diff --git a/docs/notebooks/subset_training.ipynb b/docs/notebooks/subset_training.ipynb index 3316d9b..2d12d04 100644 --- a/docs/notebooks/subset_training.ipynb +++ b/docs/notebooks/subset_training.ipynb @@ -6,7 +6,13 @@ "source": [ "# 🍰 Parameter subset training\n", "\n", - "The following example demonstrates how to train a subset of parameters" + "The following example demonstrates how to train a subset of parameters. \n", + "\n", + "\n", + "One approach is to split the parameters into two groups and train them separately, the set that is trainable and the set that is not trainable, then change the loss function signature from `loss_func(net, *args)` to \n", + "`loss_func(trainable_net, non_trainable_net, *args)`, and use `jax` transformations on the new loss function to select only the trainable parameters via `argnums`. The problem with this approach is that it requires changing the loss function signature, which requires a rewrite of the loss function and all the functions that call it plus a function that splits the parameters into two groups.\n", + "\n", + "`serket` adopts a different approach that does not change the loss function signature. The idea is to use a pytree wrapper that hides un wanted parameters from tree input to the loss function. By doing so, selecting single parameters or groups of parameters to set untrainable becomes as simple as just wrapping them, instead of having to use `jax.tree_map` to split the parameters into two groups and then rewrite the loss function." ] }, { @@ -18,6 +24,54 @@ "!pip install git+https://github.com/ASEM000/serket --quiet" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import serket as sk\n", + "import jax\n", + "import jax.numpy as jnp\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we continue with the example, let's first see how to wrap a parameter or a group of parameters using `serket.tree_mask`\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1]\n", + "{'a': 101, 'b': #1.0}\n", + "[1, 1.0]\n", + "{'a': 101, 'b': 101.0}\n" + ] + } + ], + "source": [ + "# always returns a frozen tree by making the mask always True\n", + "freeze = lambda node: sk.tree_mask(node, mask=lambda _: True)\n", + "\n", + "tree = dict(a=1, b=freeze(1.0))\n", + "print(jax.tree_util.tree_leaves(tree)) # -> b is excluded\n", + "print(jax.tree_util.tree_map(lambda x: x + 100, tree)) # only a is updated\n", + "# now lets unfreeze b by removing the mask\n", + "tree[\"b\"] = sk.tree_unmask(tree[\"b\"])\n", + "print(jax.tree_util.tree_leaves(tree)) # -> b is included\n", + "print(jax.tree_util.tree_map(lambda x: x + 100, tree)) # now b is updated" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -27,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -35,39 +89,39 @@ "output_type": "stream", "text": [ "linear1.bias before training\n", - "[[ 1.5566283 -1.6665335 1.4389884 -1.0703712 0.2445326 -1.0884298\n", - " -1.2858775 1.6523795 -1.1615381 -0.1716814]]\n", + "[[ 0.49224907]\n", + " [-0.5270041 ]\n", + " [ 0.45504808]\n", + " [-0.33848107]\n", + " [ 0.077328 ]\n", + " [-0.34419173]\n", + " [-0.40663013]\n", + " [ 0.5225283 ]\n", + " [-0.36731058]\n", + " [-0.05429043]]\n", "linear2.bias before training\n", - "[[ 0.4221825 ]\n", - " [-0.2719926 ]\n", - " [-0.1374367 ]\n", - " [-0.04014226]\n", - " [ 0.39653325]\n", - " [ 0.6115301 ]\n", - " [ 0.25633797]\n", - " [-0.28208828]\n", - " [-0.57896185]\n", - " [-0.84625894]]\n", + "[[ 1.3350583 -0.8601161 -0.43461302 -0.126941 1.2539482 1.9338281\n", + " 0.8106119 -0.8920415 -1.8308382 -2.6761057 ]]\n", "====================================================================================================\n", "linear1.weight after training\n", - "[[ 1.5091101 -1.6033007 1.5654734 -1.091966 0.32660955 -1.4726009\n", - " -1.542354 1.8473767 -1.0972728 -0.34225878]]\n", + "[[ 0.50442326]\n", + " [-0.56349325]\n", + " [ 0.9291051 ]\n", + " [-0.3272876 ]\n", + " [ 0.1763043 ]\n", + " [-1.3267784 ]\n", + " [-0.90045315]\n", + " [ 1.3376616 ]\n", + " [-0.41753256]\n", + " [-0.22012867]]\n", "linear2.weight after training\n", - "[[ 0.7347251 ]\n", - " [-0.26030147]\n", - " [-0.7558785 ]\n", - " [ 0.2405407 ]\n", - " [ 0.50954366]\n", - " [ 1.4544754 ]\n", - " [ 1.0004135 ]\n", - " [-1.2522283 ]\n", - " [-0.81760883]\n", - " [-0.9254845 ]]\n" + "[[ 1.3854882 -0.8917761 -0.9622724 -0.1221064 1.2699872 2.4034615\n", + " 1.1609921 -1.6427239 -1.8600159 -2.6882167]]\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -77,10 +131,7 @@ } ], "source": [ - "import serket as sk\n", - "import jax\n", - "import jax.numpy as jnp\n", - "import matplotlib.pyplot as plt\n", + "\n", "\n", "\n", "class Net(sk.TreeClass):\n", @@ -144,12 +195,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, lets train only `linear2` and exclude `linear1.weight`, by simply applying `sk.freeze` on `linear1.weight` without changing anything else." + "Now, lets exclude `linear1.weight`, by simply applying `sk.tree_mask(..., lambda _:True)` on `linear1.weight` without changing anything else. " ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -157,39 +208,39 @@ "output_type": "stream", "text": [ "linear1.weight before training\n", - "[[ 1.5566283 -1.6665335 1.4389884 -1.0703712 0.2445326 -1.0884298\n", - " -1.2858775 1.6523795 -1.1615381 -0.1716814]]\n", + "[[ 0.49224907]\n", + " [-0.5270041 ]\n", + " [ 0.45504808]\n", + " [-0.33848107]\n", + " [ 0.077328 ]\n", + " [-0.34419173]\n", + " [-0.40663013]\n", + " [ 0.5225283 ]\n", + " [-0.36731058]\n", + " [-0.05429043]]\n", "linear2.bias before training\n", - "[[ 0.4221825 ]\n", - " [-0.2719926 ]\n", - " [-0.1374367 ]\n", - " [-0.04014226]\n", - " [ 0.39653325]\n", - " [ 0.6115301 ]\n", - " [ 0.25633797]\n", - " [-0.28208828]\n", - " [-0.57896185]\n", - " [-0.84625894]]\n", + "[[ 1.3350583 -0.8601161 -0.43461302 -0.126941 1.2539482 1.9338281\n", + " 0.8106119 -0.8920415 -1.8308382 -2.6761057 ]]\n", "====================================================================================================\n", "linear1.weight after training\n", - "[[ 1.5566283 -1.6665335 1.4389884 -1.0703712 0.2445326 -1.0884298\n", - " -1.2858775 1.6523795 -1.1615381 -0.1716814]]\n", + "[[ 0.49224907]\n", + " [-0.5270041 ]\n", + " [ 0.45504808]\n", + " [-0.33848107]\n", + " [ 0.077328 ]\n", + " [-0.34419173]\n", + " [-0.40663013]\n", + " [ 0.5225283 ]\n", + " [-0.36731058]\n", + " [-0.05429043]]\n", "linear2.weight after training\n", - "[[ 0.7734523 ]\n", - " [-0.23901251]\n", - " [-0.85610443]\n", - " [ 0.38495493]\n", - " [ 0.48911053]\n", - " [ 1.5550724 ]\n", - " [ 1.1922117 ]\n", - " [-1.3470343 ]\n", - " [-0.80064476]\n", - " [-0.8950437 ]]\n" + "[[ 1.6160113 -0.84949774 -1.4579179 0.49293056 1.3003095 2.8833873\n", + " 1.9283237 -2.0790179 -1.8294345 -2.676439 ]]\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -207,8 +258,8 @@ "print(\"linear2.bias before training\")\n", "print(net.linear2.weight)\n", "\n", - "# exclude `linear1` by freezing it\n", - "net = net.at[\"linear1\"][\"weight\"].apply(sk.freeze)\n", + "# exclude `linear1` by freezing it (wrapping by leafless pytree wrapper)\n", + "net = net.at[\"linear1\"][\"weight\"].apply(freeze)\n", "\n", "for i in range(10_000):\n", " net = train_step(net, x, y)\n", diff --git a/serket/_src/containers.py b/serket/_src/containers.py index b864a32..e8bc34a 100644 --- a/serket/_src/containers.py +++ b/serket/_src/containers.py @@ -25,7 +25,7 @@ @ft.singledispatch -def sequential(key: jax.Array, _, __): +def sequential(key: jax.Array, _1, _2): raise TypeError(f"Invalid {type(key)=}") @@ -73,6 +73,7 @@ class Sequential(sk.TreeClass): """ def __init__(self, *layers): + # use var args to enforce tuple type to maintain immutability self.layers = layers def __call__(self, input: jax.Array, *, key: jax.Array | None = None) -> jax.Array: diff --git a/serket/_src/nn/attention.py b/serket/_src/nn/attention.py index 4cbe993..791529d 100644 --- a/serket/_src/nn/attention.py +++ b/serket/_src/nn/attention.py @@ -147,18 +147,19 @@ class MultiHeadAttention(sk.TreeClass): >>> kv_length = 2 >>> mask = jr.uniform(jr.PRNGKey(0), (batch, num_heads, q_length, kv_length)) >>> mask = (mask > 0.5).astype(jnp.float32) - >>> q = jr.uniform(jr.PRNGKey(1), (batch, q_length, q_features)) - >>> k = jr.uniform(jr.PRNGKey(2), (batch, kv_length, k_features)) - >>> v = jr.uniform(jr.PRNGKey(3), (batch, kv_length, v_features)) + >>> k1, k2, k3, k4 = jr.split(jr.PRNGKey(0), 4) + >>> q = jr.uniform(k1, (batch, q_length, q_features)) + >>> k = jr.uniform(k2, (batch, kv_length, k_features)) + >>> v = jr.uniform(k3, (batch, kv_length, v_features)) >>> layer = sk.nn.MultiHeadAttention( ... num_heads, ... q_features, ... k_features, ... v_features, ... drop_rate=0.0, - ... key=jr.PRNGKey(4), + ... key=k4, ... ) - >>> print(layer(q, k, v, mask=mask, key=jr.PRNGKey(0)).shape) + >>> print(layer(q, k, v, mask=mask, key=jr.PRNGKey(1)).shape) (3, 4, 4) Note: @@ -184,13 +185,13 @@ class MultiHeadAttention(sk.TreeClass): >>> import jax.random as jr >>> import serket as sk - >>> q = jr.uniform(jr.PRNGKey(0), (3, 2, 6)) - >>> k = jr.uniform(jr.PRNGKey(1), (3, 2, 6)) - >>> v = jr.uniform(jr.PRNGKey(2), (3, 2, 6)) - >>> key = jr.PRNGKey(0) - >>> lazy = sk.nn.MultiHeadAttention(2, None, key=key) - >>> _, material = sk.value_and_tree(lambda lazy: lazy(q, k, v, key=key))(lazy) - >>> material(q, k, v, key=key).shape + >>> k1, k2, k3, k4, k5 = jr.split(jr.PRNGKey(0), 5) + >>> q = jr.uniform(k1, (3, 2, 6)) + >>> k = jr.uniform(k2, (3, 2, 6)) + >>> v = jr.uniform(k3, (3, 2, 6)) + >>> lazy = sk.nn.MultiHeadAttention(2, None, key=k4) + >>> _, material = sk.value_and_tree(lambda lazy: lazy(q, k, v, key=k4))(lazy) + >>> material(q, k, v, key=k5).shape (3, 2, 6) Reference: diff --git a/serket/_src/nn/convolution.py b/serket/_src/nn/convolution.py index 835231e..8ea274b 100644 --- a/serket/_src/nn/convolution.py +++ b/serket/_src/nn/convolution.py @@ -49,7 +49,6 @@ Weight = Annotated[jax.Array, "OI..."] -@ft.partial(jax.jit, static_argnums=(2, 3, 4, 5), inline=True) def fft_conv_general_dilated( lhs: jax.Array, rhs: jax.Array, diff --git a/serket/_src/utils.py b/serket/_src/utils.py index e1667c4..4ec2852 100644 --- a/serket/_src/utils.py +++ b/serket/_src/utils.py @@ -309,7 +309,7 @@ def get_params(func: MethodType) -> tuple[inspect.Parameter, ...]: return tuple(inspect.signature(func).parameters.values()) -# TODO: maybe expose this as a public API +# Maybe expose this as a public API # Handling lazy layers """ Creating a _lazy_ ``Linear`` layer example: @@ -329,32 +329,67 @@ def get_params(func: MethodType) -> tuple[inspect.Parameter, ...]: translate code from both explicit and implicit shaped layer found in libraries like ``pytorch`` and ``tensorflow``. +As quick sketch how this work is in the following example: + +>>> import jax +>>> class Lazy: +... def __init__(self, dim_size: int | None): +... # let dim size be the array size +... # and if we dont have the array size +... # we can set it to None to be inferred later +... self.dim_size = dim_size +... def __call__(self, x): +... return x * self.dim_size +>>> def maybe_lazy_init(func): +... def wrapper(self, dim_size): +... if input is not None: +... return func(self, dim_size) +... # we do not execute the init function +... # because its lazy +... return None +... return wrapper +>>> def maybe_lazy_call(func): +... def wrapper(self, x): +... if self.dim_size is not None: +... return func(self, x) +... # the input is lazy , so we do infer the dim size +... # here. because `TreeClass` is immutable we need to +... # return a new instance of the class with the updated +... # dim size, but here we are just updating the dim size +... # of the current instance that is not immutable +... self.dim_size = x.size +... return func(self, x) +... return wrapper +>>> # now lets decorate our lazy class +>>> Lazy.__init__ = maybe_lazy_init(Lazy.__init__) +>>> Lazy.__call__ = maybe_lazy_call(Lazy.__call__) +>>> print(Lazy(2)(jax.numpy.ones([2]))) +>>> print(Lazy(None)(jax.numpy.ones([2]))) + + +Now lets create a lazy ``Linear`` layer using ``serket``: + >>> import functools as ft >>> import serket as sk >>> import jax.numpy as jnp >>> from serket._src.utils import maybe_lazy_call, maybe_lazy_init - >>> def is_lazy_init(self, in_features, out_features): ... # we need to define how to tell if the layer is lazy ... # based on the inputs ... return in_features is None # or anything else really - >>> def is_lazy_call(self, x): ... # we need to define how to tell if the layer is lazy ... # at the call time ... # replicating the lazy init condition ... return getattr(self, "in_features", False) is None - >>> def infer_in_features(self, x): ... # we need to define how to infer the in_features ... # based on the inputs at call time ... # for linear layers, we can infer the in_features as the last dimension ... return x.shape[-1] - >>> # lastly we need to assign this function to a dictionary that has the name >>> # of the feature we want to infer >>> updates = dict(in_features=infer_in_features) - >>> class SimpleLinear(sk.TreeClass): ... @ft.partial(maybe_lazy_init, is_lazy=is_lazy_init) ... def __init__(self, in_features, out_features): @@ -365,20 +400,17 @@ def get_params(func: MethodType) -> tuple[inspect.Parameter, ...]: >>> @ft.partial(maybe_lazy_call, is_lazy=is_lazy_call, updates=updates) ... def __call__(self, x): ... return x - >>> simple_lazy = SimpleLinear(None, 1) >>> x = jnp.ones([10, 2]) # last dimension is the in_features of the layer >>> print(repr(simple_lazy)) SimpleLinear(in_features=None, out_features=1) - ->>> _, material = simple_lazy.at["__call__"](x) - +>>> _, material = sk.value_and_tree(lambda layer: layer(x))(simple_lazy) >>> print(repr(material)) SimpleLinear( - in_features=2, - out_features=1, - weight=f32[2,1](μ=1.00, σ=0.00, ∈[1.00,1.00]), - bias=f32[1](μ=0.00, σ=0.00, ∈[0.00,0.00]) + in_features=2, + out_features=1, + weight=f32[2,1](μ=1.00, σ=0.00, ∈[1.00,1.00]), + bias=f32[1](μ=0.00, σ=0.00, ∈[0.00,0.00]) ) """ @@ -527,6 +559,8 @@ def inner(instance, *a, **k): raise RuntimeError(LAZY_CALL_ERROR.format(**kwargs)) # re-initialize the instance with the resolved arguments + # this will only works under `value_and_tree` that allows + # the instance to be mutable with it's context after being copied first getattr(type(instance), "__init__")(instance, **kwargs) # call the decorated function return func(instance, *a, **k)