[CustomDevice] adapt tensor fusion helper to npu device

python/paddle/distributed/fleet/utils/tensor_fusion_helper.py

@@ -20,6 +20,7 @@
 
 import paddle
 from paddle.framework import (
+    _current_expected_place_,
     base as imperative_base,
     core,
 )
@@ -33,6 +34,7 @@ class HOOK_ACTION:
 
 alignment = {
     "gpu": 256,
+    "npu": 256,
 }
 
 align = {
@@ -42,6 +44,28 @@ class HOOK_ACTION:
 }
 
 
+__current_device_type__ = None
+
+
+def get_current_device_type():
+    global __current_device_type__
+    if __current_device_type__ is None:
+        if paddle.is_compiled_with_cuda():
+            device_type = "gpu"
+        elif paddle.is_compiled_with_xpu():
+            device_type = "xpu"
+        elif paddle.is_compiled_with_custom_device():
+            current_device = _current_expected_place_()
+            device_type = current_device.get_device_type()
+        else:
+            device_type = "unknown"
+        assert (
+            device_type in alignment.keys()
+        ), f"tensor fusion helper now only support {alignment.keys()}, but got device {device_type} instead."
+        __current_device_type__ = device_type
+    return __current_device_type__
+
+
 def assign_group_by_size(parameters, group_size=128 * 1024 * 1024):
     is_sparse_gradient = [False] * len(parameters)
 
@@ -76,8 +100,12 @@ def flatten_dense_tensors(
     for param in parameters:
         assert param.trainable, "param must be trainable..."
         size = np.prod(param.shape) * align[dtype]
-        remaining = size % alignment["gpu"]
-        ali = 0 if remaining == 0 else alignment["gpu"] - remaining
+        remaining = size % alignment[get_current_device_type()]
+        ali = (
+            0
+            if remaining == 0
+            else alignment[get_current_device_type()] - remaining
+        )
         align_ = ali // align[dtype]
         _param2offset[param.name] = _buffer_size
         _buffer_size += np.prod(param.shape) + align_
@@ -88,7 +116,7 @@ def flatten_dense_tensors(
 
     if fuse_param:
         param_storage = ParamStorage(
-            size=_buffer_size, dtype=dtype, device="gpu"
+            size=_buffer_size, dtype=dtype, device=get_current_device_type()
         )
         param_storage.add_rank_params(parameters, _param2align)
 
@@ -97,7 +125,7 @@ def flatten_dense_tensors(
     grad_storage = GradStorage(
         size=_buffer_size,
         dtype=grad_dtype,
-        device="gpu",
+        device=get_current_device_type(),
         destination="0",
         parm2align=_param2align,
     )
@@ -261,7 +289,7 @@ def build_reduce_scatter_buffer(
 
     def get_padded_size(param):
         size = np.prod(param.shape)
-        align_size = alignment["gpu"] // align[dtype]
+        align_size = alignment[get_current_device_type()] // align[dtype]
         align_size = align_size * sharding_degree
         padded_size = ((size + align_size - 1) // align_size) * align_size
         return padded_size