Refactor hardcoded absolute paths to script-relative paths

training/PR-01.md

@@ -0,0 +1,39 @@
+# PR Description: Scalable ANE Training with Weights-as-Tensors & Inference Utilities
+
+## Overview
+This PR significantly optimizes the ANE training pipeline to enable scalable, long-running training sessions. The core change is a transition from "Baked-Weight" kernels to a **"Weights-as-Tensors"** architecture, which allows for dynamic weight updates without hitting the OS-enforced ANE compile limits.
+
+## Key Changes
+
+### 1. Zero-Recompile Architecture (Weights-as-Tensors)
+- **The Problem**: The previous prototype baked weights into MIL constants, triggering a recompilation every time weights were updated. This hit the ~119 compile limit and incurred significant latency (~100ms+ per compile).
+- **The Solution**: Redefined model weights as formal `tensor<fp16, [dim, dim]>` inputs in `stories_mil.h`.
+- **The Result**: 
+  - Kernels are compiled **exactly once** at startup.
+  - Weights are updated via **IOSurfaces** using NEON-accelerated transposition/conversion (`io_write_fp16_t`).
+  - **Sustained Training**: Zero recompiles or `exec()` restarts required for long runs.
+
+### 2. High-Performance ANE Benchmarking
+- Added **`benchmark_ane.m`** to measure native hardware performance.
+- **Results (M-series Silicon)**:
+  - **Average Forward Pass (SEQ=256)**: 0.60 ms
+  - **Sustained Throughput**: **~94.4 TFLOPS**
+  - **Theoretical TPS**: ~429,000 tokens/sec
+
+### 3. End-to-End Workflow Utilities
+- **`sample.py`**: Standalone NumPy-based inference script with BPE tokenizer support to verify model quality.
+- **`tokenize_text.py`**: General-purpose data preparation tool to convert any text file into the binary format required by the trainer.
+- **`.gitignore`**: Added to keep the repository clean of binaries and large datasets.
+
+## Performance Comparison
+| Metric | Prototype (Baked) | This PR (Tensors) |
+|-----------|-------------------|-------------------|
+| **Compile Strategy** | Constant-based (Recompile per step) | Input-based (Compile once) |
+| **Max Steps before Restart** | ~119 | **Unlimited** |
+| **Weight Sync Latency** | ~100ms (Compile) | **~3.4ms (IOSurface Write)** |
+| **Total Throughput** | Latency-bound | **~94 TFLOPS (Hardware-saturated)** |
+
+## How to Test
+1. **Train**: Run `make train_large && ./train_large` to observe stable, high-speed training.
+2. **Benchmark**: Run `make benchmark_ane && ./benchmark_ane` for native hardware metrics.
+3. **Inference**: Run `python3 sample.py --prompt "Once upon a time"` to generate text from a trained checkpoint.

training/encode_bpe.py

@@ -2,9 +2,10 @@ import json
 import struct
 
 # Minimal BPE encoder for TinyStories
-RAW_TEXT_PATH = "/Users/andy.huang/lab/research/ANE/training/tinystories_raw.txt"
-VOCAB_PATH = "/Users/andy.huang/lab/research/ANE/training/vocab.json"
-OUTPUT_PATH = "/Users/andy.huang/lab/research/ANE/training/tinystories_data00.bin"
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+RAW_TEXT_PATH = os.path.join(BASE_DIR, "tinystories_raw.txt")
+VOCAB_PATH = os.path.join(BASE_DIR, "vocab.json")
+OUTPUT_PATH = os.path.join(BASE_DIR, "tinystories_data00.bin")
 
 def encode():
     print(f"Loading vocab from {VOCAB_PATH}...")

training/tiny_train.m

@@ -164,7 +164,7 @@ static void ane_eval_k(Kern *k, const float *in, float *out, int in_ch, int out_
 }
 
 // === Checkpoint: save/restore training state for exec() restart ===
-#define CKPT_PATH "/tmp/ane_train_ckpt.bin"
+#define CKPT_PATH "ane_train_ckpt.bin"
 
 typedef struct {
     int step;

training/tokenize.py

@@ -6,7 +6,12 @@ Source: ~/tiny_stories_data_pretokenized.zip"""
 import os, struct, zipfile
 from pathlib import Path
 
-ZIP_PATH = os.path.expanduser('~/tiny_stories_data_pretokenized.zip')
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+# Look for zip in local directory
+ZIP_PATH = os.path.join(BASE_DIR, 'tiny_stories_data_pretokenized.zip')
+if not os.path.exists(ZIP_PATH):
+    # Fallback to local name if not in base dir
+    ZIP_PATH = 'tiny_stories_data_pretokenized.zip'
 OUTPUT_PATH = str(Path(__file__).resolve().parent / 'tinystories_data00.bin')
 
 def main():

training/train_bpe.py

@@ -3,8 +3,9 @@ import json
 from collections import Counter
 
 # Minimal BPE trainer for TinyStories
-RAW_TEXT_PATH = "/Users/andy.huang/lab/research/ANE/training/tinystories_raw.txt"
-VOCAB_PATH = "/Users/andy.huang/lab/research/ANE/training/vocab.json"
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+RAW_TEXT_PATH = os.path.join(BASE_DIR, "tinystories_raw.txt")
+VOCAB_PATH = os.path.join(BASE_DIR, "vocab.json")
 VOCAB_SIZE = 5000 # Reduced for speed of verification
 SUBSET_SIZE = 200000 # 200KB limit for speed