[BACKEND] Fix a missed transpose optimization during refactor (#5236)

Author: ThomasRaoux

lib/Dialect/TritonGPU/Transforms/OptimizeDotOperands.cpp

@@ -148,16 +148,16 @@ class SwizzleShmemConvert : public OpRewritePattern<ConvertLayoutOp> {
   LogicalResult matchAndRewrite(ConvertLayoutOp cvtOp,
                                 PatternRewriter &rewriter) const override {
     // Match outerCvt(trans(innerCvt(x))).
-    auto trans = cvtOp.getSrc().getDefiningOp<MemDescTransOp>();
+    auto trans = cvtOp.getSrc().getDefiningOp<TransOp>();
     if (!trans || trans.getOrder() != ArrayRef<int32_t>{1, 0})
       return failure();
 
-    auto srcTy = dyn_cast<RankedTensorType>(trans.getSrc().getType());
+    RankedTensorType srcTy = trans.getSrc().getType();
 
     if (auto srcCvt = trans.getSrc().getDefiningOp<ConvertLayoutOp>()) {
       srcTy = srcCvt.getSrc().getType();
     }
-    auto sharedLoadTy = cast<RankedTensorType>(cvtOp.getType());
+    RankedTensorType sharedLoadTy = cvtOp.getType();
     auto cvtEncoding =
         dyn_cast<DotOperandEncodingAttr>(sharedLoadTy.getEncoding());
     if (!cvtEncoding)

test/TritonGPU/dot-operands.mlir

@@ -276,3 +276,22 @@ module attributes {"triton_gpu.target" = "cuda:90", "triton_gpu.num-ctas" = 1 :
     tt.return %r : tensor<128x64xf32, #mma>
   }
 }
+
+// -----
+
+#blocked = #triton_gpu.blocked<{sizePerThread = [16, 1], threadsPerWarp = [32, 1], warpsPerCTA = [4, 1], order = [1, 0]}>
+#blocked1 = #triton_gpu.blocked<{sizePerThread = [1, 16], threadsPerWarp = [1, 32], warpsPerCTA = [1, 4], order = [0, 1]}>
+#mma = #triton_gpu.nvidia_mma<{versionMajor = 2, versionMinor = 0, warpsPerCTA = [4, 1], instrShape = [16, 8]}>
+module attributes {"triton_gpu.target" = "cuda:90", "triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 4 : i32, "triton_gpu.threads-per-warp" = 32 : i32} {
+// CHECK-LABEL: mmav2_reorder_transpose
+// CHECK: triton_gpu.local_alloc
+// CHECK: triton_gpu.memdesc_trans
+// CHECK: triton_gpu.local_load
+// CHECK: tt.dot
+  tt.func @mmav2_reorder_transpose(%t: tensor<32x128xf16, #blocked1>, %dotb: tensor<32x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma, kWidth = 2}>>, %dotc: tensor<128x64xf32, #mma>) -> tensor<128x64xf32, #mma>{
+    %a = tt.trans %t {order = array<i32: 1, 0>} : tensor<32x128xf16, #blocked1> -> tensor<128x32xf16, #blocked>
+    %cv = triton_gpu.convert_layout %a : tensor<128x32xf16, #blocked> -> tensor<128x32xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma, kWidth = 2}>>
+    %r = tt.dot %cv, %dotb, %dotc, inputPrecision = tf32 : tensor<128x32xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma, kWidth = 2}>> * tensor<32x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma, kWidth = 2}>> -> tensor<128x64xf32, #mma>
+    tt.return %r : tensor<128x64xf32, #mma>
+  }
+}