto [size, dimension] = sizeAndShape;
+    return size != dimension;
+  });
+  if (firstDifferentSize == sizesAndShape.end())
+    return true;
+
+  ++firstDifferentSize;
+  return std::all_of(firstDifferentSize, sizesAndShape.end(), [](auto sizeAndShape) {
+    auto [size, _dimension] = sizeAndShape;
+    return size == 1;
+  });
+}
+
 SmallVector<Value> sliceTensor(
   const Value& tensorToSlice, size_t axis, int64_t sliceSize, ConversionPatternRewriter& rewriter, Location loc) {
   ArrayRef<long> shape = getTensorShape(tensorToSlice);
@@ -123,4 +193,87 @@ Value broadcastToVector(Value scalarToBroadcast, int64_t length, ConversionPatte
   return broadcastCompute.getResult(0);
 }
 
+Value materializeContiguousTensorSlice(Value source,
+                                       RankedTensorType resultType,
+                                       ArrayRef<OpFoldResult> offsets,
+                                       ArrayRef<OpFoldResult> strides,
+                                       ConversionPatternRewriter& rewriter,
+                                       Location loc) {
+  assert(resultType.hasStaticShape() && "expected static result type");
+  size_t rank = static_cast<size_t>(resultType.getRank());
+  assert(offsets.size() == rank && "expected rank-matching offsets");
+  assert(strides.size() == rank && "expected rank-matching strides");
+
+  SmallVector<OpFoldResult> sizes;
+  sizes.reserve(resultType.getRank());
+  for (int64_t size : resultType.getShape())
+    sizes.push_back(rewriter.getIndexAttr(size));
+
+  if (isContiguousTensorSlice(source, resultType, strides))
+    return tensor::ExtractSliceOp::create(rewriter, loc, resultType, source, offsets, sizes, strides).getResult();
+
+  if (resultType.getRank() == 0)
+    return tensor::ExtractSliceOp::create(rewriter, loc, resultType, source, offsets, sizes, strides).getResult();
+
+  Value init = tensor::EmptyOp::create(rewriter, loc, resultType.getShape(), resultType.getElementType()).getResult();
+  SmallVector<Value> zeroIndices(resultType.getRank());
+  for (Value& zeroIndex : zeroIndices)
+    zeroIndex = arith::ConstantIndexOp::create(rewriter, loc, 0).getResult();
+
+  SmallVector<Value> resultIndices;
+  resultIndices.reserve(resultType.getRank());
+
+  auto buildLoopNest = [&](auto&& self, unsigned dim, Value accumulator) -> Value {
+    if (dim == resultType.getRank()) {
+      SmallVector<Value> sourceIndices;
+      sourceIndices.reserve(resultType.getRank());
+      for (unsigned idx = 0; idx < resultType.getRank(); ++idx) {
+        Value offsetValue = getIndexValue(offsets[idx], rewriter, loc);
+        Value scaledIndex = multiplyIndexValue(resultIndices[idx], strides[idx], rewriter, loc);
+        sourceIndices.push_back(addIndexValues(offsetValue, scaledIndex, rewriter, loc));
+      }
+
+      SmallVector<OpFoldResult> sourceOffsets;
+      SmallVector<OpFoldResult> destinationOffsets;
+      SmallVector<OpFoldResult> unitSizes;
+      SmallVector<OpFoldResult> unitStrides;
+      sourceOffsets.reserve(resultType.getRank());
+      destinationOffsets.reserve(resultType.getRank());
+      unitSizes.reserve(resultType.getRank());
+      unitStrides.reserve(resultType.getRank());
+      for (Value index : sourceIndices)
+        sourceOffsets.push_back(index);
+      for (Value index : resultIndices)
+        destinationOffsets.push_back(index);
+      for (int64_t idx = 0; idx < resultType.getRank(); ++idx) {
+        unitSizes.push_back(rewriter.getIndexAttr(1));
+        unitStrides.push_back(rewriter.getIndexAttr(1));
+      }
+
+      auto elementTensorType =
+        RankedTensorType::get(SmallVector<int64_t>(resultType.getRank(), 1), resultType.getElementType());
+      Value elementSlice =
+        tensor::ExtractSliceOp::create(rewriter, loc, elementTensorType, source, sourceOffsets, unitSizes, unitStrides)
+          .getResult();
+      return tensor::InsertSliceOp::create(
+               rewriter, loc, elementSlice, accumulator, destinationOffsets, unitSizes, unitStrides)
+        .getResult();
+    }
+
+    Value lower = zeroIndices[dim];
+    Value upper = arith::ConstantIndexOp::create(rewriter, loc, resultType.getDimSize(dim)).getResult();
+    Value step = arith::ConstantIndexOp::create(rewriter, loc, 1).getResult();
+    auto loop = scf::ForOp::create(rewriter, loc, lower, upper, step, ValueRange {accumulator});
+    rewriter.setInsertionPointToStart(loop.getBody());
+    resultIndices.push_back(loop.getInductionVar());
+    Value updated = self(self, dim + 1, loop.getRegionIterArgs().front());
+    resultIndices.pop_back();
+    scf::YieldOp::create(rewriter, loc, updated);
+    rewriter.setInsertionPointAfter(loop);
+    return loop.getResult(0);
+  };
+
+  return buildLoopNest(buildLoopNest, 0, init);
+}
+
 } // namespace onnx_mlir

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

@@ -141,4 +141,11 @@ mlir::Value broadcastToVector(mlir::Value scalarToBroadcast,
                               mlir::ConversionPatternRewriter& rewriter,
                               mlir::Location loc);
 
+mlir::Value materializeContiguousTensorSlice(mlir::Value source,
+                                             mlir::RankedTensorType resultType,
+                                             llvm::ArrayRef<mlir::OpFoldResult> offsets,
+                                             llvm::ArrayRef<mlir::OpFoldResult> strides,
+                                             mlir::ConversionPatternRewriter& rewriter,
+                                             mlir::Location loc);
+
 } // namespace onnx_mlir

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

@@ -111,6 +111,32 @@ static Value buildPackedWeight(DenseElementsAttr wDenseAttr,
   return arith::ConstantOp::create(rewriter, loc, packedWeightType, packedAttr);
 }
 
+static Value createConvWeightMatrix(Value w,
+                                    RankedTensorType wFlatType,
+                                    RankedTensorType wTransType,
+                                    ConversionPatternRewriter& rewriter,
+                                    Location loc) {
+  auto buildWeightMatrix = [&](Value weight) -> Value {
+    Value wFlat = tensor::CollapseShapeOp::create(rewriter,
+                                                  loc,
+                                                  wFlatType,
+                                                  weight,
+                                                  SmallVector<ReassociationIndices> {
+                                                    {0},
+                                                    {1, 2, 3}
+    });
+    return ONNXTransposeOp::create(rewriter, loc, wTransType, wFlat, rewriter.getI64ArrayAttr({1, 0})).getResult();
+  };
+
+  if (isCompileTimeComputable(w))
+    return buildWeightMatrix(w);
+
+  auto computeOp = createSpatCompute<1>(rewriter, loc, TypeRange {wTransType}, {}, ValueRange {w}, [&](Value weight) {
+    spatial::SpatYieldOp::create(rewriter, loc, buildWeightMatrix(weight));
+  });
+  return computeOp.getResult(0);
+}
+
 static Value buildPackedBias(bool hasBias,
                              Value gemmBias,
                              Value biasMatrix,
@@ -395,15 +421,7 @@ static Value lowerSingleConvGroup(Value x,
 
   // Prepare weight matrix W for crossbar storage:
   // W: [numChannelsOut, numChannelsIn, wHeight, wWidth] -> [numChannelsOut, patchSize] -> [patchSize, numChannelsOut]
-  Value wFlat = tensor::CollapseShapeOp::create(rewriter,
-                                                loc,
-                                                wFlatType,
-                                                w,
-                                                SmallVector<ReassociationIndices> {
-                                                  {0},
-                                                  {1, 2, 3}
-  });
-  Value wTrans = ONNXTransposeOp::create(rewriter, loc, wTransType, wFlat, rewriter.getI64ArrayAttr({1, 0}));
+  Value wTrans = createConvWeightMatrix(w, wFlatType, wTransType, rewriter, loc);
 
   // Pass bias through directly; Gemm handles rank-1 C canonicalization.
   bool hasB = !isa<ONNXNoneOp>(b.getDefiningOp());

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

@@ -73,38 +73,11 @@ static Value createIndexConstant(ConversionPatternRewriter& rewriter, int64_t va
   return getOrCreateHostIndexConstant(anchorOp, value, rewriter);
 }
 
-static std::optional<int64_t> getConstantIndexValue(Value value) {
-  if (auto constantIndex = value.getDefiningOp<arith::ConstantIndexOp>())
-    return constantIndex.value();
-
-  APInt constantValue;
-  if (matchPattern(value, m_ConstantInt(&constantValue)))
-    return constantValue.getSExtValue();
-
-  return std::nullopt;
-}
-
 static Value
 createAffineApply(ConversionPatternRewriter& rewriter, Location loc, AffineExpr expr, ValueRange operands) {
   AffineMap map = AffineMap::get(/*dimCount=*/operands.size(), /*symbolCount=*/0, expr);
-
-  SmallVector<Attribute> operandConstants;
-  operandConstants.reserve(operands.size());
-  for (Value operand : operands) {
-    std::optional<int64_t> constantValue = getConstantIndexValue(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))) {
-    auto constantResult = dyn_cast<IntegerAttr>(foldedResults.front());
-    if (constantResult)
-      return createIndexConstant(rewriter, constantResult.getInt());
-  }
-
-  return affine::AffineApplyOp::create(rewriter, loc, map, operands).getResult();
+  Operation* anchorOp = rewriter.getInsertionBlock()->getParentOp();
+  return createAffineApplyOrFoldedConstant(rewriter, loc, map, operands, anchorOp);
 }
 
 static Value
@@ -379,6 +352,233 @@ static spatial::SpatComputeBatch createVmmBatch(Value a,
   return batchOp;
 }
 
+static Value createDynamicGemmBatchRow(
+  Value lane, int64_t numOutCols, ConversionPatternRewriter& rewriter, Location loc) {
+  if (numOutCols == 1)
+    return lane;
+
+  MLIRContext* context = rewriter.getContext();
+  AffineExpr d0 = getAffineDimExpr(0, context);
+  return createAffineApply(rewriter, loc, d0.floorDiv(numOutCols), ValueRange {lane});
+}
+
+static Value createDynamicGemmBatchColumn(
+  Value lane, int64_t numOutCols, ConversionPatternRewriter& rewriter, Location loc) {
+  return modIndexByConstant(lane, numOutCols, rewriter, loc);
+}
+
+static Value
+extractDynamicGemmBColumn(Value matrix, Value column, RankedTensorType vectorType, ConversionPatternRewriter& rewriter, Location loc) {
+  SmallVector<OpFoldResult> offsets {rewriter.getIndexAttr(0), column};
+  SmallVector<OpFoldResult> strides {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+  auto columnSliceType = RankedTensorType::get({vectorType.getDimSize(1), 1}, vectorType.getElementType());
+  Value columnSlice = materializeContiguousTensorSlice(matrix, columnSliceType, offsets, strides, rewriter, loc);
+  SmallVector<ReassociationIndices> collapseReassociation {ReassociationIndices {0, 1}};
+  auto collapsedType = RankedTensorType::get({vectorType.getDimSize(1)}, vectorType.getElementType());
+  Value collapsed =
+    tensor::CollapseShapeOp::create(rewriter, loc, collapsedType, columnSlice, collapseReassociation).getResult();
+  SmallVector<ReassociationIndices> expandReassociation {ReassociationIndices {0, 1}};
+  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)};
+  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, matrix, offsets, sizes, strides).getResult();
+}
+
+static FailureOr<RankedTensorType> verifyDynamicGemmBiasType(RankedTensorType cType, RankedTensorType outType) {
+  if (!cType.hasStaticShape() || cType.getRank() > 2)
+    return failure();
+
+  if (cType.getRank() == 0)
+    return cType;
+
+  int64_t numOutRows = outType.getDimSize(0);
+  int64_t numOutCols = outType.getDimSize(1);
+  if (cType.getRank() == 1) {
+    int64_t cols = cType.getDimSize(0);
+    if (cols == 1 || cols == numOutCols)
+      return cType;
+    return failure();
+  }
+
+  int64_t rows = cType.getDimSize(0);
+  int64_t cols = cType.getDimSize(1);
+  if ((rows == 1 || rows == numOutRows) && (cols == 1 || cols == numOutCols))
+    return cType;
+  return failure();
+}
+
+static bool hasGemmBias(Value c) {
+  Operation* definingOp = c.getDefiningOp();
+  return !definingOp || !isa<ONNXNoneOp>(definingOp);
+}
+
+static Value createScalarTensorConstant(RankedTensorType scalarType,
+                                        float value,
+                                        ConversionPatternRewriter& rewriter,
+                                        Location loc) {
+  auto elementType = scalarType.getElementType();
+  auto scalarAttr = rewriter.getFloatAttr(elementType, value);
+  auto denseAttr = DenseElementsAttr::get(scalarType, scalarAttr);
+  return arith::ConstantOp::create(rewriter, loc, scalarType, denseAttr).getResult();
+}
+
+static Value createBroadcastedBiasScalar(Value bias,
+                                         RankedTensorType biasType,
+                                         Value row,
+                                         Value column,
+                                         RankedTensorType scalarType,
+                                         ConversionPatternRewriter& rewriter,
+                                         Location loc) {
+  SmallVector<OpFoldResult> unitStrides(biasType.getRank(), rewriter.getIndexAttr(1));
+  if (biasType.getRank() == 1) {
+    SmallVector<OpFoldResult> offsets {
+      biasType.getDimSize(0) == 1 ? OpFoldResult(rewriter.getIndexAttr(0)) : OpFoldResult(column)};
+    SmallVector<OpFoldResult> sizes {rewriter.getIndexAttr(1)};
+    auto vectorType = RankedTensorType::get({1}, scalarType.getElementType());
+    Value vector = tensor::ExtractSliceOp::create(rewriter, loc, vectorType, bias, offsets, sizes, unitStrides)
+                     .getResult();
+    SmallVector<ReassociationIndices> reassociation {ReassociationIndices {0, 1}};
+    return tensor::ExpandShapeOp::create(rewriter, loc, scalarType, vector, reassociation).getResult();
+  }
+
+  if (biasType.getRank() == 2) {
+    SmallVector<OpFoldResult> offsets {
+      biasType.getDimSize(0) == 1 ? OpFoldResult(rewriter.getIndexAttr(0)) : OpFoldResult(row),
+      biasType.getDimSize(1) == 1 ? OpFoldResult(rewriter.getIndexAttr(0)) : OpFoldResult(column)};
+    SmallVector<OpFoldResult> sizes {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+    return tensor::ExtractSliceOp::create(rewriter, loc, scalarType, bias, offsets, sizes, unitStrides).getResult();
+  }
+
+  Value scalar = tensor::ExtractOp::create(rewriter, loc, bias, ValueRange {}).getResult();
+  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) {
+  const int64_t numOutRows = outType.getDimSize(0);
+  const int64_t numOutCols = outType.getDimSize(1);
+  const int64_t reductionSize = aType.getDimSize(1);
+  const int64_t laneCount = numOutRows * numOutCols;
+  auto batchOp = spatial::SpatComputeBatch::create(rewriter,
+                                                   loc,
+                                                   TypeRange {scalarPiecesType},
+                                                   rewriter.getI32IntegerAttr(static_cast<int32_t>(laneCount)),
+                                                   ValueRange {},
+                                                   ValueRange {a, b});
+
+  SmallVector<Type> blockArgTypes {rewriter.getIndexType(), aType, bType, scalarPiecesType};
+  SmallVector<Location> blockArgLocs(blockArgTypes.size(), loc);
+  Block* body =
+    rewriter.createBlock(&batchOp.getBody(), batchOp.getBody().end(), TypeRange(blockArgTypes), blockArgLocs);
+  rewriter.setInsertionPointToEnd(body);
+
+  auto lane = batchOp.getLaneArgument();
+  auto inputA = batchOp.getInputArgument(0);
+  auto inputB = batchOp.getInputArgument(1);
+  auto output = batchOp.getOutputArgument(0);
+  assert(lane && inputA && inputB && output && "malformed dynamic Gemm compute_batch body");
+
+  Value row = createDynamicGemmBatchRow(*lane, numOutCols, rewriter, loc);
+  Value column = createDynamicGemmBatchColumn(*lane, numOutCols, rewriter, loc);
+
+  auto vectorType = RankedTensorType::get({1, reductionSize}, aType.getElementType());
+  auto scalarType = RankedTensorType::get({1, 1}, outType.getElementType());
+  Value aVector = extractDynamicGemmRowVector(*inputA, row, vectorType, rewriter, loc);
+  Value bVector = bAlreadyTransposed
+                    ? extractTransposedBRow(*inputB, column, vectorType, rewriter, loc)
+                    : extractDynamicGemmBColumn(*inputB, column, vectorType, rewriter, loc);
+  Value scalar = spatial::SpatVVDMulOp::create(rewriter, loc, scalarType, aVector, bVector).getResult();
+
+  auto inParallelOp = spatial::SpatInParallelOp::create(rewriter, loc);
+  rewriter.setInsertionPointToStart(&inParallelOp.getRegion().front());
+  SmallVector<OpFoldResult> outputOffsets {*lane, rewriter.getIndexAttr(0)};
+  SmallVector<OpFoldResult> scalarSizes {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+  SmallVector<OpFoldResult> unitStrides {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+  tensor::ParallelInsertSliceOp::create(rewriter, loc, scalar, *output, outputOffsets, scalarSizes, unitStrides);
+
+  rewriter.setInsertionPointAfter(batchOp);
+  return batchOp;
+}
+
+static 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) {
+    Value pieces = blockArgs[0];
+    Value biasArg = bias ? blockArgs[1] : Value();
+    auto scalarType = RankedTensorType::get({1, 1}, outType.getElementType());
+    Value outputInit = tensor::EmptyOp::create(rewriter, loc, outType.getShape(), outType.getElementType()).getResult();
+    Value c0 = createIndexConstant(rewriter, 0);
+    Value c1 = createIndexConstant(rewriter, 1);
+    Value cLaneCount = createIndexConstant(rewriter, laneCount);
+    auto loop = scf::ForOp::create(rewriter, loc, c0, cLaneCount, c1, ValueRange {outputInit});
+    rewriter.setInsertionPointToStart(loop.getBody());
+
+    Value lane = loop.getInductionVar();
+    Value outputAcc = loop.getRegionIterArgs().front();
+    Value row = createDynamicGemmBatchRow(lane, numOutCols, rewriter, loc);
+    Value column = createDynamicGemmBatchColumn(lane, numOutCols, rewriter, loc);
+    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));
+  });
+}
+
 static Value createPartialGroupOffset(Value hSlice,
                                       int64_t kSlice,
                                       int64_t numKSlices,
@@ -570,9 +770,50 @@ LogicalResult GemmToSpatialComputes::matchAndRewrite(ONNXGemmOp gemmOp,
     return failure();
   }
 
+  const int64_t numOutRows = outType.getDimSize(0);
+  const int64_t numOutCols = outType.getDimSize(1);
+  const int64_t reductionSize = aType.getDimSize(1);
+
   if (!isCompileTimeComputable(b)) {
-    gemmOp.emitOpError("requires Gemm input B to be statically computed from constants");
-    return failure();
+    bool hasC = hasGemmBias(c);
+    float alpha = gemmOpAdaptor.getAlpha().convertToFloat();
+    float beta = gemmOpAdaptor.getBeta().convertToFloat();
+    RankedTensorType biasType;
+    if (hasC) {
+      auto cType = dyn_cast<RankedTensorType>(c.getType());
+      if (!cType || !cType.hasStaticShape()) {
+        pim::emitUnsupportedStaticShapeDiagnostic(gemmOp, "Gemm bias");
+        return failure();
+      }
+      auto verifiedBiasType = verifyDynamicGemmBiasType(cType, outType);
+      if (failed(verifiedBiasType)) {
+        gemmOp.emitOpError("requires Gemm bias C to be broadcastable to the output shape");
+        return failure();
+      }
+      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) {
+      gemmOp.emitOpError("has inconsistent A, B, and output shapes");
+      return failure();
+    }
+
+    const int64_t laneCount64 = numOutRows * numOutCols;
+    if (laneCount64 > std::numeric_limits<int32_t>::max()) {
+      gemmOp.emitOpError("requires Gemm dynamic batch lane count to fit in i32");
+      return failure();
+    }
+
+    auto scalarPiecesType = RankedTensorType::get({laneCount64, 1}, outType.getElementType());
+    auto batchOp = createVvdmulBatch(
+      a, b, aType, bType, scalarPiecesType, outType, gemmOpAdaptor.getTransB(), rewriter, loc);
+    auto outputCompute = createDynamicGemmOutputCompute(
+      batchOp.getResult(0), hasC ? c : Value(), scalarPiecesType, biasType, outType, alpha, beta, rewriter, loc);
+    rewriter.replaceOp(gemmOp, outputCompute.getResults());
+    return success();
   }
 
   auto scaledB = materializeScaledConstantTensor(b, gemmOpAdaptor.getAlpha().convertToFloat(), rewriter, loc);
@@ -590,9 +831,6 @@ LogicalResult GemmToSpatialComputes::matchAndRewrite(ONNXGemmOp gemmOp,
     bType = cast<RankedTensorType>(b.getType());
   }
 
-  const int64_t numOutRows = outType.getDimSize(0);
-  const int64_t numOutCols = outType.getDimSize(1);
-  const int64_t reductionSize = aType.getDimSize(1);
   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();
@@ -615,7 +853,7 @@ LogicalResult GemmToSpatialComputes::matchAndRewrite(ONNXGemmOp gemmOp,
   aType = paddedAType;
 
   Value bias;
-  bool hasC = !isa<ONNXNoneOp>(c.getDefiningOp());
+  bool hasC = hasGemmBias(c);
   auto paddedOutType = RankedTensorType::get({numOutRows, paddedOutCols}, outType.getElementType());
   if (hasC) {
     auto cType = dyn_cast<RankedTensorType>(c.getType());

src/PIM/Conversion/SpatialToPim/SpatialToPim.td

@@ -21,6 +21,12 @@ def spatToPimVMM : Pat<
     (NativeCodeCall<"onnx_mlir::getBestOutputTensorFromOperandsOrAllocate($_builder, $0.getDefiningOp())"> $srcOpRes))
 >;
 
+def spatToPimVVDMul : Pat<
+  (SpatVVDMulOp:$srcOpRes $a, $b),
+  (PimVVDMulOp $a, $b,
+    (NativeCodeCall<"onnx_mlir::getBestOutputTensorFromOperandsOrAllocate($_builder, $0.getDefiningOp())"> $srcOpRes))
+>;
+
 def spatToPimVVAdd : Pat<
   (SpatVAddOp:$srcOpRes $a, $b),
   (PimVVAddOp $a, $b,

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

@@ -524,6 +524,39 @@ struct BinaryDstOpInterface : DstBufferizableOpInterfaceExternalModel<BinaryDstO
   }
 };
 
+struct VVDMulOpInterface : DstBufferizableOpInterfaceExternalModel<VVDMulOpInterface, PimVVDMulOp> {
+  bool bufferizesToMemoryRead(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
+    return !cast<DestinationStyleOpInterface>(op).isDpsInit(&opOperand);
+  }
+
+  LogicalResult bufferize(Operation* op,
+                          RewriterBase& rewriter,
+                          const BufferizationOptions& options,
+                          BufferizationState& state) const {
+    auto vvdmulOp = cast<PimVVDMulOp>(op);
+
+    auto lhsOpt = getBufferOrValue(rewriter, vvdmulOp.getLhs(), options, state);
+    if (failed(lhsOpt))
+      return failure();
+
+    auto rhsOpt = getBufferOrValue(rewriter, vvdmulOp.getRhs(), options, state);
+    if (failed(rhsOpt))
+      return failure();
+
+    auto outputBufferOpt = getBufferOrValue(rewriter, vvdmulOp.getOutputBuffer(), options, state);
+    if (failed(outputBufferOpt))
+      return failure();
+
+    Value contiguousLhs = materializeContiguousMemRef(*lhsOpt, op->getLoc(), rewriter);
+    Value contiguousRhs = materializeContiguousMemRef(*rhsOpt, op->getLoc(), rewriter);
+    Value contiguousOutput = allocateContiguousMemRefLike(*outputBufferOpt, op->getLoc(), rewriter);
+
+    replaceOpWithNewBufferizedOp<PimVVDMulOp>(
+      rewriter, op, contiguousOutput.getType(), contiguousLhs, contiguousRhs, contiguousOutput);
+    return success();
+  }
+};
+
 template <typename OpTy>
 struct UnaryDstOpInterface : DstBufferizableOpInterfaceExternalModel<UnaryDstOpInterface<OpTy>, OpTy> {
   bool bufferizesToMemoryRead(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
@@ -576,6 +609,7 @@ void registerOpBufferizationInterfaces(DialectRegistry& registry) {
     PimVVSubOp::attachInterface<BinaryDstOpInterface<PimVVSubOp>>(*ctx);
     PimVVMulOp::attachInterface<BinaryDstOpInterface<PimVVMulOp>>(*ctx);
     PimVVMaxOp::attachInterface<BinaryDstOpInterface<PimVVMaxOp>>(*ctx);
+    PimVVDMulOp::attachInterface<VVDMulOpInterface>(*ctx);
 
     PimVAvgOp::attachInterface<UnaryDstOpInterface<PimVAvgOp>>(*ctx);
     PimVReluOp::attachInterface<UnaryDstOpInterface<PimVReluOp>>(*ctx);

src/PIM/Dialect/Spatial/CMakeLists.txt

@@ -6,6 +6,7 @@ add_pim_library(SpatialOps
   SpatialOpsAsm.cpp
   SpatialOpsVerify.cpp
   SpatialOpsCanonicalization.cpp
+  ${PIM_SRC_ROOT}/Conversion/ONNXToSpatial/CompileTime.cpp
   Transforms/MergeComputeNodes/MergeComputeNodesPass.cpp
   Transforms/MergeComputeNodes/MaterializeMergeSchedule.cpp
   Transforms/MergeComputeNodes/Scheduling/ComputeGraph.cpp

src/PIM/Dialect/Spatial/Spatial.td

@@ -219,6 +219,25 @@ def SpatVMMOp : SpatOp<"wvmm", []> {
   }];
 }
 
+def SpatVVDMulOp : SpatOp<"vvdmul", []> {
+  let summary = "Dot product between two runtime vectors";
+
+  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 SpatVAddOp : SpatOp<"vadd", []> {
   let summary = "Element-wise addition between two tensors; rhs must match lhs or be 1x1";
 

src/PIM/Dialect/Spatial/SpatialOpsVerify.cpp

@@ -249,6 +249,32 @@ LogicalResult SpatVMMOp::verify() {
   return success();
 }
 
+LogicalResult SpatVVDMulOp::verify() {
+  auto lhsType = dyn_cast<ShapedType>(getLhs().getType());
+  auto rhsType = dyn_cast<ShapedType>(getRhs().getType());
+  auto outputType = dyn_cast<ShapedType>(getOutput().getType());
+  if (!lhsType || !rhsType || !outputType)
+    return emitError("lhs, rhs, and output must be shaped values");
+  if (!lhsType.hasRank() || !rhsType.hasRank() || !outputType.hasRank())
+    return emitError("lhs, rhs, and output must have ranked types");
+
+  ArrayRef<int64_t> lhsShape = lhsType.getShape();
+  ArrayRef<int64_t> rhsShape = rhsType.getShape();
+  ArrayRef<int64_t> outputShape = outputType.getShape();
+  if (lhsShape.size() != 2 || rhsShape.size() != 2 || outputShape.size() != 2)
+    return emitError("lhs, rhs, and output must have rank 2");
+  if (lhsType.getElementType() != rhsType.getElementType() || lhsType.getElementType() != outputType.getElementType())
+    return emitError("lhs, rhs, and output must have the same element type");
+  if (lhsShape != rhsShape)
+    return emitError("lhs and rhs vector shapes must match");
+  if (lhsShape[0] != 1 || lhsShape[1] <= 0)
+    return emitError("lhs and rhs vector shape must be (1, N) with N > 0");
+  if (outputShape[0] != 1 || outputShape[1] != 1)
+    return emitError("output shape must be (1, 1)");
+
+  return success();
+}
+
 LogicalResult SpatVAddOp::verify() {
   if (failed(OpTrait::impl::verifyAtLeastNOperands(*this, 2)))
     return failure();

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

@@ -1019,27 +1019,6 @@ std::optional<IndexedIndexPattern> getIndexedIndexPattern(ArrayRef<int64_t> valu
   return std::nullopt;
 }
 
-Value createAffineApplyOrConstant(
-  MaterializerState& state, Location loc, AffineMap map, ValueRange operands, Operation* anchor) {
-  SmallVector<Attribute> operandConstants;
-  operandConstants.reserve(operands.size());
-  for (Value operand : operands) {
-    auto constantValue = getConstantIntValue(operand);
-    if (!constantValue)
-      return affine::AffineApplyOp::create(state.rewriter, loc, map, operands).getResult();
-    operandConstants.push_back(state.rewriter.getIndexAttr(*constantValue));
-  }
-
-  SmallVector<Attribute> foldedResults;
-  if (succeeded(map.constantFold(operandConstants, foldedResults))) {
-    auto constantResult = dyn_cast<IntegerAttr>(foldedResults.front());
-    if (constantResult)
-      return createIndexConstant(state, anchor, constantResult.getInt());
-  }
-
-  return affine::AffineApplyOp::create(state.rewriter, loc, map, operands).getResult();
-}
-
 Value createAffineIndexValue(MaterializerState& state, const IndexedIndexPattern& pattern, Value index, Location loc) {
   MLIRContext* context = state.func.getContext();
   AffineExpr d0 = getAffineDimExpr(0, context);
@@ -1054,7 +1033,7 @@ Value createAffineIndexValue(MaterializerState& state, const IndexedIndexPattern
   }
 
   AffineMap map = AffineMap::get(/*dimCount=*/1, /*symbolCount=*/0, expr);
-  return createAffineApplyOrConstant(state, loc, map, ValueRange {index}, state.func);
+  return createAffineApplyOrFoldedConstant(state.rewriter, loc, map, ValueRange {index}, state.func);
 }
 
 Value createIndexedIndexValue(
@@ -3396,7 +3375,7 @@ Value createBatchRunFlatIndex(MaterializerState& state, MaterializedClass& targe
 
   int64_t laneCount = static_cast<int64_t>(targetClass.cpus.size());
   AffineMap map = AffineMap::get(/*dimCount=*/2, /*symbolCount=*/0, d0 * laneCount + d1);
-  return createAffineApplyOrConstant(state, loc, map, ValueRange {slotIndex, *laneArg}, state.func);
+  return createAffineApplyOrFoldedConstant(state.rewriter, loc, map, ValueRange {slotIndex, *laneArg}, state.func);
 }
 
 Value createBatchClassRunSourceLane(MaterializerState& state,

validation/operations/README.md

@@ -25,10 +25,11 @@ python3 validation/operations/gen_tests.py
 | Large spatial    | `conv/large_spatial`    | [1,1,8,8] | [1,1,6,6] | 3x3    | 1      | none       | no   | Larger spatial input               |
 | Grouped two groups | `conv/grouped_two_groups` | [1,4,4,4] | [1,4,4,4] | 1x1 | 1 | none | yes | group=2 channel partitioning       |
 | Depthwise grouped | `conv/depthwise_grouped` | [1,3,4,4] | [1,3,2,2] | 3x3 | 1 | none | no | group=3, one input channel per group |
+| Dynamic          | `conv/dynamic`          | [1,1,4,4] | [1,1,2,2] | 3x3    | 1      | none       | no   | Runtime input and weight           |
 
 ## Gemm
 
-| Test          | Directory               | A (input) | W (weight) | Output   | transB | alpha | beta | Bias  | Notes                        |
+| Test          | Directory               | A (input) | B/W tensor | Output   | transB | alpha | beta | Bias  | Notes                        |
 |---------------|-------------------------|-----------|------------|----------|--------|-------|------|-------|------------------------------|
 | Simple        | `gemm/simple`           | [10,132]  | [132,132]  | [10,132] | no     | 1     | 1    | no    | Square weights               |
 | Non-square    | `gemm/non_square`       | [4,128]   | [128,64]   | [4,64]   | no     | 1     | 1    | no    | K != N                       |
@@ -38,12 +39,20 @@ python3 validation/operations/gen_tests.py
 | Small         | `gemm/small`            | [2,8]     | [8,4]      | [2,4]    | no     | 1     | 1    | no    | Tiny matrices                |
 | Large         | `gemm/large`            | [8,256]   | [256,128]  | [8,128]  | no     | 1     | 1    | no    | Larger matrices              |
 | transB + bias | `gemm/transB_with_bias` | [4,128]   | [64,128]   | [4,64]   | yes    | 1     | 1    | [64]  | Combined                     |
+| Dynamic       | `gemm/dynamic`          | [2,8]     | [8,4]      | [2,4]    | no     | 1     | 1    | no    | Runtime matrix operands      |
+| Dynamic transB | `gemm/dynamic_transB` | [2,8] | [4,8] | [2,4] | yes | 1 | 1 | no | Runtime transpose handling |
+| Dynamic bias  | `gemm/dynamic_bias`     | [2,8]     | [8,4]      | [2,4]    | no     | 1     | 1    | [4]   | Runtime bias broadcast       |
+| Dynamic alpha | `gemm/dynamic_alpha`    | [2,8]     | [8,4]      | [2,4]    | no     | 0.5   | 1    | no    | Runtime alpha scaling        |
+| Dynamic beta  | `gemm/dynamic_beta`     | [2,8]     | [8,4]      | [2,4]    | no     | 1     | 2    | [4]   | Runtime beta scaling         |
+| Dynamic bias + scale | `gemm/dynamic_bias_alpha_beta` | [2,8] | [8,4] | [2,4] | no | 0.5 | 2 | [4] | Runtime operands and bias |
 
 ## MatMul
 
-| Test       | Directory           | A input | B weight | Output  | Notes                              |
+| Test       | Directory           | A input | B tensor | Output  | Notes                              |
 |------------|---------------------|---------|----------|---------|------------------------------------|
 | Basic      | `matmul/basic`      | [2,3]   | [3,4]    | [2,4]   | Direct 2D MatMul rewrite path      |
+| Left constant | `matmul/left_constant` | [2,3] | [3,4] | [2,4] | Constant LHS transpose rewrite path |
+| Dynamic    | `matmul/dynamic`    | [2,3]   | [3,4]    | [2,4]   | Runtime matrix operands            |
 | Batched 3D | `matmul/batched_3d` | [2,2,3] | [2,3,4]  | [2,2,4] | Matching-batch MatMul rewrite path |
 
 ## Gemv

validation/operations/gen_tests.py

@@ -181,6 +181,18 @@ def conv_depthwise_grouped():
     save_model(model, "conv/depthwise_grouped", "conv_depthwise_grouped.onnx")
 
 
+def conv_dynamic():
+    """Conv with input and weight both provided at runtime."""
+    X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 4, 4])
+    W = helper.make_tensor_value_info("W", TensorProto.FLOAT, [1, 1, 3, 3])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 2, 2])
+    node = helper.make_node("Conv", ["X", "W"], ["Y"],
+                            kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0])
+    graph = helper.make_graph([node], "conv_dynamic", [X, W], [Y])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "conv/dynamic", "conv_dynamic.onnx")
+
+
 # ---------------------------------------------------------------------------
 # GEMM tests
 # ---------------------------------------------------------------------------
@@ -291,6 +303,75 @@ def gemm_transB_with_bias():
     save_model(model, "gemm/transB_with_bias", "gemm_transB_with_bias.onnx")
 
 
+def gemm_dynamic():
+    """GEMM with both matrix operands provided at runtime."""
+    A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 8])
+    B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [8, 4])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
+    node = helper.make_node("Gemm", ["A", "B"], ["Y"])
+    graph = helper.make_graph([node], "gemm_dynamic", [A, B], [Y])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "gemm/dynamic", "gemm_dynamic.onnx")
+
+
+def gemm_dynamic_transB():
+    """GEMM with runtime matrix operands and transposed runtime B."""
+    A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 8])
+    B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [4, 8])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
+    node = helper.make_node("Gemm", ["A", "B"], ["Y"], transB=1)
+    graph = helper.make_graph([node], "gemm_dynamic_transB", [A, B], [Y])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "gemm/dynamic_transB", "gemm_dynamic_transB.onnx")
+
+
+def gemm_dynamic_bias():
+    """GEMM with runtime matrix operands and runtime bias."""
+    A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 8])
+    B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [8, 4])
+    C = helper.make_tensor_value_info("C", TensorProto.FLOAT, [4])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
+    node = helper.make_node("Gemm", ["A", "B", "C"], ["Y"])
+    graph = helper.make_graph([node], "gemm_dynamic_bias", [A, B, C], [Y])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "gemm/dynamic_bias", "gemm_dynamic_bias.onnx")
+
+
+def gemm_dynamic_alpha():
+    """GEMM with runtime matrix operands and runtime alpha scaling."""
+    A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 8])
+    B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [8, 4])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
+    node = helper.make_node("Gemm", ["A", "B"], ["Y"], alpha=0.5)
+    graph = helper.make_graph([node], "gemm_dynamic_alpha", [A, B], [Y])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "gemm/dynamic_alpha", "gemm_dynamic_alpha.onnx")
+
+
+def gemm_dynamic_beta():
+    """GEMM with runtime matrix operands, runtime bias, and runtime beta scaling."""
+    A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 8])
+    B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [8, 4])
+    C = helper.make_tensor_value_info("C", TensorProto.FLOAT, [4])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
+    node = helper.make_node("Gemm", ["A", "B", "C"], ["Y"], beta=2.0)
+    graph = helper.make_graph([node], "gemm_dynamic_beta", [A, B, C], [Y])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "gemm/dynamic_beta", "gemm_dynamic_beta.onnx")
+
+
+def gemm_dynamic_bias_alpha_beta():
+    """GEMM with runtime matrix operands, runtime bias, alpha, and beta."""
+    A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 8])
+    B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [8, 4])
+    C = helper.make_tensor_value_info("C", TensorProto.FLOAT, [4])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
+    node = helper.make_node("Gemm", ["A", "B", "C"], ["Y"], alpha=0.5, beta=2.0)
+    graph = helper.make_graph([node], "gemm_dynamic_bias_alpha_beta", [A, B, C], [Y])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "gemm/dynamic_bias_alpha_beta", "gemm_dynamic_bias_alpha_beta.onnx")
+
+
 # ---------------------------------------------------------------------------
 # MatMul tests
 # ---------------------------------------------------------------------------
@@ -306,6 +387,28 @@ def matmul_basic():
     save_model(model, "matmul/basic", "matmul_basic.onnx")
 
 
+def matmul_left_constant():
+    """Direct 2D MatMul with constant LHS."""
+    A = numpy_helper.from_array(np.random.default_rng(69).uniform(-1, 1, (2, 3)).astype(np.float32), name="A")
+    B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [3, 4])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
+    node = helper.make_node("MatMul", ["A", "B"], ["Y"])
+    graph = helper.make_graph([node], "matmul_left_constant", [B], [Y], initializer=[A])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "matmul/left_constant", "matmul_left_constant.onnx")
+
+
+def matmul_dynamic():
+    """Direct 2D MatMul with both operands provided at runtime."""
+    A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 3])
+    B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [3, 4])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
+    node = helper.make_node("MatMul", ["A", "B"], ["Y"])
+    graph = helper.make_graph([node], "matmul_dynamic", [A, B], [Y])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "matmul/dynamic", "matmul_dynamic.onnx")
+
+
 def matmul_batched_3d():
     """Batched 3D MatMul with matching batch dimensions."""
     rng = np.random.default_rng(50)
@@ -843,6 +946,12 @@ if __name__ == "__main__":
     gemm_small()
     gemm_large()
     gemm_transB_with_bias()
+    gemm_dynamic()
+    gemm_dynamic_transB()
+    gemm_dynamic_bias()
+    gemm_dynamic_alpha()
+    gemm_dynamic_beta()
+    gemm_dynamic_bias_alpha_beta()
 
     print("\nGenerating Conv tests:")
     conv_3x3_kernel()
@@ -856,9 +965,12 @@ if __name__ == "__main__":
     conv_large_spatial()
     conv_grouped_two_groups()
     conv_depthwise_grouped()
+    conv_dynamic()
 
     print("\nGenerating MatMul tests:")
     matmul_basic()
+    matmul_left_constant()
+    matmul_dynamic()
     matmul_batched_3d()
 
     print("\nGenerating Pooling tests:")