mirror of
https://github.com/bigscience-workshop/petals
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47 lines
1.9 KiB
Python
47 lines
1.9 KiB
Python
import random
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import pytest
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import torch
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import transformers
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from tensor_parallel import TensorParallel
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from tensor_parallel.slicing_configs import get_bloom_config
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from test_utils import MODEL_NAME
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from petals.bloom.from_pretrained import load_pretrained_block
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@pytest.mark.forked
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@pytest.mark.parametrize("custom_config", [True, False])
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@pytest.mark.parametrize("devices", [("cpu",) * 2, ("cpu",) * 3, ("cpu",) * 4])
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def test_tp_block(devices, custom_config):
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block_index = random.randint(0, 10)
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model_config = transformers.AutoConfig.from_pretrained(MODEL_NAME)
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block = load_pretrained_block(MODEL_NAME, block_index=block_index, torch_dtype=torch.float32).to(devices[0])
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tp_config = None
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if custom_config:
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tp_config = get_bloom_config(model_config, devices)
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batch_size = 2
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prefix_length = 5
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test_inputs1 = torch.randn(batch_size, 3, 1024, requires_grad=True, device=devices[0])
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test_inputs2 = test_inputs1.detach().clone().requires_grad_(True)
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test_prefix1 = torch.randn(batch_size, prefix_length, 1024, requires_grad=True, device=devices[0])
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test_prefix2 = test_prefix1.detach().clone().requires_grad_(True)
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grad_proj = torch.rand_like(test_inputs1)
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y_prefix_ref, layer_past = block(test_prefix1, use_cache=True)
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y_ref, cache_ref = block(test_inputs1, use_cache=True, layer_past=layer_past)
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y_ref.backward(grad_proj)
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block_tp = TensorParallel(block, devices, config=tp_config)
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y_prefix, layer_past = block_tp(test_prefix2, use_cache=True)
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y_ours, cache_ours = block_tp(test_inputs2, use_cache=True, layer_past=layer_past)
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y_ours.backward(grad_proj)
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assert torch.allclose(y_prefix, y_prefix_ref, atol=1e-5)
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assert torch.allclose(y_ours, y_ref, atol=1e-5)
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assert torch.allclose(test_inputs1.grad, test_inputs2.grad, atol=1e-4)
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assert torch.allclose(test_prefix1.grad, test_prefix2.grad, atol=1e-4)
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