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solarcarsim/notebooks/testing.ipynb
saji 7ad7070129 final bit
2024-12-20 16:58:51 -06:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Array([ 0.0000000e+00, -2.5544850e-07, -1.4012958e-06, ...,\n",
" -1.1142221e-02, -1.1067827e-02, -1.1001030e-02], dtype=float32)"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import jax\n",
"import jax.numpy as jnp\n",
"from solarcarsim.physsim import CarParams, fractal_noise_1d\n",
"from solarcarsim.simv2 import Snax, SimParams\n",
"import chex\n",
"\n",
"\n",
"key = jax.random.key(0)\n",
"\n",
"slope = fractal_noise_1d(key, 10000, scale=1200, height_scale=0.08)\n",
"\n",
"slope"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# make a simple triangle-type hill. Slope is 0.2 and then -0.2\n",
"dist = 1000 # it's 1 km long\n",
"xcoords = jnp.arange(dist)\n",
"slope = jnp.concat([jnp.full(int(dist/2), 0.2), jnp.full(int(dist/2), -0.2)])\n",
"def lerp(x):\n",
" return jnp.interp(x, xcoords, slope)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"time_scale = 0.1\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import jax\n",
"import jax.numpy as jnp\n",
"import flax.linen as nn\n",
"import numpy as np\n",
"import optax\n",
"from flax.linen.initializers import constant, orthogonal\n",
"from typing import Sequence, NamedTuple, Any\n",
"from flax.training.train_state import TrainState\n",
"import distrax\n",
"from gymnax.wrappers.purerl import LogWrapper, FlattenObservationWrapper\n",
"\n",
"\n",
"class GymnaxWrapper(object):\n",
" \"\"\"Base class for Gymnax wrappers.\"\"\"\n",
"\n",
" def __init__(self, env):\n",
" self._env = env\n",
"\n",
" # provide proxy access to regular attributes of wrapped object\n",
" def __getattr__(self, name):\n",
" return getattr(self._env, name)\n",
"\n",
"\n",
"class VecEnv(GymnaxWrapper):\n",
" def __init__(self, env):\n",
" super().__init__(env)\n",
" self.reset = jax.vmap(self._env.reset, in_axes=(0, None))\n",
" self.step = jax.vmap(self._env.step, in_axes=(0, 0, 0, None))\n",
"\n",
"class ClipAction(GymnaxWrapper):\n",
" def __init__(self, env, low=-1.0, high=1.0):\n",
" super().__init__(env)\n",
" self.low = low\n",
" self.high = high\n",
"\n",
" def step(self, key, state, action, params=None):\n",
" \"\"\"TODO: In theory the below line should be the way to do this.\"\"\"\n",
" # action = jnp.clip(action, self.env.action_space.low, self.env.action_space.high)\n",
" action = jnp.clip(action, self.low, self.high)\n",
" return self._env.step(key, state, action, params)\n",
"\n",
"\n",
"\n",
"class ActorCritic(nn.Module):\n",
" action_dim: Sequence[int]\n",
" activation: str = \"tanh\"\n",
"\n",
" @nn.compact\n",
" def __call__(self, x):\n",
" if self.activation == \"relu\":\n",
" activation = nn.relu\n",
" else:\n",
" activation = nn.tanh\n",
" actor_mean = nn.Dense(\n",
" 256, kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0)\n",
" )(x)\n",
" actor_mean = activation(actor_mean)\n",
" actor_mean = nn.Dense(\n",
" 256, kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0)\n",
" )(actor_mean)\n",
" actor_mean = activation(actor_mean)\n",
" actor_mean = nn.Dense(\n",
" self.action_dim, kernel_init=orthogonal(0.01), bias_init=constant(0.0)\n",
" )(actor_mean)\n",
" actor_logtstd = self.param(\"log_std\", nn.initializers.zeros, (self.action_dim,))\n",
" pi = distrax.MultivariateNormalDiag(actor_mean, jnp.exp(actor_logtstd))\n",
"\n",
" critic = nn.Dense(\n",
" 256, kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0)\n",
" )(x)\n",
" critic = activation(critic)\n",
" critic = nn.Dense(\n",
" 256, kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0)\n",
" )(critic)\n",
" critic = activation(critic)\n",
" critic = nn.Dense(1, kernel_init=orthogonal(1.0), bias_init=constant(0.0))(\n",
" critic\n",
" )\n",
"\n",
" return pi, jnp.squeeze(critic, axis=-1)\n",
"\n",
"\n",
"class Transition(NamedTuple):\n",
" done: jnp.ndarray\n",
" action: jnp.ndarray\n",
" value: jnp.ndarray\n",
" reward: jnp.ndarray\n",
" log_prob: jnp.ndarray\n",
" obs: jnp.ndarray\n",
" info: jnp.ndarray\n",
"\n",
"\n",
"def make_train(config):\n",
" config[\"NUM_UPDATES\"] = (\n",
" config[\"TOTAL_TIMESTEPS\"] // config[\"NUM_STEPS\"] // config[\"NUM_ENVS\"]\n",
" )\n",
" config[\"MINIBATCH_SIZE\"] = (\n",
" config[\"NUM_ENVS\"] * config[\"NUM_STEPS\"] // config[\"NUM_MINIBATCHES\"]\n",
" )\n",
" env = Snax()\n",
" env_params = env.default_params\n",
" env = LogWrapper(env)\n",
" env = VecEnv(env)\n",
" #env = ClipAction(env)\n",
" # if config[\"NORMALIZE_ENV\"]:\n",
" # env = NormalizeVecObservation(env)\n",
" # env = NormalizeVecReward(env, config[\"GAMMA\"])\n",
"\n",
" def linear_schedule(count):\n",
" frac = (\n",
" 1.0\n",
" - (count // (config[\"NUM_MINIBATCHES\"] * config[\"UPDATE_EPOCHS\"]))\n",
" / config[\"NUM_UPDATES\"]\n",
" )\n",
" return config[\"LR\"] * frac\n",
"\n",
" def train(rng):\n",
" # INIT NETWORK\n",
" network = ActorCritic(\n",
" env.action_space(env_params).shape[0], activation=config[\"ACTIVATION\"]\n",
" )\n",
" rng, _rng = jax.random.split(rng)\n",
" init_x = jnp.zeros(env.observation_space(env_params).shape)\n",
" network_params = network.init(_rng, init_x)\n",
" if config[\"ANNEAL_LR\"]:\n",
" tx = optax.chain(\n",
" optax.clip_by_global_norm(config[\"MAX_GRAD_NORM\"]),\n",
" optax.adam(learning_rate=linear_schedule, eps=1e-5),\n",
" )\n",
" else:\n",
" tx = optax.chain(\n",
" optax.clip_by_global_norm(config[\"MAX_GRAD_NORM\"]),\n",
" optax.adam(config[\"LR\"], eps=1e-5),\n",
" )\n",
" train_state = TrainState.create(\n",
" apply_fn=network.apply,\n",
" params=network_params,\n",
" tx=tx,\n",
" )\n",
"\n",
" # INIT ENV\n",
" rng, _rng = jax.random.split(rng)\n",
" reset_rng = jax.random.split(_rng, config[\"NUM_ENVS\"])\n",
" obsv, env_state = env.reset(reset_rng, env_params)\n",
"\n",
" # TRAIN LOOP\n",
" def _update_step(runner_state, unused):\n",
" # COLLECT TRAJECTORIES\n",
" def _env_step(runner_state, unused):\n",
" train_state, env_state, last_obs, rng = runner_state\n",
"\n",
" # SELECT ACTION\n",
" rng, _rng = jax.random.split(rng)\n",
" pi, value = network.apply(train_state.params, last_obs)\n",
" action = pi.sample(seed=_rng)\n",
" log_prob = pi.log_prob(action)\n",
"\n",
" # STEP ENV\n",
" rng, _rng = jax.random.split(rng)\n",
" rng_step = jax.random.split(_rng, config[\"NUM_ENVS\"])\n",
" obsv, env_state, reward, done, info = env.step(\n",
" rng_step, env_state, action, env_params\n",
" )\n",
" transition = Transition(\n",
" done, action, value, reward, log_prob, last_obs, info\n",
" )\n",
" runner_state = (train_state, env_state, obsv, rng)\n",
" return runner_state, transition\n",
"\n",
" runner_state, traj_batch = jax.lax.scan(\n",
" _env_step, runner_state, None, config[\"NUM_STEPS\"]\n",
" )\n",
"\n",
" # CALCULATE ADVANTAGE\n",
" train_state, env_state, last_obs, rng = runner_state\n",
" _, last_val = network.apply(train_state.params, last_obs)\n",
"\n",
" def _calculate_gae(traj_batch, last_val):\n",
" def _get_advantages(gae_and_next_value, transition):\n",
" gae, next_value = gae_and_next_value\n",
" done, value, reward = (\n",
" transition.done,\n",
" transition.value,\n",
" transition.reward,\n",
" )\n",
" delta = reward + config[\"GAMMA\"] * next_value * (1 - done) - value\n",
" gae = (\n",
" delta\n",
" + config[\"GAMMA\"] * config[\"GAE_LAMBDA\"] * (1 - done) * gae\n",
" )\n",
" return (gae, value), gae\n",
"\n",
" _, advantages = jax.lax.scan(\n",
" _get_advantages,\n",
" (jnp.zeros_like(last_val), last_val),\n",
" traj_batch,\n",
" reverse=True,\n",
" unroll=16,\n",
" )\n",
" return advantages, advantages + traj_batch.value\n",
"\n",
" advantages, targets = _calculate_gae(traj_batch, last_val)\n",
"\n",
" # UPDATE NETWORK\n",
" def _update_epoch(update_state, unused):\n",
" def _update_minbatch(train_state, batch_info):\n",
" traj_batch, advantages, targets = batch_info\n",
"\n",
" def _loss_fn(params, traj_batch, gae, targets):\n",
" # RERUN NETWORK\n",
" pi, value = network.apply(params, traj_batch.obs)\n",
" log_prob = pi.log_prob(traj_batch.action)\n",
"\n",
" # CALCULATE VALUE LOSS\n",
" value_pred_clipped = traj_batch.value + (\n",
" value - traj_batch.value\n",
" ).clip(-config[\"CLIP_EPS\"], config[\"CLIP_EPS\"])\n",
" value_losses = jnp.square(value - targets)\n",
" value_losses_clipped = jnp.square(value_pred_clipped - targets)\n",
" value_loss = (\n",
" 0.5 * jnp.maximum(value_losses, value_losses_clipped).mean()\n",
" )\n",
"\n",
" # CALCULATE ACTOR LOSS\n",
" ratio = jnp.exp(log_prob - traj_batch.log_prob)\n",
" gae = (gae - gae.mean()) / (gae.std() + 1e-8)\n",
" loss_actor1 = ratio * gae\n",
" loss_actor2 = (\n",
" jnp.clip(\n",
" ratio,\n",
" 1.0 - config[\"CLIP_EPS\"],\n",
" 1.0 + config[\"CLIP_EPS\"],\n",
" )\n",
" * gae\n",
" )\n",
" loss_actor = -jnp.minimum(loss_actor1, loss_actor2)\n",
" loss_actor = loss_actor.mean()\n",
" entropy = pi.entropy().mean()\n",
"\n",
" total_loss = (\n",
" loss_actor\n",
" + config[\"VF_COEF\"] * value_loss\n",
" - config[\"ENT_COEF\"] * entropy\n",
" )\n",
" return total_loss, (value_loss, loss_actor, entropy)\n",
"\n",
" grad_fn = jax.value_and_grad(_loss_fn, has_aux=True)\n",
" total_loss, grads = grad_fn(\n",
" train_state.params, traj_batch, advantages, targets\n",
" )\n",
" train_state = train_state.apply_gradients(grads=grads)\n",
" return train_state, total_loss\n",
"\n",
" train_state, traj_batch, advantages, targets, rng = update_state\n",
" rng, _rng = jax.random.split(rng)\n",
" batch_size = config[\"MINIBATCH_SIZE\"] * config[\"NUM_MINIBATCHES\"]\n",
" assert (\n",
" batch_size == config[\"NUM_STEPS\"] * config[\"NUM_ENVS\"]\n",
" ), \"batch size must be equal to number of steps * number of envs\"\n",
" permutation = jax.random.permutation(_rng, batch_size)\n",
" batch = (traj_batch, advantages, targets)\n",
" batch = jax.tree_util.tree_map(\n",
" lambda x: x.reshape((batch_size,) + x.shape[2:]), batch\n",
" )\n",
" shuffled_batch = jax.tree_util.tree_map(\n",
" lambda x: jnp.take(x, permutation, axis=0), batch\n",
" )\n",
" minibatches = jax.tree_util.tree_map(\n",
" lambda x: jnp.reshape(\n",
" x, [config[\"NUM_MINIBATCHES\"], -1] + list(x.shape[1:])\n",
" ),\n",
" shuffled_batch,\n",
" )\n",
" train_state, total_loss = jax.lax.scan(\n",
" _update_minbatch, train_state, minibatches\n",
" )\n",
" update_state = (train_state, traj_batch, advantages, targets, rng)\n",
" return update_state, total_loss\n",
"\n",
" update_state = (train_state, traj_batch, advantages, targets, rng)\n",
" update_state, loss_info = jax.lax.scan(\n",
" _update_epoch, update_state, None, config[\"UPDATE_EPOCHS\"]\n",
" )\n",
" train_state = update_state[0]\n",
" metric = traj_batch.info\n",
" rng = update_state[-1]\n",
" if config.get(\"DEBUG\"):\n",
"\n",
" def callback(info):\n",
" return_values = info[\"returned_episode_returns\"][\n",
" info[\"returned_episode\"]\n",
" ]\n",
" timesteps = (\n",
" info[\"timestep\"][info[\"returned_episode\"]] * config[\"NUM_ENVS\"]\n",
" )\n",
" for t in range(len(timesteps)):\n",
" print(\n",
" f\"global step={timesteps[t]}, episodic return={return_values[t]}\"\n",
" )\n",
"\n",
" jax.debug.callback(callback, metric)\n",
"\n",
" runner_state = (train_state, env_state, last_obs, rng)\n",
" return runner_state, metric\n",
"\n",
" rng, _rng = jax.random.split(rng)\n",
" runner_state = (train_state, env_state, obsv, _rng)\n",
" metrics = []\n",
" for i in range(config[\"NUM_MAINLOOPS\"]):\n",
" runner_state, metric = jax.lax.scan(\n",
" _update_step, runner_state, None, config[\"NUM_UPDATES\"]\n",
" )\n",
" metrics.append(metric)\n",
" return {\"runner_state\": runner_state, \"metrics\": metrics}\n",
"\n",
" return train\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"config = {\n",
" \"LR\": 3e-4,\n",
" \"NUM_ENVS\": 2048,\n",
" \"NUM_STEPS\": 10,\n",
" \"TOTAL_TIMESTEPS\": 5e7,\n",
" \"UPDATE_EPOCHS\": 4,\n",
" \"NUM_MINIBATCHES\": 64,\n",
" \"GAMMA\": 0.99,\n",
" \"GAE_LAMBDA\": 0.95,\n",
" \"CLIP_EPS\": 0.2,\n",
" \"ENT_COEF\": 0.0,\n",
" \"VF_COEF\": 0.5,\n",
" \"MAX_GRAD_NORM\": 0.5,\n",
" \"ACTIVATION\": \"tanh\",\n",
" \"ENV_NAME\": \"hopper\",\n",
" \"ANNEAL_LR\": False,\n",
" \"NORMALIZE_ENV\": True,\n",
" \"DEBUG\": False,\n",
" \"NUM_MAINLOOPS\": 3,\n",
"}\n",
"config_light = {\n",
" \"LR\": 3e-4,\n",
" \"NUM_ENVS\": 128,\n",
" \"NUM_STEPS\": 10,\n",
" \"TOTAL_TIMESTEPS\": 5e3,\n",
" \"UPDATE_EPOCHS\": 2,\n",
" \"NUM_MINIBATCHES\": 16,\n",
" \"GAMMA\": 0.99,\n",
" \"GAE_LAMBDA\": 0.95,\n",
" \"CLIP_EPS\": 0.2,\n",
" \"ENT_COEF\": 0.0,\n",
" \"VF_COEF\": 0.5,\n",
" \"MAX_GRAD_NORM\": 0.5,\n",
" \"ACTIVATION\": \"tanh\",\n",
" \"ENV_NAME\": \"hopper\",\n",
" \"ANNEAL_LR\": False,\n",
" \"NORMALIZE_ENV\": True,\n",
" \"DEBUG\": False,\n",
"}\n",
"\n",
"rng = jax.random.key(42)\n",
"train_jit = jax.jit(make_train(config))\n",
"out = train_jit(rng)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"data = jnp.concatenate([out[\"metrics\"][x][\"returned_episode_returns\"].mean(-1).reshape(-1) for x in range(3)])\n",
"plt.plot(data)\n",
"ax = plt.gca()\n",
"\n",
"plt.xlabel(\"Update Step\")\n",
"plt.ylabel(\"Return\")\n",
"plt.title(\"PPO Agent Returns\")\n",
"plt.savefig(\"PPO_results.pdf\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-12-17 20:40:06.756483: W external/xla/xla/tsl/framework/bfc_allocator.cc:497] Allocator (GPU_0_bfc) ran out of memory trying to allocate 7.63GiB (rounded to 8192000000)requested by op \n",
"2024-12-17 20:40:06.756558: W external/xla/xla/tsl/framework/bfc_allocator.cc:508] ***************************************************_________________________________________________\n",
"E1217 20:40:06.756588 512196 pjrt_stream_executor_client.cc:3086] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 8192000000 bytes.\n"
]
},
{
"ename": "XlaRuntimeError",
"evalue": "RESOURCE_EXHAUSTED: Out of memory while trying to allocate 8192000000 bytes.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mXlaRuntimeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[19], line 33\u001b[0m\n\u001b[1;32m 28\u001b[0m runner_state \u001b[38;5;241m=\u001b[39m (train_state, env_state, obsv, rng)\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m runner_state, env_state\n\u001b[0;32m---> 33\u001b[0m runner_state, env_logs \u001b[38;5;241m=\u001b[39m \u001b[43mjax\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlax\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscan\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 34\u001b[0m \u001b[43m \u001b[49m\u001b[43m_env_step\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrunner_state\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m100\u001b[39;49m\n\u001b[1;32m 35\u001b[0m \u001b[43m)\u001b[49m\n",
" \u001b[0;31m[... skipping hidden 11 frame]\u001b[0m\n",
"File \u001b[0;32m~/Documents/Code/solarcarsim/.venv/lib/python3.12/site-packages/jax/_src/interpreters/pxla.py:1298\u001b[0m, in \u001b[0;36mExecuteReplicated.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1296\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_handle_token_bufs(result_token_bufs, sharded_runtime_token)\n\u001b[1;32m 1297\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1298\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxla_executable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute_sharded\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_bufs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1300\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dispatch\u001b[38;5;241m.\u001b[39mneeds_check_special():\n\u001b[1;32m 1301\u001b[0m out_arrays \u001b[38;5;241m=\u001b[39m results\u001b[38;5;241m.\u001b[39mdisassemble_into_single_device_arrays()\n",
"\u001b[0;31mXlaRuntimeError\u001b[0m: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 8192000000 bytes."
]
}
],
"source": [
"tstate = out['runner_state'][0]\n",
"lastobs = out['runner_state'][2]\n",
"# tstate.apply_fn(tstate.params, lastobs)\n",
"\n",
"env = Snax()\n",
"env_params = env.default_params\n",
"env = LogWrapper(env)\n",
"env = VecEnv(env)\n",
"\n",
"def _env_step(runner_state, unused):\n",
" train_state, env_state, last_obs, rng = runner_state\n",
"\n",
" # SELECT ACTION\n",
" rng, _rng = jax.random.split(rng)\n",
" pi, value = train_state.apply_fn(train_state.params, last_obs)\n",
" action = pi.sample(seed=_rng)\n",
" log_prob = pi.log_prob(action)\n",
"\n",
" # STEP ENV\n",
" rng, _rng = jax.random.split(rng)\n",
" rng_step = jax.random.split(_rng, config[\"NUM_ENVS\"])\n",
" obsv, env_state, reward, done, info = env.step(\n",
" rng_step, env_state, action, env_params\n",
" )\n",
" transition = Transition(\n",
" done, action, value, reward, log_prob, last_obs, info\n",
" )\n",
" runner_state = (train_state, env_state, obsv, rng)\n",
" return runner_state, env_state\n",
"\n",
"\n",
"\n",
"runner_state, env_logs = jax.lax.scan(\n",
" _env_step, out['runner_state'], None, 100\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TrainState(step=Array(1874688, dtype=int32, weak_type=True), apply_fn=<bound method Module.apply of ActorCritic(\n",
" # attributes\n",
" action_dim = 1\n",
" activation = 'tanh'\n",
")>, params={'params': {'Dense_0': {'bias': Array([-5.3029938e-04, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 1.9191748e-03, 0.0000000e+00, -1.7144637e-04, 2.5281330e-05,\n",
" 1.4641370e-03, 0.0000000e+00, -1.3245111e-03, 0.0000000e+00,\n",
" 0.0000000e+00, 8.3523680e-04, 0.0000000e+00, 0.0000000e+00,\n",
" 6.0847404e-05, -4.7602013e-04, 0.0000000e+00, 1.3794134e-03,\n",
" 0.0000000e+00, -7.6553306e-06, 0.0000000e+00, 2.1930558e-04,\n",
" 0.0000000e+00, -2.2188693e-03, 1.4511290e-03, 1.9425271e-03,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 1.1256059e-03,\n",
" -5.8398215e-04, 0.0000000e+00, -1.8540439e-03, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, -6.9769150e-05, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 6.8441045e-04, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, -8.8643702e-04, 1.3925527e-03,\n",
" -1.4111768e-04, 0.0000000e+00, 0.0000000e+00, -2.0299070e-03,\n",
" 6.8957923e-04, -1.2412324e-03, 5.5068691e-04, -5.2116567e-04,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 6.2125333e-04,\n",
" 9.1447402e-04, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 5.2126928e-04, 0.0000000e+00, 5.8222190e-04, 0.0000000e+00,\n",
" 0.0000000e+00, 2.2282777e-03, 1.3834524e-03, -1.0245681e-03,\n",
" 1.7563089e-04, -2.4510939e-03, 0.0000000e+00, -1.6243160e-03,\n",
" -9.4084017e-04, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" -9.0897578e-04, 0.0000000e+00, -1.0096998e-03, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 3.1322434e-03,\n",
" 0.0000000e+00, 0.0000000e+00, 2.2586577e-03, -2.4337969e-03,\n",
" 5.6456536e-04, 3.0193112e-03, 0.0000000e+00, 0.0000000e+00,\n",
" -2.9874803e-03, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" -3.7863411e-03, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, -3.2294096e-04,\n",
" 2.7185181e-04, 1.4245387e-08, -1.9866133e-03, -2.2128341e-03,\n",
" 0.0000000e+00, -1.7956822e-04, 1.4105116e-03, 0.0000000e+00,\n",
" -6.7686941e-04, 0.0000000e+00, 0.0000000e+00, 4.0784094e-04,\n",
" 0.0000000e+00, 0.0000000e+00, -1.0925150e-03, -9.4625680e-04,\n",
" -1.0735401e-03, 1.7537770e-03, -1.4924963e-03, -1.0218532e-03,\n",
" 0.0000000e+00, -3.4914708e-03, -4.7835559e-04, 1.9216866e-03,\n",
" 0.0000000e+00, 7.1237684e-04, -3.2128301e-03, -1.7769258e-03,\n",
" 2.2146765e-03, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 6.1193650e-04, 0.0000000e+00, 0.0000000e+00, 1.7334922e-04,\n",
" -2.6329695e-03, -1.3060468e-03, 3.4790873e-03, 0.0000000e+00,\n",
" 1.2544972e-03, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, -1.2978822e-03, -3.8260728e-04, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, -4.5437412e-04, 0.0000000e+00,\n",
" 0.0000000e+00, 7.1664042e-05, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 7.0464790e-05,\n",
" -8.9069502e-04, 0.0000000e+00, 6.6835230e-04, 0.0000000e+00,\n",
" 6.8251284e-05, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" -1.0040104e-03, -2.3794189e-07, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 2.3229128e-04, 1.5453034e-03, -5.0179427e-05,\n",
" 2.2772038e-03, -2.0145031e-03, 9.4736563e-03, 7.9449776e-05,\n",
" -7.4491683e-05, 0.0000000e+00, 2.5824676e-04, 0.0000000e+00,\n",
" 1.2736652e-03, 0.0000000e+00, 3.0285574e-04, 2.4916211e-04,\n",
" 0.0000000e+00, 0.0000000e+00, 7.7232631e-04, 5.4959516e-04,\n",
" -1.9705489e-03, 0.0000000e+00, 1.1921050e-03, -3.9399034e-04,\n",
" 0.0000000e+00, 3.7107369e-04, -4.8485940e-04, 0.0000000e+00,\n",
" 1.5376735e-04, 1.8611597e-03, -6.0213718e-04, -2.2827655e-03,\n",
" 0.0000000e+00, 0.0000000e+00, -2.4243153e-04, 0.0000000e+00,\n",
" -2.2830942e-03, 4.1451986e-04, -2.6722133e-04, 2.5362780e-04,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, -6.0310279e-04, 0.0000000e+00,\n",
" 0.0000000e+00, -4.7796275e-04, 0.0000000e+00, 0.0000000e+00], dtype=float32), 'kernel': Array([[-0.04059862, -0.05616716, 0.05436023, ..., -0.01495443,\n",
" -0.09417699, -0.03651878],\n",
" [-0.04345348, 0.0237919 , 0.13925986, ..., -0.03692472,\n",
" 0.03002992, -0.11007421],\n",
" [ 0.09764732, -0.03158703, -0.02001419, ..., -0.14909488,\n",
" 0.02449407, 0.01721021],\n",
" ...,\n",
" [ 0.11338337, -0.04206258, 0.11930869, ..., -0.01594234,\n",
" 0.0031549 , 0.07585711],\n",
" [-0.09437636, -0.10784481, -0.13662401, ..., 0.0185915 ,\n",
" 0.04616122, -0.02840761],\n",
" [-0.03904481, -0.04892467, 0.05431201, ..., 0.1215561 ,\n",
" -0.19458926, 0.04553339]], dtype=float32)}, 'Dense_1': {'bias': Array([ 0.00027806, 0.04620876, -0.00658554, -0.04454401, -0.00277375,\n",
" 0.03381715, 0.03254296, 0.00331175, -0.04037352, 0.04094049,\n",
" 0.00339541, -0.0373533 , -0.03950972, 0.00459826, 0.00176061,\n",
" -0.04285378, 0.03781348, 0.04358079, 0.03816335, 0.03898 ,\n",
" 0.03793908, -0.0067359 , -0.0411862 , 0.00432372, -0.03645715,\n",
" 0.03726813, -0.00039956, 0.03472266, -0.00593952, -0.04233544,\n",
" -0.00230428, -0.00433152, 0.03425088, -0.02485432, -0.0286654 ,\n",
" -0.00356171, 0.03057408, 0.03985466, 0.00293833, 0.00458675,\n",
" 0.02603373, -0.03051696, 0.03516515, 0.00130115, 0.0465891 ,\n",
" -0.04041 , -0.04491264, 0.01050323, -0.03533997, 0.03221814,\n",
" -0.00215372, 0.04099191, 0.03301746, 0.04153331, -0.00470519,\n",
" -0.00428926, -0.05097796, -0.03040344, -0.03735339, -0.03491513,\n",
" 0.01251048, 0.03140776, 0.00783039, 0.00627123, -0.02943583,\n",
" 0.03217795, -0.03619361, 0.04503313, 0.00422618, 0.03048726,\n",
" -0.04530523, -0.05115746, 0.00748269, 0.04205422, 0.04298782,\n",
" 0.03045189, -0.03948338, 0.00261836, 0.03464689, 0.00538703,\n",
" -0.00227268, -0.03553307, 0.00145856, 0.02827597, -0.03024151,\n",
" -0.04150978, -0.03633608, -0.00803869, -0.00474264, -0.03425602,\n",
" 0.03940321, 0.03186259, -0.00434997, 0.00952879, 0.04427051,\n",
" 0.00112212, -0.01438359, 0.02742064, 0.00732329, -0.03644897,\n",
" 0.02770537, -0.00053647, -0.03326581, 0.03436317, -0.03182915,\n",
" 0.00353186, -0.0258979 , -0.00346143, -0.00030614, 0.00350122,\n",
" 0.04173567, -0.04064614, -0.01026691, -0.04184798, -0.0351134 ,\n",
" 0.02159838, 0.03210129, 0.02979252, -0.00021149, -0.03641422,\n",
" -0.00728356, -0.00786742, 0.03055024, -0.0375047 , 0.03636042,\n",
" -0.03306672, 0.03809343, -0.00785372, 0.03908067, -0.02755055,\n",
" -0.0311247 , -0.03837723, 0.03915109, 0.00293649, -0.02566425,\n",
" -0.03642197, 0.00442762, -0.0285236 , -0.03219024, -0.03771985,\n",
" 0.03348036, 0.04192547, 0.04804754, -0.00390566, 0.03801316,\n",
" -0.0048022 , -0.02534208, 0.00532888, -0.00960828, 0.0399802 ,\n",
" 0.00194347, 0.04182222, -0.02789724, -0.03810298, -0.0374799 ,\n",
" -0.00092357, -0.03994033, -0.03276777, 0.0271517 , -0.03100638,\n",
" -0.04451067, -0.00705037, -0.02453071, 0.03519889, 0.03063235,\n",
" -0.03020622, -0.03580233, -0.03554969, 0.03846539, -0.00717684,\n",
" 0.0024833 , 0.0319246 , 0.02931642, 0.03770342, -0.03255963,\n",
" 0.03416643, 0.01054269, -0.03945149, -0.00351726, 0.04458899,\n",
" -0.00589401, -0.00648886, 0.0339095 , 0.02671813, 0.04369878,\n",
" 0.03415771, 0.03542192, 0.00600839, -0.0109551 , -0.037458 ,\n",
" -0.03981134, 0.03337134, 0.00489916, 0.00273095, -0.00618493,\n",
" 0.04161482, 0.03454573, -0.01003036, 0.00425193, 0.01817769,\n",
" 0.03138407, -0.03242443, 0.00188284, -0.00314224, 0.03295113,\n",
" 0.04222574, 0.03434506, 0.04464654, -0.03982402, -0.04468929,\n",
" 0.04249624, 0.00770373, -0.03094419, 0.00289513, 0.04945203,\n",
" 0.03187467, -0.04758247, 0.00177818, 0.00270677, -0.04519045,\n",
" -0.0402201 , -0.0426771 , 0.00278877, -0.03111694, -0.03696601,\n",
" -0.03909003, -0.03003668, 0.00376004, 0.03456468, 0.01219636,\n",
" 0.00517881, -0.0396665 , 0.04550315, -0.04317153, -0.00225738,\n",
" -0.02796482, -0.00212918, 0.0041019 , -0.03426032, -0.04060804,\n",
" 0.00500863, 0.0303803 , -0.03806275, -0.0432169 , 0.03569881,\n",
" 0.04669258, -0.00873193, -0.00346405, 0.02921363, 0.03867232,\n",
" 0.04459479, -0.03213866, -0.03638731, -0.03427472, 0.04575037,\n",
" -0.00120791], dtype=float32), 'kernel': Array([[ 0.0554822 , -0.07003432, 0.09695947, ..., -0.17977887,\n",
" 0.0366156 , 0.02077846],\n",
" [ 0.17207822, 0.09883893, 0.01466036, ..., -0.02480283,\n",
" 0.08038498, -0.09478654],\n",
" [ 0.00384633, 0.1082558 , 0.02827624, ..., -0.02226412,\n",
" 0.05913283, 0.08880645],\n",
" ...,\n",
" [ 0.0052855 , 0.08274142, -0.02554986, ..., 0.08520137,\n",
" 0.01621614, -0.10647655],\n",
" [ 0.08706143, 0.09899756, -0.05577984, ..., -0.05286903,\n",
" -0.06213011, -0.07769346],\n",
" [ 0.11625612, 0.03126499, -0.13369204, ..., -0.05385393,\n",
" -0.092626 , -0.06382608]], dtype=float32)}, 'Dense_2': {'bias': Array([0.47612667], dtype=float32), 'kernel': Array([[-5.04823923e-01],\n",
" [ 4.65460658e-01],\n",
" [-5.04793406e-01],\n",
" [-4.72569168e-01],\n",
" [ 5.04825413e-01],\n",
" [ 4.79185104e-01],\n",
" [ 4.80066299e-01],\n",
" [-5.04783750e-01],\n",
" [-4.73151356e-01],\n",
" [ 4.51244444e-01],\n",
" [ 5.04835188e-01],\n",
" [-4.73466516e-01],\n",
" [-4.83210683e-01],\n",
" [ 5.04850090e-01],\n",
" [ 5.04784822e-01],\n",
" [-4.91237015e-01],\n",
" [ 4.74481404e-01],\n",
" [ 4.75008428e-01],\n",
" [ 4.78206635e-01],\n",
" [ 4.73737508e-01],\n",
" [ 4.78371084e-01],\n",
" [ 5.04845023e-01],\n",
" [-4.80819106e-01],\n",
" [-4.72271591e-01],\n",
" [-4.73856926e-01],\n",
" [ 4.75976706e-01],\n",
" [ 9.55915993e-07],\n",
" [ 4.79922265e-01],\n",
" [ 5.04790187e-01],\n",
" [-4.81541991e-01],\n",
" [ 5.04812837e-01],\n",
" [ 5.04815102e-01],\n",
" [ 4.71088231e-01],\n",
" [-4.76686895e-01],\n",
" [-4.73729491e-01],\n",
" [-5.04812241e-01],\n",
" [ 4.79393840e-01],\n",
" [ 4.72649992e-01],\n",
" [-5.04822433e-01],\n",
" [-5.04823506e-01],\n",
" [ 4.76308912e-01],\n",
" [-4.75529820e-01],\n",
" [ 4.74255502e-01],\n",
" [-5.04849136e-01],\n",
" [ 4.87328082e-01],\n",
" [-4.72647727e-01],\n",
" [-4.74320143e-01],\n",
" [ 5.04813969e-01],\n",
" [-4.75550056e-01],\n",
" [ 4.75366712e-01],\n",
" [-5.04834890e-01],\n",
" [ 4.62294102e-01],\n",
" [ 4.76273477e-01],\n",
" [ 4.81204808e-01],\n",
" [ 5.04829705e-01],\n",
" [-4.72716808e-01],\n",
" [-4.76078093e-01],\n",
" [-4.71584499e-01],\n",
" [-4.79490250e-01],\n",
" [-4.75963056e-01],\n",
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" [ 4.72023100e-01],\n",
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" [ 4.74489599e-01],\n",
" [-5.04848897e-01],\n",
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" [1.58691948e-09]], dtype=float32)}, 'log_std': Array([3.184018e-06], dtype=float32)}}), EmptyState())))"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"out['runner_state'][0]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "Destination /tmp/flax_ckpt/orbax/single_save already exists.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[9], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m ckpt \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m'\u001b[39m: out[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrunner_state\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m]}\n\u001b[1;32m 5\u001b[0m save_args \u001b[38;5;241m=\u001b[39m orbax_utils\u001b[38;5;241m.\u001b[39msave_args_from_target(ckpt)\n\u001b[0;32m----> 6\u001b[0m \u001b[43morbax_checkpointer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/tmp/flax_ckpt/orbax/single_save\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_args\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/Documents/Code/solarcarsim/.venv/lib/python3.12/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py:201\u001b[0m, in \u001b[0;36mCheckpointer.save\u001b[0;34m(self, directory, force, *args, **kwargs)\u001b[0m\n\u001b[1;32m 199\u001b[0m directory\u001b[38;5;241m.\u001b[39mrmtree() \u001b[38;5;66;03m# Post-sync handled by create_tmp_directory.\u001b[39;00m\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 201\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDestination \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdirectory\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m already exists.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 202\u001b[0m ckpt_args \u001b[38;5;241m=\u001b[39m construct_checkpoint_args(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_handler, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 203\u001b[0m tmpdir \u001b[38;5;241m=\u001b[39m asyncio_utils\u001b[38;5;241m.\u001b[39mrun_sync(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcreate_temporary_path(directory))\n",
"\u001b[0;31mValueError\u001b[0m: Destination /tmp/flax_ckpt/orbax/single_save already exists."
]
}
],
"source": [
"import orbax.checkpoint\n",
"from flax.training import orbax_utils\n",
"orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n",
"ckpt = {'model': out['runner_state'][0]}\n",
"save_args = orbax_utils.save_args_from_target(ckpt)\n",
"orbax_checkpointer.save('/tmp/flax_ckpt/orbax/single_save', ckpt, save_args=save_args)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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