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44 lines
1.7 KiB
Python
44 lines
1.7 KiB
Python
import os
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import hivemind
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import torch
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from src.bloom.from_pretrained import load_pretrained_block
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from src.client.remote_block import RemoteTransformerBlock
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from src.dht_utils import get_remote_module
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INITIAL_PEERS = os.environ.get("INITIAL_PEERS")
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if not INITIAL_PEERS:
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raise RuntimeError("Must specify INITIAL_PEERS environment variable with one or more peer ids")
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INITIAL_PEERS = INITIAL_PEERS.split()
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BLOCK_UID = os.environ.get("BLOCK_UID")
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if not BLOCK_UID:
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raise RuntimeError("Must specify BLOCK_UID as an index of a transformer block to be tested")
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REF_NAME = os.environ.get("REF_NAME", "bigscience/test-bloomd-6b3")
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REF_INDEX = int(os.environ.get("REF_INDEX", BLOCK_UID[-1].split(".")[-1]))
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def test_remote_block_exact_match(atol_forward=1e-5, atol_inference=1e-3):
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dht = hivemind.DHT(initial_peers=INITIAL_PEERS, client_mode=True, start=True)
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remote_block = get_remote_module(dht, BLOCK_UID)
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assert remote_block is not None, f"Could not find {BLOCK_UID} in DHT"
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assert isinstance(remote_block, RemoteTransformerBlock)
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inputs = torch.randn(1, 8, 4096)
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(outputs_forward,) = remote_block(inputs)
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outputs_inference = []
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with remote_block.begin_inference_session() 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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outputs_inference = torch.cat(outputs_inference, dim=1)
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ref_block = load_pretrained_block(REF_NAME, REF_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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