# Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Testing suite for the PyTorch PatchTST model.""" import inspect import random import tempfile import unittest from huggingface_hub import hf_hub_download from transformers import is_torch_available from transformers.models.auto import get_values from transformers.testing_utils import is_flaky, require_read_token, require_torch, slow, torch_device from transformers.utils import check_torch_load_is_safe from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin TOLERANCE = 1e-4 if is_torch_available(): import torch from transformers import ( MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING, MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING, PatchTSTConfig, PatchTSTForClassification, PatchTSTForPrediction, PatchTSTForPretraining, PatchTSTForRegression, PatchTSTModel, ) @require_torch class PatchTSTModelTester: def __init__( self, parent, batch_size=13, prediction_length=7, context_length=14, patch_length=5, patch_stride=5, num_input_channels=1, num_time_features=1, is_training=True, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, distil=False, seed=42, num_targets=2, mask_type="random", random_mask_ratio=0, ): self.parent = parent self.batch_size = batch_size self.prediction_length = prediction_length self.context_length = context_length self.patch_length = patch_length self.patch_stride = patch_stride self.num_input_channels = num_input_channels self.num_time_features = num_time_features self.is_training = is_training self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.mask_type = mask_type self.random_mask_ratio = random_mask_ratio self.seed = seed self.num_targets = num_targets self.distil = distil self.num_patches = (max(self.context_length, self.patch_length) - self.patch_length) // self.patch_stride + 1 # define seq_length so that it can pass the test_attention_outputs self.seq_length = self.num_patches def get_config(self): return PatchTSTConfig( prediction_length=self.prediction_length, patch_length=self.patch_length, patch_stride=self.patch_stride, num_input_channels=self.num_input_channels, d_model=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, context_length=self.context_length, activation_function=self.hidden_act, seed=self.seed, num_targets=self.num_targets, mask_type=self.mask_type, random_mask_ratio=self.random_mask_ratio, ) def prepare_patchtst_inputs_dict(self, config): _past_length = config.context_length # bs, num_input_channels, num_patch, patch_len # [bs x seq_len x num_input_channels] past_values = floats_tensor([self.batch_size, _past_length, self.num_input_channels]) future_values = floats_tensor([self.batch_size, config.prediction_length, self.num_input_channels]) inputs_dict = { "past_values": past_values, "future_values": future_values, } return inputs_dict def prepare_config_and_inputs(self): config = self.get_config() inputs_dict = self.prepare_patchtst_inputs_dict(config) return config, inputs_dict def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict @require_torch class PatchTSTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( PatchTSTModel, PatchTSTForPrediction, PatchTSTForPretraining, PatchTSTForClassification, PatchTSTForRegression, ) if is_torch_available() else () ) pipeline_model_mapping = {"feature-extraction": PatchTSTModel} if is_torch_available() else {} is_encoder_decoder = False test_missing_keys = True test_inputs_embeds = False test_resize_embeddings = True test_resize_position_embeddings = False test_mismatched_shapes = True has_attentions = True def setUp(self): self.model_tester = PatchTSTModelTester(self) self.config_tester = ConfigTester( self, config_class=PatchTSTConfig, has_text_modality=False, prediction_length=self.model_tester.prediction_length, ) def test_config(self): self.config_tester.run_common_tests() def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) # Get the actual batch size from the inputs (may differ from model_tester.batch_size in some tests) batch_size = inputs_dict["past_values"].shape[0] # if PatchTSTForPretraining if model_class == PatchTSTForPretraining: inputs_dict.pop("future_values", None) # else if classification model: elif model_class in get_values(MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING): rng = random.Random(self.model_tester.seed) labels = ids_tensor([batch_size], self.model_tester.num_targets, rng=rng) inputs_dict["target_values"] = labels inputs_dict.pop("future_values", None) elif model_class in get_values(MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING): rng = random.Random(self.model_tester.seed) target_values = floats_tensor([batch_size, self.model_tester.num_targets], rng=rng) inputs_dict["target_values"] = target_values inputs_dict.pop("future_values", None) return inputs_dict def test_save_load_strict(self): config, _ = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(info["missing_keys"], set()) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers ) self.assertEqual(len(hidden_states), expected_num_layers) num_patch = self.model_tester.num_patches self.assertListEqual( list(hidden_states[0].shape[-2:]), [num_patch, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) @unittest.skip(reason="we have no tokens embeddings") def test_resize_tokens_embeddings(self): pass def test_model_main_input_name(self): model_signature = inspect.signature(getattr(PatchTSTModel, "forward")) # The main input is the name of the argument after `self` observed_main_input_name = list(model_signature.parameters.keys())[1] self.assertEqual(PatchTSTModel.main_input_name, observed_main_input_name) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] if model_class == PatchTSTForPretraining: expected_arg_names = [ "past_values", "past_observed_mask", ] elif model_class in get_values(MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING) or model_class in get_values( MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING ): expected_arg_names = ["past_values", "target_values", "past_observed_mask"] else: expected_arg_names = [ "past_values", "past_observed_mask", "future_values", ] expected_arg_names.extend( [ "output_hidden_states", "output_attentions", "return_dict", ] ) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) @is_flaky() def test_retain_grad_hidden_states_attentions(self): super().test_retain_grad_hidden_states_attentions() @unittest.skip(reason="Model does not have input embeddings") def test_model_get_set_embeddings(self): pass def prepare_batch(repo_id="hf-internal-testing/etth1-hourly-batch", file="train-batch.pt"): file = hf_hub_download(repo_id=repo_id, filename=file, repo_type="dataset") check_torch_load_is_safe() batch = torch.load(file, map_location=torch_device, weights_only=True) return batch # Note: Pretrained model is not yet downloadable. @require_torch @slow class PatchTSTModelIntegrationTests(unittest.TestCase): # Publishing of pretrained weights are under internal review. Pretrained model is not yet downloadable. def test_pretrain_head(self): model = PatchTSTForPretraining.from_pretrained("namctin/patchtst_etth1_pretrain").to(torch_device) batch = prepare_batch() torch.manual_seed(0) with torch.no_grad(): output = model(past_values=batch["past_values"].to(torch_device)).prediction_output num_patch = ( max(model.config.context_length, model.config.patch_length) - model.config.patch_length ) // model.config.patch_stride + 1 expected_shape = torch.Size([64, model.config.num_input_channels, num_patch, model.config.patch_length]) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[-0.0173]], [[-1.0379]], [[-0.1030]], [[0.3642]], [[0.1601]], [[-1.3136]], [[0.8780]]], device=torch_device, ) torch.testing.assert_close(output[0, :7, :1, :1], expected_slice, rtol=TOLERANCE, atol=TOLERANCE) @require_read_token def test_prediction_head(self): model = PatchTSTForPrediction.from_pretrained("namctin/patchtst_etth1_forecast").to(torch_device) batch = prepare_batch(file="test-batch.pt") torch.manual_seed(0) with torch.no_grad(): output = model( past_values=batch["past_values"].to(torch_device), future_values=batch["future_values"].to(torch_device), ).prediction_outputs expected_shape = torch.Size([64, model.config.prediction_length, model.config.num_input_channels]) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[0.5142, 0.6928, 0.6118, 0.5724, -0.3735, -0.1336, -0.7124]], device=torch_device, ) torch.testing.assert_close(output[0, :1, :7], expected_slice, rtol=TOLERANCE, atol=TOLERANCE) @require_read_token def test_prediction_generation(self): model = PatchTSTForPrediction.from_pretrained("namctin/patchtst_etth1_forecast").to(torch_device) batch = prepare_batch(file="test-batch.pt") torch.manual_seed(0) with torch.no_grad(): outputs = model.generate(past_values=batch["past_values"].to(torch_device)) expected_shape = torch.Size((64, 1, model.config.prediction_length, model.config.num_input_channels)) self.assertEqual(outputs.sequences.shape, expected_shape) expected_slice = torch.tensor( [[0.4075, 0.3716, 0.4786, 0.2842, -0.3107, -0.0569, -0.7489]], device=torch_device, ) mean_prediction = outputs.sequences.mean(dim=1) torch.testing.assert_close(mean_prediction[0, -1:], expected_slice, rtol=TOLERANCE, atol=TOLERANCE) def test_regression_generation(self): model = PatchTSTForRegression.from_pretrained("ibm/patchtst-etth1-regression-distribution").to(torch_device) batch = prepare_batch(repo_id="ibm/patchtst-etth1-test-data", file="regression_distribution_batch.pt") torch.manual_seed(0) model.eval() with torch.no_grad(): outputs = model.generate(past_values=batch["past_values"].to(torch_device)) expected_shape = torch.Size((64, model.config.num_parallel_samples, model.config.num_targets)) self.assertEqual(outputs.sequences.shape, expected_shape) expected_slice = torch.tensor( [[-0.08046409], [-0.06570087], [-0.28218266], [-0.20636195], [-0.11787311]], device=torch_device, ) mean_prediction = outputs.sequences.mean(dim=1) torch.testing.assert_close(mean_prediction[-5:], expected_slice, rtol=TOLERANCE, atol=TOLERANCE)