def test_loss_calculation(self):
from lpot.strategy.tpe import TpeTuneStrategy
from lpot import Quantization
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy', (100, 3, 3, 1), label=True)
dataloader = quantizer.dataloader(dataset)
testObject = TpeTuneStrategy(self.constant_graph, quantizer.conf,
dataloader)
testObject._calculate_loss_function_scaling_components(
0.01, 2, testObject.loss_function_config)
# check if latency difference between min and max corresponds to 10 points of loss function
tmp_val = testObject.calculate_loss(0.01, 2,
testObject.loss_function_config)
tmp_val2 = testObject.calculate_loss(0.01, 1,
testObject.loss_function_config)
self.assertTrue(True if int(tmp_val2 - tmp_val) == 10 else False)
# check if 1% of acc difference corresponds to 10 points of loss function
tmp_val = testObject.calculate_loss(0.02, 2,
testObject.loss_function_config)
tmp_val2 = testObject.calculate_loss(0.03, 2,
testObject.loss_function_config)
self.assertTrue(True if int(tmp_val2 - tmp_val) == 10 else False)
def test_matmul_biasadd_requantize_dequantize_fusion_with_softmax(self):
tf.disable_v2_behavior()
g = tf.Graph()
with g.as_default():
from lpot import Quantization
x_data = np.array([[0.1, 0.2], [0.2, 0.3]])
y_data = np.array([[1, 2], [3, 4]], dtype=np.float)
x = tf.placeholder(tf.float32, shape=[2, 2], name='x')
y = tf.constant(y_data, dtype=tf.float32, shape=[2, 2])
z = tf.matmul(x, y)
z = tf.nn.bias_add(z, [1, 2])
z = tf.nn.softmax(z, name='op_to_store')
found_quantized_matmul = False
if tf.version.VERSION < "2.2.0":
found_quantized_matmul = False
else:
with tf.Session() as sess:
sess.run(z, feed_dict={x: x_data, y: y_data})
float_graph_def = sess.graph.as_graph_def()
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy',
shape=(2, 2),
label=True)
dataloader = quantizer.dataloader(dataset, batch_size=2)
output_graph = quantizer(float_graph_def,
q_dataloader=dataloader,
eval_dataloader=dataloader)
for i in output_graph.as_graph_def().node:
if i.op == 'QuantizedMatMulWithBiasAndDequantize':
found_quantized_matmul = True
break
self.assertEqual(found_quantized_matmul, False)
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--model_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: " +
", ".join(MODEL_CLASSES.keys()))
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: "
+ ", ".join(ALL_MODELS))
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model checkpoints and predictions will be written."
)
# Other parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
help=
"The input data dir. Should contain the .json files for the task. If not specified, will run with tensorflow_datasets."
)
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(
'--version_2_with_negative',
action='store_true',
help=
'If true, the SQuAD examples contain some that do not have an answer.')
parser.add_argument(
'--null_score_diff_threshold',
type=float,
default=0.0,
help=
"If null_score - best_non_null is greater than the threshold predict null."
)
parser.add_argument(
"--max_seq_length",
default=384,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. Sequences "
"longer than this will be truncated, and sequences shorter than this will be padded."
)
parser.add_argument(
"--doc_stride",
default=128,
type=int,
help=
"When splitting up a long document into chunks, how much stride to take between chunks."
)
parser.add_argument(
"--max_query_length",
default=64,
type=int,
help=
"The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument(
"--evaluate_during_training",
action='store_true',
help="Rul evaluation during training at each logging step.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--per_gpu_train_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--per_gpu_eval_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for evaluation.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument("--weight_decay",
default=0.0,
type=float,
help="Weight decay if we apply some.")
parser.add_argument("--adam_epsilon",
default=1e-8,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm",
default=1.0,
type=float,
help="Max gradient norm.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help=
"If > 0: set total number of training steps to perform. Override num_train_epochs."
)
parser.add_argument("--warmup_steps",
default=0,
type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument(
"--n_best_size",
default=20,
type=int,
help=
"The total number of n-best predictions to generate in the nbest_predictions.json output file."
)
parser.add_argument(
"--max_answer_length",
default=30,
type=int,
help=
"The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another.")
parser.add_argument(
"--verbose_logging",
action='store_true',
help=
"If true, all of the warnings related to data processing will be printed. "
"A number of warnings are expected for a normal SQuAD evaluation.")
parser.add_argument('--logging_steps',
type=int,
default=50,
help="Log every X updates steps.")
parser.add_argument('--save_steps',
type=int,
default=50,
help="Save checkpoint every X updates steps.")
parser.add_argument(
"--eval_all_checkpoints",
action='store_true',
help=
"Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"
)
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument('--overwrite_output_dir',
action='store_true',
help="Overwrite the content of the output directory")
parser.add_argument(
'--overwrite_cache',
action='store_true',
help="Overwrite the cached training and evaluation sets")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument(
'--fp16',
action='store_true',
help=
"Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"
)
parser.add_argument(
'--fp16_opt_level',
type=str,
default='O1',
help=
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
parser.add_argument('--server_ip',
type=str,
default='',
help="Can be used for distant debugging.")
parser.add_argument('--server_port',
type=str,
default='',
help="Can be used for distant debugging.")
parser.add_argument("--do_calibration",
action='store_true',
help="Whether to do calibration.")
parser.add_argument("--do_int8_inference",
action='store_true',
help="Whether to run int8 inference.")
parser.add_argument("--do_fp32_inference",
action='store_true',
help="Whether to run fp32 inference.")
parser.add_argument("--mkldnn_eval",
action='store_true',
help="evaluation with MKLDNN")
parser.add_argument(
"--tune",
action='store_true',
help="run Low Precision Optimization Tool to tune int8 acc.")
parser.add_argument("--task_name",
default=None,
type=str,
required=True,
help="SQuAD task")
parser.add_argument("--warmup",
type=int,
default=5,
help="warmup for performance")
parser.add_argument('-i',
"--iter",
default=0,
type=int,
help='For accuracy measurement only.')
parser.add_argument('--benchmark',
dest='benchmark',
action='store_true',
help='run benchmark')
parser.add_argument('-r',
"--accuracy_only",
dest='accuracy_only',
action='store_true',
help='For accuracy measurement only.')
parser.add_argument(
"--tuned_checkpoint",
default='./',
type=str,
metavar='PATH',
help=
'path to checkpoint tuned by Low Precision Optimization Tool (default: ./)'
)
parser.add_argument('--int8',
dest='int8',
action='store_true',
help='run benchmark')
args = parser.parse_args()
args.predict_file = os.path.join(
args.output_dir, 'predictions_{}_{}.txt'.format(
list(filter(None, args.model_name_or_path.split('/'))).pop(),
str(args.max_seq_length)))
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir
) and args.do_train and not args.overwrite_output_dir:
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome."
.format(args.output_dir))
mix_qkv = False
if args.do_calibration or args.do_int8_inference or args.tune:
mix_qkv = True
# Setup distant debugging if needed
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port),
redirect_output=True)
ptvsd.wait_for_attach()
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend='nccl')
args.n_gpu = 1
args.device = device
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank, device, args.n_gpu, bool(args.local_rank != -1),
args.fp16)
# Set seed
set_seed(args)
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
args.model_type = args.model_type.lower()
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_or_path,
cache_dir=args.cache_dir if args.cache_dir else None)
tokenizer = tokenizer_class.from_pretrained(
args.tokenizer_name
if args.tokenizer_name else args.model_name_or_path,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None)
model = model_class.from_pretrained(
args.model_name_or_path,
from_tf=bool('.ckpt' in args.model_name_or_path),
config=config,
mix_qkv=mix_qkv,
cache_dir=args.cache_dir if args.cache_dir else None)
if args.local_rank == 0:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum if args.fp16 is set.
# Otherwise it'll default to "promote" mode, and we'll get fp32 operations. Note that running `--fp16_opt_level="O2"` will
# remove the need for this code, but it is still valid.
if args.fp16:
try:
import apex
apex.amp.register_half_function(torch, 'einsum')
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args,
tokenizer,
evaluate=False,
output_examples=False)
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
logger.info(" global_step = %s, average loss = %s", global_step,
tr_loss)
# Save the trained model and the tokenizer
if args.do_train and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
# Create output directory if needed
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = model.module if hasattr(
model,
'module') else model # Take care of distributed/parallel training
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
# Load a trained model and vocabulary that you have fine-tuned
model = model_class.from_pretrained(args.output_dir,
force_download=True,
mix_qkv=mix_qkv)
tokenizer = tokenizer_class.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
model.to(args.device)
# Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
checkpoints = [args.output_dir]
if args.eval_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME,
recursive=True)))
logging.getLogger("transformers.modeling_utils").setLevel(
logging.WARN) # Reduce model loading logs
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
# Reload the model
global_step = checkpoint.split(
'-')[-1] if len(checkpoints) > 1 else ""
if args.mkldnn_eval or args.do_fp32_inference:
model = model_class.from_pretrained(checkpoint,
force_download=True)
model.to(args.device)
# Evaluate
result, _ = evaluate(args,
model,
tokenizer,
prefix=global_step)
result = dict(
(k + ('_{}'.format(global_step) if global_step else ''), v)
for k, v in result.items())
results.update(result)
if args.tune:
def eval_func_for_lpot(model):
result, _ = evaluate(args, model, tokenizer)
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
bert_task_acc_keys = [
'best_f1', 'f1', 'mcc', 'spearmanr', 'acc'
]
for key in bert_task_acc_keys:
if key in result.keys():
logger.info("Finally Eval {}:{}".format(
key, result[key]))
acc = result[key]
break
return acc
model = model_class.from_pretrained(checkpoint,
force_download=True,
mix_qkv=True)
model.to(args.device)
dataset = load_and_cache_examples(args,
tokenizer,
evaluate=True,
output_examples=False)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(
1, args.n_gpu)
eval_task = "squad"
from lpot import Quantization
quantizer = Quantization("./conf.yaml")
dataset = quantizer.dataset('bert',
dataset=dataset,
task=eval_task,
model_type=args.model_type)
test_dataloader = quantizer.dataloader(
dataset, batch_size=args.eval_batch_size)
quantizer(model, test_dataloader, eval_func=eval_func_for_lpot)
exit(0)
if args.benchmark or args.accuracy_only:
model = model_class.from_pretrained(checkpoint, mix_qkv=True)
model.to(args.device)
if args.int8:
from lpot.utils.pytorch import load
new_model = load(
os.path.abspath(
os.path.expanduser(args.tuned_checkpoint)), model)
else:
new_model = model
result, _ = evaluate(args,
new_model,
tokenizer,
prefix=global_step)
exit(0)
if args.do_calibration:
model = model_class.from_pretrained(checkpoint,
force_download=True,
mix_qkv=True)
model.to(args.device)
model.qconfig = default_per_channel_qconfig
propagate_qconfig_(model)
add_observer_(model)
# Evaluate
evaluate(args,
model,
tokenizer,
prefix=global_step,
calibration=True)
convert(model, inplace=True)
quantized_model_path = "squad" + str(
global_step) + "_quantized_model"
if not os.path.exists(quantized_model_path):
os.makedirs(quantized_model_path)
model.save_pretrained(quantized_model_path)
result, _ = evaluate(args,
model,
tokenizer,
prefix=global_step)
result = dict(
(k + ('_{}'.format(global_step) if global_step else ''), v)
for k, v in result.items())
results.update(result)
if args.do_int8_inference:
model = model_class.from_pretrained(checkpoint,
force_download=True,
mix_qkv=True)
model.to(args.device)
model.qconfig = default_per_channel_qconfig
propagate_qconfig_(model)
add_observer_(model)
convert(model, inplace=True)
quantized_model_path = "squad" + str(
global_step) + "_quantized_model"
if not os.path.exists(quantized_model_path):
logger.info("Please run calibration first!")
return
model_bin_file = os.path.join(quantized_model_path,
"pytorch_model.bin")
state_dict = torch.load(model_bin_file)
model.load_state_dict(state_dict)
print(model)
with torch.autograd.profiler.profile() as prof:
result, _ = evaluate(args,
model,
tokenizer,
prefix=global_step)
print(prof.key_averages().table(sort_by="cpu_time_total"))
result = dict(
(k + ('_{}'.format(global_step) if global_step else ''), v)
for k, v in result.items())
results.update(result)
logger.info("Results: {}".format(results))
return results
else:
# TODO: wait scalar support in dummy dataset
inputs_shape.append((1,))
inputs_dtype.append('bool')
logger.info("***** Final benchmark input name: {}, shape: {}".format( \
model_detail['input'].keys(), inputs_shape))
logger.info("***** Final benchmark output name: {}".format(model_detail['output']))
batch_size = inputs_shape[0][0]
if args.tune:
# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from lpot import Quantization
from lpot.adaptor.tf_utils.util import write_graph
inputs = model_detail['input']
outputs = model_detail['output']
_write_inputs_outputs_to_yaml(args.yaml, "./config_tmp.yaml", list(inputs.keys()), outputs)
quantizer = Quantization("./config_tmp.yaml")
# generate dummy data
dataset = quantizer.dataset(dataset_type='dummy', shape=inputs_shape,
low=1.0, high=20.0, dtype=inputs_dtype, label=True)
data_loader = quantizer.dataloader(dataset=dataset,
batch_size=batch_size,
collate_fn=oob_collate_data_func)
q_model = quantizer(args.model_path, q_dataloader=data_loader)
write_graph(q_model.as_graph_def(), args.output_path)
else:
run_benchmark(model_detail, num_iter, num_warmup,
disable_optimize, batch_size, args.timeline)
def main():
parser = argparse.ArgumentParser()
## Required parameters
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_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: " +
", ".join(MODEL_CLASSES.keys()))
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name 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."
)
## 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(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument(
"--evaluate_during_training",
action='store_true',
help="Rul evaluation during training at each logging step.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--per_gpu_train_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--per_gpu_eval_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for evaluation.")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay",
default=0.0,
type=float,
help="Weight deay if we apply some.")
parser.add_argument("--adam_epsilon",
default=1e-8,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm",
default=1.0,
type=float,
help="Max gradient norm.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help=
"If > 0: set total number of training steps to perform. Override num_train_epochs."
)
parser.add_argument("--warmup_steps",
default=0,
type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument('--logging_steps',
type=int,
default=50,
help="Log every X updates steps.")
parser.add_argument('--save_steps',
type=int,
default=50,
help="Save checkpoint every X updates steps.")
parser.add_argument(
"--eval_all_checkpoints",
action='store_true',
help=
"Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"
)
parser.add_argument("--no_cuda",
action='store_true',
help="Avoid using CUDA when available")
parser.add_argument("--mkldnn_eval",
action='store_true',
help="evaluation with MKLDNN")
parser.add_argument("--mkldnn_train",
action='store_true',
help="training with MKLDNN")
parser.add_argument('--overwrite_output_dir',
action='store_true',
help="Overwrite the content of the output directory")
parser.add_argument(
'--overwrite_cache',
action='store_true',
help="Overwrite the cached training and evaluation sets")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--fp16',
action='store_true',
help=
"Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"
)
parser.add_argument(
'--fp16_opt_level',
type=str,
default='O1',
help=
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="For distributed training: local_rank")
parser.add_argument('--server_ip',
type=str,
default='',
help="For distant debugging.")
parser.add_argument('--server_port',
type=str,
default='',
help="For distant debugging.")
parser.add_argument("--do_fp32_inference",
action='store_true',
help="Whether to run fp32 inference.")
parser.add_argument("--do_calibration",
action='store_true',
help="Whether to do calibration.")
parser.add_argument("--do_int8_inference",
action='store_true',
help="Whether to run int8 inference.")
parser.add_argument("--do_bf16",
action='store_true',
help="run bf16 evaluation / training.")
parser.add_argument("--tune",
action='store_true',
help="run lpot to tune int8 acc.")
parser.add_argument("--warmup",
type=int,
default=2,
help="warmup for performance")
args = parser.parse_args()
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir
) and args.do_train and not args.overwrite_output_dir:
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome."
.format(args.output_dir))
# Setup distant debugging if needed
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port),
redirect_output=True)
ptvsd.wait_for_attach()
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend='nccl')
args.n_gpu = 1
args.device = device
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank, device, args.n_gpu, bool(args.local_rank != -1),
args.fp16)
# Set seed
set_seed(args)
# Prepare GLUE task
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()
num_labels = len(label_list)
mix_qkv = False
if args.do_calibration or args.do_int8_inference or args.tune:
mix_qkv = True
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
args.model_type = args.model_type.lower()
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_or_path,
num_labels=num_labels,
finetuning_task=args.task_name,
cache_dir=args.cache_dir if args.cache_dir else None)
tokenizer = tokenizer_class.from_pretrained(
args.tokenizer_name
if args.tokenizer_name else args.model_name_or_path,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None)
model = model_class.from_pretrained(
args.model_name_or_path,
from_tf=bool('.ckpt' in args.model_name_or_path),
config=config,
mix_qkv=mix_qkv,
bf16=args.do_bf16,
mkldnn_train=args.mkldnn_train,
cache_dir=args.cache_dir if args.cache_dir else None)
if args.local_rank == 0:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args,
args.task_name,
tokenizer,
evaluate=False)
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
logger.info(" global_step = %s, average loss = %s", global_step,
tr_loss)
# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
if args.do_train and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
# Create output directory if needed
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = model.module if hasattr(
model,
'module') else model # Take care of distributed/parallel training
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
# Load a trained model and vocabulary that you have fine-tuned
model = model_class.from_pretrained(args.output_dir)
tokenizer = tokenizer_class.from_pretrained(args.output_dir)
model.to(args.device)
# Evaluation
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
tokenizer = tokenizer_class.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
checkpoints = [args.output_dir]
if args.eval_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME,
recursive=True)))
logging.getLogger("transformers.modeling_utils").setLevel(
logging.WARN) # Reduce logging
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
global_step = checkpoint.split(
'-')[-1] if len(checkpoints) > 1 else ""
prefix = checkpoint.split(
'/')[-1] if checkpoint.find('checkpoint') != -1 else ""
logger.info("Evaluate:" + args.task_name)
if args.mkldnn_eval or args.do_fp32_inference or args.do_bf16:
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
result = evaluate(args, model, tokenizer, prefix=prefix)
result = dict((k + '_{}'.format(global_step), v)
for k, v in result.items())
results.update(result)
if args.tune:
def eval_func_for_lpot(model):
result, perf = evaluate(args,
model,
tokenizer,
prefix=prefix)
bert_task_acc_keys = [
'acc_and_f1', 'f1', 'mcc', 'spearmanr', 'acc'
]
for key in bert_task_acc_keys:
if key in result.keys():
logger.info("Finally Eval {}:{}".format(
key, result[key]))
acc = result[key]
break
return acc
model = model_class.from_pretrained(checkpoint, mix_qkv=True)
model.to(args.device)
eval_task_names = (
"mnli", "mnli-mm") if args.task_name == "mnli" else (
args.task_name, )
for eval_task in eval_task_names:
eval_dataset = load_and_cache_examples(args,
eval_task,
tokenizer,
evaluate=True)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(
1, args.n_gpu)
# multi-gpu eval
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
if args.mkldnn_eval:
from torch.utils import mkldnn as mkldnn_utils
model = mkldnn_utils.to_mkldnn(model)
print(model)
from lpot import Quantization
quantizer = Quantization("./conf.yaml")
if eval_task != "squad":
eval_task = 'classifier'
eval_dataset = quantizer.dataset(
'bert',
dataset=eval_dataset,
task=eval_task,
model_type=args.model_type)
test_dataloader = quantizer.dataloader(
eval_dataset, batch_size=args.eval_batch_size)
quantizer(model,
test_dataloader,
eval_func=eval_func_for_lpot)
exit(0)
if args.do_calibration:
model = model_class.from_pretrained(checkpoint, mix_qkv=True)
model.to(args.device)
model.qconfig = default_per_channel_qconfig
fallback_layers = {}
if args.model_name_or_path == "bert-base-uncased" and args.task_name == "mrpc":
fallback_layers = {"bert.encoder.layer.9.output.dense."}
propagate_qconfig_(model)
fallback_layer(model,
layer_name="",
exculde_layers=fallback_layers)
add_observer_(model)
result, _ = evaluate(args,
model,
tokenizer,
prefix=global_step,
calibration=True)
convert(model, inplace=True)
quantized_model_path = args.task_name + "_quantized_model"
if not os.path.exists(quantized_model_path):
os.makedirs(quantized_model_path)
model.save_pretrained(quantized_model_path)
print(model)
result, _ = evaluate(args, model, tokenizer, prefix=prefix)
if args.do_int8_inference:
model = model_class.from_pretrained(checkpoint, mix_qkv=True)
model.to(args.device)
model.qconfig = default_per_channel_qconfig
fallback_layers = {}
if args.model_name_or_path == "bert-base-uncased" and args.task_name == "mrpc":
fallback_layers = {"bert.encoder.layer.9.output.dense."}
propagate_qconfig_(model)
fallback_layer(model,
layer_name="",
exculde_layers=fallback_layers)
add_observer_(model)
convert(model, inplace=True)
quantized_model_path = args.task_name + "_quantized_model"
if not os.path.exists(quantized_model_path):
logger.error(
"please do calibrantion befor run int8 inference")
return
prepare(model, inplace=True)
convert(model, inplace=True)
model_bin_file = os.path.join(quantized_model_path,
"pytorch_model.bin")
state_dict = torch.load(model_bin_file)
model.load_state_dict(state_dict)
result, _ = evaluate(args, model, tokenizer, prefix=prefix)
return results
arg_parser.add_argument('-w',
"--warmup_iter",
help='For benchmark measurement only.',
dest='warmup_iter',
default=200,
type=int)
arg_parser.add_argument('--config', type=str, default='')
arg_parser.add_argument('--output_model', type=str, default='')
arg_parser.add_argument('--tune', action='store_true', default=False)
arg_parser.add_argument('--benchmark',
dest='benchmark',
action='store_true',
help='run benchmark')
args = arg_parser.parse_args()
infer = model_infer(args)
if args.tune:
quantizer = Quantization(args.config)
q_dataloader = quantizer.dataloader(infer, args.batch_size)
output_graph = quantizer(args.input_graph,
q_dataloader=q_dataloader,
eval_func=infer.accuracy_check)
try:
write_graph(output_graph.as_graph_def(), args.output_model)
except Exception as e:
logging.getLogger().info("Failed to save model due to {}".format(
str(e)))
else:
infer.run()
def test_fold_pad_conv3(self):
tf.compat.v1.disable_eager_execution()
tf.compat.v1.reset_default_graph()
x = tf.compat.v1.placeholder(tf.float32, [1, 56, 56, 16], name="input")
x = tf.nn.relu(x)
xw = tf.constant(np.random.random((2, 2, 16, 16)),
dtype=tf.float32,
name='y')
x = tf.nn.conv2d(input=x,
filters=xw,
strides=[1, 1, 1, 1],
padding='VALID')
y = tf.constant(np.random.random((1, 55, 55, 16)),
dtype=tf.float32,
name='y')
z = tf.math.add(x, y, name='add')
conv_weights = tf.compat.v1.get_variable(
"weight", [3, 3, 16, 16],
initializer=tf.compat.v1.random_normal_initializer())
conv = tf.nn.conv2d(z,
conv_weights,
strides=[1, 2, 2, 1],
padding="VALID")
normed = tf.compat.v1.layers.batch_normalization(conv)
relu = tf.nn.relu(normed)
conv_weights2 = tf.compat.v1.get_variable(
"weight2", [3, 3, 16, 16],
initializer=tf.compat.v1.random_normal_initializer())
conv2 = tf.nn.conv2d(z,
conv_weights2,
strides=[1, 2, 2, 1],
padding="VALID")
normed2 = tf.compat.v1.layers.batch_normalization(conv2)
relu2 = tf.nn.relu(normed2)
add = tf.math.add(relu, relu2, name='op_to_store')
out_name = add.name.split(':')[0]
with tf.compat.v1.Session() as sess:
sess.run(tf.compat.v1.global_variables_initializer())
output_graph_def = graph_util.convert_variables_to_constants(
sess=sess,
input_graph_def=sess.graph_def,
output_node_names=[out_name])
from lpot import Quantization
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy',
shape=(100, 56, 56, 16),
label=True)
dataloader = quantizer.dataloader(dataset)
output_graph = quantizer(output_graph_def,
q_dataloader=dataloader,
eval_dataloader=dataloader)
quantize_v2_count = 0
for i in output_graph.as_graph_def().node:
if i.op == 'QuantizeV2':
quantize_v2_count += 1
if self.disable_s8:
self.assertEqual(quantize_v2_count, 1)
else:
self.assertEqual(quantize_v2_count, 2)
