def test_compare_model_outputs_lstm_dynamic_fx(self):
r"""Compare the output of LSTM layer in dynamic quantized model and corresponding
output of linear layer in float model
"""
qconfig_dict = {"object_type": [(nn.LSTM, default_dynamic_qconfig)]}
float_model = LSTMwithHiddenDynamicModel()
float_model.eval()
prepared_model = prepare_fx(float_model, qconfig_dict)
prepared_float_model = copy.deepcopy(prepared_model)
q_model = convert_fx(prepared_model)
lstm_input = torch.rand((1, 1, 2))
lstm_hidden = (torch.rand(1, 1, 2), torch.rand(1, 1, 2))
expected_act_compare_dict_keys = {
"x.stats", "hid.stats", "lstm_1.stats"
}
self.compare_and_validate_model_outputs_results_fx(
prepared_float_model,
q_model,
expected_act_compare_dict_keys,
lstm_input,
lstm_hidden,
)
def test_compare_model_stub_lstm_dynamic_fx(self):
r"""Compare the output of dynamic quantized linear layer and its float shadow module"""
qconfig_dict = {"object_type": [(nn.LSTM, default_dynamic_qconfig)]}
float_model = LSTMwithHiddenDynamicModel()
float_model.eval()
prepared_model = prepare_fx(float_model, qconfig_dict)
prepared_float_model = copy.deepcopy(prepared_model)
prepared_float_model.eval()
q_model = convert_fx(prepared_model)
module_swap_list = [nn.LSTM]
lstm_input = torch.rand((1, 1, 2))
lstm_hidden = (torch.rand(1, 1, 2), torch.rand(1, 1, 2))
expected_ob_dict_keys = {"lstm.stats"}
self.compare_and_validate_model_stub_results_fx(
prepared_float_model,
q_model,
module_swap_list,
expected_ob_dict_keys,
lstm_input,
lstm_hidden,
)
def test_compare_model_stub_lstm_dynamic(self):
r"""Compare the output of dynamic quantized LSTM layer and its float shadow module"""
qengine = torch.backends.quantized.engine
def compare_and_validate_results(float_model, q_model,
module_swap_list, input, hidden):
ob_dict = compare_model_stub(float_model, q_model,
module_swap_list, input, hidden)
self.assertEqual(len(ob_dict), 1)
for k, v in ob_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)
lstm_input = torch.rand((1, 1, 2))
lstm_hidden = (torch.rand(1, 1, 2), torch.rand(1, 1, 2))
model_list = [LSTMwithHiddenDynamicModel(qengine)]
module_swap_list = [nn.Linear, nn.LSTM]
for model in model_list:
model.eval()
if hasattr(model, "fuse_model"):
model.fuse_model()
q_model = quantize_dynamic(model)
compare_and_validate_results(model, q_model, module_swap_list,
lstm_input, lstm_hidden)
def test_compare_model_outputs_lstm_dynamic(self):
r"""Compare the output of LSTM layer in dynamic quantized model and corresponding
output of conv layer in float model
"""
qengine = torch.backends.quantized.engine
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)
lstm_input = torch.rand((1, 1, 2))
lstm_hidden = (torch.rand(1, 1, 2), torch.rand(1, 1, 2))
model_list = [LSTMwithHiddenDynamicModel(qengine)]
for model in model_list:
model.eval()
if hasattr(model, "fuse_model"):
model.fuse_model()
q_model = quantize_dynamic(model)
compare_and_validate_results(model, q_model, lstm_input,
lstm_hidden)
def test_compare_weights_lstm_dynamic_fx(self):
r"""Compare the weights of float and dynamic quantized lstm layer"""
qconfig_dict = {"object_type": [(nn.LSTM, default_dynamic_qconfig)]}
float_model = LSTMwithHiddenDynamicModel()
float_model.eval()
prepared_model = prepare_fx(float_model, qconfig_dict)
prepared_float_model = copy.deepcopy(prepared_model)
prepared_float_model.eval()
q_model = convert_fx(prepared_model)
expected_weight_dict_keys = {"lstm._all_weight_values.0.param"}
self.compare_and_validate_model_weights_results_fx(
prepared_float_model, q_model, expected_weight_dict_keys)
def test_compare_shadow_activations_lstm_dynamic(self):
qconfig_dict = {"object_type": [(nn.LSTM, default_dynamic_qconfig)]}
m = LSTMwithHiddenDynamicModel().eval()
lstm_input = torch.rand((1, 1, 2))
lstm_hidden = (torch.rand(1, 1, 2), torch.rand(1, 1, 2))
# TODO(future PR): enable scripting (quant prepared LSTM not scriptable)
res = self._test_match_shadow_activations(
m, (lstm_input, lstm_hidden), results_len=1, qconfig_dict=qconfig_dict,
skip_scripting=True)
def test_compare_weights_lstm_dynamic(self):
r"""Compare the weights of float and dynamic quantized LSTM layer
"""
qengine = torch.backends.quantized.engine
def compare_and_validate_results(float_model, q_model):
weight_dict = compare_weights(float_model.state_dict(),
q_model.state_dict())
self.assertEqual(len(weight_dict), 1)
for k, v in weight_dict.items():
self.assertTrue(v["float"].shape == v["quantized"].shape)
model_list = [LSTMwithHiddenDynamicModel(qengine)]
for model in model_list:
model.eval()
if hasattr(model, "fuse_model"):
model.fuse_model()
q_model = quantize_dynamic(model)
compare_and_validate_results(model, q_model)
def test_compare_weights_lstm_dynamic(self):
qconfig_dict = {"object_type": [(nn.LSTM, default_dynamic_qconfig)]}
m = LSTMwithHiddenDynamicModel().eval()
res = self._test_extract_weights(m,
results_len=1,
qconfig_dict=qconfig_dict)