def partitioned_additive_weight_decay(
conv_weight_decay: float, # wd1 from original repo
linear_weight_decay: float, # wd2 from original repo
) -> optax.GradientTransformation:
def predicate(layer_name, param_name, value):
del param_name, value
return layer_name.split("/")[-1].startswith("linear")
return partition(
predicate,
optax.additive_weight_decay(linear_weight_decay),
optax.additive_weight_decay(conv_weight_decay),
)
def partitioned_additive_weight_decay(weight_decay: float):
def predicate(layer_name, param_name, value):
del layer_name, value
return param_name == "w"
return optax_utils.partition(predicate,
optax.additive_weight_decay(weight_decay))
def create_train_state(rng, config: ml_collections.ConfigDict, model):
"""Create initial training state."""
params = get_initial_params(rng, model)
tx = optax.chain(
optax.sgd(learning_rate=config.learning_rate,
momentum=config.momentum),
optax.additive_weight_decay(weight_decay=config.weight_decay))
state = TrainState.create(apply_fn=model.apply, params=params, tx=tx)
return state
def create_train_state(rng, model, img_size, lr_schedule_fn, weight_decay,
max_norm):
tx = optax.chain(optax.clip_by_global_norm(max_norm),
optax.scale_by_adam(),
optax.additive_weight_decay(weight_decay),
optax.scale_by_schedule(lr_schedule_fn))
params = model.init(rng,
jax.numpy.ones((1, img_size, img_size, 3)),
is_training=False)
train_state = TrainState.create(
apply_fn=model.apply,
params=params,
tx=tx,
)
return train_state
assert cores_per_replica <= 8
bucket = params["bucket"]
model_dir = params["model_dir"]
layers = params["layers"]
d_model = params["d_model"]
n_heads = params["n_heads"]
n_vocab = params["n_vocab"]
seq = params["seq"]
norm = params["norm"]
params["sampler"] = nucleaus_sample
opt = optax.chain(optax.scale(1 / gradient_accumulation_steps),
clip_by_global_norm(1), optax.scale_by_adam(),
optax.additive_weight_decay(0), optax.scale(-1),
optax.scale_by_schedule(util.gpt3_schedule(0, 1, 0, 0)))
params["optimizer"] = opt
start = time.time()
print(f"jax devices: {jax.device_count()}")
print(f"jax runtime initialized in {time.time() - start:.06}s")
mesh_shape = (jax.device_count() // cores_per_replica, cores_per_replica)
devices = np.array(jax.devices()).reshape(mesh_shape)
with open(f"gs://{bucket}/{model_dir}/meta.json", "r") as f:
meta = json.load(f)
ckpt_step = meta["checkpoints"][-1]
bucket = params["bucket"]
model_dir = params["model_dir"]
layers = params["layers"]
d_model = params["d_model"]
n_heads = params["n_heads"]
n_vocab = params["n_vocab"]
seq = params["seq"]
norm = params["norm"]
params["sampler"] = nucleaus_sample
opt = optax.chain(
optax.scale(1 / gradient_accumulation_steps),
clip_by_global_norm(1),
optax.scale_by_adam(),
optax.additive_weight_decay(0),
optax.scale(-1),
optax.scale_by_schedule(util.gpt3_schedule(0, 1, 0, 0))
)
params["optimizer"] = opt
start = time.time()
print(f"jax devices: {jax.device_count()}")
print(f"jax runtime initialized in {time.time() - start:.06}s")
mesh_shape = (jax.device_count() // cores_per_replica, cores_per_replica)
devices = np.array(jax.devices()).reshape(mesh_shape)
with open(f"gs://{bucket}/{model_dir}/meta.json", "r") as f:
meta = json.load(f)
def selective_additive_weight_decay(predicate, weight_decay: float):
return partition(
predicate,
optax.additive_weight_decay(weight_decay),
)
