def saveDQ(self, input_sample=None):
if self.ts is None:
self.export(input_sample)
LOG.logI("Pytorch model dynamic quantize starting, will save model in {}".format(self.dq_output_file))
quantized_model = quantize_dynamic_jit(self.ts, self.d_qconfig_dict)
torch.jit.save(quantized_model, self.dq_output_file)
LOG.logI("Pytorch model dynamic quantize succeeded, saved model in {}".format(self.dq_output_file))
def checkGraphModeOp(self,
module,
data,
quantized_op,
tracing=False,
debug=False,
check=True,
eval_mode=True,
dynamic=False):
if debug:
print('Testing:', str(module))
qconfig_dict = {
'': get_default_qconfig(torch.backends.quantized.engine)
}
if eval_mode:
module = module.eval()
if dynamic:
qconfig_dict = {'': default_dynamic_qconfig}
inputs = data
else:
*inputs, target = data[0]
model = get_script_module(module, tracing, inputs).eval()
if debug:
print('input graph:', model.graph)
models = {}
outputs = {}
for d in [True, False]:
# TODO: _test_only_eval_fn --> default_eval_fn
if dynamic:
models[d] = quantize_dynamic_jit(model, qconfig_dict, debug=d)
# make sure it runs
outputs[d] = models[d](inputs)
else:
# module under test can contain in-place ops, and we depend on
# input data staying constant for comparisons
data_copy = copy.deepcopy(data)
models[d] = quantize_jit(model,
qconfig_dict,
test_only_eval_fn, [data_copy],
inplace=False,
debug=d)
# make sure it runs
outputs[d] = models[d](*inputs)
if debug:
print('debug graph:', models[True].graph)
print('non debug graph:', models[False].graph)
if check:
# debug and non-debug option should have the same numerics
self.assertEqual(outputs[True], outputs[False])
# non debug graph should produce quantized op
FileCheck().check(quantized_op) \
.run(models[False].graph)
return models[False]
def saveDQ(self):
LOG.logI(
"Pytorch model dynamic quantize starting, will save model in {}".
format(self.dq_output_file))
with torch.no_grad():
quantized_model = quantize_dynamic_jit(self._jit(),
self.d_qconfig_dict)
torch.jit.save(quantized_model, self.dq_output_file)
LOG.logI("Pytorch model dynamic quantize succeeded, saved model in {}".
format(self.dq_output_file))