Solves #1512

python/mlx/nn/layers/__init__.py

@@ -70,7 +70,14 @@
     LayerNorm,
     RMSNorm,
 )
-from mlx.nn.layers.pooling import AvgPool1d, AvgPool2d, MaxPool1d, MaxPool2d
+from mlx.nn.layers.pooling import (
+    AvgPool1d,
+    AvgPool2d,
+    AvgPool3d,
+    MaxPool1d,
+    MaxPool2d,
+    MaxPool3d,
+)
 from mlx.nn.layers.positional_encoding import ALiBi, RoPE, SinusoidalPositionalEncoding
 from mlx.nn.layers.quantized import QuantizedEmbedding, QuantizedLinear, quantize
 from mlx.nn.layers.recurrent import GRU, LSTM, RNN

python/mlx/nn/layers/pooling.py

@@ -158,6 +158,30 @@ def __init__(
         super().__init__(pooling_function, kernel_size, stride, padding, padding_value)
 
 
+class _Pool3d(_Pool):
+    def __init__(
+        self,
+        pooling_function,
+        padding_value,
+        kernel_size: Union[int, Tuple[int, int, int]],
+        stride: Optional[Union[int, Tuple[int, int, int]]] = None,
+        padding: Optional[Union[int, Tuple[int, int, int]]] = 0,
+    ):
+        class_name = type(self).__name__
+        msg = "[{}] '{}' must be an integer or a tuple containing 3 integers"
+        kernel_size = _value_or_list(
+            kernel_size, 3, msg.format(class_name, "kernel_size")
+        )
+        if stride is not None:
+            stride = _value_or_list(stride, 3, msg.format(class_name, "stride"))
+        else:
+            stride = kernel_size
+        padding = _value_or_list(padding, 3, msg.format(class_name, "padding"))
+        padding = [(p, p) for p in padding]
+
+        super().__init__(pooling_function, kernel_size, stride, padding, padding_value)
+
+
 class MaxPool1d(_Pool1d):
     r"""Applies 1-dimensional max pooling.
 
@@ -332,3 +356,104 @@ def __init__(
         padding: Optional[Union[int, Tuple[int, int]]] = 0,
     ):
         super().__init__(mx.mean, 0, kernel_size, stride, padding)
+
+
+class MaxPool3d(_Pool3d):
+    """
+        Assuming an input of shape :math:`(N, D, H, W, C)` and ``kernel_size`` is
+    :math:`(k_D, k_H, k_W)`, the output is a tensor of shape :math:`(N, D_{out},
+    H_{out}, W_{out}, C)`, given by:
+
+    .. math::
+        \begin{aligned}
+            \text{out}(N_i, d, h, w, C_j) = & \max_{l=0, \ldots, k_D-1} \max_{m=0, \ldots, k_H-1} \max_{n=0, \ldots, k_W-1} \\
+                                    & \text{input}(N_i, \text{stride[0]} \times d + l,
+                                                \text{stride[1]} \times h + m,
+                                                \text{stride[2]} \times w + n, C_j),
+        \end{aligned}
+
+    where :math:`D_{out} = \left\lfloor\frac{D + 2 * \text{padding[0]} - \text{kernel\_size[0]}}{\text{stride[0]}}\right\rfloor + 1`,
+    :math:`H_{out} = \left\lfloor\frac{H + 2 * \text{padding[1]} - \text{kernel\_size[1]}}{\text{stride[1]}}\right\rfloor + 1`,
+    :math:`W_{out} = \left\lfloor\frac{W + 2 * \text{padding[2]} - \text{kernel\_size[2]}}{\text{stride[2]}}\right\rfloor + 1`.
+
+    The parameters ``kernel_size``, ``stride``, ``padding``, can either be:
+
+        - a single ``int`` -- in which case the same value is used for the depth,
+        height and width axis;
+        - a ``tuple`` of three ``int`` s -- in which case, the first ``int`` is used
+        for the depth axis, the second ``int`` for the height axis, and the third
+        ``int`` for the width axis.
+
+    Args:
+        kernel_size (int or tuple(int, int, int)): The size of the pooling window.
+        stride (int or tuple(int, int, int), optional): The stride of the pooling
+            window. Default: ``kernel_size``.
+        padding (int or tuple(int, int, int), optional): How much negative infinity
+            padding to apply to the input. The padding is applied on both sides
+            of the depth, height and width axis. Default: ``0``.
+
+    Examples:
+        >>> import mlx.core as mx
+        >>> import mlx.nn.layers as nn
+        >>> x = mx.random.normal(shape=(8, 16, 32, 32, 4))
+        >>> pool = nn.MaxPool3d(kernel_size=2, stride=2)
+        >>> pool(x)
+    """
+
+    def __init__(
+        self,
+        kernel_size: Union[int, Tuple[int, int, int]],
+        stride: Optional[Union[int, Tuple[int, int, int]]] = None,
+        padding: Optional[Union[int, Tuple[int, int, int]]] = 0,
+    ):
+        super().__init__(mx.max, -float("inf"), kernel_size, stride, padding)
+
+
+class AvgPool3d(_Pool3d):
+    """
+        Assuming an input of shape :math:`(N, D, H, W, C)` and ``kernel_size`` is
+        :math:`(k_D, k_H, k_W)`, the output is a tensor of shape :math:`(N, D_{out},
+        H_{out}, W_{out}, C)`, given by:
+
+        .. math::
+        \begin{aligned}
+            \text{out}(N_i, d, h, w, C_j) = & \frac{1}{k_D k_H k_W} \sum_{l=0, \ldots, k_D-1} \sum_{m=0, \ldots, k_H-1} \sum_{n=0, \ldots, k_W-1} \\
+                                    & \text{input}(N_i, \text{stride[0]} \times d + l,
+                                                \text{stride[1]} \times h + m,
+                                                \text{stride[2]} \times w + n, C_j),
+        \end{aligned}
+
+        where :math:`D_{out} = \left\lfloor\frac{D + 2 * \text{padding[0]} - \text{kernel\_size[0]}}{\text{stride[0]}}\right\rfloor + 1`,
+        :math:`H_{out} = \left\lfloor\frac{H + 2 * \text{padding[1]} - \text{kernel\_size[1]}}{\text{stride[1]}}\right\rfloor + 1`,
+        :math:`W_{out} = \left\lfloor\frac{W + 2 * \text{padding[2]} - \text{kernel\_size[2]}}{\text{stride[2]}}\right\rfloor + 1`.
+
+        The parameters ``kernel_size``, ``stride``, ``padding``, can either be:
+
+        - a single ``int`` -- in which case the same value is used for the depth,
+            height and width axis;
+        - a ``tuple`` of three ``int`` s -- in which case, the first ``int`` is used
+            for the depth axis, the second ``int`` for the height axis, and the third
+            ``int`` for the width axis.
+
+        Args:
+        kernel_size (int or tuple(int, int, int)): The size of the pooling window.
+        stride (int or tuple(int, int, int), optional): The stride of the pooling
+            window. Default: ``kernel_size``.
+        padding (int or tuple(int, int, int), optional): How much zero
+            padding to apply to the input. The padding is applied on both sides
+            of the depth, height and width axis. Default: ``0``.
+
+        Examples:
+        >>> import mlx.core as mx
+        >>> import mlx.nn.layers as nn
+        >>> x = mx.random.normal(shape=(8, 16, 32, 32, 4))
+        >>> pool = nn.AvgPool3d(kernel_size=2, stride=2)
+        >>> pool(x)
+    """
+    def __init__(
+        self,
+        kernel_size: Union[int, Tuple[int, int, int]],
+        stride: Optional[Union[int, Tuple[int, int, int]]] = None,
+        padding: Optional[Union[int, Tuple[int, int, int]]] = 0,
+    ):
+        super().__init__(mx.mean, 0, kernel_size, stride, padding)

python/tests/test_nn.py

@@ -1583,6 +1583,123 @@ def test_pooling(self):
             str(nn.AvgPool2d(kernel_size=(1, 2), stride=2, padding=(1, 2))),
             "AvgPool2d(kernel_size=(1, 2), stride=(2, 2), padding=(1, 2))",
         )
+        # Test 3d pooling
+        x = mx.array(
+            [
+                [
+                    [
+                        [[0, 1, 2], [3, 4, 5], [6, 7, 8]],
+                        [[9, 10, 11], [12, 13, 14], [15, 16, 17]],
+                        [[18, 19, 20], [21, 22, 23], [24, 25, 26]],
+                    ],
+                    [
+                        [[27, 28, 29], [30, 31, 32], [33, 34, 35]],
+                        [[36, 37, 38], [39, 40, 41], [42, 43, 44]],
+                        [[45, 46, 47], [48, 49, 50], [51, 52, 53]],
+                    ],
+                ]
+            ]
+        )
+        expected_max_pool_output_no_padding_stride_1 = [
+            [[[[39, 40, 41], [42, 43, 44]], [[48, 49, 50], [51, 52, 53]]]]
+        ]
+
+        expected_max_pool_output_no_padding_stride_2 = [[[[[39, 40, 41]]]]]
+        expected_max_pool_output_padding_1 = [
+            [
+                [[[0, 1, 2], [6, 7, 8]], [[18, 19, 20], [24, 25, 26]]],
+                [[[27, 28, 29], [33, 34, 35]], [[45, 46, 47], [51, 52, 53]]],
+            ]
+        ]
+        expected_irregular_max_pool_output = [
+            [
+                [[[9, 10, 11], [12, 13, 14], [15, 16, 17]]],
+                [[[36, 37, 38], [39, 40, 41], [42, 43, 44]]],
+            ]
+        ]
+
+        self.assertTrue(
+            np.array_equal(
+                nn.MaxPool3d(kernel_size=2, stride=1, padding=0)(x),
+                expected_max_pool_output_no_padding_stride_1,
+            )
+        )
+        self.assertTrue(
+            np.array_equal(
+                nn.MaxPool3d(kernel_size=2, stride=2, padding=0)(x),
+                expected_max_pool_output_no_padding_stride_2,
+            )
+        )
+        self.assertTrue(
+            np.array_equal(
+                nn.MaxPool3d(kernel_size=2, stride=2, padding=1)(x),
+                expected_max_pool_output_padding_1,
+            )
+        )
+        self.assertTrue(
+            np.array_equal(
+                nn.MaxPool3d(kernel_size=(1, 2, 1), stride=(1, 2, 1))(x),
+                expected_irregular_max_pool_output,
+            )
+        )
+        self.assertEqual(
+            str(nn.MaxPool3d(kernel_size=3, stride=3, padding=2)),
+            "MaxPool3d(kernel_size=(3, 3, 3), stride=(3, 3, 3), padding=(2, 2, 2))",
+        )
+
+        expected_avg_pool_output_no_padding_stride_1 = [[[[[19.5, 20.5, 21.5],
+                                                        [22.5, 23.5, 24.5]],
+                                                        [[28.5, 29.5, 30.5],
+                                                        [31.5, 32.5, 33.5]]]]
+                                                        ]
+
+        expected_avg_pool_output_no_padding_stride_2 = [[[[[19.5, 20.5, 21.5]]]]]
+        expected_avg_pool_output_padding_1 = [
+                                                    [[[[0, 0.125, 0.25],
+                                                    [1.125, 1.375, 1.625]],
+                                                    [[3.375, 3.625, 3.875],
+                                                    [9, 9.5, 10]]],
+                                                    [[[3.375, 3.5, 3.625],
+                                                    [7.875, 8.125, 8.375]],
+                                                    [[10.125, 10.375, 10.625],
+                                                    [22.5, 23, 23.5]]]]
+            ]
+        expected_irregular_avg_pool_output = [[[[[4.5, 5.5, 6.5],
+                                                [7.5, 8.5, 9.5],
+                                                [10.5, 11.5, 12.5]]],
+                                                [[[31.5, 32.5, 33.5],
+                                                [34.5, 35.5, 36.5],
+                                                [37.5, 38.5, 39.5]]]]
+                                                ]
+
+        self.assertTrue(
+            np.array_equal(
+                nn.AvgPool3d(kernel_size=2, stride=1, padding=0)(x),
+                expected_avg_pool_output_no_padding_stride_1,
+            )
+        )
+        self.assertTrue(
+            np.array_equal(
+                nn.AvgPool3d(kernel_size=2, stride=2, padding=0)(x),
+                expected_avg_pool_output_no_padding_stride_2,
+            )
+        )
+        self.assertTrue(
+            np.array_equal(
+                nn.AvgPool3d(kernel_size=2, stride=2, padding=1)(x),
+                expected_avg_pool_output_padding_1,
+            )
+        )
+        self.assertTrue(
+            np.array_equal(
+                nn.AvgPool3d(kernel_size=(1, 2, 1), stride=(1, 2, 1))(x),
+                expected_irregular_avg_pool_output,
+            )
+        )
+        self.assertEqual(
+            str(nn.AvgPool3d(kernel_size=3, stride=3, padding=2)),
+            "AvgPool3d(kernel_size=(3, 3, 3), stride=(3, 3, 3), padding=(2, 2, 2))",
+        )
 
     def test_set_dtype(self):
         def assert_dtype(layer, dtype):