Conv3d (#993)

* added conv3d

added conv3d

implemented explicit_gemm_conv_ND_cpu and bounds checks for slow_conv_3D

* incorporated reviewer comments

* fixed test

* reduced tensor shapes in test for conv3d

* Reviewer suggestion

Co-authored-by: Awni Hannun <awni.hannun@gmail.com>

Reviewer suggestion

Co-authored-by: Awni Hannun <awni.hannun@gmail.com>

Reviewer suggestion

Co-authored-by: Awni Hannun <awni.hannun@gmail.com>

Reviewer suggestion

python/mlx/nn/layers/__init__.py

@@ -48,7 +48,7 @@ from mlx.nn.layers.activations import (
 )
 from mlx.nn.layers.base import Module
 from mlx.nn.layers.containers import Sequential
-from mlx.nn.layers.convolution import Conv1d, Conv2d
+from mlx.nn.layers.convolution import Conv1d, Conv2d, Conv3d
 from mlx.nn.layers.dropout import Dropout, Dropout2d, Dropout3d
 from mlx.nn.layers.embedding import Embedding
 from mlx.nn.layers.linear import Bilinear, Identity, Linear

python/mlx/nn/layers/convolution.py

@@ -132,3 +132,66 @@ class Conv2d(Module):
         if "bias" in self:
             y = y + self.bias
         return y
+
+
+class Conv3d(Module):
+    """Applies a 3-dimensional convolution over the multi-channel input image.
+    The channels are expected to be last i.e. the input shape should be ``NDHWC`` where:
+        - ``N`` is the batch dimension
+        - ``D`` is the input image depth
+        - ``H`` is the input image height
+        - ``W`` is the input image width
+        - ``C`` is the number of input channels
+    Args:
+        in_channels (int): The number of input channels.
+        out_channels (int): The number of output channels.
+        kernel_size (int or tuple): The size of the convolution filters.
+        stride (int or tuple, optional): The size of the stride when
+            applying the filter. Default: ``1``.
+        padding (int or tuple, optional): How many positions to 0-pad
+            the input with. Default: ``0``.
+        bias (bool, optional): If ``True`` add a learnable bias to the
+            output. Default: ``True``
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: Union[int, tuple],
+        stride: Union[int, tuple] = 1,
+        padding: Union[int, tuple] = 0,
+        bias: bool = True,
+    ):
+        super().__init__()
+
+        kernel_size, stride, padding = map(
+            lambda x: (x, x, x) if isinstance(x, int) else x,
+            (kernel_size, stride, padding),
+        )
+        scale = math.sqrt(
+            1 / (in_channels * kernel_size[0] * kernel_size[1] * kernel_size[2])
+        )
+        self.weight = mx.random.uniform(
+            low=-scale,
+            high=scale,
+            shape=(out_channels, *kernel_size, in_channels),
+        )
+        if bias:
+            self.bias = mx.zeros((out_channels,))
+
+        self.padding = padding
+        self.stride = stride
+
+    def _extra_repr(self):
+        return (
+            f"{self.weight.shape[-1]}, {self.weight.shape[0]}, "
+            f"kernel_size={self.weight.shape[1:3]}, stride={self.stride}, "
+            f"padding={self.padding}, bias={'bias' in self}"
+        )
+
+    def __call__(self, x):
+        y = mx.conv3d(x, self.weight, self.stride, self.padding)
+        if "bias" in self:
+            y = y + self.bias
+        return y