update fluid api. (#49731)

python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_optimizer_stage2.py

@@ -29,7 +29,7 @@ from collections import OrderedDict
 import paddle
 import paddle.distributed as dist
 from paddle.distributed import ParallelMode, fleet
-from paddle.fluid import core
+from paddle.framework import core
 from paddle.nn import ClipGradByGlobalNorm
 from paddle.optimizer import Optimizer
 

python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_stage3.py

@@ -20,12 +20,11 @@ import numpy as np
 
 import paddle
 import paddle.distributed as dist
-import paddle.fluid.core as core
-import paddle.fluid.framework as framework
-from paddle import nn
+from paddle import framework, nn
 from paddle.autograd import PyLayer
 from paddle.distributed import collective
 from paddle.fluid.framework import EagerParamBase
+from paddle.framework import core
 from paddle.nn import ClipGradByGlobalNorm
 
 from .group_sharded_storage import GradStorage

python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_storage.py

@@ -25,7 +25,7 @@
 import numpy as np
 
 import paddle
-from paddle.fluid import core
+from paddle.framework import core
 
 from .group_sharded_utils import Type, cvt_to_device, device_guard
 

python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_utils.py

@@ -20,9 +20,9 @@ import numpy as np
 
 import paddle
 from paddle import _legacy_C_ops
-from paddle.fluid import core, layers
+from paddle.common_ops_import import dygraph_only
 from paddle.fluid.dygraph import to_variable
-from paddle.fluid.framework import dygraph_only
+from paddle.framework import core
 from paddle.nn import clip
 
 
@@ -87,7 +87,7 @@ class GroupShardedClipGrad:
         if len(sum_square_fp16) == 0:
             global_norm_fp16 = paddle.to_tensor([0.0], dtype=paddle.float32)
         else:
-            global_norm_fp16 = layers.concat(sum_square_fp16)
+            global_norm_fp16 = paddle.concat(sum_square_fp16)
             global_norm_fp16 = paddle.sum(global_norm_fp16)
             global_norm_fp16 = paddle.cast(
                 global_norm_fp16, dtype=paddle.float32
@@ -97,7 +97,7 @@ class GroupShardedClipGrad:
         if len(unslice_params_fp16) == 0:
             global_unslice_fp16 = paddle.to_tensor([0.0], dtype=paddle.float32)
         else:
-            global_unslice_fp16 = layers.concat(unslice_params_fp16)
+            global_unslice_fp16 = paddle.concat(unslice_params_fp16)
             global_unslice_fp16 = paddle.sum(global_unslice_fp16)
             global_unslice_fp16 = paddle.cast(
                 global_unslice_fp16, dtype=paddle.float32
@@ -105,7 +105,7 @@ class GroupShardedClipGrad:
 
         # global norm of non-distributed FP32 params_and_grads
         global_norm_fp32 = (
-            layers.concat(sum_square_fp32)
+            paddle.concat(sum_square_fp32)
             if len(sum_square_fp32) != 0
             else paddle.to_tensor([0.0], dtype=paddle.float32)
         )
@@ -113,7 +113,7 @@ class GroupShardedClipGrad:
 
         # global norm of non-distributed FP32 params_and_grads for unslice parameters
         global_unslice_fp32 = (
-            layers.concat(unslice_params_fp32)
+            paddle.concat(unslice_params_fp32)
             if len(unslice_params_fp32) != 0
             else paddle.to_tensor([0.0], dtype=paddle.float32)
         )
@@ -131,8 +131,8 @@ class GroupShardedClipGrad:
             paddle.distributed.all_reduce(global_norm_var, group=self._group)
 
         global_norm_var = paddle.sqrt(global_norm_var + global_unslice_var)
-        max_global_norm = layers.fill_constant(
-            shape=[1], dtype=global_norm_var.dtype, value=self.clip_norm
+        max_global_norm = paddle.full(
+            shape=[1], dtype=global_norm_var.dtype, fill_value=self.clip_norm
         )
 
         clip_var = paddle.divide(

python/paddle/distributed/fleet/recompute/recompute_hybrid.py

@@ -13,8 +13,9 @@
 # limitations under the License.
 
 import paddle
+from paddle import framework
 from paddle.autograd import PyLayer
-from paddle.fluid import core, framework
+from paddle.framework import core
 
 from ..meta_parallel.parallel_layers.random import get_rng_state_tracker
 from ..meta_parallel.pp_utils import utils

python/paddle/distributed/models/moe/utils.py

@@ -13,9 +13,8 @@
 # limitations under the License.
 
 from paddle import _legacy_C_ops
-from paddle.fluid.data_feeder import check_variable_and_dtype
-from paddle.fluid.framework import in_dygraph_mode
-from paddle.fluid.layer_helper import LayerHelper
+from paddle.common_ops_import import check_variable_and_dtype
+from paddle.framework import LayerHelper, in_dygraph_mode
 
 
 def _number_count(numbers, upper_range):

python/paddle/distributed/utils/launch_utils.py

@@ -66,7 +66,7 @@ def get_cluster_from_args(args, selected_gpus):
 
 def get_gpus(selected_gpus):
     if selected_gpus is None:
-        from paddle.fluid import core
+        from paddle.framework import core
 
         gpus_num = core.get_cuda_device_count()
         gpus = [str(x) for x in range(0, gpus_num)]

python/paddle/distributed/utils/moe_utils.py

@@ -13,9 +13,8 @@
 # limitations under the License.
 
 from paddle import _legacy_C_ops
-from paddle.fluid.data_feeder import check_variable_and_dtype
-from paddle.fluid.framework import in_dygraph_mode
-from paddle.fluid.layer_helper import LayerHelper
+from paddle.common_ops_import import check_variable_and_dtype
+from paddle.framework import LayerHelper, in_dygraph_mode
 
 
 def global_scatter(

python/paddle/incubate/distributed/models/moe/grad_clip.py

@@ -14,8 +14,8 @@
 
 import paddle
 import paddle.distributed as dist
-from paddle.fluid import core, layers
-from paddle.fluid.dygraph import base as imperative_base
+from paddle.autograd import no_grad
+from paddle.framework import core
 from paddle.nn import clip
 from paddle.nn.clip import ClipGradBase, _squared_l2_norm
 
@@ -142,25 +142,25 @@ class ClipGradForMOEByGlobalNorm(ClipGradBase):
 
         global_norm_var = []
         if len(sum_square_list_fp16) > 0:
-            global_norm_var_fp16 = layers.concat(sum_square_list_fp16)
+            global_norm_var_fp16 = paddle.concat(sum_square_list_fp16)
             global_norm_var_fp16 = paddle.sum(global_norm_var_fp16)
             global_norm_var.append(global_norm_var_fp16.astype(sum_dtype))
         if len(sum_square_list_fp32) > 0:
-            global_norm_var_fp32 = layers.concat(sum_square_list_fp32)
+            global_norm_var_fp32 = paddle.concat(sum_square_list_fp32)
             global_norm_var_fp32 = paddle.sum(global_norm_var_fp32)
             if sum_dtype == 'float32':
                 global_norm_var.append(global_norm_var_fp32)
             else:
                 global_norm_var.append(global_norm_var_fp32.astype(sum_dtype))
         if len(sum_square_list) > 0:
-            global_norm_var_fp64 = layers.concat(sum_square_list)
+            global_norm_var_fp64 = paddle.concat(sum_square_list)
             global_norm_var_fp64 = paddle.sum(global_norm_var_fp64)
             global_norm_var.append(global_norm_var_fp64)
-        global_norm_var = layers.concat(global_norm_var)
+        global_norm_var = paddle.concat(global_norm_var)
         global_norm_var = paddle.sum(global_norm_var)
         return global_norm_var, sum_dtype
 
-    @imperative_base.no_grad
+    @no_grad()
     def _dygraph_clip(self, params_grads):
         normal_params_grads = []
         moe_params_grads = []
@@ -210,8 +210,8 @@ class ClipGradForMOEByGlobalNorm(ClipGradBase):
 
         params_and_grads = []
         global_norm_var = paddle.sqrt(global_norm_var)
-        max_global_norm = layers.fill_constant(
-            shape=[1], dtype=global_norm_var.dtype, value=self.clip_norm
+        max_global_norm = paddle.full(
+            shape=[1], dtype=global_norm_var.dtype, fill_value=self.clip_norm
         )
         clip_var = paddle.divide(
             x=max_global_norm,

python/paddle/incubate/distributed/models/moe/moe_layer.py

@@ -25,7 +25,7 @@ import paddle
 import paddle.nn as nn
 from paddle.autograd import PyLayer
 from paddle.distributed.utils.moe_utils import global_gather, global_scatter
-from paddle.fluid.framework import in_dygraph_mode
+from paddle.framework import in_dygraph_mode
 from paddle.incubate.distributed.fleet import recompute_hybrid
 
 from .gate import BaseGate, GShardGate, NaiveGate, SwitchGate

python/paddle/incubate/distributed/models/moe/utils.py

@@ -26,7 +26,7 @@ from paddle.distributed.models.moe.utils import (
     _number_count,
     _prune_gate_by_capacity,
 )
-from paddle.fluid.framework import in_dygraph_mode
+from paddle.framework import in_dygraph_mode
 
 
 def _alltoall(in_tensor_list, group=None, use_calc_stream=True):