time series forecasting model (#17965)
* initial files * initial model via cli * typos * make a start on the model config * ready with configuation * remove tokenizer ref. * init the transformer * added initial model forward to return dec_output * require gluonts * update dep. ver table and add as extra * fixed typo * add type for prediction_length * use num_time_features * use config * more config * typos * opps another typo * freq can be none * default via transformation is 1 * initial transformations * fix imports * added transform_start_field * add helper to create pytorch dataloader * added inital val and test data loader * added initial distr head and loss * training working * remove TimeSeriesTransformerTokenizer Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/__init__.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/__init__.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * fixed copyright * removed docs * remove time series tokenizer * fixed docs * fix text * fix second * fix default * fix order * use config directly * undo change * fix comment * fix year * fix import * add additional arguments for training vs. test * initial greedy inference loop * fix inference * comment out token inputs to enc dec * Use HF encoder/decoder * fix inference * Use Seq2SeqTSModelOutput output * return Seq2SeqTSPredictionOutput * added default arguments * fix return_dict true * scale is a tensor * output static_features for inference * clean up some unused bits * fixed typo * set return_dict if none * call model once for both train/predict * use cache if future_target is none * initial generate func * generate arguments * future_time_feat is required * return SampleTSPredictionOutput * removed unneeded classes * fix when params is none * fix return dict * fix num_attention_heads * fix arguments * remove unused shift_tokens_right * add different dropout configs * implement FeatureEmbedder, Scaler and weighted_average * remove gluonts dependency * fix class names * avoid _variable names * remove gluonts dependency * fix imports * remove gluonts from configuration * fix docs * fixed typo * move utils to examples * add example requirements * config has no freq * initial run_ts_no_trainer * remove from ignore * fix output_attentions and removed unsued getters/setters * removed unsed tests * add dec seq len * add test_attention_outputs * set has_text_modality=False * add config attribute_map * make style * make fix-copies * add encoder_outputs to TimeSeriesTransformerForPrediction forward * Improve docs, add model to README * added test_forward_signature * More improvements * Add more copied from * Fix README * Fix remaining quality issues * updated encoder and decoder * fix generate * output_hidden_states and use_cache are optional * past key_values returned too * initialize weights of distribution_output module * fixed more tests * update test_forward_signature * fix return_dict outputs * Update src/transformers/models/time_series_transformer/configuration_time_series_transformer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/configuration_time_series_transformer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/configuration_time_series_transformer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/configuration_time_series_transformer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/modeling_time_series_transformer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/modeling_time_series_transformer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/modeling_time_series_transformer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * removed commented out tests * added neg. bin and normal output * Update src/transformers/models/time_series_transformer/configuration_time_series_transformer.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * move to one line * Add docstrings * Update src/transformers/models/time_series_transformer/configuration_time_series_transformer.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * add try except for assert and raise * try and raise exception * fix the documentation formatting * fix assert call * fix docstring formatting * removed input_ids from DOCSTRING * Update input docstring * Improve variable names * Update order of inputs * Improve configuration * Improve variable names * Improve docs * Remove key_length from tests * Add extra docs * initial unittests * added test_inference_no_head test * added test_inference_head * add test_seq_to_seq_generation * make style * one line * assert mean prediction * removed comments * Update src/transformers/models/time_series_transformer/modeling_time_series_transformer.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/modeling_time_series_transformer.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * fix order of args * make past_observed_mask optional as well * added Amazon license header * updated utils with new fieldnames * make style * cleanup * undo position of past_observed_mask * fix import * typo * more typo * rename example files * remove example for now * Update docs/source/en/_toctree.yml Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/configuration_time_series_transformer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/modeling_time_series_transformer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/modeling_time_series_transformer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update modeling_time_series_transformer.py fix style * fixed typo * fix typo and grammer * fix style Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> Co-authored-by: NielsRogge <niels.rogge1@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Kashif Rasul
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tests/models/time_series_transformer/__init__.py
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tests/models/time_series_transformer/__init__.py
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# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Testing suite for the PyTorch TimeSeriesTransformer model. """
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import inspect
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import tempfile
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import unittest
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from huggingface_hub import hf_hub_download
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from transformers import is_torch_available
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from transformers.testing_utils import require_torch, slow, torch_device
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
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TOLERANCE = 1e-4
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if is_torch_available():
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import torch
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from transformers import (
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TimeSeriesTransformerConfig,
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TimeSeriesTransformerForPrediction,
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TimeSeriesTransformerModel,
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)
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from transformers.models.time_series_transformer.modeling_time_series_transformer import (
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TimeSeriesTransformerDecoder,
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TimeSeriesTransformerEncoder,
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)
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@require_torch
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class TimeSeriesTransformerModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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prediction_length=7,
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context_length=14,
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cardinality=19,
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embedding_dimension=5,
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num_time_features=4,
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is_training=True,
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hidden_size=16,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=4,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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lags_sequence=[1, 2, 3, 4, 5],
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):
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self.parent = parent
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self.batch_size = batch_size
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self.prediction_length = prediction_length
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self.context_length = context_length
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self.cardinality = cardinality
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self.num_time_features = num_time_features
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self.lags_sequence = lags_sequence
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self.embedding_dimension = embedding_dimension
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self.is_training = is_training
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.encoder_seq_length = context_length
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self.decoder_seq_length = prediction_length
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def get_config(self):
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return TimeSeriesTransformerConfig(
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encoder_layers=self.num_hidden_layers,
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decoder_layers=self.num_hidden_layers,
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encoder_attention_heads=self.num_attention_heads,
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decoder_attention_heads=self.num_attention_heads,
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encoder_ffn_dim=self.intermediate_size,
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decoder_ffn_dim=self.intermediate_size,
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dropout=self.hidden_dropout_prob,
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attention_dropout=self.attention_probs_dropout_prob,
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prediction_length=self.prediction_length,
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context_length=self.context_length,
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lags_sequence=self.lags_sequence,
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num_time_features=self.num_time_features,
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num_static_categorical_features=1,
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cardinality=[self.cardinality],
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embedding_dimension=[self.embedding_dimension],
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)
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def prepare_time_series_transformer_inputs_dict(self, config):
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_past_length = config.context_length + max(config.lags_sequence)
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static_categorical_features = ids_tensor([self.batch_size, 1], config.cardinality[0])
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static_real_features = floats_tensor([self.batch_size, 1])
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past_time_features = floats_tensor([self.batch_size, _past_length, config.num_time_features])
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past_values = floats_tensor([self.batch_size, _past_length])
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past_observed_mask = floats_tensor([self.batch_size, _past_length])
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# decoder inputs
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future_time_features = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features])
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future_values = floats_tensor([self.batch_size, config.prediction_length])
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inputs_dict = {
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"past_values": past_values,
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"static_categorical_features": static_categorical_features,
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"static_real_features": static_real_features,
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"past_time_features": past_time_features,
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"past_observed_mask": past_observed_mask,
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"future_time_features": future_time_features,
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"future_values": future_values,
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}
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return inputs_dict
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def prepare_config_and_inputs(self):
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config = self.get_config()
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inputs_dict = self.prepare_time_series_transformer_inputs_dict(config)
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return config, inputs_dict
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def prepare_config_and_inputs_for_common(self):
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config, inputs_dict = self.prepare_config_and_inputs()
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return config, inputs_dict
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def check_encoder_decoder_model_standalone(self, config, inputs_dict):
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model = TimeSeriesTransformerModel(config=config).to(torch_device).eval()
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outputs = model(**inputs_dict)
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encoder_last_hidden_state = outputs.encoder_last_hidden_state
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last_hidden_state = outputs.last_hidden_state
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with tempfile.TemporaryDirectory() as tmpdirname:
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encoder = model.get_encoder()
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encoder.save_pretrained(tmpdirname)
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encoder = TimeSeriesTransformerEncoder.from_pretrained(tmpdirname).to(torch_device)
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transformer_inputs, _, _ = model.create_network_inputs(**inputs_dict)
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enc_input = transformer_inputs[:, : config.context_length, ...]
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dec_input = transformer_inputs[:, config.context_length :, ...]
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encoder_last_hidden_state_2 = encoder(inputs_embeds=enc_input)[0]
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self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
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with tempfile.TemporaryDirectory() as tmpdirname:
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decoder = model.get_decoder()
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decoder.save_pretrained(tmpdirname)
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decoder = TimeSeriesTransformerDecoder.from_pretrained(tmpdirname).to(torch_device)
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last_hidden_state_2 = decoder(
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inputs_embeds=dec_input,
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encoder_hidden_states=encoder_last_hidden_state,
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)[0]
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self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
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@require_torch
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class TimeSeriesTransformerModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(TimeSeriesTransformerModel, TimeSeriesTransformerForPrediction) if is_torch_available() else ()
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)
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all_generative_model_classes = (TimeSeriesTransformerForPrediction,) if is_torch_available() else ()
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is_encoder_decoder = True
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test_pruning = False
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test_head_masking = False
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test_missing_keys = False
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test_torchscript = False
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test_inputs_embeds = False
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test_model_common_attributes = False
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def setUp(self):
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self.model_tester = TimeSeriesTransformerModelTester(self)
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self.config_tester = ConfigTester(self, config_class=TimeSeriesTransformerConfig, has_text_modality=False)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_save_load_strict(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs()
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for model_class in self.all_model_classes:
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model = model_class(config)
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
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self.assertEqual(info["missing_keys"], [])
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def test_encoder_decoder_model_standalone(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
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self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
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# Ignore since we have no tokens embeddings
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def test_resize_tokens_embeddings(self):
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pass
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# # Input is 'static_categorical_features' not 'input_ids'
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def test_model_main_input_name(self):
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model_signature = inspect.signature(getattr(TimeSeriesTransformerModel, "forward"))
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# The main input is the name of the argument after `self`
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observed_main_input_name = list(model_signature.parameters.keys())[1]
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self.assertEqual(TimeSeriesTransformerModel.main_input_name, observed_main_input_name)
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.forward)
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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 = [
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"past_values",
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"past_time_features",
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"past_observed_mask",
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"static_categorical_features",
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"static_real_features",
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"future_values",
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"future_time_features",
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]
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expected_arg_names.extend(
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[
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"future_observed_mask",
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"decoder_attention_mask",
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"head_mask",
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"decoder_head_mask",
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"cross_attn_head_mask",
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"encoder_outputs",
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"past_key_values",
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"output_hidden_states",
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"output_attentions",
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"use_cache",
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"return_dict",
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]
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if "future_observed_mask" in arg_names
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else [
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"decoder_attention_mask",
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"head_mask",
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"decoder_head_mask",
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"cross_attn_head_mask",
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"encoder_outputs",
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"past_key_values",
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"output_hidden_states",
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"output_attentions",
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"use_cache",
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"return_dict",
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]
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)
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self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
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def test_attention_outputs(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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seq_len = getattr(self.model_tester, "seq_length", None)
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decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
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encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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config.return_dict = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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# check that output_attentions also work using config
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del inputs_dict["output_attentions"]
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config.output_attentions = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.encoder_attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, encoder_seq_length],
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)
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out_len = len(outputs)
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correct_outlen = 6
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if "last_hidden_state" in outputs:
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correct_outlen += 1
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if "past_key_values" in outputs:
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correct_outlen += 1 # past_key_values have been returned
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if "loss" in outputs:
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correct_outlen += 1
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if "params" in outputs:
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correct_outlen += 1
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self.assertEqual(out_len, correct_outlen)
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# decoder attentions
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decoder_attentions = outputs.decoder_attentions
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self.assertIsInstance(decoder_attentions, (list, tuple))
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self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(decoder_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, decoder_seq_length, decoder_seq_length],
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)
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# cross attentions
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cross_attentions = outputs.cross_attentions
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self.assertIsInstance(cross_attentions, (list, tuple))
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self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(cross_attentions[0].shape[-3:]),
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[
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self.model_tester.num_attention_heads,
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decoder_seq_length,
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encoder_seq_length,
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],
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)
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# Check attention is always last and order is fine
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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, encoder_seq_length],
|
||||
)
|
||||
|
||||
|
||||
def prepare_batch(filename="train-batch.pt"):
|
||||
file = hf_hub_download(repo_id="kashif/tourism-monthly-batch", filename=filename, repo_type="dataset")
|
||||
batch = torch.load(file, map_location=torch_device)
|
||||
return batch
|
||||
|
||||
|
||||
@require_torch
|
||||
@slow
|
||||
class TimeSeriesTransformerModelIntegrationTests(unittest.TestCase):
|
||||
def test_inference_no_head(self):
|
||||
model = TimeSeriesTransformerModel.from_pretrained("huggingface/time-series-transformer-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"],
|
||||
static_real_features=batch["static_real_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.d_model))
|
||||
self.assertEqual(output.shape, expected_shape)
|
||||
|
||||
expected_slice = torch.tensor(
|
||||
[[-0.3125, -1.2884, -1.1118], [-0.5801, -1.4907, -0.7782], [0.0849, -1.6557, -0.9755]], device=torch_device
|
||||
)
|
||||
self.assertTrue(torch.allclose(output[0, :3, :3], expected_slice, atol=TOLERANCE))
|
||||
|
||||
def test_inference_head(self):
|
||||
model = TimeSeriesTransformerForPrediction.from_pretrained(
|
||||
"huggingface/time-series-transformer-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"],
|
||||
static_real_features=batch["static_real_features"],
|
||||
future_time_features=batch["future_time_features"],
|
||||
)[1]
|
||||
expected_shape = torch.Size((64, model.config.prediction_length, model.config.d_model))
|
||||
self.assertEqual(output.shape, expected_shape)
|
||||
|
||||
expected_slice = torch.tensor(
|
||||
[[0.9127, -0.2056, -0.5259], [1.0572, 1.4104, -0.1964], [0.1358, 2.0348, 0.5739]], device=torch_device
|
||||
)
|
||||
self.assertTrue(torch.allclose(output[0, :3, :3], expected_slice, atol=TOLERANCE))
|
||||
|
||||
def test_seq_to_seq_generation(self):
|
||||
model = TimeSeriesTransformerForPrediction.from_pretrained(
|
||||
"huggingface/time-series-transformer-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"],
|
||||
static_real_features=batch["static_real_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([2289.5203, 2778.3054, 4648.1313], device=torch_device)
|
||||
mean_prediction = outputs.sequences.mean(dim=1)
|
||||
self.assertTrue(torch.allclose(mean_prediction[0, -3:], expected_slice, rtol=1e-1))
|
||||
Reference in New Issue
Block a user