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91 lines
3.6 KiB
Python
91 lines
3.6 KiB
Python
import random
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from typing import Union
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import hivemind
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import pytest
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import torch
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from transformers.models.bloom.configuration_bloom import BloomConfig
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from petals.bloom.block import WrappedBloomBlock
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from petals.bloom.from_pretrained import DTYPE_MAP, _load_state_dict, load_pretrained_block
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from petals.client import DistributedBloomConfig
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from petals.client.remote_sequential import RemoteTransformerBlock
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from petals.data_structures import UID_DELIMITER
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from petals.dht_utils import get_remote_module
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from test_utils import *
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@pytest.mark.forked
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def test_remote_block_exact_match(atol_forward=1e-4, atol_inference=1e-3):
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dht = hivemind.DHT(initial_peers=INITIAL_PEERS, client_mode=True, start=True)
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config = DistributedBloomConfig.from_pretrained(MODEL_NAME)
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for block_index in random.sample(range(config.n_layer), 3):
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remote_block = get_remote_module(dht, f"{MODEL_NAME}{UID_DELIMITER}{block_index}", config)
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assert isinstance(remote_block, RemoteTransformerBlock)
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inputs = torch.randn(1, 8, config.hidden_size)
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outputs_forward = remote_block(inputs)
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outputs_inference = []
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with remote_block.inference_session(max_length=inputs.shape[1]) as sess:
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for i in range(inputs.shape[1]):
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outputs_inference.append(sess.step(inputs[:, i : i + 1, :]))
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# test that max length is respected
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with pytest.raises(ValueError, match=r"Maximum length exceeded") as exc_info:
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sess.step(inputs[:, -1:, :])
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assert "Maximum length exceeded" in repr(exc_info.value)
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outputs_inference = torch.cat(outputs_inference, dim=1)
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ref_block = load_pretrained_block(MODEL_NAME, block_index, torch_dtype=torch.float32)
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(outputs_local,) = ref_block(inputs)
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assert torch.allclose(outputs_local, outputs_forward, rtol=0, atol=atol_forward)
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assert torch.allclose(outputs_local, outputs_inference, rtol=0, atol=atol_inference)
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def _old_load_pretrained_block(
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converted_model_name_or_path: str,
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block_index: int,
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torch_dtype: Union[torch.dtype, str] = "auto",
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) -> WrappedBloomBlock:
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"""Load the BLOOM block by directly initializing the weights.
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This test is used to check consistency with the previous implementation and can be removed in the future."""
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config = BloomConfig.from_pretrained(converted_model_name_or_path)
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block = WrappedBloomBlock(config)
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state_dict = _load_state_dict(
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converted_model_name_or_path,
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block_index,
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config,
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cache_dir=None,
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)
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if torch_dtype == "auto":
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with torch.no_grad():
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for name, param in block.named_parameters():
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assert name in state_dict, f"{name} not in state dict"
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param.data = param.data.to(state_dict[name].dtype)
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else:
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assert torch_dtype in DTYPE_MAP.values(), f"torch_dtype must be one of {list(DTYPE_MAP.values())}"
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block = block.to(dtype=torch_dtype)
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block.load_state_dict(state_dict, strict=True)
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return block
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@pytest.mark.forked
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def test_init_pretrained_block(torch_dtype=torch.float32, atol_forward=1e-8):
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config = DistributedBloomConfig.from_pretrained(MODEL_NAME)
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torch.random.manual_seed(0)
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inputs = torch.randn(1, 16, config.hidden_size, dtype=torch_dtype)
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block = load_pretrained_block(MODEL_NAME, 3, torch_dtype=torch_dtype)
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ref_block = _old_load_pretrained_block(MODEL_NAME, 3, torch_dtype=torch_dtype)
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outputs = block.forward(inputs)[0]
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outputs_ref = ref_block.forward(inputs)[0]
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assert torch.allclose(outputs, outputs_ref, rtol=0, atol=atol_forward)
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