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updating examples and doc

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thomwolf
2019-07-14 23:20:10 +02:00
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examples/run_bertology.py
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@@ -1,4 +1,24 @@
#!/usr/bin/env python3
# Copyright 2018 CMU and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Bertology: this script shows how you can explore the internals of the models in the library to:
- compute the entropy of the head attentions
- compute the importance of each head
- prune (remove) the low importance head.
Some parts of this script are adapted from the code of Michel et al. (http://arxiv.org/abs/1905.10650)
which is available at https://github.com/pmichel31415/are-16-heads-really-better-than-1
"""
import os
import argparse
import logging
@@ -12,43 +32,49 @@ from torch.utils.data import DataLoader, SequentialSampler, TensorDataset, Subse
from torch.utils.data.distributed import DistributedSampler
from torch.nn import CrossEntropyLoss, MSELoss
from pytorch_transformers import BertForSequenceClassification, BertTokenizer
from pytorch_transformers import (WEIGHTS_NAME,
BertConfig, BertForSequenceClassification, BertTokenizer,
XLMConfig, XLMForSequenceClassification, XLMTokenizer,
XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer)
from utils_glue import processors, output_modes, convert_examples_to_features, compute_metrics
from run_glue import set_seed, load_and_cache_examples, ALL_MODELS, MODEL_CLASSES
from utils_glue import (compute_metrics, convert_examples_to_features,
output_modes, processors)
logger = logging.getLogger(__name__)
def entropy(p):
""" Compute the entropy of a probability distribution """
plogp = p * torch.log(p)
plogp[p == 0] = 0
return -plogp.sum(dim=-1)
def print_1d_tensor(tensor, prefix=""):
if tensor.dtype != torch.long:
logger.info(prefix + "\t".join(f"{x:.5f}" for x in tensor.cpu().data))
else:
logger.info(prefix + "\t".join(f"{x:d}" for x in tensor.cpu().data))
def print_2d_tensor(tensor):
""" Print a 2D tensor """
logger.info("lv, h >\t" + "\t".join(f"{x + 1}" for x in range(len(tensor))))
for row in range(len(tensor)):
print_1d_tensor(tensor[row], prefix=f"layer {row + 1}:\t")
if tensor.dtype != torch.long:
logger.info(f"layer {row + 1}:\t" + "\t".join(f"{x:.5f}" for x in tensor[row].cpu().data))
else:
logger.info(f"layer {row + 1}:\t" + "\t".join(f"{x:d}" for x in tensor[row].cpu().data))
def compute_heads_importance(args, model, eval_dataloader, compute_entropy=True, compute_importance=True, head_mask=None):
""" Example on how to use model outputs to compute:
- head attention entropy (activated by setting output_attentions=True when we created the model
""" This method shows how to compute:
- head attention entropy
- head importance scores according to http://arxiv.org/abs/1905.10650
(activated by setting keep_multihead_output=True when we created the model)
"""
# Prepare our tensors
n_layers, n_heads = model.bert.config.num_hidden_layers, model.bert.config.num_attention_heads
head_importance = torch.zeros(n_layers, n_heads).to(args.device)
attn_entropy = torch.zeros(n_layers, n_heads).to(args.device)
if head_mask is None:
head_mask = torch.ones(n_layers, n_heads).to(args.device)
head_mask.requires_grad_(requires_grad=True)
preds = None
labels = None
tot_tokens = 0.0
@@ -58,29 +84,17 @@ def compute_heads_importance(args, model, eval_dataloader, compute_entropy=True,
input_ids, input_mask, segment_ids, label_ids = batch
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
all_attentions, logits = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, head_mask=head_mask)
outputs = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids, head_mask=head_mask)
loss, logits, all_attentions = outputs[0], outputs[1], outputs[-1] # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
if compute_entropy:
# Update head attention entropy
for layer, attn in enumerate(all_attentions):
masked_entropy = entropy(attn.detach()) * input_mask.float().unsqueeze(1)
attn_entropy[layer] += masked_entropy.sum(-1).sum(0).detach()
if compute_importance:
# Update head importance scores with regards to our loss
# First, backpropagate to populate the gradients
if args.output_mode == "classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, args.num_labels), label_ids.view(-1))
elif args.output_mode == "regression":
loss_fct = MSELoss()
loss = loss_fct(logits.view(-1), label_ids.view(-1))
loss.backward()
# Second, compute importance scores according to http://arxiv.org/abs/1905.10650
multihead_outputs = model.bert.get_multihead_outputs()
for layer, mh_layer_output in enumerate(multihead_outputs):
dot = torch.einsum("bhli,bhli->bhl", [mh_layer_output.grad, mh_layer_output])
head_importance[layer] += dot.abs().sum(-1).sum(0).detach()
head_importance += head_mask.grad.abs().detach()
# Also store our logits/labels if we want to compute metrics afterwards
if preds is None:
@@ -104,30 +118,137 @@ def compute_heads_importance(args, model, eval_dataloader, compute_entropy=True,
if not args.dont_normalize_global_importance:
head_importance = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print/save matrices
np.save(os.path.join(args.output_dir, 'attn_entropy.npy'), attn_entropy.detach().cpu().numpy())
np.save(os.path.join(args.output_dir, 'head_importance.npy'), head_importance.detach().cpu().numpy())
logger.info("Attention entropies")
print_2d_tensor(attn_entropy)
logger.info("Head importance scores")
print_2d_tensor(head_importance)
logger.info("Head ranked by importance scores")
head_ranks = torch.zeros(head_importance.numel(), dtype=torch.long, device=args.device)
head_ranks[head_importance.view(-1).sort(descending=True)[1]] = torch.arange(head_importance.numel(), device=args.device)
head_ranks = head_ranks.view_as(head_importance)
print_2d_tensor(head_ranks)
return attn_entropy, head_importance, preds, labels
def run_model():
def mask_heads(args, model, eval_dataloader):
""" This method shows how to mask head (set some heads to zero), to test the effect on the network,
based on the head importance scores, as described in Michel et al. (http://arxiv.org/abs/1905.10650)
"""
_, head_importance, preds, labels = compute_heads_importance(args, model, eval_dataloader, compute_entropy=False)
preds = np.argmax(preds, axis=1) if args.output_mode == "classification" else np.squeeze(preds)
original_score = compute_metrics(args.task_name, preds, labels)[args.metric_name]
logger.info("Pruning: original score: %f, threshold: %f", original_score, original_score * args.masking_threshold)
new_head_mask = torch.ones_like(head_importance)
num_to_mask = max(1, int(new_head_mask.numel() * args.masking_amount))
current_score = original_score
while current_score >= original_score * args.masking_threshold:
head_mask = new_head_mask.clone() # save current head mask
# heads from least important to most - keep only not-masked heads
head_importance[head_mask == 0.0] = float('Inf')
current_heads_to_mask = head_importance.view(-1).sort()[1]
if len(current_heads_to_mask) <= num_to_mask:
break
# mask heads
current_heads_to_mask = current_heads_to_mask[:num_to_mask]
logger.info("Heads to mask: %s", str(current_heads_to_mask.tolist()))
new_head_mask = new_head_mask.view(-1)
new_head_mask[current_heads_to_mask] = 0.0
new_head_mask = new_head_mask.view_as(head_mask)
print_2d_tensor(new_head_mask)
# Compute metric and head importance again
_, head_importance, preds, labels = compute_heads_importance(args, model, eval_dataloader, compute_entropy=False, head_mask=new_head_mask)
preds = np.argmax(preds, axis=1) if args.output_mode == "classification" else np.squeeze(preds)
current_score = compute_metrics(args.task_name, preds, labels)[args.metric_name]
logger.info("Masking: current score: %f, remaning heads %d (%.1f percents)", current_score, new_head_mask.sum(), new_head_mask.sum()/new_head_mask.numel() * 100)
logger.info("Final head mask")
print_2d_tensor(head_mask)
np.save(os.path.join(args.output_dir, 'head_mask.npy'), head_mask.detach().cpu().numpy())
return head_mask
def prune_heads(args, model, eval_dataloader, head_mask):
""" This method shows how to prune head (remove heads weights) based on
the head importance scores as described in Michel et al. (http://arxiv.org/abs/1905.10650)
"""
# Try pruning and test time speedup
# Pruning is like masking but we actually remove the masked weights
before_time = datetime.now()
_, _, preds, labels = compute_heads_importance(args, model, eval_dataloader,
compute_entropy=False, compute_importance=False, head_mask=head_mask)
preds = np.argmax(preds, axis=1) if args.output_mode == "classification" else np.squeeze(preds)
score_masking = compute_metrics(args.task_name, preds, labels)[args.metric_name]
original_time = datetime.now() - before_time
original_num_params = sum(p.numel() for p in model.parameters())
heads_to_prune = dict((layer, (1 - head_mask[layer].long()).nonzero().tolist()) for layer in range(len(head_mask)))
assert sum(len(h) for h in heads_to_prune.values()) == (1 - head_mask.long()).sum().item()
model.prune_heads(heads_to_prune)
pruned_num_params = sum(p.numel() for p in model.parameters())
before_time = datetime.now()
_, _, preds, labels = compute_heads_importance(args, model, eval_dataloader,
compute_entropy=False, compute_importance=False, head_mask=None)
preds = np.argmax(preds, axis=1) if args.output_mode == "classification" else np.squeeze(preds)
score_pruning = compute_metrics(args.task_name, preds, labels)[args.metric_name]
new_time = datetime.now() - before_time
logger.info("Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)", original_num_params, pruned_num_params, pruned_num_params/original_num_params * 100)
logger.info("Pruning: score with masking: %f score with pruning: %f", score_masking, score_pruning)
logger.info("Pruning: speed ratio (new timing / original timing): %f percents", original_time/new_time * 100)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model_name_or_path', type=str, default='bert-base-cased-finetuned-mrpc', help='pretrained model name or path to local checkpoint')
parser.add_argument("--task_name", type=str, default='mrpc', help="The name of the task to train.")
parser.add_argument("--data_dir", type=str, required=True, help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--output_dir", type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument("--data_subset", type=int, default=-1, help="If > 0: limit the data to a subset of data_subset instances.")
parser.add_argument("--overwrite_output_dir", action='store_true', help="Whether to overwrite data in output directory")
parser.add_argument("--data_dir", default=None, type=str, required=True,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--model_name", default=None, type=str, required=True,
help="Bert/XLNet/XLM pre-trained model selected in the list: " + ", ".join(ALL_MODELS))
parser.add_argument("--task_name", default=None, type=str, required=True,
help="The name of the task to train selected in the list: " + ", ".join(processors.keys()))
parser.add_argument("--output_dir", default=None, type=str, required=True,
help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument("--dont_normalize_importance_by_layer", action='store_true', help="Don't normalize importance score by layers")
parser.add_argument("--dont_normalize_global_importance", action='store_true', help="Don't normalize all importance scores between 0 and 1")
## Other parameters
parser.add_argument("--config_name", default="", type=str,
help="Pretrained config name or path if not the same as model_name")
parser.add_argument("--tokenizer_name", default="", type=str,
help="Pretrained tokenizer name or path if not the same as model_name")
parser.add_argument("--cache_dir", default="", type=str,
help="Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument("--data_subset", type=int, default=-1,
help="If > 0: limit the data to a subset of data_subset instances.")
parser.add_argument("--overwrite_output_dir", action='store_true',
help="Whether to overwrite data in output directory")
parser.add_argument("--try_masking", action='store_true', help="Whether to try to mask head until a threshold of accuracy.")
parser.add_argument("--masking_threshold", default=0.9, type=float, help="masking threshold in term of metrics"
"(stop masking when metric < threshold * original metric value).")
parser.add_argument("--masking_amount", default=0.1, type=float, help="Amount to heads to masking at each masking step.")
parser.add_argument("--metric_name", default="acc", type=str, help="Metric to use for head masking.")
parser.add_argument("--dont_normalize_importance_by_layer", action='store_true',
help="Don't normalize importance score by layers")
parser.add_argument("--dont_normalize_global_importance", action='store_true',
help="Don't normalize all importance scores between 0 and 1")
parser.add_argument("--max_seq_length", default=128, type=int, help="The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--try_masking", action='store_true',
help="Whether to try to mask head until a threshold of accuracy.")
parser.add_argument("--masking_threshold", default=0.9, type=float,
help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value).")
parser.add_argument("--masking_amount", default=0.1, type=float,
help="Amount to heads to masking at each masking step.")
parser.add_argument("--metric_name", default="acc", type=str,
help="Metric to use for head masking.")
parser.add_argument("--max_seq_length", default=128, type=int,
help="The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, sequences shorter padded.")
parser.add_argument("--batch_size", default=1, type=int, help="Batch size.")
parser.add_argument("--seed", type=int, default=42)
@@ -147,164 +268,79 @@ def run_model():
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
args.n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
args.device = torch.device("cuda", args.local_rank)
n_gpu = 1
args.n_gpu = 1
torch.distributed.init_process_group(backend='nccl') # Initializes the distributed backend
# Setup logging
logging.basicConfig(level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device, n_gpu, bool(args.local_rank != -1)))
logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device, args.n_gpu, bool(args.local_rank != -1)))
# Set seeds
np.random.seed(args.seed)
torch.random.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed(args.seed)
set_seed(args)
# Prepare GLUE task
task_name = args.task_name.lower()
processor = processors[task_name]()
args.task_name = args.task_name.lower()
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name]()
args.output_mode = output_modes[args.task_name]
label_list = processor.get_labels()
args.output_mode = output_modes[task_name]
args.num_labels = len(label_list)
num_labels = len(label_list)
# Prepare output directory
if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and not args.overwrite_output_dir:
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
# Load model & tokenizer
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Make sure only one distributed process download model & vocab
tokenizer = BertTokenizer.from_pretrained(args.model_name_or_path)
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
args.model_type = ""
for key in MODEL_CLASSES:
if key in args.model_name.lower():
args.model_type = key # take the first match in model types
break
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name,
num_labels=num_labels, finetuning_task=args.task_name,
output_attentions=True)
tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name)
model = model_class.from_pretrained(args.model_name, from_tf=bool('.ckpt' in args.model_name), config=config)
# Load a model with all BERTology options on:
# output_attentions => will output attention weights
# keep_multihead_output => will store gradient of attention head outputs for head importance computation
# see: http://arxiv.org/abs/1905.10650
model = BertForSequenceClassification.from_pretrained(args.model_name_or_path,
num_labels=args.num_labels,
output_attentions=True,
keep_multihead_output=True)
if args.local_rank == 0:
torch.distributed.barrier() # Make sure only one distributed process download model & vocab
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
# Distributed and parallel training
model.to(args.device)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True)
model.eval()
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
elif args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Print/save training arguments
torch.save(args, os.path.join(args.output_dir, 'run_args.bin'))
logger.info("Training/evaluation parameters %s", args)
# Prepare dataset for the GLUE task
eval_examples = processor.get_dev_examples(args.data_dir)
cached_eval_features_file = os.path.join(args.data_dir, 'dev_{0}_{1}_{2}'.format(
list(filter(None, args.model_name_or_path.split('/'))).pop(), str(args.max_seq_length), str(task_name)))
try:
eval_features = torch.load(cached_eval_features_file)
except:
eval_features = convert_examples_to_features(eval_examples, label_list, args.max_seq_length, tokenizer, args.output_mode)
if args.local_rank in [-1, 0]:
logger.info("Saving eval features to cache file %s", cached_eval_features_file)
torch.save(eval_features, cached_eval_features_file)
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long if args.output_mode == "classification" else torch.float)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
eval_data = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=True)
if args.data_subset > 0:
eval_data = Subset(eval_data, list(range(min(args.data_subset, len(eval_data)))))
eval_sampler = SequentialSampler(eval_data) if args.local_rank == -1 else DistributedSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.batch_size)
# Print/save training arguments
print(args)
torch.save(args, os.path.join(args.output_dir, 'run_args.bin'))
# Compute head entropy and importance score
attn_entropy, head_importance, _, _ = compute_heads_importance(args, model, eval_dataloader)
compute_heads_importance(args, model, eval_dataloader)
# Print/save matrices
np.save(os.path.join(args.output_dir, 'attn_entropy.npy'), attn_entropy.detach().cpu().numpy())
np.save(os.path.join(args.output_dir, 'head_importance.npy'), head_importance.detach().cpu().numpy())
logger.info("Attention entropies")
print_2d_tensor(attn_entropy)
logger.info("Head importance scores")
print_2d_tensor(head_importance)
logger.info("Head ranked by importance scores")
head_ranks = torch.zeros(head_importance.numel(), dtype=torch.long, device=args.device)
head_ranks[head_importance.view(-1).sort(descending=True)[1]] = torch.arange(head_importance.numel(), device=args.device)
head_ranks = head_ranks.view_as(head_importance)
print_2d_tensor(head_ranks)
# Do masking if we want to
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
_, head_importance, preds, labels = compute_heads_importance(args, model, eval_dataloader, compute_entropy=False)
preds = np.argmax(preds, axis=1) if args.output_mode == "classification" else np.squeeze(preds)
original_score = compute_metrics(task_name, preds, labels)[args.metric_name]
logger.info("Pruning: original score: %f, threshold: %f", original_score, original_score * args.masking_threshold)
head_mask = mask_heads(args, model, eval_dataloader)
prune_heads(args, model, eval_dataloader, head_mask)
new_head_mask = torch.ones_like(head_importance)
num_to_mask = max(1, int(new_head_mask.numel() * args.masking_amount))
current_score = original_score
while current_score >= original_score * args.masking_threshold:
head_mask = new_head_mask.clone() # save current head mask
# heads from least important to most - keep only not-masked heads
head_importance[head_mask == 0.0] = float('Inf')
current_heads_to_mask = head_importance.view(-1).sort()[1]
if len(current_heads_to_mask) <= num_to_mask:
break
# mask heads
current_heads_to_mask = current_heads_to_mask[:num_to_mask]
logger.info("Heads to mask: %s", str(current_heads_to_mask.tolist()))
new_head_mask = new_head_mask.view(-1)
new_head_mask[current_heads_to_mask] = 0.0
new_head_mask = new_head_mask.view_as(head_mask)
print_2d_tensor(new_head_mask)
# Compute metric and head importance again
_, head_importance, preds, labels = compute_heads_importance(args, model, eval_dataloader, compute_entropy=False, head_mask=new_head_mask)
preds = np.argmax(preds, axis=1) if args.output_mode == "classification" else np.squeeze(preds)
current_score = compute_metrics(task_name, preds, labels)[args.metric_name]
logger.info("Masking: current score: %f, remaning heads %d (%.1f percents)", current_score, new_head_mask.sum(), new_head_mask.sum()/new_head_mask.numel() * 100)
logger.info("Final head mask")
print_2d_tensor(head_mask)
np.save(os.path.join(args.output_dir, 'head_mask.npy'), head_mask.detach().cpu().numpy())
# Try pruning and test time speedup
# Pruning is like masking but we actually remove the masked weights
before_time = datetime.now()
_, _, preds, labels = compute_heads_importance(args, model, eval_dataloader,
compute_entropy=False, compute_importance=False, head_mask=head_mask)
preds = np.argmax(preds, axis=1) if args.output_mode == "classification" else np.squeeze(preds)
score_masking = compute_metrics(task_name, preds, labels)[args.metric_name]
original_time = datetime.now() - before_time
original_num_params = sum(p.numel() for p in model.parameters())
heads_to_prune = dict((layer, (1 - head_mask[layer].long()).nonzero().tolist()) for layer in range(len(head_mask)))
assert sum(len(h) for h in heads_to_prune.values()) == (1 - head_mask.long()).sum().item()
model.bert.prune_heads(heads_to_prune)
pruned_num_params = sum(p.numel() for p in model.parameters())
before_time = datetime.now()
_, _, preds, labels = compute_heads_importance(args, model, eval_dataloader,
compute_entropy=False, compute_importance=False, head_mask=None)
preds = np.argmax(preds, axis=1) if args.output_mode == "classification" else np.squeeze(preds)
score_pruning = compute_metrics(task_name, preds, labels)[args.metric_name]
new_time = datetime.now() - before_time
logger.info("Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)", original_num_params, pruned_num_params, pruned_num_params/original_num_params * 100)
logger.info("Pruning: score with masking: %f score with pruning: %f", score_masking, score_pruning)
logger.info("Pruning: speed ratio (new timing / original timing): %f percents", original_time/new_time * 100)
if __name__ == '__main__':
run_model()
main()