custom_torch_ppg.py 18.3 KB
Newer Older
Dipam Chakraborty's avatar
Dipam Chakraborty committed
1
2
3
4
5
6
7
8
9
10
11
from ray.rllib.policy.torch_policy import TorchPolicy
import numpy as np
from ray.rllib.utils.torch_ops import convert_to_non_torch_type, convert_to_torch_tensor
from ray.rllib.utils import try_import_torch
from ray.rllib.models import ModelCatalog
from ray.rllib.utils.annotations import override
from collections import deque
from .utils import *
import time

torch, nn = try_import_torch()
12
import torch.distributions as td
Dipam Chakraborty's avatar
Dipam Chakraborty committed
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45

class CustomTorchPolicy(TorchPolicy):
    """Example of a random policy
    If you are using tensorflow/pytorch to build custom policies,
    you might find `build_tf_policy` and `build_torch_policy` to
    be useful.
    Adopted from examples from https://docs.ray.io/en/master/rllib-concepts.html
    """

    def __init__(self, observation_space, action_space, config):
        self.config = config

        self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
        dist_class, logit_dim = ModelCatalog.get_action_dist(
            action_space, self.config["model"], framework="torch")
        self.model = ModelCatalog.get_model_v2(
                        obs_space=observation_space,
                        action_space=action_space,
                        num_outputs=logit_dim,
                        model_config=self.config["model"],
                        framework="torch",
                        device=self.device,
                     )

        TorchPolicy.__init__(
            self,
            observation_space=observation_space,
            action_space=action_space,
            config=config,
            model=self.model,
            loss=None,
            action_distribution_class=dist_class,
        )
46

Dipam Chakraborty's avatar
Dipam Chakraborty committed
47
        self.framework = "torch"
Dipam Chakraborty's avatar
Dipam Chakraborty committed
48
49
50
51
52
53
54
55
        aux_params = set(self.model.aux_vf.parameters())
        value_params = set(self.model.value_fc.parameters())
        network_params = set(self.model.parameters())
        aux_optim_params = list(network_params - value_params)
        ppo_optim_params = list(network_params - aux_params - value_params)
        self.optimizer = torch.optim.Adam(ppo_optim_params, lr=5e-4)
        self.aux_optimizer = torch.optim.Adam(aux_optim_params, lr=5e-4)
        self.value_optimizer = torch.optim.Adam(value_params, lr=1e-3)
Dipam Chakraborty's avatar
Dipam Chakraborty committed
56
57
58
59
60
61
62
63
64
65
66
        self.max_reward = self.config['env_config']['return_max']
        self.rewnorm = RewardNormalizer(cliprew=self.max_reward) ## TODO: Might need to go to custom state
        self.reward_deque = deque(maxlen=100)
        self.best_reward = -np.inf
        self.best_weights = None
        self.time_elapsed = 0
        self.batch_end_time = time.time()
        self.timesteps_total = 0
        self.best_rew_tsteps = 0
        
        nw = self.config['num_workers'] if self.config['num_workers'] > 0 else 1
Dipam Chakraborty's avatar
Dipam Chakraborty committed
67
68
69
70
        nenvs = nw * self.config['num_envs_per_worker']
        nsteps = self.config['rollout_fragment_length']
        n_pi = self.config['n_pi']
        self.nbatch = nenvs * nsteps
Dipam Chakraborty's avatar
Dipam Chakraborty committed
71
72
73
74
75
76
77
        self.actual_batch_size = self.nbatch // self.config['updates_per_batch']
        self.accumulate_train_batches = int(np.ceil( self.actual_batch_size / self.config['max_minibatch_size'] ))
        self.mem_limited_batch_size = self.actual_batch_size // self.accumulate_train_batches
        if self.nbatch % self.actual_batch_size != 0 or self.nbatch % self.mem_limited_batch_size != 0:
            print("#################################################")
            print("WARNING: MEMORY LIMITED BATCHING NOT SET PROPERLY")
            print("#################################################")
Dipam Chakraborty's avatar
Dipam Chakraborty committed
78
79
        replay_shape = (n_pi, nsteps, nenvs)
        self.retune_selector = RetuneSelector(nenvs, observation_space, action_space, replay_shape,
Dipam Chakraborty's avatar
Dipam Chakraborty committed
80
81
                                              skips = self.config['skips'], 
                                              n_pi = n_pi,
Dipam Chakraborty's avatar
Dipam Chakraborty committed
82
83
                                              num_retunes = self.config['num_retunes'],
                                              flat_buffer = self.config['flattened_buffer'])
84
        self.save_success = 0
Dipam Chakraborty's avatar
Dipam Chakraborty committed
85
86
        self.target_timesteps = 8_000_000
        self.buffer_time = 20 # TODO: Could try to do a median or mean time step check instead
87
        self.max_time = 100000000
Dipam Chakraborty's avatar
Dipam Chakraborty committed
88
89
90
91
92
93
94
95
        self.maxrewep_lenbuf = deque(maxlen=100)
        self.gamma = self.config['gamma']
        self.adaptive_discount_tuner = AdaptiveDiscountTuner(self.gamma, momentum=0.98, eplenmult=3)
        
        self.lr = config['lr']
        self.ent_coef = config['entropy_coeff']
        
        self.last_dones = np.zeros((nw * self.config['num_envs_per_worker'],))
Dipam Chakraborty's avatar
Dipam Chakraborty committed
96
        self.make_distr = dist_build(action_space)
97
        self.retunes_completed = 0
Dipam Chakraborty's avatar
Dipam Chakraborty committed
98
99
100
        
    def to_tensor(self, arr):
        return torch.from_numpy(arr).to(self.device)
101
102
103
104
    
    @override(TorchPolicy)
    def extra_action_out(self, input_dict, state_batches, model, action_dist):
        return {'values': model._value.tolist()}
Dipam Chakraborty's avatar
Dipam Chakraborty committed
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
        
    @override(TorchPolicy)
    def learn_on_batch(self, samples):
        """Fused compute gradients and apply gradients call.
        Either this or the combination of compute/apply grads must be
        implemented by subclasses.
        Returns:
            grad_info: dictionary of extra metadata from compute_gradients().
        Examples:
            >>> batch = ev.sample()
            >>> ev.learn_on_batch(samples)
        Reference: https://github.com/ray-project/ray/blob/master/rllib/policy/policy.py#L279-L316
        """
        ## Config data values
        nbatch = self.nbatch
        nbatch_train = self.mem_limited_batch_size 
        gamma, lam = self.gamma, self.config['lambda']
        nsteps = self.config['rollout_fragment_length']
        nenvs = nbatch//nsteps
        ts = (nenvs, nsteps)
        mb_dones = unroll(samples['dones'], ts)
        
        ## Reward Normalization - No reward norm works well for many envs
        if self.config['standardize_rewards']:
            mb_origrewards = unroll(samples['rewards'], ts)
            mb_rewards =  np.zeros_like(mb_origrewards)
Dipam Chakraborty's avatar
Dipam Chakraborty committed
131
            mb_rewards[0] = self.rewnorm.normalize(mb_origrewards[0], self.last_dones, self.config["reset_returns"])
Dipam Chakraborty's avatar
Dipam Chakraborty committed
132
133
134
135
136
            for ii in range(1, nsteps):
                mb_rewards[ii] = self.rewnorm.normalize(mb_origrewards[ii], mb_dones[ii-1])
            self.last_dones = mb_dones[-1]
        else:
            mb_rewards = unroll(samples['rewards'], ts)
Dipam Chakraborty's avatar
Dipam Chakraborty committed
137
       
138
        # Weird hack that helps in many envs (Yes keep it after reward normalization)
Dipam Chakraborty's avatar
Dipam Chakraborty committed
139
140
141
        rew_scale = self.config["scale_reward"]
        if rew_scale != 1.0:
            mb_rewards *= rew_scale
Dipam Chakraborty's avatar
Dipam Chakraborty committed
142
143
144
145
146
147
148
149
150
151
152
        
        should_skip_train_step = self.best_reward_model_select(samples)
        if should_skip_train_step:
            self.update_batch_time()
            return {} # Not doing last optimization step - This is intentional due to noisy gradients
          
        obs = samples['obs']

        ## Value prediction
        next_obs = unroll(samples['new_obs'], ts)[-1]
        last_values, _ = self.model.vf_pi(next_obs, ret_numpy=True, no_grad=True, to_torch=True)
153
        values = samples['values']
Dipam Chakraborty's avatar
Dipam Chakraborty committed
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
        
        ## GAE
        mb_values = unroll(values, ts)
        mb_returns = np.zeros_like(mb_rewards)
        mb_advs = np.zeros_like(mb_rewards)
        lastgaelam = 0
        for t in reversed(range(nsteps)):
            if t == nsteps - 1:
                nextvalues = last_values
            else:
                nextvalues = mb_values[t+1]
            nextnonterminal = 1.0 - mb_dones[t]
            delta = mb_rewards[t] + gamma * nextvalues * nextnonterminal - mb_values[t]
            mb_advs[t] = lastgaelam = delta + gamma * lam * nextnonterminal * lastgaelam
        mb_returns = mb_advs + mb_values
        
        ## Data from config
        cliprange, vfcliprange = self.config['clip_param'], self.config['vf_clip_param']
        lrnow = self.lr
        max_grad_norm = self.config['grad_clip']
        ent_coef, vf_coef = self.ent_coef, self.config['vf_loss_coeff']
        
176
        logp_actions = samples['action_logp'] ## np.isclose seems to be True always, otherwise compute again if needed
Dipam Chakraborty's avatar
Dipam Chakraborty committed
177
178
179
180
        noptepochs = self.config['num_sgd_iter']
        actions = samples['actions']
        returns = roll(mb_returns)
        
181
182
183
        advs = returns - values
        normalized_advs = (advs - np.mean(advs)) / (np.std(advs) + 1e-8) 
        
Dipam Chakraborty's avatar
Dipam Chakraborty committed
184
185
186
187
188
189
190
191
        ## Train multiple epochs
        optim_count = 0
        inds = np.arange(nbatch)
        for _ in range(noptepochs):
            np.random.shuffle(inds)
            for start in range(0, nbatch, nbatch_train):
                end = start + nbatch_train
                mbinds = inds[start:end]
192
                slices = (self.to_tensor(arr[mbinds]) for arr in (obs, returns, actions, values, logp_actions, normalized_advs))
Dipam Chakraborty's avatar
Dipam Chakraborty committed
193
194
195
196
197
                optim_count += 1
                apply_grad = (optim_count % self.accumulate_train_batches) == 0
                self._batch_train(apply_grad, self.accumulate_train_batches,
                                  lrnow, cliprange, vfcliprange, max_grad_norm, ent_coef, vf_coef, *slices)

Dipam Chakraborty's avatar
Dipam Chakraborty committed
198
        ## Distill with aux head
Dipam Chakraborty's avatar
Dipam Chakraborty committed
199
        should_retune = self.retune_selector.update(unroll(obs, ts), mb_returns)
Dipam Chakraborty's avatar
Dipam Chakraborty committed
200
201
202
        if should_retune:
            self.aux_train()
        
Dipam Chakraborty's avatar
Dipam Chakraborty committed
203
204
205
206
207
208
209
210
211
212
213
214
215
216
        self.update_gamma(samples)
        self.update_lr()
        self.update_ent_coef()
            
        self.update_batch_time()
        return {}
    
    def update_batch_time(self):
        self.time_elapsed += time.time() - self.batch_end_time
        self.batch_end_time = time.time()
        
    def _batch_train(self, apply_grad, num_accumulate, 
                     lr, cliprange, vfcliprange, max_grad_norm,
                     ent_coef, vf_coef,
217
                     obs, returns, actions, values, logp_actions_old, advs):
Dipam Chakraborty's avatar
Dipam Chakraborty committed
218
219
220
221
        
        for g in self.optimizer.param_groups:
            g['lr'] = lr
        vpred, pi_logits = self.model.vf_pi(obs, ret_numpy=False, no_grad=False, to_torch=False)
222
        pd = self.make_distr(pi_logits)
Dipam Chakraborty's avatar
Dipam Chakraborty committed
223
        logp_actions = pd.log_prob(actions[...,None]).squeeze(1)
224
        entropy = torch.mean(pd.entropy())
Dipam Chakraborty's avatar
Dipam Chakraborty committed
225

Dipam Chakraborty's avatar
Dipam Chakraborty committed
226
        vf_loss = .5 * torch.mean(torch.pow((vpred - returns), 2)) * vf_coef
Dipam Chakraborty's avatar
Dipam Chakraborty committed
227

228
        ratio = torch.exp(logp_actions - logp_actions_old)
Dipam Chakraborty's avatar
Dipam Chakraborty committed
229
230
231
232
        pg_losses1 = -advs * ratio
        pg_losses2 = -advs * torch.clamp(ratio, 1-cliprange, 1+cliprange)
        pg_loss = torch.mean(torch.max(pg_losses1, pg_losses2))

Dipam Chakraborty's avatar
Dipam Chakraborty committed
233
        loss = pg_loss - entropy * ent_coef
Dipam Chakraborty's avatar
Dipam Chakraborty committed
234
235
        
        loss = loss / num_accumulate
Dipam Chakraborty's avatar
Dipam Chakraborty committed
236
        vf_loss = vf_loss / num_accumulate
Dipam Chakraborty's avatar
Dipam Chakraborty committed
237
238

        loss.backward()
Dipam Chakraborty's avatar
Dipam Chakraborty committed
239
        vf_loss.backward()
Dipam Chakraborty's avatar
Dipam Chakraborty committed
240
241
        if apply_grad:
            self.optimizer.step()
Dipam Chakraborty's avatar
Dipam Chakraborty committed
242
            self.value_optimizer.step()
Dipam Chakraborty's avatar
Dipam Chakraborty committed
243
            self.optimizer.zero_grad()
Dipam Chakraborty's avatar
Dipam Chakraborty committed
244
            self.value_optimizer.zero_grad()
Dipam Chakraborty's avatar
Dipam Chakraborty committed
245
246

        
Dipam Chakraborty's avatar
Dipam Chakraborty committed
247
248
    def aux_train(self):
        for g in self.aux_optimizer.param_groups:
Dipam Chakraborty's avatar
Dipam Chakraborty committed
249
            g['lr'] = self.config['aux_lr']
Dipam Chakraborty's avatar
Dipam Chakraborty committed
250
251
        nbatch_train = self.mem_limited_batch_size 
        retune_epochs = self.config['retune_epochs']
Dipam Chakraborty's avatar
Dipam Chakraborty committed
252
        replay_shape = self.retune_selector.vtarg_replay.shape
Dipam Chakraborty's avatar
Dipam Chakraborty committed
253
        replay_pi = np.empty((*replay_shape, self.retune_selector.ac_space.n), dtype=np.float32)
Dipam Chakraborty's avatar
Dipam Chakraborty committed
254

Dipam Chakraborty's avatar
Dipam Chakraborty committed
255
256
        for nnpi in range(self.retune_selector.n_pi):
            for ne in range(self.retune_selector.nenvs):
Dipam Chakraborty's avatar
Dipam Chakraborty committed
257
                _, replay_pi[nnpi, :, ne] = self.model.vf_pi(self.retune_selector.exp_replay[nnpi, :, ne], 
Dipam Chakraborty's avatar
Dipam Chakraborty committed
258
                                                             ret_numpy=True, no_grad=True, to_torch=True)
Dipam Chakraborty's avatar
Dipam Chakraborty committed
259
        
Dipam Chakraborty's avatar
Dipam Chakraborty committed
260
        # Tune vf and pi heads to older predictions with (augmented?) observations
Dipam Chakraborty's avatar
Dipam Chakraborty committed
261
        for ep in range(retune_epochs):
Dipam Chakraborty's avatar
Dipam Chakraborty committed
262
            for slices in self.retune_selector.make_minibatches(replay_pi):
Dipam Chakraborty's avatar
Dipam Chakraborty committed
263
                self.tune_policy(slices[0], self.to_tensor(slices[1]), self.to_tensor(slices[2]))
264
                
265
        self.retunes_completed += 1
Dipam Chakraborty's avatar
Dipam Chakraborty committed
266
267
        self.retune_selector.retune_done()
 
Dipam Chakraborty's avatar
Dipam Chakraborty committed
268
    def tune_policy(self, obs, target_vf, target_pi):
Dipam Chakraborty's avatar
Dipam Chakraborty committed
269
270
        if self.config['augment_buffer']:
            obs_aug = np.empty(obs.shape, obs.dtype)
Dipam Chakraborty's avatar
Dipam Chakraborty committed
271
            aug_idx = np.random.randint(self.config['augment_randint_num'], size=len(obs))
Dipam Chakraborty's avatar
Dipam Chakraborty committed
272
273
274
275
276
277
            obs_aug[aug_idx == 0] = pad_and_random_crop(obs[aug_idx == 0], 64, 10)
            obs_aug[aug_idx == 1] = random_cutout_color(obs[aug_idx == 1], 10, 30)
            obs_aug[aug_idx >= 2] = obs[aug_idx >= 2]
            obs_in = self.to_tensor(obs_aug)
        else:
            obs_in = self.to_tensor(obs)
278
        
Dipam Chakraborty's avatar
Dipam Chakraborty committed
279
        vpred, pi_logits = self.model.vf_pi(obs_in, ret_numpy=False, no_grad=False, to_torch=False)
Dipam Chakraborty's avatar
Dipam Chakraborty committed
280
281
        aux_vpred = self.model.aux_value_function()
        aux_loss = .5 * torch.mean(torch.pow(aux_vpred - target_vf, 2))
Dipam Chakraborty's avatar
Dipam Chakraborty committed
282
        
283
284
285
286
        target_pd = self.make_distr(target_pi)
        pd = self.make_distr(pi_logits)
        pi_loss = td.kl_divergence(target_pd, pd).mean()
        
Dipam Chakraborty's avatar
Dipam Chakraborty committed
287
        loss = pi_loss + aux_loss
Dipam Chakraborty's avatar
Dipam Chakraborty committed
288
289
        
        loss.backward()
Dipam Chakraborty's avatar
Dipam Chakraborty committed
290
291
        self.aux_optimizer.step()
        self.aux_optimizer.zero_grad()
Dipam Chakraborty's avatar
Dipam Chakraborty committed
292
        
Dipam Chakraborty's avatar
Dipam Chakraborty committed
293
294
295
296
297
298
        vf_loss = .5 * torch.mean(torch.pow(vpred - target_vf, 2))

        vf_loss.backward()
        self.value_optimizer.step()
        self.value_optimizer.zero_grad()
        
Dipam Chakraborty's avatar
Dipam Chakraborty committed
299
    def best_reward_model_select(self, samples):
300
        self.timesteps_total += len(samples['dones'])
Dipam Chakraborty's avatar
Dipam Chakraborty committed
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
        
        ## Best reward model selection
        eprews = [info['episode']['r'] for info in samples['infos'] if 'episode' in info]
        self.reward_deque.extend(eprews)
        mean_reward = safe_mean(eprews) if len(eprews) >= 100 else safe_mean(self.reward_deque)
        if self.best_reward < mean_reward:
            self.best_reward = mean_reward
            self.best_weights = self.get_weights()["current_weights"]
            self.best_rew_tsteps = self.timesteps_total
           
        if self.timesteps_total > self.target_timesteps or (self.time_elapsed + self.buffer_time) > self.max_time:
            if self.best_weights is not None:
                self.set_model_weights(self.best_weights)
                return True
            
        return False
    
    def update_lr(self):
        if self.config['lr_schedule'] == 'linear':
            self.lr = linear_schedule(initial_val=self.config['lr'],
                                      final_val=self.config['final_lr'],
                                      current_steps=self.timesteps_total,
                                      total_steps=self.target_timesteps)
            
        elif self.config['lr_schedule'] == 'exponential':
            self.lr = 0.997 * self.lr 

    
    def update_ent_coef(self):
        if self.config['entropy_schedule']:
            self.ent_coef = linear_schedule(initial_val=self.config['entropy_coeff'], 
                                            final_val=self.config['final_entropy_coeff'], 
                                            current_steps=self.timesteps_total, 
                                            total_steps=self.target_timesteps)
    
    def update_gamma(self, samples):
        if self.config['adaptive_gamma']:
            epinfobuf = [info['episode'] for info in samples['infos'] if 'episode' in info]
            self.maxrewep_lenbuf.extend([epinfo['l'] for epinfo in epinfobuf if epinfo['r'] >= self.max_reward])
            sorted_nth = lambda buf, n: np.nan if len(buf) < 100 else sorted(self.maxrewep_lenbuf.copy())[n]
            target_horizon = sorted_nth(self.maxrewep_lenbuf, 80)
            self.gamma = self.adaptive_discount_tuner.update(target_horizon)

        
    def get_custom_state_vars(self):
        return {
            "time_elapsed": self.time_elapsed,
            "timesteps_total": self.timesteps_total,
            "best_weights": self.best_weights,
            "reward_deque": self.reward_deque,
            "batch_end_time": self.batch_end_time,
            "gamma": self.gamma,
            "maxrewep_lenbuf": self.maxrewep_lenbuf,
            "lr": self.lr,
            "ent_coef": self.ent_coef,
            "rewnorm": self.rewnorm,
            "best_rew_tsteps": self.best_rew_tsteps,
            "best_reward": self.best_reward,
            "last_dones": self.last_dones,
360
            "retunes_completed": self.retunes_completed,
Dipam Chakraborty's avatar
Dipam Chakraborty committed
361
362
363
364
365
366
367
368
369
370
        }
    
    def set_custom_state_vars(self, custom_state_vars):
        self.time_elapsed = custom_state_vars["time_elapsed"]
        self.timesteps_total = custom_state_vars["timesteps_total"]
        self.best_weights = custom_state_vars["best_weights"]
        self.reward_deque = custom_state_vars["reward_deque"]
        self.batch_end_time = custom_state_vars["batch_end_time"]
        self.gamma = self.adaptive_discount_tuner.gamma = custom_state_vars["gamma"]
        self.maxrewep_lenbuf = custom_state_vars["maxrewep_lenbuf"]
371
        self.lr = custom_state_vars["lr"]
Dipam Chakraborty's avatar
Dipam Chakraborty committed
372
373
374
375
376
        self.ent_coef = custom_state_vars["ent_coef"]
        self.rewnorm = custom_state_vars["rewnorm"]
        self.best_rew_tsteps = custom_state_vars["best_rew_tsteps"]
        self.best_reward = custom_state_vars["best_reward"]
        self.last_dones = custom_state_vars["last_dones"]
377
        self.retunes_completed = custom_state_vars["retunes_completed"]
Dipam Chakraborty's avatar
Dipam Chakraborty committed
378
379
380
381
382
383
384
385
    
    @override(TorchPolicy)
    def get_weights(self):
        weights = {}
        weights["current_weights"] = {
            k: v.cpu().detach().numpy()
            for k, v in self.model.state_dict().items()
        }
386
387
388
389
390
391
392
393
394
#         weights["optimizer_state"] = {
#             k: v
#             for k, v in self.optimizer.state_dict().items()
#         }
#         weights["aux_optimizer_state"] = {
#             k: v
#             for k, v in self.aux_optimizer.state_dict().items()
#         }
#         weights["custom_state_vars"] = self.get_custom_state_vars()
Dipam Chakraborty's avatar
Dipam Chakraborty committed
395
396
397
398
399
400
        return weights
        
    
    @override(TorchPolicy)
    def set_weights(self, weights):
        self.set_model_weights(weights["current_weights"])
401
402
403
#         self.set_optimizer_state(weights["optimizer_state"])
#         self.set_aux_optimizer_state(weights["aux_optimizer_state"])
#         self.set_custom_state_vars(weights["custom_state_vars"])
Dipam Chakraborty's avatar
Dipam Chakraborty committed
404
        
Dipam Chakraborty's avatar
Dipam Chakraborty committed
405
406
407
408
    def set_aux_optimizer_state(self, aux_optimizer_state):
        aux_optimizer_state = convert_to_torch_tensor(aux_optimizer_state, device=self.device)
        self.aux_optimizer.load_state_dict(aux_optimizer_state)
        
Dipam Chakraborty's avatar
Dipam Chakraborty committed
409
410
411
412
413
414
415
    def set_optimizer_state(self, optimizer_state):
        optimizer_state = convert_to_torch_tensor(optimizer_state, device=self.device)
        self.optimizer.load_state_dict(optimizer_state)
        
    def set_model_weights(self, model_weights):
        model_weights = convert_to_torch_tensor(model_weights, device=self.device)
        self.model.load_state_dict(model_weights)