[mlir][sparse] enable more sparse convolution kernels.

[Peiming]

revert debug code.

[Peiming]

rebase.

[Peiming]

rebase.

[aartbik]

mlir/lib/Dialect/SparseTensor/Transforms/LoopEmitter.cpp

1620

typo: outermost

then, in terms of reading the code, seeing

if (!outermost)

this is outermost

is very confusing ;-)
Can you rephrase so that your logic is more clear

[Peiming]

address comments.

mlir/lib/Dialect/SparseTensor/Transforms/LoopEmitter.h

private:
   std::pair<Operation *, ValueRange>
   genSliceLvlTraverseLoop(OpBuilder &builder, Location loc, Value pLo,
                           Value pHi, Value offset, Value size, TensorId tid,
                           Level lvl, ValueRange userReduc, bool genYield,
                           LoopBodyBuilder bodyBuilder);
 
   /// Generates a nested loop that iterates over tid on all the coordinates on
   /// lvl.
-  ValueRange
-  genUnResolvedSliceTreeTraverse(OpBuilder &builder, Location loc, TensorId tid,
-                                 ArrayRef<const SliceInfo *> unResLvls,
-                                 ValueRange userReduc,
-                                 LoopBodyBuilder bodyBuilder);
+  ValueRange genUnResolvedSliceTreeTraverse(
+      OpBuilder &builder, Location loc, TensorId tid,
+      std::optional<std::pair<TensorId, Level>> firstResLvl,
+      ValueRange userReduc, LoopBodyBuilder bodyBuilder);
 
   /// Generates code to get the first non-empty slice of tid on lvl, when all
   /// the previous level before `lvl` are resolved (or lvl is the first level).
   ///
   /// This is the simple case because the previous level are resolved into a
   /// single node in the storage tree.
   void genResolvedSliceBegin(OpBuilder &builder, Location loc, TensorId tid,
                              Level lvl);

mlir/lib/Dialect/SparseTensor/Transforms/LoopEmitter.cpp

 //     } else {
 //       break;
 //     }
 //     loopLo ++;
 //   }
 // }
 ValueRange LoopEmitter::genUnResolvedSliceTreeTraverse(
     OpBuilder &builder, Location loc, TensorId tid,
-    ArrayRef<const SliceInfo *> unResLvls, ValueRange userReduc,
+    ArrayRef<const SliceInfo *> unResLvls,
+    std::optional<std::pair<TensorId, Level>> firstResLvl, ValueRange userReduc,
     LoopBodyBuilder bodyBuilder) {
-  // assert(unResLvls.size() == 1 && "TODO");
-  Value c0 = C_IDX(0), c1 = C_IDX(1), c2 = C_IDX(2);
 
-  const SliceInfo &frontSlice = *unResLvls.back();
-  Level firstLvl = *frontSlice.slicedOnLvl;
-  assert(!lvlFullyResolved(tid, firstLvl) && "TODO");
-
-  // FIXME: it is not zero when the first level is fully resolved.
+  Value c0 = C_IDX(0), c1 = C_IDX(1), c2 = C_IDX(2);
   Value pos = c0;
   OpBuilder::InsertPoint ip;
   SmallVector<Value> innerArgs(userReduc.begin(), userReduc.end());
-  scf::ForOp outerMost = nullptr;
+  scf::ForOp outerMost = nullptr; // the outtermost loop.
+  if (firstResLvl.has_value()) {
+    // Overwrite position when the first level is fully resolved.
+    pos = posits[firstResLvl->first][firstResLvl->second];
+    ip = builder.saveInsertionPoint();
+  } else {
+    const SliceInfo &frontSlice = *unResLvls.back();
+    Level firstLvl = *frontSlice.slicedOnLvl;
     if (!lvlFullyResolved(tid, firstLvl)) {
       if (isCompressedDLT(lvlTypes[tid][firstLvl])) {
         unsigned depth = frontSlice.depth - 1;
         Value offset = frontSlice.offset;
         Value sPtrBuf = slicePosBuffer[tid][firstLvl][depth];
         Value mSz = genIndexLoad(builder, loc, sPtrBuf, c0); // memSize
         outerMost = builder.create<scf::ForOp>(
             loc, c2, mSz, c2, innerArgs,
-          [this, c1, tid, firstLvl, offset, sPtrBuf, &ip, &pos, &innerArgs](
-              OpBuilder &builder, Location loc, Value iv, ValueRange iterArgs) {
+            [this, c1, tid, firstLvl, offset, sPtrBuf, &ip, &pos,
+             &innerArgs](OpBuilder &builder, Location loc, Value iv,
+                         ValueRange iterArgs) {
               // generate traversal for each level.
               Value loopLo = genIndexLoad(builder, loc, sPtrBuf, iv);
               Value loopHi = genIndexLoad(builder, loc, sPtrBuf, ADDI(iv, c1));
               ValueRange itArgs =
                   genSliceLvlTraverseLoop(
                       builder, loc, loopLo, loopHi, offset,
                       sliceSizes[tid][firstLvl].back(), tid, firstLvl, iterArgs,
                       false,
                       [&](OpBuilder &builder, Location, Value iv,
                           MutableArrayRef<Value> reduc) {
                         ip = builder.saveInsertionPoint();
                         pos = iv;
                         innerArgs.assign(reduc.begin(), reduc.end());
                       })
                       .second;
               YIELD(itArgs);
             });
       } else if (isDenseDLT(lvlTypes[tid][firstLvl])) {
         assert(firstLvl == 0); // This must be the first level.
         Value lb = frontSlice.offset;
         Value sliceSz =
             sliceSizes[tid][*frontSlice.slicedOnLvl][frontSlice.depth - 1];
         Value ub = ADDI(lb, sliceSz);
         outerMost = builder.create<scf::ForOp>(
             loc, lb, ub, c1, innerArgs,
-          [&](OpBuilder &builder, Location loc, Value iv, ValueRange iterArgs) {
+            [&](OpBuilder &builder, Location loc, Value iv,
+                ValueRange iterArgs) {
               ip = builder.saveInsertionPoint();
               pos = iv;
               innerArgs.assign(iterArgs.begin(), iterArgs.end());
             });
       }
       // We generated the loop for the first slice above, now remove it.
       unResLvls = unResLvls.drop_back();
     }
-
+  }
   // Reset the insertion point into the loop body.
   builder.restoreInsertionPoint(ip);
   if (!unResLvls.empty()) {
     // Fills in dense slices levels in between.
     SmallVector<Value> lbs, ubs, steps, lvlSzs;
     for (const SliceInfo *slice : llvm::reverse(unResLvls)) {
       Level sliceLvl = *slice->slicedOnLvl;
       assert(isDenseDLT(lvlTypes[tid][sliceLvl]));
auto denseNest =
                                pos = MULI(pos, lvlSzs[em.index()]);
                                pos = ADDI(pos, em.value());
                              }
                              innerArgs.assign(iterArgs.begin(), iterArgs.end());
                              // Generates user request loop body.
                              bodyBuilder(builder, loc, pos, innerArgs);
                              return innerArgs;
                            });
+
+    if (!outerMost) {
+      // If the outermost loop has not been set, this is the outermost loop.
+      outerMost = denseNest.loops.front();
+    } else {
+      // Otherwise we need to generate yield operations to link the SSA chain.
       YIELD(denseNest.results);
+    }
   } else {
+    assert(outerMost);
     // Generates user request loop body.
     bodyBuilder(builder, loc, pos, innerArgs);
     YIELD(innerArgs);
   }
+  assert(outerMost);
   // Insert after current while operation.
   builder.setInsertionPointAfter(outerMost);
   return outerMost.getResults();
 }
 
 void LoopEmitter::genResolvedSliceBegin(OpBuilder &builder, Location loc,
                                         TensorId tid, Level lvl) {
-  assert(lvl == 0 && "TODO: handle non-first level");
   Value c0 = C_IDX(0), c1 = C_IDX(1), c2 = C_IDX(2), c3 = C_IDX(3),
         c4 = C_IDX(4);
   if (isDenseDLT(lvlTypes[tid][lvl])) {
     // Dense slice begin is trivial.
     sliceStack[tid].emplace_back(/*minCoord=*/c0, /*offset=*/c0,
                                  /*nonEmpty=*/constantI1(builder, loc, true),
                                  lvl, /*depth=*/1);
     return;
   }
-  Value size = sliceSizes[tid][0][0];
-  Value sPtrBuf = slicePosBuffer[tid][0][0];
-  Value pHi = genIndexLoad(builder, loc, positionsBuffers[tid][0], c1);
+  Value size = sliceSizes[tid][lvl][0];
+  Value sPtrBuf = slicePosBuffer[tid][lvl][0];
+  Value pHi, pLo;
+  if (lvl == 0) {
+    pLo = c0;
+    pHi = genIndexLoad(builder, loc, positionsBuffers[tid][0], c1);
+  } else {
+    pLo = genIndexLoad(builder, loc, positionsBuffers[tid][lvl],
+                       posits[tid][lvl - 1]);
+    pHi = genIndexLoad(builder, loc, positionsBuffers[tid][lvl],
+                       ADDI(posits[tid][lvl - 1], c1));
+  }
   // Fills out pIdxBuffer[tid][lvl][0] with [/*memSize =*/4, 0, 0, pHi]
   builder.create<memref::StoreOp>(loc, c4, sPtrBuf, c0);  // memSize = 4
   builder.create<memref::StoreOp>(loc, c0, sPtrBuf, c1);  // index = 0
-  builder.create<memref::StoreOp>(loc, c0, sPtrBuf, c2);  // pLo = 0;
-  builder.create<memref::StoreOp>(loc, pHi, sPtrBuf, c3); // loaded pHi.
+  builder.create<memref::StoreOp>(loc, pLo, sPtrBuf, c2); // pLo
+  builder.create<memref::StoreOp>(loc, pHi, sPtrBuf, c3); // pHi
 
   // This is an non empty tensor if 0 < pHi.
   Value isNonEmpty = CMPI(ult, c0, pHi);
   // The minimal coord must be at the first on ordered level.
   // FIXME: Technically we should load the coord only when the slice is
   // nonempty. though we assume that even on empty sparse tensors, a non-empty
   // ptr/idx buffer is allocated for each level so it would not cause OOB to
   // avoid generating a ifOp here.
void LoopEmitter::genUnResolvedSliceBegin(OpBuilder &builder, Location loc,
   // 2nd, lvl > prevSliceLvl + 1, i.e., t[..., d2, d3 + d4] (having a
   // simple dim expression in between).
   assert(lvl == *sliceStack[tid].back().slicedOnLvl + 1);
 
   // Check slice stack integrity.
   assert(slicePosBuffer[tid][lvl - 1].size() == sliceStack[tid].back().depth);
 
   SmallVector<const SliceInfo *> unResSlices;
+  std::optional<std::pair<TensorId, Level>> firstResLvl;
   for (Level curLvl = lvl; curLvl >= 1; curLvl--) {
     Level prevLvl = curLvl - 1;
+    if (lvlFullyResolved(tid, prevLvl)) {
+      firstResLvl = std::make_pair(tid, prevLvl);
+      break;
+    }
     unResSlices.push_back(&getMostRecentSliceOnLvl(tid, prevLvl));
-    if (!isDenseDLT(lvlTypes[tid][prevLvl]) || lvlFullyResolved(tid, prevLvl)) {
+    if (!isDenseDLT(lvlTypes[tid][prevLvl])) {
       break;
     }
   }
 
   assert(!unResSlices.empty() &&
          !lvlFullyResolved(tid, *unResSlices.front()->slicedOnLvl));
 
   Value sPtrBuf = slicePosBuffer[tid][lvl].back();
   SmallVector<Value, 3> reduc = {
       constantI1(builder, loc, false), // isNonEmpty
       lvlSizes[tid][lvl],              // minCoord
       c2,                              // memSize
   };
 
   ValueRange result = genUnResolvedSliceTreeTraverse(
-      builder, loc, tid, unResSlices, reduc,
+      builder, loc, tid, unResSlices, firstResLvl, reduc,
       [this, c1, c2, tid, lvl, sPtrBuf](OpBuilder &builder, Location loc,
                                         Value iv,
                                         MutableArrayRef<Value> reduc) {
         Value &nonEmpty = reduc[0];
         Value &minCrd = reduc[1];
         Value &curMemSz = reduc[2];
 
         Value pHi = ADDI(iv, c1);
if (sliceInfo.isInitialTensor() ||
     genUnResolvedSliceBegin(builder, loc, tid, lvl);
   }
   return false;
 }
 
 void LoopEmitter::invalidateSliceIterIdx(OpBuilder &builder, Location loc,
                                          TensorId tid, Level lvl) {
   for (unsigned i = 0; i <= lvl; i++) {
-    if (!isDenseDLT(lvlTypes[tid][i])) {
+    if (!isDenseDLT(lvlTypes[tid][i]) && !dependentLvlMap[tid][i].empty()) {
       builder.create<memref::StoreOp>(loc, C_IDX(0),
                                       slicePosBuffer[tid][i].back(), C_IDX(1));
     }
   }
 }
 
 void LoopEmitter::genSliceNextInduction(OpBuilder &builder, Location loc,
                                         const Operation *op, TensorId tid,

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_conv_1d_nwc_wcf.mlir

-// UNSUPPORTED: target={{.*}}
-// FIXME: The test case is disabled (for now) because affine index on sparse tensor
-// are not handled efficiently by sparse compiler, the test case will be re-enabled
-// after new algorithm is implemented.
-
-// DEFINE: %{option} = enable-runtime-library=true
+// DEFINE: %{option} = "enable-runtime-library=true enable-index-reduction=true"
 // DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
 // DEFINE: %{run} = mlir-cpu-runner \
 // DEFINE:  -e entry -entry-point-result=void  \
 // DEFINE:  -shared-libs=%mlir_c_runner_utils | \
 // DEFINE: FileCheck %s
 //
 // RUN: %{compile} | %{run}
 //
 // Do the same run, but now with direct IR generation.
-// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true"
+// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true enable-index-reduction=true"
 // RUN: %{compile} | %{run}
 //
 // Do the same run, but now with direct IR generation and vectorization.
-// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
+// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true vl=2 reassociate-fp-reductions=true enable-index-optimizations=true enable-index-reduction=true"
 // RUN: %{compile} | %{run}
 
 // Do the same run, but now with direct IR generation and, if available, VLA
 // vectorization.
-// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
+// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA enable-index-reduction=true"
 // REDEFINE: %{run} = %lli \
 // REDEFINE:   --entry-function=entry_lli \
 // REDEFINE:   --extra-module=%S/Inputs/main_for_lli.ll \
 // REDEFINE:   %VLA_ARCH_ATTR_OPTIONS \
 // REDEFINE:   --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
 // REDEFINE: FileCheck %s
 // RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
 

 func.func @conv_1d_nwc_wcf(%arg0: tensor<?x?x?xf32>, %arg1: tensor<?x?x?xf32>, %arg2: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
   %ret = linalg.conv_1d_nwc_wcf {dilations = dense<1> : tensor<1xi64>,
                                    strides = dense<1> : tensor<1xi64>}
      ins (%arg0, %arg1: tensor<?x?x?xf32>, tensor<?x?x?xf32>)
     outs (%arg2: tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
   return %ret : tensor<?x?x?xf32>
 }
 
-func.func @conv_1d_nwc_wcf_CCC(%arg0: tensor<?x?x?xf32, #CCC>, %arg1: tensor<?x?x?xf32, #CCC>) -> tensor<?x?x?xf32, #CCC> {
+func.func @conv_1d_nwc_wcf_CCC(%arg0: tensor<?x?x?xf32, #CCC>, %arg1: tensor<?x?x?xf32>) -> tensor<?x?x?xf32, #CCC> {
   %c1 = arith.constant 1 : index
   %c3 = arith.constant 3 : index
   %c6 = arith.constant 6 : index
   %s = bufferization.alloc_tensor(%c3, %c6, %c1) : tensor<?x?x?xf32, #CCC>
   %ret = linalg.conv_1d_nwc_wcf {dilations = dense<1> : tensor<1xi64>,
                                    strides = dense<1> : tensor<1xi64>}
-     ins (%arg0, %arg1: tensor<?x?x?xf32, #CCC>, tensor<?x?x?xf32, #CCC>)
+     ins (%arg0, %arg1: tensor<?x?x?xf32, #CCC>, tensor<?x?x?xf32>)
     outs (%s: tensor<?x?x?xf32, #CCC>) -> tensor<?x?x?xf32, #CCC>
   return %ret : tensor<?x?x?xf32, #CCC>
 }
 
-func.func @conv_1d_nwc_wcf_CDC(%arg0: tensor<?x?x?xf32, #CDC>, %arg1: tensor<?x?x?xf32, #CDC>) -> tensor<?x?x?xf32, #CDC> {
+func.func @conv_1d_nwc_wcf_CDC(%arg0: tensor<?x?x?xf32, #CDC>, %arg1: tensor<?x?x?xf32>) -> tensor<?x?x?xf32, #CDC> {
   %c1 = arith.constant 1 : index
   %c3 = arith.constant 3 : index
   %c6 = arith.constant 6 : index
   %s = bufferization.alloc_tensor(%c3, %c6, %c1) : tensor<?x?x?xf32, #CDC>
   %ret = linalg.conv_1d_nwc_wcf {dilations = dense<1> : tensor<1xi64>,
                                    strides = dense<1> : tensor<1xi64>}
-     ins (%arg0, %arg1: tensor<?x?x?xf32, #CDC>, tensor<?x?x?xf32, #CDC>)
+     ins (%arg0, %arg1: tensor<?x?x?xf32, #CDC>, tensor<?x?x?xf32>)
     outs (%s: tensor<?x?x?xf32, #CDC>) -> tensor<?x?x?xf32, #CDC>
   return %ret : tensor<?x?x?xf32, #CDC>
 }
 
 func.func @entry() {
   %c0 = arith.constant 0 : index
   %c1 = arith.constant 1 : index
   %c3 = arith.constant 3 : index
   %c6 = arith.constant 6 : index
   %c8 = arith.constant 8 : index
   %f10 = arith.constant 10.00000e+00 : f32
   %val = arith.constant 2.00000e+00 : f32
   %zero = arith.constant 0.00000e+00 : f32
 
   %in1D_tmp = call @alloc_3d_filled_f32(%c3, %c8, %c1, %val) : (index, index, index, f32) -> (tensor<?x?x?xf32>)
   %in1D_nwc = tensor.insert %f10 into %in1D_tmp[%c0, %c3, %c0] : tensor<?x?x?xf32>
+
   %filter1D_nwc = call @alloc_3d_filled_f32(%c3, %c1, %c1, %val) : (index, index, index, f32) -> (tensor<?x?x?xf32>)
   %out1D_nwc = call @alloc_3d_filled_f32(%c3, %c6, %c1, %zero) : (index, index, index, f32) -> (tensor<?x?x?xf32>)
 
   %in1D_nwc_CCC = sparse_tensor.convert %in1D_nwc
     : tensor<?x?x?xf32> to tensor<?x?x?xf32, #CCC>
-  %filter1D_nwc_CCC = sparse_tensor.convert %filter1D_nwc
-    : tensor<?x?x?xf32> to tensor<?x?x?xf32, #CCC>
-
   %in1D_nwc_CDC = sparse_tensor.convert %in1D_nwc
     : tensor<?x?x?xf32> to tensor<?x?x?xf32, #CDC>
-  %filter1D_nwc_CDC = sparse_tensor.convert %filter1D_nwc
-    : tensor<?x?x?xf32> to tensor<?x?x?xf32, #CDC>
 
   %dense_ret = call @conv_1d_nwc_wcf(%in1D_nwc, %filter1D_nwc, %out1D_nwc) : (tensor<?x?x?xf32>, tensor<?x?x?xf32>, tensor<?x?x?xf32>) -> (tensor<?x?x?xf32>)
-  %CCC_ret = call @conv_1d_nwc_wcf_CCC(%in1D_nwc_CCC, %filter1D_nwc_CCC) : (tensor<?x?x?xf32, #CCC>, tensor<?x?x?xf32, #CCC>) -> (tensor<?x?x?xf32, #CCC>)
-  %CDC_ret = call @conv_1d_nwc_wcf_CDC(%in1D_nwc_CDC, %filter1D_nwc_CDC) : (tensor<?x?x?xf32, #CDC>, tensor<?x?x?xf32, #CDC>) -> (tensor<?x?x?xf32, #CDC>)
+  %CCC_ret = call @conv_1d_nwc_wcf_CCC(%in1D_nwc_CCC, %filter1D_nwc) : (tensor<?x?x?xf32, #CCC>, tensor<?x?x?xf32>) -> (tensor<?x?x?xf32, #CCC>)
+  %CDC_ret = call @conv_1d_nwc_wcf_CDC(%in1D_nwc_CDC, %filter1D_nwc) : (tensor<?x?x?xf32, #CDC>, tensor<?x?x?xf32>) -> (tensor<?x?x?xf32, #CDC>)
 
   //      CHECK: ( ( ( 12 ), ( 28 ), ( 28 ), ( 28 ), ( 12 ), ( 12 ) ),
   // CHECK-SAME:   ( ( 12 ), ( 12 ), ( 12 ), ( 12 ), ( 12 ), ( 12 ) ),
   // CHECK-SAME:   ( ( 12 ), ( 12 ), ( 12 ), ( 12 ), ( 12 ), ( 12 ) ) )
   %dense_v = vector.transfer_read %dense_ret[%c0, %c0, %c0], %zero
       : tensor<?x?x?xf32>, vector<3x6x1xf32>
   vector.print %dense_v : vector<3x6x1xf32>
 
func.func @entry() {
   vector.print %v2 : vector<3x6x1xf32>
 
   // Free the resources
   bufferization.dealloc_tensor %in1D_nwc : tensor<?x?x?xf32>
   bufferization.dealloc_tensor %filter1D_nwc : tensor<?x?x?xf32>
   bufferization.dealloc_tensor %out1D_nwc : tensor<?x?x?xf32>
 
   bufferization.dealloc_tensor %in1D_nwc_CDC : tensor<?x?x?xf32, #CDC>
-  bufferization.dealloc_tensor %filter1D_nwc_CDC : tensor<?x?x?xf32, #CDC>
   bufferization.dealloc_tensor %in1D_nwc_CCC : tensor<?x?x?xf32, #CCC>
-  bufferization.dealloc_tensor %filter1D_nwc_CCC : tensor<?x?x?xf32, #CCC>
 
   bufferization.dealloc_tensor %CCC_ret : tensor<?x?x?xf32, #CCC>
   bufferization.dealloc_tensor %CDC_ret : tensor<?x?x?xf32, #CDC>
 
   return
 }

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_conv_2d_nhwc_hwcf.mlir

-// UNSUPPORTED: target={{.*}}
-// FIXME: The test case is disabled (for now) because affine index on sparse tensor
-// are not handled efficiently by sparse compiler, the test case will be re-enabled
-// after new algorithm is implemented.
-
-// DEFINE: %{option} = enable-runtime-library=true
+// DEFINE: %{option} = "enable-runtime-library=true enable-index-reduction=true"
 // DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
 // DEFINE: %{run} = mlir-cpu-runner \
 // DEFINE:  -e entry -entry-point-result=void  \
 // DEFINE:  -shared-libs=%mlir_c_runner_utils | \
 // DEFINE: FileCheck %s
 //
 // RUN: %{compile} | %{run}
 //
 // Do the same run, but now with direct IR generation.
-// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true"
+// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true enable-index-reduction=true"
 // RUN: %{compile} | %{run}
 //
 // Do the same run, but now with direct IR generation and vectorization.
-// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
+// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true vl=2 reassociate-fp-reductions=true enable-index-optimizations=true  enable-index-reduction=true"
 // RUN: %{compile} | %{run}
 
 // Do the same run, but now with direct IR generation and, if available, VLA
 // vectorization.
-// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
+// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA  enable-index-reduction=true"
 // REDEFINE: %{run} = %lli \
 // REDEFINE:   --entry-function=entry_lli \
 // REDEFINE:   --extra-module=%S/Inputs/main_for_lli.ll \
 // REDEFINE:   %VLA_ARCH_ATTR_OPTIONS \
 // REDEFINE:   --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
 // REDEFINE: FileCheck %s
 // RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
 

 func.func @conv_2d_nhwc_hwcf(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<?x?x?x?xf32>, %arg2: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
   %ret = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>,
                                      strides = dense<1> : tensor<2xi64>}
      ins (%arg0, %arg1: tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>)
     outs (%arg2: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
   return %ret : tensor<?x?x?x?xf32>
 }
 
-func.func @conv_2d_nhwc_hwcf_CCCC(%arg0: tensor<?x?x?x?xf32, #CCCC>, %arg1: tensor<?x?x?x?xf32, #CCCC>) -> tensor<?x?x?x?xf32, #CCCC> {
+func.func @conv_2d_nhwc_hwcf_CCCC(%arg0: tensor<?x?x?x?xf32, #CCCC>, %arg1: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32, #CCCC> {
   %c1 = arith.constant 1 : index
   %c3 = arith.constant 3 : index
   %c6 = arith.constant 6 : index
   %s = bufferization.alloc_tensor(%c3, %c6, %c6, %c1) : tensor<?x?x?x?xf32, #CCCC>
   %ret = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>,
                                      strides = dense<1> : tensor<2xi64>}
-     ins (%arg0, %arg1: tensor<?x?x?x?xf32, #CCCC>, tensor<?x?x?x?xf32, #CCCC>)
+     ins (%arg0, %arg1: tensor<?x?x?x?xf32, #CCCC>, tensor<?x?x?x?xf32>)
     outs (%s: tensor<?x?x?x?xf32, #CCCC>) -> tensor<?x?x?x?xf32, #CCCC>
   return %ret : tensor<?x?x?x?xf32, #CCCC>
 }
 
-func.func @conv_2d_nhwc_hwcf_CDCD(%arg0: tensor<?x?x?x?xf32, #CDCD>, %arg1: tensor<?x?x?x?xf32, #CDCD>) -> tensor<?x?x?x?xf32, #CDCD> {
+func.func @conv_2d_nhwc_hwcf_CDCD(%arg0: tensor<?x?x?x?xf32, #CDCD>, %arg1: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32, #CDCD> {
   %c1 = arith.constant 1 : index
   %c3 = arith.constant 3 : index
   %c6 = arith.constant 6 : index
   %s = bufferization.alloc_tensor(%c3, %c6, %c6, %c1) : tensor<?x?x?x?xf32, #CDCD>
   %ret = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>,
                                      strides = dense<1> : tensor<2xi64>}
-     ins (%arg0, %arg1: tensor<?x?x?x?xf32, #CDCD>, tensor<?x?x?x?xf32, #CDCD>)
+     ins (%arg0, %arg1: tensor<?x?x?x?xf32, #CDCD>, tensor<?x?x?x?xf32>)
     outs (%s: tensor<?x?x?x?xf32, #CDCD>) -> tensor<?x?x?x?xf32, #CDCD>
   return %ret : tensor<?x?x?x?xf32, #CDCD>
 }
 
 func.func @entry() {
   %c0 = arith.constant 0 : index
   %c1 = arith.constant 1 : index
   %c3 = arith.constant 3 : index
   %c6 = arith.constant 6 : index
   %c8 = arith.constant 8 : index
   %f10 = arith.constant 10.00000e+00 : f32
   %val = arith.constant 2.00000e+00 : f32
   %zero = arith.constant 0.00000e+00 : f32
 
   %filter2D_nhwc = call @alloc_4d_filled_f32(%c3, %c3, %c3, %c1, %val) :(index, index, index, index, f32) -> (tensor<?x?x?x?xf32>)
   %in2D_tmp = call @alloc_4d_filled_f32(%c3, %c8, %c8, %c3, %val) : (index, index, index, index, f32) -> (tensor<?x?x?x?xf32>)
   %in2D_nhwc = tensor.insert %f10 into %in2D_tmp[%c0, %c0, %c3, %c0] : tensor<?x?x?x?xf32>
   %out2D_nhwc = call @alloc_4d_filled_f32(%c3, %c6, %c6, %c1, %zero) : (index, index, index, index, f32) -> (tensor<?x?x?x?xf32>)
 
   %in2D_nhwc_CCCC = sparse_tensor.convert %in2D_nhwc
     : tensor<?x?x?x?xf32> to tensor<?x?x?x?xf32, #CCCC>
-  %filter2D_nhwc_CCCC = sparse_tensor.convert %filter2D_nhwc
-    : tensor<?x?x?x?xf32> to tensor<?x?x?x?xf32, #CCCC>
-
   %in2D_nhwc_CDCD = sparse_tensor.convert %in2D_nhwc
     : tensor<?x?x?x?xf32> to tensor<?x?x?x?xf32, #CDCD>
-  %filter2D_nhwc_CDCD = sparse_tensor.convert %filter2D_nhwc
-    : tensor<?x?x?x?xf32> to tensor<?x?x?x?xf32, #CDCD>
 
   %dense_ret = call @conv_2d_nhwc_hwcf(%in2D_nhwc, %filter2D_nhwc, %out2D_nhwc) : (tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) -> (tensor<?x?x?x?xf32>)
-  %CCCC_ret = call @conv_2d_nhwc_hwcf_CCCC(%in2D_nhwc_CCCC, %filter2D_nhwc_CCCC) : (tensor<?x?x?x?xf32, #CCCC>, tensor<?x?x?x?xf32, #CCCC>) -> (tensor<?x?x?x?xf32, #CCCC>)
-  %CDCD_ret = call @conv_2d_nhwc_hwcf_CDCD(%in2D_nhwc_CDCD, %filter2D_nhwc_CDCD) : (tensor<?x?x?x?xf32, #CDCD>, tensor<?x?x?x?xf32, #CDCD>) -> (tensor<?x?x?x?xf32, #CDCD>)
+  %CCCC_ret = call @conv_2d_nhwc_hwcf_CCCC(%in2D_nhwc_CCCC, %filter2D_nhwc) : (tensor<?x?x?x?xf32, #CCCC>, tensor<?x?x?x?xf32>) -> (tensor<?x?x?x?xf32, #CCCC>)
+  %CDCD_ret = call @conv_2d_nhwc_hwcf_CDCD(%in2D_nhwc_CDCD, %filter2D_nhwc) : (tensor<?x?x?x?xf32, #CDCD>, tensor<?x?x?x?xf32>) -> (tensor<?x?x?x?xf32, #CDCD>)
 
   // CHECK:     ( ( ( ( 108 ), ( 124 ), ( 124 ), ( 124 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:    ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:    ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:    ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:    ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:    ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
   // CHECK-SAME:  ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
func.func @entry() {
   vector.print %v2 : vector<3x6x6x1xf32>
 
   // Free the resources
   bufferization.dealloc_tensor %in2D_nhwc : tensor<?x?x?x?xf32>
   bufferization.dealloc_tensor %filter2D_nhwc : tensor<?x?x?x?xf32>
   bufferization.dealloc_tensor %out2D_nhwc : tensor<?x?x?x?xf32>
 
   bufferization.dealloc_tensor %in2D_nhwc_CDCD : tensor<?x?x?x?xf32, #CDCD>
-  bufferization.dealloc_tensor %filter2D_nhwc_CDCD : tensor<?x?x?x?xf32, #CDCD>
   bufferization.dealloc_tensor %in2D_nhwc_CCCC : tensor<?x?x?x?xf32, #CCCC>
-  bufferization.dealloc_tensor %filter2D_nhwc_CCCC : tensor<?x?x?x?xf32, #CCCC>
 
   bufferization.dealloc_tensor %CCCC_ret : tensor<?x?x?x?xf32, #CCCC>
   bufferization.dealloc_tensor %CDCD_ret : tensor<?x?x?x?xf32, #CDCD>
 
   return
 }

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_conv_3d_ndhwc_dhwcf.mlir

-// UNSUPPORTED: target={{.*}}
-// FIXME: The test case is disabled (for now) because affine index on sparse tensor
-// are not handled efficiently by sparse compiler, the test case will be re-enabled
-// after new algorithm is implemented.
-
-// DEFINE: %{option} = enable-runtime-library=true
+// DEFINE: %{option} = "enable-runtime-library=true enable-index-reduction=true"
 // DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
 // DEFINE: %{run} = mlir-cpu-runner \
 // DEFINE:  -e entry -entry-point-result=void  \
 // DEFINE:  -shared-libs=%mlir_c_runner_utils | \
 // DEFINE: FileCheck %s
 //
 // RUN: %{compile} | %{run}
 //
 // Do the same run, but now with direct IR generation.
-// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true"
+// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true enable-index-reduction=true"
 // RUN: %{compile} | %{run}
 //
 // Do the same run, but now with direct IR generation and vectorization.
-// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
+// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true vl=2 reassociate-fp-reductions=true enable-index-optimizations=true enable-index-reduction=true"
 // RUN: %{compile} | %{run}
 
 // Do the same run, but now with direct IR generation and, if available, VLA
 // vectorization.
-// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
+// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA enable-index-reduction=true"
 // REDEFINE: %{run} = %lli \
 // REDEFINE:   --entry-function=entry_lli \
 // REDEFINE:   --extra-module=%S/Inputs/main_for_lli.ll \
 // REDEFINE:   %VLA_ARCH_ATTR_OPTIONS \
 // REDEFINE:   --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
 // REDEFINE: FileCheck %s
 // RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
 
func.func @conv_3d_ndhwc_dhwcf(%arg0: tensor<?x?x?x?x?xf32>,
   %ret = linalg.conv_3d_ndhwc_dhwcf {dilations = dense<1> : tensor<3xi64>,
                                        strides = dense<1> : tensor<3xi64>}
      ins (%arg0, %arg1: tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>)
     outs (%arg2: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
   return %ret : tensor<?x?x?x?x?xf32>
 }
 
 func.func @conv_3d_ndhwc_dhwcf_CCCCC(%arg0: tensor<?x?x?x?x?xf32, #CCCCC>,
-                                     %arg1: tensor<?x?x?x?x?xf32, #CCCCC>)
+                                     %arg1: tensor<?x?x?x?x?xf32>)
                                      -> tensor<?x?x?x?x?xf32, #CCCCC> {
   %c1 = arith.constant 1 : index
   %c6 = arith.constant 6 : index
   %s = bufferization.alloc_tensor(%c1, %c6, %c6, %c6, %c1)
     : tensor<?x?x?x?x?xf32, #CCCCC>
   %ret = linalg.conv_3d_ndhwc_dhwcf {dilations = dense<1> : tensor<3xi64>,
                                        strides = dense<1> : tensor<3xi64>}
-     ins (%arg0, %arg1: tensor<?x?x?x?x?xf32, #CCCCC>, tensor<?x?x?x?x?xf32, #CCCCC>)
+     ins (%arg0, %arg1: tensor<?x?x?x?x?xf32, #CCCCC>, tensor<?x?x?x?x?xf32>)
     outs (%s: tensor<?x?x?x?x?xf32, #CCCCC>) -> tensor<?x?x?x?x?xf32, #CCCCC>
   return %ret : tensor<?x?x?x?x?xf32, #CCCCC>
 }
 
 func.func @conv_3d_ndhwc_dhwcf_CDCDC(%arg0: tensor<?x?x?x?x?xf32, #CDCDC>,
-                                     %arg1: tensor<?x?x?x?x?xf32, #CDCDC>)
+                                     %arg1: tensor<?x?x?x?x?xf32>)
                                      -> tensor<?x?x?x?x?xf32, #CDCDC> {
   %c1 = arith.constant 1 : index
   %c6 = arith.constant 6 : index
   %s = bufferization.alloc_tensor(%c1, %c6, %c6, %c6, %c1)
     : tensor<?x?x?x?x?xf32, #CDCDC>
   %ret = linalg.conv_3d_ndhwc_dhwcf {dilations = dense<1> : tensor<3xi64>,
                                        strides = dense<1> : tensor<3xi64>}
-     ins (%arg0, %arg1: tensor<?x?x?x?x?xf32, #CDCDC>, tensor<?x?x?x?x?xf32, #CDCDC>)
+     ins (%arg0, %arg1: tensor<?x?x?x?x?xf32, #CDCDC>, tensor<?x?x?x?x?xf32>)
     outs (%s: tensor<?x?x?x?x?xf32, #CDCDC>) -> tensor<?x?x?x?x?xf32, #CDCDC>
   return %ret : tensor<?x?x?x?x?xf32, #CDCDC>
 }
 
 func.func @entry() {
   %c0 = arith.constant 0 : index
   %c1 = arith.constant 1 : index
   %c3 = arith.constant 3 : index
   %c6 = arith.constant 6 : index
   %c8 = arith.constant 8 : index
   %f10 = arith.constant 10.00000e+00 : f32
   %val = arith.constant 2.00000e+00 : f32
   %zero = arith.constant 0.00000e+00 : f32
 
   %in3D_tmp = call @alloc_5d_filled_f32(%c1, %c8, %c8, %c8, %c1, %val) : (index, index, index, index, index, f32) -> (tensor<?x?x?x?x?xf32>)
   %in3D_ndhwc = tensor.insert %f10 into %in3D_tmp[%c0, %c0, %c0, %c3, %c0] : tensor<?x?x?x?x?xf32>
 
   %filter3D_ndhwc = call @alloc_5d_filled_f32(%c3, %c3, %c3, %c1, %c1, %val) : (index, index, index, index, index, f32) -> (tensor<?x?x?x?x?xf32>)
   %out3D_ndhwc = call @alloc_5d_filled_f32(%c1, %c6, %c6, %c6, %c1, %zero) : (index, index, index, index, index, f32) -> (tensor<?x?x?x?x?xf32>)
 
   %in3D_ndhwc_CCCCC = sparse_tensor.convert %in3D_ndhwc
     : tensor<?x?x?x?x?xf32> to tensor<?x?x?x?x?xf32, #CCCCC>
-  %filter3D_ndhwc_CCCCC = sparse_tensor.convert %filter3D_ndhwc
-    : tensor<?x?x?x?x?xf32> to tensor<?x?x?x?x?xf32, #CCCCC>
-
   %in3D_ndhwc_CDCDC = sparse_tensor.convert %in3D_ndhwc
     : tensor<?x?x?x?x?xf32> to tensor<?x?x?x?x?xf32, #CDCDC>
-  %filter3D_ndhwc_CDCDC = sparse_tensor.convert %filter3D_ndhwc
-    : tensor<?x?x?x?x?xf32> to tensor<?x?x?x?x?xf32, #CDCDC>
 
   //      CHECK:( ( ( ( ( 108 ), ( 124 ), ( 124 ), ( 124 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:      ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:      ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:      ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:      ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:      ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
   // CHECK-SAME:    ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
func.func @entry() {
   // CHECK-SAME:      ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:      ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ) ) )
   %dense_ret = call @conv_3d_ndhwc_dhwcf(%in3D_ndhwc, %filter3D_ndhwc, %out3D_ndhwc)
       : (tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>) -> (tensor<?x?x?x?x?xf32>)
   %dense_v = vector.transfer_read %dense_ret[%c0, %c0, %c0, %c0, %c0], %zero
       : tensor<?x?x?x?x?xf32>, vector<1x6x6x6x1xf32>
   vector.print %dense_v : vector<1x6x6x6x1xf32>
 
-  %CCCCC_ret = call @conv_3d_ndhwc_dhwcf_CCCCC(%in3D_ndhwc_CCCCC, %filter3D_ndhwc_CCCCC)
+  %CCCCC_ret = call @conv_3d_ndhwc_dhwcf_CCCCC(%in3D_ndhwc_CCCCC, %filter3D_ndhwc)
       : (tensor<?x?x?x?x?xf32, #CCCCC>,
-         tensor<?x?x?x?x?xf32, #CCCCC>) -> (tensor<?x?x?x?x?xf32, #CCCCC>)
+         tensor<?x?x?x?x?xf32>) -> (tensor<?x?x?x?x?xf32, #CCCCC>)
 
   // CHECK-NEXT:( ( ( ( ( 108 ), ( 124 ), ( 124 ), ( 124 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:      ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:      ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:      ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:      ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:      ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
   // CHECK-SAME:    ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
func.func @entry() {
   // CHECK-SAME:      ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:      ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ) ) )
   %1 = sparse_tensor.convert %CCCCC_ret
     : tensor<?x?x?x?x?xf32, #CCCCC> to tensor<?x?x?x?x?xf32>
   %v1 = vector.transfer_read %1[%c0, %c0, %c0, %c0, %c0], %zero
       : tensor<?x?x?x?x?xf32>, vector<1x6x6x6x1xf32>
   vector.print %v1 : vector<1x6x6x6x1xf32>
 
-  %CDCDC_ret = call @conv_3d_ndhwc_dhwcf_CDCDC(%in3D_ndhwc_CDCDC, %filter3D_ndhwc_CDCDC)
+  %CDCDC_ret = call @conv_3d_ndhwc_dhwcf_CDCDC(%in3D_ndhwc_CDCDC, %filter3D_ndhwc)
       : (tensor<?x?x?x?x?xf32, #CDCDC>,
-         tensor<?x?x?x?x?xf32, #CDCDC>) -> (tensor<?x?x?x?x?xf32, #CDCDC>)
+         tensor<?x?x?x?x?xf32>) -> (tensor<?x?x?x?x?xf32, #CDCDC>)
 
   // CHECK-NEXT:( ( ( ( ( 108 ), ( 124 ), ( 124 ), ( 124 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:      ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:      ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:      ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:      ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
   // CHECK-SAME:      ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
   // CHECK-SAME:    ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
func.func @entry() {
   vector.print %v2 : vector<1x6x6x6x1xf32>
 
   // Free the resources
   bufferization.dealloc_tensor %in3D_ndhwc : tensor<?x?x?x?x?xf32>
   bufferization.dealloc_tensor %filter3D_ndhwc : tensor<?x?x?x?x?xf32>
   bufferization.dealloc_tensor %out3D_ndhwc : tensor<?x?x?x?x?xf32>
 
   bufferization.dealloc_tensor %in3D_ndhwc_CDCDC : tensor<?x?x?x?x?xf32, #CDCDC>
-  bufferization.dealloc_tensor %filter3D_ndhwc_CDCDC : tensor<?x?x?x?x?xf32, #CDCDC>
   bufferization.dealloc_tensor %in3D_ndhwc_CCCCC : tensor<?x?x?x?x?xf32, #CCCCC>
-  bufferization.dealloc_tensor %filter3D_ndhwc_CCCCC : tensor<?x?x?x?x?xf32, #CCCCC>
 
   bufferization.dealloc_tensor %CCCCC_ret : tensor<?x?x?x?x?xf32, #CCCCC>
   bufferization.dealloc_tensor %CDCDC_ret : tensor<?x?x?x?x?xf32, #CDCDC>
 
   return
 }