Add LoRA to ConvND layers (#19516)

* Add LoRA to `BaseConv`

* Add tests

* Fix typo

* Fix tests

* Fix tests

keras/layers/convolutional/base_conv.py

@@ -71,6 +71,15 @@ class BaseConv(Layer):
             are not safe to use when doing asynchronous distributed training.
         bias_constraint: Optional projection function to be applied to the
             bias after being updated by an `Optimizer`.
+        lora_rank: Optional integer. If set, the layer's forward pass
+            will implement LoRA (Low-Rank Adaptation)
+            with the provided rank. LoRA sets the layer's kernel
+            to non-trainable and replaces it with a delta over the
+            original kernel, obtained via multiplying two lower-rank
+            trainable matrices. This can be useful to reduce the
+            computation cost of fine-tuning large dense layers.
+            You can also enable LoRA on an existing layer by calling
+            `layer.enable_lora(rank)`.
     """
 
     def __init__(
@@ -92,16 +101,10 @@ def __init__(
         activity_regularizer=None,
         kernel_constraint=None,
         bias_constraint=None,
-        trainable=True,
-        name=None,
+        lora_rank=None,
         **kwargs,
     ):
-        super().__init__(
-            trainable=trainable,
-            name=name,
-            activity_regularizer=activity_regularizer,
-            **kwargs,
-        )
+        super().__init__(activity_regularizer=activity_regularizer, **kwargs)
         self.rank = rank
         self.filters = filters
         self.groups = groups
@@ -120,6 +123,8 @@ def __init__(
         self.bias_regularizer = regularizers.get(bias_regularizer)
         self.kernel_constraint = constraints.get(kernel_constraint)
         self.bias_constraint = constraints.get(bias_constraint)
+        self.lora_rank = lora_rank
+        self.lora_enabled = False
         self.input_spec = InputSpec(min_ndim=self.rank + 2)
         self.data_format = self.data_format
 
@@ -187,7 +192,7 @@ def build(self, input_shape):
         # shape, and make sure the output shape has all positive dimensions.
         self.compute_output_shape(input_shape)
 
-        self.kernel = self.add_weight(
+        self._kernel = self.add_weight(
             name="kernel",
             shape=kernel_shape,
             initializer=self.kernel_initializer,
@@ -209,6 +214,20 @@ def build(self, input_shape):
         else:
             self.bias = None
         self.built = True
+        if self.lora_rank:
+            self.enable_lora(self.lora_rank)
+
+    @property
+    def kernel(self):
+        if not self.built:
+            raise AttributeError(
+                "You must build the layer before accessing `kernel`."
+            )
+        if self.lora_enabled:
+            return self._kernel + ops.matmul(
+                self.lora_kernel_a, self.lora_kernel_b
+            )
+        return self._kernel
 
     def convolution_op(self, inputs, kernel):
         return ops.conv(
@@ -248,6 +267,63 @@ def compute_output_shape(self, input_shape):
             dilation_rate=self.dilation_rate,
         )
 
+    def enable_lora(
+        self, rank, a_initializer="he_uniform", b_initializer="zeros"
+    ):
+        if self.kernel_constraint:
+            raise ValueError(
+                "Lora is incompatible with kernel constraints. "
+                "In order to enable lora on this layer, remove the "
+                "`kernel_constraint` argument."
+            )
+        if not self.built:
+            raise ValueError(
+                "Cannot enable lora on a layer that isn't yet built."
+            )
+        if self.lora_enabled:
+            raise ValueError(
+                "lora is already enabled. "
+                "This can only be done once per layer."
+            )
+        self._tracker.unlock()
+        self.lora_kernel_a = self.add_weight(
+            name="lora_kernel_a",
+            shape=self._kernel.shape[:-1] + (rank,),
+            initializer=initializers.get(a_initializer),
+            regularizer=self.kernel_regularizer,
+        )
+        self.lora_kernel_b = self.add_weight(
+            name="lora_kernel_b",
+            shape=(rank, self.filters),
+            initializer=initializers.get(b_initializer),
+            regularizer=self.kernel_regularizer,
+        )
+        self._kernel.trainable = False
+        self._tracker.lock()
+        self.lora_enabled = True
+        self.lora_rank = rank
+
+    def save_own_variables(self, store):
+        # Do nothing if the layer isn't yet built
+        if not self.built:
+            return
+        store["0"] = self.kernel
+        if self.use_bias:
+            store["1"] = self.bias
+
+    def load_own_variables(self, store):
+        if not self.lora_enabled:
+            self._check_load_own_variables(store)
+        # Do nothing if the layer isn't yet built
+        if not self.built:
+            return
+        self._kernel.assign(store["0"])
+        if self.use_bias:
+            self.bias.assign(store["1"])
+        if self.lora_enabled:
+            self.lora_kernel_a.assign(ops.zeros(self.lora_kernel_a.shape))
+            self.lora_kernel_b.assign(ops.zeros(self.lora_kernel_b.shape))
+
     def get_config(self):
         config = super().get_config()
         config.update(
@@ -282,4 +358,40 @@ def get_config(self):
                 "bias_constraint": constraints.serialize(self.bias_constraint),
             }
         )
+        if self.lora_rank:
+            config["lora_rank"] = self.lora_rank
         return config
+
+    def _check_load_own_variables(self, store):
+        all_vars = self._trainable_variables + self._non_trainable_variables
+        if len(store.keys()) != len(all_vars):
+            if len(all_vars) == 0 and not self.built:
+                raise ValueError(
+                    f"Layer '{self.name}' was never built "
+                    "and thus it doesn't have any variables. "
+                    f"However the weights file lists {len(store.keys())} "
+                    "variables for this layer.\n"
+                    "In most cases, this error indicates that either:\n\n"
+                    "1. The layer is owned by a parent layer that "
+                    "implements a `build()` method, but calling the "
+                    "parent's `build()` method did NOT create the state of "
+                    f"the child layer '{self.name}'. A `build()` method "
+                    "must create ALL state for the layer, including "
+                    "the state of any children layers.\n\n"
+                    "2. You need to implement "
+                    "the `def build_from_config(self, config)` method "
+                    f"on layer '{self.name}', to specify how to rebuild "
+                    "it during loading. "
+                    "In this case, you might also want to implement the "
+                    "method that generates the build config at saving time, "
+                    "`def get_build_config(self)`. "
+                    "The method `build_from_config()` is meant "
+                    "to create the state "
+                    "of the layer (i.e. its variables) upon deserialization.",
+                )
+            raise ValueError(
+                f"Layer '{self.name}' expected {len(all_vars)} variables, "
+                "but received "
+                f"{len(store.keys())} variables during loading. "
+                f"Expected: {[v.name for v in all_vars]}"
+            )