2024-07-25 21:41:45 *Starting idea: 20240725_214144_rl_lr_adaptation* 2024-07-25 21:41:45 *Starting Experiments* Experiment Plan We will conduct the following experiments to evaluate the effectiveness of using Q-learning for dynamic learning rate adaptation in transformer training: 1 Run 1: Q-learning with initial learning rate 1e-3 • Dataset: shakespeare_char • Initial learning rate: 1e-3 • Q-learning parameters: • Learning rate for Q-learning: 0.1 • Discount factor: 0.9 • Exploration rate: 0.1 2 Run 2: Q-learning with initial learning rate 5e-4 • Dataset: enwik8 • Initial learning rate: 5e-4 • Q-learning parameters: • Learning rate for Q-learning: 0.1 • Discount factor: 0.9 • Exploration rate: 0.1 3 Run 3: Q-learning with initial learning rate 1e-3 • Dataset: text8 • Initial learning rate: 1e-3 • Q-learning parameters: • Learning rate for Q-learning: 0.1 • Discount factor: 0.9 • Exploration rate: 0.1 4 Run 4: Q-learning with initial learning rate 5e-4 • Dataset: shakespeare_char • Initial learning rate: 5e-4 • Q-learning parameters: • Learning rate for Q-learning: 0.1 • Discount factor: 0.9 • Exploration rate: 0.1 5 Run 5: Q-learning with initial learning rate 1e-3 • Dataset: enwik8 • Initial learning rate: 1e-3 • Q-learning parameters: • Learning rate for Q-learning: 0.1 • Discount factor: 0.9 • Exploration rate: 0.1 Implementation Steps 1 Implement Q-learning agent: Create a Q-learning agent that will adjust the learning rate based on the current state (validation loss, current learning rate). 2 Integrate Q-learning agent into training loop: Modify the training loop to incorporate the Q-learning agent's adjustments to the learning rate at each evaluation interval. 3 Run experiments: Execute the planned experiments and compare the results with the baseline. Step 1: Implement Q-learning Agent Let's start by implementing the Q-learning agent. q_learning_agent.py <<<<<<< SEARCH ======= import numpy as np class QLearningAgent: def __init__(self, lr=0.1, gamma=0.9, epsilon=0.1): self.lr = lr self.gamma = gamma self.epsilon = epsilon self.q_table = {} def get_state(self, val_loss, current_lr): return (round(val_loss, 2), round(current_lr, 5)) def choose_action(self, state): if np.random.rand() < self.epsilon: return np.random.choice([-1, 0, 1]) if state not in self.q_table: self.q_table[state] = [0, 0, 0] return np.argmax(self.q_table[state]) - 1 def update_q_values(self, state, action, reward, next_state): if state not in self.q_table: self.q_table[state] = [0, 0, 0] if next_state not in self.q_table: self.q_table[next_state] = [0, 0, 0] best_next_action = np.argmax(self.q_table[next_state]) td_target = reward + self.gamma * self.q_table[next_state][best_next_action] td_error = td_target - self.q_table[state][action + 1] self.q_table[state][action + 1] += self.lr * td_error >>>>>>> REPLACE Step 2: Integrate Q-learning Agent into Training Loop Next, we will modify the training loop in experiment.py to incorporate the Q-learning agent's adjustments to the learning rate at each evaluation interval. experiment.py <<<<<<< SEARCH import numpy as np import torch import torch.nn as nn from torch.nn import functional as F import argparse ======= from q_learning_agent import QLearningAgent >>>>>>> REPLACE experiment.py <<<<<<< SEARCH # model init ======= # Q-learning agent init q_agent = QLearningAgent(lr=0.1, gamma=0.9, epsilon=0.1) >>>>>>> REPLACE experiment.py <<<<<<< SEARCH val_log_info.append( ======= # Q-learning: Update learning rate based on Q-learning agent state = q_agent.get_state(losses["val"], lr) action = q_agent.choose_action(state) lr = max(min_lr, lr * (1 + action * 0.1)) # Adjust learning rate next_state = q_agent.get_state(losses["val"], lr) reward = -losses["val"] # Reward is negative validation loss q_agent.update_q_values(state, action, reward, next_state) val_log_info.append( >>>>>>> REPLACE Step 3: Run Experiments After implementing the Q-learning agent and integrating it into the training loop, we will run the experiments using the command python experiment.py --out_dir=run_i where i is the run number. Please proceed to run the first experiment using the command: python experiment.py --out_dir=run_1 Once the first experiment is complete, we can proceed with the subsequent experiments. 10207 prompt tokens, 1220 completion tokens, $0.069335 cost Applied edit to q_learning_agent.py Applied edit to experiment.py # Fix any errors below, if possible. ## Running: /home/anon/ai_scientist/venv/bin/python3 -m flake8 --select=E9,F821,F823,F831,F406,F407,F701,F702,F704,F706 --show-source --isolated experiment.py experiment.py:12:17: F821 undefined name 'nn' class LayerNorm(nn.Module): ^ experiment.py:17:23: F821 undefined name 'nn' self.weight = nn.Parameter(torch.ones(ndim)) ^ experiment.py:17:36: F821 undefined name 'torch' self.weight = nn.Parameter(torch.ones(ndim)) ^ experiment.py:18:21: F821 undefined name 'nn' self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None ^ experiment.py:18:34: F821 undefined name 'torch' self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None ^ experiment.py:21:16: F821 undefined name 'F' return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5) ^ experiment.py:24:27: F821 undefined name 'nn' class CausalSelfAttention(nn.Module): ^ experiment.py:30:23: F821 undefined name 'nn' self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias) ^ experiment.py:32:23: F821 undefined name 'nn' self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias) ^ experiment.py:34:29: F821 undefined name 'nn' self.attn_dropout = nn.Dropout(config.dropout) ^ experiment.py:35:30: F821 undefined name 'nn' self.resid_dropout = nn.Dropout(config.dropout) ^ experiment.py:40:30: F821 undefined name 'torch' self.flash = hasattr(torch.nn.functional, "scaled_dot_product_attention") ^ experiment.py:48:17: F821 undefined name 'torch' torch.tril(torch.ones(config.block_size, config.block_size)).view( ^ experiment.py:48:28: F821 undefined name 'torch' torch.tril(torch.ones(config.block_size, config.block_size)).view( ^ experiment.py:73:17: F821 undefined name 'torch' y = torch.nn.functional.scaled_dot_product_attention( ^ experiment.py:85:19: F821 undefined name 'F' att = F.softmax(att, dim=-1) ^ experiment.py:97:11: F821 undefined name 'nn' class MLP(nn.Module): ^ experiment.py:101:21: F821 undefined name 'nn' self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias) ^ experiment.py:102:21: F821 undefined name 'nn' self.gelu = nn.GELU() ^ experiment.py:103:23: F821 undefined name 'nn' self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias) ^ experiment.py:104:24: F821 undefined name 'nn' self.dropout = nn.Dropout(config.dropout) ^ experiment.py:114:13: F821 undefined name 'nn' class Block(nn.Module): ^ experiment.py:144:11: F821 undefined name 'nn' class GPT(nn.Module): ^ experiment.py:152:28: F821 undefined name 'nn' self.transformer = nn.ModuleDict( ^ experiment.py:154:21: F821 undefined name 'nn' wte=nn.Embedding(config.vocab_size, config.n_embd), ^ experiment.py:155:21: F821 undefined name 'nn' wpe=nn.Embedding(config.block_size, config.n_embd), ^ experiment.py:156:22: F821 undefined name 'nn' drop=nn.Dropout(config.dropout), ^ experiment.py:157:19: F821 undefined name 'nn' h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]), ^ experiment.py:161:24: F821 undefined name 'nn' self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) ^ experiment.py:175:17: F821 undefined name 'torch' torch.nn.init.normal_( ^ experiment.py:195:31: F821 undefined name 'nn' if isinstance(module, nn.Linear): ^ experiment.py:196:13: F821 undefined name 'torch' torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) ^ experiment.py:198:17: F821 undefined name 'torch' torch.nn.init.zeros_(module.bias) ^ experiment.py:199:33: F821 undefined name 'nn' elif isinstance(module, nn.Embedding): ^ experiment.py:200:13: F821 undefined name 'torch' torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) ^ experiment.py:208:15: F821 undefined name 'torch' pos = torch.arange(0, t, dtype=torch.long, device=device) # shape (t) ^ experiment.py:208:40: F821 undefined name 'torch' pos = torch.arange(0, t, dtype=torch.long, device=device) # shape (t) ^ experiment.py:221:20: F821 undefined name 'F' loss = F.cross_entropy( ^ experiment.py:239:39: F821 undefined name 'nn' self.transformer.wpe.weight = nn.Parameter( ^ experiment.py:268:56: F821 undefined name 'torch' fused_available = "fused" in inspect.signature(torch.optim.AdamW).parameters ^ experiment.py:271:21: F821 undefined name 'torch' optimizer = torch.optim.AdamW( ^ experiment.py:278:6: F821 undefined name 'torch' @torch.no_grad() ^ experiment.py:298:24: F821 undefined name 'torch' v, _ = torch.topk(logits, min(top_k, logits.size(-1))) ^ experiment.py:301:21: F821 undefined name 'F' probs = F.softmax(logits, dim=-1) ^ experiment.py:303:24: F821 undefined name 'torch' idx_next = torch.multinomial(probs, num_samples=1) ^ experiment.py:305:19: F821 undefined name 'torch' idx = torch.cat((idx, idx_next), dim=1) ^ experiment.py:351:27: F821 undefined name 'torch' dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else 'float16' # 'float32', 'bfloat16', or 'float16', the latter will auto implement a GradScaler ^ experiment.py:351:57: F821 undefined name 'torch' dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else 'float16' # 'float32', 'bfloat16', or 'float16', the latter will auto implement a GradScaler ^ experiment.py:363:5: F821 undefined name 'torch' torch.manual_seed(1337 + seed_offset) ^ experiment.py:364:5: F821 undefined name 'torch' torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul ^ experiment.py:365:5: F821 undefined name 'torch' torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn ^ experiment.py:369:20: F821 undefined name 'torch' "float32": torch.float32, ^ experiment.py:370:21: F821 undefined name 'torch' "bfloat16": torch.bfloat16, ^ experiment.py:371:20: F821 undefined name 'torch' "float16": torch.float16, ^ experiment.py:376:14: F821 undefined name 'torch' else torch.amp.autocast(device_type=device_type, dtype=ptdtype) ^ experiment.py:387:20: F821 undefined name 'np' data = np.memmap(os.path.join(data_dir, "train.bin"), dtype=np.uint16, mode="r") ^ experiment.py:387:73: F821 undefined name 'np' data = np.memmap(os.path.join(data_dir, "train.bin"), dtype=np.uint16, mode="r") ^ experiment.py:389:20: F821 undefined name 'np' data = np.memmap(os.path.join(data_dir, "val.bin"), dtype=np.uint16, mode="r") ^ experiment.py:389:71: F821 undefined name 'np' data = np.memmap(os.path.join(data_dir, "val.bin"), dtype=np.uint16, mode="r") ^ experiment.py:390:14: F821 undefined name 'torch' ix = torch.randint(len(data) - block_size, (batch_size,)) ^ experiment.py:391:13: F821 undefined name 'torch' x = torch.stack( ^ experiment.py:392:14: F821 undefined name 'torch' [torch.from_numpy((data[i : i + block_size]).astype(np.int64)) for i in ix] ^ experiment.py:392:65: F821 undefined name 'np' [torch.from_numpy((data[i : i + block_size]).astype(np.int64)) for i in ix] ^ experiment.py:394:13: F821 undefined name 'torch' y = torch.stack( ^ experiment.py:396:17: F821 undefined name 'torch' torch.from_numpy((data[i + 1 : i + 1 + block_size]).astype(np.int64)) ^ experiment.py:396:76: F821 undefined name 'np' torch.from_numpy((data[i + 1 : i + 1 + block_size]).astype(np.int64)) ^ experiment.py:451:14: F821 undefined name 'torch' scaler = torch.cuda.amp.GradScaler(enabled=(dtype == "float16")) ^ experiment.py:463:17: F821 undefined name 'torch' model = torch.compile(model) # requires PyTorch 2.0 ^ experiment.py:467:6: F821 undefined name 'torch' @torch.no_grad() ^ experiment.py:472:22: F821 undefined name 'torch' losses = torch.zeros(eval_iters) ^ experiment.py:548:21: F821 undefined name 'torch' torch.save(checkpoint, os.path.join(out_dir, "ckpt.pt")) ^ experiment.py:567:13: F821 undefined name 'torch' torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip) ^ experiment.py:632:10: F821 undefined name 'torch' x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...]) ^ experiment.py:632:40: F821 undefined name 'torch' x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...]) ^ experiment.py:637:10: F821 undefined name 'torch' with torch.no_grad(): ^ experiment.py:671:10: F821 undefined name 'argparse' parser = argparse.ArgumentParser(description='Run experiment') ^ experiment.py:694:31: F821 undefined name 'np' means = {f"{k}_mean": np.mean(v) for k, v in final_info_dict.items()} ^ experiment.py:695:35: F821 undefined name 'np' stderrs = {f"{k}_stderr": np.std(v) / len(v) for k, v in final_info_dict.items()} ^ experiment.py:706:9: F821 undefined name 'np' np.save(f, all_results) ^ ## See relevant lines below marked with █. experiment.py: ...⋮... 9│from q_learning_agent import QLearningAgent 10│ 11│# --- BEGIN model.py --- 12█class LayerNorm(nn.Module): 13│ """LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False""" 14│ 15│ def __init__(self, ndim, bias): 16│ super().__init__() 17█ self.weight = nn.Parameter(torch.ones(ndim)) 18█ self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None 19│ 20│ def forward(self, input): 21█ return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5) 22│ 23│ 24█class CausalSelfAttention(nn.Module): 25│ 26│ def __init__(self, config): 27│ super().__init__() 28│ assert config.n_embd % config.n_head == 0 29│ # key, query, value projections for all heads, but in a batch 30█ self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias) 31│ # output projection 32█ self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias) 33│ # regularization 34█ self.attn_dropout = nn.Dropout(config.dropout) 35█ self.resid_dropout = nn.Dropout(config.dropout) 36│ self.n_head = config.n_head 37│ self.n_embd = config.n_embd 38│ self.dropout = config.dropout 39│ # flash attention make GPU go brrrrr but support is only in PyTorch >= 2.0 40█ self.flash = hasattr(torch.nn.functional, "scaled_dot_product_attention") 41│ if not self.flash: 42│ print( 43│ "WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0" 44│ ) 45│ # causal mask to ensure that attention is only applied to the left in the input sequence 46│ self.register_buffer( 47│ "bias", 48█ torch.tril(torch.ones(config.block_size, config.block_size)).view( 49│ 1, 1, config.block_size, config.block_size 50│ ), 51│ ) 52│ 53│ def forward(self, x): 54│ B, T, C = ( 55│ x.size() ...⋮... 70│ # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T) 71│ if self.flash: 72│ # efficient attention using Flash Attention CUDA kernels 73█ y = torch.nn.functional.scaled_dot_product_attention( 74│ q, 75│ k, 76│ v, 77│ attn_mask=None, 78│ dropout_p=self.dropout if self.training else 0, 79│ is_causal=True, 80│ ) 81│ else: 82│ # manual implementation of attention 83│ att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) 84│ att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float("-inf")) 85█ att = F.softmax(att, dim=-1) 86│ att = self.attn_dropout(att) 87│ y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs) 88│ y = ( ...⋮... 94│ return y 95│ 96│ 97█class MLP(nn.Module): 98│ 99│ def __init__(self, config): 100│ super().__init__() 101█ self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias) 102█ self.gelu = nn.GELU() 103█ self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias) 104█ self.dropout = nn.Dropout(config.dropout) 105│ 106│ def forward(self, x): 107│ x = self.c_fc(x) ...⋮... 111│ return x 112│ 113│ 114█class Block(nn.Module): 115│ 116│ def __init__(self, config): 117│ super().__init__() ...⋮... 141│ ) 142│ 143│ 144█class GPT(nn.Module): 145│ 146│ def __init__(self, config): 147│ super().__init__() 148│ assert config.vocab_size is not None 149│ assert config.block_size is not None 150│ self.config = config 151│ 152█ self.transformer = nn.ModuleDict( 153│ dict( 154█ wte=nn.Embedding(config.vocab_size, config.n_embd), 155█ wpe=nn.Embedding(config.block_size, config.n_embd), 156█ drop=nn.Dropout(config.dropout), 157█ h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]), 158│ ln_f=LayerNorm(config.n_embd, bias=config.bias), 159│ ) 160│ ) 161█ self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) 162│ # with weight tying when using torch.compile() some warnings get generated: 163│ # "UserWarning: functional_call was passed multiple values for tied weights. 164│ # This behavior is deprecated and will be an error in future versions" ...⋮... 172│ # apply special scaled init to the residual projections, per GPT-2 paper 173│ for pn, p in self.named_parameters(): 174│ if pn.endswith("c_proj.weight"): 175█ torch.nn.init.normal_( 176│ p, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer) 177│ ) 178│ ...⋮... 192│ return n_params 193│ 194│ def _init_weights(self, module): 195█ if isinstance(module, nn.Linear): 196█ torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) 197│ if module.bias is not None: 198█ torch.nn.init.zeros_(module.bias) 199█ elif isinstance(module, nn.Embedding): 200█ torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) 201│ 202│ def forward(self, idx, targets=None): 203│ device = idx.device 204│ b, t = idx.size() 205│ assert ( 206│ t <= self.config.block_size 207│ ), f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}" 208█ pos = torch.arange(0, t, dtype=torch.long, device=device) # shape (t) 209│ 210│ # forward the GPT model itself 211│ tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd) ...⋮... 218│ if targets is not None: 219│ # if we are given some desired targets also calculate the loss 220│ logits = self.lm_head(x) 221█ loss = F.cross_entropy( 222│ logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1 223│ ) 224│ else: ...⋮... 233│ def crop_block_size(self, block_size): ...⋮... 236│ # but want to use a smaller block size for some smaller, simpler model 237│ assert block_size <= self.config.block_size 238│ self.config.block_size = block_size 239█ self.transformer.wpe.weight = nn.Parameter( 240│ self.transformer.wpe.weight[:block_size] 241│ ) 242│ for block in self.transformer.h: ...⋮... 246│ def configure_optimizers(self, weight_decay, learning_rate, betas, device_type): 247│ # start with all of the candidate parameters 248│ param_dict = {pn: p for pn, p in self.named_parameters()} ...⋮... 265│ f"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters" 266│ ) 267│ # Create AdamW optimizer and use the fused version if it is available 268█ fused_available = "fused" in inspect.signature(torch.optim.AdamW).parameters 269│ use_fused = fused_available and device_type == "cuda" 270│ extra_args = dict(fused=True) if use_fused else dict() 271█ optimizer = torch.optim.AdamW( 272│ optim_groups, lr=learning_rate, betas=betas, **extra_args 273│ ) 274│ print(f"using fused AdamW: {use_fused}") 275│ 276│ return optimizer 277│ 278█ @torch.no_grad() 279│ def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None): 280│ """ 281│ Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete 282│ the sequence max_new_tokens times, feeding the predictions back into the model each time. 283│ Most likely you'll want to make sure to be in model.eval() mode of operation for this. 284│ """ 285│ for _ in range(max_new_tokens): 286│ # if the sequence context is growing too long we must crop it at block_size 287│ idx_cond = ( 288│ idx 289│ if idx.size(1) <= self.config.block_size 290│ else idx[:, -self.config.block_size :] ...⋮... 295│ logits = logits[:, -1, :] / temperature 296│ # optionally crop the logits to only the top k options 297│ if top_k is not None: 298█ v, _ = torch.topk(logits, min(top_k, logits.size(-1))) 299│ logits[logits < v[:, [-1]]] = -float("Inf") 300│ # apply softmax to convert logits to (normalized) probabilities 301█ probs = F.softmax(logits, dim=-1) 302│ # sample from the distribution 303█ idx_next = torch.multinomial(probs, num_samples=1) 304│ # append sampled index to the running sequence and continue 305█ idx = torch.cat((idx, idx_next), dim=1) 306│ 307│ return idx 308│ ...⋮... 311│def train(dataset="shakespeare_char", out_dir="run_0", seed_offset=0): ...⋮... 315│ gradient_accumulation_steps = 1 ...⋮... 348│ backend = "nccl" # 'nccl', 'gloo', etc. 349│ # system 350│ device = "cuda" # Always use CUDA 351█ dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else 'float16' # 'float32', 'bfloat16', or 'float16', the latter will auto implement a GradScaler 352│ compile = True # do not torch compile the model on macbooks 353│ 354│ ...⋮... 360│ 361│ if master_process: 362│ os.makedirs(out_dir, exist_ok=True) 363█ torch.manual_seed(1337 + seed_offset) 364█ torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul 365█ torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn 366│ device_type = "cuda" if "cuda" in device else "cpu" # for later use in torch.autocast 367│ # note: float16 data type will automatically use a GradScaler 368│ ptdtype = { 369█ "float32": torch.float32, 370█ "bfloat16": torch.bfloat16, 371█ "float16": torch.float16, 372│ }[dtype] 373│ ctx = ( 374│ nullcontext() 375│ if device_type == "cpu" 376█ else torch.amp.autocast(device_type=device_type, dtype=ptdtype) 377│ ) 378│ 379│ # poor man's data loader ...⋮... 383│ def get_batch(split): 384│ # We recreate np.memmap every batch to avoid a memory leak, as per 385│ # https://stackoverflow.com/questions/45132940/numpy-memmap-memory-usage-want-to-i terate-once/61472122#61472122 386│ if split == "train": 387█ data = np.memmap(os.path.join(data_dir, "train.bin"), dtype=np.uint16, mode="r") 388│ else: 389█ data = np.memmap(os.path.join(data_dir, "val.bin"), dtype=np.uint16, mode="r") 390█ ix = torch.randint(len(data) - block_size, (batch_size,)) 391█ x = torch.stack( 392█ [torch.from_numpy((data[i : i + block_size]).astype(np.int64)) for i in ix] 393│ ) 394█ y = torch.stack( 395│ [ 396█ torch.from_numpy((data[i + 1 : i + 1 + block_size]).astype(np.int64)) 397│ for i in ix 398│ ] 399│ ) ...⋮... 448│ model.to(device) 449│ 450│ # initialize a GradScaler. If enabled=False scaler is a no-op 451█ scaler = torch.cuda.amp.GradScaler(enabled=(dtype == "float16")) 452│ 453│ # optimizer 454│ optimizer = model.configure_optimizers( ...⋮... 460│ if compile: 461│ print("compiling the model... (takes a ~minute)") 462│ unoptimized_model = model 463█ model = torch.compile(model) # requires PyTorch 2.0 464│ 465│ 466│ # helps estimate an arbitrarily accurate loss over either split using many batches 467█ @torch.no_grad() 468│ def estimate_loss(): 469│ out = {} 470│ model.eval() 471│ for split in ["train", "val"]: 472█ losses = torch.zeros(eval_iters) 473│ for k in range(eval_iters): 474│ X, Y = get_batch(split) 475│ with ctx: ...⋮... 508│ while True: 509│ ...⋮... 511│ lr = get_lr(iter_num) if decay_lr else learning_rate ...⋮... 516│ if iter_num % eval_interval == 0 and master_process: 517│ losses = estimate_loss() ...⋮... 537│ if losses["val"] < best_val_loss or always_save_checkpoint: 538│ best_val_loss = losses["val"] 539│ if iter_num > 0 and not never_save_checkpoint: 540│ checkpoint = { 541│ "model": raw_model.state_dict(), 542│ "optimizer": optimizer.state_dict(), 543│ "model_args": model_args, 544│ "iter_num": iter_num, 545│ "best_val_loss": best_val_loss, 546│ } 547│ print(f"saving checkpoint to {out_dir}") 548█ torch.save(checkpoint, os.path.join(out_dir, "ckpt.pt")) 549│ if iter_num == 0 and eval_only: 550│ break 551│ ...⋮... 564│ # clip the gradient 565│ if grad_clip != 0.0: 566│ scaler.unscale_(optimizer) 567█ torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip) 568│ # step the optimizer and scaler if training in fp16 569│ scaler.step(optimizer) 570│ scaler.update() ...⋮... 629│ with open(start[5:], 'r', encoding='utf-8') as f: 630│ start = f.read() 631│ start_ids = encode(start) 632█ x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...]) 633│ 634│ # Run generation 635│ model.eval() 636│ results = [] 637█ with torch.no_grad(): 638│ with ctx: 639│ for k in range(num_samples): 640│ start_time = time.time() ...⋮... 668│ json.dump(final_info, f) 669│ return final_info, train_log_info, val_log_info 670│ 671█parser = argparse.ArgumentParser(description='Run experiment') 672│parser.add_argument('--out_dir', type=str, default='run_0', help='Output directory') 673│args = parser.parse_args() 674│ 675│if __name__ == "__main__": 676│ num_seeds = { 677│ "shakespeare_char": 3, 678│ "enwik8": 1, 679│ "text8": 1, ...⋮... 685│ for dataset in ["shakespeare_char", "enwik8", "text8"]: 686│ final_info_list = [] ...⋮... 691│ all_results[f"{dataset}_{seed_offset}_val_info"] = val_info 692│ final_info_list.append(final_info) 693│ final_info_dict = {k: [d[k] for d in final_info_list] for k in final_info_list[0].keys()} 694█ means = {f"{k}_mean": np.mean(v) for k, v in final_info_dict.items()} 695█ stderrs = {f"{k}_stderr": np.std(v) / len(v) for k, v in final_info_dict.items()} 696│ final_infos[dataset] = { 697│ "means": means, 698│ "stderrs": stderrs, ...⋮... 703│ json.dump(final_infos, f) 704│ 705│ with open(os.path.join(out_dir, "all_results.npy"), "wb") as f: 706█ np.save(f, all_results) To fix the errors, we need to ensure that the necessary imports for torch, torch.nn, torch.nn.functional, numpy, and argparse are included in experiment.py. Here are the SEARCH/REPLACE blocks to fix the errors: experiment.py <<<<<<< SEARCH import os import time import math import pickle import inspect import json from contextlib import nullcontext from dataclasses import dataclass from q_learning_agent import QLearningAgent ======= import os import time import math import pickle import inspect import json import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import argparse from contextlib import nullcontext from dataclasses import dataclass from q_learning_agent import QLearningAgent >>>>>>> REPLACE Please apply these changes and then re-run the command python experiment.py --out_dir=run_1. 18550 prompt tokens, 200 completion tokens, $0.095750 cost Applied edit to experiment.py To fix the errors, we need to ensure that the necessary imports for `torch`, `torch.nn`, `torch.nn.functional`, `numpy`, and `argparse` are included in `experiment.py`. Here are the *SEARCH/REPLACE* blocks to fix the errors: experiment.py ```python <<<<<<< SEARCH import os import time import math import pickle import inspect import json from contextlib import nullcontext from dataclasses import dataclass from q_learning_agent import QLearningAgent ======= import os import time import math import pickle import inspect import json import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import argparse from contextlib import nullcontext from dataclasses import dataclass from q_learning_agent import QLearningAgent >>>>>>> REPLACE ``` Please apply these changes and then re-run the command `python experiment.py --out_dir=run_1`. Traceback (most recent call last): File "/home/anon/ai_scientist/results/nanoGPT/20240725_214144_rl_lr_adaptation/experiment.py", line 693, in final_info, train_info, val_info = train(dataset, out_dir, seed_offset) File "/home/anon/ai_scientist/results/nanoGPT/20240725_214144_rl_lr_adaptation/experiment.py", line 527, in train state = q_agent.get_state(losses["val"], lr) File "/home/anon/ai_scientist/results/nanoGPT/20240725_214144_rl_lr_adaptation/q_learning_agent.py", line 11, in get_state return (round(val_loss, 2), round(current_lr, 5)) TypeError: type Tensor doesn't define __round__ method Run 1 failed with return code 1 Run failed with the following error Traceback (most recent call last): File "/home/anon/ai_scientist/results/nanoGPT/20240725_214144_rl_lr_adaptation/experiment.py", line 693, in final_info, train_info, val_info = train(dataset, out_dir, seed_offset) File "/home/anon/ai_scientist/results/nanoGPT/20240725_214144_rl_lr_adaptation/experiment.py", line 527, in train state = q_agent.get_state(losses["val"], lr) File "/home/anon/ai_scientist/results/nanoGPT/20240725_214144_rl_lr_adaptation/q_learning_agent.py", line 11, in get_state return (round(val_loss, 2), round(current_lr, 5)) TypeError: type Tensor doesn't define __round__ method To fix the error, we need to convert the val_loss tensor to a Python float before rounding it. We can do this by calling the .item() method on the tensor. Here are the necessary changes: q_learning_agent.py <<<<<<< SEARCH return (round(val_loss, 2), round(current_lr, 5)) ======= return (round(val_loss.item(), 2), round(current_lr, 5)) >>>>>>> REPLACE This change will ensure that val_loss is converted to a float before rounding, which should resolve the TypeError. 10543 prompt tokens, 122 completion tokens, $0.054545 cost Applied edit to q_learning_agent.py To fix the error, we need to convert the `val_loss` tensor to a Python float before rounding it. We can do this by calling the `.item()` method on the tensor. Here are the necessary changes: q_learning_agent.py ```python <<<<<<< SEARCH return (round(val_loss, 2), round(current_lr, 5)) ======= return (round(val_loss.item(), 2), round(current_lr, 5)) >>>>>>> REPLACE ``` This change will ensure that `val_loss` is converted to a float before rounding, which should resolve the `TypeError`. First, let's update the notes.txt file to include the results and description for Run 1. notes.txt <<<<<<< SEARCH ## Run 0: Baseline Results: {'shakespeare_char': {'final_train_loss_mean': 0.8186181902885437, 'best_val_loss_mean': 1.4654763221740723, 'total_train_time_mean': 77.26942734718322, 'avg_inference_tokens_per_second_mean': 666.5076153519527}, 'enwik8': {'final_train_loss_mean': 0.930223822593689, 'best_val_loss_mean': 1.0055421590805054, 'total_train_time_mean': 819.4551751613617, 'avg_inference_tokens_per_second_mean': 671.9918599180683}, 'text8': {'final_train_loss_mean': 1.0013301372528076, 'best_val_loss_mean': 0.979989230632782, 'total_train_time_mean': 801.224205493927, 'avg_inference_tokens_per_second_mean': 671.5678332249411}} Description: Baseline results. ======= ## Run 0: Baseline Results: {'shakespeare_char': {'final_train_loss_mean': 0.8186181902885437, 'best_val_loss_mean': 1.4654763221740723, 'total_train_time_mean': 77.26942734718322, 'avg_inference_tokens_per_second_mean': 666.5076153519527}, 'enwik8': {'final_train_loss_mean': 0.930223822593689, 'best_val_loss_mean': 1.0055421590805054, 'total_train_time_mean': 819.4551751613617, 'avg_inference_tokens_per_second_mean': 671.9918599180683}, 'text8': {'final_train_loss_mean': 1.0013301372528076, 'best_val_loss_mean': 0.979989230632782, 'total_train_time_mean': 801.224205493927, 'avg_inference_tokens_per_second_mean': 671.5678332249411}} Description: Baseline results. ## Run 1: Q-learning with initial learning rate adaptation Results: {'shakespeare_char': {'final_train_loss_mean': 0.8112714489301046, 'best_val_loss_mean': 1.4664853016535442, 'total_train_time_mean': 76.33582202593486, 'avg_inference_tokens_per_second_mean': 680.220956113138}, 'enwik8': {'final_train_loss_mean': 0.9324554204940796, 'best_val_loss_mean': 1.0050768852233887, 'total_train_time_mean': 799.199625492096, 'avg_inference_tokens_per_second_mean': 690.1664700419294}, 'text8': {'final_train_loss_mean': 0.9926028251647949, 'best_val_loss_mean': 0.9795507192611694, 'total_train_time_mean': 796.1075961589813, 'avg_inference_tokens_per_second_mean': 691.9504174462957}} Description: This run implemented Q-learning for dynamic learning rate adaptation. The Q-learning agent adjusted the learning rate based on the curre state (validation loss, current learning rate) and used a reward signal derive from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at eac evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). >>>>>>> REPLACE Next, let's implement the next experiment. For this experiment, we will use a different initial learning rate to see how it affects the Q-learning adaptation. We will set the initial learning rate to 2e-3 for shakespeare_char and 1e-3 for enwik8 and text8. experiment.py <<<<<<< SEARCH learning_rate = ( 1e-3 if dataset == "shakespeare_char" else 5e-4 ) ======= learning_rate = ( 2e-3 if dataset == "shakespeare_char" else 1e-3 ) >>>>>>> REPLACE Now, you can run the next experiment with the command: python experiment.py --out_dir=run_2 11043 prompt tokens, 973 completion tokens, $0.069810 cost Applied edit to experiment.py Applied edit to notes.txt First, let's update the `notes.txt` file to include the results and description for Run 1. notes.txt ```python <<<<<<< SEARCH ## Run 0: Baseline Results: {'shakespeare_char': {'final_train_loss_mean': 0.8186181902885437, 'best_val_loss_mean': 1.4654763221740723, 'total_train_time_mean': 77.26942734718322, 'avg_inference_tokens_per_second_mean': 666.5076153519527}, 'enwik8': {'final_train_loss_mean': 0.930223822593689, 'best_val_loss_mean': 1.0055421590805054, 'total_train_time_mean': 819.4551751613617, 'avg_inference_tokens_per_second_mean': 671.9918599180683}, 'text8': {'final_train_loss_mean': 1.0013301372528076, 'best_val_loss_mean': 0.979989230632782, 'total_train_time_mean': 801.224205493927, 'avg_inference_tokens_per_second_mean': 671.5678332249411}} Description: Baseline results. ======= ## Run 0: Baseline Results: {'shakespeare_char': {'final_train_loss_mean': 0.8186181902885437, 'best_val_loss_mean': 1.4654763221740723, 'total_train_time_mean': 77.26942734718322, 'avg_inference_tokens_per_second_mean': 666.5076153519527}, 'enwik8': {'final_train_loss_mean': 0.930223822593689, 'best_val_loss_mean': 1.0055421590805054, 'total_train_time_mean': 819.4551751613617, 'avg_inference_tokens_per_second_mean': 671.9918599180683}, 'text8': {'final_train_loss_mean': 1.0013301372528076, 'best_val_loss_mean': 0.979989230632782, 'total_train_time_mean': 801.224205493927, 'avg_inference_tokens_per_second_mean': 671.5678332249411}} Description: Baseline results. ## Run 1: Q-learning with initial learning rate adaptation Results: {'shakespeare_char': {'final_train_loss_mean': 0.8112714489301046, 'best_val_loss_mean': 1.4664853016535442, 'total_train_time_mean': 76.33582202593486, 'avg_inference_tokens_per_second_mean': 680.220956113138}, 'enwik8': {'final_train_loss_mean': 0.9324554204940796, 'best_val_loss_mean': 1.0050768852233887, 'total_train_time_mean': 799.199625492096, 'avg_inference_tokens_per_second_mean': 690.1664700419294}, 'text8': {'final_train_loss_mean': 0.9926028251647949, 'best_val_loss_mean': 0.9795507192611694, 'total_train_time_mean': 796.1075961589813, 'avg_inference_tokens_per_second_mean': 691.9504174462957}} Description: This run implemented Q-learning for dynamic learning rate adaptation. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used a reward signal derived from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). >>>>>>> REPLACE ``` Next, let's implement the next experiment. For this experiment, we will use a different initial learning rate to see how it affects the Q-learning adaptation. We will set the initial learning rate to `2e-3` for `shakespeare_char` and `1e-3` for `enwik8` and `text8`. experiment.py ```python <<<<<<< SEARCH learning_rate = ( 1e-3 if dataset == "shakespeare_char" else 5e-4 ) ======= learning_rate = ( 2e-3 if dataset == "shakespeare_char" else 1e-3 ) >>>>>>> REPLACE ``` Now, you can run the next experiment with the command: ```sh python experiment.py --out_dir=run_2 ``` First, let's update the notes.txt file to include the results and description for Run 2. notes.txt <<<<<<< SEARCH ## Run 1: Q-learning with initial learning rate adaptation Results: {'shakespeare_char': {'final_train_loss_mean': 0.8112714489301046, 'best_val_loss_mean': 1.4664853016535442, 'total_train_time_mean': 76.33582202593486, 'avg_inference_tokens_per_second_mean': 680.220956113138}, 'enwik8': {'final_train_loss_mean': 0.9324554204940796, 'best_val_loss_mean': 1.0050768852233887, 'total_train_time_mean': 799.199625492096, 'avg_inference_tokens_per_second_mean': 690.1664700419294}, 'text8': {'final_train_loss_mean': 0.9926028251647949, 'best_val_loss_mean': 0.9795507192611694, 'total_train_time_mean': 796.1075961589813, 'avg_inference_tokens_per_second_mean': 691.9504174462957}} Description: This run implemented Q-learning for dynamic learning rate adaptation. The Q-learning agent adjusted the learning rate based on the curre state (validation loss, current learning rate) and used a reward signal derive from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at eac evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). ======= ## Run 1: Q-learning with initial learning rate adaptation Results: {'shakespeare_char': {'final_train_loss_mean': 0.8112714489301046, 'best_val_loss_mean': 1.4664853016535442, 'total_train_time_mean': 76.33582202593486, 'avg_inference_tokens_per_second_mean': 680.220956113138}, 'enwik8': {'final_train_loss_mean': 0.9324554204940796, 'best_val_loss_mean': 1.0050768852233887, 'total_train_time_mean': 799.199625492096, 'avg_inference_tokens_per_second_mean': 690.1664700419294}, 'text8': {'final_train_loss_mean': 0.9926028251647949, 'best_val_loss_mean': 0.9795507192611694, 'total_train_time_mean': 796.1075961589813, 'avg_inference_tokens_per_second_mean': 691.9504174462957}} Description: This run implemented Q-learning for dynamic learning rate adaptation. The Q-learning agent adjusted the learning rate based on the curre state (validation loss, current learning rate) and used a reward signal derive from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at eac evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). ## Run 2: Q-learning with different initial learning rates Results: {'shakespeare_char': {'final_train_loss_mean': 0.8047561645507812, 'best_val_loss_mean': 1.4602874914805095, 'total_train_time_mean': 76.26222737630208, 'avg_inference_tokens_per_second_mean': 675.5019470493302}, 'enwik8': {'final_train_loss_mean': 0.9224221706390381, 'best_val_loss_mean': 0.9933806657791138, 'total_train_time_mean': 806.1875951290131, 'avg_inference_tokens_per_second_mean': 682.6881990162254}, 'text8': {'final_train_loss_mean': 0.9798105955123901, 'best_val_loss_mean': 0.9613448977470398, 'total_train_time_mean': 807.7686207294464, 'avg_inference_tokens_per_second_mean': 652.3187905322042}} Description: This run implemented Q-learning for dynamic learning rate adaptation with different initial learning rates. The initial learning rate wa set to 2e-3 for shakespeare_char and 1e-3 for enwik8 and text8. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used a reward signal derived from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char enwik8, text8). >>>>>>> REPLACE Next, let's implement the next experiment. For this experiment, we will use a different reward signal for the Q-learning agent. Instead of using the negative validation loss as the reward, we will use the improvement in validation loss as the reward. q_learning_agent.py <<<<<<< SEARCH reward = -losses["val"] # Reward is negative validation loss ======= reward = best_val_loss - losses["val"] # Reward is improvement in validation loss >>>>>>> REPLACE Now, you can run the next experiment with the command: python experiment.py --out_dir=run_3 12718 prompt tokens, 1173 completion tokens, $0.081185 cost Applied edit to q_learning_agent.py Applied edit to notes.txt First, let's update the `notes.txt` file to include the results and description for Run 2. notes.txt ```python <<<<<<< SEARCH ## Run 1: Q-learning with initial learning rate adaptation Results: {'shakespeare_char': {'final_train_loss_mean': 0.8112714489301046, 'best_val_loss_mean': 1.4664853016535442, 'total_train_time_mean': 76.33582202593486, 'avg_inference_tokens_per_second_mean': 680.220956113138}, 'enwik8': {'final_train_loss_mean': 0.9324554204940796, 'best_val_loss_mean': 1.0050768852233887, 'total_train_time_mean': 799.199625492096, 'avg_inference_tokens_per_second_mean': 690.1664700419294}, 'text8': {'final_train_loss_mean': 0.9926028251647949, 'best_val_loss_mean': 0.9795507192611694, 'total_train_time_mean': 796.1075961589813, 'avg_inference_tokens_per_second_mean': 691.9504174462957}} Description: This run implemented Q-learning for dynamic learning rate adaptation. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used a reward signal derived from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). ======= ## Run 1: Q-learning with initial learning rate adaptation Results: {'shakespeare_char': {'final_train_loss_mean': 0.8112714489301046, 'best_val_loss_mean': 1.4664853016535442, 'total_train_time_mean': 76.33582202593486, 'avg_inference_tokens_per_second_mean': 680.220956113138}, 'enwik8': {'final_train_loss_mean': 0.9324554204940796, 'best_val_loss_mean': 1.0050768852233887, 'total_train_time_mean': 799.199625492096, 'avg_inference_tokens_per_second_mean': 690.1664700419294}, 'text8': {'final_train_loss_mean': 0.9926028251647949, 'best_val_loss_mean': 0.9795507192611694, 'total_train_time_mean': 796.1075961589813, 'avg_inference_tokens_per_second_mean': 691.9504174462957}} Description: This run implemented Q-learning for dynamic learning rate adaptation. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used a reward signal derived from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). ## Run 2: Q-learning with different initial learning rates Results: {'shakespeare_char': {'final_train_loss_mean': 0.8047561645507812, 'best_val_loss_mean': 1.4602874914805095, 'total_train_time_mean': 76.26222737630208, 'avg_inference_tokens_per_second_mean': 675.5019470493302}, 'enwik8': {'final_train_loss_mean': 0.9224221706390381, 'best_val_loss_mean': 0.9933806657791138, 'total_train_time_mean': 806.1875951290131, 'avg_inference_tokens_per_second_mean': 682.6881990162254}, 'text8': {'final_train_loss_mean': 0.9798105955123901, 'best_val_loss_mean': 0.9613448977470398, 'total_train_time_mean': 807.7686207294464, 'avg_inference_tokens_per_second_mean': 652.3187905322042}} Description: This run implemented Q-learning for dynamic learning rate adaptation with different initial learning rates. The initial learning rate was set to 2e-3 for shakespeare_char and 1e-3 for enwik8 and text8. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used a reward signal derived from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). >>>>>>> REPLACE ``` Next, let's implement the next experiment. For this experiment, we will use a different reward signal for the Q-learning agent. Instead of using the negative validation loss as the reward, we will use the improvement in validation loss as the reward. q_learning_agent.py ```python <<<<<<< SEARCH reward = -losses["val"] # Reward is negative validation loss ======= reward = best_val_loss - losses["val"] # Reward is improvement in validation loss >>>>>>> REPLACE ``` Now, you can run the next experiment with the command: ```sh python experiment.py --out_dir=run_3 ``` Let's first update the notes.txt file with the results and description for Run 3. notes.txt <<<<<<< SEARCH Run 3: Q-learning with reward signal based on improvement in validation loss Results: {'shakespeare_char': {'final_train_loss_mean': 0.8062439958254496, 'best_val_loss_mean': 1.461962143580119, 'total_train_time_mean': 75.80110216140747, 'avg_inference_tokens_per_second_mean': 668.3102066342188}, 'enwik8': {'final_train_loss_mean': 0.9246289730072021, 'best_val_loss_mean': 0.9944368004798889, 'total_train_time_mean': 796.9592888355255, 'avg_inference_tokens_per_second_mean': 688.6266631351763}, 'text8': {'final_train_loss_mean': 0.9843199849128723, 'best_val_loss_mean': 0.961367666721344, 'total_train_time_mean': 791.6123127937317, 'avg_inference_tokens_per_second_mean': 658.961942825521}} Description: This run implemented Q-learning for dynamic learning rate adaptation with a different reward signal. Instead of using the negative validation loss as the reward, the improvement in validation loss was used. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used the reward signal derived from the improvement in validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). ▌ ▌ ▌ ▌ ▌ ▌ ▌ REPLACE Next, let's propose the next experiment. For Run 4, we will use a different exploration strategy for the Q-learning agent. Instead of using a fixed epsilo value for exploration, we will use an epsilon decay strategy where epsilon decreases over time. Here are the changes needed: q_learning_agent.py ```python <<<<<<< SEARCH self.epsilon = epsilon ======= self.epsilon = epsilon self.initial_epsilon = epsilon self.epsilon_decay = 0.99 # Decay rate for epsilon >>>>>>> REPLACE q_learning_agent.py <<<<<<< SEARCH if np.random.rand() < self.epsilon: ======= if np.random.rand() < self.epsilon: self.epsilon *= self.epsilon_decay # Decay epsilon >>>>>>> REPLACE Now, you can run the next experiment with the command: python experiment.py --out_dir=run_4 11638 prompt tokens, 584 completion tokens, $0.066950 cost The LLM did not conform to the edit format. https://aider.chat/docs/troubleshooting/edit-errors.html Let's first update the `notes.txt` file with the results and description for Run 3. notes.txt ```python <<<<<<< SEARCH ``` ## Run 3: Q-learning with reward signal based on improvement in validation loss Results: {'shakespeare_char': {'final_train_loss_mean': 0.8062439958254496, 'best_val_loss_mean': 1.461962143580119, 'total_train_time_mean': 75.80110216140747, 'avg_inference_tokens_per_second_mean': 668.3102066342188}, 'enwik8': {'final_train_loss_mean': 0.9246289730072021, 'best_val_loss_mean': 0.9944368004798889, 'total_train_time_mean': 796.9592888355255, 'avg_inference_tokens_per_second_mean': 688.6266631351763}, 'text8': {'final_train_loss_mean': 0.9843199849128723, 'best_val_loss_mean': 0.961367666721344, 'total_train_time_mean': 791.6123127937317, 'avg_inference_tokens_per_second_mean': 658.961942825521}} Description: This run implemented Q-learning for dynamic learning rate adaptation with a different reward signal. Instead of using the negative validation loss as the reward, the improvement in validation loss was used. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used the reward signal derived from the improvement in validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). >>>>>>> REPLACE ^^^ Expected `=======` not >>>>>>> REPLACE Let's update the notes.txt file with the results and description for Run 3 correctly. notes.txt <<<<<<< SEARCH ## Run 2: Q-learning with different initial learning rates Results: {'shakespeare_char': {'final_train_loss_mean': 0.8047561645507812, 'best_val_loss_mean': 1.4602874914805095, 'total_train_time_mean': 76.26222737630208, 'avg_inference_tokens_per_second_mean': 675.5019470493302}, 'enwik8': {'final_train_loss_mean': 0.9224221706390381, 'best_val_loss_mean': 0.9933806657791138, 'total_train_time_mean': 806.1875951290131, 'avg_inference_tokens_per_second_mean': 682.6881990162254}, 'text8': {'final_train_loss_mean': 0.9798105955123901, 'best_val_loss_mean': 0.9613448977470398, 'total_train_time_mean': 807.7686207294464, 'avg_inference_tokens_per_second_mean': 652.3187905322042}} Description: This run implemented Q-learning for dynamic learning rate adaptation with different initial learning rates. The initial learning rate wa set to 2e-3 for shakespeare_char and 1e-3 for enwik8 and text8. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used a reward signal derived from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char enwik8, text8). ======= ## Run 2: Q-learning with different initial learning rates Results: {'shakespeare_char': {'final_train_loss_mean': 0.8047561645507812, 'best_val_loss_mean': 1.4602874914805095, 'total_train_time_mean': 76.26222737630208, 'avg_inference_tokens_per_second_mean': 675.5019470493302}, 'enwik8': {'final_train_loss_mean': 0.9224221706390381, 'best_val_loss_mean': 0.9933806657791138, 'total_train_time_mean': 806.1875951290131, 'avg_inference_tokens_per_second_mean': 682.6881990162254}, 'text8': {'final_train_loss_mean': 0.9798105955123901, 'best_val_loss_mean': 0.9613448977470398, 'total_train_time_mean': 807.7686207294464, 'avg_inference_tokens_per_second_mean': 652.3187905322042}} Description: This run implemented Q-learning for dynamic learning rate adaptation with different initial learning rates. The initial learning rate wa set to 2e-3 for shakespeare_char and 1e-3 for enwik8 and text8. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used a reward signal derived from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char enwik8, text8). ## Run 3: Q-learning with reward signal based on improvement in validation los Results: {'shakespeare_char': {'final_train_loss_mean': 0.8062439958254496, 'best_val_loss_mean': 1.461962143580119, 'total_train_time_mean': 75.80110216140747, 'avg_inference_tokens_per_second_mean': 668.3102066342188}, 'enwik8': {'final_train_loss_mean': 0.9246289730072021, 'best_val_loss_mean': 0.9944368004798889, 'total_train_time_mean': 796.9592888355255, 'avg_inference_tokens_per_second_mean': 688.6266631351763}, 'text8': {'final_train_loss_mean': 0.9843199849128723, 'best_val_loss_mean': 0.961367666721344, 'total_train_time_mean': 791.6123127937317, 'avg_inference_tokens_per_second_mean': 658.961942825521}} Description: This run implemented Q-learning for dynamic learning rate adaptation with a different reward signal. Instead of using the negative validation loss as the reward, the improvement in validation loss was used. Th Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used the reward signal derived fr the improvement in validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning ra at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). >>>>>>> REPLACE Next, let's propose the next experiment. For Run 4, we will use a different exploration strategy for the Q-learning agent. Instead of using a fixed epsilon value for exploration, we will use an epsilon decay strategy where epsilon decreases over time. Here are the changes needed: q_learning_agent.py <<<<<<< SEARCH self.epsilon = epsilon ======= self.epsilon = epsilon self.initial_epsilon = epsilon self.epsilon_decay = 0.99 # Decay rate for epsilon >>>>>>> REPLACE q_learning_agent.py <<<<<<< SEARCH if np.random.rand() < self.epsilon: ======= if np.random.rand() < self.epsilon: self.epsilon *= self.epsilon_decay # Decay epsilon >>>>>>> REPLACE Now, you can run the next experiment with the command: python experiment.py --out_dir=run_4 12629 prompt tokens, 1303 completion tokens, $0.082690 cost Applied edit to q_learning_agent.py Applied edit to notes.txt Let's update the `notes.txt` file with the results and description for Run 3 correctly. notes.txt ```python <<<<<<< SEARCH ## Run 2: Q-learning with different initial learning rates Results: {'shakespeare_char': {'final_train_loss_mean': 0.8047561645507812, 'best_val_loss_mean': 1.4602874914805095, 'total_train_time_mean': 76.26222737630208, 'avg_inference_tokens_per_second_mean': 675.5019470493302}, 'enwik8': {'final_train_loss_mean': 0.9224221706390381, 'best_val_loss_mean': 0.9933806657791138, 'total_train_time_mean': 806.1875951290131, 'avg_inference_tokens_per_second_mean': 682.6881990162254}, 'text8': {'final_train_loss_mean': 0.9798105955123901, 'best_val_loss_mean': 0.9613448977470398, 'total_train_time_mean': 807.7686207294464, 'avg_inference_tokens_per_second_mean': 652.3187905322042}} Description: This run implemented Q-learning for dynamic learning rate adaptation with different initial learning rates. The initial learning rate was set to 2e-3 for shakespeare_char and 1e-3 for enwik8 and text8. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used a reward signal derived from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). ======= ## Run 2: Q-learning with different initial learning rates Results: {'shakespeare_char': {'final_train_loss_mean': 0.8047561645507812, 'best_val_loss_mean': 1.4602874914805095, 'total_train_time_mean': 76.26222737630208, 'avg_inference_tokens_per_second_mean': 675.5019470493302}, 'enwik8': {'final_train_loss_mean': 0.9224221706390381, 'best_val_loss_mean': 0.9933806657791138, 'total_train_time_mean': 806.1875951290131, 'avg_inference_tokens_per_second_mean': 682.6881990162254}, 'text8': {'final_train_loss_mean': 0.9798105955123901, 'best_val_loss_mean': 0.9613448977470398, 'total_train_time_mean': 807.7686207294464, 'avg_inference_tokens_per_second_mean': 652.3187905322042}} Description: This run implemented Q-learning for dynamic learning rate adaptation with different initial learning rates. The initial learning rate was set to 2e-3 for shakespeare_char and 1e-3 for enwik8 and text8. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used a reward signal derived from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). ## Run 3: Q-learning with reward signal based on improvement in validation loss Results: {'shakespeare_char': {'final_train_loss_mean': 0.8062439958254496, 'best_val_loss_mean': 1.461962143580119, 'total_train_time_mean': 75.80110216140747, 'avg_inference_tokens_per_second_mean': 668.3102066342188}, 'enwik8': {'final_train_loss_mean': 0.9246289730072021, 'best_val_loss_mean': 0.9944368004798889, 'total_train_time_mean': 796.9592888355255, 'avg_inference_tokens_per_second_mean': 688.6266631351763}, 'text8': {'final_train_loss_mean': 0.9843199849128723, 'best_val_loss_mean': 0.961367666721344, 'total_train_time_mean': 791.6123127937317, 'avg_inference_tokens_per_second_mean': 658.961942825521}} Description: This run implemented Q-learning for dynamic learning rate adaptation with a different reward signal. Instead of using the negative validation loss as the reward, the improvement in validation loss was used. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used the reward signal derived from the improvement in validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). >>>>>>> REPLACE ``` Next, let's propose the next experiment. For Run 4, we will use a different exploration strategy for the Q-learning agent. Instead of using a fixed epsilon value for exploration, we will use an epsilon decay strategy where epsilon decreases over time. Here are the changes needed: q_learning_agent.py ```python <<<<<<< SEARCH self.epsilon = epsilon ======= self.epsilon = epsilon self.initial_epsilon = epsilon self.epsilon_decay = 0.99 # Decay rate for epsilon >>>>>>> REPLACE ``` q_learning_agent.py ```python <<<<<<< SEARCH if np.random.rand() < self.epsilon: ======= if np.random.rand() < self.epsilon: self.epsilon *= self.epsilon_decay # Decay epsilon >>>>>>> REPLACE ``` Now, you can run the next experiment with the command: ```bash python experiment.py --out_dir=run_4 ``` Let's first update the notes.txt file with the results and description for Run 4. notes.txt <<<<<<< SEARCH Run 3: Q-learning with reward signal based on improvement in validation loss Results: {'shakespeare_char': {'final_train_loss_mean': 0.8062439958254496, 'best_val_loss_mean': 1.461962143580119, 'total_train_time_mean': 75.80110216140747, 'avg_inference_tokens_per_second_mean': 668.3102066342188}, 'enwik8': {'final_train_loss_mean': 0.9246289730072021, 'best_val_loss_mean': 0.9944368004798889, 'total_train_time_mean': 796.9592888355255, 'avg_inference_tokens_per_second_mean': 688.6266631351763}, 'text8': {'final_train_loss_mean': 0.9843199849128723, 'best_val_loss_mean': 0.961367666721344, 'total_train_time_mean': 791.6123127937317, 'avg_inference_tokens_per_second_mean': 658.961942825521}} Description: This run implemented Q-learning for dynamic learning rate adaptation with a different reward signal. Instead of using the negative validation loss as the reward, the improvement in validation loss was used. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used the reward signal derived from the improvement in validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). Run 4: Q-learning with epsilon decay strategy Results: {'shakespeare_char': {'final_train_loss_mean': 0.7984780073165894, 'best_val_loss_mean': 1.463551680246989, 'total_train_time_mean': 79.24612506230672, 'avg_inference_tokens_per_second_mean': 617.9132836431749}, 'enwik8': {'final_train_loss_mean': 0.925983190536499, 'best_val_loss_mean': 0.9917866587638855, 'total_train_time_mean': 852.1484353542328, 'avg_inference_tokens_per_second_mean': 605.0617699125265}, 'text8': {'final_train_loss_mean': 0.9827583432197571, 'best_val_loss_mean': 0.9615200161933899, 'total_train_time_mean': 846.4471461772919, 'avg_inference_tokens_per_second_mean': 613.2623906747798}} Description: This run implemented Q-learning for dynamic learning rate adaptation with an epsilon decay strategy. Instead of using a fixed epsilon value for exploration, an epsilon decay strategy was used where epsilon decreases over time. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used a reward signal derived from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). Next, let's propose the next experiment. We will use a different discount factor (gamma) for the Q-learning agent to see how it affects the learning rate adaptation. q_learning_agent.py <<<<<<< SEARCH def __init__(self, lr=0.1, gamma=0.9, epsilon=0.1): ======= def __init__(self, lr=0.1, gamma=0.95, epsilon=0.1): >>>>>>> REPLACE Now, you can run the next experiment with the command: python experiment.py --out_dir=run_5 12168 prompt tokens, 864 completion tokens, $0.073800 cost The LLM did not conform to the edit format. https://aider.chat/docs/troubleshooting/edit-errors.html Let's first update the `notes.txt` file with the results and description for Run 4. notes.txt ```python <<<<<<< SEARCH ``` ## Run 3: Q-learning with reward signal based on improvement in validation loss Results: {'shakespeare_char': {'final_train_loss_mean': 0.8062439958254496, 'best_val_loss_mean': 1.461962143580119, 'total_train_time_mean': 75.80110216140747, 'avg_inference_tokens_per_second_mean': 668.3102066342188}, 'enwik8': {'final_train_loss_mean': 0.9246289730072021, 'best_val_loss_mean': 0.9944368004798889, 'total_train_time_mean': 796.9592888355255, 'avg_inference_tokens_per_second_mean': 688.6266631351763}, 'text8': {'final_train_loss_mean': 0.9843199849128723, 'best_val_loss_mean': 0.961367666721344, 'total_train_time_mean': 791.6123127937317, 'avg_inference_tokens_per_second_mean': 658.961942825521}} Description: This run implemented Q-learning for dynamic learning rate adaptation with a different reward signal. Instead of using the negative validation loss as the reward, the improvement in validation loss was used. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used the reward signal derived from the improvement in validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). ## Run 4: Q-learning with epsilon decay strategy Results: {'shakespeare_char': {'final_train_loss_mean': 0.7984780073165894, 'best_val_loss_mean': 1.463551680246989, 'total_train_time_mean': 79.24612506230672, 'avg_inference_tokens_per_second_mean': 617.9132836431749}, 'enwik8': {'final_train_loss_mean': 0.925983190536499, 'best_val_loss_mean': 0.9917866587638855, 'total_train_time_mean': 852.1484353542328, 'avg_inference_tokens_per_second_mean': 605.0617699125265}, 'text8': {'final_train_loss_mean': 0.9827583432197571, 'best_val_loss_mean': 0.9615200161933899, 'total_train_time_mean': 846.4471461772919, 'avg_inference_tokens_per_second_mean': 613.2623906747798}} Description: This run implemented Q-learning for dynamic learning rate adaptation with an epsilon decay strategy. Instead of using a fixed epsilon value for exploration, an epsilon decay strategy was used where epsilon decreases over time. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used a reward signal derived from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). ``` ``` Next, let's propose the next experiment. We will use a different discount factor (gamma) for the Q-learning agent to see how it affects the learning rate adaptation. q_learning_agent.py ```python <<<<<<< SEARCH ^^^ Expected `=======` not <<<<<<< SEARCH Let's first update the notes.txt file with the results and description for Run 4. notes.txt <<<<<<< SEARCH ## Run 3: Q-learning with reward signal based on improvement in validation los Results: {'shakespeare_char': {'final_train_loss_mean': 0.8062439958254496, 'best_val_loss_mean': 1.461962143580119, 'total_train_time_mean': 75.80110216140747, 'avg_inference_tokens_per_second_mean': 668.3102066342188}, 'enwik8': {'final_train_loss_mean': 0.9246289730072021, 'best_val_loss_mean': 0.9944368004798889, 'total_train_time_mean': 796.9592888355255, 'avg_inference_tokens_per_second_mean': 688.6266631351763}, 'text8': {'final_train_loss_mean': 0.9843199849128723, 'best_val_loss_mean': 0.961367666721344, 'total_train_time_mean': 791.6123127937317, 'avg_inference_tokens_per_second_mean': 658.961942825521}} Description: This run implemented Q-learning for dynamic learning rate adaptation with a different reward signal. Instead of using the negative validation loss as the reward, the improvement in validation loss was used. Th Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used the reward signal derived fr the improvement in validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning ra at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). ======= ## Run 3: Q-learning with reward signal based on improvement in validation los Results: {'shakespeare_char': {'final_train_loss_mean': 0.8062439958254496, 'best_val_loss_mean': 1.461962143580119, 'total_train_time_mean': 75.80110216140747, 'avg_inference_tokens_per_second_mean': 668.3102066342188}, 'enwik8': {'final_train_loss_mean': 0.9246289730072021, 'best_val_loss_mean': 0.9944368004798889, 'total_train_time_mean': 796.9592888355255, 'avg_inference_tokens_per_second_mean': 688.6266631351763}, 'text8': {'final_train_loss_mean': 0.9843199849128723, 'best_val_loss_mean': 0.961367666721344, 'total_train_time_mean': 791.6123127937317, 'avg_inference_tokens_per_second_mean': 658.961942825521}} Description: This run implemented Q-learning for dynamic learning rate adaptation with a different reward signal. Instead of using the negative validation loss as the reward, the improvement in validation loss was used. Th Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used the reward signal derived fr the improvement in validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning ra at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). ## Run 4: Q-learning with epsilon decay strategy Results: {'shakespeare_char': {'final_train_loss_mean': 0.7984780073165894, 'best_val_loss_mean': 1.463551680246989, 'total_train_time_mean': 79.24612506230672, 'avg_inference_tokens_per_second_mean': 617.9132836431749}, 'enwik8': {'final_train_loss_mean': 0.925983190536499, 'best_val_loss_mean': 0.9917866587638855, 'total_train_time_mean': 852.1484353542328, 'avg_inference_tokens_per_second_mean': 605.0617699125265}, 'text8': {'final_train_loss_mean': 0.9827583432197571, 'best_val_loss_mean': 0.9615200161933899, 'total_train_time_mean': 846.4471461772919, 'avg_inference_tokens_per_second_mean': 613.2623906747798}} Description: This run implemented Q-learning for dynamic learning rate adaptation with an epsilon decay strategy. Instead of using a fixed epsilon value for exploration, an epsilon decay strategy was used where epsilon decreases over time. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used a reward signal derived from validation performance to update the Q-values. The trainin loop was modified to incorporate the RL agent's adjustments to the learning ra at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). >>>>>>> REPLACE Next, let's propose the next experiment. We will use a different discount factor (gamma) for the Q-learning agent to see how it affects the learning rate adaptation. q_learning_agent.py <<<<<<< SEARCH def __init__(self, lr=0.1, gamma=0.9, epsilon=0.1): ======= def __init__(self, lr=0.1, gamma=0.95, epsilon=0.1): >>>>>>> REPLACE Now, you can run the next experiment with the command: python experiment.py --out_dir=run_5 13832 prompt tokens, 1218 completion tokens, $0.087430 cost Applied edit to q_learning_agent.py Applied edit to notes.txt Let's first update the `notes.txt` file with the results and description for Run 4. notes.txt ```python <<<<<<< SEARCH ## Run 3: Q-learning with reward signal based on improvement in validation loss Results: {'shakespeare_char': {'final_train_loss_mean': 0.8062439958254496, 'best_val_loss_mean': 1.461962143580119, 'total_train_time_mean': 75.80110216140747, 'avg_inference_tokens_per_second_mean': 668.3102066342188}, 'enwik8': {'final_train_loss_mean': 0.9246289730072021, 'best_val_loss_mean': 0.9944368004798889, 'total_train_time_mean': 796.9592888355255, 'avg_inference_tokens_per_second_mean': 688.6266631351763}, 'text8': {'final_train_loss_mean': 0.9843199849128723, 'best_val_loss_mean': 0.961367666721344, 'total_train_time_mean': 791.6123127937317, 'avg_inference_tokens_per_second_mean': 658.961942825521}} Description: This run implemented Q-learning for dynamic learning rate adaptation with a different reward signal. Instead of using the negative validation loss as the reward, the improvement in validation loss was used. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used the reward signal derived from the improvement in validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). ======= ## Run 3: Q-learning with reward signal based on improvement in validation loss Results: {'shakespeare_char': {'final_train_loss_mean': 0.8062439958254496, 'best_val_loss_mean': 1.461962143580119, 'total_train_time_mean': 75.80110216140747, 'avg_inference_tokens_per_second_mean': 668.3102066342188}, 'enwik8': {'final_train_loss_mean': 0.9246289730072021, 'best_val_loss_mean': 0.9944368004798889, 'total_train_time_mean': 796.9592888355255, 'avg_inference_tokens_per_second_mean': 688.6266631351763}, 'text8': {'final_train_loss_mean': 0.9843199849128723, 'best_val_loss_mean': 0.961367666721344, 'total_train_time_mean': 791.6123127937317, 'avg_inference_tokens_per_second_mean': 658.961942825521}} Description: This run implemented Q-learning for dynamic learning rate adaptation with a different reward signal. Instead of using the negative validation loss as the reward, the improvement in validation loss was used. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used the reward signal derived from the improvement in validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). ## Run 4: Q-learning with epsilon decay strategy Results: {'shakespeare_char': {'final_train_loss_mean': 0.7984780073165894, 'best_val_loss_mean': 1.463551680246989, 'total_train_time_mean': 79.24612506230672, 'avg_inference_tokens_per_second_mean': 617.9132836431749}, 'enwik8': {'final_train_loss_mean': 0.925983190536499, 'best_val_loss_mean': 0.9917866587638855, 'total_train_time_mean': 852.1484353542328, 'avg_inference_tokens_per_second_mean': 605.0617699125265}, 'text8': {'final_train_loss_mean': 0.9827583432197571, 'best_val_loss_mean': 0.9615200161933899, 'total_train_time_mean': 846.4471461772919, 'avg_inference_tokens_per_second_mean': 613.2623906747798}} Description: This run implemented Q-learning for dynamic learning rate adaptation with an epsilon decay strategy. Instead of using a fixed epsilon value for exploration, an epsilon decay strategy was used where epsilon decreases over time. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used a reward signal derived from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8). >>>>>>> REPLACE ``` Next, let's propose the next experiment. We will use a different discount factor (gamma) for the Q-learning agent to see how it affects the learning rate adaptation. q_learning_agent.py ```python <<<<<<< SEARCH def __init__(self, lr=0.1, gamma=0.9, epsilon=0.1): ======= def __init__(self, lr=0.1, gamma=0.95, epsilon=0.1): >>>>>>> REPLACE ``` Now, you can run the next experiment with the command: ```sh python experiment.py --out_dir=run_5 ``` To modify plot.py to generate the most relevant plots for the final writeup and fill in the "labels" dictionary with the correct names for each run, we will: 1 Update the labels dictionary to include all relevant runs. 2 Ensure that the plots are generated for each dataset and saved with appropriate filenames. Here are the SEARCH/REPLACE blocks: plot.py <<<<<<< SEARCH labels = { "run_0": "Baselines", ======= labels = { "run_0": "Baseline", "run_1": "Q-learning with initial learning rate adaptation", "run_2": "Q-learning with different initial learning rates", "run_3": "Q-learning with reward signal based on improvement in validation loss", "run_4": "Q-learning with epsilon decay strategy", >>>>>>> REPLACE } plot.py <<<<<<< SEARCH # Plot 1: Line plot of training loss for each dataset across the runs with labels for dataset in datasets: plt.figure(figsize=(10, 6)) for i, run in enumerate(runs): iters = results_info[run][dataset]["iters"] mean = results_info[run][dataset]["train_loss"] sterr = results_info[run][dataset]["train_loss_sterr"] plt.plot(iters, mean, label=labels[run], color=colors[i]) plt.fill_between(iters, mean - sterr, mean + sterr, color=colors[i], alpha=0.2) plt.title(f"Training Loss Across Runs for {dataset} Dataset") plt.xlabel("Iteration") plt.ylabel("Training Loss") plt.legend() plt.grid(True, which="both", ls="-", alpha=0.2) plt.tight_layout() plt.savefig(f"train_loss_{dataset}.png") plt.close() # Plot 2: Line plot of validation loss for each dataset across the runs with labels for dataset in datasets: plt.figure(figsize=(10, 6)) for i, run in enumerate(runs): iters = results_info[run][dataset]["iters"] mean = results_info[run][dataset]["val_loss"] sterr = results_info[run][dataset]["val_loss_sterr"] plt.plot(iters, mean, label=labels[run], color=colors[i]) plt.fill_between(iters, mean - sterr, mean + sterr, color=colors[i], alpha=0.2) plt.title(f"Validation Loss Across Runs for {dataset} Dataset") plt.xlabel("Iteration") plt.ylabel("Validation Loss") plt.legend() plt.grid(True, which="both", ls="-", alpha=0.2) plt.tight_layout() plt.savefig(f"val_loss_{dataset}.png") plt.close() ======= # Plot 1: Line plot of training loss for each dataset across the runs with labels for dataset in datasets: plt.figure(figsize=(10, 6)) for i, run in enumerate(runs): iters = results_info[run][dataset]["iters"] mean = results_info[run][dataset]["train_loss"] sterr = results_info[run][dataset]["train_loss_sterr"] plt.plot(iters, mean, label=labels[run], color=colors[i]) plt.fill_between(iters, mean - sterr, mean + sterr, color=colors[i], alpha=0.2) plt.title(f"Training Loss Across Runs for {dataset} Dataset") plt.xlabel("Iteration") plt.ylabel("Training Loss") plt.legend() plt.grid(True, which="both", ls="-", alpha=0.2) plt.tight_layout() plt.savefig(f"train_loss_{dataset}.png") plt.close() # Plot 2: Line plot of validation loss for each dataset across the runs with labels for dataset in datasets: plt.figure(figsize=(10, 6)) for i, run in enumerate(runs): iters = results_info[run][dataset]["iters"] mean = results_info[run][dataset]["val_loss"] sterr = results_info[run][dataset]["val_loss_sterr"] plt.plot(iters, mean, label=labels[run], color=colors[i]) plt.fill_between(iters, mean - sterr, mean + sterr, color=colors[i], alpha=0.2) plt.title(f"Validation Loss Across Runs for {dataset} Dataset") plt.xlabel("Iteration") plt.ylabel("Validation Loss") plt.legend() plt.grid(True, which="both", ls="-", alpha=0.2) plt.tight_layout() plt.savefig(f"val_loss_{dataset}.png") plt.close() >>>>>>> REPLACE These changes will ensure that the plots are generated for each dataset and saved with appropriate filenames, and the labels dictionary will include all relevant runs. 12063 prompt tokens, 1043 completion tokens, $0.075960 cost Applied edit to plot.py /home/anon/ai_scientist/venv/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, /home/anon/ai_scientist/venv/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide ret = ret.dtype.type(ret / rcount) Here are the changes to notes.txt to include descriptions of what each plot shows along with the filename of the figure: notes.txt <<<<<<< SEARCH ## Run 0: Baseline Results: {'shakespeare_char': {'final_train_loss_mean': 0.8186181902885437, 'best_val_loss_mean': 1.4654763221740723, 'total_train_time_mean': 77.26942734718322, 'avg_inference_tokens_per_second_mean': 666.5076153519527}, 'enwik8': {'final_train_loss_mean': 0.930223822593689, 'best_val_loss_mean': 1.0055421590805054, 'total_train_time_mean': 819.4551751613617, 'avg_inference_tokens_per_second_mean': 671.9918599180683}, 'text8': {'final_train_loss_mean': 1.0013301372528076, 'best_val_loss_mean': 0.979989230632782, 'total_train_time_mean': 801.224205493927, 'avg_inference_tokens_per_second_mean': 671.5678332249411}} Description: Baseline results. ======= ## Run 0: Baseline Results: {'shakespeare_char': {'final_train_loss_mean': 0.8186181902885437, 'best_val_loss_mean': 1.4654763221740723, 'total_train_time_mean': 77.26942734718322, 'avg_inference_tokens_per_second_mean': 666.5076153519527}, 'enwik8': {'final_train_loss_mean': 0.930223822593689, 'best_val_loss_mean': 1.0055421590805054, 'total_train_time_mean': 819.4551751613617, 'avg_inference_tokens_per_second_mean': 671.9918599180683}, 'text8': {'final_train_loss_mean': 1.0013301372528076, 'best_val_loss_mean': 0.979989230632782, 'total_train_time_mean': 801.224205493927, 'avg_inference_tokens_per_second_mean': 671.5678332249411}} Description: Baseline results. Plot Descriptions: 1. Training Loss Across Runs for shakespeare_char Dataset: This plot shows the training loss over iterations for the shakespeare_char dataset across differen runs. It helps in understanding how the training loss decreases over time for each run. Filename: train_loss_shakespeare_char.png 2. Validation Loss Across Runs for shakespeare_char Dataset: This plot shows t validation loss over iterations for the shakespeare_char dataset across different runs. It helps in understanding how the validation loss decreases ov time for each run. Filename: val_loss_shakespeare_char.png 3. Training Loss Across Runs for enwik8 Dataset: This plot shows the training loss over iterations for the enwik8 dataset across different runs. It helps in understanding how the training loss decreases over time for each run. Filename train_loss_enwik8.png 4. Validation Loss Across Runs for enwik8 Dataset: This plot shows the validation loss over iterations for the enwik8 dataset across different runs. helps in understanding how the validation loss decreases over time for each ru Filename: val_loss_enwik8.png 5. Training Loss Across Runs for text8 Dataset: This plot shows the training loss over iterations for the text8 dataset across different runs. It helps in understanding how the training loss decreases over time for each run. Filename train_loss_text8.png 6. Validation Loss Across Runs for text8 Dataset: This plot shows the validati loss over iterations for the text8 dataset across different runs. It helps in understanding how the validation loss decreases over time for each run. Filename: val_loss_text8.png >>>>>>> REPLACE This update will ensure that the notes.txt file contains detailed descriptions of what each plot shows along with the filenames of the figures. 13240 prompt tokens, 834 completion tokens, $0.078710 cost Applied edit to notes.txt