petals/src/petals/models/llama/__init__.py

from petals.models.llama.block import WrappedLlamaBlock
from petals.models.llama.config import DistributedLlamaConfig
from petals.models.llama.model import (
    DistributedLlamaForCausalLM,
    DistributedLlamaForSequenceClassification,
    DistributedLlamaModel,
)
from petals.utils.auto_config import register_model_classes

register_model_classes(
    config=DistributedLlamaConfig,
    model=DistributedLlamaModel,
    model_for_causal_lm=DistributedLlamaForCausalLM,
    model_for_sequence_classification=DistributedLlamaForSequenceClassification,
)
Add LLaMA support (#323) 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). 11 months ago			`from petals.models.llama.block import WrappedLlamaBlock`
			`from petals.models.llama.config import DistributedLlamaConfig`
			`from petals.models.llama.model import (`
			`DistributedLlamaForCausalLM,`
			`DistributedLlamaForSequenceClassification,`
			`DistributedLlamaModel,`
			`)`
Add AutoDistributed{Model, ModelForCausalLM, ModelForSequenceClassification} (#329) This PR adds `petals.AutoDistributed{Model, ModelForCausalLM, ModelForSequenceClassification}` classes, similar to their `transformers.Auto{Model, ModelForCausalLM, ModelForSequenceClassification}` counterparts. 11 months ago			`from petals.utils.auto_config import register_model_classes`

			`register_model_classes(`
			`config=DistributedLlamaConfig,`
			`model=DistributedLlamaModel,`
			`model_for_causal_lm=DistributedLlamaForCausalLM,`
			`model_for_sequence_classification=DistributedLlamaForSequenceClassification,`
			`)`