From 644741335806b331a9f5c1d57a91048263214cd8 Mon Sep 17 00:00:00 2001 From: Scenic Authors Date: Fri, 25 Aug 2023 02:54:28 -0700 Subject: [PATCH] Opensource REVEAL (https://arxiv.org/abs/2212.05221) PiperOrigin-RevId: 560033039 --- README.md | 1 + scenic/projects/README.md | 8 + .../knowledge_visual_language/README.md | 36 + .../knowledge_visual_language/__init__.py | 0 .../configs/__init__.py | 0 .../configs/finetune_okvqa_base.py | 127 +++ .../configs/wit_memory_G.py | 119 +++ .../configs/wit_memory_base.py | 120 +++ .../configs/wit_memory_g.py | 124 +++ .../configs/wit_memory_large.py | 116 +++ .../wit_retrieval_soft_G_froze_config.py | 101 ++ .../configs/wit_retrieval_soft_base_config.py | 100 ++ .../wit_retrieval_soft_base_froze_config.py | 101 ++ .../wit_retrieval_soft_g_froze_config.py | 101 ++ .../wit_retrieval_soft_large_config.py | 99 ++ .../wit_retrieval_soft_large_froze_config.py | 100 ++ .../data/__init__.py | 0 .../data/cc12m_generation_dataset.py | 262 ++++++ 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.../knowledge_visual_language/trainer.py | 494 ++++++++++ .../trainer_memory.py | 719 ++++++++++++++ .../trainer_utils.py | 379 ++++++++ 43 files changed, 8409 insertions(+) create mode 100644 scenic/projects/knowledge_visual_language/README.md create mode 100644 scenic/projects/knowledge_visual_language/__init__.py create mode 100644 scenic/projects/knowledge_visual_language/configs/__init__.py create mode 100644 scenic/projects/knowledge_visual_language/configs/finetune_okvqa_base.py create mode 100644 scenic/projects/knowledge_visual_language/configs/wit_memory_G.py create mode 100644 scenic/projects/knowledge_visual_language/configs/wit_memory_base.py create mode 100644 scenic/projects/knowledge_visual_language/configs/wit_memory_g.py create mode 100644 scenic/projects/knowledge_visual_language/configs/wit_memory_large.py create mode 100644 scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_G_froze_config.py create mode 100644 scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_base_config.py create mode 100644 scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_base_froze_config.py create mode 100644 scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_g_froze_config.py create mode 100644 scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_large_config.py create mode 100644 scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_large_froze_config.py create mode 100644 scenic/projects/knowledge_visual_language/data/__init__.py create mode 100644 scenic/projects/knowledge_visual_language/data/cc12m_generation_dataset.py create mode 100644 scenic/projects/knowledge_visual_language/data/cc12m_table_dataset.py create mode 100644 scenic/projects/knowledge_visual_language/data/data_utils.py create mode 100644 scenic/projects/knowledge_visual_language/data/okvqa_dataset.py create mode 100644 scenic/projects/knowledge_visual_language/data/vqa.png create mode 100644 scenic/projects/knowledge_visual_language/data/vqa_dataset.py create mode 100644 scenic/projects/knowledge_visual_language/data/vqa_table_dataset.py create mode 100644 scenic/projects/knowledge_visual_language/data/web_image_text_generation_dataset.py create mode 100644 scenic/projects/knowledge_visual_language/data/wiki_image_text_generation_dataset.py create mode 100644 scenic/projects/knowledge_visual_language/data/wikidata/data_util.py create mode 100644 scenic/projects/knowledge_visual_language/data/wikidata/data_util_test.py create mode 100644 scenic/projects/knowledge_visual_language/data/wikidata/extract_wikigraph.py create mode 100644 scenic/projects/knowledge_visual_language/data/wit_table_dataset.py create mode 100644 scenic/projects/knowledge_visual_language/main.py create mode 100644 scenic/projects/knowledge_visual_language/models/constants.py create mode 100644 scenic/projects/knowledge_visual_language/models/fusion_in_decoder_soft.py create mode 100644 scenic/projects/knowledge_visual_language/models/knowledge_fid.py create mode 100644 scenic/projects/knowledge_visual_language/models/layers.py create mode 100644 scenic/projects/knowledge_visual_language/models/local_memory.py create mode 100644 scenic/projects/knowledge_visual_language/models/losses.py create mode 100644 scenic/projects/knowledge_visual_language/models/metrics.py create mode 100644 scenic/projects/knowledge_visual_language/models/metrics_vqa.py create mode 100644 scenic/projects/knowledge_visual_language/models/vit.py create mode 100644 scenic/projects/knowledge_visual_language/trainer.py create mode 100644 scenic/projects/knowledge_visual_language/trainer_memory.py create mode 100644 scenic/projects/knowledge_visual_language/trainer_utils.py diff --git a/README.md b/README.md index ed09b9d76..a18b13b69 100644 --- a/README.md +++ b/README.md @@ -66,6 +66,7 @@ Projects that were developed in Scenic or used it for their experiments: * [How can objects help action recognition?](https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_How_Can_Objects_Help_Action_Recognition_CVPR_2023_paper.html) * [Verbs in Action: Improving verb understanding in video-language models](https://arxiv.org/abs/2304.06708) * [Unified Visual Relationship Detection with Vision and Language Models](https://arxiv.org/abs/2303.08998) +* [REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory](https://arxiv.org/abs/2212.05221) More information can be found in [projects](https://github.com/google-research/scenic/tree/main/scenic/projects#list-of-projects-hosted-in-scenic). diff --git a/scenic/projects/README.md b/scenic/projects/README.md index 338d441d3..e88cc0555 100644 --- a/scenic/projects/README.md +++ b/scenic/projects/README.md @@ -139,6 +139,14 @@ > vision and language models. > Details can be found in the [paper](https://arxiv.org/abs/2303.08998). + +* [REVEAL](knowledge_visual_language) + + > REVEAL is an Retrieval-Augmented Visual Language Model that + > learns to retrieve world knowledge from a diverse set of multimodal + > knowledge sources, through end-to-end pre-training. + > Details can be found in the [paper](https://arxiv.org/abs/2212.05221). + ## Scenic projects A typical project consists of models, trainers, configs, a runner, and some diff --git a/scenic/projects/knowledge_visual_language/README.md b/scenic/projects/knowledge_visual_language/README.md new file mode 100644 index 000000000..e688e8cb0 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/README.md @@ -0,0 +1,36 @@ +# Repository for REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory +![REVEAL is an End-to-End Retrieval-Augmented VLM](data/vivit.png) + + +### [Project Page](https://reveal-cvpr.github.io/) | [arXiv](https://arxiv.org/abs/2212.05221) + + +## What is REVEAL? + +We propose an end-to-end Retrieval-Augmented Visual Language Model (REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve from it to answer knowledge-intensive queries + +REVEAL consists of four key components: the memory, the encoder, the retriever and the generator. The large-scale memory encodes various sources of multimodal world knowledge (e.g. image-text pairs, question answering pairs, knowledge graph triplets, etc) via a unified encoder. The retriever finds the most relevant knowledge entries in the memory, and the generator fuses the retrieved knowledge with the input query to produce the output. A key novelty in our approach is that the memory, encoder, retriever and generator are all pre-trained end-to-end on a massive amount of data. Furthermore, our approach can use a diverse set of multimodal knowledge sources, which is shown to result in significant gains. We show that REVEAL achieves state-of-the-art results on visual question answering and image captioning. + +More details can be found in the [paper](https://arxiv.org/abs/2212.05221) published at CVPR 2023 (Highlight). + + +## Citation + +If you use REVEAL, please use the following BibTeX entry. + +``` +@inproceedings{reveal, + title={{REVEAL:} Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory}, + author={Ziniu Hu and + Ahmet Iscen and + Chen Sun and + Zirui Wang and + Kai{-}Wei Chang and + Yizhou Sun and + Cordelia Schmid and + David A. Ross and + Alireza Fathi}, + booktitle={CVPR}, + year={2023} +} +``` diff --git a/scenic/projects/knowledge_visual_language/__init__.py b/scenic/projects/knowledge_visual_language/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/scenic/projects/knowledge_visual_language/configs/__init__.py b/scenic/projects/knowledge_visual_language/configs/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/scenic/projects/knowledge_visual_language/configs/finetune_okvqa_base.py b/scenic/projects/knowledge_visual_language/configs/finetune_okvqa_base.py new file mode 100644 index 000000000..aed611e44 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/configs/finetune_okvqa_base.py @@ -0,0 +1,127 @@ +r"""WIT Retrieval + Captioning Pre-Training.""" + +import ml_collections + +TRAIN_DATA_SIZE = 10000 + + +def get_config() -> ml_collections.ConfigDict: + """Returns the base experiment configuration.""" + config = ml_collections.ConfigDict() + config.experiment_name = 'image_caption_debug' + + config.optimizer = 'adafactor' + n_device = 128 + batch_size = 6 * 2 * n_device + config.optimizer_configs = ml_collections.ConfigDict() + config.optimizer_configs.momentum = None + # config.optimizer_configs.momentum = 0.9 + # config.optimizer_configs.dtype_momentum = 'bfloat16' + config.optimizer_configs.weight_decay_rate = 0 + config.optimizer_configs.clipping_threshold = 10.0 + config.optimizer_configs.skip_scale_and_bias_regularization = False + + config.frozen_patterns = [] + config.not_frozen_patterns = [ + ('value_perceiver/.*', 0.1), + # ('text_encoder/.*', 0.05), + # ('img_encoder/.*', 0.05), + # ('shared_token_embedder/.*', 0.02), + ('query_head/.*', 0.3), + ('out_decoder/.*', 1.0), + ('key_head/.*', 0.3), + ('head_out/.*', 0.2), + ('fusion_encoder/.*', 0.5), + ('att_transform/.*', 0.3), + ('dataset_gate/.*', 0.5), + ] + + config.grad_clip_configs = ml_collections.ConfigDict() + config.grad_clip_configs.clip_method = 'clip_by_global_norm' + config.grad_clip_configs.clip_value = 1.0 + + config.kb_dataset_names = ['wit_table', 'cc12m_table', 'vqa_table'] + config.kb_dataset_configs = [{}, {}, {}] + + config.batch_size = batch_size + config.eval_batch_size = batch_size + config.rng_seed = 0 + config.update_num = True + config.num_training_epochs = 1000 + config.data_dtype_str = 'bfloat16' + # Model + config.model_name = 'knowledge_fid' + config.model = ml_collections.ConfigDict() + config.model.image_model = 'vit' + config.model.t5_name = 't5_1_1_base' + # ['t5_1_1_small', 't5_1_1_base', 't5_1_1_large', 't5_1_1_xl', 't5_1_1_xxl'] + config.model.num_fusion_layers = 6 + config.model.n_compressed_tokens = 32 + config.model.key_dim = 512 + config.model.dropout_rate = 0.1 + config.model.temperature = 0.2 + config.model.retr_k = 50 + config.model.retr_data_ratio = 0.1 + config.model.label_smoothing = 1e-2 + config.model.vit_name = 'B/16' + config.model.vit_model_path = 'JFT3b-B/16' + # [JFT3b-B/32, JFT3b-B/16, JFT3b-L/16, JFT3b-g/14, JFT3b-G/14] + config.model.t5_frozen_base = True + config.model.vit_num_frozen_layers = 1 / 2 + config.model.retrieve_local = False + config.model.use_psudo_retr = True + config.model.disentangle = False + config.model.gap = False + config.model.retrieval_ratio = 0.2 + config.model.n_knowledge_source = len(config.kb_dataset_names) + config.model.qa = True + config.frozen_memory = False + + config.vocab_size = 32120 + config.autoregressive_decoding = ml_collections.ConfigDict() + config.autoregressive_decoding.num_decodes = 1 + config.autoregressive_decoding.beam_search = False + + # Dataset. + config.dataset_name = 'okvqa' + config.dataset_configs = ml_collections.ConfigDict() + + # Learning rate. + config.num_train_examples = TRAIN_DATA_SIZE + steps_per_epoch = TRAIN_DATA_SIZE // config.batch_size + config.lr_configs = ml_collections.ConfigDict() + config.lr_configs.total_steps = int( + config.num_training_epochs * steps_per_epoch + ) + config.lr_configs.learning_rate_schedule = 'compound' + config.lr_configs.factors = 'constant * rsqrt_decay * linear_warmup' + config.lr_configs.warmup_steps = 2000 + config.lr_configs.timescale = 5000 + # config.lr_configs.steps_per_cycle = config.lr_configs.total_steps + config.lr_configs.base_learning_rate = 1e-5 + config.lr_configs.end_learning_rate = 1e-6 + + # Logging. + config.log_summary_steps = 100 + config.log_eval_steps = 500 + config.checkpoint_steps = 1000 + config.write_summary = True + config.xprof = True # Profile using xprof + config.checkpoint = True # Do checkpointing. + config.debug_train = False # Debug mode during training. + config.debug_eval = False # Debug mode during eval. + + # Initalisation configs + config.init_from = ml_collections.ConfigDict() + # Initializing from a vidcap model. + config.init_from.xm = None + config.init_from.xm = (54461417, 1) # compress=32 + # config.init_from.xm = (51839031, 1) # compress=32 + # config.init_from.xm = (46499437, 1) # compress=64 + # config.init_from.resume = (50154314, 1) # compress=32 + config.init_from.only_params = False + config.init_from.load_key_encoder = False + config.init_from.encoder = ml_collections.ConfigDict() + config.init_from.encoder.init_from_vit = False + config.init_from.encoder.checkpoint_path = None + return config diff --git a/scenic/projects/knowledge_visual_language/configs/wit_memory_G.py b/scenic/projects/knowledge_visual_language/configs/wit_memory_G.py new file mode 100644 index 000000000..52c54e42f --- /dev/null +++ b/scenic/projects/knowledge_visual_language/configs/wit_memory_G.py @@ -0,0 +1,119 @@ +r"""WIT Retrieval + Captioning Pre-Training.""" + +import ml_collections + +TRAIN_DATA_SIZE = 1_000_000_000 + + +def get_config() -> ml_collections.ConfigDict: + """Returns the base experiment configuration.""" + config = ml_collections.ConfigDict() + config.experiment_name = 'image_caption_debug' + + config.optimizer = 'adafactor' + n_device = 256 + batch_size = 4 * 2 * n_device + config.optimizer_configs = ml_collections.ConfigDict() + config.optimizer_configs.momentum = None + # config.optimizer_configs.momentum = 0.9 + # config.optimizer_configs.dtype_momentum = 'bfloat16' + config.optimizer_configs.weight_decay_rate = 2e-3 + config.optimizer_configs.clipping_threshold = 5.0 + config.optimizer_configs.skip_scale_and_bias_regularization = True + + config.frozen_patterns = [] + config.not_frozen_patterns = [('value_perceiver/.*', 0.3), + ('text_encoder/.*', 0.1), + ('img_encoder/.*', 0.1), + ('shared_token_embedder/.*', 0.1), + ('query_head/.*', 0.2), ('out_decoder/.*', 1), + ('key_head/.*', 0.2), ('head_out/.*', 0.2), + ('fusion_encoder/.*', 0.5), + ('att_transform/.*', 0.3), + ('dataset_gate/.*', 0.5)] + + config.grad_clip_configs = ml_collections.ConfigDict() + config.grad_clip_configs.clip_method = 'clip_by_global_norm' + config.grad_clip_configs.clip_value = 1.0 + + config.kb_dataset_names = ['wit_table', 'cc12m_table', 'vqa_table'] + config.kb_dataset_configs = [{}, {}, {}] + + config.batch_size = batch_size + config.eval_batch_size = batch_size + config.rng_seed = 0 + config.update_num = False + config.num_training_epochs = 2 + config.data_dtype_str = 'bfloat16' + # Model + config.model_name = 'knowledge_fid' + config.model = ml_collections.ConfigDict() + config.model.image_model = 'vit' + config.model.t5_name = 't5_1_1_large' + # ['t5_1_1_small', 't5_1_1_base', 't5_1_1_large', 't5_1_1_xl', 't5_1_1_xxl'] + config.model.num_fusion_layers = 8 + config.model.n_compressed_tokens = 32 + config.model.key_dim = 512 + config.model.dropout_rate = 0.0 + config.model.temperature = 0.2 + config.model.retr_k = 10 + config.model.retr_data_ratio = 0.2 + config.model.label_smoothing = 1e-2 + config.model.vit_name = 'G/14' + config.model.vit_model_path = 'JFT3b-G/14' + # [JFT3b-B/32, JFT3b-B/16, JFT3b-L/16, JFT3b-g/14, JFT3b-G/14] + config.model.t5_frozen_base = False + config.model.vit_num_frozen_layers = 5 / 6 + config.model.retrieve_local = False + config.model.use_psudo_retr = True + config.model.disentangle = True + config.model.gap = True + config.model.retrieval_ratio = 1e-2 + config.model.n_knowledge_source = len(config.kb_dataset_names) + config.model.qa = False + config.frozen_memory = False + + config.vocab_size = 32120 + config.autoregressive_decoding = ml_collections.ConfigDict() + config.autoregressive_decoding.num_decodes = 1 + config.autoregressive_decoding.beam_search = False + # Dataset. + config.dataset_name = 'web_image_text_generation' + config.dataset_configs = ml_collections.ConfigDict() + + # Learning rate. + config.num_train_examples = TRAIN_DATA_SIZE + steps_per_epoch = TRAIN_DATA_SIZE // config.batch_size + config.lr_configs = ml_collections.ConfigDict() + config.lr_configs.total_steps = int(config.num_training_epochs * + steps_per_epoch) + config.lr_configs.learning_rate_schedule = 'compound' + config.lr_configs.factors = 'constant * rsqrt_decay * linear_warmup' + config.lr_configs.warmup_steps = 20000 + config.lr_configs.timescale = 10000 + # config.lr_configs.steps_per_cycle = config.lr_configs.total_steps + config.lr_configs.base_learning_rate = 1e-3 + config.lr_configs.end_learning_rate = 1e-6 + + # Logging. + config.log_summary_steps = 100 + config.log_eval_steps = 1000 + config.checkpoint_steps = 5000 + config.write_summary = True + config.xprof = True # Profile using xprof + config.checkpoint = True # Do checkpointing. + config.debug_train = False # Debug mode during training. + config.debug_eval = False # Debug mode during eval. + + # Initalisation configs + config.init_from = ml_collections.ConfigDict() + # Initializing from a vidcap model. + # config.init_from.xm = None + # config.init_from.xm = (46234383, 1) # compress=32 + config.init_from.resume = (49645684, 1) # compress=64 + config.init_from.only_params = False + config.init_from.load_key_encoder = False + config.init_from.encoder = ml_collections.ConfigDict() + config.init_from.encoder.init_from_vit = False + config.init_from.encoder.checkpoint_path = None + return config diff --git a/scenic/projects/knowledge_visual_language/configs/wit_memory_base.py b/scenic/projects/knowledge_visual_language/configs/wit_memory_base.py new file mode 100644 index 000000000..7c76a871e --- /dev/null +++ b/scenic/projects/knowledge_visual_language/configs/wit_memory_base.py @@ -0,0 +1,120 @@ +r"""WIT Retrieval + Captioning Pre-Training.""" + +import ml_collections + +TRAIN_DATA_SIZE = 1_000_000_000 + + +def get_config() -> ml_collections.ConfigDict: + """Returns the base experiment configuration.""" + config = ml_collections.ConfigDict() + config.experiment_name = 'image_caption_debug' + + config.optimizer = 'adafactor' + n_device = 128 + batch_size = 12 * 2 * n_device + config.optimizer_configs = ml_collections.ConfigDict() + config.optimizer_configs.momentum = None + # config.optimizer_configs.momentum = 0.9 + # config.optimizer_configs.dtype_momentum = 'bfloat16' + config.optimizer_configs.weight_decay_rate = 2e-3 + config.optimizer_configs.clipping_threshold = 5.0 + config.optimizer_configs.skip_scale_and_bias_regularization = True + + config.frozen_patterns = [] + config.not_frozen_patterns = [('value_perceiver/.*', 0.3), + # ('text_encoder/.*', 0.1), + # ('img_encoder/.*', 0.1), + ('shared_token_embedder/.*', 0.1), + ('query_head/.*', 0.2), ('out_decoder/.*', 1), + ('key_head/.*', 0.2), ('head_out/.*', 0.2), + ('fusion_encoder/.*', 0.5), + ('att_transform/.*', 0.3), + ('dataset_gate/.*', 0.5)] + + config.grad_clip_configs = ml_collections.ConfigDict() + config.grad_clip_configs.clip_method = 'clip_by_global_norm' + config.grad_clip_configs.clip_value = 1.0 + + config.kb_dataset_names = ['wit_table', 'cc12m_table', 'vqa_table'] + config.kb_dataset_configs = [{}, {}, {}] + + config.batch_size = batch_size + config.eval_batch_size = batch_size + config.rng_seed = 0 + config.update_num = False + config.num_training_epochs = 1 + config.data_dtype_str = 'bfloat16' + # Model + config.model_name = 'knowledge_fid' + config.model = ml_collections.ConfigDict() + config.model.image_model = 'vit' + config.model.t5_name = 't5_1_1_base' + # ['t5_1_1_small', 't5_1_1_base', 't5_1_1_large', 't5_1_1_xl', 't5_1_1_xxl'] + config.model.num_fusion_layers = 6 + config.model.n_compressed_tokens = 32 + config.model.key_dim = 512 + config.model.dropout_rate = 0.0 + config.model.temperature = 0.2 + config.model.retr_k = 10 + config.model.retr_data_ratio = 0.2 + config.model.label_smoothing = 1e-2 + config.model.vit_name = 'B/16' + config.model.vit_model_path = 'JFT3b-B/16' + # [JFT3b-B/32, JFT3b-B/16, JFT3b-L/16, JFT3b-g/14, JFT3b-G/14] + config.model.t5_frozen_base = False + config.model.vit_num_frozen_layers = 1 / 2 + config.model.retrieve_local = False + config.model.use_psudo_retr = True + config.model.disentangle = True + config.model.gap = True + config.model.retrieval_ratio = 1e-2 + config.model.n_knowledge_source = len(config.kb_dataset_names) + config.model.qa = False + config.frozen_memory = False + + config.vocab_size = 32120 + config.autoregressive_decoding = ml_collections.ConfigDict() + config.autoregressive_decoding.num_decodes = 1 + config.autoregressive_decoding.beam_search = False + # Dataset. + config.dataset_name = 'web_image_text_generation' + config.dataset_configs = ml_collections.ConfigDict() + + # Learning rate. + config.num_train_examples = TRAIN_DATA_SIZE + steps_per_epoch = TRAIN_DATA_SIZE // config.batch_size + config.lr_configs = ml_collections.ConfigDict() + config.lr_configs.total_steps = int(config.num_training_epochs * + steps_per_epoch) + config.lr_configs.learning_rate_schedule = 'compound' + config.lr_configs.factors = 'constant * rsqrt_decay * linear_warmup' + config.lr_configs.warmup_steps = 10000 + config.lr_configs.timescale = 10000 + # config.lr_configs.steps_per_cycle = config.lr_configs.total_steps + config.lr_configs.base_learning_rate = 1e-4 + config.lr_configs.end_learning_rate = 1e-6 + + # Logging. + config.log_summary_steps = 100 + config.log_eval_steps = 1000 + config.checkpoint_steps = 5000 + config.write_summary = True + config.xprof = True # Profile using xprof + config.checkpoint = True # Do checkpointing. + config.debug_train = False # Debug mode during training. + config.debug_eval = False # Debug mode during eval. + + # Initalisation configs + config.init_from = ml_collections.ConfigDict() + # Initializing from a vidcap model. + config.init_from.xm = None + config.init_from.xm = (54354459, 1) # compress=32 + # config.init_from.xm = (46499437, 1) # compress=64 + # config.init_from.resume = (50154314, 1) # compress=32 + config.init_from.only_params = False + config.init_from.load_key_encoder = False + config.init_from.encoder = ml_collections.ConfigDict() + config.init_from.encoder.init_from_vit = False + config.init_from.encoder.checkpoint_path = None + return config diff --git a/scenic/projects/knowledge_visual_language/configs/wit_memory_g.py b/scenic/projects/knowledge_visual_language/configs/wit_memory_g.py new file mode 100644 index 000000000..7ac41d8e0 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/configs/wit_memory_g.py @@ -0,0 +1,124 @@ +r"""WIT Retrieval + Captioning Pre-Training.""" + +import ml_collections + +TRAIN_DATA_SIZE = 1_000_000_000 + + +def get_config() -> ml_collections.ConfigDict: + """Returns the base experiment configuration.""" + config = ml_collections.ConfigDict() + config.experiment_name = 'image_caption_debug' + + config.optimizer = 'adafactor' + n_device = 256 + batch_size = 4 * 2 * n_device + config.optimizer_configs = ml_collections.ConfigDict() + config.optimizer_configs.momentum = None + # config.optimizer_configs.momentum = 0.9 + # config.optimizer_configs.dtype_momentum = 'bfloat16' + config.optimizer_configs.weight_decay_rate = 2e-3 + config.optimizer_configs.clipping_threshold = 5.0 + config.optimizer_configs.skip_scale_and_bias_regularization = True + + config.frozen_patterns = [] + config.not_frozen_patterns = [('value_perceiver/.*', 0.3), + ('text_encoder/.*', 0.1), + ('img_encoder/.*', 0.1), + ('shared_token_embedder/.*', 0.1), + ('query_head/.*', 0.2), ('out_decoder/.*', 1), + ('key_head/.*', 0.2), ('head_out/.*', 0.2), + ('fusion_encoder/.*', 0.5), + ('att_transform/.*', 0.3), + ('dataset_gate/.*', 0.5)] + + config.grad_clip_configs = ml_collections.ConfigDict() + config.grad_clip_configs.clip_method = 'clip_by_global_norm' + config.grad_clip_configs.clip_value = 1.0 + + config.kb_dataset_names = ['wit_table', 'cc12m_table', 'vqa_table'] + # config.kb_dataset_configs = [{ + # 'train_split': 'train' + # }, { + # 'train_split': 'full[:%d]' % (25000 * n_device) + # }, {}] + config.kb_dataset_configs = [{}, {}, {}] + + config.batch_size = batch_size + config.eval_batch_size = batch_size + config.rng_seed = 0 + config.update_num = False + config.num_training_epochs = 2 + config.data_dtype_str = 'bfloat16' + # Model + config.model_name = 'knowledge_fid' + config.model = ml_collections.ConfigDict() + config.model.image_model = 'vit' + config.model.t5_name = 't5_1_1_large' + # ['t5_1_1_small', 't5_1_1_base', 't5_1_1_large', 't5_1_1_xl', 't5_1_1_xxl'] + config.model.num_fusion_layers = 8 + config.model.n_compressed_tokens = 32 + config.model.key_dim = 512 + config.model.dropout_rate = 0.0 + config.model.temperature = 0.2 + config.model.retr_k = 10 + config.model.retr_data_ratio = 0.2 + config.model.label_smoothing = 1e-2 + config.model.vit_name = 'g/14' + config.model.vit_model_path = 'JFT3b-g/14' + # [JFT3b-B/32, JFT3b-B/16, JFT3b-L/16, JFT3b-g/14, JFT3b-G/14] + config.model.t5_frozen_base = False + config.model.vit_num_frozen_layers = 4 / 5 + config.model.retrieve_local = False + config.model.use_psudo_retr = True + config.model.disentangle = True + config.model.gap = True + config.model.retrieval_ratio = 1e-2 + config.model.n_knowledge_source = len(config.kb_dataset_names) + config.model.qa = False + config.frozen_memory = False + + config.vocab_size = 32120 + config.autoregressive_decoding = ml_collections.ConfigDict() + config.autoregressive_decoding.num_decodes = 1 + config.autoregressive_decoding.beam_search = False + # Dataset. + config.dataset_name = 'web_image_text_generation' + config.dataset_configs = ml_collections.ConfigDict() + + # Learning rate. + config.num_train_examples = TRAIN_DATA_SIZE + steps_per_epoch = TRAIN_DATA_SIZE // config.batch_size + config.lr_configs = ml_collections.ConfigDict() + config.lr_configs.total_steps = int(config.num_training_epochs * + steps_per_epoch) + config.lr_configs.learning_rate_schedule = 'compound' + config.lr_configs.factors = 'constant * rsqrt_decay * linear_warmup' + config.lr_configs.warmup_steps = 20000 + config.lr_configs.timescale = 10000 + # config.lr_configs.steps_per_cycle = config.lr_configs.total_steps + config.lr_configs.base_learning_rate = 1e-3 + config.lr_configs.end_learning_rate = 1e-6 + + # Logging. + config.log_summary_steps = 100 + config.log_eval_steps = 1000 + config.checkpoint_steps = 5000 + config.write_summary = True + config.xprof = True # Profile using xprof + config.checkpoint = True # Do checkpointing. + config.debug_train = False # Debug mode during training. + config.debug_eval = False # Debug mode during eval. + + # Initalisation configs + config.init_from = ml_collections.ConfigDict() + # Initializing from a vidcap model. + # config.init_from.xm = None + # config.init_from.xm = (46259342, 1) # compress=32 + config.init_from.resume = (49611879, 1) # compress=64 + config.init_from.only_params = False + config.init_from.load_key_encoder = False + config.init_from.encoder = ml_collections.ConfigDict() + config.init_from.encoder.init_from_vit = False + config.init_from.encoder.checkpoint_path = None + return config diff --git a/scenic/projects/knowledge_visual_language/configs/wit_memory_large.py b/scenic/projects/knowledge_visual_language/configs/wit_memory_large.py new file mode 100644 index 000000000..d6810b018 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/configs/wit_memory_large.py @@ -0,0 +1,116 @@ +r"""WIT Retrieval + Captioning Pre-Training.""" + +import ml_collections + +TRAIN_DATA_SIZE = 1_000_000_000 + + +def get_config() -> ml_collections.ConfigDict: + """Returns the base experiment configuration.""" + config = ml_collections.ConfigDict() + config.experiment_name = 'image_caption_debug' + + config.optimizer = 'adafactor' + n_device = 128 + batch_size = 12 * 2 * n_device + config.optimizer_configs = ml_collections.ConfigDict() + config.optimizer_configs.momentum = None + # config.optimizer_configs.momentum = 0.9 + # config.optimizer_configs.dtype_momentum = 'bfloat16' + config.optimizer_configs.weight_decay_rate = 1e-3 + config.optimizer_configs.clipping_threshold = 5.0 + config.optimizer_configs.skip_scale_and_bias_regularization = False + + config.not_frozen_patterns = [('value_perceiver/.*', 0.3), + ('text_encoder/.*', 0.1), + ('img_encoder/.*', 0.1), + ('shared_token_embedder/.*', 0.1), + ('query_head/.*', 0.2), ('out_decoder/.*', 1), + ('key_head/.*', 0.2), ('head_out/.*', 0.2), + ('fusion_encoder/.*', 0.5), + ('att_transform/.*', 0.3), + ('dataset_gate/.*', 0.5)] + + config.grad_clip_configs = ml_collections.ConfigDict() + config.grad_clip_configs.clip_method = 'clip_by_global_norm' + config.grad_clip_configs.clip_value = 1.0 + + config.kb_dataset_names = ['wit_table', 'cc12m_table', 'vqa_table'] + config.kb_dataset_configs = [{ + 'train_split': 'train' + }, { + 'train_split': 'full[:%d]' % (50000 * n_device) + }, {}] + + config.batch_size = batch_size + config.eval_batch_size = batch_size + config.rng_seed = 0 + config.update_num = False + config.num_training_epochs = 5 + config.data_dtype_str = 'bfloat16' + # Model + config.model_name = 'knowledge_fid' + config.model = ml_collections.ConfigDict() + config.model.image_model = 'vit' + config.model.t5_name = 't5_1_1_large' + # ['t5_1_1_small', 't5_1_1_base', 't5_1_1_large', 't5_1_1_xl', 't5_1_1_xxl'] + config.model.num_fusion_layers = 8 + config.model.n_compressed_tokens = 64 + config.model.key_dim = 512 + config.model.dropout_rate = 0.0 + config.model.temperature = 0.2 + config.model.retr_k = 10 + config.model.retr_data_ratio = 0.2 + config.model.label_smoothing = 1e-2 + config.model.vit_name = 'L/16' + config.model.vit_model_path = 'JFT3b-L/16' + # [JFT3b-B/32, JFT3b-B/16, JFT3b-L/16, JFT3b-g/14, JFT3b-G/14] + config.model.t5_frozen_base = True + config.model.vit_num_frozen_layers = 5 / 6 + config.model.retrieve_local = True + config.model.disentangle = True + config.model.gap = True + config.model.retrieval_ratio = 1e-2 + config.model.n_knowledge_source = len(config.kb_dataset_names) + + # Dataset. + config.dataset_name = 'web_image_text_generation' + config.dataset_configs = ml_collections.ConfigDict() + + # Learning rate. + config.num_train_examples = TRAIN_DATA_SIZE + steps_per_epoch = TRAIN_DATA_SIZE // config.batch_size + config.lr_configs = ml_collections.ConfigDict() + config.lr_configs.total_steps = int(config.num_training_epochs * + steps_per_epoch) + config.lr_configs.learning_rate_schedule = 'compound' + config.lr_configs.factors = 'constant * rsqrt_decay * linear_warmup' + config.lr_configs.warmup_steps = 20000 + config.lr_configs.timescale = 10000 + # config.lr_configs.steps_per_cycle = config.lr_configs.total_steps + config.lr_configs.base_learning_rate = 1e-3 + config.lr_configs.end_learning_rate = 1e-3 + + # Logging. + config.log_summary_steps = 100 + config.log_eval_steps = 1000 + config.checkpoint_steps = 5000 + config.write_summary = True + config.xprof = True # Profile using xprof + config.checkpoint = True # Do checkpointing. + config.debug_train = False # Debug mode during training. + config.debug_eval = False # Debug mode during eval. + + # Initalisation configs + config.init_from = ml_collections.ConfigDict() + # Initializing from a vidcap model. + config.init_from.xm = None + config.init_from.xm = (45474590, 1) # compress=16 + # config.init_from.xm = (46499437, 1) # compress=64 + # config.init_from.resume = (47241443, 1) # compress=32 + config.init_from.only_params = False + config.init_from.load_key_encoder = False + config.init_from.encoder = ml_collections.ConfigDict() + config.init_from.encoder.init_from_vit = False + config.init_from.encoder.checkpoint_path = None + return config diff --git a/scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_G_froze_config.py b/scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_G_froze_config.py new file mode 100644 index 000000000..4949a4685 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_G_froze_config.py @@ -0,0 +1,101 @@ +r"""WIT Retrieval + Captioning Pre-Training.""" + +import ml_collections + +TRAIN_DATA_SIZE = 5000000 + + +def get_config() -> ml_collections.ConfigDict: + """Returns the base experiment configuration.""" + config = ml_collections.ConfigDict() + config.experiment_name = 'image_caption_debug' + + config.optimizer = 'adafactor' + batch_size = 5 * 2 * 256 + config.optimizer_configs = ml_collections.ConfigDict() + config.optimizer_configs.momentum = None + config.optimizer_configs.weight_decay_rate = 1e-2 + config.optimizer_configs.clipping_threshold = 1.0 + config.optimizer_configs.skip_scale_and_bias_regularization = False + + config.frozen_patterns = [] + config.not_frozen_patterns = [('value_perceiver/.*', 1.5), + ('text_encoder/.*', 1), + ('shared_token_embedder/.*', 0.5), + ('query_head/.*', 1.2), ('out_decoder/.*', 1), + ('key_head/.*', 1.2), ('img_encoder/.*', 1), + ('head_out/.*', 1.2), ('fusion_encoder/.*', 1), + ('att_transform/.*', 1)] + + config.grad_clip_configs = ml_collections.ConfigDict() + config.grad_clip_configs.clip_method = 'clip_by_global_norm' + config.grad_clip_configs.clip_value = 1.0 + + config.batch_size = batch_size + config.eval_batch_size = batch_size + config.rng_seed = 0 + config.update_num = True + config.num_training_epochs = 50 + config.data_dtype_str = 'float32' + + # Model + config.model_name = 'retrieval_image_captioner_soft' + config.model = ml_collections.ConfigDict() + config.model.image_model = 'vit' + config.model.t5_name = 't5_1_1_large' + # ['t5_1_1_small', 't5_1_1_base', 't5_1_1_large', 't5_1_1_xl', 't5_1_1_xxl'] + config.model.num_fusion_layers = 8 + config.model.n_compressed_tokens = 64 + config.model.key_dim = 512 + config.model.dropout_rate = 0.0 + config.model.temperature = 0.2 + config.model.fuse_retrieval = True + config.model.supervised_retrieval = True + config.model.in_batch_neg = True + config.model.retrieval_ratio = 1 / 2 + config.model.label_smoothing = 1e-2 + config.model.vit_name = 'G/14' + config.model.vit_model_path = 'JFT3b-G/14' + config.model.vit_num_frozen_layers = 0.6 + # [JFT3b-B/32, JFT3b-B/16, JFT3b-L/16, JFT3b-g/14, JFT3b-G/14] + + # Dataset. + config.dataset_name = 'wiki_image_text_generation' + config.dataset_configs = ml_collections.ConfigDict() + + # Learning rate. + config.num_train_examples = TRAIN_DATA_SIZE + steps_per_epoch = TRAIN_DATA_SIZE // config.batch_size + config.lr_configs = ml_collections.ConfigDict() + config.lr_configs.total_steps = int(config.num_training_epochs * + steps_per_epoch) + config.lr_configs.learning_rate_schedule = 'compound' + config.lr_configs.factors = 'constant * rsqrt_decay * linear_warmup' + config.lr_configs.warmup_steps = int(0.1 * config.lr_configs.total_steps) + config.lr_configs.timescale = 5000 + # config.lr_configs.steps_per_cycle = config.lr_configs.total_steps + config.lr_configs.base_learning_rate = 3e-4 + config.lr_configs.end_learning_rate = 1e-6 + + # Logging. + config.log_summary_steps = 100 + config.log_eval_steps = 1000 + config.checkpoint_steps = 5000 + config.write_summary = True + config.xprof = True # Profile using xprof + config.checkpoint = True # Do checkpointing. + config.debug_train = False # Debug mode during training. + config.debug_eval = False # Debug mode during eval. + + # Initalisation configs + config.init_from = ml_collections.ConfigDict() + # Initializing from a vidcap model. + # config.init_from.xm = None + config.init_from.xm = (46234383, 1) # adam_before + # config.init_from.xm = (41448318, 1) # image_caption_lamb_5 + config.init_from.only_params = False + config.init_from.load_key_encoder = False + config.init_from.encoder = ml_collections.ConfigDict() + config.init_from.encoder.init_from_vit = False + config.init_from.encoder.checkpoint_path = None + return config diff --git a/scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_base_config.py b/scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_base_config.py new file mode 100644 index 000000000..cc694ce23 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_base_config.py @@ -0,0 +1,100 @@ +r"""WIT Retrieval + Captioning Pre-Training.""" + +import ml_collections + +TRAIN_DATA_SIZE = 5000000 + + +def get_config() -> ml_collections.ConfigDict: + """Returns the base experiment configuration.""" + config = ml_collections.ConfigDict() + config.experiment_name = 'image_caption_debug' + + # config.optimizer = 'adam' + # batch_size = 30 * 2 * 64 + # config.optimizer_configs = ml_collections.ConfigDict() + # config.optimizer_configs.beta1 = 0.9 + # config.optimizer_configs.beta2 = 0.999 + # config.optimizer_configs.epsilon = 1e-6 + # config.optimizer_configs.weight_decay = 1e-2 + # config.optimizer_configs.mu_dtype = 'bfloat16' + + config.optimizer = 'adafactor' + batch_size = 48 * 2 * 64 + config.optimizer_configs = ml_collections.ConfigDict() + config.optimizer_configs.momentum = None + config.optimizer_configs.dtype_momentum = 'bfloat16' + config.optimizer_configs.weight_decay_rate = 1e-2 + config.optimizer_configs.clipping_threshold = 1.0 + config.optimizer_configs.skip_scale_and_bias_regularization = True + + config.grad_clip_configs = ml_collections.ConfigDict() + config.grad_clip_configs.clip_method = 'clip_by_global_norm' + config.grad_clip_configs.clip_value = 1.0 + + config.batch_size = batch_size + config.eval_batch_size = batch_size + config.rng_seed = 0 + config.update_num = True + config.num_training_epochs = 100 + config.data_dtype_str = 'bfloat16' + + # Model + config.model_name = 'retrieval_image_captioner_soft' + config.model = ml_collections.ConfigDict() + config.model.image_model = 'vit' + config.model.t5_name = 't5_1_1_base' + # ['t5_1_1_small', 't5_1_1_base', 't5_1_1_large', 't5_1_1_xl', 't5_1_1_xxl'] + config.model.num_fusion_layers = 6 + config.model.key_dim = 512 + config.model.dropout_rate = 0.0 + config.model.temperature = 0.2 + config.model.fuse_retrieval = True + config.model.supervised_retrieval = True + config.model.in_batch_neg = True + config.model.retrieval_ratio = 1 / 2 + config.model.label_smoothing = 1e-2 + config.model.vit_name = 'B/16' + config.model.vit_model_path = 'JFT3b-B/16' + # [JFT3b-B/32, JFT3b-B/16, JFT3b-L/16, JFT3b-g/14, JFT3b-G/14] + + # Dataset. + config.dataset_name = 'wiki_image_text_generation' + config.dataset_configs = ml_collections.ConfigDict() + + # Learning rate. + config.num_train_examples = TRAIN_DATA_SIZE + steps_per_epoch = TRAIN_DATA_SIZE // config.batch_size + config.lr_configs = ml_collections.ConfigDict() + config.lr_configs.total_steps = int(config.num_training_epochs * + steps_per_epoch) + config.lr_configs.learning_rate_schedule = 'compound' + config.lr_configs.factors = 'constant * rsqrt_decay * linear_warmup' + config.lr_configs.warmup_steps = int(0.1 * config.lr_configs.total_steps) + config.lr_configs.timescale = 5000 + # config.lr_configs.steps_per_cycle = config.lr_configs.total_steps + config.lr_configs.base_learning_rate = 6e-4 + config.lr_configs.end_learning_rate = 1e-6 + + # Logging. + config.log_summary_steps = 100 + config.log_eval_steps = 1000 + config.checkpoint_steps = 5000 + config.write_summary = True + config.xprof = True # Profile using xprof + config.checkpoint = True # Do checkpointing. + config.debug_train = False # Debug mode during training. + config.debug_eval = False # Debug mode during eval. + + # Initalisation configs + config.init_from = ml_collections.ConfigDict() + # Initializing from a vidcap model. + config.init_from.xm = None + # config.init_from.xm = (44775926, 1) # adam_before + # config.init_from.xm = (41448318, 1) # image_caption_lamb_5 + config.init_from.only_params = False + config.init_from.load_key_encoder = False + config.init_from.encoder = ml_collections.ConfigDict() + config.init_from.encoder.init_from_vit = False + config.init_from.encoder.checkpoint_path = None + return config diff --git a/scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_base_froze_config.py b/scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_base_froze_config.py new file mode 100644 index 000000000..3c9419976 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_base_froze_config.py @@ -0,0 +1,101 @@ +r"""WIT Retrieval + Captioning Pre-Training.""" + +import ml_collections + +TRAIN_DATA_SIZE = 5000000 + + +def get_config() -> ml_collections.ConfigDict: + """Returns the base experiment configuration.""" + config = ml_collections.ConfigDict() + config.experiment_name = 'image_caption_debug' + + config.optimizer = 'adafactor' + batch_size = 36 * 2 * 64 + config.optimizer_configs = ml_collections.ConfigDict() + config.optimizer_configs.momentum = None + config.optimizer_configs.weight_decay_rate = 1e-2 + config.optimizer_configs.clipping_threshold = 1.0 + config.optimizer_configs.skip_scale_and_bias_regularization = False + + config.frozen_patterns = [] + config.not_frozen_patterns = [('value_perceiver/.*', 2), + ('text_encoder/.*', 1), + ('shared_token_embedder/.*', 0.5), + ('query_head/.*', 1.5), ('out_decoder/.*', 1), + ('key_head/.*', 1.5), ('img_encoder/.*', 1), + ('head_out/.*', 1.5), ('fusion_encoder/.*', 1), + ('att_transform/.*', 1)] + + config.grad_clip_configs = ml_collections.ConfigDict() + config.grad_clip_configs.clip_method = 'clip_by_global_norm' + config.grad_clip_configs.clip_value = 1.0 + + config.batch_size = batch_size + config.eval_batch_size = batch_size + config.rng_seed = 0 + config.update_num = True + config.num_training_epochs = 100 + config.data_dtype_str = 'float32' + + # Model + config.model_name = 'retrieval_image_captioner_soft' + config.model = ml_collections.ConfigDict() + config.model.image_model = 'vit' + config.model.t5_name = 't5_1_1_base' + # ['t5_1_1_small', 't5_1_1_base', 't5_1_1_large', 't5_1_1_xl', 't5_1_1_xxl'] + config.model.num_fusion_layers = 6 + config.model.n_compressed_tokens = 64 + config.model.key_dim = 512 + config.model.dropout_rate = 0.0 + config.model.temperature = 0.2 + config.model.fuse_retrieval = True + config.model.supervised_retrieval = True + config.model.in_batch_neg = True + config.model.retrieval_ratio = 1 / 2 + config.model.label_smoothing = 1e-2 + config.model.vit_name = 'B/16' + config.model.vit_model_path = 'JFT3b-B/16' + config.model.vit_num_frozen_layers = -1 + # [JFT3b-B/32, JFT3b-B/16, JFT3b-L/16, JFT3b-g/14, JFT3b-G/14] + + # Dataset. + config.dataset_name = 'wiki_image_text_generation' + config.dataset_configs = ml_collections.ConfigDict() + + # Learning rate. + config.num_train_examples = TRAIN_DATA_SIZE + steps_per_epoch = TRAIN_DATA_SIZE // config.batch_size + config.lr_configs = ml_collections.ConfigDict() + config.lr_configs.total_steps = int(config.num_training_epochs * + steps_per_epoch) + config.lr_configs.learning_rate_schedule = 'compound' + config.lr_configs.factors = 'constant * rsqrt_decay * linear_warmup' + config.lr_configs.warmup_steps = int(0.1 * config.lr_configs.total_steps) + config.lr_configs.timescale = 5000 + # config.lr_configs.steps_per_cycle = config.lr_configs.total_steps + config.lr_configs.base_learning_rate = 6e-4 + config.lr_configs.end_learning_rate = 1e-6 + + # Logging. + config.log_summary_steps = 100 + config.log_eval_steps = 1000 + config.checkpoint_steps = 5000 + config.write_summary = True + config.xprof = True # Profile using xprof + config.checkpoint = True # Do checkpointing. + config.debug_train = False # Debug mode during training. + config.debug_eval = False # Debug mode during eval. + + # Initalisation configs + config.init_from = ml_collections.ConfigDict() + # Initializing from a vidcap model. + config.init_from.xm = None + # config.init_from.xm = (44775926, 1) # adam_before + # config.init_from.xm = (41448318, 1) # image_caption_lamb_5 + config.init_from.only_params = False + config.init_from.load_key_encoder = False + config.init_from.encoder = ml_collections.ConfigDict() + config.init_from.encoder.init_from_vit = False + config.init_from.encoder.checkpoint_path = None + return config diff --git a/scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_g_froze_config.py b/scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_g_froze_config.py new file mode 100644 index 000000000..f3195d363 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_g_froze_config.py @@ -0,0 +1,101 @@ +r"""WIT Retrieval + Captioning Pre-Training.""" + +import ml_collections + +TRAIN_DATA_SIZE = 5000000 + + +def get_config() -> ml_collections.ConfigDict: + """Returns the base experiment configuration.""" + config = ml_collections.ConfigDict() + config.experiment_name = 'image_caption_debug' + + config.optimizer = 'adafactor' + batch_size = 8 * 2 * 128 + config.optimizer_configs = ml_collections.ConfigDict() + config.optimizer_configs.momentum = None + config.optimizer_configs.weight_decay_rate = 1e-2 + config.optimizer_configs.clipping_threshold = 1.0 + config.optimizer_configs.skip_scale_and_bias_regularization = False + + config.frozen_patterns = [] + config.not_frozen_patterns = [('value_perceiver/.*', 1.5), + ('text_encoder/.*', 1), + ('shared_token_embedder/.*', 0.5), + ('query_head/.*', 1.2), ('out_decoder/.*', 1), + ('key_head/.*', 1.2), ('img_encoder/.*', 1), + ('head_out/.*', 1.2), ('fusion_encoder/.*', 1), + ('att_transform/.*', 1)] + + config.grad_clip_configs = ml_collections.ConfigDict() + config.grad_clip_configs.clip_method = 'clip_by_global_norm' + config.grad_clip_configs.clip_value = 1.0 + + config.batch_size = batch_size + config.eval_batch_size = batch_size + config.rng_seed = 0 + config.update_num = True + config.num_training_epochs = 50 + config.data_dtype_str = 'float32' + + # Model + config.model_name = 'retrieval_image_captioner_soft' + config.model = ml_collections.ConfigDict() + config.model.image_model = 'vit' + config.model.t5_name = 't5_1_1_large' + # ['t5_1_1_small', 't5_1_1_base', 't5_1_1_large', 't5_1_1_xl', 't5_1_1_xxl'] + config.model.num_fusion_layers = 8 + config.model.n_compressed_tokens = 64 + config.model.key_dim = 512 + config.model.dropout_rate = 0.0 + config.model.temperature = 0.2 + config.model.fuse_retrieval = True + config.model.supervised_retrieval = True + config.model.in_batch_neg = True + config.model.retrieval_ratio = 1 / 2 + config.model.label_smoothing = 1e-2 + config.model.vit_name = 'g/14' + config.model.vit_model_path = 'JFT3b-g/14' + config.model.vit_num_frozen_layers = 0.5 + # [JFT3b-B/32, JFT3b-B/16, JFT3b-L/16, JFT3b-g/14, JFT3b-G/14] + + # Dataset. + config.dataset_name = 'wiki_image_text_generation' + config.dataset_configs = ml_collections.ConfigDict() + + # Learning rate. + config.num_train_examples = TRAIN_DATA_SIZE + steps_per_epoch = TRAIN_DATA_SIZE // config.batch_size + config.lr_configs = ml_collections.ConfigDict() + config.lr_configs.total_steps = int(config.num_training_epochs * + steps_per_epoch) + config.lr_configs.learning_rate_schedule = 'compound' + config.lr_configs.factors = 'constant * rsqrt_decay * linear_warmup' + config.lr_configs.warmup_steps = int(0.1 * config.lr_configs.total_steps) + config.lr_configs.timescale = 5000 + # config.lr_configs.steps_per_cycle = config.lr_configs.total_steps + config.lr_configs.base_learning_rate = 4e-4 + config.lr_configs.end_learning_rate = 1e-6 + + # Logging. + config.log_summary_steps = 100 + config.log_eval_steps = 1000 + config.checkpoint_steps = 5000 + config.write_summary = True + config.xprof = True # Profile using xprof + config.checkpoint = True # Do checkpointing. + config.debug_train = False # Debug mode during training. + config.debug_eval = False # Debug mode during eval. + + # Initalisation configs + config.init_from = ml_collections.ConfigDict() + # Initializing from a vidcap model. + config.init_from.xm = None + # config.init_from.xm = (44775926, 1) # adam_before + # config.init_from.xm = (41448318, 1) # image_caption_lamb_5 + config.init_from.only_params = False + config.init_from.load_key_encoder = False + config.init_from.encoder = ml_collections.ConfigDict() + config.init_from.encoder.init_from_vit = False + config.init_from.encoder.checkpoint_path = None + return config diff --git a/scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_large_config.py b/scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_large_config.py new file mode 100644 index 000000000..f0822f760 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_large_config.py @@ -0,0 +1,99 @@ +r"""WIT Retrieval + Captioning Pre-Training.""" + +import ml_collections + +TRAIN_DATA_SIZE = 5000000 + + +def get_config() -> ml_collections.ConfigDict: + """Returns the base experiment configuration.""" + config = ml_collections.ConfigDict() + config.experiment_name = 'image_caption_debug' + + # config.optimizer = 'adam' + # batch_size = 30 * 2 * 64 + # config.optimizer_configs = ml_collections.ConfigDict() + # config.optimizer_configs.beta1 = 0.9 + # config.optimizer_configs.beta2 = 0.999 + # config.optimizer_configs.epsilon = 1e-6 + # config.optimizer_configs.weight_decay = 1e-2 + # config.optimizer_configs.mu_dtype = 'bfloat16' + + config.optimizer = 'adafactor' + batch_size = 12 * 2 * 128 + config.optimizer_configs = ml_collections.ConfigDict() + config.optimizer_configs.momentum = 0.9 + config.dtype_momentum = 'bfloat16' + config.optimizer_configs.weight_decay_rate = 1e-2 + config.optimizer_configs.skip_scale_and_bias_regularization = True + + config.grad_clip_configs = ml_collections.ConfigDict() + config.grad_clip_configs.clip_method = 'clip_by_global_norm' + config.grad_clip_configs.clip_value = 1.0 + + config.batch_size = batch_size + config.eval_batch_size = batch_size + config.rng_seed = 0 + config.update_num = True + config.num_training_epochs = 100 + config.data_dtype_str = 'bfloat16' + + # Model + config.model_name = 'retrieval_image_captioner_soft' + config.model = ml_collections.ConfigDict() + config.model.image_model = 'vit' + config.model.t5_name = 't5_1_1_large' + # ['t5_1_1_small', 't5_1_1_base', 't5_1_1_large', 't5_1_1_xl', 't5_1_1_xxl'] + config.model.num_fusion_layers = 8 + config.model.key_dim = 512 + config.model.dropout_rate = 0.0 + config.model.temperature = 0.2 + config.model.fuse_retrieval = True + config.model.supervised_retrieval = True + config.model.in_batch_neg = True + config.model.retrieval_ratio = 1 / 3 + config.model.label_smoothing = 1e-2 + config.model.vit_name = 'B/16' + config.model.vit_model_path = 'JFT3b-L/16' + # [JFT3b-B/32, JFT3b-B/16, JFT3b-L/16, JFT3b-g/14, JFT3b-G/14] + + # Dataset. + config.dataset_name = 'wiki_image_text_generation' + config.dataset_configs = ml_collections.ConfigDict() + + # Learning rate. + config.num_train_examples = TRAIN_DATA_SIZE + steps_per_epoch = TRAIN_DATA_SIZE // config.batch_size + config.lr_configs = ml_collections.ConfigDict() + config.lr_configs.total_steps = int(config.num_training_epochs * + steps_per_epoch) + config.lr_configs.learning_rate_schedule = 'compound' + config.lr_configs.factors = 'constant * rsqrt_decay * linear_warmup' + config.lr_configs.warmup_steps = 10000 + config.lr_configs.timescale = 5000 + # config.lr_configs.steps_per_cycle = config.lr_configs.total_steps + config.lr_configs.base_learning_rate = 4e-4 + config.lr_configs.end_learning_rate = 1e-6 + + # Logging. + config.log_summary_steps = 100 + config.log_eval_steps = 1000 + config.checkpoint_steps = 5000 + config.write_summary = True + config.xprof = True # Profile using xprof + config.checkpoint = True # Do checkpointing. + config.debug_train = False # Debug mode during training. + config.debug_eval = False # Debug mode during eval. + + # Initalisation configs + config.init_from = ml_collections.ConfigDict() + # Initializing from a vidcap model. + config.init_from.xm = None + # config.init_from.xm = (41988362, 1) # adam_before + # config.init_from.xm = (41448318, 1) # image_caption_lamb_5 + config.init_from.only_params = False + config.init_from.load_key_encoder = False + config.init_from.encoder = ml_collections.ConfigDict() + config.init_from.encoder.init_from_vit = False + config.init_from.encoder.checkpoint_path = None + return config diff --git a/scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_large_froze_config.py b/scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_large_froze_config.py new file mode 100644 index 000000000..67698c1ad --- /dev/null +++ b/scenic/projects/knowledge_visual_language/configs/wit_retrieval_soft_large_froze_config.py @@ -0,0 +1,100 @@ +r"""WIT Retrieval + Captioning Pre-Training.""" + +import ml_collections + +TRAIN_DATA_SIZE = 5000000 + + +def get_config() -> ml_collections.ConfigDict: + """Returns the base experiment configuration.""" + config = ml_collections.ConfigDict() + config.experiment_name = 'image_caption_debug' + + config.optimizer = 'adafactor' + batch_size = 12 * 2 * 128 + config.optimizer_configs = ml_collections.ConfigDict() + config.optimizer_configs.momentum = None + config.optimizer_configs.weight_decay_rate = 1e-2 + config.optimizer_configs.clipping_threshold = 1.0 + config.optimizer_configs.skip_scale_and_bias_regularization = False + + config.frozen_patterns = [] + config.not_frozen_patterns = [('value_perceiver/.*', 2), + ('text_encoder/.*', 1), + ('shared_token_embedder/.*', 0.5), + ('query_head/.*', 1.5), ('out_decoder/.*', 1), + ('key_head/.*', 1.5), ('img_encoder/.*', 1), + ('head_out/.*', 1.5), ('fusion_encoder/.*', 1), + ('att_transform/.*', 1)] + + config.grad_clip_configs = ml_collections.ConfigDict() + config.grad_clip_configs.clip_method = 'clip_by_global_norm' + config.grad_clip_configs.clip_value = 1.0 + + config.batch_size = batch_size + config.eval_batch_size = batch_size + config.rng_seed = 0 + config.update_num = True + config.num_training_epochs = 100 + config.data_dtype_str = 'float32' + + # Model + config.model_name = 'retrieval_image_captioner_soft' + config.model = ml_collections.ConfigDict() + config.model.image_model = 'vit' + config.model.t5_name = 't5_1_1_large' + # ['t5_1_1_small', 't5_1_1_base', 't5_1_1_large', 't5_1_1_xl', 't5_1_1_xxl'] + config.model.num_fusion_layers = 8 + config.model.key_dim = 512 + config.model.dropout_rate = 0.0 + config.model.temperature = 0.2 + config.model.fuse_retrieval = True + config.model.supervised_retrieval = True + config.model.in_batch_neg = True + config.model.retrieval_ratio = 1 / 2 + config.model.label_smoothing = 1e-2 + config.model.vit_name = 'L/16' + config.model.vit_model_path = 'JFT3b-L/16' + config.model.vit_num_frozen_layers = 1 / 2 + # [JFT3b-B/32, JFT3b-B/16, JFT3b-L/16, JFT3b-g/14, JFT3b-G/14] + + # Dataset. + config.dataset_name = 'wiki_image_text_generation' + config.dataset_configs = ml_collections.ConfigDict() + + # Learning rate. + config.num_train_examples = TRAIN_DATA_SIZE + steps_per_epoch = TRAIN_DATA_SIZE // config.batch_size + config.lr_configs = ml_collections.ConfigDict() + config.lr_configs.total_steps = int(config.num_training_epochs * + steps_per_epoch) + config.lr_configs.learning_rate_schedule = 'compound' + config.lr_configs.factors = 'constant * rsqrt_decay * linear_warmup' + config.lr_configs.warmup_steps = int(0.1 * config.lr_configs.total_steps) + config.lr_configs.timescale = 5000 + # config.lr_configs.steps_per_cycle = config.lr_configs.total_steps + config.lr_configs.base_learning_rate = 8e-4 + config.lr_configs.end_learning_rate = 1e-6 + + # Logging. + config.log_summary_steps = 100 + config.log_eval_steps = 1000 + config.checkpoint_steps = 5000 + config.write_summary = True + config.xprof = True # Profile using xprof + config.checkpoint = True # Do checkpointing. + config.debug_train = False # Debug mode during training. + config.debug_eval = False # Debug mode during eval. + + # Initalisation configs + config.init_from = ml_collections.ConfigDict() + # Initializing from a vidcap model. + config.init_from.xm = None + # config.init_from.xm = (44775926, 1) # adam_before + # config.init_from.xm = (41448318, 1) # image_caption_lamb_5 + config.init_from.only_params = False + config.init_from.load_key_encoder = False + config.init_from.encoder = ml_collections.ConfigDict() + config.init_from.encoder.init_from_vit = False + config.init_from.encoder.checkpoint_path = None + return config diff --git a/scenic/projects/knowledge_visual_language/data/__init__.py b/scenic/projects/knowledge_visual_language/data/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/scenic/projects/knowledge_visual_language/data/cc12m_generation_dataset.py b/scenic/projects/knowledge_visual_language/data/cc12m_generation_dataset.py new file mode 100644 index 000000000..063e0e853 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/data/cc12m_generation_dataset.py @@ -0,0 +1,262 @@ +"""Dataset and Loader for CC12M dataset for caption generation pre-training. + +Key Changes: 1) use T5 tokenizer; 2) Use text prefix as enoder input; +3) add autoregressive output. + +TODO(ziniu): probably consider image masking? +""" +import functools +from typing import Optional + +from absl import logging +from flax import jax_utils +# import jax +import jax.numpy as jnp +import ml_collections +from scenic.dataset_lib import dataset_utils +from scenic.dataset_lib import datasets +from scenic.dataset_lib.big_transfer import bit +from scenic.dataset_lib.big_transfer import builder +from scenic.dataset_lib import web_image_text_dataset +from scenic.projects.knowledge_visual_language.data import data_utils + +FILTER_LENGTH = 16 +PREFIX_MAX_LENGTH = 12 +OUTPUT_MAX_LENGTH = 48 +IMAGE_SIZE = 224 + + +def get_default_dataset_config(runlocal=False, additional_valid_dataset=True): + """Gets default configs for CC12M dataset.""" + dataset_configs = ml_collections.ConfigDict() + # Add path to your data here: + dataset_configs.dataset_dir = '' + dataset_configs.train_split = 'full[10000:]' + MAX_LENGTH = OUTPUT_MAX_LENGTH # pylint: disable=invalid-name + pp_common = ( + f'|t5_tokenize(max_num_tokens={MAX_LENGTH}, inkey="texts",' + f' prompt="{data_utils.CAPTION_PREFIX}")|keep("image", "tokens")' + ) + dataset_configs.max_num_tokens = MAX_LENGTH + dataset_configs.image_size = IMAGE_SIZE + dataset_configs.pp_train = ( + f'decode|resize(resize_size={IMAGE_SIZE})|value_range(-1,1)' + pp_common + ) + dataset_configs.shuffle_buffer_size = 250000 if not runlocal else 50 + pp_common_eval = f'decode|resize(resize_size={IMAGE_SIZE})|value_range(-1,1)' + pp_argus_eval = pp_common_eval + pp_common + sub = '[:4]' if runlocal else '' + if additional_valid_dataset: + pp_coco_eval = ( + pp_common_eval + + '|coco_captions(inkey="captions", outkey="texts")' + + pp_common + ) + # pp_imagenet_eval = pp_common_eval + ( + # '|clip_i1k_label_names(inkey="label", outkey="texts")') + pp_common + dataset_configs.val_split = [ + ( + 'val', + dataset_configs.dataset, + ['full[:10000]', f'full{sub}'][runlocal], + pp_argus_eval, + ), + ('coco', 'coco_captions', f'val{sub}', pp_coco_eval), + # ('imagenet', 'imagenet2012', f'validation{sub}', pp_imagenet_eval), + ] + else: + dataset_configs.val_split = f'full{sub}' if runlocal else 'full[:50000]' + dataset_configs.pp_eval = pp_argus_eval + + dataset_configs.val_cache = 'loaded' # Unfortunately, "batched" gets us OOM. + dataset_configs.vocab_size = data_utils.VOCAB_SIZE_T5 + dataset_configs.prefetch_to_device = 2 + return dataset_configs + + +@datasets.add_dataset('cc12m_generation') +def get_dataset( + *, + batch_size, + eval_batch_size, + num_shards, + dtype_str='float32', + shuffle_seed=0, + rng=None, + dataset_configs=None, + dataset_service_address: Optional[str] = None, +): + """Returns generators for the CC12M train, validation and test sets. + + Args: + batch_size: int; Determines the train batch size. + eval_batch_size: int; Determines the evaluation batch size. + num_shards: int; Number of shards --> batch shape: [num_shards, bs, ...]. + dtype_str: Data type of the image (e.g. 'float32'). + shuffle_seed: int; Seed for shuffling the training data. Not used. + rng: JAX rng key, which can be used for augmentation, shuffling, etc. + dataset_configs: dict; Dataset specific configurations. + dataset_service_address: If set, will distribute the training dataset using + the given tf.data service at the given address. + + Returns: + A dataset_utils.Dataset() which includes a train_iter, a valid_iter, + a test_iter, and a dict of meta_data. + """ + default_dataset_config = get_default_dataset_config( + runlocal=False, additional_valid_dataset=True + ) + if dataset_configs: + default_dataset_config.update(dataset_configs) + + dataset_configs = default_dataset_config + + del rng + assert dataset_configs is not None + logging.info( + 'Loading train split of the %sfrom cc12m dataset.', + dataset_configs.dataset, + ) + + def pp_fn(x, how): + pp = builder.get_preprocess_fn(how) + example = pp(x) + return {'image': example['image'], 'tokens': example['tokens']} + + # E.g. for testing with TAP. + shuffle_buffer_size = ( + 1000 if num_shards == 1 else dataset_configs.shuffle_buffer_size + ) + + train_ds = data_utils.get_data( + dataset=dataset_configs.dataset, + split=dataset_configs.train_split, + data_dir=dataset_configs.get('dataset_dir'), + batch_size=batch_size, + filter_fn=functools.partial( + data_utils.filter_text_length, filter_len=FILTER_LENGTH + ), + preprocess_fn=functools.partial(pp_fn, how=dataset_configs.pp_train), + shuffle_buffer_size=shuffle_buffer_size, + prefetch=dataset_configs.get('prefetch_to_host', 2), + cache='loaded', + ignore_errors=True, + ) + + if dataset_service_address: + if shuffle_seed is not None: + raise ValueError( + 'Using dataset service with a random seed causes each ' + 'worker to produce exactly the same data. Add ' + 'config.shuffle_seed = None to your config if you ' + 'want to run with dataset service.' + ) + logging.info('Using the tf.data service at %s', dataset_service_address) + assert shuffle_buffer_size is not None + train_ds = dataset_utils.distribute(train_ds, dataset_service_address) + + n_train_ex = dataset_utils.get_num_examples( + dataset_configs.dataset, + dataset_configs.train_split, + data_dir=dataset_configs.get('dataset_dir'), + ) + + maybe_pad_batches_train = functools.partial( + dataset_utils.maybe_pad_batch, + inputs_key='encoder_input_image', + train=True, + batch_size=batch_size, + ) + shard_batches = functools.partial(dataset_utils.shard, n_devices=num_shards) + map_generation_split_batches = functools.partial( + data_utils.map_generation_split, prefix_h=PREFIX_MAX_LENGTH + ) + + train_iter = iter(train_ds) + train_iter = map(map_generation_split_batches, train_iter) + train_iter = map(dataset_utils.tf_to_numpy, train_iter) + train_iter = map(maybe_pad_batches_train, train_iter) + if num_shards > 0: + train_iter = map(shard_batches, train_iter) + if dataset_configs.prefetch_to_device: + train_iter = jax_utils.prefetch_to_device( + train_iter, dataset_configs.prefetch_to_device + ) + + logging.info( + 'Loading validation split of the %sfrom cc12m dataset.', + dataset_configs.dataset, + ) + maybe_pad_batches_eval = functools.partial( + dataset_utils.maybe_pad_batch, + inputs_key='encoder_input_image', + train=False, + batch_size=eval_batch_size, + ) + + def _get_eval_iter(dataset, split, pp_eval): + val_ds = data_utils.get_data( + dataset=dataset, + split=split, + data_dir=dataset_configs.get('dataset_dir'), + batch_size=eval_batch_size, + filter_fn=functools.partial( + data_utils.filter_text_length, filter_len=FILTER_LENGTH + ), + preprocess_fn=functools.partial(pp_fn, how=pp_eval), + cache='batched', + repeat_after_batching=True, + drop_remainder=False, + ) + + valid_iter = iter(val_ds) + valid_iter = map(map_generation_split_batches, valid_iter) + valid_iter = map(bit.tf_to_numpy, valid_iter) + valid_iter = map(maybe_pad_batches_eval, valid_iter) + if num_shards > 0: + valid_iter = map(shard_batches, valid_iter) + if dataset_configs.prefetch_to_device: + valid_iter = jax_utils.prefetch_to_device( + valid_iter, dataset_configs.prefetch_to_device + ) + + return valid_iter + + def _get_num_eval_examples(dataset, split, data_dir): + return dataset_utils.get_num_examples(dataset, split, data_dir) + + if isinstance(dataset_configs.val_split, str): + valid_iter = _get_eval_iter( + dataset_configs.dataset, + dataset_configs.val_split, + dataset_configs.pp_eval, + ) + n_eval_ex = _get_num_eval_examples( + dataset_configs.dataset, + dataset_configs.val_split, + data_dir=dataset_configs.get('dataset_dir'), + ) + else: + valid_iter, n_eval_ex = {}, {} + for eval_spec in dataset_configs.val_split: + name, dataset, split, pp_eval = eval_spec + valid_iter[name] = _get_eval_iter(dataset, split, pp_eval) + n_eval_ex[name] = _get_num_eval_examples( + dataset, split, data_dir=dataset_configs.get('dataset_dir') + ) + + meta_data = {'num_train_examples': n_train_ex, 'num_eval_examples': n_eval_ex} + + if dataset_configs.get('extra_meta_data'): + for k, v in dataset_configs.extra_meta_data.items(): + meta_data[k] = v + + image_shape = (-1, dataset_configs.image_size, dataset_configs.image_size, 3) + predix_shape = (-1, PREFIX_MAX_LENGTH) + input_shape = (-1, OUTPUT_MAX_LENGTH) + + meta_data['encoder_input_image_spec'] = (image_shape, getattr(jnp, dtype_str)) + meta_data['encoder_input_tokens_spec'] = (predix_shape, jnp.int16) + meta_data['decoder_input_tokens_spec'] = (input_shape, jnp.int16) + meta_data['decoder_target_tokens_spec'] = (input_shape, jnp.int16) + return dataset_utils.Dataset(train_iter, valid_iter, None, meta_data) diff --git a/scenic/projects/knowledge_visual_language/data/cc12m_table_dataset.py b/scenic/projects/knowledge_visual_language/data/cc12m_table_dataset.py new file mode 100644 index 000000000..d4369f24b --- /dev/null +++ b/scenic/projects/knowledge_visual_language/data/cc12m_table_dataset.py @@ -0,0 +1,149 @@ +"""Dataset and Loader for cc12m dataset for retrieval training. + +Only prepare paired with knowledge (contextualalized passages) +""" +import functools +from typing import Optional + +from absl import logging +import jax +import jax.numpy as jnp +import ml_collections +from scenic.dataset_lib import dataset_utils +from scenic.dataset_lib import datasets +from scenic.dataset_lib.big_transfer import builder +from scenic.projects.knowledge_visual_language.data import data_utils +import tensorflow as tf + +SPAN_MAX_LENGTH = 5 +OUTPUT_MAX_LENGTH = 36 +KNOWLEDGE_MAX_LENGTH = 320 +IMAGE_SIZE = 224 + + +def get_default_dataset_config(): + """Gets default configs for wit_internal (en) dataset.""" + dataset_configs = ml_collections.ConfigDict() + # Add path to your data here: + dataset_configs.dataset_dir = '' + dataset_configs.train_split = 'full' + dataset_configs.output_max_num_tokens = OUTPUT_MAX_LENGTH + dataset_configs.knowledge_max_num_tokens = OUTPUT_MAX_LENGTH + dataset_configs.image_size = IMAGE_SIZE + dataset_configs.pp_train = ( + f'decode|resize(resize_size={IMAGE_SIZE})|value_range(-1,1)|t5_tokenize(max_num_tokens={KNOWLEDGE_MAX_LENGTH},' + ' inkey="texts", outkey="knowledge_tokens",' + f' prompt="{data_utils.KNOWLEDGE_PREFIX}")|keep("image",' + ' "knowledge_tokens", "canonical_doc_id")' + ) + dataset_configs.vocab_size = data_utils.VOCAB_SIZE_T5 + dataset_configs.prefetch_to_device = 2 + return dataset_configs + + +@datasets.add_dataset('cc12m_table') +def get_dataset( + *, + batch_size, + eval_batch_size, + num_shards, + dtype_str='float32', + shuffle_seed=None, + rng=None, + dataset_configs=None, + dataset_service_address: Optional[str] = None, +): + """Returns generators for the CC12M train, validation and test sets. + + Args: + batch_size: int; Determines the train batch size. + eval_batch_size: int; Determines the evaluation batch size. + num_shards: int; Number of shards --> batch shape: [num_shards, bs, ...]. + dtype_str: Data type of the image (e.g. 'float32'). + shuffle_seed: int; Seed for shuffling the training data. Not used. + rng: JAX rng key, which can be used for augmentation, shuffling, etc. + dataset_configs: dict; Dataset specific configurations. + dataset_service_address: If set, will distribute the training dataset using + the given tf.data service at the given address. + + Returns: + A dataset_utils.Dataset() which includes a train_iter, a valid_iter, + a test_iter, and a dict of meta_data. + """ + del batch_size + default_dataset_config = get_default_dataset_config() + if dataset_configs: + default_dataset_config.update(dataset_configs) + + dataset_configs = default_dataset_config + + del rng + assert dataset_configs is not None + logging.info('Loading train split of the %s', dataset_configs.dataset) + + def pp_fn(x, how): + pp = builder.get_preprocess_fn(how, remove_tpu_dtypes=False) + example = pp(x) + example['image'] = tf.cast(example['image'], dtype=dtype_str) + return example + + # E.g. for testing with TAP. + shuffle_buffer_size = None + + train_ds = data_utils.get_data( + dataset=dataset_configs.dataset, + split=dataset_configs.train_split, + batch_size=eval_batch_size, + preprocess_fn=functools.partial(pp_fn, how=dataset_configs.pp_train), + shuffle_buffer_size=None, + shuffle_files=False, + prefetch=dataset_configs.get('prefetch_to_host', 2), + cache='loaded', + ignore_errors=False, + drop_remainder=True, + ) + + if dataset_service_address: + if shuffle_seed is not None: + raise ValueError( + 'Using dataset service with a random seed causes each ' + 'worker to produce exactly the same data. Add ' + 'config.shuffle_seed = None to your config if you ' + 'want to run with dataset service.' + ) + logging.info('Using the tf.data service at %s', dataset_service_address) + assert shuffle_buffer_size is not None + train_ds = dataset_utils.distribute(train_ds, dataset_service_address) + + n_train_ex = dataset_utils.get_num_examples( + dataset_configs.dataset, + dataset_configs.train_split, + data_dir=dataset_configs.get('dataset_dir'), + ) + + shard_batches = functools.partial(dataset_utils.shard, n_devices=num_shards) + maybe_pad_batches_train = functools.partial( + dataset_utils.maybe_pad_batch, + inputs_key='image', + train=True, + batch_size=eval_batch_size, + ) + + train_iter = iter(train_ds) + train_iter = map(dataset_utils.tf_to_numpy, train_iter) + train_iter = map(maybe_pad_batches_train, train_iter) + if num_shards > 0: + train_iter = map(shard_batches, train_iter) + + meta_data = { + 'num_train_examples': n_train_ex, + 'example_per_shard': int(n_train_ex // jax.process_count()), + 'batch_size': eval_batch_size, + } + + image_shape = (dataset_configs.image_size, dataset_configs.image_size, 3) + knowledge_shape = (KNOWLEDGE_MAX_LENGTH + data_utils.PROMPT_LENGTH,) + + meta_data['image_spec'] = (image_shape, getattr(jnp, dtype_str)) + meta_data['knowledge_spec'] = (knowledge_shape, jnp.int16) + return dataset_utils.Dataset(train_iter, None, None, meta_data) diff --git a/scenic/projects/knowledge_visual_language/data/data_utils.py b/scenic/projects/knowledge_visual_language/data/data_utils.py new file mode 100644 index 000000000..60bbe7995 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/data/data_utils.py @@ -0,0 +1,308 @@ +"""Util functions for preparing dataset wrapper in scenic.""" +import functools +import multiprocessing as mp + +from absl import logging +import apache_beam as beam +from big_vision.datasets.imagenet import class_names as imagenet_class_names +import numpy as np +from scenic.dataset_lib import dataset_utils +from scenic.dataset_lib.big_transfer.preprocessing import utils +from scenic.dataset_lib.big_transfer.registry import registry +from scenic.projects.t5 import tokenizer as t5_tokenizer +import tensorflow as tf +import tqdm + +import sstable +import shards + + +# import numpy as np + +CAPTION_PREFIX = 'Please describe this image:' +VQA_PREFIX = 'Please based on this image to answer the question:' +KNOWLEDGE_PREFIX = 'Please summarize this knowledge:' + +PROMPT_LENGTH = 6 +VOCAB_SIZE_T5 = 32128 +MASK_TOKEN_ID = 32099 +BOS_ID = 32001 +EOS_ID = 1 +SEP_ID = 32000 + + +@registry.Registry.register('preprocess_ops.clip_i1k_label_names', 'function') +@utils.InKeyOutKey(indefault='label', outdefault='texts') +def get_pp_clip_i1k_label_names(): + """Convert i1k label numbers to strings, using CLIP's class names.""" + + def _pp_imagenet_labels(label): + return tf.reshape( + tf.gather(imagenet_class_names.CLIP_IMAGENET_CLASS_NAMES, label), (-1,) + ) + + return _pp_imagenet_labels + + +@registry.Registry.register('preprocess_ops.coco_captions', 'function') +@utils.InKeyOutKey(indefault='captions', outdefault='texts') +def get_coco_captions(): + """Extracts coco's captions from nested dict.""" + + def _pp_coco_captions(captions, sample=False): + t = captions['text'] + if sample: + ts = tf.concat([t, ['']], axis=0) + num_texts = tf.reduce_max([tf.shape(ts)[0] - 1, 1]) + idx = tf.random.uniform([], 0, num_texts, dtype=tf.int16) + else: + idx = tf.argmax(tf.strings.length(t)) + return tf.reshape(tf.strings.lower(t[idx]), (-1,)) + + return _pp_coco_captions + + +@registry.Registry.register('preprocess_ops.t5_tokenize', 'function') +@utils.InKeyOutKey(indefault='texts', outdefault='tokens') +def get_t5_tokenize(max_num_tokens, append_eos=True, prompt=None): + """Tokenizes a text using T5 Tokenizer.""" + + tokenizer = t5_tokenizer.build_dmvr_sp_model() + tokenizer.initialize() + if prompt is None: + prompt = [BOS_ID] + else: + prompt = tokenizer.string_to_indices(prompt, max_num_tokens=None) + prompt = tf.concat([[BOS_ID], prompt], axis=-1) + + def _t5_tokenize(texts): + if texts.shape.ndims == 0: + texts = tf.reshape(texts, (-1,)) + tokens = tokenizer.string_tensor_to_indices( + string_tensor=texts, + max_num_tokens=max_num_tokens, + append_eos=append_eos, + )[0] + return tf.cast(tf.concat([prompt, tokens], axis=-1), tf.int16) + + return _t5_tokenize + + +@registry.Registry.register('preprocess_ops.list_t5_tokenize', 'function') +@utils.InKeyOutKey(indefault='texts', outdefault='tokens') +def get_list_t5_tokenize(max_num_tokens, prompt=None): + """Tokenizes a text using T5 Tokenizer.""" + + tokenizer = t5_tokenizer.build_dmvr_sp_model() + tokenizer.initialize() + if prompt is None: + prompt = [BOS_ID] + else: + prompt = tokenizer.string_to_indices(prompt, max_num_tokens=None) + prompt = tf.concat([[BOS_ID], prompt], axis=-1) + + def add_prompt(tokens): + return tf.concat([prompt, tokens], axis=-1) + + def _list_t5_tokenize(texts): + if texts.shape.ndims == 0: + texts = tf.reshape(texts, (-1,)) + token_list = tokenizer.string_tensor_to_indices( + string_tensor=texts, + max_num_tokens=max_num_tokens, + append_eos=True, + ) + token_list = tf.stack(tf.map_fn(add_prompt, token_list), axis=0) + return tf.cast(token_list, tf.int16) + + return _list_t5_tokenize + + +@registry.Registry.register('preprocess_ops.multi_t5_tokenize', 'function') +@utils.InKeyOutKey(indefault='texts', outdefault='tokens') +def get_multi_t5_tokenize(max_num_tokens, append_eos=True): + """Tokenizes a text using T5 Tokenizer.""" + + tokenizer = t5_tokenizer.build_dmvr_sp_model() + tokenizer.initialize() + max_answers = 10 + + def _multi_t5_tokenize(texts): + parse = functools.partial( + tokenizer.string_tensor_to_indices, + max_num_tokens=max_num_tokens, + append_eos=append_eos, + ) + # if texts.shape.ndims == 1: + # tokens = parse(string_tensor=texts) + # else: + # tokens = tf.map_fn(parse, texts) + tokens = parse(string_tensor=texts)[:max_answers] + return tf.cast(tokens, tf.int16) + + return _multi_t5_tokenize + + +def inception_crop(image, resize_size=224, area_min=20, area_max=80): + """Random crop input image.""" + begin, size, _ = tf.image.sample_distorted_bounding_box( + tf.shape(image), + tf.zeros([0, 0, 4], tf.float32), + area_range=(area_min / 100, area_max / 100), + min_object_covered=0, # Don't enforce a minimum area. + use_image_if_no_bounding_boxes=True, + ) + crop = tf.slice(image, begin, size) + # Unfortunately, the above operation loses the depth-dimension. So we need + # to restore it the manual way. + crop.set_shape([None, None, image.shape[-1]]) + if resize_size: + crop = tf.cast( + tf.image.resize(crop, [resize_size, resize_size]), image.dtype + ) + return crop + + +def sample_retr_image(batch): + """Sample image from similar sample by tfidf.""" + + crops = [] + for img in batch['encoder_input_image']: + crops += [inception_crop(img, area_min=10, area_max=80)] + batch['retr_images'] = np.stack(crops, axis=0) + return batch + + +def map_generation_split( + batch, span_len, output_max_len, split_key='tokens', add_retr=False +): + """Split tokens into prefix, decoder_input and decoder_output.""" + full_tokens = batch.pop(split_key) + full_masks = tf.greater(full_tokens, 0) + min_length = tf.reduce_max([ + tf.reduce_min(tf.reduce_sum(tf.cast(full_masks, tf.int16), axis=1)), + PROMPT_LENGTH + 4, + ]).numpy() + max_length = PROMPT_LENGTH + span_len + 1 + bsz = full_tokens.shape[0] + idx = tf.experimental.numpy.random.randint( + low=PROMPT_LENGTH, high=tf.reduce_min([min_length, max_length]).numpy() + ).numpy() + input_tokens = [ + full_tokens[..., :idx], + tf.ones([bsz, 1], dtype=tf.int16) * MASK_TOKEN_ID, + tf.zeros([bsz, max_length - idx - 1], dtype=tf.int16), + ] + output_tokens = [ + tf.ones([bsz, 1], dtype=tf.int16) * BOS_ID, + full_tokens[..., idx : idx + output_max_len], + ] + batch['encoder_input_tokens'] = tf.concat(input_tokens, axis=1) + batch['encoder_input_image'] = batch.pop('image') + output_tokens = tf.concat(output_tokens, axis=1) + batch['decoder_input_tokens'] = output_tokens[..., :-1] + batch['decoder_target_tokens'] = output_tokens[..., 1:] + if add_retr: + if 'retr_texts' in batch: + batch['retr_texts'] = tf.expand_dims(batch['retr_texts'], axis=1) + else: + batch['retr_texts'] = tf.expand_dims( + batch['decoder_input_tokens'], axis=1 + ) + return batch + + +def get_data( + dataset, + split, + batch_size, + filter_fn=None, + preprocess_fn=lambda x: x, + repeats=None, + shuffle_buffer_size=None, + prefetch=2, + cache='loaded', + repeat_after_batching=False, + drop_remainder=True, + data_dir=None, + ignore_errors=False, + shuffle_files=True, + dataset_service_address=None, +): + """API kept for backwards compatibility.""" + dataset = dataset_utils.get_dataset_tfds( + dataset=dataset, + split=split, + shuffle_files=shuffle_files, + data_dir=data_dir, + ) + if 'train' not in split: + dataset_service_address = None + if filter_fn: + dataset = dataset.filter(filter_fn) + return dataset_utils.make_pipeline( + data=dataset, + preprocess_fn=preprocess_fn, + batch_size=batch_size, + drop_remainder=drop_remainder, + cache=cache, + repeats=repeats, + prefetch=prefetch, + shuffle_buffer_size=shuffle_buffer_size, + repeat_after_batching=repeat_after_batching, + ignore_errors=ignore_errors, + dataset_service_address=dataset_service_address, + ) + + +def filter_text_length(d, filter_len): + if 'texts' in d: + return tf.strings.length(d['texts'][0]) > filter_len + elif 'caption' in d: + return tf.strings.length(d['caption']) > filter_len + elif 'alt_texts' in d: + return tf.strings.length(d['alt_texts'][0]) > filter_len + return True + + +def _get_bytes_feature(example, name): + return example.features.feature[name].bytes_list.value[0] + + +def _get_integer_list_feature(example, name): + return list(example.features.feature[name].int64_list.value) + + +def _extract_wit_features(example): + return [ + _get_integer_list_feature(example, 'knowledge'), + _get_integer_list_feature(example, 'caption'), + _get_bytes_feature(example, 'image'), + ] + + +def load_wit_table(pattern, n_pool=2048): + """Load the whole wit dataset as in-memory dict.""" + global_wit_dict = {} + + def read_from_table(filename): + value_coder = beam.coders.ProtoCoder(tf.train.Example) + table = sstable.SSTable(filename) + for key, val in table.iteritems(): + example = value_coder.decode(val) + global_wit_dict[key] = _extract_wit_features(example) + return filename + + if '@' in pattern: + jobs = [] + filenames = shards.GenerateShardedFilenames(pattern) + with mp.pool.ThreadPool(len(filenames)) as pool: + logging.info('Start loading data with %d proccesses', n_pool) + for filename in tqdm.tqdm(filenames): + jobs += [pool.apply_async(read_from_table, args=(filename,))] + for job in tqdm.tqdm(jobs): + file_name = job.get() + logging.info('finish loading %s', file_name) + else: + read_from_table(pattern) + return global_wit_dict diff --git a/scenic/projects/knowledge_visual_language/data/okvqa_dataset.py b/scenic/projects/knowledge_visual_language/data/okvqa_dataset.py new file mode 100644 index 000000000..da7e86603 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/data/okvqa_dataset.py @@ -0,0 +1,271 @@ +"""Dataset and Loader for OK-VQA dataset.""" + +import functools +from typing import Optional + +from absl import logging +from flax import jax_utils +import jax.numpy as jnp +import ml_collections +from scenic.dataset_lib import dataset_utils +from scenic.dataset_lib import datasets +from scenic.dataset_lib.big_transfer import bit +from scenic.dataset_lib.big_transfer import builder +from scenic.dataset_lib.big_transfer import registry +from scenic.dataset_lib import web_image_text_dataset +from scenic.projects.knowledge_visual_language.data import data_utils +import tensorflow as tf + +# import jax +OUTPUT_MAX_LENGTH = 64 +IMAGE_SIZE = 224 +QUESTION_LENGTH = 64 +ANSWER_LENGTH = 32 +KNOWLEDGE_MAX_LENGTH = 320 + + +@registry.Registry.register('preprocess_ops.get_qa_pair', 'function') +def get_qa_pair(): + """Concat title passage and document together to form knowledge.""" + + def get_qa_pair_fn(data): + """Prepare Knowledge by concating hierarchy, passage and first-paragraph.""" + data['question'] = data['question/answers']['question_text'] + data['answers'] = tf.reshape(data['question/answers']['answers'], (-1,)) + data['answer'] = data['answers'][0] + data['top_answers'] = tf.strings.reduce_join( + data['answers'], separator=', ' + ) + return data + + return get_qa_pair_fn + + +def map_vqa_split(batch): + """Split answer into decoder_input and decoder_output.""" + + full_tokens = batch.pop('answer') + batch['decoder_input_tokens'] = full_tokens[..., :-1] + batch['decoder_target_tokens'] = full_tokens[..., 1:] + return batch + + +def get_default_dataset_config(runlocal=False): + """Gets default configs for CC12M dataset.""" + dataset_configs = ml_collections.ConfigDict() + dataset_configs.dataset = 'okvqa' + # Add path to your data here: + dataset_configs.dataset_dir = '' + dataset_configs.train_split = 'train' + dataset_configs.question_max_num_tokens = QUESTION_LENGTH + dataset_configs.answer_max_num_tokens = ANSWER_LENGTH + dataset_configs.image_size = IMAGE_SIZE + dataset_configs.pp_train = ( + f'decode|resize(resize_size={IMAGE_SIZE})|value_range(-1,1)|get_qa_pair|t5_tokenize(max_num_tokens={KNOWLEDGE_MAX_LENGTH},inkey="top_answers",' + ' outkey="retr_texts",' + f' prompt="{data_utils.KNOWLEDGE_PREFIX}")|t5_tokenize(max_num_tokens={ANSWER_LENGTH},' + f' inkey="answer",outkey="answer")|multi_t5_tokenize(max_num_tokens={ANSWER_LENGTH},inkey="answers",outkey="answers")|t5_tokenize(max_num_tokens={QUESTION_LENGTH},' + ' inkey="question", outkey="question",' + f' prompt="{data_utils.VQA_PREFIX}")|keep("image", "question", "answer",' + ' "retr_texts", "answers")' + ) + + dataset_configs.val_split = [( + 'val', + dataset_configs.dataset, + 'validation', + dataset_configs.pp_train, + )] + + dataset_configs.shuffle_buffer_size = 10000 if not runlocal else 50 + dataset_configs.val_cache = 'loaded' # Unfortunately, "batched" gets us OOM. + dataset_configs.vocab_size = data_utils.VOCAB_SIZE_T5 + dataset_configs.prefetch_to_device = 2 + return dataset_configs + + +@datasets.add_dataset('okvqa') +def get_dataset( + *, + batch_size, + eval_batch_size, + num_shards, + dtype_str='float32', + shuffle_seed=None, + rng=None, + dataset_configs=None, + dataset_service_address: Optional[str] = None, +): + """Returns generators for the OKVQA train and validation sets. + + Args: + batch_size: int; Determines the train batch size. + eval_batch_size: int; Determines the evaluation batch size. + num_shards: int; Number of shards --> batch shape: [num_shards, bs, ...]. + dtype_str: Data type of the image (e.g. 'float32'). + shuffle_seed: int; Seed for shuffling the training data. Not used. + rng: JAX rng key, which can be used for augmentation, shuffling, etc. + dataset_configs: dict; Dataset specific configurations. + dataset_service_address: If set, will distribute the training dataset using + the given tf.data service at the given address. + + Returns: + A dataset_utils.Dataset() which includes a train_iter, a valid_iter, + a test_iter, and a dict of meta_data. + """ + default_dataset_config = get_default_dataset_config(runlocal=False) + if dataset_configs: + default_dataset_config.update(dataset_configs) + + dataset_configs = default_dataset_config + + del rng + assert dataset_configs is not None + logging.info('Loading train split of the %s', dataset_configs.dataset) + + def pp_fn(x, how): + pp = builder.get_preprocess_fn(how, remove_tpu_dtypes=False) + example = pp(x) + return { + 'encoder_input_image': example['image'], + 'encoder_input_tokens': example['question'], + 'answer': example['answer'], + 'answers': example['answers'], + 'retr_texts': example['retr_texts'], + } + + # E.g. for testing with TAP. + shuffle_buffer_size = ( + 1000 if num_shards == 1 else dataset_configs.shuffle_buffer_size + ) + + train_ds = data_utils.get_data( + dataset=dataset_configs.dataset, + split=dataset_configs.train_split, + data_dir=dataset_configs.get('dataset_dir'), + batch_size=batch_size, + preprocess_fn=functools.partial(pp_fn, how=dataset_configs.pp_train), + shuffle_buffer_size=shuffle_buffer_size, + prefetch=dataset_configs.get('prefetch_to_host', 2), + cache=dataset_configs.val_cache, + ignore_errors=True, + ) + + if dataset_service_address: + if shuffle_seed is not None: + raise ValueError( + 'Using dataset service with a random seed causes each ' + 'worker to produce exactly the same data. Add ' + 'config.shuffle_seed = None to your config if you ' + 'want to run with dataset service.' + ) + logging.info('Using the tf.data service at %s', dataset_service_address) + assert shuffle_buffer_size is not None + train_ds = dataset_utils.distribute(train_ds, dataset_service_address) + + n_train_ex = dataset_utils.get_num_examples( + dataset_configs.dataset, + dataset_configs.train_split, + data_dir=dataset_configs.get('dataset_dir'), + ) + + maybe_pad_batches_train = functools.partial( + dataset_utils.maybe_pad_batch, + inputs_key='encoder_input_image', + train=True, + batch_size=batch_size, + ) + shard_batches = functools.partial(dataset_utils.shard, n_devices=num_shards) + + train_iter = iter(train_ds) + train_iter = map(map_vqa_split, train_iter) + train_iter = map(dataset_utils.tf_to_numpy, train_iter) + train_iter = map(data_utils.sample_retr_image, train_iter) + train_iter = map(maybe_pad_batches_train, train_iter) + if num_shards > 0: + train_iter = map(shard_batches, train_iter) + if dataset_configs.prefetch_to_device: + train_iter = jax_utils.prefetch_to_device( + train_iter, dataset_configs.prefetch_to_device + ) + + logging.info('Loading validation split of the %s', dataset_configs.dataset) + maybe_pad_batches_eval = functools.partial( + dataset_utils.maybe_pad_batch, + inputs_key='encoder_input_image', + train=False, + batch_size=eval_batch_size, + ) + + def _get_eval_iter(dataset, split, pp_eval): + val_ds = data_utils.get_data( + dataset=dataset, + split=split, + data_dir=dataset_configs.get('dataset_dir'), + batch_size=eval_batch_size, + preprocess_fn=functools.partial(pp_fn, how=pp_eval), + cache='batched', + repeat_after_batching=True, + drop_remainder=False, + ) + + valid_iter = iter(val_ds) + valid_iter = map(map_vqa_split, valid_iter) + valid_iter = map(bit.tf_to_numpy, valid_iter) + valid_iter = map(data_utils.sample_retr_image, valid_iter) + valid_iter = map(maybe_pad_batches_eval, valid_iter) + if num_shards > 0: + valid_iter = map(shard_batches, valid_iter) + if dataset_configs.prefetch_to_device: + valid_iter = jax_utils.prefetch_to_device( + valid_iter, dataset_configs.prefetch_to_device + ) + + return valid_iter + + def _get_num_eval_examples(dataset, split, data_dir): + return dataset_utils.get_num_examples(dataset, split, data_dir) + + if isinstance(dataset_configs.val_split, str): + valid_iter = _get_eval_iter( + dataset_configs.dataset, + dataset_configs.val_split, + dataset_configs.pp_eval, + ) + n_eval_ex = _get_num_eval_examples( + dataset_configs.dataset, + dataset_configs.val_split, + data_dir=dataset_configs.get('dataset_dir'), + ) + else: + valid_iter, n_eval_ex = {}, {} + for eval_spec in dataset_configs.val_split: + name, dataset, split, pp_eval = eval_spec + valid_iter[name] = _get_eval_iter(dataset, split, pp_eval) + n_eval_ex[name] = _get_num_eval_examples( + dataset, split, data_dir=dataset_configs.get('dataset_dir') + ) + + meta_data = {'num_train_examples': n_train_ex, 'num_eval_examples': n_eval_ex} + + if dataset_configs.get('extra_meta_data'): + for k, v in dataset_configs.extra_meta_data.items(): + meta_data[k] = v + + image_shape = (-1, dataset_configs.image_size, dataset_configs.image_size, 3) + predix_shape = (-1, QUESTION_LENGTH) + input_shape = (-1, ANSWER_LENGTH) + retr_texts_shape = (-1, KNOWLEDGE_MAX_LENGTH + data_utils.PROMPT_LENGTH) + retr_image_shape = ( + -1, + dataset_configs.image_size, + dataset_configs.image_size, + 3, + ) + meta_data['encoder_input_image_spec'] = (image_shape, getattr(jnp, dtype_str)) + meta_data['encoder_input_tokens_spec'] = (predix_shape, jnp.int16) + meta_data['decoder_input_tokens_spec'] = (input_shape, jnp.int16) + meta_data['decoder_target_tokens_spec'] = (input_shape, jnp.int16) + meta_data['retr_texts_spec'] = (retr_texts_shape, jnp.int16) + meta_data['retr_images_spec'] = (retr_image_shape, getattr(jnp, dtype_str)) + return dataset_utils.Dataset(train_iter, valid_iter, None, meta_data) diff --git 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b/scenic/projects/knowledge_visual_language/data/vqa_dataset.py @@ -0,0 +1,273 @@ +"""Dataset and Loader for VQA dataset.""" + +import functools +from typing import Optional + +from absl import logging +from flax import jax_utils +import jax.numpy as jnp +import ml_collections +from scenic.dataset_lib import dataset_utils +from scenic.dataset_lib import datasets +from scenic.dataset_lib.big_transfer import bit +from scenic.dataset_lib.big_transfer import builder +from scenic.dataset_lib.big_transfer import registry +from scenic.dataset_lib import web_image_text_dataset +from scenic.projects.knowledge_visual_language.data import data_utils + +import tensorflow as tf + +# import jax +OUTPUT_MAX_LENGTH = 64 +IMAGE_SIZE = 224 +QUESTION_LENGTH = 64 +ANSWER_LENGTH = 32 +KNOWLEDGE_MAX_LENGTH = 320 +n_qa = 5 + + +@registry.Registry.register('preprocess_ops.get_vqa_pair', 'function') +def get_vqa_pair(): + """Concat title passage and document together to form knowledge.""" + + def get_vqa_pair_fn(data): + """Prepare Knowledge by concating hierarchy, passage and first-paragraph.""" + data['question'] = data['question/answers']['question_text'] + data['answers'] = tf.reshape(data['question/answers']['answers'], [5, -1]) + data['answer'] = data['answers'][:, 0] + data['top_answers'] = tf.strings.reduce_join( + data['answers'], separator=', ', axis=-1 + ) + return data + + return get_vqa_pair_fn + + +def map_vqa_split(batch): + """Split answer into decoder_input and decoder_output.""" + + full_tokens = batch.pop('answer') + batch['decoder_input_tokens'] = full_tokens[..., :-1] + batch['decoder_target_tokens'] = full_tokens[..., 1:] + return batch + + +def get_default_dataset_config(runlocal=False): + """Gets default configs for CC12M dataset.""" + dataset_configs = ml_collections.ConfigDict() + dataset_configs.dataset = 'vqa' + # Add path to your data here: + dataset_configs.dataset_dir = '' + dataset_configs.train_split = 'train+validation[5000:]' + dataset_configs.question_max_num_tokens = QUESTION_LENGTH + dataset_configs.answer_max_num_tokens = ANSWER_LENGTH + dataset_configs.image_size = IMAGE_SIZE + dataset_configs.pp_train = ( + f'decode|resize(resize_size={IMAGE_SIZE})|value_range(-1,1)|get_vqa_pair|list_t5_tokenize(max_num_tokens={KNOWLEDGE_MAX_LENGTH},inkey="top_answers",' + ' outkey="retr_texts",' + f' prompt="{data_utils.KNOWLEDGE_PREFIX}")|list_t5_tokenize(max_num_tokens={ANSWER_LENGTH},' + f' inkey="answer",outkey="answer")|multi_t5_tokenize(max_num_tokens={ANSWER_LENGTH},inkey="answers",outkey="answers")|list_t5_tokenize(max_num_tokens={QUESTION_LENGTH},' + ' inkey="question", outkey="question",' + f' prompt="{data_utils.VQA_PREFIX}")|keep("image", "question", "answer",' + ' "retr_texts", "answers")' + ) + + dataset_configs.val_split = [( + 'val', + dataset_configs.dataset, + 'validation[:5000]', + dataset_configs.pp_train, + )] + + dataset_configs.shuffle_buffer_size = 10000 if not runlocal else 50 + dataset_configs.val_cache = 'loaded' # Unfortunately, "batched" gets us OOM. + dataset_configs.vocab_size = data_utils.VOCAB_SIZE_T5 + dataset_configs.prefetch_to_device = 2 + return dataset_configs + + +@datasets.add_dataset('vqa') +def get_dataset( + *, + batch_size, + eval_batch_size, + num_shards, + dtype_str='float32', + shuffle_seed=None, + rng=None, + dataset_configs=None, + dataset_service_address: Optional[str] = None, +): + """Returns generators for the VQA train and validation sets. + + Args: + batch_size: int; Determines the train batch size. + eval_batch_size: int; Determines the evaluation batch size. + num_shards: int; Number of shards --> batch shape: [num_shards, bs, ...]. + dtype_str: Data type of the image (e.g. 'float32'). + shuffle_seed: int; Seed for shuffling the training data. Not used. + rng: JAX rng key, which can be used for augmentation, shuffling, etc. + dataset_configs: dict; Dataset specific configurations. + dataset_service_address: If set, will distribute the training dataset using + the given tf.data service at the given address. + + Returns: + A dataset_utils.Dataset() which includes a train_iter, a valid_iter, + a test_iter, and a dict of meta_data. + """ + default_dataset_config = get_default_dataset_config(runlocal=False) + if dataset_configs: + default_dataset_config.update(dataset_configs) + + dataset_configs = default_dataset_config + + del rng + assert dataset_configs is not None + logging.info('Loading train split of the %s', dataset_configs.dataset) + + def pp_fn(x, how): + pp = builder.get_preprocess_fn(how, remove_tpu_dtypes=False) + example = pp(x) + return { + 'encoder_input_image': example['image'], + 'encoder_input_tokens': example['question'], + 'answer': example['answer'], + 'answers': example['answers'], + 'retr_texts': example['retr_texts'], + } + + # E.g. for testing with TAP. + shuffle_buffer_size = ( + 1000 if num_shards == 1 else dataset_configs.shuffle_buffer_size + ) + + train_ds = data_utils.get_data( + dataset=dataset_configs.dataset, + split=dataset_configs.train_split, + data_dir=dataset_configs.get('dataset_dir'), + batch_size=batch_size, + preprocess_fn=functools.partial(pp_fn, how=dataset_configs.pp_train), + shuffle_buffer_size=shuffle_buffer_size, + prefetch=dataset_configs.get('prefetch_to_host', 2), + cache=dataset_configs.val_cache, + ignore_errors=True, + ) + + if dataset_service_address: + if shuffle_seed is not None: + raise ValueError( + 'Using dataset service with a random seed causes each ' + 'worker to produce exactly the same data. Add ' + 'config.shuffle_seed = None to your config if you ' + 'want to run with dataset service.' + ) + logging.info('Using the tf.data service at %s', dataset_service_address) + assert shuffle_buffer_size is not None + train_ds = dataset_utils.distribute(train_ds, dataset_service_address) + + n_train_ex = dataset_utils.get_num_examples( + dataset_configs.dataset, + dataset_configs.train_split, + data_dir=dataset_configs.get('dataset_dir'), + ) + + maybe_pad_batches_train = functools.partial( + dataset_utils.maybe_pad_batch, + inputs_key='encoder_input_image', + train=True, + batch_size=batch_size, + ) + shard_batches = functools.partial(dataset_utils.shard, n_devices=num_shards) + + train_iter = iter(train_ds) + train_iter = map(map_vqa_split, train_iter) + train_iter = map(dataset_utils.tf_to_numpy, train_iter) + train_iter = map(data_utils.sample_retr_image, train_iter) + train_iter = map(maybe_pad_batches_train, train_iter) + if num_shards > 0: + train_iter = map(shard_batches, train_iter) + if dataset_configs.prefetch_to_device: + train_iter = jax_utils.prefetch_to_device( + train_iter, dataset_configs.prefetch_to_device + ) + + logging.info('Loading validation split of the %s', dataset_configs.dataset) + maybe_pad_batches_eval = functools.partial( + dataset_utils.maybe_pad_batch, + inputs_key='encoder_input_image', + train=False, + batch_size=eval_batch_size, + ) + + def _get_eval_iter(dataset, split, pp_eval): + val_ds = data_utils.get_data( + dataset=dataset, + split=split, + data_dir=dataset_configs.get('dataset_dir'), + batch_size=eval_batch_size, + preprocess_fn=functools.partial(pp_fn, how=pp_eval), + cache='batched', + repeat_after_batching=True, + drop_remainder=False, + ) + + valid_iter = iter(val_ds) + valid_iter = map(map_vqa_split, valid_iter) + valid_iter = map(bit.tf_to_numpy, valid_iter) + valid_iter = map(data_utils.sample_retr_image, valid_iter) + valid_iter = map(maybe_pad_batches_eval, valid_iter) + if num_shards > 0: + valid_iter = map(shard_batches, valid_iter) + if dataset_configs.prefetch_to_device: + valid_iter = jax_utils.prefetch_to_device( + valid_iter, dataset_configs.prefetch_to_device + ) + + return valid_iter + + def _get_num_eval_examples(dataset, split, data_dir): + return dataset_utils.get_num_examples(dataset, split, data_dir) + + if isinstance(dataset_configs.val_split, str): + valid_iter = _get_eval_iter( + dataset_configs.dataset, + dataset_configs.val_split, + dataset_configs.pp_eval, + ) + n_eval_ex = _get_num_eval_examples( + dataset_configs.dataset, + dataset_configs.val_split, + data_dir=dataset_configs.get('dataset_dir'), + ) + else: + valid_iter, n_eval_ex = {}, {} + for eval_spec in dataset_configs.val_split: + name, dataset, split, pp_eval = eval_spec + valid_iter[name] = _get_eval_iter(dataset, split, pp_eval) + n_eval_ex[name] = _get_num_eval_examples( + dataset, split, data_dir=dataset_configs.get('dataset_dir') + ) + + meta_data = {'num_train_examples': n_train_ex, 'num_eval_examples': n_eval_ex} + + if dataset_configs.get('extra_meta_data'): + for k, v in dataset_configs.extra_meta_data.items(): + meta_data[k] = v + + image_shape = (-1, dataset_configs.image_size, dataset_configs.image_size, 3) + predix_shape = (-1, QUESTION_LENGTH) + input_shape = (-1, ANSWER_LENGTH) + retr_texts_shape = (-1, KNOWLEDGE_MAX_LENGTH + data_utils.PROMPT_LENGTH) + retr_image_shape = ( + -1, + dataset_configs.image_size, + dataset_configs.image_size, + 3, + ) + meta_data['encoder_input_image_spec'] = (image_shape, getattr(jnp, dtype_str)) + meta_data['encoder_input_tokens_spec'] = (predix_shape, jnp.int16) + meta_data['decoder_input_tokens_spec'] = (input_shape, jnp.int16) + meta_data['decoder_target_tokens_spec'] = (input_shape, jnp.int16) + meta_data['retr_texts_spec'] = (retr_texts_shape, jnp.int16) + meta_data['retr_images_spec'] = (retr_image_shape, getattr(jnp, dtype_str)) + return dataset_utils.Dataset(train_iter, valid_iter, None, meta_data) diff --git a/scenic/projects/knowledge_visual_language/data/vqa_table_dataset.py b/scenic/projects/knowledge_visual_language/data/vqa_table_dataset.py new file mode 100644 index 000000000..75c26150c --- /dev/null +++ b/scenic/projects/knowledge_visual_language/data/vqa_table_dataset.py @@ -0,0 +1,182 @@ +"""Dataset and Loader for Wikipedia Image-Text (WIT) dataset for retrieval training. + +Only prepare paired with knowledge (contextualalized passages) +""" + +import functools +from typing import Optional + +from absl import logging +import jax +import jax.numpy as jnp +import ml_collections +from scenic.dataset_lib import dataset_utils +from scenic.dataset_lib import datasets +from scenic.dataset_lib.big_transfer import builder +from scenic.dataset_lib.big_transfer import registry + +from scenic.dataset_lib import web_image_text_dataset + + +from scenic.projects.knowledge_visual_language.data import data_utils + +import tensorflow as tf + +SPAN_MAX_LENGTH = 5 +OUTPUT_MAX_LENGTH = 36 +KNOWLEDGE_MAX_LENGTH = 320 +IMAGE_SIZE = 224 + + +@registry.Registry.register('preprocess_ops.get_vqa_knowledge', 'function') +def get_vqa_knowledge(): + """Concat title passage and document together to form knowledge.""" + + def get_vqa_knowledge_fn(data): + """Prepare Knowledge by concating hierarchy, passage and first-paragraph.""" + + questions = data['question/answers']['question_text'] + answers = tf.strings.reduce_join( + data['question/answers']['top_answers'] + ', ', axis=1 + ) + q_prefix = tf.repeat(['Question: '], repeats=tf.shape(questions)[0]) + a_prefix = tf.repeat([' Answer: '], repeats=tf.shape(questions)[0]) + sep_token = tf.repeat([' '], repeats=tf.shape(questions)[0]) + knowledges = tf.strings.join( + [q_prefix, questions, a_prefix, answers, sep_token] + ) + # data['knowledge'] = tf.strings.reduce_join(knowledges, axis=0) + data['knowledge'] = tf.strings.reduce_join(knowledges, axis=0) + return data + + return get_vqa_knowledge_fn + + +def get_default_dataset_config(): + """Gets default configs for wit_internal (en) dataset.""" + dataset_configs = ml_collections.ConfigDict() + dataset_configs.dataset = 'vqa' + # Add path to your data here: + dataset_configs.dataset_dir = '' + dataset_configs.train_split = 'train+validation' + dataset_configs.output_max_num_tokens = OUTPUT_MAX_LENGTH + dataset_configs.knowledge_max_num_tokens = OUTPUT_MAX_LENGTH + dataset_configs.image_size = IMAGE_SIZE + dataset_configs.pp_train = ( + f'get_vqa_knowledge|decode|resize(resize_size={IMAGE_SIZE})|value_range(-1,1)|t5_tokenize(max_num_tokens={KNOWLEDGE_MAX_LENGTH},' + ' inkey="knowledge", outkey="knowledge_tokens",' + f' prompt="{data_utils.KNOWLEDGE_PREFIX}")|keep("image",' + ' "knowledge_tokens")' + ) + dataset_configs.vocab_size = data_utils.VOCAB_SIZE_T5 + dataset_configs.prefetch_to_device = 2 + return dataset_configs + + +@datasets.add_dataset('vqa_table') +def get_dataset( + *, + batch_size, + eval_batch_size, + num_shards, + dtype_str='float32', + shuffle_seed=None, + rng=None, + dataset_configs=None, + dataset_service_address: Optional[str] = None, +): + """Returns generators for the CC12M train, validation and test sets. + + Args: + batch_size: int; Determines the train batch size. + eval_batch_size: int; Determines the evaluation batch size. + num_shards: int; Number of shards --> batch shape: [num_shards, bs, ...]. + dtype_str: Data type of the image (e.g. 'float32'). + shuffle_seed: int; Seed for shuffling the training data. Not used. + rng: JAX rng key, which can be used for augmentation, shuffling, etc. + dataset_configs: dict; Dataset specific configurations. + dataset_service_address: If set, will distribute the training dataset using + the given tf.data service at the given address. + + Returns: + A dataset_utils.Dataset() which includes a train_iter, a valid_iter, + a test_iter, and a dict of meta_data. + """ + del batch_size + default_dataset_config = get_default_dataset_config() + if dataset_configs: + default_dataset_config.update(dataset_configs) + + dataset_configs = default_dataset_config + + del rng + assert dataset_configs is not None + logging.info('Loading train split of the %s', dataset_configs.dataset) + + def pp_fn(x, how): + pp = builder.get_preprocess_fn(how, remove_tpu_dtypes=False) + example = pp(x) + example['image'] = tf.cast(example['image'], dtype=dtype_str) + return example + + # E.g. for testing with TAP. + shuffle_buffer_size = None + + train_ds = data_utils.get_data( + dataset=dataset_configs.dataset, + split=dataset_configs.train_split, + data_dir=dataset_configs.get('dataset_dir'), + batch_size=eval_batch_size, + preprocess_fn=functools.partial(pp_fn, how=dataset_configs.pp_train), + shuffle_buffer_size=None, + shuffle_files=False, + prefetch=dataset_configs.get('prefetch_to_host', 2), + cache='loaded', + ignore_errors=False, + drop_remainder=True, + ) + + if dataset_service_address: + if shuffle_seed is not None: + raise ValueError( + 'Using dataset service with a random seed causes each ' + 'worker to produce exactly the same data. Add ' + 'config.shuffle_seed = None to your config if you ' + 'want to run with dataset service.' + ) + logging.info('Using the tf.data service at %s', dataset_service_address) + assert shuffle_buffer_size is not None + train_ds = dataset_utils.distribute(train_ds, dataset_service_address) + + n_train_ex = dataset_utils.get_num_examples( + dataset_configs.dataset, + dataset_configs.train_split, + data_dir=dataset_configs.get('dataset_dir'), + ) + + maybe_pad_batches_train = functools.partial( + dataset_utils.maybe_pad_batch, + inputs_key='image', + train=True, + batch_size=eval_batch_size, + ) + shard_batches = functools.partial(dataset_utils.shard, n_devices=num_shards) + + train_iter = iter(train_ds) + train_iter = map(dataset_utils.tf_to_numpy, train_iter) + train_iter = map(maybe_pad_batches_train, train_iter) + if num_shards > 0: + train_iter = map(shard_batches, train_iter) + + meta_data = { + 'num_train_examples': n_train_ex, + 'example_per_shard': int(n_train_ex // jax.process_count()), + 'batch_size': eval_batch_size, + } + + image_shape = (dataset_configs.image_size, dataset_configs.image_size, 3) + knowledge_shape = (KNOWLEDGE_MAX_LENGTH + data_utils.PROMPT_LENGTH,) + + meta_data['image_spec'] = (image_shape, getattr(jnp, dtype_str)) + meta_data['knowledge_spec'] = (knowledge_shape, jnp.int16) + return dataset_utils.Dataset(train_iter, None, None, meta_data) diff --git a/scenic/projects/knowledge_visual_language/data/web_image_text_generation_dataset.py b/scenic/projects/knowledge_visual_language/data/web_image_text_generation_dataset.py new file mode 100644 index 000000000..ab8d4e247 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/data/web_image_text_generation_dataset.py @@ -0,0 +1,290 @@ +"""Dataset and Loader for Argus Web-Image-Text dataset for caption generation pre-training.""" +import functools +from typing import Optional + +from absl import logging +from flax import jax_utils +# import jax +import jax.numpy as jnp +import ml_collections +from scenic.dataset_lib import dataset_utils +from scenic.dataset_lib import datasets +from scenic.dataset_lib.big_transfer import builder +from scenic.dataset_lib import web_image_text_dataset +from scenic.projects.knowledge_visual_language.data import data_utils +import tensorflow as tf + +SPAN_MAX_LENGTH = 5 +FILTER_LENGTH = 32 +OUTPUT_MAX_LENGTH = 32 +KNOWLEDGE_MAX_LENGTH = 320 +IMAGE_SIZE = 224 + + +def get_default_dataset_config(runlocal=False, additional_valid_dataset=True): + """Gets default configs for argus dataset.""" + dataset_configs = ml_collections.ConfigDict() + # Add path to your data here: + dataset_configs.dataset = '' + dataset_configs.train_split = 'full[50000:]' + MAX_LENGTH = OUTPUT_MAX_LENGTH # pylint: disable=invalid-name + pp_common = f'decode|resize(resize_size={IMAGE_SIZE})|value_range(-1,1)' + pp_argus = ( + f'|t5_tokenize(max_num_tokens={KNOWLEDGE_MAX_LENGTH}, inkey="alt_texts",' + ' outkey="retr_texts",' + f' prompt="{data_utils.KNOWLEDGE_PREFIX}")|t5_tokenize(max_num_tokens={OUTPUT_MAX_LENGTH * 3},' + ' inkey="alt_texts", outkey="caption_tokens",' + f' prompt="{data_utils.CAPTION_PREFIX}")|keep("image", "caption_tokens",' + ' "retr_texts")' + ) + pp_cc = ( + f'|t5_tokenize(max_num_tokens={OUTPUT_MAX_LENGTH * 3}, inkey="texts",' + ' outkey="caption_tokens",' + f' prompt="{data_utils.CAPTION_PREFIX}")|t5_tokenize(max_num_tokens={KNOWLEDGE_MAX_LENGTH},' + ' inkey="texts", outkey="retr_texts",' + f' prompt="{data_utils.KNOWLEDGE_PREFIX}")|keep("image",' + ' "caption_tokens", "retr_texts")' + ) + pp_coco_eval = ( + pp_common + + f'|coco_captions|t5_tokenize(max_num_tokens={OUTPUT_MAX_LENGTH * 3},' + ' inkey="texts", outkey="caption_tokens",' + f' prompt="{data_utils.CAPTION_PREFIX}")|t5_tokenize(max_num_tokens={KNOWLEDGE_MAX_LENGTH},' + ' inkey="texts", outkey="retr_texts",' + f' prompt="{data_utils.KNOWLEDGE_PREFIX}")|keep("image",' + ' "caption_tokens", "retr_texts")' + ) + dataset_configs.max_num_tokens = MAX_LENGTH + dataset_configs.image_size = IMAGE_SIZE + dataset_configs.pp_train = pp_common + pp_argus + dataset_configs.shuffle_buffer_size = 250000 if not runlocal else 50 + + pp_argus_eval = pp_common + pp_argus + pp_cc_eval = pp_common + pp_cc + sub = '[:4]' if runlocal else '' + if additional_valid_dataset: + dataset_configs.val_split = [ + ( + 'val_argus', + dataset_configs.dataset, + ['full[:50000]', f'full{sub}'][runlocal], + pp_argus_eval, + ), + ( + 'val_cc', + 'argus:cc12m/cc12m', + ['full[:50000]', f'full{sub}'][runlocal], + pp_cc_eval, + ), + ('coco', 'coco_captions', 'val', pp_coco_eval), + ] + else: + dataset_configs.val_split = f'full{sub}' if runlocal else 'full[:50000]' + dataset_configs.pp_eval = pp_argus_eval + + dataset_configs.val_cache = 'loaded' # Unfortunately, "batched" gets us OOM. + dataset_configs.vocab_size = data_utils.VOCAB_SIZE_T5 + dataset_configs.prefetch_to_device = 2 + return dataset_configs + + +@datasets.add_dataset('web_image_text_generation') +def get_dataset( + *, + batch_size, + eval_batch_size, + num_shards, + dtype_str='float32', + shuffle_seed=0, + rng=None, + dataset_configs=None, + dataset_service_address: Optional[str] = None, +): + """Returns generators for the argus train, validation and test sets. + + Args: + batch_size: int; Determines the train batch size. + eval_batch_size: int; Determines the evaluation batch size. + num_shards: int; Number of shards --> batch shape: [num_shards, bs, ...]. + dtype_str: Data type of the image (e.g. 'float32'). + shuffle_seed: int; Seed for shuffling the training data. Not used. + rng: JAX rng key, which can be used for augmentation, shuffling, etc. + dataset_configs: dict; Dataset specific configurations. + dataset_service_address: If set, will distribute the training dataset using + the given tf.data service at the given address. + + Returns: + A dataset_utils.Dataset() which includes a train_iter, a valid_iter, + a test_iter, and a dict of meta_data. + """ + del eval_batch_size + default_dataset_config = get_default_dataset_config( + runlocal=False, additional_valid_dataset=True + ) + if dataset_configs: + default_dataset_config.update(dataset_configs) + + dataset_configs = default_dataset_config + + del rng + assert dataset_configs is not None + logging.info( + 'Loading train split of the %sfrom argus dataset.', + dataset_configs.dataset, + ) + + def pp_fn(x, how): + pp = builder.get_preprocess_fn(how, remove_tpu_dtypes=False) + example = pp(x) + example['image'] = tf.cast(example['image'], dtype=dtype_str) + return example + + # E.g. for testing with TAP. + shuffle_buffer_size = ( + 1000 if num_shards == 1 else dataset_configs.shuffle_buffer_size + ) + + train_ds = data_utils.get_data( + dataset=dataset_configs.dataset, + split=dataset_configs.train_split, + data_dir=dataset_configs.get('dataset_dir'), + batch_size=batch_size, + filter_fn=functools.partial( + data_utils.filter_text_length, filter_len=FILTER_LENGTH + ), + preprocess_fn=functools.partial(pp_fn, how=dataset_configs.pp_train), + shuffle_buffer_size=shuffle_buffer_size, + prefetch=dataset_configs.get('prefetch_to_host', 2), + cache='loaded', + ignore_errors=True, + ) + + if dataset_service_address: + if shuffle_seed is not None: + raise ValueError( + 'Using dataset service with a random seed causes each ' + 'worker to produce exactly the same data. Add ' + 'config.shuffle_seed = None to your config if you ' + 'want to run with dataset service.' + ) + logging.info('Using the tf.data service at %s', dataset_service_address) + assert shuffle_buffer_size is not None + train_ds = dataset_utils.distribute(train_ds, dataset_service_address) + + n_train_ex = dataset_utils.get_num_examples( + dataset_configs.dataset, + dataset_configs.train_split, + data_dir=dataset_configs.get('dataset_dir'), + ) + + maybe_pad_batches_train = functools.partial( + dataset_utils.maybe_pad_batch, + inputs_key='encoder_input_image', + train=True, + batch_size=batch_size, + ) + shard_batches = functools.partial(dataset_utils.shard, n_devices=num_shards) + map_generation_split_batches = functools.partial( + data_utils.map_generation_split, + span_len=SPAN_MAX_LENGTH, + output_max_len=OUTPUT_MAX_LENGTH, + split_key='caption_tokens', + add_retr=False, + ) + + train_iter = iter(train_ds) + train_iter = map(map_generation_split_batches, train_iter) + train_iter = map(dataset_utils.tf_to_numpy, train_iter) + train_iter = map(data_utils.sample_retr_image, train_iter) + train_iter = map(maybe_pad_batches_train, train_iter) + if num_shards > 0: + train_iter = map(shard_batches, train_iter) + if dataset_configs.prefetch_to_device: + train_iter = jax_utils.prefetch_to_device( + train_iter, dataset_configs.prefetch_to_device + ) + + logging.info( + 'Loading validation split of the %sfrom argus dataset.', + dataset_configs.dataset, + ) + maybe_pad_batches_eval = functools.partial( + dataset_utils.maybe_pad_batch, + inputs_key='encoder_input_image', + train=False, + batch_size=batch_size, + ) + + def _get_eval_iter(dataset, split, pp_eval): + val_ds = data_utils.get_data( + dataset=dataset, + split=split, + data_dir=dataset_configs.get('dataset_dir'), + batch_size=batch_size, + preprocess_fn=functools.partial(pp_fn, how=pp_eval), + cache=dataset_configs.val_cache, + repeat_after_batching=True, + drop_remainder=False, + ) + + valid_iter = iter(val_ds) + valid_iter = map(map_generation_split_batches, valid_iter) + valid_iter = map(dataset_utils.tf_to_numpy, valid_iter) + valid_iter = map(data_utils.sample_retr_image, valid_iter) + valid_iter = map(maybe_pad_batches_eval, valid_iter) + if num_shards > 0: + valid_iter = map(shard_batches, valid_iter) + if dataset_configs.prefetch_to_device: + valid_iter = jax_utils.prefetch_to_device( + valid_iter, dataset_configs.prefetch_to_device + ) + + return valid_iter + + def _get_num_eval_examples(dataset, split, data_dir): + return dataset_utils.get_num_examples(dataset, split, data_dir) + + if isinstance(dataset_configs.val_split, str): + valid_iter = _get_eval_iter( + dataset_configs.dataset, + dataset_configs.val_split, + dataset_configs.pp_eval, + ) + n_eval_ex = _get_num_eval_examples( + dataset_configs.dataset, + dataset_configs.val_split, + data_dir=dataset_configs.get('dataset_dir'), + ) + else: + valid_iter, n_eval_ex = {}, {} + for eval_spec in dataset_configs.val_split: + name, dataset, split, pp_eval = eval_spec + valid_iter[name] = _get_eval_iter(dataset, split, pp_eval) + n_eval_ex[name] = _get_num_eval_examples( + dataset, split, data_dir=dataset_configs.get('dataset_dir') + ) + + meta_data = {'num_train_examples': n_train_ex, 'num_eval_examples': n_eval_ex} + + if dataset_configs.get('extra_meta_data'): + for k, v in dataset_configs.extra_meta_data.items(): + meta_data[k] = v + + image_shape = (-1, dataset_configs.image_size, dataset_configs.image_size, 3) + predix_shape = (-1, data_utils.PROMPT_LENGTH + SPAN_MAX_LENGTH + 1) + input_shape = (-1, OUTPUT_MAX_LENGTH) + retr_texts_shape = (-1, KNOWLEDGE_MAX_LENGTH + data_utils.PROMPT_LENGTH) + retr_image_shape = ( + -1, + dataset_configs.image_size, + dataset_configs.image_size, + 3, + ) + + meta_data['encoder_input_image_spec'] = (image_shape, getattr(jnp, dtype_str)) + meta_data['encoder_input_tokens_spec'] = (predix_shape, jnp.int16) + meta_data['decoder_input_tokens_spec'] = (input_shape, jnp.int16) + meta_data['decoder_target_tokens_spec'] = (input_shape, jnp.int16) + meta_data['retr_texts_spec'] = (retr_texts_shape, jnp.int16) + meta_data['retr_images_spec'] = (retr_image_shape, getattr(jnp, dtype_str)) + return dataset_utils.Dataset(train_iter, valid_iter, None, meta_data) diff --git a/scenic/projects/knowledge_visual_language/data/wiki_image_text_generation_dataset.py b/scenic/projects/knowledge_visual_language/data/wiki_image_text_generation_dataset.py new file mode 100644 index 000000000..546b7d807 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/data/wiki_image_text_generation_dataset.py @@ -0,0 +1,345 @@ +"""Dataset and Loader for Wikipedia Image-Text (WIT) dataset for retrieval training. + +Only prepare paired with knowledge (contextualalized passages) +""" +import functools +from typing import Optional + +from absl import logging +from flax import jax_utils +import jax +import jax.numpy as jnp +import ml_collections +import numpy as np +from scenic.dataset_lib import dataset_utils +from scenic.dataset_lib import datasets +from scenic.dataset_lib.big_transfer import builder +from scenic.dataset_lib.big_transfer.registry import registry +from scenic.dataset_lib import web_image_text_dataset +from scenic.projects.knowledge_visual_language.data import data_utils +import tensorflow as tf + +SPAN_MAX_LENGTH = 5 +OUTPUT_MAX_LENGTH = 36 +KNOWLEDGE_MAX_LENGTH = 320 +IMAGE_SIZE = 224 + + +@registry.Registry.register('preprocess_ops.get_wit_knowledge', 'function') +def get_wit_knowledge(): + """Concat title passage and document together to form knowledge.""" + + def get_wit_knowledge_fn(data): + """Prepare Knowledge by concating hierarchy, passage and first-paragraph.""" + + knowledges = [ + data['hierarchical_section_title'], + data['context_section_description'], + data['context_page_description'], + ] + data['knowledge'] = tf.strings.join(knowledges, separator=' ') + + caption = data['caption_reference_description_canonicalized'] + if tf.strings.length(caption) < 1: + caption = data['caption_alt_text_description_canonicalized'] + if tf.strings.length(caption) < 1: + caption = tf.strings.regex_replace( + data['caption_attribution_description_canonicalized'], '^english ', '' + ) + data['caption'] = caption + return data + + return get_wit_knowledge_fn + + +def get_default_dataset_config(runlocal=False): + """Gets default configs for wit_internal (en) dataset.""" + dataset_configs = ml_collections.ConfigDict() + # Add path to your data here: + dataset_configs.dataset = '' + dataset_configs.train_split = 'train[1000:]' + dataset_configs.output_max_num_tokens = OUTPUT_MAX_LENGTH + dataset_configs.knowledge_max_num_tokens = OUTPUT_MAX_LENGTH + dataset_configs.image_size = IMAGE_SIZE + dataset_configs.pp_train = ( + f'decode|resize(resize_size={IMAGE_SIZE})|value_range(-1,1)|get_wit_knowledge|t5_tokenize(max_num_tokens={KNOWLEDGE_MAX_LENGTH},' + ' inkey="knowledge", outkey="retr_texts",' + f' prompt="{data_utils.KNOWLEDGE_PREFIX}")|t5_tokenize(max_num_tokens={OUTPUT_MAX_LENGTH * 3},' + ' inkey="caption", outkey="caption_tokens",' + f' prompt="{data_utils.CAPTION_PREFIX}")|keep("image", "caption_tokens",' + ' "retr_texts")' + ) + + dataset_configs.val_split = [( + 'val', + dataset_configs.dataset, + 'train[:1000]', + dataset_configs.pp_train, + )] + + dataset_configs.shuffle_buffer_size = 250000 if not runlocal else 50 + dataset_configs.val_cache = 'loaded' # Unfortunately, "batched" gets us OOM. + dataset_configs.vocab_size = data_utils.VOCAB_SIZE_T5 + dataset_configs.prefetch_to_device = 2 + return dataset_configs + + +def inception_crop(image, resize_size=224, area_min=20, area_max=80): + """Random crop input image.""" + begin, size, _ = tf.image.sample_distorted_bounding_box( + tf.shape(image), + tf.zeros([0, 0, 4], tf.float32), + area_range=(area_min / 100, area_max / 100), + min_object_covered=0, # Don't enforce a minimum area. + use_image_if_no_bounding_boxes=True, + ) + crop = tf.slice(image, begin, size) + # Unfortunately, the above operation loses the depth-dimension. So we need + # to restore it the manual way. + crop.set_shape([None, None, image.shape[-1]]) + if resize_size: + crop = tf.cast( + tf.image.resize(crop, [resize_size, resize_size]), image.dtype + ) + return crop + + +# def sample_retr_image(batch, random_ratio=0.): +# """Sample image from similar sample by tfidf.""" + +# rds = np.random.random(size=len(batch['encoder_input_image'])) +# passages = [p.decode('utf-8')[:256] for p in batch.pop('knowledge')] +# passages_tfidf = TfidfVectorizer().fit_transform(passages) +# sim = cosine_similarity(passages_tfidf) +# np.fill_diagonal(sim, 0) +# rd_imgs = batch['encoder_input_image'][np.argmax(sim, axis=1)] +# del passages_tfidf, passages + +# batch['retr_images'] = [] +# for rd_img, rd, img in zip(rd_imgs, rds, batch['encoder_input_image']): +# if rd < random_ratio: +# batch['retr_images'] += [rd_img] +# else: +# batch['retr_images'] += [inception_crop(img, area_min=5, area_max=60)] +# batch['retr_images'] = np.expand_dims(batch['retr_images'], axis=1) + +# crops = [] +# for img in batch['encoder_input_image']: +# crop = inception_crop(img, area_min=40, area_max=100) +# crops += [crop] +# batch['encoder_input_image'] = np.stack(crops, axis=0) +# return batch + + +def sample_retr_image(batch): + """Sample image from similar sample by tfidf.""" + + crops = [] + for img in batch['encoder_input_image']: + crops += [inception_crop(img, area_min=5, area_max=60)] + batch['retr_images'] = np.stack(crops, axis=0) + return batch + + +@datasets.add_dataset('wiki_image_text_generation') +def get_dataset( + *, + batch_size, + eval_batch_size, + num_shards, + dtype_str='float32', + shuffle_seed=None, + rng=None, + dataset_configs=None, + dataset_service_address: Optional[str] = None, +): + """Returns generators for the CC12M train, validation and test sets. + + Args: + batch_size: int; Determines the train batch size. + eval_batch_size: int; Determines the evaluation batch size. + num_shards: int; Number of shards --> batch shape: [num_shards, bs, ...]. + dtype_str: Data type of the image (e.g. 'float32'). + shuffle_seed: int; Seed for shuffling the training data. Not used. + rng: JAX rng key, which can be used for augmentation, shuffling, etc. + dataset_configs: dict; Dataset specific configurations. + dataset_service_address: If set, will distribute the training dataset using + the given tf.data service at the given address. + + Returns: + A dataset_utils.Dataset() which includes a train_iter, a valid_iter, + a test_iter, and a dict of meta_data. + """ + default_dataset_config = get_default_dataset_config(runlocal=False) + if dataset_configs: + default_dataset_config.update(dataset_configs) + + dataset_configs = default_dataset_config + + del rng + assert dataset_configs is not None + logging.info('Loading train split of the %s', dataset_configs.dataset) + + def pp_fn(x, how): + pp = builder.get_preprocess_fn(how, remove_tpu_dtypes=False) + example = pp(x) + example['image'] = tf.cast(example['image'], dtype=dtype_str) + return example + + # E.g. for testing with TAP. + shuffle_buffer_size = ( + 1000 if num_shards == 1 else dataset_configs.shuffle_buffer_size + ) + + train_ds = data_utils.get_data( + dataset=dataset_configs.dataset, + split=dataset_configs.train_split, + batch_size=batch_size, + preprocess_fn=functools.partial(pp_fn, how=dataset_configs.pp_train), + filter_fn=functools.partial(data_utils.filter_text_length, filter_len=4), + shuffle_buffer_size=shuffle_buffer_size, + prefetch=dataset_configs.get('prefetch_to_host', 2), + cache='loaded', + ignore_errors=True, + ) + + if dataset_service_address: + if shuffle_seed is not None: + raise ValueError( + 'Using dataset service with a random seed causes each ' + 'worker to produce exactly the same data. Add ' + 'config.shuffle_seed = None to your config if you ' + 'want to run with dataset service.' + ) + logging.info('Using the tf.data service at %s', dataset_service_address) + assert shuffle_buffer_size is not None + train_ds = dataset_utils.distribute(train_ds, dataset_service_address) + + n_train_ex = dataset_utils.get_num_examples( + dataset_configs.dataset, + dataset_configs.train_split, + data_dir=dataset_configs.get('dataset_dir'), + ) + + maybe_pad_batches_train = functools.partial( + dataset_utils.maybe_pad_batch, + inputs_key='encoder_input_image', + train=True, + batch_size=batch_size, + ) + shard_batches = functools.partial(dataset_utils.shard, n_devices=num_shards) + map_generation_split_batches = functools.partial( + data_utils.map_generation_split, + span_len=SPAN_MAX_LENGTH, + output_max_len=OUTPUT_MAX_LENGTH, + split_key='caption_tokens', + add_retr=False, + ) + + train_iter = iter(train_ds) + train_iter = map(map_generation_split_batches, train_iter) + train_iter = map(dataset_utils.tf_to_numpy, train_iter) + train_iter = map(sample_retr_image, train_iter) + train_iter = map(maybe_pad_batches_train, train_iter) + if num_shards > 0: + train_iter = map(shard_batches, train_iter) + if dataset_configs.prefetch_to_device: + train_iter = jax_utils.prefetch_to_device( + train_iter, dataset_configs.prefetch_to_device + ) + + logging.info('Loading validation split of the %s', dataset_configs.dataset) + maybe_pad_batches_eval = functools.partial( + dataset_utils.maybe_pad_batch, + inputs_key='encoder_input_image', + train=False, + batch_size=eval_batch_size, + ) + + def _get_eval_iter(dataset, split, pp_eval): + val_ds = data_utils.get_data( + dataset=dataset, + split=split, + data_dir=dataset_configs.get('dataset_dir'), + batch_size=eval_batch_size, + preprocess_fn=functools.partial(pp_fn, how=pp_eval), + filter_fn=functools.partial( + data_utils.filter_text_length, filter_len=4 + ), + cache=dataset_configs.val_cache, + repeat_after_batching=True, + drop_remainder=False, + ) + + valid_iter = iter(val_ds) + valid_iter = map(map_generation_split_batches, valid_iter) + valid_iter = map(dataset_utils.tf_to_numpy, valid_iter) + valid_iter = map(sample_retr_image, valid_iter) + valid_iter = map(maybe_pad_batches_eval, valid_iter) + if num_shards > 0: + valid_iter = map(shard_batches, valid_iter) + if dataset_configs.prefetch_to_device: + valid_iter = jax_utils.prefetch_to_device( + valid_iter, dataset_configs.prefetch_to_device + ) + + return valid_iter + + def _get_num_eval_examples(dataset, split, data_dir): + return dataset_utils.get_num_examples(dataset, split, data_dir) + + if isinstance(dataset_configs.val_split, str): + valid_iter = _get_eval_iter( + dataset_configs.dataset, + dataset_configs.val_split, + dataset_configs.pp_eval, + ) + n_eval_ex = _get_num_eval_examples( + dataset_configs.dataset, + dataset_configs.val_split, + data_dir=dataset_configs.get('dataset_dir'), + ) + else: + valid_iter, n_eval_ex = {}, {} + for eval_spec in dataset_configs.val_split: + name, dataset, split, pp_eval = eval_spec + valid_iter[name] = _get_eval_iter(dataset, split, pp_eval) + n_eval_ex[name] = _get_num_eval_examples( + dataset, split, data_dir=dataset_configs.get('dataset_dir') + ) + + # builder, split, host_id, host_count + + # splitname, host_start, host_end = dataset_utils._get_data_range( + # dataset_configs.dataset, + # dataset_configs.train_split, + # data_dir=dataset_configs.get('dataset_dir')) + + meta_data = { + 'num_train_examples': n_train_ex, + 'example_per_shard': int(n_train_ex // jax.process_count()), + 'num_eval_examples': n_eval_ex, + } + + if dataset_configs.get('extra_meta_data'): + for k, v in dataset_configs.extra_meta_data.items(): + meta_data[k] = v + + image_shape = (-1, dataset_configs.image_size, dataset_configs.image_size, 3) + predix_shape = (-1, data_utils.PROMPT_LENGTH + SPAN_MAX_LENGTH + 1) + input_shape = (-1, OUTPUT_MAX_LENGTH) + retr_texts_shape = (-1, KNOWLEDGE_MAX_LENGTH + data_utils.PROMPT_LENGTH) + retr_image_shape = ( + -1, + dataset_configs.image_size, + dataset_configs.image_size, + 3, + ) + + meta_data['encoder_input_image_spec'] = (image_shape, getattr(jnp, dtype_str)) + meta_data['encoder_input_tokens_spec'] = (predix_shape, jnp.int16) + meta_data['decoder_input_tokens_spec'] = (input_shape, jnp.int16) + meta_data['retr_texts_spec'] = (retr_texts_shape, jnp.int16) + meta_data['retr_images_spec'] = (retr_image_shape, getattr(jnp, dtype_str)) + meta_data['decoder_target_tokens_spec'] = (input_shape, jnp.int16) + return dataset_utils.Dataset(train_iter, valid_iter, None, meta_data) diff --git a/scenic/projects/knowledge_visual_language/data/wikidata/data_util.py b/scenic/projects/knowledge_visual_language/data/wikidata/data_util.py new file mode 100644 index 000000000..ca7237c34 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/data/wikidata/data_util.py @@ -0,0 +1,162 @@ +"""Functions for processing the data.""" + +import json +from typing import Any, Dict, List + +import matplotlib.pyplot as plt +import networkx as nx +import numpy as np +import tqdm + +import gfile + +MAX_REL_PER_ENTITY = 10000 +# Add path to your data here: +WIKIPEDIA_LINK_PATH = '' +WIKIPEDIA_GRAPH_PATH = '' +WIKIDATA_EDGE_PATH = '' +WIKIDATA_ENTITY_PATH = '' + +NUM_SPLIT = 1024 + + +def construct_kg_graph(rel_list) -> Dict[str, Dict[str, List[str]]]: + """construct a kg graph with reverse link. + + Args: + rel_list: KG edge list, in the format of triplets. + + Returns: + + """ + kg_graph = {} + kg_rel = {} + for se, r, te in tqdm.tqdm(rel_list): + if se == te: + continue + if se not in kg_graph: + kg_graph[se] = {} + if te not in kg_graph[se]: + kg_graph[se][te] = [] + if r not in kg_graph[se][te]: + kg_graph[se][te].append(r) + + if r not in kg_rel: + kg_rel[r] = {} + if te not in kg_rel[r]: + kg_rel[r][te] = 0 + kg_rel[r][te] += 1 + + for se, r, te in tqdm.tqdm(rel_list): + if se == te or kg_rel[r][te] >= MAX_REL_PER_ENTITY: + continue + if te not in kg_graph: + kg_graph[te] = {} + if se not in kg_graph[te]: + kg_graph[te][se] = [] + if r + '_R' not in kg_graph[te][se]: + kg_graph[te][se].append(r + '_R') + + return kg_graph + + +def load_wiki_from_file(file_path) -> List[Dict[str, None]]: + data_list = [] + with gfile.Open(file_path, 'r') as fopen: + lines = fopen.readlines() + for line in lines: + data = json.loads(line) + data_list += [data] + del lines + return data_list + + +def extract_2hop_graph(in_context_ents, kg_graph) -> Dict[Any, Dict[Any, bool]]: + """For each wikipedia page with N in-context entities, extract a subgraph that contains only 2hop paths between any pair of nodes. + + Args: + in_context_ents: all entities within each wiki-page, stored as dict. + kg_graph: the global KG (i.e. WikiData Knowledge Graph), stored as dict. + + Returns: + Extracted 2-hop subgraph for each page. + """ + all_nodes = {se: [se] for se in in_context_ents} + for se in in_context_ents: + if se in kg_graph: + for te in kg_graph[se]: + if te not in in_context_ents: + if te not in all_nodes: + all_nodes[te] = [se] + else: + all_nodes[te] += [se] + remain_nodes = {e: True for e in all_nodes if len(all_nodes[e]) > 1} + for e in in_context_ents: + remain_nodes[e] = True + + two_graph = {} + for se in remain_nodes: + if se in kg_graph: + for te in kg_graph[se]: + if te in remain_nodes: + if se not in two_graph: + two_graph[se] = {} + two_graph[se][te] = kg_graph[se][te] + if te in kg_graph and se in kg_graph[te]: + if te not in two_graph: + two_graph[te] = {} + two_graph[te][se] = kg_graph[te][se] + return two_graph + + +def plot_graph(in_graph_ents, graph, entity_dict, print_out_label=True) -> None: + """Function to plot each wikipedia's subgraph. + + Args: + in_graph_ents: all in-context entities + graph: subgraph of each wikipage. + entity_dict: entityID to name + print_out_label: whether to print the intermediate label. + """ + if not graph: + return + g = nx.Graph() + all_label = {} + in_label = {} + for se in graph: + all_label[entity_dict[se]] = entity_dict[se] + if se in in_graph_ents: + g.add_node(entity_dict[se], color='red', size=2000) + in_label[entity_dict[se]] = entity_dict[se] + if se not in in_graph_ents: + g.add_node(entity_dict[se], color='blue', size=100) + for te in graph[se]: + all_label[entity_dict[te]] = entity_dict[te] + if te in in_graph_ents: + g.add_node(entity_dict[te], color='red', size=2000) + in_label[entity_dict[te]] = entity_dict[te] + if te not in in_graph_ents: + g.add_node(entity_dict[te], color='blue', size=100) + g.add_edge(entity_dict[se], entity_dict[te]) + plt.figure(figsize=(10, 10)) + layout = nx.kamada_kawai_layout(g) + if print_out_label: + nx.draw_networkx_nodes( + g, + pos=layout, + node_color=nx.get_node_attributes(g, 'color').values(), + node_size=list(nx.get_node_attributes(g, 'size').values())) + nx.draw_networkx_labels(g, pos=layout, labels=all_label) + nx.draw_networkx_edges(g, pos=layout, alpha=0.3, arrows=False) + else: + nx.draw_networkx_nodes( + g, + pos=layout, + node_color=nx.get_node_attributes(g, 'color').values(), + node_size=list(nx.get_node_attributes(g, 'size').values())) + nx.draw_networkx_labels(g, pos=layout, labels=in_label) + nx.draw_networkx_edges(g, pos=layout, alpha=0.3, arrows=False) + xs = np.array(list(layout.values()))[:, 0] + xmin, xmax = np.min(xs), np.max(xs) + plt.xlim(xmin - (xmax - xmin) * 0.2, xmax + (xmax - xmin) * 0.2) + plt.show() diff --git a/scenic/projects/knowledge_visual_language/data/wikidata/data_util_test.py b/scenic/projects/knowledge_visual_language/data/wikidata/data_util_test.py new file mode 100644 index 000000000..becf230fe --- /dev/null +++ b/scenic/projects/knowledge_visual_language/data/wikidata/data_util_test.py @@ -0,0 +1,103 @@ +"""Tests for Extract WikiGraph.""" +from absl import logging +from absl.testing import absltest +from scenic.projects.knowledge_visual_language.data.wikidata import data_util + + +class ClipModelsTest(absltest.TestCase): + """Single Test based on a dummy input.""" + + def test_graph_extraction(self): + """Tests whether it could correctly extract 2-hop graph.""" + rel_list = [ + ["Q1066517", "r1", "Qdummy"], + ["Q35581", "r2", "Qdummy"], + ["Q2339039", "r2", "Qdummy"], + ["Q753878", "r2", "Qdummy"], + ] + logging.info("Construct KG Graph from edge list.") + kg_graph = data_util.construct_kg_graph(rel_list) + logging.info("Extract 2-hop subgraphs within wiki-pages.") + demo_data = self.demo_input() + for d in demo_data: + in_context_ents = {ent: True for ent in d["link"] if ent != "UNK"} + graph = data_util.extract_2hop_graph(in_context_ents, kg_graph) + print(graph) + logging.info(len(graph)) + + def demo_input(self): + d_list = [{ + "txt": "yukigassen\n is a snowball fighting-competition from " + "japan. today there are annual tournaments in " + "s\u014dbetsu, hokkaid\u014d in japan, kemij\u00e4rvi in" + " finland, vard\u00f8 in norway, murmansk in russia, " + "mount buller, victoria in australia, lule\u00e5 in " + "sweden, anchorage in alaska, aparan in armenia, jasper," + " alberta and saskatoon, saskatchewan in canada.\nthe " + "word consists of the japanese words yuki (snow) and " + "kassen (battle) with rendaku. hence \"yukigassen\" " + "means snow battle, but is a common term for 'snowball " + "fight' in japanese.\n......", + "link": { + "Q1066517": [[17, 46], [483, 497]], + "Q35581": [[106, 114]], + "Q744704": [[125, 134]], + "Q33": [[138, 145]], + "Q108983": [[147, 152], [1720, 1725], [1907, 1912], [2089, 2094]], + "Q1763": [[164, 172]], + "Q984117": [[184, 206]], + "Q26268": [[221, 226], [1534, 1539], [1015, 1020], [1175, 1180], + [1363, 1368]], + "Q39450": [[238, 247]], + "Q797": [[251, 257]], + "Q39618": [[259, 265]], + "Q399": [[269, 276]], + "Q999429": [[278, 293]], + "Q10566": [[298, 321]], + "Q5287": [[358, 366]], + "Q3943791": [[379, 383], [444, 448], [1627, 1631]], + "Q178561": [[397, 403]], + "Q1192464": [[410, 417]], + "Q1035213": [[744, 760]], + "Q131647": [[964, 969], [1118, 1123], [1305, 1310], [1477, 1482], + [1669, 1674], [1865, 1870], [2047, 2052], [925, 930]], + "Q406039": [[1029, 1039], [1189, 1199], [1377, 1387], [1548, 1558], + [1734, 1744], [1922, 1926], [2104, 2108]], + "Q847956": [[1052, 1064], [1211, 1223], [1404, 1416], [1584, 1596], + [1766, 1778], [1951, 1957], [2146, 2152]], + "Q873364": [[1073, 1085], [1243, 1255], [1429, 1441], [1608, 1620], + [1808, 1820], [1975, 1981], [2175, 2181]], + "Q44853": [[1745, 1753]], + "Q218082": [[0, 10], [426, 436], [522, 532], [704, 714], [831, + 841]], + "UNK": [[97, 104], [698, 725], [1040, 1051], [1200, 1210], + [1442, 1452], [1597, 1607], [1224, 1242], [1256, 1280], + [1559, 1583], [1388, 1403], [1621, 1640], [1779, 1795], + [1821, 1826], [1959, 1964], [1928, 1940], [1983, 2006]] + }, + "entity": "Yukigassen" + }, { + "txt": + "harry hyams\nharry john hyams (2 january 1928 \u2013 19 december " + "2015) was a british millionaire who initially made his money as a" + " speculative property (real estate) developer. he was best known " + "as the developer of the centre point office building in " + "london.\n......", + "link": { + "Q2339039": [[213, 225], [961, 973]], + "Q149787": [[283, 289]], + "Q19186": [[291, 300]], + "Q753878": [[633, 642]], + "Q743535": [[1139, 1146]], + "Q43747844": [[1301, 1310]], + "Q7290153": [[1388, 1402], [1566, 1580]], + "Q7743416": [[1584, 1600]], + "Q5669913": [[0, 11], [12, 28]] + }, + "entity": "Harry Hyams" + }] + return d_list + + +if __name__ == "__main__": + absltest.main() diff --git a/scenic/projects/knowledge_visual_language/data/wikidata/extract_wikigraph.py b/scenic/projects/knowledge_visual_language/data/wikidata/extract_wikigraph.py new file mode 100644 index 000000000..793fa8690 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/data/wikidata/extract_wikigraph.py @@ -0,0 +1,70 @@ +"""Extract 2-hop subgraph from each wikipedia page.""" + +import json +import multiprocessing as mp +import os + +from absl import app +from absl import logging +from scenic.projects.knowledge_visual_language.data.wikidata import data_util +import tqdm + +import gfile + + +def extract_graph_from_file(kg_graph, i) -> None: + """Extract 2hop graphs for all pages in each file. + + Args: + kg_graph: the global KG (i.e. WikiData Knowledge Graph), stored as dict. + i: index of wiki file. + + Returns: + A list of wikipedia pages with extracted graph. + """ + d_list = data_util.load_wiki_from_file( + os.path.join(data_util.WIKIPEDIA_LINK_PATH, 'wiki_%d.txt' % i)) + with gfile.Open( + os.path.join(data_util.WIKIPEDIA_GRAPH_PATH, 'wiki_graph_%d.txt' % i), + 'w') as fopen: + for d in d_list: + if 'link' in d and isinstance(d['link'], dict): + in_context_ents = {ent: True for ent in d['link'] if ent != 'UNK'} + d['graph'] = data_util.extract_2hop_graph(in_context_ents, kg_graph) + fopen.write(json.dumps(d) + '\n') + del d_list + + +def extract_graph_mp(kg_graph, n_pool=1) -> None: + """Use multiprocessing to parallize graph extraction. + + Args: + kg_graph: the global KG (i.e. WikiData Knowledge Graph), stored as dict. + n_pool: number of process (worker) + """ + jobs = [] + if not gfile.Exists(data_util.WIKIPEDIA_GRAPH_PATH): + gfile.MakeDirs(data_util.WIKIPEDIA_GRAPH_PATH) + if n_pool == 1: + for i in range(data_util.NUM_SPLIT): + extract_graph_from_file(kg_graph, i) + else: + with mp.Pool(n_pool) as pool: + for i in range(data_util.NUM_SPLIT): + jobs += [pool.apply_async(extract_graph_from_file, args=(kg_graph, i))] + for job in tqdm.tqdm(jobs): + job.get() + + +def main(_) -> None: + logging.info('Load WikiData relational edges.') + with gfile.Open(data_util.WIKIDATA_EDGE_PATH) as f: + rel_list = json.load(f) + logging.info('Construct KG Graph from edge list.') + kg_graph = data_util.construct_kg_graph(rel_list) + logging.info('Extract 2-hop subgraphs within wiki-pages.') + extract_graph_mp(kg_graph, n_pool=64) + + +if __name__ == '__main__': + app.run(main) diff --git a/scenic/projects/knowledge_visual_language/data/wit_table_dataset.py b/scenic/projects/knowledge_visual_language/data/wit_table_dataset.py new file mode 100644 index 000000000..29a62d120 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/data/wit_table_dataset.py @@ -0,0 +1,177 @@ +"""Dataset and Loader for Wikipedia Image-Text (WIT) dataset for retrieval training. + +Only prepare paired with knowledge (contextualalized passages) +""" +import functools +from typing import Optional + +from absl import logging +import jax +import jax.numpy as jnp +import ml_collections +from scenic.dataset_lib import dataset_utils +from scenic.dataset_lib import datasets +from scenic.dataset_lib.big_transfer import builder +from scenic.dataset_lib.big_transfer.registry import registry +from scenic.dataset_lib import web_image_text_dataset +from scenic.projects.knowledge_visual_language.data import data_utils +import tensorflow as tf + +SPAN_MAX_LENGTH = 5 +OUTPUT_MAX_LENGTH = 36 +KNOWLEDGE_MAX_LENGTH = 320 +IMAGE_SIZE = 224 + + +@registry.Registry.register('preprocess_ops.get_table_knowledge', 'function') +def get_table_knowledge(): + """Concat title passage and document together to form knowledge.""" + + def get_table_knowledge_fn(data): + """Prepare Knowledge by concating hierarchy, passage and first-paragraph.""" + + knowledges = [ + data['hierarchical_section_title'], + data['context_section_description'], + data['context_page_description'], + data['caption_reference_description_canonicalized'], + data['caption_alt_text_description_canonicalized'], + tf.strings.regex_replace( + data['caption_attribution_description_canonicalized'], + '^english ', + '', + ), + ] + data['knowledge'] = tf.strings.join(knowledges, separator=' ') + # data['raw_image'] = data['image'] + return data + + return get_table_knowledge_fn + + +def get_default_dataset_config(): + """Gets default configs for wit_internal (en) dataset.""" + dataset_configs = ml_collections.ConfigDict() + # Add path to your data here: + dataset_configs.dataset = '' + dataset_configs.train_split = 'train' + dataset_configs.output_max_num_tokens = OUTPUT_MAX_LENGTH + dataset_configs.knowledge_max_num_tokens = OUTPUT_MAX_LENGTH + dataset_configs.image_size = IMAGE_SIZE + dataset_configs.pp_train = ( + f'get_table_knowledge|decode|resize(resize_size={IMAGE_SIZE})|value_range(-1,1)|t5_tokenize(max_num_tokens={KNOWLEDGE_MAX_LENGTH},' + ' inkey="knowledge", outkey="knowledge_tokens",' + f' prompt="{data_utils.KNOWLEDGE_PREFIX}")|keep("image",' + ' "knowledge_tokens", "canonical_doc_id")' + ) + dataset_configs.vocab_size = data_utils.VOCAB_SIZE_T5 + dataset_configs.prefetch_to_device = 2 + return dataset_configs + + +@datasets.add_dataset('wit_table') +def get_dataset( + *, + batch_size, + eval_batch_size, + num_shards, + dtype_str='float32', + shuffle_seed=None, + rng=None, + dataset_configs=None, + dataset_service_address: Optional[str] = None, +): + """Returns generators for the CC12M train, validation and test sets. + + Args: + batch_size: int; Determines the train batch size. + eval_batch_size: int; Determines the evaluation batch size. + num_shards: int; Number of shards --> batch shape: [num_shards, bs, ...]. + dtype_str: Data type of the image (e.g. 'float32'). + shuffle_seed: int; Seed for shuffling the training data. Not used. + rng: JAX rng key, which can be used for augmentation, shuffling, etc. + dataset_configs: dict; Dataset specific configurations. + dataset_service_address: If set, will distribute the training dataset using + the given tf.data service at the given address. + + Returns: + A dataset_utils.Dataset() which includes a train_iter, a valid_iter, + a test_iter, and a dict of meta_data. + """ + del batch_size + default_dataset_config = get_default_dataset_config() + if dataset_configs: + default_dataset_config.update(dataset_configs) + + dataset_configs = default_dataset_config + + del rng + assert dataset_configs is not None + logging.info('Loading train split of the %s', dataset_configs.dataset) + + def pp_fn(x, how): + pp = builder.get_preprocess_fn(how, remove_tpu_dtypes=False) + example = pp(x) + example['image'] = tf.cast(example['image'], dtype=dtype_str) + return example + + # E.g. for testing with TAP. + shuffle_buffer_size = None + + train_ds = data_utils.get_data( + dataset=dataset_configs.dataset, + split=dataset_configs.train_split, + batch_size=eval_batch_size, + preprocess_fn=functools.partial(pp_fn, how=dataset_configs.pp_train), + shuffle_buffer_size=None, + shuffle_files=False, + prefetch=dataset_configs.get('prefetch_to_host', 2), + cache='loaded', + ignore_errors=True, + drop_remainder=True, + ) + + if dataset_service_address: + if shuffle_seed is not None: + raise ValueError( + 'Using dataset service with a random seed causes each ' + 'worker to produce exactly the same data. Add ' + 'config.shuffle_seed = None to your config if you ' + 'want to run with dataset service.' + ) + logging.info('Using the tf.data service at %s', dataset_service_address) + assert shuffle_buffer_size is not None + train_ds = dataset_utils.distribute(train_ds, dataset_service_address) + + n_train_ex = dataset_utils.get_num_examples( + dataset_configs.dataset, + dataset_configs.train_split, + data_dir=dataset_configs.get('dataset_dir'), + ) + + maybe_pad_batches_train = functools.partial( + dataset_utils.maybe_pad_batch, + inputs_key='image', + train=True, + batch_size=eval_batch_size, + ) + shard_batches = functools.partial(dataset_utils.shard, n_devices=num_shards) + + train_iter = iter(train_ds) + train_iter = map(dataset_utils.tf_to_numpy, train_iter) + train_iter = map(maybe_pad_batches_train, train_iter) + if num_shards > 0: + train_iter = map(shard_batches, train_iter) + + meta_data = { + 'num_train_examples': n_train_ex, + 'example_per_shard': int(n_train_ex // jax.process_count()), + 'batch_size': eval_batch_size, + } + + image_shape = (dataset_configs.image_size, dataset_configs.image_size, 3) + knowledge_shape = (KNOWLEDGE_MAX_LENGTH + data_utils.PROMPT_LENGTH,) + + meta_data['image_spec'] = (image_shape, getattr(jnp, dtype_str)) + meta_data['knowledge_spec'] = (knowledge_shape, jnp.int16) + return dataset_utils.Dataset(train_iter, None, None, meta_data) diff --git a/scenic/projects/knowledge_visual_language/main.py b/scenic/projects/knowledge_visual_language/main.py new file mode 100644 index 000000000..eeae78875 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/main.py @@ -0,0 +1,91 @@ +"""Main file for start training.""" + +from absl import flags +from absl import logging +from clu import metric_writers +import jax +import jax.numpy as jnp +import ml_collections +from scenic import app +from scenic.projects.knowledge_visual_language import trainer +from scenic.projects.knowledge_visual_language import trainer_memory +from scenic.projects.knowledge_visual_language import trainer_utils +from scenic.projects.knowledge_visual_language.data import cc12m_table_dataset # pylint: disable=unused-import +from scenic.projects.knowledge_visual_language.data import vqa_dataset # pylint: disable=unused-import +from scenic.projects.knowledge_visual_language.data import vqa_table_dataset # pylint: disable=unused-import +from scenic.projects.knowledge_visual_language.data import web_image_text_generation_dataset # pylint: disable=unused-import +from scenic.projects.knowledge_visual_language.data import wiki_image_text_generation_dataset # pylint: disable=unused-import +from scenic.projects.knowledge_visual_language.data import wit_table_dataset # pylint: disable=unused-import +from scenic.projects.knowledge_visual_language.models import fusion_in_decoder_soft +from scenic.projects.knowledge_visual_language.models import knowledge_fid +from scenic.train_lib import train_utils + +FLAGS = flags.FLAGS + + +def main( + rng: jnp.ndarray, + config: ml_collections.ConfigDict, + workdir: str, + writer: metric_writers.MetricWriter, +) -> None: + """Main function for the knowledge project.""" + data_rng, rng = jax.random.split(rng) + + dataset = train_utils.get_dataset( + config, data_rng, dataset_service_address=FLAGS.dataset_service_address + ) + + logging.info(FLAGS.dataset_service_address) + logging.info('workdir= %s', workdir) + logging.info('global_num_shards= %d', jax.device_count()) + logging.info('num_shards= %d', jax.local_device_count()) + logging.info('cpu info') + logging.info(jax.devices(backend='cpu')) + logging.info(jax.local_devices(backend='cpu')) + logging.info('****************** Dataset metadata *****************') + logging.info(dataset.meta_data) + + if config.update_num: + trainer_utils.update_config(config, dataset.meta_data) + if config.model_name == 'retrieval_image_captioner_soft': + model_cls = fusion_in_decoder_soft.FIDSoftModel + trainer.train_and_eval( + rng=rng, + config=config, + model_cls=model_cls, + dataset=dataset, + workdir=workdir, + writer=writer, + ) + elif config.model_name == 'knowledge_fid': + model_cls = knowledge_fid.KnowledgeFIDModel + kb_datasets = {} + for kb_dataset_name, kb_dataset_config in zip( + config.kb_dataset_names, config.kb_dataset_configs + ): + kb_datasets[kb_dataset_name] = train_utils.get_dataset( + config, + data_rng, + dataset_service_address=FLAGS.dataset_service_address, + dataset_name=kb_dataset_name, + dataset_configs=kb_dataset_config, + ) + + trainer_memory.train_and_eval( + rng=rng, + config=config, + model_cls=model_cls, + dataset=dataset, + workdir=workdir, + writer=writer, + kb_datasets=kb_datasets, + ) + else: + raise ValueError(('Unknown model name %s' % config.model_name)) + + return None + + +if __name__ == '__main__': + app.run(main=main) diff --git a/scenic/projects/knowledge_visual_language/models/constants.py b/scenic/projects/knowledge_visual_language/models/constants.py new file mode 100644 index 000000000..b67e5fb84 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/models/constants.py @@ -0,0 +1,16 @@ +"""Constant value and types' definitions.""" + +from typing import Dict, Callable, Any, Tuple, Iterable + +import jax.numpy as jnp + +PyTree = Any +DType = jnp.dtype +JTensor = jnp.ndarray +JTensorDict = Dict[str, JTensor] +Batch = Dict[str, JTensor] +Shape = Iterable[int] +MetricFn = Callable[[JTensor, Batch], Dict[str, Tuple[float, int]]] +LossFn = Callable[[JTensorDict, Batch], Dict[str, float]] +Initializer = Callable[[JTensor, Shape, DType], JTensor] + diff --git a/scenic/projects/knowledge_visual_language/models/fusion_in_decoder_soft.py b/scenic/projects/knowledge_visual_language/models/fusion_in_decoder_soft.py new file mode 100644 index 000000000..67abf480c --- /dev/null +++ b/scenic/projects/knowledge_visual_language/models/fusion_in_decoder_soft.py @@ -0,0 +1,504 @@ +"""Baseline for Image to Text Models. + +B = batch size +H = height +W = width +N = number of image tokens +I = Input sequence length +O = Ouput sequence length +d = hidden dims +C = number of vocabulary +K = number of candidate +L = sequence length of retrieved document +M = sequence length of compressed tokens +""" +from typing import Any, Dict, Optional + +import flax.linen as nn +import jax +import jax.numpy as jnp +import ml_collections +from scenic.model_lib.base_models import base_model +from scenic.projects.knowledge_visual_language.models import constants +from scenic.projects.knowledge_visual_language.models import layers +from scenic.projects.knowledge_visual_language.models import losses +from scenic.projects.knowledge_visual_language.models import metrics +from scenic.projects.knowledge_visual_language.models import vit as vit_model +from scenic.projects.t5 import layers as t5_model +from scenic.projects.t5 import model as t5_pretrained + + +class VisionLanguageModule(nn.Module): + """Basic ViT + T5 vision language model.""" + + config: ml_collections.ConfigDict + + def setup(self): + t5_config = t5_pretrained.CONFIGS[self.config.t5_name] + self.t5_config = t5_config + t5_config['dropout_rate'] = self.config.dropout_rate + self.ndim = t5_config['emb_dim'] + self.dropout_rate = t5_config['dropout_rate'] + self.key_dim = self.config.key_dim + self.dtype = t5_config['dtype'] + # Shared token embedding for T5 encoder & Decoder + self.shared_token_embedder = t5_model.t5_layers.Embed( + num_embeddings=t5_config['vocab_size'], + features=self.ndim, + dtype=self.dtype, + attend_dtype=self.dtype, # For logit training stability. + embedding_init=nn.initializers.normal(stddev=1.0), + one_hot=True, + name='shared_token_embedder', + ) + # Pre-Trained Lower T5 Decoder + self.out_decoder = t5_model.T5Decoder( + **t5_config, + shared_embedding=self.shared_token_embedder, + name='out_decoder' + ) + # Uni-Modal Text Encoding (Pre-Trained Lower T5 Encoder) + self.text_encoder = layers.LowerT5Encoder( + **t5_config, + num_fusion_layers=self.config.num_fusion_layers, + shared_embedding=self.shared_token_embedder, + name='text_encoder' + ) + # Multi-Modal Fusion Encoder (Pre-Trained Upper T5 Encoder) + self.fusion_encoder = layers.FusedT5Encoder( + **t5_config, + num_fusion_layers=self.config.num_fusion_layers, + name='fusion_encoder' + ) + # Visual Encoding (Pre-Trained ViT) + self.img_encoder = vit_model.Model( + num_classes=self.ndim, + dropout=self.dropout_rate, + name='img_encoder', + variant=self.config.vit_name, + head_zeroinit=False, + dtype=jnp.bfloat16, + num_frozen_layers=self.config.get('vit_num_frozen_layers', -1), + pool_type='gap', + ) + self.dropout = nn.Dropout(rate=0.2) + + def get_base_encoded( + self, + image=None, + text_tokens=None, + train=False, + random_drop_image=False, + bsz=None, + frozen_base=True, + ): + if bsz is None: + if text_tokens is not None: + bsz = len(text_tokens) + elif image is not None: + bsz = len(image) + if text_tokens is not None: + text_query, text_mask = self.text_encoder( + encoder_input_tokens=text_tokens, + use_dropout=train, + frozen_base=frozen_base, + ) # B×I×d + else: + text_query = jnp.zeros([bsz, 1, self.ndim], dtype=self.dtype) + text_mask = jnp.zeros([bsz, 1], dtype=self.dtype) + if image is not None: + img_query, img_emb = self.encode_image(image, train=train) + n_img_tokens = img_query.shape[1] + else: + n_img_tokens = 1 + img_query = jnp.zeros([bsz, n_img_tokens, self.ndim], dtype=self.dtype) + img_emb = jnp.zeros([bsz, self.ndim], dtype=self.dtype) + if train and random_drop_image: + image_mask = jax.random.bernoulli( + self.make_rng('dropout'), p=1 - 0.2, shape=(bsz, 1) + ).astype(self.dtype) + img_emb = img_emb * image_mask + image_mask = jnp.repeat(image_mask, repeats=n_img_tokens, axis=1) + else: + image_mask = jnp.ones([bsz, n_img_tokens], dtype=self.dtype) + base_masks = jnp.concatenate([text_mask, image_mask], axis=1) + return [text_query, img_query], base_masks, img_emb + + +class FusionInDecoderSoftModule(VisionLanguageModule): + """Modification of FID (https://arxiv.org/pdf/2007.01282.pdf) model. + + Take continous embedding of retrieved document at middle fusion layer + instead of whole sequence at input. + """ + + config: ml_collections.ConfigDict + + def setup(self): + super().setup() + self.n_compressed_tokens = self.config.n_compressed_tokens + # Project retrieved knowledge into encoder space + self.value_perceiver = layers.PerceiverEncoder( + **self.t5_config, + num_fusion_layers=self.config.num_fusion_layers, + perceiver_output_dim=self.n_compressed_tokens, + name='value_perceiver' + ) + # Query & Key Head for Retrieval + self.compress_head = nn.Dense( + features=self.key_dim, dtype=self.dtype, name='head_out', use_bias=False + ) + self.query_head = layers.TransformerHead( + **self.t5_config, + num_head_layers=self.config.num_fusion_layers, + out_head=self.compress_head, + key_dim=self.key_dim, + name='query_head' + ) + self.key_head = layers.TransformerHead( + **self.t5_config, + num_head_layers=self.config.num_fusion_layers, + out_head=self.compress_head, + key_dim=self.key_dim, + name='key_head' + ) + self.att_transform = layers.AffineTransform() + + def compress_and_pool_key(self, h, mask): + window_size = self.n_stride + pooled_tokens = nn.avg_pool( + h[:, self.n_compressed_tokens :, :], + window_shape=(window_size,), + strides=(self.n_stride,), + ) + pooled_tokens = jnp.concatenate( + (h[:, : self.n_compressed_tokens, :], pooled_tokens), axis=1 + ) + pooled_mask = jnp.squeeze( + -nn.max_pool( + jnp.expand_dims(-mask[:, self.n_compressed_tokens :], axis=-1), + window_shape=(window_size,), + strides=(self.n_stride,), + ) + ) + pooled_mask = jnp.concatenate( + (mask[:, : self.n_compressed_tokens], pooled_mask), axis=1 + ) + # Total: 10 + 512 / n_stride = 42 tokens + return pooled_tokens, pooled_mask + + def compress_key(self, h, mask): + pooled_tokens = h[:, : self.n_compressed_tokens, :] + pooled_mask = mask[:, : self.n_compressed_tokens] + return pooled_tokens, pooled_mask + + def encode_knowledge( + self, + retr_texts, + retr_images=None, + bsz=None, + train=False, + random_drop_image=False, + frozen_base=True, + ): + retr_tokens, retr_masks, retr_img_emb = self.get_base_encoded( + bsz=bsz, + image=retr_images, + text_tokens=retr_texts, + train=train, + random_drop_image=random_drop_image, + frozen_base=frozen_base, + ) + retr_tokens = jnp.concatenate(retr_tokens, axis=1) # B×(I+N)×d + retr_keys = self.key_head( + encoded_emb=retr_tokens, encoder_mask=retr_masks, use_dropout=train + ) # B×(I+N)×d -> B×d + compressed_val, compressed_mask, disentangle_reg = self.value_perceiver( + encoded=retr_tokens, encoded_mask=retr_masks, use_dropout=train + ) + + return ( + retr_keys, + compressed_val, + compressed_mask, + retr_img_emb, + disentangle_reg, + ) + + def encode_query( + self, + encoder_input_image, + encoder_input_tokens, + train=False, + frozen_base=True, + ): + bsz = encoder_input_image.shape[0] + base_vals, base_masks, _ = self.get_base_encoded( + bsz=bsz, + image=encoder_input_image, + text_tokens=encoder_input_tokens, + train=train, + frozen_base=frozen_base, + ) + base_vals = self.dropout( + jnp.concatenate(base_vals, axis=1), deterministic=not train + ) # B×(I+N)×d + base_query = self.query_head( + encoded_emb=base_vals, encoder_mask=base_masks, use_dropout=train + ) + return base_vals, base_masks, base_query + + def encode_topk_knowledge( + self, + bsz, + retr_texts, + retr_images=None, + train=False, + random_drop_image=False, + frozen_base=True, + ): + k, l = retr_texts.shape[1], retr_texts.shape[2] + retr_texts = jnp.reshape(retr_texts, (bsz * k, l)) + if retr_images is not None: + image_shape = (bsz * k,) + retr_images.shape[2:] + retr_images = jnp.reshape(retr_images, image_shape) + ( + retr_keys, + compressed_val, + compressed_mask, + retr_img_emb, + disentangle_reg, + ) = self.encode_knowledge( + retr_texts, + retr_images, + bsz=bsz * k, + train=train, + random_drop_image=random_drop_image, + frozen_base=frozen_base, + ) + n_tokens = compressed_val.shape[1] + retr_keys = jnp.reshape(retr_keys, (bsz, k, self.key_dim)) + compressed_val = jnp.reshape( + compressed_val, (bsz, k, n_tokens, self.ndim) + ) # B×K×M×d + compressed_mask = jnp.reshape(compressed_mask, (bsz, k, n_tokens)) + return ( + retr_keys, + compressed_val, + compressed_mask, + retr_img_emb, + disentangle_reg, + ) + + def encode_image(self, image, train=False): + _, out = self.img_encoder(image, train=train) # B×W×H×3 -> B×N×d + img_query = jnp.asarray(out['logits_2d'] * 4, self.dtype) + n_img_tokens = img_query.shape[1] * img_query.shape[2] + img_query = jnp.reshape(img_query, [-1, n_img_tokens, self.ndim]) + img_emb = jnp.asarray(out['head_input'], self.dtype) + return img_query, img_emb + + def fuse_topk_knowledge( + self, + base_query, + base_vals, + base_masks, + retr_keys, + retr_vals, + retr_masks, + train=False, + ): + (bsz, k, n_tokens) = retr_vals.shape[:3] + retr_vals = jnp.reshape( + retr_vals, (bsz, k * n_tokens, self.ndim) + ) # B×(M*K)×d + retr_scores = jnp.einsum('bd,bkd->bk', base_query, retr_keys) + retr_scores = jax.nn.softmax(self.att_transform(retr_scores), axis=-1) * k + retr_masks = jnp.reshape(retr_masks, (bsz, k * n_tokens)) + att_mask = [ + jnp.ones([bsz, base_vals.shape[1]]), + jnp.repeat(retr_scores, repeats=n_tokens, axis=-1), + ] + att_mask = jnp.expand_dims(jnp.concatenate(att_mask, axis=-1), axis=-1) + fused_query, fused_mask, attn_weights_all_layers = self.fusion_encoder( + encoder_input_embs=base_vals, + fused_input_embs=retr_vals, + encoder_mask=base_masks, + fused_mask=retr_masks, + att_mask=att_mask, + use_dropout=train, + output=True, + ) # B×(I+N+M*K)×d + return fused_query, fused_mask, retr_scores, attn_weights_all_layers + + def __call__( + self, + decoder_input_tokens, # B×O + decoder_target_tokens, # B×O + encoder_input_image=None, # B×W×H×3 + encoder_input_tokens=None, # B×I + retr_texts=None, # B×K×L + retr_images=None, # B×K×W×H×3 + train=False, + decode=False, + fuse_retrieval=True, + max_decode_length=None, + debug: bool = False, + in_batch_neg: bool = False, + frozen_base=True, + **args + ): + """Conduct supervised retrieval-augmented training with given retrieved documents. + + Args: + decoder_input_tokens: # B×O. + decoder_target_tokens: # B×O. + encoder_input_image: # B×W×H×3. + encoder_input_tokens: # B×I. + retr_texts: # B×K×L. + retr_images: # B×K×W×H×3. + train: whether using train mode. + decode: whether in decode mode. + fuse_retrieval: whether use input retrieval docs. + max_decode_length: maximum decode token length. + debug: whether use debug mode. + in_batch_neg: whether use in-batch contastive learning. + frozen_base: whether froze the whole encoder. + **args: other possible arguments. + + Returns: + output dictionary containing final and intermediate results. + """ + bsz = decoder_input_tokens.shape[0] + base_vals, base_masks, query_img_emb = self.get_base_encoded( + bsz=bsz, + image=encoder_input_image, + text_tokens=encoder_input_tokens, + train=train, + frozen_base=frozen_base, + ) # B×N×d, B×I×d + out_dict = { + 'query_img_emb': query_img_emb, + 'text_query': base_vals[0], + 'image_query': base_vals[1], + } + base_vals = jnp.concatenate(base_vals, axis=1) # B×(I+N)×d + if retr_texts is not None: + retr_keys, retr_vals, retr_masks, retr_img_emb, disentangle_reg = ( + self.encode_topk_knowledge( + bsz=bsz, + retr_images=retr_images, + retr_texts=retr_texts, + train=train, + random_drop_image=True, + ) + ) + base_query = self.query_head( + encoded_emb=base_vals, encoder_mask=base_masks, use_dropout=train + ) # B×(I+N)×d -> B×d + out_dict['disentangle_reg'] = disentangle_reg + out_dict['retr_img_emb'] = retr_img_emb + out_dict['base_query'] = base_query + out_dict['retr_keys'] = retr_keys + out_dict['retr_vals'] = retr_vals + + if fuse_retrieval and retr_texts is not None: + # fuse top-k retrieved knowledge (or no fusion) + if in_batch_neg and retr_vals.shape[1] == 1: + # retr_vals: B×1×M×d -> B×2×M×d, retr_keys: B×1×d -> B×2×M×d + retr_vals = jnp.concatenate( + (retr_vals, jnp.roll(retr_vals, shift=1, axis=0)), axis=1 + ) + retr_keys = jnp.concatenate( + (retr_keys, jnp.roll(retr_keys, shift=1, axis=0)), axis=1 + ) + retr_masks = jnp.concatenate( + (retr_masks, jnp.roll(retr_masks, shift=1, axis=0)), axis=1 + ) + + fused_emb, fused_mask, retr_scores, attn_weights_all_layers = ( + self.fuse_topk_knowledge( + base_query=base_query, + base_vals=base_vals, + base_masks=base_masks, + retr_keys=retr_keys, + retr_vals=retr_vals, + retr_masks=retr_masks, + train=train, + ) + ) # B×(I+N+M*K)×d + out_dict['retr_scores'] = retr_scores + else: + # only fuse input image and text + fused_emb, fused_mask, attn_weights_all_layers = self.fusion_encoder( + fused_input_embs=base_vals, fused_mask=base_masks, use_dropout=train + ) # B×(I+N)×d + # generate decoding results. + out_dict['attn_weights_all_layers'] = attn_weights_all_layers + out_dict['predicted_logits'] = self.out_decoder( + encoded=fused_emb, + decoder_input_tokens=decoder_input_tokens, + encoder_input_tokens=fused_mask, + decoder_target_tokens=decoder_target_tokens, + enable_dropout=train, + decode=decode, + max_decode_length=max_decode_length, + encoder_segment_ids=None, + decoder_segment_ids=None, + ) + return out_dict + + +class FIDSoftModel(base_model.BaseModel): + """FID model.""" + + def build_flax_model(self) -> nn.Module: + return FusionInDecoderSoftModule(self.config.model) + + def loss_function_dict( + self, output: constants.JTensorDict, batch: constants.JTensorDict + ) -> Dict[str, Any]: + """Returns negative loglikelihood (NLL) of the target sentence. + + Args: + output: Output of model in OrderedDict. + batch: Batch of data that has 'decoder_target' as ground-truth. + + Returns: + Total loss. + """ + gen_loss = losses.nll_loss( + targets=batch['decoder_target_tokens'], + pred=output['predicted_logits'], + target_masks=batch['decoder_target_tokens'] > 0, + label_smoothing=self.config.model.get('label_smoothing'), + ) + loss_dict = {'gen_loss': gen_loss} + if output['supervised_retrieval']: + retr_loss, (retr_acc, s0, s1) = losses.contrastive_loss( + query_emb=output['base_query'], + key_emb=output['retr_keys'], + temperature=self.config.model.get('temperature'), + ) + loss_dict['retr_loss'] = retr_loss + loss_dict['retr_acc'] = retr_acc + loss_dict['s0'] = s0 + loss_dict['s1'] = s1 + else: + loss_dict['retr_loss'] = -1 + loss_dict['retr_acc'] = -1 + loss_dict['s0'] = -1 + loss_dict['s1'] = -1 + return loss_dict + + def get_metrics_fn(self, split: Optional[str] = None) -> base_model.MetricFn: + """Returns a callable metric function for the model. + + Args: + split: The split for which we calculate the metrics. It should be one of + the ['train', 'validation', 'test']. + Returns: A metric function with the following API: ```metrics_fn(outputs, + batch)``` + """ + + return metrics.token_accuracy diff --git a/scenic/projects/knowledge_visual_language/models/knowledge_fid.py b/scenic/projects/knowledge_visual_language/models/knowledge_fid.py new file mode 100644 index 000000000..4f4522dd2 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/models/knowledge_fid.py @@ -0,0 +1,884 @@ +"""Baseline for Image to Text Models. + +B = batch size +H = height +W = width +N = number of image tokens +I = Input sequence length +O = Ouput sequence length +d = hidden dims +C = number of vocabulary +K = number of candidate +L = sequence length of retrieved document +M = sequence length of compressed tokens +""" +import functools +from typing import Any, Dict, Mapping, Optional, Tuple, List + +from absl import logging +import flax.linen as nn +import jax +from jax.experimental import host_callback # pylint: disable=unused-import +import jax.numpy as jnp +import ml_collections +import numpy as np +from scenic.dataset_lib import dataset_utils +from scenic.model_lib.base_models import base_model +from scenic.projects.knowledge_visual_language.data import data_utils +from scenic.projects.knowledge_visual_language.models import constants +from scenic.projects.knowledge_visual_language.models import fusion_in_decoder_soft +from scenic.projects.knowledge_visual_language.models import layers +from scenic.projects.knowledge_visual_language.models import local_memory +from scenic.projects.knowledge_visual_language.models import losses +from scenic.projects.knowledge_visual_language.models import metrics +from t5x import decoding + +local_kb = local_memory.kb + + +class KnowledgeFIDModule(fusion_in_decoder_soft.FusionInDecoderSoftModule): + """FID model (https://arxiv.org/pdf/2007.01282.pdf) with a retrieval module over a knowledge memory.""" + + retr_k: int + data_k: int + axis_index_groups: Optional[List[List[int]]] = None + across_index_groups: Optional[List[List[int]]] = None + + def setup(self): + super().setup() + self.local_keys = self.variable( + 'memory', + 'keys', + functools.partial(jnp.zeros, dtype=jnp.bfloat16), + (local_kb.n_data_per_shard, self.key_dim), + ) + self.local_dataset_idxs = self.variable( + 'memory', + 'idxs', + functools.partial(jnp.zeros, dtype=jnp.int16), + (local_kb.n_data_per_shard * local_kb.n_local_device), + ) + self.dataset_gate = nn.Dense( + features=local_kb.n_kb_dataset, dtype=self.dtype, name='dataset_gate' + ) + + def _get_corpus_scores(self, corpus_scores, topk_ids): + corpus_ids = jnp.take(self.local_dataset_idxs.value, topk_ids, axis=0) + return layers.batch_index_select(corpus_scores, corpus_ids), corpus_ids + + def _dist_mips_local( + self, + query, + corpus_scores, + local_device_id, + recall_target=0.99, + exact=False, + ): + logging.info('mips local!!!') + logging.info(self.local_keys.value.shape) + logging.info(local_kb.n_data) + host_query = jax.lax.all_gather( + x=query, + axis_name='batch', + axis=0, + axis_index_groups=self.axis_index_groups, + tiled=True, + ) + logging.info(host_query.shape) + + local_scores = jax.lax.dot(host_query, self.local_keys.value.transpose()) + if exact: + local_topk_scores, local_topk_ids = jax.lax.top_k( + local_scores, k=self.retr_k + ) + else: + local_topk_scores, local_topk_ids = jax.lax.approx_max_k( + local_scores, + k=self.retr_k, + recall_target=recall_target, + reduction_input_size_override=local_kb.n_data, + aggregate_to_topk=True, + ) + local_topk_ids_offset = ( + local_topk_ids + local_device_id * local_kb.n_data_per_shard + ) + # local_topk_scores: bsz * k + logging.info(local_topk_ids.shape) + host_topk_scores = jax.lax.all_to_all( + x=local_topk_scores, + axis_name='batch', + split_axis=0, + concat_axis=1, + axis_index_groups=self.axis_index_groups, + tiled=True, + ) + host_topk_ids = jax.lax.all_to_all( + x=local_topk_ids_offset, + axis_name='batch', + split_axis=0, + concat_axis=1, + axis_index_groups=self.axis_index_groups, + tiled=True, + ) + # host_topk_scores: per_bsz * (n_device * k) + logging.info(host_topk_scores.shape) + + host_corpus_scores, host_corpus_ids = self._get_corpus_scores( + corpus_scores, host_topk_ids + ) + host_topk_scores, host_rank_ids = jax.lax.top_k( + host_topk_scores * host_corpus_scores, k=self.retr_k + ) + # host_topk_scores: per_bsz * retr_k + + host_data_ids = layers.batch_index_select( + host_topk_ids, host_rank_ids[:, : self.data_k] + ) + host_memory_ids = layers.batch_index_select( + host_topk_ids, host_rank_ids[:, self.data_k :] + ) + host_corpus_ids = layers.batch_index_select(host_corpus_ids, host_rank_ids) + + logging.info('data and memory shape!!!') + logging.info(host_data_ids.shape) + logging.info(host_memory_ids.shape) + + # retrieve memory + args = (host_data_ids, host_memory_ids) + + ret_memory, ret_data = host_callback.call( + local_memory.local_retrieve_memory, + args, + result_shape=local_kb.local_ret_specs, + ) + + ret_memory['masks'] = jnp.ones(ret_memory['values'].shape[:3]).astype(bool) + + for k in ret_memory: + logging.info(k) + logging.info(ret_memory[k].shape) + logging.info(ret_memory[k].dtype) + + return ( + host_topk_scores, + ret_memory, + ret_data, + host_topk_ids, + host_rank_ids, + host_corpus_ids, + ) + + def _dist_mips_across( + self, + query, + corpus_scores, + local_device_id, + recall_target=0.99, + exact=False, + ): + # must have n_host > retr_k + logging.info('mips global!!!') + logging.info(self.local_keys.value.shape) + logging.info(local_kb.n_data) + n_local_device = len(self.across_index_groups) + logging.info(n_local_device) + global_query = jax.lax.all_gather( + x=query, axis_name='batch', axis=0, tiled=True + ) + logging.info(global_query.shape) + # global_query: (per_bsz * n_local_device * n_hosts) * d + global_corpus_scores = jax.lax.all_gather( + x=corpus_scores, axis_name='batch', axis=0, tiled=True + ) + logging.info(global_corpus_scores.shape) + # global_corpus_scores: (per_bsz * n_local_device * n_hosts) * n_kb + + local_scores = jax.lax.dot(global_query, self.local_keys.value.transpose()) + local_k = local_kb.k + if exact: + local_topk_scores, local_topk_ids = jax.lax.top_k(local_scores, k=local_k) + else: + local_topk_scores, local_topk_ids = jax.lax.approx_max_k( + local_scores, + k=local_k, + recall_target=recall_target, + reduction_input_size_override=local_kb.n_data, + aggregate_to_topk=True, + ) + + local_topk_ids_offset = ( + local_topk_ids + local_device_id * local_kb.n_data_per_shard + ) + logging.info(local_topk_ids.shape) + # local_topk_ids: (per_bsz * n_local_device * n_hosts) * K + host_topk_scores = jax.lax.all_gather( + x=local_topk_scores, + axis_name='batch', + axis=1, + axis_index_groups=self.axis_index_groups, + tiled=True, + ) + logging.info(host_topk_scores.shape) + # host_topk_scores: (per_bsz * n_local_device * n_hosts) * (n_hosts * K) + host_topk_ids = jax.lax.all_gather( + x=local_topk_ids_offset, + axis_name='batch', + axis=1, + axis_index_groups=self.axis_index_groups, + tiled=True, + ) + # host_topk_ids: (per_bsz * n_local_device * n_hosts) * (n_hosts * K) + + host_corpus_scores, host_corpus_ids = self._get_corpus_scores( + global_corpus_scores, host_topk_ids + ) + # host_corpus_scores: (per_bsz * n_local_device * n_hosts) * (n_hosts * K) + host_topk_scores, host_rank_ids = jax.lax.top_k( + host_topk_scores * host_corpus_scores, k=local_k + ) + # host_topk_scores: (per_bsz * n_local_device * n_hosts) * K + host_topk_ids = layers.batch_index_select(host_topk_ids, host_rank_ids) + logging.info(host_topk_ids.shape) + # host_topk_ids: (per_bsz * n_local_device * n_hosts) * K + host_topk_ids = jnp.reshape(host_topk_ids, (-1, n_local_device, local_k)) + host_topk_ids = host_topk_ids[:, local_device_id] + logging.info(host_topk_ids.shape) + # host_topk_ids: (per_bsz * n_hosts) + host_topk_scores = jnp.reshape( + host_topk_scores, (-1, n_local_device, local_k) + ) + host_topk_scores = host_topk_scores[:, local_device_id] + logging.info('host_topk_scores') + logging.info(host_topk_scores.shape) + + ret_memory, ret_data = host_callback.call( + local_memory.retrieve_top_memory, + (host_topk_ids), + result_shape=local_kb.ret_top_specs, + ) + + global_topk_scores = jax.lax.all_to_all( + x=host_topk_scores, + axis_name='batch', + split_axis=0, + concat_axis=1, + axis_index_groups=self.across_index_groups, + tiled=True, + ) + logging.info('global_topk_scores') + logging.info(global_topk_scores.shape) + # global_topk_scores: per_bsz * (n_device * k) + global_topk_scores, global_rank_ids = jax.lax.top_k( + global_topk_scores, k=self.retr_k + ) + logging.info(global_topk_scores.shape) + # global_topk_scores: per_bsz * retr_k + global_data_ids = global_rank_ids[:, : int(self.data_k)] + global_memory_ids = global_rank_ids[:, int(self.data_k) :] + + def _gather_val(local_ret_vals, top_ids): + logging.info(local_ret_vals.shape) + global_ret_vals = jax.lax.all_to_all( + x=local_ret_vals, + axis_name='batch', + split_axis=0, + concat_axis=1, + axis_index_groups=self.across_index_groups, + tiled=True, + ) + logging.info(global_ret_vals.shape) + # global_ret_vals: per_bsz * (n_device * k) * dshape + global_ret_vals = layers.batch_index_select(global_ret_vals, top_ids) + logging.info(global_ret_vals.shape) + # global_ret_vals: per_bsz * retr_k * dshape + return global_ret_vals + + logging.info('_gather_val!!!') + + ret_memory = jax.tree_map( + lambda local_val: _gather_val(local_val, global_memory_ids), ret_memory + ) + + ret_data = jax.tree_map( + lambda local_val: _gather_val(local_val, global_data_ids), ret_data + ) + + ret_memory['masks'] = jnp.ones(ret_memory['values'].shape[:3]).astype(bool) + + for k in ret_memory: + logging.info(k) + logging.info(ret_memory[k].shape) + logging.info(ret_memory[k].dtype) + + host_corpus_ids = layers.batch_index_select(host_corpus_ids, host_rank_ids) + host_corpus_ids = jnp.reshape( + host_corpus_ids, (-1, n_local_device, local_k) + )[:, local_device_id] + logging.info('corpus_ids') + logging.info(host_corpus_ids.shape) + # host_corpus_ids: (per_bsz * n_local_device, k) + + global_corpus_ids = jax.lax.all_to_all( + x=host_corpus_ids, + axis_name='batch', + split_axis=0, + concat_axis=1, + axis_index_groups=self.across_index_groups, + tiled=True, + ) + logging.info(global_corpus_ids.shape) + # global_corpus_ids: (per_bsz, n_local_device) + global_corpus_ids = layers.batch_index_select( + global_corpus_ids, global_rank_ids + ) + logging.info(global_corpus_ids.shape) + # global_corpus_ids: (per_bsz, 10) + return ( + global_topk_scores, + ret_memory, + ret_data, + local_topk_ids, + global_rank_ids, + global_corpus_ids, + ) + + def t5_decode( + self, + encoded, + encoder_input_tokens: jnp.ndarray, # Only needed for masks. + decoder_input_tokens: jnp.ndarray, + decoder_target_tokens: jnp.ndarray, + enable_dropout: bool = True, + decode: bool = False, + max_decode_length: Optional[int] = None, + ): + """wraps _t5_decoder call (no packing) to enable autoregressive decoding.""" + # Without this wrapper flax.model.apply does not know self._t5_decoder yet + # when doing a single (autoregressive) decode step. + return self.out_decoder( + encoded=encoded, + encoder_input_tokens=encoder_input_tokens, + decoder_input_tokens=decoder_input_tokens, + decoder_target_tokens=decoder_target_tokens, + enable_dropout=enable_dropout, + decode=decode, + max_decode_length=max_decode_length, + ) + + def __call__( + self, + decoder_input_tokens, + decoder_target_tokens, + encoder_input_image=None, + encoder_input_tokens=None, + retr_texts=None, + retr_images=None, + device_id=0, + train=False, + decode=False, + max_decode_length=None, + use_memory=False, + use_psudo_retr=False, + retrieve_local=False, + no_memory=False, + debug=False, + frozen_base=True, + only_encode=False, + **args + ): + """Conduct online retrieval and retrieval-augmented generataion. + + Args: + decoder_input_tokens: # B×O. + decoder_target_tokens: # B×O. + encoder_input_image: # B×W×H×3. + encoder_input_tokens: # B×I. + retr_texts: # B×K×L. + retr_images: # B×K×W×H×3. + device_id: index of TPU device. + train: whether using train mode. + decode: whether in decode mode. + max_decode_length: maximum decode token length. + use_memory: whether use on-device memory. + use_psudo_retr: whether to use psudo retrieved groundtruth for guidance. + retrieve_local: whether only retrieve in local host or across hosts. + no_memory: whether not using any retrieval. + debug: whether use debug mode. + frozen_base: whether froze the whole encoder. + only_encode: skip decoding and only return encoded tokens. + **args: other possible arguments. + + Returns: + output dictionary containing final and intermediate results. + """ + bsz = decoder_input_tokens.shape[0] + + out_dict = {} + base_vals, base_masks, base_query = self.encode_query( + encoder_input_image=encoder_input_image, + encoder_input_tokens=encoder_input_tokens, + frozen_base=frozen_base, + ) + base_query = self.dropout(base_query, deterministic=not train) + base_vals = self.dropout(base_vals, deterministic=not train) + if debug: + out_dict['base_query'] = base_query + out_dict['base_masks'] = base_masks + corpus_scores = jax.nn.softmax(self.dataset_gate(base_query), axis=-1) + out_dict['corpus_scores'] = corpus_scores + if no_memory: + fused_emb, fused_mask, attn_weights_all_layers = self.fusion_encoder( + fused_input_embs=base_vals, fused_mask=base_masks, use_dropout=train + ) # B×(I+N)×d + else: + if use_memory: + detached_query = jax.lax.stop_gradient(base_query) + if retrieve_local: + ( + topk_scores, + ret_memory, + ret_data, + local_topk_ids, + global_topk_ids, + global_corpus_ids, + ) = self._dist_mips_local( + query=detached_query, + corpus_scores=corpus_scores, + local_device_id=device_id, + ) + else: + ( + topk_scores, + ret_memory, + ret_data, + local_topk_ids, + global_topk_ids, + global_corpus_ids, + ) = self._dist_mips_across( + query=detached_query, + corpus_scores=corpus_scores, + local_device_id=device_id, + ) + out_dict['topk_scores'] = topk_scores + + # encode the retrieved data + retr_keys, retr_vals, retr_masks, _, disentangle_reg = ( + self.encode_topk_knowledge( + bsz=bsz, + retr_images=ret_data['image'], + retr_texts=ret_data['text_tokens'], + train=train, + random_drop_image=False, + frozen_base=frozen_base, + ) + ) + + global_corpus_scores = layers.batch_index_select( + corpus_scores, global_corpus_ids + ) + + if debug: + out_dict['detached_query'] = detached_query + out_dict['global_corpus_scores'] = global_corpus_scores + out_dict['global_corpus_ids'] = global_corpus_ids + out_dict['local_topk_ids'] = local_topk_ids + out_dict['global_topk_ids'] = global_topk_ids + out_dict['retr_keys'] = retr_keys + out_dict['retr_masks'] = retr_masks + out_dict['base_vals'] = base_vals + out_dict['retr_vals'] = retr_vals + + out_dict['retr_data'] = ret_data + out_dict['base_norm'] = layers.l2_norm(base_vals).mean() + out_dict['data_norm'] = layers.l2_norm(retr_vals).mean() + out_dict['vals_norm'] = layers.l2_norm(ret_memory['values'][0]).mean() + out_dict['gap'] = jnp.abs( + 1 - jnp.divide(out_dict['data_norm'], out_dict['base_norm']) + ) + + if train and retr_texts is not None and use_psudo_retr: + logging.info('global keys!!!') + ground_truth_keys, ground_truth_vals, _, _, _ = self.encode_knowledge( + retr_texts=retr_texts, + retr_images=retr_images, + bsz=bsz, + train=train, + random_drop_image=True, + frozen_base=frozen_base, + ) + global_keys = jnp.concatenate( + jax.lax.all_gather( + x=ground_truth_keys, axis_name='batch', axis=0 + ), + axis=0, + ) + logging.info(global_keys.shape) + inbatch_sim = jax.lax.dot(base_query, global_keys.transpose()) + out_dict['inbatch_sim'] = inbatch_sim + if debug: + out_dict['global_keys'] = global_keys + out_dict['ground_truth_keys'] = ground_truth_keys + out_dict['ground_truth_vals'] = ground_truth_vals + # replace retrieved knowledge as ground-truth ones for stablization. + k = retr_keys.shape[1] + ground_truth_keys = jnp.repeat( + jnp.expand_dims(ground_truth_keys, axis=1), axis=1, repeats=k + ) + ground_truth_vals = jnp.repeat( + jnp.expand_dims(ground_truth_vals, axis=1), axis=1, repeats=k + ) + replace_mask = jax.random.bernoulli( + self.make_rng('dropout'), p=0.02, shape=(bsz, 1, 1) + ) + keys_mask = jnp.broadcast_to(replace_mask, retr_keys.shape) + retr_keys = jax.lax.select(keys_mask, ground_truth_keys, retr_keys) + vals_mask = jnp.broadcast_to( + jnp.expand_dims(replace_mask, axis=-1), retr_vals.shape + ) + retr_vals = jax.lax.select(vals_mask, ground_truth_vals, retr_vals) + + logging.info('Concat memory and data!!!') + logging.info(retr_keys.shape) + logging.info(ret_memory['keys'].shape) + logging.info(global_corpus_scores.shape) + # concat retrieved memory (90%) with re-encoded ones (10%) + + retr_keys = jnp.concatenate([retr_keys, ret_memory['keys']], axis=1) + retr_keys = retr_keys * jnp.expand_dims(global_corpus_scores, axis=-1) + retr_vals = jnp.concatenate([retr_vals, ret_memory['values']], axis=1) + retr_masks = jnp.concatenate([retr_masks, ret_memory['masks']], axis=1) + elif retr_texts is not None: + retr_keys, retr_vals, retr_masks, _, disentangle_reg = ( + self.encode_topk_knowledge( + bsz=bsz, + retr_images=jnp.expand_dims(retr_images, axis=1), + retr_texts=jnp.expand_dims(retr_texts, axis=1), + train=train, + random_drop_image=False, + ) + ) + else: + retr_keys, retr_vals, retr_masks, _, disentangle_reg = ( + self.encode_topk_knowledge( + bsz=bsz, + retr_images=jnp.expand_dims(encoder_input_image, axis=1), + retr_texts=jnp.expand_dims(encoder_input_tokens, axis=1), + train=train, + random_drop_image=False, + ) + ) + + fused_emb, fused_mask, retr_scores, attn_weights_all_layers = ( + self.fuse_topk_knowledge( + base_query=base_query, + base_vals=base_vals, + base_masks=base_masks, + retr_keys=retr_keys, + retr_vals=retr_vals, + retr_masks=retr_masks, + train=train, + ) + ) # B×(I+N+M*K)×d + out_dict['disentangle_reg'] = jnp.mean(disentangle_reg) + out_dict['retr_scores'] = retr_scores + + out_dict['fused_emb'] = fused_emb + out_dict['fused_mask'] = fused_mask + logging.info('fused_emb.shape') + logging.info(fused_emb.shape) + out_dict['attn_weights_all_layers'] = attn_weights_all_layers + + if not only_encode: + # decode: generate decoding results. + out_dict['predicted_logits'] = self.t5_decode( + encoded=fused_emb, + encoder_input_tokens=fused_mask, + decoder_input_tokens=decoder_input_tokens, + decoder_target_tokens=decoder_target_tokens, + enable_dropout=train, + decode=decode, + max_decode_length=max_decode_length, + ) + return out_dict + + +class KnowledgeFIDModel(base_model.BaseModel): + """FID model with a retrieval module over a knowledge memory.""" + + def __init__( + self, + config: Optional[ml_collections.ConfigDict], + dataset_meta_data: Dict[str, Any], + kb_datasets: Dict[str, dataset_utils.Dataset], + ) -> None: + self.config = config + self.dataset_meta_data = dataset_meta_data + self.retr_k = self.config.model.retr_k + self.retr_data_ratio = self.config.model.retr_data_ratio + n_device = jax.device_count() + self.data_k = int(np.ceil(self.retr_k * self.retr_data_ratio)) + device_per_axis = jax.local_device_count() + if n_device < device_per_axis: + self.axis_index_groups = None + self.across_index_groups = None + else: + self.axis_index_groups = np.arange(n_device).reshape( + [n_device // device_per_axis, device_per_axis] + ) + self.across_index_groups = self.axis_index_groups.T.tolist() + self.axis_index_groups = self.axis_index_groups.tolist() + logging.info('axis_index_groups') + logging.info(self.axis_index_groups) + logging.info(self.across_index_groups) + local_kb.initialize(kb_datasets=kb_datasets) + self.flax_model = self.build_flax_model() + + def build_flax_model(self) -> nn.Module: + return KnowledgeFIDModule( + self.config.model, + retr_k=self.retr_k, + data_k=self.data_k, + axis_index_groups=self.axis_index_groups, + across_index_groups=self.across_index_groups, + ) + + def loss_function_dict( + self, output: constants.JTensorDict, batch: constants.JTensorDict + ) -> Dict[str, Any]: + """Returns negative loglikelihood (NLL) of the target sentence. + + Args: + output: Output of model in OrderedDict. + batch: Batch of data that has 'decoder_target' as ground-truth. + + Returns: + Total loss. + """ + model_config = self.config.model + gen_loss = losses.nll_loss( + targets=batch['decoder_target_tokens'], + pred=output['predicted_logits'], + target_masks=batch['decoder_target_tokens'] > 0, + label_smoothing=self.config.model.get('label_smoothing'), + ) + loss_dict = {'gen_loss': gen_loss} + + if 'inbatch_sim' in output: + score_matrix = output['inbatch_sim'] + bsz = score_matrix.shape[0] + labels = jnp.arange(bsz) + bsz * jax.lax.axis_index(axis_name='batch') + contra_loss = losses.nll_loss( + pred=score_matrix / self.config.model.get('temperature'), + targets=labels, + ) + loss_dict['contra_loss'] = contra_loss + r = model_config.retrieval_ratio + loss = gen_loss * (1 - r) + contra_loss * r + accs = jnp.equal(jnp.argmax(score_matrix, axis=1), labels) + loss_dict['contra_accs'] = accs + else: + loss_dict['contra_loss'] = 0.0 + loss_dict['contra_accs'] = 0.0 + loss = gen_loss + + if 'disentangle' in model_config and 'disentangle_reg' in output: + loss += output['disentangle_reg'] * 1e-2 + if 'gap' in model_config and 'gap' in output: + loss += output['gap'] * 1e-4 + loss_dict['total_loss'] = loss + return loss_dict + + def get_metrics_fn(self, split: Optional[str] = None) -> Any: + """Returns a callable metric function for the model. + + Args: + split: The split for which we calculate the metrics. It should be one of + the ['train', 'validation', 'test']. + Returns: A metric function with the following API: ```metrics_fn(outputs, + batch)``` + """ + return metrics.token_accuracy + + def get_vqa_metrics( + self, + logits: jnp.ndarray, + batch: constants.JTensorDict, + split: Optional[str] = None, + ) -> dict[str, float]: + """Returns the VQA Accuracy for the validation / test set. + + Args: + logits: Output of model in shape [B, L, C]. + batch: Batch of data that has 'decoder_target' as ground-truth. + split: The split for which we calculate the metrics. It should be one of + the ['train', 'validation', 'test']. + Returns: VQA accuracy``` + """ + + return metrics.vqa_accuracy(logits, batch) + + def single_decode_step( + self, + decoding_state: decoding.DecodingState, + variables: constants.PyTree, + encoded_inputs: jnp.ndarray, + input_masks: jnp.ndarray, + max_decode_length: int, + ) -> Tuple[jnp.ndarray, Mapping[str, jnp.ndarray]]: + """Single autoregressive decode step with caching.""" + flat_ids = decoding_state.cur_token + flat_cache = decoding_state.cache + # flat_ids: [batch * beam, seq_len=1] + # cache is expanded inside beam_search to become flat_cache + # flat_cache: [batch * beam, num_heads, depth_per_head, max_decode_len] + # flat_logits: [batch * beam, seq_len=1, vocab] + flat_logits, new_vars = self.flax_model.apply( + {'cache': flat_cache, **variables}, + encoded=encoded_inputs, + encoder_input_tokens=input_masks, + decoder_input_tokens=flat_ids, + decoder_target_tokens=flat_ids, + decode=True, + enable_dropout=False, + max_decode_length=max_decode_length, + mutable=['cache'], + method=self.flax_model.t5_decode, + ) + # Remove sequence length dimension since it's always 1 during decoding. + flat_logits = jnp.squeeze(flat_logits, axis=1) + new_flat_cache = new_vars['cache'] + return flat_logits, new_flat_cache + + def apply_with_autoregressive_decoding( + self, + variables: constants.PyTree, + decoder_input_tokens: jnp.ndarray, + decoder_target_tokens: jnp.ndarray, + encoder_input_image: Optional[jnp.ndarray] = None, + encoder_input_tokens: Optional[jnp.ndarray] = None, + num_decodes: int = 1, + debug: bool = False, + beam_search: bool = True, + decoder_params: Optional[dict[str, Any]] = None, + return_all_decodes: bool = False, + use_memory=False, + retrieve_local=False, + **args + ): + """Apply inference with autoregressive decoding. + + Apply t5x autoregressive decoding with cache using either their + beam_search or temperature_sample decoding technique. + + Args: + variables: variables of the models. + decoder_input_tokens: # B×O. + decoder_target_tokens: # B×O. + encoder_input_image: # B×W×H×3. + encoder_input_tokens: # B×I. + num_decodes: number of outputs generated per input for the decode search. + debug: Whether in debug mode or not. + beam_search: If True, do beam search. If False, do temperature sampling. + decoder_params: Additional decoding parameters. These provide additional + parameters to beam_search or temperature_sample (see decoder module). + return_all_decodes: If True, return all decodes. Otherwise only return the + top scored decoding. + use_memory: whether use on-device memory. + retrieve_local: whether only retrieve in local host or across hosts. + **args: other possible arguments. + + Returns: + logits array from the final decoder. + """ + # Prepare zeroed-out autoregressive cache. + _, model_state_with_cache = self.flax_model.apply( + variables=variables, + encoder_input_image=encoder_input_image, + encoder_input_tokens=encoder_input_tokens, + decoder_input_tokens=decoder_input_tokens, + decoder_target_tokens=decoder_target_tokens, + train=False, + only_encode=False, + decode=True, + mutable=['cache'], + debug=debug, + use_memory=use_memory, + retrieve_local=retrieve_local, + ) + + # Call model to get the features consumed by the decoder. Skip the + # the decoding part itself. + out_dict = self.flax_model.apply( + variables=variables, + encoder_input_image=encoder_input_image, + encoder_input_tokens=encoder_input_tokens, + decoder_input_tokens=decoder_input_tokens, + decoder_target_tokens=decoder_target_tokens, + train=False, + only_encode=True, + debug=debug, + use_memory=use_memory, + retrieve_local=retrieve_local, + ) + retr_top_image = out_dict['retr_data']['image'][:, 0] + + # Prepare transformer fast-decoder call for beam search: for beam search, we + # need to set up our decoder model to handle a batch size equal to + # batch_size * num_decodes, where each batch item's data is expanded + # in-place rather than tiled. + # i.e. if we denote each batch element subtensor as el[n]: + # [el0, el1, el2] --> beamsize=2 --> [el0,el0,el1,el1,el2,el2] + # [batch * num_decodes, input_len, emb_dim] + beam_expand_fn = functools.partial( + decoding.flat_batch_beam_expand, beam_size=num_decodes + ) + encoded_inputs = jax.tree_map(beam_expand_fn, out_dict['fused_emb']) + encoded_masks = jax.tree_util.tree_map( + beam_expand_fn, out_dict['fused_mask'] + ) + bsz = decoder_input_tokens.shape[0] + max_decode_length = decoder_input_tokens.shape[-1] + # Define the token2logit function for a single decoding step. + tokens_ids_to_logits = functools.partial( + self.single_decode_step, + variables=variables, + encoded_inputs=encoded_inputs, + input_masks=encoded_masks, + max_decode_length=decoder_input_tokens.shape[-1], + ) + + if decoder_params is None: + decoder_params = {} + # For beam search, `decoder_prompt_inputs` is only used to obtain batch size + # and max decode length information. For temperature sampling, + # `decod_prompt_inputs` will be filled with the sampled ids. + decoder_prompt_inputs = jnp.zeros([bsz, max_decode_length - 1]) + bos_inputs = jnp.ones([bsz, 1]) * data_utils.BOS_ID + decoder_prompt_inputs = jnp.concatenate( + (bos_inputs, decoder_prompt_inputs), axis=-1, dtype=jnp.int32 + ) + if beam_search: + decodes, scores = decoding.beam_search( + inputs=decoder_prompt_inputs, + cache=model_state_with_cache['cache'], + tokens_to_logits=tokens_ids_to_logits, + eos_id=data_utils.EOS_ID, + num_decodes=num_decodes, + cache_offset=0, + **decoder_params + ) + else: + decodes, scores = decoding.temperature_sample( + inputs=decoder_prompt_inputs, + cache=model_state_with_cache['cache'], + tokens_to_logits=tokens_ids_to_logits, + eos_id=data_utils.EOS_ID, + num_decodes=num_decodes, + cache_offset=0, + initial_index=jnp.zeros([bsz], dtype=jnp.int32), + **decoder_params + ) + if return_all_decodes: + return decodes, scores, retr_top_image + else: + return decodes[:, -1, :], scores[:, -1], retr_top_image diff --git a/scenic/projects/knowledge_visual_language/models/layers.py b/scenic/projects/knowledge_visual_language/models/layers.py new file mode 100644 index 000000000..84ab92497 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/models/layers.py @@ -0,0 +1,827 @@ +"""Layers and Modules for Knowledge-FID.""" + +import functools +from typing import Optional, Sequence + +from flax import linen as nn +import jax +import jax.numpy as jnp +from scenic.projects.knowledge_visual_language.models import constants +from t5x.examples.t5 import layers as t5_layers +from t5x.examples.t5 import network as t5_network + + +@jax.vmap +def batch_index_select(data, idx): + return jnp.take(data, idx, axis=0) + + +def _mask_select(data, mask): + return jax.lax.select( + mask > 0, data, jnp.full(data.shape, 0).astype(data.dtype) + ) + + +def l2_norm(x): + """Compute the l2 norm of a vector.""" + return jnp.sqrt((x * x).sum(axis=-1)) + + +def l2_normalize(x, axis=-1, eps=1e-10): + """Normalizes along dimension `axis` using an L2 norm. + + This specialized function exists for numerical stability reasons. + Args: + x: An input ndarray. + axis: Dimension along which to normalize, e.g. `1` to separately normalize + vectors in a batch. Passing `None` views `t` as a flattened vector when + calculating the norm (equivalent to Frobenius norm). + eps: Epsilon to avoid dividing by zero. + + Returns: + An array of the same shape as 'x' L2-normalized along 'axis'. + """ + denorm = (x * x).sum(axis=axis, keepdims=True) + eps + return (x * jax.lax.rsqrt(denorm)).astype(x.dtype) + + +class AffineTransform(nn.Module): + """Do affine Transform for modulating attention score.""" + + @nn.compact + def __call__(self, x): + scale = self.param('scale', nn.initializers.ones, (1,), jnp.float32) + bias = self.param('bias', nn.initializers.zeros, (1,), jnp.float32) + return x * nn.sigmoid(scale) * 5 + bias + + +class TransformerHead(nn.Module): + """A stack of encoder layers.""" + + num_head_layers: int + key_dim: int + vocab_size: int + emb_dim: int + num_heads: int + num_encoder_layers: int + num_decoder_layers: int + head_dim: int + mlp_dim: int + dropout_rate: float + out_head: nn.Module + dtype: str = 'bfloat16' + mlp_activations: Sequence[str] = ('gelu', 'linear') + logits_via_embedding: bool = False + + def setup(self): + self.t5_config = t5_network.T5Config( + vocab_size=self.vocab_size, + emb_dim=self.emb_dim, + num_heads=self.num_heads, + num_encoder_layers=self.num_encoder_layers, + num_decoder_layers=self.num_decoder_layers, + head_dim=self.head_dim, + mlp_dim=self.mlp_dim, + dropout_rate=self.dropout_rate, + dtype=self.dtype, + mlp_activations=self.mlp_activations, + logits_via_embedding=self.logits_via_embedding, + ) + + @nn.compact + def __call__(self, encoded_emb, encoder_mask=None, use_dropout=True): + """transform the encoded representation.""" + cfg = self.t5_config + assert encoded_emb.ndim == 3 # [batch, length, emb_dim] + x = encoded_emb + if encoder_mask is not None: + encoder_mask = t5_layers.make_attention_mask( + encoder_mask, encoder_mask, dtype=cfg.dtype + ) + + rel_emb = t5_layers.RelativePositionBiases( + num_buckets=32, + max_distance=128, + num_heads=self.num_heads, + dtype=self.dtype, + embedding_init=nn.initializers.variance_scaling( + 1.0, 'fan_avg', 'uniform' + ), + ) + for _ in range( + cfg.num_encoder_layers - self.num_head_layers, cfg.num_encoder_layers + ): + # [batch, length, emb_dim] -> [batch, length, emb_dim] + x = t5_network.EncoderLayer(config=cfg, relative_embedding=rel_emb)( + x, encoder_mask, deterministic=not use_dropout + ) + x = t5_layers.LayerNorm(dtype=cfg.dtype)(x[:, 0, :]) + return l2_normalize(self.out_head(x), axis=-1) + + +class LowerT5Encoder(nn.Module): + """T5 encoder as a separate model which fuse multi-modal input. + + This module contains the encoder part of a pretrained T5. It is useful when + adopting the pretrained T5 encoder as a part of a larger network. Note that + the embedding layer should be created outside the module and provided as a + parameter `shared_embedding` to share it in other parts of the network (e.g., + text encoder). If `shared_embedding` is not provided, the embedding layer is + created within the module. + + Attributes: + vocab_size: Size of the vocabulary. + emb_dim: Size of the embeddings. + num_heads: Number of attention heads. + num_encoder_layers: Number of encoder layers. + num_decoder_layers: Number of decoder layers. + head_dim: Size of the embeddings in each head. + mlp_dim: Size of the MLP output embeddings. + dropout_rate: Dropout rate. + dtype: Data type. + mlp_activations: Sequence of activations in MLP. + logits_via_embedding: Use the embedding weights for computing logits. + shared_embedding: Optional. Embedding layer that is shared outside this + module. If not given, a non-shared embedding layer will be created within + the module. + """ + + vocab_size: int + emb_dim: int + num_heads: int + num_encoder_layers: int + num_decoder_layers: int + num_fusion_layers: int + head_dim: int + mlp_dim: int + dropout_rate: float + dtype: str = 'bfloat16' + mlp_activations: Sequence[str] = ('gelu', 'linear') + logits_via_embedding: bool = False + shared_embedding: Optional[nn.Module] = None + + def setup(self): + self.t5_config = t5_network.T5Config( + vocab_size=self.vocab_size, + emb_dim=self.emb_dim, + num_heads=self.num_heads, + num_encoder_layers=self.num_encoder_layers, + num_decoder_layers=self.num_decoder_layers, + head_dim=self.head_dim, + mlp_dim=self.mlp_dim, + dropout_rate=self.dropout_rate, + dtype=self.dtype, + mlp_activations=self.mlp_activations, + logits_via_embedding=self.logits_via_embedding, + ) + if self.shared_embedding is None: + self.shared_embedding = t5_layers.Embed( + num_embeddings=self.vocab_size, + features=self.emb_dim, + dtype=self.dtype, + attend_dtype=jnp.float32, # For logit training stability. + embedding_init=nn.initializers.normal(stddev=1.0), + one_hot=True, + ) + + @nn.compact + def __call__( + self, + encoder_input_tokens, + encoder_segment_ids=None, + use_dropout=True, + frozen_base=True, + ): + """encode the text sentence only. + + Args: + encoder_input_tokens: input text tokens + encoder_segment_ids: segmend ID in packing mode + use_dropout: whether to use dropout during Training + frozen_base: whether froze the text encoder + + Returns: + Sequence of token embedding with or without fusion + """ + cfg = self.t5_config + assert encoder_input_tokens.ndim == 2 # (batch, len) + # Make padding attention mask. + encoder_mask = encoder_input_tokens > 0 + mask_matrix = t5_layers.make_attention_mask( + encoder_input_tokens > 0, encoder_input_tokens > 0, dtype=cfg.dtype + ) + # Add segmentation block-diagonal attention mask if using segmented data. + if encoder_segment_ids is not None: + mask_matrix = t5_layers.combine_masks( + mask_matrix, + t5_layers.make_attention_mask( + encoder_segment_ids, + encoder_segment_ids, + jnp.equal, + dtype=cfg.dtype, + ), + ) + + rel_emb = t5_layers.RelativePositionBiases( + num_buckets=32, + max_distance=128, + num_heads=self.t5_config.num_heads, + dtype=self.t5_config.dtype, + embedding_init=nn.initializers.variance_scaling( + 1.0, 'fan_avg', 'uniform' + ), + ) + + # [batch, length] -> [batch, length, emb_dim] + x = self.shared_embedding(encoder_input_tokens.astype('int32')) + x = nn.Dropout(rate=cfg.dropout_rate, broadcast_dims=(-2,))( + x, deterministic=not use_dropout + ) + x = x.astype(cfg.dtype) + n_layer = cfg.num_encoder_layers - self.num_fusion_layers + frozen_layer_id = int(n_layer * 0.8) - 1 + for lyr in range(n_layer): + # [batch, length, emb_dim] -> [batch, length, emb_dim] + x = t5_network.EncoderLayer(config=cfg, relative_embedding=rel_emb)( + x, mask_matrix, deterministic=not use_dropout + ) + if frozen_base and lyr == frozen_layer_id: + x = jax.lax.stop_gradient(x) + return x, encoder_mask + + +class FusedT5Encoder(nn.Module): + """T5 encoder as a separate model which fuse multi-modal input. + + This module contains the encoder part of a pretrained T5. It is useful when + adopting the pretrained T5 encoder as a part of a larger network. Note that + the embedding layer should be created outside the module and provided as a + parameter `shared_embedding` to share it in other parts of the network (e.g., + text encoder). If `shared_embedding` is not provided, the embedding layer is + created within the module. + + Attributes: + vocab_size: Size of the vocabulary. + emb_dim: Size of the embeddings. + num_heads: Number of attention heads. + num_encoder_layers: Number of encoder layers. + num_decoder_layers: Number of decoder layers. + head_dim: Size of the embeddings in each head. + mlp_dim: Size of the MLP output embeddings. + dropout_rate: Dropout rate. + dtype: Data type. + mlp_activations: Sequence of activations in MLP. + logits_via_embedding: Use the embedding weights for computing logits. + """ + + vocab_size: int + emb_dim: int + num_heads: int + num_encoder_layers: int + num_decoder_layers: int + num_fusion_layers: int + head_dim: int + mlp_dim: int + dropout_rate: float + dtype: str = 'bfloat16' + mlp_activations: Sequence[str] = ('gelu', 'linear') + logits_via_embedding: bool = False + + def setup(self): + self.t5_config = t5_network.T5Config( + vocab_size=self.vocab_size, + emb_dim=self.emb_dim, + num_heads=self.num_heads, + num_encoder_layers=self.num_encoder_layers, + num_decoder_layers=self.num_decoder_layers, + head_dim=self.head_dim, + mlp_dim=self.mlp_dim, + dropout_rate=self.dropout_rate, + dtype=self.dtype, + mlp_activations=self.mlp_activations, + logits_via_embedding=self.logits_via_embedding, + ) + + @nn.compact + def __call__( + self, + fused_input_embs, + encoder_input_embs=None, + encoder_mask=None, + fused_mask=None, + att_mask=None, + use_dropout=True, + output=False, + ): + """Function to fuse text and imaget embedding. + + encode both the encoded text embedding (encoder_input_embs) and + encoded image embedding (fused_input_embs) together using + self-attentive Transformer. + + Args: + fused_input_embs: pre-encoded embeddings of other modalities + encoder_input_embs: encoded text embedding sequence + encoder_mask: mask for encoding part + fused_mask: mask for fusion part + att_mask: pre-computed attention product to each layer's output + use_dropout: whether to use dropout. + output: whether it's output layer. + + Returns: + Sequence of token embedding after fusion + """ + cfg = self.t5_config + if encoder_input_embs is not None: + x = jnp.concatenate([encoder_input_embs, fused_input_embs], axis=1) + else: + x = fused_input_embs + rel_emb = t5_layers.RelativePositionBiases( + num_buckets=32, + max_distance=128, + num_heads=self.t5_config.num_heads, + dtype=self.t5_config.dtype, + embedding_init=nn.initializers.variance_scaling( + 1.0, 'fan_avg', 'uniform' + ), + ) + + if encoder_mask is not None: + if fused_mask is None: + pad_width = fused_input_embs.shape[1] + fused_mask = jnp.pad( + array=encoder_mask, + pad_width=((0, 0), (0, pad_width)), + mode='constant', + constant_values=1.0, + ) + else: + fused_mask = jnp.concatenate([encoder_mask, fused_mask], axis=1) + + mask_matrix = t5_layers.make_attention_mask( + fused_mask, fused_mask, dtype=cfg.dtype + ) + attn_weights_all_layers = [] + for _ in range( + cfg.num_encoder_layers - self.num_fusion_layers, cfg.num_encoder_layers + ): + # [batch, length, emb_dim] -> [batch, length, emb_dim] + x, attn_weights = FusionEncoderLayer( + config=cfg, relative_embedding=rel_emb + )( + x, + encoder_mask=mask_matrix, + att_mask=att_mask, + deterministic=not use_dropout, + ) + attn_weights_all_layers += [attn_weights] + if output: + x = t5_layers.LayerNorm(dtype=cfg.dtype)(x) + if att_mask is not None: + x = x * att_mask + x = nn.Dropout(rate=cfg.dropout_rate)(x, deterministic=not use_dropout) + return x, fused_mask, attn_weights_all_layers + + +class FusionEncoderLayer(nn.Module): + """Transformer encoder layer.""" + + config: t5_network.T5Config + relative_embedding: nn.Module + + @nn.compact + def __call__( + self, inputs, att_mask=None, encoder_mask=None, deterministic=False + ): + cfg = self.config + + # Relative position embedding as attention biases. + encoder_bias = self.relative_embedding( + inputs.shape[-2], inputs.shape[-2], True + ) + + # Attention block. + assert inputs.ndim == 3 + x = t5_layers.LayerNorm(dtype=cfg.dtype)(inputs) + if att_mask is not None: + x = x * att_mask + # [batch, length, emb_dim] -> [batch, length, emb_dim] + x, attn_weights = MultiHeadDotProductAttention( + num_heads=cfg.num_heads, + dtype=cfg.dtype, + head_dim=cfg.head_dim, + dropout_rate=cfg.dropout_rate, + float32_logits=cfg.float32_attention_logits, + )(x, x, encoder_mask, encoder_bias, deterministic=deterministic) + x = nn.Dropout(rate=cfg.dropout_rate, broadcast_dims=(-2,))( + x, deterministic=deterministic + ) + x = x + inputs + + # MLP block. + y = t5_layers.LayerNorm(dtype=cfg.dtype)(x) + # [batch, length, emb_dim] -> [batch, length, emb_dim] + y = t5_layers.MlpBlock( + intermediate_dim=cfg.mlp_dim, + activations=cfg.mlp_activations, + intermediate_dropout_rate=cfg.dropout_rate, + dtype=cfg.dtype, + )(y, deterministic=deterministic) + y = nn.Dropout(rate=cfg.dropout_rate, broadcast_dims=(-2,))( + y, deterministic=deterministic + ) + y = y + x + + return y, attn_weights + + +class PerceiverEncoder(nn.Module): + """Reimplementation of Perceiver. + + Perceiver: General Perception with Iterative Attention + (https://arxiv.org/abs/2103.03206) + """ + + perceiver_output_dim: int + vocab_size: int + emb_dim: int + num_heads: int + num_encoder_layers: int + num_decoder_layers: int + num_fusion_layers: int + head_dim: int + mlp_dim: int + dropout_rate: float + dtype: str = 'bfloat16' + mlp_activations: Sequence[str] = ('gelu', 'linear') + logits_via_embedding: bool = False + + def setup(self): + self.t5_config = t5_network.T5Config( + vocab_size=self.vocab_size, + emb_dim=self.emb_dim, + num_heads=self.num_heads, + num_encoder_layers=self.num_encoder_layers, + num_decoder_layers=self.num_decoder_layers, + head_dim=self.head_dim, + mlp_dim=self.mlp_dim, + dropout_rate=self.dropout_rate, + dtype=self.dtype, + mlp_activations=self.mlp_activations, + logits_via_embedding=self.logits_via_embedding, + ) + + self.perceive_embedding = self.param( + 'perceive_embedding', + nn.initializers.normal(stddev=1.0), + (1, self.perceiver_output_dim, self.emb_dim), + jnp.float32, + ) + v = jnp.arange(self.perceiver_output_dim) + self.batch_triangle_select = jax.vmap( + functools.partial(_mask_select, mask=v < v.reshape([-1, 1])) + ) + + def linear_disentangle(self, y): + mean = y.mean(axis=-2, keepdims=True) + norm_y = l2_normalize(y - mean) + pairwise_mat = jnp.square(jnp.einsum('bqd,btd->bqt', norm_y, norm_y)) + masked_mat = self.batch_triangle_select(pairwise_mat) + return jnp.mean(masked_mat) + + @nn.compact + def __call__(self, encoded, encoded_mask, use_dropout=False): + cfg = self.t5_config + rel_emb = t5_layers.RelativePositionBiases( + num_buckets=32, + max_distance=128, + num_heads=cfg.num_heads, + dtype=cfg.dtype, + embedding_init=nn.initializers.variance_scaling( + 1.0, 'fan_avg', 'uniform' + ), + ) + + # [batch, length] -> [batch, length, emb_dim] + encoded = t5_layers.LayerNorm(dtype=cfg.dtype)(encoded) + bsz = encoded.shape[0] + y = jnp.asarray(self.perceive_embedding, dtype=cfg.dtype) + y = jnp.repeat(y, bsz, axis=0) + y = nn.Dropout(rate=cfg.dropout_rate, broadcast_dims=(-2,))( + y, deterministic=not use_dropout + ) + y = y.astype(cfg.dtype) + + mask = jnp.ones([bsz, self.perceiver_output_dim]).astype(bool) + encoder_decoder_mask = t5_layers.make_attention_mask( + mask, encoded_mask, dtype=self.dtype + ) + + for _ in range(self.num_fusion_layers): + # [batch, length, emb_dim] -> [batch, length, emb_dim] + y = t5_network.DecoderLayer(config=cfg, relative_embedding=rel_emb)( + y, + encoded, + deterministic=not use_dropout, + encoder_decoder_mask=encoder_decoder_mask, + decode=False, + ) + + return y * 4, mask, self.linear_disentangle(y) + + +def dot_product_attention( + query: constants.JTensor, + key: constants.JTensor, + value: constants.JTensor, + bias: Optional[constants.JTensor] = None, + dropout_rng: Optional[constants.JTensor] = None, + dropout_rate: float = 0.0, + deterministic: bool = False, + dtype: constants.DType = jnp.float32, + float32_logits: bool = False, +): + """Computes dot-product attention given query, key, and value. + + This is the core function for applying attention based on + https://arxiv.org/abs/1706.03762. It calculates the attention weights given + query and key and combines the values using the attention weights. + + Args: + query: queries for calculating attention with shape of `[batch, q_length, + num_heads, qk_depth_per_head]`. + key: keys for calculating attention with shape of `[batch, kv_length, + num_heads, qk_depth_per_head]`. + value: values to be used in attention with shape of `[batch, kv_length, + num_heads, v_depth_per_head]`. + bias: bias for the attention weights. This should be broadcastable to the + shape `[batch, num_heads, q_length, kv_length]` This can be used for + incorporating causal masks, padding masks, proximity bias, etc. + dropout_rng: JAX PRNGKey: to be used for dropout + dropout_rate: dropout rate + deterministic: bool, deterministic or not (to apply dropout) + dtype: the dtype of the computation (default: float32) + float32_logits: bool, if True then compute logits in float32 to avoid + numerical issues with bfloat16. + + Returns: + Output of shape `[batch, length, num_heads, v_depth_per_head]`. + """ + assert key.ndim == query.ndim == value.ndim, 'q, k, v must have same rank.' + assert ( + query.shape[:-3] == key.shape[:-3] == value.shape[:-3] + ), 'q, k, v batch dims must match.' + assert ( + query.shape[-2] == key.shape[-2] == value.shape[-2] + ), 'q, k, v num_heads must match.' + assert key.shape[-3] == value.shape[-3], 'k, v lengths must match.' + assert query.shape[-1] == key.shape[-1], 'q, k depths must match.' + + # Casting logits and softmax computation for float32 for model stability. + if float32_logits: + query = query.astype(jnp.float32) + key = key.astype(jnp.float32) + + # `attn_weights`: [batch, num_heads, q_length, kv_length] + attn_weights = jnp.einsum('bqhd,bkhd->bhqk', query, key) + + # Apply attention bias: masking, dropout, proximity bias, etc. + if bias is not None: + attn_weights = attn_weights + bias.astype(attn_weights.dtype) + + # Normalize the attention weights across `kv_length` dimension. + attn_weights = jax.nn.softmax(attn_weights).astype(dtype) + + # Apply attention dropout. + if not deterministic and dropout_rate > 0.0: + keep_prob = 1.0 - dropout_rate + # T5 broadcasts along the "length" dim, but unclear which one that + # corresponds to in positional dimensions here, assuming query dim. + dropout_shape = list(attn_weights.shape) + dropout_shape[-2] = 1 + keep = jax.random.bernoulli(dropout_rng, keep_prob, dropout_shape) + keep = jnp.broadcast_to(keep, attn_weights.shape) + multiplier = keep.astype(attn_weights.dtype) / jnp.asarray( + keep_prob, dtype=dtype + ) + attn_weights = attn_weights * multiplier + + # Take the linear combination of `value`. + return jnp.einsum('bhqk,bkhd->bqhd', attn_weights, value), attn_weights + + +class MultiHeadDotProductAttention(nn.Module): + """Multi-head dot-product attention. + + Attributes: + num_heads: number of attention heads. Features (i.e. inputs_q.shape[-1]) + should be divisible by the number of heads. + head_dim: dimension of each head. + dtype: the dtype of the computation. + dropout_rate: dropout rate + kernel_init: initializer for the kernel of the Dense layers. + float32_logits: bool, if True then compute logits in float32 to avoid + numerical issues with bfloat16. + """ + + num_heads: int + head_dim: int + dtype: constants.DType = jnp.float32 + dropout_rate: float = 0.0 + kernel_init: constants.Initializer = nn.initializers.variance_scaling( + 1.0, 'fan_in', 'normal' + ) + float32_logits: bool = False # computes logits in float32 for stability. + + @nn.compact + def __call__( + self, + inputs_q: constants.JTensor, + inputs_kv: constants.JTensor, + mask: Optional[constants.JTensor] = None, + bias: Optional[constants.JTensor] = None, + *, + decode: bool = False, + deterministic: bool = False, + ) -> constants.JTensor: + """Applies multi-head dot product attention on the input data. + + Projects the inputs into multi-headed query, key, and value vectors, + applies dot-product attention and project the results to an output vector. + + There are two modes: decoding and non-decoding (e.g., training). The mode is + determined by `decode` argument. For decoding, this method is called twice, + first to initialize the cache and then for an actual decoding process. The + two calls are differentiated by the presence of 'cached_key' in the variable + dict. In the cache initialization stage, the cache variables are initialized + as zeros and will be filled in the subsequent decoding process. + + In the cache initialization call, `inputs_q` has a shape [batch, length, + q_features] and `inputs_kv`: [batch, length, kv_features]. During the + incremental decoding stage, query, key and value all have the shape [batch, + 1, qkv_features] corresponding to a single step. + + Args: + inputs_q: input queries of shape `[batch, q_length, q_features]`. + inputs_kv: key/values of shape `[batch, kv_length, kv_features]`. + mask: attention mask of shape `[batch, num_heads, q_length, kv_length]`. + bias: attention bias of shape `[batch, num_heads, q_length, kv_length]`. + decode: Whether to prepare and use an autoregressive cache. + deterministic: Disables dropout if set to True. + + Returns: + output of shape `[batch, length, q_features]`. + """ + projection = functools.partial( + t5_layers.DenseGeneral, + axis=-1, + features=(self.num_heads, self.head_dim), + kernel_axes=('embed', 'joined_kv'), + dtype=self.dtype, + ) + + # NOTE: T5 does not explicitly rescale the attention logits by + # 1/sqrt(depth_kq)! This is folded into the initializers of the + # linear transformations, which is equivalent under Adafactor. + depth_scaling = jnp.sqrt(self.head_dim).astype(self.dtype) + query_init = lambda *args: self.kernel_init(*args) / depth_scaling + + # Project inputs_q to multi-headed q/k/v + # dimensions are then [batch, length, num_heads, head_dim] + query = projection(kernel_init=query_init)(inputs_q) + key = projection(kernel_init=self.kernel_init)(inputs_kv) + value = projection(kernel_init=self.kernel_init)(inputs_kv) + + query = t5_layers.with_sharding_constraint( + query, ('batch', 'length', 'heads', 'kv') + ) + key = t5_layers.with_sharding_constraint( + key, ('batch', 'length', 'heads', 'kv') + ) + value = t5_layers.with_sharding_constraint( + value, ('batch', 'length', 'heads', 'kv') + ) + + if decode: + # Detect if we're initializing by absence of existing cache data. + is_initialized = self.has_variable('cache', 'cached_key') + # The key and value have dimension [batch, length, num_heads, head_dim], + # but we cache them as [batch, num_heads, head_dim, length] as a TPU + # fusion optimization. This also enables the "scatter via one-hot + # broadcast" trick, which means we do a one-hot broadcast instead of a + # scatter/gather operations, resulting in a 3-4x speedup in practice. + swap_dims = lambda x: x[:-3] + tuple(x[i] for i in [-2, -1, -3]) + cached_key = self.variable( + 'cache', 'cached_key', jnp.zeros, swap_dims(key.shape), key.dtype + ) + cached_value = self.variable( + 'cache', + 'cached_value', + jnp.zeros, + swap_dims(value.shape), + value.dtype, + ) + cache_index = self.variable( + 'cache', 'cache_index', lambda: jnp.array(0, dtype=jnp.int32) + ) + if is_initialized: + batch, num_heads, head_dim, length = cached_key.value.shape + # During fast autoregressive decoding, we feed one position at a time, + # and cache the keys and values step by step. + # Sanity shape check of cached key against input query. + expected_shape = (batch, 1, num_heads, head_dim) + if expected_shape != query.shape: + raise ValueError( + 'Autoregressive cache shape error, ' + 'expected query shape %s instead got %s.' + % (expected_shape, query.shape) + ) + + # Create a OHE of the current index. NOTE: the index is increased below. + cur_index = cache_index.value + one_hot_indices = jax.nn.one_hot(cur_index, length, dtype=key.dtype) + # In order to update the key, value caches with the current key and + # value, we move the length axis to the back, similar to what we did for + # the cached ones above. + # Note these are currently the key and value of a single position, since + # we feed one position at a time. + one_token_key = jnp.moveaxis(key, -3, -1) + one_token_value = jnp.moveaxis(value, -3, -1) + # Update key, value caches with our new 1d spatial slices. + # We implement an efficient scatter into the cache via one-hot + # broadcast and addition. + key = cached_key.value + one_token_key * one_hot_indices + value = cached_value.value + one_token_value * one_hot_indices + cached_key.value = key + cached_value.value = value + cache_index.value = cache_index.value + 1 + # Move the keys and values back to their original shapes. + key = jnp.moveaxis(key, -1, -3) + value = jnp.moveaxis(value, -1, -3) + + # Causal mask for cached decoder self-attention: our single query + # position should only attend to those key positions that have already + # been generated and cached, not the remaining zero elements. + mask = t5_layers.combine_masks( + mask, + jnp.broadcast_to( + jnp.arange(length) <= cur_index, + # (1, 1, length) represent (head dim, query length, key length) + # query length is 1 because during decoding we deal with one + # index. + # The same mask is applied to all batch elements and heads. + (batch, 1, 1, length), + ), + ) + + # Grab the correct relative attention bias during decoding. This is + # only required during single step decoding. + if bias is not None: + # The bias is a full attention matrix, but during decoding we only + # have to take a slice of it. + # This is equivalent to bias[..., cur_index:cur_index+1, :]. + bias = t5_layers.dynamic_vector_slice_in_dim( + jnp.squeeze(bias, axis=0), jnp.reshape(cur_index, (-1)), 1, -2 + ) + + # Convert the boolean attention mask to an attention bias. + if mask is not None: + # attention mask in the form of attention bias + attention_bias = jax.lax.select( + mask > 0, + jnp.full(mask.shape, 0.0).astype(self.dtype), + jnp.full(mask.shape, -1e10).astype(self.dtype), + ) + else: + attention_bias = None + + # Add provided bias term (e.g. relative position embedding). + if bias is not None: + attention_bias = t5_layers.combine_biases(attention_bias, bias) + + dropout_rng = None + if not deterministic and self.dropout_rate > 0.0: + dropout_rng = self.make_rng('dropout') + + # Apply attention. + x, attn_weights = dot_product_attention( + query, + key, + value, + bias=attention_bias, + dropout_rng=dropout_rng, + dropout_rate=self.dropout_rate, + deterministic=deterministic, + dtype=self.dtype, + float32_logits=self.float32_logits, + ) + + # Back to the original inputs dimensions. + out = t5_layers.DenseGeneral( + features=inputs_q.shape[-1], # output dim is set to the input dim. + axis=(-2, -1), + kernel_init=self.kernel_init, + kernel_axes=('joined_kv', 'embed'), + dtype=self.dtype, + )(x) + return out, attn_weights diff --git a/scenic/projects/knowledge_visual_language/models/local_memory.py b/scenic/projects/knowledge_visual_language/models/local_memory.py new file mode 100644 index 000000000..773d33f86 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/models/local_memory.py @@ -0,0 +1,258 @@ +"""Global file to store local knowledge base.""" + +import functools + +from absl import logging +from flax.core.frozen_dict import FrozenDict +import jax +import numpy as np +import tqdm + + +def static_encode_knowledge(knowledge_batch, train_state, *, flax_model): + """Function to encode KB knowledge. + + Args: + knowledge_batch: A single batch of data. The buffer of this argument can be + donated to the computation. + train_state: The state of training including the current global_step, + model_state, rng, and optimizer. The buffer of this argument can be + donated to the computation. + flax_model: A Flax model. + + Returns: + Key (single embedding), Val (compressed list of embedding) and Mask. + """ + variables = {'params': train_state.params, **train_state.model_state} + retr_tokens, retr_images = knowledge_batch[ + 'knowledge_tokens'], knowledge_batch['image'] + batch_size = retr_images.shape[0] + keys_head, compressed_val, compressed_mask, _, _ = flax_model.apply( + variables, + retr_texts=retr_tokens, + retr_images=retr_images, + bsz=batch_size, + train=False, + random_drop_image=False, + method=flax_model.encode_knowledge) + return keys_head, compressed_val, compressed_mask + + +class KnowledgeBase: + """Local Knowledge Base stored in CPU.""" + + def __init__(self): + self.memory = {} + self.memory_flatten = {} + self.specs = {} + self.n_data_per_shard = -1 + self.n_data = -1 + self.ret_specs = None + self.n_kb_dataset = 1 + self.n_local_device = 1 + self.k = 1 + + def set_encode_fn(self, flax_model): + encode_fn = functools.partial( + static_encode_knowledge, flax_model=flax_model) + self.encode_knowledge_pmap = jax.pmap( + encode_fn, + axis_name='batch', + donate_argnums=(0, 1), + ) + + def initialize(self, kb_datasets): + """Load sharded dataset into CPU.""" + memory_image = [] + memory_text = [] + memory_idxs = [] + self.n_kb_dataset = len(kb_datasets) + logging.info('Start Loading sharded dataset into CPU.') + for idx, dataset_name in enumerate(kb_datasets): + logging.info(dataset_name) + dataset = kb_datasets[dataset_name] + n_iter = int(dataset.meta_data['example_per_shard'] // + dataset.meta_data['batch_size']) + for _ in tqdm.tqdm(range(n_iter)): + kb_batch = next(dataset.train_iter) + memory_image += [np.asarray(kb_batch['image'])] + memory_text += [np.asarray(kb_batch['knowledge_tokens'])] + memory_idxs += [ + idx * np.ones(shape=kb_batch['image'].shape[:2]).astype('int16') + ] + del kb_batch + self.memory['image'] = np.concatenate(memory_image, axis=1) + self.memory['text'] = np.concatenate(memory_text, axis=1) + self.n_local_device = self.memory['image'].shape[0] + self.memory_flatten['idxs'] = np.repeat( + np.reshape(np.concatenate(memory_idxs, axis=1), (1, -1)), + self.n_local_device, + axis=0) + self.n_data_per_shard = self.memory['image'].shape[1] + self.n_data = self.n_data_per_shard * self.n_local_device + + self.specs = { + 'image': dataset.meta_data['image_spec'], + 'text': dataset.meta_data['knowledge_spec'] + } + + def update_memory(self, pmap_train_state, bsz, retr_k, data_k, + axis_index_groups): + """Function to update stale embedding as memory. + + Args: + pmap_train_state: pmaped train state. + bsz: Global batch size. + retr_k: number of returned data for retrieval. + data_k: number of returned data for ranking. + axis_index_groups: axis groups to gather data. + + Returns: + updated train_state + """ + per_bsz = bsz // jax.device_count() + if axis_index_groups is None: + per_shard_bsz = bsz + else: + per_shard_bsz = bsz // len(axis_index_groups[0]) + logging.info('update memory!!!') + logging.info(per_bsz) + memory_key = [] + memory_val = [] + # memory_mask = [] + eval_per_bsz = per_bsz * 4 + for idx in range(int(np.ceil(self.n_data_per_shard / eval_per_bsz))): + kb_batch = { + 'knowledge_tokens': + self.memory['text'][:, + idx * eval_per_bsz:(idx + 1) * eval_per_bsz], + 'image': + self.memory['image'][:, + idx * eval_per_bsz:(idx + 1) * eval_per_bsz], + } + keys_head, compressed_val, _ = self.encode_knowledge_pmap( + kb_batch, pmap_train_state) + memory_key += [np.asarray(keys_head)] + memory_val += [np.asarray(compressed_val)] + # memory_mask += [np.asarray(mask)] + del kb_batch + + for kw in ['keys', 'values']: + if kw in self.memory: + del self.memory[kw] + if kw in self.memory_flatten: + del self.memory_flatten[kw] + + self.memory['keys'] = np.concatenate(memory_key, axis=1) + self.memory['values'] = np.concatenate(memory_val, axis=1) + # self.memory['masks'] = np.concatenate(memory_mask, axis=1) + + for kw in ['keys', 'values', 'image', 'text']: + mem = self.memory[kw] + self.memory_flatten[kw] = mem.reshape((mem.shape[0] * mem.shape[1],) + + mem.shape[2:]) + + self.specs['keys'] = (keys_head.shape[2:], keys_head.dtype.name) + self.specs['values'] = (compressed_val.shape[2:], compressed_val.dtype.name) + # self.specs['masks'] = (mask.shape[2:], mask.dtype.name) + + self.local_ret_specs = [{ + 'keys': + jax.ShapeDtypeStruct( + shape=(per_bsz, retr_k - data_k) + self.specs['keys'][0], + dtype=self.specs['values'][1]), + 'values': + jax.ShapeDtypeStruct( + shape=(per_bsz, retr_k - data_k) + self.specs['values'][0], + dtype=self.specs['values'][1]) + }, { + 'image': + jax.ShapeDtypeStruct( + shape=(per_bsz, data_k) + self.specs['image'][0], + dtype=self.specs['image'][1]), + 'text_tokens': + jax.ShapeDtypeStruct( + shape=(per_bsz, data_k) + self.specs['text'][0], + dtype=self.specs['text'][1]) + }] + self.k = int(np.ceil(retr_k / len(axis_index_groups)) + 1) + self.ret_top_specs = [{ + 'keys': + jax.ShapeDtypeStruct( + shape=(per_shard_bsz, self.k) + self.specs['keys'][0], + dtype=self.specs['values'][1]), + 'values': + jax.ShapeDtypeStruct( + shape=(per_shard_bsz, self.k) + self.specs['values'][0], + dtype=self.specs['values'][1]) + }, { + 'image': + jax.ShapeDtypeStruct( + shape=(per_shard_bsz, self.k) + self.specs['image'][0], + dtype=self.specs['image'][1]), + 'text_tokens': + jax.ShapeDtypeStruct( + shape=(per_shard_bsz, self.k) + self.specs['text'][0], + dtype=self.specs['text'][1]) + }] + + logging.info(self.local_ret_specs) + logging.info(self.ret_specs) + logging.info(self.memory['keys'].shape) + new_model_state = pmap_train_state.model_state.unfreeze() + if 'keys' in new_model_state['memory']: + del new_model_state['memory']['keys'] + del new_model_state['memory']['idxs'] + new_model_state['memory']['keys'] = self.memory['keys'] + new_model_state['memory']['idxs'] = self.memory_flatten['idxs'] + pmap_train_state = pmap_train_state.replace( + model_state=FrozenDict(new_model_state)) + logging.info('finish update memory!!!') + return pmap_train_state + + +def retrieve_memory(args): + device_id, indexs = args + return [ + { + 'values': kb.memory['values'][device_id][indexs], + # 'masks': kb.memory['masks'][device_id][indexs] + }, + { + 'image': kb.memory['image'][device_id][indexs], + 'text_tokens': kb.memory['text'][device_id][indexs] + } + ] + + +def local_retrieve_memory(args): + global_data_ids, global_memory_ids = args + return [ + { + 'keys': kb.memory_flatten['keys'][global_memory_ids], + 'values': kb.memory_flatten['values'][global_memory_ids], + # 'masks': kb.memory['masks'][device_id][indexs] + }, + { + 'image': kb.memory_flatten['image'][global_data_ids], + 'text_tokens': kb.memory_flatten['text'][global_data_ids] + } + ] + + +def retrieve_top_memory(args): + top1_ids = args + return [ + { + 'keys': kb.memory_flatten['keys'][top1_ids], + 'values': kb.memory_flatten['values'][top1_ids], + # 'masks': kb.memory['masks'][device_id][indexs] + }, + { + 'image': kb.memory_flatten['image'][top1_ids], + 'text_tokens': kb.memory_flatten['text'][top1_ids] + } + ] + + +kb = KnowledgeBase() diff --git a/scenic/projects/knowledge_visual_language/models/losses.py b/scenic/projects/knowledge_visual_language/models/losses.py new file mode 100644 index 000000000..8128d07f8 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/models/losses.py @@ -0,0 +1,65 @@ +"""Loss functions.""" + +from absl import logging +from flax.training import common_utils +import jax +import jax.numpy as jnp +from scenic.model_lib.base_models import model_utils as base_model_utils + + +def nll_loss(targets, pred, target_masks=None, label_smoothing=0): + """Negative Log-loglikelihood loss (perplexity). + + Args: + targets: ground-truth labels + pred: predicted logits + target_masks: mask that don't count + label_smoothing: factor to smooth label. + + Returns: + loss value + """ + + vocab_size = pred.shape[-1] + onehot_targets = common_utils.onehot(targets, vocab_size) + + return base_model_utils.weighted_softmax_cross_entropy( + pred, onehot_targets, target_masks, label_smoothing=label_smoothing) + + +def contrastive_loss(query_emb: jnp.ndarray, + key_emb: jnp.ndarray, + temperature: float = 1.0): + """Contrastive loss with hard negative samples & other in-batch negatives. + + Args: + query_emb: An array of shape [bsz, n_dim]. + key_emb: An array of shape [bsz, n_knowledge, n_dim]. Only the first one is + true positive sample, and the others are hard negatives. + temperature: A scalar that the temprature is divided by it. + + Returns: + Computed loss value. + """ + if query_emb.shape[0] != key_emb.shape[0]: + raise ValueError('query_emb and key_emb should have the same batch size.') + if query_emb.shape[-1] != key_emb.shape[-1]: + raise ValueError( + 'query_emb and key_emb should have the same embedding size.') + per_device_bsz, k = query_emb.shape[0], key_emb.shape[1] + global_key_emb = jnp.concatenate(jax.lax.all_gather(key_emb, 'batch'), 0) + labels = jax.lax.axis_index( + axis_name='batch') * per_device_bsz * k + jnp.arange(per_device_bsz) + + # bsz×d @ (bsz*n_device)×K×d -> bsz×(bsz * k * n_device) + # positive pairs are on first diagonal. + score_matrix = jnp.reshape( + jnp.einsum('bd,nkd->bkn', query_emb, global_key_emb), + [per_device_bsz, -1]) + + loss = nll_loss(pred=score_matrix / temperature, targets=labels) + accs = jnp.equal(jnp.argmax(score_matrix, axis=1), labels) + s0, s1 = score_matrix[0][0], score_matrix[0][1] # debug purpose + logging.info('backward host_id : %d', jax.process_index()) + logging.info(jax.lax.axis_index(axis_name='batch')) + return loss, (jnp.mean(accs), s0, s1) diff --git a/scenic/projects/knowledge_visual_language/models/metrics.py b/scenic/projects/knowledge_visual_language/models/metrics.py new file mode 100644 index 000000000..bf7a124d3 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/models/metrics.py @@ -0,0 +1,33 @@ +"""Metric functions.""" + +from typing import Dict, Tuple +from flax.training import common_utils +from scenic.model_lib.base_models import model_utils as base_model_utils +from scenic.projects.knowledge_visual_language.models import constants + +JTensor = constants.JTensor +JTensorDict = constants.JTensorDict + + +def token_accuracy(logits, batch: JTensorDict) -> Dict[str, Tuple[float, int]]: + """Return the accuracy for LM prediction. + + Args: + logits: Output of model in shape [B, L, C]. + batch: Batch of data that has 'decoder_outputs' as ground-truth. + + Returns: + Accuracy stored as Dict. + """ + targets = batch['decoder_target_tokens'] + vocab_size = logits.shape[-1] + onehot_targets = common_utils.onehot(targets, vocab_size) + masks = targets > 0 + n_corrects = base_model_utils.weighted_correctly_classified( + logits, onehot_targets, masks) + n_valids = base_model_utils.num_examples(logits, onehot_targets, masks) + return { # pytype: disable=bad-return-type # jax-ndarray + 'token_accuracy': + base_model_utils.psum_metric_normalizer((n_corrects, n_valids)) + } + diff --git a/scenic/projects/knowledge_visual_language/models/metrics_vqa.py b/scenic/projects/knowledge_visual_language/models/metrics_vqa.py new file mode 100644 index 000000000..4bf2582f5 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/models/metrics_vqa.py @@ -0,0 +1,94 @@ +"""Metric functions for VQA.""" +import string +from typing import Any + +from clu import metrics +import clu.values as clu_values +import flax +import jax.numpy as jnp +import numpy as np +from scenic.projects.knowledge_visual_language.data import data_utils +from scenic.projects.t5 import tokenizer as t5_tokenizer +from scenic.projects.vit_vqa.models import qa_utils + + +_PUNCTUATION = string.punctuation + '‘’´`_' +# Recheck the total number of ground truth answers +# received from the data loader. +MAX_GT_ANSWERS = 10 + + +def normalize_answer(prediction: str) -> str: + answer = qa_utils.normalize_answer( + prediction, punc_chars=_PUNCTUATION, punc_repl='' + ) + return answer.strip() + + +@flax.struct.dataclass +class VQAMetrics( + metrics.CollectingMetric.from_outputs(('predictions', 'targets')) +): + """Computes VQA accuracy and F1 metrics. + + Since jax does not support strings, during the compute phase, which is done + outside of pmap and other jax functions, converts the integer word/text + predictions into words/strings which are used to compute the scores. + + Attributes: + predictions: jnp.ndarray of integers representing word/text predictions. + targets: jnp.ndarray of integers representing ground truth words/text. + """ + + def compute(self) -> dict[str, Any]: + values = super().compute() + tokenizer = t5_tokenizer.build_dmvr_sp_model() + tokenizer.initialize() + # Moves all values into the batch dimension when run with pmap. + predictions = jnp.reshape( + values['predictions'], (-1, values['predictions'].shape[-1]) + ) + # Targets has multiple answers per-question, so we keep that structure when + # reshaping. + targets = values['targets'] + targets = jnp.reshape(targets, (-1, targets.shape[-2], targets.shape[-1])) + assert len(targets.shape) == 3 + assert len(predictions.shape) == 2 + + # Convert the values into text. + prediction_tokens = [] + for p in predictions.tolist(): + if data_utils.EOS_ID in p: + p = p[: p.index(data_utils.EOS_ID)] + prediction_tokens += [tokenizer.indices_to_string(p)] + # Each question has multiple answers, so we need to decode a list-of-list. + + target_tokens = [] + for target_answers in targets.tolist(): + tokens = [] + for answer in target_answers: + if data_utils.EOS_ID in answer: + answer = answer[: answer.index(data_utils.EOS_ID)] + tokens += [tokenizer.indices_to_string(answer)] + target_tokens += [tokens] + # Normalize answers, which expects a string. + predictions = [normalize_answer(p) for p in prediction_tokens] + targets = [[normalize_answer(a) for a in t] for t in target_tokens] + + if len(targets[0]) > 1: + # VizWiz and VQA2.0 style metric with multiple GT answers. + qa = qa_utils.vqa_metrics(targets, predictions) + else: + # SNLI-VE, GQA, NLVR single GT answer metric. + qa = qa_utils.qa_metrics(targets, predictions) + qa['acc'] = 100 * np.average( + [p in ans for p, ans in zip(predictions, targets)] + ) + return qa + + def compute_value(self) -> dict[str, clu_values.Value]: + metric_values = self.compute() + metric_results = { + key: clu_values.Scalar(value) for key, value in metric_values.items() + } + return metric_results diff --git a/scenic/projects/knowledge_visual_language/models/vit.py b/scenic/projects/knowledge_visual_language/models/vit.py new file mode 100644 index 000000000..ae6e088ac --- /dev/null +++ b/scenic/projects/knowledge_visual_language/models/vit.py @@ -0,0 +1,200 @@ +"""A refactored and simplified ViT. + +However, the names of modules are made to match the old ones for easy loading. +""" + +from typing import Optional, Sequence, Union + +from big_vision.models import vit as bv_vit +import flax.linen as nn +import jax +import jax.numpy as jnp + + +class MlpBlock(nn.Module): + """Transformer MLP / feed-forward block.""" + mlp_dim: Optional[int] = None # Defaults to 4x input dim + dropout: float = 0.0 + dtype: str = jnp.bfloat16 + + @nn.compact + def __call__(self, x, deterministic=True): + """Applies Transformer MlpBlock module.""" + inits = dict( + kernel_init=nn.initializers.xavier_uniform(), + bias_init=nn.initializers.normal(stddev=1e-6), + ) + + n, l, d = x.shape # pylint: disable=unused-variable + x = jnp.array(x, dtype=self.dtype) + x = nn.Dense(self.mlp_dim or 4 * d, **inits, dtype=self.dtype)(x) + x = nn.gelu(x) + x = nn.Dropout(rate=self.dropout)(x, deterministic) + x = nn.Dense(d, **inits, dtype=self.dtype)(x) + return x + + +class Encoder1DBlock(nn.Module): + """Single transformer encoder block (MHSA + MLP).""" + mlp_dim: Optional[int] = None # Defaults to 4x input dim + num_heads: int = 12 + dropout: float = 0.0 + dtype: str = jnp.bfloat16 + + @nn.compact + def __call__(self, x, deterministic=True): + out = {} + y = nn.LayerNorm()(x) + y = out["sa"] = nn.MultiHeadDotProductAttention( + num_heads=self.num_heads, + kernel_init=nn.initializers.xavier_uniform(), + deterministic=deterministic, + dtype=self.dtype)(y, y) + y = nn.Dropout(rate=self.dropout)(y, deterministic) + x = out["+sa"] = x + y + + y = nn.LayerNorm()(x) + y = out["mlp"] = MlpBlock( + mlp_dim=self.mlp_dim, dropout=self.dropout, + dtype=self.dtype)(y, deterministic) + y = nn.Dropout(rate=self.dropout)(y, deterministic) + x = out["+mlp"] = x + y + return x, out + + +class Encoder(nn.Module): + """Transformer Model Encoder for sequence to sequence translation.""" + depth: int + mlp_dim: Optional[int] = None # Defaults to 4x input dim + num_heads: int = 12 + dropout: float = 0.0 + dtype: str = jnp.bfloat16 + num_frozen_layers: int = -1 + + @nn.compact + def __call__(self, x, deterministic=True): + out = {} + + # Input Encoder + for lyr in range(self.depth): + block = Encoder1DBlock( + name=f"encoderblock_{lyr}", + mlp_dim=self.mlp_dim, + num_heads=self.num_heads, + dropout=self.dropout, + dtype=self.dtype) + x, out[f"block{lyr:02d}"] = block(x, deterministic) + if self.num_frozen_layers > 0 and lyr == self.num_frozen_layers - 1: + x = jax.lax.stop_gradient(x) + out["pre_ln"] = x # Alias for last block, but without the number in it. + + return nn.LayerNorm(name="encoder_norm")(x), out + + +class _Model(nn.Module): + """ViT model.""" + + num_classes: int + patch_size: Sequence[int] = (16, 16) + width: int = 768 + depth: int = 12 + mlp_dim: Optional[int] = None # Defaults to 4x input dim + num_heads: int = 12 + posemb: str = "learn" # Can also be "sincos2d" + rep_size: Union[int, bool] = False + dropout: float = 0.0 + pool_type: str = "gap" # Can also be "map" or "tok" + head_zeroinit: bool = True + dtype: str = jnp.bfloat16 + num_frozen_layers: int = -1 + + @nn.compact + def __call__(self, image, *, train=False): + out = {} + + # Patch extraction + x = out["stem"] = nn.Conv( + self.width, + self.patch_size, + strides=self.patch_size, + padding="VALID", + name="embedding")( + image) + n, h, w, c = x.shape + x = jnp.reshape(x, [n, h * w, c]) + + # Add posemb before adding extra token. + x = out["with_posemb"] = x + bv_vit.get_posemb(self, self.posemb, (h, w), c, + "pos_embedding", x.dtype) + + if self.pool_type == "tok": + cls = self.param("cls", nn.initializers.zeros, (1, 1, c), x.dtype) + x = jnp.concatenate([jnp.tile(cls, [n, 1, 1]), x], axis=1) + + n, l, c = x.shape # pylint: disable=unused-variable + x = nn.Dropout(rate=self.dropout)(x, not train) + x, out["encoder"] = Encoder( + depth=self.depth, + mlp_dim=self.mlp_dim, + num_heads=self.num_heads, + dropout=self.dropout, + dtype=self.dtype, + num_frozen_layers=self.num_frozen_layers, + name="Transformer")( + x, deterministic=not train) + + encoded = out["encoded"] = x + + if self.pool_type == "map": + x = out["head_input"] = bv_vit.MAPHead( + num_heads=self.num_heads, mlp_dim=self.mlp_dim)( + x) + elif self.pool_type == "gap": + x = out["head_input"] = jnp.mean(x, axis=1) + elif self.pool_type == "0": + x = out["head_input"] = x[:, 0] + elif self.pool_type == "tok": + x = out["head_input"] = x[:, 0] + encoded = encoded[:, 1:] + else: + raise ValueError(f"Unknown pool type: '{self.pool_type}'") + + x_2d = jnp.reshape(encoded, [n, h, w, -1]) + + if self.rep_size: + rep_size = self.width if self.rep_size is True else self.rep_size # pylint: disable=g-bool-id-comparison + hid = nn.Dense(rep_size, name="pre_logits", dtype=self.dtype) + # NOTE: In the past we did not include tanh in pre_logits. + # For few-shot, it should not matter much, as it whitens anyways. + x_2d = nn.tanh(hid(x_2d)) + x = nn.tanh(hid(x)) + + out["pre_logits_2d"] = x_2d + out["pre_logits"] = x + + if self.num_classes: + kw = {"kernel_init": nn.initializers.zeros} if self.head_zeroinit else {} + head = nn.Dense(self.num_classes, name="head", dtype=self.dtype, **kw) + x_2d = out["logits_2d"] = head(x_2d) + x = out["logits"] = head(x) + x = jnp.array(x, dtype=self.dtype) + return x, out + + +def Model(num_classes, + num_frozen_layers=-1, + dtype=jnp.bfloat16, + variant=None, + **kw): # pylint: disable=invalid-name + """Factory function, because linen really don't like what I'm doing!""" + vit_config = bv_vit.decode_variant(variant) + if isinstance(num_frozen_layers, float): + num_frozen_layers = int(num_frozen_layers * vit_config["depth"]) + return _Model( + num_classes=num_classes, + num_frozen_layers=num_frozen_layers, + dtype=dtype, + **{ + **vit_config, + **kw + }) diff --git a/scenic/projects/knowledge_visual_language/trainer.py b/scenic/projects/knowledge_visual_language/trainer.py new file mode 100644 index 000000000..5f3b7ba71 --- /dev/null +++ b/scenic/projects/knowledge_visual_language/trainer.py @@ -0,0 +1,494 @@ +"""Training Script for knowledge-based models.""" + +import functools +from typing import Any, Dict, Optional, Tuple + +from absl import logging +from clu import metric_writers +from clu import periodic_actions +from clu import platform +from flax import jax_utils +import flax.linen as nn +import jax +import jax.example_libraries.optimizers as jax_optimizers +import jax.numpy as jnp +import jax.profiler +import ml_collections +import numpy as np +import optax +from scenic.dataset_lib import dataset_utils +from scenic.xm import xm_utils +from scenic.model_lib.base_models import base_model +from scenic.projects.knowledge_visual_language import trainer_utils +from scenic.projects.knowledge_visual_language.models import constants +from scenic.projects.knowledge_visual_language.models import losses +from scenic.train_lib import lr_schedules +from scenic.train_lib import optimizers +from scenic.train_lib import pretrain_utils +from scenic.train_lib import train_utils + + +def init_state( + model: base_model.BaseModel, + dataset: dataset_utils.Dataset, + config: ml_collections.ConfigDict, + workdir: str, + rng: jnp.ndarray, + writer: metric_writers.MetricWriter, +): + """Initialize the train state.""" + + input_spec = { + key[:-5]: dataset.meta_data[key] + for key in dataset.meta_data + if key[-5:] == '_spec' + } + # Initialize model. + rng, init_rng = jax.random.split(rng) + params, model_state, num_params, gflops = ( + train_utils.initialize_model_with_pytree( + model_def=model.flax_model, + input_spec=input_spec, + config=config, + rngs=init_rng, + ) + ) + logging.info('The model has %d params', num_params) + + if gflops: + logging.info('uses %d gflops', gflops or -1) + lr_fn = lr_schedules.get_learning_rate_fn(config) + # Create the optimizer. + # We jit this, such that the arrays that are created are created on the same + # device as the input is, in this case the CPU. Else they'd be on device[0]. + tx = optimizers.get_optimizer( + optimizer_config=optimizers.get_optax_optimizer_config(config), + learning_rate_fn=lr_fn, + ) + tx = trainer_utils.froze_param_optax( + params=params, + tx=tx, + frozen_patterns=config.get('frozen_patterns', None), + not_frozen_patterns=config.get('not_frozen_patterns', None), + ) + opt_state = jax.jit(tx.init, backend='cpu')(params) + # del params # Do not keep a copy of the initial params. + + _, train_rng = jax.random.split(rng) + chrono = train_utils.Chrono() + train_state = train_utils.TrainState( + global_step=0, + opt_state=opt_state, + tx=tx, + params=params, + model_state=model_state, + rng=train_rng, + metadata={'chrono': chrono.save()}, + ) + if config.checkpoint: + logging.info('Continuing training from the checkpoint') + logging.info('workdir= %s', workdir) + train_state, params_axes = trainer_utils.pop_axes_names( + train_state, axes_name='params_axes' + ) + train_state, _ = train_utils.restore_checkpoint(workdir, train_state) + train_state = trainer_utils.re_add_axis_names( + train_state, params_axes, axes_name='params_axes' + ) + + start_step = int(train_state.global_step) + chrono.load(train_state.metadata['chrono']) + + if start_step == 0: + if config.get('init_from', False): + if config.init_from.get('resume', False): + workdir = config.init_from.get('resume') + logging.info('Resuming training from the checkpoint') + logging.info('workdir= %s', workdir) + train_state, params_axes = trainer_utils.pop_axes_names( + train_state, axes_name='params_axes' + ) + train_state, _ = train_utils.restore_checkpoint(workdir, train_state) + train_state = trainer_utils.re_add_axis_names( + train_state, params_axes, axes_name='params_axes' + ) + start_step = int(train_state.global_step) + chrono.load(train_state.metadata['chrono']) + + elif config.init_from.get('xm', False): + if config.init_from.load_key_encoder: + params = trainer_utils.load_key_params(params, config) + train_state = train_state.replace( # pytype: disable=attribute-error + params=params + ) + else: + logging.info('Loading T5 & ViT Parameter from Pre-Trained Model') + params = trainer_utils.load_visual_params(params, config) + params = trainer_utils.load_text_params(params, config) + train_state = train_state.replace( # pytype: disable=attribute-error + params=params + ) + step0_log = {'num_trainable_params': num_params} + if gflops: + step0_log['gflops'] = gflops + writer.write_scalars(1, step0_log) + + return train_state, start_step, chrono, lr_fn + + +def train_step( + train_state: train_utils.TrainState, + batch: constants.Batch, + *, + flax_model: nn.Module, + loss_fn: constants.LossFn, + metrics_fn: constants.MetricFn, + model_config: ml_collections.ConfigDict, + debug: Optional[bool] = False, +) -> Tuple[ + train_utils.TrainState, Dict[str, Tuple[float, int]], Dict[str, float] +]: + """Runs a single step of training. + + Given the state of the training and a batch of data, computes + the loss and updates the parameters of the model. + + Note that in this code, the buffers of the first (train_state) and second + (batch) arguments are donated to the computation. + + Args: + train_state: The state of training including the current global_step, + model_state, rng, and optimizer. The buffer of this argument can be + donated to the computation. + batch: A single batch of data. The buffer of this argument can be donated to + the computation. + flax_model: A Flax model. + loss_fn: A loss function that given logits and batch of data, calculates the + training loss. + metrics_fn: A metrics function that given logits and batch of data, + calculates the metrics as well as the loss. + model_config: Config for model architecture. + debug: Whether the debug mode is enabled during training. `debug=True` + enables model specific logging/storing some values using + jax.host_callback. + + Returns: + Updated state of training, loss, computed metrics, and learning rate for + logging. + """ + new_rng, rng = jax.random.split(train_state.rng) + + # Bind the rng to the host/device we are on for dropout. + dropout_rng = train_utils.bind_rng_to_host_device( + rng, axis_name='batch', bind_to='device' + ) + + def training_loss_fn(params): + variables = {'params': params, **train_state.model_state} + output_dict, new_model_state = flax_model.apply( + variables, + **batch, + mutable=['batch_stats'], + train=True, + fuse_retrieval=model_config.fuse_retrieval, + in_batch_neg=model_config.in_batch_neg, + rngs={'dropout': dropout_rng}, + debug=debug, + ) + if debug: + logging.info( + 'Shape of token_logits in train step is: %s', + output_dict['predicted_logits'].shape, + ) + r = model_config.retrieval_ratio + output_dict['supervised_retrieval'] = model_config.supervised_retrieval + loss_dict = loss_fn(output_dict, batch) + train_loss = loss_dict['gen_loss'] * (1 - r) + loss_dict['retr_loss'] * r + return train_loss, (new_model_state, output_dict, loss_dict) + + compute_gradient_fn = jax.value_and_grad(training_loss_fn, has_aux=True) + + (train_loss, (new_model_state, output_dict, loss_dict)), grad = ( + compute_gradient_fn(train_state.params) + ) + grad = jax.lax.pmean(grad, axis_name='batch') + grad_norm = jax_optimizers.l2_norm(grad).astype(float) + updates, new_opt_state = train_state.tx.update( + grad, train_state.opt_state, train_state.params + ) + update_norm = jax_optimizers.l2_norm(updates).astype(float) + new_params = optax.apply_updates(train_state.params, updates) + param_norm = jax_optimizers.l2_norm(train_state.params).astype(float) + + metrics = metrics_fn(output_dict['predicted_logits'], batch) + + new_train_state = train_state.replace( # pytype: disable=attribute-error + global_step=train_state.global_step + 1, + opt_state=new_opt_state, + params=new_params, + model_state=new_model_state, + rng=new_rng, + ) + + logs = { + 'train/train_loss': train_loss, + 'train/gen_loss': loss_dict['gen_loss'], + 'train/retr_loss': loss_dict['retr_loss'], + 'train/retr_acc': loss_dict['retr_acc'], + 'train/s0': loss_dict['s0'], + 'train/s1': loss_dict['s1'], + 'grad_norm': grad_norm, + 'update_norm': update_norm, + 'param_norm': param_norm, + 'bias': train_state.params['att_transform']['bias'][0], + 'scale': train_state.params['att_transform']['scale'][0], + } + + if 'retr_scores' in output_dict: + logs['train/a0'] = output_dict['retr_scores'][0][0] + logs['train/a1'] = output_dict['retr_scores'][0][1] + + return new_train_state, metrics, logs # pytype: disable=bad-return-type # jax-types + + +def eval_step( + train_state: train_utils.TrainState, + batch: constants.Batch, + *, + flax_model: nn.Module, + metrics_fn: constants.MetricFn, + model_config: ml_collections.ConfigDict, + debug: Optional[bool] = False, +) -> Tuple[Dict[str, Tuple[float, int]], Dict[str, float]]: + """Runs a single step of evaluation, TODO(ziniu): Add beam search decoding. + + Given the state of the training and a batch of data, computes + the loss and updates the parameters of the model. + + Note that in this code, the buffers of the first (train_state) and second + (batch) arguments are donated to the computation. + + Args: + train_state: The state of training including the current global_step, + model_state, rng, and optimizer. The buffer of this argument can be + donated to the computation. + batch: A single batch of data. The buffer of this argument can be donated to + the computation. + flax_model: A Flax model. + metrics_fn: A metrics function that given logits and batch of data, + calculates the metrics as well as the loss. + model_config: Config for model architecture. + debug: Whether the debug mode is enabled during training. `debug=True` + enables model specific logging/storing some values using + jax.host_callback. + + Returns: + Updated state of training, loss, computed metrics, and learning rate for + logging. + """ + variables = {'params': train_state.params, **train_state.model_state} + output_dict = flax_model.apply( + variables, + **batch, + train=False, + fuse_retrieval=model_config.fuse_retrieval, + in_batch_neg=model_config.in_batch_neg, + debug=debug, + ) + metrics = metrics_fn(output_dict['predicted_logits'], batch) + + retr_loss, (retr_acc, _, _) = losses.contrastive_loss( + query_emb=output_dict['base_query'], + key_emb=output_dict['retr_keys'], + temperature=model_config.get('temperature'), + ) + logs = {'eval/retr_loss': retr_loss, 'eval/retr_acc': retr_acc} + + return metrics, logs + + +def train_and_eval( + *, + rng: jnp.ndarray, + config: ml_collections.ConfigDict, + model_cls: Any, + dataset: dataset_utils.Dataset, + workdir: str, + writer: metric_writers.MetricWriter, +) -> Tuple[train_utils.TrainState, Dict[str, Any], Dict[str, Any]]: + """Main training loop lives in this function. + + Given the model class and dataset, it prepares the items needed to run the + training, including the TrainState. + + Args: + rng: Jax rng key. + config: Configurations of the experiment. + model_cls: Model class; A model has a flax_module, a loss_fn, and a + metrics_fn associated with it. + dataset: The dataset that has train_iter, eval_iter, meta_data, and + optionally, test_iter. + workdir: Directory for checkpointing. + writer: CLU metrics writer instance. + + Returns: + A tuple with: + * the state that has the state of training (including current + global_step, model_state, rng, and the optimizer) + * a dictionary with the train_summary + * a dictionary with the evaluation summary + """ + host_id = jax.process_index() + lead_host = host_id == 0 + # Build the loss_fn, metrics, and flax_model. + model = model_cls(config, dataset.meta_data) + train_state, start_step, chrono, lr_fn = init_state( + model, dataset, config, workdir, rng, writer + ) + + logging.info('Complete initialization. Start Training.') + train_state = jax_utils.replicate(train_state) + logging.info('Number of processes is %s', jax.process_count()) + + # Get the pmapped train and eval steps. + train_step_pmapped = jax.pmap( + functools.partial( + train_step, + flax_model=model.flax_model, + loss_fn=model.loss_function, + metrics_fn=model.get_metrics_fn('train'), + debug=config.debug_train, + model_config=config.model, + ), + axis_name='batch', + # We can donate both buffers of train_state and train_batch. + donate_argnums=(0, 1), + ) + eval_step_pmapped = jax.pmap( + functools.partial( + eval_step, + flax_model=model.flax_model, + metrics_fn=model.get_metrics_fn('validation'), + debug=config.debug_eval, + model_config=config.model, + ), + axis_name='batch', + # We can donate the eval_batch's buffer. + donate_argnums=(1,), + ) + train_summary, eval_summary, e_metrics = {}, {}, {} + # Calculate the total number of training steps. + total_steps, steps_per_epoch = train_utils.get_num_training_steps( + config, dataset.meta_data + ) + log_eval_steps = config.get('log_eval_steps', steps_per_epoch) + checkpoint_steps = config.get('checkpoint_steps', steps_per_epoch) + log_summary_steps = config.get('log_summary_steps', log_eval_steps) + + logging.info('Start training from step %d', start_step + 1) + report_progress = periodic_actions.ReportProgress( + num_train_steps=total_steps, writer=writer + ) + hooks = [] + if lead_host: + hooks.append(report_progress) + + if config.get('xprof', True) and lead_host: + hooks.append(periodic_actions.Profile(num_profile_steps=5, logdir=workdir)) + + chrono.inform( + int(start_step), + int(total_steps), + int(config.batch_size), + int(steps_per_epoch), + ) + + summary_builder = trainer_utils.SummaryBuilder([], []) + + def write_note(note): + if lead_host: + platform.work_unit().set_notes(note) + + for step in range(start_step + 1, total_steps + 1): + if lead_host: + logging.info('training for step %d', step) + ###################### Training ######################## + with jax.profiler.StepTraceAnnotation('train', step_num=step): + train_batch = next(dataset.train_iter) + train_state, t_metrics, logs = train_step_pmapped( + train_state=train_state, batch=train_batch + ) + for hook in hooks: + hook(step) + logs['learning_rate'] = lr_fn(step).reshape([-1, 1]) + summary_builder.update(metrics_update=t_metrics, extra_logs_update=logs) + if lead_host: + logging.info('finish training for step %d', step) + ###################### LOG TRAIN SUMMARY ######################## + if (step % log_summary_steps == 10) or (step == total_steps): + chrono.pause() + if lead_host: + logging.info('log training summary') + chrono.tick(step, writer, write_note) + train_summary = summary_builder.write(writer, step) + chrono.resume() + ################### EVALUATION ################################ + should_eval = (step % log_eval_steps == 10) or (step == total_steps) + if should_eval: + logging.info('Start validation!') + chrono.pause(wait_for=(train_state.params)) + # Sync model state across replicas. + train_state = train_utils.sync_model_state_across_replicas(train_state) + for ds_name in dataset.valid_iter: + logging.info('Validate on %s', ds_name) + # Compute the number of evaluation steps per dataset. + num_eval_examples = dataset.meta_data['num_eval_examples'][ds_name] + total_eval_steps = int( + np.ceil(num_eval_examples / (config.get('eval_batch_size'))) + ) + steps_per_eval = config.get('steps_per_eval', total_eval_steps) + eval_metrics_all = [] + for _ in range(steps_per_eval): + eval_batch = next(dataset.valid_iter[ds_name]) + e_metrics, logs = eval_step_pmapped( + train_state=train_state, batch=eval_batch + ) + eval_metrics_all.append(train_utils.unreplicate_and_get(e_metrics)) + logging.info(e_metrics) + logging.info(logs) + eval_summary = train_utils.log_eval_summary( + step=step, + eval_metrics=eval_metrics_all, + writer=writer, + prefix=ds_name, + extra_eval_summary=jax.tree_util.tree_map( + train_utils.unreplicate_and_get, logs + ), + ) + chrono.resume() + + ##################### CHECKPOINTING ########################### + if not config.checkpoint: + continue + elif step % checkpoint_steps == 0 and step > 0 or (step == total_steps): + logging.info('Save checkpoint!') + chrono.pause(wait_for=(train_state.params, train_state.opt_state)) + with report_progress.timed('checkpoint'): + # Sync model state across replicas. + train_state = train_utils.sync_model_state_across_replicas(train_state) + if lead_host: + logging.info('checkpointing (training step: %d)', step) + # Take the first replica. + unrep_train_state = jax_utils.unreplicate(train_state) + metadata = unrep_train_state.metadata + metadata['chrono'] = chrono.save() + unrep_train_state.replace(metadata=metadata) # pytype: disable=attribute-error + train_utils.save_checkpoint(workdir, unrep_train_state) + del unrep_train_state + chrono.resume() + logging.info('Checkpoint saved!') + + # Wait until computations are done before exiting. + train_utils.barrier_across_hosts() + # Return the train and eval summary after last step. + return train_state, train_summary, eval_summary diff --git a/scenic/projects/knowledge_visual_language/trainer_memory.py b/scenic/projects/knowledge_visual_language/trainer_memory.py new file mode 100644 index 000000000..b6e58868b --- /dev/null +++ b/scenic/projects/knowledge_visual_language/trainer_memory.py @@ -0,0 +1,719 @@ +"""Training Script for knowledge-based models.""" + +import functools +import gc +from typing import Any, Dict, Optional, Tuple + +from absl import logging +from clu import metric_writers +from clu import periodic_actions +from clu import platform +from flax import jax_utils +import flax.linen as nn +from flax.training import common_utils +import jax +import jax.example_libraries.optimizers as jax_optimizers +import jax.numpy as jnp +import jax.profiler +import ml_collections +import numpy as np +import optax +from scenic.dataset_lib import dataset_utils +from scenic.xm import xm_utils +from scenic.model_lib.base_models import base_model +from scenic.projects.knowledge_visual_language import trainer_utils +from scenic.projects.knowledge_visual_language.models import constants +from scenic.projects.knowledge_visual_language.models import local_memory +from scenic.train_lib import lr_schedules +from scenic.train_lib import optimizers +from scenic.train_lib import pretrain_utils +from scenic.train_lib import train_utils + +local_kb = local_memory.kb + + +def init_state( + model: base_model.BaseModel, + dataset: dataset_utils.Dataset, + config: ml_collections.ConfigDict, + workdir: str, + rng: jnp.ndarray, + writer: metric_writers.MetricWriter, +): + """Initialize the train state.""" + + input_spec = { + key[:-5]: dataset.meta_data[key] + for key in dataset.meta_data + if key[-5:] == '_spec' + } + # Initialize model. + rng, init_rng = jax.random.split(rng) + params, model_state, num_params, gflops = ( + train_utils.initialize_model_with_pytree( + model_def=model.flax_model, + input_spec=input_spec, + config=config, + rngs=init_rng, + ) + ) + logging.info('The model has %d params', num_params) + + if gflops: + logging.info('uses %d gflops', gflops or -1) + lr_fn = lr_schedules.get_learning_rate_fn(config) + # Create the optimizer. + # We jit this, such that the arrays that are created are created on the same + # device as the input is, in this case the CPU. Else they'd be on device[0]. + tx = optimizers.get_optimizer( + optimizer_config=optimizers.get_optax_optimizer_config(config), + learning_rate_fn=lr_fn, + ) + tx = trainer_utils.froze_param_optax( + params=params, + tx=tx, + frozen_patterns=config.get('frozen_patterns', None), + not_frozen_patterns=config.get('not_frozen_patterns', None), + ) + opt_state = jax.jit(tx.init, backend='cpu')(params) + + _, train_rng = jax.random.split(rng) + chrono = train_utils.Chrono() + train_state = train_utils.TrainState( + global_step=0, + opt_state=opt_state, + tx=tx, + params=params, + model_state=model_state, + rng=train_rng, + metadata={'chrono': chrono.save()}, + ) + if config.checkpoint: + logging.info('Continuing training from the checkpoint') + logging.info('workdir= %s', workdir) + train_state, params_axes = trainer_utils.pop_axes_names( + train_state, axes_name='params_axes' + ) + train_state, memory_axes = trainer_utils.pop_axes_names( + train_state, axes_name='memory' + ) + train_state, _ = train_utils.restore_checkpoint(workdir, train_state) + train_state = trainer_utils.re_add_axis_names( + train_state, memory_axes, axes_name='memory' + ) + train_state = trainer_utils.re_add_axis_names( + train_state, params_axes, axes_name='params_axes' + ) + + start_step = int(train_state.global_step) + chrono.load(train_state.metadata['chrono']) + + if start_step == 0: + if config.get('init_from', False): + if config.init_from.get('resume', False): + xid, wid = config.init_from.get('resume') + (_, workdir) = xm_utils.get_info_from_xmanager(xid, wid) + logging.info('Resuming training from the checkpoint') + logging.info('workdir= %s', workdir) + train_state, params_axes = trainer_utils.pop_axes_names( + train_state, axes_name='params_axes' + ) + train_state, memory_axes = trainer_utils.pop_axes_names( + train_state, axes_name='memory' + ) + train_state, _ = train_utils.restore_checkpoint(workdir, train_state) + train_state = trainer_utils.re_add_axis_names( + train_state, memory_axes, axes_name='memory' + ) + train_state = trainer_utils.re_add_axis_names( + train_state, params_axes, axes_name='params_axes' + ) + start_step = int(train_state.global_step) + chrono.load(train_state.metadata['chrono']) + + elif config.init_from.get('xm', False): + if config.init_from.load_key_encoder: + params = trainer_utils.load_key_params(params, config) + train_state = train_state.replace( # pytype: disable=attribute-error + params=params + ) + else: + logging.info('Loading T5 & ViT Parameter from Pre-Trained Model') + params = trainer_utils.load_visual_params(params, config) + params = trainer_utils.load_text_params(params, config) + train_state = train_state.replace( # pytype: disable=attribute-error + params=params + ) + step0_log = {'num_trainable_params': num_params} + if gflops: + step0_log['gflops'] = gflops + writer.write_scalars(1, step0_log) + train_state, _ = trainer_utils.pop_axes_names( + train_state, axes_name='params_axes' + ) + return train_state, start_step, chrono, lr_fn + + +def train_step( + train_state: train_utils.TrainState, + batch: constants.Batch, + *, + flax_model: nn.Module, + loss_fn: constants.LossFn, + metrics_fn: constants.MetricFn, + model_config: ml_collections.ConfigDict, + debug: Optional[bool] = False, +) -> Tuple[ + train_utils.TrainState, + Dict[str, Tuple[float, int]], + Dict[str, float], + constants.JTensor, +]: + """Runs a single step of training. + + Given the state of the training and a batch of data, computes + the loss and updates the parameters of the model. + + Note that in this code, the buffers of the first (train_state) and second + (batch) arguments are donated to the computation. + + Args: + train_state: The state of training including the current global_step, + model_state, rng, and optimizer. The buffer of this argument can be + donated to the computation. + batch: A single batch of data. The buffer of this argument can be donated to + the computation. + flax_model: A Flax model. + loss_fn: A loss function that given logits and batch of data, calculates the + training loss. + metrics_fn: A metrics function that given logits and batch of data, + calculates the metrics as well as the loss. + model_config: Config for model architecture. + debug: Whether the debug mode is enabled during training. `debug=True` + enables model specific logging/storing some values using + jax.host_callback. + + Returns: + Updated state of training, loss, computed metrics, and learning rate for + logging. + """ + new_rng, rng = jax.random.split(train_state.rng) + + # Bind the rng to the host/device we are on for dropout. + dropout_rng = train_utils.bind_rng_to_host_device( + rng, axis_name='batch', bind_to='device' + ) + + def training_loss_fn(params): + variables = {'params': params, **train_state.model_state} + logging.info(variables.keys()) + output_dict = flax_model.apply( + variables, + **batch, + train=True, + rngs={'dropout': dropout_rng}, + debug=debug, + use_memory=True, + use_psudo_retr=model_config.use_psudo_retr, + retrieve_local=model_config.retrieve_local, + frozen_base=model_config.t5_frozen_base, + ) + if debug: + logging.info( + 'Shape of token_logits in train step is: %s', + output_dict['predicted_logits'].shape, + ) + loss_dict = loss_fn(output_dict, batch) + return loss_dict['total_loss'], (output_dict, loss_dict) + + compute_gradient_fn = jax.value_and_grad(training_loss_fn, has_aux=True) + + (train_loss, (output_dict, loss_dict)), grad = compute_gradient_fn( + train_state.params + ) + grad = jax.lax.pmean(grad, axis_name='batch') + grad_norm = jax_optimizers.l2_norm(grad).astype(float) + updates, new_opt_state = train_state.tx.update( + grad, train_state.opt_state, train_state.params + ) + update_norm = jax_optimizers.l2_norm(updates).astype(float) + new_params = optax.apply_updates(train_state.params, updates) + param_norm = jax_optimizers.l2_norm(train_state.params).astype(float) + + logging.info(output_dict['predicted_logits'].shape) + logging.info(batch) + metrics = metrics_fn(output_dict['predicted_logits'], batch) + + new_train_state = train_state.replace( # pytype: disable=attribute-error + global_step=train_state.global_step + 1, + opt_state=new_opt_state, + params=new_params, + rng=new_rng, + ) + + logs = { + 'train/train_loss': train_loss, + 'train/gen_loss': loss_dict['gen_loss'], + 'train/contra_loss': loss_dict['contra_loss'], + 'train/contra_accs': loss_dict['contra_accs'], + 'grad_norm': grad_norm, + 'update_norm': update_norm, + 'param_norm': param_norm, + 'bias': train_state.params['att_transform']['bias'][0], + 'scale': train_state.params['att_transform']['scale'][0], + } + + if 'retr_scores' in output_dict: + logs['train/a0'] = output_dict['retr_scores'][0][0] + logs['train/a1'] = output_dict['retr_scores'][0][1] + logs['train/a2'] = output_dict['retr_scores'][0][2] + logs['train/a3'] = output_dict['retr_scores'][0][3] + + if 'topk_scores' in output_dict: + logs['train/s0'] = output_dict['topk_scores'][0][0] + logs['train/s1'] = output_dict['topk_scores'][0][1] + + if 'inbatch_sim' in output_dict: + logs['train/i0'] = output_dict['inbatch_sim'][0][0] + logs['train/i1'] = output_dict['inbatch_sim'][0][1] + + if 'base_norm' in output_dict: + logs['train/base_norm'] = output_dict['base_norm'] + if 'data_norm' in output_dict: + logs['train/data_norm'] = output_dict['data_norm'] + if 'vals_norm' in output_dict: + logs['train/memory_norm'] = output_dict['vals_norm'] + if 'disentangle_reg' in output_dict: + logs['train/disentangle'] = output_dict['disentangle_reg'] + if 'gap' in output_dict: + logs['train/gap'] = output_dict['gap'] + retr_top_image = output_dict['retr_data']['image'][:, 0] + return new_train_state, metrics, logs, retr_top_image # pytype: disable=bad-return-type # jax-types + + +def eval_step( + train_state: train_utils.TrainState, + batch: constants.Batch, + *, + flax_model: nn.Module, + metrics_fn: constants.MetricFn, + model_config: ml_collections.ConfigDict, + debug: Optional[bool] = False, +) -> Tuple[Dict[str, Tuple[float, int]], constants.JTensor, Any]: + """Runs a single step of evaluation. + + Given the state of the training and a batch of data, computes + the loss and updates the parameters of the model. + + Note that in this code, the buffers of the first (train_state) and second + (batch) arguments are donated to the computation. + + Args: + train_state: The state of training including the current global_step, + model_state, rng, and optimizer. The buffer of this argument can be + donated to the computation. + batch: A single batch of data. The buffer of this argument can be donated to + the computation. + flax_model: A Flax model. + metrics_fn: A metrics function that given logits and batch of data, + calculates the metrics as well as the loss. + model_config: Config for model architecture. + debug: Whether the debug mode is enabled during training. `debug=True` + enables model specific logging/storing some values using + jax.host_callback. + + Returns: + Updated state of training, loss, computed metrics, and learning rate for + logging. + """ + variables = {'params': train_state.params, **train_state.model_state} + output_dict = flax_model.apply( + variables, + **batch, + train=False, + debug=debug, + use_memory=True, + retrieve_local=model_config.retrieve_local, + ) + retr_top_image = output_dict['retr_data']['image'][:, 0] + metrics = metrics_fn(output_dict['predicted_logits'], batch) + return metrics, retr_top_image, output_dict['predicted_logits'] + + +def eval_step_autoregressive_decoding( + train_state: train_utils.TrainState, + batch: constants.Batch, + *, + model: Any, + metrics_fn: Any, + model_config: ml_collections.ConfigDict, + vocab_size: int, + num_decodes: int, + beam_search: bool = True, + debug: Optional[bool] = False, +) -> Tuple[Dict[str, Tuple[float, int]], constants.JTensor, Any]: + """Evaluate autoregressive generation. + + Args: + train_state: The state of training including the current global_step, + model_state, rng, and optimizer. The buffer of this argument are donated + to the computation. + batch: A single batch of data. The buffer of this argument can be donated to + the computation. + model: The scenic model. + metrics_fn: A metrics function that given logits and batch of data, + calculates the metrics as well as the loss. + model_config: Config for model architecture. + vocab_size: size of the vocabulary. + num_decodes: number of decoding attempts. A larger number means longer + inference time but better performance. + beam_search: if True, perform beam search. If False, perform temperature + sampling. + debug: Whether the debug mode is enabled during training. `debug=True` + enables model specific logging/storing some values using + jax.host_callback. + + Returns: + metrics: dictionary mapping metrics to values. + logits: predicted logits of the model. + """ + variables = {'params': train_state.params, **train_state.model_state} + + # Loop per example since decoding only works for single examples. + predicted_tokens, _, retr_top_image = ( + model.apply_with_autoregressive_decoding( + variables, + **batch, + num_decodes=num_decodes, + beam_search=beam_search, + debug=debug, + use_memory=True, + retrieve_local=model_config.retrieve_local, + ) + ) + + # The autoregressive decoder yields tokens. However, the metrics want + # logits. So make the predictions into one-hot predictions. + logits = common_utils.onehot(predicted_tokens, vocab_size) + metrics = metrics_fn(logits, batch) + + return metrics, retr_top_image, logits + + +def train_and_eval( + *, + rng: jnp.ndarray, + config: ml_collections.ConfigDict, + model_cls: Any, + dataset: dataset_utils.Dataset, + workdir: str, + writer: metric_writers.MetricWriter, + kb_datasets: Dict[str, dataset_utils.Dataset], +) -> Tuple[train_utils.TrainState, Dict[str, Any], Dict[str, Any]]: + """Main training loop lives in this function. + + Given the model class and dataset, it prepares the items needed to run the + training, including the TrainState. + + Args: + rng: Jax rng key. + config: Configurations of the experiment. + model_cls: Model class; A model has a flax_module, a loss_fn, and a + metrics_fn associated with it. + dataset: The dataset that has train_iter, eval_iter, meta_data, and + optionally, test_iter. + workdir: Directory for checkpointing. + writer: CLU metrics writer instance. + kb_datasets: dictionary of datasets served as knowledge base. + + Returns: + A tuple with: + * the state that has the state of training (including current + global_step, model_state, rng, and the optimizer) + * a dictionary with the train_summary + * a dictionary with the evaluation summary + """ + host_id = jax.process_index() + lead_host = host_id == 0 + # Build the loss_fn, metrics, and flax_model. + + model = model_cls(config, dataset.meta_data, kb_datasets=kb_datasets) + train_state, start_step, chrono, lr_fn = init_state( + model, dataset, config, workdir, rng, writer + ) + + logging.info('Complete initialization for %s. Start Training.', host_id) + train_state = jax_utils.replicate(train_state) + local_kb.set_encode_fn(flax_model=model.flax_model) + train_state = local_kb.update_memory( + train_state, + bsz=config.batch_size, + data_k=model.data_k, + retr_k=model.retr_k, + axis_index_groups=model.axis_index_groups, + ) + logging.info('Number of processes is %s', jax.process_count()) + + # Get the pmapped train and eval steps. + train_step_pmapped = jax.pmap( + functools.partial( + train_step, + flax_model=model.flax_model, + loss_fn=model.loss_function_dict, + metrics_fn=model.get_metrics_fn('train'), + debug=config.debug_train, + model_config=config.model, + ), + axis_name='batch', + # We can donate both buffers of train_state and train_batch. + donate_argnums=(0, 1), + ) + eval_step_pmapped = jax.pmap( + functools.partial( + eval_step, + flax_model=model.flax_model, + metrics_fn=model.get_metrics_fn('validation'), + debug=config.debug_eval, + model_config=config.model, + ), + axis_name='batch', + # We can donate the eval_batch's buffer. + donate_argnums=(1), + ) + train_summary, eval_summary, e_metrics = {}, {}, {} + eval_step_autoregressive_decoding_pmapped = jax.pmap( + functools.partial( + eval_step_autoregressive_decoding, + model=model, + metrics_fn=model.get_metrics_fn('validation'), + model_config=config.model, + vocab_size=config.vocab_size, + num_decodes=config.autoregressive_decoding.num_decodes, + beam_search=config.autoregressive_decoding.beam_search, + debug=config.debug_eval, + ), + axis_name='batch', + # We can donate the eval_batch's buffer. + donate_argnums=(1), + ) + + # Calculate the total number of training steps. + total_steps, steps_per_epoch = train_utils.get_num_training_steps( + config, dataset.meta_data + ) + log_eval_steps = config.get('log_eval_steps', steps_per_epoch) + checkpoint_steps = config.get('checkpoint_steps', steps_per_epoch) + log_summary_steps = config.get('log_summary_steps', log_eval_steps) + frozen_memory = config.get('frozen_memory', False) + + logging.info('Start training from step %d', start_step + 1) + report_progress = periodic_actions.ReportProgress( + num_train_steps=total_steps, writer=writer + ) + hooks = [report_progress] + + # if config.get('xprof', True) and lead_host: + # hooks.append(periodic_actions.Profile(num_profile_steps=5, logdir=workdir)) + + chrono.inform( + int(start_step), + int(total_steps), + int(config.batch_size), + int(steps_per_epoch), + ) + + summary_builder = trainer_utils.SummaryBuilder([], []) + + def write_note(note): + if lead_host: + platform.work_unit().set_notes(note) + + for step in range(start_step + 1, total_steps + 1): + if lead_host: + logging.info('training for step %d', step) + ###################### Training ######################## + with jax.profiler.StepTraceAnnotation('train', step_num=step): + train_batch = next(dataset.train_iter) + inp_image_sample = np.asarray(train_batch['encoder_input_image'][0][0]) + train_batch['device_id'] = jnp.arange( + train_batch['encoder_input_image'].shape[0] + ) + train_state, t_metrics, logs, retr_top_image = train_step_pmapped( + train_state, train_batch + ) + retr_top_image = np.asarray(retr_top_image[0][0]) + for hook in hooks: + hook(step) + logs['learning_rate'] = lr_fn(step).reshape([-1, 1]) + summary_builder.update(metrics_update=t_metrics, extra_logs_update=logs) + jax.tree_map(lambda h: h.delete(), train_batch) + if lead_host: + logging.info('finish training for step %d', step) + ###################### LOG TRAIN SUMMARY ######################## + if (step % log_summary_steps == 2) or (step == total_steps): + chrono.pause() + if lead_host: + logging.info('log training summary for step %d', step) + chrono.tick(step, writer, write_note) + writer.write_images( + step, + { + 'train/retr_image': np.expand_dims(retr_top_image, axis=0), + 'train/query_image': np.expand_dims(inp_image_sample, axis=0), + }, + ) + writer.flush() + train_summary = summary_builder.write(writer, step) + chrono.resume() + del inp_image_sample, retr_top_image + ################### EVALUATION ################################ + should_eval = (step % log_eval_steps == 2) or (step == total_steps) + if should_eval: + # update the KB memory + if not frozen_memory: + train_state = local_kb.update_memory( + train_state, + bsz=config.batch_size, + data_k=model.data_k, + retr_k=model.retr_k, + axis_index_groups=model.axis_index_groups, + ) + logging.info('Start validation!') + chrono.pause(wait_for=(train_state.params)) + # Sync model state across replicas. + for ds_name in dataset.valid_iter: + logging.info('Validate on %s', ds_name) + # Compute the number of evaluation steps per dataset. + num_eval_examples = dataset.meta_data['num_eval_examples'][ds_name] + total_eval_steps = int( + np.ceil(num_eval_examples / (config.get('eval_batch_size'))) + ) + steps_per_eval = config.get('steps_per_eval', total_eval_steps) + for test_mode in range(2): + eval_metrics_all = [] + eval_vqa_metrics_all = {} + for step_id in range(steps_per_eval): + eval_batch = next(dataset.valid_iter[ds_name]) + inp_image_sample = np.asarray( + eval_batch['encoder_input_image'][0][0] + ) + if test_mode == 0: + eval_batch['device_id'] = jnp.arange( + eval_batch['encoder_input_image'].shape[0] + ) + e_metrics, retr_top_image, predicted_logits = eval_step_pmapped( + train_state=train_state, batch=eval_batch + ) + eval_metrics_all.append(train_utils.unreplicate_and_get(e_metrics)) + # add vqa accuracy metric if it's a vqa dataset + if config.model.get('qa', False): + eval_vqa_metric_fn = model.get_vqa_metrics( + predicted_logits, eval_batch + ) + eval_vqa_metrics = eval_vqa_metric_fn.compute() + for key in eval_vqa_metrics: + eval_vqa_metrics_all.setdefault(key, 0.0) + eval_vqa_metrics_all[key] += eval_vqa_metrics[key] + + if test_mode == 0 and step_id == 0 and lead_host: + logging.info(e_metrics) + if ds_name == 'val_cc': + retr_top_image = np.asarray(retr_top_image[0][0]) + writer.write_images( + step, + { + 'eval/retr_image': np.expand_dims( + retr_top_image, axis=0 + ), + 'eval/query_image': np.expand_dims( + inp_image_sample, axis=0 + ), + }, + ) + writer.flush() + del retr_top_image + jax.tree_map(lambda h: h.delete(), eval_batch) + del inp_image_sample + for key in eval_vqa_metrics_all: + eval_vqa_metrics_all[key] /= float(steps_per_eval) + if test_mode == 0: + eval_summary = train_utils.log_eval_summary( + step=step, + eval_metrics=eval_metrics_all, + extra_eval_summary=eval_vqa_metrics_all, + writer=writer, + prefix=ds_name, + ) + else: + eval_summary = train_utils.log_eval_summary( + step=step, + eval_metrics=eval_metrics_all, + extra_eval_summary=eval_vqa_metrics_all, + writer=writer, + prefix='random_retrieve_' + ds_name, + ) + # autoregressive eval + eval_metrics_all = [] + eval_vqa_metrics_all = {} + for step_id in range(steps_per_eval): + eval_batch = next(dataset.valid_iter[ds_name]) + eval_batch['device_id'] = jnp.arange( + eval_batch['encoder_input_image'].shape[0] + ) + logging.log_first_n( + logging.INFO, 'Peforming eval with autoregressive decode', 3 + ) + e_metrics, retr_top_image, predicted_logits = ( + eval_step_autoregressive_decoding_pmapped( + train_state=train_state, batch=eval_batch + ) + ) + eval_metrics_all.append(train_utils.unreplicate_and_get(e_metrics)) + # add vqa accuracy metric if it's a vqa dataset + if config.model.get('qa', False): + eval_vqa_metric_fn = model.get_vqa_metrics( + predicted_logits, eval_batch + ) + eval_vqa_metrics = eval_vqa_metric_fn.compute() + for key in eval_vqa_metrics: + eval_vqa_metrics_all.setdefault(key, 0.0) + eval_vqa_metrics_all[key] += eval_vqa_metrics[key] + for key in eval_vqa_metrics_all: + eval_vqa_metrics_all[key] /= float(steps_per_eval) + eval_summary = train_utils.log_eval_summary( + step=step, + eval_metrics=eval_metrics_all, + extra_eval_summary=eval_vqa_metrics_all, + writer=writer, + prefix=ds_name + '_autoregressive', + ) + + chrono.resume() + gc.collect() + ##################### CHECKPOINTING ########################### + if not config.checkpoint: + continue + elif step % checkpoint_steps == 0 and step > 0 or (step == total_steps): + logging.info('Save checkpoint!') + chrono.pause(wait_for=(train_state.params, train_state.opt_state)) + with report_progress.timed('checkpoint'): + if lead_host: + logging.info('checkpointing (training step: %d)', step) + # Take the first replica. + unrep_train_state = jax_utils.unreplicate(train_state) + unrep_train_state, mem = trainer_utils.pop_axes_names( + unrep_train_state, axes_name='memory' + ) + metadata = unrep_train_state.metadata + metadata['chrono'] = chrono.save() + unrep_train_state.replace(metadata=metadata) # pytype: disable=attribute-error + train_utils.save_checkpoint(workdir, unrep_train_state) + del unrep_train_state, mem + chrono.resume() + logging.info('Checkpoint saved!') + + # Wait until computations are done before exiting. + train_utils.barrier_across_hosts() + + # Return the train and eval summary after last step. + return train_state, train_summary, eval_summary diff --git a/scenic/projects/knowledge_visual_language/trainer_utils.py b/scenic/projects/knowledge_visual_language/trainer_utils.py new file mode 100644 index 000000000..53ecd45ff --- /dev/null +++ b/scenic/projects/knowledge_visual_language/trainer_utils.py @@ -0,0 +1,379 @@ +"""Train/eval/model utility functions.""" + +import dataclasses +import operator +import re +from typing import Any, Dict, List, Mapping, Optional, Tuple, Union + +from absl import logging +from big_vision import utils as bv_utils +from big_vision.models import vit as vit_model +from clu import metric_writers +import flax +from flax import jax_utils +from flax.core import frozen_dict +import jax +import jax.numpy as jnp +import ml_collections +import optax +from scenic.dataset_lib import dataset_utils +from scenic.dataset_lib import datasets +from scenic.projects.knowledge_visual_language.models import constants +from scenic.projects.t5 import model as t5_model +from scenic.train_lib import train_utils +from scenic.train_lib.train_utils import TrainState + +# Note this list must be in the exact order of the inputs required by the model. +PyTree = Union[Mapping[str, Mapping], Any] + + +def froze_param_optax( + params: optax.Params, + tx: optax.GradientTransformation, + frozen_patterns: Optional[List[str]] = None, + not_frozen_patterns: Optional[List[str]] = None, +) -> optax.GradientTransformation: + r"""change optax optimizer to not optimize frozen parameter. + + Args: + params: model parameter in FrozenDict (Tree) + tx: optax GradientTransform function + frozen_patterns: a list of re patterns for frozen parameter + not_frozen_patterns: a tupe of re patterns for trainable parameter with + scale + + Returns: + optax GradientTransform function that mask out frozen parameters. + + Example: + class Encoder(nn.Module): + @nn.compact + def __call__(self, x, train=True): + h = nn.Dense(8, name='dense_init')(x) + return nn.Dense(1, name='dense_output')(h) + + ......(after model initialization) + tx = optax.adamw(learning_rate=1e-3, weight_decay=1e-2) + tx = froze_param_optax(params=params, tx = tx,\ + frozen_patterns=["params/dense_init/.*"]) + opt_state = tx.init(params) + ...... + """ + if frozen_patterns and not_frozen_patterns is None: + frozen_masks = bv_utils.make_mask_trees(params, frozen_patterns) + frozen_mask = jax.tree_map(lambda *bools: any(bools), *frozen_masks) + not_frozen_mask = jax.tree_map(operator.not_, frozen_mask) + tx = optax.chain( + optax.masked(tx, not_frozen_mask), + optax.masked(optax.set_to_zero(), frozen_mask)) + elif not_frozen_patterns: + not_frozen_keys = [] + scale_vals = [] + for pattern, val in not_frozen_patterns: + if frozen_patterns is not None and pattern in frozen_patterns: + continue + not_frozen_keys += [pattern] + scale_vals += [val] + not_frozen_masks = bv_utils.make_mask_trees(params, not_frozen_keys) + not_frozen_mask = jax.tree_map(lambda *bools: any(bools), *not_frozen_masks) + frozen_mask = jax.tree_map(operator.not_, not_frozen_mask) + scale_txs = [ + optax.masked(optax.scale(scale_val), mask) + for scale_val, mask in zip(scale_vals, not_frozen_masks) + ] + tx = optax.chain( + optax.masked(tx, not_frozen_mask), + optax.masked(optax.set_to_zero(), frozen_mask), *scale_txs) + return tx + + +def update_config(config: ml_collections.ConfigDict, meta_data: Dict[str, Any]): + config.num_train_examples = meta_data['num_train_examples'] + steps_per_epoch = config.num_train_examples // config.batch_size + config.lr_configs.total_steps = int(config.num_training_epochs * + steps_per_epoch) + + +def get_dataset( + config: ml_collections.ConfigDict, + data_rng: jnp.ndarray, + *, + dataset_service_address: Optional[str] = None, + dataset_name: Optional[str] = None, + dataset_configs: Optional[ml_collections.ConfigDict] = None +) -> dataset_utils.Dataset: + """Creates dataset. + + Copy from scenic.train_lib.train_utils.get_dataset. Only use` + this alternative function when your dataset is not registered in + secnic library, and you need to add dependency into BUILD. + + By default, the values in the config file are used. + However, if the optional `dataset_name` and `dataset_configs` are passed, + those are used instead. + + Args: + config: The configuration of the experiment. + data_rng: Random number generator key to use for the dataset. + dataset_service_address: Used when using the tf.data.experimental.service + dataset_name: Name of dataset to load, if not reading from the config. + dataset_configs: Configuration of the dataset, if not reading directly from + the config. + + Returns: + A dataset_utils.Dataset object. + """ + device_count = jax.device_count() + logging.info('device_count: %d', device_count) + logging.info('num_hosts : %d', jax.process_count()) + logging.info('host_id : %d', jax.process_index()) + + dataset_name = dataset_name or config.dataset_name + dataset_builder = datasets.get_dataset(dataset_name) + + batch_size = config.batch_size + if batch_size % device_count > 0: + raise ValueError(f'Batch size ({batch_size}) must be divisible by the ' + f'number of devices ({device_count})') + + eval_batch_size = config.get('eval_batch_size', batch_size) + if eval_batch_size % device_count > 0: + raise ValueError(f'Eval batch size ({eval_batch_size}) must be divisible ' + f'by the number of devices ({device_count})') + + local_batch_size = batch_size // jax.process_count() + eval_local_batch_size = eval_batch_size // jax.process_count() + device_batch_size = batch_size // device_count + logging.info('local_batch_size : %d', local_batch_size) + logging.info('device_batch_size : %d', device_batch_size) + + shuffle_seed = config.get('shuffle_seed', None) + if dataset_service_address and shuffle_seed is not None: + raise ValueError('Using dataset service with a random seed causes each ' + 'worker to produce exactly the same data. Add ' + 'config.shuffle_seed = None to your config if you want ' + 'to run with dataset service.') + + dataset_configs = dataset_configs or config.get('dataset_configs') + dataset = dataset_builder( + batch_size=local_batch_size, + eval_batch_size=eval_local_batch_size, + num_shards=jax.local_device_count(), + dtype_str=config.data_dtype_str, + rng=data_rng, + shuffle_seed=shuffle_seed, + dataset_configs=dataset_configs, + dataset_service_address=dataset_service_address) + return dataset + + +@dataclasses.dataclass +class SummaryBuilder: + """A helper class to build the summary over the training iterations.""" + metrics: List[Dict[str, Tuple[float, int]]] + extra_logs: List[Dict[str, Any]] + + def update(self, metrics_update, extra_logs_update): + """Update with the given per-step metrics.""" + self.metrics.append(metrics_update) + self.extra_logs.append(extra_logs_update) + + def write(self, writer: metric_writers.MetricWriter, step: int): + """Write to the given writer and training step. + + After writing, the state gets reset. + + Args: + writer: The summary will be written with this writer. + step: The current training step. + + Returns: + The summary since the last write. + """ + summary = train_utils.log_train_summary( + step=step, + train_metrics=jax.tree_util.tree_map(train_utils.unreplicate_and_get, + self.metrics), + extra_training_logs=jax.tree_util.tree_map( + train_utils.unreplicate_and_get, self.extra_logs), + writer=writer, + key_separator='/') + self.metrics = [] + self.extra_logs = [] + return summary + + +def all_gather_and_unreplicate(tensor): + return jax_utils.unreplicate( + jax.pmap(lambda x: jax.lax.all_gather(x, 'batch'), 'batch')(tensor)) + + +def replace_dict(model: PyTree, + restored: PyTree, + ckpt_prefix_path: Optional[List[str]] = None, + model_prefix_path: Optional[List[str]] = None, + name_mapping: Optional[Mapping[str, str]] = None, + skip_regex: Optional[str] = None) -> PyTree: + """Replaces values in model dictionary with restored ones from checkpoint.""" + model = flax.core.unfreeze(model) # pytype: disable=wrong-arg-types + restored = flax.core.unfreeze(restored) # pytype: disable=wrong-arg-types + + if ckpt_prefix_path: + for p in ckpt_prefix_path: + restored = restored[p] + + if model_prefix_path: + for p in reversed(model_prefix_path): + restored = {p: restored} + + # Flatten nested parameters to a dict of str -> tensor. Keys are tuples + # from the path in the nested dictionary to the specific tensor. E.g., + # {'a1': {'b1': t1, 'b2': t2}, 'a2': t3} + # -> {('a1', 'b1'): t1, ('a1', 'b2'): t2, ('a2',): t3}. + restored_flat = flax.traverse_util.flatten_dict( + dict(restored), keep_empty_nodes=True) + model_flat = flax.traverse_util.flatten_dict( + dict(model), keep_empty_nodes=True) + + for m_key, m_params in restored_flat.items(): + # pytype: disable=attribute-error + for name, to_replace in name_mapping.items(): + m_key = tuple(to_replace if k == name else k for k in m_key) + # pytype: enable=attribute-error + m_key_str = '/'.join(m_key) + if m_key not in model_flat: + logging.warning('%s in checkpoint doesn\'t exist in model. Skip.', + m_key_str) + continue + if skip_regex and re.findall(skip_regex, m_key_str): + logging.info('Skip loading parameter %s.', m_key_str) + continue + logging.info('Loading %s from checkpoint into model', m_key_str) + model_flat[m_key] = m_params + + return flax.core.freeze(flax.traverse_util.unflatten_dict(model_flat)) + + +def pop_axes_names( + train_state: TrainState, + axes_name: str = 'params_axes') -> Tuple[TrainState, Optional[Any]]: + """Removes axes_names from model_state for a train state. + + Args: + train_state: Training state. + axes_name: the string specifying the name in the model_state + + Returns: + New train state without axes_names in model_state, axes_names metadata if it + was removed (so it can be re-added). + """ + model_state = train_state.model_state + if axes_name in train_state.model_state: + model_state, params_axes = frozen_dict.freeze(model_state).pop(axes_name) + return train_state.replace(model_state=model_state), params_axes + else: + return train_state, None + + +def re_add_axis_names(train_state: TrainState, + params_axes: Any, + axes_name: str = 'params_axes') -> TrainState: + """Adds axes_names to model_state for a train state. + + Args: + train_state: Training state. + params_axes: Model axes metadata to re-add. + axes_name: the string specifying the name in the model_state + + Returns: + New train state without axes_names in model_state, axes_names metadata if it + was removed (so it can be re-added). + """ + if params_axes: + model_state = frozen_dict.unfreeze(train_state.model_state) + model_state[axes_name] = params_axes + return train_state.replace(model_state=frozen_dict.freeze(model_state)) + else: + return train_state + + +def load_key_params(params: constants.PyTree, + config: ml_collections.ConfigDict): + """Load T5 & ViT for Key Encoders.""" + t5_params = t5_model.load_pretrained_weights(config.model.t5_name) + if 'key_text_encoder' in params: + params = replace_dict( + params, + t5_params, + ckpt_prefix_path=['params', 't5_module', 'encoder'], + model_prefix_path=['key_text_encoder'], + name_mapping={}) + return params + + +def load_text_params(params: constants.PyTree, + config: ml_collections.ConfigDict): + """Load T5 params from a checkpoint.""" + t5_params = t5_model.load_pretrained_weights(config.model.t5_name) + logging.info('T5 params are:') + logging.info(jax.tree_util.tree_map(lambda x: x.shape, t5_params)) + # first load the shared token embeddings + + params = replace_dict( + params, + t5_params, + ckpt_prefix_path=['params', 't5_module', 'token_embedder'], + model_prefix_path=['shared_token_embedder'], + name_mapping={}) + + # then load the encoder and decoder weights + params = replace_dict( + params, + t5_params, + ckpt_prefix_path=['params', 't5_module', 'encoder'], + model_prefix_path=['text_encoder'], + name_mapping={}) + + params = replace_dict( + params, + t5_params, + ckpt_prefix_path=['params', 't5_module', 'encoder'], + model_prefix_path=['fusion_encoder'], + name_mapping={}) + + params = replace_dict( + params, + t5_params, + ckpt_prefix_path=['params', 't5_module', 'encoder'], + model_prefix_path=['query_head'], + name_mapping={}) + + params = replace_dict( + params, + t5_params, + ckpt_prefix_path=['params', 't5_module', 'encoder'], + model_prefix_path=['key_head'], + name_mapping={}) + + params = replace_dict( + params, + t5_params, + ckpt_prefix_path=['params', 't5_module', 'decoder'], + model_prefix_path=['out_decoder', 'decoder_module'], + name_mapping={}) + + return params + + +def load_visual_params(params: constants.PyTree, + config: ml_collections.ConfigDict): + """Load encoder parameters.""" + load_params = vit_model.load( + init_params=params.get('img_encoder'), + init_file=config.model.vit_model_path, + model_cfg='', + dont_load='MAPHead(.+)|head(.+)') + + params = replace_dict( + params, load_params, model_prefix_path=['img_encoder'], name_mapping={}) + return params