[Paddle-ASP] Revise 4d tensor sparsity mask pattern for conv2d sparsity (#36054)

Sparse tensor core for convolution requires the input channel dimension is 2:4 structed sparse.
So we have to mask the input channel dimension for using sparse tensor core

python/paddle/fluid/contrib/sparsity/utils.py

@@ -518,9 +518,13 @@ def create_mask(tensor, func_name=MaskAlgo.MASK_1D, n=2, m=4):
         t = t.reshape(shape[0], shape[1])
     elif len(shape) == 3:
         t = t.reshape(shape[0] * shape[1], shape[2])
-    # 4d-tensor conv (out, in, h, w) -> (out, in*h*w) in GemmConvKernel Op
+    # 4d-tensor conv (h, w, in, out) -> (h*w*out, in) in GemmConvKernel Op
     elif len(shape) == 4:
-        t = t.reshape(shape[0], shape[1] * shape[2] * shape[3])
+        t = t.transpose([0, 1, 3, 2]).reshape(shape[0] * shape[1] * shape[3],
+                                              shape[2])
+        mask = func(t, n=n, m=m)
+        return mask.reshape([shape[0], shape[1], shape[3],
+                             shape[2]]).transpose([0, 1, 3, 2]).astype(dtype)
     else:
         raise ValueError("The dimension of input tensor is not supported in create_mask, " \
                          "Only dimension < 4 is supported but got {}".format(len(shape)))
@@ -572,9 +576,10 @@ def check_sparsity(tensor, func_name=CheckMethod.CHECK_1D, n=2, m=4):
         t = t.reshape(shape[0], shape[1])
     elif len(shape) == 3:
         t = t.reshape(shape[0] * shape[1], shape[2])
-    # 4d-tensor conv (out, in, h, w) -> (out, in*h*w) in GemmConvKernel Op
+    # 4d-tensor conv (h, w, in, out) -> (h*w*out, in) in GemmConvKernel Op
     elif len(shape) == 4:
-        t = t.reshape(shape[0], shape[1] * shape[2] * shape[3])
+        t = t.transpose([0, 1, 3, 2]).reshape(
+            [shape[0] * shape[1] * shape[3], shape[2]])
     else:
         raise ValueError("The dimension of input tensor is not supported in create_mask, " \
                          "Only dimension < 4 is supported but got {}".format(len(shape)))