[Cherry-pick]cherry-pick to Release/2.0 (#30076)

* add fp16 check into max and avg pool (#29479)

* Add ReserveSpace in dygraph batch_norm. (#29221)

* Add ReserveSpace in dygraph batch_norm.

* Add unittest for reservespace

* add float16 into adaptive_avg_pool2d check list. (#29547)

python/paddle/fluid/dygraph/nn.py

@@ -31,6 +31,7 @@ from ..data_feeder import check_variable_and_dtype, check_type
 import numpy as np
 import numbers
 import logging
+import os
 import paddle.utils.deprecated as deprecated
 
 __all__ = [
@@ -1308,6 +1309,12 @@ class BatchNorm(layers.Layer):
             dtype=self._dtype)
         self._variance.stop_gradient = True
 
+        self._has_reserve_space = False
+        if data_layout == 'NHWC':
+            flag = os.environ.get('FLAGS_cudnn_batchnorm_spatial_persistent')
+            if flag is not None and flag.lower() in ['true', '1']:
+                self._has_reserve_space = True
+
         self._in_place = in_place
         self._data_layout = data_layout
         self._momentum = momentum
@@ -1364,6 +1371,12 @@ class BatchNorm(layers.Layer):
             dtype=self._dtype, stop_gradient=True)
         saved_variance = self._helper.create_variable_for_type_inference(
             dtype=self._dtype, stop_gradient=True)
+
+        reserve_space = None
+        if self._has_reserve_space:
+            reserve_space = self._helper.create_variable_for_type_inference(
+                dtype=core.VarDesc.VarType.FP16, stop_gradient=True)
+
         batch_norm_out = input if self._in_place else self._helper.create_variable_for_type_inference(
             self._dtype)
 
@@ -1374,6 +1387,8 @@ class BatchNorm(layers.Layer):
             "SavedMean": [saved_mean],
             "SavedVariance": [saved_variance]
         }
+        if reserve_space is not None:
+            outputs["ReserveSpace"] = reserve_space
 
         self._helper.append_op(
             type="batch_norm", inputs=inputs, outputs=outputs, attrs=attrs)

python/paddle/fluid/tests/unittests/test_batch_norm_op.py

@@ -17,6 +17,7 @@ from __future__ import print_function
 import os
 import unittest
 import numpy as np
+import paddle
 import paddle.fluid.core as core
 from paddle.fluid.op import Operator
 import paddle.fluid as fluid
@@ -671,5 +672,18 @@ class TestDygraphBatchNormTrainableStats(unittest.TestCase):
             self.assertTrue(np.allclose(y1, y2))
 
 
+class TestDygraphBatchNormOpenReserveSpace(unittest.TestCase):
+    def test_reservespace(self):
+        with program_guard(Program(), Program()):
+            paddle.enable_static()
+            x = np.random.random(size=(3, 10, 3, 7)).astype('float32')
+            x = fluid.data(name='x', shape=x.shape, dtype=x.dtype)
+            # Set this FLAG, the BatchNorm API will pass "reserve_space" argument into batch_norm op.
+            os.environ['FLAGS_cudnn_batchnorm_spatial_persistent'] = '1'
+            batch_norm = fluid.dygraph.BatchNorm(7, data_layout="NHWC")
+            hidden1 = batch_norm(x)
+            os.environ['FLAGS_cudnn_batchnorm_spatial_persistent'] = '0'
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/nn/functional/pooling.py

@@ -692,7 +692,8 @@ def max_pool2d(x,
                                                return_mask=True)
             # out.shape [1, 3, 16, 16], max_indices.shape [1, 3, 16, 16],
     """
-    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'max_pool2d')
+    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
+                             'max_pool2d')
     kernel_size = utils.convert_to_list(kernel_size, 2, 'pool_size')
     if stride is None:
         stride = kernel_size
@@ -933,7 +934,8 @@ def adaptive_avg_pool1d(x, output_size, name=None):
               # pool_out shape: [1, 3, 16])
     """
     pool_type = 'avg'
-    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'adaptive_pool2d')
+    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
+                             'adaptive_pool2d')
     _check_input(x, 3)
     check_type(output_size, 'pool_size', (int), 'adaptive_pool1d')
 
@@ -1015,7 +1017,7 @@ def adaptive_avg_pool2d(x, output_size, data_format='NCHW', name=None):
             # out.shape is [2, 3, 3, 3]
     """
     if not in_dygraph_mode():
-        check_variable_and_dtype(x, 'x', ['float32', 'float64'],
+        check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
                                  'adaptive_avg_pool2d')
     check_type(data_format, 'data_format', str, 'adaptive_avg_pool2d')