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save_activations.py
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save_activations.py
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import os
import torch
import argparse
from utils import *
from load import load_model
from make_prompt_datasets import ENTITY_PROMPTS
from feature_datasets import common
import os
from tqdm import tqdm
import torch
import einops
import numpy as np
from torch.utils.data import DataLoader
def load_activation_probing_dataset_args(args, prompt_name, layer_ix):
activation_save_path = os.path.join(
os.getenv('ACTIVATION_DATASET_DIR', 'activation_datasets'),
args.model,
args.entity_type
)
save_name = f'{args.entity_type}.{args.activation_aggregation}.{prompt_name}.{layer_ix}.pt'
save_path = os.path.join(activation_save_path, save_name)
activations = torch.load(save_path)
return activations
def load_activation_probing_dataset(model, entity_type, prompt_name, layer_ix, activation_aggregation='last'):
activation_save_path = os.path.join(
'activation_datasets', model, entity_type)
save_name = f'{entity_type}.{activation_aggregation}.{prompt_name}.{layer_ix}.pt'
save_path = os.path.join(activation_save_path, save_name)
activations = torch.load(save_path)
return activations
def process_activation_batch(args, batch_activations, step, batch_mask=None):
cur_batch_size = batch_activations.shape[0]
if args.activation_aggregation is None:
# only save the activations for the required indices
batch_activations = einops.rearrange(
batch_activations, 'b c d -> (b c) d') # batch, context, dim
processed_activations = batch_activations[batch_mask]
if args.activation_aggregation == 'last':
last_ix = batch_activations.shape[1] - 1
batch_mask = batch_mask.to(int)
last_entity_token = last_ix - \
torch.argmax(batch_mask.flip(dims=[1]), dim=1)
d_act = batch_activations.shape[2]
expanded_mask = last_entity_token.unsqueeze(-1).expand(-1, d_act)
processed_activations = batch_activations[
torch.arange(cur_batch_size).unsqueeze(-1),
expanded_mask,
torch.arange(d_act)
]
assert processed_activations.shape == (cur_batch_size, d_act)
elif args.activation_aggregation == 'mean':
# average over the context dimension for valid tokens only
masked_activations = batch_activations * batch_mask
batch_valid_ixs = batch_mask.sum(dim=1)
processed_activations = masked_activations.sum(
dim=1) / batch_valid_ixs[:, None]
elif args.activation_aggregation == 'max':
# max over the context dimension for valid tokens only (set invalid tokens to -1)
batch_mask = batch_mask[:, :, None].to(int)
# set masked tokens to -1
masked_activations = batch_activations * batch_mask + (batch_mask - 1)
processed_activations = masked_activations.max(dim=1)[0]
return processed_activations
def save_activation_hook(tensor, hook):
hook.ctx['activation'] = tensor.detach().cpu().to(torch.float16)
@torch.no_grad()
def get_layer_activations_tl(
args, model, tokenized_dataset, layers='all', device=None,
):
if layers == 'all':
layers = list(range(model.cfg.n_layers))
if device is None:
device = model.cfg.device
hooks = [
(f'blocks.{layer_ix}.hook_resid_post', save_activation_hook)
for layer_ix in layers
]
entity_mask = torch.tensor(tokenized_dataset['entity_mask'])
n_seq, ctx_len = tokenized_dataset['input_ids'].shape
activation_rows = entity_mask.sum().item() \
if args.activation_aggregation is None \
else n_seq
layer_activations = {
l: torch.zeros(activation_rows, model.cfg.d_model, dtype=torch.float16)
for l in layers
}
layer_offsets = {l: 0 for l in layers}
bs = args.batch_size
dataloader = DataLoader(
tokenized_dataset['input_ids'], batch_size=bs, shuffle=False)
for step, batch in enumerate(tqdm(dataloader, disable=False)):
# clip batch to remove excess padding
batch_entity_mask = entity_mask[step*bs:(step+1)*bs]
last_valid_ix = torch.argmax(
(batch_entity_mask.sum(dim=0) > 0) * torch.arange(ctx_len)) + 1
batch = batch[:, :last_valid_ix].to(device)
batch_entity_mask = batch_entity_mask[:, :last_valid_ix]
model.run_with_hooks(
batch,
fwd_hooks=hooks,
stop_at_layer=max(layers) + 1,
)
for lix, (hook_pt, _) in enumerate(hooks):
batch_activations = model.hook_dict[hook_pt].ctx['activation']
processed_activations = process_activation_batch(
args, batch_activations, step, batch_entity_mask)
offset = layer_offsets[layers[lix]]
save_rows = processed_activations.shape[0]
layer_activations[layers[lix]][
offset:offset+save_rows] = processed_activations
layer_offsets[layers[lix]] += save_rows
model.reset_hooks()
return layer_activations
@torch.no_grad()
def get_layer_activations_hf(
args, model, tokenized_dataset, layers='all', device=None,
):
if layers == 'all':
layers = list(range(model.config.num_hidden_layers))
if device is None:
device = model.device
entity_mask = torch.tensor(tokenized_dataset['entity_mask'])
n_seq, ctx_len = tokenized_dataset['input_ids'].shape
activation_rows = entity_mask.sum().item() \
if args.activation_aggregation is None \
else n_seq
layer_activations = {
l: torch.zeros(activation_rows, model.config.hidden_size,
dtype=torch.float16)
for l in layers
}
assert args.activation_aggregation == 'last' # code assumes this
offset = 0
bs = args.batch_size
dataloader = DataLoader(
tokenized_dataset['input_ids'], batch_size=bs, shuffle=False)
for step, batch in enumerate(tqdm(dataloader, disable=False)):
# clip batch to remove excess padding
batch_entity_mask = entity_mask[step*bs:(step+1)*bs]
last_valid_ix = torch.argmax(
(batch_entity_mask.sum(dim=0) > 0) * torch.arange(ctx_len)) + 1
batch = batch[:, :last_valid_ix].to(device)
batch_entity_mask = batch_entity_mask[:, :last_valid_ix]
out = model(batch, output_hidden_states=True,
output_attentions=False, return_dict=True, use_cache=False)
# do not save post embedding layer activations
for lix, activation in enumerate(out.hidden_states[1:]):
if lix not in layer_activations:
continue
activation = activation.cpu().to(torch.float16)
processed_activations = process_activation_batch(
args, activation, step, batch_entity_mask)
save_rows = processed_activations.shape[0]
layer_activations[lix][offset:offset +
save_rows] = processed_activations
offset += batch.shape[0]
return layer_activations
if __name__ == '__main__':
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# experiment params
parser.add_argument(
'--model', default='pythia-70m',
help='Name of model from TransformerLens')
parser.add_argument(
'--entity_type',
help='Name of entity_type (should be dir under data/entity_datasets/)')
parser.add_argument(
'--activation_aggregation', default='last',
help='Average activations across all tokens in a sequence')
# base experiment params
parser.add_argument(
'--device', default="cuda" if torch.cuda.is_available() else "cpu",
help='device to use for computation')
parser.add_argument(
'--batch_size', type=int, default=128,
help='batch size to use for model.forward')
parser.add_argument(
'--save_precision', type=int, default=8, choices=[8, 16, 32],
help='Number of bits to use for saving activations')
parser.add_argument(
'--n_threads', type=int,
default=int(os.getenv('SLURM_CPUS_PER_TASK', 8)),
help='number of threads to use for pytorch cpu parallelization')
parser.add_argument(
'--layers', nargs='+', type=int, default=None)
parser.add_argument(
'--use_tl', action='store_true',
help='Use TransformerLens model instead of HuggingFace model')
parser.add_argument(
'--is_test', action='store_true')
parser.add_argument(
'--prompt_name', default='all')
args = parser.parse_args()
print(timestamp(), 'Begin loading model')
model = load_model(
args.model, device=args.device,
use_hf=not args.use_tl,
dtype=torch.float16 if args.device.startswith(
"cuda") else torch.float32
)
print(timestamp(), 'Finished loading model')
model_family = get_model_family(args.model)
torch.set_grad_enabled(False)
if args.prompt_name == 'all':
prompt_list = list(ENTITY_PROMPTS[args.entity_type].keys())
else:
prompt_list = [args.prompt_name]
for prompt_name in prompt_list:
tokenized_dataset = common.load_tokenized_dataset(
args.entity_type, prompt_name, model_family)
if args.is_test:
tokenized_dataset = tokenized_dataset.select(range(10))
activation_save_path = os.path.join(
os.getenv('ACTIVATION_DATASET_DIR', 'activation_datasets'),
args.model,
args.entity_type
)
os.makedirs(activation_save_path, exist_ok=True)
print(timestamp(),
f'Begin processing {args.model} {args.entity_type} {prompt_name}')
if args.use_tl:
layer_activations = get_layer_activations_tl(
args, model, tokenized_dataset,
device=args.device,
)
else:
layer_activations = get_layer_activations_hf(
args, model, tokenized_dataset,
device=args.device,
)
for layer_ix, activations in layer_activations.items():
save_name = f'{args.entity_type}.{args.activation_aggregation}.{prompt_name}.{layer_ix}.pt'
save_path = os.path.join(activation_save_path, save_name)
activations = adjust_precision(
activations.to(torch.float32), args.save_precision, per_channel=True)
torch.save(activations, save_path)