Take/Put_along_axis more input size support (#39072)

Support the cases that the indices shape size is larger than the arr shape size

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

@@ -82,7 +82,7 @@ class TestPutAlongAxisAPI(unittest.TestCase):
         if core.is_compiled_with_cuda():
             self.place.append(paddle.CUDAPlace(0))
 
-    def test_api_static_case1(self):
+    def test_api_static(self):
         paddle.enable_static()
 
         def run(place):
@@ -110,7 +110,7 @@ class TestPutAlongAxisAPI(unittest.TestCase):
         for place in self.place:
             run(place)
 
-    def test_api_dygraph_case1(self):
+    def test_api_dygraph(self):
         def run(place):
             paddle.disable_static(place)
             x_tensor = paddle.to_tensor(self.x_np)
@@ -137,33 +137,7 @@ class TestPutAlongAxisAPI(unittest.TestCase):
         for place in self.place:
             run(place)
 
-    def test_api_dygraph_case2(self):
-        def run(place):
-            paddle.disable_static(place)
-            self.shape = [2, 2]
-            self.index_shape = [2, 2]
-            self.index_np = np.array([[0, 0], [1, 0]]).astype('int64')
-            self.x_np = np.random.random(self.shape).astype(np.float32)
-
-            x_tensor = paddle.to_tensor(self.x_np)
-            index_tensor = paddle.to_tensor(self.index_np)
-            value_tensor = paddle.to_tensor(self.value_np)
-            out = paddle.put_along_axis(x_tensor, index_tensor, value_tensor,
-                                        self.axis)
-            np.array(
-                np.put_along_axis(self.x_np, self.index_np, self.value_np,
-                                  self.axis))
-            out_ref = self.x_np
-            self.assertEqual(
-                np.allclose(
-                    out.numpy(), out_ref, rtol=1e-03), True)
-
-            paddle.enable_static()
-
-        for place in self.place:
-            run(place)
-
-    def test_inplace_dygraph_case3(self):
+    def test_inplace_dygraph(self):
         def run(place):
             paddle.disable_static(place)
             x_tensor = paddle.to_tensor(self.x_np)
@@ -186,6 +160,42 @@ class TestPutAlongAxisAPI(unittest.TestCase):
             run(place)
 
 
+class TestPutAlongAxisAPICase2(TestPutAlongAxisAPI):
+    def setUp(self):
+        np.random.seed(0)
+        self.shape = [2, 2]
+        self.index_shape = [2, 2]
+        self.index_np = np.array([[0, 0], [1, 0]]).astype('int64')
+        self.x_np = np.random.random(self.shape).astype(np.float32)
+        self.place = [paddle.CPUPlace()]
+        self.axis = 0
+        self.value_np = 99.0
+        self.value_shape = [1]
+        self.x_feed = copy.deepcopy(self.x_np)
+        if core.is_compiled_with_cuda():
+            self.place.append(paddle.CUDAPlace(0))
+
+
+class TestPutAlongAxisAPICase3(TestPutAlongAxisAPI):
+    def setUp(self):
+        np.random.seed(0)
+        self.shape = [2, 2]
+        self.index_shape = [4, 2]
+        self.index_np = np.array(
+            [[0, 0], [1, 0], [0, 0], [1, 0]]).astype('int64')
+        self.x_np = np.random.random(self.shape).astype(np.float32)
+        self.place = [paddle.CPUPlace()]
+        self.axis = 0
+        self.value_np = 99.0
+        self.value_shape = [1]
+        self.x_feed = copy.deepcopy(self.x_np)
+        if core.is_compiled_with_cuda():
+            self.place.append(paddle.CUDAPlace(0))
+
+    def test_inplace_dygraph(self):
+        pass
+
+
 if __name__ == "__main__":
     paddle.enable_static()
     unittest.main()

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

@@ -106,6 +106,20 @@ class TestTakeAlongAxisAPI(unittest.TestCase):
         paddle.enable_static()
 
 
+class TestTakeAlongAxisAPICase1(TestTakeAlongAxisAPI):
+    def setUp(self):
+        np.random.seed(0)
+        self.shape = [2, 2]
+        self.index_shape = [4, 2]
+        self.index_np = np.array(
+            [[0, 0], [1, 0], [0, 0], [1, 0]]).astype('int64')
+        self.x_np = np.random.random(self.shape).astype(np.float32)
+        self.place = [paddle.CPUPlace()]
+        self.axis = 0
+        if core.is_compiled_with_cuda():
+            self.place.append(paddle.CUDAPlace(0))
+
+
 if __name__ == "__main__":
     paddle.enable_static()
     unittest.main()

python/paddle/tensor/manipulation.py

@@ -2751,6 +2751,31 @@ def moveaxis(x, source, destination, name=None):
     return out
 
 
+def non_negative_axis(arr, axis):
+    ndim = len(arr.shape)
+    if axis >= 0:
+        assert axis < ndim, "'axis'  must be in the range of [-{0}, {0})".format(
+            ndim)
+    else:
+        assert axis >= -ndim, "'axis'  must be in the range of [-{0}, {0})".format(
+            ndim)
+        axis += ndim
+
+    return axis
+
+
+def infer_broadcast_shape(arr, indices, axis):
+    # This function is used in take/put_along_axis 
+    broadcast_shape_list = list(arr.shape)
+    broadcast_shape_list[axis] = list(indices.shape)[axis]
+    broadcast_shape = tuple(broadcast_shape_list)
+    for i in range(len(arr.shape)):
+        if arr.shape[i] < indices.shape[i]:
+            # if indices matrix has larger size than arr matrix, do not broadcast.
+            return None
+    return broadcast_shape
+
+
 def take_along_axis(arr, indices, axis):
     """
     Take values from the input array by given indices matrix along the designated axis.
@@ -2779,14 +2804,20 @@ def take_along_axis(arr, indices, axis):
             print(result)
             # [[1, 2, 3]]
     """
-    if (arr.shape == indices.shape):
-        broadcast_shape = arr.shape
-    else:
-        broadcast_shape_list = list(arr.shape)
-        broadcast_shape_list[axis] = 1
-        broadcast_shape = tuple(broadcast_shape_list)
+    if (len(arr.shape) != len(indices.shape)):
+        raise ValueError(
+            "`indices` and `arr` must have the same number of dimensions!")
+    axis = non_negative_axis(arr, axis)
+    broadcast_shape = infer_broadcast_shape(arr, indices, axis)
+    if not broadcast_shape:
+        # if indices matrix have larger size than arr, arr should broadcast into indices shape.
+        broadcast_shape = indices.shape
     if in_dygraph_mode():
         indices = paddle.broadcast_to(indices, broadcast_shape)
+        broadcast_shape_list = list(broadcast_shape)
+        broadcast_shape_list[axis] = list(arr.shape)[axis]
+        broadcast_shape = tuple(broadcast_shape_list)
+        arr = paddle.broadcast_to(arr, broadcast_shape)
         return _C_ops.take_along_axis(arr, indices, 'Axis', axis)
     check_variable_and_dtype(
         arr, 'x', ['float16', 'float32', 'float64', 'int32', 'int64', 'uint8'],
@@ -2794,6 +2825,10 @@ def take_along_axis(arr, indices, axis):
     check_variable_and_dtype(indices, 'index', ['int32', 'int64'],
                              'take_along_axis')
     indices = paddle.broadcast_to(indices, broadcast_shape)
+    broadcast_shape_list = list(broadcast_shape)
+    broadcast_shape_list[axis] = list(arr.shape)[axis]
+    broadcast_shape = tuple(broadcast_shape_list)
+    arr = paddle.broadcast_to(arr, broadcast_shape)
     helper = LayerHelper('take_along_axis', **locals())
     dtype = helper.input_dtype()
     result = helper.create_variable_for_type_inference(dtype)
@@ -2837,17 +2872,17 @@ def put_along_axis(arr, indices, values, axis, reduce='assign'):
             # [60, 40, 50]]
 
     """
-    if (arr.shape == indices.shape):
-        broadcast_shape = arr.shape
-    else:
-        broadcast_shape_list = list(arr.shape)
-        broadcast_shape_list[axis] = 1
-        broadcast_shape = tuple(broadcast_shape_list)
+    if (len(arr.shape) != len(indices.shape)):
+        raise ValueError(
+            "`indices` and `arr` must have the same number of dimensions!")
+    axis = non_negative_axis(arr, axis)
+    broadcast_shape = infer_broadcast_shape(arr, indices, axis)
     if in_dygraph_mode():
-        indices = paddle.broadcast_to(indices, broadcast_shape)
         values = paddle.to_tensor(values) if not isinstance(
             values, paddle.Tensor) else values
-        values = paddle.broadcast_to(values, broadcast_shape)
+        if broadcast_shape:
+            indices = paddle.broadcast_to(indices, broadcast_shape)
+        values = paddle.broadcast_to(values, indices.shape)
         return _C_ops.put_along_axis(arr, indices, values, "Axis", axis,
                                      "Reduce", reduce)
 
@@ -2856,8 +2891,9 @@ def put_along_axis(arr, indices, values, axis, reduce='assign'):
         'put_along_axis')
     check_variable_and_dtype(indices, 'index', ['int32', 'int64'],
                              'put_along_axis')
+    if broadcast_shape:
         indices = paddle.broadcast_to(indices, broadcast_shape)
-    values = paddle.broadcast_to(values, broadcast_shape)
+    values = paddle.broadcast_to(values, indices.shape)
     helper = LayerHelper('put_along_axis', **locals())
     dtype = helper.input_dtype()
     result = helper.create_variable_for_type_inference(dtype)
@@ -2875,19 +2911,18 @@ def put_along_axis(arr, indices, values, axis, reduce='assign'):
 @inplace_apis_in_dygraph_only
 def put_along_axis_(arr, indices, values, axis, reduce='assign'):
     r"""
-    Inplace version of ``put_along_axis`` API, the output Tensor will be inplaced with input ``x``.
+    Inplace version of ``put_along_axis`` API, the output Tensor will be inplaced with input ``arr``.
     Please refer to :ref:`api_tensor_put_along_axis`.
     """
-    if (arr.shape == indices.shape):
-        broadcast_shape = arr.shape
-    else:
-        broadcast_shape_list = list(arr.shape)
-        broadcast_shape_list[axis] = 1
-        broadcast_shape = tuple(broadcast_shape_list)
-
-    indices = paddle.broadcast_to(indices, broadcast_shape)
+    if (len(arr.shape) != len(indices.shape)):
+        raise ValueError(
+            "`indices` and `arr` must have the same number of dimensions!")
+    axis = non_negative_axis(arr, axis)
+    broadcast_shape = infer_broadcast_shape(arr, indices, axis)
     values = paddle.to_tensor(values) if not isinstance(
         values, paddle.Tensor) else values
-    values = paddle.broadcast_to(values, broadcast_shape)
+    if broadcast_shape:
+        indices = paddle.broadcast_to(indices, broadcast_shape)
+    values = paddle.broadcast_to(values, indices.shape)
     return _C_ops.put_along_axis_(arr, indices, values, "Axis", axis, "Reduce",
                                   reduce)

python/paddle/tensor/stat.py

@@ -437,17 +437,29 @@ def quantile(x, q, axis=None, keepdim=False):
     indices_upper = paddle.ceil(indices).astype(paddle.int32)
     outputs = []
 
+    def expand_dim(indices, sorted_tensor_shape, axis):
+        assert axis < len(list(sorted_tensor_shape))
+        expanded_shape = [1] * len(list(sorted_tensor_shape))
+        expanded_shape[axis] = len(indices)
+        expanded_shape = tuple(expanded_shape)
+        indices = indices.reshape(expanded_shape)
+        return indices
+
     # TODO(chenjianye): replace the for-loop to directly take elements.
     for i in range(len(indices)):
         if (indices_upper[i] != indices_below[i]):
-            tensor_below = paddle.take_along_axis(sorted_tensor,
-                                                  indices_below[i], axis)
-            tensor_upper = paddle.take_along_axis(sorted_tensor,
-                                                  indices_upper[i], axis)
+            tensor_below = paddle.take_along_axis(
+                sorted_tensor,
+                expand_dim(indices_below[i], sorted_tensor.shape, axis), axis)
+            tensor_upper = paddle.take_along_axis(
+                sorted_tensor,
+                expand_dim(indices_upper[i], sorted_tensor.shape, axis), axis)
             weights = (indices[i] - indices_below[i]).astype(x.dtype)
             out = paddle.lerp(tensor_below, tensor_upper, weights)
         else:
-            out = paddle.take_along_axis(sorted_tensor, indices_below[i], axis)
+            out = paddle.take_along_axis(
+                sorted_tensor,
+                expand_dim(indices_below[i], sorted_tensor.shape, axis), axis)
         if not keepdim:
             out = paddle.squeeze(out, axis=axis)
         else: