tensor: Generate fp16-packed matrix in script

tests/kernel/tensor/generate_matrix.py

@@ -1,7 +1,10 @@
 import numpy as np
-A_array = np.random.rand(16, 8)
-B_array = np.random.rand(8, 16)
-C_array = np.random.rand(16, 16)
+
+# A_array = np.random.rand(8, 16)
+A_array = np.arange(8 * 8).reshape([8, 8])
+B_array = np.arange(8 * 8).reshape([8, 8])
+# C_array = np.random.rand(16, 16)
+C_array = np.zeros([8, 8])
 # A_array = np.zeros((16, 8))
 # B_array = np.zeros((8, 16))
 # A_array[0,:] = 1.0
@@ -11,22 +14,52 @@ C_array = np.random.rand(16, 16)
 #     for j in range(16):
 #         C_array[i,j] = i * 16 + j
 
-with open('a_matrix.h', 'w') as f:
-    for i in range(A_array.shape[0]):
-        for j in range(A_array.shape[1]):
-            f.write(f'{A_array[i,j]}f, ')
-        f.write('\n')
+# Reorder array in a way that groups two adjacent elements along the column to
+# be now adjacent along the row.  This way, when the resulting fp16 array is
+# read in column-major order with 32-bit granularity, the fp16 elements will be
+# read in the same order as regular fp32 elements in column-major.
+#
+# For example:
+# [[1 2]
+#  [3 4]
+#  [5 6]
+#  [7 8]]
+# becomes
+# [[1 3 2 4]
+#  [5 7 6 8]]
+def pack_fp16_by_column(array):
+    rows = array.shape[0]
+    cols = array.shape[1]
 
-with open('b_matrix.h', 'w') as f:
-    for i in range(B_array.shape[0]):
-        for j in range(B_array.shape[1]):
-            f.write(f'{B_array[i,j]}f, ')
-        f.write('\n')
+    T = array.transpose([1, 0])
+    T_packed = T.reshape([cols, -1, 2])
+    result = T_packed.transpose([1, 0, 2]).reshape([rows // 2, cols * 2])
+    return result
 
-with open('c_matrix.h', 'w') as f:
-    for i in range(C_array.shape[0]):
-        for j in range(C_array.shape[1]):
-            f.write(f'{C_array[i,j]}f, ')
-        f.write('\n')
 
-np.savez("abc", A_array=A_array, B_array=B_array, C_array=C_array)
+if __name__ == "__main__":
+    with open('a_matrix.h', 'w') as f:
+        for i in range(A_array.shape[0]):
+            for j in range(A_array.shape[1]):
+                f.write(f'{A_array[i,j]:f}f, ')
+            f.write('\n')
+    with open('b_matrix.h', 'w') as f:
+        for i in range(B_array.shape[0]):
+            for j in range(B_array.shape[1]):
+                f.write(f'{B_array[i,j]:f}f, ')
+            f.write('\n')
+    with open('c_matrix.h', 'w') as f:
+        for i in range(C_array.shape[0]):
+            for j in range(C_array.shape[1]):
+                f.write(f'{C_array[i,j]:f}f, ')
+            f.write('\n')
+
+    np.savez("abc", A_array=A_array, B_array=B_array, C_array=C_array)
+
+    A_array.astype('float32').tofile("input.a.bin")
+    B_array.astype('float32').tofile("input.b.bin")
+
+    # A_array.astype('float16').tofile("input.a.bin")
+    # B_array = pack_fp16_by_column(B_array)
+    # B_array.astype('float16').tofile("input.b.bin")
+    print(B_array)