[TOPI] Reduce code redundancy in conv2d weights transformation

python/tvm/topi/arm_cpu/arm_utils.py

@@ -73,3 +73,41 @@ def get_tiling_B_interleaved_t(interleave_A):
         tile_cols_B = 16
 
     return tile_rows_B, tile_cols_B
+
+
+def get_conv2d_weights_padding(N, K, tile_rows, tile_cols):
+    """Compute the necessary padding for matrix B', where B'
+    is the transposed and interleaved version of matrix B in C=A*B.
+
+    Parameters
+    ----------
+    N : int
+        Number of rows in B' = OC
+    K : int
+        Number of columns in B' = KW * KH * IC
+    tile_rows : int
+                tile rows of B'
+    tile_cols : int
+                tile columns of B'
+
+    Returns
+    ----------
+    pad_N : padding for N axis
+    pad_K : padding for K axis
+    """
+    pad_N = 0
+    pad_K = 0
+
+    if N % tile_rows != 0:
+        pad_N = tile_rows - (N % tile_rows)
+
+    # Tensorize will later make use of 4 tiles at once across the columns so make sure we pad such
+    # that the columns is multiple of 4
+    column_multiplier = 4
+    tile_cols_multiplied = tile_cols * column_multiplier
+    K_misalignment = K % tile_cols_multiplied
+
+    if K_misalignment != 0:
+        pad_K = tile_cols_multiplied - K_misalignment
+
+    return pad_N, pad_K

python/tvm/topi/arm_cpu/conv2d_alter_op.py

@@ -32,7 +32,7 @@
 from ..x86.conv2d import _get_default_config as _get_x86_default_config
 from ..x86.conv2d_int8 import _get_default_config_int8
 from .conv2d_int8 import is_int8_hw_support
-from .arm_utils import get_tiling_B_interleaved_t
+from .arm_utils import get_tiling_B_interleaved_t, get_conv2d_weights_padding
 from ..generic.conv2d import conv2d_alter_int8_common
 from .mprofile.dsp.micro_kernel.common import num_simd_lanes_per_word
 
@@ -72,21 +72,7 @@ def interleave_transpose_weights(inputs, data, kernel, interleave_A):
 
     # Get tiling information for the interleaved transposed version of B
     tile_rows_B, tile_cols_B = get_tiling_B_interleaved_t(interleave_A)
-
-    pad_K = 0
-    pad_N = 0
-
-    if N % tile_rows_B != 0:
-        pad_N = tile_rows_B - (N % tile_rows_B)
-
-    # Tensorize will later make use of 4 tiles at once across the columns so make sure we pad such
-    # that the columns is multiple of 4
-    column_multiplier = 4
-    tile_cols_multiplied = tile_cols_B * column_multiplier
-    K_misalignment = K % tile_cols_multiplied
-
-    if K_misalignment != 0:
-        pad_K = tile_cols_multiplied - K_misalignment
+    pad_N, pad_K = get_conv2d_weights_padding(N, K, tile_rows_B, tile_cols_B)
 
     N_padded = N + pad_N
     K_padded = K + pad_K

python/tvm/topi/nn/conv2d.py

@@ -617,20 +617,7 @@ def conv2d_gemm_weight_transform(kernel, tile_rows, tile_cols):
         (K, N), lambda x, y: kernel[(x // IC) // KW, (x // IC) % KW, x % IC, y], "weight_flatten"
     )
 
-    pad_K = 0
-    pad_N = 0
-
-    if N % tile_rows != 0:
-        pad_N = tile_rows - (N % tile_rows)
-
-    # Tensorize will later make use of 4 tiles at once across the columns so make sure we pad such
-    # that the columns is multiple of 4
-    column_multiplier = 4
-    tile_cols_multiplied = tile_cols * column_multiplier
-    K_misalignment = K % tile_cols_multiplied
-
-    if K_misalignment != 0:
-        pad_K = tile_cols_multiplied - K_misalignment
+    pad_N, pad_K = tvm.topi.arm_cpu.arm_utils.get_conv2d_weights_padding(N, K, tile_rows, tile_cols)
 
     N_padded = N + pad_N
     K_padded = K + pad_K