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Running
on
Zero
Running
on
Zero
| import logging | |
| import os | |
| from collections import OrderedDict | |
| from functools import partial | |
| import diffusers | |
| import torch | |
| from torch import nn | |
| import re | |
| class FeedForwardHooker: | |
| def __init__( | |
| self, | |
| pipeline: nn.Module, | |
| regex: str, | |
| dtype: torch.dtype, | |
| masking: str, | |
| dst: str, | |
| epsilon: float = 0.0, | |
| eps: float = 1e-6, | |
| use_log: bool = False, | |
| binary: bool = False, | |
| ): | |
| self.pipeline = pipeline | |
| self.net = pipeline.unet if hasattr(pipeline, "unet") else pipeline.transformer | |
| self.logger = logging.getLogger(__name__) | |
| self.dtype = dtype | |
| self.regex = regex | |
| self.hook_dict = {} | |
| self.masking = masking | |
| self.dst = dst | |
| self.epsilon = epsilon | |
| self.eps = eps | |
| self.use_log = use_log | |
| self.lambs = [] | |
| self.lambs_module_names = [] # store the module names for each lambda block | |
| self.hook_counter = 0 | |
| self.module_neurons = OrderedDict() | |
| self.binary = binary # default, need to discuss if we need to keep this attribute or not | |
| def add_hooks_to_ff(self, hook_fn: callable): | |
| total_hooks = 0 | |
| for name, module in self.net.named_modules(): | |
| name_last_word = name.split(".")[-1] | |
| if "ff" in name_last_word: | |
| if re.match(self.regex, name): | |
| hook_fn_with_name = partial(hook_fn, name=name) | |
| actual_module = module.net[0] | |
| hook = actual_module.register_forward_hook(hook_fn_with_name, with_kwargs=True) | |
| self.hook_dict[name] = hook | |
| if isinstance(actual_module, diffusers.models.activations.GEGLU): # geglu | |
| # due to the GEGLU chunking, we need to divide by 2 | |
| self.module_neurons[name] = actual_module.proj.out_features // 2 | |
| elif isinstance(actual_module, diffusers.models.activations.GELU): # gelu | |
| self.module_neurons[name] = actual_module.proj.out_features | |
| else: | |
| raise NotImplementedError(f"Module {name} is not implemented, please check") | |
| self.logger.info(f"Adding hook to {name}, neurons: {self.module_neurons[name]}") | |
| total_hooks += 1 | |
| self.logger.info(f"Total hooks added: {total_hooks}") | |
| return self.hook_dict | |
| def add_hooks(self, init_value=1.0): | |
| hook_fn = self.get_ff_masking_hook(init_value) | |
| self.add_hooks_to_ff(hook_fn) | |
| # initialize the lambda | |
| self.lambs = [None] * len(self.hook_dict) | |
| # initialize the lambda module names | |
| self.lambs_module_names = [None] * len(self.hook_dict) | |
| def clear_hooks(self): | |
| """clear all hooks""" | |
| for hook in self.hook_dict.values(): | |
| hook.remove() | |
| self.hook_dict.clear() | |
| def save(self, name: str = None): | |
| if name is not None: | |
| dst = os.path.join(os.path.dirname(self.dst), name) | |
| else: | |
| dst = self.dst | |
| dst_dir = os.path.dirname(dst) | |
| if not os.path.exists(dst_dir): | |
| self.logger.info(f"Creating directory {dst_dir}") | |
| os.makedirs(dst_dir) | |
| torch.save(self.lambs, dst) | |
| def get_lambda_block_names(self): | |
| return self.lambs_module_names | |
| def load(self, device, threshold=2.5): | |
| if os.path.exists(self.dst): | |
| self.logger.info(f"loading lambda from {self.dst}") | |
| self.lambs = torch.load(self.dst, weights_only=True, map_location=device) | |
| if self.binary: | |
| # set binary masking for each lambda by using clamp | |
| self.lambs = [(torch.relu(lamb - threshold) > 0).float() for lamb in self.lambs] | |
| else: | |
| self.lambs = [torch.clamp(lamb, min=0.0) for lamb in self.lambs] | |
| # self.lambs_module_names = [None for _ in self.lambs] | |
| else: | |
| self.logger.info("skipping loading, training from scratch") | |
| def binarize(self, scope: str, ratio: float): | |
| assert scope in ["local", "global"], "scope must be either local or global" | |
| assert not self.binary, "binarization is not supported when using binary mask already" | |
| if scope == "local": | |
| # Local binarization | |
| for i, lamb in enumerate(self.lambs): | |
| num_heads = lamb.size(0) | |
| num_activate_heads = int(num_heads * ratio) | |
| # Sort the lambda values with stable sorting to maintain order for equal values | |
| sorted_lamb, sorted_indices = torch.sort(lamb, descending=True, stable=True) | |
| # Find the threshold value | |
| threshold = sorted_lamb[num_activate_heads - 1] | |
| # Create a mask based on the sorted indices | |
| mask = torch.zeros_like(lamb) | |
| mask[sorted_indices[:num_activate_heads]] = 1.0 | |
| # Binarize the lambda based on the threshold and the mask | |
| self.lambs[i] = torch.where(lamb > threshold, torch.ones_like(lamb), mask) | |
| else: | |
| # Global binarization | |
| all_lambs = torch.cat([lamb.flatten() for lamb in self.lambs]) | |
| num_total = all_lambs.numel() | |
| num_activate = int(num_total * ratio) | |
| # Sort all lambda values globally with stable sorting | |
| sorted_lambs, sorted_indices = torch.sort(all_lambs, descending=True, stable=True) | |
| # Find the global threshold value | |
| threshold = sorted_lambs[num_activate - 1] | |
| # Create a global mask based on the sorted indices | |
| global_mask = torch.zeros_like(all_lambs) | |
| global_mask[sorted_indices[:num_activate]] = 1.0 | |
| # Binarize all lambdas based on the global threshold and mask | |
| start_idx = 0 | |
| for i in range(len(self.lambs)): | |
| end_idx = start_idx + self.lambs[i].numel() | |
| lamb_mask = global_mask[start_idx:end_idx].reshape(self.lambs[i].shape) | |
| self.lambs[i] = torch.where(self.lambs[i] > threshold, torch.ones_like(self.lambs[i]), lamb_mask) | |
| start_idx = end_idx | |
| self.binary = True | |
| def masking_fn(hidden_states, **kwargs): | |
| hidden_states_dtype = hidden_states.dtype | |
| lamb = kwargs["lamb"].view(1, 1, kwargs["lamb"].shape[0]) | |
| if kwargs.get("masking", None) == "sigmoid": | |
| mask = torch.sigmoid(lamb) | |
| elif kwargs.get("masking", None) == "binary": | |
| mask = lamb | |
| elif kwargs.get("masking", None) == "continues2binary": | |
| # TODO: this might cause potential issue as it hard threshold at 0 | |
| mask = (lamb > 0).float() | |
| elif kwargs.get("masking", None) == "no_masking": | |
| mask = torch.ones_like(lamb) | |
| else: | |
| raise NotImplementedError | |
| epsilon = kwargs.get("epsilon", 0.0) | |
| hidden_states = hidden_states * mask + torch.randn_like(hidden_states) * epsilon * (1 - mask) | |
| return hidden_states.to(hidden_states_dtype) | |
| def get_ff_masking_hook(self, init_value=1.0): | |
| """ | |
| Get a hook function to mask feed forward layer | |
| """ | |
| def hook_fn(module, args, kwargs, output, name): | |
| # initialize lambda with acual head dim in the first run | |
| if self.lambs[self.hook_counter] is None: | |
| self.lambs[self.hook_counter] = ( | |
| torch.ones(self.module_neurons[name], device=self.pipeline.device, dtype=self.dtype) * init_value | |
| ) | |
| self.lambs[self.hook_counter].requires_grad = True | |
| # load ff lambda module name for logging | |
| self.lambs_module_names[self.hook_counter] = name | |
| # perform masking | |
| output = self.masking_fn( | |
| output, | |
| masking=self.masking, | |
| lamb=self.lambs[self.hook_counter], | |
| epsilon=self.epsilon, | |
| eps=self.eps, | |
| use_log=self.use_log, | |
| ) | |
| self.hook_counter += 1 | |
| self.hook_counter %= len(self.lambs) | |
| return output | |
| return hook_fn | |