mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
122 lines
5.9 KiB
122 lines
5.9 KiB
import torch.nn.functional as F
|
|
from typing import Optional
|
|
from torch import Tensor
|
|
from colossalai.tensor.op_wrapper import colo_op_impl
|
|
from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ComputeSpec, ColoTensor, distspec
|
|
from ._utils import GeneralTensor, convert_to_colo_tensor
|
|
|
|
|
|
def colo_embedding_bag_1Dcol(input_tensor: ColoTensor,
|
|
weight: ColoTensor,
|
|
offsets: Optional[Tensor] = None,
|
|
max_norm: Optional[float] = None,
|
|
norm_type: float = 2,
|
|
scale_grad_by_freq: bool = False,
|
|
mode: str = "mean",
|
|
sparse: bool = False,
|
|
per_sample_weights: Optional[Tensor] = None,
|
|
include_last_offset: bool = False,
|
|
padding_idx: Optional[int] = None) -> ColoTensor:
|
|
# embedding_bag_1Dcol split the weight(lookup table) to (num_embeddings, embedding_dim/P)
|
|
# Gather splitted lookup table
|
|
input_tensor = input_tensor.convert_to_dist_spec(distspec.replicate(weight.spec.get_process_group()))
|
|
|
|
output_parallel = F.embedding_bag(input_tensor,
|
|
weight,
|
|
offsets=offsets,
|
|
max_norm=max_norm,
|
|
norm_type=norm_type,
|
|
scale_grad_by_freq=scale_grad_by_freq,
|
|
mode=mode,
|
|
sparse=sparse,
|
|
per_sample_weights=per_sample_weights,
|
|
include_last_offset=include_last_offset,
|
|
padding_idx=padding_idx)
|
|
output_spec = TensorSpec(
|
|
distspec.shard(weight.spec.get_process_group(), [-1], [weight.spec.get_process_group_size()]),
|
|
ComputeSpec(ComputePattern.TP1D))
|
|
output = ColoTensor.from_torch_tensor(output_parallel, spec=output_spec)
|
|
|
|
return output.to_replicate()
|
|
|
|
|
|
def colo_embedding_bag_1d(tp_mode: str,
|
|
input_tensor: ColoTensor,
|
|
weight: ColoTensor,
|
|
offsets: Optional[Tensor] = None,
|
|
max_norm: Optional[float] = None,
|
|
norm_type: float = 2,
|
|
scale_grad_by_freq: bool = False,
|
|
mode: str = "mean",
|
|
sparse: bool = False,
|
|
per_sample_weights: Optional[Tensor] = None,
|
|
include_last_offset: bool = False,
|
|
padding_idx: Optional[int] = None) -> ColoTensor:
|
|
assert tp_mode in ('col',)
|
|
funcs = {'col': colo_embedding_bag_1Dcol}
|
|
return funcs[tp_mode](input_tensor,
|
|
weight,
|
|
offsets=offsets,
|
|
max_norm=max_norm,
|
|
norm_type=norm_type,
|
|
scale_grad_by_freq=scale_grad_by_freq,
|
|
mode=mode,
|
|
sparse=sparse,
|
|
per_sample_weights=per_sample_weights,
|
|
include_last_offset=include_last_offset,
|
|
padding_idx=padding_idx)
|
|
|
|
|
|
@colo_op_impl(F.embedding_bag)
|
|
def colo_embedding_bag(input_tensor: GeneralTensor,
|
|
weight: GeneralTensor,
|
|
offsets: Optional[Tensor] = None,
|
|
max_norm: Optional[float] = None,
|
|
norm_type: float = 2,
|
|
scale_grad_by_freq: bool = False,
|
|
mode: str = "mean",
|
|
sparse: bool = False,
|
|
per_sample_weights: Optional[Tensor] = None,
|
|
include_last_offset: bool = False,
|
|
padding_idx: Optional[int] = None):
|
|
"""Handles ``__torch_function__`` dispatch for ``torch.nn.functional.embedding_bag``.
|
|
This method looks up an embedding table.
|
|
"""
|
|
input_tensor, weight = tuple(map(convert_to_colo_tensor, (input_tensor, weight)))
|
|
|
|
# Handle differen parallel actions.
|
|
|
|
if not weight.has_spec(): # No Model Parallel Applied
|
|
assert weight.spec.is_gathered(), 'Invalid weight spec for native embedding op'
|
|
return ColoTensor.from_torch_tensor(
|
|
F.embedding_bag(input_tensor,
|
|
weight,
|
|
offsets=offsets,
|
|
max_norm=max_norm,
|
|
norm_type=norm_type,
|
|
scale_grad_by_freq=scale_grad_by_freq,
|
|
mode=mode,
|
|
sparse=sparse,
|
|
per_sample_weights=per_sample_weights,
|
|
include_last_offset=include_last_offset,
|
|
padding_idx=padding_idx))
|
|
elif weight.spec.has_compute_pattern(ComputePattern.TP1D): # Single Model Parallel Applied
|
|
if weight.spec.is_1D_col():
|
|
tp_mode = 'col'
|
|
else:
|
|
raise NotImplementedError
|
|
return colo_embedding_bag_1d(tp_mode,
|
|
input_tensor,
|
|
weight,
|
|
offsets=offsets,
|
|
max_norm=max_norm,
|
|
norm_type=norm_type,
|
|
scale_grad_by_freq=scale_grad_by_freq,
|
|
mode=mode,
|
|
sparse=sparse,
|
|
per_sample_weights=per_sample_weights,
|
|
include_last_offset=include_last_offset,
|
|
padding_idx=padding_idx)
|
|
else:
|
|
raise NotImplementedError
|