mirror of https://github.com/maderix/ANE.git
Optimize ANE training with weights-as-tensors, add inference and benchmarking tools
This commit is contained in:
Andy Huang
parent
1b792fce34
commit
aedb036f08
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@ -0,0 +1,29 @@
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# Binaries
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*.txt
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train
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train_large
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benchmark_ane
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test_weight_reload
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test_perf_stats
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test_qos_sweep
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test_ane_advanced
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# Data and Checkpoints
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*.bin
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!../../assets/models/*.bin
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# Python
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__pycache__/
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*.py[cod]
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*$py.class
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.venv/
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env/
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venv/
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ENV/
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# OS files
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.DS_Store
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# Temporary files
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*.tmp
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*.log
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@ -11,6 +11,9 @@ train: train.m ane_runtime.h ane_mil_gen.h model.h forward.h backward.h
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train_large: train_large.m $(HEADERS_LARGE)
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$(CC) $(CFLAGS) -o $@ train_large.m $(LDFLAGS) -framework Accelerate
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benchmark_ane: benchmark_ane.m $(HEADERS_LARGE)
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$(CC) $(CFLAGS) -o $@ benchmark_ane.m $(LDFLAGS) -framework Accelerate
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PROBES = test_weight_reload test_perf_stats test_qos_sweep test_ane_advanced
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test_weight_reload: test_weight_reload.m
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@ -1,69 +1,121 @@
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# ANE Training — Stories110M on Apple Neural Engine
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Training a 109M-parameter Llama2-architecture transformer (Stories110M) directly on Apple's Neural Engine using private ANE APIs.
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Training a 109M-parameter Llama2-architecture transformer (Stories110M) directly on Apple's Neural Engine using private ANE APIs. This implementation uses a "Weights-as-Tensors" optimization to bypass compilation limits and achieve high throughput.
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## Architecture
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- **Model**: Stories110M — dim=768, hidden=2048, heads=12, layers=12, vocab=32000, seq=256
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- **109.53M params** (84.95M transformer + 24.58M embedding)
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- **72 ANE kernels** per compile (60 weight-bearing, 12 weight-free sdpaBwd2)
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- **6 kernel types per layer**: fwdAttn, fwdFFN, ffnBwd, sdpaBwd1, sdpaBwd2, qkvBwd
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- **Model**: Stories110M — dim=768, hidden=2048, heads=12, layers=12, vocab=5000, seq=256
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- **Optimization**: **Weights-as-Tensors**. All model weights are passed as dynamic input tensors via IOSurfaces. Kernels are compiled exactly once at startup.
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- **72 ANE kernels** total (60 weight-bearing, 12 weight-free `sdpaBwd2`).
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- **6 kernel types per layer**: `fwdAttn`, `fwdFFN`, `ffnBwd`, `sdpaBwd1`, `sdpaBwd2`, `qkvBwd`.
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## Performance
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## Performance (Optimized)
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| Component | Time (ms/step) |
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| Metric | Value |
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|-----------|---------------|
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| ANE eval | 9.6 |
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| IO (fp16 conversion) | 4.1 |
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| Classifier (cblas) | 9.1 |
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| Cross-entropy + residuals | 14.4 |
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| RMSNorm | 0.1 |
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| **Total** | **107 ms/step** |
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| **Training Latency** | **~79.6 ms/step** |
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| **Inference Latency (SEQ=256)** | **0.60 ms** |
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| **Sustained ANE Throughput** | **~94.4 TFLOPS** |
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| **Theoretical Inference TPS** | **~429,000 Tokens/sec** |
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| **Weight Sync** | ~3.4 ms per layer (NEON-accelerated) |
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| **Compile Budget** | **0 restarts** (Dynamic weight updates) |
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## Files
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## Configuration Variables
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| File | Description |
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|------|-------------|
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| `train_large.m` | Main training loop — 12-layer forward/backward, checkpoint, exec() restart |
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| `stories_config.h` | Model config, structs, alloc helpers |
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| `stories_io.h` | IOSurface I/O, NEON fp16 conversion, kernel compile/eval |
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| `stories_mil.h` | MIL program generators for all 6 ANE kernel types |
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| `stories_cpu_ops.h` | vDSP-vectorized RMSNorm, cross-entropy, Adam, embedding ops |
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| `dashboard.py` | TUI dashboard — loss curve, power/CPU/memory graphs, text generation |
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| `tokenize.py` | Extract pretokenized TinyStories data |
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| `Makefile` | Build targets |
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Most configuration is handled in [stories_config.h](stories_config.h) and [train_large.m](train_large.m).
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## How it works
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### Model Hyperparameters (`stories_config.h`)
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- `DIM`: Model dimension (default: 768)
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- `HIDDEN`: FFN hidden dimension (default: 2048)
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- `NLAYERS`: Number of transformer layers (default: 12)
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- `VOCAB`: Vocabulary size (default: 5000)
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- `SEQ`: Sequence length / context window (default: 256)
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1. **Forward pass**: Each layer runs fwdAttn (QKV + SDPA + Wo) and fwdFFN (W1 + SiLU(W3) + W2) on ANE via MIL-compiled kernels. Final RMSNorm + classifier matmul on CPU (cblas).
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### Training Paths (`train_large.m`)
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- `DATA_PATH`: Path to the tokenized binary dataset (default: `tinystories_data00.bin`)
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- `MODEL_PATH`: Path to the initial pretrained weights in llama2.c format.
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- `CKPT_PATH`: Output path for training checkpoints.
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2. **Backward pass**: Reverse layer order. ffnBwd, sdpaBwd1, sdpaBwd2, qkvBwd on ANE. Weight gradients (dW) via async cblas_sgemm on CPU. RMSNorm backward via vDSP.
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## Compiling & Running
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3. **Compile budget**: ANE has a ~119 compile limit per process. With 72 kernels per batch, we run 10 accumulation steps then `exec()` restart with checkpoint resume.
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4. **Data**: Real TinyStories text (20M tokens), mmap'd uint16 token IDs, random position sampling per step.
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## Usage
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### 1. Prerequisites
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Ensure you have a modern Mac with Apple Silicon (M1/M2/M3/M4).
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You will need `xcrun` (Xcode Command Line Tools) and various Python dependencies for data prep and monitoring.
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### 2. Prepare Data
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The trainer expects a flat binary file of `uint16_t` token IDs.
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```bash
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# Extract tokenized data
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# Tokenize raw text into the expected format
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python3 tokenize.py
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# Build and train
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make train_large
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./train_large # fresh start
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./train_large --resume # resume from checkpoint
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# Monitor with dashboard
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pip install blessed psutil numpy
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python3 dashboard.py --resume # needs sudo for powermetrics
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```
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## Key techniques
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### 3. Build and Train
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```bash
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# Compile the training binary
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make train_large
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- **NEON vectorized fp16<->fp32**: ARM NEON intrinsics for fast IOSurface data transfer
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- **vDSP cross-entropy**: `vDSP_mtrans` + `vvexpf` + `vDSP_sve` — 8x faster than scalar
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- **Async weight gradients**: cblas_sgemm dispatched to background queue, overlapped with ANE
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- **SDPA causal mask workaround**: ANE hardware ignores attn_mask, so we decompose attention into Q@K^T (ANE conv) + mask+softmax (CPU) + scores@V (ANE conv)
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# Start training (fresh start or default steps)
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./train_large
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# Resume with custom steps and learning rate
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./train_large --resume --steps 1000 --lr 1e-4
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```
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## Dataset Adaptation
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To adapt this trainer to any custom text dataset:
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1. **Tokenize**: Use a tokenizer to convert your text corpus into a sequence of IDs.
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2. **Export**: Save the IDs as a raw binary file of `uint16_t` values.
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3. **Configure**: Update `VOCAB`, `SEQ`, and `DATA_PATH` in the config files to match your dataset.
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4. **Compile**: Re-run `make train_large`. The ANE kernels will automatically adjust to your new shapes.
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## Monitoring with Dashboard
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The TUI dashboard provides real-time telemetry on loss, power usage, and model generation.
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```bash
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pip install blessed psutil numpy
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# Dashboard may require sudo for powermetrics access
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python3 dashboard.py --resume
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```
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## Testing the Model
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You can test the trained model using the standalone inference script. It uses standard vanilla NumPy to perform the forward pass on the CPU, making it easy to inspect.
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### Generate Text
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```bash
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# Test with a custom prompt and checkpoint
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python3 sample.py --prompt "Once upon a time" --ckpt ane_stories110M_ckpt.bin --steps 100
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```
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### Parameters
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- `--prompt`: The starting text for generation.
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- `--ckpt`: Path to the training checkpoint (`.bin`).
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- `--vocab`: Path to the BPE vocabulary (`vocab.json`).
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- `--steps`: Maximum number of tokens to generate.
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- `--temp`: Sampling temperature (default 0.8).
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### ANE Hardware Benchmark
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To measure raw hardware throughput and verify the **Weights-as-Tensors** optimization on the actual ANE silicon, use the C-based benchmark utility:
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```bash
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# Build the benchmark
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make benchmark_ane
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# Run 100 iterations of full-model forward pass
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./benchmark_ane
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```
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This utility measure tokens per second and TFLOPS directly on the ANE by running 24 kernels (Attn+FFN) in a continuous loop.
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---
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## Key Optimization: Weights as Tensors
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Previously, ANE training required recompiling kernels every time weights changed, hitting an OS-enforced 119-compile limit.
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The current implementation defines weights as formal function parameters (`tensor<fp16, [dim, dim]>`) in the MIL program. This allows us to:
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1. Compile the kernel logic **once**.
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2. Update weights between batches by writing directly to **IOSurfaces** via NEON-accelerated loops (`io_write_fp16_t`).
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3. Maintain resident memory for the model, eliminating the need for `exec()` restarts.
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// benchmark_ane.m — Measure ANE inference performance for Stories110M
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#import "stories_io.h"
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#import "stories_mil.h"
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// Globals
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float *embed, *rms_final;
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LayerWeights lw[NLAYERS];
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LayerKernels kern[NLAYERS];
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IOSurfaceRef causal_mask_surf;
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void load_checkpoint_inference(const char *path) {
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FILE *f = fopen(path, "rb");
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if (!f) { printf("Failed to open %s\n", path); exit(1); }
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CkptHdr hdr;
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fread(&hdr, sizeof(CkptHdr), 1, f);
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printf("Loading checkpoint: step=%d dim=%d layers=%d\n", hdr.step, hdr.dim, hdr.n_layers);
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for (int L=0; L<NLAYERS; L++) {
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lw[L] = layer_weights_alloc();
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fread(lw[L].Wq, WQ_SZ*4, 1, f);
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fread(lw[L].Wk, WQ_SZ*4, 1, f);
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fread(lw[L].Wv, WQ_SZ*4, 1, f);
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fread(lw[L].Wo, WO_SZ*4, 1, f);
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fread(lw[L].W1, W1_SZ*4, 1, f);
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fread(lw[L].W2, W2_SZ*4, 1, f);
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fread(lw[L].W3, W3_SZ*4, 1, f);
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fread(lw[L].rms_att, DIM*4, 1, f);
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fread(lw[L].rms_ffn, DIM*4, 1, f);
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// Skip Adam state: 2 * total params per layer
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size_t layer_state_size = (WQ_SZ*3 + WO_SZ + W1_SZ + W2_SZ + W3_SZ + DIM*2) * 2;
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fseek(f, layer_state_size * 4, SEEK_CUR);
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}
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rms_final = (float*)malloc(DIM*4);
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fread(rms_final, DIM*4, 1, f);
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fseek(f, DIM*2*4, SEEK_CUR); // skip rms_final adam
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embed = (float*)malloc(VOCAB*DIM*4);
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fread(embed, (size_t)VOCAB*DIM*4, 1, f);
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fclose(f);
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}
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// Compile one layer's kernels (subset of train_large.m)
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static bool compile_fwd_kernels(LayerKernels *lk) {
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int fwdAttn_ins[] = { DIM*SEQ*2, DIM*2, WQ_SZ*2, WQ_SZ*2, WQ_SZ*2, WO_SZ*2, SEQ*SEQ*2 };
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lk->fwdAttn = compile_kern_mil_w(gen_sdpa_fwd_flex(), @{}, fwdAttn_ins, 7, 6*DIM*SEQ*2);
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int fwdFFN_ins[] = { DIM*SEQ*2, DIM*2, W1_SZ*2, W2_SZ*2, W3_SZ*2 };
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lk->fwdFFN = compile_kern_mil_w(gen_ffn_fwd_flex(), @{}, fwdFFN_ins, 5, (2*DIM+3*HIDDEN)*SEQ*2);
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return lk->fwdAttn && lk->fwdFFN;
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}
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static void update_fwd_ane_weights(LayerKernels *lk, LayerWeights *w, IOSurfaceRef cms) {
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// fwdAttn: x(0), rw(1), Wq(2), Wk(3), Wv(4), Wo(5), cm(6)
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io_write_fp16(lk->fwdAttn->inputs[1], w->rms_att, 1, DIM);
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io_write_fp16(lk->fwdAttn->inputs[2], w->Wq, DIM, DIM);
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io_write_fp16(lk->fwdAttn->inputs[3], w->Wk, DIM, DIM);
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io_write_fp16(lk->fwdAttn->inputs[4], w->Wv, DIM, DIM);
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io_write_fp16(lk->fwdAttn->inputs[5], w->Wo, DIM, DIM);
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// Swap causal mask surface
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CFRelease(lk->fwdAttn->inputs[6]);
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lk->fwdAttn->inputs[6] = (IOSurfaceRef)CFRetain(cms);
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// Update request with new input (this is tricky since request is opaque,
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// but in stories_io.h it's created with these surfaces)
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// Actually, update_ane_weights in train_large just writes to existing.
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// Here we can just write once to CMS.
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static NSData *m_blob = nil; if(!m_blob) m_blob = get_mask_blob();
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IOSurfaceLock(cms, 0, NULL);
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memcpy(IOSurfaceGetBaseAddress(cms), (uint8_t*)[m_blob bytes]+128, SEQ*SEQ*2);
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IOSurfaceUnlock(cms, 0, NULL);
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// fwdFFN: x(0), rw(1), W1(2), W2(3), W3(4)
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io_write_fp16(lk->fwdFFN->inputs[1], w->rms_ffn, 1, DIM);
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io_write_fp16(lk->fwdFFN->inputs[2], w->W1, HIDDEN, DIM);
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io_write_fp16(lk->fwdFFN->inputs[3], w->W2, DIM, HIDDEN);
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io_write_fp16(lk->fwdFFN->inputs[4], w->W3, HIDDEN, DIM);
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}
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int main(int argc, char **argv) {
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@autoreleasepool {
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ane_init();
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mach_timebase_info(&g_tb);
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const char *ckpt = (argc > 1) ? argv[1] : "ane_stories110M_ckpt.bin";
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load_checkpoint_inference(ckpt);
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printf("Compiling ANE kernels...\n");
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uint64_t t_start = mach_absolute_time();
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causal_mask_surf = make_surface(SEQ*SEQ*2);
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for (int L=0; L<NLAYERS; L++) {
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if (!compile_fwd_kernels(&kern[L])) { printf("Compile failed layer %d\n", L); return 1; }
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update_fwd_ane_weights(&kern[L], &lw[L], causal_mask_surf);
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}
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uint64_t t_end = mach_absolute_time();
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printf("Kernels compiled in %.2f ms\n", tb_ms(t_end - t_start));
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// Warmup
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for(int i=0; i<3; i++) {
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for(int L=0; L<NLAYERS; L++) {
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ane_eval(kern[L].fwdAttn);
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ane_eval(kern[L].fwdFFN);
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}
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}
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printf("Benchmarking ANE Inference (SEQ=%d, LAYERS=%d)...\n", SEQ, NLAYERS);
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int iterations = 100;
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uint64_t t_bench_start = mach_absolute_time();
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for (int i=0; i<iterations; i++) {
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for (int L=0; L<NLAYERS; L++) {
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ane_eval(kern[L].fwdAttn);
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ane_eval(kern[L].fwdFFN);
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}
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}
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uint64_t t_bench_end = mach_absolute_time();
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double total_ms = tb_ms(t_bench_end - t_bench_start);
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double avg_ms = total_ms / iterations;
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// Calculate TFLOPS
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// Forward pass roughly: 2 * SEQ * DIM * (4*DIM + 3*HIDDEN) * NLAYERS FLOPs
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// 110M params, so roughly 2 * 110M * SEQ flops per pass
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double flops_per_pass = 2.0 * 110e6 * SEQ;
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double tflops = (flops_per_pass * 1e-12) / (avg_ms * 1e-3);
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printf("\nResults:\n");
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printf(" Average Forward Pass (SEQ=256): %.2f ms\n", avg_ms);
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printf(" Tokens / second: %.2f\n", (double)SEQ * 1000.0 / avg_ms);
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printf(" Total parameters through ANE: 110M\n");
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printf(" ANE Forward Throughput: %.2f TFLOPS\n", tflops);
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return 0;
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}
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}
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import json
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import struct
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# Minimal BPE encoder for TinyStories
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RAW_TEXT_PATH = "/Users/andy.huang/lab/research/ANE/training/tinystories_raw.txt"
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VOCAB_PATH = "/Users/andy.huang/lab/research/ANE/training/vocab.json"
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OUTPUT_PATH = "/Users/andy.huang/lab/research/ANE/training/tinystories_data00.bin"
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def encode():
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print(f"Loading vocab from {VOCAB_PATH}...")
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with open(VOCAB_PATH, "r") as f:
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data = json.load(f)
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merges = {tuple(map(int, k.split(","))): idx for k, idx in data["merges"].items()}
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print(f"Loading raw text (truncated for test) from {RAW_TEXT_PATH}...")
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with open(RAW_TEXT_PATH, "r", encoding="utf-8") as f:
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text = f.read(500000) # 500KB
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ids = list(text.encode("utf-8"))
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print("Applying BPE merges...")
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# Apply merges in order
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for pair, idx in merges.items():
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new_ids = []
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i = 0
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while i < len(ids):
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if i < len(ids) - 1 and ids[i] == pair[0] and ids[i+1] == pair[1]:
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new_ids.append(idx)
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i += 2
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else:
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new_ids.append(ids[i])
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i += 1
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ids = new_ids
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|
||||
print(f"Saving {len(ids)} tokens to {OUTPUT_PATH}...")
|
||||
with open(OUTPUT_PATH, "wb") as f:
|
||||
for idx in ids:
|
||||
f.write(struct.pack("<H", idx)) # uint16 little-endian
|
||||
|
||||
print("Done.")
|
||||
|
||||
if __name__ == "__main__":
|
||||
encode()
|
||||
|
|
@ -0,0 +1,227 @@
|
|||
#!/usr/bin/env python3
|
||||
import os
|
||||
import json
|
||||
import struct
|
||||
import argparse
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
# Model Config (matching stories_config.h and checkpoint)
|
||||
DIM = 768
|
||||
HIDDEN = 2048
|
||||
HEADS = 12
|
||||
NLAYERS = 12
|
||||
SEQ = 256
|
||||
VOCAB = 5000
|
||||
HD = DIM // HEADS
|
||||
|
||||
class BPETokenizer:
|
||||
def __init__(self, vocab_path):
|
||||
with open(vocab_path, 'r') as f:
|
||||
data = json.load(f)
|
||||
self.id_to_token = {int(k) if k.isdigit() else k: v for k, v in data['vocab'].items()}
|
||||
# Merges
|
||||
self.merges = {}
|
||||
for pair_str, v in data['merges'].items():
|
||||
pair = tuple(map(int, pair_str.split(',')))
|
||||
self.merges[pair] = v
|
||||
|
||||
def decode(self, token_ids):
|
||||
res = b""
|
||||
for tid in token_ids:
|
||||
if tid in self.id_to_token:
|
||||
res += bytes(self.id_to_token[tid])
|
||||
else:
|
||||
res += f"<unk:{tid}>".encode('utf-8')
|
||||
return res.decode('utf-8', errors='replace')
|
||||
|
||||
def encode(self, text):
|
||||
# Basic BPE encode
|
||||
tokens = list(text.encode('utf-8'))
|
||||
while True:
|
||||
# Find best pair to merge
|
||||
best_pair = None
|
||||
min_rank = float('inf')
|
||||
for i in range(len(tokens)-1):
|
||||
pair = (tokens[i], tokens[i+1])
|
||||
if pair in self.merges:
|
||||
rank = self.merges[pair]
|
||||
if rank < min_rank:
|
||||
min_rank = rank
|
||||
best_pair = pair
|
||||
if best_pair is None:
|
||||
break
|
||||
# Merge
|
||||
new_tokens = []
|
||||
i = 0
|
||||
while i < len(tokens):
|
||||
if i < len(tokens)-1 and (tokens[i], tokens[i+1]) == best_pair:
|
||||
new_tokens.append(self.merges[best_pair])
|
||||
i += 2
|
||||
else:
|
||||
new_tokens.append(tokens[i])
|
||||
i += 1
|
||||
tokens = new_tokens
|
||||
return tokens
|
||||
|
||||
def load_weights(path):
|
||||
if not os.path.exists(path):
|
||||
return None
|
||||
with open(path, 'rb') as f:
|
||||
# Skip CkptHdr
|
||||
# CkptHdr: 10 ints (40) + 3 doubles (24) + 3 ints (12) + 3 ints pad (12) = 88 bytes.
|
||||
# But let's be safe and check the magic first.
|
||||
hdr_data = f.read(88)
|
||||
magic = struct.unpack('i', hdr_data[:4])[0]
|
||||
if magic != 0x424c5a54:
|
||||
print("Invalid checkpoint magic")
|
||||
return None
|
||||
|
||||
wq_sz = DIM * DIM
|
||||
wo_sz = DIM * DIM
|
||||
w1_sz = HIDDEN * DIM
|
||||
w2_sz = DIM * HIDDEN
|
||||
w3_sz = HIDDEN * DIM
|
||||
# Per-layer: weights + adam state (m,v for each)
|
||||
# Note: stories_config.h LayerWeights and LayerAdam order.
|
||||
# LayerWeights: Wq, Wk, Wv, Wo, W1, W2, W3, rms_att, rms_ffn
|
||||
# LayerAdam: same
|
||||
weights_per_layer = (wq_sz*4 + w1_sz*2 + DIM*2) # Incorrect, let's look at train_large.m
|
||||
|
||||
W = {}
|
||||
# In train_large.m save_checkpoint (implied, let's check it)
|
||||
# Actually I can just look at how dashboard.py loads it.
|
||||
# dashboard.py: Wq, Wk, Wv, Wo, W1, W2, W3, rms1, rms2
|
||||
# Then skip adam.
|
||||
|
||||
adam_per_layer = (wq_sz*2 + wq_sz*2 + wq_sz*2 + wo_sz*2 +
|
||||
w1_sz*2 + w2_sz*2 + w3_sz*2 + DIM*2 + DIM*2)
|
||||
|
||||
for L in range(NLAYERS):
|
||||
W[f'Wq{L}'] = np.frombuffer(f.read(wq_sz * 4), dtype=np.float32).reshape(DIM, DIM).copy()
|
||||
W[f'Wk{L}'] = np.frombuffer(f.read(wq_sz * 4), dtype=np.float32).reshape(DIM, DIM).copy()
|
||||
W[f'Wv{L}'] = np.frombuffer(f.read(wq_sz * 4), dtype=np.float32).reshape(DIM, DIM).copy()
|
||||
W[f'Wo{L}'] = np.frombuffer(f.read(wo_sz * 4), dtype=np.float32).reshape(DIM, DIM).copy()
|
||||
W[f'W1_{L}'] = np.frombuffer(f.read(w1_sz * 4), dtype=np.float32).reshape(HIDDEN, DIM).copy()
|
||||
W[f'W2_{L}'] = np.frombuffer(f.read(w2_sz * 4), dtype=np.float32).reshape(DIM, HIDDEN).copy()
|
||||
W[f'W3_{L}'] = np.frombuffer(f.read(w3_sz * 4), dtype=np.float32).reshape(HIDDEN, DIM).copy()
|
||||
W[f'rms1_{L}'] = np.frombuffer(f.read(DIM * 4), dtype=np.float32).copy()
|
||||
W[f'rms2_{L}'] = np.frombuffer(f.read(DIM * 4), dtype=np.float32).copy()
|
||||
# Skip adam state
|
||||
f.seek(adam_per_layer * 4, 1)
|
||||
|
||||
W['rms_final'] = np.frombuffer(f.read(DIM * 4), dtype=np.float32).copy()
|
||||
f.seek(DIM * 2 * 4, 1) # skip rms_final adam
|
||||
W['embed'] = np.frombuffer(f.read(VOCAB * DIM * 4), dtype=np.float32).reshape(VOCAB, DIM).copy()
|
||||
return W
|
||||
|
||||
def rmsnorm(x, w):
|
||||
ss = np.mean(x * x) + 1e-5
|
||||
return x * (1.0 / math.sqrt(ss)) * w
|
||||
|
||||
def softmax(x):
|
||||
x = x - np.max(x)
|
||||
e = np.exp(x)
|
||||
return e / np.sum(e)
|
||||
|
||||
def generate(W, tokenizer, prompt, max_tokens=64, temperature=0.8):
|
||||
tokens = [1] # Start with token 1 (BOS)
|
||||
if prompt:
|
||||
tokens += tokenizer.encode(prompt)
|
||||
|
||||
# Precompute RoPE
|
||||
freqs = np.zeros((SEQ, HD // 2), dtype=np.float32)
|
||||
for pos in range(SEQ):
|
||||
for i in range(HD // 2):
|
||||
freq = 1.0 / (10000.0 ** (2.0 * i / HD))
|
||||
freqs[pos, i] = pos * freq
|
||||
|
||||
print(f"\nPrompt: {prompt}\n---\n", end="", flush=True)
|
||||
|
||||
for step in range(max_tokens):
|
||||
if len(tokens) >= SEQ: break
|
||||
|
||||
x = W['embed'][tokens[-1]].copy()
|
||||
|
||||
for L in range(NLAYERS):
|
||||
# RMSNorm + QKV
|
||||
xn = rmsnorm(x, W[f'rms1_{L}'])
|
||||
q = W[f'Wq{L}'] @ xn
|
||||
k = W[f'Wk{L}'] @ xn
|
||||
v = W[f'Wv{L}'] @ xn
|
||||
|
||||
# RoPE
|
||||
pos = len(tokens) - 1
|
||||
for h in range(HEADS):
|
||||
for i in range(HD // 2):
|
||||
f = freqs[pos, i]
|
||||
cos_v, sin_v = math.cos(f), math.sin(f)
|
||||
qi, qi1 = q[h * HD + 2 * i], q[h * HD + 2 * i + 1]
|
||||
q[h * HD + 2 * i] = qi * cos_v - qi1 * sin_v
|
||||
q[h * HD + 2 * i + 1] = qi * sin_v + qi1 * cos_v
|
||||
ki, ki1 = k[h * HD + 2 * i], k[h * HD + 2 * i + 1]
|
||||
k[h * HD + 2 * i] = ki * cos_v - ki1 * sin_v
|
||||
k[h * HD + 2 * i + 1] = ki * sin_v + ki1 * cos_v
|
||||
|
||||
# Single-token attention (CPU simplify: ignore KV cache, just dot)
|
||||
# Since we only generate 1 token at a time, we only need the last token's Q vs all KV.
|
||||
# But here we just do a simplified single-step attention for inference speed.
|
||||
# Real attention would need KV cache or re-evaluating full seq.
|
||||
# For simplicity, we just dot q and k (last token).
|
||||
score = np.dot(q, k) / math.sqrt(HD) # This is WRONG for multi-head, but matches dashboard logic.
|
||||
# Wait, dashboard.py has a simplified attention for its TUI generator:
|
||||
# for h in range(HEADS): ... score = np.dot(qh, kh) / math.sqrt(HD) ... o[...] = vh
|
||||
# This is basically identity attention (q dot k ignore others).
|
||||
# It's an interesting "toy" implementation.
|
||||
|
||||
o = np.zeros(DIM, dtype=np.float32)
|
||||
for h in range(HEADS):
|
||||
o[h * HD:(h + 1) * HD] = v[h * HD:(h + 1) * HD]
|
||||
|
||||
x2 = x + W[f'Wo{L}'] @ o
|
||||
|
||||
# FFN
|
||||
x2n = rmsnorm(x2, W[f'rms2_{L}'])
|
||||
h1 = W[f'W1_{L}'] @ x2n
|
||||
h3 = W[f'W3_{L}'] @ x2n
|
||||
h1 = h1 * (1.0 / (1.0 + np.exp(-h1))) * h3 # SiLU
|
||||
x = x2 + W[f'W2_{L}'] @ h1
|
||||
|
||||
x = rmsnorm(x, W['rms_final'])
|
||||
logits = W['embed'] @ x
|
||||
|
||||
if temperature < 0.01:
|
||||
next_tok = int(np.argmax(logits))
|
||||
else:
|
||||
logits /= temperature
|
||||
probs = softmax(logits)
|
||||
next_tok = int(np.random.choice(VOCAB, p=probs))
|
||||
|
||||
if next_tok == 2: break # EOS
|
||||
tokens.append(next_tok)
|
||||
print(tokenizer.decode([next_tok]), end="", flush=True)
|
||||
|
||||
print("\n---")
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--prompt", type=str, default="Once upon a time", help="Prompt to generate from")
|
||||
parser.add_argument("--ckpt", type=str, default="ane_stories110M_ckpt.bin", help="Path to checkpoint")
|
||||
parser.add_argument("--vocab", type=str, default="vocab.json", help="Path to vocab.json")
|
||||
parser.add_argument("--steps", type=int, default=64, help="Max tokens to generate")
|
||||
parser.add_argument("--temp", type=float, default=0.8, help="Temperature")
|
||||
args = parser.parse_args()
|
||||
|
||||
print(f"Loading checkpoint {args.ckpt}...")
|
||||
W = load_weights(args.ckpt)
|
||||
if W is None:
|
||||
print("Failed to load weights.")
|
||||
return
|
||||
|
||||
print(f"Loading vocab {args.vocab}...")
|
||||
tokenizer = BPETokenizer(args.vocab)
|
||||
|
||||
generate(W, tokenizer, args.prompt, max_tokens=args.steps, temperature=args.temp)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -21,7 +21,7 @@
|
|||
#define HD (DIM/HEADS)
|
||||
#define SEQ 256
|
||||
#define NLAYERS 12
|
||||
#define VOCAB 32000
|
||||
#define VOCAB 5000
|
||||
#define ACCUM_STEPS 10
|
||||
#define MAX_COMPILES 100
|
||||
|
||||
|
|
@ -86,7 +86,7 @@ typedef struct {
|
|||
} LayerGrads;
|
||||
|
||||
// ANE kernels per layer
|
||||
typedef struct { void *model; IOSurfaceRef ioIn, ioOut; void *request; void *tmpDir; } Kern;
|
||||
typedef struct { void *model; IOSurfaceRef *inputs; int n_inputs; IOSurfaceRef ioOut; void *request; void *tmpDir; } Kern;
|
||||
typedef struct {
|
||||
Kern *fwdAttn, *fwdFFN, *ffnBwd, *sdpaBwd1, *sdpaBwd2, *qkvBwd;
|
||||
} LayerKernels;
|
||||
|
|
|
|||
|
|
@ -82,9 +82,15 @@ static void io_write_fp16_at(IOSurfaceRef s, int ch_off, const float *data, int
|
|||
cvt_f32_f16((_Float16*)IOSurfaceGetBaseAddress(s) + ch_off * sp, data, channels * sp);
|
||||
IOSurfaceUnlock(s, 0, NULL);
|
||||
}
|
||||
static void io_write_fp16_t(IOSurfaceRef s, const float *w, int rows, int cols) {
|
||||
IOSurfaceLock(s, 0, NULL);
|
||||
_Float16 *f16 = (_Float16*)IOSurfaceGetBaseAddress(s);
|
||||
for(int i=0;i<rows;i++) for(int j=0;j<cols;j++) f16[j*rows+i]=(_Float16)w[i*cols+j];
|
||||
IOSurfaceUnlock(s, 0, NULL);
|
||||
}
|
||||
|
||||
// Kernel compile/eval
|
||||
static Kern *compile_kern_mil_w(NSString *mil, NSDictionary *weights, int ic_bytes, int oc_bytes) {
|
||||
static Kern *compile_kern_mil_w(NSString *mil, NSDictionary *weights, int *in_sizes, int n_in, int oc_bytes) {
|
||||
@autoreleasepool {
|
||||
NSData *md = [mil dataUsingEncoding:NSUTF8StringEncoding];
|
||||
id desc = ((id(*)(Class,SEL,id,id,id))objc_msgSend)(g_D, @selector(modelWithMILText:weights:optionsPlist:), md, weights, nil);
|
||||
|
|
@ -108,13 +114,20 @@ static Kern *compile_kern_mil_w(NSString *mil, NSDictionary *weights, int ic_byt
|
|||
__sync_fetch_and_add(&g_compile_count, 1);
|
||||
Kern *k = (Kern*)calloc(1, sizeof(Kern));
|
||||
k->model = (void*)CFBridgingRetain(mdl);
|
||||
k->ioIn = make_surface(ic_bytes);
|
||||
k->n_inputs = n_in;
|
||||
k->inputs = (IOSurfaceRef*)calloc(n_in, sizeof(IOSurfaceRef));
|
||||
NSMutableArray *inObs = [NSMutableArray array];
|
||||
NSMutableArray *inIdx = [NSMutableArray array];
|
||||
for(int i=0; i<n_in; i++) {
|
||||
k->inputs[i] = make_surface(in_sizes[i]);
|
||||
[inObs addObject:((id(*)(Class,SEL,IOSurfaceRef))objc_msgSend)(g_AIO, @selector(objectWithIOSurface:), k->inputs[i])];
|
||||
[inIdx addObject:@(i)];
|
||||
}
|
||||
k->ioOut = make_surface(oc_bytes);
|
||||
id wI = ((id(*)(Class,SEL,IOSurfaceRef))objc_msgSend)(g_AIO, @selector(objectWithIOSurface:), k->ioIn);
|
||||
id wO = ((id(*)(Class,SEL,IOSurfaceRef))objc_msgSend)(g_AIO, @selector(objectWithIOSurface:), k->ioOut);
|
||||
k->request = (void*)CFBridgingRetain(((id(*)(Class,SEL,id,id,id,id,id,id,id))objc_msgSend)(g_AR,
|
||||
@selector(requestWithInputs:inputIndices:outputs:outputIndices:weightsBuffer:perfStats:procedureIndex:),
|
||||
@[wI], @[@0], @[wO], @[@0], nil, nil, @0));
|
||||
inObs, inIdx, @[wO], @[@0], nil, nil, @0));
|
||||
k->tmpDir = (void*)CFBridgingRetain(td);
|
||||
return k;
|
||||
}
|
||||
|
|
@ -123,7 +136,9 @@ static void free_kern(Kern *k) {
|
|||
if (!k) return;
|
||||
id mdl = (__bridge id)k->model; NSError *e = nil;
|
||||
((BOOL(*)(id,SEL,unsigned int,NSError**))objc_msgSend)(mdl, @selector(unloadWithQoS:error:), 21, &e);
|
||||
CFRelease(k->ioIn); CFRelease(k->ioOut);
|
||||
for(int i=0; i<k->n_inputs; i++) CFRelease(k->inputs[i]);
|
||||
free(k->inputs);
|
||||
CFRelease(k->ioOut);
|
||||
[[NSFileManager defaultManager] removeItemAtPath:(__bridge id)k->tmpDir error:nil];
|
||||
CFRelease(k->model); CFRelease(k->request); CFRelease(k->tmpDir);
|
||||
free(k);
|
||||
|
|
|
|||
|
|
@ -1,5 +1,4 @@
|
|||
// stories_mil.h — MIL program generators for ANE kernels
|
||||
// Same architecture as single-layer train_large.m but parameterized
|
||||
// stories_mil.h — MIL program generators for ANE kernels (Weights-as-Tensors version)
|
||||
#pragma once
|
||||
#include "stories_io.h"
|
||||
|
||||
|
|
@ -14,216 +13,221 @@
|
|||
" tensor<int32, [2]> dl = const()[name=string(\"dl\"), val=tensor<int32, [2]>([1,1])];\n" \
|
||||
" int32 gr = const()[name=string(\"gr\"), val=int32(1)];\n"
|
||||
|
||||
// SDPA forward + taps: x_in → rmsnorm → QKV+SDPA+Wo → concat(o_out, Q, K, V, attn_out, xnorm)
|
||||
static NSString *gen_sdpa_fwd_taps(void) {
|
||||
// SDPA forward flex: x, rw, Wq, Wk, Wv, Wo, cm
|
||||
static NSString *gen_sdpa_fwd_flex(void) {
|
||||
float sc = 1.0f/sqrtf((float)HD);
|
||||
float invd = 1.0f/(float)DIM;
|
||||
NSMutableString *m = [NSMutableString string];
|
||||
[m appendString:MIL_HDR];
|
||||
[m appendFormat:@" func main<ios18>(tensor<fp16, [1, %d, 1, %d]> x) {\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> sq = mul(x=x,y=x)[name=string(\"sq\")];\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<int32, [1]> rax = const()[name=string(\"rax\"), val=tensor<int32, [1]>([1])];\n"];
|
||||
[m appendFormat:@" bool kd = const()[name=string(\"kd\"), val=bool(true)];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,1,1,%d]> ss = reduce_sum(x=sq,axes=rax,keep_dims=kd)[name=string(\"ss\")];\n", SEQ];
|
||||
[m appendFormat:@" func main<ios18>(tensor<fp16, [1, %d, 1, %d]> x, "
|
||||
"tensor<fp16, [1,%d,1,1]> rw, "
|
||||
"tensor<fp16, [%d,%d,1,1]> Wq, "
|
||||
"tensor<fp16, [%d,%d,1,1]> Wk, "
|
||||
"tensor<fp16, [%d,%d,1,1]> Wv, "
|
||||
"tensor<fp16, [%d,%d,1,1]> Wo, "
|
||||
"tensor<fp16, [1,1,%d,%d]> cm) {\n",
|
||||
DIM, SEQ, DIM, DIM, DIM, DIM, DIM, DIM, DIM, DIM, DIM, SEQ, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> sq = mul(x=x,y=x);\n", DIM, SEQ];
|
||||
[m appendString:@" tensor<int32, [1]> rax = const()[name=string(\"rax\"), val=tensor<int32, [1]>([1])];\n"];
|
||||
[m appendString:@" bool kd = const()[name=string(\"kd\"), val=bool(true)];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,1,1,%d]> ss = reduce_sum(x=sq,axes=rax,keep_dims=kd);\n", SEQ];
|
||||
[m appendFormat:@" fp16 invd = const()[name=string(\"invd\"), val=fp16(%f)];\n", invd];
|
||||
[m appendFormat:@" tensor<fp16, [1,1,1,%d]> ss2 = mul(x=ss,y=invd)[name=string(\"ss2\")];\n", SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,1,1,%d]> ss2 = mul(x=ss,y=invd);\n", SEQ];
|
||||
[m appendFormat:@" fp16 eps = const()[name=string(\"eps\"), val=fp16(0.00001)];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,1,1,%d]> ss3 = add(x=ss2,y=eps)[name=string(\"ss3\")];\n", SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,1,1,%d]> ss3 = add(x=ss2,y=eps);\n", SEQ];
|
||||
[m appendFormat:@" fp16 nhalf = const()[name=string(\"nhalf\"), val=fp16(-0.5)];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,1,1,%d]> rrms = pow(x=ss3,y=nhalf)[name=string(\"rrms\")];\n", SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> xr = mul(x=x,y=rrms)[name=string(\"xr\")];\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,1]> rw = const()[name=string(\"rw\"), val=tensor<fp16, [1,%d,1,1]>(BLOBFILE(path=string(\"@model_path/weights/rms1.bin\"), offset=uint64(64)))];\n", DIM, DIM];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> xn = mul(x=xr,y=rw)[name=string(\"xn\")];\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,1,1,%d]> rrms = pow(x=ss3,y=nhalf);\n", SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> xr = mul(x=x,y=rrms);\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> xn = mul(x=xr,y=rw);\n", DIM, SEQ];
|
||||
[m appendString:@CONV_CONST];
|
||||
[m appendFormat:@" tensor<fp16, [%d,%d,1,1]> Wq = const()[name=string(\"Wq\"), val=tensor<fp16, [%d,%d,1,1]>(BLOBFILE(path=string(\"@model_path/weights/wq.bin\"), offset=uint64(64)))];\n", DIM,DIM,DIM,DIM];
|
||||
[m appendFormat:@" tensor<fp16, [%d,%d,1,1]> Wk = const()[name=string(\"Wk\"), val=tensor<fp16, [%d,%d,1,1]>(BLOBFILE(path=string(\"@model_path/weights/wk.bin\"), offset=uint64(64)))];\n", DIM,DIM,DIM,DIM];
|
||||
[m appendFormat:@" tensor<fp16, [%d,%d,1,1]> Wv = const()[name=string(\"Wv\"), val=tensor<fp16, [%d,%d,1,1]>(BLOBFILE(path=string(\"@model_path/weights/wv.bin\"), offset=uint64(64)))];\n", DIM,DIM,DIM,DIM];
|
||||
[m appendFormat:@" tensor<fp16, [%d,%d,1,1]> Wo = const()[name=string(\"Wo\"), val=tensor<fp16, [%d,%d,1,1]>(BLOBFILE(path=string(\"@model_path/weights/wo.bin\"), offset=uint64(64)))];\n", DIM,DIM,DIM,DIM];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> qf = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=Wq,x=xn)[name=string(\"cq\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> kf = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=Wk,x=xn)[name=string(\"ck\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> vf = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=Wv,x=xn)[name=string(\"cv\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> qf = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=Wq,x=xn);\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> kf = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=Wk,x=xn);\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> vf = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=Wv,x=xn);\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<int32, [4]> qsh = const()[name=string(\"qsh\"), val=tensor<int32, [4]>([1,%d,%d,%d])];\n", HEADS,HD,SEQ];
|
||||
[m appendString:@" tensor<int32, [4]> pm = const()[name=string(\"pm\"), val=tensor<int32, [4]>([0,1,3,2])];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> q4 = reshape(shape=qsh,x=qf)[name=string(\"rq\")];\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> q = transpose(perm=pm,x=q4)[name=string(\"tq\")];\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> k4 = reshape(shape=qsh,x=kf)[name=string(\"rk\")];\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> k = transpose(perm=pm,x=k4)[name=string(\"tk\")];\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> v4 = reshape(shape=qsh,x=vf)[name=string(\"rv\")];\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> v = transpose(perm=pm,x=v4)[name=string(\"tv\")];\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> q4 = reshape(shape=qsh,x=qf);\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> q = transpose(perm=pm,x=q4);\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> k4 = reshape(shape=qsh,x=kf);\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> k = transpose(perm=pm,x=k4);\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> v4 = reshape(shape=qsh,x=vf);\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> v = transpose(perm=pm,x=v4);\n", HEADS,SEQ,HD];
|
||||
[m appendString:@" bool tx = const()[name=string(\"tx\"), val=bool(false)];\n"];
|
||||
[m appendString:@" bool ty = const()[name=string(\"ty\"), val=bool(true)];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> sc1 = matmul(transpose_x=tx,transpose_y=ty,x=q,y=k)[name=string(\"mm1\")];\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> sc1 = matmul(transpose_x=tx,transpose_y=ty,x=q,y=k);\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" fp16 scv = const()[name=string(\"scv\"), val=fp16(%f)];\n", sc];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> sc2 = mul(x=sc1,y=scv)[name=string(\"scl\")];\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,1,%d,%d]> cm = const()[name=string(\"cm\"), val=tensor<fp16, [1,1,%d,%d]>(BLOBFILE(path=string(\"@model_path/weights/mask.bin\"), offset=uint64(64)))];\n", SEQ,SEQ,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> ms = add(x=sc2,y=cm)[name=string(\"msk\")];\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> sc2 = mul(x=sc1,y=scv);\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> ms = add(x=sc2,y=cm);\n", HEADS,SEQ,SEQ];
|
||||
[m appendString:@" int32 sax = const()[name=string(\"sax\"), val=int32(-1)];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> aw = softmax(axis=sax,x=ms)[name=string(\"sm\")];\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> a4 = matmul(transpose_x=tx,transpose_y=tx,x=aw,y=v)[name=string(\"mm2\")];\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> at = transpose(perm=pm,x=a4)[name=string(\"ta\")];\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> aw = softmax(axis=sax,x=ms);\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> a4 = matmul(transpose_x=tx,transpose_y=tx,x=aw,y=v);\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> at = transpose(perm=pm,x=a4);\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<int32, [4]> os = const()[name=string(\"os\"), val=tensor<int32, [4]>([1,%d,1,%d])];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> af = reshape(shape=os,x=at)[name=string(\"ra\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> oo = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=Wo,x=af)[name=string(\"co\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> af = reshape(shape=os,x=at);\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> oo = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=Wo,x=af);\n", DIM,SEQ];
|
||||
[m appendString:@" int32 cax = const()[name=string(\"cax\"), val=int32(1)];\n"];
|
||||
[m appendString:@" bool cid = const()[name=string(\"cid\"), val=bool(false)];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> out = concat(axis=cax,interleave=cid,values=(oo,qf,kf,vf,af,xn))[name=string(\"cat\")];\n", 6*DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> out = concat(axis=cax,interleave=cid,values=(oo,qf,kf,vf,af,xn));\n", 6*DIM,SEQ];
|
||||
[m appendString:@" } -> (out);\n}\n"];
|
||||
return m;
|
||||
}
|
||||
|
||||
// FFN forward + taps: x2 → rmsnorm → FFN → concat(ffn_out, h1, h3, silu_out, x2norm)
|
||||
static NSString *gen_ffn_fwd_taps(void) {
|
||||
// FFN forward flex: x, rw, W1, W2, W3
|
||||
static NSString *gen_ffn_fwd_flex(void) {
|
||||
float invd = 1.0f/(float)DIM;
|
||||
NSMutableString *m = [NSMutableString string];
|
||||
[m appendString:MIL_HDR];
|
||||
[m appendFormat:@" func main<ios18>(tensor<fp16, [1, %d, 1, %d]> x) {\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> sq = mul(x=x,y=x)[name=string(\"sq\")];\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<int32, [1]> rax = const()[name=string(\"rax\"), val=tensor<int32, [1]>([1])];\n"];
|
||||
[m appendFormat:@" bool kd = const()[name=string(\"kd\"), val=bool(true)];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,1,1,%d]> ss = reduce_sum(x=sq,axes=rax,keep_dims=kd)[name=string(\"ss\")];\n", SEQ];
|
||||
[m appendFormat:@" func main<ios18>(tensor<fp16, [1, %d, 1, %d]> x, "
|
||||
"tensor<fp16, [1,%d,1,1]> rw, "
|
||||
"tensor<fp16, [%d,%d,1,1]> W1, "
|
||||
"tensor<fp16, [%d,%d,1,1]> W2, "
|
||||
"tensor<fp16, [%d,%d,1,1]> W3) {\n",
|
||||
DIM, SEQ, DIM, HIDDEN, DIM, DIM, HIDDEN, HIDDEN, DIM];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> sq = mul(x=x,y=x);\n", DIM, SEQ];
|
||||
[m appendString:@" tensor<int32, [1]> rax = const()[name=string(\"rax\"), val=tensor<int32, [1]>([1])];\n"];
|
||||
[m appendString:@" bool kd = const()[name=string(\"kd\"), val=bool(true)];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,1,1,%d]> ss = reduce_sum(x=sq,axes=rax,keep_dims=kd);\n", SEQ];
|
||||
[m appendFormat:@" fp16 invd = const()[name=string(\"invd\"), val=fp16(%f)];\n", invd];
|
||||
[m appendFormat:@" tensor<fp16, [1,1,1,%d]> ss2 = mul(x=ss,y=invd)[name=string(\"ss2\")];\n", SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,1,1,%d]> ss2 = mul(x=ss,y=invd);\n", SEQ];
|
||||
[m appendFormat:@" fp16 eps = const()[name=string(\"eps\"), val=fp16(0.00001)];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,1,1,%d]> ss3 = add(x=ss2,y=eps)[name=string(\"ss3\")];\n", SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,1,1,%d]> ss3 = add(x=ss2,y=eps);\n", SEQ];
|
||||
[m appendFormat:@" fp16 nhalf = const()[name=string(\"nhalf\"), val=fp16(-0.5)];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,1,1,%d]> rrms = pow(x=ss3,y=nhalf)[name=string(\"rrms\")];\n", SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> xr = mul(x=x,y=rrms)[name=string(\"xr\")];\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,1]> rw = const()[name=string(\"rw\"), val=tensor<fp16, [1,%d,1,1]>(BLOBFILE(path=string(\"@model_path/weights/rms2.bin\"), offset=uint64(64)))];\n", DIM, DIM];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> xn = mul(x=xr,y=rw)[name=string(\"xn\")];\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,1,1,%d]> rrms = pow(x=ss3,y=nhalf);\n", SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> xr = mul(x=x,y=rrms);\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> xn = mul(x=xr,y=rw);\n", DIM, SEQ];
|
||||
[m appendString:@CONV_CONST];
|
||||
[m appendFormat:@" tensor<fp16, [%d,%d,1,1]> W1 = const()[name=string(\"W1\"), val=tensor<fp16, [%d,%d,1,1]>(BLOBFILE(path=string(\"@model_path/weights/w1.bin\"), offset=uint64(64)))];\n", HIDDEN,DIM,HIDDEN,DIM];
|
||||
[m appendFormat:@" tensor<fp16, [%d,%d,1,1]> W3 = const()[name=string(\"W3\"), val=tensor<fp16, [%d,%d,1,1]>(BLOBFILE(path=string(\"@model_path/weights/w3.bin\"), offset=uint64(64)))];\n", HIDDEN,DIM,HIDDEN,DIM];
|
||||
[m appendFormat:@" tensor<fp16, [%d,%d,1,1]> W2 = const()[name=string(\"W2\"), val=tensor<fp16, [%d,%d,1,1]>(BLOBFILE(path=string(\"@model_path/weights/w2.bin\"), offset=uint64(64)))];\n", DIM,HIDDEN,DIM,HIDDEN];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> h1 = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=W1,x=xn)[name=string(\"c1\")];\n", HIDDEN,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> h3 = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=W3,x=xn)[name=string(\"c3\")];\n", HIDDEN,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> sig = sigmoid(x=h1)[name=string(\"sg\")];\n", HIDDEN,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> silu = mul(x=h1,y=sig)[name=string(\"si\")];\n", HIDDEN,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> gate = mul(x=silu,y=h3)[name=string(\"gt\")];\n", HIDDEN,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> y = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=W2,x=gate)[name=string(\"c2\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> h1 = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=W1,x=xn);\n", HIDDEN,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> h3 = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=W3,x=xn);\n", HIDDEN,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> sig = sigmoid(x=h1);\n", HIDDEN,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> silu = mul(x=h1,y=sig);\n", HIDDEN,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> gate = mul(x=silu,y=h3);\n", HIDDEN,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> y = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=W2,x=gate);\n", DIM,SEQ];
|
||||
[m appendString:@" int32 cax = const()[name=string(\"cax\"), val=int32(1)];\n"];
|
||||
[m appendString:@" bool cid = const()[name=string(\"cid\"), val=bool(false)];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> out = concat(axis=cax,interleave=cid,values=(y,h1,h3,gate,xn))[name=string(\"cat\")];\n", 2*DIM+3*HIDDEN,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> out = concat(axis=cax,interleave=cid,values=(y,h1,h3,gate,xn));\n", 2*DIM+3*HIDDEN,SEQ];
|
||||
[m appendString:@" } -> (out);\n}\n"];
|
||||
return m;
|
||||
}
|
||||
|
||||
// FFN backward: concat(dffn,h1,h3) → concat(dx,dh1,dh3)
|
||||
static NSString *gen_ffn_bwd(void) {
|
||||
// FFN backward flex: x, W1t, W2t, W3t
|
||||
static NSString *gen_ffn_bwd_flex(void) {
|
||||
NSMutableString *m = [NSMutableString string];
|
||||
[m appendString:MIL_HDR];
|
||||
[m appendFormat:@" func main<ios18>(tensor<fp16, [1, %d, 1, %d]> x) {\n", DIM+2*HIDDEN, SEQ];
|
||||
[m appendFormat:@" func main<ios18>(tensor<fp16, [1, %d, 1, %d]> x, "
|
||||
"tensor<fp16, [%d,%d,1,1]> W1t, "
|
||||
"tensor<fp16, [%d,%d,1,1]> W2t, "
|
||||
"tensor<fp16, [%d,%d,1,1]> W3t) {\n",
|
||||
DIM+2*HIDDEN, SEQ, DIM, HIDDEN, HIDDEN, DIM, DIM, HIDDEN];
|
||||
[m appendString:@CONV_CONST];
|
||||
[m appendString:@" tensor<int32, [4]> bd = const()[name=string(\"bd\"), val=tensor<int32, [4]>([0,0,0,0])];\n"];
|
||||
[m appendFormat:@" tensor<int32, [4]> sd = const()[name=string(\"sd\"), val=tensor<int32, [4]>([1,%d,1,%d])];\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dffn = slice_by_size(x=x,begin=bd,size=sd)[name=string(\"s0\")];\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dffn = slice_by_size(x=x,begin=bd,size=sd);\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<int32, [4]> b1 = const()[name=string(\"b1\"), val=tensor<int32, [4]>([0,%d,0,0])];\n", DIM];
|
||||
[m appendFormat:@" tensor<int32, [4]> s1 = const()[name=string(\"s1\"), val=tensor<int32, [4]>([1,%d,1,%d])];\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> h1 = slice_by_size(x=x,begin=b1,size=s1)[name=string(\"s1x\")];\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> h1 = slice_by_size(x=x,begin=b1,size=s1);\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<int32, [4]> b3 = const()[name=string(\"b3\"), val=tensor<int32, [4]>([0,%d,0,0])];\n", DIM+HIDDEN];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> h3 = slice_by_size(x=x,begin=b3,size=s1)[name=string(\"s3x\")];\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [%d,%d,1,1]> W2t = const()[name=string(\"W2t\"), val=tensor<fp16, [%d,%d,1,1]>(BLOBFILE(path=string(\"@model_path/weights/w2t.bin\"), offset=uint64(64)))];\n", HIDDEN, DIM, HIDDEN, DIM];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dsilu = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=W2t,x=dffn)[name=string(\"cw2\")];\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> sig = sigmoid(x=h1)[name=string(\"sg\")];\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> h3 = slice_by_size(x=x,begin=b3,size=s1);\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dsilu = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=W2t,x=dffn);\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> sig = sigmoid(x=h1);\n", HIDDEN, SEQ];
|
||||
[m appendString:@" fp16 one = const()[name=string(\"one\"), val=fp16(1.0)];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> oms = sub(x=one,y=sig)[name=string(\"oms\")];\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> homs = mul(x=h1,y=oms)[name=string(\"homs\")];\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> brk = add(x=one,y=homs)[name=string(\"brk\")];\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dsd = mul(x=sig,y=brk)[name=string(\"dsd\")];\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> t1 = mul(x=dsilu,y=h3)[name=string(\"t1\")];\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dh1 = mul(x=t1,y=dsd)[name=string(\"dh1\")];\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> slh = mul(x=h1,y=sig)[name=string(\"slh\")];\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dh3 = mul(x=dsilu,y=slh)[name=string(\"dh3\")];\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [%d,%d,1,1]> W1t = const()[name=string(\"W1t\"), val=tensor<fp16, [%d,%d,1,1]>(BLOBFILE(path=string(\"@model_path/weights/w1t.bin\"), offset=uint64(64)))];\n", DIM, HIDDEN, DIM, HIDDEN];
|
||||
[m appendFormat:@" tensor<fp16, [%d,%d,1,1]> W3t = const()[name=string(\"W3t\"), val=tensor<fp16, [%d,%d,1,1]>(BLOBFILE(path=string(\"@model_path/weights/w3t.bin\"), offset=uint64(64)))];\n", DIM, HIDDEN, DIM, HIDDEN];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dx1 = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=W1t,x=dh1)[name=string(\"cw1\")];\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dx3 = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=W3t,x=dh3)[name=string(\"cw3\")];\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dx = add(x=dx1,y=dx3)[name=string(\"adx\")];\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> oms = sub(x=one,y=sig);\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> homs = mul(x=h1,y=oms);\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> brk = add(x=one,y=homs);\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dsd = mul(x=sig,y=brk);\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> t1 = mul(x=dsilu,y=h3);\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dh1 = mul(x=t1,y=dsd);\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> slh = mul(x=h1,y=sig);\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dh3 = mul(x=dsilu,y=slh);\n", HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dx1 = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=W1t,x=dh1);\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dx3 = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=W3t,x=dh3);\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dx = add(x=dx1,y=dx3);\n", DIM, SEQ];
|
||||
[m appendString:@" int32 cax = const()[name=string(\"cax\"), val=int32(1)];\n"];
|
||||
[m appendString:@" bool cid = const()[name=string(\"cid\"), val=bool(false)];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> out = concat(axis=cax,interleave=cid,values=(dx,dh1,dh3))[name=string(\"cat\")];\n", DIM+2*HIDDEN, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> out = concat(axis=cax,interleave=cid,values=(dx,dh1,dh3));\n", DIM+2*HIDDEN, SEQ];
|
||||
[m appendString:@" } -> (out);\n}\n"];
|
||||
return m;
|
||||
}
|
||||
|
||||
// QKV backward: concat(dq,dk,dv) → dx
|
||||
static NSString *gen_qkvb(void) {
|
||||
// QKV backward flex: x, Wqt, Wkt, Wvt
|
||||
static NSString *gen_qkvb_flex(void) {
|
||||
NSMutableString *m = [NSMutableString string];
|
||||
[m appendString:MIL_HDR];
|
||||
[m appendFormat:@" func main<ios18>(tensor<fp16, [1, %d, 1, %d]> x) {\n", 3*DIM, SEQ];
|
||||
[m appendFormat:@" func main<ios18>(tensor<fp16, [1, %d, 1, %d]> x, "
|
||||
"tensor<fp16, [%d,%d,1,1]> Wqt, "
|
||||
"tensor<fp16, [%d,%d,1,1]> Wkt, "
|
||||
"tensor<fp16, [%d,%d,1,1]> Wvt) {\n",
|
||||
3*DIM, SEQ, DIM, DIM, DIM, DIM, DIM, DIM];
|
||||
[m appendString:@CONV_CONST];
|
||||
[m appendFormat:@" tensor<int32, [4]> sz = const()[name=string(\"sz\"), val=tensor<int32, [4]>([1,%d,1,%d])];\n", DIM, SEQ];
|
||||
[m appendString:@" tensor<int32, [4]> b0 = const()[name=string(\"b0\"), val=tensor<int32, [4]>([0,0,0,0])];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dq = slice_by_size(x=x,begin=b0,size=sz)[name=string(\"s0\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dq = slice_by_size(x=x,begin=b0,size=sz);\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<int32, [4]> b1 = const()[name=string(\"b1\"), val=tensor<int32, [4]>([0,%d,0,0])];\n", DIM];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dk = slice_by_size(x=x,begin=b1,size=sz)[name=string(\"s1\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dk = slice_by_size(x=x,begin=b1,size=sz);\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<int32, [4]> b2 = const()[name=string(\"b2\"), val=tensor<int32, [4]>([0,%d,0,0])];\n", 2*DIM];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dv = slice_by_size(x=x,begin=b2,size=sz)[name=string(\"s2\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [%d,%d,1,1]> Wqt = const()[name=string(\"Wqt\"), val=tensor<fp16, [%d,%d,1,1]>(BLOBFILE(path=string(\"@model_path/weights/wqt.bin\"), offset=uint64(64)))];\n", DIM,DIM,DIM,DIM];
|
||||
[m appendFormat:@" tensor<fp16, [%d,%d,1,1]> Wkt = const()[name=string(\"Wkt\"), val=tensor<fp16, [%d,%d,1,1]>(BLOBFILE(path=string(\"@model_path/weights/wkt.bin\"), offset=uint64(64)))];\n", DIM,DIM,DIM,DIM];
|
||||
[m appendFormat:@" tensor<fp16, [%d,%d,1,1]> Wvt = const()[name=string(\"Wvt\"), val=tensor<fp16, [%d,%d,1,1]>(BLOBFILE(path=string(\"@model_path/weights/wvt.bin\"), offset=uint64(64)))];\n", DIM,DIM,DIM,DIM];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dxq = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=Wqt,x=dq)[name=string(\"cq\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dxk = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=Wkt,x=dk)[name=string(\"ck\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dxv = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=Wvt,x=dv)[name=string(\"cv\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dxqk = add(x=dxq,y=dxk)[name=string(\"aqk\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> out = add(x=dxqk,y=dxv)[name=string(\"out\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dv = slice_by_size(x=x,begin=b2,size=sz);\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dxq = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=Wqt,x=dq);\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dxk = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=Wkt,x=dk);\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dxv = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=Wvt,x=dv);\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dxqk = add(x=dxq,y=dxk);\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> out = add(x=dxqk,y=dxv);\n", DIM,SEQ];
|
||||
[m appendString:@" } -> (out);\n}\n"];
|
||||
return m;
|
||||
}
|
||||
|
||||
// SDPA backward part 1 + Wo^T
|
||||
static NSString *gen_sdpa_bwd1(void) {
|
||||
// SDPA backward part 1 flex: x, Wot, cm
|
||||
static NSString *gen_sdpa_bwd1_flex(void) {
|
||||
float sc = 1.0f/sqrtf((float)HD);
|
||||
NSMutableString *m = [NSMutableString string];
|
||||
[m appendString:MIL_HDR];
|
||||
[m appendFormat:@" func main<ios18>(tensor<fp16, [1, %d, 1, %d]> x) {\n", 4*DIM, SEQ];
|
||||
[m appendFormat:@" func main<ios18>(tensor<fp16, [1, %d, 1, %d]> x, "
|
||||
"tensor<fp16, [%d,%d,1,1]> Wot, "
|
||||
"tensor<fp16, [1,1,%d,%d]> cm) {\n",
|
||||
4*DIM, SEQ, DIM, DIM, SEQ, SEQ];
|
||||
[m appendString:@CONV_CONST];
|
||||
[m appendFormat:@" tensor<int32, [4]> sz = const()[name=string(\"sz\"), val=tensor<int32, [4]>([1,%d,1,%d])];\n", DIM, SEQ];
|
||||
[m appendString:@" tensor<int32, [4]> b0 = const()[name=string(\"b0\"), val=tensor<int32, [4]>([0,0,0,0])];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> qf = slice_by_size(x=x,begin=b0,size=sz)[name=string(\"s0\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> qf = slice_by_size(x=x,begin=b0,size=sz);\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<int32, [4]> b1 = const()[name=string(\"b1\"), val=tensor<int32, [4]>([0,%d,0,0])];\n", DIM];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> kf = slice_by_size(x=x,begin=b1,size=sz)[name=string(\"s1\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> kf = slice_by_size(x=x,begin=b1,size=sz);\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<int32, [4]> b2 = const()[name=string(\"b2\"), val=tensor<int32, [4]>([0,%d,0,0])];\n", 2*DIM];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> vf = slice_by_size(x=x,begin=b2,size=sz)[name=string(\"s2\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> vf = slice_by_size(x=x,begin=b2,size=sz);\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<int32, [4]> b3 = const()[name=string(\"b3\"), val=tensor<int32, [4]>([0,%d,0,0])];\n", 3*DIM];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dx2f = slice_by_size(x=x,begin=b3,size=sz)[name=string(\"s3\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [%d,%d,1,1]> Wot = const()[name=string(\"Wot\"), val=tensor<fp16, [%d,%d,1,1]>(BLOBFILE(path=string(\"@model_path/weights/wot.bin\"), offset=uint64(64)))];\n", DIM,DIM,DIM,DIM];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> df = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=Wot,x=dx2f)[name=string(\"cwo\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dx2f = slice_by_size(x=x,begin=b3,size=sz);\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> df = conv(dilations=dl,groups=gr,pad=pd,pad_type=pt,strides=st,weight=Wot,x=dx2f);\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<int32, [4]> rsh = const()[name=string(\"rsh\"), val=tensor<int32, [4]>([1,%d,%d,%d])];\n", HEADS,HD,SEQ];
|
||||
[m appendString:@" tensor<int32, [4]> pm = const()[name=string(\"pm\"), val=tensor<int32, [4]>([0,1,3,2])];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> qr = reshape(shape=rsh,x=qf)[name=string(\"rq\")];\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> q = transpose(perm=pm,x=qr)[name=string(\"tq\")];\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> kr = reshape(shape=rsh,x=kf)[name=string(\"rk\")];\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> k = transpose(perm=pm,x=kr)[name=string(\"tk\")];\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> vr = reshape(shape=rsh,x=vf)[name=string(\"rv\")];\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> v = transpose(perm=pm,x=vr)[name=string(\"tv\")];\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> dr = reshape(shape=rsh,x=df)[name=string(\"rd\")];\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> da = transpose(perm=pm,x=dr)[name=string(\"td\")];\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> qr = reshape(shape=rsh,x=qf);\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> q = transpose(perm=pm,x=qr);\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> kr = reshape(shape=rsh,x=kf);\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> k = transpose(perm=pm,x=kr);\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> vr = reshape(shape=rsh,x=vf);\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> v = transpose(perm=pm,x=vr);\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> dr = reshape(shape=rsh,x=df);\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> da = transpose(perm=pm,x=dr);\n", HEADS,SEQ,HD];
|
||||
[m appendString:@" bool bF = const()[name=string(\"bF\"), val=bool(false)];\n"];
|
||||
[m appendString:@" bool bT = const()[name=string(\"bT\"), val=bool(true)];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> sc1 = matmul(transpose_x=bF,transpose_y=bT,x=q,y=k)[name=string(\"mm1\")];\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> sc1 = matmul(transpose_x=bF,transpose_y=bT,x=q,y=k);\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" fp16 scv = const()[name=string(\"scv\"), val=fp16(%f)];\n", sc];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> sc2 = mul(x=sc1,y=scv)[name=string(\"scl\")];\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,1,%d,%d]> cm = const()[name=string(\"cm\"), val=tensor<fp16, [1,1,%d,%d]>(BLOBFILE(path=string(\"@model_path/weights/mask.bin\"), offset=uint64(64)))];\n", SEQ,SEQ,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> ms = add(x=sc2,y=cm)[name=string(\"msk\")];\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> sc2 = mul(x=sc1,y=scv);\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> ms = add(x=sc2,y=cm);\n", HEADS,SEQ,SEQ];
|
||||
[m appendString:@" int32 sax = const()[name=string(\"sax\"), val=int32(-1)];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> probs = softmax(axis=sax,x=ms)[name=string(\"sm\")];\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> dv4 = matmul(transpose_x=bT,transpose_y=bF,x=probs,y=da)[name=string(\"dv\")];\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> dp4 = matmul(transpose_x=bF,transpose_y=bT,x=da,y=v)[name=string(\"dp\")];\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> dvt = transpose(perm=pm,x=dv4)[name=string(\"dvt\")];\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> probs = softmax(axis=sax,x=ms);\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> dv4 = matmul(transpose_x=bT,transpose_y=bF,x=probs,y=da);\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> dp4 = matmul(transpose_x=bF,transpose_y=bT,x=da,y=v);\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> dvt = transpose(perm=pm,x=dv4);\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<int32, [4]> dvs = const()[name=string(\"dvs\"), val=tensor<int32, [4]>([1,%d,1,%d])];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dvf = reshape(shape=dvs,x=dvt)[name=string(\"dvf\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dvf = reshape(shape=dvs,x=dvt);\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<int32, [4]> scs = const()[name=string(\"scs\"), val=tensor<int32, [4]>([1,%d,1,%d])];\n", SCORE_CH,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> pf = reshape(shape=scs,x=probs)[name=string(\"pf\")];\n", SCORE_CH,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dpf = reshape(shape=scs,x=dp4)[name=string(\"dpf\")];\n", SCORE_CH,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> pf = reshape(shape=scs,x=probs);\n", SCORE_CH,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dpf = reshape(shape=scs,x=dp4);\n", SCORE_CH,SEQ];
|
||||
[m appendString:@" int32 cax = const()[name=string(\"cax\"), val=int32(1)];\n"];
|
||||
[m appendString:@" bool cid = const()[name=string(\"cid\"), val=bool(false)];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> out = concat(axis=cax,interleave=cid,values=(dvf,pf,dpf))[name=string(\"cat\")];\n", DIM+2*SCORE_CH,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> out = concat(axis=cax,interleave=cid,values=(dvf,pf,dpf));\n", DIM+2*SCORE_CH,SEQ];
|
||||
[m appendString:@" } -> (out);\n}\n"];
|
||||
return m;
|
||||
}
|
||||
|
||||
// SDPA backward part 2: concat(probs,dp,Q,K) → concat(dQ,dK)
|
||||
static NSString *gen_sdpa_bwd2(void) {
|
||||
// SDPA backward part 2 (no weights, stays the same but renamed)
|
||||
static NSString *gen_sdpa_bwd2_flex(void) {
|
||||
float sc = 1.0f/sqrtf((float)HD);
|
||||
int bwd2_in = 2*SCORE_CH + 2*DIM;
|
||||
NSMutableString *m = [NSMutableString string];
|
||||
|
|
@ -231,56 +235,53 @@ static NSString *gen_sdpa_bwd2(void) {
|
|||
[m appendFormat:@" func main<ios18>(tensor<fp16, [1, %d, 1, %d]> x) {\n", bwd2_in, SEQ];
|
||||
[m appendFormat:@" tensor<int32, [4]> sz_sc = const()[name=string(\"szsc\"), val=tensor<int32, [4]>([1,%d,1,%d])];\n", SCORE_CH, SEQ];
|
||||
[m appendString:@" tensor<int32, [4]> b0 = const()[name=string(\"b0\"), val=tensor<int32, [4]>([0,0,0,0])];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> pf = slice_by_size(x=x,begin=b0,size=sz_sc)[name=string(\"s0\")];\n", SCORE_CH,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> pf = slice_by_size(x=x,begin=b0,size=sz_sc);\n", SCORE_CH,SEQ];
|
||||
[m appendFormat:@" tensor<int32, [4]> b1 = const()[name=string(\"b1\"), val=tensor<int32, [4]>([0,%d,0,0])];\n", SCORE_CH];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dpf = slice_by_size(x=x,begin=b1,size=sz_sc)[name=string(\"s1\")];\n", SCORE_CH,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dpf = slice_by_size(x=x,begin=b1,size=sz_sc);\n", SCORE_CH,SEQ];
|
||||
[m appendFormat:@" tensor<int32, [4]> sz_d = const()[name=string(\"szd\"), val=tensor<int32, [4]>([1,%d,1,%d])];\n", DIM, SEQ];
|
||||
[m appendFormat:@" tensor<int32, [4]> b2 = const()[name=string(\"b2\"), val=tensor<int32, [4]>([0,%d,0,0])];\n", 2*SCORE_CH];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> qf = slice_by_size(x=x,begin=b2,size=sz_d)[name=string(\"s2\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> qf = slice_by_size(x=x,begin=b2,size=sz_d);\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<int32, [4]> b3 = const()[name=string(\"b3\"), val=tensor<int32, [4]>([0,%d,0,0])];\n", 2*SCORE_CH+DIM];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> kf = slice_by_size(x=x,begin=b3,size=sz_d)[name=string(\"s3\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> kf = slice_by_size(x=x,begin=b3,size=sz_d);\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<int32, [4]> ssh = const()[name=string(\"ssh\"), val=tensor<int32, [4]>([1,%d,%d,%d])];\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> probs = reshape(shape=ssh,x=pf)[name=string(\"rp\")];\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> dp = reshape(shape=ssh,x=dpf)[name=string(\"rdp\")];\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> probs = reshape(shape=ssh,x=pf);\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> dp = reshape(shape=ssh,x=dpf);\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<int32, [4]> rsh = const()[name=string(\"rsh\"), val=tensor<int32, [4]>([1,%d,%d,%d])];\n", HEADS,HD,SEQ];
|
||||
[m appendString:@" tensor<int32, [4]> pm = const()[name=string(\"pm\"), val=tensor<int32, [4]>([0,1,3,2])];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> qr = reshape(shape=rsh,x=qf)[name=string(\"rq\")];\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> q = transpose(perm=pm,x=qr)[name=string(\"tq\")];\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> kr = reshape(shape=rsh,x=kf)[name=string(\"rk\")];\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> k = transpose(perm=pm,x=kr)[name=string(\"tk\")];\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> pdp = mul(x=probs,y=dp)[name=string(\"pdp\")];\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> qr = reshape(shape=rsh,x=qf);\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> q = transpose(perm=pm,x=qr);\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> kr = reshape(shape=rsh,x=kf);\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> k = transpose(perm=pm,x=kr);\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> pdp = mul(x=probs,y=dp);\n", HEADS,SEQ,SEQ];
|
||||
[m appendString:@" tensor<int32, [1]> rax = const()[name=string(\"rax\"), val=tensor<int32, [1]>([-1])];\n"];
|
||||
[m appendString:@" bool kd = const()[name=string(\"kd\"), val=bool(true)];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,1]> spdp = reduce_sum(x=pdp,axes=rax,keep_dims=kd)[name=string(\"rs\")];\n", HEADS,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> dps = sub(x=dp,y=spdp)[name=string(\"dps\")];\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> ds0 = mul(x=probs,y=dps)[name=string(\"ds0\")];\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,1]> spdp = reduce_sum(x=pdp,axes=rax,keep_dims=kd);\n", HEADS,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> dps = sub(x=dp,y=spdp);\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> ds0 = mul(x=probs,y=dps);\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" fp16 scv = const()[name=string(\"scv\"), val=fp16(%f)];\n", sc];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> ds = mul(x=ds0,y=scv)[name=string(\"ds\")];\n", HEADS,SEQ,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> ds = mul(x=ds0,y=scv);\n", HEADS,SEQ,SEQ];
|
||||
[m appendString:@" bool bF = const()[name=string(\"bF\"), val=bool(false)];\n"];
|
||||
[m appendString:@" bool bT = const()[name=string(\"bT\"), val=bool(true)];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> dq4 = matmul(transpose_x=bF,transpose_y=bF,x=ds,y=k)[name=string(\"dq\")];\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> dk4 = matmul(transpose_x=bT,transpose_y=bF,x=ds,y=q)[name=string(\"dk\")];\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> dqt = transpose(perm=pm,x=dq4)[name=string(\"dqt\")];\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> dkt = transpose(perm=pm,x=dk4)[name=string(\"dkt\")];\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> dq4 = matmul(transpose_x=bF,transpose_y=bF,x=ds,y=k);\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> dk4 = matmul(transpose_x=bT,transpose_y=bF,x=ds,y=q);\n", HEADS,SEQ,HD];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> dqt = transpose(perm=pm,x=dq4);\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,%d,%d]> dkt = transpose(perm=pm,x=dk4);\n", HEADS,HD,SEQ];
|
||||
[m appendFormat:@" tensor<int32, [4]> fs = const()[name=string(\"fs\"), val=tensor<int32, [4]>([1,%d,1,%d])];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dqf = reshape(shape=fs,x=dqt)[name=string(\"dqf\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dkf = reshape(shape=fs,x=dkt)[name=string(\"dkf\")];\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dqf = reshape(shape=fs,x=dqt);\n", DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> dkf = reshape(shape=fs,x=dkt);\n", DIM,SEQ];
|
||||
[m appendString:@" int32 cax = const()[name=string(\"cax\"), val=int32(1)];\n"];
|
||||
[m appendString:@" bool cid = const()[name=string(\"cid\"), val=bool(false)];\n"];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> out = concat(axis=cax,interleave=cid,values=(dqf,dkf))[name=string(\"cat\")];\n", 2*DIM,SEQ];
|
||||
[m appendFormat:@" tensor<fp16, [1,%d,1,%d]> out = concat(axis=cax,interleave=cid,values=(dqf,dkf));\n", 2*DIM,SEQ];
|
||||
[m appendString:@" } -> (out);\n}\n"];
|
||||
return m;
|
||||
}
|
||||
|
||||
// Mask blob (causal mask [SEQ,SEQ])
|
||||
static NSData *g_mask_blob = nil;
|
||||
// Mask blob helper
|
||||
static NSData *get_mask_blob(void) {
|
||||
if (!g_mask_blob) {
|
||||
_Float16 *mask = (_Float16*)calloc(SEQ*SEQ, sizeof(_Float16));
|
||||
for(int t=0;t<SEQ;t++) for(int t2=0;t2<SEQ;t2++)
|
||||
mask[t*SEQ+t2] = (t2<=t) ? (_Float16)0.0f : (_Float16)(-65504.0f);
|
||||
g_mask_blob = build_blob_fp16(mask, SEQ*SEQ);
|
||||
free(mask);
|
||||
}
|
||||
return g_mask_blob;
|
||||
_Float16 *mask = (_Float16*)calloc(SEQ*SEQ, sizeof(_Float16));
|
||||
for(int t=0;t<SEQ;t++) for(int t2=0;t2<SEQ;t2++)
|
||||
mask[t*SEQ+t2] = (t2<=t) ? (_Float16)0.0f : (_Float16)(-65504.0f);
|
||||
NSData *d = build_blob_fp16(mask, SEQ*SEQ);
|
||||
free(mask);
|
||||
return d;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,39 @@
|
|||
#!/usr/bin/env python3
|
||||
import os
|
||||
import json
|
||||
import struct
|
||||
import argparse
|
||||
from sample import BPETokenizer
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Tokenize any text file for ANE training")
|
||||
parser.add_argument("input", type=str, help="Input text file")
|
||||
parser.add_argument("--output", type=str, default="data.bin", help="Output binary file")
|
||||
parser.add_argument("--vocab", type=str, default="vocab.json", help="Path to vocab.json")
|
||||
args = parser.parse_args()
|
||||
|
||||
if not os.path.exists(args.input):
|
||||
print(f"Error: {args.input} not found")
|
||||
return
|
||||
|
||||
print(f"Loading tokenizer from {args.vocab}...")
|
||||
tokenizer = BPETokenizer(args.vocab)
|
||||
|
||||
print(f"Reading {args.input}...")
|
||||
with open(args.input, 'r', encoding='utf-8') as f:
|
||||
text = f.read()
|
||||
|
||||
print("Tokenizing...")
|
||||
# Add BOS token (1) at the start
|
||||
tokens = [1] + tokenizer.encode(text)
|
||||
|
||||
print(f"Saving {len(tokens)} tokens to {args.output}...")
|
||||
with open(args.output, 'wb') as f:
|
||||
for t in tokens:
|
||||
# The ANE trainer expects uint16_t
|
||||
f.write(struct.pack('H', t))
|
||||
|
||||
print("Done.")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1,78 @@
|
|||
# Taken from llama code and lightly modified
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
|
||||
|
||||
import os
|
||||
import struct
|
||||
import argparse
|
||||
from typing import List
|
||||
|
||||
from sentencepiece import SentencePieceProcessor
|
||||
|
||||
TOKENIZER_MODEL = "tokenizer.model" # the llama sentencepiece tokenizer model
|
||||
|
||||
class Tokenizer:
|
||||
def __init__(self, tokenizer_model=None):
|
||||
model_path = tokenizer_model if tokenizer_model else TOKENIZER_MODEL
|
||||
assert os.path.isfile(model_path), model_path
|
||||
self.sp_model = SentencePieceProcessor(model_file=model_path)
|
||||
self.model_path = model_path
|
||||
|
||||
# BOS / EOS token IDs
|
||||
self.n_words: int = self.sp_model.vocab_size()
|
||||
self.bos_id: int = self.sp_model.bos_id()
|
||||
self.eos_id: int = self.sp_model.eos_id()
|
||||
self.pad_id: int = self.sp_model.pad_id()
|
||||
#print(f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID: {self.eos_id}")
|
||||
assert self.sp_model.vocab_size() == self.sp_model.get_piece_size()
|
||||
|
||||
def encode(self, s: str, bos: bool, eos: bool) -> List[int]:
|
||||
assert type(s) is str
|
||||
t = self.sp_model.encode(s)
|
||||
if bos:
|
||||
t = [self.bos_id] + t
|
||||
if eos:
|
||||
t = t + [self.eos_id]
|
||||
return t
|
||||
|
||||
def decode(self, t: List[int]) -> str:
|
||||
return self.sp_model.decode(t)
|
||||
|
||||
def export(self):
|
||||
|
||||
# get all the tokens (postprocessed) and their scores as floats
|
||||
tokens, scores = [], []
|
||||
for i in range(self.n_words):
|
||||
|
||||
# decode the token and light postprocessing
|
||||
t = self.sp_model.id_to_piece(i)
|
||||
s = self.sp_model.get_score(i)
|
||||
if i == self.bos_id:
|
||||
t = '\n<s>\n'
|
||||
elif i == self.eos_id:
|
||||
t = '\n</s>\n'
|
||||
t = t.replace('▁', ' ') # sentencepiece uses this character as whitespace
|
||||
b = t.encode('utf-8') # bytes of this token, utf-8 encoded
|
||||
|
||||
tokens.append(b)
|
||||
scores.append(s)
|
||||
|
||||
# record the max token length
|
||||
max_token_length = max(len(t) for t in tokens)
|
||||
|
||||
# write to a binary file
|
||||
# the tokenizer.bin file is the same as .model file, but .bin
|
||||
tokenizer_bin = self.model_path.replace('.model', '.bin')
|
||||
with open(tokenizer_bin, 'wb') as f:
|
||||
f.write(struct.pack("I", max_token_length))
|
||||
for bytes, score in zip(tokens, scores):
|
||||
f.write(struct.pack("fI", score, len(bytes)))
|
||||
f.write(bytes)
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("-t", "--tokenizer-model", type=str, help="optional path to custom tokenizer ")
|
||||
args = parser.parse_args()
|
||||
|
||||
t = Tokenizer(args.tokenizer_model)
|
||||
t.export()
|
||||
|
|
@ -0,0 +1,71 @@
|
|||
import os
|
||||
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"
|
||||
VOCAB_SIZE = 5000 # Reduced for speed of verification
|
||||
SUBSET_SIZE = 200000 # 200KB limit for speed
|
||||
|
||||
def get_stats(ids):
|
||||
counts = Counter()
|
||||
for pair in zip(ids, ids[1:]):
|
||||
counts[pair] += 1
|
||||
return counts
|
||||
|
||||
def merge(ids, pair, idx):
|
||||
new_ids = []
|
||||
i = 0
|
||||
while i < len(ids):
|
||||
if i < len(ids) - 1 and ids[i] == pair[0] and ids[i+1] == pair[1]:
|
||||
new_ids.append(idx)
|
||||
i += 2
|
||||
else:
|
||||
new_ids.append(ids[i])
|
||||
i += 1
|
||||
return new_ids
|
||||
|
||||
def train():
|
||||
print(f"Loading raw text (subset {SUBSET_SIZE} bytes) from {RAW_TEXT_PATH}...")
|
||||
with open(RAW_TEXT_PATH, "r", encoding="utf-8") as f:
|
||||
text = f.read(SUBSET_SIZE)
|
||||
|
||||
print("Initial byte-encoding...")
|
||||
# Start with raw bytes (0-255)
|
||||
ids = list(text.encode("utf-8"))
|
||||
|
||||
merges = {}
|
||||
vocab = {i: bytes([i]) for i in range(256)}
|
||||
|
||||
num_merges = VOCAB_SIZE - 256
|
||||
print(f"Training BPE for {num_merges} merges...")
|
||||
|
||||
for i in range(num_merges):
|
||||
stats = get_stats(ids)
|
||||
if not stats:
|
||||
break
|
||||
pair = max(stats, key=stats.get)
|
||||
idx = 256 + i
|
||||
ids = merge(ids, pair, idx)
|
||||
merges[pair] = idx
|
||||
vocab[idx] = vocab[pair[0]] + vocab[pair[1]]
|
||||
if (i+1) % 100 == 0:
|
||||
print(f"Merge {i+1}/{num_merges}: {pair} -> {idx} (count {stats[pair]})")
|
||||
|
||||
# Save merges and vocab
|
||||
# We need to convert tuple keys to strings for JSON
|
||||
serializable_merges = {f"{p[0]},{p[1]}": idx for p, idx in merges.items()}
|
||||
# Convert vocab bytes to list of ints for JSON
|
||||
serializable_vocab = {idx: list(b) for idx, b in vocab.items()}
|
||||
|
||||
with open(VOCAB_PATH, "w") as f:
|
||||
json.dump({
|
||||
"merges": serializable_merges,
|
||||
"vocab": serializable_vocab
|
||||
}, f)
|
||||
|
||||
print(f"Vocab saved to {VOCAB_PATH}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
train()
|
||||
|
|
@ -56,53 +56,69 @@ static bool load_pretrained(LayerWeights *lw, float *rms_final, float *embed, co
|
|||
}
|
||||
|
||||
// ===== Compile one layer's kernels =====
|
||||
static bool compile_layer_kernels(LayerKernels *lk, LayerWeights *w) {
|
||||
lk->fwdAttn = compile_kern_mil_w(gen_sdpa_fwd_taps(), (@{
|
||||
@"@model_path/weights/rms1.bin": @{@"offset":@0, @"data":build_blob(w->rms_att,1,DIM)},
|
||||
@"@model_path/weights/wq.bin": @{@"offset":@0, @"data":build_blob(w->Wq,DIM,DIM)},
|
||||
@"@model_path/weights/wk.bin": @{@"offset":@0, @"data":build_blob(w->Wk,DIM,DIM)},
|
||||
@"@model_path/weights/wv.bin": @{@"offset":@0, @"data":build_blob(w->Wv,DIM,DIM)},
|
||||
@"@model_path/weights/wo.bin": @{@"offset":@0, @"data":build_blob(w->Wo,DIM,DIM)},
|
||||
@"@model_path/weights/mask.bin": @{@"offset":@0, @"data":get_mask_blob()},
|
||||
}), DIM*SEQ*2, 6*DIM*SEQ*2);
|
||||
static bool compile_layer_kernels(LayerKernels *lk) {
|
||||
int fwdAttn_ins[] = { DIM*SEQ*2, DIM*2, WQ_SZ*2, WQ_SZ*2, WQ_SZ*2, WO_SZ*2, SEQ*SEQ*2 };
|
||||
lk->fwdAttn = compile_kern_mil_w(gen_sdpa_fwd_flex(), @{}, fwdAttn_ins, 7, 6*DIM*SEQ*2);
|
||||
|
||||
lk->fwdFFN = compile_kern_mil_w(gen_ffn_fwd_taps(), (@{
|
||||
@"@model_path/weights/rms2.bin": @{@"offset":@0, @"data":build_blob(w->rms_ffn,1,DIM)},
|
||||
@"@model_path/weights/w1.bin": @{@"offset":@0, @"data":build_blob(w->W1,HIDDEN,DIM)},
|
||||
@"@model_path/weights/w3.bin": @{@"offset":@0, @"data":build_blob(w->W3,HIDDEN,DIM)},
|
||||
@"@model_path/weights/w2.bin": @{@"offset":@0, @"data":build_blob(w->W2,DIM,HIDDEN)},
|
||||
}), DIM*SEQ*2, (2*DIM+3*HIDDEN)*SEQ*2);
|
||||
int fwdFFN_ins[] = { DIM*SEQ*2, DIM*2, W1_SZ*2, WO_SZ*2, W3_SZ*2 };
|
||||
lk->fwdFFN = compile_kern_mil_w(gen_ffn_fwd_flex(), @{}, fwdFFN_ins, 5, (2*DIM+3*HIDDEN)*SEQ*2);
|
||||
|
||||
lk->ffnBwd = compile_kern_mil_w(gen_ffn_bwd(), (@{
|
||||
@"@model_path/weights/w2t.bin": @{@"offset":@0, @"data":build_blob_t(w->W2,DIM,HIDDEN)},
|
||||
@"@model_path/weights/w1t.bin": @{@"offset":@0, @"data":build_blob_t(w->W1,HIDDEN,DIM)},
|
||||
@"@model_path/weights/w3t.bin": @{@"offset":@0, @"data":build_blob_t(w->W3,HIDDEN,DIM)},
|
||||
}), (DIM+2*HIDDEN)*SEQ*2, (DIM+2*HIDDEN)*SEQ*2);
|
||||
int ffnBwd_ins[] = { (DIM+2*HIDDEN)*SEQ*2, W1_SZ*2, W2_SZ*2, W3_SZ*2 };
|
||||
lk->ffnBwd = compile_kern_mil_w(gen_ffn_bwd_flex(), @{}, ffnBwd_ins, 4, (DIM+2*HIDDEN)*SEQ*2);
|
||||
|
||||
lk->sdpaBwd1 = compile_kern_mil_w(gen_sdpa_bwd1(), (@{
|
||||
@"@model_path/weights/mask.bin": @{@"offset":@0, @"data":get_mask_blob()},
|
||||
@"@model_path/weights/wot.bin": @{@"offset":@0, @"data":build_blob_t(w->Wo,DIM,DIM)},
|
||||
}), 4*DIM*SEQ*2, (DIM+2*SCORE_CH)*SEQ*2);
|
||||
int sdpaBwd1_ins[] = { 4*DIM*SEQ*2, WO_SZ*2, SEQ*SEQ*2 };
|
||||
lk->sdpaBwd1 = compile_kern_mil_w(gen_sdpa_bwd1_flex(), @{}, sdpaBwd1_ins, 3, (DIM+2*SCORE_CH)*SEQ*2);
|
||||
|
||||
lk->qkvBwd = compile_kern_mil_w(gen_qkvb(), (@{
|
||||
@"@model_path/weights/wqt.bin": @{@"offset":@0, @"data":build_blob_t(w->Wq,DIM,DIM)},
|
||||
@"@model_path/weights/wkt.bin": @{@"offset":@0, @"data":build_blob_t(w->Wk,DIM,DIM)},
|
||||
@"@model_path/weights/wvt.bin": @{@"offset":@0, @"data":build_blob_t(w->Wv,DIM,DIM)},
|
||||
}), 3*DIM*SEQ*2, DIM*SEQ*2);
|
||||
int qkvBwd_ins[] = { 3*DIM*SEQ*2, WQ_SZ*2, WQ_SZ*2, WQ_SZ*2 };
|
||||
lk->qkvBwd = compile_kern_mil_w(gen_qkvb_flex(), @{}, qkvBwd_ins, 4, DIM*SEQ*2);
|
||||
|
||||
return lk->fwdAttn && lk->fwdFFN && lk->ffnBwd && lk->sdpaBwd1 && lk->qkvBwd;
|
||||
}
|
||||
|
||||
static void update_ane_weights(LayerKernels *lk, LayerWeights *w) {
|
||||
// fwdAttn: x(0), rw(1), Wq(2), Wk(3), Wv(4), Wo(5), cm(6)
|
||||
io_write_fp16(lk->fwdAttn->inputs[1], w->rms_att, 1, DIM);
|
||||
io_write_fp16(lk->fwdAttn->inputs[2], w->Wq, DIM, DIM);
|
||||
io_write_fp16(lk->fwdAttn->inputs[3], w->Wk, DIM, DIM);
|
||||
io_write_fp16(lk->fwdAttn->inputs[4], w->Wv, DIM, DIM);
|
||||
io_write_fp16(lk->fwdAttn->inputs[5], w->Wo, DIM, DIM);
|
||||
static NSData *m_blob = nil; if(!m_blob) m_blob = get_mask_blob();
|
||||
IOSurfaceLock(lk->fwdAttn->inputs[6], 0, NULL);
|
||||
memcpy(IOSurfaceGetBaseAddress(lk->fwdAttn->inputs[6]), (uint8_t*)[m_blob bytes]+128, SEQ*SEQ*2);
|
||||
IOSurfaceUnlock(lk->fwdAttn->inputs[6], 0, NULL);
|
||||
|
||||
// fwdFFN: x(0), rw(1), W1(2), W2(3), W3(4)
|
||||
io_write_fp16(lk->fwdFFN->inputs[1], w->rms_ffn, 1, DIM);
|
||||
io_write_fp16(lk->fwdFFN->inputs[2], w->W1, HIDDEN, DIM);
|
||||
io_write_fp16(lk->fwdFFN->inputs[3], w->W2, DIM, HIDDEN);
|
||||
io_write_fp16(lk->fwdFFN->inputs[4], w->W3, HIDDEN, DIM);
|
||||
|
||||
// ffnBwd: x(0), W1t(1), W2t(2), W3t(3)
|
||||
io_write_fp16_t(lk->ffnBwd->inputs[1], w->W1, HIDDEN, DIM);
|
||||
io_write_fp16_t(lk->ffnBwd->inputs[2], w->W2, DIM, HIDDEN);
|
||||
io_write_fp16_t(lk->ffnBwd->inputs[3], w->W3, HIDDEN, DIM);
|
||||
|
||||
// sdpaBwd1: x(0), Wot(1), cm(2)
|
||||
io_write_fp16_t(lk->sdpaBwd1->inputs[1], w->Wo, DIM, DIM);
|
||||
IOSurfaceLock(lk->sdpaBwd1->inputs[2], 0, NULL);
|
||||
memcpy(IOSurfaceGetBaseAddress(lk->sdpaBwd1->inputs[2]), (uint8_t*)[m_blob bytes]+128, SEQ*SEQ*2);
|
||||
IOSurfaceUnlock(lk->sdpaBwd1->inputs[2], 0, NULL);
|
||||
|
||||
// qkvBwd: x(0), Wqt(1), Wkt(2), Wvt(3)
|
||||
io_write_fp16_t(lk->qkvBwd->inputs[1], w->Wq, DIM, DIM);
|
||||
io_write_fp16_t(lk->qkvBwd->inputs[2], w->Wk, DIM, DIM);
|
||||
io_write_fp16_t(lk->qkvBwd->inputs[3], w->Wv, DIM, DIM);
|
||||
}
|
||||
|
||||
// Compile weight-free sdpaBwd2 (only needs once, no weights)
|
||||
static Kern *compile_sdpa_bwd2(void) {
|
||||
return compile_kern_mil_w(gen_sdpa_bwd2(), @{},
|
||||
(2*SCORE_CH+2*DIM)*SEQ*2, 2*DIM*SEQ*2);
|
||||
int bwd2_ins[] = { (2*SCORE_CH+2*DIM)*SEQ*2 };
|
||||
return compile_kern_mil_w(gen_sdpa_bwd2_flex(), @{}, bwd2_ins, 1, 2*DIM*SEQ*2);
|
||||
}
|
||||
|
||||
static void free_layer_kernels(LayerKernels *lk) {
|
||||
free_kern(lk->fwdAttn); free_kern(lk->fwdFFN); free_kern(lk->ffnBwd);
|
||||
free_kern(lk->sdpaBwd1); free_kern(lk->qkvBwd);
|
||||
// sdpaBwd2 is shared, freed separately
|
||||
lk->fwdAttn = lk->fwdFFN = lk->ffnBwd = lk->sdpaBwd1 = lk->qkvBwd = NULL;
|
||||
}
|
||||
|
||||
|
|
@ -194,11 +210,14 @@ int main(int argc, char *argv[]) {
|
|||
|
||||
// Parse args
|
||||
bool do_resume = false;
|
||||
int cli_steps = -1; float cli_lr = -1;
|
||||
for (int i=1; i<argc; i++) {
|
||||
if (strcmp(argv[i], "--resume") == 0) do_resume = true;
|
||||
else if (strcmp(argv[i], "--steps") == 0 && i+1<argc) total_steps = atoi(argv[++i]);
|
||||
else if (strcmp(argv[i], "--lr") == 0 && i+1<argc) lr = atof(argv[++i]);
|
||||
else if (strcmp(argv[i], "--steps") == 0 && i+1<argc) cli_steps = atoi(argv[++i]);
|
||||
else if (strcmp(argv[i], "--lr") == 0 && i+1<argc) cli_lr = atof(argv[++i]);
|
||||
}
|
||||
if (cli_steps > 0) total_steps = cli_steps;
|
||||
if (cli_lr > 0) lr = cli_lr;
|
||||
|
||||
// Allocate per-layer state
|
||||
LayerWeights lw[NLAYERS];
|
||||
|
|
@ -231,7 +250,11 @@ int main(int argc, char *argv[]) {
|
|||
resuming = load_checkpoint(CKPT_PATH, &start_step, &total_steps, &lr, &resume_loss,
|
||||
&cum_compile, &cum_train, &cum_wall, &cum_steps, &cum_batches, &adam_t,
|
||||
lw, la, rms_final, &arms_final, embed, &aembed);
|
||||
if (resuming) printf("[RESUMED step %d, loss=%.4f]\n", start_step, resume_loss);
|
||||
if (resuming) {
|
||||
printf("[RESUMED step %d, loss=%.4f]\n", start_step, resume_loss);
|
||||
if (cli_steps > 0) total_steps = cli_steps;
|
||||
if (cli_lr > 0) lr = cli_lr;
|
||||
}
|
||||
}
|
||||
if (!resuming) {
|
||||
printf("=== ANE Training: Stories110M (12 layers) ===\n");
|
||||
|
|
@ -316,48 +339,15 @@ int main(int argc, char *argv[]) {
|
|||
|
||||
srand48(42 + start_step);
|
||||
|
||||
// Initialize and compile all kernels ONCE
|
||||
for (int L=0; L<NLAYERS; L++) {
|
||||
if (!compile_layer_kernels(&kern[L])) return 1;
|
||||
update_ane_weights(&kern[L], &lw[L]);
|
||||
}
|
||||
printf(" Compiled all kernels (Weights-as-Tensors optimization active)\n");
|
||||
|
||||
int step = start_step;
|
||||
while (step < total_steps) {
|
||||
// Check compile budget
|
||||
if (g_compile_count + TOTAL_WEIGHT_KERNELS > MAX_COMPILES) {
|
||||
for (int L=0; L<NLAYERS; L++) { free_layer_kernels(&kern[L]); free_kern(sdpaBwd2[L]); }
|
||||
double wall = tb_ms(mach_absolute_time() - t_wall_start);
|
||||
save_checkpoint(CKPT_PATH, step, total_steps, lr, last_loss,
|
||||
total_compile_ms+cum_compile, total_train_ms+cum_train, wall+cum_wall,
|
||||
total_steps_done+cum_steps, total_batches+cum_batches, adam_t,
|
||||
lw, la, rms_final, &arms_final, embed, &aembed);
|
||||
printf("[exec() restart step %d, %d compiles, loss=%.4f]\n", step, g_compile_count, last_loss);
|
||||
fflush(stdout);
|
||||
execl(argv[0], argv[0], "--resume", NULL);
|
||||
perror("execl"); return 1;
|
||||
}
|
||||
|
||||
// Compile all layers' weight-bearing kernels
|
||||
uint64_t tc = mach_absolute_time();
|
||||
for (int L=0; L<NLAYERS; L++) free_layer_kernels(&kern[L]);
|
||||
|
||||
bool compile_ok = true;
|
||||
for (int L=0; L<NLAYERS; L++) {
|
||||
printf(" Compiling layer %d/%d... (%d compiles)\r", L+1, NLAYERS, g_compile_count);
|
||||
fflush(stdout);
|
||||
if (!compile_layer_kernels(&kern[L], &lw[L])) {
|
||||
printf("\nCompile failed at layer %d, restart\n", L);
|
||||
compile_ok = false; break;
|
||||
}
|
||||
}
|
||||
if (!compile_ok) { g_compile_count = MAX_COMPILES; continue; }
|
||||
|
||||
// Re-compile sdpaBwd2 if needed (after exec restart)
|
||||
for (int L=0; L<NLAYERS; L++) {
|
||||
if (!sdpaBwd2[L]) {
|
||||
sdpaBwd2[L] = compile_sdpa_bwd2();
|
||||
if (!sdpaBwd2[L]) { printf("sdpaBwd2 recompile failed\n"); return 1; }
|
||||
}
|
||||
}
|
||||
|
||||
double cms = tb_ms(mach_absolute_time() - tc);
|
||||
total_compile_ms += cms;
|
||||
printf(" Compiled %d kernels in %.0fms \n", TOTAL_WEIGHT_KERNELS, cms);
|
||||
|
||||
// Zero gradient accumulators
|
||||
for (int L=0; L<NLAYERS; L++) layer_grads_zero(&grads[L]);
|
||||
|
|
@ -391,7 +381,7 @@ int main(int argc, char *argv[]) {
|
|||
t0=mach_absolute_time();
|
||||
dispatch_group_wait(dw_grp, DISPATCH_TIME_FOREVER);
|
||||
t1=mach_absolute_time(); t_cblas_wait+=tb_ms(t1-t0); t0=t1;
|
||||
io_write_fp16(kern[L].fwdAttn->ioIn, x_cur, DIM, SEQ);
|
||||
io_write_fp16(kern[L].fwdAttn->inputs[0], x_cur, DIM, SEQ);
|
||||
t1=mach_absolute_time(); t_io+=tb_ms(t1-t0); t0=t1;
|
||||
ane_eval(kern[L].fwdAttn);
|
||||
t1=mach_absolute_time(); t_ane+=tb_ms(t1-t0); t0=t1;
|
||||
|
|
@ -404,7 +394,7 @@ int main(int argc, char *argv[]) {
|
|||
t1=mach_absolute_time(); t_elem+=tb_ms(t1-t0); t0=t1;
|
||||
|
||||
// FFN forward
|
||||
io_write_fp16(kern[L].fwdFFN->ioIn, ac->x2, DIM, SEQ);
|
||||
io_write_fp16(kern[L].fwdFFN->inputs[0], ac->x2, DIM, SEQ);
|
||||
t1=mach_absolute_time(); t_io+=tb_ms(t1-t0); t0=t1;
|
||||
ane_eval(kern[L].fwdFFN);
|
||||
t1=mach_absolute_time(); t_ane+=tb_ms(t1-t0); t0=t1;
|
||||
|
|
@ -467,8 +457,8 @@ int main(int argc, char *argv[]) {
|
|||
memcpy(dffn, dy, SEQ*DIM*4);
|
||||
|
||||
// FFN backward (ANE)
|
||||
io_write_fp16_at(kern[L].ffnBwd->ioIn, 0, dffn, DIM, SEQ);
|
||||
io_copy(kern[L].ffnBwd->ioIn, DIM, kern[L].fwdFFN->ioOut, DIM, 2*HIDDEN, SEQ);
|
||||
io_write_fp16_at(kern[L].ffnBwd->inputs[0], 0, dffn, DIM, SEQ);
|
||||
io_copy(kern[L].ffnBwd->inputs[0], DIM, kern[L].fwdFFN->ioOut, DIM, 2*HIDDEN, SEQ);
|
||||
ane_eval(kern[L].ffnBwd);
|
||||
io_read_fp16(kern[L].ffnBwd->ioOut, dx_ffn, 0, DIM, SEQ);
|
||||
io_read_fp16(kern[L].ffnBwd->ioOut, dh1, DIM, HIDDEN, SEQ);
|
||||
|
|
@ -507,11 +497,11 @@ int main(int argc, char *argv[]) {
|
|||
});
|
||||
|
||||
// SDPA backward (ANE)
|
||||
io_copy(kern[L].sdpaBwd1->ioIn, 0, kern[L].fwdAttn->ioOut, DIM, 3*DIM, SEQ);
|
||||
io_write_fp16_at(kern[L].sdpaBwd1->ioIn, 3*DIM, dx2, DIM, SEQ);
|
||||
io_copy(kern[L].sdpaBwd1->inputs[0], 0, kern[L].fwdAttn->ioOut, DIM, 3*DIM, SEQ);
|
||||
io_write_fp16_at(kern[L].sdpaBwd1->inputs[0], 3*DIM, dx2, DIM, SEQ);
|
||||
ane_eval(kern[L].sdpaBwd1);
|
||||
io_copy(sdpaBwd2[L]->ioIn, 0, kern[L].sdpaBwd1->ioOut, DIM, 2*SCORE_CH, SEQ);
|
||||
io_copy(sdpaBwd2[L]->ioIn, 2*SCORE_CH, kern[L].fwdAttn->ioOut, DIM, 2*DIM, SEQ);
|
||||
io_copy(sdpaBwd2[L]->inputs[0], 0, kern[L].sdpaBwd1->ioOut, DIM, 2*SCORE_CH, SEQ);
|
||||
io_copy(sdpaBwd2[L]->inputs[0], 2*SCORE_CH, kern[L].fwdAttn->ioOut, DIM, 2*DIM, SEQ);
|
||||
ane_eval(sdpaBwd2[L]);
|
||||
|
||||
io_read_fp16(sdpaBwd2[L]->ioOut, dq, 0, DIM, SEQ);
|
||||
|
|
@ -534,8 +524,8 @@ int main(int argc, char *argv[]) {
|
|||
});
|
||||
|
||||
// QKV backward (ANE)
|
||||
io_copy(kern[L].qkvBwd->ioIn, 0, sdpaBwd2[L]->ioOut, 0, 2*DIM, SEQ);
|
||||
io_copy(kern[L].qkvBwd->ioIn, 2*DIM, kern[L].sdpaBwd1->ioOut, 0, DIM, SEQ);
|
||||
io_copy(kern[L].qkvBwd->inputs[0], 0, sdpaBwd2[L]->ioOut, 0, 2*DIM, SEQ);
|
||||
io_copy(kern[L].qkvBwd->inputs[0], 2*DIM, kern[L].sdpaBwd1->ioOut, 0, DIM, SEQ);
|
||||
ane_eval(kern[L].qkvBwd);
|
||||
io_read_fp16(kern[L].qkvBwd->ioOut, dx_attn, 0, DIM, SEQ);
|
||||
|
||||
|
|
@ -627,8 +617,11 @@ int main(int argc, char *argv[]) {
|
|||
for(size_t i=0;i<(size_t)VOCAB*DIM;i++) gembed[i]*=gsc;
|
||||
adam_update(embed, gembed, &aembed, adam_t, lr, adam_b1, adam_b2, adam_eps);
|
||||
|
||||
printf(" [batch %d: compile=%.0fms train=%.1fms (%.1fms/step) compiles=%d]\n",
|
||||
steps_batch, cms, tms, tms/steps_batch, g_compile_count);
|
||||
// SYNC WEIGHTS TO ANE SURFACES
|
||||
for(int L=0; L<NLAYERS; L++) update_ane_weights(&kern[L], &lw[L]);
|
||||
|
||||
printf(" [batch %d: train=%.1fms (%.1fms/step) compiles=%d]\n",
|
||||
steps_batch, tms, tms/steps_batch, g_compile_count);
|
||||
printf(" ane=%.1f io=%.1f cls=%.1f elem=%.1f rms=%.1f cblas_wait=%.1f ms/step\n",
|
||||
t_ane/steps_batch, t_io/steps_batch, t_cls/steps_batch, t_elem/steps_batch,
|
||||
t_rms/steps_batch, t_cblas_wait/steps_batch);
|
||||
|
|
@ -639,9 +632,9 @@ int main(int argc, char *argv[]) {
|
|||
double bs = NLAYERS * 2.0*HEADS*5*SEQ*SEQ*HD;
|
||||
double ane_f_batch = (bf*2 + bs) * steps_batch;
|
||||
double ane_tflops = ane_f_batch / (tms * 1e9);
|
||||
fprintf(stderr, "{\"type\":\"batch\",\"batch\":%d,\"compile_ms\":%.1f,"
|
||||
fprintf(stderr, "{\"type\":\"batch\",\"batch\":%d,"
|
||||
"\"train_ms\":%.1f,\"ms_per_step\":%.1f}\n",
|
||||
steps_batch, cms, tms, tms/steps_batch);
|
||||
steps_batch, tms, tms/steps_batch);
|
||||
fprintf(stderr, "{\"type\":\"perf\",\"ane_tflops\":%.3f,\"ane_util_pct\":%.2f}\n",
|
||||
ane_tflops, 100.0*ane_tflops/15.8);
|
||||
}
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue