def test_weight_only_activation_only_fakequant(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(67)
calib_data = torch.rand(2048, 3, 15, 15, dtype=torch.float32)
eval_data = torch.rand(10, 3, 15, 15, dtype=torch.float32)
qconfigset = set([torch.quantization.default_weight_only_quant_qconfig,
torch.quantization.default_activation_only_quant_qconfig])
SQNRTarget = [35, 45]
for idx, qconfig in enumerate(qconfigset):
my_model = ModelMultipleOpsNoAvgPool().to(torch.float32)
my_model.eval()
out_ref = my_model(eval_data)
fq_model = torch.quantization.QuantWrapper(my_model)
fq_model.train()
fq_model.qconfig = qconfig
torch.quantization.fuse_modules(fq_model.module, [['conv1', 'bn1', 'relu1']], inplace=True)
torch.quantization.prepare_qat(fq_model)
fq_model.eval()
fq_model.apply(torch.quantization.disable_fake_quant)
fq_model.apply(torch.nn.intrinsic.qat.freeze_bn_stats)
fq_model(calib_data)
fq_model.apply(torch.quantization.enable_fake_quant)
fq_model.apply(torch.quantization.disable_observer)
out_fq = fq_model(eval_data)
SQNRdB = 20 * torch.log10(torch.norm(out_ref) / torch.norm(out_ref - out_fq))
self.assertGreater(SQNRdB, SQNRTarget[idx], msg='Quantized model numerics diverge from float')
def test_fake_quant_true_quant_compare(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(67)
my_model = ModelMultipleOpsNoAvgPool().to(torch.float32)
calib_data = torch.rand(2048, 3, 15, 15, dtype=torch.float32)
eval_data = torch.rand(10, 3, 15, 15, dtype=torch.float32)
my_model.eval()
out_ref = my_model(eval_data)
fq_model = torch.quantization.QuantWrapper(my_model)
fq_model.train()
fq_model.qconfig = torch.quantization.default_qat_qconfig
torch.quantization.fuse_modules(fq_model.module, [['conv1', 'bn1', 'relu1']], inplace=True)
torch.quantization.prepare_qat(fq_model)
fq_model.eval()
fq_model.apply(torch.quantization.disable_fake_quant)
fq_model.apply(torch.nn.intrinsic.qat.freeze_bn_stats)
fq_model(calib_data)
fq_model.apply(torch.quantization.enable_fake_quant)
fq_model.apply(torch.quantization.disable_observer)
out_fq = fq_model(eval_data)
SQNRdB = 20 * torch.log10(torch.norm(out_ref) / torch.norm(out_ref - out_fq))
# 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')
torch.quantization.convert(fq_model)
out_q = fq_model(eval_data)
SQNRdB = 20 * torch.log10(torch.norm(out_fq) / (torch.norm(out_fq - out_q) + 1e-10))
self.assertGreater(SQNRdB, 60, msg='Fake quant and true quant numerics diverge, expect SQNR > 60 dB')
def test_weight_only_activation_only_fakequant(self):
torch.manual_seed(67)
calib_data = torch.rand(2048, 3, 15, 15, dtype=torch.float32)
eval_data = torch.rand(10, 3, 15, 15, dtype=torch.float32)
qconfigset = set([
torch.quantization.default_weight_only_quant_qconfig,
torch.quantization.default_activation_only_quant_qconfig
])
SQNRTarget = [35, 45]
for idx, qconfig in enumerate(qconfigset):
myModel = ModelMultipleOpsNoAvgPool().to(torch.float32)
myModel.eval()
out_ref = myModel(eval_data)
fqModel = torch.quantization.QuantWrapper(myModel)
fqModel.train()
fqModel.qconfig = qconfig
torch.quantization.fuse_modules(fqModel.module,
[['conv1', 'bn1', 'relu1']])
torch.quantization.prepare_qat(fqModel)
fqModel.eval()
fqModel.apply(torch.quantization.disable_fake_quant)
fqModel.apply(torch.nn._intrinsic.qat.freeze_bn_stats)
fqModel(calib_data)
fqModel.apply(torch.quantization.enable_fake_quant)
fqModel.apply(torch.quantization.disable_observer)
out_fq = fqModel(eval_data)
SQNRdB = 20 * torch.log10(
torch.norm(out_ref) / torch.norm(out_ref - out_fq))
self.assertGreater(
SQNRdB,
SQNRTarget[idx],
msg='Quantized model numerics diverge from float')
def test_fake_quant_true_quant_compare(self):
torch.manual_seed(67)
myModel = ModelMultipleOpsNoAvgPool().to(torch.float32)
calib_data = torch.rand(2048, 3, 15, 15, dtype=torch.float32)
eval_data = torch.rand(10, 3, 15, 15, dtype=torch.float32)
myModel.eval()
out_ref = myModel(eval_data)
fqModel = torch.quantization.QuantWrapper(myModel)
fqModel.train()
fqModel.qconfig = torch.quantization.default_qat_qconfig
torch.quantization.fuse_modules(fqModel.module,
[['conv1', 'bn1', 'relu1']])
torch.quantization.prepare_qat(fqModel)
fqModel.eval()
fqModel.apply(torch.quantization.disable_fake_quant)
fqModel.apply(torch.nn._intrinsic.qat.freeze_bn_stats)
fqModel(calib_data)
fqModel.apply(torch.quantization.enable_fake_quant)
fqModel.apply(torch.quantization.disable_observer)
out_fq = fqModel(eval_data)
SQNRdB = 20 * torch.log10(
torch.norm(out_ref) / torch.norm(out_ref - out_fq))
# 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')
torch.quantization.convert(fqModel)
out_q = fqModel(eval_data)
SQNRdB = 20 * torch.log10(
torch.norm(out_fq) / (torch.norm(out_fq - out_q) + 1e-10))
self.assertGreater(
SQNRdB,
60,
msg=
'Fake quant and true quant numerics diverge, expect SQNR > 60 dB')