[Mlir-commits] [mlir] f58fb8c - [mlir][tosa] Fix lowering of tosa.conv2d (#73240)

[llvmlistbot at llvm.org]

Author: Spenser Bauman
Date: 2023-12-01T15:33:14Z
New Revision: f58fb8c209a5179f8f2e02e2a0816c9b1f1edb1b

URL: https://github.com/llvm/llvm-project/commit/f58fb8c209a5179f8f2e02e2a0816c9b1f1edb1b
DIFF: https://github.com/llvm/llvm-project/commit/f58fb8c209a5179f8f2e02e2a0816c9b1f1edb1b.diff

LOG: [mlir][tosa] Fix lowering of tosa.conv2d (#73240)

The lowering of tosa.conv2d produces an illegal tensor.empty operation
where the number of inputs do not match the number of dynamic dimensions
in the output type.

The fix is to base the generation of tensor.dim operations off the
result type of the conv2d operation, rather than the input type. The
problem and fix are very similar to this fix

https://github.com/llvm/llvm-project/pull/72724

but for convolution.

Added: 
    

Modified: 
    mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
    mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir

Removed: 
    


################################################################################
diff  --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
index b30651976eeb939..0accd9d1986a1ed 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
@@ -179,7 +179,7 @@ static SmallVector<Value> inferDynamicDimsForConv(
   for (uint32_t i = 0, s = inputSizeDims.size(); i < s; ++i) {
     int64_t inputDim = inputSizeDims[i];
     int64_t kernelDim = kernelSizeDims[i];
-    if (inputTy.isDynamicDim(inputDim)) {
+    if (resultTy.isDynamicDim(inputDim)) {
       auto padTop = padAttr[i * 2];
       auto padBottom = padAttr[i * 2 + 1];
       auto stride = strideAttr[i];
@@ -196,7 +196,7 @@ static SmallVector<Value> inferDynamicDimsForConv(
 
   // Get the batch/channels dimensions.
   for (int i = 0; i < inputRank; i++) {
-    if (inputTy.isDynamicDim(i) && !dynDims[i])
+    if (resultTy.isDynamicDim(i) && !dynDims[i])
       dynDims[i] = rewriter.create<tensor::DimOp>(loc, input, i);
   }
 

diff  --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
index bbdd1bad799865d..230001f7633b570 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
@@ -495,6 +495,29 @@ func.func @conv2d_dyn_w_h(%input: tensor<1x?x?x27xf32>, %weights: tensor<28x3x3x
 
 // -----
 
+// CHECK: [[$MAP1:.+]] = affine_map<(d0, d1, d2, d3) -> (d3)>
+// CHECK: [[$MAP2:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+
+func.func @conv2d_dyn_output(%input: tensor<2x6x5x4xf32>, %weights: tensor<4x3x3x4xf32>, %bias: tensor<4xf32>) {
+  // %[[C0:.+]] = arith.constant 0 : index
+  // %[[DIM0:.+]] = tensor.dim %input, %[[C0]] : tensor<2x6x5x4xf32>
+  // %[[INIT_CONV:.+]] = tensor.empty(%[[DIM0]]) : tensor<?x4x3x4xf32>
+  // %[[ZERO:.+]] = arith.constant 0.000000e+00 : f32
+  // %[[FILL:.+]] = linalg.fill
+  // %[[INIT_GENERIC:.+]] = tensor.empty([[DIM0]]) : tensor<?x4x3x4xf32>
+
+  // %[[CONV:.+]] = linalg.conv_2d_nhwc_fhwc {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<2x6x5x4xf32>, tensor<4x3x3x4xf32>) outs(%[[INIT_CONV]] : tensor<?x4x3x4xf32>) -> tensor<?x4x3x4xf32>
+  // linalg.generic {indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2, %[[CONV]] : tensor<4xf32>, tensor<?x4x3x4xf32>) outs(%[[INIT_GENERIC]] : tensor<?x4x3x4xf32>) {
+  //   %[[ADD:.+]] = arith.addf
+  //   linalg.yield %[[ADD]] : f32
+  // } -> tensor<?x4x3x4xf32>
+
+  %0 = tosa.conv2d %input, %weights, %bias {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x6x5x4xf32    >, tensor<4x3x3x4xf32>, tensor<4xf32>) -> tensor<?x4x3x4xf32>
+  return
+}
+
+// -----
+
 // CHECK-LABEL: @conv2d_padded_f32
 func.func @conv2d_padded_f32(%input: tensor<1x47x40x28xf32>, %weights: tensor<28x3x3x28xf32>, %bias: tensor<28xf32>) -> () {
   // CHECK: %[[C0:.+]] = arith.constant 0