ColossalAI/colossalai/nn/_ops/linear.py

172 lines
7.0 KiB
Python
Raw Normal View History

from copy import deepcopy
from typing import Optional
import torch.nn.functional as F
from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ReplicaSpec, ShardSpec
2022-04-21 06:15:48 +00:00
from colossalai.tensor.op_wrapper import colo_op_impl
from colossalai.tensor.sharding_spec import ShardingSpec
from ._utils import GeneralTensor, convert_to_colo_tensor, reduce_grad, reduce_input
2022-04-25 02:06:53 +00:00
def colo_linear_1drow(input_tensor: ColoTensor, weight: ColoTensor, bias: Optional[ColoTensor]) -> 'ColoTensor':
# Input:S[1] x Weight:S[0] = Output:P
# All-Reduce(Output) + bias = res
# Input:S[1]
2022-07-08 06:18:30 +00:00
pg = weight.get_process_group()
input_tensor = input_tensor.redistribute(ShardSpec([-1], [weight.get_tp_world_size()]), pg)
# Output:P
partial_output = F.linear(input_tensor, weight)
# Reduce(Output)
2022-07-08 06:18:30 +00:00
output = reduce_input(partial_output, pg)
# Bias
if bias is not None:
assert not bias.has_compute_spec(), 'Invalid bias spec for 1Drow Linear op'
output = output + bias
output = ColoTensor.from_torch_tensor(output, spec=ColoTensorSpec(pg, ReplicaSpec()))
return output
def colo_linear_1dcol(input_tensor: ColoTensor, weight: ColoTensor, bias: Optional[ColoTensor]) -> 'ColoTensor':
# Input:B x Weight:S[1] + Bias:S[1] = Output:S[1]
# All-Gather(Output)
# Input:B
compute_spec = weight.compute_spec
input_tensor = input_tensor.redistribute(ReplicaSpec())
input_parallel = reduce_grad(input_tensor, weight.get_process_group())
output_parallel = F.linear(input_parallel, weight, bias)
output = ColoTensor.from_torch_tensor(output_parallel,
spec=ColoTensorSpec(weight.get_process_group(),
ShardSpec([-1], [weight.get_tp_world_size()]),
ComputeSpec(ComputePattern.TP1D)))
if compute_spec.output_replicate:
return output.to_replicate()
else:
return output
def colo_linear_1d(mode: str, input_tensor: ColoTensor, weight: ColoTensor, bias: Optional[ColoTensor]) -> 'ColoTensor':
assert mode in ('row', 'col')
funcs = {'row': colo_linear_1drow, 'col': colo_linear_1dcol}
return funcs[mode](input_tensor, weight, bias)
# @register_colo_graph(input_pos=[1], param_pos=[2, 3])
def colo_linear_imp(input_tensor: GeneralTensor,
weight: GeneralTensor,
bias: Optional[GeneralTensor] = None) -> 'ColoTensor':
"""Handles ``__torch_function__`` dispatch for ``torch.nn.functional.linear``.
This method computes a linear.
"""
assert isinstance(weight, ColoTensor)
pg = weight.get_process_group()
assert pg
input_tensor = convert_to_colo_tensor(input_tensor, pg)
bias = convert_to_colo_tensor(bias, pg)
# input_tensor, weight, bias = tuple(map(convert_to_colo_tensor, (input_tensor, weight, bias)))
# Add communication logic before and after linear call.
ret_tensor = None
if not weight.has_compute_spec(): # No Model Parallel Applied
assert weight.is_replicate(), 'Invalid weight spec for native Linear op'
assert bias is None or bias.is_replicate(), 'Invalid bias spec for native Linear op'
ret_tensor = ColoTensor.from_torch_tensor(F.linear(input_tensor, weight, bias), spec=ColoTensorSpec(pg))
elif weight.has_compute_pattern(ComputePattern.TP1D): # Single Model Parallel Applied
if weight.is_shard_1dcol() and (bias is None or bias.is_replicate()):
mode = 'row'
elif weight.is_shard_1drow() and (bias is None or bias.is_shard_1drow() or bias.is_shard_1dcol()):
mode = 'col'
2022-04-24 05:43:12 +00:00
else:
raise RuntimeError(f"the weight or bias tensor spec is not valid, weight {weight}, bias {bias}")
ret_tensor = colo_linear_1d(mode, input_tensor, weight, bias)
else:
raise NotImplementedError
return ret_tensor
def _new_colo_linear_imp(input_tensor: GeneralTensor,
weight: GeneralTensor,
bias: Optional[GeneralTensor] = None) -> 'ColoTensor':
"""
A tentative function to compute the distributed linear layer with the latest sharding spec.
This function is subject to future change as the current sharding API is not stable.
"""
# get mesh info
input_sharding_seq = input_tensor.sharding_spec.sharding_sequence
weight_sharding_seq = weight.sharding_spec.sharding_sequence
if bias is not None:
bias_sharding_seq = bias.sharding_spec.sharding_sequence
device_mesh = weight.sharding_spec.device_mesh
pg_axis0 = weight.pg_axis0
pg_axis1 = weight.pg_axis1
# the last dim of input should have the same spec as the first dim of weight
# the weight is transposed, so we look at the second dimension
assert input_sharding_seq[-1] == weight_sharding_seq[1]
if bias is not None:
assert bias_sharding_seq[0] == weight_sharding_seq[0]
# compute the output sharding sequence
# as weight is transposed, so we look at the first dimension
output_shard_seq = input_sharding_seq[:-1] + weight_sharding_seq[:1]
output_shard_seq = deepcopy(output_shard_seq)
# TODO: add reduce grad logic
# handle column and row parallel linear
# by reusing the implementation above
out = F.linear(input_tensor, weight)
# run all reduce if necessary
last_dim_spec = input_sharding_seq[-1]
if last_dim_spec.is_replica:
pass
elif last_dim_spec.shard_list is not None:
for dim in last_dim_spec.shard_list:
if dim == 0:
reduce_input(out, pg_axis0)
elif dim == 1:
reduce_input(out, pg_axis1)
else:
raise RuntimeError("Found invalid sharding axis {dim}, only 0 or 1 is expected")
# add bias
if bias is not None:
out += bias
# convert shard seq to partition dict
output_partition_dict = {}
for index, dim_spec in enumerate(output_shard_seq):
if not dim_spec.is_replica:
if index not in output_partition_dict:
output_partition_dict[index] = []
output_partition_dict[index].extend(dim_spec.shard_list)
entire_shape = out.shape
output_sharding_spec = ShardingSpec(device_mesh, entire_shape, output_partition_dict)
ret_tensor = ColoTensor.from_torch_tensor(out)
setattr(ret_tensor, 'sharding_spec', output_sharding_spec)
return ret_tensor
def _has_sharding_spec(tensor):
"""
A tentative function to check whether the tensor is using the new sharding spec API. We assume that the sharding spec object is
set as the attribute `sharding_spec` on a tensor.
"""
return hasattr(tensor, 'sharding_spec')
@colo_op_impl(F.linear)
def colo_linear(input: GeneralTensor, weight: GeneralTensor, bias: Optional[GeneralTensor] = None) -> 'ColoTensor':
if _has_sharding_spec(weight):
return _new_colo_linear_imp(input, weight, bias)
else:
return colo_linear_imp(input, weight, bias)