mirror of https://github.com/hpcaitech/ColossalAI
98 lines
3.2 KiB
Python
98 lines
3.2 KiB
Python
import torch
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from colossalai.context.parallel_mode import ParallelMode
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from colossalai.tensor import ColoTensor
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from functools import partial
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import colossalai
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import pytest
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import torch
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import torch.multiprocessing as mp
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from colossalai.testing import parameterize, rerun_if_address_is_in_use
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from colossalai.utils.cuda import get_current_device
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from colossalai.utils import free_port
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from colossalai.core import global_context as gpc
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from colossalai.tensor import TensorSpec, ComputePattern, ParallelAction
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from _utils import check_equal, replace_parameter_add_grad, broadcast_tensor_chunk
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def run_linear_tp1d_row_test():
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device = get_current_device()
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dtype = torch.float32
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DEPTH = gpc.get_world_size(ParallelMode.PARALLEL_1D)
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in_features = 4
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out_features = 5
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local_rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
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layer_master = torch.nn.Linear(in_features, out_features)
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layer = torch.nn.Linear(in_features, out_features)
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A_shape = (2, in_features)
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A_master = torch.randn(A_shape, dtype=dtype, device=device)
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A = broadcast_tensor_chunk(A_master, chunk_size=1)
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A.requires_grad = True
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W_shape = (out_features, in_features)
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W_master = torch.randn(W_shape, dtype=dtype, device=device)
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W = broadcast_tensor_chunk(W_master, chunk_size=1)
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W.requires_grad = True
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B_shape = (out_features)
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B_master = torch.randn(B_shape, dtype=dtype, device=device)
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B = broadcast_tensor_chunk(B_master, chunk_size=1)
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B.requires_grad = True
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# replace the torch nn.Parameters with ColoTensor
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sharded_weight = ColoTensor.init_from_torch_tensor(W)
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parallel_action_list = [
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ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow, parallel_mode=ParallelMode.PARALLEL_1D)
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]
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spec = TensorSpec(parallel_action_list)
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sharded_weight.set_spec(spec=spec) # reshard
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sharded_bias = ColoTensor.init_from_torch_tensor(B)
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replace_parameter_add_grad(layer, sharded_weight, sharded_bias)
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out = layer(A)
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replace_parameter_add_grad(layer_master, W_master, B_master)
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A_master.requires_grad = True
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#C_master = torch.matmul(A_master, W_master.transpose(0, 1)) + B_master
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C_master = layer_master(A_master)
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C = C_master.clone()
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check_equal(out, C)
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grad_shape = C_master.shape
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grad_master = torch.randn(grad_shape, dtype=dtype, device=get_current_device())
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grad = broadcast_tensor_chunk(grad_master, chunk_size=1)
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out.backward(grad)
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grad_master = grad_master.clone()
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C_master.backward(grad_master)
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W_grad = W_master.grad
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W_grad = torch.chunk(W_grad, DEPTH, dim=-1)[local_rank]
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check_equal(W_grad, layer.weight.grad)
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B_grad = B_master.grad
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check_equal(B_grad, layer.bias.grad)
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def run_dist(rank, world_size, port):
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config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
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colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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run_linear_tp1d_row_test()
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@pytest.mark.dist
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@parameterize('world_size', [1, 4])
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@rerun_if_address_is_in_use()
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def test_linear_1d(world_size):
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run_func = partial(run_dist, world_size=world_size, port=free_port())
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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test_linear_1d()
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