PatchtTST and PatchTSMixer fixes (#28083)
* 🐛 fix .max bug * remove prediction_length from regression output dimensions * fix parameter names, fix output names, update tests * ensure shape for PatchTST * ensure output shape for PatchTSMixer * update model, batch, and expected for regression distribution test * update test expected Signed-off-by: Wesley M. Gifford <wmgifford@us.ibm.com> * Update tests/models/patchtst/test_modeling_patchtst.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update tests/models/patchtst/test_modeling_patchtst.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update tests/models/patchtst/test_modeling_patchtst.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/patchtsmixer/modeling_patchtsmixer.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update tests/models/patchtsmixer/test_modeling_patchtsmixer.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update tests/models/patchtsmixer/test_modeling_patchtsmixer.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * standardize on patch_length Signed-off-by: Wesley M. Gifford <wmgifford@us.ibm.com> * Update tests/models/patchtsmixer/test_modeling_patchtsmixer.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update tests/models/patchtsmixer/test_modeling_patchtsmixer.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Make arguments more explicit Signed-off-by: Wesley M. Gifford <wmgifford@us.ibm.com> * adjust prepared inputs Signed-off-by: Wesley M. Gifford <wmgifford@us.ibm.com> --------- Signed-off-by: Wesley M. Gifford <wmgifford@us.ibm.com> Co-authored-by: Wesley M. Gifford <wmgifford@us.ibm.com> Co-authored-by: Kashif Rasul <kashif.rasul@gmail.com> Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
Wesley Gifford
@@ -191,11 +191,8 @@ class PatchTSMixerModelTester:
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# [bs x context_length x n_vars]
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past_values = floats_tensor([self.batch_size, _past_length, self.num_input_channels])
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future_values = floats_tensor([self.batch_size, config.prediction_length, self.num_input_channels])
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inputs_dict = {
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"past_values": past_values,
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"future_values": future_values,
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}
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return inputs_dict
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@@ -256,21 +253,25 @@ class PatchTSMixerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.Test
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
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# if classification model:
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if model_class in get_values(MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING):
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if model_class == PatchTSMixerForPrediction:
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rng = random.Random(self.model_tester.seed_number)
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labels = floats_tensor(
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[
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self.model_tester.batch_size,
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self.model_tester.prediction_length,
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self.model_tester.num_input_channels,
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],
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rng=rng,
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)
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inputs_dict["future_values"] = labels
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elif model_class in get_values(MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING):
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rng = random.Random(self.model_tester.seed_number)
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labels = ids_tensor([self.model_tester.batch_size], self.model_tester.num_targets, rng=rng)
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# inputs_dict["labels"] = labels
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inputs_dict["future_values"] = labels
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# inputs_dict.pop("future_values")
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inputs_dict["target_values"] = labels
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elif model_class in get_values(MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING):
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rng = random.Random(self.model_tester.seed_number)
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labels = floats_tensor([self.model_tester.batch_size, self.model_tester.num_targets], rng=rng)
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# inputs_dict["labels"] = labels
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inputs_dict["future_values"] = labels
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# inputs_dict.pop("future_values")
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elif model_class in [PatchTSMixerModel, PatchTSMixerForPretraining]:
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inputs_dict.pop("future_values")
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inputs_dict["target_values"] = labels
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inputs_dict["output_hidden_states"] = True
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return inputs_dict
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@@ -409,28 +410,37 @@ class PatchTSMixerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.Test
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names_with_target = [
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"past_values",
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"observed_mask",
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"future_values",
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"output_hidden_states",
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"return_loss",
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]
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expected_arg_names_without_target = [
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"past_values",
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"observed_mask",
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"output_hidden_states",
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]
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expected_arg_names = expected_arg_names_with_target
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if model_class == PatchTSMixerForPretraining:
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expected_arg_names = expected_arg_names_without_target + ["return_loss"]
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if model_class == PatchTSMixerModel:
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expected_arg_names = expected_arg_names_without_target
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if model_class in get_values(MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING) or model_class in get_values(
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expected_arg_names = [
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"past_values",
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"observed_mask",
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"output_hidden_states",
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"return_loss",
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]
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elif model_class == PatchTSMixerModel:
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expected_arg_names = [
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"past_values",
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"observed_mask",
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"output_hidden_states",
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]
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elif model_class in get_values(MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING) or model_class in get_values(
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MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING
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):
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expected_arg_names.remove("observed_mask")
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expected_arg_names = [
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"past_values",
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"target_values",
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"output_hidden_states",
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"return_loss",
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]
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else:
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# PatchTSMixerForPrediction
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expected_arg_names = [
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"past_values",
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"observed_mask",
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"future_values",
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"output_hidden_states",
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"return_loss",
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]
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self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
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@@ -686,20 +696,27 @@ class PatchTSMixerFunctionalTests(unittest.TestCase):
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else:
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target_output = target_input
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ref_samples = target_output.unsqueeze(1).expand(-1, config.num_parallel_samples, -1, -1)
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ground_truth_arg = "future_values"
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output_predictions_arg = "prediction_outputs"
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elif task == "classification":
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mdl = PatchTSMixerForTimeSeriesClassification(config)
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target_input = self.__class__.correct_classification_classes
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target_output = self.__class__.correct_classification_output
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ground_truth_arg = "target_values"
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output_predictions_arg = "prediction_outputs"
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elif task == "regression":
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mdl = PatchTSMixerForRegression(config)
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target_input = self.__class__.correct_regression_output
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target_output = self.__class__.correct_regression_output
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ref_samples = target_output.unsqueeze(1).expand(-1, config.num_parallel_samples, -1)
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ground_truth_arg = "target_values"
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output_predictions_arg = "regression_outputs"
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elif task == "pretrain":
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mdl = PatchTSMixerForPretraining(config)
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target_input = None
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target_output = self.__class__.correct_pretrain_output
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ground_truth_arg = None
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output_predictions_arg = "prediction_outputs"
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else:
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print("invalid task")
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@@ -710,15 +727,18 @@ class PatchTSMixerFunctionalTests(unittest.TestCase):
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else:
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output = mdl(
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self.__class__.data,
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future_values=target_input,
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output_hidden_states=output_hidden_states,
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**{
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ground_truth_arg: target_input,
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"output_hidden_states": output_hidden_states,
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},
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)
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if isinstance(output.prediction_outputs, tuple):
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for t in output.prediction_outputs:
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prediction_outputs = getattr(output, output_predictions_arg)
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if isinstance(prediction_outputs, tuple):
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for t in prediction_outputs:
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self.assertEqual(t.shape, target_output.shape)
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else:
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self.assertEqual(output.prediction_outputs.shape, target_output.shape)
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self.assertEqual(prediction_outputs.shape, target_output.shape)
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self.assertEqual(output.last_hidden_state.shape, enc_output.shape)
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@@ -980,7 +1000,7 @@ class PatchTSMixerFunctionalTests(unittest.TestCase):
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mdl = PatchTSMixerForTimeSeriesClassification(config)
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output = mdl(
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self.__class__.data,
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future_values=self.__class__.correct_classification_classes,
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target_values=self.__class__.correct_classification_classes,
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)
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self.assertEqual(
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output.prediction_outputs.shape,
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@@ -994,7 +1014,7 @@ class PatchTSMixerFunctionalTests(unittest.TestCase):
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mdl = PatchTSMixerForTimeSeriesClassification(config)
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output = mdl(
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self.__class__.data,
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future_values=self.__class__.correct_classification_classes,
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target_values=self.__class__.correct_classification_classes,
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return_dict=False,
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)
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if isinstance(output, tuple):
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@@ -1017,9 +1037,9 @@ class PatchTSMixerFunctionalTests(unittest.TestCase):
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def test_regression_full(self):
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config = PatchTSMixerConfig(**self.__class__.params)
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mdl = PatchTSMixerForRegression(config)
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output = mdl(self.__class__.data, future_values=self.__class__.correct_regression_output)
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output = mdl(self.__class__.data, target_values=self.__class__.correct_regression_output)
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self.assertEqual(
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output.prediction_outputs.shape,
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output.regression_outputs.shape,
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self.__class__.correct_regression_output.shape,
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)
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self.assertEqual(output.last_hidden_state.shape, self.__class__.enc_output.shape)
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@@ -1030,13 +1050,13 @@ class PatchTSMixerFunctionalTests(unittest.TestCase):
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mdl = PatchTSMixerForRegression(config)
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output = mdl(
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self.__class__.data,
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future_values=self.__class__.correct_regression_output,
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target_values=self.__class__.correct_regression_output,
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return_dict=False,
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)
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if isinstance(output, tuple):
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output = PatchTSMixerForRegressionOutput(*output)
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self.assertEqual(
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output.prediction_outputs.shape,
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output.regression_outputs.shape,
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self.__class__.correct_regression_output.shape,
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)
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self.assertEqual(output.last_hidden_state.shape, self.__class__.enc_output.shape)
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@@ -1049,13 +1069,13 @@ class PatchTSMixerFunctionalTests(unittest.TestCase):
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config = PatchTSMixerConfig(**params)
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mdl = PatchTSMixerForRegression(config)
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output = mdl(self.__class__.data, future_values=self.__class__.correct_regression_output)
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output = mdl(self.__class__.data, target_values=self.__class__.correct_regression_output)
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self.assertEqual(
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output.prediction_outputs[0].shape,
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output.regression_outputs[0].shape,
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self.__class__.correct_regression_output.shape,
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)
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self.assertEqual(
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output.prediction_outputs[1].shape,
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output.regression_outputs[1].shape,
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self.__class__.correct_regression_output.shape,
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)
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self.assertEqual(output.last_hidden_state.shape, self.__class__.enc_output.shape)
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@@ -1075,13 +1095,13 @@ class PatchTSMixerFunctionalTests(unittest.TestCase):
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config = PatchTSMixerConfig(**params)
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mdl = PatchTSMixerForRegression(config)
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output = mdl(self.__class__.data, future_values=self.__class__.correct_regression_output)
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output = mdl(self.__class__.data, target_values=self.__class__.correct_regression_output)
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self.assertEqual(
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output.prediction_outputs[0].shape,
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output.regression_outputs[0].shape,
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self.__class__.correct_regression_output.shape,
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)
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self.assertEqual(
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output.prediction_outputs[1].shape,
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output.regression_outputs[1].shape,
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self.__class__.correct_regression_output.shape,
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)
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self.assertEqual(output.last_hidden_state.shape, self.__class__.enc_output.shape)
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@@ -367,19 +367,19 @@ class PatchTSTModelIntegrationTests(unittest.TestCase):
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self.assertTrue(torch.allclose(mean_prediction[0, -1:], expected_slice, atol=TOLERANCE))
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def test_regression_generation(self):
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model = PatchTSTForRegression.from_pretrained("namctin/patchtst_etth1_regression").to(torch_device)
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batch = prepare_batch(file="test-batch.pt")
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model = PatchTSTForRegression.from_pretrained("ibm/patchtst-etth1-regression-distribution").to(torch_device)
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batch = prepare_batch(repo_id="ibm/patchtst-etth1-test-data", file="regression_distribution_batch.pt")
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torch.manual_seed(0)
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model.eval()
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with torch.no_grad():
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outputs = model.generate(past_values=batch["past_values"].to(torch_device))
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expected_shape = torch.Size((64, model.config.num_parallel_samples, model.config.num_targets))
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self.assertEqual(outputs.sequences.shape, expected_shape)
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expected_slice = torch.tensor(
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[[0.3228, 0.4320, 0.4591, 0.4066, -0.3461, 0.3094, -0.8426]],
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[[-0.08046409], [-0.06570087], [-0.28218266], [-0.20636195], [-0.11787311]],
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device=torch_device,
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)
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mean_prediction = outputs.sequences.mean(dim=1)
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self.assertTrue(torch.allclose(mean_prediction[0, -1:], expected_slice, rtol=TOLERANCE))
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self.assertTrue(torch.allclose(mean_prediction[-5:], expected_slice, rtol=TOLERANCE))
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