ColossalAI/colossalai/legacy/nn/layer/parallel_3d/_operation.py

591 lines
22 KiB
Python
Executable File

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from typing import Optional, Tuple
import torch
from torch import Tensor
from torch.cuda.amp import custom_bwd, custom_fwd
from colossalai.constants import INPUT_GROUP_3D, WEIGHT_GROUP_3D
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.legacy.communication import all_gather, all_reduce, broadcast, reduce, reduce_scatter
from ._utils import get_parallel_mode_from_env, push_async_grad
class _Linear3D(torch.autograd.Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(
ctx,
input_: Tensor,
weight: Tensor,
weight_id: int,
input_parallel_mode: ParallelMode,
weight_parallel_mode: ParallelMode,
output_parallel_mode: ParallelMode,
) -> Tensor:
ctx.weight_id = weight_id
ctx.input_parallel_mode = input_parallel_mode
ctx.weight_parallel_mode = weight_parallel_mode
ctx.output_parallel_mode = output_parallel_mode
input_ = all_gather(input_, 0, input_parallel_mode)
weight = all_gather(weight, 0, weight_parallel_mode)
ctx.save_for_backward(input_, weight)
output = torch.matmul(input_, weight)
output = reduce_scatter(output, 0, output_parallel_mode)
return output
@staticmethod
@custom_bwd
def backward(ctx, output_grad: Tensor) -> Tuple[Tensor, ...]:
input_, weight = ctx.saved_tensors
output_grad = all_gather(output_grad, 0, ctx.output_parallel_mode)
input_grad = torch.matmul(output_grad, weight.transpose(0, 1))
input_grad, input_op = reduce_scatter(input_grad, 0, ctx.input_parallel_mode, async_op=True)
weight_grad = torch.matmul(
input_.reshape(-1, input_.shape[-1]).transpose(0, 1), output_grad.reshape(-1, output_grad.shape[-1]))
weight_grad, op = reduce_scatter(weight_grad, 0, ctx.weight_parallel_mode, async_op=True)
weight_grad = push_async_grad(op, weight_grad, ctx.weight_id)
input_op.wait()
return input_grad, weight_grad, None, None, None, None
def linear_3d(
input_: Tensor,
weight: Tensor,
input_parallel_mode: ParallelMode,
weight_parallel_mode: ParallelMode,
output_parallel_mode: ParallelMode,
) -> Tensor:
r"""Linear layer for 3D parallelism.
Args:
input_ (:class:`torch.tensor`): input matrix.
weight (:class:`torch.tensor`): matrix of weight.
input_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): input parallel mode.
weight_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): weight parallel mode.
output_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): output parallel mode.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
return _Linear3D.apply(
input_,
weight,
id(weight),
input_parallel_mode,
weight_parallel_mode,
output_parallel_mode,
)
class _Classifier3D(torch.autograd.Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(
ctx,
input_: Tensor,
weight: Tensor,
bias: Optional[Tensor],
weight_id: int,
bias_id: Optional[int],
input_parallel_mode: ParallelMode,
weight_parallel_mode: ParallelMode,
output_parallel_mode: ParallelMode,
) -> Tensor:
ctx.use_bias = bias is not None
ctx.weight_id = weight_id
src_rank = gpc.get_ranks_in_group(input_parallel_mode)[gpc.get_local_rank(output_parallel_mode)]
weight = broadcast(weight, src_rank, input_parallel_mode)
ctx.save_for_backward(input_, weight)
output = torch.matmul(input_, weight.transpose(0, 1))
output = all_reduce(output, output_parallel_mode)
if bias is not None:
ctx.bias_id = bias_id
output += bias
ctx.src_rank = src_rank
ctx.input_parallel_mode = input_parallel_mode
ctx.weight_parallel_mode = weight_parallel_mode
ctx.output_parallel_mode = output_parallel_mode
return output
@staticmethod
@custom_bwd
def backward(ctx, output_grad: Tensor) -> Tuple[Tensor, ...]:
input_, weight = ctx.saved_tensors
weight_grad = torch.matmul(
output_grad.reshape(-1, output_grad.shape[-1]).transpose(0, 1), input_.reshape(-1, input_.shape[-1]))
weight_grad = reduce(weight_grad, ctx.src_rank, ctx.input_parallel_mode)
if gpc.get_local_rank(ctx.input_parallel_mode) == gpc.get_local_rank(ctx.output_parallel_mode):
weight_grad, op = all_reduce(weight_grad, ctx.weight_parallel_mode, async_op=True)
weight_grad = push_async_grad(op, weight_grad, ctx.weight_id)
else:
weight_grad = None
if ctx.use_bias:
bias_grad = torch.sum(output_grad, dim=tuple(range(len(output_grad.shape))[:-1]))
bias_grad = all_reduce(bias_grad, ctx.input_parallel_mode)
bias_grad, op = all_reduce(bias_grad, ctx.weight_parallel_mode, async_op=True)
bias_grad = push_async_grad(op, bias_grad, ctx.bias_id)
else:
bias_grad = None
input_grad = torch.matmul(output_grad, weight)
return input_grad, weight_grad, bias_grad, None, None, None, None, None
def classifier_3d(
input_: Tensor,
weight: Tensor,
bias: Optional[Tensor],
input_parallel_mode: ParallelMode,
weight_parallel_mode: ParallelMode,
output_parallel_mode: ParallelMode,
) -> Tensor:
r"""3D parallel classifier.
Args:
input_ (:class:`torch.tensor`): input matrix.
weight (:class:`torch.tensor`): matrix of weight.
bias (:class:`torch.tensor`): matrix of bias.
input_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): input parallel mode.
weight_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): weight parallel mode.
output_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): output parallel mode.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
return _Classifier3D.apply(
input_,
weight,
bias,
id(weight),
id(bias) if bias is not None else None,
input_parallel_mode,
weight_parallel_mode,
output_parallel_mode,
)
class _VocabParallelClassifier3D(torch.autograd.Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(
ctx,
input_: Tensor,
weight: Tensor,
bias: Optional[Tensor],
weight_id: int,
bias_id: Optional[int],
input_parallel_mode: ParallelMode,
weight_parallel_mode: ParallelMode,
output_parallel_mode: ParallelMode,
) -> Tensor:
ctx.use_bias = bias is not None
ctx.weight_id = weight_id
input_ = all_gather(input_, 0, input_parallel_mode)
weight = all_gather(weight, 0, weight_parallel_mode).transpose(0, 1)
ctx.save_for_backward(input_, weight)
output = torch.matmul(input_, weight)
output = reduce_scatter(output, 0, output_parallel_mode)
if bias is not None:
ctx.bias_id = bias_id
output += bias
ctx.input_parallel_mode = input_parallel_mode
ctx.weight_parallel_mode = weight_parallel_mode
ctx.output_parallel_mode = output_parallel_mode
return output
@staticmethod
@custom_bwd
def backward(ctx, output_grad: Tensor) -> Tuple[Tensor, ...]:
input_, weight = ctx.saved_tensors
output_grad = all_gather(output_grad, 0, ctx.output_parallel_mode)
input_grad = torch.matmul(output_grad, weight.transpose(0, 1))
input_grad, input_op = reduce_scatter(input_grad, 0, ctx.input_parallel_mode, async_op=True)
weight_grad = torch.matmul(
input_.reshape(-1, input_.shape[-1]).transpose(0, 1), output_grad.reshape(-1, output_grad.shape[-1]))
weight_grad, op = reduce_scatter(weight_grad.transpose(0, 1), 0, ctx.weight_parallel_mode, async_op=True)
weight_grad = push_async_grad(op, weight_grad, ctx.weight_id)
if ctx.use_bias:
bias_grad = torch.sum(output_grad, dim=tuple(range(len(output_grad.shape))[:-1]))
bias_grad, op = all_reduce(bias_grad, ctx.weight_parallel_mode, async_op=True)
bias_grad = push_async_grad(op, bias_grad, ctx.bias_id)
else:
bias_grad = None
input_op.wait()
return input_grad, weight_grad, bias_grad, None, None, None, None, None
def vocab_parallel_classifier_3d(
input_: Tensor,
weight: Tensor,
bias: Optional[Tensor],
input_parallel_mode: ParallelMode,
weight_parallel_mode: ParallelMode,
output_parallel_mode: ParallelMode,
) -> Tensor:
r"""3D vocab parallel classifier.
Args:
input_ (:class:`torch.tensor`): input matrix.
weight (:class:`torch.tensor`): matrix of weight.
bias (:class:`torch.tensor`): matrix of bias.
input_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): input parallel mode.
weight_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): weight parallel mode.
output_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): output parallel mode.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
return _VocabParallelClassifier3D.apply(
input_,
weight,
bias,
id(weight),
id(bias) if bias is not None else None,
input_parallel_mode,
weight_parallel_mode,
output_parallel_mode,
)
@torch.jit.script
def norm_forward(x: Tensor, mean: Tensor, sqr_mean: Tensor, weight: Tensor, bias: Tensor, eps: float):
mu = x - mean
var = sqr_mean - mean**2
sigma = torch.sqrt(var + eps)
z = mu / sigma
output = weight * z + bias
return output, mu, sigma
@torch.jit.script
def norm_backward(grad: Tensor, mu: Tensor, sigma: Tensor, weight: Tensor):
# dbias, dweight = grad, grad * mu / sigma
dz = grad * weight
dmu = dz / sigma
dvar = dz * mu * (-0.5) * sigma**(-3)
dmean = -dmu
dvar = torch.sum(dvar, -1, keepdim=True)
dmean = torch.sum(dmean, -1, keepdim=True)
return dmu, dmean, dvar
class _Layernorm3D(torch.autograd.Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float32)
def forward(
ctx,
input_: Tensor,
weight: Tensor,
bias: Tensor,
weight_id: int,
bias_id: int,
normalized_shape: int,
eps: float,
output_parallel_mode: ParallelMode,
input_x_weight_parallel_mode: ParallelMode,
) -> Tensor:
ctx.weight_id = weight_id
ctx.bias_id = bias_id
sum_ = torch.sum(input_, dim=-1, keepdim=True)
sqr_sum = torch.sum(input_**2, dim=-1, keepdim=True)
mean, sqr_mean = all_reduce(torch.stack((sum_, sqr_sum)), output_parallel_mode) / normalized_shape
output, mu, sigma = norm_forward(input_, mean, sqr_mean, weight, bias, eps)
ctx.save_for_backward(mu, sigma, weight)
ctx.normalized_shape = normalized_shape
ctx.output_parallel_mode = output_parallel_mode
ctx.input_x_weight_parallel_mode = input_x_weight_parallel_mode
return output
@staticmethod
@custom_bwd
def backward(ctx, output_grad: Tensor) -> Tuple[Tensor, ...]:
mu, sigma, weight = ctx.saved_tensors
bias_grad, weight_grad = output_grad, output_grad * mu / sigma
bias_grad = torch.sum(bias_grad, dim=tuple(range(len(bias_grad.shape))[:-1]))
bias_grad, op = all_reduce(bias_grad, ctx.input_x_weight_parallel_mode, async_op=True)
bias_grad = push_async_grad(op, bias_grad, ctx.bias_id)
weight_grad = torch.sum(weight_grad, dim=tuple(range(len(weight_grad.shape))[:-1]))
weight_grad, op = all_reduce(weight_grad, ctx.input_x_weight_parallel_mode, async_op=True)
weight_grad = push_async_grad(op, weight_grad, ctx.weight_id)
dmu, dmean, dvar = norm_backward(output_grad, mu, sigma, weight)
dvar, dmean = all_reduce(torch.stack((dvar, dmean)), ctx.output_parallel_mode)
input_grad = dmu + (dmean + 2 * dvar * mu) / ctx.normalized_shape
return input_grad, weight_grad, bias_grad, None, None, None, None, None, None, None, None
def layernorm_3d(
input_: Tensor,
weight: Tensor,
bias: Tensor,
normalized_shape: int,
eps: float,
output_parallel_mode: ParallelMode,
input_x_weight_parallel_mode: ParallelMode,
) -> Tensor:
r"""3D parallel Layernorm.
Args:
input_ (:class:`torch.tensor`): input matrix.
weight (:class:`torch.tensor`): matrix of weight.
bias (:class:`torch.tensor`): matrix of bias.
normalized_shape (int): input shape from an expected input of size.
:math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1]
\times \ldots \times \text{normalized_shape}[-1]]`
If a single integer is used, it is treated as a singleton list, and this module will
normalize over the last dimension which is expected to be of that specific size.
eps (float): a value added to the denominator for numerical stability
output_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): output parallel mode.
input_x_weight_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): input x weight parallel mode.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
return _Layernorm3D.apply(
input_,
weight,
bias,
id(weight),
id(bias),
normalized_shape,
eps,
output_parallel_mode,
input_x_weight_parallel_mode,
)
def split_tensor_3d(tensor: Tensor, dim: int, parallel_mode: ParallelMode) -> Tensor:
r"""Splits 3D parallel tensor in specified dimension.
Args:
tensor (:class:`torch.tensor`): Input tensor.
dim (int): Specified dimension in which to split.
parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`, optional): Parallel mode.
Returns:
:class:`torch.tensor`: The tensor has been split.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_.
"""
dim_size = tensor.size(dim)
world_size = gpc.get_world_size(parallel_mode)
assert dim_size % world_size == 0, \
f'The dimension {dim} to split, size ({dim_size}) is not a multiple of world size ({world_size}), ' \
f'cannot split tensor evenly'
if tensor.size(dim) <= 1:
return tensor
output = torch.chunk(tensor, gpc.get_world_size(parallel_mode),
dim=dim)[gpc.get_local_rank(parallel_mode)].contiguous()
return output
def split_batch_3d(input_: Tensor,
dim: int = 0,
input_parallel_mode: ParallelMode = ParallelMode.PARALLEL_3D_INPUT,
weight_parallel_mode: ParallelMode = ParallelMode.PARALLEL_3D_WEIGHT) -> Tensor:
r"""Splits 3D tensor in batch.
Args:
input_ (:class:`torch.tensor`): Input tensor.
dim (int): Specified dimension in which to split.
input_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`, optional): input parallel mode.
weight_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`, optional): weight parallel mode.
Returns:
:class:`torch.tensor`: The tensor has been split.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_.
"""
if input_.size(dim) <= 1:
return input_
weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
weight_world_size = gpc.get_world_size(weight_parallel_mode)
input_world_size = gpc.get_world_size(input_parallel_mode)
output = torch.chunk(input_, weight_world_size, dim=dim)[gpc.get_local_rank(weight_parallel_mode)].contiguous()
output = torch.chunk(output, input_world_size, dim=dim)[gpc.get_local_rank(input_parallel_mode)].contiguous()
return output
class _ReduceTensor3D(torch.autograd.Function):
@staticmethod
def forward(ctx, input_, parallel_mode):
return all_reduce(input_, parallel_mode)
@staticmethod
def backward(ctx, output_grad):
return output_grad, None
def reduce_tensor_3d(tensor: Tensor, parallel_mode: ParallelMode) -> Tensor:
r"""All-reduce the input
Args:
tensor (:class:`torch.tensor`): Input tensor.
parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): Parallel mode.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_.
"""
return _ReduceTensor3D.apply(tensor, parallel_mode)
class _AllGatherTensor3D(torch.autograd.Function):
@staticmethod
def forward(ctx, input_, dim, parallel_mode):
ctx.dim = dim
ctx.parallel_mode = parallel_mode
output = all_gather(input_, dim, parallel_mode)
return output
@staticmethod
def backward(ctx, output_grad):
input_grad = reduce_scatter(output_grad, ctx.dim, ctx.parallel_mode)
return input_grad, None, None
def all_gather_tensor_3d(tensor: Tensor, dim: int, parallel_mode: ParallelMode) -> Tensor:
r"""All-reduce the gradient in backward pass.
Args:
tensor (:class:`torch.tensor`): Input tensor.
dim (int): Dimension to gather.
parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): Parallel mode.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_.
"""
return _AllGatherTensor3D.apply(tensor, dim, parallel_mode)
class _ReduceScatterTensor3D(torch.autograd.Function):
@staticmethod
def forward(ctx, input_, dim, parallel_mode):
ctx.dim = dim
ctx.parallel_mode = parallel_mode
return reduce_scatter(input_, dim, parallel_mode)
@staticmethod
def backward(ctx, output_grad):
input_grad = all_gather(output_grad, ctx.dim, ctx.parallel_mode)
return input_grad, None, None
def reduce_scatter_tensor_3d(tensor: Tensor, dim: int, parallel_mode: ParallelMode) -> Tensor:
r"""Reduce-scatter the input.
Args:
tensor (:class:`torch.tensor`): Input tensor.
dim (int): Dimension to scatter.
parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): Parallel mode.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
dim_size = tensor.size(dim)
world_size = gpc.get_world_size(parallel_mode)
assert dim_size % world_size == 0, \
f'The batch size ({dim_size}) is not a multiple of square of 3D depth ({world_size}).'
return _ReduceScatterTensor3D.apply(tensor, dim, parallel_mode)
class _ReduceByBatch3D(torch.autograd.Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float32)
def forward(ctx,
input_: Tensor,
input_parallel_mode: ParallelMode,
weight_parallel_mode: ParallelMode,
reduce_mean: bool = False) -> Tensor:
output = all_reduce(input_, input_parallel_mode)
output = all_reduce(output, weight_parallel_mode)
ctx.reduce_mean = reduce_mean
if reduce_mean:
reduce_size = gpc.get_world_size(input_parallel_mode) * gpc.get_world_size(weight_parallel_mode)
ctx.reduce_size = reduce_size
return output.clone() / reduce_size
return output.clone()
@staticmethod
@custom_bwd
def backward(ctx, output_grad: Tensor) -> Tuple[Tensor, ...]:
if ctx.reduce_mean:
return output_grad / ctx.reduce_size, None, None, None
else:
return output_grad, None, None, None
def reduce_by_batch_3d(tensor: Tensor,
input_parallel_mode: ParallelMode,
weight_parallel_mode: ParallelMode,
reduce_mean: bool = False) -> Tensor:
r"""All-reduce the input from the model parallel region.
Args:
input_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): input parallel mode.
weight_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): weight parallel mode.
reduce_mean (bool, optional): If set to ``True``, it will divide the output by
(input parallel size * weight parallel size), default to False.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
return _ReduceByBatch3D.apply(tensor, input_parallel_mode, weight_parallel_mode, reduce_mean)