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From 3bfbe71fcdc138764408a5e1646b66c464308b8b Mon Sep 17 00:00:00 2001
From: YurenHao0426 <Blackhao0426@gmail.com>
Date: Mon, 27 Jul 2026 13:09:05 -0500
Subject: [PATCH 04/19] crossover: add architecture-compatible PEPITA
---
config/cli_config.py | 8 ++++--
src/models.py | 37 ++++++++++++++++++++++++++++
src/training_utils.py | 50 ++++++++++++++++++++++++++++++++++++--
tests/local_rules_smoke.py | 32 ++++++++++++++++++++++++
train.py | 3 ++-
5 files changed, 125 insertions(+), 5 deletions(-)
diff --git a/config/cli_config.py b/config/cli_config.py
index e1756e1..3088436 100644
--- a/config/cli_config.py
+++ b/config/cli_config.py
@@ -42,12 +42,16 @@ parser.add_argument('--experiment-name', default='test', help='A string denoting
parser.add_argument('--model', default='VGG16', choices=['VGG16', 'VGGlike', 'CNN', 'miniCNN', 'MLP'], help='')
-parser.add_argument('--learning-algorithm', default='dualprop-lagr-ff', choices=['backprop', 'fa', 'dfa', 'dualprop-lagr-ff', 'dualprop-raovr-ff', 'dualprop-raovr-dampened-ff'])
+parser.add_argument('--learning-algorithm', default='dualprop-lagr-ff', choices=['backprop', 'fa', 'dfa', 'pepita', 'dualprop-lagr-ff', 'dualprop-raovr-ff', 'dualprop-raovr-dampened-ff'])
parser.add_argument(
'--feedback-seed', default=1729, type=int,
help='Independent fixed-feedback initialization seed for FA/DFA.')
+parser.add_argument(
+ '--pepita-projection-scale', default=0.05, type=float,
+ help='Multiplier on PEPITA He-uniform output-error-to-input projection.')
+
parser.add_argument(
'--gradient-diagnostics', default='full', choices=['none', 'full'],
help=('Compute the exact BP reference gradient and layerwise cosine on '
@@ -128,7 +132,7 @@ elif config.model == "MLP":
dense_features = [1024, 1024, config.num_classes]
# Load model
-modeltype = {"backprop":cnn_abstract, "fa": cnn_abstract, "dfa": cnn_abstract, "dualprop-lagr-ff": cnn_dualprop_Lagr_ff, "dualprop-raovr-ff": cnn_dualprop_RAOVR_ff, "dualprop-raovr-dampened-ff": cnn_dualprop_RAOVR_dampened_ff}
+modeltype = {"backprop":cnn_abstract, "fa": cnn_abstract, "dfa": cnn_abstract, "pepita": cnn_abstract, "dualprop-lagr-ff": cnn_dualprop_Lagr_ff, "dualprop-raovr-ff": cnn_dualprop_RAOVR_ff, "dualprop-raovr-dampened-ff": cnn_dualprop_RAOVR_dampened_ff}
activation={"relu": relu, "hs": hs, "sigmoid": sigmoid, "tanh": tanh}
config.model = modeltype[config.learning_algorithm](loss_func, Conv, Dense, activation[config.activation], config.num_classes, config.beta, config.alpha, config.dtype, config.param_dtype,
kernels=kernels, strides=strides, features=features, mp = mp,
diff --git a/src/models.py b/src/models.py
index edc432c..127268a 100644
--- a/src/models.py
+++ b/src/models.py
@@ -194,6 +194,43 @@ class cnn_abstract(nn.Module, ABC):
_, linear_pullback = jax.vjp(linear_map, s[i])
fields[i] = linear_pullback(linear_field)[0]
return fields
+
+ def pepita_correlation_objective(self, modulated_states, original_linear,
+ modulated_linear, one_hot):
+ """Architecture-compatible PEPITA/ERIN two-presentation update.
+
+ Hidden fields are the first-minus-second post-ReLU activity
+ differences from the PEPITA equation. They multiply the modulated
+ presynaptic state directly, without differentiating a nonlinearity.
+ Convolutional correlations follow the reference code's additional
+ average over spatial positions. The readout uses the second-pass
+ softmax error.
+ """
+ objective = 0.0
+ batch_size = modulated_states[0].shape[0]
+ for i, layer in enumerate(self.layers):
+ x = jax.lax.stop_gradient(modulated_states[i])
+ if i == self.num_layers - 1:
+ field = jax.lax.stop_gradient(
+ jax.nn.softmax(modulated_states[-1], axis=-1) - one_hot)
+ prediction = layer(x)
+ objective += jnp.sum(prediction * field) / batch_size
+ elif i < self.num_convlayers:
+ field = jax.lax.stop_gradient(
+ self.act(original_linear[i])
+ - self.act(modulated_linear[i]))
+ prediction = layer.call_without_pooling(x)
+ spatial_positions = prediction.shape[1] * prediction.shape[2]
+ objective += (
+ jnp.sum(prediction * field)
+ / (batch_size * spatial_positions))
+ else:
+ field = jax.lax.stop_gradient(
+ self.act(original_linear[i])
+ - self.act(modulated_linear[i]))
+ prediction = layer(x)
+ objective += jnp.sum(prediction * field) / batch_size
+ return objective
def init_states_to_zero(self, x0):
s = [x0]
diff --git a/src/training_utils.py b/src/training_utils.py
index 2b6b18a..f5fae62 100644
--- a/src/training_utils.py
+++ b/src/training_utils.py
@@ -219,17 +219,23 @@ def create_train_state(rng, model, image_dims, lr, wlr, lrf, momentum, weight_de
def create_local_feedback(rng, model, params, image_dims, learning_algorithm,
- num_classes):
+ num_classes, pepita_projection_scale=0.05):
"""Create fixed feedback without reading a forward parameter value.
FA uses an independently initialized model-shaped linear parameter tree.
DFA uses one independent output-to-hidden tensor per hidden population.
The forward tree is used only to infer activation shapes for DFA.
"""
- if learning_algorithm not in ("fa", "dfa"):
+ if learning_algorithm not in ("fa", "dfa", "pepita"):
return None
w, h, channels = image_dims
dummy = jnp.ones([1, w, h, channels])
+ if learning_algorithm == "pepita":
+ input_units = w * h * channels
+ limit = jnp.sqrt(6.0 / input_units) * pepita_projection_scale
+ return jax.random.uniform(
+ rng, (num_classes, w, h, channels),
+ minval=-limit, maxval=limit, dtype=dummy.dtype)
if learning_algorithm == "fa":
return unfreeze(model.init(rng, dummy)["params"])
@@ -301,6 +307,10 @@ def train_epoch(state, train_ds, batch_size, rng, augmentation_on,
state, local_feedback, learning_algorithm, image,
labels_onehot, labels, batch_rng, augmentation_on,
gradient_diagnostics)
+ elif learning_algorithm == "pepita":
+ state, metrics = train_step_pepita(
+ state, local_feedback, image, labels_onehot, labels,
+ batch_rng, augmentation_on, gradient_diagnostics)
elif learning_algorithm != "backprop":
state, metrics = train_step(
state, image, labels_onehot, labels, batch_rng, inf_rng,
@@ -332,6 +342,42 @@ def direct_feedback_fields(states, output_field, direct_feedback):
return fields
+@jax.jit
+def train_step_pepita(state, input_feedback, image, labels_onehot, labels,
+ batch_rng, augmentation_on, gradient_diagnostics):
+ """One PEPITA step: ordinary forward, error-modulated forward, local rule."""
+ image = jax.lax.cond(
+ augmentation_on, vmap_augment_train, no_aug, image, batch_rng)
+ states, linear = state.apply_fn(
+ {"params": state.params}, image, method="ff_with_local_cache")
+ output_error = jax.lax.stop_gradient(
+ jax.nn.softmax(states[-1], axis=-1) - labels_onehot)
+ input_error = jnp.tensordot(
+ output_error, input_feedback, axes=((-1,), (0,)))
+ modulated_image = jax.lax.stop_gradient(image + input_error)
+ modulated_states, modulated_linear = state.apply_fn(
+ {"params": state.params}, modulated_image,
+ method="ff_with_local_cache")
+ states = tree_map(jax.lax.stop_gradient, states)
+ linear = tree_map(jax.lax.stop_gradient, linear)
+ modulated_states = tree_map(jax.lax.stop_gradient, modulated_states)
+ modulated_linear = tree_map(jax.lax.stop_gradient, modulated_linear)
+
+ def local_objective(params):
+ return state.apply_fn(
+ {"params": params}, modulated_states, linear, modulated_linear,
+ labels_onehot, method="pepita_correlation_objective")
+
+ grads = jax.grad(local_objective)(state.params)
+ metrics = compute_metrics(
+ image=image, labels_onehot=labels_onehot, labels=labels, state=state)
+ metrics = jax.lax.cond(
+ gradient_diagnostics, ref_grad_and_angle, no_ref_grad_and_angle,
+ state, grads, image, labels_onehot, metrics)
+ state = state.apply_gradients(grads=grads)
+ return state, metrics
+
+
@partial(jax.jit, static_argnames=("learning_algorithm",))
def train_step_local(state, local_feedback, learning_algorithm, image,
labels_onehot, labels, batch_rng, augmentation_on,
diff --git a/tests/local_rules_smoke.py b/tests/local_rules_smoke.py
index 2366e61..e7da52e 100644
--- a/tests/local_rules_smoke.py
+++ b/tests/local_rules_smoke.py
@@ -123,12 +123,44 @@ def main():
)
assert all(bool(jnp.all(jnp.isfinite(field))) for field in dfa_fields)
+ pepita_projection = create_local_feedback(
+ jax.random.PRNGKey(1731), model, params, (8, 8, 2), "pepita", 3,
+ pepita_projection_scale=0.05)
+ pepita_error = jax.nn.softmax(states[-1], axis=-1) - one_hot
+ modulated_x = x + jnp.tensordot(
+ pepita_error, pepita_projection, axes=((-1,), (0,)))
+ modulated_states, modulated_linear = model.apply(
+ {"params": params}, modulated_x, method="ff_with_local_cache")
+ pepita_grads = jax.grad(
+ lambda candidate: model.apply(
+ {"params": candidate}, modulated_states, linear,
+ modulated_linear, one_hot,
+ method="pepita_correlation_objective")
+ )(params)
+ output_error = jax.nn.softmax(modulated_states[-1], axis=-1) - one_hot
+ output_input = modulated_states[-2].reshape((x.shape[0], -1))
+ manual_kernel = output_input.T @ output_error / x.shape[0]
+ manual_bias = output_error.mean(axis=0)
+ readout_leaves = jax.tree_util.tree_leaves(pepita_grads["d00"])
+ readout_kernel = next(value for value in readout_leaves if value.ndim == 2)
+ readout_bias = next(value for value in readout_leaves if value.ndim == 1)
+ pepita_readout_error = max(
+ float(jnp.max(jnp.abs(readout_kernel - manual_kernel))),
+ float(jnp.max(jnp.abs(readout_bias - manual_bias))),
+ )
+ assert pepita_readout_error < 2e-12, pepita_readout_error
+ projection_limit = (6.0 / (8 * 8 * 2)) ** 0.5 * 0.05
+ assert float(jnp.max(jnp.abs(pepita_projection))) <= projection_limit
+ assert bool(jnp.all(jnp.isfinite(flat(pepita_grads))))
+
report = {
"symmetric_fa_bp_relative_error": symmetric_error,
"independent_feedback_forward_cosine": feedback_cosine,
"feedback_independence_error": feedback_independence_error,
"detached_local_boundary_error": local_boundary_error,
"dfa_hidden_maps": len(direct),
+ "pepita_readout_equation_max_error": pepita_readout_error,
+ "pepita_projection_shape": list(pepita_projection.shape),
"status": "passed",
}
print(json.dumps(report, indent=2, sort_keys=True))
diff --git a/train.py b/train.py
index c030158..ba5f6ac 100644
--- a/train.py
+++ b/train.py
@@ -48,7 +48,8 @@ for experiment_index, seed in enumerate(config.seeds):
state = create_train_state(init_rng, config.model, config.image_dims, config.learning_rate, config.warmup_learning_rate, config.learning_rate_final, config.momentum, config.weight_decay, config.num_epochs, config.warmup_epochs, config.decay_epochs, steps_per_epoch)
local_feedback = create_local_feedback(
jax.random.PRNGKey(config.feedback_seed), config.model, state.params,
- config.image_dims, config.learning_algorithm, config.num_classes)
+ config.image_dims, config.learning_algorithm, config.num_classes,
+ pepita_projection_scale=config.pepita_projection_scale)
del init_rng # Must not be used anymore.
--
2.54.0
|
