fix: restore all removed bundled skills + fix skills sync system

- Restored 21 skills removed in commits 757d012 and 740dd92: accelerate, audiocraft, code-review, faiss, flash-attention, gguf, grpo-rl-training, guidance, llava, nemo-curator, obliteratus, peft, pytorch-fsdp, pytorch-lightning, simpo, slime, stable-diffusion, tensorrt-llm, torchtitan, trl-fine-tuning, whisper - Rewrote sync_skills() with proper update semantics: * New skills (not in manifest): copied to user dir * Existing skills (in manifest + on disk): updated via hash comparison * User-deleted skills (in manifest, not on disk): respected, not re-added * Stale manifest entries (removed from bundled): cleaned from manifest - Added sync_skills() to CLI startup (cmd_chat) and gateway startup (start_gateway) — previously only ran during 'hermes update' - Updated cmd_update output to show new/updated/cleaned counts - Rewrote tests: 20 tests covering manifest CRUD, dir hashing, fresh install, user deletion respect, update detection, stale cleanup, and name collision handling 75 bundled skills total. 2002 tests pass.
2026-03-06 15:57:12 -08:00 · 2026-03-06 15:57:12 -08:00 · ab0f4126cf
commit ab0f4126cf
parent 68fbae5692
74 changed files with 27881 additions and 44 deletions
--- a/skills/mlops/torchtitan/references/checkpoint.md
+++ b/skills/mlops/torchtitan/references/checkpoint.md
@ -0,0 +1,181 @@
+# Checkpointing in TorchTitan
+
+TorchTitan uses PyTorch Distributed Checkpoint (DCP) for fault-tolerant, interoperable checkpointing.
+
+## Basic Configuration
+
+```toml
+[checkpoint]
+enable = true
+folder = "checkpoint"
+interval = 500
+```
+
+## Save Model Only (Smaller Checkpoints)
+
+Exclude optimizer state and training metadata:
+
+```toml
+[checkpoint]
+enable = true
+last_save_model_only = true
+export_dtype = "bfloat16"  # Optional: export in lower precision
+```
+
+## Excluding Keys from Loading
+
+Partial checkpoint loading for modified settings:
+
+```toml
+[checkpoint]
+enable = true
+exclude_from_loading = ["data_loader", "lr_scheduler"]
+```
+
+CLI equivalent:
+```bash
+--checkpoint.exclude_from_loading data_loader,lr_scheduler
+```
+
+## Creating Seed Checkpoints
+
+Required for Pipeline Parallelism to ensure consistent initialization:
+
+```bash
+NGPU=1 CONFIG_FILE=<path_to_config> ./run_train.sh \
+  --checkpoint.enable \
+  --checkpoint.create_seed_checkpoint \
+  --parallelism.data_parallel_replicate_degree 1 \
+  --parallelism.data_parallel_shard_degree 1 \
+  --parallelism.tensor_parallel_degree 1 \
+  --parallelism.pipeline_parallel_degree 1 \
+  --parallelism.context_parallel_degree 1 \
+  --parallelism.expert_parallel_degree 1
+```
+
+This initializes on single CPU for reproducible initialization across any GPU count.
+
+## Async Checkpointing
+
+Reduce checkpoint overhead with async writes:
+
+```toml
+[checkpoint]
+enable = true
+async_mode = "async"  # Options: "disabled", "async", "async_with_pinned_mem"
+```
+
+## HuggingFace Conversion
+
+### During Training
+
+Save directly in HuggingFace format:
+
+```toml
+[checkpoint]
+last_save_in_hf = true
+last_save_model_only = true
+```
+
+Load from HuggingFace:
+
+```toml
+[checkpoint]
+initial_load_in_hf = true
+
+[model]
+hf_assets_path = "./path/to/hf/checkpoint"
+```
+
+### Offline Conversion
+
+Convert without running training:
+
+```bash
+# HuggingFace -> TorchTitan
+python ./scripts/checkpoint_conversion/convert_from_hf.py \
+  <input_dir> <output_dir> \
+  --model_name llama3 \
+  --model_flavor 8B
+
+# TorchTitan -> HuggingFace
+python ./scripts/checkpoint_conversion/convert_to_hf.py \
+  <input_dir> <output_dir> \
+  --hf_assets_path ./assets/hf/Llama3.1-8B \
+  --model_name llama3 \
+  --model_flavor 8B
+```
+
+### Example
+
+```bash
+python ./scripts/convert_from_hf.py \
+  ~/.cache/huggingface/hub/models--meta-llama--Meta-Llama-3-8B/snapshots/8cde5ca8380496c9a6cc7ef3a8b46a0372a1d920/ \
+  ./initial_load_path/ \
+  --model_name llama3 \
+  --model_flavor 8B
+```
+
+## Converting to Single .pt File
+
+Convert DCP sharded checkpoint to single PyTorch file:
+
+```bash
+python -m torch.distributed.checkpoint.format_utils \
+  dcp_to_torch \
+  torchtitan/outputs/checkpoint/step-1000 \
+  checkpoint.pt
+```
+
+## Checkpoint Structure
+
+DCP saves sharded checkpoints that can be resharded for different parallelism configurations:
+
+```
+checkpoint/
+├── step-500/
+│   ├── .metadata
+│   ├── __0_0.distcp
+│   ├── __0_1.distcp
+│   └── ...
+└── step-1000/
+    └── ...
+```
+
+## Resume Training
+
+Training auto-resumes from the latest checkpoint in the configured folder. To resume from a specific step:
+
+```toml
+[checkpoint]
+load_step = 500  # Resume from step 500
+```
+
+## Interoperability with TorchTune
+
+Checkpoints saved with `last_save_model_only = true` can be loaded directly into [torchtune](https://github.com/pytorch/torchtune) for fine-tuning.
+
+## Full Configuration Example
+
+```toml
+[checkpoint]
+enable = true
+folder = "checkpoint"
+interval = 500
+load_step = -1  # -1 = latest, or specify step number
+last_save_model_only = true
+export_dtype = "bfloat16"
+async_mode = "async"
+exclude_from_loading = []
+last_save_in_hf = false
+initial_load_in_hf = false
+create_seed_checkpoint = false
+```
+
+## Best Practices
+
+1. **Large models**: Use `async_mode = "async"` to overlap checkpoint saves with training
+2. **Fine-tuning export**: Enable `last_save_model_only` and `export_dtype = "bfloat16"` for smaller files
+3. **Pipeline parallelism**: Always create seed checkpoint first
+4. **Debugging**: Save frequent checkpoints during development, reduce for production
+5. **HF interop**: Use conversion scripts for offline conversion, direct save/load for training workflows
--- a/skills/mlops/torchtitan/references/custom-models.md
+++ b/skills/mlops/torchtitan/references/custom-models.md
@ -0,0 +1,258 @@
+# Adding Custom Models to TorchTitan
+
+This guide explains how to add a new model to TorchTitan following the established patterns.
+
+## Directory Structure
+
+```
+torchtitan/models/your_model/
+├── model/
+│   ├── __init__.py
+│   ├── args.py          # Model arguments
+│   ├── model.py         # Model definition
+│   └── state_dict_adapter.py  # HF conversion (optional)
+├── infra/
+│   ├── __init__.py
+│   ├── parallelize.py   # TP, FSDP, compile application
+│   └── pipeline.py      # PP application (optional)
+├── train_configs/
+│   ├── debug_model.toml
+│   └── your_model_XB.toml
+├── __init__.py          # TrainSpec registration
+└── README.md
+```
+
+## Step 1: Define Model Arguments
+
+Inherit from `BaseModelArgs`:
+
+```python
+# model/args.py
+from torchtitan.protocols.model import BaseModelArgs
+from dataclasses import dataclass
+
+@dataclass
+class YourModelArgs(BaseModelArgs):
+    dim: int = 4096
+    n_layers: int = 32
+    n_heads: int = 32
+    vocab_size: int = 128256
+
+    def get_nparams_and_flops(self, seq_len: int) -> tuple[int, int]:
+        """Return (num_params, flops_per_token) for throughput calculation."""
+        nparams = self.vocab_size * self.dim + ...  # Calculate params
+        flops = 6 * nparams  # Approximate: 6 * params for forward+backward
+        return nparams, flops
+
+    def update_from_config(self, job_config) -> "YourModelArgs":
+        """Update args from training config."""
+        # Override specific args from job_config if needed
+        return self
+```
+
+## Step 2: Define Model
+
+Inherit from `ModelProtocol`:
+
+```python
+# model/model.py
+import torch.nn as nn
+from torchtitan.protocols.model import ModelProtocol
+from .args import YourModelArgs
+
+class YourModel(ModelProtocol):
+    def __init__(self, args: YourModelArgs):
+        super().__init__()
+        self.args = args
+        self.tok_embeddings = nn.Embedding(args.vocab_size, args.dim)
+        self.layers = nn.ModuleDict({
+            str(i): TransformerBlock(args) for i in range(args.n_layers)
+        })
+        self.norm = RMSNorm(args.dim)
+        self.output = nn.Linear(args.dim, args.vocab_size, bias=False)
+
+    def forward(self, tokens: torch.Tensor) -> torch.Tensor:
+        h = self.tok_embeddings(tokens)
+        for layer in self.layers.values():
+            h = layer(h)
+        h = self.norm(h)
+        return self.output(h)
+
+    def init_weights(self):
+        """Initialize weights recursively."""
+        for module in self.modules():
+            if hasattr(module, 'init_weights') and module is not self:
+                module.init_weights()
+            elif isinstance(module, nn.Linear):
+                nn.init.normal_(module.weight, std=0.02)
+```
+
+**Important guidelines**:
+- Write single-device model code (parallelism applied externally)
+- Use `nn.ModuleDict` for layers (preserves FQNs when deleting for PP)
+- Make input/output layers optional for PP compatibility
+- Define `init_weights()` recursively
+
+## Step 3: Parallelize Function
+
+```python
+# infra/parallelize.py
+from torch.distributed._composable.fsdp import fully_shard
+from torch.distributed.tensor.parallel import parallelize_module
+
+def parallelize_your_model(
+    model: YourModel,
+    world_mesh: DeviceMesh,
+    parallel_dims: ParallelDims,
+    job_config: JobConfig,
+):
+    # Apply in this order: TP -> AC -> compile -> FSDP
+
+    # 1. Tensor Parallelism
+    if parallel_dims.tp_enabled:
+        apply_tp(model, world_mesh["tp"], job_config)
+
+    # 2. Activation Checkpointing
+    if job_config.activation_checkpoint.mode == "full":
+        apply_ac(model, job_config)
+
+    # 3. torch.compile
+    if job_config.compile.enable:
+        model = torch.compile(model)
+
+    # 4. FSDP
+    if parallel_dims.dp_enabled:
+        apply_fsdp(model, world_mesh["dp"], job_config)
+
+    return model
+```
+
+## Step 4: Create TrainSpec
+
+```python
+# __init__.py
+from torchtitan.protocols.train_spec import TrainSpec, register_train_spec
+from .model.model import YourModel
+from .model.args import YourModelArgs
+from .infra.parallelize import parallelize_your_model
+
+MODEL_CONFIGS = {
+    "8B": YourModelArgs(dim=4096, n_layers=32, n_heads=32),
+    "70B": YourModelArgs(dim=8192, n_layers=80, n_heads=64),
+}
+
+def get_train_spec(flavor: str) -> TrainSpec:
+    return TrainSpec(
+        model_cls=YourModel,
+        model_args=MODEL_CONFIGS[flavor],
+        parallelize_fn=parallelize_your_model,
+        pipeline_fn=None,  # Or your_pipeline_fn for PP
+        build_optimizer_fn=build_optimizer,  # Reuse existing
+        build_lr_scheduler_fn=build_lr_scheduler,  # Reuse existing
+        build_dataloader_fn=build_dataloader,  # Reuse existing
+        build_tokenizer_fn=build_tokenizer,  # Reuse existing
+        build_loss_fn=build_loss,  # Reuse existing
+        state_dict_adapter=None,  # Or YourStateDictAdapter
+    )
+
+# Register so train.py can find it
+register_train_spec("your_model", get_train_spec)
+```
+
+## Step 5: State Dict Adapter (Optional)
+
+For HuggingFace checkpoint conversion:
+
+```python
+# model/state_dict_adapter.py
+from torchtitan.protocols.state_dict_adapter import BaseStateDictAdapter
+
+class YourStateDictAdapter(BaseStateDictAdapter):
+    def to_hf(self, state_dict: dict) -> dict:
+        """Convert torchtitan state dict to HF format."""
+        hf_state_dict = {}
+        for key, value in state_dict.items():
+            hf_key = self._convert_key_to_hf(key)
+            hf_state_dict[hf_key] = value
+        return hf_state_dict
+
+    def from_hf(self, state_dict: dict) -> dict:
+        """Convert HF state dict to torchtitan format."""
+        tt_state_dict = {}
+        for key, value in state_dict.items():
+            tt_key = self._convert_key_from_hf(key)
+            tt_state_dict[tt_key] = value
+        return tt_state_dict
+```
+
+## Step 6: Training Config
+
+```toml
+# train_configs/your_model_8b.toml
+[job]
+dump_folder = "./outputs"
+description = "Your Model 8B training"
+
+[model]
+name = "your_model"
+flavor = "8B"
+
+[optimizer]
+name = "AdamW"
+lr = 3e-4
+
+[training]
+local_batch_size = 2
+seq_len = 8192
+steps = 1000
+dataset = "c4"
+
+[parallelism]
+data_parallel_shard_degree = -1
+tensor_parallel_degree = 1
+```
+
+## Step 7: Register Model
+
+Add to `torchtitan/models/__init__.py`:
+
+```python
+from .your_model import get_train_spec as get_your_model_train_spec
+
+MODEL_REGISTRY["your_model"] = get_your_model_train_spec
+```
+
+## Testing
+
+### Numerics Test
+
+Compare output with HuggingFace implementation:
+
+```python
+def test_numerics():
+    # Load same checkpoint into both implementations
+    tt_model = YourModel(args).load_checkpoint(...)
+    hf_model = HFYourModel.from_pretrained(...)
+
+    # Compare outputs
+    input_ids = torch.randint(0, vocab_size, (1, 128))
+    tt_output = tt_model(input_ids)
+    hf_output = hf_model(input_ids).logits
+
+    torch.testing.assert_close(tt_output, hf_output, atol=1e-4, rtol=1e-4)
+```
+
+### Loss Convergence
+
+Compare loss curves with verified baseline (see `docs/converging.md`).
+
+### Performance Benchmark
+
+Add benchmark config to `benchmarks/` folder.
+
+## Guiding Principles
+
+1. **Readability over flexibility**: Don't over-abstract
+2. **Minimal model changes**: Parallelism applied externally
+3. **Clean, minimal codebase**: Reuse existing components where possible
+4. **Single-device semantics**: Model code should work on single GPU
--- a/skills/mlops/torchtitan/references/float8.md
+++ b/skills/mlops/torchtitan/references/float8.md
@ -0,0 +1,133 @@
+# Float8 Training in TorchTitan
+
+Float8 training provides substantial speedups for models where GEMMs are large enough that the FP8 tensorcore speedup outweighs dynamic quantization overhead.
+
+## Hardware Requirements
+
+- NVIDIA H100 or newer GPUs (FP8 Tensor Cores)
+- Blackwell GPUs for MXFP8 training
+
+## Installation
+
+```bash
+USE_CPP=0 pip install git+https://github.com/pytorch/ao.git
+```
+
+## Usage: Tensorwise Scaling
+
+Standard Float8 with tensorwise dynamic scaling:
+
+```bash
+CONFIG_FILE="./torchtitan/models/llama3/train_configs/llama3_8b.toml" ./run_train.sh \
+  --model.converters="quantize.linear.float8" \
+  --quantize.linear.float8.enable_fsdp_float8_all_gather \
+  --quantize.linear.float8.precompute_float8_dynamic_scale_for_fsdp \
+  --compile.enable
+```
+
+### Key Arguments
+
+| Argument | Description |
+|----------|-------------|
+| `--model.converters="quantize.linear.float8"` | Swap `nn.Linear` with `Float8Linear` |
+| `--quantize.linear.float8.enable_fsdp_float8_all_gather` | Communicate in float8 to save bandwidth |
+| `--quantize.linear.float8.precompute_float8_dynamic_scale_for_fsdp` | Single all-reduce for all AMAX/scales |
+| `--compile.enable` | Required - fuses float8 scaling/casting kernels |
+
+## Usage: Rowwise Scaling
+
+Higher accuracy than tensorwise scaling:
+
+```bash
+CONFIG_FILE="./torchtitan/models/llama3/train_configs/llama3_8b.toml" ./run_train.sh \
+  --model.converters="quantize.linear.float8" \
+  --quantize.linear.float8.recipe_name rowwise \
+  --compile.enable
+```
+
+## Filtering Layers
+
+Not all layers benefit from Float8. Filter small layers:
+
+```bash
+--quantize.linear.float8.filter_fqns="attention.wk,attention.wv,output"
+```
+
+### Auto-filtering
+
+Automatically skip layers too small to benefit:
+
+```bash
+--quantize.linear.float8.filter_fqns="auto_filter_small_kn"
+```
+
+Thresholds based on H100 microbenchmarks where speedup > overhead.
+
+## TOML Configuration
+
+```toml
+[model]
+converters = ["quantize.linear.float8"]
+
+[quantize.linear.float8]
+enable_fsdp_float8_all_gather = true
+precompute_float8_dynamic_scale_for_fsdp = true
+filter_fqns = ["output", "auto_filter_small_kn"]
+
+[compile]
+enable = true
+components = ["model", "loss"]
+```
+
+## How Float8 Works with Distributed Training
+
+### Single Device
+
+Cast input and weight to float8 inside forward before calling `torch._scaled_mm`:
+
+```python
+# Float8 matmul requires scales
+torch._scaled_mm(input_fp8, weight_fp8, scale_a=scale_input, scale_b=scale_weight)
+```
+
+### FSDP + Float8
+
+1. Cast sharded high-precision weights (1/N per rank) to float8
+2. Perform float8 all-gather (saves bandwidth vs bf16/fp32)
+3. Communicate `max(abs)` across ranks for scale computation
+4. At forward start, have unsharded float8 weights ready
+
+**Net benefit**: Float8 all-gather + amax communication can beat bf16/fp32 all-gather, depending on world size and message size.
+
+### TP + Float8
+
+- **Input**: Cast sharded input to float8, all-gather in float8
+- **Weights**: Communicate `max(abs)` for sharded weights
+- **Matmul**: Float8 input (unsharded) x float8 weight (sharded) with global scales
+
+## Scaling Strategies
+
+| Strategy | Status | Description |
+|----------|--------|-------------|
+| Tensorwise dynamic | Stable | Single scale per tensor |
+| Rowwise dynamic | Alpha | Scale per row, higher accuracy |
+
+## Performance Gains
+
+From benchmarks on H100:
+
+| Configuration | TPS/GPU | vs Baseline |
+|---------------|---------|-------------|
+| FSDP only | 5,762 | - |
+| FSDP + compile | 6,667 | +16% |
+| FSDP + compile + Float8 | 8,532 | +48% |
+
+## Determining Float8 Benefit
+
+Check [torchao microbenchmarks](https://github.com/pytorch/ao/tree/main/torchao/float8#performance) for forward+backward pass speedups on "layer norm => linear => sigmoid" for different M,N,K sizes.
+
+Rule of thumb: GEMMs with K,N > 4096 typically benefit from Float8.
+
+## MXFP8 Training (Blackwell)
+
+For NVIDIA Blackwell GPUs, TorchTitan supports MXFP8 (Microscaling FP8) for both dense and MoE models. See [docs/mxfp8.md](https://github.com/pytorch/torchtitan/blob/main/docs/mxfp8.md) for details.
--- a/skills/mlops/torchtitan/references/fsdp.md
+++ b/skills/mlops/torchtitan/references/fsdp.md
@ -0,0 +1,126 @@
+# FSDP2 in TorchTitan
+
+## Why FSDP2?
+
+FSDP2 is a rewrite of PyTorch's Fully Sharded Data Parallel (FSDP) API, removing the `FlatParameter` abstraction for better composability and simpler implementation.
+
+### Key improvements over FSDP1
+
+- **DTensor-based sharding**: Sharded parameters are `DTensor`s on dim-0, enabling easy manipulation and communication-free sharded state dicts
+- **Better memory management**: Deterministic and lower GPU memory (7% reduction) by avoiding `recordStream`
+- **Simplified API**: Fewer arguments, no wrapper class
+
+### Performance
+
+On Llama-7B with 8x H100s, FSDP2 achieves higher MFU with 7% lower peak memory than FSDP1, matching the same loss curve.
+
+## API Reference
+
+```python
+from torch.distributed._composable.fsdp import fully_shard, MixedPrecisionPolicy, OffloadPolicy
+
+@contract(state_cls=FSDPState)
+def fully_shard(
+    module: nn.Module,
+    *,
+    mesh: Optional[DeviceMesh] = None,
+    reshard_after_forward: Union[bool, int] = True,
+    mp_policy: MixedPrecisionPolicy = MixedPrecisionPolicy(),
+    offload_policy: OffloadPolicy = OffloadPolicy(),
+) -> nn.Module:
+```
+
+## Sharding Strategies (ZeRO Equivalents)
+
+| FSDP2 Configuration | FSDP1 Equivalent | DeepSpeed |
+|---------------------|------------------|-----------|
+| 1D mesh + `reshard_after_forward=True` | FULL_SHARD | ZeRO-3 |
+| 1D mesh + `reshard_after_forward=False` | SHARD_GRAD_OP | ZeRO-2 |
+| 2D mesh + `reshard_after_forward=True` | HYBRID_SHARD | MiCS |
+| 1D/2D mesh + `reshard_after_forward=8` (int) | - | ZeRO++ hpZ |
+
+## Meta-Device Initialization
+
+FSDP2 supports materializing tensors onto GPU _after_ sharding:
+
+```python
+# Initialize on meta device (no memory)
+with torch.device("meta"):
+    model = Transformer()
+
+# Apply FSDP2 sharding
+for module in model.modules():
+    if isinstance(module, TransformerBlock):
+        fully_shard(module)
+fully_shard(model)
+
+# Parameters still on meta device
+for tensor in itertools.chain(model.parameters(), model.buffers()):
+    assert tensor.device == torch.device("meta")
+
+# Allocate sharded parameters on GPU
+model.to_empty(device="cuda")
+
+# Initialize weights
+model.init_weights()
+```
+
+## State Dict Differences
+
+| Operation | FSDP1 | FSDP2 |
+|-----------|-------|-------|
+| `model.state_dict()` | Full state dict | Sharded state dict (no communication) |
+| `optim.state_dict()` | Local state dict | Sharded state dict (no communication) |
+| `summon_full_params()` | Supported | Use `DTensor` APIs like `full_tensor()` |
+| Gradient clipping | `FSDP.clip_grad_norm_()` | `nn.utils.clip_grad_norm_()` |
+
+## Mixed Precision
+
+```python
+from torch.distributed._composable.fsdp import MixedPrecisionPolicy
+
+mp_policy = MixedPrecisionPolicy(
+    param_dtype=torch.bfloat16,
+    reduce_dtype=torch.float32,
+    output_dtype=torch.bfloat16,
+    cast_forward_inputs=True,
+)
+
+fully_shard(model, mp_policy=mp_policy)
+```
+
+## HSDP (Hybrid Sharded Data Parallel)
+
+For 2D parallelism with replication + sharding:
+
+```python
+from torch.distributed.device_mesh import init_device_mesh
+
+# Replicate across 4 groups, shard within 8 GPUs each
+mesh = init_device_mesh("cuda", (4, 8), mesh_dim_names=("replicate", "shard"))
+
+fully_shard(model, mesh=mesh)
+```
+
+## Configuration in TorchTitan
+
+```toml
+[parallelism]
+# FSDP sharding degree (-1 = auto, use all available GPUs)
+data_parallel_shard_degree = -1
+
+# HSDP replication degree (1 = pure FSDP, >1 = HSDP)
+data_parallel_replicate_degree = 1
+```
+
+## Removed Arguments from FSDP1
+
+These FSDP1 arguments are no longer needed:
+
+- `auto_wrap_policy`: Apply `fully_shard` directly to modules
+- `backward_prefetch`: Always uses BACKWARD_PRE
+- `param_init_fn`: Use meta-device initialization
+- `device_id`: Uses mesh's device automatically
+- `sync_module_states`: Not needed with DTensor
+- `limit_all_gathers`: New memory management doesn't need it
+- `use_orig_params`: Always true (no FlatParameter)