mirror of
https://github.com/bigscience-workshop/petals
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0be21775af
* remove transformer block, implement as sequence size 1 * reimplement get_remote_module * fix readme Co-authored-by: Alexander Borzunov <hxrussia@gmail.com> Co-authored-by: Aleksandr Borzunov <borzunov.alexander@gmail.com>
80 lines
3.0 KiB
Python
80 lines
3.0 KiB
Python
######
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# Warning:torch this test is a work in progress. It will be modified soon.
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# - if you want more stable tests, see test_block_exact_match
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# - if you want to figure out chained inference, ask yozh
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import hivemind
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import pytest
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import torch
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from test_utils import *
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import src
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from src.bloom.from_pretrained import load_pretrained_block
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from src.client.remote_sequential import RemoteSequential
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from src.dht_utils import get_remote_sequence
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@pytest.mark.forked
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def test_forward_backward_exact_match(atol_forward=1e-4, atol_backward=1e-4, seq_length=1):
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dht = hivemind.DHT(initial_peers=INITIAL_PEERS, client_mode=True, start=True)
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config = src.DistributedBloomConfig.from_pretrained(MODEL_NAME)
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remote_blocks = get_remote_sequence(dht, 3, 6, config)
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assert isinstance(remote_blocks, RemoteSequential)
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ref_blocks = [
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load_pretrained_block(MODEL_NAME, 3, torch_dtype=torch.float32),
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load_pretrained_block(MODEL_NAME, 4, torch_dtype=torch.float32),
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load_pretrained_block(MODEL_NAME, 5, torch_dtype=torch.float32),
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]
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inputs = torch.randn(1, seq_length, config.hidden_size, requires_grad=True)
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outputs_rpc = remote_blocks.forward(inputs)
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outputs_rpc.sum().backward()
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grads_rpc = inputs.grad
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inputs.grad = None
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hidden_states = inputs
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for ref_block in ref_blocks:
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hidden_states = ref_block.forward(hidden_states)[0]
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outputs_ref = hidden_states
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outputs_ref.sum().backward()
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grads_ref = inputs.grad
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assert torch.allclose(outputs_ref, outputs_rpc, rtol=0, atol=atol_forward)
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assert torch.allclose(grads_ref, grads_rpc, rtol=0, atol=atol_backward)
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@pytest.mark.forked
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def test_chained_inference_exact_match(atol_inference=1e-4):
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dht = hivemind.DHT(initial_peers=INITIAL_PEERS, client_mode=True, start=True)
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config = src.DistributedBloomConfig.from_pretrained(MODEL_NAME)
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remote_blocks = get_remote_sequence(dht, 3, 5, config)
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assert isinstance(remote_blocks, RemoteSequential)
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inputs = torch.randn(1, 8, config.hidden_size)
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outputs_inference = []
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with remote_blocks.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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outputs_inference = torch.cat(outputs_inference, dim=1)
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ref_blocks = [
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load_pretrained_block(MODEL_NAME, 3, torch_dtype=torch.float32),
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load_pretrained_block(MODEL_NAME, 4, torch_dtype=torch.float32),
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]
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outputs_ref = []
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caches = [None, None]
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for i in range(inputs.shape[1]):
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new_caches = []
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hidden_states = inputs[:, i : i + 1, :]
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for ref_block, cache in zip(ref_blocks, caches):
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with torch.no_grad():
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hidden_states, new_cache = ref_block.forward(hidden_states, use_cache=True, layer_past=cache)
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new_caches.append(new_cache)
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outputs_ref.append(hidden_states)
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caches = new_caches
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outputs_ref = torch.cat(outputs_ref, dim=1)
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assert torch.allclose(outputs_ref, outputs_inference, rtol=0, atol=atol_inference)
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