# 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 Autoformer model.""" import inspect import tempfile import unittest from huggingface_hub import hf_hub_download from transformers import is_torch_available from transformers.testing_utils import is_flaky, 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 AutoformerConfig, AutoformerForPrediction, AutoformerModel from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder @require_torch class AutoformerModelTester: def __init__( self, parent, d_model=16, batch_size=13, prediction_length=7, context_length=14, label_length=10, cardinality=19, embedding_dimension=5, num_time_features=4, 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, lags_sequence=[1, 2, 3, 4, 5], moving_average=25, autocorrelation_factor=5, ): self.d_model = d_model self.parent = parent self.batch_size = batch_size self.prediction_length = prediction_length self.context_length = context_length self.cardinality = cardinality self.num_time_features = num_time_features self.lags_sequence = lags_sequence self.embedding_dimension = embedding_dimension 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.encoder_seq_length = context_length self.decoder_seq_length = prediction_length + label_length self.label_length = label_length self.moving_average = moving_average self.autocorrelation_factor = autocorrelation_factor def get_config(self): return AutoformerConfig( d_model=self.d_model, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, prediction_length=self.prediction_length, context_length=self.context_length, label_length=self.label_length, lags_sequence=self.lags_sequence, num_time_features=self.num_time_features, num_static_categorical_features=1, cardinality=[self.cardinality], embedding_dimension=[self.embedding_dimension], moving_average=self.moving_average, scaling="std", # we need std to get non-zero `loc` ) def prepare_autoformer_inputs_dict(self, config): _past_length = config.context_length + max(config.lags_sequence) static_categorical_features = ids_tensor([self.batch_size, 1], config.cardinality[0]) past_time_features = floats_tensor([self.batch_size, _past_length, config.num_time_features]) past_values = floats_tensor([self.batch_size, _past_length]) past_observed_mask = floats_tensor([self.batch_size, _past_length]) > 0.5 # decoder inputs future_time_features = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]) future_values = floats_tensor([self.batch_size, config.prediction_length]) inputs_dict = { "past_values": past_values, "static_categorical_features": static_categorical_features, "past_time_features": past_time_features, "past_observed_mask": past_observed_mask, "future_time_features": future_time_features, "future_values": future_values, } return inputs_dict def prepare_config_and_inputs(self): config = self.get_config() inputs_dict = self.prepare_autoformer_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 def check_encoder_decoder_model_standalone(self, config, inputs_dict): model = AutoformerModel(config=config).to(torch_device).eval() outputs = model(**inputs_dict) encoder_last_hidden_state = outputs.encoder_last_hidden_state last_hidden_state = outputs.last_hidden_state with tempfile.TemporaryDirectory() as tmpdirname: encoder = model.get_encoder() encoder.save_pretrained(tmpdirname) encoder = AutoformerEncoder.from_pretrained(tmpdirname).to(torch_device) transformer_inputs, feature, _, _, _ = model.create_network_inputs(**inputs_dict) seasonal_input, trend_input = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]) enc_input = torch.cat( (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]), dim=-1, ) encoder_last_hidden_state_2 = encoder(inputs_embeds=enc_input)[0] self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3) mean = ( torch.mean(transformer_inputs[:, : config.context_length, ...], dim=1) .unsqueeze(1) .repeat(1, config.prediction_length, 1) ) zeros = torch.zeros( [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]], device=enc_input.device, ) dec_input = torch.cat( ( torch.cat((seasonal_input[:, -config.label_length :, ...], zeros), dim=1), feature[:, config.context_length - config.label_length :, ...], ), dim=-1, ) trend_init = torch.cat( ( torch.cat((trend_input[:, -config.label_length :, ...], mean), dim=1), feature[:, config.context_length - config.label_length :, ...], ), dim=-1, ) with tempfile.TemporaryDirectory() as tmpdirname: decoder = model.get_decoder() decoder.save_pretrained(tmpdirname) decoder = AutoformerDecoder.from_pretrained(tmpdirname).to(torch_device) last_hidden_state_2 = decoder( trend=trend_init, inputs_embeds=dec_input, encoder_hidden_states=encoder_last_hidden_state, )[0] self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3) @require_torch class AutoformerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else () pipeline_model_mapping = {"feature-extraction": AutoformerModel} if is_torch_available() else {} test_missing_keys = False test_inputs_embeds = False def setUp(self): self.model_tester = AutoformerModelTester(self) self.config_tester = ConfigTester(self, config_class=AutoformerConfig, has_text_modality=False) # TODO: (ydshieh) Fix the wrong logic for `tmp_delay` is possible @unittest.skip( reason="The computation of `tmp_delay` in `AutoformerAttention.forward` seems wrong, see PR #12345. Also `topk` is used to compute indices which is not stable." ) def test_batching_equivalence(self): super().test_batching_equivalence() def test_config(self): self.config_tester.run_common_tests() def test_save_load_strict(self): config, inputs_dict = 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_encoder_decoder_model_standalone(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs) @unittest.skip(reason="Model has no tokens embeddings") def test_resize_tokens_embeddings(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass # # Input is 'static_categorical_features' not 'input_ids' def test_model_main_input_name(self): model_signature = inspect.signature(getattr(AutoformerModel, "forward")) # The main input is the name of the argument after `self` observed_main_input_name = list(model_signature.parameters.keys())[1] self.assertEqual(AutoformerModel.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()] expected_arg_names = [ "past_values", "past_time_features", "past_observed_mask", "static_categorical_features", "static_real_features", "future_values", "future_time_features", ] if model.__class__.__name__ == "AutoformerForPrediction": expected_arg_names.append("future_observed_mask") expected_arg_names.extend( [ "decoder_attention_mask", "encoder_outputs", "past_key_values", "output_hidden_states", "output_attentions", "use_cache", "return_dict", ] ) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) d_model = getattr(self.model_tester, "d_model", None) num_attention_heads = getattr(self.model_tester, "num_attention_heads", None) dim = d_model // num_attention_heads for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class._from_config(config, attn_implementation="eager") config = model.config model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, dim], ) out_len = len(outputs) correct_outlen = 7 if "last_hidden_state" in outputs: correct_outlen += 1 if "trend" in outputs: correct_outlen += 1 if "past_key_values" in outputs: correct_outlen += 1 # past_key_values have been returned if "loss" in outputs: correct_outlen += 1 if "params" in outputs: correct_outlen += 1 self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions self.assertIsInstance(decoder_attentions, (list, tuple)) self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, decoder_seq_length, dim], ) # cross attentions cross_attentions = outputs.cross_attentions self.assertIsInstance(cross_attentions, (list, tuple)) self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(cross_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, decoder_seq_length, dim], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(out_len + 2, len(outputs)) self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, dim], ) @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(filename="train-batch.pt"): file = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch", filename=filename, repo_type="dataset") check_torch_load_is_safe() batch = torch.load(file, map_location=torch_device, weights_only=True) return batch @require_torch @slow class AutoformerModelIntegrationTests(unittest.TestCase): def test_inference_no_head(self): model = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly").to(torch_device) batch = prepare_batch() with torch.no_grad(): output = model( past_values=batch["past_values"], past_time_features=batch["past_time_features"], past_observed_mask=batch["past_observed_mask"], static_categorical_features=batch["static_categorical_features"], future_values=batch["future_values"], future_time_features=batch["future_time_features"], )[0] expected_shape = torch.Size( (64, model.config.prediction_length + model.config.label_length, model.config.feature_size) ) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]], device=torch_device ) torch.testing.assert_close(output[0, :3, :3], expected_slice, rtol=TOLERANCE, atol=TOLERANCE) def test_inference_head(self): model = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly").to(torch_device) batch = prepare_batch("val-batch.pt") with torch.no_grad(): output = model( past_values=batch["past_values"], past_time_features=batch["past_time_features"], past_observed_mask=batch["past_observed_mask"], static_categorical_features=batch["static_categorical_features"], ).encoder_last_hidden_state expected_shape = torch.Size((64, model.config.context_length, model.config.d_model)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]], device=torch_device ) torch.testing.assert_close(output[0, :3, :3], expected_slice, rtol=TOLERANCE, atol=TOLERANCE) def test_seq_to_seq_generation(self): model = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly").to(torch_device) batch = prepare_batch("val-batch.pt") with torch.no_grad(): outputs = model.generate( static_categorical_features=batch["static_categorical_features"], past_time_features=batch["past_time_features"], past_values=batch["past_values"], future_time_features=batch["future_time_features"], past_observed_mask=batch["past_observed_mask"], ) expected_shape = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)) self.assertEqual(outputs.sequences.shape, expected_shape) expected_slice = torch.tensor([3130.6763, 4056.5293, 7053.0786], device=torch_device) mean_prediction = outputs.sequences.mean(dim=1) torch.testing.assert_close(mean_prediction[0, -3:], expected_slice, rtol=1e-1, atol=1e-1)