# Copyright © 2023 Apple Inc. import textwrap from typing import Any, Callable, List, Optional, Tuple, Union import mlx.core as mx from mlx.utils import tree_flatten, tree_unflatten class Module(dict): """Base class for building neural networks with MLX. All the layers provided in :mod:`mlx.nn.layers` subclass this class and your models should do the same. A ``Module`` can contain other ``Module`` instances or :class:`mlx.core.array` instances in arbitrary nesting of python lists or dicts. The ``Module`` then allows recursively extracting all the :class:`mlx.core.array` instances using :meth:`mlx.nn.Module.parameters`. In addition, the ``Module`` has the concept of trainable and non trainable parameters (called "frozen"). When using :func:`mlx.nn.value_and_grad` the gradients are returned only with respect to the trainable parameters. All arrays in a module are trainable unless they are added in the "frozen" set by calling :meth:`freeze`. .. code-block:: python import mlx.core as mx import mlx.nn as nn class MyMLP(nn.Module): def __init__(self, in_dims: int, out_dims: int, hidden_dims: int = 16): super().__init__() self.in_proj = nn.Linear(in_dims, hidden_dims) self.out_proj = nn.Linear(hidden_dims, out_dims) def __call__(self, x): x = self.in_proj(x) x = mx.maximum(x, 0) return self.out_proj(x) model = MyMLP(2, 1) # All the model parameters are created but since MLX is lazy by # default, they are not evaluated yet. Calling `mx.eval` actually # allocates memory and initializes the parameters. mx.eval(model.parameters()) # Setting a parameter to a new value is as simply as accessing that # parameter and assigning a new array to it. model.in_proj.weight = model.in_proj.weight * 2 mx.eval(model.parameters()) """ def __init__(self): """Should be called by the subclasses of ``Module``.""" self._no_grad = set() self._training = True @property def training(self): """Boolean indicating if the model is in training mode.""" return self._training def _extra_repr(self): return "" def __repr__(self): children = tree_flatten(self.children(), is_leaf=self.is_module) value = f"{type(self).__name__}({self._extra_repr()}" for k, v in children: value += "\n" value += textwrap.indent(f"({k}): {repr(v)}", prefix=" ") if children: value += "\n" value += ")" return value def __getattr__(self, key: str): if key in self: return self[key] else: raise AttributeError(f"{type(self)!r} has no attribute {key!r}") def __setattr__(self, key: str, val: Any): self[key] = val def load_weights( self, file_or_weights: Union[str, List[Tuple[str, mx.array]]], strict: bool = True, ): """ Update the model's weights from a ``.npz`` or a list. Args: file_or_weights (str or list(tuple(str, mx.array))): The path to the weights ``.npz`` file or a list of pairs of parameter names and arrays. strict (bool, optional): If ``True`` then checks that the provided weights exactly match the parameters of the model. Otherwise, only the weights actually contained in the model are loaded and shapes are not checked. Default: ``True``. Example: .. code-block:: python import mlx.core as mx import mlx.nn as nn model = nn.Linear(10, 10) # Load from file model.load_weights("weights.npz") # Load from list weights = [ ("weight", mx.random.uniform(shape=(10, 10))), ("bias", mx.zeros((10,))), ] model.load_weights(weights) # Missing weight weights = [ ("weight", mx.random.uniform(shape=(10, 10))), ] # Raises a ValueError exception model.load_weights(weights) # Ok, only updates the weight but not the bias model.load_weights(weights, strict=False) """ weights = file_or_weights if isinstance(weights, str): weights = list(mx.load(weights).items()) if strict: new_weights = dict(weights) curr_weights = dict(tree_flatten(self.parameters())) if extras := (new_weights.keys() - curr_weights.keys()): extras = " ".join(extras) raise ValueError(f"Received parameters not in model: {extras}.") if missing := (curr_weights.keys() - new_weights.keys()): missing = " ".join(missing) raise ValueError(f"Missing parameters: {missing}.") for k, v in curr_weights.items(): v_new = new_weights[k] if not isinstance(v_new, mx.array): raise ValueError( "Expected mx.array but received " f"{type(v_new)} for parameter {k}" ) if v_new.shape != v.shape: raise ValueError( f"Expected shape {v.shape} but received " f" shape {v_new.shape} for parameter {k}" ) self.update(tree_unflatten(weights)) def save_weights(self, file: str): """ Save the model's weights to a ``.npz`` file. """ mx.savez(file, **dict(tree_flatten(self.parameters()))) @staticmethod def is_module(value): return isinstance(value, Module) @staticmethod def valid_child_filter(module, key, value): return isinstance(value, (dict, list)) @staticmethod def valid_parameter_filter(module, key, value): return isinstance(value, (dict, list, mx.array)) and not key.startswith("_") @staticmethod def trainable_parameter_filter(module, key, value): return ( Module.valid_parameter_filter(module, key, value) and key not in module._no_grad ) def filter_and_map( self, filter_fn: Callable[["mlx.nn.Module", str, Any], bool], map_fn: Optional[Callable] = None, is_leaf_fn: Optional[Callable[["mlx.nn.Module", str, Any], bool]] = None, ): """Recursively filter the contents of the module using ``filter_fn``, namely only select keys and values where ``filter_fn`` returns true. This is used to implement :meth:`parameters` and :meth:`trainable_parameters` but it can also be used to extract any subset of the module's parameters. Args: filter_fn (Callable): Given a value, the key in which it is found and the containing module, decide whether to keep the value or drop it. map_fn (Callable, optional): Optionally transform the value before returning it. is_leaf_fn (Callable, optional): Given a value, the key in which it is found and the containing module decide if it is a leaf. Returns: A dictionary containing the contents of the module recursively filtered """ map_fn = map_fn or (lambda x: x) is_leaf_fn = is_leaf_fn or ( lambda m, k, v: not isinstance(v, (Module, dict, list)) ) def unwrap(vk, v): if is_leaf_fn(self, vk, v): return map_fn(v) if isinstance(v, Module): return v.filter_and_map(filter_fn, map_fn, is_leaf_fn) if isinstance(v, dict): nd = {} for k, v in v.items(): tk = f"{vk}.{k}" nd[k] = unwrap(tk, v) if filter_fn(self, tk, v) else {} return nd if isinstance(v, list): nl = [] for i, vi in enumerate(v): tk = f"{vk}.{i}" nl.append(unwrap(tk, vi) if filter_fn(self, tk, vi) else {}) return nl raise RuntimeError("Unexpected leaf found while traversing the module") return {k: unwrap(k, v) for k, v in self.items() if filter_fn(self, k, v)} def parameters(self): """Recursively return all the :class:`mlx.core.array` members of this Module as a dict of dicts and lists.""" return self.filter_and_map(self.valid_parameter_filter) def trainable_parameters(self): """Recursively return all the non frozen :class:`mlx.core.array` members of this Module as a dict of dicts and lists.""" return self.filter_and_map(self.trainable_parameter_filter) def children(self): """Return the direct descendants of this Module instance.""" return self.filter_and_map( self.valid_child_filter, is_leaf_fn=lambda m, k, v: isinstance(v, Module) ) def leaf_modules(self): """Return the submodules that do not contain other modules.""" def _is_leaf_module(m, k, v): return isinstance(v, Module) and len(tree_flatten(v.children())) == 0 return self.filter_and_map(self.valid_child_filter, is_leaf_fn=_is_leaf_module) def update(self, parameters: dict): """Replace the parameters of this Module with the provided ones in the dict of dicts and lists. Commonly used by the optimizer to change the model to the updated (optimized) parameters. Also used by the :meth:`mlx.nn.value_and_grad` to set the tracers in the model in order to compute gradients. The passed in parameters dictionary need not be a full dictionary similar to :meth:`parameters`. Only the provided locations will be updated. Args: parameters (dict): A complete or partial dictionary of the modules parameters. """ def apply(dst, parameters): if isinstance(parameters, dict): for k in parameters: if k in dst: current_value = dst[k] new_value = parameters[k] if isinstance(current_value, mx.array): dst[k] = new_value elif isinstance(current_value, Module): current_value.update(new_value) elif isinstance(current_value, (dict, list)): apply(current_value, new_value) elif isinstance(parameters, list): for i in range(len(dst)): current_value = dst[i] new_value = parameters[i] if isinstance(current_value, mx.array): dst[i] = new_value elif isinstance(current_value, Module): current_value.update(new_value) elif isinstance(current_value, (dict, list)): apply(current_value, new_value) apply(self, parameters) def apply( self, map_fn: Callable[[mx.array], mx.array], filter_fn: Optional[Callable[["mlx.nn.Module", str, Any], bool]] = None, ): """Map all the parameters using the provided ``map_fn`` and immediately update the module with the mapped parameters. For instance running ``model.apply(lambda x: x.astype(mx.float16))`` casts all parameters to 16 bit floats. Args: map_fn (Callable): Maps an array to another array filter_fn (Callable, optional): Filter to select which arrays to map (default: :meth:`Module.valid_parameter_filter`). """ filter_fn = filter_fn or Module.valid_parameter_filter self.update(self.filter_and_map(filter_fn, map_fn)) def update_modules(self, modules: dict): """Replace the child modules of this :class:`Module` instance with the provided ones in the dict of dicts and lists. It is the equivalent of :meth:`Module.update` but for modules instead of parameters and allows us to flexibly edit complex architectures by programmatically swapping layers. The passed in parameters dictionary need not be a full dictionary similar to :meth:`parameters`. Only the provided locations will be updated. Args: modules (dict): A complete or partial dictionary of the modules submodules. """ def apply(dst, modules): if isinstance(modules, dict): for k in modules: if k in dst: current_value = dst[k] new_value = modules[k] if self.is_module(current_value) and self.is_module(new_value): dst[k] = new_value elif isinstance(current_value, (dict, list)): apply(current_value, new_value) elif isinstance(modules, list): for i in range(len(dst)): current_value = dst[i] new_value = modules[i] if self.is_module(current_value) and self.is_module(new_value): dst[i] = new_value elif isinstance(current_value, (dict, list)): apply(current_value, new_value) apply(self, modules) def apply_to_modules(self, apply_fn: Callable[[str, "mlx.nn.Module"], Any]): """Apply a function to all the modules in this instance (including this instance). Args: apply_fn (Callable): The function to apply to the modules. """ module_stack = [("", self)] while module_stack: prefix, mod = module_stack.pop() apply_fn(prefix, mod) prefix = "." + prefix if prefix else "" module_stack.extend( tree_flatten(mod.children(), prefix=prefix, is_leaf=self.is_module) ) def modules(self): """Return a list with all the modules in this instance. Returns: A list of :class:`mlx.nn.Module` instances. """ modulelist = [] self.apply_to_modules(lambda k, m: modulelist.append(m)) return modulelist def named_modules(self): """Return a list with all the modules in this instance and their name with dot notation. Returns: A list of tuples (str, :class:`mlx.nn.Module`). """ modulelist = [] self.apply_to_modules(lambda k, m: modulelist.append((k, m))) return modulelist def _validate_keys(self, keys, strict): keys = keys if isinstance(keys, list) else [keys] if strict: for k in keys: if k not in self: raise KeyError(f"Module doesn't contain member {k}.") return keys def freeze( self, *, recurse: bool = True, keys: Optional[Union[str, List[str]]] = None, strict: bool = False, ): """Freeze the Module's parameters or some of them. Freezing a parameter means not computing gradients for it. This function is idempotent i.e. freezing a frozen model is a no-op. Example: For instance to only train the attention parameters from a Transformer: .. code-block:: python model = nn.Transformer() model.freeze() model.apply_to_modules(lambda k, v: v.unfreeze() if k.endswith("attention") else None) Args: recurse (bool, optional): If True then freeze the parameters of the submodules as well. Default: ``True``. keys (str or list[str], optional): If provided then only these parameters will be frozen otherwise all the parameters of a module. For instance freeze all biases by calling ``module.freeze(keys="bias")``. strict (bool, optional): If set to ``True`` validate that the passed keys exist. Default: ``False``. """ def _freeze_impl(_, m): local_keys = keys if local_keys is None: local_keys = tree_flatten( m.filter_and_map( lambda m, k, v: (not isinstance(v, Module)) and m.valid_parameter_filter(m, k, v) ) ) local_keys = [k for (k, v) in local_keys] local_keys = m._validate_keys(local_keys, strict) m._no_grad.update(local_keys) if recurse: self.apply_to_modules(_freeze_impl) else: _freeze_impl("", self) def unfreeze( self, *, recurse: bool = True, keys: Optional[Union[str, List[str]]] = None, strict: bool = False, ): """Unfreeze the Module's parameters or some of them. This function is idempotent ie unfreezing a model that is not frozen is a noop. Example: For instance to only train the biases of a Transformer one can do: .. code-block:: python model = nn.Transformer() model.freeze() model.unfreeze(keys="bias") Args: recurse (bool, optional): If True then unfreeze the parameters of the submodules as well. Default: ``True``. keys (str or list[str], optional): If provided then only these parameters will be unfrozen otherwise all the parameters of a module. For instance unfreeze all biases by calling ``module.unfreeze(keys="bias")``. strict (bool, optional): If set to ``True`` validate that the passed keys exist. Default: ``False``. """ def _unfreeze_impl(_, m): if keys is None: m._no_grad.clear() else: local_keys = m._validate_keys(keys, strict) m._no_grad.difference_update(local_keys) if recurse: self.apply_to_modules(_unfreeze_impl) else: _unfreeze_impl("", self) def train(self, mode: bool = True): """Set the model in or out of training mode. Training mode only applies to certain layers. For example :obj:`Dropout` applies a random mask in training mode, but is the identity in evaluation mode. Args: mode (bool): Indicate if the model should be in training or evaluation mode. Default: ``True``. """ def _set_train(_, m): m._training = mode self.apply_to_modules(_set_train) def eval(self): """Set the model to evaluation mode. See :func:`train`. """ self.train(False)