[MLIR][XeGPU] Allow load/store/prefetch uses [memref+offset] instead of tdesc

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

@@ -628,35 +628,71 @@ def XeGPU_PrefetchOp : XeGPU_Op<"prefetch", []> {
     As compared to prefetch_nd, which works on non-scattered TensorDesc,
     it works on scattered TensorDesc instead.
 
-    Example:
+    Example 1:
     ```mlir
       xegpu.prefetch %tdesc {l1_hint = #xegpu.cache_hint<cached>,
                              l2_hint = #xegpu.cache_hint<cached>,
                              l3_hint = #xegpu.cache_hint<cached>}
         : !xegpu.tensor_desc<16xf16>
     ```
+
+    Example 2:
+    A variant accepts memref as base pointer and an offset instead of scattered TensorTdesc.
+    It combines "create scattered TensorTdesc" and "prefetch with scattered TensorTdesc".
+    The source operand could be a raw pointer (uint64_t).
+    Please refer to create_tdesc for the restriction of memref. 
+    ```mlir
+      %a = memref.alloc() : memref<1024xf32>
+      %0 = arith.constant dense<[0, 16, 32, 64]> : vector<4xindex>
+      xegpu.prefetch %a[%0] {l1_hint = #xegpu.cache_hint<cached>,
+                             l2_hint = #xegpu.cache_hint<cached>,
+                             l3_hint = #xegpu.cache_hint<cached>}
+        : memref<1024xf32>, vector<4xindex>
+    ```
 
   }];
 
-  let arguments = (ins XeGPU_TensorDesc: $TensorDesc,
+  let arguments = (ins XeGPU_GatherScatterSourceType: $source,
+                       Optional<XeGPU_OffsetType>: $offsets,
                        OptionalAttr<XeGPU_CacheHintAttr>: $l1_hint,
                        OptionalAttr<XeGPU_CacheHintAttr>: $l2_hint,
                        OptionalAttr<XeGPU_CacheHintAttr>: $l3_hint);
 
   let extraClassDeclaration = extraBaseClassDeclaration # [{
+    Type getSourceType() {
+      return getSource().getType();
+    }
+
+    TypedValue<xegpu::TensorDescType> getTensorDesc() {
+      if (auto tdescType = getTensorDescType()) {
+        return llvm::cast<TypedValue<xegpu::TensorDescType>>(getSource());
+      }
+      return TypedValue<xegpu::TensorDescType>();
+    }
+
     xegpu::TensorDescType getTensorDescType() {
-      return getTensorDesc().getType();
+      return dyn_cast<xegpu::TensorDescType>(getSourceType());
     }
   }];
 
-  let assemblyFormat = "$TensorDesc prop-dict attr-dict `:` qualified(type($TensorDesc))";
+  let assemblyFormat = [{
+    $source 
+    (`[` $offsets^ `]`)?
+    prop-dict
+    attr-dict `:` type(operands) 
+  }];
+
+  let builders = [
+    OpBuilder<(ins "Value": $source,
+                    "xegpu::CachePolicyAttr": $l1_hint, 
+                    "xegpu::CachePolicyAttr": $l2_hint, 
+                    "xegpu::CachePolicyAttr": $l3_hint)>
+  ];
 
   let hasVerifier = 1;
 }
 
-def XeGPU_LoadGatherOp : XeGPU_Op<"load", [
-  AllElementTypesMatch<["value", "TensorDesc"]>, MemoryEffects<[MemRead]>
-  ]> {
+def XeGPU_LoadGatherOp : XeGPU_Op<"load", [MemoryEffects<[MemRead]>]> {
   let summary = "load a set of scattered data points from memory.";
 
   let description = [{ It (aka. load) load data per each work-item. The output
@@ -687,6 +723,7 @@ def XeGPU_LoadGatherOp : XeGPU_Op<"load", [
           : !xegpu.tensor_desc<16x8xf32, #xegpu.scatter_tdesc_attr<memory_space=global, chunk_size=8>>,
             vector<16xi1> -> vector<16x8xf32>
   ```
+
   Example 3 (SIMT mode):
   ```mlir
     %2 = xegpu.load %1, %0 <{l1_hint = #xegpu.cache_hint<cached>,
@@ -695,19 +732,48 @@ def XeGPU_LoadGatherOp : XeGPU_Op<"load", [
           : !xegpu.tensor_desc<16x8xf32, #xegpu.scatter_tdesc_attr<memory_space=global, chunk_size=8>>
             vector<16xi1> -> vector<8xf32>
   ```
+
+  Example 4:
+  A variant accepts memref as base pointer and an offset instead of scattered TensorTdesc.
+  It combines "create scattered TensorTdesc" and "load with scattered TensorTdesc".
+  The source operand could be a raw pointer (uint64_t). Please refer to create_tdesc
+  for the restriction of memref. 
+  ```mlir
+    %a = memref.alloc() : memref<1024xf32>
+    %offsets = vector.step : vector<16xindex>
+    %mask = vector.constant_mask [16]: vector<16xi1>
+    %val = xegpu.load %a[%offsets], %mask {l1_hint = #xegpu.cache_hint<cached>,
+                           l2_hint = #xegpu.cache_hint<cached>,
+                           l3_hint = #xegpu.cache_hint<cached>}
+      : memref<1024xf32>, vector<16xi1>, vector<16xindex> -> vector<16xf32>
+  ```
 
   }];
 
-  let arguments = (ins XeGPU_TensorDesc: $TensorDesc,
+  let arguments = (ins XeGPU_GatherScatterSourceType: $source,
+                       Optional<XeGPU_OffsetType>: $offsets,
                        XeGPU_MaskType: $mask,
+                       OptionalAttr<I64Attr>: $chunk_size,
                        OptionalAttr<XeGPU_CacheHintAttr>: $l1_hint,
                        OptionalAttr<XeGPU_CacheHintAttr>: $l2_hint,
                        OptionalAttr<XeGPU_CacheHintAttr>: $l3_hint);
   let results = (outs XeGPU_ValueType: $value);
 
   let extraClassDeclaration = extraBaseClassDeclaration # [{
+
+    Type getSourceType() {
+      return getSource().getType();
+    }
+
+    TypedValue<xegpu::TensorDescType> getTensorDesc() {
+      if (auto tdescType = getTensorDescType()) {
+        return llvm::cast<TypedValue<xegpu::TensorDescType>>(getSource());
+      }
+      return TypedValue<xegpu::TensorDescType>();
+    }
+
     xegpu::TensorDescType getTensorDescType() {
-      return getTensorDesc().getType();
+       return dyn_cast<xegpu::TensorDescType>(getSourceType());
     }
 
     mlir::Type getElementType() {
@@ -725,15 +791,24 @@ def XeGPU_LoadGatherOp : XeGPU_Op<"load", [
 
   }];
 
-  let assemblyFormat = [{$TensorDesc `,` $mask prop-dict attr-dict
-      `:` qualified(type($TensorDesc)) `,` type($mask) `->` type($value)}];
+  let assemblyFormat = [{
+    $source
+    (`[` $offsets^ `]`)? `,`
+    $mask prop-dict 
+    attr-dict `:` type(operands) `->` type($value)
+  }];
+
+  let builders = [
+    OpBuilder<(ins "Type": $value, "Value": $source, "Value": $mask,
+                    "xegpu::CachePolicyAttr": $l1_hint, 
+                    "xegpu::CachePolicyAttr": $l2_hint, 
+                    "xegpu::CachePolicyAttr": $l3_hint)>
+   ];
 
   let hasVerifier = 1;
 }
 
-def XeGPU_StoreScatterOp : XeGPU_Op<"store", [
-  AllElementTypesMatch<["value", "TensorDesc"]>, MemoryEffects<[MemWrite]>
-  ]> {
+def XeGPU_StoreScatterOp : XeGPU_Op<"store", [MemoryEffects<[MemWrite]>]> {
   let summary = "store data to scattered memory locations.";
   let description = [{ It (aka. store) stores data to scattered memory locations. The value is
   typically a 1D vector. But when the chunk size of the TensorDesc is larger than 1, it will be
@@ -768,19 +843,49 @@ def XeGPU_StoreScatterOp : XeGPU_Op<"store", [
                              l3_hint = #xegpu.cache_hint<write_through>}>
           : vector<8xf32>, !xegpu.tensor_desc<16x8xf32, #xegpu.scattered_tdesc_attr<chunk_size=8>> vector<16xi1>
   ```
+
+  Example 4:
+  A variant accepts memref as base pointer and an offset instead of scattered TensorTdesc.
+  It combines "create scattered TensorTdesc" and "store with scattered TensorTdesc".
+  The dest operand could be a raw pointer (uint64_t).
+  Please refer to create_tdesc for the restriction of memref. 
+  ```mlir
+    %a = memref.alloc() : memref<1024xf32>
+    %val = arith.constant dense<0.0> : vector<16xf32>
+    %offsets = vector.step : vector<16xindex>
+    %mask = vector.constant_mask [16]: vector<16xi1>
+    xegpu.store %val, %a[%offsets], %mask {l1_hint = #xegpu.cache_hint<cached>,
+                           l2_hint = #xegpu.cache_hint<cached>,
+                           l3_hint = #xegpu.cache_hint<cached>}
+      : memref<1024xf32>, vector<16xi1>, vector<16xindex> -> vector<16xf32>
+  ```
+
   }];
 
   let arguments = (ins
     XeGPU_ValueType: $value,
-    XeGPU_TensorDesc: $TensorDesc,
+    XeGPU_GatherScatterSourceType: $dest,
+    Optional<XeGPU_OffsetType>: $offsets,
     XeGPU_MaskType: $mask,
+    OptionalAttr<I64Attr>: $chunk_size,
     OptionalAttr<XeGPU_CacheHintAttr>: $l1_hint,
     OptionalAttr<XeGPU_CacheHintAttr>: $l2_hint,
     OptionalAttr<XeGPU_CacheHintAttr>: $l3_hint);
 
   let extraClassDeclaration = extraBaseClassDeclaration # [{
+    Type getDestType() {
+      return getDest().getType();
+    }
+
+    TypedValue<xegpu::TensorDescType> getTensorDesc() {
+      if (auto tdescType = getTensorDescType()) {
+        return llvm::cast<TypedValue<xegpu::TensorDescType>>(getDest());
+      }
+      return TypedValue<xegpu::TensorDescType>();
+    }
+
     xegpu::TensorDescType getTensorDescType() {
-      return getTensorDesc().getType();
+      return dyn_cast<xegpu::TensorDescType>(getDestType());
     }
 
     VectorType getValueType() {
@@ -792,8 +897,21 @@ def XeGPU_StoreScatterOp : XeGPU_Op<"store", [
     }
   }];
 
-  let assemblyFormat = [{$value `,` $TensorDesc `,` $mask prop-dict attr-dict
-            `:` type($value) `,` qualified(type($TensorDesc)) `,` type($mask)}];
+  let assemblyFormat = [{
+    $value `,`
+    $dest
+    (`[` $offsets^ `]`)? `,`
+    $mask 
+    prop-dict 
+    attr-dict `:`  type(operands)
+  }];
+
+  let builders = [
+    OpBuilder<(ins "Value": $value, "Value": $dest, "Value": $mask,
+                    "xegpu::CachePolicyAttr": $l1_hint, 
+                    "xegpu::CachePolicyAttr": $l2_hint, 
+                    "xegpu::CachePolicyAttr": $l3_hint)>
+   ];
 
   let hasVerifier = 1;
 }

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

@@ -189,6 +189,7 @@ def XeGPU_TensorDesc: XeGPUTypeDef<"TensorDesc", "tensor_desc",
   let genVerifyDecl = 1;
 }
 
+def XeGPU_GatherScatterSourceType : AnyTypeOf<[XeGPU_TensorDesc,Non0RankedMemRefOf<[XeGPU_ScalarType]>, UI64]>;
 
 def XeGPU_Nbarrier: XeGPUTypeDef<"Nbarrier", "nbarrier", [], "mlir::Type"> {
   let summary = "!xegpu.nbarrier a custom XeGPU type representing a barrier.";