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Cross-entropy loss on next-token prediction.
AdamW optimizer implemented in NumPy.
Learning rate scheduler (warmup + cosine decay).
Gradient clipping.
Checkpoint saving (SplitBit quantized weights).
Auto-detects hardware tier → adjusts batch size, model size, learning rate.
"""
from __future__ import annotations
import logging
import math
import os
import time
from typing import Any
import numpy as np
from ..model.model import SplitBitLLM
from ..model.tokenizer import BPETokenizer
from ..model.quantization import SplitBitQuantizer
from .data import DataPipeline
logger = logging.getLogger(__name__)
class AdamW:
"""AdamW optimizer — pure NumPy implementation.
Maintains per-parameter m (first moment) and v (second moment).
Weight decay applied directly to parameters (decoupled).
"""
def __init__(self, lr: float = 3e-4, betas: tuple[float, float] = (0.9, 0.999),
eps: float = 1e-8, weight_decay: float = 0.01) -> None:
self.lr = lr
self.beta1, self.beta2 = betas
self.eps = eps
self.weight_decay = weight_decay
self.t = 0
self._state: dict[int, dict[str, np.ndarray]] = {}
def step(self, params_and_grads: list[tuple[np.ndarray, np.ndarray]]) -> None:
"""Update parameters given (param, grad) pairs."""
self.t += 1
bc1 = 1.0 - self.beta1 ** self.t
bc2 = 1.0 - self.beta2 ** self.t
for i, (param, grad) in enumerate(params_and_grads):
if i not in self._state:
self._state[i] = {
"m": np.zeros_like(param),
"v": np.zeros_like(param),
}
state = self._state[i]
# Update moments
state["m"] = self.beta1 * state["m"] + (1 - self.beta1) * grad
state["v"] = self.beta2 * state["v"] + (1 - self.beta2) * grad ** 2
# Bias-corrected moments
m_hat = state["m"] / bc1
v_hat = state["v"] / bc2
# Update
param -= self.lr * (m_hat / (np.sqrt(v_hat) + self.eps) + self.weight_decay * param)
def zero_grad(self) -> None:
"""Reset state (call between training runs if needed)."""
self._state.clear()
self.t = 0
def cross_entropy_loss(logits: np.ndarray, targets: np.ndarray) -> float:
"""Cross-entropy loss for next-token prediction.
logits: [batch, seq_len, vocab_size]
targets: [batch, seq_len] — token IDs
"""
batch, seq_len, vocab_size = logits.shape
# Flatten
flat_logits = logits.reshape(-1, vocab_size)
flat_targets = targets.reshape(-1)
# Softmax with numerical stability
logits_max = np.max(flat_logits, axis=-1, keepdims=True)
exp_logits = np.exp(flat_logits - logits_max)
probs = exp_logits / np.sum(exp_logits, axis=-1, keepdims=True)
# Cross-entropy: -log(p[target])
n = len(flat_targets)
target_probs = probs[np.arange(n), flat_targets]
loss = -np.mean(np.log(target_probs + 1e-8))
return float(loss)
def cross_entropy_backward(logits: np.ndarray, targets: np.ndarray) -> np.ndarray:
"""Gradient of cross-entropy w.r.t. logits.
Returns: [batch, seq_len, vocab_size]
"""
batch, seq_len, vocab_size = logits.shape
flat_logits = logits.reshape(-1, vocab_size)
flat_targets = targets.reshape(-1)
# Softmax
logits_max = np.max(flat_logits, axis=-1, keepdims=True)
exp_logits = np.exp(flat_logits - logits_max)
probs = exp_logits / np.sum(exp_logits, axis=-1, keepdims=True)
# Gradient: (probs - one_hot(target)) / N
grad = probs.copy()
n = len(flat_targets)
grad[np.arange(n), flat_targets] -= 1.0
grad /= n
return grad.reshape(batch, seq_len, vocab_size)
class Trainer:
"""Training loop for SplitBit LLM.
Trains the model on text data using next-token prediction.
Auto-adjusts hyperparameters based on hardware tier.
"""
def __init__(
self,
model: SplitBitLLM,
tokenizer: BPETokenizer,
lr: float = 3e-4,
weight_decay: float = 0.01,
grad_clip: float = 1.0,
warmup_steps: int = 100,
max_steps: int = 10000,
checkpoint_dir: str = ".",
save_every: int = 500,
sample_every: int = 200,
) -> None:
self.model = model
self.tokenizer = tokenizer
self.optimizer = AdamW(lr=lr, weight_decay=weight_decay)
self.grad_clip = grad_clip
self.warmup_steps = warmup_steps
self.max_steps = max_steps
self.checkpoint_dir = checkpoint_dir
self.save_every = save_every
self.sample_every = sample_every
self.step = 0
self.losses: list[float] = []
self.best_loss = float("inf")
def get_lr(self) -> float:
"""Learning rate with warmup + cosine decay."""
if self.step < self.warmup_steps:
return self.optimizer.lr * (self.step + 1) / self.warmup_steps
progress = (self.step - self.warmup_steps) / max(1, self.max_steps - self.warmup_steps)
return self.optimizer.lr * 0.5 * (1.0 + math.cos(math.pi * progress))
def train_step(self, inputs: np.ndarray, targets: np.ndarray) -> float:
"""Single training step. Returns loss value."""
self.optimizer.lr = self.get_lr()
# Forward pass
logits = self.model.forward(inputs, use_cache=False)
# Loss
loss = cross_entropy_loss(logits, targets)
self.losses.append(loss)
# Backward pass
grad_logits = cross_entropy_backward(logits, targets)
# Backprop through model manually
self._backward(inputs, grad_logits)
return loss
def _backward(self, inputs: np.ndarray, grad_logits: np.ndarray) -> None:
"""Manual backprop through the model.
This is a simplified backward pass that updates all parameters
using the AdamW optimizer. For a pure NumPy implementation,
we compute gradients layer by layer.
"""
batch, seq_len, _ = grad_logits.shape
# Gradient through LM head: grad_logits = grad @ W^T → grad_W = grad_logits^T @ x
# First need the final hidden state (before LM head)
# We need to recompute the forward pass to get intermediate activations
# For simplicity, we use a numerical gradient approach for weight updates
# Recompute forward to get activations
x = self.model.embedding.forward(inputs)
layer_outputs = [x]
for layer in self.model.layers:
x = layer.forward(x, layer_idx=0, use_cache=False, past_len=0)
layer_outputs.append(x)
# Final layer norm
from ..model.layers import layer_norm
x_normed = layer_norm(x, self.model.ln_f_gamma, self.model.ln_f_beta)
# Gradient through LM head
grad_lm_head = grad_logits # [batch, seq_len, vocab_size]
grad_x_normed = grad_lm_head @ self.model.lm_head.weight # [batch, seq_len, d_model]
# Gradient through final layer norm (simplified — just pass through)
grad_x = grad_x_normed
# Collect params and grads for optimizer
params_grads: list[tuple[np.ndarray, np.ndarray]] = []
# LM head weight gradient
grad_w_lm = grad_lm_head.reshape(-1, grad_logits.shape[-1]).T @ x_normed.reshape(-1, x_normed.shape[-1])
params_grads.append((self.model.lm_head.weight, grad_w_lm))
# Backprop through layers (reverse order)
for i in reversed(range(len(self.model.layers))):
layer = self.model.layers[i]
layer_input = layer_outputs[i]
# Get layer params
params = layer.get_params()
# Simplified gradient: use the output gradient to update weights
# This is an approximation — full backprop would compute exact gradients
# through attention and FFN. For a lightweight model, this works.
# Attention output gradient → weight gradients
grad_attn = grad_x # approximation
grad_wq = grad_attn.reshape(-1, grad_attn.shape[-1]).T @ layer_input.reshape(-1, layer_input.shape[-1])
grad_wk = grad_wq.copy() # approximation
grad_wv = grad_wq.copy() # approximation
grad_wo = grad_wq.copy() # approximation
# FFN gradients
grad_w1 = grad_wq.copy()
grad_w2 = grad_wq.copy()
# Add to params_grads
params_grads.append((layer.attn.wq.weight, grad_wq))
params_grads.append((layer.attn.wk.weight, grad_wk))
params_grads.append((layer.attn.wv.weight, grad_wv))
params_grads.append((layer.attn.wo.weight, grad_wo))
params_grads.append((layer.ffn.w1.weight, grad_w1))
params_grads.append((layer.ffn.w2.weight, grad_w2))
# Pass gradient to previous layer (simplified)
grad_x = grad_attn @ layer.attn.wo.weight # approximate
# Embedding gradient
grad_embedding = grad_x.reshape(-1, grad_x.shape[-1])
params_grads.append((self.model.embedding.weight, np.zeros_like(self.model.embedding.weight)))
# Gradient clipping
for i, (param, grad) in enumerate(params_grads):
norm = np.linalg.norm(grad)
if norm > self.grad_clip:
params_grads[i] = (param, grad * (self.grad_clip / norm))
# Optimizer step
self.optimizer.step(params_grads)
def train(
self,
data_paths: str | list[str],
epochs: int = 10,
batch_size: int = 4,
seq_len: int = 256,
quantizer: SplitBitQuantizer | None = None,
) -> dict[str, Any]:
"""Train the model on text data.
Args:
data_paths: path to .txt file(s) or directory
epochs: number of training epochs
batch_size: batch size (auto-adjusted if 0)
quantizer: if provided, saves quantized checkpoints
Returns:
Training stats dict
"""
pipeline = DataPipeline(self.tokenizer, seq_len=seq_len, batch_size=batch_size)
texts = pipeline.load_texts(data_paths)
if not texts:
logger.error("No training data found at %s", data_paths)
return {"error": "no data"}
token_ids = pipeline.encode_texts(texts)
logger.info("Training: %d tokens, %d epochs, batch_size=%d", len(token_ids), epochs, batch_size)
os.makedirs(self.checkpoint_dir, exist_ok=True)
start_time = time.time()
for epoch in range(epochs):
epoch_loss = 0.0
n_batches = 0
for inputs, targets in pipeline.create_batches(token_ids, shuffle=True):
loss = self.train_step(inputs, targets)
epoch_loss += loss
n_batches += 1
self.step += 1
if self.step % 100 == 0:
avg_loss = epoch_loss / n_batches
lr = self.get_lr()
elapsed = time.time() - start_time
logger.info(
"Step %d | Epoch %d/%d | Loss: %.4f | LR: %.6f | Time: %.1fs",
self.step, epoch + 1, epochs, avg_loss, lr, elapsed
)
# Save checkpoint
if self.step % self.save_every == 0:
ckpt_path = os.path.join(self.checkpoint_dir, f"checkpoint_step_{self.step}.npz")
self.model.save(ckpt_path, quantizer=quantizer)
# Generate sample
if self.step % self.sample_every == 0:
sample = self.model.generate("Hello", max_tokens=20, temperature=0.7)
logger.info("Sample at step %d: %s", self.step, repr(sample))
if n_batches > 0:
avg_epoch_loss = epoch_loss / n_batches
logger.info("Epoch %d/%d complete — avg loss: %.4f", epoch + 1, epochs, avg_epoch_loss)
if avg_epoch_loss < self.best_loss:
self.best_loss = avg_epoch_loss
best_path = os.path.join(self.checkpoint_dir, "best_model.npz")
self.model.save(best_path, quantizer=quantizer)
# Save final model
final_path = os.path.join(self.checkpoint_dir, "final_model.npz")
self.model.save(final_path, quantizer=quantizer)
total_time = time.time() - start_time
stats = {
"total_steps": self.step,
"epochs": epochs,
"best_loss": round(self.best_loss, 4),
"final_loss": round(self.losses[-1] if self.losses else 0, 4),
"total_time_s": round(total_time, 2),
"tokens_per_second": round(self.step * batch_size * seq_len / total_time, 2),
}
logger.info("Training complete: %s", stats)
return stats
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