msadapter/pytorch/nn/functional.py

@@ -3,6 +3,7 @@
 """Functional interface"""
 import math
 import warnings
+from typing import Iterable
 from functools import lru_cache
 import numpy as np
 import mindspore as ms
@@ -12,7 +13,7 @@ from mindspore.ops.operations.nn_ops import TripletMarginLoss as TripletMarginLo
 from mindspore.ops._primitive_cache import _get_cache_prim
 from mindspore.ops.function.math_func import _expand, _check_same_type
 
-from msadapter.utils import unsupported_attr, _GLOBAL_LRU_CACHE_SIZE_NN
+from msadapter.utils import unsupported_attr, _GLOBAL_LRU_CACHE_SIZE_NN, is_under_ascend_context
 from msadapter.pytorch.tensor import Tensor, cast_to_ms_tensor, cast_to_adapter_tensor
 from msadapter.pytorch.common._inner import _inplace_assign_pynative
 from msadapter.pytorch.common.dtype import all_int_type
@@ -165,9 +166,65 @@ def adaptive_max_pool2d(input, output_size, return_indices=False):
     output = ms.ops.adaptive_max_pool2d(input, output_size, return_indices)
     return cast_to_adapter_tensor(output)
 
+@constexpr
+@lru_cache(_GLOBAL_LRU_CACHE_SIZE_NN)
+def _get_adaptive_max_pool3d_output_size(input_shape, output_size):
+    if not isinstance(output_size, Iterable):
+        output_size = [output_size, ] * 3
+    condition = [0,] * 3
+    if None in output_size:
+        output_size = list(output_size)
+        if output_size[0] is None:
+            condition[0] = 1
+            output_size[0] = 0
+        if output_size[1] is None:
+            condition[1] = 1
+            output_size[1] = 0
+        if output_size[2] is None:
+            condition[2] = 1
+            output_size[2] = 0
+    _, _, d, h, w = input_shape
+    out_d = output_size[0] + condition[0] * d
+    out_h = output_size[1] + condition[1] * h
+    out_w = output_size[2] + condition[2] * w
+    return out_d, out_h, out_w
+
+@constexpr
+@lru_cache(_GLOBAL_LRU_CACHE_SIZE_NN)
+def _get_adaptive_max_pool3d_stride(input_shape, output_size):
+    out_d, out_h, out_w = output_size
+    _, _, d, h, w = input_shape
+    stride_d = d // out_d
+    kernel_d = d - (out_d - 1) * stride_d
+    stride_h = h // out_h
+    kernel_h = h - (out_h - 1) * stride_h
+    stride_w = w // out_w
+    kernel_w = w - (out_w - 1) * stride_w
+
+    return kernel_d, kernel_h, kernel_w, stride_d, stride_h, stride_w
+
 def adaptive_max_pool3d(input, output_size, return_indices=False):
     input = cast_to_ms_tensor(input)
-    output = ms.ops.adaptive_max_pool3d(input, output_size, return_indices)
+    input_shape = ms.ops.shape(input)
+    _output_size = _get_adaptive_max_pool3d_output_size(input_shape, output_size)
+
+    if is_under_ascend_context():
+        # TODO: Ascend not support ms.ops.adaptive_max_pool3d, use MaxPool3D instead
+        # MaxPool3D result is not the same as adaptive_max_pool3d, but the shape.
+        # Implement below do not affect the converge of trainning.
+        if return_indices:
+            raise NotImplementedError("For adaptive_max_pool3d, return_indices is not supported yet.")
+
+        kernel_d, kernel_h, kernel_w, stride_d, stride_h, stride_w = \
+            _get_adaptive_max_pool3d_stride(input_shape, _output_size)
+
+        avg_pool = ms.ops.MaxPool3D(kernel_size=(kernel_d, kernel_h, kernel_w),
+                                    strides=(stride_d, stride_h, stride_w),
+                                    pad_mode="valid", data_format="NCDHW")
+        output = avg_pool(input)
+    else:
+        output = ms.ops.adaptive_max_pool3d(input, _output_size, return_indices)
+
     return cast_to_adapter_tensor(output)
 
 def pad(input, pad, mode="constant", value=0):

msadapter/pytorch/nn/modules/pooling.py

@@ -279,45 +279,16 @@ class AdaptiveMaxPool2d(_AdaptiveMaxPoolNd):
 
 
 class AdaptiveMaxPool3d(_AdaptiveMaxPoolNd):
-    def __init__(self, output_size, return_indices = False):
+    def __init__(self, output_size, return_indices=False):
+        # TODO: not support `return_indices` yet
+        if return_indices and is_under_ascend_context():
+            raise NotImplementedError('AdaptiveMaxPool3d doesn\'t  support return_indices on Ascend now.')
         super(AdaptiveMaxPool3d, self).__init__(output_size, return_indices)
-        self.output_size = output_size
-        self.shape = P.Shape()
-        if not isinstance(self.output_size, Iterable):
-            self.output_size = [self.output_size, ] * 3
-        self.condition = [0,] * 3
-        if None in self.output_size:
-            self.output_size = list(self.output_size)
-            if self.output_size[0] is None:
-                self.condition [0] = 1
-                self.output_size[0] = 0
-            if self.output_size[1] is None:
-                self.condition [1] = 1
-                self.output_size[1] = 0
-            if self.output_size[2] is None:
-                self.condition[2] = 1
-                self.output_size[2] = 0
-        if return_indices:
-            raise NotImplementedError('AdaptiveMaxPool3d doesn\'t  support return_indices now.')
-
+        self.return_indices = return_indices
 
     def forward(self, input):
-        input = cast_to_ms_tensor(input)
-        _, _, d, h, w = self.shape(input)
-        out_d = self.output_size[0] + self.condition[0] * d
-        out_h = self.output_size[1] + self.condition[1] * h
-        out_w = self.output_size[2] + self.condition[2] * w
-        stride_d = d // out_d
-        kernel_d = d - (out_d - 1) * stride_d
-        stride_h = h // out_h
-        kernel_h = h - (out_h - 1) * stride_h
-        stride_w = w // out_w
-        kernel_w = w - (out_w - 1) * stride_w
-        avg_pool = P.MaxPool3D(kernel_size=(kernel_d, kernel_h, kernel_w),
-                                strides=(stride_d, stride_h, stride_w),
-                                pad_mode="valid", data_format="NCDHW")
-        outputs = avg_pool(input)
-        return cast_to_adapter_tensor(outputs)
+        outputs = Adapter_F.adaptive_max_pool3d(input, self.output_size, self.return_indices)
+        return outputs
 
 
 class _LPPoolNd(Module):

testing/ut/pytorch/nn/test_adaptivepool.py

@@ -4,6 +4,7 @@ import torch
 from mindspore import Tensor
 from msadapter.pytorch.nn import AdaptiveAvgPool1d, AdaptiveAvgPool2d, AdaptiveAvgPool3d
 from msadapter.pytorch.nn import AdaptiveMaxPool1d, AdaptiveMaxPool2d, AdaptiveMaxPool3d
+from msadapter.utils import is_under_ascend_context
 from mindspore import context
 import mindspore as ms
 context.set_context(mode=ms.GRAPH_MODE)
@@ -278,6 +279,9 @@ def test_adaptivemaxpool3d_compare1():
     torch_output = torch_net(torch_input)
     print(ms_output.shape, torch_output.shape)
     assert np.allclose(ms_output.shape, torch_output.shape)
+    if not is_under_ascend_context():
+        assert np.allclose(ms_output.numpy(), torch_output.numpy())
+
 
 def test_adaptivemaxpool3d_compare2():
     ms_net = AdaptiveMaxPool3d(3)
@@ -291,6 +295,8 @@ def test_adaptivemaxpool3d_compare2():
     torch_output = torch_net(torch_input)
     print(ms_output.shape, torch_output.shape)
     assert np.allclose(ms_output.shape, torch_output.shape)
+    if not is_under_ascend_context():
+        assert np.allclose(ms_output.numpy(), torch_output.numpy())
 
 def test_adaptivemaxpool3d_compare3():
     ms_net = AdaptiveMaxPool3d(3)
@@ -304,6 +310,8 @@ def test_adaptivemaxpool3d_compare3():
     torch_output = torch_net(torch_input)
     print(ms_output.shape, torch_output.shape)
     assert np.allclose(ms_output.shape, torch_output.shape)
+    if not is_under_ascend_context():
+        assert np.allclose(ms_output.numpy(), torch_output.numpy())
 
 def test_adaptivemaxpool3d_compare4():
     ms_net = AdaptiveMaxPool3d((3, None, 5))
@@ -317,6 +325,8 @@ def test_adaptivemaxpool3d_compare4():
     torch_output = torch_net(torch_input)
     print(ms_output.shape, torch_output.shape)
     assert np.allclose(ms_output.shape, torch_output.shape)
+    if not is_under_ascend_context():
+        assert np.allclose(ms_output.numpy(), torch_output.numpy())
 
 def test_adaptivemaxpool3d_compare5():
     ms_net = AdaptiveMaxPool3d((None, None, 5))
@@ -330,6 +340,8 @@ def test_adaptivemaxpool3d_compare5():
     torch_output = torch_net(torch_input)
     print(ms_output.shape, torch_output.shape)
     assert np.allclose(ms_output.shape, torch_output.shape)
+    if not is_under_ascend_context():
+        assert np.allclose(ms_output.numpy(), torch_output.numpy())
 
 if __name__ == '__main__':
     test_adaptiveavgpool2d_compare1()

testing/ut/pytorch/nn/test_pooling.py

@@ -5,6 +5,7 @@ from mindspore import Tensor
 from msadapter.pytorch.nn import MaxPool1d, MaxPool2d, MaxPool3d, AvgPool1d, AvgPool2d, AvgPool3d, \
     AdaptiveAvgPool1d, AdaptiveAvgPool2d, AdaptiveAvgPool3d, AdaptiveMaxPool1d, AdaptiveMaxPool2d, AdaptiveMaxPool3d, \
     LPPool1d, LPPool2d, FractionalMaxPool2d, FractionalMaxPool3d
+from msadapter.utils import is_under_ascend_context
 
 import mindspore as ms
 ms.context.set_context(mode=ms.GRAPH_MODE)
@@ -305,7 +306,8 @@ def test_adaptive_maxpool3d_compare1():
     ms_output = ms_net(ms_input)
 
     assert (torch_output.shape == ms_output.shape)
-    # TODO: assert np.allclose(ms_output.asnumpy(), torch_output.numpy())
+    if not is_under_ascend_context():
+        assert np.allclose(ms_output.asnumpy(), torch_output.numpy())
 
 
 def test_adaptive_avgpool1d_compare1():