This PR fixes problems related to #569:
- block initialization
- throughput calculation and cache usage
- mixtral in tests
Beam search is removed for Mixtral and Llama for now. Those models use DynamicCache, which requires special function to change: (see https://github.com/huggingface/transformers/blob/main/src/transformers/cache_utils.py#L161)
---------
Co-authored-by: Max Ryabinin <mryabinin0@gmail.com>
Before:
```python
Aug 23 23:51:31.394 [INFO] Loaded Maykeye/TinyLLama-v0 block 0, _IncompatibleKeys(missing_keys=[], unexpected_keys=['self_attn.rotary_emb.inv_freq'])
```
After:
```python
Aug 23 23:51:31.394 [INFO] Loaded Maykeye/TinyLLama-v0 block 0
```
Hiding this since the excess keys in Llama-based models are okay since the latest transformers release.
* Hide GeneratorExit in _iterate_inference_steps()
* Update README.md about `--public_name`
* Use .from_pretrained(..., use_auth_token=token) instead of token=token
until it's fully supported across HF libs
* Use default network speed 25 Mbit/s
* Apply relay penalty in max-throughput routing
* Replace RPS with "tokens/sec per block" in logs
* Increase default expiration
Before this PR, `free_disk_space_for()` was able to remove **(a)** only entire cached revisions (= git commits/branches) and **(b)** only from the repository we're loading right now.
This PR allows this functions to remove arbitrary files separately from any repositories.
This is useful for transition to Petals 1.2.0+, since it now uses original repos instead of the ones with converted models (see #323). In particular, the cache for `bigscience/bloom-petals` is now deprecated and should be removed in favor of `bigscience/bloom`. This is also useful as a way to free space before loading LoRA adapters (#335).
This PR:
- Adds benchmark scripts for inference, forward pass, and full training step (e.g. used for experiments in our paper).
- Fixes bug with dtypes in `petals.DistributedBloomForSequenceClassification`.
- (minor refactor) Moves `DTYPE_MAP` to `petals.constants` as a useful constant.
This PR:
1. **Abolishes the model conversion procedure.** Now, models are downloaded directly from original repositories like https://huggingface.co/bigscience/bloom. Servers download only shards with blocks to be hosted, and clients download only shards with input/output embeddings and layernorms.
- BLOOM is loaded from `bigscience/bloom`, but we use the DHT prefix `bigscience/bloom-petals` for backward compatibility. Same with smaller BLOOMs and BLOOMZ.
- LLaMA can be loaded from any repo like `username/llama-65b-hf`, but we use the DHT prefix `llama-65b-hf` (without the username) to accomodate blocks from different repos (there're a few of them with minor differences, such as `Llama` vs. `LLaMA` in the class name).
2. **Refactors the client to generalize it for multiple models.** Now, we have `petals.models` packages that contain model-specific code (e.g. `petals.models.bloom`, `petals.models.llama`). General code (e.g. CPU-efficient LM head, p-tuning) is kept in `petals.client`.
3. **Introduces** `WrappedLlamaBlock`, `DistributedLlamaConfig`, `DistributedLlamaForCausalLM`, `DistributedLlamaForSequenceClassification`, and `DistributedLlamaModel` compatible with Petals functionality (p-tuning, adapters, etc.).
4. **Introduces** `AutoDistributedConfig` that automatically chooses the correct config class (`DistributedLlamaConfig` or `DistributedBloomConfig`). The refactored configs contain all model-specific info for both clients and servers.
Upgrade instructions:
- Remove disk caches for blocks in old (converted) format to save disk space. That is, remove `~/.cache/petals/model--bigscience--bloom-petals` and `~/.cache/petals/model--bigscience--bloomz-petals` directories (if present).
Artem Chumachenko
Alexander Borzunov
justheuristic