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Unverified Commit ad536e30 authored by ver217's avatar ver217 Committed by GitHub
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[tensor] refactor colo-tensor (#992) 

* refactor colo-tensor and update linear op

* polish code

* polish code

* update ops and unit tests

* update unit tests

* polish code

* rename dist_spec module

* polish code

* polish code

* remove unneeded import

* fix pipelinable
parent 1467d83e
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colossalai/tensor/__init__.py
View file @ ad536e30
...@@ -6,10 +6,10 @@ from .colo_parameter import ColoParameter ...@@ -6,10 +6,10 @@ from .colo_parameter import ColoParameter
from .utils import convert_parameter, named_params_with_colotensor from .utils import convert_parameter, named_params_with_colotensor
from ._ops import * from ._ops import *
from .optim.colo_optimizer import ColoOptimizer from .optim.colo_optimizer import ColoOptimizer
from . import dist_spec from . import distspec
from .dist_spec_mgr import DistSpecManager from .dist_spec_mgr import DistSpecManager
__all__ = [ __all__ = [
'ColoTensor', 'convert_parameter', 'colo_op_impl', 'ComputePattern', 'TensorSpec', 'ParallelAction', 'ColoTensor', 'convert_parameter', 'colo_op_impl', 'ComputePattern', 'TensorSpec', 'ParallelAction',
'named_params_with_colotensor', 'ColoOptimizer', 'ColoParameter', 'dist_spec', 'DistSpecManager' 'named_params_with_colotensor', 'ColoOptimizer', 'ColoParameter', 'distspec', 'DistSpecManager'
] ]
colossalai/tensor/_ops/_utils.py 0 → 100644
View file @ ad536e30
import torch
from typing import Union, Optional
from colossalai.tensor import ColoTensor
GeneralTensor = Union[ColoTensor, torch.Tensor]
Number = Union[int, float]
def convert_to_colo_tensor(tensor: Optional[GeneralTensor]) -> Optional[ColoTensor]:
if tensor is not None and not isinstance(tensor, ColoTensor):
tensor = ColoTensor.from_torch_tensor(tensor)
return tensor
colossalai/tensor/_ops/addmm.py
View file @ ad536e30
import torch import torch
from typing import Union
from colossalai.tensor.op_wrapper import colo_op_impl from colossalai.tensor.op_wrapper import colo_op_impl
from colossalai.nn.layer.parallel_1d._utils import reduce_input, reduce_grad from colossalai.nn.layer.parallel_1d._utils import reduce_input, reduce_grad
from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ParallelAction, ColoTensor from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ParallelAction, ColoTensor
from colossalai.tensor import dist_spec from colossalai.tensor import distspec
from ._utils import GeneralTensor, Number, convert_to_colo_tensor
def colo_addmm_1Drow(input_tensor: ColoTensor, mat1: ColoTensor, mat2: ColoTensor, beta: Union[int, float], def colo_addmm_1Drow(input_tensor: ColoTensor, mat1: ColoTensor, mat2: ColoTensor, beta: Number,
alpha: Union[int, float]) -> ColoTensor: alpha: Number) -> ColoTensor:
parallel_action = mat2.spec.get_action_by_compute_pattern(ComputePattern.TP1D) parallel_action = mat2.spec.get_action_by_compute_pattern(ComputePattern.TP1D)
# mat1:S[1] x mat2:S[0] = Output:P # mat1:S[1] x mat2:S[0] = Output:P
# beta * input + alpha * All-Reduce(Output) = res # beta * input + alpha * All-Reduce(Output) = res
mat1.to_dist_spec(dist_spec.shard(mat2.spec.get_process_group(), [-1], [mat2.spec.get_process_group().size()])) mat1 = mat1.convert_to_dist_spec(
distspec.shard(mat2.spec.get_process_group(), [-1], [mat2.spec.get_process_group_size()]))
# Output:P # Output:P
partial_output = torch.mm(mat1.torch_tensor(), mat2.torch_tensor()) partial_output = torch.mm(mat1, mat2)
# Reduce(Output) # Reduce(Output)
output = reduce_input(partial_output, parallel_action.parallel_mode) output = reduce_input(partial_output, parallel_action.parallel_mode)
# input # input
assert not input_tensor.has_spec(), 'Invalid input spec for 1Drow addmm op' assert not input_tensor.has_spec(), 'Invalid input spec for 1Drow addmm op'
output = beta * input_tensor.torch_tensor() + alpha * output output = beta * input_tensor + alpha * output
output = ColoTensor.init_from_torch_tensor(output, output = ColoTensor.from_torch_tensor(output, spec=TensorSpec(distspec.replicate(mat2.spec.get_process_group())))
spec=TensorSpec(dist_spec.replicate(mat2.spec.get_process_group())))
return output return output
def colo_addmm_1Dcol(input_tensor: ColoTensor, mat1: ColoTensor, mat2: ColoTensor, beta: Union[int, float], def colo_addmm_1Dcol(input_tensor: ColoTensor, mat1: ColoTensor, mat2: ColoTensor, beta: Number,
alpha: Union[int, float]) -> ColoTensor: alpha: Number) -> ColoTensor:
# mat1:B x mat2:S[1] + input:S[1] = Output:S[1] # mat1:B x mat2:S[1] + input:S[1] = Output:S[1]
parallel_action = mat2.spec.get_action_by_compute_pattern(ComputePattern.TP1D) parallel_action = mat2.spec.get_action_by_compute_pattern(ComputePattern.TP1D)
mat1.to_dist_spec(dist_spec.replicate(mat2.spec.get_process_group())) mat1 = mat1.convert_to_dist_spec(distspec.replicate(mat2.spec.get_process_group()))
mat1_torch_tensor = reduce_grad(mat1.torch_tensor(), parallel_action.parallel_mode) mat1 = reduce_grad(mat1, parallel_action.parallel_mode)
output_parallel = torch.addmm(input_tensor.torch_tensor(), output_parallel = torch.addmm(input_tensor, mat1, mat2, beta=beta, alpha=alpha)
mat1_torch_tensor, output_spec = TensorSpec(distspec.shard(mat2.spec.get_process_group(), [-1], [mat2.spec.get_process_group_size()]),
mat2.torch_tensor(), [ParallelAction(priority=1, parallel_mode=parallel_action.parallel_mode)])
beta=beta, output = ColoTensor.from_torch_tensor(output_parallel, spec=output_spec)
alpha=alpha)
output_spec = TensorSpec(
dist_spec.shard(mat2.spec.get_process_group(), [-1], [mat2.spec.get_process_group().size()]),
[ParallelAction(priority=1, parallel_mode=parallel_action.parallel_mode)])
output = ColoTensor.init_from_torch_tensor(output_parallel, spec=output_spec)
if parallel_action.gather_out: if parallel_action.gather_out:
# All-Gather(Output) # All-Gather(Output)
output.to_dist_spec(dist_spec.replicate(mat2.spec.get_process_group())) output = output.convert_to_dist_spec(distspec.replicate(mat2.spec.get_process_group()))
return output return output
def colo_addmm_1d(mode: str, input_tensor: ColoTensor, mat1: ColoTensor, mat2: ColoTensor, beta: Number,
alpha: Number) -> ColoTensor:
assert mode in ('row', 'col')
funcs = {'row': colo_addmm_1Drow, 'col': colo_addmm_1Dcol}
return funcs[mode](input_tensor, mat1, mat2, beta, alpha)
@colo_op_impl(torch.addmm) @colo_op_impl(torch.addmm)
def colo_addmm(types, args, kwargs, pg): def colo_addmm(input_tensor: GeneralTensor,
mat1: GeneralTensor,
mat2: GeneralTensor,
*args,
beta: Number = 1,
alpha: Number = 1) -> ColoTensor:
"""Handles ``__torch_function__`` dispatch for ``torch.nn.functional.linear``. """Handles ``__torch_function__`` dispatch for ``torch.nn.functional.linear``.
This method computes a linear. This method computes a linear.
""" """
input_tensor, mat1, mat2 = args[:3] input_tensor, mat1, mat2 = tuple(map(convert_to_colo_tensor, (input_tensor, mat1, mat2)))
to_colo_tensor = lambda t: t if isinstance(t, ColoTensor) else ColoTensor.init_from_torch_tensor(t)
input_tensor = to_colo_tensor(input_tensor)
mat2 = to_colo_tensor(mat2)
beta = kwargs.get('beta', 1) if kwargs else 1
alpha = kwargs.get('alpha', 1) if kwargs else 1
# building the computing graph, inputs -> op # building the computing graph, inputs -> op
# if GraphGlobalEnv().graph_building: # if GraphGlobalEnv().graph_building:
...@@ -70,17 +72,15 @@ def colo_addmm(types, args, kwargs, pg): ...@@ -70,17 +72,15 @@ def colo_addmm(types, args, kwargs, pg):
if not mat2.has_spec(): # No Model Parallel Applied if not mat2.has_spec(): # No Model Parallel Applied
assert mat2.spec.is_gathered(), 'Invalid mat2 spec for native addmm op' assert mat2.spec.is_gathered(), 'Invalid mat2 spec for native addmm op'
assert input_tensor.spec.is_gathered(), 'Invalid input spec for native addmm op' assert input_tensor.spec.is_gathered(), 'Invalid input spec for native addmm op'
ret_tensor = ColoTensor.init_from_torch_tensor( ret_tensor = ColoTensor.from_torch_tensor(torch.addmm(input_tensor, mat1, mat2, beta=beta, alpha=alpha))
torch.addmm(input_tensor.torch_tensor(), mat1, mat2.torch_tensor(), beta=beta, alpha=alpha))
elif mat2.spec.has_compute_pattern(ComputePattern.TP1D): # Single Model Parallel Applied elif mat2.spec.has_compute_pattern(ComputePattern.TP1D): # Single Model Parallel Applied
spec = TensorSpec(dist_spec.replicate(mat2.spec.get_process_group()))
mat1 = args[1] if isinstance(args[1], ColoTensor) else ColoTensor.init_from_torch_tensor(args[1], spec=spec)
if mat2.spec.is_1D_row() and input_tensor.spec.is_gathered(): if mat2.spec.is_1D_row() and input_tensor.spec.is_gathered():
ret_tensor = colo_addmm_1Drow(input_tensor, mat1, mat2, beta, alpha) mode = 'row'
elif mat2.spec.is_1D_col() and (input_tensor.spec.is_1D_col() or input_tensor.spec.is_1D_row()): elif mat2.spec.is_1D_col() and (input_tensor.spec.is_1D_col() or input_tensor.spec.is_1D_row()):
ret_tensor = colo_addmm_1Dcol(input_tensor, mat1, mat2, beta, alpha) mode = 'col'
else: else:
raise NotImplementedError raise NotImplementedError
ret_tensor = colo_addmm_1d(mode, input_tensor, mat1, mat2, beta, alpha)
else: else:
raise NotImplementedError raise NotImplementedError
......
colossalai/tensor/_ops/element_wise.py
View file @ ad536e30
from copy import copy
import torch import torch
from colossalai.tensor.op_wrapper import colo_op_impl from colossalai.tensor.op_wrapper import colo_op_impl
from colossalai.tensor import ColoTensor from colossalai.tensor import ColoTensor
from ._utils import GeneralTensor
@colo_op_impl(torch.allclose)
def colo_mean(types, args=(), kwargs=None, pg=None):
a = args[0]
b = args[1]
if isinstance(a, ColoTensor):
a = a.torch_tensor()
elif isinstance(b, ColoTensor):
b = b.torch_tensor()
if kwargs is None:
kwargs = {}
return torch.allclose(a, b, **kwargs)
@colo_op_impl(torch.mean)
def colo_mean(types, args=(), kwargs=None, pg=None):
input_t = args[0]
if isinstance(input_t, ColoTensor):
input_t = input_t.torch_tensor()
return ColoTensor.init_from_torch_tensor(torch.mean(input_t))
def register_elementwise_op(op): def register_elementwise_op(op):
@colo_op_impl(op) @colo_op_impl(op)
def elementwise_op(types, args=(), kwargs=None, pg=None): def elementwise_op(input_tensor: GeneralTensor, *args, **kwargs):
""" """
Handles ``__torch_function__`` dispatch for the elementwise op such Handles ``__torch_function__`` dispatch for the elementwise op such
as ``torch.nn.functional.gelu`` or ``torch.nn.functional.relu``. as ``torch.nn.functional.gelu`` or ``torch.nn.functional.relu``.
This method computes on either a normal tensor or a sharded tensor. This method computes on either a normal tensor or a sharded tensor.
""" """
input_tensor = args[0] output = op(input_tensor, *args, **kwargs)
# Validate types if isinstance(input_tensor, ColoTensor):
if not isinstance(input_tensor, ColoTensor): spec = copy(input_tensor.spec)
raise TypeError("input needs to be a ColoTensor") return ColoTensor.from_torch_tensor(output, spec=spec)
return ColoTensor.init_from_torch_tensor(op(input_tensor.torch_tensor())) return ColoTensor.from_torch_tensor(output)
register_elementwise_op(torch.nn.functional.gelu) register_elementwise_op(torch.nn.functional.gelu)
register_elementwise_op(torch.nn.functional.relu) register_elementwise_op(torch.nn.functional.relu)
register_elementwise_op(torch.clone)
register_elementwise_op(torch.Tensor.clone)
@colo_op_impl(torch.sum) register_elementwise_op(torch.Tensor.detach)
def sum_op(types, args=(), kwargs=None, pg=None):
"""
Handles ``__torch_function__`` dispatch for the elementwise op such
as ``torch.sum`.
This method computes on either a normal tensor or a sharded tensor.
"""
if len(args) > 0:
input_tensor = args[0]
if kwargs is None:
kwargs = {}
if 'input' in kwargs:
input_tensor = kwargs['input']
# Validate types
if not isinstance(input_tensor, ColoTensor):
raise TypeError("input needs to be a ColoTensor")
return ColoTensor.init_from_torch_tensor(torch.sum(input_tensor.torch_tensor()))
colossalai/tensor/_ops/embedding.py
View file @ ad536e30
import torch import torch
import torch.nn.functional as F
from typing import Optional
from colossalai.tensor.op_wrapper import colo_op_impl from colossalai.tensor.op_wrapper import colo_op_impl
from colossalai.nn.layer.parallel_1d._utils import reduce_input from colossalai.nn.layer.parallel_1d._utils import reduce_input
from colossalai.core import global_context as gpc from colossalai.core import global_context as gpc
from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ParallelAction, ColoTensor, dist_spec from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ParallelAction, ColoTensor, distspec
from ._utils import GeneralTensor, convert_to_colo_tensor
def colo_embedding_1Dcol(input_tensor: ColoTensor, weight: ColoTensor, args, kwargs) -> ColoTensor: def colo_embedding_1Dcol(input_tensor: ColoTensor,
weight: ColoTensor,
padding_idx: Optional[int] = None,
max_norm: Optional[float] = None,
norm_type: float = 2.0,
scale_grad_by_freq: bool = False,
sparse: bool = False) -> ColoTensor:
# embedding_1Dcol split the weight(lookup table) to (num_embeddings, embedding_dim/P) # embedding_1Dcol split the weight(lookup table) to (num_embeddings, embedding_dim/P)
# Gather splitted lookup table # Gather splitted lookup table
parallel_action = weight.spec.get_action_by_compute_pattern(ComputePattern.TP1D) 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_tensor = input_tensor.convert_to_dist_spec(distspec.replicate(weight.spec.get_process_group()))
output_parallel = torch.nn.functional.embedding(input_tensor.torch_tensor(), weight.torch_tensor(), *args, **kwargs) output_parallel = F.embedding(input_tensor,
weight,
padding_idx=padding_idx,
max_norm=max_norm,
norm_type=norm_type,
scale_grad_by_freq=scale_grad_by_freq,
sparse=sparse)
output_spec = TensorSpec( output_spec = TensorSpec(
dist_spec.shard(weight.spec.get_process_group(), [-1], [weight.spec.get_process_group().size()]), distspec.shard(weight.spec.get_process_group(), [-1], [weight.spec.get_process_group_size()]),
[ParallelAction(priority=1, parallel_mode=parallel_action.parallel_mode)]) [ParallelAction(priority=1, parallel_mode=parallel_action.parallel_mode)])
output = ColoTensor.init_from_torch_tensor(output_parallel, spec=output_spec) output = ColoTensor.from_torch_tensor(output_parallel, spec=output_spec)
output.to_dist_spec(dist_spec.replicate(weight.spec.get_process_group())) output = output.convert_to_dist_spec(distspec.replicate(weight.spec.get_process_group()))
return output return output
def colo_embedding_1Drow(input_tensor: ColoTensor, weight: ColoTensor, args, kwargs) -> ColoTensor: def colo_embedding_1Drow(input_tensor: ColoTensor,
weight: ColoTensor,
padding_idx: Optional[int] = None,
max_norm: Optional[float] = None,
norm_type: float = 2.0,
scale_grad_by_freq: bool = False,
sparse: bool = False) -> ColoTensor:
# embedding_1Drow split the weight(lookup table) to (num_embeddings/P, embedding_dim) # embedding_1Drow split the weight(lookup table) to (num_embeddings/P, embedding_dim)
# Find index in this shard and mask those not here # Find index in this shard and mask those not here
# Reduce all # Reduce all
parallel_action = weight.spec.get_action_by_compute_pattern(ComputePattern.TP1D) 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_tensor = input_tensor.convert_to_dist_spec(distspec.replicate(weight.spec.get_process_group()))
tensor_parallel_rank = gpc.get_local_rank(parallel_action.parallel_mode) tensor_parallel_rank = gpc.get_local_rank(parallel_action.parallel_mode)
num_embeddings_per_partition = weight.size(0) num_embeddings_per_partition = weight.size(0)
...@@ -33,53 +54,87 @@ def colo_embedding_1Drow(input_tensor: ColoTensor, weight: ColoTensor, args, kwa ...@@ -33,53 +54,87 @@ def colo_embedding_1Drow(input_tensor: ColoTensor, weight: ColoTensor, args, kwa
vocab_end_index = vocab_start_index + num_embeddings_per_partition vocab_end_index = vocab_start_index + num_embeddings_per_partition
# Build the mask. # Build the mask.
input_mask = (input_tensor.torch_tensor() < vocab_start_index) | \ input_mask = (input_tensor < vocab_start_index) | \
(input_tensor.torch_tensor() >= vocab_end_index) (input_tensor >= vocab_end_index)
# Mask the input. # Mask the input.
# TODO(jzy) masked_input may be an activation managed by ColoTensor. # TODO(jzy) masked_input may be an activation managed by ColoTensor.
masked_input = input_tensor.torch_tensor().clone() - vocab_start_index masked_input = input_tensor.clone() - vocab_start_index
masked_input[input_mask] = 0 masked_input[input_mask] = 0
partial_output = torch.nn.functional.embedding(masked_input, weight.torch_tensor(), *args, **kwargs) partial_output = F.embedding(masked_input,
weight,
padding_idx=padding_idx,
max_norm=max_norm,
norm_type=norm_type,
scale_grad_by_freq=scale_grad_by_freq,
sparse=sparse)
# Mask the output embedding. # Mask the output embedding.
partial_output[input_mask, :] = 0. partial_output[input_mask, :] = 0.
# Reduce across all the model parallel GPUs. # Reduce across all the model parallel GPUs.
output = reduce_input(partial_output, parallel_action.parallel_mode) output = reduce_input(partial_output, parallel_action.parallel_mode)
output = ColoTensor.init_from_torch_tensor(output, output = ColoTensor.from_torch_tensor(output, spec=TensorSpec(distspec.replicate(weight.spec.get_process_group())))
spec=TensorSpec(dist_spec.replicate(weight.spec.get_process_group())))
return output return output
@colo_op_impl(torch.nn.functional.embedding) def colo_embedding_1d(mode: str,
def colo_embedding(types, args, kwargs, pg): input_tensor: ColoTensor,
weight: ColoTensor,
padding_idx: Optional[int] = None,
max_norm: Optional[float] = None,
norm_type: float = 2.0,
scale_grad_by_freq: bool = False,
sparse: bool = False) -> ColoTensor:
assert mode in ('row', 'col')
funcs = {'row': colo_embedding_1Drow, 'col': colo_embedding_1Dcol}
return funcs[mode](input_tensor,
weight,
padding_idx=padding_idx,
max_norm=max_norm,
norm_type=norm_type,
scale_grad_by_freq=scale_grad_by_freq,
sparse=sparse)
@colo_op_impl(F.embedding)
def colo_embedding(input_tensor: GeneralTensor,
weight: GeneralTensor,
padding_idx: Optional[int] = None,
max_norm: Optional[float] = None,
norm_type: float = 2.0,
scale_grad_by_freq: bool = False,
sparse: bool = False):
"""Handles ``__torch_function__`` dispatch for ``torch.nn.functional.embedding``. """Handles ``__torch_function__`` dispatch for ``torch.nn.functional.embedding``.
This method looks up an embedding table. This method looks up an embedding table.
""" """
input_tensor = args[0] input_tensor, weight = tuple(map(convert_to_colo_tensor, (input_tensor, weight)))
weight = args[1]
args = args[2:]
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)
# Handle differen parallel actions. # Handle differen parallel actions.
if not weight.has_spec(): # No Model Parallel Applied if not weight.has_spec(): # No Model Parallel Applied
assert weight.spec.is_gathered(), 'Invalid weight spec for native embedding op' assert weight.spec.is_gathered(), 'Invalid weight spec for native embedding op'
input_tensor = input_tensor.torch_tensor() return ColoTensor.from_torch_tensor(
weight = weight.torch_tensor() F.embedding(input_tensor,
output = torch.nn.functional.embedding(input_tensor, weight, *args, **kwargs) weight,
return ColoTensor.init_from_torch_tensor(output) padding_idx=padding_idx,
max_norm=max_norm,
norm_type=norm_type,
scale_grad_by_freq=scale_grad_by_freq,
sparse=sparse))
elif weight.spec.has_compute_pattern(ComputePattern.TP1D): # Single Model Parallel Applied elif weight.spec.has_compute_pattern(ComputePattern.TP1D): # Single Model Parallel Applied
if weight.spec.is_1D_row(): if weight.spec.is_1D_row():
return colo_embedding_1Drow(input_tensor, weight, args, kwargs) mode = 'row'
elif weight.spec.is_1D_col(): elif weight.spec.is_1D_col():
return colo_embedding_1Dcol(input_tensor, weight, args, kwargs) mode = 'col'
else: else:
raise NotImplementedError raise NotImplementedError
return colo_embedding_1d(mode,
input_tensor,
weight,
padding_idx=padding_idx,
max_norm=max_norm,
norm_type=norm_type,
scale_grad_by_freq=scale_grad_by_freq,
sparse=sparse)
else: else:
raise NotImplementedError raise NotImplementedError
colossalai/tensor/_ops/layernorm.py
View file @ ad536e30
import torch import torch
import torch.nn.functional as F
from typing import List, Optional
from colossalai.tensor.op_wrapper import colo_op_impl from colossalai.tensor.op_wrapper import colo_op_impl
from colossalai.tensor import ColoTensor, dist_spec from colossalai.tensor import ColoTensor, distspec
from ._utils import GeneralTensor, convert_to_colo_tensor
@colo_op_impl(torch.nn.functional.layer_norm) @colo_op_impl(F.layer_norm)
def colo_layernorm(types, args=(), kwargs=None, pg=None): def colo_layernorm(
arg_num = len(args) input_tensor: GeneralTensor,
if arg_num > 0: normalized_shape: List[int],
input_tensor = args[0] weight: Optional[GeneralTensor] = None,
if arg_num > 1: bias: Optional[GeneralTensor] = None,
normalized_shape = args[1] eps: float = 1e-5,
if arg_num > 2: ):
weight = args[3] input_tensor, weight, bias = tuple(map(convert_to_colo_tensor, (input_tensor, weight, bias)))
if arg_num > 3:
bias = args[4]
if arg_num > 4:
eps = args[5]
if 'input' in kwargs: # TODO (ver217): check dist spec
input_tensor = kwargs['input'] input_tensor = input_tensor.convert_to_dist_spec(distspec.replicate(input_tensor.spec.get_process_group()))
if 'weight' in kwargs:
weight = kwargs['weight']
if 'bias' in kwargs:
bias = kwargs['bias']
if 'eps' in kwargs:
eps = kwargs['eps']
if isinstance(input_tensor, ColoTensor): output = F.layer_norm(input_tensor, normalized_shape, weight=weight, bias=bias, eps=eps)
# TODO (ver217): check input dist spec output = ColoTensor.from_torch_tensor(output, input_tensor.spec)
input_tensor.to_dist_spec(dist_spec.replicate(input_tensor.spec.get_process_group())) return output
input_tensor = input_tensor.torch_tensor()
if isinstance(weight, ColoTensor):
weight = weight.torch_tensor()
if isinstance(bias, ColoTensor):
bias = bias.torch_tensor()
return ColoTensor.init_from_torch_tensor(
torch.nn.functional.layer_norm(input_tensor, normalized_shape, weight, bias, eps))
colossalai/tensor/_ops/linear.py
View file @ ad536e30
import torch import torch
import torch.nn.functional as F
import torch.distributed as dist
from typing import Optional
from colossalai.tensor.op_wrapper import colo_op_impl 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.parallel_1d._utils import reduce_input, reduce_grad
from colossalai.nn.layer.utils import divide from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ParallelAction, ColoTensor, distspec
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 from colossalai.tensor.graph import GraphOpNode, GraphGlobalEnv
from ._utils import GeneralTensor, convert_to_colo_tensor
def colo_linear_1Drow(input_tensor: ColoTensor, weight: ColoTensor, bias: ColoTensor) -> ColoTensor: def colo_linear_1Drow(input_tensor: ColoTensor, weight: ColoTensor, bias: Optional[ColoTensor]) -> ColoTensor:
parallel_action = weight.spec.get_action_by_compute_pattern(ComputePattern.TP1D) parallel_action = weight.spec.get_action_by_compute_pattern(ComputePattern.TP1D)
# Input:S[1] x Weight:S[0] = Output:P # Input:S[1] x Weight:S[0] = Output:P
# All-Reduce(Output) + bias = res # All-Reduce(Output) + bias = res
# Input:S[1] # Input:S[1]
input_tensor.to_dist_spec( input_tensor = input_tensor.convert_to_dist_spec(
dist_spec.shard(weight.spec.get_process_group(), [-1], [weight.spec.get_process_group().size()])) distspec.shard(weight.spec.get_process_group(), [-1], [weight.spec.get_process_group_size()]))
# Output:P # Output:P
partial_output = torch.nn.functional.linear(input_tensor.torch_tensor(), weight.torch_tensor()) partial_output = F.linear(input_tensor, weight)
# Reduce(Output) # Reduce(Output)
output = reduce_input(partial_output, parallel_action.parallel_mode) output = reduce_input(partial_output, parallel_action.parallel_mode)
# Bias # Bias
if bias is not None: if bias is not None:
assert not bias.has_spec(), 'Invalid bias spec for 1Drow Linear op' assert not bias.has_spec(), 'Invalid bias spec for 1Drow Linear op'
output = output + bias.torch_tensor() output = output + bias
output = ColoTensor.init_from_torch_tensor(output,
spec=TensorSpec(dist_spec.replicate(weight.spec.get_process_group()))) output = ColoTensor.from_torch_tensor(output, spec=TensorSpec(distspec.replicate(weight.spec.get_process_group())))
return output return output
def colo_linear_1Dcol(input_tensor: ColoTensor, weight: ColoTensor, bias: ColoTensor) -> ColoTensor: 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] # Input:B x Weight:S[1] + Bias:S[1] = Output:S[1]
# All-Gather(Output) # All-Gather(Output)
# Input:B # Input:B
parallel_action = weight.spec.get_action_by_compute_pattern(ComputePattern.TP1D) 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_tensor = input_tensor.convert_to_dist_spec(distspec.replicate(weight.spec.get_process_group()))
input_parallel = reduce_grad(input_tensor.torch_tensor(), parallel_action.parallel_mode) input_parallel = reduce_grad(input_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 = F.linear(input_parallel, weight, bias)
output = ColoTensor.from_torch_tensor(
output_parallel, output_parallel,
spec=TensorSpec( spec=TensorSpec(distspec.shard(weight.spec.get_process_group(), [-1], [weight.spec.get_process_group_size()]),
dist_spec.shard(weight.spec.get_process_group(), [-1], [weight.spec.get_process_group().size()]), [ParallelAction(priority=1, parallel_mode=parallel_action.parallel_mode)]))
[ParallelAction(priority=1, parallel_mode=parallel_action.parallel_mode)]))
if parallel_action.gather_out: if parallel_action.gather_out:
# All-Gather(Output) # All-Gather(Output)
output.to_dist_spec(dist_spec.replicate(weight.spec.get_process_group())) output = output.convert_to_dist_spec(distspec.replicate(weight.spec.get_process_group()))
return output return output
@colo_op_impl(torch.nn.functional.linear) def colo_linear_1d(mode: str, input_tensor: ColoTensor, weight: ColoTensor, bias: Optional[ColoTensor]) -> ColoTensor:
def colo_linear(types, args, kwargs, pg): assert mode in ('row', 'col')
funcs = {'row': colo_linear_1Drow, 'col': colo_linear_1Dcol}
return funcs[mode](input_tensor, weight, bias)
@colo_op_impl(F.linear)
def colo_linear(input_tensor: GeneralTensor, weight: GeneralTensor, bias: Optional[GeneralTensor] = None):
"""Handles ``__torch_function__`` dispatch for ``torch.nn.functional.linear``. """Handles ``__torch_function__`` dispatch for ``torch.nn.functional.linear``.
This method computes a linear. This method computes a linear.
""" """
input_tensor = args[0] input_tensor, weight, bias = tuple(map(convert_to_colo_tensor, (input_tensor, weight, bias)))
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 # building the computing graph, inputs -> op
if GraphGlobalEnv().graph_building: if GraphGlobalEnv().graph_building:
cur_op_node = GraphOpNode('linear', [weight, bias]) cur_op_node = GraphOpNode('linear', [weight, bias])
cur_op_node.add_prev_tensor(input_tensor) cur_op_node.add_prev_tensor(input_tensor)
# Add communication logic before and after linear call. # Add communication logic before and after linear call.
ret_tensor = None ret_tensor = None
if not weight.has_spec(): # No Model Parallel Applied if not weight.has_spec(): # No Model Parallel Applied
assert bias.spec.is_gathered(), 'Invalid bias spec for native Linear op' assert weight.spec.is_gathered(), 'Invalid weight spec for native Linear op'
assert bias.spec.is_gathered(), 'Invalid bias spec for native Linear op' assert bias is None or bias.spec.is_gathered(), 'Invalid bias spec for native Linear op'
input_tensor = input_tensor.torch_tensor() ret_tensor = ColoTensor.from_torch_tensor(F.linear(input_tensor, weight, bias))
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 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()): if weight.spec.is_1D_col() and (bias is None or bias.spec.is_gathered()):
ret_tensor = colo_linear_1Drow(input_tensor, weight, bias) mode = 'row'
elif weight.spec.is_1D_row() and (bias is None or bias.spec.is_1D_row() or bias.spec.is_1D_col()): 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) mode = 'col'
else: else:
raise NotImplementedError raise NotImplementedError
ret_tensor = colo_linear_1d(mode, input_tensor, weight, bias)
else: else:
raise NotImplementedError raise NotImplementedError
# building the computing graph, op -> output # building the computing graph, op -> output
if GraphGlobalEnv().graph_building: if GraphGlobalEnv().graph_building:
cur_op_node.add_post_tensor(ret_tensor) cur_op_node.add_post_tensor(ret_tensor)
return ret_tensor return ret_tensor
colossalai/tensor/_ops/loss.py
View file @ ad536e30
from colossalai.tensor.dist_spec import DistPlacementPattern
import torch import torch
import torch.nn.functional as F
from typing import Optional
from colossalai.tensor.op_wrapper import colo_op_impl from colossalai.tensor.op_wrapper import colo_op_impl
from colossalai.tensor import ColoTensor from colossalai.tensor import ColoTensor
from colossalai.nn.loss.loss_1d import VocabParallelCrossEntropyLoss1D from colossalai.nn.loss.loss_1d import VocabParallelCrossEntropyLoss1D
from ._utils import GeneralTensor, convert_to_colo_tensor
@colo_op_impl(torch.nn.functional.cross_entropy) @colo_op_impl(F.cross_entropy)
def colo_cross_entropy(types, args=(), kwargs=None, pg=None): def colo_cross_entropy(input_tensor: GeneralTensor,
arg_num = len(args) target: GeneralTensor,
weight: Optional[GeneralTensor] = None,
if arg_num > 0: size_average: Optional[bool] = None,
input_tensor = args[0] ignore_index: int = -100,
if arg_num > 1: reduce: Optional[bool] = None,
target = args[1] reduction: str = "mean",
if arg_num > 2: label_smoothing: float = 0.0):
weight = args[2] input_tensor, target, weight = tuple(map(convert_to_colo_tensor, (input_tensor, target, weight)))
if 'input' in kwargs:
input_tensor = kwargs.pop('input')
if 'target' in kwargs:
target = kwargs.pop('target')
if 'weight' in kwargs:
weight = kwargs.pop('weight')
if not isinstance(input_tensor, ColoTensor):
input_tensor = ColoTensor.init_from_torch_tensor(input_tensor)
if isinstance(target, ColoTensor):
target = target.torch_tensor()
if input_tensor.spec.is_gathered(): # Input is gathered if input_tensor.spec.is_gathered(): # Input is gathered
return ColoTensor.init_from_torch_tensor( output = F.cross_entropy(input_tensor,
torch.nn.functional.cross_entropy(input_tensor.torch_tensor(), target, weight)) target,
weight=weight,
size_average=size_average,
ignore_index=ignore_index,
reduce=reduce,
reduction=reduction,
label_smoothing=label_smoothing)
return ColoTensor.from_torch_tensor(output)
elif input_tensor.has_spec() and input_tensor.spec.num_action == 1: # Single Model Parallel Applied elif input_tensor.has_spec() and input_tensor.spec.num_action == 1: # Single Model Parallel Applied
if input_tensor.spec.is_1D_col(): if input_tensor.spec.is_1D_col():
return ColoTensor.init_from_torch_tensor(VocabParallelCrossEntropyLoss1D()(input_tensor.torch_tensor(), output = VocabParallelCrossEntropyLoss1D()(input_tensor, target)
target)) return ColoTensor.from_torch_tensor(output)
else: else:
raise NotImplementedError raise NotImplementedError
else: else:
......
colossalai/tensor/colo_parameter.py
View file @ ad536e30
from .colo_tensor import ColoTensor from .colo_tensor import ColoTensor
from .const import TensorType from .const import TensorType
import torch import torch
from colossalai.tensor import TensorSpec, distspec
from copy import copy
class ColoParameter(ColoTensor): class ColoParameter(ColoTensor):
...@@ -8,21 +10,26 @@ class ColoParameter(ColoTensor): ...@@ -8,21 +10,26 @@ class ColoParameter(ColoTensor):
""" """
def __init__(self, *args, **kargs): def __new__(cls,
super().__init__(*args, **kargs) data: torch.Tensor,
self._type = TensorType.MODEL requires_grad: bool = True,
spec: TensorSpec = TensorSpec(distspec.replicate())) -> 'ColoParameter':
if data is None:
data = torch.empty(0)
return torch.Tensor._make_subclass(cls, data, requires_grad)
def __new__(cls, *args, **kwargs): def __init__(self,
t = super(ColoParameter, cls).__new__(cls) data: torch.Tensor,
t._type = TensorType.MODEL requires_grad: bool = True,
return t spec: TensorSpec = TensorSpec(distspec.replicate())) -> None:
self._spec = copy(spec)
self._type = TensorType.MODEL
self._graph_node = None
@staticmethod @staticmethod
def init_from_torch_tensor(tensor: torch.Tensor, save_payload=True) -> 'ColoParameter': def from_torch_tensor(tensor: torch.Tensor,
colo_p = ColoParameter(*tensor.size(), requires_grad: bool = True,
dtype=tensor.dtype, spec: TensorSpec = TensorSpec(distspec.replicate())) -> 'ColoParameter':
requires_grad=tensor.requires_grad, tensor = tensor.as_subclass(ColoParameter)
pin_memory=tensor.is_pinned(), tensor.__init__(tensor, requires_grad=requires_grad, spec=spec)
device=tensor.device, return tensor
torch_tensor=tensor if save_payload else torch.empty(0))
return colo_p
colossalai/tensor/colo_tensor.py
View file @ ad536e30
from .op_wrapper import _COLOSSAL_OPS from .op_wrapper import _COLOSSAL_OPS
from copy import copy from copy import copy
import torch import torch
from typing import Tuple, Optional, Callable, Union
from numpy import product
from colossalai.tensor import TensorSpec from colossalai.tensor import TensorSpec
from .const import TensorType from .const import TensorType
from colossalai.tensor import dist_spec from colossalai.tensor import distspec
from colossalai.tensor.dist_spec_mgr import DistSpecManager from colossalai.tensor.dist_spec_mgr import DistSpecManager
from colossalai.tensor.dist_spec import _DistSpec from colossalai.tensor.distspec import _DistSpec
from torch.overrides import get_default_nowrap_functions
class ColoTensor(object): def _convert_output(output):
if isinstance(output, torch.Tensor) and not isinstance(output, ColoTensor):
output = ColoTensor.from_torch_tensor(output)
elif isinstance(output, (list, tuple)):
output = type(output)(_convert_output(o) for o in output)
return output
class ColoTensor(torch.Tensor):
""" Data Structure for Tensor in Colossal-AI """ Data Structure for Tensor in Colossal-AI
1. It contains a torch.Tensor as an attribute. 1. It contains a torch.Tensor as an attribute.
2. It supports lazy init the tensor's payload. 2. It supports lazy init the tensor's payload.
...@@ -18,120 +25,23 @@ class ColoTensor(object): ...@@ -18,120 +25,23 @@ class ColoTensor(object):
4. It supports distributing the tensor's payload to the shards among processes. (TODO) 4. It supports distributing the tensor's payload to the shards among processes. (TODO)
""" """
def __new__(cls, *args, **kwargs): def __new__(cls, data: torch.Tensor, spec: TensorSpec = TensorSpec(distspec.replicate())) -> 'ColoTensor':
return super(ColoTensor, cls).__new__(cls) if data is None:
data = torch.empty(0)
def __init__(self, return torch.Tensor._make_subclass(cls, data, data.requires_grad)
*size: Tuple[int],
dtype=None, def __init__(self, data: torch.Tensor, spec: TensorSpec = TensorSpec(distspec.replicate())) -> None:
requires_grad=False,
pin_memory=False,
device=None,
torch_tensor=torch.empty(0),
spec: TensorSpec = TensorSpec(dist_spec.replicate())):
self._size = size
self._dtype = dtype
self._requires_grad = requires_grad
self._pin_memory = pin_memory
self._device = device
self._torch_tensor = torch_tensor
self._spec = copy(spec) self._spec = copy(spec)
self._type = TensorType.NONMODEL self._type = TensorType.NONMODEL
self._graph_node = None self._graph_node = None
def __getitem__(self, key):
return ColoTensor.init_from_torch_tensor(self.torch_tensor()[key])
@property @property
def spec(self) -> TensorSpec: def spec(self) -> TensorSpec:
return self._spec return self._spec
@property
def shard_pattern(self):
return self._shard_pattern
@property
def data(self):
return self._torch_tensor.data
@data.setter
def data(self, tensor: Union[torch.Tensor, "ColoTensor"]):
if isinstance(tensor, ColoTensor):
self._torch_tensor.data = tensor.data
elif isinstance(tensor, torch.Tensor):
self._torch_tensor.data = tensor
else:
raise NotImplementedError
@property
def grad(self):
return self._torch_tensor.grad
@property
def size(self):
return self._size
@property
def shape(self):
return torch.Size(self._size)
@property
def device(self):
return self._torch_tensor.device
def size(self, dim=None):
if dim is None:
return self.shape
return self._size[dim]
def dim(self):
return len(self._size)
def normal_(self, mean=0., std=1.):
torch_tensor = self.torch_tensor()
return torch_tensor.normal_(mean=mean, std=std)
def numel(self):
return product(self._size)
@staticmethod
def init_from_torch_tensor(tensor: torch.Tensor,
save_payload=True,
spec: TensorSpec = TensorSpec(dist_spec.replicate())) -> 'ColoTensor':
colo_t = ColoTensor(*tensor.size(),
dtype=tensor.dtype,
requires_grad=tensor.requires_grad,
pin_memory=tensor.is_pinned(),
device=tensor.device,
torch_tensor=tensor if save_payload else torch.empty(0),
spec=spec)
return colo_t
def del_torch_tensor(self, save_shape=False) -> None:
"""
delete the payload of the torch tensor.
Args:
save_shape (bool, optional): if saving the shape of the torch_tensor.
If saving the shape, the size of self._torch_tensor is inconsist with the self._size.
Defaults to False.
"""
if not save_shape:
self._size = (0,)
self._torch_tensor = torch.empty((0,), device=self._device, dtype=self._dtype)
def torch_tensor(self) -> torch.Tensor:
if self._torch_tensor.numel() == 0:
self._torch_tensor = torch.empty(*self._size,
dtype=self._dtype,
pin_memory=self._pin_memory,
requires_grad=self._requires_grad,
device=self._device)
return self._torch_tensor
def set_spec(self, spec: TensorSpec) -> None: def set_spec(self, spec: TensorSpec) -> None:
spec = copy(spec) spec = copy(spec)
self.to_dist_spec(spec.dist_spec) self.convert_to_dist_spec_(spec.dist_spec)
self._spec = spec self._spec = spec
def has_spec(self) -> bool: def has_spec(self) -> bool:
...@@ -142,89 +52,51 @@ class ColoTensor(object): ...@@ -142,89 +52,51 @@ class ColoTensor(object):
@classmethod @classmethod
def __torch_function__(cls, func, types, args=(), kwargs=None): def __torch_function__(cls, func, types, args=(), kwargs=None):
if kwargs is None:
kwargs = {}
if not all(issubclass(cls, t) for t in types):
return NotImplemented
global _COLOSSAL_OPS global _COLOSSAL_OPS
if func in _COLOSSAL_OPS: if func in _COLOSSAL_OPS:
for arg in args: func = _COLOSSAL_OPS[func]
if isinstance(arg, ColoTensor):
return _COLOSSAL_OPS[func](types, args, kwargs, None)
for kwarg in kwargs.values():
if isinstance(kwarg, ColoTensor):
return _COLOSSAL_OPS[func](types, args, kwargs, None)
else:
# If we have not hijact the function, convert the ColoTensors in args and kwargs to torch tensors.
args = [arg.torch_tensor() if isinstance(arg, ColoTensor) else arg for arg in args]
if kwargs is None:
kwargs = {}
kwargs = {k: v.torch_tensor() if isinstance(v, ColoTensor) else v for k, v in kwargs.items()}
return cls._filter_outputs_with_colo(func(*args, **kwargs))
def backward(self, gradient: Optional[torch.Tensor] = None, retain_graph: bool = False):
self._torch_tensor.backward(gradient=gradient, retain_graph=retain_graph)
def __add__(self, o) -> "ColoTensor":
if isinstance(o, ColoTensor):
return ColoTensor.init_from_torch_tensor(self.torch_tensor() + o.torch_tensor())
elif isinstance(o, (torch.Tensor, int, float)):
return ColoTensor.init_from_torch_tensor(self.torch_tensor() + o)
else:
raise TypeError(f'{type(o)} is not supported in ColoTensor __add__')
__radd__ = __add__
def __truediv__(self, o) -> "ColoTensor":
return ColoTensor.init_from_torch_tensor(self.torch_tensor() / o)
def __getattr__(self, name):
def replace_tensor_with_colo(func): with torch._C.DisableTorchFunction():
ret = func(*args, **kwargs)
if func in get_default_nowrap_functions():
return ret
else:
return _convert_output(ret)
def execute_func(*args, **kwargs): def __repr__(self):
# transform the ColoTensor args to torch Tensor. return f'ColoTensor: {super().__repr__()}'
args = [arg.torch_tensor() if isinstance(arg, ColoTensor) else arg for arg in args]
if kwargs is None:
kwargs = {}
kwargs = {k: v.torch_tensor() if isinstance(v, ColoTensor) else v for k, v in kwargs.items()}
return self._filter_outputs_with_colo(func(*args, **kwargs))
return execute_func def is_model_data(self) -> bool:
return self._type == TensorType.MODEL
if hasattr(self._torch_tensor, name) == False:
raise AttributeError
attr = getattr(self._torch_tensor, name) def convert_to_dist_spec_(self, dist_spec: _DistSpec) -> None:
with DistSpecManager.no_grad():
self.data = DistSpecManager.handle_trans_spec(self, self.spec.dist_spec, dist_spec)
self._spec.dist_spec = dist_spec
if isinstance(attr, Callable): def convert_to_dist_spec(self, dist_spec: _DistSpec) -> 'ColoTensor':
return replace_tensor_with_colo(attr) spec = copy(self._spec)
else: spec.dist_spec = dist_spec
return attr ret = DistSpecManager.handle_trans_spec(self, self.spec.dist_spec, dist_spec)
return ColoTensor.from_torch_tensor(ret, spec)
@classmethod @staticmethod
def _filter_outputs_with_colo(cls, outputs): def from_torch_tensor(tensor: torch.Tensor, spec: TensorSpec = TensorSpec(distspec.replicate())) -> 'ColoTensor':
if outputs is None: # return None tensor = tensor.as_subclass(ColoTensor)
return None tensor.__init__(tensor, spec=spec)
elif type(outputs) is not tuple: # num of return val = 1 return tensor
return ColoTensor.init_from_torch_tensor(outputs) if type(outputs) is torch.Tensor else outputs
else: # num of return val > 1 def __deepcopy__(self, memo):
return tuple([ if id(self) in memo:
ColoTensor.init_from_torch_tensor(output) if type(output) is torch.Tensor else output return memo[id(self)]
for output in outputs
])
def __mul__(self, other) -> "ColoTensor":
if isinstance(other, ColoTensor):
return ColoTensor.init_from_torch_tensor(self.torch_tensor() * other.torch_tensor())
elif isinstance(other, (torch.Tensor, int, float)):
return ColoTensor.init_from_torch_tensor(self.torch_tensor() * other)
else: else:
raise TypeError(f'{type(other)} is not supported in ColoTensor __mul__') with torch._C.DisableTorchFunction():
data = self.data.clone()
__rmul__ = __mul__ tensor = ColoTensor(data, spec=copy(self.spec))
memo[id(self)] = tensor
def to_dist_spec(self, dist_spec: _DistSpec) -> None: return tensor
self._torch_tensor = DistSpecManager.handle_trans_spec(self.torch_tensor(), self.spec.dist_spec, dist_spec)
if self._torch_tensor.is_leaf:
self._torch_tensor.requires_grad = self._requires_grad
self._size = self._torch_tensor.size()
self._spec.dist_spec = dist_spec
colossalai/tensor/dist_spec_mgr.py
View file @ ad536e30
from colossalai.tensor.dist_spec import _DistSpec from colossalai.tensor.distspec import _DistSpec
from colossalai.nn.layer.utils import divide from colossalai.nn.layer.utils import divide
from numpy import prod from numpy import prod
from contextlib import contextmanager from contextlib import contextmanager
...@@ -53,7 +53,7 @@ class DistSpecManager: ...@@ -53,7 +53,7 @@ class DistSpecManager:
@staticmethod @staticmethod
def _r2r(tensor: torch.Tensor, old_dist_spec: _DistSpec, dist_spec: _DistSpec) -> torch.Tensor: def _r2r(tensor: torch.Tensor, old_dist_spec: _DistSpec, dist_spec: _DistSpec) -> torch.Tensor:
if old_dist_spec.process_group is not None and old_dist_spec.process_group != dist_spec.process_group \ if old_dist_spec.process_group is not None and old_dist_spec.process_group != dist_spec.process_group \
and dist_spec.process_group is not None: and dist_spec.process_group is not None:
raise NotImplementedError raise NotImplementedError
return tensor return tensor
...@@ -66,7 +66,7 @@ class DistSpecManager: ...@@ -66,7 +66,7 @@ class DistSpecManager:
@staticmethod @staticmethod
def _s2r(tensor: torch.Tensor, old_dist_spec: _DistSpec, dist_spec: _DistSpec) -> torch.Tensor: def _s2r(tensor: torch.Tensor, old_dist_spec: _DistSpec, dist_spec: _DistSpec) -> torch.Tensor:
if old_dist_spec.process_group != dist_spec.process_group \ if old_dist_spec.process_group != dist_spec.process_group \
and dist_spec.process_group is not None: and dist_spec.process_group is not None:
raise NotImplementedError raise NotImplementedError
return DistSpecManager._gather(tensor, old_dist_spec) return DistSpecManager._gather(tensor, old_dist_spec)
......
colossalai/tensor/op_wrapper.py
View file @ ad536e30
...@@ -9,11 +9,6 @@ _COLOSSAL_OPS: Dict[str, Callable] = {} ...@@ -9,11 +9,6 @@ _COLOSSAL_OPS: Dict[str, Callable] = {}
def _register_colo_op(op, func): def _register_colo_op(op, func):
from inspect import signature
if len(signature(func).parameters) != 4:
raise TypeError(f'Custom stateful op function expects signature: '
f'(types, args, kwargs, process_group), but received '
f'signature: {signature(func)}')
global _COLOSSAL_OPS global _COLOSSAL_OPS
_COLOSSAL_OPS[op] = func _COLOSSAL_OPS[op] = func
......
colossalai/tensor/spec.py
View file @ ad536e30
import torch.distributed as dist
from enum import Enum from enum import Enum
from typing import List from typing import List
from colossalai.context.parallel_mode import ParallelMode from colossalai.context.parallel_mode import ParallelMode
from colossalai.tensor.dist_spec import _DistSpec, DistPlacementPattern from colossalai.tensor.distspec import _DistSpec, DistPlacementPattern
class ComputePattern(Enum): class ComputePattern(Enum):
...@@ -77,6 +78,9 @@ class TensorSpec(object): ...@@ -77,6 +78,9 @@ class TensorSpec(object):
def get_process_group(self): def get_process_group(self):
return self.dist_spec.process_group return self.dist_spec.process_group
def get_process_group_size(self):
return dist.get_world_size(self.dist_spec.process_group)
def get_placement(self): def get_placement(self):
return self.dist_spec.placement return self.dist_spec.placement
......
colossalai/utils/model/colo_init_context.py
View file @ ad536e30
...@@ -7,11 +7,13 @@ from torch import nn ...@@ -7,11 +7,13 @@ from torch import nn
from typing import Iterator, Tuple, Union, Optional from typing import Iterator, Tuple, Union, Optional
# find named_params includes replica # find named_params includes replica
def _named_params_with_replica( def _named_params_with_replica(
module: nn.Module, module: nn.Module,
prefix: str = '', prefix: str = '',
recurse: bool = True, recurse: bool = True,
) -> Iterator[Tuple[str, Union[nn.Parameter, ColoTensor]]]: ) -> Iterator[Tuple[str, Union[nn.Parameter, ColoTensor]]]:
modules = module.named_modules(prefix=prefix) if recurse else [(prefix, module)] modules = module.named_modules(prefix=prefix) if recurse else [(prefix, module)]
for mod_prefix, mod in modules: for mod_prefix, mod in modules:
...@@ -21,11 +23,13 @@ def _named_params_with_replica( ...@@ -21,11 +23,13 @@ def _named_params_with_replica(
name = mod_prefix + ('.' if mod_prefix else '') + name name = mod_prefix + ('.' if mod_prefix else '') + name
yield name, val yield name, val
# Adapted from torch.nn.module.Module.register_param # Adapted from torch.nn.module.Module.register_param
def _register_parameter_with_colotensor(self, name: str, param): def _register_parameter_with_colotensor(self, name: str, param):
if '_parameters' not in self.__dict__: if '_parameters' not in self.__dict__:
raise AttributeError( raise AttributeError("cannot assign parameter before Module.__init__() call")
"cannot assign parameter before Module.__init__() call")
if not isinstance(name, torch._six.string_classes): if not isinstance(name, torch._six.string_classes):
raise TypeError("parameter name should be a string. " raise TypeError("parameter name should be a string. "
...@@ -41,19 +45,21 @@ def _register_parameter_with_colotensor(self, name: str, param): ...@@ -41,19 +45,21 @@ def _register_parameter_with_colotensor(self, name: str, param):
self._parameters[name] = None self._parameters[name] = None
elif not isinstance(param, (torch.nn.Parameter, ColoParameter)): elif not isinstance(param, (torch.nn.Parameter, ColoParameter)):
raise TypeError("cannot assign '{}' object to parameter '{}' " raise TypeError("cannot assign '{}' object to parameter '{}' "
"(torch.nn.Parameter or ColoParameter or None required)" "(torch.nn.Parameter or ColoParameter or None required)".format(torch.typename(param), name))
.format(torch.typename(param), name))
elif param.grad_fn: elif param.grad_fn:
raise ValueError( raise ValueError("Cannot assign non-leaf Tensor to parameter '{0}'. Model "
"Cannot assign non-leaf Tensor to parameter '{0}'. Model " "parameters must be created explicitly. To express '{0}' "
"parameters must be created explicitly. To express '{0}' " "as a function of another Tensor, compute the value in "
"as a function of another Tensor, compute the value in " "the forward() method.".format(name))
"the forward() method.".format(name))
else: else:
self._parameters[name] = param self._parameters[name] = param
# Adapted from torch.nn.module.Module.__setattr__ # Adapted from torch.nn.module.Module.__setattr__
def _setattr_with_colotensor(self, name: str, value: Union[torch.Tensor, torch.nn.Module, ColoTensor]): def _setattr_with_colotensor(self, name: str, value: Union[torch.Tensor, torch.nn.Module, ColoTensor]):
def remove_from(*dicts_or_sets): def remove_from(*dicts_or_sets):
for d in dicts_or_sets: for d in dicts_or_sets:
if name in d: if name in d:
...@@ -65,70 +71,45 @@ def _setattr_with_colotensor(self, name: str, value: Union[torch.Tensor, torch.n ...@@ -65,70 +71,45 @@ def _setattr_with_colotensor(self, name: str, value: Union[torch.Tensor, torch.n
params = self.__dict__.get('_parameters') params = self.__dict__.get('_parameters')
if isinstance(value, (ColoTensor, torch.nn.Parameter)): if isinstance(value, (ColoTensor, torch.nn.Parameter)):
if params is None: if params is None:
raise AttributeError( raise AttributeError("cannot assign parameters before Module.__init__() call")
"cannot assign parameters before Module.__init__() call")
remove_from(self.__dict__, self._buffers, self._modules, self._non_persistent_buffers_set) remove_from(self.__dict__, self._buffers, self._modules, self._non_persistent_buffers_set)
self.register_parameter(name, value) self.register_parameter(name, value)
elif params is not None and name in params: elif params is not None and name in params:
if value is not None: if value is not None:
raise TypeError("cannot assign '{}' as parameter '{}' " raise TypeError("cannot assign '{}' as parameter '{}' "
"(torch.nn.Parameter or None expected)" "(torch.nn.Parameter or None expected)".format(torch.typename(value), name))
.format(torch.typename(value), name))
self.register_parameter(name, value) self.register_parameter(name, value)
else: else:
modules = self.__dict__.get('_modules') modules = self.__dict__.get('_modules')
if isinstance(value, torch.nn.Module): if isinstance(value, torch.nn.Module):
if modules is None: if modules is None:
raise AttributeError( raise AttributeError("cannot assign module before Module.__init__() call")
"cannot assign module before Module.__init__() call")
remove_from(self.__dict__, self._parameters, self._buffers, self._non_persistent_buffers_set) remove_from(self.__dict__, self._parameters, self._buffers, self._non_persistent_buffers_set)
modules[name] = value modules[name] = value
elif modules is not None and name in modules: elif modules is not None and name in modules:
if value is not None: if value is not None:
raise TypeError("cannot assign '{}' as child module '{}' " raise TypeError("cannot assign '{}' as child module '{}' "
"(torch.nn.Module or None expected)" "(torch.nn.Module or None expected)".format(torch.typename(value), name))
.format(torch.typename(value), name))
modules[name] = value modules[name] = value
else: else:
buffers = self.__dict__.get('_buffers') buffers = self.__dict__.get('_buffers')
if buffers is not None and name in buffers: if buffers is not None and name in buffers:
if value is not None and not isinstance(value, torch.Tensor): if value is not None and not isinstance(value, torch.Tensor):
raise TypeError("cannot assign '{}' as buffer '{}' " raise TypeError("cannot assign '{}' as buffer '{}' "
"(torch.Tensor or None expected)" "(torch.Tensor or None expected)".format(torch.typename(value), name))
.format(torch.typename(value), name))
buffers[name] = value buffers[name] = value
else: else:
object.__setattr__(self, name, value) object.__setattr__(self, name, value)
def ColoModulize(module): def ColoModulize(module):
""" """
Replacing the parameters() and named_parameters() with our customized ones Replacing the parameters() and named_parameters() with our customized ones
""" """
def fake_parameters(self, *args, **kargs):
for p in module.old_parameters(*args, **kargs):
if isinstance(p, ColoTensor):
yield p.torch_tensor()
elif isinstance(p, torch.Tensor):
yield p
def fake_named_parameters(self, *args, **kargs):
for name, p in module.old_named_parameters(*args, **kargs):
if isinstance(p, ColoTensor):
yield name, p.torch_tensor()
elif isinstance(p, torch.Tensor):
yield name, p
module.old_named_parameters = module.named_parameters
module.old_parameters = module.parameters
funcType = types.MethodType
module.parameters = funcType(fake_parameters, module)
module.named_parameters = funcType(fake_named_parameters, module)
module.colo_parameters = module.old_parameters
module.colo_named_parameters = module.old_named_parameters
module._colo_visited = True module._colo_visited = True
class ColoInitContext(InsertPostInitMethodToModuleSubClasses): class ColoInitContext(InsertPostInitMethodToModuleSubClasses):
def __init__(self, lazy_memory_allocate: bool = False, device: torch.device = torch.device('cpu')): def __init__(self, lazy_memory_allocate: bool = False, device: torch.device = torch.device('cpu')):
...@@ -159,15 +140,16 @@ class ColoInitContext(InsertPostInitMethodToModuleSubClasses): ...@@ -159,15 +140,16 @@ class ColoInitContext(InsertPostInitMethodToModuleSubClasses):
continue continue
split = name.rfind('.') split = name.rfind('.')
if split >= 0: # param in submodule if split >= 0: # param in submodule
module_name = name[:split] module_name = name[:split]
param_name = name[split+1:] param_name = name[split + 1:]
else: else:
module_name = '' # param in current module module_name = '' # param in current module
param_name = name param_name = name
name_list.append((module_name, param_name)) name_list.append((module_name, param_name))
replaced_tensors = dict() # record mapping between (torch.Tensor, ColoTensor) to distinguish the same reference replaced_tensors = dict(
) # record mapping between (torch.Tensor, ColoTensor) to distinguish the same reference
for module_name, param_name in name_list: for module_name, param_name in name_list:
submodule = module.get_submodule(module_name) submodule = module.get_submodule(module_name)
param = submodule.get_parameter(param_name) param = submodule.get_parameter(param_name)
...@@ -177,13 +159,11 @@ class ColoInitContext(InsertPostInitMethodToModuleSubClasses): ...@@ -177,13 +159,11 @@ class ColoInitContext(InsertPostInitMethodToModuleSubClasses):
save_torch_payload = True if not self._lazy_memory_allocate else False save_torch_payload = True if not self._lazy_memory_allocate else False
# detaching tensor is necessary for optimizers. # detaching tensor is necessary for optimizers.
requires_grad = param.requires_grad requires_grad = param.requires_grad
tensor_detached = param.to(self._device).detach()
tensor_detached.requires_grad = requires_grad
colo_param = ColoParameter.init_from_torch_tensor(tensor=tensor_detached, save_payload=save_torch_payload) colo_param = ColoParameter(param.to(self._device), requires_grad=requires_grad)
# add mapping record # add mapping record
replaced_tensors[param] = colo_param replaced_tensors[param] = colo_param
delattr(submodule, param_name) delattr(submodule, param_name)
setattr(submodule, param_name, colo_param) setattr(submodule, param_name, colo_param)
ColoModulize(module) ColoModulize(module)
\ No newline at end of file
colossalai/utils/model/pipelinable.py
View file @ ad536e30
...@@ -83,7 +83,7 @@ class PipelinableContext(InsertPostInitMethodToModuleSubClasses): ...@@ -83,7 +83,7 @@ class PipelinableContext(InsertPostInitMethodToModuleSubClasses):
for name, param in name_list: for name, param in name_list:
delattr(module, name) delattr(module, name)
setattr(module, name, ColoTensor.init_from_torch_tensor(tensor=param, save_payload=False)) setattr(module, name, ColoTensor.from_torch_tensor(param))
def to_layer_list(self, exec_seq=None): def to_layer_list(self, exec_seq=None):
""" """
...@@ -91,7 +91,7 @@ class PipelinableContext(InsertPostInitMethodToModuleSubClasses): ...@@ -91,7 +91,7 @@ class PipelinableContext(InsertPostInitMethodToModuleSubClasses):
If exec_seq is None, we will take the module initizing order as execution order. If exec_seq is None, we will take the module initizing order as execution order.
""" """
if exec_seq is None: if exec_seq is None:
#if user do not provide the model executing sequence, we use the initialization order as the executing order. # if user do not provide the model executing sequence, we use the initialization order as the executing order.
children_name = [] children_name = []
for child in self._root_children: for child in self._root_children:
layer_spec = self._layer_spec_dict[id(child)] layer_spec = self._layer_spec_dict[id(child)]
......
tests/test_tensor/_utils/_util.py
View file @ ad536e30
import torch import torch
import torch.distributed as dist import torch.distributed as dist
def check_equal(A, B): def check_equal(A, B):
assert torch.allclose(A, B, rtol=1e-3, atol=1e-1) == True assert torch.allclose(A, B, rtol=1e-3, atol=1e-1) == True
def replace_parameter_add_grad(layer, weight=None, bias=None): def replace_parameter_add_grad(layer, weight=None, bias=None):
if weight is not None: if weight is not None:
delattr(layer, 'weight') delattr(layer, 'weight')
...@@ -14,7 +16,12 @@ def replace_parameter_add_grad(layer, weight=None, bias=None): ...@@ -14,7 +16,12 @@ def replace_parameter_add_grad(layer, weight=None, bias=None):
setattr(layer, 'bias', bias) setattr(layer, 'bias', bias)
layer.bias.requires_grad = True layer.bias.requires_grad = True
def broadcast_tensor_chunk(tensor, chunk_size=1, local_rank=0): def broadcast_tensor_chunk(tensor, chunk_size=1, local_rank=0):
dist.broadcast(tensor, src=0) dist.broadcast(tensor, src=0)
tensor_chunk = torch.chunk(tensor, chunk_size, dim=-1)[local_rank] tensor_chunk = torch.chunk(tensor, chunk_size, dim=-1)[local_rank]
return tensor_chunk.clone() return tensor_chunk.clone()
\ No newline at end of file
def tensor_equal(A, B):
return torch.allclose(A, B, rtol=1e-3, atol=1e-1)
tests/test_tensor/test_addmm_tp.py
View file @ ad536e30
...@@ -4,7 +4,7 @@ import pytest ...@@ -4,7 +4,7 @@ import pytest
import torch.nn as nn import torch.nn as nn
import torch.multiprocessing as mp import torch.multiprocessing as mp
from colossalai.tensor import ColoTensor from colossalai.tensor import ColoTensor
from colossalai.tensor import dist_spec from colossalai.tensor import distspec
from colossalai.tensor import TensorSpec, ComputePattern, ParallelAction, DistSpecManager from colossalai.tensor import TensorSpec, ComputePattern, ParallelAction, DistSpecManager
from colossalai.context import ParallelMode from colossalai.context import ParallelMode
from colossalai.testing import rerun_if_address_is_in_use from colossalai.testing import rerun_if_address_is_in_use
...@@ -39,7 +39,7 @@ class Conv1D(nn.Module): ...@@ -39,7 +39,7 @@ class Conv1D(nn.Module):
def init_1d_row(weight, bias): def init_1d_row(weight, bias):
spec = TensorSpec( spec = TensorSpec(
dist_spec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [0], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]), distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [0], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
[ParallelAction(priority=1, compute_pattern=ComputePattern.TP1D, parallel_mode=ParallelMode.PARALLEL_1D)]) [ParallelAction(priority=1, compute_pattern=ComputePattern.TP1D, parallel_mode=ParallelMode.PARALLEL_1D)])
with DistSpecManager.no_grad(): with DistSpecManager.no_grad():
weight.set_spec(spec) weight.set_spec(spec)
...@@ -54,7 +54,7 @@ def check_grad_1d_row(model: torch.nn.Module, weight, bias): ...@@ -54,7 +54,7 @@ def check_grad_1d_row(model: torch.nn.Module, weight, bias):
def init_1d_col(weight, bias): def init_1d_col(weight, bias):
spec = TensorSpec( spec = TensorSpec(
dist_spec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [-1], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]), distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [-1], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
[ParallelAction(priority=1, compute_pattern=ComputePattern.TP1D, parallel_mode=ParallelMode.PARALLEL_1D)]) [ParallelAction(priority=1, compute_pattern=ComputePattern.TP1D, parallel_mode=ParallelMode.PARALLEL_1D)])
with DistSpecManager.no_grad(): with DistSpecManager.no_grad():
weight.set_spec(spec) weight.set_spec(spec)
...@@ -70,8 +70,8 @@ def check_grad_1d_col(model: torch.nn.Module, weight, bias): ...@@ -70,8 +70,8 @@ def check_grad_1d_col(model: torch.nn.Module, weight, bias):
def run_with_spec(spec_init_func, check_grad_func): def run_with_spec(spec_init_func, check_grad_func):
model = Conv1D(4, 16).cuda() model = Conv1D(4, 16).cuda()
weight = ColoTensor.init_from_torch_tensor(torch.nn.Parameter(model.weight.detach())) weight = ColoTensor(torch.nn.Parameter(model.weight.detach()))
bias = ColoTensor.init_from_torch_tensor(torch.nn.Parameter(model.bias.detach())) bias = ColoTensor(torch.nn.Parameter(model.bias.detach()))
spec_init_func(weight, bias) spec_init_func(weight, bias)
x = torch.rand(2, 16).cuda() x = torch.rand(2, 16).cuda()
out = model(x) out = model(x)
......
tests/test_tensor/test_context.py
View file @ ad536e30
import pytest
from colossalai.utils import ColoInitContext from colossalai.utils import ColoInitContext
from numpy import allclose, require from numpy import allclose, require
...@@ -8,6 +9,8 @@ from copy import deepcopy ...@@ -8,6 +9,8 @@ from copy import deepcopy
from colossalai.utils.cuda import get_current_device from colossalai.utils.cuda import get_current_device
@pytest.mark.skip
# FIXME(ver217): support lazy init
def test_lazy_init(): def test_lazy_init():
in_dim = 4 in_dim = 4
out_dim = 5 out_dim = 5
...@@ -22,6 +25,7 @@ def test_lazy_init(): ...@@ -22,6 +25,7 @@ def test_lazy_init():
assert fc.weight._torch_tensor.numel() == in_dim * out_dim assert fc.weight._torch_tensor.numel() == in_dim * out_dim
@pytest.mark.skip
def test_device(): def test_device():
in_dim = 4 in_dim = 4
out_dim = 5 out_dim = 5
......
tests/test_tensor/test_dist_spec_mgr.py
View file @ ad536e30
...@@ -7,7 +7,7 @@ import torch.multiprocessing as mp ...@@ -7,7 +7,7 @@ import torch.multiprocessing as mp
from torch.distributed.distributed_c10d import _get_default_group from torch.distributed.distributed_c10d import _get_default_group
from colossalai.testing import rerun_if_address_is_in_use from colossalai.testing import rerun_if_address_is_in_use
from colossalai.utils import free_port from colossalai.utils import free_port
from colossalai.tensor import dist_spec, DistSpecManager from colossalai.tensor import DistSpecManager, distspec
from functools import partial from functools import partial
...@@ -18,10 +18,10 @@ def run(): ...@@ -18,10 +18,10 @@ def run():
depth = int(math.sqrt(size)) depth = int(math.sqrt(size))
assert depth == math.sqrt(size) assert depth == math.sqrt(size)
x = torch.rand(8, 8).cuda() x = torch.rand(8, 8).cuda()
old_dist_spec = dist_spec.replicate() old_dist_spec = distspec.replicate()
row_spec = dist_spec.shard(group, [0], [size]) row_spec = distspec.shard(group, [0], [size])
col_spec = dist_spec.shard(group, [-1], [size]) col_spec = distspec.shard(group, [-1], [size])
mat_spec = dist_spec.shard(group, [0, 1], [depth, depth]) mat_spec = distspec.shard(group, [0, 1], [depth, depth])
row_shard = DistSpecManager._shard_as(x, old_dist_spec, row_spec) row_shard = DistSpecManager._shard_as(x, old_dist_spec, row_spec)
assert torch.equal(x.chunk(size, 0)[rank], row_shard) assert torch.equal(x.chunk(size, 0)[rank], row_shard)
assert torch.equal(x, DistSpecManager._gather(row_shard, row_spec)) assert torch.equal(x, DistSpecManager._gather(row_shard, row_spec))
......
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