[INTERPRETER][NFC] Rename tensor_shape -> block_shape in interpre…

…ter (#5195)

`tensor_shape` is a confusing name and doesn't match block pointer's
semantic.
`block_shape` is much clearer.

python/triton/runtime/interpreter.py

@@ -21,7 +21,7 @@ def __init__(self, data, dtype):
         '''
             data: numpy array
             dtype: triton type, either pointer_type or scalar_type.
-            we don't store block_type here because the shape information is already availale in the data field
+            we don't store block_type here because the shape information is already available in the data field
             attr: a dictionary of attributes
         '''
         self.data = data
@@ -46,24 +46,23 @@ def set_attr(self, key, value):
 
 class BlockPointerHandle:
 
-    def __init__(self, base, shape, strides, offsets, tensor_shape, order):
+    def __init__(self, base, shape, strides, offsets, block_shape, order):
         self.base = base
         self.shape = shape
         self.strides = strides
         self.offsets = offsets
-        self.tensor_shape = tensor_shape
+        self.block_shape = block_shape
         self.order = order
 
     def materialize_pointers(self, boundary_check):
         dtype_tt = self.base.get_element_ty()
         n_bytes = dtype_tt.primitive_bitwidth // 8
-        tensor_shape = self.tensor_shape
-        ptrs = np.broadcast_to(self.base.data, self.tensor_shape)
-        masks = np.ones(self.tensor_shape, dtype=bool)
-        for dim in range(len(tensor_shape)):
-            bcast_dims = [1] * len(tensor_shape)
-            bcast_dims[dim] = tensor_shape[dim]
-            off = (self.offsets[dim].data + np.arange(tensor_shape[dim])).reshape(bcast_dims)
+        ptrs = np.broadcast_to(self.base.data, self.block_shape)
+        masks = np.ones(self.block_shape, dtype=bool)
+        for dim in range(len(self.block_shape)):
+            bcast_dims = [1] * len(self.block_shape)
+            bcast_dims[dim] = self.block_shape[dim]
+            off = (self.offsets[dim].data + np.arange(self.block_shape[dim])).reshape(bcast_dims)
             ptrs = ptrs + (n_bytes * off * self.strides[dim].data).astype(np.uint64)
             if dim in boundary_check:
                 masks = np.logical_and(masks, off < self.shape[dim].data)
@@ -655,17 +654,17 @@ def create_barrier(self):
         # Triton's barrier applies to each program in a grid, so it's a no-op in the interpreter
         pass
 
-    def create_make_block_ptr(self, base, shape, strides, offsets, tensor_shape, order):
+    def create_make_block_ptr(self, base, shape, strides, offsets, block_shape, order):
         # Create new offsets to avoid modifying the original
         new_offsets = [offset.clone() for offset in offsets]
-        return BlockPointerHandle(base, shape, strides, new_offsets, tensor_shape, order)
+        return BlockPointerHandle(base, shape, strides, new_offsets, block_shape, order)
 
     def create_advance(self, ptr, offsets):
         if len(ptr.offsets) != len(offsets):
             raise ValueError("len(ptr.offsets) != len(offsets)")
         # Create new offsets to avoid modifying the original
         new_offsets = [offset.clone() for offset in ptr.offsets]
-        ret = BlockPointerHandle(ptr.base, ptr.shape, ptr.strides, new_offsets, ptr.tensor_shape, ptr.order)
+        ret = BlockPointerHandle(ptr.base, ptr.shape, ptr.strides, new_offsets, ptr.block_shape, ptr.order)
         for i in range(len(offsets)):
             ret.offsets[i].data += offsets[i].data
         return ret