import torch
import torch.nn.functional as F
from typing import List, Optional, Iterator, Tuple, Union
from .base_embedding import BaseEmbeddingBag
from .cache_mgr import CachedParamMgr, EvictionStrategy
from torch.nn.parameter import Parameter
class FreqAwareEmbeddingBag(BaseEmbeddingBag):
"""FreqAwareEmbeddingBag
Frequency Aware Embedding. Apply a GPU-based software cache approaches to dynamically manage the embedding table in the CPU and GPU memory space.
It can leverage the id's frequency statistics of the target dataset, by passing a frequency list to param `ids_freq_mapping`.
You can also apply a navie LFU cache eviction strategy by setting `evict_strategy` as EvictionStrategy.LFU.
Args:
num_embeddings (int): size of the dictionary of embeddings
embedding_dim (int): the size of each embedding vector
padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient; therefore, the embedding vector at padding_idx is not updated during training, i.e. it remains as a fixed “pad”. For a newly constructed EmbeddingBag, the embedding vector at padding_idx will default to all zeros, but can be updated to another value to be used as the padding vector. Note that the embedding vector at padding_idx is excluded from the reduction.
max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm
norm_type (str, optional): The p of the p-norm to compute for the max_norm option. Defaults to 2..
scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default False. Note: this option is not supported when mode="max". Defaults to False.
sparse (bool, optional): if True, gradient w.r.t. weight matrix will be a sparse tensor. See Notes for more details regarding sparse gradients. Note: this option is not supported when mode="max".. Defaults to False.
_weight (torch.Tensor, optional): an embedding weight tensor. Concate multiple tables in a embedding bag as a single one. Defaults to None.
mode (str, optional): "sum", "mean" or "max". Specifies the way to reduce the bag. "sum" computes the weighted sum, taking per_sample_weights into consideration. "mean" computes the average of the values in the bag, "max" computes the max value over each bag. Default: "mean". Defaults to 'mean'.
include_last_offset (bool, optional): if True, offsets has one additional element, where the last element is equivalent to the size of indices. This matches the CSR format.. Defaults to False.
dtype (torch.dtype, optional): data type of the cpu weight initialization. Defaults to None meaning float32.
device (torch.device, optional): device type to the cpu weight. Defaults to None meaning cpu.
cache_ratio (float, float): cache ratio of the #cuda_weight_row / #cpu_weight_row
ids_freq_mapping (Union[List, torch.Tensor], optional): the frequency of each embedding vector occures in dataset. Defaults to None.
warmup_ratio (float, optional): the ratio of cuda cache is warmuped with. Defaults to 0.7.
buffer_size (int, optional): the max number of vectors in transmitter buffer. If set to 0, the buffer is not used. Defaults to 0.
pin_weight (bool, optional): pin the cpu weight. Defaults to False.
evict_strategy (EvictionStrategy, optional): evict strategy of the software cache. Defaults to EvictionStrategy.DATASET.
"""
def __init__(self,
num_embeddings: int,
embedding_dim: int,
padding_idx: int = None,
max_norm: float = None,
norm_type: float = 2.,
scale_grad_by_freq: bool = False,
sparse: bool = False,
_weight: Optional[torch.Tensor] = None,
mode: str = 'mean',
include_last_offset: bool = False,
dtype: Optional[torch.dtype] = None,
device: Optional[torch.device] = None,
cache_ratio: float = 0.01,
ids_freq_mapping: Optional[Union[List, torch.Tensor]] = None,
warmup_ratio: float = 0.7,
buffer_size: int = 0,
pin_weight: bool = False,
evict_strategy: EvictionStrategy = EvictionStrategy.LFU):
super(FreqAwareEmbeddingBag, self).__init__(num_embeddings, embedding_dim, padding_idx, max_norm, norm_type,
scale_grad_by_freq, sparse, mode, include_last_offset)
assert cache_ratio <= 1.0, f"cache ratio {cache_ratio} must less than 1.0"
self.evict_strategy = evict_strategy
if _weight is None:
_weight = self._weight_alloc(dtype, device)
cuda_row_num = int(num_embeddings * cache_ratio)
# configure weight & cache
self._preprocess(_weight, cuda_row_num, ids_freq_mapping, warmup_ratio, buffer_size, pin_weight)
def _weight_alloc(self, dtype, device):
weight = torch.empty(self.num_embeddings, self.embedding_dim, dtype=dtype, device=device)
with torch.no_grad():
weight.data.uniform_(-1 / self.num_embeddings, 1 / self.num_embeddings)
if self.padding_idx is not None:
weight[self.padding_idx].fill_(0)
return weight
def _preprocess(self,
weight,
cuda_row_num: int,
ids_freq_mapping: Optional[List[int]] = None,
warmup_ratio=0.7,
buffer_size=50_000,
pin_weight=False):
"""
Called after initialized.
Reorder the weight rows according to the ids_freq_mapping.
Then, let the weights of the Module be managed by a CachedParamMgr.
Args:
cuda_row_num (int): number of rows can be hosted in CUDA memory
ids_freq_mapping (List[int]): a list, idx is id number, value is freq
warmup_ratio (float): the amount of rows preloaded in cuda cache
"""
self.cache_weight_mgr = CachedParamMgr(weight,
cuda_row_num,
buffer_size,
pin_weight,
evict_strategy=self.evict_strategy)
self.cache_weight_mgr.reorder(ids_freq_mapping, warmup_ratio)
def forward(self, input, offsets=None, per_sample_weights=None, shape_hook=None, cache_op=True):
if cache_op:
with torch.no_grad():
input = self.cache_weight_mgr.prepare_ids(input)
embeddings = F.embedding_bag(input.cuda(), self.cache_weight_mgr.cuda_cached_weight, offsets, self.max_norm,
self.norm_type, self.scale_grad_by_freq, self.mode, self.sparse,
per_sample_weights, self.include_last_offset, self.padding_idx)
if shape_hook is not None:
embeddings = shape_hook(embeddings)
return embeddings
@property
def weight(self):
return self.cache_weight_mgr.weight
def named_parameters(self, prefix: str = '', recurse: bool = True) -> Iterator[Tuple[str, Parameter]]:
yield 'weight', self.cache_weight_mgr.cuda_cached_weight
def parameters(self, recurse: bool = True) -> Iterator[Parameter]:
yield self.cache_weight_mgr.cuda_cached_weight
############################# Perf Log ###################################
@property
def num_hits_history(self):
return self.cache_weight_mgr.num_hits_history
@property
def num_miss_history(self):
return self.cache_weight_mgr.num_miss_history
@property
def num_write_back_history(self):
return self.cache_weight_mgr.num_write_back_history
@property
def swap_in_bandwidth(self):
if self.cache_weight_mgr._cpu_to_cuda_numel > 0:
return self.cache_weight_mgr._cpu_to_cuda_numel * self.cache_weight_mgr.elem_size_in_byte / 1e6 / \
self.cache_weight_mgr._cpu_to_cuda_elpase
else:
return 0
@property
def swap_out_bandwidth(self):
if self.cache_weight_mgr._cuda_to_cpu_numel > 0:
return self.cache_weight_mgr._cuda_to_cpu_numel * self.cache_weight_mgr.elem_size_in_byte / 1e6 / \
self.cache_weight_mgr._cuda_to_cpu_elapse
return 0