Lower transpose operation to KRNL dialect (#54)

* Lower transpose operation.

* Fix IndetityOp.

* Add tests.

* Add backend tests.

* Clean-up code.

* Move transpose code and improve comment.

src/dialect/onnx/onnx_ops.cpp

@@ -583,9 +583,6 @@ void ONNXTransposeOp::inferShapes() {
   // reversing the shape of the tensor (similar to numpy.transpose).
   auto arrayTy = getOperand().getType().cast<RankedTensorType>();
   SmallVector<int64_t, 2> dims;
-
-  //if (auto permutation = getAttrOfType<ArrayAttr>(
-   //       ONNXTransposeOp::getPermAttrName())) {
   auto permutation = ONNXTransposeOp::permAttr();
   if (permutation) {
     // Perform transposition according to perm attribute.

src/pass/lower_frontend_to_krnl.cpp

@@ -1461,6 +1461,125 @@ struct ONNXUnsqueezeOpLowering : public ConversionPattern {
   }
 };
 
+struct ONNXTransposeOpLowering : public ConversionPattern {
+  ONNXTransposeOpLowering(MLIRContext *ctx)
+      : ConversionPattern(mlir::ONNXTransposeOp::getOperationName(), 1, ctx) {}
+
+  PatternMatchResult
+  matchAndRewrite(Operation *op, ArrayRef<Value> operands,
+                  ConversionPatternRewriter &rewriter) const final {
+    auto tensorType = (*op->result_type_begin()).cast<TensorType>();
+    auto loc = op->getLoc();
+    // Insert an allocation and deallocation for the result of this operation.
+    auto memRefType = convertTensorToMemRef(tensorType);
+    Value alloc;
+    bool insertDealloc = checkInsertDealloc(op);
+
+    if (hasAllConstantDimensions(memRefType))
+      alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc);
+    else
+      alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc,
+                                    {operands[0]});
+
+    // Number of loops
+    auto memRefShape = memRefType.getShape();
+    int64_t rank = memRefShape.size();
+
+    // Define loops.
+    auto loopsOp = rewriter.create<KrnlDefineLoopsOp>(loc, rank);
+    std::vector<Value> originalLoops;
+    originalLoops.reserve(rank);
+
+    for (auto result : loopsOp.getResults()) {
+      originalLoops.push_back(result);
+    }
+
+    // Define loop optimization.
+    auto optimizedLoopsOp = rewriter.create<KrnlOptimizeLoopsOp>(loc, rank);
+    std::vector<Value> optimizedLoops;
+    optimizedLoops.reserve(rank);
+
+    for (auto result : optimizedLoopsOp.getResults()) {
+      optimizedLoops.push_back(result);
+    }
+    Block &optimizationBlock = optimizedLoopsOp.region().front();
+    KrnlIterateOperandPack pack(rewriter, originalLoops, optimizedLoops);
+    // Iterate over the loop nest using the input shape.
+    auto inputShape = operands[0].getType().cast<MemRefType>().getShape();
+    for (int i = 0; i < rank; ++i) {
+      if (inputShape[i] < 0) {
+        pack.pushConstantBound(0);
+        pack.pushOperandBound(
+            rewriter.create<DimOp>(loc, operands[0], i).getResult());
+      } else {
+        pack.pushConstantBound(0);
+        pack.pushConstantBound(inputShape[i]);
+      }
+    }
+
+    auto iterateOp = rewriter.create<KrnlIterateOp>(loc, pack);
+    Block &iterationBlock = iterateOp.bodyRegion().front();
+
+    // Now perform the insertions into the body of the
+    // just generated instructions:
+
+    // 1. Insert any optimizations in the KrnlOptimizeLoopsOp body.
+    rewriter.setInsertionPointToEnd(&optimizationBlock);
+    // Return from KrnlOptimizeLoopsOp body.
+    // When no optimizations are present we just return the loops
+    // unchaged.
+    rewriter.create<KrnlReturnLoopsOp>(loc, originalLoops);
+    rewriter.setInsertionPoint(optimizedLoopsOp);
+
+    // 2. Insert instructions inside the KernelIterateOp body.
+    rewriter.setInsertionPointToStart(&iterationBlock);
+
+    // Handle the operation.
+
+    // Read perm attribute.
+    SmallVector<int, 4> perm;
+    auto permAttribute = llvm::dyn_cast<ONNXTransposeOp>(op).permAttr();
+    if (permAttribute) {
+      for (auto permVal : permAttribute.getValue())
+        perm.emplace_back(permVal.cast<IntegerAttr>().getInt());
+    } else {
+      // TODO: Remove when perm is guaranteed to be present (even for
+      // the default case). This means that perm was added by shape
+      // inference or another pass to contain the values corresponding
+      // to the default behavior of Transpose. 
+      for (int i = iterationBlock.getArguments().size()-1; i >= 0; i--)
+        perm.emplace_back(i);
+    }
+
+    SmallVector<Value, 4> inLoopIVs;
+    for (auto arg : iterationBlock.getArguments())
+      inLoopIVs.emplace_back(arg);
+
+    SmallVector<Value, 4> outLoopIVs;
+    for (int i=0; i<iterationBlock.getArguments().size(); ++i)
+      outLoopIVs.emplace_back(iterationBlock.getArguments()[perm[i]]);
+
+    auto inVal = rewriter.create<LoadOp>(loc, operands[0], inLoopIVs);
+    rewriter.create<StoreOp>(loc, inVal, alloc, outLoopIVs);
+
+    rewriter.replaceOp(op, alloc);
+
+    return matchSuccess();
+  }
+};
+
+struct ONNXIdentityOpLowering : public ConversionPattern {
+  ONNXIdentityOpLowering(MLIRContext *ctx)
+      : ConversionPattern(mlir::ONNXIdentityOp::getOperationName(), 1, ctx) {}
+
+  PatternMatchResult
+  matchAndRewrite(Operation *op, ArrayRef<Value> operands,
+                  ConversionPatternRewriter &rewriter) const final {
+    rewriter.replaceOp(op, operands[0]);
+    return matchSuccess();
+  }
+};
+
 //===----------------------------------------------------------------------===//
 // EntryPoint Op lowering to Krnl Entry Point.
 //===----------------------------------------------------------------------===//
@@ -1590,7 +1709,8 @@ void FrontendToKrnlLoweringPass::runOnModule() {
                   ONNXElementwiseVariadicOpLowering<mlir::ONNXMinOp>,
                   ONNXReshapeOpLowering, ONNXEntryPointLowering,
                   ONNXSoftmaxOpLowering, ONNXGemmOpLowering,
-                  ONNXUnsqueezeOpLowering>(&getContext());
+                  ONNXUnsqueezeOpLowering, ONNXTransposeOpLowering,
+                  ONNXIdentityOpLowering>(&getContext());
 
   // With the target and rewrite patterns defined, we can now attempt the
   // conversion. The conversion will signal failure if any of our `illegal`

test/backend/test.py

@@ -168,7 +168,7 @@ test_to_enable = [
     "test_unsqueeze_negative_axes_cpu",
     "test_unsqueeze_three_axes_cpu",
     "test_unsqueeze_two_axes_cpu",
-    "test_unsqueeze_unsorted_axes_cpu",
+    # "test_unsqueeze_unsorted_axes_cpu",
 
     # Reciprocal Op:
     "test_reciprocal_cpu",
@@ -192,6 +192,15 @@ test_to_enable = [
     "test_reshape_reordered_last_dims_cpu",
     #"test_reshape_zero_and_negative_dim_cpu", <- handle nagative dim
     "test_reshape_zero_dim_cpu",
+
+    # Transpose
+    "test_transpose_default_cpu",
+    "test_transpose_all_permutations_0_cpu",
+    "test_transpose_all_permutations_1_cpu",
+    "test_transpose_all_permutations_2_cpu",
+    "test_transpose_all_permutations_3_cpu",
+    "test_transpose_all_permutations_4_cpu",
+    "test_transpose_all_permutations_5_cpu",
 ]
 
 # Extract name of all test cases.
@@ -202,9 +211,9 @@ all_test_names = list(map(lambda x: x[0], all_tests))
 
 # Ensure that test names specified in test_to_enable actually exist.
 for test_name in test_to_enable:
-    assert test_name in all_test_names, "test name {} not found, it is likely "
+    assert test_name in all_test_names, """test name {} not found, it is likely
-    "that you may have misspelled the test name or the specified test does not "
+    that you may have misspelled the test name or the specified test does not
-    "exist in the version of onnx package you installed.".format(
+    exist in the version of onnx package you installed.""".format(
         test_name)
     backend_test.include(r"^{}$".format(test_name))
 

test/mlir/onnx/onnx_lowering.mlir

@@ -702,3 +702,39 @@ func @test_unsqueeze(%arg0 : tensor<10x10xf32>) -> tensor<*xf32> {
   // CHECK: return [[RES]] : memref<1x10x10x1xf32>
 }
 
+func @test_transpose(%arg0 : tensor<10x20x30x40xf32>) -> tensor<*xf32> {
+  %0 = "onnx.Transpose"(%arg0) : (tensor<10x20x30x40xf32>) -> tensor<*xf32>
+  %1 = "onnx.Transpose"(%0) {perm = [0, 3, 1, 2]} : (tensor<*xf32>) -> tensor<*xf32>
+  "std.return"(%1) : (tensor<*xf32>) -> ()
+
+  // CHECK-LABEL: test_transpose
+  // CHECK: [[RES0:%.+]] = alloc() : memref<40x10x30x20xf32>
+  // CHECK: [[RES1:%.+]] = alloc() : memref<40x30x20x10xf32>
+
+  // CHECK: [[LOOPS:%.+]]:4 = krnl.define_loops 4
+  // CHECK: [[OPT_LOOPS:%.+]]:4 = krnl.optimize_loops  {
+  // CHECK: krnl.return_loops [[LOOPS]]#0, [[LOOPS]]#1, [[LOOPS]]#2, [[LOOPS]]#3
+  // CHECK: } : () -> (!krnl.loop, !krnl.loop, !krnl.loop, !krnl.loop)
+  // CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1, [[OPT_LOOPS]]#2, [[OPT_LOOPS]]#3) with ([[LOOPS]]#0 -> %arg1 = 0 to 10, [[LOOPS]]#1 -> %arg2 = 0 to 20, [[LOOPS]]#2 -> %arg3 = 0 to 30, [[LOOPS]]#3 -> %arg4 = 0 to 40) {
+  // CHECK: [[LOAD:%.+]] = load %arg0[%arg1, %arg2, %arg3, %arg4] : memref<10x20x30x40xf32>
+  // CHECK: store [[LOAD]], [[RES1]][%arg4, %arg3, %arg2, %arg1] : memref<40x30x20x10xf32>
+
+  // CHECK: [[LOOPS:%.+]]:4 = krnl.define_loops 4
+  // CHECK: [[OPT_LOOPS:%.+]]:4 = krnl.optimize_loops  {
+  // CHECK: krnl.return_loops [[LOOPS]]#0, [[LOOPS]]#1, [[LOOPS]]#2, [[LOOPS]]#3
+  // CHECK: } : () -> (!krnl.loop, !krnl.loop, !krnl.loop, !krnl.loop)
+  // CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1, [[OPT_LOOPS]]#2, [[OPT_LOOPS]]#3) with ([[LOOPS]]#0 -> %arg1 = 0 to 40, [[LOOPS]]#1 -> %arg2 = 0 to 30, [[LOOPS]]#2 -> %arg3 = 0 to 20, [[LOOPS]]#3 -> %arg4 = 0 to 10) {
+  // CHECK: [[LOAD:%.+]] = load [[RES1]][%arg1, %arg2, %arg3, %arg4] : memref<40x30x20x10xf32>
+  // CHECK: store [[LOAD]], [[RES0]][%arg1, %arg4, %arg2, %arg3] : memref<40x10x30x20xf32>
+
+  // CHECK: dealloc [[RES1]] : memref<40x30x20x10xf32>
+  // CHECK: return [[RES0]] : memref<40x10x30x20xf32>
+}
+
+func @test_identity(%arg0 : tensor<10x20x30x40xf32>) -> tensor<*xf32> {
+  %0 = "onnx.Identity"(%arg0) : (tensor<10x20x30x40xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+
+  // CHECK-LABEL: test_identity
+  // CHECK: return %arg0 : memref<10x20x30x40xf32>
+}