feat: Added GPT Model Code

Fix: Changed .pre-commit-confit.yaml to stop conflicts
docs: README.md changed due to the pre-commits

.pre-commit-config.yaml

@@ -1,11 +1,19 @@
 repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v3.4.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-yaml
+
   - repo: https://github.com/psf/black
-    rev: 24.4.2
+    rev: 22.3.0
     hooks:
       - id: black
-        language_version: python3.10.6
+        args: [--line-length=79]
 
   - repo: https://github.com/pycqa/flake8
-    rev: 7.0.0  # Use the latest revision
+    rev: 4.0.1
     hooks:
       - id: flake8
+        args: [--max-line-length=79, --ignore=E203]

README.md

@@ -2,4 +2,4 @@
 ![Phoebe](/Phoebe.png)
 
 # About Me
-Hi there! My name is Phoebe! I am a 20 year old college student who is currently working on my degree in Machine Learning. I am a bit of a shy gal, and like to obverse everyone from the distance. My best friend is Daniel (@advtech as he goes by on Discord). I am looking forward to getting to know you!
+Hi there! My name is Phoebe! I am a 20 year old college student who is currently working on my degree in Machine Learning. I am a bit of a shy gal, and like to obverse everyone from the distance. My best friend is Daniel (@advtech as he goes by on Discord). I am looking forward to getting to know you!

phoebe/neural/gpt_model.py

@@ -0,0 +1,216 @@
+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):
+        super().__init__()
+        self.key = nn.Linear(num_embed, head_size)
+        self.query = nn.Linear(num_embed, head_size)
+        self.value = nn.Linear(num_embed, head_size)
+        self.register_buffer(
+            "tril", torch.tril(torch.ones(block_size, block_size))
+        )
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        B, T, C = x.shape
+        k = self.key(x)
+        q = self.query(x)
+        wei = q @ k.transpose(-2, -1) * C**-0.5
+        wei = wei.masked_fill(self.tril[:T, :T] == 0, float("-inf"))
+        wei = F.softmax(wei, dim=-1)
+        wei = self.dropout(wei)
+        v = self.value(x)
+        out = wei @ v
+        return out
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, num_heads, head_size):
+        super().__init__()
+        self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
+        self.proj = nn.Linear(num_embed, num_embed)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        out = torch.cat([h(x) for h in self.heads], dim=-1)
+        out = self.dropout(self.proj(out))
+        return out
+
+
+class FeedForward(nn.Module):
+    def __init__(self, num_embed):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(num_embed, 4 * num_embed),
+            nn.ReLU(),
+            nn.Linear(4 * num_embed, num_embed),
+            nn.Dropout(dropout),
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class Block(nn.Module):
+    def __init__(self, num_embed, num_head):
+        super().__init__()
+        head_size = num_embed // num_head
+        self.sa = MultiHeadAttention(num_head, head_size)
+        self.ff = FeedForward(num_embed)
+        self.ln1 = nn.LayerNorm(num_embed)
+        self.ln2 = nn.LayerNorm(num_embed)
+
+    def forward(self, x):
+        y = self.sa(x)
+        x = self.ln1(x + y)
+        y = self.ff(x)
+        x = self.ln2(x + y)
+        return x
+
+
+class GPT(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.token_embedding_table = nn.Embedding(vocab_size, num_embed)
+        self.position_embedding_table = nn.Embedding(block_size, num_embed)
+        self.blocks = nn.Sequential(
+            *[Block(num_embed, num_heads) for _ in range(num_layers)]
+        )
+        self.ln = nn.LayerNorm(num_embed)
+        self.lm_head = nn.Linear(num_embed, vocab_size)
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    def forward(self, idx, targets=None):
+        B, T = idx.shape
+        tok_emb = self.token_embedding_table(idx)
+        pos_emb = self.position_embedding_table(
+            torch.arange(T, device=idx.device)
+        )
+        x = tok_emb + pos_emb
+        x = self.blocks(x)
+        x = self.ln(x)
+        logits = self.lm_head(x)
+
+        if targets is None:
+            loss = None
+        else:
+            B, T, C = logits.shape
+            logits = logits.view(B * T, C)
+            targets = targets.view(B * T)
+            loss = F.cross_entropy(logits, targets)
+        return logits, loss
+
+    def generate(self, idx, max_new_tokens):
+        for _ in range(max_new_tokens):
+            idx_cond = idx[:, -block_size:]
+            logits, loss = self(idx_cond)
+            logits = logits[:, -1, :]
+            probs = F.softmax(logits, dim=-1)
+            idx_next = torch.multinomial(probs, num_samples=1)
+            idx = torch.cat((idx, idx_next), dim=1)
+        return idx
+
+
+model = GPT().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!")