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"VPTQ (Vector Post-Training Quantization) integration file" |
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import torch.nn as nn |
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from accelerate import init_empty_weights |
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from vptq import VQuantLinear |
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def replace_with_vptq_linear( |
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model, |
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quantization_config=None, |
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modules_to_not_convert=None, |
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current_key_name=None, |
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has_been_replaced=False, |
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): |
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""" |
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Public method that recursively replaces the Linear layers of the given model with VPTQ quantized layers. |
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`accelerate` is needed to use this method. Returns the converted model and a boolean that indicates if the |
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conversion has been successful or not. |
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Args: |
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model (`torch.nn.Module`): |
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The model to convert, can be any `torch.nn.Module` instance. |
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quantization_config (`VptqConfig`): |
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The quantization config object that contains the quantization parameters. |
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modules_to_not_convert (`list[`str`]`, *optional*, defaults to `["lm_head"]`): |
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Names of the modules to not convert in `VQuantLinear`. In practice we keep the `lm_head` in full precision |
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for numerical stability reasons. |
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current_key_name (`list`, *optional*): |
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A list that contains the current key name. This is used for recursion and should not be passed by the user. |
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has_been_replaced (`bool`, *optional*): |
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A boolean that indicates if the conversion has been successful or not. This is used for recursion and |
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should not be passed by the user. |
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""" |
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modules_to_not_convert = modules_to_not_convert if modules_to_not_convert else ["lm_head"] |
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for name, module in model.named_children(): |
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if current_key_name is None: |
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current_key_name = [] |
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current_key_name.append(name) |
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layer_name = ".".join(current_key_name) |
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shared_layer_config = quantization_config.shared_layer_config |
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config_for_layers = quantization_config.config_for_layers |
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if ( |
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isinstance(module, nn.Linear) |
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and layer_name not in modules_to_not_convert |
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and ((layer_name in config_for_layers) or (current_key_name[-1] in shared_layer_config)) |
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): |
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layer_params = config_for_layers.get(layer_name, None) or shared_layer_config.get( |
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current_key_name[-1], None |
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) |
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with init_empty_weights(): |
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in_features = module.in_features |
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out_features = module.out_features |
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model._modules[name] = VQuantLinear( |
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in_features, |
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out_features, |
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vector_lens=layer_params["vector_lens"], |
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num_centroids=layer_params["num_centroids"], |
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num_res_centroids=layer_params["num_res_centroids"], |
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group_num=layer_params["group_num"], |
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group_size=layer_params["group_size"], |
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outlier_size=layer_params["outlier_size"], |
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indices_as_float=layer_params["indices_as_float"], |
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enable_norm=layer_params["enable_norm"], |
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enable_perm=layer_params["enable_perm"], |
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is_indice_packed=True, |
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enable_proxy_error=False, |
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bias=module.bias is not None, |
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) |
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has_been_replaced = True |
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model._modules[name].requires_grad_(False) |
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if len(list(module.children())) > 0: |
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_, has_been_replaced = replace_with_vptq_linear( |
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module, |
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quantization_config=quantization_config, |
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modules_to_not_convert=modules_to_not_convert, |
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current_key_name=current_key_name, |
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has_been_replaced=has_been_replaced, |
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) |
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current_key_name.pop(-1) |
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return model, has_been_replaced |
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