v1.0

examples/MMPT/mmpt/losses/fairseqmmloss.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+TODO (huxu): a general fairseq criterion for all your pre-defined losses.
+"""
+
+from fairseq.criterions import FairseqCriterion, register_criterion
+from fairseq.logging import metrics
+
+
+@register_criterion("mmloss")
+class MMCriterion(FairseqCriterion):
+    def __init__(self, task):
+        super().__init__(task)
+        # TODO (huxu): wrap forward call of loss_fn and eval_fn into task.
+        self.mmtask = task.mmtask
+
+    def forward(self, model, sample):
+        """Compute the loss for the given sample.
+        Returns a tuple with three elements:
+        1) the loss
+        2) the sample size, which is used as the denominator for the gradient
+        3) logging outputs to display while training
+        """
+        outputs = self.mmtask(model, sample)
+
+        loss, loss_scalar, max_len, batch_size, sample_size = (
+            outputs["loss"],
+            outputs["loss_scalar"],
+            outputs["max_len"],
+            outputs["batch_size"],
+            outputs["sample_size"],
+        )
+
+        logging_output = {
+            "loss": loss_scalar,
+            "ntokens": max_len * batch_size,  # dummy report.
+            "nsentences": batch_size,  # dummy report.
+            "sample_size": sample_size,
+        }
+
+        return loss, 1, logging_output
+
+    @staticmethod
+    def reduce_metrics(logging_outputs) -> None:
+        """Aggregate logging outputs from data parallel training."""
+        """since we use NCE, our actual batch_size is 1 per GPU.
+        Then we take the mean of each worker."""
+        loss_sum = sum(log.get("loss", 0.0) for log in logging_outputs)
+        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
+        metrics.log_scalar("loss", loss_sum / sample_size, round=3)
+
+    @staticmethod
+    def logging_outputs_can_be_summed() -> bool:
+        """
+        Whether the logging outputs returned by `forward` can be summed
+        across workers prior to calling `reduce_metrics`. Setting this
+        to True will improves distributed training speed.
+        """
+        return True

examples/MMPT/mmpt/losses/loss.py

+# Copyright (c) Facebook, Inc. All Rights Reserved
+
+import torch
+
+from torch import nn
+
+
+class Loss(object):
+    def __call__(self, *args, **kwargs):
+        raise NotImplementedError
+
+
+# Dummy Loss for testing.
+class DummyLoss(Loss):
+    def __init__(self):
+        self.loss = nn.CrossEntropyLoss()
+
+    def __call__(self, logits, targets, **kwargs):
+        return self.loss(logits, targets)
+
+
+class DummyK400Loss(Loss):
+    """dummy k400 loss for MViT."""
+    def __init__(self):
+        self.loss = nn.CrossEntropyLoss()
+
+    def __call__(self, logits, targets, **kwargs):
+        return self.loss(
+            logits, torch.randint(0, 400, (logits.size(0),), device=logits.device))
+
+
+class CrossEntropy(Loss):
+    def __init__(self):
+        self.loss = nn.CrossEntropyLoss()
+
+    def __call__(self, logits, targets, **kwargs):
+        return self.loss(logits.reshape(-1, logits.size(-1)), targets.reshape(-1))
+
+
+class ArgmaxCrossEntropy(Loss):
+    def __init__(self):
+        self.loss = nn.CrossEntropyLoss()
+
+    def __call__(self, logits, targets, **kwargs):
+        return self.loss(logits, targets.argmax(dim=1))
+
+
+class BCE(Loss):
+    def __init__(self):
+        self.loss = nn.BCEWithLogitsLoss()
+
+    def __call__(self, logits, targets, **kwargs):
+        targets = targets.squeeze(0)
+        return self.loss(logits, targets)
+
+
+class NLGLoss(Loss):
+    def __init__(self):
+        self.loss = nn.CrossEntropyLoss()
+
+    def __call__(self, logits, text_label, **kwargs):
+        targets = text_label[text_label != -100]
+        return self.loss(logits, targets)
+
+
+class MSE(Loss):
+    def __init__(self):
+        self.loss = nn.MSELoss()
+
+    def __call__(self, logits, targets, **kwargs):
+        return self.loss(logits, targets)
+
+
+class L1(Loss):
+    def __init__(self):
+        self.loss = nn.L1Loss()
+
+    def __call__(self, logits, targets, **kwargs):
+        return self.loss(logits, targets)
+
+
+class SmoothL1(Loss):
+    def __init__(self):
+        self.loss = nn.SmoothL1Loss()
+
+    def __call__(self, logits, targets, **kwargs):
+        return self.loss(logits, targets)

examples/MMPT/mmpt/losses/nce.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+softmax-based NCE loss, used by this project.
+"""
+
+import torch
+
+from torch import nn
+
+from .loss import Loss
+
+
+class NCE(Loss):
+    def __init__(self):
+        # TODO (huxu): define temperature.
+        self.loss = nn.CrossEntropyLoss()
+
+    def __call__(self, align_scores, **kargs):
+        # note: we reuse the same shape as cls head in BERT (batch_size, 2)
+        # but NCE only needs one logits.
+        # (so we drop all weights in the second neg logits.)
+        align_scores = align_scores[:, :1]
+        # duplicate negative examples
+        batch_size = align_scores.size(0) // 2
+        pos_scores = align_scores[:batch_size]
+        neg_scores = align_scores[batch_size:].view(1, batch_size).repeat(
+            batch_size, 1)
+        scores = torch.cat([pos_scores, neg_scores], dim=1)
+        return self.loss(
+            scores,
+            torch.zeros(
+                (batch_size,),
+                dtype=torch.long,
+                device=align_scores.device),
+        )
+
+
+class T2VContraLoss(Loss):
+    """NCE for MM joint space, on softmax text2video matrix.
+    """
+    def __init__(self):
+        # TODO (huxu): define temperature.
+        self.loss = nn.CrossEntropyLoss()
+
+    def __call__(self, pooled_video, pooled_text, **kargs):
+        batch_size = pooled_video.size(0)
+        logits = torch.mm(pooled_text, pooled_video.transpose(1, 0))
+        targets = torch.arange(
+            batch_size,
+            dtype=torch.long,
+            device=pooled_video.device)
+        return self.loss(logits, targets)
+
+
+class V2TContraLoss(Loss):
+    """NCE for MM joint space, with softmax on video2text matrix."""
+
+    def __init__(self):
+        # TODO (huxu): define temperature.
+        self.loss = nn.CrossEntropyLoss()
+
+    def __call__(self, pooled_video, pooled_text, **kargs):
+        batch_size = pooled_video.size(0)
+        logits = torch.mm(pooled_video, pooled_text.transpose(1, 0))
+        targets = torch.arange(
+            batch_size,
+            dtype=torch.long,
+            device=pooled_video.device)
+        return self.loss(logits, targets)
+
+
+class MMContraLoss(Loss):
+    def __init__(self):
+        self.loss = nn.CrossEntropyLoss()
+
+    def __call__(self, pooled_video, pooled_text, **kwargs):
+        logits_per_video = pooled_video @ pooled_text.t()
+        logits_per_text = pooled_text @ pooled_video.t()
+
+        targets = torch.arange(
+            pooled_video.size(0),
+            dtype=torch.long,
+            device=pooled_video.device)
+        loss_video = self.loss(logits_per_video, targets)
+        loss_text = self.loss(logits_per_text, targets)
+        return loss_video + loss_text
+
+
+class MTM(Loss):
+    """Combination of MFM and MLM."""
+
+    def __init__(self):
+        self.loss = nn.CrossEntropyLoss()
+
+    def __call__(
+        self,
+        video_logits,
+        text_logits,
+        video_label,
+        text_label,
+        **kwargs
+    ):
+        text_logits = torch.cat([
+            text_logits,
+            torch.zeros(
+                (text_logits.size(0), 1), device=text_logits.device)
+        ], dim=1)
+        vt_logits = torch.cat([video_logits, text_logits], dim=0)
+        # loss for video.
+        video_label = torch.zeros(
+            (video_logits.size(0),),
+            dtype=torch.long,
+            device=video_logits.device
+        )
+
+        # loss for text.
+        text_label = text_label.reshape(-1)
+        labels_mask = text_label != -100
+        selected_text_label = text_label[labels_mask]
+
+        vt_label = torch.cat([video_label, selected_text_label], dim=0)
+        return self.loss(vt_logits, vt_label)
+
+
+class MFMMLM(Loss):
+    """Combination of MFM and MLM."""
+
+    def __init__(self):
+        self.loss = nn.CrossEntropyLoss()
+
+    def __call__(
+        self,
+        video_logits,
+        text_logits,
+        video_label,
+        text_label,
+        **kwargs
+    ):
+        # loss for video.
+        video_label = torch.zeros(
+            (video_logits.size(0),),
+            dtype=torch.long,
+            device=video_logits.device
+        )
+        masked_frame_loss = self.loss(video_logits, video_label)
+
+        # loss for text.
+        text_label = text_label.reshape(-1)
+        labels_mask = text_label != -100
+        selected_text_label = text_label[labels_mask]
+        masked_lm_loss = self.loss(text_logits, selected_text_label)
+        return masked_frame_loss + masked_lm_loss

examples/MMPT/mmpt/models/__init__.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+from .mmfusion import *
+from .transformermodel import *
+from .mmfusionnlg import *
+
+try:
+    from .fairseqmmmodel import *
+except ImportError:
+    pass
+
+try:
+    from .expmmfusion import *
+except ImportError:
+    pass

examples/MMPT/mmpt/models/fairseqmmmodel.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from fairseq.models import (
+    BaseFairseqModel,
+    register_model,
+    register_model_architecture
+)
+
+
+@register_model("mmmodel")
+class FairseqMMModel(BaseFairseqModel):
+    """a fairseq wrapper of model built by `task`."""
+
+    @classmethod
+    def build_model(cls, args, task):
+        return FairseqMMModel(task.mmtask.model)
+
+    def __init__(self, mmmodel):
+        super().__init__()
+        self.mmmodel = mmmodel
+
+    def forward(self, *args, **kwargs):
+        return self.mmmodel(*args, **kwargs)
+
+    def upgrade_state_dict_named(self, state_dict, name):
+
+        super().upgrade_state_dict_named(state_dict, name)
+
+        keys_to_delete = []
+
+        for key in state_dict:
+            if key not in self.state_dict():
+                keys_to_delete.append(key)
+        for key in keys_to_delete:
+            print("[INFO]", key, "not used anymore.")
+            del state_dict[key]
+
+        # copy any newly defined parameters.
+        for key in self.state_dict():
+            if key not in state_dict:
+                print("[INFO] adding", key)
+                state_dict[key] = self.state_dict()[key]
+
+
+# a dummy arch, we config the model.
+@register_model_architecture("mmmodel", "mmarch")
+def mmarch(args):
+    pass

examples/MMPT/mmpt/models/mmfusion.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Copyright (c) Facebook, Inc. All Rights Reserved
+
+
+import torch
+
+from torch import nn
+
+try:
+    from transformers import AutoConfig, AutoTokenizer
+except ImportError:
+    pass
+
+from . import transformermodel
+
+
+class MMPTModel(nn.Module):
+    """An e2e wrapper of inference model.
+    """
+    @classmethod
+    def from_pretrained(cls, config, checkpoint="checkpoint_best.pt"):
+        import os
+        from ..utils import recursive_config
+        from ..tasks import Task
+        config = recursive_config(config)
+        mmtask = Task.config_task(config)
+        checkpoint_path = os.path.join(config.eval.save_path, checkpoint)
+        mmtask.build_model(checkpoint=checkpoint_path)
+        # TODO(huxu): make the video encoder configurable.
+        from ..processors.models.s3dg import S3D
+        video_encoder = S3D('pretrained_models/s3d_dict.npy', 512)
+        video_encoder.load_state_dict(
+            torch.load('pretrained_models/s3d_howto100m.pth'))
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(
+            config.dataset.bert_name, use_fast=config.dataset.use_fast
+        )
+        from ..processors import Aligner
+        aligner = Aligner(config.dataset)
+        return (
+            MMPTModel(config, mmtask.model, video_encoder),
+            tokenizer,
+            aligner
+        )
+
+    def __init__(self, config, model, video_encoder, **kwargs):
+        super().__init__()
+        self.max_video_len = config.dataset.max_video_len
+        self.video_encoder = video_encoder
+        self.model = model
+
+    def forward(self, video_frames, caps, cmasks, return_score=False):
+        bsz = video_frames.size(0)
+        assert bsz == 1, "only bsz=1 is supported now."
+        seq_len = video_frames.size(1)
+        video_frames = video_frames.view(-1, *video_frames.size()[2:])
+        vfeats = self.video_encoder(video_frames.permute(0, 4, 1, 2, 3))
+        vfeats = vfeats['video_embedding']
+        vfeats = vfeats.view(bsz, seq_len, vfeats.size(-1))
+        padding = torch.zeros(
+            bsz, self.max_video_len - seq_len, vfeats.size(-1))
+        vfeats = torch.cat([vfeats, padding], dim=1)
+        vmasks = torch.cat([
+            torch.ones((bsz, seq_len), dtype=torch.bool),
+            torch.zeros((bsz, self.max_video_len - seq_len), dtype=torch.bool)
+            ],
+            dim=1
+        )
+        output = self.model(caps, cmasks, vfeats, vmasks)
+        if return_score:
+            output = {"score": torch.bmm(
+                output["pooled_video"][:, None, :],
+                output["pooled_text"][:, :, None]
+            ).squeeze(-1).squeeze(-1)}
+        return output
+
+
+class MMFusion(nn.Module):
+    """a MMPT wrapper class for MMBert style models.
+    TODO: move isolated mask to a subclass.
+    """
+    def __init__(self, config, **kwargs):
+        super().__init__()
+        transformer_config = AutoConfig.from_pretrained(
+            config.dataset.bert_name)
+        self.hidden_size = transformer_config.hidden_size
+        self.is_train = False
+        if config.dataset.train_path is not None:
+            self.is_train = True
+        # 0 means no iso; 1-12 means iso up to that layer.
+        self.num_hidden_layers = transformer_config.num_hidden_layers
+        self.last_iso_layer = 0
+        if config.dataset.num_iso_layer is not None:
+            self.last_iso_layer = config.dataset.num_iso_layer - 1 + 1
+
+        if config.model.mm_encoder_cls is not None:
+            mm_encoder_cls = getattr(transformermodel, config.model.mm_encoder_cls)
+            model_config = AutoConfig.from_pretrained(config.dataset.bert_name)
+            model_config.max_video_len = config.dataset.max_video_len
+            # TODO: a general way to add parameter for a model.
+            model_config.use_seg_emb = config.model.use_seg_emb
+            self.mm_encoder = mm_encoder_cls.from_pretrained(
+                config.dataset.bert_name, config=model_config)
+        elif config.model.video_encoder_cls is not None\
+                and config.model.text_encoder_cls is not None:
+            video_encoder_cls = getattr(transformermodel, config.model.video_encoder_cls)
+            model_config = AutoConfig.from_pretrained(config.dataset.bert_name)
+            model_config.max_video_len = config.dataset.max_video_len
+            # TODO: make each model a set of config class.
+            if hasattr(model_config, "num_layers"):
+                model_config.num_layers = config.model.num_hidden_video_layers
+            else:
+                model_config.num_hidden_layers = config.model.num_hidden_video_layers
+            self.video_encoder = video_encoder_cls.from_pretrained(
+                config.dataset.bert_name, config=model_config)
+            # exact same NLP model from Huggingface.
+            text_encoder_cls = getattr(transformermodel, config.model.text_encoder_cls)
+            self.text_encoder = text_encoder_cls.from_pretrained(
+                config.dataset.bert_name)
+        else:
+            raise ValueError("the encoder must be either MM or two backbones.")
+
+    def forward(
+        self,
+        caps,
+        cmasks,
+        vfeats,
+        vmasks,
+        **kwargs
+    ):
+        raise NotImplementedError(
+            "Please derive MMFusion module."
+        )
+
+    def _mm_on_the_fly(
+        self,
+        cmasks,
+        vmasks,
+        attention_mask
+    ):
+        """helper function for mask, seg_ids and token_type_ids."""
+        if attention_mask is None:
+            attention_mask = self._mm_attention_mask(cmasks, vmasks)
+
+        """
+        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+        | first sequence    | second sequence |
+        """
+        token_type_ids = torch.cat(
+            [
+                torch.zeros(
+                    (vmasks.size(0), vmasks.size(1) + 2),
+                    dtype=torch.long,
+                    device=vmasks.device,
+                ),
+                torch.ones(
+                    (cmasks.size(0), cmasks.size(1) - 2),
+                    dtype=torch.long,
+                    device=cmasks.device,
+                ),
+            ],
+            dim=1,
+        )
+        return attention_mask, token_type_ids
+
+    def _mm_attention_mask(self, cmasks, vmasks):
+        assert cmasks.size(0) == vmasks.size(0), "{}, {}, {}, {}".format(
+            str(cmasks.size()),
+            str(vmasks.size()),
+            str(cmasks.size(0)),
+            str(vmasks.size(0)),
+        )
+
+        mm_mask = torch.cat([cmasks[:, :1], vmasks, cmasks[:, 1:]], dim=1)
+        if self.last_iso_layer == 0:
+            # hard attention mask.
+            return mm_mask
+        else:
+            # a gpu iso mask; 0 : num_iso_layer is isolated;
+            # num_iso_layer: are MM-fused.
+            # make an iso layer
+            batch_size = cmasks.size(0)
+            iso_mask = self._make_iso_mask(batch_size, cmasks, vmasks)
+            mm_mask = mm_mask[:, None, :].repeat(1, mm_mask.size(-1), 1)
+            iso_mm_masks = []
+            # hard attention mask.
+            iso_mask = iso_mask[:, None, :, :].repeat(
+                1, self.last_iso_layer, 1, 1)
+            iso_mm_masks.append(iso_mask)
+            if self.last_iso_layer < self.num_hidden_layers:
+                mm_mask = mm_mask[:, None, :, :].repeat(
+                    1, self.num_hidden_layers - self.last_iso_layer, 1, 1
+                )
+                iso_mm_masks.append(mm_mask)
+            iso_mm_masks = torch.cat(iso_mm_masks, dim=1)
+            return iso_mm_masks
+
+    def _make_iso_mask(self, batch_size, cmasks, vmasks):
+        cls_self_mask = torch.cat(
+            [
+                torch.ones(
+                    (batch_size, 1), dtype=torch.bool, device=cmasks.device),
+                torch.zeros(
+                    (batch_size, cmasks.size(1) + vmasks.size(1) - 1),
+                    dtype=torch.bool, device=cmasks.device)
+            ], dim=1)
+
+        iso_video_mask = torch.cat(
+            [
+                # [CLS] is not used.
+                torch.zeros(
+                    (batch_size, 1), dtype=torch.bool, device=cmasks.device
+                ),
+                vmasks,
+                # assume to be 1.
+                cmasks[:, 1:2],
+                # 2 means [CLS] + [SEP]
+                torch.zeros(
+                    (batch_size, cmasks.size(1) - 2),
+                    dtype=torch.bool,
+                    device=cmasks.device,
+                ),
+            ],
+            dim=1,
+        )
+        iso_text_mask = torch.cat(
+            [
+                torch.zeros(
+                    (batch_size, 2 + vmasks.size(1)),
+                    dtype=torch.bool,
+                    device=cmasks.device,
+                ),  # [CLS] is not used.
+                cmasks[:, 2:],  # assume to be 1.
+            ],
+            dim=1,
+        )
+        cls_self_mask = cls_self_mask[:, None, :]
+        iso_video_mask = iso_video_mask[:, None, :].repeat(
+            1, vmasks.size(1) + 1, 1)
+        iso_text_mask = iso_text_mask[:, None, :].repeat(
+            1, cmasks.size(1) - 2, 1)
+        return torch.cat([cls_self_mask, iso_video_mask, iso_text_mask], dim=1)
+
+    def _pooling_vt_layer(
+        self,
+        layered_sequence_output,
+        cmasks,
+        vmasks
+    ):
+        layer_idx = self.last_iso_layer \
+                if self.last_iso_layer > 0 else self.num_hidden_layers
+        hidden_state = layered_sequence_output[layer_idx]
+        # also output pooled_video and pooled_text.
+        batch_size = cmasks.size(0)
+        # pool the modality.
+        text_offset = vmasks.size(1) + 2  # [CLS] + [SEP]
+        # video tokens + [SEP]
+        video_outputs = hidden_state[:, 1:text_offset]
+        video_attention_mask = torch.cat(
+            [
+                vmasks,
+                torch.ones(
+                    (batch_size, 1), dtype=torch.bool, device=vmasks.device),
+            ],
+            dim=1,
+        )
+        assert video_outputs.size(1) == video_attention_mask.size(1)
+        pooled_video = torch.sum(
+            video_outputs * video_attention_mask.unsqueeze(-1), dim=1
+        ) / video_attention_mask.sum(1, keepdim=True)
+        # pooled_video = torch.mean(video_outputs[0], dim=1)
+
+        # text tokens + [SEP]
+        text_attention_mask = cmasks[:, 2:]
+        text_outputs = hidden_state[:, text_offset:]
+        assert text_outputs.size(1) == text_attention_mask.size(1)
+        pooled_text = torch.sum(
+            text_outputs * text_attention_mask.unsqueeze(-1), dim=1
+        ) / text_attention_mask.sum(1, keepdim=True)
+        return pooled_video, pooled_text
+
+
+class MMFusionMFMMLM(MMFusion):
+    """forward function for MFM and MLM."""
+    def forward(
+        self,
+        caps,
+        cmasks,
+        vfeats,
+        vmasks,
+        attention_mask=None,
+        video_label=None,
+        text_label=None,
+        **kwargs
+    ):
+        output_hidden_states = False if self.is_train else True
+
+        target_vfeats, non_masked_frame_mask = None, None
+        if video_label is not None:
+            target_vfeats = vfeats.masked_select(
+                video_label.unsqueeze(-1)).view(
+                -1, vfeats.size(-1)
+            )
+            # mask video token.
+            vfeats[video_label] = 0.0
+            non_masked_frame_mask = vmasks.clone()
+            non_masked_frame_mask[video_label] = False
+
+        attention_mask, token_type_ids = self._mm_on_the_fly(
+            cmasks, vmasks, attention_mask)
+
+        outputs = self.mm_encoder(
+            input_ids=caps,
+            input_video_embeds=vfeats,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            masked_frame_labels=video_label,
+            target_video_hidden_states=target_vfeats,
+            non_masked_frame_mask=non_masked_frame_mask,
+            masked_lm_labels=text_label,
+            output_hidden_states=output_hidden_states,
+        )
+
+        video_logits, text_logits = outputs[0], outputs[1]
+
+        if self.is_train:  # return earlier for training.
+            return {
+                "video_logits": video_logits,
+                "text_logits": text_logits,
+            }
+
+        pooled_video, pooled_text = self._pooling_vt_layer(
+            outputs[2], cmasks, vmasks)
+        return {"pooled_video": pooled_video, "pooled_text": pooled_text}
+
+
+class MMFusionMTM(MMFusionMFMMLM):
+    def __init__(self, config, **kwargs):
+        super().__init__(config)
+        """
+        For reproducibility:
+        self.mm_encoder will be initialized then discarded.
+        """
+        from .transformermodel import MMBertForMTM
+        model_config = AutoConfig.from_pretrained(config.dataset.bert_name)
+        model_config.max_video_len = config.dataset.max_video_len
+        model_config.use_seg_emb = config.model.use_seg_emb
+        self.mm_encoder = MMBertForMTM.from_pretrained(
+            config.dataset.bert_name, config=model_config)
+
+
+class MMFusionShare(MMFusion):
+    """A retrival wrapper using mm_encoder as both video/text backbone.
+    TODO: move formally.
+    """
+    def forward(
+        self,
+        caps,
+        cmasks,
+        vfeats,
+        vmasks,
+        attention_mask=None,
+        video_label=None,
+        text_label=None,
+        output_hidden_states=False,
+        **kwargs
+    ):
+        pooled_video = self.forward_video(
+            vfeats,
+            vmasks,
+            caps,
+            cmasks,
+            output_hidden_states
+        )
+
+        pooled_text = self.forward_text(
+            caps,
+            cmasks,
+            output_hidden_states
+        )
+
+        return {"pooled_video": pooled_video, "pooled_text": pooled_text}
+
+    def forward_video(
+        self,
+        vfeats,
+        vmasks,
+        caps,
+        cmasks,
+        output_hidden_states=False,
+        **kwargs
+    ):
+        input_ids = caps[:, :2]
+
+        attention_mask = torch.cat([
+            cmasks[:, :1],
+            vmasks,
+            cmasks[:, 1:2]
+        ], dim=1)
+
+        token_type_ids = torch.zeros(
+            (vmasks.size(0), vmasks.size(1) + 2),
+            dtype=torch.long,
+            device=vmasks.device)
+
+        outputs = self.mm_encoder(
+            input_ids=input_ids,
+            input_video_embeds=vfeats,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            output_hidden_states=True
+        )
+        video_outputs = outputs[0]
+
+        if output_hidden_states:
+            return video_outputs
+
+        batch_size = cmasks.size(0)
+
+        video_attention_mask = torch.cat(
+            [
+                torch.zeros(
+                    (batch_size, 1), dtype=torch.bool, device=vmasks.device),
+                vmasks,
+                torch.ones(
+                    (batch_size, 1), dtype=torch.bool, device=vmasks.device),
+            ],
+            dim=1,
+        )
+        assert video_outputs.size(1) == video_attention_mask.size(1)
+
+        video_attention_mask = video_attention_mask.type(video_outputs.dtype) \
+            / video_attention_mask.sum(1, keepdim=True)
+
+        pooled_video = torch.bmm(
+            video_outputs.transpose(2, 1),
+            video_attention_mask.unsqueeze(2)
+        ).squeeze(-1)
+        return pooled_video  # video_outputs
+
+    def forward_text(
+        self,
+        caps,
+        cmasks,
+        output_hidden_states=False,
+        **kwargs
+    ):
+        input_ids = torch.cat([
+            caps[:, :1], caps[:, 2:],
+            ], dim=1)
+
+        attention_mask = torch.cat([
+            cmasks[:, :1],
+            cmasks[:, 2:]
+        ], dim=1)
+
+        token_type_ids = torch.cat([
+            torch.zeros(
+                (cmasks.size(0), 1),
+                dtype=torch.long,
+                device=cmasks.device),
+            torch.ones(
+                (cmasks.size(0), cmasks.size(1) - 2),
+                dtype=torch.long,
+                device=cmasks.device)
+            ], dim=1)
+
+        outputs = self.mm_encoder(
+            input_ids=input_ids,
+            input_video_embeds=None,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            output_hidden_states=True
+        )
+        text_outputs = outputs[0]
+
+        if output_hidden_states:
+            return text_outputs
+
+        batch_size = caps.size(0)
+        # text tokens + [SEP]
+        text_attention_mask = torch.cat([
+            torch.zeros(
+                (batch_size, 1), dtype=torch.bool, device=cmasks.device),
+            cmasks[:, 2:]
+        ], dim=1)
+
+        assert text_outputs.size(1) == text_attention_mask.size(1)
+
+        text_attention_mask = text_attention_mask.type(text_outputs.dtype) \
+            / text_attention_mask.sum(1, keepdim=True)
+
+        pooled_text = torch.bmm(
+            text_outputs.transpose(2, 1),
+            text_attention_mask.unsqueeze(2)
+        ).squeeze(-1)
+        return pooled_text  # text_outputs
+
+
+class MMFusionSeparate(MMFusionShare):
+    def forward_video(
+        self,
+        vfeats,
+        vmasks,
+        caps,
+        cmasks,
+        output_hidden_states=False,
+        **kwargs
+    ):
+        input_ids = caps[:, :2]
+
+        attention_mask = torch.cat([
+            cmasks[:, :1],
+            vmasks,
+            cmasks[:, 1:2]
+        ], dim=1)
+
+        token_type_ids = torch.zeros(
+            (vmasks.size(0), vmasks.size(1) + 2),
+            dtype=torch.long,
+            device=vmasks.device)
+
+        outputs = self.video_encoder(
+            input_ids=input_ids,
+            input_video_embeds=vfeats,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            output_hidden_states=True
+        )
+        video_outputs = outputs[0]
+
+        if output_hidden_states:
+            return video_outputs
+
+        batch_size = cmasks.size(0)
+
+        video_attention_mask = torch.cat(
+            [
+                torch.zeros(
+                    (batch_size, 1), dtype=torch.bool, device=vmasks.device),
+                vmasks,
+                torch.ones(
+                    (batch_size, 1), dtype=torch.bool, device=vmasks.device),
+            ],
+            dim=1,
+        )
+        assert video_outputs.size(1) == video_attention_mask.size(1)
+
+        video_attention_mask = video_attention_mask.type(video_outputs.dtype) \
+            / video_attention_mask.sum(1, keepdim=True)
+
+        pooled_video = torch.bmm(
+            video_outputs.transpose(2, 1),
+            video_attention_mask.unsqueeze(2)
+        ).squeeze(-1)
+        return pooled_video  # video_outputs
+
+    def forward_text(
+        self,
+        caps,
+        cmasks,
+        output_hidden_states=False,
+        **kwargs
+    ):
+        input_ids = torch.cat([
+            caps[:, :1], caps[:, 2:],
+            ], dim=1)
+
+        attention_mask = torch.cat([
+            cmasks[:, :1],
+            cmasks[:, 2:]
+        ], dim=1)
+        # different from sharing, we use all-0 type.
+        token_type_ids = torch.zeros(
+            (cmasks.size(0), cmasks.size(1) - 1),
+            dtype=torch.long,
+            device=cmasks.device)
+
+        outputs = self.text_encoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            output_hidden_states=True
+        )
+        text_outputs = outputs[0]
+
+        if output_hidden_states:
+            return text_outputs
+
+        batch_size = caps.size(0)
+        # text tokens + [SEP]
+        text_attention_mask = torch.cat([
+            torch.zeros(
+                (batch_size, 1), dtype=torch.bool, device=cmasks.device),
+            cmasks[:, 2:]
+        ], dim=1)
+
+        assert text_outputs.size(1) == text_attention_mask.size(1)
+
+        text_attention_mask = text_attention_mask.type(text_outputs.dtype) \
+            / text_attention_mask.sum(1, keepdim=True)
+
+        pooled_text = torch.bmm(
+            text_outputs.transpose(2, 1),
+            text_attention_mask.unsqueeze(2)
+        ).squeeze(-1)
+        return pooled_text  # text_outputs
+
+
+class MMFusionJoint(MMFusion):
+    """fine-tuning wrapper for retrival task."""
+
+    def forward(
+        self,
+        caps,
+        cmasks,
+        vfeats,
+        vmasks,
+        attention_mask=None,
+        video_label=None,
+        text_label=None,
+        **kwargs
+    ):
+        # TODO (huxu): other ways to do negative examples; move the following
+        # into your criterion forward.
+        output_hidden_states = True
+
+        attention_mask, token_type_ids = self._mm_on_the_fly(
+            cmasks, vmasks, attention_mask)
+
+        separate_forward_split = (
+            None if self.is_train else vmasks.size(1) + 2
+        )  # [CLS] + [SEP]
+
+        outputs = self.mm_encoder(
+            input_ids=caps,
+            input_video_embeds=vfeats,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            output_hidden_states=output_hidden_states,
+            separate_forward_split=separate_forward_split,
+        )
+
+        pooled_video, pooled_text = self._pooling_vt_layer(
+            outputs[2], cmasks, vmasks)
+        return {"pooled_video": pooled_video, "pooled_text": pooled_text}
+
+
+class MMFusionActionSegmentation(MMFusion):
+    """Fine-tuning wrapper for action segmentation.
+    TODO: rename this for VLM.
+    """
+    def forward(
+        self,
+        caps,
+        cmasks,
+        vfeats,
+        vmasks,
+        attention_mask=None,
+        **kwargs
+    ):
+        # ActionLocalization assume of batch_size=1, squeeze it.
+        caps = caps.view(-1, caps.size(-1))
+        cmasks = cmasks.view(-1, cmasks.size(-1))
+        vfeats = vfeats.view(-1, vfeats.size(2), vfeats.size(3))
+        vmasks = vmasks.view(-1, vmasks.size(-1))
+
+        # this may not cover all shapes of attention_mask.
+        attention_mask = attention_mask.view(
+            -1, attention_mask.size(2), attention_mask.size(3)) \
+            if attention_mask is not None else None
+
+        # TODO (huxu): other ways to do negative examples; move the following
+        # into your criterion forward.
+        output_hidden_states = True
+
+        #  video forwarding, text is dummy; never use attention_mask.
+        attention_mask, token_type_ids = self._mm_on_the_fly(
+            cmasks, vmasks, attention_mask)
+
+        logits = self.mm_encoder(
+            input_ids=caps,
+            input_video_embeds=vfeats,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            output_hidden_states=output_hidden_states,
+        )
+        return {"logits": logits[0][:, 1:vmasks.size(1)+1]}
+
+
+class MMFusionActionLocalization(MMFusion):
+    """fine-tuning model for retrival task."""
+
+    def __init__(self, config, **kwargs):
+        super().__init__(config)
+        tokenizer = AutoTokenizer.from_pretrained(
+            config.dataset.bert_name)
+        self.cls_token_id = tokenizer.cls_token_id
+        self.sep_token_id = tokenizer.sep_token_id
+        self.pad_token_id = tokenizer.pad_token_id
+
+    def forward(
+        self,
+        caps,
+        cmasks,
+        vfeats,
+        vmasks,
+        attention_mask=None,
+        **kwargs
+    ):
+        # ActionLocalization assume of batch_size=1, squeeze it.
+        caps = caps.squeeze(0)
+        cmasks = cmasks.squeeze(0)
+        vfeats = vfeats.squeeze(0)
+        vmasks = vmasks.squeeze(0)
+        attention_mask = attention_mask.squeeze(0) if attention_mask is not None else None
+
+        # TODO (huxu): other ways to do negative examples; move the following
+        # into your criterion forward.
+        output_hidden_states = True
+
+        # a len1 dummy video token.
+        dummy_vfeats = torch.zeros(
+            (caps.size(0), 1, vfeats.size(-1)), device=vfeats.device, dtype=vfeats.dtype)
+        dummy_vmasks = torch.ones(
+            (caps.size(0), 1), dtype=torch.bool,
+            device=vfeats.device)
+
+        dummy_caps = torch.LongTensor(
+            [[self.cls_token_id, self.sep_token_id,
+              self.pad_token_id, self.sep_token_id]],
+            ).to(caps.device).repeat(vfeats.size(0), 1)
+        dummy_cmasks = torch.BoolTensor(
+            [[0, 1, 0, 1]]  # pad are valid for attention.
+            ).to(caps.device).repeat(vfeats.size(0), 1)
+
+        #  video forwarding, text is dummy; never use attention_mask.
+        attention_mask, token_type_ids = self._mm_on_the_fly(
+            dummy_cmasks, vmasks, None)
+
+        outputs = self.mm_encoder(
+            input_ids=dummy_caps,
+            input_video_embeds=vfeats,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            output_hidden_states=output_hidden_states,
+        )
+
+        layer_idx = self.last_iso_layer \
+                if self.last_iso_layer > 0 else self.num_hidden_layers
+
+        video_seq = outputs[2][layer_idx][:, 1:vmasks.size(1)+1].masked_select(
+                vmasks.unsqueeze(-1)
+            ).view(-1, self.hidden_size)
+
+        # text forwarding, video is dummy
+        attention_mask, token_type_ids = self._mm_on_the_fly(
+            cmasks, dummy_vmasks, None)
+
+        outputs = self.mm_encoder(
+            input_ids=caps,
+            input_video_embeds=dummy_vfeats,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            output_hidden_states=output_hidden_states,
+        )
+
+        _, pooled_text = self._pooling_vt_layer(
+            outputs[2], cmasks, dummy_vmasks)
+        # this line is not right.
+        logits = torch.mm(video_seq, pooled_text.transpose(1, 0))
+        return {"logits": logits}
+
+
+# --------------- MMFusionSeparate for end tasks ---------------
+
+class MMFusionSeparateActionSegmentation(MMFusionSeparate):
+    """Fine-tuning wrapper for action segmentation."""
+    def forward(
+        self,
+        caps,
+        cmasks,
+        vfeats,
+        vmasks,
+        attention_mask=None,
+        **kwargs
+    ):
+        # ActionLocalization assume of batch_size=1, squeeze it.
+        caps = caps.view(-1, caps.size(-1))
+        cmasks = cmasks.view(-1, cmasks.size(-1))
+        vfeats = vfeats.view(-1, vfeats.size(2), vfeats.size(3))
+        vmasks = vmasks.view(-1, vmasks.size(-1))
+        logits = self.forward_video(
+            vfeats,
+            vmasks,
+            caps,
+            cmasks,
+            output_hidden_states=True
+        )
+        return {"logits": logits[:, 1:vmasks.size(1)+1]}
+
+
+class MMFusionSeparateActionLocalization(MMFusionSeparate):
+    def __init__(self, config, **kwargs):
+        super().__init__(config)
+        tokenizer = AutoTokenizer.from_pretrained(
+            config.dataset.bert_name)
+        self.cls_token_id = tokenizer.cls_token_id
+        self.sep_token_id = tokenizer.sep_token_id
+        self.pad_token_id = tokenizer.pad_token_id
+
+    def forward(
+        self,
+        caps,
+        cmasks,
+        vfeats,
+        vmasks,
+        **kwargs
+    ):
+        # ActionLocalization assume of batch_size=1, squeeze it.
+        caps = caps.squeeze(0)
+        cmasks = cmasks.squeeze(0)
+        vfeats = vfeats.squeeze(0)
+        vmasks = vmasks.squeeze(0)
+
+        # TODO (huxu): other ways to do negative examples; move the following
+        # into your criterion forward.
+        dummy_caps = torch.LongTensor(
+            [[self.cls_token_id, self.sep_token_id,
+              self.pad_token_id, self.sep_token_id]],
+            ).to(caps.device).repeat(vfeats.size(0), 1)
+        dummy_cmasks = torch.BoolTensor(
+            [[0, 1, 0, 1]]  # pad are valid for attention.
+            ).to(caps.device).repeat(vfeats.size(0), 1)
+
+        outputs = self.forward_video(
+            vfeats,
+            vmasks,
+            dummy_caps,
+            dummy_cmasks,
+            output_hidden_states=True
+        )
+
+        video_seq = outputs[:, 1:vmasks.size(1)+1].masked_select(
+                vmasks.unsqueeze(-1)
+            ).view(-1, self.hidden_size)
+
+        pooled_text = self.forward_text(
+            caps,
+            cmasks,
+            output_hidden_states=False
+        )
+
+        # this line is not right.
+        logits = torch.mm(video_seq, pooled_text.transpose(1, 0))
+        return {"logits": logits}
+
+
+class MMFusionShareActionLocalization(MMFusionShare):
+    def __init__(self, config, **kwargs):
+        super().__init__(config)
+        tokenizer = AutoTokenizer.from_pretrained(
+            config.dataset.bert_name)
+        self.cls_token_id = tokenizer.cls_token_id
+        self.sep_token_id = tokenizer.sep_token_id
+        self.pad_token_id = tokenizer.pad_token_id
+
+    def forward(
+        self,
+        caps,
+        cmasks,
+        vfeats,
+        vmasks,
+        **kwargs
+    ):
+        # ActionLocalization assume of batch_size=1, squeeze it.
+        caps = caps.squeeze(0)
+        cmasks = cmasks.squeeze(0)
+        vfeats = vfeats.squeeze(0)
+        vmasks = vmasks.squeeze(0)
+
+        # TODO (huxu): other ways to do negative examples; move the following
+        # into your criterion forward.
+        dummy_caps = torch.LongTensor(
+            [[self.cls_token_id, self.sep_token_id,
+              self.pad_token_id, self.sep_token_id]],
+            ).to(caps.device).repeat(vfeats.size(0), 1)
+        dummy_cmasks = torch.BoolTensor(
+            [[0, 1, 0, 1]]  # pad are valid for attention.
+            ).to(caps.device).repeat(vfeats.size(0), 1)
+
+        outputs = self.forward_video(
+            vfeats,
+            vmasks,
+            dummy_caps,
+            dummy_cmasks,
+            output_hidden_states=True
+        )
+
+        video_seq = outputs[:, 1:vmasks.size(1)+1].masked_select(
+                vmasks.unsqueeze(-1)
+            ).view(-1, self.hidden_size)
+
+        pooled_text = self.forward_text(
+            caps,
+            cmasks,
+            output_hidden_states=False
+        )
+
+        # this line is not right.
+        logits = torch.mm(video_seq, pooled_text.transpose(1, 0))
+        return {"logits": logits}

examples/MMPT/mmpt/models/mmfusionnlg.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors, Facebook AI Research authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Copyright (c) Facebook, Inc. All Rights Reserved
+
+
+import torch
+
+from torch.nn import functional as F
+
+from typing import Optional, Iterable
+
+try:
+    from transformers import BertPreTrainedModel
+    from transformers.modeling_bert import BertOnlyMLMHead
+
+    from transformers.file_utils import ModelOutput
+    from transformers.modeling_outputs import CausalLMOutput
+    from transformers.generation_utils import (
+        BeamHypotheses,
+        top_k_top_p_filtering
+    )
+except ImportError:
+    pass
+
+from .mmfusion import MMFusion
+from .transformermodel import MMBertModel
+from ..modules import VideoTokenMLP
+
+
+class MMFusionNLG(MMFusion):
+    def __init__(self, config, **kwargs):
+        super().__init__(config)
+        if config.model.max_decode_length is not None:
+            self.max_length = min(
+                config.model.max_decode_length,
+                config.dataset.max_len - config.dataset.max_video_len - 3
+            )
+        else:
+            self.max_length = \
+                config.dataset.max_len - config.dataset.max_video_len - 3
+        self.gen_param = config.gen_param if config.gen_param is not None \
+            else {}
+
+    def forward(
+        self,
+        caps,
+        cmasks,
+        vfeats,
+        vmasks,
+        attention_mask,
+        video_label=None,
+        text_label=None,
+        **kwargs
+    ):
+        """use pre-trained LM header for generation."""
+        attention_mask, token_type_ids = self._mm_on_the_fly(
+            cmasks, vmasks, attention_mask)
+
+        outputs = self.mm_encoder(
+            input_ids=caps,
+            input_video_embeds=vfeats,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            masked_lm_labels=text_label,
+        )
+        return {"logits": outputs[0]}
+
+    @torch.no_grad()
+    def generate(
+        self,
+        caps, cmasks, vfeats, vmasks,
+        attention_mask=None,
+        bos_token_id=None,
+        eos_token_id=None,
+        **kwargs
+    ):
+        # a simplified interface from
+        # https://huggingface.co/transformers/v3.4.0/_modules/transformers/generation_utils.html#GenerationMixin.generate
+
+        # caps now only have
+        # [CLS], [SEP] (for video) and [CLS] (as bos_token)
+        assert caps.size(1) == 3
+
+        attention_mask, token_type_ids = self._mm_on_the_fly(
+            cmasks, vmasks, attention_mask)
+
+        output = self.mm_encoder.generate(
+            input_ids=caps,
+            input_video_embeds=vfeats,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            max_length=self.max_length,
+            **self.gen_param
+        )
+        return output
+
+
+class MMBertForNLG(BertPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.bert = MMBertModel(config)
+        self.videomlp = VideoTokenMLP(config)
+        # we do not use `BertGenerationOnlyLMHead`
+        # because we can reuse pretraining.
+        self.cls = BertOnlyMLMHead(config)
+        self.hidden_size = config.hidden_size
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    def forward(
+        self,
+        input_ids=None,
+        input_video_embeds=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        masked_lm_labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        # similar to MMBertForMFMMLM without MFM.
+        video_tokens = self.videomlp(input_video_embeds)
+        outputs = self.bert(
+            input_ids,
+            video_tokens,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        prediction_scores = None
+        if masked_lm_labels is not None:
+            text_offset = input_video_embeds.size(1) + 1  # [CLS]
+            # recover caps format: [CLS] [SEP] text [SEP]
+            text_sequence_output = torch.cat(
+                [sequence_output[:, :1], sequence_output[:, text_offset:]],
+                dim=1
+            )
+
+            # only compute select tokens to training to speed up.
+            hidden_size = text_sequence_output.size(-1)
+            # masked_lm_labels = masked_lm_labels.reshape(-1)
+            labels_mask = masked_lm_labels != -100
+
+            selected_text_output = text_sequence_output.masked_select(
+                labels_mask.unsqueeze(-1)
+            ).view(-1, hidden_size)
+            prediction_scores = self.cls(selected_text_output)
+
+        if not return_dict:
+            output = (
+                prediction_scores,
+            ) + outputs[2:]
+            return output
+
+        # for generation.
+        text_offset = input_video_embeds.size(1) + 2  # [CLS]
+        text_sequence_output = sequence_output[:, text_offset:]
+        prediction_scores = self.cls(text_sequence_output)
+        return CausalLMOutput(
+            loss=None,
+            logits=prediction_scores,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        input_video_embeds,
+        attention_mask=None,
+        token_type_ids=None,
+        **model_kwargs
+    ):
+        # must return a dictionary.
+        seq_len = input_ids.size(1) + input_video_embeds.size(1)
+        if attention_mask is not None:
+            if len(attention_mask.size()) == 4:
+                attention_mask = attention_mask[:, :, :seq_len, :seq_len]
+            elif len(attention_mask.size()) == 3:
+                attention_mask = attention_mask[:, :seq_len, :seq_len]
+            else:
+                attention_mask = attention_mask[:, :seq_len]
+        if token_type_ids is not None:
+            token_type_ids = token_type_ids[:, :seq_len]
+
+        return {
+            "input_ids": input_ids,
+            "input_video_embeds": input_video_embeds,
+            "attention_mask": attention_mask,
+            "token_type_ids": token_type_ids,
+        }
+
+    @torch.no_grad()
+    def generate(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        decoder_input_ids: Optional[torch.LongTensor] = None,
+        max_length: Optional[int] = None,
+        min_length: Optional[int] = None,
+        do_sample: Optional[bool] = None,
+        early_stopping: Optional[bool] = None,
+        num_beams: Optional[int] = None,
+        temperature: Optional[float] = None,
+        top_k: Optional[int] = None,
+        top_p: Optional[float] = None,
+        repetition_penalty: Optional[float] = None,
+        bad_words_ids: Optional[Iterable[int]] = None,
+        bos_token_id: Optional[int] = None,
+        pad_token_id: Optional[int] = None,
+        eos_token_id: Optional[int] = None,
+        length_penalty: Optional[float] = None,
+        no_repeat_ngram_size: Optional[int] = None,
+        num_return_sequences: Optional[int] = None,
+        attention_mask: Optional[torch.LongTensor] = None,
+        decoder_start_token_id: Optional[int] = None,
+        use_cache: Optional[bool] = None,
+        **model_kwargs
+    ) -> torch.LongTensor:
+        r"""
+        Generates sequences for models with a language modeling head. The method currently supports greedy decoding,
+        beam-search decoding, sampling with temperature, sampling with top-k or nucleus sampling.
+        Adapted in part from `Facebook's XLM beam search code
+        <https://github.com/facebookresearch/XLM/blob/9e6f6814d17be4fe5b15f2e6c43eb2b2d76daeb4/src/model/transformer.py#L529>`__.
+        Apart from :obj:`input_ids` and :obj:`attention_mask`, all the arguments below will default to the value of the
+        attribute of the same name inside the :class:`~transformers.PretrainedConfig` of the model. The default values
+        indicated are the default values of those config.
+        Most of these parameters are explained in more detail in `this blog post
+        <https://huggingface.co/blog/how-to-generate>`__.
+        Parameters:
+            input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+                The sequence used as a prompt for the generation. If :obj:`None` the method initializes
+                it as an empty :obj:`torch.LongTensor` of shape :obj:`(1,)`.
+            decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+                initial input_ids for the decoder of encoder-decoder type models. If :obj:`None` then only
+                decoder_start_token_id is passed as the first token to the decoder.
+            max_length (:obj:`int`, `optional`, defaults to 20):
+                The maximum length of the sequence to be generated.
+            min_length (:obj:`int`, `optional`, defaults to 10):
+                The minimum length of the sequence to be generated.
+            do_sample (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Whether or not to use sampling ; use greedy decoding otherwise.
+            early_stopping (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Whether to stop the beam search when at least ``num_beams`` sentences are finished per batch or not.
+            num_beams (:obj:`int`, `optional`, defaults to 1):
+                Number of beams for beam search. 1 means no beam search.
+            temperature (:obj:`float`, `optional`, defaults tp 1.0):
+                The value used to module the next token probabilities.
+            top_k (:obj:`int`, `optional`, defaults to 50):
+                The number of highest probability vocabulary tokens to keep for top-k-filtering.
+            top_p (:obj:`float`, `optional`, defaults to 1.0):
+                If set to float < 1, only the most probable tokens with probabilities that add up to ``top_p`` or
+                higher are kept for generation.
+            repetition_penalty (:obj:`float`, `optional`, defaults to 1.0):
+                The parameter for repetition penalty. 1.0 means no penalty. See `this paper
+                <https://arxiv.org/pdf/1909.05858.pdf>`__ for more details.
+            pad_token_id (:obj:`int`, `optional`):
+                The id of the `padding` token.
+            bos_token_id (:obj:`int`, `optional`):
+                The id of the `beginning-of-sequence` token.
+            eos_token_id (:obj:`int`, `optional`):
+                The id of the `end-of-sequence` token.
+            length_penalty (:obj:`float`, `optional`, defaults to 1.0):
+                Exponential penalty to the length. 1.0 means no penalty.
+                Set to values < 1.0 in order to encourage the model to generate shorter sequences, to a value > 1.0 in
+                order to encourage the model to produce longer sequences.
+            no_repeat_ngram_size (:obj:`int`, `optional`, defaults to 0):
+                If set to int > 0, all ngrams of that size can only occur once.
+            bad_words_ids(:obj:`List[int]`, `optional`):
+                List of token ids that are not allowed to be generated. In order to get the tokens of the words that
+                should not appear in the generated text, use :obj:`tokenizer.encode(bad_word, add_prefix_space=True)`.
+            num_return_sequences(:obj:`int`, `optional`, defaults to 1):
+                The number of independently computed returned sequences for each element in the batch.
+            attention_mask (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+                Mask to avoid performing attention on padding token indices. Mask values are in ``[0, 1]``, 1 for
+                tokens that are not masked, and 0 for masked tokens.
+                If not provided, will default to a tensor the same shape as :obj:`input_ids` that masks the pad token.
+                `What are attention masks? <../glossary.html#attention-mask>`__
+            decoder_start_token_id (:obj:`int`, `optional`):
+                If an encoder-decoder model starts decoding with a different token than `bos`, the id of that token.
+            use_cache: (:obj:`bool`, `optional`, defaults to :obj:`True`):
+                Whether or not the model should use the past last key/values attentions (if applicable to the model) to
+                speed up decoding.
+            model_kwargs:
+                Additional model specific kwargs will be forwarded to the :obj:`forward` function of the model.
+        Return:
+            :obj:`torch.LongTensor` of shape :obj:`(batch_size * num_return_sequences, sequence_length)`:
+            The generated sequences. The second dimension (sequence_length) is either equal to :obj:`max_length` or
+            shorter if all batches finished early due to the :obj:`eos_token_id`.
+        Examples::
+            tokenizer = AutoTokenizer.from_pretrained('distilgpt2')   # Initialize tokenizer
+            model = AutoModelWithLMHead.from_pretrained('distilgpt2')    # Download model and configuration from S3 and cache.
+            outputs = model.generate(max_length=40)  # do greedy decoding
+            print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))
+            tokenizer = AutoTokenizer.from_pretrained('openai-gpt')   # Initialize tokenizer
+            model = AutoModelWithLMHead.from_pretrained('openai-gpt')    # Download model and configuration from S3 and cache.
+            input_context = 'The dog'
+            input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
+            outputs = model.generate(input_ids=input_ids, num_beams=5, num_return_sequences=3, temperature=1.5)  # generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog'
+            for i in range(3): #  3 output sequences were generated
+                print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))
+            tokenizer = AutoTokenizer.from_pretrained('distilgpt2')   # Initialize tokenizer
+            model = AutoModelWithLMHead.from_pretrained('distilgpt2')    # Download model and configuration from S3 and cache.
+            input_context = 'The dog'
+            input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
+            outputs = model.generate(input_ids=input_ids, max_length=40, temperature=0.7, num_return_sequences=3, do_sample=True)  # generate 3 candidates using sampling
+            for i in range(3): #  3 output sequences were generated
+                print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))
+            tokenizer = AutoTokenizer.from_pretrained('ctrl')   # Initialize tokenizer
+            model = AutoModelWithLMHead.from_pretrained('ctrl')    # Download model and configuration from S3 and cache.
+            input_context = 'Legal My neighbor is'  # "Legal" is one of the control codes for ctrl
+            input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
+            outputs = model.generate(input_ids=input_ids, max_length=50, temperature=0.7, repetition_penalty=1.2)  # generate sequences
+            print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))
+            tokenizer = AutoTokenizer.from_pretrained('gpt2')   # Initialize tokenizer
+            model = AutoModelWithLMHead.from_pretrained('gpt2')    # Download model and configuration from S3 and cache.
+            input_context = 'My cute dog'  # "Legal" is one of the control codes for ctrl
+            bad_words_ids = [tokenizer.encode(bad_word, add_prefix_space=True) for bad_word in ['idiot', 'stupid', 'shut up']]
+            input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
+            outputs = model.generate(input_ids=input_ids, max_length=100, do_sample=True, bad_words_ids=bad_words_ids)  # generate sequences without allowing bad_words to be generated
+        """
+
+        # We cannot generate if the model does not have a LM head
+        if self.get_output_embeddings() is None:
+            raise AttributeError(
+                "You tried to generate sequences with a model that does not have a LM Head."
+                "Please use another model class (e.g. `OpenAIGPTLMHeadModel`, `XLNetLMHeadModel`, `GPT2LMHeadModel`, `CTRLLMHeadModel`, `T5WithLMHeadModel`, `TransfoXLLMHeadModel`, `XLMWithLMHeadModel`, `BartForConditionalGeneration` )"
+            )
+
+        max_length = max_length if max_length is not None else self.config.max_length
+        min_length = min_length if min_length is not None else self.config.min_length
+        do_sample = do_sample if do_sample is not None else self.config.do_sample
+        early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        num_beams = num_beams if num_beams is not None else self.config.num_beams
+        temperature = temperature if temperature is not None else self.config.temperature
+        top_k = top_k if top_k is not None else self.config.top_k
+        top_p = top_p if top_p is not None else self.config.top_p
+        repetition_penalty = repetition_penalty if repetition_penalty is not None else self.config.repetition_penalty
+        bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id
+        pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
+        eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
+        length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty
+        no_repeat_ngram_size = (
+            no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size
+        )
+        bad_words_ids = bad_words_ids if bad_words_ids is not None else self.config.bad_words_ids
+        num_return_sequences = (
+            num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
+        )
+        decoder_start_token_id = (
+            decoder_start_token_id if decoder_start_token_id is not None else self.config.decoder_start_token_id
+        )
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]  # overriden by the input batch_size
+        else:
+            batch_size = 1
+
+        assert isinstance(max_length, int) and max_length > 0, "`max_length` should be a strictly positive integer."
+        assert isinstance(min_length, int) and min_length >= 0, "`min_length` should be a positive integer."
+        assert isinstance(do_sample, bool), "`do_sample` should be a boolean."
+        assert isinstance(early_stopping, bool), "`early_stopping` should be a boolean."
+        assert isinstance(use_cache, bool), "`use_cache` should be a boolean."
+        assert isinstance(num_beams, int) and num_beams > 0, "`num_beams` should be a strictly positive integer."
+        assert temperature > 0, "`temperature` should be strictly positive."
+        assert isinstance(top_k, int) and top_k >= 0, "`top_k` should be a positive integer."
+        assert 0 <= top_p <= 1, "`top_p` should be between 0 and 1."
+        assert repetition_penalty >= 1.0, "`repetition_penalty` should be >= 1."
+        assert input_ids is not None or (
+            isinstance(bos_token_id, int) and bos_token_id >= 0
+        ), "If input_ids is not defined, `bos_token_id` should be a positive integer."
+        assert pad_token_id is None or (
+            isinstance(pad_token_id, int) and (pad_token_id >= 0)
+        ), "`pad_token_id` should be a positive integer."
+        assert (eos_token_id is None) or (
+            isinstance(eos_token_id, int) and (eos_token_id >= 0)
+        ), "`eos_token_id` should be a positive integer."
+        assert length_penalty > 0, "`length_penalty` should be strictly positive."
+        assert (
+            isinstance(no_repeat_ngram_size, int) and no_repeat_ngram_size >= 0
+        ), "`no_repeat_ngram_size` should be a positive integer."
+        assert (
+            isinstance(num_return_sequences, int) and num_return_sequences > 0
+        ), "`num_return_sequences` should be a strictly positive integer."
+        assert (
+            bad_words_ids is None or isinstance(bad_words_ids, list) and isinstance(bad_words_ids[0], list)
+        ), "`bad_words_ids` is either `None` or a list of lists of tokens that should not be generated"
+
+        if input_ids is None:
+            assert isinstance(bos_token_id, int) and bos_token_id >= 0, (
+                "you should either supply a context to complete as `input_ids` input "
+                "or a `bos_token_id` (integer >= 0) as a first token to start the generation."
+            )
+            input_ids = torch.full(
+                (batch_size, 1),
+                bos_token_id,
+                dtype=torch.long,
+                device=next(self.parameters()).device,
+            )
+        else:
+            assert input_ids.dim() == 2, "Input prompt should be of shape (batch_size, sequence length)."
+
+        # not allow to duplicate outputs when greedy decoding
+        if do_sample is False:
+            if num_beams == 1:
+                # no_beam_search greedy generation conditions
+                assert (
+                    num_return_sequences == 1
+                ), "Greedy decoding will always produce the same output for num_beams == 1 and num_return_sequences > 1. Please set num_return_sequences = 1"
+
+            else:
+                # beam_search greedy generation conditions
+                assert (
+                    num_beams >= num_return_sequences
+                ), "Greedy beam search decoding cannot return more sequences than it has beams. Please set num_beams >= num_return_sequences"
+
+        # create attention mask if necessary
+        # TODO (PVP): this should later be handled by the forward fn() in each model in the future see PR 3140
+        if (attention_mask is None) and (pad_token_id is not None) and (pad_token_id in input_ids):
+            attention_mask = input_ids.ne(pad_token_id).long()
+        elif attention_mask is None:
+            attention_mask = input_ids.new_ones(input_ids.shape)
+
+        # set pad_token_id to eos_token_id if not set. Important that this is done after
+        # attention_mask is created
+        if pad_token_id is None and eos_token_id is not None:
+            print(
+                "Setting `pad_token_id` to {} (first `eos_token_id`) to generate sequence".format(eos_token_id)
+            )
+            pad_token_id = eos_token_id
+
+        # vocab size
+        if hasattr(self.config, "vocab_size"):
+            vocab_size = self.config.vocab_size
+        elif (
+            self.config.is_encoder_decoder
+            and hasattr(self.config, "decoder")
+            and hasattr(self.config.decoder, "vocab_size")
+        ):
+            vocab_size = self.config.decoder.vocab_size
+        else:
+            raise ValueError("either self.config.vocab_size or self.config.decoder.vocab_size needs to be defined")
+
+        # set effective batch size and effective batch multiplier according to do_sample
+        if do_sample:
+            effective_batch_size = batch_size * num_return_sequences
+            effective_batch_mult = num_return_sequences
+        else:
+            effective_batch_size = batch_size
+            effective_batch_mult = 1
+
+        if self.config.is_encoder_decoder:
+            if decoder_start_token_id is None:
+                # see if BOS token can be used for decoder_start_token_id
+                if bos_token_id is not None:
+                    decoder_start_token_id = bos_token_id
+                elif (
+                    hasattr(self.config, "decoder")
+                    and hasattr(self.config.decoder, "bos_token_id")
+                    and self.config.decoder.bos_token_id is not None
+                ):
+                    decoder_start_token_id = self.config.decoder.bos_token_id
+                else:
+                    raise ValueError(
+                        "decoder_start_token_id or bos_token_id has to be defined for encoder-decoder generation"
+                    )
+
+            assert hasattr(self, "get_encoder"), "{} should have a 'get_encoder' function defined".format(self)
+            assert callable(self.get_encoder), "{} should be a method".format(self.get_encoder)
+
+            # get encoder and store encoder outputs
+            encoder = self.get_encoder()
+            encoder_outputs: ModelOutput = encoder(input_ids, attention_mask=attention_mask, return_dict=True)
+
+        # Expand input ids if num_beams > 1 or num_return_sequences > 1
+        if num_return_sequences > 1 or num_beams > 1:
+            # TODO: make this a call-back function.
+            # input_ids=caps,
+            # input_video_embeds=vfeats,
+            # attention_mask=attention_mask,
+            # token_type_ids=token_type_ids,
+            input_video_embeds = model_kwargs.pop("input_video_embeds", None)
+            token_type_ids = model_kwargs.pop("token_type_ids", None)
+
+            input_ids_len = input_ids.shape[-1]
+            input_ids = input_ids.unsqueeze(1).expand(
+                 batch_size, effective_batch_mult * num_beams, input_ids_len)
+
+            input_video_embeds_len, input_video_embeds_hidden = input_video_embeds.size(1), input_video_embeds.size(2)
+            input_video_embeds = input_video_embeds.unsqueeze(1).expand(
+                batch_size, effective_batch_mult * num_beams, input_video_embeds_len, input_video_embeds_hidden)
+
+            attention_mask_from_len, attention_mask_to_len = attention_mask.size(1), attention_mask.size(2)
+            attention_mask = attention_mask.unsqueeze(1).expand(
+                batch_size, effective_batch_mult * num_beams, attention_mask_from_len, attention_mask_to_len
+            )
+
+            token_type_ids_len = token_type_ids.size(1)
+            token_type_ids = token_type_ids.unsqueeze(1).expand(
+                batch_size, effective_batch_mult * num_beams, token_type_ids_len
+            )
+
+            # contiguous ...
+            input_ids = input_ids.contiguous().view(
+                effective_batch_size * num_beams, input_ids_len
+            )  # shape: (batch_size * num_return_sequences * num_beams, cur_len)
+
+            input_video_embeds = input_video_embeds.contiguous().view(
+                effective_batch_size * num_beams, input_video_embeds_len, input_video_embeds_hidden)
+
+            attention_mask = attention_mask.contiguous().view(
+                effective_batch_size * num_beams, attention_mask_from_len, attention_mask_to_len
+            )  # shape: (batch_size * num_return_sequences * num_beams, cur_len)
+
+            token_type_ids = token_type_ids.contiguous().view(
+                effective_batch_size * num_beams, token_type_ids_len
+            )
+
+            model_kwargs["input_video_embeds"] = input_video_embeds
+            model_kwargs["token_type_ids"] = token_type_ids
+
+        if self.config.is_encoder_decoder:
+            device = next(self.parameters()).device
+            if decoder_input_ids is not None:
+                # give initial decoder input ids
+                input_ids = decoder_input_ids.repeat(effective_batch_size * num_beams, 1).to(device)
+            else:
+                # create empty decoder input_ids
+                input_ids = torch.full(
+                    (effective_batch_size * num_beams, 1),
+                    decoder_start_token_id,
+                    dtype=torch.long,
+                    device=device,
+                )
+            cur_len = input_ids.shape[-1]
+
+            assert (
+                batch_size == encoder_outputs.last_hidden_state.shape[0]
+            ), f"expected encoder_outputs.last_hidden_state to have 1st dimension bs={batch_size}, got {encoder_outputs.last_hidden_state.shape[0]} "
+
+            # expand batch_idx to assign correct encoder output for expanded input_ids (due to num_beams > 1 and num_return_sequences > 1)
+            expanded_batch_idxs = (
+                torch.arange(batch_size)
+                .view(-1, 1)
+                .repeat(1, num_beams * effective_batch_mult)
+                .view(-1)
+                .to(input_ids.device)
+            )
+
+            # expand encoder_outputs
+            encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.index_select(
+                0, expanded_batch_idxs
+            )
+
+            # save encoder_outputs in `model_kwargs`
+            model_kwargs["encoder_outputs"] = encoder_outputs
+
+        else:
+            cur_len = input_ids.shape[-1]
+
+        assert (
+            cur_len < max_length
+        ), f"The context has {cur_len} number of tokens, but `max_length` is only {max_length}. Please make sure that `max_length` is bigger than the number of tokens, by setting either `generate(max_length=...,...)` or `config.max_length = ...`"
+
+        if num_beams > 1:
+            output = self._generate_beam_search(
+                input_ids,
+                cur_len=cur_len,
+                max_length=max_length,
+                min_length=min_length,
+                do_sample=do_sample,
+                early_stopping=early_stopping,
+                temperature=temperature,
+                top_k=top_k,
+                top_p=top_p,
+                repetition_penalty=repetition_penalty,
+                no_repeat_ngram_size=no_repeat_ngram_size,
+                bad_words_ids=bad_words_ids,
+                pad_token_id=pad_token_id,
+                eos_token_id=eos_token_id,
+                batch_size=effective_batch_size,
+                num_return_sequences=num_return_sequences,
+                length_penalty=length_penalty,
+                num_beams=num_beams,
+                vocab_size=vocab_size,
+                attention_mask=attention_mask,
+                use_cache=use_cache,
+                model_kwargs=model_kwargs,
+            )
+        else:
+            output = self._generate_no_beam_search(
+                input_ids,
+                cur_len=cur_len,
+                max_length=max_length,
+                min_length=min_length,
+                do_sample=do_sample,
+                temperature=temperature,
+                top_k=top_k,
+                top_p=top_p,
+                repetition_penalty=repetition_penalty,
+                no_repeat_ngram_size=no_repeat_ngram_size,
+                bad_words_ids=bad_words_ids,
+                pad_token_id=pad_token_id,
+                eos_token_id=eos_token_id,
+                batch_size=effective_batch_size,
+                attention_mask=attention_mask,
+                use_cache=use_cache,
+                model_kwargs=model_kwargs,
+            )
+
+        return output
+
+    def _generate_beam_search(
+        self,
+        input_ids,
+        cur_len,
+        max_length,
+        min_length,
+        do_sample,
+        early_stopping,
+        temperature,
+        top_k,
+        top_p,
+        repetition_penalty,
+        no_repeat_ngram_size,
+        bad_words_ids,
+        pad_token_id,
+        eos_token_id,
+        batch_size,
+        num_return_sequences,
+        length_penalty,
+        num_beams,
+        vocab_size,
+        attention_mask,
+        use_cache,
+        model_kwargs,
+    ):
+        """Generate sequences for each example with beam search."""
+
+        # generated hypotheses
+        generated_hyps = [
+            BeamHypotheses(num_beams, max_length, length_penalty, early_stopping=early_stopping)
+            for _ in range(batch_size)
+        ]
+
+        # scores for each sentence in the beam
+        beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)
+
+        # for greedy decoding it is made sure that only tokens of the first beam are considered to avoid sampling the exact same tokens three times
+        if do_sample is False:
+            beam_scores[:, 1:] = -1e9
+        beam_scores = beam_scores.view(-1)  # shape (batch_size * num_beams,)
+
+        # cache compute states
+        past = None
+
+        # done sentences
+        done = [False for _ in range(batch_size)]
+
+        while cur_len < max_length:
+            model_inputs = self.prepare_inputs_for_generation(
+                input_ids, past=past, attention_mask=attention_mask, use_cache=use_cache, **model_kwargs
+            )
+            outputs = self(**model_inputs, return_dict=True)  # (batch_size * num_beams, cur_len, vocab_size)
+            next_token_logits = outputs.logits[:, -1, :]  # (batch_size * num_beams, vocab_size)
+
+            # if model has past, then set the past variable to speed up decoding
+            if "past_key_values" in outputs:
+                past = outputs.past_key_values
+            elif "mems" in outputs:
+                past = outputs.mems
+
+            if self.config.is_encoder_decoder and do_sample is False:
+                # TODO (PVP) still a bit hacky here - there might be a better solution
+                next_token_logits = self.adjust_logits_during_generation(
+                    next_token_logits, cur_len=cur_len, max_length=max_length
+                )
+
+            scores = F.log_softmax(next_token_logits, dim=-1)  # (batch_size * num_beams, vocab_size)
+
+            scores = self.postprocess_next_token_scores(
+                scores=scores,
+                input_ids=input_ids,
+                no_repeat_ngram_size=no_repeat_ngram_size,
+                bad_words_ids=bad_words_ids,
+                cur_len=cur_len,
+                min_length=min_length,
+                max_length=max_length,
+                eos_token_id=eos_token_id,
+                repetition_penalty=repetition_penalty,
+                batch_size=batch_size,
+                num_beams=num_beams,
+            )
+
+            assert scores.shape == (batch_size * num_beams, vocab_size), "Shapes of scores: {} != {}".format(
+                scores.shape, (batch_size * num_beams, vocab_size)
+            )
+
+            if do_sample:
+                _scores = scores + beam_scores[:, None].expand_as(scores)  # (batch_size * num_beams, vocab_size)
+                # Temperature
+                if temperature != 1.0:
+                    _scores = _scores / temperature
+                # Top-p/top-k filtering
+                _scores = top_k_top_p_filtering(
+                    _scores, top_k=top_k, top_p=top_p, min_tokens_to_keep=2
+                )  # (batch_size * num_beams, vocab_size)
+                # re-organize to group the beam together to sample from all beam_idxs
+                _scores = _scores.contiguous().view(
+                    batch_size, num_beams * vocab_size
+                )  # (batch_size, num_beams * vocab_size)
+
+                # Sample 2 next tokens for each beam (so we have some spare tokens and match output of greedy beam search)
+                probs = F.softmax(_scores, dim=-1)
+                next_tokens = torch.multinomial(probs, num_samples=2 * num_beams)  # (batch_size, num_beams * 2)
+                # Compute next scores
+                next_scores = torch.gather(_scores, -1, next_tokens)  # (batch_size, num_beams * 2)
+                # sort the sampled vector to make sure that the first num_beams samples are the best
+                next_scores, next_scores_indices = torch.sort(next_scores, descending=True, dim=1)
+                next_tokens = torch.gather(next_tokens, -1, next_scores_indices)  # (batch_size, num_beams * 2)
+
+            else:
+                next_scores = scores + beam_scores[:, None].expand_as(scores)  # (batch_size * num_beams, vocab_size)
+
+                # re-organize to group the beam together (we are keeping top hypothesis accross beams)
+                next_scores = next_scores.view(
+                    batch_size, num_beams * vocab_size
+                )  # (batch_size, num_beams * vocab_size)
+
+                next_scores, next_tokens = torch.topk(next_scores, 2 * num_beams, dim=1, largest=True, sorted=True)
+
+            assert next_scores.size() == next_tokens.size() == (batch_size, 2 * num_beams)
+
+            # next batch beam content
+            next_batch_beam = []
+
+            # for each sentence
+            for batch_idx in range(batch_size):
+
+                # if we are done with this sentence, add a pad token
+                if done[batch_idx]:
+                    assert (
+                        len(generated_hyps[batch_idx]) >= num_beams
+                    ), "Batch can only be done if at least {} beams have been generated".format(num_beams)
+                    assert (
+                        eos_token_id is not None and pad_token_id is not None
+                    ), "generated beams >= num_beams -> eos_token_id and pad_token have to be defined"
+                    next_batch_beam.extend([(0, pad_token_id, 0)] * num_beams)  # pad the batch
+                    continue
+
+                # next sentence beam content, this will get added to next_batch_beam
+                next_sent_beam = []
+
+                # next tokens for this sentence
+                for beam_token_rank, (beam_token_id, beam_token_score) in enumerate(
+                    zip(next_tokens[batch_idx], next_scores[batch_idx])
+                ):
+                    # get beam and token IDs
+                    beam_id = beam_token_id // vocab_size
+                    token_id = beam_token_id % vocab_size
+
+                    effective_beam_id = batch_idx * num_beams + beam_id
+                    # add to generated hypotheses if end of sentence
+                    if (eos_token_id is not None) and (token_id.item() == eos_token_id):
+                        # if beam_token does not belong to top num_beams tokens, it should not be added
+                        is_beam_token_worse_than_top_num_beams = beam_token_rank >= num_beams
+                        if is_beam_token_worse_than_top_num_beams:
+                            continue
+                        generated_hyps[batch_idx].add(
+                            input_ids[effective_beam_id].clone(),
+                            beam_token_score.item(),
+                        )
+                    else:
+                        # add next predicted token since it is not eos_token
+                        next_sent_beam.append((beam_token_score, token_id, effective_beam_id))
+
+                    # once the beam for next step is full, don't add more tokens to it.
+                    if len(next_sent_beam) == num_beams:
+                        break
+
+                # Check if we are done so that we can save a pad step if all(done)
+                done[batch_idx] = done[batch_idx] or generated_hyps[batch_idx].is_done(
+                    next_scores[batch_idx].max().item(), cur_len
+                )
+
+                # update next beam content
+                assert len(next_sent_beam) == num_beams, "Beam should always be full"
+                next_batch_beam.extend(next_sent_beam)
+                assert len(next_batch_beam) == num_beams * (batch_idx + 1), "We should have added num_beams each step"
+
+            # stop when we are done with each sentence
+            if all(done):
+                break
+
+            # sanity check / prepare next batch
+            assert len(next_batch_beam) == batch_size * num_beams
+            beam_scores = beam_scores.new([x[0] for x in next_batch_beam])
+            beam_tokens = input_ids.new([x[1] for x in next_batch_beam])
+            beam_idx = input_ids.new([x[2] for x in next_batch_beam])
+
+            # re-order batch and update current length
+            input_ids = input_ids[beam_idx, :]
+            input_ids = torch.cat([input_ids, beam_tokens.unsqueeze(1)], dim=-1)
+            cur_len = cur_len + 1
+
+            # re-order internal states
+            if past is not None:
+                past = self._reorder_cache(past, beam_idx)
+
+            # extend attention_mask for new generated input if only decoder
+            # (huxu): move out since we trim attention_mask by ourselves.
+            # if self.config.is_encoder_decoder is False:
+            #    attention_mask = torch.cat(
+            #        [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
+            #    )
+
+        # finalize all open beam hypotheses and add to generated hypotheses
+        for batch_idx in range(batch_size):
+            if done[batch_idx]:
+                continue
+
+            # test that beam scores match previously calculated scores if not eos and batch_idx not done
+            if eos_token_id is not None and all(
+                (token_id % vocab_size).item() != eos_token_id for token_id in next_tokens[batch_idx]
+            ):
+                assert torch.all(
+                    next_scores[batch_idx, :num_beams] == beam_scores.view(batch_size, num_beams)[batch_idx]
+                ), "If batch_idx is not done, final next scores: {} have to equal to accumulated beam_scores: {}".format(
+                    next_scores[:, :num_beams][batch_idx],
+                    beam_scores.view(batch_size, num_beams)[batch_idx],
+                )
+
+            # need to add best num_beams hypotheses to generated hyps
+            for beam_id in range(num_beams):
+                effective_beam_id = batch_idx * num_beams + beam_id
+                final_score = beam_scores[effective_beam_id].item()
+                final_tokens = input_ids[effective_beam_id]
+                generated_hyps[batch_idx].add(final_tokens, final_score)
+
+        # depending on whether greedy generation is wanted or not define different output_batch_size and output_num_return_sequences_per_batch
+        output_batch_size = batch_size if do_sample else batch_size * num_return_sequences
+        output_num_return_sequences_per_batch = 1 if do_sample else num_return_sequences
+
+        # select the best hypotheses
+        sent_lengths = input_ids.new(output_batch_size)
+        best = []
+
+        # retrieve best hypotheses
+        for i, hypotheses in enumerate(generated_hyps):
+            sorted_hyps = sorted(hypotheses.beams, key=lambda x: x[0])
+            for j in range(output_num_return_sequences_per_batch):
+                effective_batch_idx = output_num_return_sequences_per_batch * i + j
+                best_hyp = sorted_hyps.pop()[1]
+                sent_lengths[effective_batch_idx] = len(best_hyp)
+                best.append(best_hyp)
+
+        # prepare for adding eos
+        sent_max_len = min(sent_lengths.max().item() + 1, max_length)
+        decoded = input_ids.new(output_batch_size, sent_max_len)
+        # shorter batches are padded if needed
+        if sent_lengths.min().item() != sent_lengths.max().item():
+            assert pad_token_id is not None, "`pad_token_id` has to be defined"
+            decoded.fill_(pad_token_id)
+
+        # fill with hypotheses and eos_token_id if the latter fits in
+        for i, hypo in enumerate(best):
+            decoded[i, : sent_lengths[i]] = hypo
+            if sent_lengths[i] < max_length:
+                decoded[i, sent_lengths[i]] = eos_token_id
+
+        return decoded
+
+    def _generate_no_beam_search(
+        self,
+        input_ids,
+        cur_len,
+        max_length,
+        min_length,
+        do_sample,
+        temperature,
+        top_k,
+        top_p,
+        repetition_penalty,
+        no_repeat_ngram_size,
+        bad_words_ids,
+        pad_token_id,
+        eos_token_id,
+        batch_size,
+        attention_mask,
+        use_cache,
+        model_kwargs,
+    ):
+        """Generate sequences for each example without beam search (num_beams == 1).
+        All returned sequence are generated independantly.
+        """
+        # length of generated sentences / unfinished sentences
+        unfinished_sents = input_ids.new(batch_size).fill_(1)
+        sent_lengths = input_ids.new(batch_size).fill_(max_length)
+
+        past = None
+        while cur_len < max_length:
+            model_inputs = self.prepare_inputs_for_generation(
+                input_ids, past=past, attention_mask=attention_mask, use_cache=use_cache, **model_kwargs
+            )
+
+            outputs = self(**model_inputs, return_dict=True)
+            next_token_logits = outputs.logits[:, -1, :]
+            scores = self.postprocess_next_token_scores(
+                scores=next_token_logits,
+                input_ids=input_ids,
+                no_repeat_ngram_size=no_repeat_ngram_size,
+                bad_words_ids=bad_words_ids,
+                cur_len=cur_len,
+                min_length=min_length,
+                max_length=max_length,
+                eos_token_id=eos_token_id,
+                repetition_penalty=repetition_penalty,
+                batch_size=batch_size,
+                num_beams=1,
+            )
+
+            # if model has past, then set the past variable to speed up decoding
+            if "past_key_values" in outputs:
+                past = outputs.past_key_values
+            elif "mems" in outputs:
+                past = outputs.mems
+
+            if do_sample:
+                # Temperature (higher temperature => more likely to sample low probability tokens)
+                if temperature != 1.0:
+                    scores = scores / temperature
+                # Top-p/top-k filtering
+                next_token_logscores = top_k_top_p_filtering(scores, top_k=top_k, top_p=top_p)
+                # Sample
+                probs = F.softmax(next_token_logscores, dim=-1)
+                next_token = torch.multinomial(probs, num_samples=1).squeeze(1)
+            else:
+                # Greedy decoding
+                next_token = torch.argmax(next_token_logits, dim=-1)
+            
+                # print(next_token_logits[0,next_token[0]], next_token_logits[0,eos_token_id])
+
+            # update generations and finished sentences
+            if eos_token_id is not None:
+                # pad finished sentences if eos_token_id exist
+                tokens_to_add = next_token * unfinished_sents + (pad_token_id) * (1 - unfinished_sents)
+            else:
+                tokens_to_add = next_token
+
+            # add token and increase length by one
+            input_ids = torch.cat([input_ids, tokens_to_add.unsqueeze(-1)], dim=-1)
+            cur_len = cur_len + 1
+
+            if eos_token_id is not None:
+                eos_in_sents = tokens_to_add == eos_token_id
+                # if sentence is unfinished and the token to add is eos, sent_lengths is filled with current length
+                is_sents_unfinished_and_token_to_add_is_eos = unfinished_sents.mul(eos_in_sents.long()).bool()
+                sent_lengths.masked_fill_(is_sents_unfinished_and_token_to_add_is_eos, cur_len)
+                # unfinished_sents is set to zero if eos in sentence
+                unfinished_sents.mul_((~eos_in_sents).long())
+
+            # stop when there is a </s> in each sentence, or if we exceed the maximul length
+            if unfinished_sents.max() == 0:
+                break
+            
+            
+            # extend attention_mask for new generated input if only decoder
+            # if self.config.is_encoder_decoder is False:
+            #     attention_mask = torch.cat(
+            #         [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
+            #     )
+
+        return input_ids

examples/MMPT/mmpt/models/transformermodel.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Copyright (c) Facebook, Inc. All Rights Reserved
+
+import torch
+
+from torch import nn
+
+try:
+    from transformers.modeling_bert import (
+        BertPreTrainedModel,
+        BertModel,
+        BertEncoder,
+        BertPredictionHeadTransform,
+    )
+except ImportError:
+    pass
+
+from ..modules import VideoTokenMLP, MMBertEmbeddings
+
+
+# --------------- fine-tuning models ---------------
+class MMBertForJoint(BertPreTrainedModel):
+    """A BertModel with isolated attention mask to separate modality."""
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.videomlp = VideoTokenMLP(config)
+        self.bert = MMBertModel(config)
+        self.init_weights()
+
+    def forward(
+        self,
+        input_ids=None,
+        input_video_embeds=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        next_sentence_label=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        separate_forward_split=None,
+    ):
+        return_dict = (
+            return_dict if return_dict is not None
+            else self.config.use_return_dict
+        )
+        video_tokens = self.videomlp(input_video_embeds)
+
+        outputs = self.bert(
+            input_ids,
+            video_tokens,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            separate_forward_split=separate_forward_split,
+        )
+
+        return outputs
+
+
+class MMBertForTokenClassification(BertPreTrainedModel):
+    """A BertModel similar to MMJointUni, with extra wrapper layer
+    to be fine-tuned from other pretrained MMFusion model."""
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.videomlp = VideoTokenMLP(config)
+        self.bert = MMBertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        # TODO(huxu): 779 is the number of classes for COIN: move to config?
+        self.classifier = nn.Linear(config.hidden_size, 779)
+        self.init_weights()
+
+    def forward(
+        self,
+        input_ids=None,
+        input_video_embeds=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        next_sentence_label=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        separate_forward_split=None,
+    ):
+        return_dict = (
+            return_dict if return_dict is not None
+            else self.config.use_return_dict
+        )
+
+        video_tokens = self.videomlp(input_video_embeds)
+        outputs = self.bert(
+            input_ids,
+            video_tokens,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            separate_forward_split=separate_forward_split,
+        )
+
+        return (self.classifier(outputs[0]),)
+
+
+# ------------ pre-training models ----------------
+
+class MMBertForEncoder(BertPreTrainedModel):
+    """A BertModel for Contrastive Learning."""
+    def __init__(self, config):
+        super().__init__(config)
+        self.videomlp = VideoTokenMLP(config)
+        self.bert = MMBertModel(config)
+        self.init_weights()
+
+    def forward(
+        self,
+        input_ids=None,
+        input_video_embeds=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        return_dict = (
+            return_dict if return_dict is not None
+            else self.config.use_return_dict
+        )
+        if input_video_embeds is not None:
+            video_tokens = self.videomlp(input_video_embeds)
+        else:
+            video_tokens = None
+
+        outputs = self.bert(
+            input_ids,
+            video_tokens,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        return outputs
+
+
+class MMBertForMFMMLM(BertPreTrainedModel):
+    """A BertModel with shared prediction head on MFM-MLM."""
+    def __init__(self, config):
+        super().__init__(config)
+        self.videomlp = VideoTokenMLP(config)
+        self.bert = MMBertModel(config)
+        self.cls = MFMMLMHead(config)
+        self.hidden_size = config.hidden_size
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    def forward(
+        self,
+        input_ids=None,
+        input_video_embeds=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        masked_frame_labels=None,
+        target_video_hidden_states=None,
+        non_masked_frame_mask=None,
+        masked_lm_labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        return_dict = (
+            return_dict if return_dict is not None
+            else self.config.use_return_dict
+        )
+        if input_video_embeds is not None:
+            video_tokens = self.videomlp(input_video_embeds)
+        else:
+            video_tokens = None
+
+        if target_video_hidden_states is not None:
+            target_video_hidden_states = self.videomlp(
+                target_video_hidden_states)
+
+            non_masked_frame_hidden_states = video_tokens.masked_select(
+                non_masked_frame_mask.unsqueeze(-1)
+            ).view(-1, self.hidden_size)
+
+        outputs = self.bert(
+            input_ids,
+            video_tokens,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        mfm_scores, prediction_scores = None, None
+        if masked_frame_labels is not None and masked_lm_labels is not None:
+            # split the sequence.
+            text_offset = masked_frame_labels.size(1) + 1  # [CLS]
+            video_sequence_output = sequence_output[
+                :, 1:text_offset
+            ]  # remove [SEP] as not in video_label.
+            text_sequence_output = torch.cat(
+                [sequence_output[:, :1], sequence_output[:, text_offset:]],
+                dim=1
+            )
+
+            hidden_size = video_sequence_output.size(-1)
+            selected_video_output = video_sequence_output.masked_select(
+                masked_frame_labels.unsqueeze(-1)
+            ).view(-1, hidden_size)
+
+            # only compute select tokens to training to speed up.
+            hidden_size = text_sequence_output.size(-1)
+            # masked_lm_labels = masked_lm_labels.reshape(-1)
+            labels_mask = masked_lm_labels != -100
+
+            selected_text_output = text_sequence_output.masked_select(
+                labels_mask.unsqueeze(-1)
+            ).view(-1, hidden_size)
+            mfm_scores, prediction_scores = self.cls(
+                selected_video_output,
+                target_video_hidden_states,
+                non_masked_frame_hidden_states,
+                selected_text_output,
+            )
+
+        output = (
+            mfm_scores,
+            prediction_scores,
+        ) + outputs
+        return output
+
+
+class BertMFMMLMPredictionHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.transform = BertPredictionHeadTransform(config)
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.decoder = nn.Linear(
+            config.hidden_size, config.vocab_size, bias=False)
+
+        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+        # Need a link between the two variables so that the bias is correctly
+        # resized with `resize_token_embeddings`
+        self.decoder.bias = self.bias
+
+    def forward(
+        self,
+        video_hidden_states=None,
+        target_video_hidden_states=None,
+        non_masked_frame_hidden_states=None,
+        text_hidden_states=None,
+    ):
+        video_logits, text_logits = None, None
+        if video_hidden_states is not None:
+            video_hidden_states = self.transform(video_hidden_states)
+            non_masked_frame_logits = torch.mm(
+                video_hidden_states,
+                non_masked_frame_hidden_states.transpose(1, 0)
+            )
+            masked_frame_logits = torch.bmm(
+                video_hidden_states.unsqueeze(1),
+                target_video_hidden_states.unsqueeze(-1),
+            ).squeeze(-1)
+            video_logits = torch.cat(
+                [masked_frame_logits, non_masked_frame_logits], dim=1
+            )
+
+        if text_hidden_states is not None:
+            text_hidden_states = self.transform(text_hidden_states)
+            text_logits = self.decoder(text_hidden_states)
+        return video_logits, text_logits
+
+
+class MFMMLMHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.predictions = BertMFMMLMPredictionHead(config)
+
+    def forward(
+        self,
+        video_hidden_states=None,
+        target_video_hidden_states=None,
+        non_masked_frame_hidden_states=None,
+        text_hidden_states=None,
+    ):
+        video_logits, text_logits = self.predictions(
+            video_hidden_states,
+            target_video_hidden_states,
+            non_masked_frame_hidden_states,
+            text_hidden_states,
+        )
+        return video_logits, text_logits
+
+
+class MMBertForMTM(MMBertForMFMMLM):
+    def __init__(self, config):
+        BertPreTrainedModel.__init__(self, config)
+        self.videomlp = VideoTokenMLP(config)
+        self.bert = MMBertModel(config)
+        self.cls = MTMHead(config)
+        self.hidden_size = config.hidden_size
+        self.init_weights()
+
+
+class BertMTMPredictionHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.transform = BertPredictionHeadTransform(config)
+        self.decoder = nn.Linear(
+            config.hidden_size, config.vocab_size, bias=False)
+
+    def forward(
+        self,
+        video_hidden_states=None,
+        target_video_hidden_states=None,
+        non_masked_frame_hidden_states=None,
+        text_hidden_states=None,
+    ):
+        non_masked_frame_hidden_states = non_masked_frame_hidden_states.transpose(1, 0)
+        video_logits, text_logits = None, None
+        if video_hidden_states is not None:
+            video_hidden_states = self.transform(video_hidden_states)
+
+            masked_frame_logits = torch.bmm(
+                video_hidden_states.unsqueeze(1),
+                target_video_hidden_states.unsqueeze(-1),
+            ).squeeze(-1)
+
+            non_masked_frame_logits = torch.mm(
+                video_hidden_states,
+                non_masked_frame_hidden_states
+            )
+            video_on_vocab_logits = self.decoder(video_hidden_states)
+            video_logits = torch.cat([
+                masked_frame_logits,
+                non_masked_frame_logits,
+                video_on_vocab_logits], dim=1)
+
+        if text_hidden_states is not None:
+            text_hidden_states = self.transform(text_hidden_states)
+            # text first so label does not need to be shifted.
+            text_on_vocab_logits = self.decoder(text_hidden_states)
+            text_on_video_logits = torch.mm(
+                text_hidden_states,
+                non_masked_frame_hidden_states
+            )
+            text_logits = torch.cat([
+                text_on_vocab_logits,
+                text_on_video_logits
+            ], dim=1)
+
+        return video_logits, text_logits
+
+
+class MTMHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.predictions = BertMTMPredictionHead(config)
+
+    def forward(
+        self,
+        video_hidden_states=None,
+        target_video_hidden_states=None,
+        non_masked_frame_hidden_states=None,
+        text_hidden_states=None,
+    ):
+        video_logits, text_logits = self.predictions(
+            video_hidden_states,
+            target_video_hidden_states,
+            non_masked_frame_hidden_states,
+            text_hidden_states,
+        )
+        return video_logits, text_logits
+
+
+class MMBertModel(BertModel):
+    """MMBertModel has MMBertEmbedding to support video tokens."""
+
+    def __init__(self, config, add_pooling_layer=True):
+        super().__init__(config)
+        # overwrite embedding
+        self.embeddings = MMBertEmbeddings(config)
+        self.encoder = MultiLayerAttentionMaskBertEncoder(config)
+        self.init_weights()
+
+    def forward(
+        self,
+        input_ids=None,
+        input_video_embeds=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        separate_forward_split=None,
+    ):
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None
+            else self.config.use_return_dict
+        )
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both input_ids "
+                "and inputs_embeds at the same time"
+            )
+        elif input_ids is not None:
+            if input_video_embeds is not None:
+                input_shape = (
+                    input_ids.size(0),
+                    input_ids.size(1) + input_video_embeds.size(1),
+                )
+            else:
+                input_shape = (
+                    input_ids.size(0),
+                    input_ids.size(1),
+                )
+        elif inputs_embeds is not None:
+            if input_video_embeds is not None:
+                input_shape = (
+                    inputs_embeds.size(0),
+                    inputs_embeds.size(1) + input_video_embeds.size(1),
+                )
+            else:
+                input_shape = (
+                    input_ids.size(0),
+                    input_ids.size(1),
+                )
+        else:
+            raise ValueError(
+                "You have to specify either input_ids or inputs_embeds")
+
+        device = input_ids.device if input_ids is not None \
+            else inputs_embeds.device
+
+        if attention_mask is None:
+            attention_mask = torch.ones(input_shape, device=device)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(
+                input_shape, dtype=torch.long, device=device)
+
+        # We can provide a self-attention mask of dimensions
+        # [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case
+        # we just need to make it broadcastable to all heads.
+        extended_attention_mask: torch.Tensor = \
+            self.get_extended_attention_mask(
+                attention_mask, input_shape, device)
+
+        # If a 2D or 3D attention mask is provided for the cross-attention
+        # we need to make broadcastable to
+        # [batch_size, num_heads, seq_length, seq_length]
+        if self.config.is_decoder and encoder_hidden_states is not None:
+            (
+                encoder_batch_size,
+                encoder_sequence_length,
+                _,
+            ) = encoder_hidden_states.size()
+            encoder_hidden_shape = (
+                encoder_batch_size, encoder_sequence_length)
+            if encoder_attention_mask is None:
+                encoder_attention_mask = torch.ones(
+                    encoder_hidden_shape, device=device)
+            encoder_extended_attention_mask = self.invert_attention_mask(
+                encoder_attention_mask
+            )
+        else:
+            encoder_extended_attention_mask = None
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or
+        # [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape
+        # [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+
+        head_mask = self.get_head_mask(
+            head_mask, self.config.num_hidden_layers)
+
+        embedding_output = self.embeddings(
+            input_ids,
+            input_video_embeds,
+            position_ids=position_ids,
+            token_type_ids=token_type_ids,
+            inputs_embeds=inputs_embeds,
+        )
+
+        if separate_forward_split is not None:
+            split_embedding_output = \
+                embedding_output[:, :separate_forward_split]
+            split_extended_attention_mask = extended_attention_mask[
+                :, :, :, :separate_forward_split, :separate_forward_split
+            ]
+            split_encoder_outputs = self.encoder(
+                split_embedding_output,
+                attention_mask=split_extended_attention_mask,
+                head_mask=head_mask,
+                encoder_hidden_states=encoder_hidden_states,
+                encoder_attention_mask=encoder_extended_attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            assert (
+                len(split_encoder_outputs) <= 2
+            ), "we do not support merge on attention for now."
+            encoder_outputs = []
+            encoder_outputs.append([split_encoder_outputs[0]])
+            if len(split_encoder_outputs) == 2:
+                encoder_outputs.append([])
+                for _all_hidden_states in split_encoder_outputs[1]:
+                    encoder_outputs[-1].append([_all_hidden_states])
+
+            split_embedding_output = \
+                embedding_output[:, separate_forward_split:]
+            split_extended_attention_mask = extended_attention_mask[
+                :, :, :, separate_forward_split:, separate_forward_split:
+            ]
+
+            split_encoder_outputs = self.encoder(
+                split_embedding_output,
+                attention_mask=split_extended_attention_mask,
+                head_mask=head_mask,
+                encoder_hidden_states=encoder_hidden_states,
+                encoder_attention_mask=encoder_extended_attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            assert (
+                len(split_encoder_outputs) <= 2
+            ), "we do not support merge on attention for now."
+            encoder_outputs[0].append(split_encoder_outputs[0])
+            encoder_outputs[0] = torch.cat(encoder_outputs[0], dim=1)
+            if len(split_encoder_outputs) == 2:
+                for layer_idx, _all_hidden_states in enumerate(
+                    split_encoder_outputs[1]
+                ):
+                    encoder_outputs[1][layer_idx].append(_all_hidden_states)
+                    encoder_outputs[1][layer_idx] = torch.cat(
+                        encoder_outputs[1][layer_idx], dim=1
+                    )
+            encoder_outputs = tuple(encoder_outputs)
+        else:
+            encoder_outputs = self.encoder(
+                embedding_output,
+                attention_mask=extended_attention_mask,
+                head_mask=head_mask,
+                encoder_hidden_states=encoder_hidden_states,
+                encoder_attention_mask=encoder_extended_attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+        sequence_output = encoder_outputs[0]
+        pooled_output = (
+            self.pooler(sequence_output) if self.pooler is not None else None
+        )
+
+        return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+    def get_extended_attention_mask(self, attention_mask, input_shape, device):
+        """This is borrowed from `modeling_utils.py` with the support of
+        multi-layer attention masks.
+        The second dim is expected to be number of layers.
+        See `MMAttentionMaskProcessor`.
+        Makes broadcastable attention and causal masks so that future
+        and masked tokens are ignored.
+
+        Arguments:
+            attention_mask (:obj:`torch.Tensor`):
+                Mask with ones indicating tokens to attend to,
+                zeros for tokens to ignore.
+            input_shape (:obj:`Tuple[int]`):
+                The shape of the input to the model.
+            device: (:obj:`torch.device`):
+                The device of the input to the model.
+
+        Returns:
+            :obj:`torch.Tensor` The extended attention mask, \
+                with a the same dtype as :obj:`attention_mask.dtype`.
+        """
+        # We can provide a self-attention mask of dimensions
+        # [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable
+        # to all heads.
+        if attention_mask.dim() == 4:
+            extended_attention_mask = attention_mask[:, :, None, :, :]
+            extended_attention_mask = extended_attention_mask.to(
+                dtype=self.dtype
+            )  # fp16 compatibility
+            extended_attention_mask = (1.0 - extended_attention_mask) \
+                * -10000.0
+            return extended_attention_mask
+        else:
+            return super().get_extended_attention_mask(
+                attention_mask, input_shape, device
+            )
+
+
+class MultiLayerAttentionMaskBertEncoder(BertEncoder):
+    """extend BertEncoder with the capability of
+    multiple layers of attention mask."""
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=False,
+        output_hidden_states=False,
+        return_dict=False,
+    ):
+        all_hidden_states = () if output_hidden_states else None
+        all_attentions = () if output_attentions else None
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+
+            layer_attention_mask = (
+                attention_mask[:, i, :, :, :]
+                if attention_mask.dim() == 5
+                else attention_mask
+            )
+
+            if getattr(self.config, "gradient_checkpointing", False):
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(layer_module),
+                    hidden_states,
+                    layer_attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    layer_attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    output_attentions,
+                )
+            hidden_states = layer_outputs[0]
+            if output_attentions:
+                all_attentions = all_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        return tuple(
+            v
+            for v in [hidden_states, all_hidden_states, all_attentions]
+            if v is not None
+        )

examples/MMPT/mmpt/modules/__init__.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+from .mm import *
+
+try:
+    from .expmm import *
+except ImportError:
+    pass

examples/MMPT/mmpt/modules/mm.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Copyright (c) Facebook, Inc. All Rights Reserved
+
+
+import torch
+
+from torch import nn
+
+try:
+    from transformers.modeling_bert import (
+        BertEmbeddings,
+        ACT2FN,
+    )
+except ImportError:
+    pass
+
+
+class VideoTokenMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        input_dim = config.input_dim if hasattr(config, "input_dim") else 512
+        self.linear1 = nn.Linear(input_dim, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size)
+        self.activation = ACT2FN[config.hidden_act]
+        self.linear2 = nn.Linear(config.hidden_size, config.hidden_size)
+
+    def forward(self, hidden_states):
+        hidden_states = self.linear1(hidden_states)
+        hidden_states = self.activation(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states)
+        hidden_states = self.linear2(hidden_states)
+        return hidden_states
+
+
+class MMBertEmbeddings(BertEmbeddings):
+    def __init__(self, config):
+        super().__init__(config)
+        self.max_video_len = config.max_video_len
+        if hasattr(config, "use_seg_emb") and config.use_seg_emb:
+            """the original VLM paper uses seg_embeddings for temporal space.
+            although not used it changed the randomness of initialization.
+            we keep it for reproducibility.
+            """
+            self.seg_embeddings = nn.Embedding(256, config.hidden_size)
+
+    def forward(
+        self,
+        input_ids,
+        input_video_embeds,
+        token_type_ids=None,
+        position_ids=None,
+        inputs_embeds=None,
+    ):
+        input_tensor = input_ids if input_ids is not None else inputs_embeds
+        if input_video_embeds is not None:
+            input_shape = (
+                input_tensor.size(0),
+                input_tensor.size(1) + input_video_embeds.size(1),
+            )
+        else:
+            input_shape = (input_tensor.size(0), input_tensor.size(1))
+
+        if position_ids is None:
+            """
+            Auto skip position embeddings for text only case.
+            use cases:
+            (1) action localization and segmentation:
+                feed in len-1 dummy video token needs text part to
+                skip input_video_embeds.size(1) for the right
+                position_ids for video [SEP] and rest text tokens.
+            (2) MMFusionShare for two forward passings:
+                in `forward_text`: input_video_embeds is None.
+                    need to skip video [SEP] token.
+
+            # video_len + 1: [CLS] + video_embed
+            # self.max_video_len + 1: [SEP] for video.
+            # self.max_video_len + 2: [SEP] for video.
+            # self.max_video_len + input_ids.size(1): rest for text.
+            """
+            if input_video_embeds is not None:
+                video_len = input_video_embeds.size(1)
+                starting_offset = self.max_video_len + 1  # video [SEP]
+                ending_offset = self.max_video_len + input_ids.size(1)
+            else:
+                video_len = 0
+                starting_offset = self.max_video_len + 2  # first text token.
+                ending_offset = self.max_video_len + input_ids.size(1) + 1
+            position_ids = torch.cat([
+                self.position_ids[:, :video_len + 1],
+                self.position_ids[:, starting_offset:ending_offset]
+                ], dim=1)
+
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(
+                input_shape, dtype=torch.long, device=self.position_ids.device
+            )
+
+        """
+        the format of input_ids is [CLS] [SEP] caption [SEP] padding.
+        the goal is to build [CLS] video tokens [SEP] caption [SEP] .
+        """
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+        if input_video_embeds is not None:
+            inputs_mm_embeds = torch.cat([
+                inputs_embeds[:, :1], input_video_embeds, inputs_embeds[:, 1:]
+            ], dim=1)
+        else:
+            # text only for `MMFusionShare`.
+            inputs_mm_embeds = inputs_embeds
+
+        position_embeddings = self.position_embeddings(position_ids)
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+        embeddings = inputs_mm_embeds + position_embeddings
+        embeddings += token_type_embeddings
+
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+class AlignHead(nn.Module):
+    """this will load pre-trained weights for NSP, which is desirable."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+    def forward(self, dropout_pooled_output):
+        logits = self.seq_relationship(dropout_pooled_output)
+        return logits

examples/MMPT/mmpt/modules/retri.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+import numpy as np
+import pickle
+import time
+
+try:
+    import faiss
+except ImportError:
+    pass
+
+from collections import defaultdict
+
+from ..utils import get_local_rank, print_on_rank0
+
+
+class VectorRetriever(object):
+    """
+    How2 Video Retriver.
+    Reference usage of FAISS:
+    https://github.com/fairinternal/fairseq-py/blob/paraphrase_pretraining/fairseq/data/multilingual_faiss_dataset.py
+    """
+
+    def __init__(self, hidden_size, cent, db_type, examples_per_cent_to_train):
+        if db_type == "flatl2":
+            quantizer = faiss.IndexFlatL2(hidden_size)  # the other index
+            self.db = faiss.IndexIVFFlat(
+                quantizer, hidden_size, cent, faiss.METRIC_L2)
+        elif db_type == "pq":
+            self.db = faiss.index_factory(
+                    hidden_size, f"IVF{cent}_HNSW32,PQ32"
+            )
+        else:
+            raise ValueError("unknown type of db", db_type)
+        self.train_thres = cent * examples_per_cent_to_train
+        self.train_cache = []
+        self.train_len = 0
+        self.videoid_to_vectoridx = {}
+        self.vectoridx_to_videoid = None
+        self.make_direct_maps_done = False
+
+    def make_direct_maps(self):
+        faiss.downcast_index(self.db).make_direct_map()
+
+    def __len__(self):
+        return self.db.ntotal
+
+    def save(self, out_dir):
+        faiss.write_index(
+            self.db,
+            os.path.join(out_dir, "faiss_idx")
+        )
+        with open(
+                os.path.join(
+                    out_dir, "videoid_to_vectoridx.pkl"),
+                "wb") as fw:
+            pickle.dump(
+                self.videoid_to_vectoridx, fw,
+                protocol=pickle.HIGHEST_PROTOCOL
+            )
+
+    def load(self, out_dir):
+        fn = os.path.join(out_dir, "faiss_idx")
+        self.db = faiss.read_index(fn)
+        with open(
+                os.path.join(out_dir, "videoid_to_vectoridx.pkl"), "rb") as fr:
+            self.videoid_to_vectoridx = pickle.load(fr)
+
+    def add(self, hidden_states, video_ids, last=False):
+        assert len(hidden_states) == len(video_ids), "{}, {}".format(
+            str(len(hidden_states)), str(len(video_ids)))
+        assert len(hidden_states.shape) == 2
+        assert hidden_states.dtype == np.float32
+
+        valid_idx = []
+        for idx, video_id in enumerate(video_ids):
+            if video_id not in self.videoid_to_vectoridx:
+                valid_idx.append(idx)
+                self.videoid_to_vectoridx[video_id] = \
+                    len(self.videoid_to_vectoridx)
+
+        hidden_states = hidden_states[valid_idx]
+        if not self.db.is_trained:
+            self.train_cache.append(hidden_states)
+            self.train_len += hidden_states.shape[0]
+            if self.train_len < self.train_thres:
+                return
+            self.finalize_training()
+        else:
+            self.db.add(hidden_states)
+
+    def finalize_training(self):
+        hidden_states = np.concatenate(self.train_cache, axis=0)
+        del self.train_cache
+        local_rank = get_local_rank()
+        if local_rank == 0:
+            start = time.time()
+            print("training db on", self.train_thres, "/", self.train_len)
+        self.db.train(hidden_states[:self.train_thres])
+        if local_rank == 0:
+            print("training db for", time.time() - start)
+        self.db.add(hidden_states)
+
+    def search(
+        self,
+        query_hidden_states,
+        orig_dist,
+    ):
+        if len(self.videoid_to_vectoridx) != self.db.ntotal:
+            raise ValueError(
+                "cannot search: size mismatch in-between index and db",
+                len(self.videoid_to_vectoridx),
+                self.db.ntotal
+            )
+
+        if self.vectoridx_to_videoid is None:
+            self.vectoridx_to_videoid = {
+                self.videoid_to_vectoridx[videoid]: videoid
+                for videoid in self.videoid_to_vectoridx
+            }
+            assert len(self.vectoridx_to_videoid) \
+                == len(self.videoid_to_vectoridx)
+
+        # MultilingualFaissDataset uses the following; not sure the purpose.
+        # faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10)
+        queried_dist, index = self.db.search(query_hidden_states, 1)
+        queried_dist, index = queried_dist[:, 0], index[:, 0]
+
+        outputs = np.array(
+            [self.vectoridx_to_videoid[_index]
+                if _index != -1 else (-1, -1, -1) for _index in index],
+            dtype=np.int32)
+        outputs[queried_dist <= orig_dist] = -1
+        return outputs
+
+    def search_by_video_ids(
+        self,
+        video_ids,
+        retri_factor
+    ):
+        if len(self.videoid_to_vectoridx) != self.db.ntotal:
+            raise ValueError(
+                len(self.videoid_to_vectoridx),
+                self.db.ntotal
+            )
+
+        if not self.make_direct_maps_done:
+            self.make_direct_maps()
+
+        if self.vectoridx_to_videoid is None:
+            self.vectoridx_to_videoid = {
+                self.videoid_to_vectoridx[videoid]: videoid
+                for videoid in self.videoid_to_vectoridx
+            }
+            assert len(self.vectoridx_to_videoid) \
+                == len(self.videoid_to_vectoridx)
+
+        query_hidden_states = []
+        vector_ids = []
+        for video_id in video_ids:
+            vector_id = self.videoid_to_vectoridx[video_id]
+            vector_ids.append(vector_id)
+            query_hidden_state = self.db.reconstruct(vector_id)
+            query_hidden_states.append(query_hidden_state)
+        query_hidden_states = np.stack(query_hidden_states)
+
+        # MultilingualFaissDataset uses the following; not sure the reason.
+        # faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10)
+        _, index = self.db.search(query_hidden_states, retri_factor)
+        outputs = []
+        for sample_idx, sample in enumerate(index):
+            # the first video_id is always the video itself.
+            cands = [video_ids[sample_idx]]
+            for vector_idx in sample:
+                if vector_idx >= 0 \
+                        and vector_ids[sample_idx] != vector_idx:
+                    cands.append(
+                        self.vectoridx_to_videoid[vector_idx]
+                    )
+            outputs.append(cands)
+        return outputs
+
+
+class VectorRetrieverDM(VectorRetriever):
+    """
+    with direct map.
+    How2 Video Retriver.
+    Reference usage of FAISS:
+    https://github.com/fairinternal/fairseq-py/blob/paraphrase_pretraining/fairseq/data/multilingual_faiss_dataset.py
+    """
+
+    def __init__(
+        self,
+        hidden_size,
+        cent,
+        db_type,
+        examples_per_cent_to_train
+    ):
+        super().__init__(
+            hidden_size, cent, db_type, examples_per_cent_to_train)
+        self.make_direct_maps_done = False
+
+    def make_direct_maps(self):
+        faiss.downcast_index(self.db).make_direct_map()
+        self.make_direct_maps_done = True
+
+    def search(
+        self,
+        query_hidden_states,
+        orig_dist,
+    ):
+        if len(self.videoid_to_vectoridx) != self.db.ntotal:
+            raise ValueError(
+                len(self.videoid_to_vectoridx),
+                self.db.ntotal
+            )
+
+        if not self.make_direct_maps_done:
+            self.make_direct_maps()
+        if self.vectoridx_to_videoid is None:
+            self.vectoridx_to_videoid = {
+                self.videoid_to_vectoridx[videoid]: videoid
+                for videoid in self.videoid_to_vectoridx
+            }
+            assert len(self.vectoridx_to_videoid) \
+                == len(self.videoid_to_vectoridx)
+
+        # MultilingualFaissDataset uses the following; not sure the reason.
+        # faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10)
+        queried_dist, index = self.db.search(query_hidden_states, 1)
+        outputs = []
+        for sample_idx, sample in enumerate(index):
+            # and queried_dist[sample_idx] < thres \
+            if sample >= 0 \
+                    and queried_dist[sample_idx] < orig_dist[sample_idx]:
+                outputs.append(self.vectoridx_to_videoid[sample])
+            else:
+                outputs.append(None)
+        return outputs
+
+    def search_by_video_ids(
+        self,
+        video_ids,
+        retri_factor=8
+    ):
+        if len(self.videoid_to_vectoridx) != self.db.ntotal:
+            raise ValueError(
+                len(self.videoid_to_vectoridx),
+                self.db.ntotal
+            )
+
+        if not self.make_direct_maps_done:
+            self.make_direct_maps()
+        if self.vectoridx_to_videoid is None:
+            self.vectoridx_to_videoid = {
+                self.videoid_to_vectoridx[videoid]: videoid
+                for videoid in self.videoid_to_vectoridx
+            }
+            assert len(self.vectoridx_to_videoid) \
+                == len(self.videoid_to_vectoridx)
+
+        query_hidden_states = []
+        vector_ids = []
+        for video_id in video_ids:
+            vector_id = self.videoid_to_vectoridx[video_id]
+            vector_ids.append(vector_id)
+            query_hidden_state = self.db.reconstruct(vector_id)
+            query_hidden_states.append(query_hidden_state)
+        query_hidden_states = np.stack(query_hidden_states)
+
+        # MultilingualFaissDataset uses the following; not sure the reason.
+        # faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10)
+        _, index = self.db.search(query_hidden_states, retri_factor)
+        outputs = []
+        for sample_idx, sample in enumerate(index):
+            # the first video_id is always the video itself.
+            cands = [video_ids[sample_idx]]
+            for vector_idx in sample:
+                if vector_idx >= 0 \
+                        and vector_ids[sample_idx] != vector_idx:
+                    cands.append(
+                        self.vectoridx_to_videoid[vector_idx]
+                    )
+            outputs.append(cands)
+        return outputs
+
+
+class MMVectorRetriever(VectorRetrieverDM):
+    """
+    multimodal vector retriver:
+    text retrieve video or video retrieve text.
+    """
+
+    def __init__(self, hidden_size, cent, db_type, examples_per_cent_to_train):
+        super().__init__(
+            hidden_size, cent, db_type, examples_per_cent_to_train)
+        video_db = self.db
+        super().__init__(
+            hidden_size, cent, db_type, examples_per_cent_to_train)
+        text_db = self.db
+        self.db = {"video": video_db, "text": text_db}
+        self.video_to_videoid = defaultdict(list)
+
+    def __len__(self):
+        assert self.db["video"].ntotal == self.db["text"].ntotal
+        return self.db["video"].ntotal
+
+    def make_direct_maps(self):
+        faiss.downcast_index(self.db["video"]).make_direct_map()
+        faiss.downcast_index(self.db["text"]).make_direct_map()
+
+    def save(self, out_dir):
+        faiss.write_index(
+            self.db["video"],
+            os.path.join(out_dir, "video_faiss_idx")
+        )
+        faiss.write_index(
+            self.db["text"],
+            os.path.join(out_dir, "text_faiss_idx")
+        )
+
+        with open(
+                os.path.join(
+                    out_dir, "videoid_to_vectoridx.pkl"),
+                "wb") as fw:
+            pickle.dump(
+                self.videoid_to_vectoridx, fw,
+                protocol=pickle.HIGHEST_PROTOCOL
+            )
+
+    def load(self, out_dir):
+        fn = os.path.join(out_dir, "video_faiss_idx")
+        video_db = faiss.read_index(fn)
+        fn = os.path.join(out_dir, "text_faiss_idx")
+        text_db = faiss.read_index(fn)
+        self.db = {"video": video_db, "text": text_db}
+        with open(
+                os.path.join(out_dir, "videoid_to_vectoridx.pkl"), "rb") as fr:
+            self.videoid_to_vectoridx = pickle.load(fr)
+        self.video_to_videoid = defaultdict(list)
+
+    def add(self, hidden_states, video_ids):
+        """hidden_states is a pair `(video, text)`"""
+        assert len(hidden_states) == len(video_ids), "{}, {}".format(
+            str(len(hidden_states)), str(len(video_ids)))
+        assert len(hidden_states.shape) == 3
+        assert len(self.video_to_videoid) == 0
+
+        valid_idx = []
+        for idx, video_id in enumerate(video_ids):
+            if video_id not in self.videoid_to_vectoridx:
+                valid_idx.append(idx)
+                self.videoid_to_vectoridx[video_id] = \
+                    len(self.videoid_to_vectoridx)
+
+        batch_size = hidden_states.shape[0]
+        hidden_states = hidden_states[valid_idx]
+
+        hidden_states = np.transpose(hidden_states, (1, 0, 2)).copy()
+        if not self.db["video"].is_trained:
+            self.train_cache.append(hidden_states)
+            train_len = batch_size * len(self.train_cache)
+            if train_len < self.train_thres:
+                return
+
+            hidden_states = np.concatenate(self.train_cache, axis=1)
+            del self.train_cache
+            self.db["video"].train(hidden_states[0, :self.train_thres])
+            self.db["text"].train(hidden_states[1, :self.train_thres])
+        self.db["video"].add(hidden_states[0])
+        self.db["text"].add(hidden_states[1])
+
+    def get_clips_by_video_id(self, video_id):
+        if not self.video_to_videoid:
+            for video_id, video_clip, text_clip in self.videoid_to_vectoridx:
+                self.video_to_videoid[video_id].append(
+                    (video_id, video_clip, text_clip))
+        return self.video_to_videoid[video_id]
+
+    def search(
+        self,
+        video_ids,
+        target_modality,
+        retri_factor=8
+    ):
+        if len(self.videoid_to_vectoridx) != len(self):
+            raise ValueError(
+                len(self.videoid_to_vectoridx),
+                len(self)
+            )
+
+        if not self.make_direct_maps_done:
+            self.make_direct_maps()
+        if self.vectoridx_to_videoid is None:
+            self.vectoridx_to_videoid = {
+                self.videoid_to_vectoridx[videoid]: videoid
+                for videoid in self.videoid_to_vectoridx
+            }
+            assert len(self.vectoridx_to_videoid) \
+                == len(self.videoid_to_vectoridx)
+
+        src_modality = "text" if target_modality == "video" else "video"
+
+        query_hidden_states = []
+        vector_ids = []
+        for video_id in video_ids:
+            vector_id = self.videoid_to_vectoridx[video_id]
+            vector_ids.append(vector_id)
+            query_hidden_state = self.db[src_modality].reconstruct(vector_id)
+            query_hidden_states.append(query_hidden_state)
+        query_hidden_states = np.stack(query_hidden_states)
+
+        # MultilingualFaissDataset uses the following; not sure the reason.
+        # faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10)
+        _, index = self.db[target_modality].search(
+            query_hidden_states, retri_factor)
+        outputs = []
+        for sample_idx, sample in enumerate(index):
+            cands = []
+            for vector_idx in sample:
+                if vector_idx >= 0:
+                    cands.append(
+                        self.vectoridx_to_videoid[vector_idx]
+                    )
+            outputs.append(cands)
+        return outputs

examples/MMPT/mmpt/modules/vectorpool.py

+# Copyright (c) Facebook, Inc. All Rights Reserved
+
+import torch
+import os
+import numpy as np
+import pickle
+
+from . import retri
+from ..utils import get_local_rank
+
+
+class VectorPool(object):
+    """
+    Base class of retrieval space.
+    """
+
+    def __init__(self, config):
+        from transformers import AutoConfig
+        self.hidden_size = AutoConfig.from_pretrained(
+            config.dataset.bert_name).hidden_size
+        self.retriever_cls = getattr(retri, config.retriever_cls)
+
+    def __call__(self, sample, **kwargs):
+        raise NotImplementedError
+
+    def build_retriver(
+        self,
+        retriever_cls=None,
+        hidden_size=None,
+        centroids=512,
+        db_type="flatl2",
+        examples_per_cent_to_train=48
+    ):
+
+        """merge results from multiple gpus and return a retriver.."""
+        self.retriver = retriever_cls(
+            hidden_size, centroids, db_type, examples_per_cent_to_train)
+        return self.retriver
+
+    def __repr__(self):
+        if hasattr(self, "retriver"):
+            retriver_name = str(len(self.retriver))
+        else:
+            retriver_name = "no retriver field yet"
+        return self.__class__.__name__ \
+            + "(" + retriver_name + ")"
+
+
+class VideoVectorPool(VectorPool):
+    """
+    average clips of a video as video representation.
+    """
+    def __init__(self, config):
+        super().__init__(config)
+        self.build_retriver(self.retriever_cls, self.hidden_size)
+
+    def __call__(self, sample, subsampling, **kwargs):
+        hidden_states = (
+            sample["pooled_video"] + sample["pooled_text"]) / 2.
+        hidden_states = hidden_states.view(
+            -1, subsampling,
+            hidden_states.size(-1))
+        hidden_states = torch.mean(hidden_states, dim=1)
+        hidden_states = hidden_states.cpu().detach().numpy()
+        video_ids = []
+        for offset_idx, video_id in enumerate(sample["video_id"]):
+            if isinstance(video_id, tuple) and len(video_id) == 3:
+                # a sharded video_id.
+                video_id = video_id[0]
+            video_ids.append(video_id)
+        assert len(video_ids) == len(hidden_states)
+        self.retriver.add(
+            hidden_states.astype("float32"),
+            video_ids
+        )
+
+
+class DistributedVectorPool(VectorPool):
+    """
+    support sync of multiple gpus/nodes.
+    """
+    def __init__(self, config):
+        super().__init__(config)
+        self.out_dir = os.path.join(
+            config.fairseq.checkpoint.save_dir,
+            "retri")
+        os.makedirs(self.out_dir, exist_ok=True)
+        self.hidden_states = []
+        self.video_ids = []
+
+    def build_retriver(
+        self,
+        retriever_cls=None,
+        hidden_size=None,
+        centroids=4096,
+        db_type="flatl2",
+        examples_per_cent_to_train=48
+    ):
+        if retriever_cls is None:
+            retriever_cls = self.retriever_cls
+        if hidden_size is None:
+            hidden_size = self.hidden_size
+        """merge results from multiple gpus and return a retriver.."""
+        if torch.distributed.is_initialized():
+            self.save()
+            # sync saving.
+            torch.distributed.barrier()
+            world_size = torch.distributed.get_world_size()
+        else:
+            world_size = 1
+        self.retriver = retriever_cls(
+            hidden_size, centroids, db_type, examples_per_cent_to_train)
+        # each gpu process has its own retriever.
+        for local_rank in range(world_size):
+            if get_local_rank() == 0:
+                print("load local_rank", local_rank)
+            hidden_states, video_ids = self.load(local_rank)
+            hidden_states = hidden_states.astype("float32")
+            self.retriver.add(hidden_states, video_ids)
+        return self.retriver
+
+    def load(self, local_rank):
+        hidden_states = np.load(
+            os.path.join(
+                self.out_dir,
+                "hidden_state" + str(local_rank) + ".npy"
+            )
+        )
+
+        with open(
+            os.path.join(
+                self.out_dir, "video_id" + str(local_rank) + ".pkl"),
+                "rb") as fr:
+            video_ids = pickle.load(fr)
+        return hidden_states, video_ids
+
+    def save(self):
+        hidden_states = np.vstack(self.hidden_states)
+        assert len(hidden_states) == len(self.video_ids), "{}, {}".format(
+            len(hidden_states),
+            len(self.video_ids)
+        )
+        local_rank = torch.distributed.get_rank() \
+            if torch.distributed.is_initialized() else 0
+
+        np.save(
+            os.path.join(
+                self.out_dir,
+                "hidden_state" + str(local_rank) + ".npy"),
+            hidden_states)
+
+        with open(
+            os.path.join(
+                self.out_dir,
+                "video_id" + str(local_rank) + ".pkl"),
+                "wb") as fw:
+            pickle.dump(
+                self.video_ids,
+                fw,
+                protocol=pickle.HIGHEST_PROTOCOL
+            )
+
+
+class DistributedVideoVectorPool(DistributedVectorPool):
+    """
+    average clips of a video as video representation.
+    """
+    def __call__(self, sample, subsampling, **kwargs):
+        hidden_states = (
+            sample["pooled_video"] + sample["pooled_text"]) / 2.
+        hidden_states = hidden_states.view(
+            -1, subsampling,
+            hidden_states.size(-1))
+        hidden_states = torch.mean(hidden_states, dim=1)
+        hidden_states = hidden_states.cpu().detach().numpy()
+        video_ids = []
+        for offset_idx, video_id in enumerate(sample["video_id"]):
+            if isinstance(video_id, tuple) and len(video_id) == 3:
+                # a sharded video_id.
+                video_id = video_id[0]
+            video_ids.append(video_id)
+        assert len(video_ids) == len(hidden_states)
+        self.hidden_states.append(hidden_states)
+        self.video_ids.extend(video_ids)
+
+
+# ------------ the following are deprecated --------------
+
+class TextClipVectorPool(VectorPool):
+    def __init__(self, config):
+        from transformers import AutoConfig
+        hidden_size = AutoConfig.from_pretrained(
+            config.dataset.bert_name).hidden_size
+        retriever_cls = getattr(retri, config.retriever_cls)
+        self.build_retriver(retriever_cls, hidden_size)
+
+    def __call__(self, sample, **kwargs):
+        clip_meta = sample["clip_meta"].cpu()
+        assert torch.all(torch.le(clip_meta[:, 4], clip_meta[:, 5]))
+        text_meta = [tuple(item.tolist()) for item in clip_meta[:, 3:]]
+
+        if hasattr(self, "retriver"):
+            # build_retriver is called.
+            self.retriver.add(
+                sample["pooled_text"].cpu().numpy().astype("float32"),
+                text_meta
+            )
+        else:
+            raise NotImplementedError
+
+
+class MMClipVectorPool(VectorPool):
+    """
+    Multimodal Clip-level vector pool.
+    """
+    def __init__(self, out_dir):
+        """use hidden_states to store `(video, text)`."""
+        """use video_ids to store `(video_id, start, end)`."""
+        super().__init__(out_dir)
+
+    def __call__(self, sample, **kwargs):
+        pooled_video = sample["pooled_video"].cpu().unsqueeze(1).numpy()
+        pooled_text = sample["pooled_text"].cpu().unsqueeze(1).numpy()
+
+        self.hidden_states.append(
+            np.concatenate([pooled_video, pooled_text], axis=1)
+        )
+
+        video_starts = sample["video_start"].cpu()
+        video_ends = sample["video_end"].cpu()
+        assert torch.all(torch.le(video_starts, video_ends))
+
+        text_starts = sample["text_start"].cpu()
+        text_ends = sample["text_end"].cpu()
+        assert torch.all(torch.le(text_starts, text_ends))
+        subsample_size = sample["pooled_video"].size(0) // len(sample["video_id"])
+        video_ids = [video_id for video_id in sample["video_id"]
+                    for _ in range(subsample_size)
+        ]
+        for video_id, video_start, video_end, text_start, text_end in zip(
+                video_ids, video_starts, video_ends, text_starts, text_ends):
+            self.video_ids.append((
+                video_id,
+                (int(video_start), int(video_end)),
+                (int(text_start), int(text_end))
+            ))

examples/MMPT/mmpt/processors/__init__.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+from .processor import *
+
+from .how2processor import *
+from .how2retriprocessor import *
+
+from .dsprocessor import *
+
+try:
+    from .rawvideoprocessor import *
+    from .codecprocessor import *
+    from .webvidprocessor import *
+    from .expprocessor import *
+    from .exphow2processor import *
+    from .exphow2retriprocessor import *
+    from .expcodecprocessor import *
+    from .expfeatureencoder import *
+    from .expdsprocessor import *
+except ImportError:
+    pass

examples/MMPT/mmpt/processors/dedupprocessor.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import random
+import json
+import pickle
+from tqdm import tqdm
+import os
+import numpy as np
+
+
+class CaptionDedupProcessor(object):
+    """remove overlapping of caption sentences(clip).
+    Some statistics:
+    caption:
+    {'t_clip_len': 246.6448431320854,
+    'video_len': 281.09174795676245,
+    'clip_tps': 0.8841283727427481,
+    'video_tps': 0.7821156477732097,
+    'min_clip_len': 0.0,
+    'max_clip_len': 398.3,
+    'mean_clip_len': 3.196580003006861,
+    'num_clip': 77.15897706301081}
+
+    raw_caption:
+    {'t_clip_len': 238.95908778424115,
+    'video_len': 267.5914859862507,
+    'clip_tps': 2.4941363624267963,
+    'video_tps': 2.258989769647173,
+    'min_clip_len': 0.0,
+    'max_clip_len': 398.3,
+    'mean_clip_len': 3.0537954186814265,
+    'num_clip': 78.24986779481756}
+    """
+
+    def __init__(self, pkl_file):
+        with open(pkl_file, "rb") as fd:
+            self.data = pickle.load(fd)
+        self.stat = {
+            "t_clip_len": [],
+            "video_len": [],
+            "clip_tps": [],
+            "video_tps": [],
+            "clip_len": [],
+        }
+
+    def __call__(self):
+        for idx, video_id in enumerate(tqdm(self.data)):
+            caption = json.loads(self.data[video_id])
+            caption = self._dedup(caption)
+            if idx < 4096:  # for the first 4096 examples, compute the statistics.
+                self.save_stat(video_id, caption)
+            self.data[video_id] = json.dumps(caption)
+        self.print_stat()
+
+    def single(self, video_id):
+        caption = json.loads(self.data[video_id])
+        for clip_idx, (start, end, text) in enumerate(
+            zip(caption["start"], caption["end"], caption["text"])
+        ):
+            print(start, end, text)
+        print("@" * 100)
+        caption = self._dedup(caption)
+        for clip_idx, (start, end, text) in enumerate(
+            zip(caption["start"], caption["end"], caption["text"])
+        ):
+            print(start, end, text)
+        print("#" * 100)
+        self.save_stat(video_id, caption)
+        self.print_stat()
+
+    def finalize(self, tgt_fn):
+        with open(tgt_fn, "wb") as fw:
+            pickle.dump(self.data, fw, pickle.HIGHEST_PROTOCOL)
+
+    def save_stat(self, video_id, caption):
+        video_fn = os.path.join(
+            "data/feat/feat_how2_s3d", video_id + ".npy"
+        )
+        if os.path.isfile(video_fn):
+            with open(video_fn, "rb", 1) as fr:  # 24 is the buffer size. buffered
+                version = np.lib.format.read_magic(fr)
+                shape, fortran, dtype = np.lib.format._read_array_header(fr, version)
+                video_len = shape[0]
+
+            t_clip_len = 0.0
+            t_tokens = 0
+            for idx, (start, end, text) in enumerate(
+                zip(caption["start"], caption["end"], caption["text"])
+            ):
+                clip_len = (
+                    (end - max(caption["end"][idx - 1], start))
+                    if idx > 0
+                    else end - start
+                )
+                t_clip_len += clip_len
+                t_tokens += len(text.split(" "))
+                self.stat["clip_len"].append(clip_len)
+            self.stat["t_clip_len"].append(t_clip_len)
+            self.stat["video_len"].append(video_len)
+            self.stat["clip_tps"].append(t_tokens / t_clip_len)
+            self.stat["video_tps"].append(t_tokens / video_len)
+
+    def print_stat(self):
+        result = {
+            "t_clip_len": np.mean(self.stat["t_clip_len"]),
+            "video_len": np.mean(self.stat["video_len"]),
+            "clip_tps": np.mean(self.stat["clip_tps"]),
+            "video_tps": np.mean(self.stat["video_tps"]),
+            "min_clip_len": min(self.stat["clip_len"]),
+            "max_clip_len": max(self.stat["clip_len"]),
+            "mean_clip_len": np.mean(self.stat["clip_len"]),
+            "num_clip": len(self.stat["clip_len"]) / len(self.stat["video_tps"]),
+        }
+        print(result)
+
+    def _dedup(self, caption):
+        def random_merge(end_idx, start, end, text, starts, ends, texts):
+            if random.random() > 0.5:
+                # print(clip_idx, "[PARTIAL INTO PREV]", end_idx)
+                # overlapped part goes to the end of previous.
+                ends[-1] = max(ends[-1], start)  # ?
+                rest_text = text[end_idx:].strip()
+                if rest_text:
+                    starts.append(max(ends[-1], start))
+                    ends.append(max(end, starts[-1]))
+                    texts.append(rest_text)
+            else:  # goes to the beginning of the current.
+                # strip the previous.
+                left_text = texts[-1][:-end_idx].strip()
+                if left_text:
+                    # print(clip_idx, "[PREV PARTIAL INTO CUR]", end_idx)
+                    ends[-1] = min(ends[-1], start)
+                    texts[-1] = left_text
+                else:
+                    # print(clip_idx, "[PREV LEFT NOTHING ALL INTO CUR]", end_idx)
+                    starts.pop(-1)
+                    ends.pop(-1)
+                    texts.pop(-1)
+                starts.append(start)
+                ends.append(end)
+                texts.append(text)
+
+        starts, ends, texts = [], [], []
+        for clip_idx, (start, end, text) in enumerate(
+            zip(caption["start"], caption["end"], caption["text"])
+        ):
+            if not isinstance(text, str):
+                continue
+            text = text.replace("\n", " ").strip()
+            if len(text) == 0:
+                continue
+            starts.append(start)
+            ends.append(end)
+            texts.append(text)
+            break
+
+        for clip_idx, (start, end, text) in enumerate(
+            zip(
+                caption["start"][clip_idx + 1:],
+                caption["end"][clip_idx + 1:],
+                caption["text"][clip_idx + 1:],
+            )
+        ):
+            if not isinstance(text, str):
+                continue
+            text = text.replace("\n", " ").strip()
+            if len(text) == 0:
+                continue
+
+            # print(clip_idx, texts[-5:])
+            # print(clip_idx, start, end, text)
+            if texts[-1].endswith(text):  # subset of prev caption -> merge
+                # print(clip_idx, "[MERGE INTO PREV]")
+                ends[-1] = max(ends[-1], end)
+            elif text.startswith(texts[-1]):  # superset of prev caption -> merge
+                # print(clip_idx, "[PREV MERGE INTO CUR]")
+                texts[-1] = text
+                starts[-1] = min(starts[-1], start)
+                ends[-1] = max(ends[-1], end)
+            else:  # overlapping or non-overlapping.
+                for end_idx in range(1, len(text) + 1):
+                    if texts[-1].endswith(text[:end_idx]):
+                        random_merge(end_idx, start, end, text, starts, ends, texts)
+                        break
+                else:
+                    starts.append(start)
+                    ends.append(end)
+                    texts.append(text)
+
+            assert (ends[-1] + 0.001) >= starts[-1] and len(
+                texts[-1]
+            ) > 0, "{} {} {} <- {} {} {}, {} {} {}".format(
+                str(starts[-1]),
+                str(ends[-1]),
+                texts[-1],
+                caption["start"][clip_idx - 1],
+                caption["end"][clip_idx - 1],
+                caption["text"][clip_idx - 1],
+                str(start),
+                str(end),
+                text,
+            )
+
+        return {"start": starts, "end": ends, "text": texts}
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="dedup how2 caption")
+    parser.add_argument('--how2dir', default="data/how2")
+    args = parser.parse_args()
+
+    raw_caption_json = os.path.join(args.how2dir, "raw_caption.json")
+    raw_caption_pickle = os.path.join(args.how2dir, "raw_caption.pkl")
+    raw_caption_dedup_pickle = os.path.join(args.how2dir, "raw_caption_dedup.pkl")
+
+    def convert_to_pickle(src_fn, tgt_fn):
+        with open(src_fn) as fd:
+            captions = json.load(fd)
+
+        for video_id in captions:
+            captions[video_id] = json.dumps(captions[video_id])
+
+        with open(tgt_fn, "wb") as fw:
+            pickle.dump(captions, fw, pickle.HIGHEST_PROTOCOL)
+
+    if not os.path.isfile(raw_caption_pickle):
+        convert_to_pickle(raw_caption_json, raw_caption_pickle)
+
+    deduper = CaptionDedupProcessor(raw_caption_pickle)
+    deduper()
+    deduper.finalize(raw_caption_dedup_pickle)
+
+    """
+    # demo
+    deduper = CaptionDedupProcessor("data/how2/raw_caption.pkl")
+    deduper.single("HfIeQ9pzL5U")
+    """

examples/MMPT/mmpt/processors/dsprocessor.py

+# Copyright (c) Facebook, Inc. All Rights Reserved
+
+"""
+Processors for all downstream (ds) tasks.
+"""
+
+import json
+import os
+import pickle
+import random
+import math
+import numpy as np
+import torch
+
+from collections import defaultdict
+
+from .processor import (
+    MetaProcessor,
+    VideoProcessor,
+    TextProcessor,
+    Aligner,
+    MMAttentionMask2DProcessor,
+)
+
+from .how2processor import TextGenerationProcessor
+
+
+# ------------- A General Aligner for all downstream tasks-----------------
+
+
+class DSAligner(Aligner):
+    """
+    Downstream (DS) aligner shared by all datasets.
+    """
+
+    def __call__(self, video_id, video_feature, text_feature, wps=0.7):
+        # random sample a starting sec for video.
+        video_start = 0
+        video_end = min(len(video_feature), self.max_video_len)
+        # the whole sequence is a single clip.
+        video_clips = {"start": [video_start], "end": [video_end]}
+
+        text_feature = {
+            "cap": [text_feature],
+            "start": [video_start],
+            "end": [len(text_feature) / wps],
+        }
+        text_clip_indexs = [0]
+
+        vfeats, vmasks = self._build_video_seq(
+            video_feature, video_clips
+        )
+        caps, cmasks = self._build_text_seq(
+            text_feature, text_clip_indexs
+        )
+
+        return {
+            "caps": caps,
+            "cmasks": cmasks,
+            "vfeats": vfeats,
+            "vmasks": vmasks,
+            "video_id": video_id,
+        }
+
+
+class NLGTextProcessor(TextProcessor):
+    """
+    Also return the original text as ref.
+    """
+    def __call__(self, text_id):
+        return super().__call__(text_id), text_id
+
+
+class DSNLGAligner(DSAligner):
+    """extend with the capability of 2d mask for generation."""
+    def __init__(self, config):
+        super().__init__(config)
+        self.attnmasker = MMAttentionMask2DProcessor()
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(
+            self.bert_name, use_fast=self.use_fast,
+            bos_token="[CLS]", eos_token="[SEP]"
+        )
+        self.tokenizer = tokenizer
+        self.bos_token_id = tokenizer.bos_token_id
+        self.eos_token_id = tokenizer.eos_token_id
+        self.textgen = TextGenerationProcessor(tokenizer)
+
+    def __call__(self, video_id, video_feature, text_feature):
+        output = super().__call__(video_id, video_feature, text_feature[0])
+        if self.split == "test":
+            # output.update({"ref": text_feature[1]})
+            output.update({"ref": self.tokenizer.decode(
+                output["caps"], skip_special_tokens=True)})
+            text_label = output["caps"]
+            cmasks = torch.BoolTensor([1] * text_label.size(0))
+            caps = torch.LongTensor([
+                self.cls_token_id,
+                self.sep_token_id,
+                self.bos_token_id])
+        else:
+            caps, text_label = self.textgen(output["caps"])
+            cmasks = output["cmasks"]
+
+        attention_mask = self.attnmasker(
+            output["vmasks"], cmasks, "textgen")
+
+        output.update({
+            "caps": caps,
+            "cmasks": cmasks,
+            "text_label": text_label,
+            "attention_mask": attention_mask,
+        })
+        return output
+
+
+# -------------------- MSRVTT ------------------------
+
+
+class MSRVTTMetaProcessor(MetaProcessor):
+    """MSRVTT dataset.
+    reference: `howto100m/msrvtt_dataloader.py`
+    """
+
+    def __init__(self, config):
+        super().__init__(config)
+        import pandas as pd
+        data = pd.read_csv(self._get_split_path(config))
+        # TODO: add a text1ka flag.
+        if config.split == "train" \
+                and config.full_test_path is not None \
+                and config.jsfusion_path is not None:
+            # add testing videos from full_test_path not used by jfusion.
+            additional_data = pd.read_csv(config.full_test_path)
+            jsfusion_data = pd.read_csv(config.jsfusion_path)
+
+            for video_id in additional_data["video_id"]:
+                if video_id not in jsfusion_data["video_id"].values:
+                    data = data.append(
+                        {"video_id": video_id}, ignore_index=True)
+
+        if config.dup is not None and config.split == "train":
+            data = data.append([data] * (config.dup - 1), ignore_index=True)
+        self.data = data
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        """slightly modify with if condition to combine train/test."""
+        vid, sentence = None, None
+        vid = self.data["video_id"].values[idx]
+        if "sentence" in self.data:  # for testing.
+            sentence = self.data["sentence"].values[idx]
+        else:  # for training.
+            sentence = vid
+        return vid, sentence
+
+
+class MSRVTTTextProcessor(TextProcessor):
+    """MSRVTT dataset.
+    reference: `msrvtt_dataloader.py` `MSRVTT_TrainDataLoader`.
+    TODO (huxu): add max_words.
+    """
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.sentences = None
+        if config.json_path is not None and config.split == "train":
+            with open(config.json_path) as fd:
+                self.data = json.load(fd)
+            self.sentences = defaultdict(list)
+            for s in self.data["sentences"]:
+                self.sentences[s["video_id"]].append(s["caption"])
+
+    def __call__(self, text_id):
+        if self.sentences is not None:
+            rind = random.randint(0, len(self.sentences[text_id]) - 1)
+            sentence = self.sentences[text_id][rind]
+        else:
+            sentence = text_id
+        caption = self.tokenizer(sentence, add_special_tokens=False)
+        return caption["input_ids"]
+
+
+class MSRVTTNLGTextProcessor(MSRVTTTextProcessor):
+    """TODO: change dsaligner and merge to avoid any NLG text processor."""
+    def __call__(self, text_id):
+        if self.sentences is not None:
+            rind = random.randint(0, len(self.sentences[text_id]) - 1)
+            sentence = self.sentences[text_id][rind]
+        else:
+            sentence = text_id
+        caption = self.tokenizer(sentence, add_special_tokens=False)
+        return caption["input_ids"], sentence
+
+
+class MSRVTTQAMetaProcessor(MetaProcessor):
+    """MSRVTT-QA: retrieval-based multi-choice QA from JSFusion dataset.
+    For simplicity, we use the train retrieval model.
+    reference: `https://github.com/yj-yu/lsmdc`
+    """
+
+    def __init__(self, config):
+        super().__init__(config)
+        import pandas as pd
+        csv_data = pd.read_csv(self._get_split_path(config), sep="\t")
+        data = []
+        for video_id, a1, a2, a3, a4, a5, answer in zip(
+                csv_data["vid_key"].values,
+                csv_data["a1"].values,
+                csv_data["a2"].values,
+                csv_data["a3"].values,
+                csv_data["a4"].values,
+                csv_data["a5"].values,
+                csv_data["answer"].values):
+            video_id = video_id.replace("msr", "video")
+            data.append((video_id, (answer, [a1, a2, a3, a4, a5])))
+        self.data = data
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        return self.data[idx]
+
+
+class MSRVTTQATextProcessor(TextProcessor):
+    """MSRVTT-QA dataset.
+    text_ans is of format `(answer, [a1, a2, a3, a4, a5])`.
+    """
+
+    def __call__(self, text_ans):
+        for ans_idx, ans in enumerate(text_ans[1]):
+            if isinstance(ans, str):
+                text_ans[1][ans_idx] = self.tokenizer(ans, add_special_tokens=False)["input_ids"]
+        return text_ans
+
+
+class MSRVTTQAAligner(DSAligner):
+    """MSRVTT dataset.
+    similar to sample in how2.
+    we call __call__ multiple times.
+    """
+
+    def __call__(self, video_id, video_feature, text_feature, wps=0.7):
+        caps = []
+        cmasks = []
+        answer = text_feature[0]
+        for ans_idx, _text_feature in enumerate(text_feature[1]):
+            output = super().__call__(
+                video_id, video_feature, _text_feature, wps)
+            caps.append(output["caps"])
+            cmasks.append(output["cmasks"])
+        output.update({
+            "caps": torch.stack(caps),
+            "cmasks": torch.stack(cmasks),
+            "answers": torch.LongTensor([answer]),
+        })
+        return output
+
+
+# -------------------- Youcook -----------------------
+
+
+class YoucookMetaProcessor(MetaProcessor):
+    """Youcook dataset.
+    reference: `howto100m/youcook_dataloader.py`
+    note that the data can be different as the
+    (1) some videos already in Howto100m are removed.
+    (2) stop words are removed from caption
+    TODO (huxu): make a flag to load the original caption.
+    (see youcookii_annotations_trainval.json).
+
+    The max_video_len can be 264 and text can be 64 tokens.
+    In reality we may not need that long. see projects/task/youcook.yaml
+    """
+
+    def __init__(self, config):
+        super().__init__(config)
+        vfeat_dir = config.vfeat_dir
+        print(self._get_split_path(config))
+        with open(self._get_split_path(config), "rb") as fd:
+            data = pickle.load(fd)
+            all_valid_video_ids = set(
+                [os.path.splitext(fn)[0] for fn in os.listdir(vfeat_dir)]
+            )
+            recs = []
+            video_ids = set()
+            valid_video_ids = set()
+            for rec in data:  # filter videos not available.
+                udl_idx = rec["id"].rindex("_")
+                video_id = rec["id"][:udl_idx]
+                video_ids.add(video_id)
+                if video_id in all_valid_video_ids:
+                    valid_video_ids.add(video_id)
+                    recs.append(rec)
+            print("total video_ids in .pkl", len(video_ids))
+            print("valid video_ids in .pkl", len(valid_video_ids))
+            print("please verify {train,val}_list.txt")
+            data = recs
+            self.data = data
+
+        with open(config.trainval_annotation) as fd:
+            self.youcook_annotation = json.load(fd)["database"]
+        if config.use_annotation_text is True:
+            print("using text in annotation.")
+            self.use_annotation_caption = True
+        else:
+            self.use_annotation_caption = False
+
+    def __getitem__(self, idx):
+        def _get_video_and_caption(rec):
+            vid = rec["id"]
+            udl_idx = vid.rindex("_")
+            video_id, clip_id = vid[:udl_idx], int(vid[udl_idx + 1:])
+            clip = self.youcook_annotation[video_id]["annotations"][clip_id]
+            start, end = clip["segment"]
+            if self.use_annotation_caption:
+                caption = clip["sentence"]
+            else:
+                caption = rec["caption"]
+            return (video_id, start, end), caption
+
+        rec = self.data[idx]
+        video_info, text_info = _get_video_and_caption(rec)
+        return video_info, text_info
+
+
+class YoucookVideoProcessor(VideoProcessor):
+    """video_fn is a tuple of (video_id, start, end) now."""
+
+    def __call__(self, video_fn):
+        video_id, start, end = video_fn
+        feat = np.load(os.path.join(self.vfeat_dir, video_id + ".npy"))
+        return feat[start:end]
+
+
+class YoucookNLGMetaProcessor(MetaProcessor):
+    """NLG uses the original split:
+    `train_list.txt` and `val_list.txt`
+    """
+
+    def __init__(self, config):
+        super().__init__(config)
+        vfeat_dir = config.vfeat_dir
+        print(self._get_split_path(config))
+        with open(self._get_split_path(config)) as fd:
+            video_ids = [
+                line.strip().split("/")[1] for line in fd.readlines()]
+            print("total video_ids in train/val_list.txt", len(video_ids))
+
+            all_valid_video_ids = set(
+                [os.path.splitext(fn)[0] for fn in os.listdir(vfeat_dir)]
+            )
+            video_ids = [
+                video_id for video_id in video_ids
+                if video_id in all_valid_video_ids]
+
+            print("valid video_ids in train/val_list.txt", len(video_ids))
+        with open(config.trainval_annotation) as fd:
+            self.youcook_annotation = json.load(fd)["database"]
+
+        data = []
+        for video_id in video_ids:
+            for clip in self.youcook_annotation[video_id]["annotations"]:
+                start, end = clip["segment"]
+                caption = clip["sentence"]
+                data.append(((video_id, start, end), caption))
+        self.data = data
+
+    def __getitem__(self, idx):
+        return self.data[idx]
+
+
+# --------------------- CrossTask -------------------------
+
+class CrossTaskMetaProcessor(MetaProcessor):
+    def __init__(self, config):
+        super().__init__(config)
+        np.random.seed(0)  # deterministic random split.
+        task_vids = self._get_vids(
+            config.train_csv_path,
+            config.vfeat_dir,
+            config.annotation_path)
+
+        val_vids = self._get_vids(
+            config.val_csv_path,
+            config.vfeat_dir,
+            config.annotation_path)
+
+        # filter out those task and vids appear in val_vids.
+        task_vids = {
+            task: [
+                vid for vid in vids
+                if task not in val_vids or vid not in val_vids[task]]
+            for task, vids in task_vids.items()}
+
+        primary_info = self._read_task_info(config.primary_path)
+        test_tasks = set(primary_info['steps'].keys())
+
+        # if args.use_related:
+        related_info = self._read_task_info(config.related_path)
+        task_steps = {**primary_info['steps'], **related_info['steps']}
+        n_steps = {**primary_info['n_steps'], **related_info['n_steps']}
+        # else:
+        #     task_steps = primary_info['steps']
+        #     n_steps = primary_info['n_steps']
+        all_tasks = set(n_steps.keys())
+        # filter and keep task in primary or related.
+        task_vids = {
+            task: vids for task, vids in task_vids.items()
+            if task in all_tasks}
+        # vocab-by-step matrix (A) and vocab (M)
+        # (huxu): we do not use BoW.
+        # A, M = self._get_A(task_steps, share="words")
+
+        train_vids, test_vids = self._random_split(
+            task_vids, test_tasks, config.n_train)
+        print("train_num_videos", sum(len(vids) for vids in train_vids.values()))
+        print("test_num_videos", sum(len(vids) for vids in test_vids.values()))
+        # added by huxu to automatically determine the split.
+        split_map = {
+            "train": train_vids,
+            "valid": test_vids,
+            "test": test_vids
+        }
+        task_vids = split_map[config.split]
+
+        self.vids = []
+        for task, vids in task_vids.items():
+            self.vids.extend([(task, vid) for vid in vids])
+        self.task_steps = task_steps
+        self.n_steps = n_steps
+
+    def __getitem__(self, idx):
+        task, vid = self.vids[idx]
+        n_steps = self.n_steps[task]
+        steps = self.task_steps[task]
+        assert len(steps) == n_steps
+        return (task, vid, steps, n_steps), (task, vid, steps, n_steps)
+
+    def __len__(self):
+        return len(self.vids)
+
+    def _random_split(self, task_vids, test_tasks, n_train):
+        train_vids = {}
+        test_vids = {}
+        for task, vids in task_vids.items():
+            if task in test_tasks and len(vids) > n_train:
+                train_vids[task] = np.random.choice(
+                    vids, n_train, replace=False).tolist()
+                test_vids[task] = [
+                    vid for vid in vids if vid not in train_vids[task]]
+            else:
+                train_vids[task] = vids
+        return train_vids, test_vids
+
+    def _get_vids(self, path, vfeat_dir, annotation_path):
+        """refactored from
+        https://github.com/DmZhukov/CrossTask/blob/master/data.py
+        changes: add `vfeat_dir` to check if the video is available.
+        add `annotation_path` to check if the video is available.
+        """
+
+        task_vids = {}
+        with open(path, 'r') as f:
+            for line in f:
+                task, vid, url = line.strip().split(',')
+                # double check the video is available.
+                if not os.path.exists(
+                        os.path.join(vfeat_dir, vid + ".npy")):
+                    continue
+                # double check the annotation is available.
+                if not os.path.exists(os.path.join(
+                        annotation_path,
+                        task + "_" + vid + ".csv")):
+                    continue
+                if task not in task_vids:
+                    task_vids[task] = []
+                task_vids[task].append(vid)
+        return task_vids
+
+    def _read_task_info(self, path):
+        titles = {}
+        urls = {}
+        n_steps = {}
+        steps = {}
+        with open(path, 'r') as f:
+            idx = f.readline()
+            while idx != '':
+                idx = idx.strip()
+                titles[idx] = f.readline().strip()
+                urls[idx] = f.readline().strip()
+                n_steps[idx] = int(f.readline().strip())
+                steps[idx] = f.readline().strip().split(',')
+                next(f)
+                idx = f.readline()
+        return {
+            'title': titles,
+            'url': urls,
+            'n_steps': n_steps,
+            'steps': steps
+        }
+
+    def _get_A(self, task_steps, share="words"):
+        raise ValueError("running get_A is not allowed for BERT.")
+        """Step-to-component matrices."""
+        if share == 'words':
+            # share words
+            task_step_comps = {
+                task: [step.split(' ') for step in steps]
+                for task, steps in task_steps.items()}
+        elif share == 'task_words':
+            # share words within same task
+            task_step_comps = {
+                task: [[task+'_'+tok for tok in step.split(' ')] for step in steps]
+                for task, steps in task_steps.items()}
+        elif share == 'steps':
+            # share whole step descriptions
+            task_step_comps = {
+                task: [[step] for step in steps] for task, steps in task_steps.items()}
+        else:
+            # no sharing
+            task_step_comps = {
+                task: [[task+'_'+step] for step in steps]
+                for task, steps in task_steps.items()}
+        # BERT tokenizer here?
+        vocab = []
+        for task, steps in task_step_comps.items():
+            for step in steps:
+                vocab.extend(step)
+        vocab = {comp: m for m, comp in enumerate(set(vocab))}
+        M = len(vocab)
+        A = {}
+        for task, steps in task_step_comps.items():
+            K = len(steps)
+            a = torch.zeros(M, K)
+            for k, step in enumerate(steps):
+                a[[vocab[comp] for comp in step], k] = 1
+            a /= a.sum(dim=0)
+            A[task] = a
+        return A, M
+
+
+class CrossTaskVideoProcessor(VideoProcessor):
+    def __call__(self, video_fn):
+        task, vid, steps, n_steps = video_fn
+        video_fn = os.path.join(self.vfeat_dir, vid + ".npy")
+        feat = np.load(video_fn)
+        return feat
+
+
+class CrossTaskTextProcessor(TextProcessor):
+    def __call__(self, text_id):
+        task, vid, steps, n_steps = text_id
+        step_ids = []
+        for step_str in steps:
+            step_ids.append(
+                self.tokenizer(step_str, add_special_tokens=False)["input_ids"]
+            )
+        return step_ids
+
+
+class CrossTaskAligner(Aligner):
+    """
+    TODO: it's not clear yet the formulation of the task; finish this later.
+    """
+    def __init__(self, config):
+        super().__init__(config)
+        self.annotation_path = config.annotation_path
+        self.sliding_window = config.sliding_window
+        self.sliding_window_size = config.sliding_window_size
+
+    def __call__(self, video_id, video_feature, text_feature):
+        task, vid, steps, n_steps = video_id
+        annot_path = os.path.join(
+            self.annotation_path, task + '_' + vid + '.csv')
+        video_len = len(video_feature)
+
+        labels = torch.from_numpy(self._read_assignment(
+            video_len, n_steps, annot_path)).float()
+
+        vfeats, vmasks, targets = [], [], []
+        # sliding window on video features and targets.
+        for window_start in range(0, video_len, self.sliding_window):
+            video_start = 0
+            video_end = min(video_len - window_start, self.sliding_window_size)
+            video_clip = {"start": [video_start], "end": [video_end]}
+
+            vfeat, vmask = self._build_video_seq(
+                video_feature[window_start: window_start + video_end],
+                video_clip
+            )
+
+            target = labels[window_start: window_start + video_end]
+            assert len(vfeat) >= len(target), "{},{}".format(len(vfeat), len(target))
+            # TODO: randomly drop all zero targets for training ?
+            # if self.split == "train" and target.sum() == 0:
+            #     continue
+            vfeats.append(vfeat)
+            vmasks.append(vmask)
+            targets.append(target)
+
+            if (video_len - window_start) <= self.sliding_window_size:
+                break
+
+        vfeats = torch.stack(vfeats)
+        vmasks = torch.stack(vmasks)
+        targets = torch.cat(targets, dim=0)
+
+        caps, cmasks = [], []
+        for step in text_feature:
+            step_text_feature = {"start": [0], "end": [1], "cap": [step]}
+            step_text_clip_index = [0]
+            cap, cmask = self._build_text_seq(
+                step_text_feature, step_text_clip_index
+            )
+            caps.append(cap)
+            cmasks.append(cmask)
+        caps = torch.stack(caps)
+        cmasks = torch.stack(cmasks)
+
+        return {
+            "caps": caps,
+            "cmasks": cmasks,
+            "vfeats": vfeats,  # X for original code.
+            "vmasks": vmasks,
+            "targets": targets,
+            "video_id": vid,
+            "task": task,
+            "video_len": video_len  # for later checking.
+        }
+
+    def _read_assignment(self, T, K, path):
+        """
+        refactored from https://github.com/DmZhukov/CrossTask/blob/master/data.py
+        Howto interpret contraints on loss that is going to be minimized:
+        lambd is a big number;
+        self.lambd * C is a big number for all valid position (csv stores invalids)
+
+        def forward(self, O, Y, C):
+            return (Y*(self.lambd * C - self.lsm(O))).mean(dim=0).sum()
+
+        This will load the csv file and fill-in the step col from start to end rows.
+        """
+
+        Y = np.zeros([T, K], dtype=np.uint8)
+        with open(path, 'r') as f:
+            for line in f:
+                step, start, end = line.strip().split(',')
+                start = int(math.floor(float(start)))
+                end = int(math.ceil(float(end)))
+                step = int(step) - 1
+                Y[start:end, step] = 1
+        return Y
+
+
+# --------------------- COIN -------------------------
+
+class MetaTextBinarizer(Aligner):
+    def __call__(self, text_feature):
+        text_feature = {
+            "cap": [text_feature],
+            "start": [0.],
+            "end": [100.],
+        }
+        text_clip_indexs = [0]
+
+        caps, cmasks = self._build_text_seq(
+            text_feature, text_clip_indexs
+        )
+        return {"caps": caps, "cmasks": cmasks}
+
+
+class COINActionSegmentationMetaProcessor(MetaProcessor):
+    split_map = {
+        "train": "training",
+        "valid": "testing",
+        "test": "testing",
+    }
+
+    def __init__(self, config):
+        super().__init__(config)
+        with open(self._get_split_path(config)) as fr:
+            database = json.load(fr)["database"]
+        id2label = {}
+        data = []
+        # filter the data by split.
+        for video_id, rec in database.items():
+            # always use testing to determine label_set
+            if rec["subset"] == "testing":
+                for segment in rec["annotation"]:
+                    id2label[int(segment["id"])] = segment["label"]
+        # text_labels is used for ZS setting
+        self.text_labels = ["none"] * len(id2label)
+        for label_id in id2label:
+            self.text_labels[label_id-1] = id2label[label_id]
+
+        id2label[0] = "O"
+        print("num of labels", len(id2label))
+
+        for video_id, rec in database.items():
+            if not os.path.isfile(os.path.join(config.vfeat_dir, video_id + ".npy")):
+                continue
+            if rec["subset"] == COINActionSegmentationMetaProcessor.split_map[self.split]:
+                starts, ends, labels = [], [], []
+                for segment in rec["annotation"]:
+                    start, end = segment["segment"]
+                    label = int(segment["id"])
+                    starts.append(start)
+                    ends.append(end)
+                    labels.append(label)
+                data.append(
+                    (video_id, {"start": starts, "end": ends, "label": labels}))
+        self.data = data
+
+    def meta_text_labels(self, config):
+        from transformers import default_data_collator
+        from ..utils import get_local_rank
+
+        text_processor = TextProcessor(config)
+        binarizer = MetaTextBinarizer(config)
+        # TODO: add prompts to .yaml.
+        text_labels = [label for label in self.text_labels]
+
+        if get_local_rank() == 0:
+            print(text_labels)
+
+        outputs = []
+        for text_label in text_labels:
+            text_feature = text_processor(text_label)
+            outputs.append(binarizer(text_feature))
+        return default_data_collator(outputs)
+
+    def __getitem__(self, idx):
+        return self.data[idx]
+
+
+class COINActionSegmentationTextProcessor(TextProcessor):
+    def __call__(self, text_label):
+        return text_label
+
+
+class COINActionSegmentationAligner(Aligner):
+    def __init__(self, config):
+        super().__init__(config)
+        self.sliding_window = config.sliding_window
+        self.sliding_window_size = config.sliding_window_size
+
+    def __call__(self, video_id, video_feature, text_feature):
+        starts, ends, label_ids = text_feature["start"], text_feature["end"], text_feature["label"]
+        # sliding window.
+        video_len = len(video_feature)
+
+        vfeats, vmasks, targets = [], [], []
+        # sliding window on video features and targets.
+        for window_start in range(0, video_len, self.sliding_window):
+            video_start = 0
+            video_end = min(video_len - window_start, self.sliding_window_size)
+            video_clip = {"start": [video_start], "end": [video_end]}
+            vfeat, vmask = self._build_video_seq(
+                video_feature[window_start: window_start + video_end],
+                video_clip
+            )
+            # covers video length only.
+            target = torch.full_like(vmask, -100, dtype=torch.long)
+            target[vmask] = 0
+            for start, end, label_id in zip(starts, ends, label_ids):
+                if (window_start < end) and (start < (window_start + video_end)):
+                    start_offset = max(0, math.floor(start) - window_start)
+                    end_offset = min(video_end, math.ceil(end) - window_start)
+                    target[start_offset:end_offset] = label_id
+            vfeats.append(vfeat)
+            vmasks.append(vmask)
+            targets.append(target)
+            if (video_len - window_start) <= self.sliding_window_size:
+                break
+
+        vfeats = torch.stack(vfeats)
+        vmasks = torch.stack(vmasks)
+        targets = torch.stack(targets)
+        video_targets = torch.full((video_len,), 0)
+        for start, end, label_id in zip(starts, ends, label_ids):
+            start_offset = max(0, math.floor(start))
+            end_offset = min(video_len, math.ceil(end))
+            video_targets[start_offset:end_offset] = label_id
+
+        caps = torch.LongTensor(
+            [[self.cls_token_id, self.sep_token_id,
+              self.pad_token_id, self.sep_token_id]],
+            ).repeat(vfeats.size(0), 1)
+        cmasks = torch.BoolTensor(
+            [[0, 1, 0, 1]]  # pad are valid for attention.
+            ).repeat(vfeats.size(0), 1)
+        return {
+            "caps": caps,
+            "cmasks": cmasks,
+            "vfeats": vfeats,  # X for original code.
+            "vmasks": vmasks,
+            "targets": targets,
+            "video_id": video_id,
+            "video_len": video_len,  # for later checking.
+            "video_targets": video_targets
+        }
+
+
+class DiDeMoMetaProcessor(MetaProcessor):
+    """reference: https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/eval.py
+    https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/data_processing.py
+    """
+    def __init__(self, config):
+        super().__init__(config)
+
+        assert "test" in self._get_split_path(config), "DiDeMo only supports zero-shot testing for now."
+
+        with open(self._get_split_path(config)) as data_file:
+            json_data = json.load(data_file)
+
+        data = []
+        for record in json_data:
+            data.append((record["video"], record["description"]))
+        self.data = data
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        return self.data[idx]
+
+
+class DiDeMoTextProcessor(TextProcessor):
+    """reference: https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/eval.py
+    https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/data_processing.py
+    """
+
+    def __call__(self, text):
+        return self.tokenizer(text, add_special_tokens=False)["input_ids"]
+
+
+class DiDeMoAligner(DSAligner):
+    """
+    check video length.
+    """
+
+    def __call__(self, video_id, video_feature, text_feature):
+        # print(video_feature.shape[0])
+        return super().__call__(video_id, video_feature, text_feature)

examples/MMPT/mmpt/processors/how2processor.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Copyright (c) Facebook, Inc. All Rights Reserved
+
+
+import torch
+import math
+import pickle
+import random
+import os
+import numpy as np
+
+from collections import deque
+from typing import Optional, Tuple, List
+from .processor import (
+    Processor,
+    MetaProcessor,
+    TextProcessor,
+    Aligner,
+    MMAttentionMask2DProcessor
+)
+
+from ..utils import ShardedTensor
+
+
+class How2MetaProcessor(MetaProcessor):
+    def __init__(self, config):
+        super().__init__(config)
+        path = self._get_split_path(config)
+        with open(path) as fd:
+            self.data = [line.strip() for line in fd]
+
+    def __getitem__(self, idx):
+        video_id = self.data[idx]
+        return video_id, video_id
+
+
+class ShardedHow2MetaProcessor(How2MetaProcessor):
+    def __init__(self, config):
+        super().__init__(config)
+        self.split = str(config.split)
+        self.vfeat_dir = config.vfeat_dir
+        self._init_shard()
+
+    def _init_shard(self):
+        if self.split == "train":
+            meta_fn = os.path.join(self.vfeat_dir, "train" + "_meta.pkl")
+            with open(meta_fn, "rb") as fr:
+                meta = pickle.load(fr)
+        elif self.split == "valid":
+            meta_fn = os.path.join(self.vfeat_dir, "val" + "_meta.pkl")
+            with open(meta_fn, "rb") as fr:
+                meta = pickle.load(fr)
+        elif self.split == "test":
+            print("use how2 val as test.")
+            meta_fn = os.path.join(self.vfeat_dir, "val" + "_meta.pkl")
+            with open(meta_fn, "rb") as fr:
+                meta = pickle.load(fr)
+        else:
+            raise ValueError("unsupported for MetaProcessor:", self.split)
+        video_id_to_shard = {}
+        for shard_id in meta:
+            for video_idx, video_id in enumerate(meta[shard_id]):
+                video_id_to_shard[video_id] = (shard_id, video_idx)
+        self.video_id_to_shard = video_id_to_shard
+
+    def __getitem__(self, idx):
+        video_id, video_id = super().__getitem__(idx)
+        shard_id, shard_idx = self.video_id_to_shard[video_id]
+        meta = (video_id, idx, shard_id, shard_idx)
+        return meta, meta
+
+
+class ShardedVideoProcessor(Processor):
+    """
+    mmaped shards of numpy video features.
+    """
+
+    def __init__(self, config):
+        self.split = str(config.split)
+        self.vfeat_dir = config.vfeat_dir
+
+    def __call__(self, video_id):
+        _, _, shard_id, video_idx = video_id
+        if self.split == "train":
+            shard = ShardedTensor.load(
+                os.path.join(self.vfeat_dir, "train" + "_" + str(shard_id)),
+                "r"
+            )
+        elif self.split == "valid":
+            shard = ShardedTensor.load(
+                os.path.join(self.vfeat_dir, "val" + "_" + str(shard_id)),
+                "r"
+            )
+        elif self.split == "test":
+            shard = ShardedTensor.load(
+                os.path.join(self.vfeat_dir, "val" + "_" + str(shard_id)),
+                "r"
+            )
+        else:
+            raise ValueError("unknown split", self.split)
+        feat = shard[video_idx]
+        return feat
+
+
+class ShardedTextProcessor(Processor):
+    def __init__(self, config):
+        self.tfeat_dir = str(config.tfeat_dir)
+        self.split = str(config.split)
+
+    def __call__(self, video_id):
+        _, _, shard_id, shard_idx = video_id
+        if self.split == "train":
+            target_path = self.tfeat_dir + "train" + "_" + str(shard_id)
+        elif self.split == "valid":
+            target_path = self.tfeat_dir + "val" + "_" + str(shard_id)
+        elif self.split == "test":
+            target_path = self.tfeat_dir + "val" + "_" + str(shard_id)
+        else:
+            raise ValueError("unknown split", self.split)
+
+        startend = ShardedTensor.load(
+            target_path + ".startends", "r")[shard_idx]
+        cap_ids = ShardedTensor.load(
+            target_path + ".caps_ids", "r")[shard_idx]
+        cap = []
+        for clip_idx in range(len(cap_ids)):
+            clip = cap_ids[clip_idx]
+            cap.append(clip[clip != -1].tolist())
+        start, end = startend[:, 0].tolist(), startend[:, 1].tolist()
+        return {"start": start, "end": end, "cap": cap}
+
+
+class FixedLenAligner(Aligner):
+    """
+    In the model we assume text is on the left (closer to BERT formulation)
+    and video is on the right.
+    We fix the total length of text + video.
+    max_video_len is in number of secs.
+    max_text_len is in number of tokens.
+
+    special tokens formats:
+    we use the format [CLS] [SEP] text tokens [SEP] [PAD] ...
+    [CLS] will be splitted out into:
+    [CLS] video tokens [SEP] text tokens [SEP] [PAD] ...
+    token_type_ids will be generated by the model (for now).
+    0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+    | first sequence    | second sequence |
+    so each sequence owns a [SEP] token for no-ops.
+    """
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.text_clip_sampler = TextClipSamplingProcessor(
+            self.max_len - self.max_video_len - 3
+        )
+        """
+        decide subsampling:
+        `config.subsampling` will change batch_size in trainer.
+        `config.clip_per_video` (used by RetriTask) doesn't
+            change batch_size in trainer.
+        """
+        subsampling = config.subsampling \
+            if config.subsampling is not None else None
+        if config.clip_per_video is not None:
+            subsampling = config.clip_per_video
+        self.subsampling = subsampling
+
+    def _get_text_maxlen(self):
+        # use max text len
+        return self.text_clip_sampler.max_text_len
+
+    def __call__(self, video_id, video_feature, text_feature):
+        from transformers import default_data_collator
+        video_idx = video_id[1]
+        if self.subsampling is not None and self.subsampling >= 1:
+            batch = []
+            for _ in range(self.subsampling):
+                centerclip_idx = random.randint(
+                                    0, len(text_feature["start"]) - 1)
+                batch.append(
+                    self.sampling(
+                        video_idx,
+                        video_feature,
+                        text_feature,
+                        centerclip_idx,
+                        self._get_text_maxlen()
+                    ))
+            batch = self.batch_post_processing(batch, video_feature)
+            batch = default_data_collator(batch)
+        else:
+            raise ValueError(
+                "dataset.subsampling must be >= 1 for efficient video loading.")
+            batch = self.sampling(video_idx, video_feature, text_feature)
+            batch = self.batch_post_processing(batch, video_feature)
+
+        batch["video_id"] = video_id if isinstance(video_id, str) \
+            else video_id[0]
+        # e2e: make sure frame ids is into tensor.
+        assert torch.is_tensor(batch["vfeats"])
+        return batch
+
+    def sampling(
+        self,
+        video_idx,
+        video_feature,
+        text_feature,
+        centerclip_idx=None,
+        sampled_max_text_len=None,
+    ):
+        text_clip_indexs = self.text_clip_sampler(
+            text_feature, centerclip_idx,
+            sampled_max_text_len
+        )
+        if isinstance(video_feature, np.ndarray):
+            video_len = len(video_feature)
+        else:
+            video_len = math.ceil(text_feature["end"][-1])
+
+        video_end = min(
+            math.ceil(text_feature["end"][text_clip_indexs[-1]]),
+            video_len
+        )
+        video_start = max(
+            min(
+                math.floor(text_feature["start"][text_clip_indexs[0]]),
+                video_end),
+            0
+        )
+
+        video_clips = {"start": [video_start], "end": [video_end]}
+
+        # tensorize.
+        vfeats, vmasks = self._build_video_seq(
+            video_feature, video_clips
+        )
+        caps, cmasks = self._build_text_seq(
+            text_feature, text_clip_indexs
+        )
+
+        text_start = text_clip_indexs[0]
+        text_end = text_clip_indexs[-1] + 1
+
+        return {
+            "caps": caps,
+            "cmasks": cmasks,
+            "vfeats": vfeats,
+            "vmasks": vmasks,
+            "video_start": video_start,
+            "video_end": video_end,
+            "text_start": text_start,
+            "text_end": text_end,
+        }
+
+
+class VariedLenAligner(FixedLenAligner):
+    def __init__(self, config):
+        super().__init__(config)
+        self.sampled_min_len = config.sampled_min_len
+        self.sampled_max_len = config.sampled_max_len
+
+    def _get_text_maxlen(self):
+        return random.randint(self.sampled_min_len, self.sampled_max_len)
+
+
+class StartClipAligner(VariedLenAligner):
+    def sampling(
+        self,
+        video_idx,
+        video_feature,
+        text_feature,
+        centerclip_idx=None,
+        sampled_max_text_len=None,
+    ):
+        return super().sampling(
+            video_idx, video_feature, text_feature, 0)
+
+
+class OverlappedAligner(VariedLenAligner):
+    """video clip and text clip has overlappings
+    but may not be the same start/end."""
+    def __init__(self, config):
+        super().__init__(config)
+        self.sampled_video_min_len = config.sampled_video_min_len
+        self.sampled_video_max_len = config.sampled_video_max_len
+
+        self.video_clip_sampler = VideoClipSamplingProcessor()
+
+    def _get_video_maxlen(self):
+        return random.randint(
+            self.sampled_video_min_len, self.sampled_video_max_len)
+
+    def sampling(
+        self,
+        video_idx,
+        video_feature,
+        text_feature,
+        centerclip_idx=None,
+        sampled_max_text_len=None,
+    ):
+        text_clip_indexs = self.text_clip_sampler(
+            text_feature, centerclip_idx,
+            sampled_max_text_len
+        )
+        if isinstance(video_feature, np.ndarray):
+            video_len = len(video_feature)
+        else:
+            video_len = math.ceil(text_feature["end"][-1])
+        low = math.floor(text_feature["start"][text_clip_indexs[0]])
+        high = math.ceil(text_feature["end"][text_clip_indexs[-1]])
+        if low < high:
+            center = random.randint(low, high)
+        else:
+            center = int((low + high) // 2)
+        center = max(0, min(video_feature.shape[0] - 1, center))
+
+        assert 0 <= center < video_feature.shape[0]
+
+        video_clips = self.video_clip_sampler(
+            video_len, self._get_video_maxlen(), center
+        )
+        video_start = video_clips["start"][0]
+        video_end = video_clips["end"][0]
+
+        # tensorize.
+        vfeats, vmasks = self._build_video_seq(
+            video_feature, video_clips
+        )
+        caps, cmasks = self._build_text_seq(
+            text_feature, text_clip_indexs
+        )
+
+        text_start = text_clip_indexs[0]
+        text_end = text_clip_indexs[-1] + 1
+
+        return {
+            "caps": caps,
+            "cmasks": cmasks,
+            "vfeats": vfeats,
+            "vmasks": vmasks,
+            "video_start": video_start,
+            "video_end": video_end,
+            "text_start": text_start,
+            "text_end": text_end,
+        }
+
+
+class MFMMLMAligner(FixedLenAligner):
+    """
+    `FixedLenAligner` with Masked Language Model and Masked Frame Model.
+    """
+
+    def __init__(self, config):
+        super().__init__(config)
+        keep_prob = config.keep_prob if config.keep_prob is not None else 1.0
+        self.text_clip_sampler = TextClipSamplingProcessor(
+            self.max_len - self.max_video_len - 3, keep_prob
+        )
+        self.sampled_min_len = config.sampled_min_len
+        self.sampled_max_len = config.sampled_max_len
+        self.masked_token_sampler = TextMaskingProcessor(config)
+        self.mm_type = config.mm_type \
+            if config.mm_type is not None else "full"
+        self.attnmasker = MMAttentionMask2DProcessor() \
+            if self.mm_type == "textgen" else None
+        self.masked_frame_sampler = FrameMaskingProcessor(config)
+        self.lazy_vfeat_mask = (
+            False if config.lazy_vfeat_mask is None else config.lazy_vfeat_mask
+        )
+        self.mm_prob = config.mm_prob if config.mm_prob is not None else 0.
+
+    def __call__(self, video_id, video_feature, text_feature):
+        from transformers import default_data_collator
+        if self.subsampling is not None and self.subsampling > 1:
+            batch = []
+            for _ in range(self.subsampling):
+                centerclip_idx = random.randint(
+                                    0, len(text_feature["start"]) - 1)
+                sampled_max_text_len = random.randint(
+                    self.sampled_min_len, self.sampled_max_len
+                )
+                batch.append(
+                    self.sampling(
+                        video_id,
+                        video_feature,
+                        text_feature,
+                        centerclip_idx,
+                        sampled_max_text_len,
+                    )
+                )
+            batch = self.batch_post_processing(batch, video_feature)
+            batch = default_data_collator(batch)
+        else:
+            batch = self.sampling(video_id, video_feature, text_feature)
+            batch = self.batch_post_processing(batch, video_feature)
+        batch["video_id"] = video_id if isinstance(video_id, str) \
+            else video_id[0]
+        return batch
+
+    def sampling(
+        self,
+        video_id,
+        video_feature,
+        text_feature,
+        centerclip_idx=None,
+        sampled_max_text_len=None,
+    ):
+        output = FixedLenAligner.sampling(self,
+            video_id, video_feature, text_feature,
+            centerclip_idx, sampled_max_text_len)
+
+        masking_text, masking_video = None, None
+        if random.random() < self.mm_prob:
+            if random.random() > 0.5:
+                masking_text, masking_video = self.mm_type, "no"
+            else:
+                masking_text, masking_video = "no", "full"
+        video_feats = output["vfeats"] if not self.lazy_vfeat_mask else None
+        video_label = self.masked_frame_sampler(
+            output["vmasks"], masking_video, vfeats=video_feats)
+        caps, text_label = self.masked_token_sampler(
+            output["caps"], masking_text)
+
+        output.update({
+            "caps": caps,
+            "video_label": video_label,
+            "text_label": text_label,
+        })
+
+        if self.attnmasker is not None:
+            attention_mask = self.attnmasker(
+                output["vmasks"], output["cmasks"], masking_text)
+            output.update({
+                "attention_mask": attention_mask
+            })
+        return output
+
+
+class FrameMaskingProcessor(Processor):
+    def __init__(self, config):
+        self.mfm_probability = 0.15
+        if config.mfm_probability is not None:
+            self.mfm_probability = config.mfm_probability
+
+    def __call__(self, vmasks, modality_masking=None, vfeats=None):
+        """
+        We perform lazy masking to save data transfer time.
+        It only generates video_labels by default and MFM model
+        will do actualy masking.
+        Return: `video_label` is a binary mask.
+        """
+        video_label = vmasks.clone()
+        if modality_masking is not None:
+            if modality_masking == "full":
+                probability_matrix = torch.full(video_label.shape, 1.)
+            elif modality_masking == "no":
+                probability_matrix = torch.full(video_label.shape, 0.)
+            elif modality_masking == "inverse":
+                probability_matrix = torch.full(
+                    video_label.shape, 1. - self.mfm_probability)
+            else:
+                raise ValueError("unknown modality masking.", modality_masking)
+        else:
+            probability_matrix = torch.full(
+                video_label.shape, self.mfm_probability)
+        masked_indices = torch.bernoulli(probability_matrix).bool()
+        # We only compute loss on masked tokens
+        video_label[~masked_indices] = 0
+        if vfeats is not None:
+            vfeats[video_label, :] = 0.0
+        return video_label
+
+
+class TextGenerationProcessor(Processor):
+    def __init__(self, tokenizer):
+        self.bos_token_id = tokenizer.bos_token_id
+        self.pad_token_id = tokenizer.pad_token_id
+
+    def __call__(self, inputs):
+        labels = inputs.clone()
+        # [CLS] [SEP] for video
+        labels[:2] = -100
+        # keep [SEP] for text.
+        pad_mask = labels == self.pad_token_id
+        labels[pad_mask] = -100
+        inputs[2:] = torch.cat([
+            torch.LongTensor([self.bos_token_id]),
+            inputs[2:-1]])
+        inputs[pad_mask] = self.pad_token_id
+        assert len(inputs) == len(labels)
+        return inputs, labels
+
+
+class TextMaskingProcessor(Processor):
+    def __init__(self, config):
+        """this function is borrowed from
+        `transformers/data/data_collator.DataCollatorForLanguageModeling`"""
+        self.mlm_probability = 0.15
+        if config.mlm_probability is not None:
+            self.mlm_probability = config.mlm_probability
+        self.bert_name = config.bert_name
+        # [CLS] is used as bos_token and [SEP] is used as eos_token.
+        # https://huggingface.co/transformers/master/model_doc/bertgeneration.html
+        from transformers import AutoTokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.bert_name, bos_token="[CLS]", eos_token="[SEP]")
+        self.textgen = TextGenerationProcessor(self.tokenizer)
+
+    def __call__(
+        self, inputs: torch.Tensor,
+        modality_masking=None,
+        special_tokens_mask: Optional[torch.Tensor] = None
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        expand modality_masking into
+            None: traditional bert masking.
+            "no": no masking.
+            "full": all [MASK] token for generation.
+            "gen": autoregressive generation.
+        """
+        """
+        Prepare masked tokens inputs/labels for masked language modeling:
+        80% MASK, 10% random, 10% original.
+        """
+        labels = inputs.clone()
+        # We sample a few tokens in each sequence for MLM training
+        # (with probability `self.mlm_probability`)
+        if modality_masking is not None:
+            if modality_masking == "full":
+                probability_matrix = torch.full(labels.shape, 1.)
+            elif modality_masking == "no":
+                probability_matrix = torch.full(labels.shape, 0.)
+            elif modality_masking.startswith("textgen"):
+                # [CLS] [SEP] <s> ...
+                inputs, labels = self.textgen(inputs)
+                if "mask" not in modality_masking:
+                    return inputs, labels
+                inputs = self.mask_input(inputs, special_tokens_mask)
+                return inputs, labels
+            elif modality_masking == "mask":
+                inputs = self.mask_input(inputs, special_tokens_mask)
+                labels = torch.full(inputs.shape, -100)
+                return inputs, labels
+            elif modality_masking == "inverse":
+                probability_matrix = torch.full(labels.shape, 1. - self.mlm_probability)
+            else:
+                raise ValueError("unknown modality masking.", modality_masking)
+        else:
+            probability_matrix = torch.full(labels.shape, self.mlm_probability)
+
+        if special_tokens_mask is None:
+            special_tokens_mask = self.get_special_tokens_mask(
+                labels.tolist(), already_has_special_tokens=True
+            )
+            special_tokens_mask = torch.tensor(
+                special_tokens_mask, dtype=torch.bool)
+        else:
+            special_tokens_mask = special_tokens_mask.bool()
+
+        probability_matrix.masked_fill_(special_tokens_mask, value=0.0)
+        masked_indices = torch.bernoulli(probability_matrix).bool()
+        labels[~masked_indices] = -100  # We only compute loss on masked tokens
+
+        # 80% of the time,
+        # we replace masked input tokens with tokenizer.mask_token ([MASK])
+        indices_replaced = (
+            torch.bernoulli(
+                torch.full(labels.shape, 0.8)).bool() & masked_indices
+        )
+        inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(
+            self.tokenizer.mask_token
+        )
+
+        # 10% of the time, we replace masked input tokens with random word
+        indices_random = (
+            torch.bernoulli(torch.full(labels.shape, 0.5)).bool()
+            & masked_indices
+            & ~indices_replaced
+        )
+        random_words = torch.randint(
+            len(self.tokenizer), labels.shape, dtype=torch.long
+        )
+        inputs[indices_random] = random_words[indices_random]
+
+        # The rest of the time (10% of the time) we keep the masked input
+        # tokens unchanged
+        return inputs, labels
+
+    def mask_input(self, inputs, special_tokens_mask=None):
+        # the following is new with masked autoregressive.
+        probability_matrix = torch.full(
+            inputs.shape, self.mlm_probability)
+        if special_tokens_mask is None:
+            special_tokens_mask = self.get_special_tokens_mask(
+                inputs.tolist(), already_has_special_tokens=True
+            )
+            special_tokens_mask = torch.tensor(
+                special_tokens_mask, dtype=torch.bool)
+        else:
+            special_tokens_mask = special_tokens_mask.bool()
+        probability_matrix.masked_fill_(special_tokens_mask, value=0.0)
+        masked_indices = torch.bernoulli(probability_matrix).bool()
+        indices_replaced = (
+            torch.bernoulli(
+                torch.full(inputs.shape, 0.8)).bool() & masked_indices
+        )
+        inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(
+            self.tokenizer.mask_token
+        )
+
+        # 10% of the time, we replace masked input tokens with random word
+        indices_random = (
+            torch.bernoulli(torch.full(inputs.shape, 0.5)).bool()
+            & masked_indices
+            & ~indices_replaced
+        )
+        random_words = torch.randint(
+            len(self.tokenizer), inputs.shape, dtype=torch.long
+        )
+        inputs[indices_random] = random_words[indices_random]
+        return inputs
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int],
+        token_ids_1: Optional[List[int]] = None,
+        already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Note: the version from transformers do not consider pad
+        as special tokens.
+        """
+
+        if already_has_special_tokens:
+            if token_ids_1 is not None:
+                raise ValueError(
+                    "You should not supply a second sequence if"
+                    "the provided sequence of "
+                    "ids is already formated with special tokens "
+                    "for the model."
+                )
+            return list(map(lambda x: 1 if x in [
+                self.tokenizer.sep_token_id,
+                self.tokenizer.cls_token_id,
+                self.tokenizer.pad_token_id] else 0, token_ids_0))
+
+        if token_ids_1 is not None:
+            return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1]
+
+
+class TextClipSamplingProcessor(Processor):
+    def __init__(self, max_text_len, keep_prob=1.0):
+        self.max_text_len = max_text_len
+        self.max_video_len = 256  # always hold.
+        self.keep_prob = keep_prob
+
+    def __call__(
+        self,
+        text_feature,
+        centerclip_idx=None,
+        sampled_max_text_len=None,
+        sampled_max_video_len=None,
+    ):
+        # Let's use all caps for now and see if 256 can cover all of them.
+        if sampled_max_text_len is not None:
+            max_text_len = sampled_max_text_len
+        else:
+            max_text_len = self.max_text_len
+        if sampled_max_video_len is not None:
+            max_video_len = sampled_max_video_len
+        else:
+            max_video_len = self.max_video_len
+
+        t_num_clips = len(text_feature["start"])
+
+        if centerclip_idx is None:
+            centerclip_idx = random.randint(0, t_num_clips - 1)
+
+        start_idx, end_idx = centerclip_idx, centerclip_idx + 1
+        text_clip_indexs = deque()
+        text_clip_indexs.append(start_idx)
+        text_len = len(text_feature["cap"][start_idx])
+
+        video_len = max(
+            0,
+            text_feature["end"][start_idx]
+            - text_feature["start"][start_idx],
+        )
+
+        while (
+            (start_idx > 0 or end_idx < t_num_clips)
+            and text_len < max_text_len
+            and video_len < max_video_len
+        ):
+            if random.random() > 0.5 and end_idx < t_num_clips:
+                # skip the next one?
+                if random.random() > self.keep_prob and (end_idx + 1) < t_num_clips:
+                    end_idx = end_idx + 1
+                text_clip_indexs.append(end_idx)
+                text_len += len(text_feature["cap"][end_idx])
+                end_idx += 1
+            elif start_idx > 0:
+                if random.random() > self.keep_prob and (start_idx - 1) > 0:
+                    start_idx = start_idx - 1
+                start_idx -= 1
+                text_clip_indexs.insert(0, start_idx)
+                text_len += len(text_feature["cap"][start_idx])
+            else:
+                if end_idx < t_num_clips:
+                    if random.random() > self.keep_prob and (end_idx + 1) < t_num_clips:
+                        end_idx = end_idx + 1
+                    text_clip_indexs.append(end_idx)
+                    text_len += len(text_feature["cap"][end_idx])
+                    end_idx += 1
+                else:
+                    return text_clip_indexs
+            video_len = max(
+                0,
+                text_feature["end"][text_clip_indexs[-1]]
+                - text_feature["start"][text_clip_indexs[0]],
+            )
+        return text_clip_indexs
+
+
+class VideoClipSamplingProcessor(Processor):
+    def __call__(self, video_len, max_video_len, center):
+        """
+        `video_len`: length of the video.
+        `max_video_len`: maximum video tokens allowd in a sequence.
+        `center`: initial starting index.
+        """
+        assert center >= 0 and center < video_len
+        t_clip_len = 0
+        start, end = center, center
+        while (start > 0 or end < video_len) and t_clip_len < max_video_len:
+            # decide the direction to grow.
+            if start <= 0:
+                end += 1
+            elif end >= video_len:
+                start -= 1
+            elif random.random() > 0.5:
+                end += 1
+            else:
+                start -= 1
+            t_clip_len += 1
+        return {"start": [start], "end": [end]}
+
+
+class How2MILNCEAligner(FixedLenAligner):
+    """reference: `antoine77340/MIL-NCE_HowTo100M/video_loader.py`"""
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_candidates = 4
+        self.min_time = 5.0
+        self.num_sec = 3.2
+        # self.num_sec = self.num_frames / float(self.fps)  num_frames=16 / fps = 5
+        # self.num_frames = 16
+
+    def sampling(
+        self,
+        video_id,
+        video_feature,
+        text_feature,
+        centerclip_idx=None,  # will be ignored.
+        sampled_max_text_len=None  # will be ignored.
+    ):
+        text, start, end = self._get_text(text_feature)
+        video = self._get_video(video_feature, start, end)
+
+        vfeats = torch.zeros((self.max_video_len, video_feature.shape[1]))
+        vmasks = torch.zeros((self.max_video_len,), dtype=torch.bool)
+        vfeats[: video.shape[0]] = torch.from_numpy(np.array(video))
+        vmasks[: video.shape[0]] = 1
+
+        caps, cmasks = [], []
+        for words in text:
+            cap, cmask = self._build_text_seq(text_feature, words)
+            caps.append(cap)
+            cmasks.append(cmask)
+        caps = torch.stack(caps)
+        cmasks = torch.stack(cmasks)
+        # video of shape: (video_len)
+        # text of shape (num_candidates, max_text_len)
+
+        return {
+            "caps": caps,
+            "cmasks": cmasks,
+            "vfeats": vfeats,
+            "vmasks": vmasks,
+            # "video_id": video_id,
+        }
+
+    def _get_video(self, video_feature, start, end):
+        start_seek = random.randint(start, int(max(start, end - self.num_sec)))
+        # duration = self.num_sec + 0.1
+        return video_feature[start_seek : int(start_seek + self.num_sec)]
+
+    def _get_text(self, cap):
+        ind = random.randint(0, len(cap["start"]) - 1)
+        if self.num_candidates == 1:
+            words = [ind]
+        else:
+            words = []
+            cap_start = self._find_nearest_candidates(cap, ind)
+            for i in range(self.num_candidates):
+                words.append([max(0, min(len(cap["cap"]) - 1, cap_start + i))])
+
+        start, end = cap["start"][ind], cap["end"][ind]
+        # TODO: May need to be improved for edge cases.
+        # expand the min time.
+        if end - start < self.min_time:
+            diff = self.min_time - end + start
+            start = max(0, start - diff / 2)
+            end = start + self.min_time
+        return words, int(start), int(end)
+
+    def _find_nearest_candidates(self, caption, ind):
+        """find the range of the clips."""
+        start, end = ind, ind
+        #diff = caption["end"][end] - caption["start"][start]
+        n_candidate = 1
+        while n_candidate < self.num_candidates:
+            # the first clip
+            if start == 0:
+                return 0
+            # we add () in the following condition to fix the bug.
+            elif end == (len(caption["start"]) - 1):
+                return start - (self.num_candidates - n_candidate)
+            elif (caption["end"][end] - caption["start"][start - 1]) < (
+                caption["end"][end + 1] - caption["start"][start]
+            ):
+                start -= 1
+            else:
+                end += 1
+            n_candidate += 1
+        return start
+
+
+class PKLJSONStrTextProcessor(TextProcessor):
+    """`caption.json` from howto100m are preprocessed as a
+    dict `[video_id, json_str]`.
+    Json parsing tokenization are conducted on-the-fly and cached into dict.
+    """
+
+    def __init__(self, config, max_clip_text_len=96):
+        print("[Warning] PKLJSONStrTextProcessor is slow for num_workers > 0.")
+        self.caption_pkl_path = str(config.caption_pkl_path)
+        with open(self.caption_pkl_path, "rb") as fd:
+            self.data = pickle.load(fd)
+        self.max_clip_text_len = max_clip_text_len
+        from transformers import AutoTokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            str(config.bert_name), use_fast=config.use_fast
+        )
+
+    def __call__(self, video_id):
+        caption = self.data[video_id]
+        if isinstance(caption, str):
+            import json
+            caption = json.loads(caption)
+            cap = []
+            for clip_idx, text_clip in enumerate(caption["text"]):
+                clip_ids = []
+                if isinstance(text_clip, str):
+                    clip_ids = self.tokenizer(
+                        text_clip[: self.max_clip_text_len],
+                        add_special_tokens=False
+                    )["input_ids"]
+                cap.append(clip_ids)
+            caption["cap"] = cap
+            caption.pop("text")  # save space.
+            self.data[video_id] = caption
+        return caption

examples/MMPT/mmpt/processors/how2retriprocessor.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .how2processor import (
+    ShardedHow2MetaProcessor,
+    ShardedVideoProcessor,
+    ShardedTextProcessor,
+    VariedLenAligner,
+    OverlappedAligner
+)
+
+
+class ShardedHow2VideoRetriMetaProcessor(ShardedHow2MetaProcessor):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_video_per_batch = config.num_video_per_batch
+        self.cands = [
+            self.data[batch_offset:batch_offset + self.num_video_per_batch]
+            for batch_offset in
+            range(0, (len(self.data) // (8 * self.num_video_per_batch)) * 8 * self.num_video_per_batch, self.num_video_per_batch)]
+
+    def __len__(self):
+        return len(self.cands)
+
+    def set_candidates(self, cands):
+        # no changes on num of batches.
+        print(len(self.cands), "->", len(cands))
+        # assert len(self.cands) == len(cands)
+        self.cands = cands
+
+    def __getitem__(self, idx):
+        video_ids = self.cands[idx]
+        assert isinstance(video_ids, list)
+        sharded_video_idxs = []
+        for video_id in video_ids:
+            shard_id, video_idx = self.video_id_to_shard[video_id]
+            sharded_video_idxs.append((video_id, -1, shard_id, video_idx))
+        return sharded_video_idxs, sharded_video_idxs
+
+
+class ShardedVideoRetriVideoProcessor(ShardedVideoProcessor):
+    """In retrival case the video_id
+    is a list of tuples: `(shard_id, video_idx)` ."""
+
+    def __call__(self, sharded_video_idxs):
+        assert isinstance(sharded_video_idxs, list)
+        cand_feats = []
+        for shared_video_idx in sharded_video_idxs:
+            feat = super().__call__(shared_video_idx)
+            cand_feats.append(feat)
+        return cand_feats
+
+
+class ShardedVideoRetriTextProcessor(ShardedTextProcessor):
+    """In retrival case the video_id
+    is a list of tuples: `(shard_id, video_idx)` ."""
+
+    def __call__(self, sharded_video_idxs):
+        assert isinstance(sharded_video_idxs, list)
+        cand_caps = []
+        for shared_video_idx in sharded_video_idxs:
+            caps = super().__call__(shared_video_idx)
+            cand_caps.append(caps)
+        return cand_caps
+
+
+class VideoRetriAligner(VariedLenAligner):
+    # Retritask will trim dim-0.
+    def __call__(self, sharded_video_idxs, video_features, text_features):
+        from transformers import default_data_collator
+        batch, video_ids = [], []
+        for video_id, video_feature, text_feature in \
+                zip(sharded_video_idxs, video_features, text_features):
+            sub_batch = super().__call__(video_id, video_feature, text_feature)
+            batch.append(sub_batch)
+            if isinstance(video_id, tuple):
+                video_id = video_id[0]
+            video_ids.append(video_id)
+        batch = default_data_collator(batch)
+        batch["video_id"] = video_ids
+        return batch
+
+
+class VideoRetriOverlappedAligner(OverlappedAligner):
+    # Retritask will trim dim-0.
+    def __call__(self, sharded_video_idxs, video_features, text_features):
+        from transformers import default_data_collator
+        batch, video_ids = [], []
+        for video_id, video_feature, text_feature in \
+                zip(sharded_video_idxs, video_features, text_features):
+            sub_batch = super().__call__(video_id, video_feature, text_feature)
+            batch.append(sub_batch)
+            if isinstance(video_id, tuple):
+                video_id = video_id[0]
+            video_ids.append(video_id)
+        batch = default_data_collator(batch)
+        batch["video_id"] = video_ids
+        return batch

examples/MMPT/mmpt/processors/models/s3dg.py

+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Contains a PyTorch definition for Gated Separable 3D network (S3D-G)
+with a text module for computing joint text-video embedding from raw text
+and video input. The following code will enable you to load the HowTo100M
+pretrained S3D Text-Video model from:
+  A. Miech, J.-B. Alayrac, L. Smaira, I. Laptev, J. Sivic and A. Zisserman,
+  End-to-End Learning of Visual Representations from Uncurated Instructional Videos.
+  https://arxiv.org/abs/1912.06430.
+
+S3D-G was proposed by:
+  S. Xie, C. Sun, J. Huang, Z. Tu and K. Murphy,
+  Rethinking Spatiotemporal Feature Learning For Video Understanding.
+  https://arxiv.org/abs/1712.04851.
+  Tensorflow code: https://github.com/tensorflow/models/blob/master/research/slim/nets/s3dg.py
+
+The S3D architecture was slightly modified with a space to depth trick for TPU
+optimization.
+"""
+
+import torch as th
+import torch.nn.functional as F
+import torch.nn as nn
+import os
+import numpy as np
+import re
+
+
+class InceptionBlock(nn.Module):
+    def __init__(
+        self,
+        input_dim,
+        num_outputs_0_0a,
+        num_outputs_1_0a,
+        num_outputs_1_0b,
+        num_outputs_2_0a,
+        num_outputs_2_0b,
+        num_outputs_3_0b,
+        gating=True,
+    ):
+        super(InceptionBlock, self).__init__()
+        self.conv_b0 = STConv3D(input_dim, num_outputs_0_0a, [1, 1, 1])
+        self.conv_b1_a = STConv3D(input_dim, num_outputs_1_0a, [1, 1, 1])
+        self.conv_b1_b = STConv3D(
+            num_outputs_1_0a, num_outputs_1_0b, [3, 3, 3], padding=1, separable=True
+        )
+        self.conv_b2_a = STConv3D(input_dim, num_outputs_2_0a, [1, 1, 1])
+        self.conv_b2_b = STConv3D(
+            num_outputs_2_0a, num_outputs_2_0b, [3, 3, 3], padding=1, separable=True
+        )
+        self.maxpool_b3 = th.nn.MaxPool3d((3, 3, 3), stride=1, padding=1)
+        self.conv_b3_b = STConv3D(input_dim, num_outputs_3_0b, [1, 1, 1])
+        self.gating = gating
+        self.output_dim = (
+            num_outputs_0_0a + num_outputs_1_0b + num_outputs_2_0b + num_outputs_3_0b
+        )
+        if gating:
+            self.gating_b0 = SelfGating(num_outputs_0_0a)
+            self.gating_b1 = SelfGating(num_outputs_1_0b)
+            self.gating_b2 = SelfGating(num_outputs_2_0b)
+            self.gating_b3 = SelfGating(num_outputs_3_0b)
+
+    def forward(self, input):
+        """Inception block
+      """
+        b0 = self.conv_b0(input)
+        b1 = self.conv_b1_a(input)
+        b1 = self.conv_b1_b(b1)
+        b2 = self.conv_b2_a(input)
+        b2 = self.conv_b2_b(b2)
+        b3 = self.maxpool_b3(input)
+        b3 = self.conv_b3_b(b3)
+        if self.gating:
+            b0 = self.gating_b0(b0)
+            b1 = self.gating_b1(b1)
+            b2 = self.gating_b2(b2)
+            b3 = self.gating_b3(b3)
+        return th.cat((b0, b1, b2, b3), dim=1)
+
+
+class SelfGating(nn.Module):
+    def __init__(self, input_dim):
+        super(SelfGating, self).__init__()
+        self.fc = nn.Linear(input_dim, input_dim)
+
+    def forward(self, input_tensor):
+        """Feature gating as used in S3D-G.
+      """
+        spatiotemporal_average = th.mean(input_tensor, dim=[2, 3, 4])
+        weights = self.fc(spatiotemporal_average)
+        weights = th.sigmoid(weights)
+        return weights[:, :, None, None, None] * input_tensor
+
+
+class STConv3D(nn.Module):
+    def __init__(
+        self, input_dim, output_dim, kernel_size, stride=1, padding=0, separable=False
+    ):
+        super(STConv3D, self).__init__()
+        self.separable = separable
+        self.relu = nn.ReLU(inplace=True)
+        assert len(kernel_size) == 3
+        if separable and kernel_size[0] != 1:
+            spatial_kernel_size = [1, kernel_size[1], kernel_size[2]]
+            temporal_kernel_size = [kernel_size[0], 1, 1]
+            if isinstance(stride, list) and len(stride) == 3:
+                spatial_stride = [1, stride[1], stride[2]]
+                temporal_stride = [stride[0], 1, 1]
+            else:
+                spatial_stride = [1, stride, stride]
+                temporal_stride = [stride, 1, 1]
+            if isinstance(padding, list) and len(padding) == 3:
+                spatial_padding = [0, padding[1], padding[2]]
+                temporal_padding = [padding[0], 0, 0]
+            else:
+                spatial_padding = [0, padding, padding]
+                temporal_padding = [padding, 0, 0]
+        if separable:
+            self.conv1 = nn.Conv3d(
+                input_dim,
+                output_dim,
+                kernel_size=spatial_kernel_size,
+                stride=spatial_stride,
+                padding=spatial_padding,
+                bias=False,
+            )
+            self.bn1 = nn.BatchNorm3d(output_dim)
+            self.conv2 = nn.Conv3d(
+                output_dim,
+                output_dim,
+                kernel_size=temporal_kernel_size,
+                stride=temporal_stride,
+                padding=temporal_padding,
+                bias=False,
+            )
+            self.bn2 = nn.BatchNorm3d(output_dim)
+        else:
+            self.conv1 = nn.Conv3d(
+                input_dim,
+                output_dim,
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=padding,
+                bias=False,
+            )
+            self.bn1 = nn.BatchNorm3d(output_dim)
+
+    def forward(self, input):
+        out = self.relu(self.bn1(self.conv1(input)))
+        if self.separable:
+            out = self.relu(self.bn2(self.conv2(out)))
+        return out
+
+
+class MaxPool3dTFPadding(th.nn.Module):
+    def __init__(self, kernel_size, stride=None, padding="SAME"):
+        super(MaxPool3dTFPadding, self).__init__()
+        if padding == "SAME":
+            padding_shape = self._get_padding_shape(kernel_size, stride)
+            self.padding_shape = padding_shape
+            self.pad = th.nn.ConstantPad3d(padding_shape, 0)
+        self.pool = th.nn.MaxPool3d(kernel_size, stride, ceil_mode=True)
+
+    def _get_padding_shape(self, filter_shape, stride):
+        def _pad_top_bottom(filter_dim, stride_val):
+            pad_along = max(filter_dim - stride_val, 0)
+            pad_top = pad_along // 2
+            pad_bottom = pad_along - pad_top
+            return pad_top, pad_bottom
+
+        padding_shape = []
+        for filter_dim, stride_val in zip(filter_shape, stride):
+            pad_top, pad_bottom = _pad_top_bottom(filter_dim, stride_val)
+            padding_shape.append(pad_top)
+            padding_shape.append(pad_bottom)
+        depth_top = padding_shape.pop(0)
+        depth_bottom = padding_shape.pop(0)
+        padding_shape.append(depth_top)
+        padding_shape.append(depth_bottom)
+        return tuple(padding_shape)
+
+    def forward(self, inp):
+        inp = self.pad(inp)
+        out = self.pool(inp)
+        return out
+
+
+class Sentence_Embedding(nn.Module):
+    def __init__(
+        self,
+        embd_dim,
+        num_embeddings=66250,
+        word_embedding_dim=300,
+        token_to_word_path="dict.npy",
+        max_words=16,
+        output_dim=2048,
+    ):
+        super(Sentence_Embedding, self).__init__()
+        self.word_embd = nn.Embedding(num_embeddings, word_embedding_dim)
+        self.fc1 = nn.Linear(word_embedding_dim, output_dim)
+        self.fc2 = nn.Linear(output_dim, embd_dim)
+        self.word_to_token = {}
+        self.max_words = max_words
+        token_to_word = np.load(token_to_word_path)
+        for i, t in enumerate(token_to_word):
+            self.word_to_token[t] = i + 1
+
+    def _zero_pad_tensor_token(self, tensor, size):
+        if len(tensor) >= size:
+            return tensor[:size]
+        else:
+            zero = th.zeros(size - len(tensor)).long()
+            return th.cat((tensor, zero), dim=0)
+
+    def _split_text(self, sentence):
+        w = re.findall(r"[\w']+", str(sentence))
+        return w
+
+    def _words_to_token(self, words):
+        words = [
+            self.word_to_token[word] for word in words if word in self.word_to_token
+        ]
+        if words:
+            we = self._zero_pad_tensor_token(th.LongTensor(words), self.max_words)
+            return we
+        else:
+            return th.zeros(self.max_words).long()
+
+    def _words_to_ids(self, x):
+        split_x = [self._words_to_token(self._split_text(sent.lower())) for sent in x]
+        return th.stack(split_x, dim=0)
+
+    def forward(self, x):
+        x = self._words_to_ids(x)
+        x = self.word_embd(x)
+        x = F.relu(self.fc1(x))
+        x = th.max(x, dim=1)[0]
+        x = self.fc2(x)
+        return {'text_embedding': x}
+
+
+class S3D(nn.Module):
+    def __init__(self, dict_path, num_classes=512, gating=True, space_to_depth=True):
+        super(S3D, self).__init__()
+        self.num_classes = num_classes
+        self.gating = gating
+        self.space_to_depth = space_to_depth
+        if space_to_depth:
+            self.conv1 = STConv3D(
+                24, 64, [2, 4, 4], stride=1, padding=(1, 2, 2), separable=False
+            )
+        else:
+            self.conv1 = STConv3D(
+                3, 64, [3, 7, 7], stride=2, padding=(1, 3, 3), separable=False
+            )
+        self.conv_2b = STConv3D(64, 64, [1, 1, 1], separable=False)
+        self.conv_2c = STConv3D(64, 192, [3, 3, 3], padding=1, separable=True)
+        self.gating = SelfGating(192)
+        self.maxpool_2a = MaxPool3dTFPadding(
+            kernel_size=(1, 3, 3), stride=(1, 2, 2), padding="SAME"
+        )
+        self.maxpool_3a = MaxPool3dTFPadding(
+            kernel_size=(1, 3, 3), stride=(1, 2, 2), padding="SAME"
+        )
+        self.mixed_3b = InceptionBlock(192, 64, 96, 128, 16, 32, 32)
+        self.mixed_3c = InceptionBlock(
+            self.mixed_3b.output_dim, 128, 128, 192, 32, 96, 64
+        )
+        self.maxpool_4a = MaxPool3dTFPadding(
+            kernel_size=(3, 3, 3), stride=(2, 2, 2), padding="SAME"
+        )
+        self.mixed_4b = InceptionBlock(
+            self.mixed_3c.output_dim, 192, 96, 208, 16, 48, 64
+        )
+        self.mixed_4c = InceptionBlock(
+            self.mixed_4b.output_dim, 160, 112, 224, 24, 64, 64
+        )
+        self.mixed_4d = InceptionBlock(
+            self.mixed_4c.output_dim, 128, 128, 256, 24, 64, 64
+        )
+        self.mixed_4e = InceptionBlock(
+            self.mixed_4d.output_dim, 112, 144, 288, 32, 64, 64
+        )
+        self.mixed_4f = InceptionBlock(
+            self.mixed_4e.output_dim, 256, 160, 320, 32, 128, 128
+        )
+        self.maxpool_5a = self.maxPool3d_5a_2x2 = MaxPool3dTFPadding(
+            kernel_size=(2, 2, 2), stride=(2, 2, 2), padding="SAME"
+        )
+        self.mixed_5b = InceptionBlock(
+            self.mixed_4f.output_dim, 256, 160, 320, 32, 128, 128
+        )
+        self.mixed_5c = InceptionBlock(
+            self.mixed_5b.output_dim, 384, 192, 384, 48, 128, 128
+        )
+        self.fc = nn.Linear(self.mixed_5c.output_dim, num_classes)
+        self.text_module = Sentence_Embedding(num_classes,
+            token_to_word_path=dict_path)
+
+    def _space_to_depth(self, input):
+        """3D space to depth trick for TPU optimization.
+      """
+        B, C, T, H, W = input.shape
+        input = input.view(B, C, T // 2, 2, H // 2, 2, W // 2, 2)
+        input = input.permute(0, 3, 5, 7, 1, 2, 4, 6)
+        input = input.contiguous().view(B, 8 * C, T // 2, H // 2, W // 2)
+        return input
+
+    def forward(self, inputs):
+        """Defines the S3DG base architecture."""
+        if self.space_to_depth:
+            inputs = self._space_to_depth(inputs)
+        net = self.conv1(inputs)
+        if self.space_to_depth:
+            # we need to replicate 'SAME' tensorflow padding
+            net = net[:, :, 1:, 1:, 1:]
+        net = self.maxpool_2a(net)
+        net = self.conv_2b(net)
+        net = self.conv_2c(net)
+        if self.gating:
+            net = self.gating(net)
+        net = self.maxpool_3a(net)
+        net = self.mixed_3b(net)
+        net = self.mixed_3c(net)
+        net = self.maxpool_4a(net)
+        net = self.mixed_4b(net)
+        net = self.mixed_4c(net)
+        net = self.mixed_4d(net)
+        net = self.mixed_4e(net)
+        net = self.mixed_4f(net)
+        net = self.maxpool_5a(net)
+        net = self.mixed_5b(net)
+        net = self.mixed_5c(net)
+        net = th.mean(net, dim=[2, 3, 4])
+        return {'video_embedding': self.fc(net), 'mixed_5c': net}

examples/MMPT/mmpt/processors/processor.py

+# Copyright (c) Facebook, Inc. All Rights Reserved
+
+import numpy as np
+import os
+import torch
+
+
+class Processor(object):
+    """
+    A generic processor for video (codec, feature etc.) and text.
+    """
+
+    def __call__(self, **kwargs):
+        raise NotImplementedError
+
+
+class MetaProcessor(Processor):
+    """
+    A meta processor is expected to load the metadata of a dataset:
+        (e.g., video_ids, or captions).
+    You must implement the `__getitem__` (meta datasets are rather diverse.).
+    """
+
+    def __init__(self, config):
+        self.split = config.split
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        raise NotImplementedError
+
+    def _get_split_path(self, config):
+        splits = {
+            "train": config.train_path,
+            "valid": config.val_path,
+            "test": config.test_path,
+        }
+        if config.split is not None:
+            return splits[config.split]
+        return config.train_path
+
+
+class TextProcessor(Processor):
+    """
+    A generic Text processor: rename this as `withTokenizer`.
+    tokenize a string of text on-the-fly.
+    Warning: mostly used for end tasks.
+        (on-the-fly tokenization is slow for how2.)
+    TODO(huxu): move this class as a subclass.
+    """
+
+    def __init__(self, config):
+        self.bert_name = str(config.bert_name)
+        self.use_fast = config.use_fast
+        from transformers import AutoTokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.bert_name, use_fast=self.use_fast
+        )
+
+    def __call__(self, text_id):
+        caption = self.tokenizer(text_id, add_special_tokens=False)
+        return caption["input_ids"]
+
+
+class VideoProcessor(Processor):
+    """
+    A generic video processor: load a numpy video tokens by default.
+    """
+
+    def __init__(self, config):
+        self.vfeat_dir = config.vfeat_dir
+
+    def __call__(self, video_fn):
+        if isinstance(video_fn, tuple):
+            video_fn = video_fn[0]
+        assert isinstance(video_fn, str)
+        video_fn = os.path.join(self.vfeat_dir, video_fn + ".npy")
+        feat = np.load(video_fn)
+        return feat
+
+
+class Aligner(object):
+    """
+    An alignprocessor align video and text and output a dict of tensors (for a model).
+    """
+    def __init__(self, config):
+        """__init__ needs to be light weight for more workers/threads."""
+        self.split = config.split
+        self.max_video_len = config.max_video_len
+        self.max_len = config.max_len
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(
+            str(config.bert_name), use_fast=config.use_fast
+        )
+        self.cls_token_id = tokenizer.cls_token_id
+        self.sep_token_id = tokenizer.sep_token_id
+        self.pad_token_id = tokenizer.pad_token_id
+        self.mask_token_id = tokenizer.mask_token_id
+
+    def __call__(self, video_id, video_feature, text_feature):
+        raise NotImplementedError
+
+    def _build_video_seq(self, video_feature, video_clips=None):
+        """
+        `video_feature`: available video tokens.
+        `video_clips`: video clip sequence to build.
+        """
+        if not isinstance(video_feature, np.ndarray):
+            raise ValueError(
+                "unsupported type of video_feature", type(video_feature)
+            )
+
+        if video_clips is None:
+            # this is borrowed from DSAligner
+            video_start = 0
+            video_end = min(len(video_feature), self.max_video_len)
+            # the whole sequence is a single clip.
+            video_clips = {"start": [video_start], "end": [video_end]}
+
+        vfeats = np.zeros(
+            (self.max_video_len, video_feature.shape[1]), dtype=np.float32
+        )
+        vmasks = torch.zeros((self.max_video_len,), dtype=torch.bool)
+        video_len = 0
+        for start, end in zip(video_clips["start"], video_clips["end"]):
+            clip_len = min(self.max_video_len - video_len, (end - start))
+            if clip_len > 0:
+                vfeats[video_len: video_len + clip_len] = video_feature[
+                    start: start + clip_len
+                ]
+                vmasks[video_len: video_len + clip_len] = 1
+                video_len += clip_len
+        vfeats = torch.from_numpy(vfeats)
+
+        return vfeats, vmasks
+
+    def _build_text_seq(self, text_feature, text_clip_indexs=None):
+        """
+        `text_feature`: all available clips.
+        `text_clip_indexes`: clip sequence to build.
+        """
+        if text_clip_indexs is None:
+            text_clip_indexs = [0]
+
+        full_caps = []
+        if isinstance(text_feature, dict):
+            for clip_idx in text_clip_indexs:
+                full_caps.extend(text_feature["cap"][clip_idx])
+        else:
+            full_caps = text_feature
+        max_text_len = self.max_len - self.max_video_len - 3
+        full_caps = full_caps[:max_text_len]
+        full_caps = (
+            [self.cls_token_id, self.sep_token_id] + full_caps + [self.sep_token_id]
+        )
+        text_pad_len = self.max_len - len(full_caps) - self.max_video_len
+        padded_full_caps = full_caps + [self.pad_token_id] * text_pad_len
+        caps = torch.LongTensor(padded_full_caps)
+        cmasks = torch.zeros((len(padded_full_caps),), dtype=torch.bool)
+        cmasks[: len(full_caps)] = 1
+
+        return caps, cmasks
+
+    def batch_post_processing(self, batch, video_feature):
+        return batch
+
+
+class MMAttentionMask2DProcessor(Processor):
+    """text generation requires 2d mask
+    that is harder to generate by GPU at this stage."""
+
+    def __call__(self, vmask, cmask, mtype):
+        if mtype == "textgen":
+            return self._build_textgeneration_mask(vmask, cmask)
+        elif mtype == "videogen":
+            return self._build_videogeneration_mask(vmask, cmask)
+        else:
+            return self._build_mm_mask(vmask, cmask)
+
+    def _build_mm_mask(self, vmask, cmask):
+        mask_1d = torch.cat([cmask[:1], vmask, cmask[1:]], dim=0)
+        return mask_1d[None, :].repeat(mask_1d.size(0), 1)
+
+    def _build_videogeneration_mask(self, vmask, cmask):
+        # cls_mask is only about text otherwise it will leak generation.
+        cls_text_mask = torch.cat([
+            # [CLS]
+            torch.ones(
+                (1,), dtype=torch.bool, device=cmask.device),
+            # video tokens and [SEP] for video.
+            torch.zeros(
+                (vmask.size(0) + 1,), dtype=torch.bool, device=cmask.device),
+            cmask[2:]
+            ], dim=0)
+
+        # concat horizontially.
+        video_len = int(vmask.sum())
+        video_masks = torch.cat([
+            # [CLS]
+            torch.ones(
+                (video_len, 1), dtype=torch.bool, device=cmask.device
+            ),
+            torch.tril(
+                torch.ones(
+                    (video_len, video_len),
+                    dtype=torch.bool, device=cmask.device)),
+            # video_padding
+            torch.zeros(
+                (video_len, vmask.size(0) - video_len),
+                dtype=torch.bool, device=cmask.device
+            ),
+            # [SEP] for video (unused).
+            torch.zeros(
+                (video_len, 1), dtype=torch.bool, device=cmask.device
+            ),
+            cmask[2:].unsqueeze(0).repeat(video_len, 1)
+            ], dim=1)
+
+        text_masks = cls_text_mask[None, :].repeat(
+            cmask.size(0) - 2, 1)
+        video_padding_masks = cls_text_mask[None, :].repeat(
+            vmask.size(0) - video_len, 1)
+
+        return torch.cat([
+            cls_text_mask[None, :],
+            video_masks,
+            video_padding_masks,
+            torch.cat([cmask[:1], vmask, cmask[1:]], dim=0)[None,:],
+            text_masks
+            ], dim=0)
+
+    def _build_textgeneration_mask(self, vmask, cmask):
+        # cls_mask is only about video otherwise it will leak generation.
+        cls_video_mask = torch.cat([
+            # [CLS]
+            torch.ones(
+                (1,), dtype=torch.bool, device=cmask.device),
+            vmask,
+            # [SEP]
+            torch.ones((1,), dtype=torch.bool, device=cmask.device),
+            torch.zeros(
+                (cmask.size(0)-2,), dtype=torch.bool, device=cmask.device)
+        ], dim=0)
+
+        # concat horizontially.
+        text_len = int(cmask[2:].sum())
+        text_masks = torch.cat([
+            # [CLS]
+            torch.ones(
+                (text_len, 1), dtype=torch.bool, device=cmask.device
+            ),
+            vmask.unsqueeze(0).repeat(text_len, 1),
+            # [SEP] for video.
+            torch.ones(
+                (text_len, 1), dtype=torch.bool, device=cmask.device
+            ),
+            torch.tril(
+                torch.ones(
+                    (text_len, text_len),
+                    dtype=torch.bool, device=cmask.device)),
+            # padding.
+            torch.zeros(
+                (text_len, cmask.size(0) - text_len - 2),
+                dtype=torch.bool, device=cmask.device
+            )
+        ], dim=1)
+
+        cls_video_masks = cls_video_mask[None, :].repeat(
+            vmask.size(0) + 2, 1)
+        text_padding_masks = cls_video_mask[None, :].repeat(
+            cmask.size(0) - text_len - 2, 1)
+        return torch.cat([
+            cls_video_masks, text_masks, text_padding_masks], dim=0)

examples/MMPT/mmpt/tasks/__init__.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+from .task import *
+from .vlmtask import *
+from .retritask import *
+
+try:
+    from .fairseqmmtask import *
+except ImportError:
+    pass
+
+try:
+    from .milncetask import *
+except ImportError:
+    pass
+
+try:
+    from .expretritask import *
+except ImportError:
+    pass