def validate_backward_pass(self, tc: TestCase):
original_model = copy.deepcopy(tc.model)
quant_op = QcQuantizeRecurrent(module_to_quantize=tc.model, weight_bw=8, activation_bw=8, is_symmetric=False,
quant_scheme=QuantScheme.post_training_tf_enhanced, round_mode='nearest')
encodings = libpymo.TfEncoding()
encodings.bw = 8
encodings.max = 3
encodings.min = -2
encodings.delta = 1
encodings.offset = 0.2
for input_quantizer in quant_op.input_quantizers.values():
input_quantizer.enabled = True
input_quantizer.encoding = encodings
for name, param in quant_op.named_parameters(recurse=False):
quant_op.param_quantizers[name].enabled = True
quant_op.param_quantizers[name].encoding = encodings
for output_quantizer in quant_op.output_quantizers.values():
output_quantizer.encoding = encodings
# Checking if param are matched
for name, param in original_model.named_parameters():
self.assertTrue(torch.allclose(param.data, getattr(quant_op, name).data, atol=1e-05))
inp = torch.rand(tc.input_shape, requires_grad=True).to(tc.device)
o_qc_rnn, _ = quant_op(inp, hx=None)
# Checking if param are matched
for name, param in original_model.named_parameters():
self.assertTrue(torch.allclose(param.data, getattr(quant_op, name).data, atol=1e-05))
optimizer = torch.optim.SGD(quant_op.parameters(), lr=0.05, momentum=0.5)
# creating a fake loss function with sum of output
loss = o_qc_rnn.flatten().sum()
loss.backward()
for name, param in quant_op.module_to_quantize.named_parameters():
self.assertTrue(param.grad is None)
self.assertTrue(getattr(quant_op, name).grad is not None)
optimizer.step()
for name, param in original_model.named_parameters():
# check if custom param have been updated
quant_param = getattr(quant_op, name)
self.assertFalse(torch.allclose(param.data, quant_param.data, atol=1e-05))
# check if 'replaced' module param are still the same
module_to_quantize_param = getattr(quant_op.module_to_quantize, name)
self.assertTrue(torch.allclose(param.data, module_to_quantize_param.data, atol=1e-05))
# updated the 'replaced' module and check for the reverse
quant_op.update_params()
for name, param in quant_op.module_to_quantize.named_parameters():
# check if 'replaced' module param have been updated
orig_param = getattr(original_model, name)
self.assertFalse(torch.allclose(param.data, orig_param.data, atol=1e-05))
# check if 'replaced' module param are matching the Custom Op
quant_param = getattr(quant_op, name)
self.assertTrue(torch.allclose(param.data, quant_param.data, atol=1e-05))
def create_encoding_from_dict(encoding_dict: dict) -> (libpymo.TfEncoding, bool):
"""
Create encoding object from encoding dictionary
:param encoding_dict: Dictionary containing encodings
:return: Encoding object, is_symmetric
"""
encoding = libpymo.TfEncoding()
encoding.bw = encoding_dict.get('bitwidth')
encoding.max = encoding_dict.get('max')
encoding.min = encoding_dict.get('min')
encoding.delta = encoding_dict.get('scale')
encoding.offset = encoding_dict.get('offset')
is_symmetric = eval(encoding_dict.get('is_symmetric')) # pylint: disable=eval-used
return encoding, is_symmetric
def __setstate__(self, state):
# Restore instance attributes
self.__dict__.update(state.dict)
# Create the c++ op
self._cppOp = AimetTensorQuantizer.AimetTensorQuantizer(
self.quant_scheme)
# Create the encoding object
if hasattr(state, 'min'):
self.encoding = libpymo.TfEncoding()
self.encoding.bw = state.bw
self.encoding.max = state.max
self.encoding.min = state.min
self.encoding.delta = state.delta
self.encoding.offset = state.offset
else:
self.encoding = None
def test_quantize_only_cpu(self):
""" Test tensor quantizer quantize only functionality """
post_training_tensor_quantizer = \
PostTrainingTensorQuantizer(bitwidth=8, round_mode='nearest',
quant_scheme=MAP_QUANT_SCHEME_TO_PYMO[QuantScheme.post_training_tf],
use_symmetric_encodings=False, enabled_by_default=True)
encodings = libpymo.TfEncoding()
encodings.bw = 8
encodings.max = 2.23
encodings.min = -5.19
post_training_tensor_quantizer.encoding = encodings
inp_tensor = torch.tensor([-7, -5, -3, 0, .1, 2.5])
quant_out = post_training_tensor_quantizer.quantize(
inp_tensor, MAP_ROUND_MODE_TO_PYMO['nearest'])
expected_out = torch.tensor([0, 6, 75, 178, 181, 255],
dtype=torch.float32)
self.assertTrue(torch.equal(quant_out, expected_out))
def test_quantsim_export(self):
torch.manual_seed(10)
model = Model2(Add())
dummy_input = torch.randn(5, 10, 10, 20)
sim = QuantizationSimModel(model, dummy_input)
encodings = libpymo.TfEncoding()
encodings.bw = 8
encodings.max = 5
encodings.min = -5
encodings.delta = 1
encodings.offset = 0.2
sim.model.op1.output_quantizer.encoding = encodings
sim.model.conv1.output_quantizer.encoding = encodings
sim.model.conv1.param_quantizers['weight'].encoding = encodings
sim.export(path='./data', filename_prefix='quant_model', dummy_input=dummy_input)
with open('./data/quant_model.encodings') as f:
data = json.load(f)
self.assertTrue(isinstance(data['activation_encodings']['3'], list))
self.assertTrue(isinstance(data['activation_encodings']['4'], list))
def get_encoding(self) -> libpymo.TfEncoding:
"""
Get encoding if valid else raise error
:return: encoding
"""
if self.is_encoding_valid():
encoding_min = self.get_variable_from_op(
QuantizeOpIndices.encoding_min)
encoding_max = self.get_variable_from_op(
QuantizeOpIndices.encoding_max)
bitwidth = self.bitwidth
# Create Encoding object
encoding = libpymo.TfEncoding()
encoding.min = encoding_min
encoding.max = encoding_max
encoding.bw = bitwidth
encoding.delta, encoding.offset = calculate_delta_offset(
encoding_min, encoding_max, bitwidth)
else:
raise AssertionError(
'Compute encoding or Set encoding must be invoked before')
return encoding
def validate_serialize_deserialize(self, tc: TestCase):
"""
helper method to run quant RNN test
"""
original_model = copy.deepcopy(tc.model)
quant_op = QcQuantizeRecurrent(module_to_quantize=tc.model, weight_bw=8, activation_bw=8, is_symmetric=False,
quant_scheme=QuantScheme.post_training_tf_enhanced, round_mode='nearest')
quant_op.eval()
inp = torch.rand(tc.input_shape, requires_grad=True).to(tc.device)
encodings = libpymo.TfEncoding()
encodings.bw = 8
encodings.max = 3
encodings.min = -2
encodings.delta = 1
encodings.offset = 0.2
for input_quantizer in quant_op.input_quantizers.values():
input_quantizer.enabled = True
input_quantizer.encoding = encodings
for name, param in quant_op.named_parameters(recurse=False):
quant_op.param_quantizers[name].enabled = True
quant_op.param_quantizers[name].encoding = encodings
for output_quantizer in quant_op.output_quantizers.values():
output_quantizer.encoding = encodings
o_qc_rnn, _ = quant_op(inp, hx=None)
optimizer = torch.optim.SGD(quant_op.parameters(), lr=0.05, momentum=0.5)
# creating a fake loss function with sum of output
loss = o_qc_rnn.flatten().sum()
loss.backward()
optimizer.step()
# Generate Quantize encodings
quant_op.compute_encoding()
quant_op.compute_weight_encodings()
o_pre, h_pre = quant_op(inp, hx=None)
# Save and loaded a quantized model
with tempfile.NamedTemporaryFile() as f:
torch.save(quant_op, f)
f.seek(0)
loaded_model = torch.load(f)
loaded_model.eval()
# compare the parameters
for name, param in quant_op.named_parameters(recurse=False):
loaded_param = getattr(loaded_model, name)
self.assertTrue(torch.equal(param, loaded_param),
msg="param mismatched recurrent op param mis-matched, TestCase:{}".format(tc.test_name))
for name, param in quant_op.module_to_quantize.named_parameters():
loaded_param = getattr(loaded_model.module_to_quantize, name)
self.assertTrue(torch.equal(param, loaded_param),
msg="original module mismatched, TestCase:{}".format(tc.test_name))
# compare the quantizers
for name, output_quantizer in quant_op.output_quantizers.items():
if output_quantizer.enabled:
self.compare_quantizer(output_quantizer, loaded_model.output_quantizers[name])
for name, quantizer in quant_op.param_quantizers.items():
if quantizer.enabled:
self.compare_quantizer(quantizer, loaded_model.param_quantizers[name])
# check if the loaded module generates the same output
o_post, h_post = loaded_model(inp, hx=None)
self.assertTrue(torch.equal(o_pre, o_post),
msg="output mismatched, Failed TestCase:{}".format(tc.test_name))
if isinstance(h_pre, tuple):
for pre, post in zip(h_pre, h_post):
self.assertTrue(torch.equal(pre, post),
msg="h or c mismatched, Failed TestCase:{}".format(tc.test_name))
else:
self.assertTrue(torch.equal(h_pre, h_post),
msg="h mis-matched, Failed TestCase:{}".format(tc.test_name))
def test_qc_post_training_wrapper(self):
torch.manual_seed(0)
encodings = libpymo.TfEncoding()
encodings.bw, encodings.max, encodings.min, encodings.delta, encodings.offset = 8, 0.5, -1, 1, 0.2
encodings_new = libpymo.TfEncoding()
encodings_new.bw, encodings_new.max, encodings_new.min, encodings_new.delta, encodings_new.offset = 8, 0.4, -0.98, 1, 0.2
output_grad = []
def hook_fn(m, _, i):
for grad in i:
try:
output_grad.append(grad)
except AttributeError:
print("None found for Gradient")
conv1 = torch.nn.Conv2d(1, 2, 1)
quantize = QcPostTrainingWrapper(
conv1,
weight_bw=8,
activation_bw=8,
round_mode='nearest',
quant_scheme=QuantScheme.post_training_tf_enhanced)
quantize.train()
quantize._module_to_wrap.register_backward_hook(hook_fn)
quantize.input_quantizer.enabled = True
quantize.output_quantizers[0].enabled = True
quantize.input_quantizer.encoding = encodings
quantize.output_quantizers[0].encoding = encodings
new_input = torch.autograd.Variable(torch.tensor([[[[0.6469]]],
[[[-0.9]]]]),
requires_grad=True)
quantize.set_mode(QcQuantizeOpMode.ACTIVE)
out = quantize(new_input)
quantize.input_quantizer.encoding = encodings_new
quantize.output_quantizers[0].encoding = encodings_new
quantize.param_quantizers['weight'].encoding = encodings_new
loss = out.flatten().sum()
loss.backward()
# Check if input gradient got clipped
for i, val in enumerate(new_input):
if encodings_new.min > val or val > encodings_new.max:
self.assertTrue(new_input.grad[0][i] == 0.0)
# Check if output gradient got clipped
output_grad = output_grad[0].flatten()
self.assertTrue(output_grad[0] == 1.0)
self.assertTrue(output_grad[1] == 1.0)
self.assertTrue(output_grad[2] == 1.0)
self.assertTrue(output_grad[3] == 0.0)
# Check if weight gradient got clipped
weight_tensor = quantize._module_to_wrap.weight.flatten()
weight_tensor_grad = quantize._module_to_wrap.weight.grad.flatten()
for i, val in enumerate(weight_tensor):
if encodings_new.min > val or val > encodings_new.max:
self.assertTrue(weight_tensor_grad[i] == 0.0)
