[TOSA] Add transposed conv support

Lower aten.conv_transpose2d into tosa.transpose_conv2d. Refresh FX importer
TOSA xfails to drop the transpose-conv cases that now pass, and document
the weight layout mapping.

Change-Id: I709579e40a1ccaf9b9188392c7c78fcb653109ce

Author: Lallapallooza

lib/Conversion/TorchToTosa/TorchToTosa.cpp

@@ -2306,9 +2306,6 @@ LogicalResult ConvertAtenOp<AtenConvolutionOp>::matchAndRewrite(
   if (!matchPattern(op.getTransposed(), m_TorchConstantBool(&transposed)))
     return rewriter.notifyMatchFailure(
         op, "Unimplemented: non-constant value for transposed not supported");
-  if (transposed)
-    return rewriter.notifyMatchFailure(
-        op, "Unimplemented: transposed convolution not supported");
 
   auto input = adaptor.getInput();
   auto weight = adaptor.getWeight();
@@ -2340,12 +2337,17 @@ LogicalResult ConvertAtenOp<AtenConvolutionOp>::matchAndRewrite(
   auto bias = adaptor.getBias();
 
   if (isa<Torch::NoneType>(bias.getType())) {
-    auto bias_result = tosa::getConvBiasForNoneType(op, rewriter, inputElemTy,
-                                                    outputElemTy, weightShape);
-    if (failed(bias_result))
+    SmallVector<int64_t, 4> biasWeightShape =
+        transposed ? SmallVector<int64_t, 4>{weightShape[1], weightShape[0],
+                                             weightShape[2], weightShape[3]}
+                   : weightShape;
+
+    auto biasResult = tosa::getConvBiasForNoneType(
+        op, rewriter, inputElemTy, outputElemTy, biasWeightShape);
+    if (failed(biasResult))
       return rewriter.notifyMatchFailure(
           op, "Failed to create bias tensor for none type.");
-    bias = bias_result.value();
+    bias = biasResult.value();
   } else {
     if (!isa<RankedTensorType>(bias.getType()))
       return rewriter.notifyMatchFailure(
@@ -2372,8 +2374,8 @@ LogicalResult ConvertAtenOp<AtenConvolutionOp>::matchAndRewrite(
                     m_TorchListOfConstantInts(padding_2d)))
     return rewriter.notifyMatchFailure(op,
                                        "non-const padding list unsupported");
-  // TOSA uses 4D padding {top, bottom, left, right} while Torch defines 2D
-  // padding {height, width}. The Torch OFM computation uses 2*pad in each
+  // TOSA uses 4D padding {top, bottom, left, right} while PyTorch defines 2D
+  // padding {height, width}. The PyTorch OFM computation uses 2*pad in each
   // spatial direction, implying the same top=bottom=height and left=right=width
   // values for TOSA.
   SmallVector<int64_t> padding(
@@ -2390,11 +2392,19 @@ LogicalResult ConvertAtenOp<AtenConvolutionOp>::matchAndRewrite(
     return rewriter.notifyMatchFailure(
         op, "failed to get accumulator type for convolution ops");
 
+  // Weight layout reference:
+  //   Conv       : PyTorch OIHW     -> TOSA OHWI
+  //   Depthwise  : PyTorch OIHW*    -> TOSA HWIM
+  //                (PyTorch depthwise uses out_ch=in_ch*depth_multiplier)
+  //   Grouped    : PyTorch O(I/G)HW -> N/A
+  //   Transposed : PyTorch IOHW     -> TOSA OHWI
   // TOSA works in NHWC and takes OHWI (conv) / HWIM (depthwise conv) weights.
   // Perform the necessary transformations.
   SmallVector<int32_t> nchwToNhwcDims({0, 2, 3, 1});
-  SmallVector<int64_t> transposedInputShape(
-      {inputShape[0], inputShape[2], inputShape[3], inputShape[1]});
+  SmallVector<int32_t> nhwcToNchwDims({0, 3, 1, 2});
+  SmallVector<int64_t, 4> transposedInputShape;
+  for (int32_t dim : nchwToNhwcDims)
+    transposedInputShape.push_back(inputShape[dim]);
   auto transposedInputType = RankedTensorType::get(
       makeShapeLLVMCompatible(transposedInputShape), inputElemTy);
   auto transposedInput =
@@ -2404,6 +2414,104 @@ LogicalResult ConvertAtenOp<AtenConvolutionOp>::matchAndRewrite(
           rewriter.getDenseI32ArrayAttr(nchwToNhwcDims))
           .getResult();
 
+  if (transposed) {
+    if (groups != 1)
+      return rewriter.notifyMatchFailure(
+          op, "Unimplemented: grouped transposed convolution not supported by "
+              "TOSA");
+    if (dilation[0] != 1 || dilation[1] != 1)
+      return rewriter.notifyMatchFailure(
+          op, "Unimplemented: dilated transposed convolution not supported by "
+              "TOSA");
+
+    SmallVector<int32_t> iohwToOhwi({1, 2, 3, 0});
+    SmallVector<int64_t, 4> ohwiWeightShape;
+    for (int32_t dim : iohwToOhwi)
+      ohwiWeightShape.push_back(weightShape[dim]);
+    auto ohwiWeightType = RankedTensorType::get(
+        makeShapeLLVMCompatible(ohwiWeightShape), weightElemTy);
+    Value transformedWeight =
+        rewriter
+            .create<tosa::TransposeOp>(
+                op->getLoc(), getTypeConverter()->convertType(ohwiWeightType),
+                weight, rewriter.getDenseI32ArrayAttr(iohwToOhwi))
+            .getResult();
+
+    // TOSA 'out_pad' is a 4D array {top,bottom,left,right}.
+    // Map from PyTorch's (padding, output_padding):
+    //   out_pad_total(H/W) = output_padding(H/W) - 2*padding(H/W)
+    // Negative values are allowed and will be handled by the TOSA
+    // decomposition.
+    SmallVector<int64_t, 2> outPadding2D;
+    if (!matchPattern(adaptor.getOutputPadding(),
+                      m_TorchListOfConstantInts(outPadding2D)))
+      return rewriter.notifyMatchFailure(
+          op, "non-const output_padding list unsupported for transposed conv");
+
+    int64_t outPadH = outPadding2D[0] - 2 * padding_2d[0];
+    int64_t outPadW = outPadding2D[1] - 2 * padding_2d[1];
+    int64_t outPadTop = outPadH / 2;
+    int64_t outPadBottom = outPadH - outPadTop;
+    int64_t outPadLeft = outPadW / 2;
+    int64_t outPadRight = outPadW - outPadLeft;
+    SmallVector<int64_t, 4> outPad(
+        {outPadTop, outPadBottom, outPadLeft, outPadRight});
+
+    // Result type is NHWC (we'll transpose back).
+    auto outNCHW = makeShapeTorchCompatible(outputTy.getShape());
+    SmallVector<int64_t, 4> outNHWC;
+    for (int32_t dim : nchwToNhwcDims)
+      outNHWC.push_back(outNCHW[dim]);
+    auto transConvOpTy =
+        RankedTensorType::get(makeShapeLLVMCompatible(outNHWC), biasElemTy);
+
+    // Zero-points.
+    auto zps = tosa::createZPsAsConst(rewriter, input, weight);
+    Value inputZp = zps.first ? zps.first
+                              : tosa::createZeroPointTensor(
+                                    rewriter, op->getLoc(), inputElemTy, 0)
+                                    .value();
+    Value weightZp = zps.second ? zps.second
+                                : tosa::createZeroPointTensor(
+                                      rewriter, op->getLoc(), weightElemTy, 0)
+                                      .value();
+
+    Value convTOut =
+        rewriter
+            .create<tosa::TransposeConv2DOp>(
+                op->getLoc(), getTypeConverter()->convertType(transConvOpTy),
+                transposedInput, transformedWeight, bias, inputZp, weightZp,
+                rewriter.getDenseI64ArrayAttr(outPad),
+                rewriter.getDenseI64ArrayAttr(stride), accType)
+            .getResult();
+
+    SmallVector<int64_t, 4> transposedOutputShape;
+    for (int32_t dim : nhwcToNchwDims)
+      transposedOutputShape.push_back(outNHWC[dim]);
+    auto transposedOutputType = RankedTensorType::get(
+        makeShapeLLVMCompatible(transposedOutputShape), biasElemTy);
+    Value transposedOutput =
+        rewriter
+            .create<tosa::TransposeOp>(
+                op->getLoc(),
+                getTypeConverter()->convertType(transposedOutputType), convTOut,
+                rewriter.getDenseI32ArrayAttr(nhwcToNchwDims))
+            .getResult();
+
+    // Quantized rescale.
+    Value rescaledResult = transposedOutput;
+    if (isa<quant::QuantizedType>(inputElemTy)) {
+      rescaledResult = tosa::buildRescaleOpConvOutput(
+          rewriter, op, transposedOutput, inputTy, weightTy, outputTy);
+    }
+
+    // Final cast to requested output type.
+    rewriter.replaceOp(
+        op, {tosa::tosaCastTensorToType(rewriter, rescaledResult, outputTy)
+                 .value()});
+    return success();
+  }
+
   SmallVector<int64_t> transformedWeightShape;
   RankedTensorType transformedWeightType;
   Value transformedWeight;
@@ -2485,7 +2593,7 @@ LogicalResult ConvertAtenOp<AtenConvolutionOp>::matchAndRewrite(
     if (remainderHDim != 0) {
       if (remainderHDim > padding[1]) {
         SmallVector<int64_t> startHSlice(inputTy.getRank(), 0);
-        SmallVector<int64_t> sizeHSlice(transposedInputShape);
+        SmallVector<int64_t, 4> sizeHSlice(transposedInputShape);
         // TOSA uses NHWC, so we will slice dim 1 for Height value
         sizeHSlice[1] = inputHDim - (remainderHDim - padding[1]);
         transposedInput = tosa::CreateOpAndInfer<tosa::SliceOp>(
@@ -2579,7 +2687,6 @@ LogicalResult ConvertAtenOp<AtenConvolutionOp>::matchAndRewrite(
     llvm_unreachable("Unhandled convolution type");
   }
 
-  SmallVector<int32_t> nhwcToNchwDims({0, 3, 1, 2});
   SmallVector<int64_t> transposedOutputShape(
       {outputShape[0], outputShape[3], outputShape[1], outputShape[2]});
   auto transposedOutputType = RankedTensorType::get(

projects/pt1/e2e_testing/xfail_sets.py

@@ -3581,7 +3581,6 @@
     "AvgPool3dCountIncludePadFalseWithoutPadding_basic",
     "Conv_Transpose1dModule_basic",
     "Conv_Transpose1dStaticModule_basic",
-    "Conv_Transpose2dStaticModule_basic",
     "Conv_Transpose3dModule_basic",
     "Conv_Transpose3dStaticModule_basic",
     "IndexPutWithNoneAndBroadcastModule_basic",
@@ -3706,16 +3705,11 @@
     "Conv3dWithValidPaddingModule_basic",
     "ConvTbcModule_basic",
     "ConvTranspose2DQInt8_basic",
-    "Conv_Transpose2dModule_basic",
     "ConvolutionBackwardModule2DPadded_basic",
-    "ConvolutionBackwardModule2DStatic_basic",
     "ConvolutionBackwardModule2DStrided_basic",
     "ConvolutionBackwardModule2D_basic",
     "ConvolutionModule2DGroups_basic",
     "ConvolutionModule2DTransposeNonUnitOutputPadding_basic",
-    "ConvolutionModule2DTransposeStridedStatic_basic",
-    "ConvolutionModule2DTransposeStrided_basic",
-    "ConvolutionModule2DTranspose_basic",
     "ConvolutionModule2DGroupedTranspose_basic",
     "ConvolutionModule3DGroups_basic",
     "ConvolutionModule3DGroupsStrided_basic",

projects/pt1/python/torch_mlir_e2e_test/tosa_backends/linalg_on_tensors.py

@@ -29,6 +29,8 @@
         # that depend on TOSA as well as TOSA-to-Standard.
         "tosa-to-arith",
         "tosa-to-scf",
+        # Required for transposed convolution support (decomposes to conv ops).
+        "tosa-optional-decompositions",
         # Named ops must be legalized prior to general tosa-to-linalg
         "tosa-to-linalg-named",
         # TOSA-to-LinAlg may generate tosa.const() ops, so we want to lower them

test/Conversion/TorchToTosa/conv2d_transpose.mlir

@@ -1,8 +1,23 @@
-// RUN: torch-mlir-opt <%s -convert-torch-to-tosa -split-input-file -verify-diagnostics
+// RUN: torch-mlir-opt <%s -convert-torch-to-tosa -split-input-file | FileCheck %s
 
-// The following test ensures that a tranposed convolution op is not
-// lowered in the torch-to-tosa conversion pass.
+// The lowering now legalizes transpose convolutions into the TOSA dialect.
+// Verify that we emit tosa.transpose_conv2d with the expected reshapes/
+// permutations.
 
+// CHECK-LABEL: func.func @forward
+// CHECK-SAME:  %[[INPUT:.*]]: !torch.vtensor<[1,64,1,100],f32>) -> !torch.vtensor<[1,64,2,200],f32> {
+// CHECK:  %[[IN_TENSOR:.*]] = torch_c.to_builtin_tensor %[[INPUT]] : !torch.vtensor<[1,64,1,100],f32> -> tensor<1x64x1x100xf32>
+// CHECK:  %[[WEIGHT:.*]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<64x64x3x3xf32>}> : () -> tensor<64x64x3x3xf32>
+// CHECK:  %[[BIAS:.*]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<64xf32>}> : () -> tensor<64xf32>
+// CHECK:  %[[TRANS_IN:.*]] = tosa.transpose %[[IN_TENSOR]] {perms = array<i32: 0, 2, 3, 1>} : (tensor<1x64x1x100xf32>) -> tensor<1x1x100x64xf32>
+// CHECK:  %[[W_OHWI:.*]] = tosa.transpose %[[WEIGHT]] {perms = array<i32: 1, 2, 3, 0>} : (tensor<64x64x3x3xf32>) -> tensor<64x3x3x64xf32>
+// CHECK:  %[[ZP0:.*]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
+// CHECK:  %[[ZP1:.*]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
+// CHECK:  %[[TCONV:.*]] = tosa.transpose_conv2d %[[TRANS_IN]], %[[W_OHWI]], %[[BIAS]], %[[ZP0]], %[[ZP1]] {acc_type = f32, out_pad = array<i64: 0, -1, 0, -1>, stride = array<i64: 2, 2>} : (tensor<1x1x100x64xf32>, tensor<64x3x3x64xf32>, tensor<64xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x2x200x64xf32>
+// CHECK:  %[[TRANS_OUT:.*]] = tosa.transpose %[[TCONV]] {perms = array<i32: 0, 3, 1, 2>} : (tensor<1x2x200x64xf32>) -> tensor<1x64x2x200xf32>
+// CHECK:  %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[TRANS_OUT]] : tensor<1x64x2x200xf32> -> !torch.vtensor<[1,64,2,200],f32>
+// CHECK:  return %[[RESULT]] : !torch.vtensor<[1,64,2,200],f32>
+// CHECK: }
 func.func @forward(%input: !torch.vtensor<[1,64,1,100],f32>) -> !torch.vtensor<[1,64,2,200],f32> {
   %true = torch.constant.bool true
   %int1 = torch.constant.int 1
@@ -11,7 +26,6 @@ func.func @forward(%input: !torch.vtensor<[1,64,1,100],f32>) -> !torch.vtensor<[
   %bias = torch.vtensor.literal(dense<0.0> : tensor<64xf32>) : !torch.vtensor<[64],f32>
   %stride = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
   %int1x1 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
-  // expected-error@+1 {{failed to legalize operation 'torch.aten.convolution' that was explicitly marked illegal}}
   %output = torch.aten.convolution %input, %weight, %bias, %stride, %int1x1, %int1x1, %true, %int1x1, %int1 : !torch.vtensor<[1,64,1,100],f32>, !torch.vtensor<[64,64,3,3],f32>, !torch.vtensor<[64],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,64,2,200],f32>
   return %output : !torch.vtensor<[1,64,2,200],f32>
 }