from transformers import TrainerCallback, Trainer from trl import SFTTrainer, DataCollatorForCompletionOnlyLM from peft import PeftModel from datasets import Dataset from transformers.utils import is_sagemaker_mp_enabled, is_sagemaker_dp_enabled from typing import Any, Dict, Union, Optional, Tuple from torch.nn import MSELoss import warnings import torch import torch.nn as nn import torch.nn.functional as F import matplotlib.pyplot as plt import numpy as np import time import os import copy from transformers.models.mistral.modeling_mistral import ( MistralMLP, MistralAttention, MistralModel, MistralDecoderLayer, MistralConfig, MISTRAL_ATTENTION_CLASSES, MistralRMSNorm, MistralForCausalLM, ) from experiments.models.sparse_mistral.svd_router import ( low_rank_approximation, SparsePredictor, ) from utils.utils import ( print_size_of_model, is_running_deepspeed, is_mainprocess, get_datetime, ds_print, ) class SparseSFTTTrainer(SFTTrainer): def __init__(self, *args, **kwargs): self.regularization_coefficient = kwargs.pop("regularization_coefficient", 10) self.use_sparse_regularization = kwargs.pop("use_sparse_regularization", False) self.use_spm_loss = False self.freeze_original_weights = False self.regularization_type = kwargs.pop( "regularization_type", "L1 positive activation" ) assert self.regularization_type in [ "L2 activation", "L1 positive activation", ], f"Invalid regularization type: {self.regularization_type}" self.sparse_layers = [] self.sparse_decoder_layers = [] super(SparseSFTTTrainer, self).__init__(*args, **kwargs) def initialize_sparse_silu_layers(self, model): self.sparse_layers = [ m for m in model.modules() if isinstance(m, MistralSparseSiluMLP) ] def initialize_sparse_decoder_layers(self, model): self.sparse_decoder_layers = [ m for m in model.modules() if isinstance(m, SparseMistralDecoderLayer) ] def training_step( self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]] ) -> torch.Tensor: """ Override the huggingface's training_step function to add a regularization term. A regularization term is computed with intermediate values, which are freed after "backward()." You need to set `retain_graph=True` inside `backward` function to keep the values. """ model.train() inputs = self._prepare_inputs(inputs) with self.compute_loss_context_manager(): loss = self.compute_loss(model, inputs) if self.args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel training if not self.freeze_original_weights: if loss is not None: self.accelerator.backward(loss, retain_graph=False) if self.use_sparse_regularization: regularization_loss = self.compute_regularization(model) if self.args.n_gpu > 1: regularization_loss = regularization_loss.mean() if regularization_loss is not None: self.accelerator.backward(regularization_loss, retain_graph=True) loss += regularization_loss if self.use_spm_loss: spm_loss = self.compute_spm_loss(model) if self.args.n_gpu > 1: spm_loss = spm_loss.mean() if spm_loss is not None: self.accelerator.backward(spm_loss, retain_graph=False) loss += spm_loss return loss.detach() / self.args.gradient_accumulation_steps def compute_regularization(self, model): """ Compute a sparse regularization loss for SiLU """ loss = 0 if len(self.sparse_layers) == 0: self.initialize_sparse_silu_layers(model) num_layers = len(self.sparse_layers) for module in self.sparse_layers: if module.activation_norm is not None: loss += module.activation_norm loss /= num_layers loss *= self.regularization_coefficient if self.state.global_step % 20 == 0 and loss != 0: print("Negative relularizer loss: ", loss.item()) return loss def compute_spm_loss(self, model): loss = 0 if len(self.sparse_decoder_layers) == 0: self.initialize_sparse_decoder_layers(model) for module in self.sparse_decoder_layers: if module.distill_loss != None: loss += module.distill_loss if self.state.global_step % 20 == 0 and loss != 0: print("Sparse Predictor Distillation loss: ", loss.item()) return loss # def compute_loss(self, model, inputs, return_outputs=False): # loss = super().compute_loss(model, inputs, return_outputs) # # if is_sagemaker_mp_enabled(): # import smdistributed.modelparallel.torch as smp # @smp.step() # def smp_forward_backward(model, inputs, gradient_accumulation_steps=1): # outputs = model(**inputs) # loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0] # loss /= gradient_accumulation_steps # model.backward(loss) # return loss # # loss_mb = smp_forward_backward( # model, inputs, self.args.gradient_accumulation_steps # ) # if self.use_sparse_regularization: # return loss_mb.reduce_mean().detach().to( # self.args.device # ) + self.regularization_coefficient * self.compute_regularization(model) # else: # return loss_mb.reduce_mean().detach().to(self) # # if return_outputs: # classification_loss, outputs = loss # else: # classification_loss = loss # # loss = classification_loss # if self.use_sparse_regularization: # regularization_loss = self.compute_regularization(model) # loss += self.regularization_coefficient * regularization_loss # # return (loss, outputs) if return_outputs else loss class SparseTrainer(Trainer): def __init__(self, *args, **kwargs): self.regularization_coefficient = kwargs.pop("regularization_coefficient", 10) self.use_sparse_regularization = kwargs.pop("use_sparse_regularization", False) self.use_spm_loss = False self.freeze_original_weights = False self.regularization_type = kwargs.pop( "regularization_type", "L1 positive activation" ) assert self.regularization_type in [ "L2 activation", "L1 positive activation", ], f"Invalid regularization type: {self.regularization_type}" self.sparse_layers = [] self.sparse_decoder_layers = [] super(SparseTrainer, self).__init__(*args, **kwargs) def initialize_sparse_silu_layers(self, model): self.sparse_layers = [ m for m in model.modules() if isinstance(m, MistralSparseSiluMLP) ] def initialize_sparse_decoder_layers(self, model): self.sparse_decoder_layers = [ m for m in model.modules() if isinstance(m, SparseMistralDecoderLayer) ] def training_step( self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]] ) -> torch.Tensor: """ Override the huggingface's training_step function to add a regularization term. A regularization term is computed with intermediate values, which are freed after "backward()." You need to set `retain_graph=True` inside `backward` function to keep the values. """ model.train() inputs = self._prepare_inputs(inputs) with self.compute_loss_context_manager(): loss = self.compute_loss(model, inputs) if self.args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel training if not self.freeze_original_weights: if loss is not None: self.accelerator.backward(loss, retain_graph=False) if self.use_sparse_regularization: regularization_loss = self.compute_regularization(model) if self.args.n_gpu > 1: regularization_loss = regularization_loss.mean() if regularization_loss is not None: self.accelerator.backward(regularization_loss, retain_graph=True) loss += regularization_loss if self.use_spm_loss: spm_loss = self.compute_spm_loss(model) if self.args.n_gpu > 1: spm_loss = spm_loss.mean() if spm_loss is not None: self.accelerator.backward(spm_loss, retain_graph=False) loss += spm_loss return loss.detach() / self.args.gradient_accumulation_steps def compute_regularization(self, model): """ Compute a sparse regularization loss for SiLU """ loss = 0 if len(self.sparse_layers) == 0: self.initialize_sparse_silu_layers(model) num_layers = len(self.sparse_layers) for module in self.sparse_layers: if module.activation_norm is not None: loss += module.activation_norm loss /= num_layers loss *= self.regularization_coefficient if self.state.global_step % 20 == 0 and loss != 0: print("Negative relularizer loss: ", loss.item()) return loss def compute_spm_loss(self, model): loss = 0 if len(self.sparse_decoder_layers) == 0: self.initialize_sparse_decoder_layers(model) for module in self.sparse_decoder_layers: if module.distill_loss != None: loss += module.distill_loss if self.state.global_step % 20 == 0 and loss != 0: print("Sparse Predictor Distillation loss: ", loss.item()) return loss class SparseSiLU(nn.SiLU): def __init__(self, threshold): super(SparseSiLU, self).__init__() self.threshold = threshold self.m = nn.Threshold(self.threshold, 0) def set_new_threshold(self, threshold): self.threshold = threshold self.m = nn.Threshold(threshold, 0) def forward(self, x): act = super(SparseSiLU, self).forward(x) return self.m(act) - self.m(-act) class MistralSparseSiluMLP(MistralMLP): def __init__(self, config, *args, **kwargs): super().__init__(config) self.swish_outputs = None self.relu = nn.ReLU() self.kill_sparse_swish_outputs = False self.dead_percentage = 0 self.is_stats = False self.visit_counts = 0 # Hyperparameters to tune self.dead_threshold = kwargs.pop("dead_threshold", 0) self.use_sparse_regularization = kwargs.pop("use_sparse_regularization", True) self.regularization_type = kwargs.pop( "regularization_type", "L1 regularization" ) self.regularization_threshold = kwargs.pop("regularization_threshold", 0.5) self.use_relu = kwargs.pop("use_relu", False) self.activation_norm = None # Activation Histograms self.is_collect_histogram = False num_bins = 1000 self.histogram_bins = torch.linspace(-1, 1, num_bins - 2) self.histogram_bins = torch.cat( [torch.tensor([-torch.inf]), self.histogram_bins, torch.tensor([torch.inf])] ) self.pre_act_hist_counts = torch.zeros(num_bins - 1) self.post_act_hist_counts = torch.zeros(num_bins - 1) self.t = 0 self.agg_sparsity = 0 # Sparse activation function self.sparse_act_fn = SparseSiLU(threshold=self.dead_threshold) def activate_stats(self, is_collect_histogram: bool = True): self.is_stats = True self.dead_percentage = 0 self.visit_counts = 0 self.is_collect_histogram = is_collect_histogram self.histogram_counts = torch.zeros(2000) # .to(self.down_proj.weight.device) def deactivate_stats(self): self.is_stats = False def collect_stats(self, pre_activation, post_activation): start_time = time.time() pre_activation = pre_activation.float().cpu().detach() post_activation = post_activation.float().cpu().detach() # self.histogram_bins=self.histogram_bins.to(pre_activation.device).type(pre_activation.dtype) self.pre_act_hist_counts += torch.histogram( pre_activation, bins=self.histogram_bins )[0] self.post_act_hist_counts += torch.histogram( torch.abs(post_activation), bins=self.histogram_bins )[0] self.t += time.time() - start_time if self.visit_counts % 30 == 0: print(f"Time taken to collect stats: {self.t}s.") def forward( self, x, sp_mask: torch.tensor = None, ): """ If kill_sparse_swish_outputs is set to False, this layer functions exactly like a normal MLP layer. """ if sp_mask != None: # When sparse mask is given return self.down_proj( self.sparse_act_fn(self.gate_proj(x) * sp_mask) * self.up_proj(x) ) # Todo: This doesn't accelerate runtime (instead slowing down) elif self.use_relu: post_act = self.relu(self.gate_proj(x)) if self.is_stats: dead_neurons = post_act == 0 dead_percentage = dead_neurons.float().mean() agg_sparsity = dead_neurons.all(dim=0).float().mean() self.dead_percentage = ( self.dead_percentage * self.visit_counts + dead_percentage ) / (self.visit_counts + 1) self.agg_sparsity = ( self.agg_sparsity * self.visit_counts + agg_sparsity ) / (self.visit_counts + 1) self.visit_counts += 1 return self.down_proj(post_act * self.up_proj(x)) else: pre_act = self.gate_proj(x) post_act = self.act_fn(pre_act) if self.kill_sparse_swish_outputs: dead_neurons = post_act.abs() <= self.dead_threshold dead_percentage = dead_neurons.float().mean() agg_sparsity = dead_neurons.all(dim=0).float().mean() if self.is_stats: self.dead_percentage = ( self.dead_percentage * self.visit_counts + dead_percentage ) / (self.visit_counts + 1) self.agg_sparsity = ( self.agg_sparsity * self.visit_counts + agg_sparsity ) / (self.visit_counts + 1) self.visit_counts += 1 # print(self.agg_sparsity) # Collect histogram stats if ( self.is_collect_histogram and pre_act.eq(0).float().mean() < 0.99 ): # Padded dataset self.collect_stats(pre_act, post_act) post_act[dead_neurons] = 0 out = self.down_proj(post_act * self.up_proj(x)) if self.use_sparse_regularization: if self.regularization_type == "L1 regularization": self.activation_norm = torch.abs(post_act)[ post_act < self.regularization_threshold ].mean() elif self.regularization_type == "L2 regularization": self.activation_norm = torch.sqrt( torch.square(post_act)[post_act < self.regularization_threshold] ).mean() return out class SparseMistralDecoderLayer(MistralDecoderLayer): def __init__( self, config: MistralConfig, layer_idx: int, decoder_layer: MistralDecoderLayer, init_svd: bool = True, *args, **kwargs, ): assert isinstance( decoder_layer.mlp, MistralSparseSiluMLP ), f"{type(decoder_layer.mlp)} should MistralSparseSiluMLP." super().__init__(config, layer_idx) self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size self.init_svd = init_svd self.self_attn = decoder_layer.self_attn self.mlp = decoder_layer.mlp self.input_layernorm = decoder_layer.input_layernorm self.post_attention_layernorm = decoder_layer.post_attention_layernorm # Sparse predictor for mlp (initialized with SVD decomposed matrix) self.low_rank = kwargs.pop("low_rank", 64) self.sparse_act_func = decoder_layer.mlp.sparse_act_fn print( f"Setting {layer_idx}th mlp layer's sparse predictor... svd init: {init_svd}" ) self.sp_mlp = low_rank_approximation( decoder_layer.mlp.gate_proj, act_func=self.sparse_act_func, init_svd=init_svd, ) self.use_async = kwargs.pop("use_async", False) self.use_sparse_predictor = False self.distill_loss = None def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, **kwargs, ) -> Tuple[ torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] ]: print("hidden_states shape: ", hidden_states.shape) if "padding_mask" in kwargs: warnings.warn( "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" ) residual = hidden_states sp_mask = None if self.use_async: sp_mask = self.sp_mlp(hidden_states) hidden_states = self.input_layernorm(hidden_states) # Self Attention hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) if not self.use_async: sp_mask = self.sp_mlp(hidden_states) # Compute distillation loss gating_output = self.mlp.sparse_act_fn(self.mlp.gate_proj(hidden_states)) loss_func = MSELoss() self.distill_loss = loss_func(sp_mask, gating_output) # Convert sp mask into binary form sp_mask = sp_mask > 0 if self.training: sp_mask = None # if not self.use_sparse_predictor: # sp_mask = None hidden_states = self.mlp(hidden_states, sp_mask) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs class SparseMistralConfig(MistralConfig): model_type = "sparse_mistral" def __init__(self, **kwargs): super().__init__(**kwargs) class SparseMistralforCausalLM(MistralForCausalLM): config_class = SparseMistralConfig def __init__(self, config): super().__init__(config) self.config = config if config.use_sparse_model: self.apply_sparse_mlp() if config.thresholds is not None: for idx, m in enumerate(self.model.layers): if isinstance(m.mlp, MistralSparseSiluMLP): m.mlp.dead_threshold = config.thresholds[idx] m.mlp.sparse_act_fn.set_new_threshold(m.mlp.dead_threshold) m.mlp.kill_sparse_swish_outputs = True m.mlp.use_relu = config.use_relu if config.use_sparse_predictor: self.apply_sparse_predictor(init_svd=config.init_svd) def apply_sparse_mlp(self): apply_mistral_sparse_silu_mlp( self, config=self.config, use_sparse_regularization=self.config.use_sparse_regularization, ) def apply_sparse_predictor(self, init_svd: bool = True): apply_mistral_sparse_decoder_layer(self, config=self.config, init_svd=init_svd) class GracefulRegularizationScheduler(TrainerCallback): def __init__( self, num_warmup_steps=40, is_enabled: bool = False, model_name: str = "mistral", test_dataset: Dataset = None, targeted_sparsity: float = 0.5, keep_regularization_with_kill: bool = False, ): """Scheduler for regularizing the model first before applying the dead threshold. :param num_warmup_steps: number of training steps required to reach the dead threshold, defaults to 40 :param increment_ratio: by how much to increase the dead threshold. For example, 0.5 means "increase the threshold by 0.5 * desired threshold """ self.num_warmup_steps = num_warmup_steps self.is_enabled = is_enabled self.model_name = model_name self.test_dataset = test_dataset self.targeted_sparsity = targeted_sparsity self.keep_regularization_with_kill = keep_regularization_with_kill self.act_hist_path = ( f"/matx/u/vxbrando/histograms/warm_up_reg_{targeted_sparsity}/act_hist.pt" ) if self.is_enabled: print("GracefulRegularizationScheduler is enabled.") self.trainer = None def set_trainer(self, trainer): self.trainer = trainer def on_step_end(self, args, state, control, **kwargs): if not self.is_enabled: return model = kwargs["model"] if isinstance(model, PeftModel): base_model = model.get_base_model() else: base_model = model if state.global_step == 1: ds_print("Setting an initial reg threshold to 0.1") set_regularization_threshold(base_model, 0.1) # if state.global_step >= self.num_warmup_steps and state.global_step % 50 == 0: if state.global_step == self.num_warmup_steps: activate_stats(base_model) enable_sparse_silu(base_model) self.trainer.evaluate() save_act_hist(base_model, self.act_hist_path) set_sparse_threshold(base_model, self.targeted_sparsity, True) deactivate_stats(base_model) self.trainer.use_sparse_regularization = self.keep_regularization_with_kill # set_layer_specific_regularization(model.get_base_model()) print_dead_neuron_stats(model.get_base_model()) if state.global_step % 2000 == 0: if is_mainprocess(): ds_print( f"Saving to /scr/lukeai/{self.model_name}_{state.global_step}.pt", ) torch.save( model.state_dict(), f"/scr/lukeai/{self.model_name}_{state.global_step}.pt", ) class GradualSparsificationScheduler(TrainerCallback): def __init__( self, num_warmup_steps=40, increment_ratio=0.5, is_enabled: bool = False, model_name: str = "mistral", ): """Scheduler for gradually increasing a dead threshold until it reaches the desired threshold. :param num_warmup_steps: number of training steps required to reach the dead threshold, defaults to 40 :param increment_ratio: by how much to increase the dead threshold. For example, 0.5 means "increase the threshold by 0.5 * desired threshold """ self.num_warmup_steps = num_warmup_steps self.increment_ratio = increment_ratio self.step_size = int(num_warmup_steps * increment_ratio) self.is_enabled = is_enabled self.model_name = model_name def on_step_end(self, args, state, control, **kwargs): model = kwargs["model"] if not self.is_enabled: if state.global_step <= 10: for module in model.modules(): if isinstance(module, MistralSparseSiluMLP): module.current_dead_threshold = module.dead_threshold return current_dead_threshold = 0 desired_dead_threshold = 0 if is_mainprocess(): ds_print(state.global_step) if state.global_step % self.step_size == 2: for module in model.modules(): if isinstance(module, MistralSparseSiluMLP): desired_dead_threshold = copy.deepcopy(module.dead_threshold) current_dead_threshold = module.current_dead_threshold current_dead_threshold += ( self.increment_ratio * desired_dead_threshold ) module.current_dead_threshold = min( desired_dead_threshold, current_dead_threshold ) if is_running_deepspeed and is_mainprocess(): ds_print( state.global_step, current_dead_threshold, desired_dead_threshold, ) if state.global_step % 2000 == 0: if is_running_deepspeed and is_mainprocess(): ds_print( f"Saving to /matx/u/lukeai/{self.model_name}_{state.global_step - 2}.pt", ) torch.save( model.state_dict(), f"/matx/u/lukeai/{self.model_name}_{state.global_step - 2}.pt", ) def get_sparse_mistral_config( config: MistralConfig, use_sparse_model=False, use_sparse_predictor=False, use_sparse_regularization=False, thresholds=None, ): new_config = SparseMistralConfig() new_config.__dict__.update(config.__dict__) config = new_config config.use_sparse_model = use_sparse_model config.use_sparse_predictor = use_sparse_predictor config.use_sparse_regularization = use_sparse_regularization config.thresholds = thresholds return config def apply_mistral_sparse_silu_mlp( model, config, use_sparse_regularization: bool = False, ): # counts = 0 for layer in model.model.layers: # counts += 1 # if counts < 4: # continue original_mlp = layer.mlp new_mlp = MistralSparseSiluMLP( config, use_sparse_regularization=use_sparse_regularization ) new_mlp.gate_proj = original_mlp.gate_proj new_mlp.up_proj = original_mlp.up_proj new_mlp.down_proj = original_mlp.down_proj layer.mlp = new_mlp def apply_mistral_sparse_decoder_layer( model, config, init_svd: bool = True, ): assert isinstance(model.model, MistralModel), "model.model must be a MistralModel." new_layers = [] for layer_idx, layer in enumerate(model.model.layers): if isinstance(layer.mlp, MistralSparseSiluMLP): new_layers.append( SparseMistralDecoderLayer( config=config, layer_idx=layer_idx, decoder_layer=layer, init_svd=init_svd, ) ) print(f"{layer_idx}th mlp layer activation: {layer.mlp.sparse_act_fn}") else: new_layers.append(layer) model.model.layers = nn.ModuleList(new_layers) def enable_sparse_predictor( model, ): for layer_idx, layer in enumerate(model.model.layers): if isinstance(layer, MistralDecoderLayer): layer.use_sparse_predictor = True def disable_sparse_predictor( model, ): for layer_idx, layer in enumerate(model.model.layers): if isinstance(layer, MistralDecoderLayer): layer.use_sparse_predictor = False def activate_stats(model, is_collect_histogram: bool = True): for layer in model.model.layers: if isinstance(layer.mlp, MistralSparseSiluMLP): layer.mlp.activate_stats(is_collect_histogram=is_collect_histogram) def deactivate_stats(model): for layer in model.model.layers: if isinstance(layer.mlp, MistralSparseSiluMLP): layer.mlp.deactivate_stats() def enable_sparse_silu(model): print("Enabling SparseSilu") for i, layer in enumerate(model.model.layers): if isinstance(layer.mlp, MistralSparseSiluMLP): layer.mlp.kill_sparse_swish_outputs = True def print_dead_neuron_stats(model): total_sparsity = 0 counts = 0 for i, layer in enumerate(model.model.layers): if isinstance(layer.mlp, MistralSparseSiluMLP): dead_percentage = layer.mlp.dead_percentage * 100 agg_sparsity = layer.mlp.agg_sparsity * 100 print(f"layer {i} sparsity: {dead_percentage:.3f}%") print(f"layer {i} agg sparsity: {agg_sparsity:.3f}%") total_sparsity += dead_percentage counts += 1 print(f"Total sparsity: {total_sparsity/counts: .3f}%") return total_sparsity / counts def get_sparse_layers(model: MistralModel): sparse_layers = [ m.mlp for m in model.layers() if isinstance(m.mlp, MistralSparseSiluMLP) ] return sparse_layers def get_threshold( bin_edges: torch.tensor, histogram_counts: torch.tensor, sparsity_level: float ): # Only for L1 Regularization assert ( len(bin_edges.shape) == len(histogram_counts.shape) == 1 ), "bin_edges and histogram are expected to be 1-dimensional." histogram_counts /= histogram_counts.sum() threshold_idx = torch.searchsorted( histogram_counts.cumsum(0), sparsity_level, side="right" ) return bin_edges[threshold_idx] def set_regularization_threshold(model, threshold: float = 0.1): for i, layer in enumerate(model.model.layers): if ( isinstance(layer.mlp, MistralSparseSiluMLP) and layer.mlp.is_stats ): # Can set the threshold only the relevant statistics is collected. layer.mlp.regularization_threshold = threshold # TODO: find better param def set_sparse_threshold(model, sparsity_level: float, use_relu: bool = False): for i, layer in enumerate(model.model.layers): if ( isinstance(layer.mlp, MistralSparseSiluMLP) and layer.mlp.is_stats ): # Can set the threshold only the relevant statistics is collected. if use_relu: layer.mlp.sparse_act_fn = nn.ReLU() layer.mlp.use_relu = True else: layer.mlp.dead_threshold = get_threshold( layer.mlp.histogram_bins, layer.mlp.post_act_hist_counts, sparsity_level, ) layer.mlp.sparse_act_fn.set_new_threshold(layer.mlp.dead_threshold) layer.mlp.regularization_threshold = ( layer.mlp.dead_threshold * 1.2 ) # TODO: find better param def plot_histogram( bin_edges, histogram_counts: torch.tensor, title: str = "Activation Distribution", fig_dir: str = "figures" ): plt.bar( bin_edges[:-1], histogram_counts, width=np.diff(bin_edges), edgecolor="black" ) plt.title(title) plt.xlabel("Activation Value") plt.ylabel("Frequency") os.makedirs(fig_dir, exist_ok=True) plt.savefig(f"{fig_dir}/{title}.png") # plt.show() plt.clf() def plot_act(model, fig_dir: str = "figures"): for i, layer in enumerate(model.model.layers): if ( isinstance(layer.mlp, MistralSparseSiluMLP) and layer.mlp.is_stats ): # Can set the threshold only the relevant statistics is collected. plot_title = f"Layer: {i} Pre-Activation Distribution" plot_histogram( layer.mlp.histogram_bins, layer.mlp.pre_act_hist_counts, plot_title ) plot_title = f"Layer: {i} Post-Activation Absolute Distribution" plot_histogram( layer.mlp.histogram_bins, layer.mlp.post_act_hist_counts, plot_title ) def save_act_hist( model, filename="/scr/jay/models/mistral/pre_finetune/cola_act_hist.pt" ): os.makedirs(os.path.dirname(filename), exist_ok=True) act_dict = {} for i, layer in enumerate(model.model.layers): if ( isinstance(layer.mlp, MistralSparseSiluMLP) and layer.mlp.is_stats ): # Can set the threshold only the relevant statistics is collected. act_dict[i] = ( layer.mlp.histogram_bins, layer.mlp.pre_act_hist_counts, layer.mlp.post_act_hist_counts, ) print("Saving activation histograms...\n\n\n") torch.save(act_dict, filename) def load_act_hist( model, filename="/scr/jay/models/mistral/pre_finetune/cola_act_hist.pt" ): assert os.path.exists( filename ), f"{filename} does not exist when loading pre/post-activation histogram of SparseMistralSiluMLP." print("Loading activation histograms...\n\n\n") act_dict = torch.load(filename) for i, layer in enumerate(model.model.layers): if ( isinstance(layer.mlp, MistralSparseSiluMLP) and layer.mlp.is_stats ): # Can set the threshold only the relevant statistics is collected. ( layer.mlp.histogram_bins, layer.mlp.pre_act_hist_counts, layer.mlp.post_act_hist_counts, ) = act_dict[i] def enable_last_k_modules(model, start_module_idx: int): assert 32 > start_module_idx >= 0 new_modules = [] new_idx = 0 for idx in range(start_module_idx, len(model.model.original_layers)): module = model.model.original_layers[idx] module.layer_idx = new_idx module.self_attn.layer_idx = new_idx new_modules.append(module) new_idx += 1 print(module.layer_idx) model.model.layers = nn.ModuleList(new_modules) def enable_first_k_modules(model, end_module_idx: int): assert 32 > end_module_idx >= 0 new_modules = [] new_idx = 0 for idx in range(0, end_module_idx + 1): module = model.model.original_layers[idx] module.layer_idx = new_idx module.self_attn.layer_idx = new_idx new_modules.append(module) new_idx += 1 print(module.layer_idx) model.model.layers = nn.ModuleList(new_modules)