Merge branch 'users/chencha3/xegpu/xegpu_simt_2d_to_1d' into xegpu_simt_dist

Author: charithaintc

mlir/include/mlir/Dialect/XeGPU/IR/XeGPUOps.td

@@ -833,30 +833,27 @@ def XeGPU_DpasOp : XeGPU_Op<"dpas", [Pure, AllElementTypesMatch<["lhs", "rhs"]>]
     data type, the matrices are `A: vector<8x16xf16>`, `B: vector<16x16xf16>`,
     and `C/D: vector<8x16xf32>`. Besides the matrix size requirements, DPAS
     also requires A and B to be loaded with the required data layout. Specially,
-
     VNNI layout is required for B operand. It is achieved via adding `packed`
     attribute to the `load_nd` operator.  Due to the VNNI transformation, B operands
     can be represented as a 3D vector, with the last dimension representing the VNNI
     factor, which is computed as `32/bit_width_of_elem_type`. Thus, `B: vector<16x16xf16>`
     can be represented as `B: vector<8x16x2xf16>`.
 
-    In SIMT mode, DpasOp expects layout attributes `a`, `b`, and `c` (only if acc is used)
-    which describe the data fragment owned by each work-item w.r.t. the tensor descriptor
-    these data are loaded from.
+    In SIMT code, each work-item from a subgroup holds a data fragment for A, B, C and the result,
+    which are represented as 1D vectors. Please refer to [OpenCL Intel extentions]
+    (https://registry.khronos.org/OpenCL/extensions/intel/cl_intel_subgroup_matrix_multiply_accumulate.html)
+    for more details about the fragment distribution.
 
     Note: on PVC, the hardware can perform load with VNNI transformation when data
           element type is 16-bit or lower precision, taking 2 or 4 elements from
           the first dimension and inserted into the newly added innermost dimension.
   }];
 
   let arguments = (ins
-    XeGPU_DpasOpType : $lhs,
-    XeGPU_DpasOpType : $rhs,
-    Optional<XeGPU_Vector2DType>: $acc,
-    OptionalAttr<XeGPU_LayoutAttr>:$a_layout,
-    OptionalAttr<XeGPU_LayoutAttr>:$b_layout,
-    OptionalAttr<XeGPU_LayoutAttr>:$c_layout);
-  let results = (outs XeGPU_Vector2DType: $result);
+    XeGPU_DpasOprType : $lhs,
+    XeGPU_DpasOprType : $rhs,
+    Optional<XeGPU_DpasResType>: $acc);
+  let results = (outs XeGPU_DpasResType: $result);
 
   let extraClassDeclaration = [{
     VectorType getLhsType() {

mlir/include/mlir/Dialect/XeGPU/IR/XeGPUTypes.td

@@ -17,7 +17,8 @@ def XeGPU_IntType: AnyTypeOf<[I1, I8, I16, I32, I64, SI1, SI8, SI16, SI32, SI64,
 def XeGPU_FloatType: AnyTypeOf<[F16, F32, F64, BF16, TF32]>;
 def XeGPU_ScalarType: AnyTypeOf<[XeGPU_IntType, XeGPU_FloatType]>;
 def XeGPU_BaseAddrType: AnyTypeOf<[Non0RankedMemRefOf<[XeGPU_ScalarType]>, UI64, UI32, I64, I32]>;
-def XeGPU_DpasOpType: VectorOfRankAndType<[2, 3], [XeGPU_ScalarType]>;
+def XeGPU_DpasOprType: VectorOfRankAndType<[1, 2, 3], [XeGPU_ScalarType]>;
+def XeGPU_DpasResType: VectorOfRankAndType<[1, 2], [XeGPU_ScalarType]>;
 def XeGPU_OffsetType: VectorOfRankAndType<[1], [Index]>;
 def XeGPU_MaskType: AnyTypeOf<[VectorOfRankAndType<[1], [I1]>, I1]>;
 def XeGPU_ValueType: AnyTypeOf<[VectorOfRankAndType<[1,2,3,4], [XeGPU_ScalarType]>, XeGPU_ScalarType]>;

mlir/lib/Dialect/XeGPU/IR/XeGPUDialect.cpp

@@ -12,6 +12,7 @@
 #include "mlir/IR/DialectImplementation.h"
 #include "llvm/ADT/SmallVector.h"
 #include "llvm/ADT/TypeSwitch.h"
+#include <numeric>
 
 namespace mlir {
 namespace xegpu {
@@ -338,32 +339,30 @@ LogicalResult TensorDescType::verify(
 //        [n_distribution_units, lane_data_size]
 FailureOr<VectorType> TensorDescType::getDistributedVectorType() {
   auto layout = llvm::dyn_cast_if_present<LayoutAttr>(getLayout());
-  // If no layout is provided, tensor desc is not used in SIMT mode.
-  if (!layout)
+  // It only works for subgroup level layout, which only has lane_layout
+  // and lane_data, and is to distribute a SIMD code into SIMT code.
+  if (!layout || !layout.isSgLayout())
     return failure();
 
   SmallVector<int64_t> laneData(layout.getLaneData().asArrayRef());
   SmallVector<int64_t> laneLayout(layout.getLaneLayout().asArrayRef());
   auto tdescShape = getShape();
 
-  auto laneDataSize = 1, sgSize = 1;
-  for (auto [laneDim, laneDataDim] : llvm::zip_equal(laneLayout, laneData)) {
-    laneDataSize *= laneDataDim;
-    sgSize *= laneDim;
-  }
+  // compute sgSize by multiply elements of laneLayout
+  // e.g. for 2D layout, sgSize = laneLayout[0] * laneLayout[1]
+  // e.g. for 1D layout, sgSize = laneLayout[0]
+  auto sgSize = std::accumulate(laneLayout.begin(), laneLayout.end(), 1,
+                                std::multiplies<int64_t>());
 
   // Case 1: regular loads/stores
   auto scatterAttr = getEncodingAsScatterTensorDescAttr();
   if (scatterAttr) {
     auto chunkSize = scatterAttr.getChunkSize().getInt();
     // Verify if the first dimension of the tensor descriptor shape is
     // distributable.
-    assert(tdescShape[0] % (laneLayout[0]) == 0 &&
+    assert(tdescShape[0] == laneLayout[0] &&
            "tensor descriptor shape is not distributable");
-    if (chunkSize > 1)
-      return VectorType::get({chunkSize / laneDataSize, laneDataSize},
-                             getElementType());
-    return VectorType::get({laneDataSize}, getElementType());
+    return VectorType::get({chunkSize}, getElementType());
   }
 
   // Case 2: block loads/stores
@@ -378,12 +377,7 @@ FailureOr<VectorType> TensorDescType::getDistributedVectorType() {
   // tensorSize must be adjusted for array_length.
   tensorSize *= getArrayLength();
 
-  if (layout.getRank() == 1) {
-    return VectorType::get({tensorSize / sgSize}, getElementType());
-  }
-
-  return VectorType::get({tensorSize / (sgSize * laneDataSize), laneDataSize},
-                         getElementType());
+  return VectorType::get({tensorSize / sgSize}, getElementType());
 }
 
 } // namespace xegpu