Add more rnn options and batch norm

scripts/cleanba.py

@@ -19,14 +19,13 @@ import numpy as np
 import optax
 import distrax
 import tyro
-from flax.training.train_state import TrainState
 from rich.pretty import pprint
 from tensorboardX import SummaryWriter
 
 from ygoai.utils import init_ygopro, load_embeddings
 from ygoai.rl.ckpt import ModelCheckpoint, sync_to_gcs, zip_files
 from ygoai.rl.jax.agent import RNNAgent, ModelArgs
-from ygoai.rl.jax.utils import RecordEpisodeStatistics, masked_normalize, categorical_sample
+from ygoai.rl.jax.utils import RecordEpisodeStatistics, masked_normalize, categorical_sample, TrainState
 from ygoai.rl.jax.eval import evaluate, battle
 from ygoai.rl.jax.switch import truncated_gae_sep as gae_sep_switch
 from ygoai.rl.jax import clipped_surrogate_pg_loss, mse_loss, entropy_loss, simple_policy_loss, \
@@ -251,6 +250,14 @@ def create_agent(args, eval=False):
         )
 
 
+def get_variables(agent_state):
+    batch_stats = getattr(agent_state, "batch_stats", None)
+    variables = {'params': agent_state.params}
+    if batch_stats is not None:
+        variables['batch_stats'] = batch_stats
+    return variables
+
+
 def init_rnn_state(num_envs, rnn_channels):
     return (
         np.zeros((num_envs, rnn_channels)),
@@ -502,11 +509,9 @@ def rollout(
             sharded_storage.append(x)
         sharded_storage = Transition(*sharded_storage)
         next_main = main_player == next_to_play
-        next_rstate = jax.tree.map(
-            lambda x1, x2: jnp.where(next_main[:, None], x1, x2), next_rstate1, next_rstate2)
         sharded_data = jax.tree.map(lambda x: jax.device_put_sharded(
                 np.split(x, len(learner_devices)), devices=learner_devices),
-                         (init_rstate1, init_rstate2, (next_obs, next_rstate), next_main))
+                         (init_rstate1, init_rstate2, next_obs, next_main))
 
         if args.eval_interval and update % args.eval_interval == 0:
             _start = time.time()
@@ -683,13 +688,17 @@ def main():
 
     agent = create_agent(args)
     rstate = agent.init_rnn_state(1)
-    params = agent.init(init_key, sample_obs, rstate)
+    variables = agent.init(init_key, sample_obs, rstate)
+    variables = flax.core.unfreeze(variables)
     if embeddings is not None:
         unknown_embed = embeddings.mean(axis=0)
         embeddings = np.concatenate([unknown_embed[None, :], embeddings], axis=0)
-        params = flax.core.unfreeze(params)
-        params['params']['Encoder_0']['Embed_0']['embedding'] = jax.device_put(embeddings)
-        params = flax.core.freeze(params)
+        variables['params']['Encoder_0']['Embed_0']['embedding'] = jax.device_put(embeddings)
+        # variables = flax.core.freeze(variables)
+    if args.checkpoint:
+        with open(args.checkpoint, "rb") as f:
+            variables = flax.serialization.from_bytes(variables, f.read())
+        print(f"loaded checkpoint from {args.checkpoint}")
 
     tx = optax.MultiSteps(
         optax.chain(
@@ -701,29 +710,29 @@ def main():
         every_k_schedule=1,
     )
     tx = optax.apply_if_finite(tx, max_consecutive_errors=10)
+
+    if 'batch_stats' not in variables:
+        # variables = flax.core.unfreeze(variables)
+        variables['batch_stats'] = {}
+        # variables = flax.core.freeze(variables)
     agent_state = TrainState.create(
         apply_fn=None,
-        params=params,
+        params=variables['params'],
         tx=tx,
+        batch_stats=variables['batch_stats'],
     )
-    if args.checkpoint:
-        with open(args.checkpoint, "rb") as f:
-            params = flax.serialization.from_bytes(params, f.read())
-            agent_state = agent_state.replace(params=params)
-        print(f"loaded checkpoint from {args.checkpoint}")
-
     agent_state = flax.jax_utils.replicate(agent_state, devices=learner_devices)
     # print(agent.tabulate(agent_key, sample_obs))
 
     if args.eval_checkpoint:
         eval_agent = create_agent(args, eval=True)
         eval_rstate = eval_agent.init_rnn_state(1)
-        eval_params = eval_agent.init(init_key, sample_obs, eval_rstate)
+        eval_variables = eval_agent.init(init_key, sample_obs, eval_rstate)
         with open(args.eval_checkpoint, "rb") as f:
-            eval_params = flax.serialization.from_bytes(eval_params, f.read())
+            eval_variables = flax.serialization.from_bytes(eval_variables, f.read())
         print(f"loaded eval checkpoint from {args.eval_checkpoint}")
     else:
-        eval_params = None
+        eval_variables = None
 
     def advantage_fn(
         new_logits, new_values, next_dones, switch_or_mains,
@@ -811,17 +820,29 @@ def main():
         loss = pg_loss - args.ent_coef * ent_loss + v_loss * args.vf_coef
         return loss, pg_loss, v_loss, ent_loss, approx_kl
 
-    def apply_fn(params, obs, rstate1, rstate2, dones, next_dones, switch_or_mains):
+    def apply_fn(variables, obs, rstate1, rstate2, dones, next_dones, switch_or_mains):
         if args.switch:
             dones = dones | next_dones
-        (rstate1, rstate2), new_logits, new_values = agent.apply(
-            params, obs, (rstate1, rstate2), dones, switch_or_mains)[:3]
+        ((rstate1, rstate2), new_logits, new_values, _), state_updates = agent.apply(
+            variables, obs, (rstate1, rstate2), dones, switch_or_mains,
+            train=True, mutable=["batch_stats"])
         new_values = jax.tree.map(lambda x: x.squeeze(-1), new_values)
-        return (rstate1, rstate2), new_logits, new_values
+        return ((rstate1, rstate2), new_logits, new_values), state_updates
+
+    def compute_next_value(
+        variables, rstate1, rstate2, next_obs, next_main):
+        rstate = jax.tree.map(
+            lambda x1, x2: jnp.where(next_main[:, None], x1, x2), rstate1, rstate2)
+        next_value = agent.apply(variables, next_obs, rstate)[2]
+        next_value = jax.tree.map(lambda x: x.squeeze(-1), next_value)
+        next_value = jax.lax.stop_gradient(next_value)
+        sign = -1 if args.switch else 1
+        next_value = jnp.where(next_main, sign * next_value, -sign * next_value)
+        return next_value
 
     def compute_advantage(
-        params, rstate1, rstate2, obs, dones, next_dones,
-        switch_or_mains, actions, logits, rewards, next_value):
+        variables, rstate1, rstate2, obs, dones, next_dones,
+        switch_or_mains, actions, logits, rewards, next_obs, next_main):
         segment_length = dones.shape[0]
 
         obs, dones, next_dones, switch_or_mains, actions, logits, rewards = \
@@ -829,8 +850,11 @@ def main():
                 lambda x: jnp.reshape(x, (-1,) + x.shape[2:]),
                 (obs, dones, next_dones, switch_or_mains, actions, logits, rewards))
 
-        new_logits, new_values = apply_fn(
-            params, obs, rstate1, rstate2, dones, next_dones, switch_or_mains)[1:3]
+        ((rstate1, rstate2), new_logits, new_values), state_updates = apply_fn(
+            variables, obs, rstate1, rstate2, dones, next_dones, switch_or_mains)
+
+        next_value = compute_next_value(
+            variables, rstate1, rstate2, next_obs, next_main)
 
         target_values, advantages = advantage_fn(
             new_logits, new_values, next_dones, switch_or_mains,
@@ -842,10 +866,11 @@ def main():
         return target_values, advantages
 
     def compute_loss(
-        params, rstate1, rstate2, obs, dones, next_dones,
+        params, batch_stats, rstate1, rstate2, obs, dones, next_dones,
         switch_or_mains, actions, logits, target_values, advantages, mask):
-        (rstate1, rstate2), new_logits, new_values = apply_fn(
-            params, obs, rstate1, rstate2, dones, next_dones, switch_or_mains)
+        variables = {'params': params, 'batch_stats': batch_stats}
+        ((rstate1, rstate2), new_logits, new_values), state_updates = apply_fn(
+            variables, obs, rstate1, rstate2, dones, next_dones, switch_or_mains)
 
         loss, pg_loss, v_loss, ent_loss, approx_kl = loss_fn(
             new_logits, new_values, actions, logits, target_values, advantages,
@@ -854,14 +879,19 @@ def main():
         loss = jnp.where(jnp.isnan(loss) | jnp.isinf(loss), 0.0, loss)
         approx_kl, rstate1, rstate2 = jax.tree.map(
             jax.lax.stop_gradient, (approx_kl, rstate1, rstate2))
-        return loss, (pg_loss, v_loss, ent_loss, approx_kl, rstate1, rstate2)
+        return loss, (state_updates, pg_loss, v_loss, ent_loss, approx_kl, rstate1, rstate2)
 
     def compute_advantage_loss(
-        params, rstate1, rstate2, obs, dones, next_dones,
-        switch_or_mains, actions, logits, rewards, next_value, mask):
-        num_envs = jax.tree.leaves(next_value)[0].shape[0]
-        new_logits, new_values = apply_fn(
-            params, obs, rstate1, rstate2, dones, next_dones, switch_or_mains)[1:3]
+        params, batch_stats, rstate1, rstate2, obs, dones, next_dones,
+        switch_or_mains, actions, logits, rewards, mask, next_obs, next_main):
+        num_envs = jax.tree.leaves(next_main)[0].shape[0]
+        variables = {'params': params, 'batch_stats': batch_stats}
+        ((rstate1, rstate2), new_logits, new_values), state_updates = apply_fn(
+            variables, obs, rstate1, rstate2, dones, next_dones, switch_or_mains)
+
+        variables = {'params': params, 'batch_stats': state_updates['batch_stats']}
+        next_value = compute_next_value(
+            variables, rstate1, rstate2, next_obs, next_main)
 
         target_values, advantages = advantage_fn(
             new_logits, new_values, next_dones, switch_or_mains,
@@ -873,22 +903,21 @@ def main():
 
         loss = jnp.where(jnp.isnan(loss) | jnp.isinf(loss), 0.0, loss)
         approx_kl = jax.lax.stop_gradient(approx_kl)
-        return loss, (pg_loss, v_loss, ent_loss, approx_kl)
+        return loss, (state_updates, pg_loss, v_loss, ent_loss, approx_kl)
 
     def single_device_update(
         agent_state: TrainState,
         sharded_storages: List,
         sharded_init_rstate1: List,
         sharded_init_rstate2: List,
-        sharded_next_inputs: List,
+        sharded_next_obs: List,
         sharded_next_main: List,
         key: jax.random.PRNGKey,
     ):
         storage = jax.tree.map(lambda *x: jnp.hstack(x), *sharded_storages)
-        # TODO: rstate will be out-date after the first update, maybe consider R2D2
-        next_inputs, init_rstate1, init_rstate2 = [
+        next_obs, init_rstate1, init_rstate2 = [
             jax.tree.map(lambda *x: jnp.concatenate(x), *x)
-            for x in [sharded_next_inputs, sharded_init_rstate1, sharded_init_rstate2]
+            for x in [sharded_next_obs, sharded_init_rstate1, sharded_init_rstate2]
         ]
         next_main = jnp.concatenate(sharded_next_main)
 
@@ -913,49 +942,45 @@ def main():
             agent_state, key = carry
             key, subkey = jax.random.split(key)
 
-            next_value = agent.apply(agent_state.params, *next_inputs)[2]
-            next_value = jax.tree.map(lambda x: jnp.squeeze(x, axis=-1), next_value)
-            sign = -1 if args.switch else 1
-            next_value = jnp.where(next_main, sign * next_value, -sign * next_value)
-
             def convert_data(x: jnp.ndarray, multi_step=True):
                 key = subkey if args.update_epochs > 1 else None
                 return reshape_minibatch(
                     x, multi_step, args.num_minibatches, num_steps, args.segment_length, key=key)
 
-            shuffled_init_rstate1, shuffled_init_rstate2 = jax.tree.map(
-                partial(convert_data, multi_step=False), (init_rstate1, init_rstate2))
-            shuffled_storage = jax.tree.map(convert_data, storage)
+            b_init_rstate1, b_init_rstate2, b_next_obs, b_next_main = \
+                jax.tree.map(partial(convert_data, multi_step=False),
+                             (init_rstate1, init_rstate2, next_obs, next_main))
+            b_storage = jax.tree.map(convert_data, storage)
             if args.switch:
                 switch_or_mains = convert_data(switch)
             else:
-                switch_or_mains = shuffled_storage.mains
-            shuffled_mask = ~shuffled_storage.dones
-            shuffled_next_value = jax.tree.map(
-                partial(convert_data, multi_step=False), next_value)
-            shuffled_rewards = shuffled_storage.rewards
+                switch_or_mains = b_storage.mains
+            b_mask = ~b_storage.dones
+            b_rewards = b_storage.rewards
 
             if args.segment_length is None:
                 def update_minibatch(agent_state, minibatch):
-                    (loss, (pg_loss, v_loss, ent_loss, approx_kl)), grads = loss_grad_fn(
-                        agent_state.params, *minibatch)
+                    (loss, (state_updates, pg_loss, v_loss, ent_loss, approx_kl)), grads = \
+                        loss_grad_fn(agent_state.params, agent_state.batch_stats, *minibatch)
                     grads = jax.lax.pmean(grads, axis_name="local_devices")
                     agent_state = agent_state.apply_gradients(grads=grads)
+                    agent_state = agent_state.replace(batch_stats=state_updates['batch_stats'])
                     return agent_state, (loss, pg_loss, v_loss, ent_loss, approx_kl)
             else:
                 def update_minibatch(carry, minibatch):
                     def update_minibatch_t(carry, minibatch_t):
                         agent_state, rstate1, rstate2 = carry
                         minibatch_t = rstate1, rstate2, *minibatch_t
-                        (loss, (pg_loss, v_loss, ent_loss, approx_kl, rstate1, rstate2)), \
-                            grads = loss_grad_fn(agent_state.params, *minibatch_t)
+                        (loss, (state_updates, pg_loss, v_loss, ent_loss, approx_kl, rstate1, rstate2)), \
+                            grads = loss_grad_fn(agent_state.params, agent_state.batch_stats, *minibatch_t)
                         grads = jax.lax.pmean(grads, axis_name="local_devices")
                         agent_state = agent_state.apply_gradients(grads=grads)
+                        agent_state = agent_state.replace(batch_stats=state_updates['batch_stats'])
                         return (agent_state, rstate1, rstate2), (loss, pg_loss, v_loss, ent_loss, approx_kl)
 
                     rstate1, rstate2, *minibatch_t, mask = minibatch
                     target_values, advantages = compute_advantage(
-                        carry.params, rstate1, rstate2, *minibatch_t)
+                        get_variables(carry), rstate1, rstate2, *minibatch_t)
                     minibatch_t = *minibatch_t[:-2], target_values, advantages, mask
 
                     (carry, _rstate1, _rstate2), \
@@ -967,17 +992,18 @@ def main():
                 update_minibatch,
                 agent_state,
                 (
-                    shuffled_init_rstate1,
-                    shuffled_init_rstate2,
-                    shuffled_storage.obs,
-                    shuffled_storage.dones,
-                    shuffled_storage.next_dones,
+                    b_init_rstate1,
+                    b_init_rstate2,
+                    b_storage.obs,
+                    b_storage.dones,
+                    b_storage.next_dones,
                     switch_or_mains,
-                    shuffled_storage.actions,
-                    shuffled_storage.logits,
-                    shuffled_rewards,
-                    shuffled_next_value,
-                    shuffled_mask,
+                    b_storage.actions,
+                    b_storage.logits,
+                    b_rewards,
+                    b_mask,
+                    b_next_obs,
+                    b_next_main,
                 ),
             )
             return (agent_state, key), (loss, pg_loss, v_loss, ent_loss, approx_kl)
@@ -1007,16 +1033,16 @@ def main():
     params_queues = []
     rollout_queues = []
 
-    unreplicated_params = flax.jax_utils.unreplicate(agent_state.params)
+    unreplicated_params = flax.jax_utils.unreplicate(get_variables(agent_state))
     for d_idx, d_id in enumerate(args.actor_device_ids):
         actor_device = local_devices[d_id]
         device_params = jax.device_put(unreplicated_params, actor_device)
         for thread_id in range(args.num_actor_threads):
             params_queues.append(queue.Queue(maxsize=1))
             rollout_queues.append(queue.Queue(maxsize=1))
-            if eval_params:
+            if eval_variables:
                 params_queues[-1].put(
-                    jax.device_put(eval_params, actor_device))
+                    jax.device_put(eval_variables, actor_device))
             actor_thread_id = d_idx * args.num_actor_threads + thread_id             
             threading.Thread(
                 target=rollout,
@@ -1070,7 +1096,7 @@ def main():
             *list(zip(*sharded_data_list)),
             learner_keys,
         )
-        unreplicated_params = flax.jax_utils.unreplicate(agent_state.params)
+        unreplicated_params = flax.jax_utils.unreplicate(get_variables(agent_state))
         params_queue_put_time = 0
         for d_idx, d_id in enumerate(args.actor_device_ids):
             device_params = jax.device_put(unreplicated_params, local_devices[d_id])

ygoai/rl/jax/agent.py

@@ -8,7 +8,7 @@ import jax.numpy as jnp
 import flax.linen as nn
 
 from ygoai.rl.jax.transformer import EncoderLayer, PositionalEncoding, LlamaEncoderLayer
-from ygoai.rl.jax.modules import MLP, GLUMlp, RMSNorm, make_bin_params, bytes_to_bin, decode_id
+from ygoai.rl.jax.modules import MLP, GLUMlp, BatchRenorm, make_bin_params, bytes_to_bin, decode_id
 from ygoai.rl.jax.rwkv import Rwkv6SelfAttention
 
 
@@ -487,7 +487,7 @@ class Critic(nn.Module):
     param_dtype: jnp.dtype = jnp.float32
     
     @nn.compact
-    def __call__(self, f_state):
+    def __call__(self, f_state, train):
         f_state = f_state.astype(self.dtype)
         mlp = partial(MLP, dtype=self.dtype, param_dtype=self.param_dtype)
         x = mlp(self.channels, last_lin=False)(f_state)
@@ -495,6 +495,33 @@ class Critic(nn.Module):
         return x
 
 
+class CrossCritic(nn.Module):
+    channels: Sequence[int] = (128, 128, 128)
+    # dropout_rate: Optional[float] = None
+    batch_norm_momentum: float = 0.99
+    dtype: Optional[jnp.dtype] = None
+    param_dtype: jnp.dtype = jnp.float32
+    
+    @nn.compact
+    def __call__(self, f_state, train):
+        x = f_state.astype(self.dtype)
+        linear = partial(nn.Dense, dtype=self.dtype, param_dtype=self.param_dtype, use_bias=False)
+        BN = partial(
+            BatchRenorm, dtype=self.dtype, param_dtype=self.param_dtype,
+            momentum=self.batch_norm_momentum, axis_name="local_devices",
+            use_running_average=not train)
+        x = BN()(x)
+        for c in self.channels:
+            x = linear(c)(x)
+            # if self.use_layer_norm:
+            #     x = nn.LayerNorm()(x)
+            x = nn.relu()(x)
+            # x = nn.leaky_relu(x, negative_slope=0.1)
+            x = BN()(x)
+        x = nn.Dense(1, dtype=jnp.float32, param_dtype=self.param_dtype)(x)
+        return x
+
+
 class GlobalCritic(nn.Module):
     channels: Sequence[int] = (128, 128)
     dtype: Optional[jnp.dtype] = None
@@ -580,6 +607,14 @@ class ModelArgs(EncoderArgs):
     """whether to use FiLM for the actor"""
     oppo_info: bool = False
     """whether to use opponent's information"""
+    rnn_shortcut: bool = False
+    """whether to use shortcut for the RNN"""
+    batch_norm: bool = False
+    """whether to use batch normalization for the critic"""
+    critic_width: int = 128
+    """the width of the critic"""
+    critic_depth: int = 3
+    """the depth of the critic"""
     rwkv_head_size: int = 32
     """the head size for the RWKV"""
 
@@ -596,6 +631,10 @@ class RNNAgent(nn.Module):
     rwkv_head_size: int = 32
     action_feats: bool = True
     oppo_info: bool = False
+    rnn_shortcut: bool = False
+    batch_norm: bool = False
+    critic_width: int = 128
+    critic_depth: int = 3
     version: int = 0
 
     switch: bool = True
@@ -606,7 +645,7 @@ class RNNAgent(nn.Module):
     param_dtype: jnp.dtype = jnp.float32
 
     @nn.compact
-    def __call__(self, x, rstate, done=None, switch_or_main=None):
+    def __call__(self, x, rstate, done=None, switch_or_main=None, train=False):
         batch_size = jax.tree.leaves(rstate)[0].shape[0]
 
         c = self.num_channels
@@ -669,6 +708,10 @@ class RNNAgent(nn.Module):
                 rstate, f_state_r = rnn_step_by_main(
                     rnn_layer, rstate, f_state, done, switch_or_main)
 
+        if self.rnn_shortcut:
+            # f_state_r = ReZero(channel_wise=True)(f_state_r)
+            f_state_r = jnp.concatenate([f_state, f_state_r], axis=-1)
+
         if self.film:
             actor = FiLMActor(
                 channels=c, dtype=jnp.float32, param_dtype=self.param_dtype, noam=self.noam)
@@ -694,13 +737,16 @@ class RNNAgent(nn.Module):
                     lambda x1, x2: jnp.where(main, x2, x1), rstate1, rstate2)
             value = critic(rstate1_t, rstate2_t, f_g)
         else:
-            critic = Critic(
-                channels=[c, c, c], dtype=self.dtype, param_dtype=self.param_dtype)
-            value = critic(f_state_r)
-        
+            CriticCls = CrossCritic if self.batch_norm else Critic
+            cs = [self.critic_width] * self.critic_depth
+            critic = CriticCls(
+                channels=cs, dtype=self.dtype, param_dtype=self.param_dtype)
+            value = critic(f_state_r, train)
+
         if self.int_head:
+            cs = [self.critic_width] * self.critic_depth
             critic_int = Critic(
-                channels=[c, c, c], dtype=self.dtype, param_dtype=self.param_dtype)
+                channels=cs, dtype=self.dtype, param_dtype=self.param_dtype)
             value_int = critic_int(f_state_r)
             value = (value, value_int)
         return rstate, logits, value, valid

ygoai/rl/jax/modules.py

-from typing import Tuple, Union, Optional
+from typing import Tuple, Union, Optional, Any
 import functools
 
 import jax
 import jax.numpy as jnp
 import flax.linen as nn
+from flax.linen.normalization import _compute_stats, _normalize, _canonicalize_axes
 
 
 def decode_id(x):
@@ -109,4 +110,145 @@ class RMSNorm(nn.Module):
             "scale", nn.initializers.ones, reduced_feature_shape, self.param_dtype
         )
         x = x * scale
-        return jnp.asarray(x, self.dtype)
+        return jnp.asarray(x, self.dtype)
\ No newline at end of file
+
+
+class ReZero(nn.Module):
+    channel_wise: bool = False
+    param_dtype: jnp.dtype = jnp.float32
+
+    @nn.compact
+    def __call__(self, x):
+        shape = (x.shape[-1],) if self.channel_wise else ()
+        scale = self.param("scale", nn.initializers.zeros, shape, self.param_dtype)
+        return x * scale
+
+
+class BatchRenorm(nn.Module):
+    """BatchRenorm Module, implemented based on the Batch Renormalization paper (https://arxiv.org/abs/1702.03275).
+    and adapted from Flax's BatchNorm implementation: 
+    https://github.com/google/flax/blob/ce8a3c74d8d1f4a7d8f14b9fb84b2cc76d7f8dbf/flax/linen/normalization.py#L228
+
+
+    Attributes:
+        use_running_average: if True, the statistics stored in batch_stats will be
+            used instead of computing the batch statistics on the input.
+        axis: the feature or non-batch axis of the input.
+        momentum: decay rate for the exponential moving average of the batch
+            statistics.
+        epsilon: a small float added to variance to avoid dividing by zero.
+        dtype: the dtype of the result (default: infer from input and params).
+        param_dtype: the dtype passed to parameter initializers (default: float32).
+        use_bias:  if True, bias (beta) is added.
+        use_scale: if True, multiply by scale (gamma). When the next layer is linear
+            (also e.g. nn.relu), this can be disabled since the scaling will be done
+            by the next layer.
+        bias_init: initializer for bias, by default, zero.
+        scale_init: initializer for scale, by default, one.
+        axis_name: the axis name used to combine batch statistics from multiple
+            devices. See `jax.pmap` for a description of axis names (default: None).
+        axis_index_groups: groups of axis indices within that named axis
+            representing subsets of devices to reduce over (default: None). For
+            example, `[[0, 1], [2, 3]]` would independently batch-normalize over the
+            examples on the first two and last two devices. See `jax.lax.psum` for
+            more details.
+        use_fast_variance: If true, use a faster, but less numerically stable,
+            calculation for the variance.
+  """
+
+    use_running_average: Optional[bool] = None
+    axis: int = -1
+    momentum: float = 0.999
+    epsilon: float = 0.001
+    dtype: Optional[jnp.dtype] = None
+    param_dtype: jnp.dtype = jnp.float32
+    use_bias: bool = True
+    use_scale: bool = True
+    bias_init: nn.initializers.Initializer = nn.initializers.zeros
+    scale_init: nn.initializers.Initializer = nn.initializers.ones
+    axis_name: Optional[str] = None
+    axis_index_groups: Any = None
+    use_fast_variance: bool = True
+
+    @nn.compact
+    def __call__(self, x, use_running_average: Optional[bool] = None):
+        """
+        Args:
+            x: the input to be normalized.
+            use_running_average: if true, the statistics stored in batch_stats will be
+            used instead of computing the batch statistics on the input.
+
+        Returns:
+            Normalized inputs (the same shape as inputs).
+        """
+
+        use_running_average = nn.merge_param(
+            'use_running_average', self.use_running_average, use_running_average
+        )
+        feature_axes = _canonicalize_axes(x.ndim, self.axis)
+        reduction_axes = tuple(i for i in range(x.ndim) if i not in feature_axes)
+        feature_shape = [x.shape[ax] for ax in feature_axes]
+
+        ra_mean = self.variable(
+            'batch_stats', 'mean', lambda s: jnp.zeros(s, jnp.float32), feature_shape)
+        ra_var = self.variable(
+            'batch_stats', 'var', lambda s: jnp.ones(s, jnp.float32), feature_shape)
+
+        r_max = self.variable('batch_stats', 'r_max', lambda s: s, 3)
+        d_max = self.variable('batch_stats', 'd_max', lambda s: s, 5)
+        steps = self.variable('batch_stats', 'steps', lambda s: s, 0)
+
+        if use_running_average:
+            mean, var = ra_mean.value, ra_var.value
+            custom_mean = mean
+            custom_var = var
+        else:
+            mean, var = _compute_stats(
+                x,
+                reduction_axes,
+                dtype=self.dtype,
+                axis_name=self.axis_name if not self.is_initializing() else None,
+                axis_index_groups=self.axis_index_groups,
+                use_fast_variance=self.use_fast_variance,
+            )
+            custom_mean = mean
+            custom_var = var
+            if not self.is_initializing():
+                # The code below is implemented following the Batch Renormalization paper
+                r = 1
+                d = 0
+                std = jnp.sqrt(var + self.epsilon)
+                ra_std = jnp.sqrt(ra_var.value + self.epsilon)
+                r = jax.lax.stop_gradient(std / ra_std)
+                r = jnp.clip(r, 1 / r_max.value, r_max.value)
+                d = jax.lax.stop_gradient((mean - ra_mean.value) / ra_std)
+                d = jnp.clip(d, -d_max.value, d_max.value)
+                tmp_var = var / (r**2)
+                tmp_mean = mean - d * jnp.sqrt(custom_var) / r
+
+                # Warm up batch renorm for 100_000 steps to build up proper running statistics
+                warmed_up = jnp.greater_equal(steps.value, 100_000).astype(jnp.float32)
+                custom_var = warmed_up * tmp_var + (1. - warmed_up) * custom_var
+                custom_mean = warmed_up * tmp_mean + (1. - warmed_up) * custom_mean
+
+                ra_mean.value = (
+                    self.momentum * ra_mean.value + (1 - self.momentum) * mean
+                )
+                ra_var.value = self.momentum * ra_var.value + (1 - self.momentum) * var
+                steps.value += 1
+
+        return _normalize(
+            self,
+            x,
+            custom_mean,
+            custom_var,
+            reduction_axes,
+            feature_axes,
+            self.dtype,
+            self.param_dtype,
+            self.epsilon,
+            self.use_bias,
+            self.use_scale,
+            self.bias_init,
+            self.scale_init,
+        )

ygoai/rl/jax/utils.py

+from typing import Any, Callable
+
 import jax
 import jax.numpy as jnp
 
+from flax import core, struct
+from flax.linen.fp8_ops import OVERWRITE_WITH_GRADIENT
+
+import optax
+
 import numpy as np
 
 from ygoai.rl.env import RecordEpisodeStatistics
@@ -67,3 +74,72 @@ def update_mean_var_count_from_moments(
     new_count = tot_count
 
     return new_mean, new_var, new_count
+
+
+class TrainState(struct.PyTreeNode):
+    step: int
+    apply_fn: Callable = struct.field(pytree_node=False)
+    params: core.FrozenDict[str, Any] = struct.field(pytree_node=True)
+    tx: optax.GradientTransformation = struct.field(pytree_node=False)
+    opt_state: optax.OptState = struct.field(pytree_node=True)
+    batch_stats: core.FrozenDict[str, Any] = struct.field(pytree_node=True)
+
+    def apply_gradients(self, *, grads, **kwargs):
+        """Updates ``step``, ``params``, ``opt_state`` and ``**kwargs`` in return value.
+
+        Note that internally this function calls ``.tx.update()`` followed by a call
+        to ``optax.apply_updates()`` to update ``params`` and ``opt_state``.
+
+        Args:
+            grads: Gradients that have the same pytree structure as ``.params``.
+            **kwargs: Additional dataclass attributes that should be ``.replace()``-ed.
+
+        Returns:
+            An updated instance of ``self`` with ``step`` incremented by one, ``params``
+            and ``opt_state`` updated by applying ``grads``, and additional attributes
+            replaced as specified by ``kwargs``.
+        """
+        if OVERWRITE_WITH_GRADIENT in grads:
+            grads_with_opt = grads['params']
+            params_with_opt = self.params['params']
+        else:
+            grads_with_opt = grads
+            params_with_opt = self.params
+
+        updates, new_opt_state = self.tx.update(
+            grads_with_opt, self.opt_state, params_with_opt
+        )
+        new_params_with_opt = optax.apply_updates(params_with_opt, updates)
+
+        # As implied by the OWG name, the gradients are used directly to update the
+        # parameters.
+        if OVERWRITE_WITH_GRADIENT in grads:
+            new_params = {
+                'params': new_params_with_opt,
+                OVERWRITE_WITH_GRADIENT: grads[OVERWRITE_WITH_GRADIENT],
+            }
+        else:
+            new_params = new_params_with_opt
+            return self.replace(
+            step=self.step + 1,
+            params=new_params,
+            opt_state=new_opt_state,
+            **kwargs,
+        )
+
+    @classmethod
+    def create(cls, *, apply_fn, params, tx, **kwargs):
+        """Creates a new instance with ``step=0`` and initialized ``opt_state``."""
+        # We exclude OWG params when present because they do not need opt states.
+        params_with_opt = (
+            params['params'] if OVERWRITE_WITH_GRADIENT in params else params
+        )
+        opt_state = tx.init(params_with_opt)
+        return cls(
+            step=0,
+            apply_fn=apply_fn,
+            params=params,
+            tx=tx,
+            opt_state=opt_state,
+            **kwargs,
+        )
\ No newline at end of file