increase M, N, K, and PP

tests/dataflow/test_packed_systolic.py

@@ -7,14 +7,16 @@
 import allo.backend.hls as hls
 import numpy as np
 
-L, D = 2, 2
-M, N, K = L, 1 * D, D
-PP = 2
+M, N, K = 8, 8, 4
+PP = 4
 P0, P1 = M // PP + 2, N + 2
 
 if PP == 2:
     np_type = np.int16
     allo_type = int16
+elif PP == 4:
+    np_type = np.int32
+    allo_type = int32
 else:
     raise ValueError(f"Unsupported packing factor: {PP}")
 
@@ -26,9 +28,9 @@ def top():
 
     @df.kernel(mapping=[P0, P1])
     def gemm(
-        X_packed: allo_type[L // PP, D],
-        W_packed: allo_type[D, 1 * D // PP],
-        Z_packed: allo_type[L // PP, 1 * D],
+        X_packed: allo_type[M, K // PP],
+        W_packed: allo_type[K // PP, N],
+        Z_packed: allo_type[M // PP, N],
     ):
         i, j = df.get_pid()
         # Peripheral kernels
@@ -37,11 +39,11 @@ def gemm(
         with allo.meta_elif(j == 0):
             # i > 0
             for k in range(K):
-                fifo_A[i, j + 1].put(X_packed[i - 1, k])
+                fifo_A[i, j + 1].put(X_packed[(i - 1) * PP, k])
         with allo.meta_elif(i == 0):
             # j > 0
             for k in range(K):
-                fifo_B[i + 1, j].put(W_packed[j // PP, 0])
+                fifo_B[i + 1, j].put(W_packed[k // PP, j - 1])
 
         # drain
         with allo.meta_elif(i == M // PP + 1 and j > 0):
@@ -68,14 +70,14 @@ def gemm(
 
 
 def test_packed_systolic():
-    X = np.random.randint(-4, 4, size=(L, D)).astype(np.int8)
-    W_A_cst = np.random.randint(-4, 4, size=(D, 1 * D)).astype(np.int8)
+    X = np.random.randint(-4, 4, size=(M, K)).astype(np.int8)
+    W_A_cst = np.random.randint(-4, 4, size=(K, N)).astype(np.int8)
 
-    packed_X = np.ascontiguousarray(np.ascontiguousarray(X).view(np_type).transpose())
+    packed_X = np.ascontiguousarray(np.ascontiguousarray(X).view(np_type))
     W_A_packed = np.ascontiguousarray(
         np.ascontiguousarray(W_A_cst.transpose()).view(np_type).transpose()
     )
-    Z_packed = np.zeros((L // PP, 1 * D), dtype=np_type)
+    Z_packed = np.zeros((M // PP, N), dtype=np_type)
     mod = df.build(top)
     if hls.is_available("vitis_hls"):
         mod(packed_X, W_A_packed, Z_packed)