refine the doc for TensorDesc

Author: chencha3

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

@@ -34,27 +34,24 @@ def XeGPU_TensorDesc: XeGPUTypeDef<"TensorDesc", "tensor_desc",
         [ShapedTypeInterface], "::mlir::TensorType"> {
   let summary = "TensorDesc describing regions of interested data.";
   let description = [{
-    TensorDesc is a type designed to describe regions of the interested data as well as some
-    features that are unique to Intel hardware. Different with the builtin tensor type in MLIR,
-    it essentially only contains the meta data, and doesn't hold the data by itself. It is designed
-    to mainly support 2D block load/store and DPAS (matrix multiplication instruction) on Intel GPU.
-    It encodes the following information:
+    TensorDesc is a type designed to describe regions of interest in data, as well as some features
+    unique to Intel hardware. Unlike the built-in tensor type in MLIR, it essentially contains only
+    metadata and does not hold the data itself. It is primarily designed to support 2D block load/store
+    and DPAS (matrix multiplication instruction) on Intel GPUs. It encodes the following information:
 
     * shape:  the sizes/shape of the intereted data block, e.g., 8x16 means 8 rows
               and each row contains 16 contiguous data element. The rows could be
-              either contiguous or not, depends on whether the encoding attribute
-              is set or not.
-    * element_type: the data type of the data element, e.g., f16, f32.
+              either contiguous or not, depends on the encoding attribute. If the
+              encoding is a BlockTensorDescAttr, rows are contiguous. If the encoding
+              is a ScatterTensorDescAttr, rows are not necessary to be contiguous. If
+              encoding is not set, it is considered as a default BlockTensorDescAttr.
 
-    Similar to the builtin tensor, it also provides an optinal attribute to encoding
-    the following information via the TensorDescAttr object:
-    * memory_space (xegpu::MemorySpace): [optional] where the data is located,
-                global memory or shared memory. It is default to Global.
-    * array_length (int): [optional] The number of contiguous blocks with size as `shape`,
-               that will be loaded by block load at a time. It is default to 1.
-    * boundary_check (bool): [optional] indicates whether the operation detects the boundary
-                and pads with zero for out-of-boundary access. It is default to do boundary check.
+    * element_type: the data type of the data element, e.g., f16, f32.
 
+    Similar to the built-in tensor, it also provides optional attributes for encoding
+    additional information via either BlockTensorDescAttr or ScatterTensorDescAttr, or
+    supporting Workgroup, Subgroup, and workitem (or SIMT) level programmings via the
+    Layout attribute. Please check their definition for details.
 
     Syntax:
 
@@ -63,7 +60,9 @@ def XeGPU_TensorDesc: XeGPUTypeDef<"TensorDesc", "tensor_desc",
     element-type ::= float-type | integer-type | index-type
     dim-list := (static-dim-list `x`)?
     static-dim-list ::= decimal-literal `x` decimal-literal
-    attr-list = (, memory_space = value)? (, arr_len = value)? (, boundary_check = value)? (, scattered = value)? (, layout `<` wi_layout = value, wi_data = value `>`)?
+    attr-list = (, encoding-attr)? (, layout-attr)?
+    enconding-attr = (, memory_space = value)? (, arr_len = value)? (, boundary_check = value)? (, scattered = value)?
+    layout-attr = (, layout `<` (scope = value,)? (sg_layout = value, sg_data = value, order = value)? wi_layout = value, wi_data = value `>`)?
     ```
 
     Examples:
@@ -78,8 +77,14 @@ def XeGPU_TensorDesc: XeGPUTypeDef<"TensorDesc", "tensor_desc",
     // A TensorDesc with 8x16 f32 elements for a memory region in shared memory space.
     xegpu.tensor_desc<8x16xf32, #xegpu.tdesc_attr<memory_space = slm>>
 
-    // A TensorDesc with a layout
-    xegpu.tensor_desc<8x16xf32, #xegpu.layout<wi_layout = [1, 16], wi_data = [1, 1]>>
+    // A TensorDesc with a layout for workgroup level programming
+    xegpu.tensor_desc<32x64xf32, #xegpu.layout<sg_layout = [2, 4], sg_data = [16, 16], wi_layout = [1, 16], wi_data = [1, 1]>>
+
+    // A TensorDesc with a layout for subgroup level programming
+    xegpu.tensor_desc<8x16xf32, #xegpu.layout<scope = sg, wi_layout = [1, 16], wi_data = [1, 1]>>
+
+    // A TensorDesc with a layout for workitem level programming
+    xegpu.tensor_desc<8x16xf32, #xegpu.layout<scope = wi, wi_layout = [1, 16], wi_data = [1, 1]>>
     ```
   }];