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- ######
- # Warning:torch this test is a work in progress. It will be modified soon.
- # - if you want more stable tests, see test_block_exact_match
- # - if you want to figure out chained inference, ask yozh
- import os
- import hivemind
- import torch
- from hivemind.moe.expert_uid import ExpertInfo
- from src.bloom.from_pretrained import load_pretrained_block
- from src.client.remote_block import RemoteTransformerBlock
- from src.dht_utils import get_remote_module
- INITIAL_PEERS = os.environ.get("INITIAL_PEERS")
- if not INITIAL_PEERS:
- raise RuntimeError("Must specify INITIAL_PEERS environment variable with one or more peer ids")
- INITIAL_PEERS = INITIAL_PEERS.split()
- BLOCK_UID = os.environ.get("BLOCK_UID")
- if not BLOCK_UID:
- raise RuntimeError("Must specify BLOCK_UID as an index of a transformer block to be tested")
- REF_NAME = os.environ.get("REF_NAME", "bigscience/test-bloomd-6b3")
- REF_INDEX = int(os.environ.get("REF_INDEX", BLOCK_UID[-1].split(".")[-1]))
- def test_remote_block_exact_match(atol_inference=1e-4):
- dht = hivemind.DHT(initial_peers=INITIAL_PEERS, client_mode=True, start=True)
- remote_block = get_remote_module(dht, BLOCK_UID)
- assert remote_block is not None, f"Could not find {BLOCK_UID} in DHT"
- assert isinstance(remote_block, RemoteTransformerBlock)
- _ = remote_block.info # lazy-init info now, because otherwise we will _break_ info init by chaning _info
- remote_block._info = ExpertInfo('bloom6b3.3 bloom6b3.4', remote_block._info.peer_id)
- inputs = torch.randn(1, 8, 4096)
- outputs_inference = []
- with remote_block.begin_inference_session() as sess:
- for i in range(inputs.shape[1]):
- outputs_inference.append(sess.step(inputs[:, i : i + 1, :]))
- outputs_inference = torch.cat(outputs_inference, dim=1)
- ref_blocks = [
- load_pretrained_block(REF_NAME, 3, torch_dtype=torch.float32),
- load_pretrained_block(REF_NAME, 4, torch_dtype=torch.float32)
- ]
- outputs_ref = []
- caches = [None, None]
- for i in range(inputs.shape[1]):
- new_caches = []
- hidden_states = inputs[:, i : i + 1, :]
- for ref_block, cache in zip(ref_blocks, caches):
- with torch.no_grad():
- hidden_states, new_cache = ref_block.forward(hidden_states, use_cache=True, layer_past=cache)
- new_caches.append(new_cache)
- outputs_ref.append(hidden_states)
- caches = new_caches
- outputs_ref = torch.cat(outputs_ref, dim=1)
- assert torch.allclose(outputs_ref, outputs_inference, rtol=0, atol=atol_inference)
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