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# take a trained pytorch model and convert it
import os
import sys
sys.path.append(os.path.dirname(os.getcwd()) + "/python_lib")
print( os.path.dirname(os.getcwd()) + "/python_lib" )
import midigpt
import time
import json
import numpy as np
import torch
import torch.quantization
from transformers import GPT2LMHeadModel, GPT2Config
from transformers.modeling_utils import Conv1D
from custom_models import *
from torch import nn
class QuantWrapper(nn.Module):
def __init__(self, module):
super(QuantWrapper, self).__init__()
qconfig = module.qconfig if hasattr(module, 'qconfig') else None
self.add_module('quant', torch.quantization.QuantStub(qconfig))
self.add_module('dequant', torch.quantization.DeQuantStub())
self.add_module('module', module)
self.train(module.training)
def forward(self, X, P):
X = self.quant(X)
P = self.quant(P)
O = self.module(X,P)
return self.dequant(O)
def _conv1d_to_linear(module):
in_size, out_size = module.weight.shape
linear = torch.nn.Linear(in_size, out_size)
linear.weight.data = module.weight.data.T.contiguous()
linear.bias.data = module.bias.data
return linear
def conv1d_to_linear(model):
for name in list(model._modules):
module = model._modules[name]
if isinstance(module, Conv1D):
linear = _conv1d_to_linear(module)
model._modules[name] = linear
else:
conv1d_to_linear(module)
def score_model(model):
targets = np.load("target.npz")["data"]
def time_model(model):
start = time.time()
pkv = None
for _ in range(1000):
input_ids = torch.ones(1,1).type(torch.LongTensor)
outputs = model(input_ids, past_key_values=pkv)
pkv = outputs[1]
print("BATCH TIME : {}".format(time.time() - start))
def print_size_of_model(model):
import os
torch.save(model.state_dict(), "temp.p")
print('Size (MB):', os.path.getsize("temp.p")/1e6)
os.remove('temp.p')
def quantize_model(model):
conv1d_to_linear(model)
model = torch.quantization.quantize_dynamic(
model, {torch.nn.Linear}, dtype=torch.qint8)
return model
def static_quantize_model(model):
conv1d_to_linear(model)
model.qconfig = torch.quantization.default_qconfig
torch.quantization.prepare(model, inplace=True)
torch.quantization.convert(model, inplace=True)
return model
def prune_model(model):
import torch.nn.utils.prune as prune
conv1d_to_linear(model)
parameters_to_prune = []
for _,module in model.named_modules():
if isinstance(module, torch.nn.Linear):
prune.l1_unstructured(module, name="weight", amount=.8)
prune.remove(module, "weight")
for _,submodule in module.named_modules():
if isinstance(submodule, torch.nn.Linear):
prune.l1_unstructured(submodule, name="weight", amount=.8)
prune.remove(submodule, "weight")
return model
def inject_metadata(path, metadata_path, encoder, new_state):
model = torch.jit.load(path)
with open(metadata_path, "r") as f:
metadata = json.load(f)
metadata["encoder"] = encoder
metadata["new_state"] = new_state
extra_files = torch._C.ExtraFilesMap()
extra_files['metadata.json'] = json.dumps(metadata)
out_path = os.path.splitext(path)[0] + "_WMETA.pt"
torch.jit.save(model, out_path, _extra_files=extra_files)
def convert(model, path, quantize=False, prune=False, force=False, control=False, ckpt_path=None, encoderX=None):
if not os.path.exists(path) or force:
model.eval()
if quantize:
model = quantize_model(model)
if prune:
model = prune_model(model)
print_size_of_model(model)
example_input = torch.zeros(1,300).type(torch.LongTensor)
example_control = torch.zeros(1,300,3).type(torch.FloatTensor)
if control:
outputs = model(input_ids=example_input, control_ids=example_control, past_key_values=None)
print(len(outputs[1]))
traced_script_module = torch.jit.trace(model, [example_input,example_control,outputs[1]])
else:
outputs = model(input_ids=example_input)
traced_script_module = torch.jit.trace(model, [example_input, outputs[1]])
num_layers = len(outputs[1])
_,num_heads,_,num_hidden = outputs[1][0][0].detach().numpy().shape
encoder = encoderX
model_metadata = {
"encoder" : encoder,
"num_heads" : num_heads,
"num_hidden" : num_hidden,
"num_layers" : num_layers,
"model_dim" : -1,
"new_state" : True
}
print(model_metadata)
extra_files = {}
extra_files['metadata.json'] = json.dumps(model_metadata)
torch.jit.save(
traced_script_module, path, _extra_files=extra_files)
class GPT2LMHeadModelWMeta(GPT2LMHeadModel):
def extra_repr(self):
return "trent is the man"
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--ckpt_path", type=str, required=True)
parser.add_argument("--output", type=str, default="")
parser.add_argument("--metadata_path", type=str, default="")
parser.add_argument("--config", type=str, default="")
parser.add_argument("--encoder", type=str, default="NONE")
parser.add_argument("--init", action="store_true")
parser.add_argument("--inject", action="store_true")
parser.add_argument("--new_state", action="store_true")
parser.add_argument("--quantize", action="store_true")
parser.add_argument("--prune", action="store_true")
parser.add_argument("--control", action="store_true")
args = parser.parse_args()
if args.inject:
assert len(args.metadata_path)
inject_metadata(
args.ckpt_path, args.metadata_path, args.encoder, True if args.new_state else False)
else:
assert len(args.output)
if args.init:
encoder_mode = midigpt.getEncoderType(args.encoder)
assert encoder_mode is not midigpt.ENCODER_TYPE.NO_ENCODER
encoder = midigpt.getEncoder(encoder_mode)
vocab_size = encoder.vocab_size()
if args.control:
config = GPT2LMHeadModelContConfig().from_json_file(args.config)
# encoder knows the size of the embedding
config.n_control_dim = encoder.config.embed_dim
model_cls = GPT2LMHeadModelCont
else:
config = GPT2Config().from_json_file(args.config)
config.vocab_size = vocab_size
model_cls = GPT2LMHeadModel
model = model_cls(config)
else:
if args.control:
model = GPT2LMHeadModelCont.from_pretrained(args.ckpt_path, torchscript=True)
else:
model = GPT2LMHeadModel.from_pretrained(args.ckpt_path, torchscript=True)
convert(model, args.output, quantize=args.quantize, prune=args.prune, control=args.control, ckpt_path=args.ckpt_path, encoderX=args.encoder)
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