mirror of https://github.com/hpcaitech/ColossalAI
109 lines
4.7 KiB
Python
109 lines
4.7 KiB
Python
import torch
|
|
from colossalai.tensor.op_wrapper import colo_op_impl
|
|
from colossalai.nn.layer.parallel_1d._utils import split_forward_gather_backward, reduce_input, 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, TensorSpec, ComputePattern, ParallelAction, ColoTensor, dist_spec
|
|
from colossalai.tensor.graph import GraphOpNode, GraphGlobalEnv
|
|
|
|
|
|
def colo_linear_1Drow(input_tensor: ColoTensor, weight: ColoTensor, bias: ColoTensor) -> ColoTensor:
|
|
parallel_action = weight.spec.get_action_by_compute_pattern(ComputePattern.TP1D)
|
|
# Input:S[1] x Weight:S[0] = Output:P
|
|
# All-Reduce(Output) + bias = res
|
|
# Input:S[1]
|
|
input_tensor.to_dist_spec(
|
|
dist_spec.shard(weight.spec.get_process_group(), [-1], [weight.spec.get_process_group().size()]))
|
|
|
|
# Output:P
|
|
partial_output = torch.nn.functional.linear(input_tensor.torch_tensor(), weight.torch_tensor())
|
|
# Reduce(Output)
|
|
output = reduce_input(partial_output, parallel_action.parallel_mode)
|
|
# Bias
|
|
if bias is not None:
|
|
assert not bias.has_spec(), 'Invalid bias spec for 1Drow Linear op'
|
|
output = output + bias.torch_tensor()
|
|
output = ColoTensor.init_from_torch_tensor(output,
|
|
spec=TensorSpec(dist_spec.replicate(weight.spec.get_process_group())))
|
|
return output
|
|
|
|
|
|
def colo_linear_1Dcol(input_tensor: ColoTensor, weight: ColoTensor, bias: ColoTensor) -> ColoTensor:
|
|
# Input:B x Weight:S[1] + Bias:S[1] = Output:S[1]
|
|
# All-Gather(Output)
|
|
# Input:B
|
|
parallel_action = weight.spec.get_action_by_compute_pattern(ComputePattern.TP1D)
|
|
input_tensor.to_dist_spec(dist_spec.replicate(weight.spec.get_process_group()))
|
|
input_parallel = reduce_grad(input_tensor.torch_tensor(), parallel_action.parallel_mode)
|
|
if bias is not None:
|
|
bias = bias.torch_tensor()
|
|
output_parallel = torch.nn.functional.linear(input_parallel, weight.torch_tensor(), bias)
|
|
|
|
output = ColoTensor.init_from_torch_tensor(
|
|
output_parallel,
|
|
spec=TensorSpec(
|
|
dist_spec.shard(weight.spec.get_process_group(), [-1], [weight.spec.get_process_group().size()]),
|
|
[ParallelAction(priority=1, parallel_mode=parallel_action.parallel_mode)]))
|
|
if parallel_action.gather_out:
|
|
# All-Gather(Output)
|
|
output.to_dist_spec(dist_spec.replicate(weight.spec.get_process_group()))
|
|
return output
|
|
|
|
|
|
@colo_op_impl(torch.nn.functional.linear)
|
|
def colo_linear(types, args, kwargs, pg):
|
|
"""Handles ``__torch_function__`` dispatch for ``torch.nn.functional.linear``.
|
|
This method computes a linear.
|
|
"""
|
|
input_tensor = args[0]
|
|
weight = args[1]
|
|
|
|
if version.parse(torch.__version__) > version.parse("1.11.0"):
|
|
if len(args) == 3:
|
|
bias = args[2]
|
|
else:
|
|
bias = None
|
|
else:
|
|
bias = kwargs.get('bias', None)
|
|
|
|
if not isinstance(input_tensor, ColoTensor):
|
|
input_tensor = ColoTensor.init_from_torch_tensor(input_tensor)
|
|
|
|
if not isinstance(weight, ColoTensor):
|
|
weight = ColoTensor.init_from_torch_tensor(weight)
|
|
|
|
if bias is not None and not isinstance(bias, ColoTensor):
|
|
bias = ColoTensor.init_from_torch_tensor(bias)
|
|
|
|
# building the computing graph, inputs -> op
|
|
if GraphGlobalEnv().graph_building:
|
|
cur_op_node = GraphOpNode('linear', [weight, bias])
|
|
cur_op_node.add_prev_tensor(input_tensor)
|
|
|
|
# Add communication logic before and after linear call.
|
|
ret_tensor = None
|
|
if not weight.has_spec(): # No Model Parallel Applied
|
|
assert bias.spec.is_gathered(), 'Invalid bias spec for native Linear op'
|
|
assert bias.spec.is_gathered(), 'Invalid bias spec for native Linear op'
|
|
input_tensor = input_tensor.torch_tensor()
|
|
weight = weight.torch_tensor()
|
|
if bias is not None:
|
|
bias = bias.torch_tensor()
|
|
ret_tensor = ColoTensor.init_from_torch_tensor(torch.nn.functional.linear(input_tensor, weight, bias))
|
|
elif weight.spec.has_compute_pattern(ComputePattern.TP1D): # Single Model Parallel Applied
|
|
if weight.spec.is_1D_col() and (bias is None or bias.spec.is_gathered()):
|
|
ret_tensor = colo_linear_1Drow(input_tensor, weight, bias)
|
|
elif weight.spec.is_1D_row() and (bias is None or bias.spec.is_1D_row() or bias.spec.is_1D_col()):
|
|
ret_tensor = colo_linear_1Dcol(input_tensor, weight, bias)
|
|
else:
|
|
raise NotImplementedError
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
# building the computing graph, op -> output
|
|
if GraphGlobalEnv().graph_building:
|
|
cur_op_node.add_post_tensor(ret_tensor)
|
|
|
|
return ret_tensor
|