Feat!: Added train_gpt_model.py

This breaks any past code as it splits the code into two files.
doc: added phoebe_model.pt (trained model for phoebe)

phoebe/neural/gpt_model.py

@@ -1,63 +1,15 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import mmap
-import random
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # Hyperparameters
 batch_size = 64
 block_size = 256
-max_iters = 200
-learning_rate = 2e-5
-eval_iters = 100
 num_embed = 384  # Ensure consistency in naming
 num_heads = 8
 num_layers = 8
 dropout = 0.2
 
-chars = ""
-with open("vocab.txt", "r", encoding="utf-8") as f:
-    text = f.read()
-    chars = sorted(list(set(text)))
-vocab_size = len(chars)
-
-string_to_int = {ch: i for i, ch in enumerate(chars)}
-int_to_string = {i: ch for i, ch in enumerate(chars)}
-
-
-def encode(s):
-    return [string_to_int[c] for c in s]
-
-
-def decode(lst):
-    return "".join([int_to_string[i] for i in lst])
-
-
-def get_random_chunk(split):
-    filename = "train_split.txt" if split == "train" else "eval_split.txt"
-    with open(filename, "rb") as f:
-        with mmap.mmap(f.fileno(), length=0, access=mmap.ACCESS_READ) as mm:
-            file_size = len(mm)
-            start = random.randint(0, file_size - block_size * batch_size)
-            mm.seek(start)
-            block = mm.read(block_size * batch_size - 1)
-            decoded_block = block.decode("utf-8", errors="ignore").replace(
-                "\r", ""
-            )
-            data = torch.tensor(encode(decoded_block), dtype=torch.long)
-            return data
-
-
-def get_batch(split):
-    data = get_random_chunk(split)
-    ix = torch.randint(len(data) - block_size, (batch_size,))
-    x = torch.stack([data[i : i + block_size] for i in ix])
-    y = torch.stack([data[i + 1 : i + block_size + 1] for i in ix])
-    x, y = x.to(device), y.to(device)
-    return x, y
-
 
 class Head(nn.Module):
     def __init__(self, head_size):
@@ -128,7 +80,7 @@ class Block(nn.Module):
 
 
 class GPT(nn.Module):
-    def __init__(self):
+    def __init__(self, vocab_size):
         super().__init__()
         self.token_embedding_table = nn.Embedding(vocab_size, num_embed)
         self.position_embedding_table = nn.Embedding(block_size, num_embed)
@@ -170,7 +122,7 @@ class GPT(nn.Module):
     def generate(self, idx, max_new_tokens):
         for _ in range(max_new_tokens):
             idx_cond = idx[:, -block_size:]
-            logits, loss = self(idx_cond)
+            logits, _ = self(idx_cond)
             logits = logits[:, -1, :]
             probs = F.softmax(logits, dim=-1)
             idx_next = torch.multinomial(probs, num_samples=1)
@@ -178,39 +130,9 @@ class GPT(nn.Module):
         return idx
 
 
-model = GPT().to(device)
+def encode(s, string_to_int):
+    return [string_to_int[c] for c in s]
 
 
-@torch.no_grad()
-def estimate_loss():
-    out = {}
-    model.eval()
-    for split in ["train", "val"]:
-        losses = torch.zeros(eval_iters)
-        for k in range(eval_iters):
-            X, Y = get_batch(split)
-            logits, loss = model(X, Y)
-            losses[k] = loss.item()
-        out[split] = losses.mean().item()
-    model.train()
-    return out
-
-
-optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
-for iter in range(max_iters):
-    if iter % eval_iters == 0:
-        losses = estimate_loss()
-        print(
-            f"step {iter}: train loss {losses['train']:.3f}, "
-            f"val loss {losses['val']:.3f}"
-        )
-    xb, yb = get_batch("train")
-    logits, loss = model(xb, yb)
-    optimizer.zero_grad(set_to_none=True)
-    loss.backward()
-    optimizer.step()
-
-print(loss.item())
-
-torch.save(model.state_dict(), "phoebe_model.pt")
-print("Model Saved!")
+def decode(lst, int_to_string):
+    return "".join([int_to_string[i] for i in lst])

phoebe/neural/train_gpt_model.py

@@ -0,0 +1,93 @@
+import torch
+import mmap
+import random
+from gpt_model import GPT, encode
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Hyperparameters
+batch_size = 64
+block_size = 256
+max_iters = 200
+learning_rate = 2e-5
+eval_iters = 100
+dropout = 0.2
+
+chars = ""
+with open("vocab.txt", "r", encoding="utf-8") as f:
+    text = f.read()
+    chars = sorted(list(set(text)))
+
+# Ensure that space and other special characters are included
+required_chars = " \n\r\t"
+for char in required_chars:
+    if char not in chars:
+        chars.append(char)
+
+vocab_size = len(chars)
+string_to_int = {ch: i for i, ch in enumerate(chars)}
+int_to_string = {i: ch for i, ch in enumerate(chars)}
+
+
+def get_random_chunk(split):
+    filename = "train_split.txt" if split == "train" else "eval_split.txt"
+    with open(filename, "rb") as f:
+        with mmap.mmap(f.fileno(), length=0, access=mmap.ACCESS_READ) as mm:
+            file_size = len(mm)
+            start = random.randint(0, file_size - block_size * batch_size)
+            mm.seek(start)
+            block = mm.read(block_size * batch_size - 1)
+            decoded_block = block.decode("utf-8", errors="ignore").replace(
+                "\r", ""
+            )
+            data = torch.tensor(
+                encode(decoded_block, string_to_int), dtype=torch.long
+            )
+            return data
+
+
+def get_batch(split):
+    data = get_random_chunk(split)
+    ix = torch.randint(len(data) - block_size, (batch_size,))
+    x = torch.stack([data[i : i + block_size] for i in ix])
+    y = torch.stack([data[i + 1 : i + block_size + 1] for i in ix])
+    x, y = x.to(device), y.to(device)
+    return x, y
+
+
+model = GPT(vocab_size).to(device)
+
+
+@torch.no_grad()
+def estimate_loss():
+    out = {}
+    model.eval()
+    for split in ["train", "val"]:
+        losses = torch.zeros(eval_iters)
+        for k in range(eval_iters):
+            X, Y = get_batch(split)
+            logits, loss = model(X, Y)
+            losses[k] = loss.item()
+        out[split] = losses.mean().item()
+    model.train()
+    return out
+
+
+optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
+for iter in range(max_iters):
+    if iter % eval_iters == 0:
+        losses = estimate_loss()
+        print(
+            f"step {iter}: train loss {losses['train']:.3f}, "
+            f"val loss {losses['val']:.3f}"
+        )
+    xb, yb = get_batch("train")
+    logits, loss = model(xb, yb)
+    optimizer.zero_grad(set_to_none=True)
+    loss.backward()
+    optimizer.step()
+
+print(loss.item())
+
+torch.save(model.state_dict(), "phoebe_model.pt")
+print("Model Saved!")