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import random
import hivemind
import pytest
import torch
import transformers
from hivemind import P2PHandlerError
from test_utils import *
import src
from src import DistributedBloomConfig
from src.bloom.from_pretrained import load_pretrained_block
from src.client.remote_sequential import RemoteTransformerBlock
from src.data_structures import UID_DELIMITER
from src.dht_utils import get_remote_module
@pytest.mark.forked
def test_remote_block_exact_match(atol_forward=1e-5, atol_inference=1e-3):
dht = hivemind.DHT(initial_peers=INITIAL_PEERS, client_mode=True, start=True)
config = DistributedBloomConfig.from_pretrained(MODEL_NAME)
for block_index in random.sample(range(config.n_layer), 3):
remote_block = get_remote_module(dht, f"{MODEL_NAME}{UID_DELIMITER}{block_index}", config)
assert isinstance(remote_block, RemoteTransformerBlock)
inputs = torch.randn(1, 8, config.hidden_size)
outputs_forward = remote_block(inputs)
outputs_inference = []
with remote_block.inference_session(max_length=inputs.shape[1]) as sess:
for i in range(inputs.shape[1]):
outputs_inference.append(sess.step(inputs[:, i : i + 1, :]))
# test that max length is respected
with pytest.raises(P2PHandlerError) as exc_info:
sess.step(inputs[:, -1:, :])
assert "Maximum length exceeded" in repr(exc_info.value)
outputs_inference = torch.cat(outputs_inference, dim=1)
ref_block = load_pretrained_block(MODEL_NAME, block_index, torch_dtype=torch.float32)
(outputs_local,) = ref_block(inputs)
assert torch.allclose(outputs_local, outputs_forward, rtol=0, atol=atol_forward)
assert torch.allclose(outputs_local, outputs_inference, rtol=0, atol=atol_inference)
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