def test_float_quant_compare(self):
torch.manual_seed(42)
myModel = ModelMultipleOps().to(torch.float32)
myModel.eval()
calib_data = torch.rand(1024, 3, 15, 15, dtype=torch.float32)
eval_data = torch.rand(1, 3, 15, 15, dtype=torch.float32)
out_ref = myModel(eval_data)
qModel = torch.quantization.QuantWrapper(myModel)
qModel.eval()
qModel.qconfig = torch.quantization.default_qconfig
torch.quantization.fuse_modules(qModel.module,
[['conv1', 'bn1', 'relu1']])
torch.quantization.prepare(qModel, inplace=True)
qModel(calib_data)
torch.quantization.convert(qModel, inplace=True)
out_q = qModel(eval_data)
SQNRdB = 20 * torch.log10(
torch.norm(out_ref) / torch.norm(out_ref - out_q))
# Quantized model output should be close to floating point model output numerically
# Setting target SQNR to be 30 dB so that relative error is 1e-3 below the desired
# output
self.assertGreater(
SQNRdB,
30,
msg=
'Quantized model numerics diverge from float, expect SQNR > 30 dB')
def test_float_quant_compare_per_tensor(self):
for qengine in ["fbgemm", "qnnpack"]:
if qengine not in torch.backends.quantized.supported_engines:
continue
if qengine == 'qnnpack':
if IS_PPC or TEST_WITH_UBSAN:
continue
with override_quantized_engine(qengine):
torch.manual_seed(42)
my_model = ModelMultipleOps().to(torch.float32)
my_model.eval()
calib_data = torch.rand(1024, 3, 15, 15, dtype=torch.float32)
eval_data = torch.rand(1, 3, 15, 15, dtype=torch.float32)
out_ref = my_model(eval_data)
qModel = torch.quantization.QuantWrapper(my_model)
qModel.eval()
qModel.qconfig = torch.quantization.default_qconfig
torch.quantization.fuse_modules(qModel.module, [['conv1', 'bn1', 'relu1']], inplace=True)
torch.quantization.prepare(qModel, inplace=True)
qModel(calib_data)
torch.quantization.convert(qModel, inplace=True)
out_q = qModel(eval_data)
SQNRdB = 20 * torch.log10(torch.norm(out_ref) / torch.norm(out_ref - out_q))
# Quantized model output should be close to floating point model output numerically
# Setting target SQNR to be 30 dB so that relative error is 1e-3 below the desired
# output
self.assertGreater(SQNRdB, 30, msg='Quantized model numerics diverge from float, expect SQNR > 30 dB')
def test_float_quant_compare_per_channel(self):
# Test for per-channel Quant
torch.manual_seed(67)
my_model = ModelMultipleOps().to(torch.float32)
my_model.eval()
calib_data = torch.rand(2048, 3, 15, 15, dtype=torch.float32)
eval_data = torch.rand(10, 3, 15, 15, dtype=torch.float32)
out_ref = my_model(eval_data)
q_model = torch.quantization.QuantWrapper(my_model)
q_model.eval()
q_model.qconfig = torch.quantization.default_per_channel_qconfig
torch.quantization.fuse_modules(q_model.module,
[['conv1', 'bn1', 'relu1']],
inplace=True)
torch.quantization.prepare(q_model)
q_model(calib_data)
torch.quantization.convert(q_model)
out_q = q_model(eval_data)
SQNRdB = 20 * torch.log10(
torch.norm(out_ref) / torch.norm(out_ref - out_q))
# Quantized model output should be close to floating point model output numerically
# Setting target SQNR to be 35 dB
self.assertGreater(
SQNRdB,
35,
msg=
'Quantized model numerics diverge from float, expect SQNR > 35 dB')