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cb3f018f9f
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).
17 lines
672 B
Python
17 lines
672 B
Python
import pytest
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import torch
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from petals.server.block_utils import resolve_block_dtype
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from petals.server.from_pretrained import load_pretrained_block
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from petals.utils.auto_config import AutoDistributedConfig
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from test_utils import MODEL_NAME
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@pytest.mark.forked
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@pytest.mark.parametrize("torch_dtype", [torch.float32, torch.float16, "auto"])
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def test_block_dtype(torch_dtype):
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config = AutoDistributedConfig.from_pretrained(MODEL_NAME)
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block = load_pretrained_block(MODEL_NAME, 0, config=config, torch_dtype=torch_dtype)
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expected_dtype = resolve_block_dtype(config, torch_dtype)
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assert all(param.dtype == expected_dtype for param in block.parameters())
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