U Kc/@sbddlZddlmZddlmZmZddlmZmZej ej hZ ddZ d eej ddd ZdS) N) module_to_fqn fqn_to_module)DictListcCs^g}|g}|rZ|}|D]8\}}t||}t|tkrL|||fq||qq |S)z>Fetches Embedding and EmbeddingBag modules from the model )popZnamed_childrenrtypeSUPPORTED_MODULESappend)modelembedding_modulesstackmodule_childfqn_namerf/tmp/pip-unpacked-wheel-gikjz4vx/torch/ao/sparsity/_experimental/data_sparsifier/quantization_utils.py_fetch_all_embeddings s  rT)select_embeddingscKs\|f|}|dkrt|}n\g}t|ts2td|D]@}t|tksNtdt||}|dk shtd|||fq6|r|D]"\} } | dd} |j | | dq| | |D]\} } t j jj| _qt jj|dd t jj|dd nf|D]\} } t j jj| _qt jj|dd t jj|dd iiiiid } |D]\} } t|| }|dk s`t|}|| d | <|| d | <t || d | <|| d| <|j| d| <|j | dd| d | dq@| | |D]h\} } t|| }|dk stt j| d | | d | | d | | d| | d| d}||qdS)aTakes in a model and applies sparsification and quantization to only embeddings & embeddingbags. The quantization step can happen before or after sparsification depending on the `sparsify_first` argument. Args: - model (nn.Module) model whose embeddings needs to be sparsified - data_sparsifier_class (type of data sparsifier) Type of sparsification that needs to be applied to model - sparsify_first (bool) if true, sparsifies first and then quantizes otherwise, quantizes first and then sparsifies. - select_embeddings (List of Embedding modules) List of embedding modules to in the model to be sparsified & quantized. If None, all embedding modules with be sparsified - sparse_config (Dict) config that will be passed to the constructor of data sparsifier object. Note: 1. When `sparsify_first=False`, quantization occurs first followed by sparsification. - before sparsifying, the embedding layers are dequantized. - scales and zero-points are saved - embedding layers are sparsified and `squash_mask` is applied - embedding weights are requantized using the saved scales and zero-points 2. When `sparsify_first=True`, sparsification occurs first followed by quantization. - embeddings are sparsified first - quantization is applied on the sparsified embeddings Nz9the embedding_modules must be a list of embedding moduleszAthe embedding_modules list must be an embedding or embedding bagsz1the embedding modules must be part of input model.r)namedataT)Zinplace)scales zero_pointsdequant_weightsaxisdtyperrrrr)rrrr)r isinstancerAssertionErrorrrrr replaceZadd_datastepZ squash_masktorchZaoZ quantizationZ!float_qparams_weight_only_qconfigZqconfigprepareconvertrZweightZq_per_channel_scalesZq_per_channel_zero_pointsZ dequantizeZq_per_channel_axisrZquantize_per_channelZ set_weight)r Zdata_sparsifier_classZsparsify_firstrZ sparse_configZdata_sparsifierr ZembrrZ emb_moduleZ valid_namerZquantize_paramsZ quantized_embZquantized_weightZrequantized_vectorrrrpost_training_sparse_quantizesf         "      r$)TN)r!Ztorch.nnnnZ"torch.ao.sparsity.sparsifier.utilsrrtypingrrZ EmbeddingZ EmbeddingBagrrModuler$rrrrs