mlx - more conv

keras/src/backend/common/backend_utils.py

@@ -187,6 +187,88 @@ def compute_conv_transpose_padding_args_for_torch(
     return torch_paddings, torch_output_paddings
 
 
+def _convert_conv_tranpose_padding_args_from_keras_to_mlx(
+    kernel_size, stride, dilation_rate, padding, output_padding
+):
+    effective_k_size = (kernel_size - 1) * dilation_rate + 1
+    if padding == "valid":
+        output_padding = (
+            max(effective_k_size, stride) - effective_k_size
+            if output_padding is None
+            else output_padding
+        )
+        pad_left = effective_k_size - 1
+        pad_right = effective_k_size - 1 + output_padding
+    elif padding == "same":
+        if output_padding is None:
+            total_pad = stride + effective_k_size - 2
+        else:
+            total_pad = (
+                effective_k_size + effective_k_size % 2 - 2 + output_padding
+            )
+        pad_left = min(total_pad // 2 + total_pad % 2, effective_k_size - 1)
+        pad_right = total_pad - pad_left
+    else:
+        raise ValueError(f"Invalid padding value: {padding}")
+    return pad_left, pad_right
+
+
+def compute_conv_transpose_padding_args_for_mlx(
+    padding,
+    num_spatial_dims,
+    kernel_spatial_shape,
+    dilation_rate,
+    strides,
+    output_padding,
+):
+    start_paddings = []
+    end_paddings = []
+    for i in range(num_spatial_dims):
+        kernel_size_i = kernel_spatial_shape[i]
+        stride_i = strides[i]
+        dilation_rate_i = dilation_rate[i]
+        output_padding_i = None if output_padding is None else output_padding[i]
+        pad_left, pad_right = (
+            _convert_conv_tranpose_padding_args_from_keras_to_mlx(
+                kernel_size_i,
+                stride_i,
+                dilation_rate_i,
+                padding,
+                output_padding_i,
+            )
+        )
+        start_paddings.append(pad_left)
+        end_paddings.append(pad_right)
+    return (start_paddings, end_paddings)
+
+
+def compute_transpose_padding_args_for_mlx(
+    padding,
+    input_spatial_shape,
+    kernel_spatial_shape,
+    dilation_rate,
+    strides,
+):
+    if padding == "valid":
+        return 0
+    elif padding == "same":
+        start_paddings = []
+        end_paddings = []
+        for dim_size, k_size, d_rate, s in zip(
+            input_spatial_shape, kernel_spatial_shape, dilation_rate, strides
+        ):
+            out_size = (dim_size + s - 1) // s
+            effective_k_size = (k_size - 1) * d_rate + 1
+            total_pad = max(0, (out_size - 1) * s + effective_k_size - dim_size)
+            pad_start = total_pad // 2
+            pad_end = total_pad - pad_start
+            start_paddings.append(pad_start)
+            end_paddings.append(pad_end)
+        return (start_paddings, end_paddings)
+    else:
+        raise ValueError(f"Invalid padding value: {padding}")
+
+
 def _get_output_shape_given_tf_padding(
     input_size, kernel_size, strides, padding, output_padding, dilation_rate
 ):

keras/src/backend/mlx/nn.py

@@ -3,6 +3,12 @@
 
 from keras.src.backend import standardize_data_format
 from keras.src.backend import standardize_dtype
+from keras.src.backend.common.backend_utils import (
+    compute_conv_transpose_padding_args_for_mlx,
+)
+from keras.src.backend.common.backend_utils import (
+    compute_transpose_padding_args_for_mlx,
+)
 from keras.src.backend.config import epsilon
 from keras.src.backend.mlx.core import convert_to_tensor
 from keras.src.backend.mlx.core import to_mlx_dtype
@@ -148,25 +154,15 @@ def conv(
     # mlx expects kernel with (out_channels, spatial..., in_channels)
     kernel = kernel.transpose(-1, *range(kernel.ndim - 2), -2)
 
-    if padding == "valid":
-        mlx_padding = 0
-    elif padding == "same":
-        kernel_spatial_shape = kernel.shape[1:-1]
-        start_paddings = []
-        end_paddings = []
-        for dim_size, k_size, d_rate, s in zip(
-            inputs.shape[1:-1], kernel_spatial_shape, dilation_rate, strides
-        ):
-            out_size = (dim_size + s - 1) // s
-            effective_k_size = (k_size - 1) * d_rate + 1
-            total_pad = max(0, (out_size - 1) * s + effective_k_size - dim_size)
-            pad_start = total_pad // 2
-            pad_end = total_pad - pad_start
-            start_paddings.append(pad_start)
-            end_paddings.append(pad_end)
-        mlx_padding = (start_paddings, end_paddings)
-    else:
-        raise ValueError(f"Invalid padding value: {padding}")
+    kernel_spatial_shape = kernel.shape[1:-1]
+    input_spatial_shape = inputs.shape[1:-1]
+    mlx_padding = compute_transpose_padding_args_for_mlx(
+        padding,
+        input_spatial_shape,
+        kernel_spatial_shape,
+        dilation_rate,
+        strides,
+    )
 
     channels = inputs.shape[-1]
     kernel_in_channels = kernel.shape[-1]
@@ -202,7 +198,53 @@ def depthwise_conv(
     data_format=None,
     dilation_rate=1,
 ):
-    raise NotImplementedError("MLX backend doesn't support depthwise conv yet")
+    inputs = convert_to_tensor(inputs)
+    kernel = convert_to_tensor(kernel)
+    data_format = standardize_data_format(data_format)
+    num_spatial_dims = inputs.ndim - 2
+
+    strides = standardize_tuple(strides, num_spatial_dims, "strides")
+    dilation_rate = standardize_tuple(
+        dilation_rate, num_spatial_dims, "dilation_rate"
+    )
+
+    if data_format == "channels_first":
+        # mlx expects channels_last
+        inputs = inputs.transpose(0, *range(2, inputs.ndim), 1)
+
+    feature_group_count = inputs.shape[-1]
+
+    # reshape first for depthwise conv, then transpose to expected mlx format
+    kernel = kernel.reshape(
+        *iter(kernel.shape[:-2]), 1, feature_group_count * kernel.shape[-1]
+    )
+    # mlx expects kernel with (out_channels, spatial..., in_channels)
+    kernel = kernel.transpose(-1, *range(kernel.ndim - 2), -2)
+
+    kernel_spatial_shape = kernel.shape[1:-1]
+    input_spatial_shape = inputs.shape[1:-1]
+    mlx_padding = compute_transpose_padding_args_for_mlx(
+        padding,
+        input_spatial_shape,
+        kernel_spatial_shape,
+        dilation_rate,
+        strides,
+    )
+
+    result = mx.conv_general(
+        inputs,
+        kernel,
+        stride=strides,
+        padding=mlx_padding,
+        kernel_dilation=dilation_rate,
+        input_dilation=1,
+        groups=feature_group_count,
+        flip=False,
+    )
+    if data_format == "channels_first":
+        result = result.transpose(0, -1, *range(1, result.ndim - 1))
+
+    return result
 
 
 def separable_conv(
@@ -214,7 +256,23 @@ def separable_conv(
     data_format=None,
     dilation_rate=1,
 ):
-    raise NotImplementedError("MLX backend doesn't support separable conv yet")
+    data_format = standardize_data_format(data_format)
+    depthwise_conv_output = depthwise_conv(
+        inputs,
+        depthwise_kernel,
+        strides,
+        padding,
+        data_format,
+        dilation_rate,
+    )
+    return conv(
+        depthwise_conv_output,
+        pointwise_kernel,
+        strides=1,
+        padding="valid",
+        data_format=data_format,
+        dilation_rate=dilation_rate,
+    )
 
 
 def conv_transpose(
@@ -226,7 +284,62 @@ def conv_transpose(
     data_format=None,
     dilation_rate=1,
 ):
-    raise NotImplementedError("MLX backend doesn't support conv transpose yet")
+    inputs = convert_to_tensor(inputs)
+    kernel = convert_to_tensor(kernel)
+    data_format = standardize_data_format(data_format)
+    num_spatial_dims = inputs.ndim - 2
+
+    strides = standardize_tuple(strides, num_spatial_dims, "strides")
+    dilation_rate = standardize_tuple(
+        dilation_rate, num_spatial_dims, "dilation_rate"
+    )
+    if output_padding is not None:
+        output_padding = standardize_tuple(
+            output_padding, num_spatial_dims, "output_padding"
+        )
+
+    if data_format == "channels_first":
+        # mlx expects channels_last
+        inputs = inputs.transpose(0, *range(2, inputs.ndim), 1)
+
+    # mlx expects kernel with (out_channels, spatial..., in_channels)
+    kernel = kernel.transpose(-2, *range(kernel.ndim - 2), -1)
+    kernel_spatial_shape = kernel.shape[1:-1]
+
+    mlx_padding = compute_conv_transpose_padding_args_for_mlx(
+        padding,
+        num_spatial_dims,
+        kernel_spatial_shape,
+        dilation_rate,
+        strides,
+        output_padding,
+    )
+
+    channels = inputs.shape[-1]
+    kernel_in_channels = kernel.shape[-1]
+    if channels % kernel_in_channels > 0:
+        raise ValueError(
+            "The number of input channels must be evenly divisible by "
+            f"kernel's in_channels. Received input channels {channels} and "
+            f"kernel in_channels {kernel_in_channels}. "
+        )
+    groups = channels // kernel_in_channels
+
+    result = mx.conv_general(
+        inputs,
+        kernel,
+        stride=1,  # stride is handled by input_dilation
+        padding=mlx_padding,
+        kernel_dilation=dilation_rate,
+        input_dilation=strides,
+        groups=groups,
+        flip=True,
+    )
+
+    if data_format == "channels_first":
+        result = result.transpose(0, -1, *range(1, result.ndim - 1))
+
+    return result
 
 
 def one_hot(x, num_classes, axis=-1, dtype="float32", sparse=False):