Merge pull request #210 from InfiniTensor/issue-209-elementwise-kunlun

Issue/209 elementwise kunlun

test/infiniop/global_avg_pool.py

@@ -51,6 +51,7 @@ def test(
     torch_device,
     x_shape,
     tensor_dtype=torch.float16,
+    sync=None
 ):
     print(
         f"Testing GlobalAvgPool on {torch_device} with input tensor_shape: {x_shape} dtype: {tensor_dtype}"
@@ -70,8 +71,11 @@ def test(
 
     x_tensor = to_tensor(x, lib)
     y_tensor = to_tensor(y, lib)
-    descriptor = infiniopGlobalAvgPoolDescriptor_t()
     
+    if sync is not None:
+        sync()
+
+    descriptor = infiniopGlobalAvgPoolDescriptor_t()
     check_error(
         lib.infiniopCreateGlobalAvgPoolDescriptor(
             handle,

test/infiniop/libinfiniop/utils.py

@@ -423,6 +423,7 @@ def test_operator(lib, device, test_func, test_cases, tensor_dtypes):
                     infiniDeviceEnum_str_map[device],
                     *test_case,
                     tensor_dtype,
+                    get_sync_func(device)
                 )
     finally:
         destroy_handle(lib, handle)
@@ -471,3 +472,14 @@ def get_test_devices(args):
         devices_to_test = [InfiniDeviceEnum.CPU]
 
     return devices_to_test
+
+
+def get_sync_func(device):
+    import torch
+    
+    if device == "cpu":
+        sync = None
+    else:
+        sync = getattr(torch, infiniDeviceEnum_str_map[device]).synchronize
+    
+    return sync

test/infiniop/max_pool.py

@@ -83,6 +83,7 @@ def test(
     padding,
     strides,
     tensor_dtype=torch.float16,
+    sync=None
 ):
     print(
         f"Testing MaxPool on {torch_device} with x_shape:{x_shape} kernel_shape:{k_shape} padding:{padding} strides:{strides} dtype:{tensor_dtype}"
@@ -104,8 +105,11 @@ def test(
 
     x_tensor = to_tensor(x, lib)
     y_tensor = to_tensor(y, lib)
-    descriptor = infiniopMaxPoolDescriptor_t()
     
+    if sync is not None:
+        sync()
+
+    descriptor = infiniopMaxPoolDescriptor_t()
     check_error(
         lib.infiniopCreateMaxPoolDescriptor(
             handle,

test/infiniop/mlp.py

@@ -65,6 +65,7 @@ def test(
     y_stride=None,
     w12_stride=None,
     w3_stride=None,
+    sync=None
 ):
     print(
         f"Testing MLP on {torch_device} with num_tokens:{num_tokens} hidden_size:{hidden_size} intermediate_size:{intermediate_size}"
@@ -97,6 +98,10 @@ def test(
     x_tensor = to_tensor(x, lib)
     w12_tensor = to_tensor(w12, lib)
     w3_tensor = to_tensor(w3, lib)
+    
+    if sync is not None:
+        sync()
+
     descriptor = infiniopMLPDescriptor_t()
     check_error(
         lib.infiniopCreateMLPDescriptor(

test/infiniop/random_sample.py

@@ -103,6 +103,7 @@ def test(
     topk,
     temperature,
     dtype=torch.float16,
+    sync=None
 ):
     print(
         f"Testing RandomSample on {torch_device} with voc:{voc} random_val:{random_val} topp:{topp} topk:{topk} temperature:{temperature} dtype:{dtype}"
@@ -122,6 +123,9 @@ def test(
 
     indices_tensor.descriptor.contents.dt = InfiniDtype.U64  # treat int64 as uint64
 
+    if sync is not None:
+        sync()
+
     descriptor = infiniopRandomSampleDescriptor_t()
     check_error(
         lib.infiniopCreateRandomSampleDescriptor(

test/infiniop/rearrange.py

@@ -131,6 +131,7 @@ def test(
     x_stride,
     y_stride,
     dtype=torch.float16,
+    sync=None
 ):
     print(
         f"Testing Rerrange on {torch_device} with shape:{shape} x_stride:{x_stride} y_stride:{y_stride} dtype:{dtype}"
@@ -146,6 +147,9 @@ def test(
 
     x_tensor, y_tensor = [to_tensor(tensor, lib) for tensor in [x, y]]
     
+    if sync is not None:
+        sync()
+
     descriptor = infiniopRearrangeDescriptor_t()
     check_error(
         lib.infiniopCreateRearrangeDescriptor(

test/infiniop/relu.py

@@ -55,6 +55,7 @@ def test(
     tensor_shape,
     tensor_dtype=torch.float16,
     inplace=Inplace.OUT_OF_PLACE,
+    sync=None
 ):
     print(
         f"Testing Relu on {torch_device} with tensor_shape:{tensor_shape} dtype:{tensor_dtype} inplace: {inplace.name}"
@@ -78,8 +79,11 @@ def test(
 
     x_tensor = to_tensor(x, lib)
     y_tensor = to_tensor(y, lib) if inplace == Inplace.OUT_OF_PLACE else x_tensor
-    descriptor = infiniopReluDescriptor_t()
 
+    if sync is not None:
+        sync()    
+
+    descriptor = infiniopReluDescriptor_t()
     check_error(
         lib.infiniopCreateReluDescriptor(
             handle,

test/infiniop/rms_norm.py

@@ -72,6 +72,7 @@ def test(
     x_stride,
     w_dtype=torch.float16,
     dtype=torch.float16,
+    sync=None
 ):
     print(
         f"Testing RMS_Norm on {torch_device} with y_shape:{y_shape} x_shape:{x_shape} w_shape:{w_shape}"
@@ -89,9 +90,11 @@ def test(
         rearrange_if_needed(tensor, stride)
         for tensor, stride in zip([x, y], [x_stride, y_stride])
     ]
-
     x_tensor, y_tensor, w_tensor = [to_tensor(tensor, lib) for tensor in [x, y, w]]
     
+    if sync is not None:
+        sync()
+    
     descriptor = infiniopRMSNormDescriptor_t()
 
     check_error(

test/infiniop/rope.py

@@ -117,6 +117,7 @@ def test(
     y_strides=None,
     inplace=Inplace.OUT_OF_PLACE,
     dtype=torch.float32,
+    sync=None
 ):
     if inplace == Inplace.INPLACE_X:
         y_strides = x_strides
@@ -147,8 +148,8 @@ def test(
     else:
         y_tensor = to_tensor(y, lib)
 
-    if torch_device == "npu":
-        synchronize_device(torch_device)
+    if sync is not None:
+        sync()
 
     check_error(
         lib.infiniopCreateRoPEDescriptor(