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ns for fx: move linear activation test case to new API (#53777)

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Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53777

Moves linear activation test case to new NS API

Test Plan:
```
python test/test_quantization.py TestFXNumericSuiteCoreAPIsModels.test_compare_activations_linear
```

Imported from OSS

Reviewed By: hx89

Differential Revision: D26967107

fbshipit-source-id: 83c4401b2bf79d15227b7fb3e59c54276ec5626b
This commit is contained in:
Vasiliy Kuznetsov 2021-03-12 09:53:02 -08:00 committed by Facebook GitHub Bot
parent 57bf13409a
commit 2912ad1324
1 changed files with 18 additions and 52 deletions
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70
test/quantization/test_numeric_suite_fx.py
View file

@ -216,57 +216,6 @@ class TestGraphModeNumericSuite(QuantizationTestCase):
v["float"][i][1].shape == v["quantized"][i][1].shape
)
@override_qengines
def test_compare_model_outputs_linear_static_fx(self):
r"""Compare the output of linear layer in static quantized model and corresponding
output of linear layer in float model
"""
qengine = torch.backends.quantized.engine
qconfig = get_default_qconfig(qengine)
qconfig_dict = {"": qconfig}
float_model = SingleLayerLinearModel()
float_model.eval()
prepared_model = prepare_fx(float_model, qconfig_dict)
prepared_float_model = copy.deepcopy(prepared_model)
# Run calibration
test_only_eval_fn(prepared_model, self.calib_data)
q_model = convert_fx(prepared_model)
linear_data = self.calib_data[0][0]
expected_act_compare_dict_keys = {"x.stats", "fc1.stats"}
self.compare_and_validate_model_outputs_results_fx(
prepared_float_model, q_model, expected_act_compare_dict_keys, linear_data
)
@override_qengines
def test_compare_model_outputs_linear_dynamic_fx(self):
r"""Compare the output of linear layer in dynamic quantized model and corresponding
output of linear layer in float model
"""
qconfig_dict = {"object_type": [(nn.Linear, default_dynamic_qconfig)]}
float_model = SingleLayerLinearDynamicModel()
float_model.eval()
prepared_model = prepare_fx(float_model, qconfig_dict)
prepared_float_model = copy.deepcopy(prepared_model)
q_model = convert_fx(prepared_model)
linear_data = self.calib_data[0][0]
expected_act_compare_dict_keys = {"x.stats", "fc1.stats"}
self.compare_and_validate_model_outputs_results_fx(
prepared_float_model, q_model, expected_act_compare_dict_keys, linear_data
)
@override_qengines
def test_compare_model_outputs_lstm_dynamic_fx(self):
r"""Compare the output of LSTM layer in dynamic quantized model and corresponding
@ -573,8 +522,11 @@ class FXNumericSuiteQuantizationTestCase(QuantizationTestCase):
def _test_match_activations(
self, m, data, prepared_expected_node_occurrence=None, results_len=0,
should_log_inputs=False,
qconfig_dict=None,
):
mp = prepare_fx(m, {'': torch.quantization.default_qconfig})
if qconfig_dict is None:
qconfig_dict = {'': torch.quantization.default_qconfig}
mp = prepare_fx(m, qconfig_dict)
mp(*data)
# TODO(future PR): prevent the need for copying here, we can copy the
# modules but should reuse the underlying tensors
@ -836,6 +788,20 @@ class TestFXNumericSuiteCoreAPIsModels(FXNumericSuiteQuantizationTestCase):
res = self._test_match_activations(
m, (torch.randn(1, 3, 4, 4),), results_len=1)
@skipIfNoFBGEMM
def test_compare_activations_linear(self):
test_cases = (
(SingleLayerLinearModel(), None),
(
SingleLayerLinearDynamicModel(),
{"object_type": [(nn.Linear, default_dynamic_qconfig)]},
),
)
for m, qconfig_dict in test_cases:
m.eval()
res = self._test_match_activations(
m, (torch.randn(5, 5),), results_len=1, qconfig_dict=qconfig_dict)
@skipIfNoFBGEMM
def test_sparsenn_compare_activations(self):
for should_log_inputs in (True, False):