sae werks with deep loss

config.toml

@@ -1,33 +1,12 @@
 batch_size = 64
-steps = 1000000
+steps = 10000
-print_every = 50000
+print_every = 500
-seed = 0
+seed = 1
 cnn_storage = "./res/cnn.eqx"
 
 [[sae]]
 layer = 6
-hidden_size = 300
+hidden_size = 256
-input_size = 64
-learning_rate = 1e-3
-l1 = 3e-4 # from Neel Nanda's sae git
-
-[[sae]]
-layer = 6
-hidden_size = 300
-input_size = 64
-learning_rate = 3e-4
-l1 = 3e-4
-
-[[sae]]
-layer = 6
-hidden_size = 300
 input_size = 64
 learning_rate = 1e-4
 l1 = 3e-4
-
-[[sae]]
-layer = 6
-hidden_size = 300
-input_size = 64
-learning_rate = 3e-5
-l1 = 3e-4

src/__main__.py

@@ -39,21 +39,25 @@ for sae_hyperparams in O.sae:
     sae_dir = run_dir(sae_hyperparams)
     if sae_dir.exists():
         continue
-
-    sae = train_sae(
-        key,
-        cnn,
-        lambda m: m.layers[sae_hyperparams.layer],
-        sae_hyperparams.input_size,
-        sae_hyperparams.hidden_size,
-        O.batch_size,
-        sae_hyperparams.learning_rate,
-        O.steps,
-        O.print_every,
-        SummaryWriter(sae_dir / "log")
-    )
-
     sae_dir.mkdir()
+
+    with SummaryWriter(sae_dir / "log") as tensorboard:
+        tensorboard.add_text("hyperparameters", str(sae_hyperparams))
+
+        sae = train_sae(
+            key,
+            cnn,
+            sae_hyperparams.layer,
+            sae_hyperparams.input_size,
+            sae_hyperparams.hidden_size,
+            O.batch_size,
+            sae_hyperparams.learning_rate,
+            O.steps,
+            O.print_every,
+            tensorboard,
+            sae_hyperparams.l1,
+        )
+
     argtoml.save(sae_hyperparams, sae_dir / "sae-hyperparams.toml")
     argtoml.save(O, sae_dir / "config.toml")
     jo3eqx.save(
@@ -70,7 +74,7 @@ for sae_hyperparams in O.sae:
     sae = jo3eqx.load(sae_dir / f"sae.eqx", SAE)
     sown_cnn = jo3eqx.sow(lambda m: m.layers[sae_hyperparams.layer], cnn)
     trainloader, testloader = jo3mnist.load(
-        batch_size=O.batch_size, shuffle=False
+        batch_size=1, shuffle=False
     )
 
     train_dir = sae_dir / f"train"

src/sae.py

@@ -11,6 +11,7 @@ from typing import Callable, List
 import equinox as eqx
 import jax
 import jax.numpy as jnp
+import jax.tree_util as jtu
 import jo3mnist
 import optax
 from jaxtyping import Array, Float, Int, PyTree
@@ -18,6 +19,12 @@ from jo3util.eqx import insert_after, sow
 from torch.utils.data import DataLoader
 from torch.utils.tensorboard import SummaryWriter
 
+from .cnn import cross_entropy
+
+
+def filter_value_and_grad_with_aux(f):
+    return eqx.filter_value_and_grad(f, has_aux=True)
+
 
 class SAE(eqx.Module):
     we: Float
@@ -27,15 +34,16 @@ class SAE(eqx.Module):
 
     def __init__(self, in_size, hidden_size, key=jax.random.PRNGKey(42)):
         k0, k1, k2, k3 = jax.random.split(key, 4)
+        initializer = jax.nn.initializers.he_uniform()
 
         # encoder weight matrix
-        self.we = jax.random.uniform(k0, (in_size, hidden_size))
+        self.we = initializer(k0, (in_size, hidden_size))
         # decoder weight matrix
-        self.wd = jax.random.uniform(k1, (hidden_size, in_size))
+        self.wd = initializer(k1, (hidden_size, in_size))
         # encoder bias
-        self.be = jax.random.uniform(k2, (hidden_size,))
+        self.be = jnp.zeros((hidden_size,))
         # decader bias
-        self.bd = jax.random.uniform(k3, (in_size,))
+        self.bd = jnp.zeros((in_size,))
 
     def __call__(self, x):
         x = self.encode(x)
@@ -49,22 +57,32 @@ class SAE(eqx.Module):
     def decode(self, fx):
         return fx @ self.wd + self.bd
 
+    def l1(self, x):
+        x = self.encode(x)
+        return jax.vmap(jnp.dot, (0, 0))(x, x)
+
     @staticmethod
-    @eqx.filter_value_and_grad
+    @filter_value_and_grad_with_aux
-    def loss(sae, x, λ):
+    def loss(diff_model, static_model, sae_pos, x, y, λ):
-        fx = jax.vmap(sae.encode)(x)
+        model = eqx.combine(diff_model, static_model)
-        x_ = jax.vmap(sae.decode)(fx)
+        original_activ, reconstructed_activ, pred = jax.vmap(model)(x)
-        sq_err = jax.vmap(jnp.dot, (0, 0))((x - x_), (x - x_))
+
-        l1 = λ * jax.vmap(jnp.dot, (0, 0))(fx, fx)
+        reconstruction_err = jnp.mean(jax.vmap(jnp.dot, (0, 0))(
-        out = jnp.mean(sq_err + l1)
+            (original_activ - reconstructed_activ),
-        return out
+            (original_activ - reconstructed_activ)
+        ))
+        l1 = λ * jnp.mean(sae_pos(model).l1(original_activ))
+        deep_err = jnp.mean(cross_entropy(y, pred))
+
+        loss = reconstruction_err + l1 + deep_err
+        return loss, (reconstruction_err, l1, deep_err)
 
 
 def sample_features(cnn, sae, loader):
     for i, (x, _) in enumerate(loader):
         x = x.numpy()
         activ = jax.vmap(cnn)(x)[0]
-        yield i, sae.encode(activ)
+        yield i, sae.encode(activ)[0]
 
 
 def evaluate(model: eqx.Module, testloader: DataLoader):
@@ -84,23 +102,25 @@ def evaluate(model: eqx.Module, testloader: DataLoader):
 
 @eqx.filter_jit
 def make_step(
-    sae: SAE,
     model: eqx.Module,
+    freeze_spec: PyTree,
+    sae_pos: Callable,
     optim,
     opt_state: PyTree,
     x: Float[Array, "batch 1 28 28"],
+    y: Float[Array, "batch"],
     λ: float,
 ):
-    activ = jax.vmap(model)(x)[0]
+    diff_model, static_model = eqx.partition(model, freeze_spec)
-    loss_value, grads = SAE.loss(sae, activ[0], λ)
+    (loss, aux), grads = SAE.loss(diff_model, static_model, sae_pos, x, y, λ)
-    updates, opt_state = optim.update(grads, opt_state, sae)
+    updates, opt_state = optim.update(grads, opt_state, model)
-    sae = eqx.apply_updates(sae, updates)
+    model = eqx.apply_updates(model, updates)
-    return sae, opt_state, loss_value
+    return model, opt_state, loss, *aux
 
 
 def train_loop(
-    sae: SAE,
+    model: eqx.Module,
-    cnn: eqx.Module,
+    freeze_spec: PyTree,
     sae_pos: Callable,
     trainloader: DataLoader,
     testloader: DataLoader,
@@ -110,34 +130,57 @@ def train_loop(
     tensorboard,
     λ,
 ) -> eqx.Module:
-    opt_state = optim.init(eqx.filter(sae, eqx.is_array))
+    opt_state = optim.init(freeze_spec)
-
-    cnn = sow(sae_pos, cnn)
-
-    print(f"test_accuracy={evaluate(cnn, testloader).item()}")
 
     # Loop over our training dataset as many times as we need.
     def infinite_trainloader():
         while True:
             yield from trainloader
 
-    for step, (x, _) in zip(range(steps), infinite_trainloader()):
+    for step, (x, y) in zip(range(steps), infinite_trainloader()):
         # PyTorch dataloaders give PyTorch tensors by default,
         # so convert them to NumPy arrays.
-        sae, opt_state, train_loss = make_step(
+        model, opt_state, loss, reconstruction_err, l1, deep_err = make_step(
-            sae, cnn, optim, opt_state, x.numpy(), λ
+            model,
+            freeze_spec,
+            sae_pos,
+            optim,
+            opt_state,
+            x.numpy(),
+            y.numpy(),
+            λ
         )
-        tensorboard.add_scalar("loss", train_loss.item(), step)
+        tensorboard.add_scalar("loss", loss.item(), step)
         if (step % print_every) == 0 or (step == steps - 1):
-            cnn_with_sae = insert_after(sae_pos, cnn, sae)
+            test_accuracy = evaluate(model, testloader)
-            test_accuracy = evaluate(cnn_with_sae, testloader)
             print(
                 datetime.now().strftime("%H:%M"),
-                f"{step=}, train_loss={train_loss.item()}, "
+                step,
-                f"test_accuracy={test_accuracy.item()}",
+                f"{loss=:.3f}",
+                f"rec={reconstruction_err.item():.3f}",
+                f"{l1=:.3f}",
+                f"{deep_err=:.3f}",
+                f"{test_accuracy=:.3f}",
             )
             tensorboard.add_scalar("accu", test_accuracy.item(), step)
-    return sae
+    return sae_pos(model)
+
+
+def compose_model(cnn, sae, layer):
+    sae_pos = lambda m: m.layers[layer]
+    model = sow(sae_pos, cnn)
+    model = insert_after(sae_pos, model, sae)
+    model = sow(sae_pos, model)
+
+    sae_pos = lambda m: m.layers[layer].children[1]
+    freeze_spec = jtu.tree_map(lambda _: False, model)
+    freeze_spec = eqx.tree_at(
+        sae_pos,
+        freeze_spec,
+        replace=jtu.tree_map(lambda leaf: eqx.is_array(leaf), sae)
+    )
+    
+    return model, freeze_spec, sae_pos
 
 
 def train_sae(
@@ -154,11 +197,14 @@ def train_sae(
     λ,
 ):
     trainloader, testloader = jo3mnist.load(batch_size=batch_size)
+    # print(f"test_accuracy={evaluate(cnn, testloader).item()}")
+
     sae = SAE(activ_size, hidden_size, key)
+    model, freeze_spec, sae_pos = compose_model(cnn, sae, sae_pos)
     optim = optax.adamw(learning_rate)
     return train_loop(
-        sae,
+        model,
-        cnn,
+        freeze_spec,
         sae_pos,
         trainloader,
         testloader,

src/temp.py

@@ -0,0 +1,37 @@
+#! /usr/bin/env python3
+# vim:fenc=utf-8
+
+import argtoml
+import equinox as eqx
+import jax
+import jax.tree_util as jtu
+from jo3util.eqx import insert_after, sow
+
+from cnn import CNN
+from sae import SAE
+
+
+O = argtoml.parse_args()
+
+key = jax.random.PRNGKey(O.seed)
+key, subkey = jax.random.split(key)
+
+cnn = eqx.tree_deserialise_leaves(O.cnn_storage, CNN(subkey))
+
+sae_hyparam = O.sae[0]
+sae_pos = lambda m: m.layers[sae_hyparam.layer]
+
+sae = SAE(sae_hyparam.input_size, sae_hyparam.hidden_size, key)
+
+model = sow(sae_pos, cnn)
+model = insert_after(sae_pos, model, sae)
+model = sow(sae_pos, model)
+
+freeze_spec = jtu.tree_map(lambda _: False, model)
+freeze_spec = eqx.tree_at(
+    lambda m: m.layers[sae_hyparam.layer].children[1],
+    freeze_spec,
+    replace=jtu.tree_map(lambda leaf: eqx.is_array(leaf), sae)
+)
+# print(model)
+print(freeze_spec)