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[tensor] add ColoTensor 1Dcol (#888)

pull/889/head
Ziyue Jiang 2022-04-27 14:13:55 +08:00 committed by GitHub
parent a0e5971692
commit 1d0aba4153
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4 changed files with 166 additions and 28 deletions
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94
colossalai/tensor/_ops/linear.py
View File

@ -1,12 +1,12 @@
import torch
from colossalai.tensor.op_wrapper import colo_op_impl
from colossalai.tensor.colo_tensor import ColoTensor
from colossalai.context import ParallelMode
from colossalai.nn.layer.parallel_1d._utils import split_forward_gather_backward, reduce_input
from colossalai.nn.layer.parallel_1d._utils import split_forward_gather_backward, reduce_input, \
gather_forward_split_backward, reduce_grad
from colossalai.nn.layer.utils import divide
from colossalai.core import global_context as gpc
from packaging import version
from colossalai.tensor import ComputePattern
from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ParallelAction, ColoTensor
@colo_op_impl(torch.nn.functional.linear)
@ -25,39 +25,107 @@ def colo_linear(types, args, kwargs, pg):
else:
bias = kwargs.get('bias', None)
bias_spec = None
if isinstance(bias, ColoTensor):
assert bias.shard_spec.num_action == 0, f"We currently only support bias is duplicated among processes in the linear operator"
bias_spec = bias.shard_spec
bias = bias.torch_tensor()
# Add communication logic before and after linear call.
if isinstance(weight, ColoTensor):
if weight.shard_spec == None or weight.shard_spec.num_action == 0:
assert bias_spec == None or bias_spec.num_action == 0, 'Invalid bias spec for native Linear op'
if isinstance(input_tensor, ColoTensor):
input_tensor = input_tensor.torch_tensor()
if isinstance(weight, ColoTensor):
weight = weight.torch_tensor()
return ColoTensor.init_from_torch_tensor(torch.nn.functional.linear(input_tensor, weight, bias))
elif weight.shard_spec.num_action == 1:
if ComputePattern.TP1DRow in weight.shard_spec.compute_patterns:
parallel_action = weight.shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DRow)
compute_patterns = weight.shard_spec.compute_patterns
if ComputePattern.TP1DRow in compute_patterns:
# Input:S[1] x Weight:S[0] = Output:P
# All-Reduce(Output) + bias = res
assert divide(input_tensor.shape[-1], gpc.tensor_parallel_size) == weight.size(-1), \
'Invalid shapes in 1Drow forward: input={}, weight={}. Expected last dim of input {}.'.format(
input_tensor.shape, weight.size, weight.size(-1) * gpc.tensor_parallel_size)
# Input:S[1]
input_spec = None
if isinstance(input_tensor, ColoTensor):
input_spec = input_tensor.shard_spec
input_tensor = input_tensor.torch_tensor()
parallel_action = weight.shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DRow)
input_per_partition = split_forward_gather_backward(input_tensor, parallel_action.parallel_mode, dim=-1)
if input_spec == None or input_spec.num_action == 0:
# Not splited yet.
assert divide(input_tensor.shape[-1], gpc.tensor_parallel_size) == weight.size(-1), \
'Invalid shapes in 1Drow forward: input={}, weight={}. Expected last dim of input {}.'.format(
input_tensor.shape, weight.size, weight.size(-1) * gpc.tensor_parallel_size)
input_per_partition = split_forward_gather_backward(input_tensor, parallel_action.parallel_mode, dim=-1)
elif input_tensor.shard_spec.num_action == 1:
if ComputePattern.TP1DCol in input_spec.compute_patterns:
# Splited by 1Dcol
assert input_tensor.shape[-1] == weight.size(-1), \
'Invalid shapes in 1Drow forward: input={}, weight={}. Expected last dim of input {}.'.format(
input_tensor.shape, weight.size, weight.size(-1))
input_per_partition = input_tensor
else:
raise NotImplementedError
else:
raise NotImplementedError
# Output:P
weight_ = weight.torch_tensor()
partial_output = torch.nn.functional.linear(input_per_partition, weight_)
# Reduce(Output)
output = reduce_input(partial_output, ParallelMode.PARALLEL_1D)
output = reduce_input(partial_output, parallel_action.parallel_mode)
# Bias
if bias is not None:
assert bias_spec == None or bias_spec.num_action == 0, 'Invalid bias spec for 1Drow Linear op'
output = output + bias
return ColoTensor.init_from_torch_tensor(output)
output = ColoTensor.init_from_torch_tensor(output)
return output
elif ComputePattern.TP1DCol in compute_patterns:
# Input:B x Weight:S[1] + Bias:S[1] = Output:S[1]
# All-Gather(Output)
# Input:B
input_spec = None
output_spec = None
parallel_action = weight.shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DCol)
if isinstance(input_tensor, ColoTensor):
input_spec = input_tensor.shard_spec
input_tensor = input_tensor.torch_tensor()
if input_spec == None or input_spec.num_action == 0:
# Not splited yet.
assert input_tensor.shape[-1] == weight.size(-1), \
'Invalid shapes in 1Dcol forward: input={}, weight={}. Expected last dim of input {}.'.format(
input_tensor.shape, weight.size, weight.size(-1))
input_parallel = reduce_grad(input_tensor, parallel_action.parallel_mode)
else:
raise NotImplementedError
# Bias:S[1]
if bias is not None:
assert bias_spec is not None and bias_spec.num_action == 1 and \
ComputePattern.TP1DCol in bias_spec.compute_patterns, \
'Invalid bias spec for 1Dcol Linear op'
weight_ = weight.torch_tensor()
output_parallel = torch.nn.functional.linear(input_parallel, weight_, bias)
if parallel_action.gather_out:
# All-Gather(Output)
output = gather_forward_split_backward(output_parallel, parallel_action.parallel_mode, dim=-1)
output = ColoTensor.init_from_torch_tensor(output)
else:
output = ColoTensor.init_from_torch_tensor(output_parallel)
out_parallel_action_list = [
ParallelAction(
priority=1, compute_pattern=ComputePattern.TP1DCol,
parallel_mode=parallel_action.parallel_mode
)
]
output_spec = TensorSpec(out_parallel_action_list)
# set ColoTensor spec
if output_spec is not None:
output.set_spec(output_spec)
return output
else:
raise NotImplementedError
else:

31
colossalai/tensor/colo_tensor.py
View File

@ -121,18 +121,25 @@ class ColoTensor(object):
assert self._shard_spec is not None, 'You should call set_spec() before _shard() ColoTensor.'
if self._shard_spec.num_action == 1:
if ComputePattern.TP1DRow in self._shard_spec.compute_patterns:
parallel_action = self._shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DRow)
num_partition = gpc.get_world_size(parallel_action.parallel_mode)
local_rank = gpc.get_local_rank(parallel_action.parallel_mode)
dim = -1
chunk_size = divide(self._size[dim], num_partition)
# Reshape to get shard for this rank and we don't want autograd
# recording here for the narrow op and 'local_shard' should be a
# leaf variable in the autograd graph.
self._torch_tensor = self._torch_tensor.narrow(dim, local_rank * chunk_size, chunk_size).detach(
).contiguous() # TODO Shall we clone() here since detach() will point to the old tensor?
self._torch_tensor.requires_grad = self._requires_grad
self._size = self._torch_tensor.size()
parallel_action = self._shard_spec.get_action_by_compute_pattern(
ComputePattern.TP1DRow)
self._shard_1d(parallel_action=parallel_action, dim=-1)
elif ComputePattern.TP1DCol in self._shard_spec.compute_patterns:
parallel_action = self._shard_spec.get_action_by_compute_pattern(
ComputePattern.TP1DCol)
self._shard_1d(parallel_action=parallel_action, dim=0)
def _shard_1d(self, parallel_action, dim=-1):
num_partition = gpc.get_world_size(parallel_action.parallel_mode)
local_rank = gpc.get_local_rank(parallel_action.parallel_mode)
chunk_size = divide(self._size[dim], num_partition)
# Reshape to get shard for this rank and we don't want autograd
# recording here for the narrow op and 'local_shard' should be a
# leaf variable in the autograd graph.
self._torch_tensor = self._torch_tensor.narrow(dim, local_rank * chunk_size, chunk_size).detach(
).contiguous() # TODO Shall we clone() here since detach() will point to the old tensor?
self._torch_tensor.requires_grad = self._requires_grad
self._size = self._torch_tensor.size()
@classmethod
def __torch_function__(cls, func, types, args=(), kwargs=None):

4
colossalai/tensor/spec.py
View File

@ -12,11 +12,11 @@ class ComputePattern(Enum):
class ParallelAction(object):
def __init__(self, priority=0, compute_pattern=ComputePattern.DP, parallel_mode=ParallelMode.DATA) -> None:
def __init__(self, priority=0, compute_pattern=ComputePattern.DP, parallel_mode=ParallelMode.DATA, gather_out=True) -> None:
self.priority = priority
self.compute_pattern = compute_pattern
self.parallel_mode = parallel_mode
self.gather_out = gather_out
class TensorSpec(object):
"""

65
tests/test_tensor/test_linear_tp.py
View File

@ -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])