def test_compare_model_outputs_functional_static(self):
r"""Compare the output of functional layer in static quantized model and corresponding
output of conv layer in float model
"""
qengine = torch.backends.quantized.engine
model = ModelWithFunctionals().eval()
model.qconfig = torch.quantization.get_default_qconfig("fbgemm")
q_model = prepare(model, inplace=False)
q_model(self.img_data_2d[0][0])
q_model = convert(q_model)
act_compare_dict = compare_model_outputs(model, q_model,
self.img_data_2d[0][0])
self.assertEqual(len(act_compare_dict), 7)
expected_act_compare_dict_keys = {
"mycat.stats",
"myadd.stats",
"mymul.stats",
"myadd_relu.stats",
"my_scalar_add.stats",
"my_scalar_mul.stats",
"quant.stats",
}
self.assertTrue(
act_compare_dict.keys() == expected_act_compare_dict_keys)
for k, v in act_compare_dict.items():
self.assertTrue(v["float"].shape == v["quantized"].shape)
def compare_and_validate_results(float_model, q_model, data):
act_compare_dict = compare_model_outputs(float_model, q_model,
data)
self.assertEqual(len(act_compare_dict), 2)
expected_act_compare_dict_keys = {"conv.stats", "quant.stats"}
self.assertTrue(
act_compare_dict.keys() == expected_act_compare_dict_keys)
for k, v in act_compare_dict.items():
self.assertTrue(v["float"].shape == v["quantized"].shape)
def compare_and_validate_results(float_model, q_model, input, hidden):
act_compare_dict = compare_model_outputs(float_model, q_model,
input, hidden)
expected_act_compare_dict_keys = {"lstm.stats"}
self.assertTrue(
act_compare_dict.keys() == expected_act_compare_dict_keys)
for k, v in act_compare_dict.items():
self.assertTrue(v["float"][0].shape == v["quantized"][0].shape)
def test_compare_model_outputs(self):
r"""Compare the output of conv layer in quantized model and corresponding
output of conv layer in float model
"""
def compare_and_validate_results(float_model, q_model, data):
act_compare_dict = compare_model_outputs(float_model, q_model,
data)
self.assertEqual(len(act_compare_dict), 2)
expected_act_compare_dict_keys = {"conv.stats", "quant.stats"}
self.assertTrue(
act_compare_dict.keys() == expected_act_compare_dict_keys)
for k, v in act_compare_dict.items():
self.assertTrue(v["float"].shape == v["quantized"].shape)
for qengine in supported_qengines:
with override_quantized_engine(qengine):
model_list = [
AnnotatedConvModel(qengine),
AnnotatedConvBnReLUModel(qengine),
]
data = self.img_data[0][0]
module_swap_list = [
nn.Conv2d, nn.intrinsic.modules.fused.ConvReLU2d
]
for model in model_list:
model.eval()
if hasattr(model, "fuse_model"):
model.fuse_model()
q_model = quantize(model, default_eval_fn, self.img_data)
compare_and_validate_results(model, q_model, data)
# Test functionals
model = ModelWithFunctionals().eval()
model.qconfig = torch.quantization.get_default_qconfig(
"fbgemm")
q_model = prepare(model, inplace=False)
q_model(data)
q_model = convert(q_model)
act_compare_dict = compare_model_outputs(model, q_model, data)
self.assertEqual(len(act_compare_dict), 7)
expected_act_compare_dict_keys = {
"mycat.stats",
"myadd.stats",
"mymul.stats",
"myadd_relu.stats",
"my_scalar_add.stats",
"my_scalar_mul.stats",
"quant.stats",
}
self.assertTrue(
act_compare_dict.keys() == expected_act_compare_dict_keys)
for k, v in act_compare_dict.items():
self.assertTrue(v["float"].shape == v["quantized"].shape)
def compare_and_validate_results(float_model, q_model, data):
act_compare_dict = compare_model_outputs(float_model, q_model,
data)
expected_act_compare_dict_keys = {"fc1.stats"}
self.assertTrue(
act_compare_dict.keys() == expected_act_compare_dict_keys)
for k, v in act_compare_dict.items():
self.assertTrue(len(v["float"]) == len(v["quantized"]))
for i, val in enumerate(v["quantized"]):
self.assertTrue(
v["float"][i].shape == v["quantized"][i].shape)
def compare_and_validate_results(float_model, q_model, input, hidden):
act_compare_dict = compare_model_outputs(float_model, q_model,
input, hidden)
expected_act_compare_dict_keys = {"lstm.stats"}
self.assertTrue(
act_compare_dict.keys() == expected_act_compare_dict_keys)
for k, v in act_compare_dict.items():
self.assertTrue(len(v["float"]) == len(v["quantized"]))
for i, val in enumerate(v["quantized"]):
self.assertTrue(
len(v["float"][i]) == len(v["quantized"][i]))
if i == 0:
self.assertTrue(v["float"][i][0].shape ==
v["quantized"][i][0].shape)
else:
self.assertTrue(v["float"][i][0].shape ==
v["quantized"][i][0].shape)
self.assertTrue(v["float"][i][1].shape ==
v["quantized"][i][1].shape)
##############################################################################
#
# 2. Compare float point and quantized models at corresponding locations
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# The second tool allows for comparison of weights and activations between float and quantized models at corresponding locations for the same input as shown in the figure below. Red arrows indicate the locations of the comparison.
#
# .. figure:: /_static/img/compare_output.png
#
# We call ``compare_model_outputs()`` from PyTorch Numeric Suite to get the activations in float model and quantized model at corresponding locations for the given input data. This API returns a dict with module names being keys. Each entry is itself a dict with two keys 'float' and 'quantized' containing the activations.
data = img_data[0][0]
# Take in floating point and quantized model as well as input data, and returns a dict, with keys
# corresponding to the quantized module names and each entry being a dictionary with two keys 'float' and
# 'quantized', containing the activations of floating point and quantized model at matching locations.
act_compare_dict = ns.compare_model_outputs(float_model, qmodel, data)
print('keys of act_compare_dict:')
print(act_compare_dict.keys())
print("\nkeys of act_compare_dict entry for conv1's output:")
print(act_compare_dict['conv1.stats'].keys())
print(act_compare_dict['conv1.stats']['float'][0].shape)
print(act_compare_dict['conv1.stats']['quantized'][0].shape)
##############################################################################
# This dict can be used to compare and compute the quantization error of the activations of float and quantized models as following.
for key in act_compare_dict:
print(key, compute_error(act_compare_dict[key]['float'][0], act_compare_dict[key]['quantized'][0].dequantize()))
##############################################################################