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[tensor] add ColoTensor 1Dcol (#888)

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Ziyue Jiang
2022-04-27 14:13:55 +08:00
committed by GitHub
parent a0e5971692
commit 1d0aba4153
4 changed files with 166 additions and 28 deletions
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65
tests/test_tensor/test_linear_tp.py
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@@ -16,6 +16,69 @@ from colossalai.tensor import TensorSpec, ComputePattern, ParallelAction
from _utils import check_equal, replace_parameter_add_grad, broadcast_tensor_chunk
def run_linear_tp1d_col_test():
device = get_current_device()
dtype = torch.float32
DEPTH = gpc.get_world_size(ParallelMode.PARALLEL_1D)
in_features = 4
out_features = 8
local_rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
layer_master = torch.nn.Linear(in_features, out_features)
layer = torch.nn.Linear(in_features, out_features)
A_shape = (2, in_features)
A_master = torch.randn(A_shape, dtype=dtype, device=device)
A = broadcast_tensor_chunk(A_master, chunk_size=1)
A.requires_grad = True
W_shape = (out_features, in_features)
W_master = torch.randn(W_shape, dtype=dtype, device=device)
W = broadcast_tensor_chunk(W_master, chunk_size=1)
W.requires_grad = True
B_shape = (out_features)
B_master = torch.randn(B_shape, dtype=dtype, device=device)
B = broadcast_tensor_chunk(B_master, chunk_size=1)
B.requires_grad = True
# replace the torch nn.Parameters with ColoTensor
sharded_weight = ColoTensor.init_from_torch_tensor(W)
sharded_bias = ColoTensor.init_from_torch_tensor(B)
parallel_action_list = [
ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol, parallel_mode=ParallelMode.PARALLEL_1D)
]
spec = TensorSpec(parallel_action_list)
sharded_weight.set_spec(spec) # reshard
sharded_bias.set_spec(spec)
replace_parameter_add_grad(layer, sharded_weight, sharded_bias)
out = layer(A)
replace_parameter_add_grad(layer_master, W_master, B_master)
A_master.requires_grad = True
#C_master = torch.matmul(A_master, W_master.transpose(0, 1)) + B_master
C_master = layer_master(A_master)
C = C_master.clone()
check_equal(out, C)
grad_shape = C_master.shape
grad_master = torch.randn(grad_shape, dtype=dtype, device=get_current_device())
grad = broadcast_tensor_chunk(grad_master, chunk_size=1)
out.backward(grad)
grad_master = grad_master.clone()
C_master.backward(grad_master)
W_grad = W_master.grad
W_grad = torch.chunk(W_grad, DEPTH, dim=0)[local_rank]
check_equal(W_grad, layer.weight.grad)
B_grad = B_master.grad
B_grad = torch.chunk(B_grad, DEPTH, dim=0)[local_rank]
check_equal(B_grad, layer.bias.grad)
def run_linear_tp1d_row_test():
device = get_current_device()
@@ -83,7 +146,7 @@ def run_dist(rank, world_size, port):
config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
run_linear_tp1d_row_test()
run_linear_tp1d_col_test()
@pytest.mark.dist
@parameterize('world_size', [1, 4])