[NFC] polish test_2p5d/checks_2p5d/check_operation_2p5d.py code style (#1718)

tests/test_layers/test_2p5d/checks_2p5d/check_operation_2p5d.py

@@ -39,16 +39,9 @@ def check_AB():
     B.requires_grad = True
 
     out_shape = (BATCH_SIZE // TESSERACT_DIM, SEQ_LENGTH, 4 * HIDDEN_SIZE // TESSERACT_DIM)
-    out = Matmul_AB_2p5D.apply(
-        A, B,
-        TESSERACT_DIM, out_shape,
-        i, j, k,
-        ParallelMode.PARALLEL_2P5D_ROW,
-        ParallelMode.PARALLEL_2P5D_COL,
-        data_parallel_rank,
-        pipeline_parallel_rank,
-        pipeline_parallel_size,
-        tensor_parallel_size)
+    out = Matmul_AB_2p5D.apply(A, B, TESSERACT_DIM, out_shape, i, j, k, ParallelMode.PARALLEL_2P5D_ROW,
+                               ParallelMode.PARALLEL_2P5D_COL, data_parallel_rank, pipeline_parallel_rank,
+                               pipeline_parallel_size, tensor_parallel_size)
 
     C_shape = (BATCH_SIZE, SEQ_LENGTH, 4 * HIDDEN_SIZE)
     A_master = A_master.clone()
@@ -116,16 +109,10 @@ def check_ABT():
     B = B.clone()
     B.requires_grad = True
 
-    out = Matmul_ABT_2p5D.apply(
-        C, B,
-        TESSERACT_DIM, (BATCH_SIZE // TESSERACT_DIM, SEQ_LENGTH, HIDDEN_SIZE // TESSERACT_DIM),
-        i, j, k,
-        ParallelMode.PARALLEL_2P5D_ROW,
-        ParallelMode.PARALLEL_2P5D_COL,
-        data_parallel_rank,
-        pipeline_parallel_rank,
-        pipeline_parallel_size,
-        tensor_parallel_size)
+    out = Matmul_ABT_2p5D.apply(C, B, TESSERACT_DIM,
+                                (BATCH_SIZE // TESSERACT_DIM, SEQ_LENGTH, HIDDEN_SIZE // TESSERACT_DIM), i, j, k,
+                                ParallelMode.PARALLEL_2P5D_ROW, ParallelMode.PARALLEL_2P5D_COL, data_parallel_rank,
+                                pipeline_parallel_rank, pipeline_parallel_size, tensor_parallel_size)
 
     A_shape = (BATCH_SIZE, SEQ_LENGTH, HIDDEN_SIZE)
     C_master = C_master.clone()
@@ -191,16 +178,10 @@ def check_ATB():
     C = C.clone()
     C.requires_grad = True
 
-    out = Matmul_ATB_2p5D.apply(
-        A, C,
-        TESSERACT_DIM, (HIDDEN_SIZE // TESSERACT_DIM, 4 * HIDDEN_SIZE // TESSERACT_DIM),
-        i, j, k,
-        ParallelMode.PARALLEL_2P5D_ROW,
-        ParallelMode.PARALLEL_2P5D_COL,
-        data_parallel_rank,
-        pipeline_parallel_rank,
-        pipeline_parallel_size,
-        tensor_parallel_size)
+    out = Matmul_ATB_2p5D.apply(A, C, TESSERACT_DIM, (HIDDEN_SIZE // TESSERACT_DIM, 4 * HIDDEN_SIZE // TESSERACT_DIM),
+                                i, j, k, ParallelMode.PARALLEL_2P5D_ROW, ParallelMode.PARALLEL_2P5D_COL,
+                                data_parallel_rank, pipeline_parallel_rank, pipeline_parallel_size,
+                                tensor_parallel_size)
 
     B_shape = (HIDDEN_SIZE, 4 * HIDDEN_SIZE)
     A_master = A_master.clone()
@@ -208,8 +189,7 @@ def check_ATB():
     C_master = C_master.clone()
     C_master.requires_grad = True
     B_master = torch.matmul(
-        A_master.view(-1, A_master.shape[-1]).transpose(0, 1),
-        C_master.view(-1, C_master.shape[-1]))
+        A_master.view(-1, A_master.shape[-1]).transpose(0, 1), C_master.view(-1, C_master.shape[-1]))
     B = torch.chunk(B_master, TESSERACT_DIM, dim=0)[i]
     B = torch.chunk(B, TESSERACT_DIM, dim=-1)[j]
     check_equal(out, B)