Delete .ipynb_checkpoints/model-checkpoint.py

.ipynb_checkpoints/model-checkpoint.py

@@ -1,334 +0,0 @@
-# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
-# Source for "Build a Large Language Model From Scratch"
-# https://github.com/rasbt/LLMs-from-scratch/blob/main/ch05/07_gpt_to_llama/standalone-llama32.ipynb
-
-
-import torch
-import torch.nn as nn
-
-
-LLAMA32_CONFIG_1B = {
-    "vocab_size": 128_256,           # Vocabulary size
-    "context_length": 8192,          # Maximum context length to use (reduced to save memory)
-    "orig_context_length": 131_072,  # Context length that was used to train the model
-    "emb_dim": 2048,                 # Embedding dimension
-    "n_heads": 32,                   # Number of attention heads
-    "n_layers": 16,                  # Number of layers
-    "hidden_dim": 8192,              # Size of the intermediate dimension in FeedForward
-    "n_kv_groups": 8,                # Key-Value groups for grouped-query attention
-    "rope_base": 500_000.0,          # The base in RoPE's "theta"
-    "dtype": torch.bfloat16,         # Lower-precision dtype to reduce memory usage
-    "rope_freq": {                   # RoPE frequency scaling
-        "factor": 32.0,
-        "low_freq_factor": 1.0,
-        "high_freq_factor": 4.0,
-        "original_context_length": 8192,
-    }
-}
-
-LLAMA32_CONFIG_3B = {
-    "vocab_size": 128_256,           # Vocabulary size
-    "context_length": 8192,          # Maximum context length to use (reduced to save memory)
-    "orig_context_length": 131_072,  # Context length that was used to train the model
-    "emb_dim": 3072,                 # Embedding dimension
-    "n_heads": 24,                   # Number of attention heads
-    "n_layers": 28,                  # Number of layers
-    "hidden_dim": 8192,              # Size of the intermediate dimension in FeedForward
-    "n_kv_groups": 8,                # Key-Value groups for grouped-query attention
-    "rope_base": 500_000.0,          # The base in RoPE's "theta"
-    "dtype": torch.bfloat16,         # Lower-precision dtype to reduce memory usage
-    "rope_freq": {                   # RoPE frequency scaling
-        "factor": 32.0,
-        "low_freq_factor": 1.0,
-        "high_freq_factor": 4.0,
-        "original_context_length": 8192,
-    }
-}
-
-
-class Llama3Model(nn.Module):
-    def __init__(self, cfg):
-        super().__init__()
-
-        # Main model parameters
-        self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"], dtype=cfg["dtype"])
-
-        self.trf_blocks = nn.ModuleList(  # ModuleList since Sequential can only accept one input, and we need `x, mask, cos, sin`
-            [TransformerBlock(cfg) for _ in range(cfg["n_layers"])]
-        )
-
-        self.final_norm = nn.RMSNorm(cfg["emb_dim"], eps=1e-5, dtype=cfg["dtype"])
-        self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False, dtype=cfg["dtype"])
-
-        # Reusuable utilities
-        self.register_buffer(
-            "mask", torch.triu(torch.ones(cfg["context_length"], cfg["context_length"]), diagonal=1).bool(),
-            persistent=False
-        )
-
-        if cfg["orig_context_length"] != cfg["context_length"]:
-            cfg["rope_base"] = rescale_theta(
-                            cfg["rope_base"],
-                            cfg["orig_context_length"],
-                            cfg["context_length"]
-                        )
-        cos, sin = compute_rope_params(
-            head_dim=cfg["emb_dim"] // cfg["n_heads"],
-            theta_base=cfg["rope_base"],
-            context_length=cfg["context_length"],
-            freq_config=cfg["rope_freq"]
-        )
-        self.register_buffer("cos", cos, persistent=False)
-        self.register_buffer("sin", sin, persistent=False)
-        self.cfg = cfg
-
-    def forward(self, in_idx):
-        # Forward pass
-        tok_embeds = self.tok_emb(in_idx)
-        x = tok_embeds
-
-        for block in self.trf_blocks:
-            x = block(x, self.mask, self.cos, self.sin)
-        x = self.final_norm(x)
-        logits = self.out_head(x.to(self.cfg["dtype"]))
-        return logits
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, cfg):
-        super().__init__()
-        self.att = GroupedQueryAttention(
-            d_in=cfg["emb_dim"],
-            d_out=cfg["emb_dim"],
-            num_heads=cfg["n_heads"],
-            num_kv_groups=cfg["n_kv_groups"],
-            dtype=cfg["dtype"]
-        )
-        self.ff = FeedForward(cfg)
-        self.norm1 = nn.RMSNorm(cfg["emb_dim"], eps=1e-5, dtype=cfg["dtype"])
-        self.norm2 = nn.RMSNorm(cfg["emb_dim"], eps=1e-5, dtype=cfg["dtype"])
-
-    def forward(self, x, mask, cos, sin):
-        # Shortcut connection for attention block
-        shortcut = x
-        x = self.norm1(x)
-        x = self.att(x, mask, cos, sin)  # Shape [batch_size, num_tokens, emb_size]
-        x = x + shortcut  # Add the original input back
-
-        # Shortcut connection for feed-forward block
-        shortcut = x
-        x = self.norm2(x)
-        x = self.ff(x)
-        x = x + shortcut  # Add the original input back
-
-        return x
-
-
-class FeedForward(nn.Module):
-    def __init__(self, cfg):
-        super().__init__()
-        self.fc1 = nn.Linear(cfg["emb_dim"], cfg["hidden_dim"], dtype=cfg["dtype"], bias=False)
-        self.fc2 = nn.Linear(cfg["emb_dim"], cfg["hidden_dim"], dtype=cfg["dtype"], bias=False)
-        self.fc3 = nn.Linear(cfg["hidden_dim"], cfg["emb_dim"], dtype=cfg["dtype"], bias=False)
-
-    def forward(self, x):
-        x_fc1 = self.fc1(x)
-        x_fc2 = self.fc2(x)
-        x = nn.functional.silu(x_fc1) * x_fc2
-        return self.fc3(x)
-
-
-class GroupedQueryAttention(nn.Module):
-    def __init__(
-            self, d_in, d_out, num_heads,
-            num_kv_groups,
-            dtype=None
-    ):
-        super().__init__()
-        assert d_out % num_heads == 0, "d_out must be divisible by num_heads"
-        assert num_heads % num_kv_groups == 0, "num_heads must be divisible by num_kv_groups"
-
-        self.d_out = d_out
-        self.num_heads = num_heads
-        self.head_dim = d_out // num_heads
-
-        self.W_key = nn.Linear(d_in, num_kv_groups * self.head_dim, bias=False, dtype=dtype)
-        self.W_value = nn.Linear(d_in, num_kv_groups * self.head_dim, bias=False, dtype=dtype)
-        self.num_kv_groups = num_kv_groups
-        self.group_size = num_heads // num_kv_groups
-
-        self.W_query = nn.Linear(d_in, d_out, bias=False, dtype=dtype)
-        self.out_proj = nn.Linear(d_out, d_out, bias=False, dtype=dtype)
-
-    def forward(self, x, mask, cos, sin):
-        b, num_tokens, d_in = x.shape
-
-        queries = self.W_query(x)  # Shape: (b, num_tokens, d_out)
-        keys = self.W_key(x)  # Shape: (b, num_tokens, num_kv_groups * head_dim)
-        values = self.W_value(x)  # Shape: (b, num_tokens, num_kv_groups * head_dim)
-
-        # Reshape queries, keys, and values
-        queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
-        keys = keys.view(b, num_tokens, self.num_kv_groups, self.head_dim)
-        values = values.view(b, num_tokens, self.num_kv_groups, self.head_dim)
-
-        # Transpose keys, values, and queries
-        keys = keys.transpose(1, 2)  # Shape: (b, num_heads, num_tokens, head_dim)
-        values = values.transpose(1, 2)  # Shape: (b, num_heads, num_tokens, head_dim)
-        queries = queries.transpose(1, 2)  # Shape: (b, num_query_groups, num_tokens, head_dim)
-
-        # Apply RoPE
-        keys = apply_rope(keys, cos, sin)
-        queries = apply_rope(queries, cos, sin)
-
-        # Expand keys and values to match the number of heads
-        # Shape: (b, num_heads, num_tokens, head_dim)
-        keys = keys.repeat_interleave(self.group_size, dim=1)  # Shape: (b, num_heads, num_tokens, head_dim)
-        values = values.repeat_interleave(self.group_size, dim=1)  # Shape: (b, num_heads, num_tokens, head_dim)
-        # For example, before repeat_interleave along dim=1 (query groups):
-        #   [K1, K2]
-        # After repeat_interleave (each query group is repeated group_size times):
-        #   [K1, K1, K2, K2]
-        # If we used regular repeat instead of repeat_interleave, we'd get:
-        #   [K1, K2, K1, K2]
-
-        # Compute scaled dot-product attention (aka self-attention) with a causal mask
-        # Shape: (b, num_heads, num_tokens, num_tokens)
-        attn_scores = queries @ keys.transpose(2, 3)  # Dot product for each head
-
-        # Use the mask to fill attention scores
-        attn_scores = attn_scores.masked_fill(mask[:num_tokens, :num_tokens], -torch.inf)
-
-        attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
-        assert keys.shape[-1] == self.head_dim
-
-        # Shape: (b, num_tokens, num_heads, head_dim)
-        context_vec = (attn_weights @ values).transpose(1, 2)
-
-        # Combine heads, where self.d_out = self.num_heads * self.head_dim
-        context_vec = context_vec.reshape(b, num_tokens, self.d_out)
-        context_vec = self.out_proj(context_vec)  # optional projection
-
-        return context_vec
-
-
-def compute_rope_params(head_dim, theta_base=10_000, context_length=4096, freq_config=None, dtype=torch.float32):
-    assert head_dim % 2 == 0, "Embedding dimension must be even"
-
-    # Compute the inverse frequencies
-    inv_freq = 1.0 / (theta_base ** (torch.arange(0, head_dim, 2, dtype=dtype)[: (head_dim // 2)].float() / head_dim))
-
-    # Frequency adjustments
-    if freq_config is not None:
-        low_freq_wavelen = freq_config["original_context_length"] / freq_config["low_freq_factor"]
-        high_freq_wavelen = freq_config["original_context_length"] / freq_config["high_freq_factor"]
-
-        wavelen = 2 * torch.pi / inv_freq
-
-        inv_freq_llama = torch.where(
-            wavelen > low_freq_wavelen, inv_freq / freq_config["factor"], inv_freq
-        )
-
-        smooth_factor = (freq_config["original_context_length"] / wavelen - freq_config["low_freq_factor"]) / (
-            freq_config["high_freq_factor"] - freq_config["low_freq_factor"]
-        )
-
-        smoothed_inv_freq = (
-            (1 - smooth_factor) * (inv_freq / freq_config["factor"]) + smooth_factor * inv_freq
-        )
-
-        is_medium_freq = (wavelen <= low_freq_wavelen) & (wavelen >= high_freq_wavelen)
-        inv_freq_llama = torch.where(is_medium_freq, smoothed_inv_freq, inv_freq_llama)
-        inv_freq = inv_freq_llama
-
-    # Generate position indices
-    positions = torch.arange(context_length, dtype=dtype)
-
-    # Compute the angles
-    angles = positions[:, None] * inv_freq[None, :]  # Shape: (context_length, head_dim // 2)
-
-    # Expand angles to match the head_dim
-    angles = torch.cat([angles, angles], dim=1)  # Shape: (context_length, head_dim)
-
-    # Precompute sine and cosine
-    cos = torch.cos(angles)
-    sin = torch.sin(angles)
-
-    return cos, sin
-
-
-def apply_rope(x, cos, sin):
-    # x: (batch_size, num_heads, seq_len, head_dim)
-    batch_size, num_heads, seq_len, head_dim = x.shape
-    assert head_dim % 2 == 0, "Head dimension must be even"
-
-    # Split x into first half and second half
-    x1 = x[..., : head_dim // 2]  # First half
-    x2 = x[..., head_dim // 2:]  # Second half
-
-    # Adjust sin and cos shapes
-    cos = cos[:seq_len, :].unsqueeze(0).unsqueeze(0)  # Shape: (1, 1, seq_len, head_dim)
-    sin = sin[:seq_len, :].unsqueeze(0).unsqueeze(0)
-
-    # Apply the rotary transformation
-    rotated = torch.cat((-x2, x1), dim=-1)
-    x_rotated = (x * cos) + (rotated * sin)
-
-    # It's ok to use lower-precision after applying cos and sin rotation
-    return x_rotated.to(dtype=x.dtype)
-
-
-def rescale_theta(theta_old, context_length_old, context_length_new):
-    scaling_factor = context_length_new / context_length_old
-    theta_new = theta_old * scaling_factor
-    return theta_new
-
-
-def text_to_token_ids(text, tokenizer):
-    encoded = tokenizer.encode(text)
-    encoded_tensor = torch.tensor(encoded).unsqueeze(0)  # add batch dimension
-    return encoded_tensor
-
-
-def token_ids_to_text(token_ids, tokenizer):
-    flat = token_ids.squeeze(0)  # remove batch dimension
-    return tokenizer.decode(flat.tolist())
-
-
-def generate(model, idx, max_new_tokens, context_size, temperature=0.0, top_k=None, eos_id=None):
-
-    # For-loop is the same as before: Get logits, and only focus on last time step
-    for _ in range(max_new_tokens):
-        idx_cond = idx[:, -context_size:]
-        with torch.no_grad():
-            logits = model(idx_cond)
-        logits = logits[:, -1, :]
-
-        # Filter logits with top_k sampling
-        if top_k is not None:
-            # Keep only top_k values
-            top_logits, _ = torch.topk(logits, top_k)
-            min_val = top_logits[:, -1]
-            logits = torch.where(logits < min_val, torch.tensor(float('-inf')).to(logits.device), logits)
-
-        # Apply temperature scaling
-        if temperature > 0.0:
-            logits = logits / temperature
-
-            # Apply softmax to get probabilities
-            probs = torch.softmax(logits, dim=-1)  # (batch_size, context_len)
-
-            # Sample from the distribution
-            idx_next = torch.multinomial(probs, num_samples=1)  # (batch_size, 1)
-
-        # Otherwise same as before: get idx of the vocab entry with the highest logits value
-        else:
-            idx_next = torch.argmax(logits, dim=-1, keepdim=True)  # (batch_size, 1)
-
-        if idx_next == eos_id:  # Stop generating early if end-of-sequence token is encountered and eos_id is specified
-            break
-
-        # Same as before: append sampled index to the running sequence
-        idx = torch.cat((idx, idx_next), dim=1)  # (batch_size, num_tokens+1)
-
-    return idx