[mlir][Linalg] Conv1D, Conv2D and Conv3D added as named ops

[limo1996]

Summary

This commit is part of a greater project which aims to add
full end-to-end support for convolutions inside mlir. The
reason behind having conv ops for each rank rather than
having one generic ConvOp is to enable better optimizations
for every N-D case which reflects memory layout of input/kernel
buffers better and simplifies code as well. We expect plain linalg.conv
to be progressively retired.

[bondhugula]

mlir/include/mlir/Dialect/Linalg/IR/LinalgStructuredOps.td

211

Nit: Please terminate with period.

262

Missing doc.

mlir/lib/Dialect/Linalg/Transforms/Loops.cpp

311

These are all missing doc comments - they do need at least a couple of lines however obvious it might appear to the authors now.

324–329

I'm missing something here. Is this diff correctly generated? Where did the existing emitScalarImplementation of linalg.conv go?

[ftynse]

This is a reasonable first take. Please make sure to document your assumptions, e.g. the order of dimensions. Also make sure the long documentation of the "base" class gets visible when you generate documentation from tablegen.

mlir/include/mlir/Dialect/Linalg/IR/LinalgStructuredOps.td

248

Don't iterator types (at least, their number) also depend on rank?

258

The "input rank * 2" as number of loops sounds fragile to me. Please add documentation about this assumption somewhere, it will have to be revisited when we want to model batch convolutions.

259

@nicolasvasilache should this use "window" type for appropriate loops?

264

This function is long enough to deserve having its definition in a .cc file.

290

typeof is a non-standard GCC extension, this will break most other compilers. Why do you even need explicit template instantiation here?

313

This does not correspond to the maps the code above constructs. The code will produce (d0,d1,d2,d3) -> (d0 + d2, d1 + d3).

mlir/lib/Dialect/Linalg/Transforms/Loops.cpp

324–329

I see it at line 390....

343

Can this at least use a consistent naming scheme: m1,m2,m3?

[limo1996]

10/12 comments resolved

[ftynse]

mlir/lib/Dialect/Linalg/Transforms/Loops.cpp

311

I think @bondhugula meant function-level comments.

315

All comments must use proper English grammar, with capitalization and periods.

328

Nit: this comment would make more sense above the first line.

mlir/test/Dialect/Linalg/loops.mlir

1301–1309

This currently generates out-of-bounds accesses. Please also update the loop bound generator to either (a) handle the access expressions properly or (b) return an error in presence of such expressions. (b) is simpler for this commit, but we will ultimately want (a). Your choice.

[bondhugula]

Could you please rewrite the commit summary? The current one isn't really one - that's more of a plan.

mlir/lib/Dialect/Linalg/Transforms/Loops.cpp

324–329

If that's still there, how is Conv2D different from that? Isn't linalg.conv the Conv2D here?

[limo1996]

evolve

[limo1996]

Most of the comments addressed

mlir/test/Dialect/Linalg/loops.mlir

1301–1309

Here I assume that input array has the padding and the output one not.. Should I do it in another way?

[limo1996]

mlir/lib/Dialect/Linalg/Transforms/Loops.cpp

324–329

There is a new approach to convolution where we will have named ops for each rank so we can optimize better for each case. For more information you can reach out to @nicolasvasilache

[ftynse]

mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

1129

I think this is sufficiently clear to avoid additional comments in the argument list. If you want to keep a comment above the expression, please use proper English grammar with capitalization and punctuation. I already mentioned this guideline in this diff, it applies to _all_ comments.

mlir/lib/Dialect/Linalg/Transforms/Loops.cpp

324–329

We expect plain linalg.conv to be progressively retired. It was mimicking TF convolution, which isn't necessarily generic or transformation-friendly enough. This will happen after we reach feature parity.

mlir/test/Dialect/Linalg/loops.mlir

18

Please avoid spurious whitespace changes.

1301–1309

This assumption should be explicit in the op documentation (in ODS).

Ideally, we should generate std.assert on sizes now that we have that operation, but it can be done consistently for all of Linalg, in a separate commit.

[limo1996]

Documentation of ConvOpBase now included comment that input buffers need to be padded
+ other minor changes

mlir/test/Dialect/Linalg/loops.mlir

18

It is not spurious. It is intended as I think an empty line after 5 lines of code increases readability. Actually I am just following the pattern set above (ever sixth line is empty)

1301–1309

Ok. It's mentioned in ODS documentation. We can add std.assert to our TODO list.

[limo1996]

Loop type of kernel iterators changed from parallel to reduction as those cannot be parallelized.

[ftynse]

Please update the diff/commit description as requested before. It should explain why you are doing the change, https://mlir.llvm.org/getting_started/Contributing/#commit-messages.

mlir/include/mlir/Dialect/Linalg/IR/LinalgStructuredOps.td

252

This sounds overly restrictive in two ways:

• there will be bad accesses _unless_ the size of the input is greater than or equal to the size of the output + size of the kernel - 1, not just when the size of the input is equal to the size of the output;
• the behavior is explained in terms of a specific code generation scheme; it should rather say something like "the behavior of the op is undefined if ...".

mlir/test/Dialect/Linalg/loops.mlir

18

It is. It isn't anyhow related to what this diff does, that is "Conv1D, Conv2D and Conv3D added as named ops". Piggy-backing on commits to do irrelevant changes is bad practice that makes it harder to track changes in the future (irrelevant commits show up in blame). If you want to make a non-functional cleanup change, submit a separate diff with a proper description and put "NFC" in the name to simplify the review.

Speaking about readability, the empty lines between the blocks seem to be attributable to https://github.com/llvm/llvm-project/commit/e36337a998a6be39d65872eab3e3e2291b6518b9, where there was a different number of lines per block. So I doubt the claim that every sixth line is intentionally empty. Even if it had been the case, I would have challenged a rule based on syntax rather than semantics. It makes more sense to separate blocks by semantics (maps with dilation / maps without dilation) than by the number of lines. Your code below has a snippet of 28 CHECK lines without vertical whitespace, why haven't those been splitted into 6 different blocks then?

[limo1996]

evolve

[limo1996]

evolve & merge

[limo1996]

mlir/test/Dialect/Linalg/loops.mlir

18

You are right. Sorry

[limo1996]

comment regarding input sizes done

[bondhugula]

mlir/lib/Dialect/Linalg/Transforms/Loops.cpp

324–329

We expect plain linalg.conv to be progressively retired. It was mimicking TF

Please add a comment on this. Ideally, this should be mentioned in the commit summary or else it wouldn't clear to those outside of your internal discussion.

There is a new approach to convolution where we will have named ops for each rank
so we can optimize better for each case. For more information you can reach out to

Yes, but I was asking what would happen to the current linalg.conv which is equivalent to this conv2d.

[limo1996]

Small refactoring

[limo1996]

Computation of indexes split into multiple to make windows tests (hopefully) pass

[ftynse]

Could you please rebase?

[limo1996]

In D83879#2178481, @ftynse wrote:

Could you please rebase?

Done

[limo1996]

rebased

mlir/include/mlir/Dialect/Linalg/IR/LinalgOps.h

 /// symbol-less identity map of `rank`.
 AffineMap extractOrIdentityMap(Optional<AffineMap> maybeMap, unsigned rank,
                                MLIRContext *context);
 
 /// Return the vector that is the concatenation of `a` and `b`.
 SmallVector<AffineExpr, 4> concat(ArrayRef<AffineExpr> a,
                                   ArrayRef<AffineExpr> b);
 
+/// Generates indexing maps for convolution with the following structure:
+/// input:   (m_1, ..., m_r, n_1, ..., n_r) -> (m_1 + n_1, ..., m_r + n_r)
+/// kernel:  (m_1, ..., m_r, n_1, ..., n_r) -> (n_1, ..., n_r)
+/// output:  (m_1, ..., m_r, n_1, ..., n_r) -> (m_1, ..., m_r)
+/// where r is the rank of the input, kernel and output
+llvm::Optional<SmallVector<AffineMap, 8>>
+createConvNDIndexingMaps(MLIRContext *context, unsigned rank);
+
 #include "mlir/Dialect/Linalg/IR/LinalgStructuredOpsInterfaces.h.inc"
 
 #define GET_OP_CLASSES
 #include "mlir/Dialect/Linalg/IR/LinalgOps.h.inc"
 
 #define GET_OP_CLASSES
 #include "mlir/Dialect/Linalg/IR/LinalgStructuredOps.h.inc"
 
 } // namespace linalg
 } // namespace mlir
 
 #endif // MLIR_DIALECT_LINALG_LINALGOPS_H_

mlir/include/mlir/Dialect/Linalg/IR/LinalgStructuredOps.td

llvm::Optional<SmallVector<AffineMap, 8>> referenceIndexingMaps() {
         AffineMap::get(1, 0, {r_i}, context),
         AffineMap::get(1, 0, {}, context)};
     }
   }];
 
   let hasFolder = 1;
 }
 
+class ConvOpBase<string mnemonic, int N>
+  : LinalgStructured_Op<mnemonic, [NInputs<2>, NOutputs<1>]> {
+  let description = [{
+    Base operation for any N-D Convolution implemented as a linalg.generic op.
+
+    Usage:
+
+    ```mlir
+    linalg.conv<N>D(%in, %filter, %out) : memref<(?x)+f32>,
+                                          memref<(?x)+f32>,
+                                          memref<(?x)+f32>
+    ```
+
+    where    %in:     input array
+             %filter: kernel or filter that will be applied on the input array
+             %out:    output array
+
+    and rank of the operands is *N*.
+
+    Every child convolution is expressed as:
+
+    ```mlir
+    #conv_trait = {
+      args_in = 2,
+      args_out = 1,
+      indexing_maps = #conv_accesses,
+      library_call  = "linalg_conv",
+      iterator_types = [("parallel", "parallel")+], // `2 * rank` iterators
+    }
+
+    linalg.generic #conv_trait %in, %filter, %out {
+      ^bb0(%a: f32, %b: f32, %c: f32) :
+        %d = mulf %a, %b : f32
+        %e = addf %c, %d : f32
+        linalg.yield %e : f32
+    } : memref<(?x)+f32>,
+        memref<(?x)+f32>,
+        memref<(?x)+f32>
+    ```
+
+    where #conv_accesses depend on the rank of the operands and thus
+    can be found in the documentation of each N-D case.
+    Please note that the input array is expected to be right-padded i.e.
+    the size of the input is greater than or equal to the size of the output
+    + size of the kernel - 1. If it is not padded the behavior of the op
+    is undefined.
+  }];
+
+  let arguments = (ins AnyStridedMemRefOfRank<N>,
+                       AnyStridedMemRefOfRank<N>,
+                       AnyStridedMemRefOfRank<N>);
+
+  let extraClassDeclaration = libraryCallName # [{
+    llvm::Optional<SmallVector<StringRef, 8>> referenceIterators() {
+      // There are always 2 loops for each dimension of the convolution. First
+      // iterates output and second kernel. Since ranks of all 3 operands must
+      // be the same it does not matter which operand is picked to get the rank.
+      // Loops iterating the output can be parallelized and thus are marked as
+      // "parallel" while loops iterating the kernel are accumulating the
+      // products and therefore are marked as "reduction".
+      unsigned rank = getInputShapedType(0).getRank();
+      SmallVector<StringRef, 8> parallel(rank, getParallelIteratorTypeName());
+      SmallVector<StringRef, 8> reduction(rank, getReductionIteratorTypeName());
+      parallel.insert(parallel.end(), reduction.begin(), reduction.end());
+      return parallel;
+    }
+
+    // Generates indexing maps with the following structure:
+    // input:   (m_1, ..., m_r, n_1, ..., n_r) -> (m_1 + n_1, ..., m_r + n_r)
+    // kernel:  (m_1, ..., m_r, n_1, ..., n_r) -> (n_1, ..., n_r)
+    // output:  (m_1, ..., m_r, n_1, ..., n_r) -> (m_1, ..., m_r)
+    // where r is the rank of the input, kernel and output
+    llvm::Optional<SmallVector<AffineMap, 8>> referenceIndexingMaps() {
+      MLIRContext *context = getContext();
+      unsigned rank = getInputShapedType(0).getRank();
+      return createConvNDIndexingMaps(context, rank);
+    }
+  }];
+
+  let hasFolder = 1;
+  let verifier = [{ return ::verify(*this); }];
+}
+
+def Conv1DOp : ConvOpBase<"conv1D", 1> {
+  let description = [{
+    *1D* convolution which uses following affine maps to access operands:
+
+    ```mlir
+    #conv_accesses = [
+      affine_map<(m, n) -> (m + n)>, // in
+      affine_map<(m, n) -> (n)>, // kernel
+      affine_map<(m, n) -> (m)> // out
+    ]
+    ```
+  }];
+}
+
+def Conv2DOp : ConvOpBase<"conv2D", 2> {
+  let description = [{
+    *2D* convolution which uses following affine maps to access operands:
+
+    ```mlir
+    #conv_accesses = [
+      affine_map<(m1, m2, n1, n2) -> (m1 + n1, m2 + n2)>, // in
+      affine_map<(m1, m2, n1, n2) -> (n1, n2)>, // kernel
+      affine_map<(m1, m2, n1, n2) -> (m1, m2) // out
+    ]
+    ```
+  }];
+}
+
+def Conv3DOp : ConvOpBase<"conv3D", 3> {
+  let description = [{
+    *3D* convolution which uses following affine maps to access operands:
+
+    ```mlir
+    #conv_accesses = [
+      affine_map<(m1, m2, m3, n1, n2, n3) -> (m1 + n1, m2 + n2, m3 + n3)>, // in
+      affine_map<(m1, m2, m3, n1, n2, n3) -> (n1, n2, n3)>, // kernel
+      affine_map<(m1, m2, m3, n1, n2, n3) -> (m1, m2, m3)> // out
+    ]
+    ```
+  }];
+}
+
 /// A base class for pooling operation such as conv. The arguments must contain
 /// optional arguments `strides`, `dilations` and `padding` with following type:
 ///   OptionalAttr<I64ArrayAttr>:$strides
 ///   OptionalAttr<I64ArrayAttr>:$dilations
 ///   OptionalAttr<I64ElementsAttr>:$padding
 /// `strides` denotes the step of each window along the dimension.
 class PoolingBase_Op<string mnemonic, list<OpTrait> props>
   : LinalgStructured_Op<mnemonic, props> {

mlir/lib/Conversion/LinalgToStandard/LinalgToStandard.cpp

void mlir::populateLinalgToStandardConversionPatterns(
   patterns.insert<
       CopyTransposeConversion,
       LinalgOpConversion<ConvOp>,
       LinalgOpConversion<PoolingMaxOp>,
       LinalgOpConversion<PoolingMinOp>,
       LinalgOpConversion<PoolingSumOp>,
       LinalgOpConversion<CopyOp>,
       LinalgOpConversion<DotOp>,
+      LinalgOpConversion<Conv1DOp>,
+      LinalgOpConversion<Conv2DOp>,
+      LinalgOpConversion<Conv3DOp>,
       LinalgOpConversion<FillOp>,
       LinalgOpConversion<GenericOp>,
       LinalgOpConversion<IndexedGenericOp>>(ctx);
   // TODO: collect all auto-generated named ops with a tblgen directive.
   patterns.insert<
       LinalgOpConversion<BatchMatmulOp>,
       LinalgOpConversion<MatvecOp>,
       LinalgOpConversion<MatmulOp>>(ctx);

mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

static LogicalResult verifyStrideOrDilation(LinalgPoolingOp op,
   if (attrs.size() != op.getNumWindowLoops())
     return op.emitOpError("expects num ")
            << strideOrDilation
            << "s equal to number of window dimensions: " << attrs.size()
            << " vs " << op.getNumWindowLoops();
   return success();
 }
 
+template <typename ConvNDOp>
+static LogicalResult verify(ConvNDOp op) {
+  auto outputType = op.getOutputShapedType(0).getElementType();
+  auto inputType = op.getInputShapedType(0).getElementType();
+  auto kernelType = op.getInputShapedType(1).getElementType();
+  if (outputType != inputType || inputType != kernelType)
+    return op.emitOpError("expected all element types of operands to match");
+
+  return success();
+}
+
 static LogicalResult verify(ConvOp op) {
   auto oType = op.output().getType().cast<MemRefType>();
   auto fType = op.filter().getType().cast<MemRefType>();
   auto iType = op.input().getType().cast<MemRefType>();
   if (oType.getElementType() != iType.getElementType() ||
       oType.getElementType() != fType.getElementType())
     return op.emitOpError("expects memref elemental types to match");
   if (oType.getRank() != iType.getRank() || oType.getRank() != fType.getRank())
for (unsigned i = 0, e = outputDims.size(); i < e; ++i) {
     // TODO: add a level of indirection to linalg.generic.
     auto expr = op.getStride(i) * outputDims[i] +
                 op.getDilation(i) * windowDims[i] - op.getLowPad(i);
     res.push_back(expr);
   }
   return res;
 }
 
+llvm::Optional<SmallVector<AffineMap, 8>>
+mlir::linalg::createConvNDIndexingMaps(MLIRContext *context, unsigned rank) {
+  unsigned numDims = rank * 2, idx = 0;
+
+  SmallVector<AffineExpr, 8> dims, in, kernel, out;
+  dims = makeAffineDimExprs(numDims, idx, context);
+  in.reserve(rank);
+  kernel.reserve(rank);
+  out.reserve(rank);
+
+  for (unsigned i = 0; i < rank; i++) {
+    in.push_back(dims[i] + dims[rank + i]);
+    kernel.push_back(dims[rank + i]);
+    out.push_back(dims[i]);
+  }
+
+  return SmallVector<AffineMap, 8>{AffineMap::get(numDims, 0, in, context),
+                                   AffineMap::get(numDims, 0, kernel, context),
+                                   AffineMap::get(numDims, 0, out, context)};
+}
+
 #define INSTANTIATE_WEIGHTED_POOLING_INPUT_INDEX(OP_TYPE)                      \
   template SmallVector<AffineExpr, 4>                                          \
   mlir::linalg::weightedPoolingInputIndex<OP_TYPE>(                            \
       OP_TYPE op, ArrayRef<AffineExpr> outputDims,                             \
       ArrayRef<AffineExpr> windowDims);
 
 INSTANTIATE_WEIGHTED_POOLING_INPUT_INDEX(ConvOp)
 INSTANTIATE_WEIGHTED_POOLING_INPUT_INDEX(PoolingMaxOp)

 LogicalResult DotOp::fold(ArrayRef<Attribute>,
                           SmallVectorImpl<OpFoldResult> &) {
   return foldMemRefCast(*this);
 }
 LogicalResult FillOp::fold(ArrayRef<Attribute>,
                            SmallVectorImpl<OpFoldResult> &) {
   return foldMemRefCast(*this);
 }
+LogicalResult Conv1DOp::fold(ArrayRef<Attribute>,
+                             SmallVectorImpl<OpFoldResult> &) {
+  return foldMemRefCast(*this);
+}
+LogicalResult Conv2DOp::fold(ArrayRef<Attribute>,
+                             SmallVectorImpl<OpFoldResult> &) {
+  return foldMemRefCast(*this);
+}
+LogicalResult Conv3DOp::fold(ArrayRef<Attribute>,
+                             SmallVectorImpl<OpFoldResult> &) {
+  return foldMemRefCast(*this);
+}
 LogicalResult GenericOp::fold(ArrayRef<Attribute>,
                               SmallVectorImpl<OpFoldResult> &) {
   return foldMemRefCast(*this);
 }
 LogicalResult IndexedGenericOp::fold(ArrayRef<Attribute>,
                                      SmallVectorImpl<OpFoldResult> &) {
   return foldMemRefCast(*this);
 }

mlir/lib/Dialect/Linalg/Transforms/Loops.cpp

void emitScalarImplementation(ArrayRef<Value> allIvs, DotOp dotOp) {
   assert(allIvs.size() == 1);
   Value r_i(allIvs[0]);
   IndexedValueType A(dotOp.getInput(0)), B(dotOp.getInput(1)),
       C(dotOp.getOutputBuffer(0));
   // Emit scalar form.
   C() = C() + A(r_i) * B(r_i);
 }
 
+/// Following functions emit scalar part of the N-D convolution op.
+/// N-D convolution has 2N loops:
+///   1-N: Iterate over the output array *O* with iterators *m1, ..., mN*.
+///   N-2N:. Iterate over the kernel *K* with iterators *n1, ..., nN*.
+///
+/// The scalar part accumulates products of input array *I* values with kernel
+/// ones. The accumulation expression therefore looks like:
+///   O[m1, ..., mN] += I[m1 + n1, ..., mN + nN] * K[n1, ..., nN].
+/// Note that the input array has to be padded in order to prevent
+/// out of bounds accesses.
+template <typename IndexedValueType>
+void emitScalarImplementation(ArrayRef<Value> allIvs, Conv1DOp convOp) {
+  assert(convOp.hasBufferSemantics() &&
+         "expected linalg op with buffer semantics");
+  assert(allIvs.size() == 2);
+  Value m1(allIvs[0]);
+  Value n1(allIvs[1]);
+  IndexedValueType I(convOp.getInput(0)), K(convOp.getInput(1)),
+      O(convOp.getOutputBuffer(0));
+  // Emit scalar form for the 1D conv case.
+  Value i1 = m1 + n1;
+  O(m1) = O(m1) + I(i1) * K(n1);
+}
+
+template <typename IndexedValueType>
+void emitScalarImplementation(ArrayRef<Value> allIvs, Conv2DOp convOp) {
+  assert(convOp.hasBufferSemantics() &&
+         "expected linalg op with buffer semantics");
+  assert(allIvs.size() == 4);
+  Value m1(allIvs[0]), m2(allIvs[1]);
+  Value n1(allIvs[2]), n2(allIvs[3]);
+  IndexedValueType I(convOp.getInput(0)), K(convOp.getInput(1)),
+      O(convOp.getOutputBuffer(0));
+  // Emit scalar form for the 2D conv case.
+  Value i1 = m1 + n1;
+  Value i2 = m2 + n2;
+  O(m1, m2) = O(m1, m2) + I(i1, i2) * K(n1, n2);
+}
+
+template <typename IndexedValueType>
+void emitScalarImplementation(ArrayRef<Value> allIvs, Conv3DOp convOp) {
+  assert(convOp.hasBufferSemantics() &&
+         "expected linalg op with buffer semantics");
+  assert(allIvs.size() == 6);
+  Value m1(allIvs[0]), m2(allIvs[1]), m3(allIvs[2]);
+  Value n1(allIvs[3]), n2(allIvs[4]), n3(allIvs[5]);
+  IndexedValueType I(convOp.getInput(0)), K(convOp.getInput(1)),
+      O(convOp.getOutputBuffer(0));
+  // Emit scalar form for the 3D conv case.
+  Value i1 = m1 + n1;
+  Value i2 = m2 + n2;
+  Value i3 = m3 + n3;
+  O(m1, m2, m3) = O(m1, m2, m3) + I(i1, i2, i3) * K(n1, n2, n3);
+}
+
 template <typename IndexedValueType>
 Value getConvOpInput(ConvOp convOp, StdIndexedValue im,
                      MutableArrayRef<Value> imIdx) {
   // TODO: add a level of indirection to linalg.generic.
   if (!convOp.padding())
     return im(imIdx);
 
   auto *context = ScopedContext::getContext();

mlir/test/Dialect/Linalg/invalid.mlir

 
 // -----
 
 func @named_ops(%a3: memref<?x?x?xf32>, %b3: memref<?x?xf32>, %c3: memref<?x?x?xf32>) {
   // expected-error @+1 {{op expected indexing_map #1 results to match view rank: 'memref<?x?xf32>'}}
   linalg.batch_matmul %a3, %b3, %c3 : (memref<?x?x?xf32>, memref<?x?xf32>, memref<?x?x?xf32>) -> ()
   return
 }
+
+// -----
+
+func @conv_type_mismatch(%in: memref<?xi32>, %filter: memref<?xf32>, %out: memref<?xf32>) {
+  // expected-error @+1 {{expected all element types of operands to match}}
+  linalg.conv1D(%in, %filter, %out) : memref<?xi32>, memref<?xf32>, memref<?xf32>
+  return
+}

mlir/test/Dialect/Linalg/loops.mlir

 // CHECKLOOP-DAG: #[[$strided4D:.*]] = affine_map<(d0, d1, d2, d3)[s0, s1, s2, s3] -> (d0 * s1 + s0 + d1 * s2 + d2 * s3 + d3)>
 // CHECKLOOP-DAG: #[[$clampMinMap:.*]] = affine_map<(d0) -> (d0, 0)>
 
 // CHECKLOOP-DAG: #[[$stride1Dilation1:.*]] = affine_map<(d0, d1) -> (d0  + d1)>
 // CHECKLOOP-DAG: #[[$stride2Dilation1:.*]] = affine_map<(d0, d1) -> (d0 * 2 + d1)>
 // CHECKLOOP-DAG: #[[$stride2Dilation4:.*]] = affine_map<(d0, d1) -> (d0 * 2 + d1 * 4)>
 // CHECKLOOP-DAG: #[[$stride3Dilation5:.*]] = affine_map<(d0, d1) -> (d0 * 3 + d1 * 5)>
 // CHECKLOOP-DAG: #[[$convLowerBound:.*]] = affine_map<()[s0] -> (s0 floordiv 2)>
 // CHECKLOOP-DAG: #[[$convUpperBound:.*]] = affine_map<()[s0, s1] -> (s1 + s0 floordiv 2 - s0 + 1)>
 // CHECKLOOP-DAG: #[[$convMap:.*]] = affine_map<(d0, d1)[s0] -> (d0 + d1 - s0 floordiv 2)>
 
 // CHECKPARALLEL-DAG: #[[$strided1D:.*]] = affine_map<(d0)[s0] -> (d0 + s0)>
 // CHECKPARALLEL-DAG: #[[$strided2D:.*]] = affine_map<(d0, d1)[s0, s1] -> (d0 * s1 + s0 + d1)>
 // CHECKPARALLEL-DAG: #[[$strided3D:.*]] = affine_map<(d0, d1, d2)[s0, s1, s2] -> (d0 * s1 + s0 + d1 * s2 + d2)>
 // CHECKPARALLEL-DAG: #[[$strided4D:.*]] = affine_map<(d0, d1, d2, d3)[s0, s1, s2, s3] -> (d0 * s1 + s0 + d1 * s2 + d2 * s3 + d3)>
 // CHECKPARALLEL-DAG: #[[$clampMinMap:.*]] = affine_map<(d0) -> (d0, 0)>
 

 //       CHECKPARALLEL:   %[[aff3:.*]] = affine.apply #[[$convMap]](%{{.*}}, %{{.*}})[%[[dim10]]]
 //       CHECKPARALLEL:   %[[aff4:.*]] = affine.apply #[[$convMap]](%{{.*}}, %{{.*}})[%[[dim11]]]
 //       CHECKPARALLEL:   %[[va:.*]] = load %[[arg0]][%[[aff1]], %[[aff2]], %[[aff3]], %[[aff4]]] : memref<?x?x?x?xf32>
 //       CHECKPARALLEL:   %[[vb:.*]] = load %[[arg1]][%[[i4]], %[[i5]], %[[i6]], %[[i7]]] : memref<?x?x?x?xf32>
 //       CHECKPARALLEL:   %[[vc:.*]] = load %[[arg2]][%[[i0]], %[[i1]], %[[i2]], %[[i3]]] : memref<?x?x?x?xf32>
 //       CHECKPARALLEL:   %[[inc:.*]] = mulf %[[va]], %[[vb]] : f32
 //       CHECKPARALLEL:   %[[res:.*]] = addf %[[vc]], %[[inc]] : f32
 //       CHECKPARALLEL:   store %[[res]], %[[arg2]][%[[i0]], %[[i1]], %[[i2]], %[[i3]]] : memref<?x?x?x?xf32>
+
+func @conv1d_no_symbols(%in : memref<?xf32>, %filter : memref<?xf32>, %out : memref<?xf32>) -> () {
+  linalg.conv1D(%in, %filter, %out) : memref<?xf32>, memref<?xf32>, memref<?xf32>
+  return
+}
+
+// CHECKLOOP-LABEL: @conv1d_no_symbols
+//  CHECKLOOP-SAME: %[[arg0:[a-zA-Z0-9]+]]: memref<?xf32>
+//  CHECKLOOP-SAME: %[[arg1:[a-zA-Z0-9]+]]: memref<?xf32>
+//  CHECKLOOP-SAME: %[[arg2:[a-zA-Z0-9]+]]: memref<?xf32>
+//       CHECKLOOP: %[[c0:.*]] = constant 0 : index
+//       CHECKLOOP: %[[c1:.*]] = constant 1 : index
+//       CHECKLOOP: %[[dim0:.*]] = dim %[[arg1]], %[[c0]] : memref<?xf32>
+//       CHECKLOOP: %[[dim1:.*]] = dim %[[arg2]], %[[c0]] : memref<?xf32>
+//       CHECKLOOP: scf.for %[[b:.*]] = %[[c0]] to %[[dim1]] step %[[c1]] {
+//       CHECKLOOP:   scf.for %[[m:.*]] = %[[c0]] to %[[dim0]] step %[[c1]] {
+//       CHECKLOOP:     %[[aff:.*]] = affine.apply #[[$stride1Dilation1]](%[[b]], %[[m]])
+//       CHECKLOOP:     %[[va:.*]] = load %[[arg1]][%[[m]]] : memref<?xf32>
+//       CHECKLOOP:     %[[vb:.*]] = load %[[arg0]][%[[aff]]] : memref<?xf32>
+//       CHECKLOOP:     %[[inc:.*]] = mulf %[[vb]], %[[va]] : f32
+//       CHECKLOOP:     %[[vc:.*]] = load %[[arg2]][%[[b]]] : memref<?xf32>
+//       CHECKLOOP:     %[[res:.*]] = addf %[[vc]], %[[inc]] : f32
+//       CHECKLOOP:     store %[[res]], %[[arg2]][%[[b]]] : memref<?xf32>
+
+// CHECKPARALLEL-LABEL: @conv1d_no_symbols
+//  CHECKPARALLEL-SAME: %[[arg0:[a-zA-Z0-9]+]]: memref<?xf32>
+//  CHECKPARALLEL-SAME: %[[arg1:[a-zA-Z0-9]+]]: memref<?xf32>
+//  CHECKPARALLEL-SAME: %[[arg2:[a-zA-Z0-9]+]]: memref<?xf32>
+//       CHECKPARALLEL: %[[c0:.*]] = constant 0 : index
+//       CHECKPARALLEL: %[[c1:.*]] = constant 1 : index
+//       CHECKPARALLEL: %[[dim0:.*]] = dim %[[arg1]], %[[c0]] : memref<?xf32>
+//       CHECKPARALLEL: %[[dim1:.*]] = dim %[[arg2]], %[[c0]] : memref<?xf32>
+//       CHECKPARALLEL: scf.parallel (%[[b:.*]]) = (%[[c0]]) to (%[[dim1]]) step (%[[c1]]) {
+//       CHECKPARALLEL:   scf.for %[[m:.*]] = %[[c0]] to %[[dim0]] step %[[c1]] {
+//       CHECKPARALLEL:     %[[aff:.*]] = affine.apply #[[$stride1Dilation1]](%[[b]], %[[m]])
+//       CHECKPARALLEL:     %[[va:.*]] = load %[[arg1]][%[[m]]] : memref<?xf32>
+//       CHECKPARALLEL:     %[[vb:.*]] = load %[[arg0]][%[[aff]]] : memref<?xf32>
+//       CHECKPARALLEL:     %[[inc:.*]] = mulf %[[vb]], %[[va]] : f32
+//       CHECKPARALLEL:     %[[vc:.*]] = load %[[arg2]][%[[b]]] : memref<?xf32>
+//       CHECKPARALLEL:     %[[res:.*]] = addf %[[vc]], %[[inc]] : f32
+//       CHECKPARALLEL:     store %[[res]], %[[arg2]][%[[b]]] : memref<?xf32>
+
+
+func @conv2d_no_symbols(%in : memref<?x?xf32>, %filter : memref<?x?xf32>, %out : memref<?x?xf32>) -> () {
+  linalg.conv2D(%in, %filter, %out) : memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>
+  return
+}
+// CHECKLOOP-LABEL: @conv2d_no_symbols
+//  CHECKLOOP-SAME: %[[arg0:[a-zA-Z0-9]+]]: memref<?x?xf32>
+//  CHECKLOOP-SAME: %[[arg1:[a-zA-Z0-9]+]]: memref<?x?xf32>
+//  CHECKLOOP-SAME: %[[arg2:[a-zA-Z0-9]+]]: memref<?x?xf32>
+//       CHECKLOOP: %[[c0:.*]] = constant 0 : index
+//       CHECKLOOP: %[[c1:.*]] = constant 1 : index
+//       CHECKLOOP: %[[dim0:.*]] = dim %[[arg1]], %[[c0]] : memref<?x?xf32>
+//       CHECKLOOP: %[[dim1:.*]] = dim %[[arg1]], %[[c1]] : memref<?x?xf32>
+//       CHECKLOOP: %[[dim2:.*]] = dim %[[arg2]], %[[c0]] : memref<?x?xf32>
+//       CHECKLOOP: %[[dim3:.*]] = dim %[[arg2]], %[[c1]] : memref<?x?xf32>
+//       CHECKLOOP: scf.for %[[arg3:.*]] = %[[c0]] to %[[dim2]] step %[[c1]] {
+//       CHECKLOOP:   scf.for %[[arg4:.*]] = %[[c0]] to %[[dim3]] step %[[c1]] {
+//       CHECKLOOP:     scf.for %[[arg5:.*]] = %[[c0]] to %[[dim0]] step %[[c1]] {
+//       CHECKLOOP:       scf.for %[[arg6:.*]] = %[[c0]] to %[[dim1]] step %[[c1]] {
+//       CHECKLOOP:         %[[aff:.*]] = affine.apply #[[$stride1Dilation1]](%[[arg3]], %[[arg5]])
+//       CHECKLOOP:         %[[aff2:.*]] = affine.apply #[[$stride1Dilation1]](%[[arg4]], %[[arg6]])
+//       CHECKLOOP:         %[[va:.*]] = load %[[arg1]][%[[arg5]], %[[arg6]]] : memref<?x?xf32>
+//       CHECKLOOP:         %[[vb:.*]] = load %[[arg0]][%[[aff]], %[[aff2]]] : memref<?x?xf32>
+//       CHECKLOOP:         %[[inc:.*]] = mulf %[[vb]], %[[va]] : f32
+//       CHECKLOOP:         %[[vc:.*]] = load %[[arg2]][%[[arg3]], %[[arg4]]] : memref<?x?xf32>
+//       CHECKLOOP:         %[[res:.*]] = addf %[[vc]], %[[inc]] : f32
+//       CHECKLOOP:         store %[[res]], %[[arg2]][%[[arg3]], %[[arg4]]] : memref<?x?xf32>
+
+// CHECKPARALLEL-LABEL: @conv2d_no_symbols
+//  CHECKPARALLEL-SAME: %[[arg0:[a-zA-Z0-9]+]]: memref<?x?xf32>
+//  CHECKPARALLEL-SAME: %[[arg1:[a-zA-Z0-9]+]]: memref<?x?xf32>
+//  CHECKPARALLEL-SAME: %[[arg2:[a-zA-Z0-9]+]]: memref<?x?xf32>
+//       CHECKPARALLEL: %[[c0:.*]] = constant 0 : index
+//       CHECKPARALLEL: %[[c1:.*]] = constant 1 : index
+//       CHECKPARALLEL: %[[dim0:.*]] = dim %[[arg1]], %[[c0]] : memref<?x?xf32>
+//       CHECKPARALLEL: %[[dim1:.*]] = dim %[[arg1]], %[[c1]] : memref<?x?xf32>
+//       CHECKPARALLEL: %[[dim2:.*]] = dim %[[arg2]], %[[c0]] : memref<?x?xf32>
+//       CHECKPARALLEL: %[[dim3:.*]] = dim %[[arg2]], %[[c1]] : memref<?x?xf32>
+//       CHECKPARALLEL: scf.parallel (%[[arg3:.*]], %[[arg4:.*]]) = (%[[c0]], %[[c0]]) to (%[[dim2]], %[[dim3]]) step (%[[c1]], %[[c1]]) {
+//       CHECKPARALLEL:   scf.for %[[arg5:.*]] = %[[c0]] to %[[dim0]] step %[[c1]] {
+//       CHECKPARALLEL:     scf.for %[[arg6:.*]] = %[[c0]] to %[[dim1]] step %[[c1]] {
+//       CHECKPARALLEL:       %[[aff:.*]] = affine.apply #[[$stride1Dilation1]](%[[arg3]], %[[arg5]])
+//       CHECKPARALLEL:       %[[aff2:.*]] = affine.apply #[[$stride1Dilation1]](%[[arg4]], %[[arg6]])
+//       CHECKPARALLEL:       %[[va:.*]] = load %[[arg1]][%[[arg5]], %[[arg6]]] : memref<?x?xf32>
+//       CHECKPARALLEL:       %[[vb:.*]] = load %[[arg0]][%[[aff]], %[[aff2]]] : memref<?x?xf32>
+//       CHECKPARALLEL:       %[[inc:.*]] = mulf %[[vb]], %[[va]] : f32
+//       CHECKPARALLEL:       %[[vc:.*]] = load %[[arg2]][%[[arg3]], %[[arg4]]] : memref<?x?xf32>
+//       CHECKPARALLEL:       %[[res:.*]] = addf %[[vc]], %[[inc]] : f32
+//       CHECKPARALLEL:       store %[[res]], %[[arg2]][%[[arg3]], %[[arg4]]] : memref<?x?xf32>
+
+
+func @conv3d_no_symbols(%in : memref<?x?x?xf32>, %filter : memref<?x?x?xf32>, %out : memref<?x?x?xf32>) -> () {
+  linalg.conv3D(%in, %filter, %out) : memref<?x?x?xf32>, memref<?x?x?xf32>, memref<?x?x?xf32>
+  return
+}
+
+// CHECKLOOP-LABEL: @conv3d_no_symbols
+//  CHECKLOOP-SAME: %[[arg0:[a-zA-Z0-9]+]]: memref<?x?x?xf32>
+//  CHECKLOOP-SAME: %[[arg1:[a-zA-Z0-9]+]]: memref<?x?x?xf32>
+//  CHECKLOOP-SAME: %[[arg2:[a-zA-Z0-9]+]]: memref<?x?x?xf32>
+//       CHECKLOOP: %[[c2:.*]] = constant 2 : index
+//       CHECKLOOP: %[[c0:.*]] = constant 0 : index
+//       CHECKLOOP: %[[c1:.*]] = constant 1 : index
+//       CHECKLOOP: %[[dim0:.*]] = dim %[[arg1]], %[[c0]] : memref<?x?x?xf32>
+//       CHECKLOOP: %[[dim1:.*]] = dim %[[arg1]], %[[c1]] : memref<?x?x?xf32>
+//       CHECKLOOP: %[[dim2:.*]] = dim %[[arg1]], %[[c2]] : memref<?x?x?xf32>
+//       CHECKLOOP: %[[dim3:.*]] = dim %[[arg2]], %[[c0]] : memref<?x?x?xf32>
+//       CHECKLOOP: %[[dim4:.*]] = dim %[[arg2]], %[[c1]] : memref<?x?x?xf32>
+//       CHECKLOOP: %[[dim5:.*]] = dim %[[arg2]], %[[c2]] : memref<?x?x?xf32>
+//       CHECKLOOP: scf.for %[[arg3:.*]] = %[[c0]] to %[[dim3]] step %[[c1]] {
+//       CHECKLOOP:   scf.for %[[arg4:.*]] = %[[c0]] to %[[dim4]] step %[[c1]] {
+//       CHECKLOOP:     scf.for %[[arg5:.*]] = %[[c0]] to %[[dim5]] step %[[c1]] {
+//       CHECKLOOP:       scf.for %[[arg6:.*]] = %[[c0]] to %[[dim0]] step %[[c1]] {
+//       CHECKLOOP:         scf.for %[[arg7:.*]] = %[[c0]] to %[[dim1]] step %[[c1]] {
+//       CHECKLOOP:           scf.for %[[arg8:.*]] = %[[c0]] to %[[dim2]] step %[[c1]] {
+//       CHECKLOOP:             %[[aff:.*]] = affine.apply #[[$stride1Dilation1]](%[[arg3]], %[[arg6]])
+//       CHECKLOOP:             %[[aff2:.*]] = affine.apply #[[$stride1Dilation1]](%[[arg4]], %[[arg7]])
+//       CHECKLOOP:             %[[aff3:.*]] = affine.apply #[[$stride1Dilation1]](%[[arg5]], %[[arg8]])
+//       CHECKLOOP:             %[[va:.*]] = load %[[arg1]][%[[arg6]], %[[arg7]], %[[arg8]]] : memref<?x?x?xf32>
+//       CHECKLOOP:             %[[vb:.*]] = load %[[arg0]][%[[aff]], %[[aff2]], %[[aff3]]] : memref<?x?x?xf32>
+//       CHECKLOOP:             %[[inc:.*]] = mulf %[[vb]], %[[va]] : f32
+//       CHECKLOOP:             %[[vc:.*]] = load %[[arg2]][%[[arg3]], %[[arg4]], %[[arg5]]] : memref<?x?x?xf32>
+//       CHECKLOOP:             %[[res:.*]] = addf %[[vc]], %[[inc]] : f32
+//       CHECKLOOP:             store %[[res]], %[[arg2]][%[[arg3]], %[[arg4]], %[[arg5]]] : memref<?x?x?xf32>
+
+// CHECKPARALLEL-LABEL: @conv3d_no_symbols
+//  CHECKPARALLEL-SAME: %[[arg0:[a-zA-Z0-9]+]]: memref<?x?x?xf32>
+//  CHECKPARALLEL-SAME: %[[arg1:[a-zA-Z0-9]+]]: memref<?x?x?xf32>
+//  CHECKPARALLEL-SAME: %[[arg2:[a-zA-Z0-9]+]]: memref<?x?x?xf32>
+//       CHECKPARALLEL: %[[c2:.*]] = constant 2 : index
+//       CHECKPARALLEL: %[[c0:.*]] = constant 0 : index
+//       CHECKPARALLEL: %[[c1:.*]] = constant 1 : index
+//       CHECKPARALLEL: %[[dim0:.*]] = dim %[[arg1]], %[[c0]] : memref<?x?x?xf32>
+//       CHECKPARALLEL: %[[dim1:.*]] = dim %[[arg1]], %[[c1]] : memref<?x?x?xf32>
+//       CHECKPARALLEL: %[[dim2:.*]] = dim %[[arg1]], %[[c2]] : memref<?x?x?xf32>
+//       CHECKPARALLEL: %[[dim3:.*]] = dim %[[arg2]], %[[c0]] : memref<?x?x?xf32>
+//       CHECKPARALLEL: %[[dim4:.*]] = dim %[[arg2]], %[[c1]] : memref<?x?x?xf32>
+//       CHECKPARALLEL: %[[dim5:.*]] = dim %[[arg2]], %[[c2]] : memref<?x?x?xf32>
+//       CHECKPARALLEL: scf.parallel (%[[arg3:.*]], %[[arg4:.*]], %[[arg5:.*]]) = (%[[c0]], %[[c0]], %[[c0]]) to (%[[dim3]], %[[dim4]], %[[dim5]]) step (%[[c1]], %[[c1]], %[[c1]]) {
+//       CHECKPARALLEL:   scf.for %[[arg6:.*]] = %[[c0]] to %[[dim0]] step %[[c1]] {
+//       CHECKPARALLEL:     scf.for %[[arg7:.*]] = %[[c0]] to %[[dim1]] step %[[c1]] {
+//       CHECKPARALLEL:       scf.for %[[arg8:.*]] = %[[c0]] to %[[dim2]] step %[[c1]] {
+//       CHECKPARALLEL:         %[[aff:.*]] = affine.apply #[[$stride1Dilation1]](%[[arg3]], %[[arg6]])
+//       CHECKPARALLEL:         %[[aff2:.*]] = affine.apply #[[$stride1Dilation1]](%[[arg4]], %[[arg7]])
+//       CHECKPARALLEL:         %[[aff3:.*]] = affine.apply #[[$stride1Dilation1]](%[[arg5]], %[[arg8]])
+//       CHECKPARALLEL:         %[[va:.*]] = load %[[arg1]][%[[arg6]], %[[arg7]], %[[arg8]]] : memref<?x?x?xf32>
+//       CHECKPARALLEL:         %[[vb:.*]] = load %[[arg0]][%[[aff]], %[[aff2]], %[[aff3]]] : memref<?x?x?xf32>
+//       CHECKPARALLEL:         %[[inc:.*]] = mulf %[[vb]], %[[va]] : f32
+//       CHECKPARALLEL:         %[[vc:.*]] = load %[[arg2]][%[[arg3]], %[[arg4]], %[[arg5]]] : memref<?x?x?xf32>
+//       CHECKPARALLEL:         %[[res:.*]] = addf %[[vc]], %[[inc]] : f32
+//       CHECKPARALLEL:         store %[[res]], %[[arg2]][%[[arg3]], %[[arg4]], %[[arg5]]] : memref<?x?x?xf32>