mirror of
https://github.com/bigscience-workshop/petals
synced 2024-10-31 09:20:41 +00:00
80 lines
3.0 KiB
Python
80 lines
3.0 KiB
Python
######
|
|
# 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 hivemind
|
|
import pytest
|
|
import torch
|
|
from test_utils import *
|
|
|
|
import src
|
|
from petals.bloom.from_pretrained import load_pretrained_block
|
|
from petals.client.remote_sequential import RemoteSequential
|
|
from petals.dht_utils import get_remote_sequence
|
|
|
|
|
|
@pytest.mark.forked
|
|
def test_forward_backward_exact_match(atol_forward=1e-4, atol_backward=1e-4, seq_length=1):
|
|
dht = hivemind.DHT(initial_peers=INITIAL_PEERS, client_mode=True, start=True)
|
|
config = src.DistributedBloomConfig.from_pretrained(MODEL_NAME)
|
|
remote_blocks = get_remote_sequence(dht, 3, 6, config)
|
|
assert isinstance(remote_blocks, RemoteSequential)
|
|
|
|
ref_blocks = [
|
|
load_pretrained_block(MODEL_NAME, 3, torch_dtype=torch.float32),
|
|
load_pretrained_block(MODEL_NAME, 4, torch_dtype=torch.float32),
|
|
load_pretrained_block(MODEL_NAME, 5, torch_dtype=torch.float32),
|
|
]
|
|
inputs = torch.randn(1, seq_length, config.hidden_size, requires_grad=True)
|
|
outputs_rpc = remote_blocks.forward(inputs)
|
|
outputs_rpc.sum().backward()
|
|
grads_rpc = inputs.grad
|
|
|
|
inputs.grad = None
|
|
hidden_states = inputs
|
|
for ref_block in ref_blocks:
|
|
hidden_states = ref_block.forward(hidden_states)[0]
|
|
outputs_ref = hidden_states
|
|
outputs_ref.sum().backward()
|
|
grads_ref = inputs.grad
|
|
|
|
assert torch.allclose(outputs_ref, outputs_rpc, rtol=0, atol=atol_forward)
|
|
assert torch.allclose(grads_ref, grads_rpc, rtol=0, atol=atol_backward)
|
|
|
|
|
|
@pytest.mark.forked
|
|
def test_chained_inference_exact_match(atol_inference=1e-4):
|
|
dht = hivemind.DHT(initial_peers=INITIAL_PEERS, client_mode=True, start=True)
|
|
config = src.DistributedBloomConfig.from_pretrained(MODEL_NAME)
|
|
remote_blocks = get_remote_sequence(dht, 3, 5, config)
|
|
assert isinstance(remote_blocks, RemoteSequential)
|
|
|
|
inputs = torch.randn(1, 8, config.hidden_size)
|
|
|
|
outputs_inference = []
|
|
with remote_blocks.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, :]))
|
|
outputs_inference = torch.cat(outputs_inference, dim=1)
|
|
|
|
ref_blocks = [
|
|
load_pretrained_block(MODEL_NAME, 3, torch_dtype=torch.float32),
|
|
load_pretrained_block(MODEL_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)
|