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 CachedEmbeddingBag(BaseEmbeddingBag): """CachedEmbeddingBag Cached 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 naive 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. Concatenate 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 occurs 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(CachedEmbeddingBag, 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) self.cache_op = True def set_cache_mgr_async_copy(self, flag): self.cache_weight_mgr._async_copy = flag 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): if self.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 def set_cache_op(self, cache_op: bool = True): self.cache_op = cache_op ############################# 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_elapse 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