modeling_manta.py

# coding=utf-8
# Copyright 2022 Mesh TensorFlow authors, Manta Authors and HuggingFace Inc. team.
#
# 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.
""" PyTorch Manta model."""


import math
from dataclasses import dataclass
import warnings
from typing import Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from transformers.modeling_outputs import BaseModelOutput, Seq2SeqLMOutput, Seq2SeqModelOutput
from transformers.modeling_utils import PreTrainedModel
from transformers.models.longformer import LongformerConfig, LongformerModel
from transformers.models.t5.configuration_t5 import T5Config
from transformers.models.t5.modeling_t5 import (
    __HEAD_MASK_WARNING_MSG,
    T5Attention,
    T5Stack,
)
from transformers.utils import (
    DUMMY_INPUTS,
    DUMMY_MASK,
    add_start_docstrings,
    add_end_docstrings,
    is_torch_fx_proxy,
    logging,
    replace_return_docstrings,
)
from configuration_manta import MantaConfig


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "MantaConfig"
_TOKENIZER_FOR_DOC = "ByT5Tokenizer"

MANTA_PRETRAINED_MODEL_ARCHIVE_LIST = []


def gaussian_pdf(x):
    return torch.exp(-x * x / 2.0)


def pad_block_embeddings(block_embeddings, pad_length):
    if not pad_length:
        return block_embeddings

    padding_tensor_len = max(pad_length - block_embeddings.size(1), 0)

    padding_tensor = torch.zeros(
        (block_embeddings.size(0), padding_tensor_len, block_embeddings.size(2)),
        device=block_embeddings.device,
        dtype=block_embeddings.dtype,
    )
    return torch.cat([block_embeddings[:, :pad_length, :], padding_tensor], dim=1)


@add_end_docstrings()
@dataclass
class MantaSeq2SeqLMOutput(Seq2SeqLMOutput):
    """
    Base class for Manta encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
    decoding.

    Args:
        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the decoder of the model.

            If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
            hidden_size)` is output.
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
        decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs.
        decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
            self-attention heads.
        cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
            weighted average in the cross-attention heads.
        encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Sequence of hidden-states at the output of the last layer of the encoder of the model.
        encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs.
        encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
            self-attention heads.
        frontier_predictions: (`torch.FloatTensor`, *optional*, of shape `(batch_size, sequence_length, 1)`):
            Probability scores of being a frontier as predicted by the FrontierPredictor module.
    """

    frontier_predictions: Optional[torch.FloatTensor] = None


@dataclass
class MantaBaseModelOutput(BaseModelOutput):
    """
    Base class for Manta's outputs, with potential hidden states, attentions and Manta's frontier predictions.

    Args:
        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
        frontier_predictions: (`torch.FloatTensor`, *optional*, of shape `(batch_size, sequence_length, 1)`):
            Probability scores of being a frontier as predicted by the FrontierPredictor module.
    """

    frontier_predictions: Optional[torch.FloatTensor] = None


class MantaFrontierPredictor(nn.Module):
    def __init__(
        self,
        hidden_size,
        num_layers,
        num_attention_heads,
        dropout_rate,
        attention_window,
        max_length,
    ):
        super().__init__()

        # First, find out what the maximum position will be after tensors are padded to a multiple of local_transformer_attention_window.
        # Then, add 1 because LongFormer position embeddings are bugged when passed inputs_embeds.
        max_position_embeddings = (max_length // attention_window + 1) * attention_window + 1
        self.hidden_size = hidden_size

        self.config = LongformerConfig(
            attention_probs_dropout_prob=dropout_rate,
            attention_window=attention_window,
            hidden_act="gelu",
            hidden_dropout_prob=dropout_rate,
            hidden_size=hidden_size,
            intermediate_size=hidden_size * 4,
            max_position_embeddings=max_position_embeddings,
            num_attention_heads=num_attention_heads,
            num_hidden_layers=num_layers,
            position_embedding_type="absolute",  # Actually cannot be changed
            vocab_size=1,  # Remove almost entirely the embeddings
            pad_token_id=0,
        )
        self.local_transformer = LongformerModel(self.config)

        self.output_projection = nn.Linear(hidden_size, 1)

    def forward(self, embeddings, attention_mask):
        longformer_output = self.local_transformer(inputs_embeds=embeddings, attention_mask=attention_mask)

        projection_outputs = self.output_projection(longformer_output.last_hidden_state)

        frontier_predictions = torch.sigmoid(projection_outputs.squeeze(-1))

        return frontier_predictions


class MantaConvFeatures(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        groups,
        padding,
    ):
        """
        This nn.Module "decomposes" the convolution in order to extract and cache feature maps. This amounts to
        computing an element-wise multiplication between weights of size (hidden_dim, kernel_size) and the input.
        """
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.groups = groups
        self.padding = padding

        if groups == in_channels:
            assert (
                in_channels == out_channels
            ), "When using `groups = in_channels`, make sure to have `in_channels == out_channels`"
            self.weight = nn.Parameter(torch.Tensor(1, 1, kernel_size, out_channels))
        elif self.groups == 1:
            self.weight = nn.Parameter(torch.Tensor(in_channels, out_channels, kernel_size))
        else:
            raise ValueError("MantaConvFeatures only supports `groups = 1` or `groups = in_channels`")

        left_pad = (kernel_size - 1) // 2
        self.pad = (left_pad, kernel_size - 1 - left_pad)

        self.reset_parameters()

    def reset_parameters(self):
        """
        See https://pytorch.org/docs/stable/_modules/torch/nn/modules/conv.html#Conv1d, in the `_ConvNd` class :
            > Setting a=sqrt(5) in kaiming_uniform is the same as initializing with
            > uniform(-1/sqrt(k), 1/sqrt(k)), where k = weight.size(1) * prod(*kernel_size)
            > For more details see: https://github.com/pytorch/pytorch/issues/15314#issuecomment-477448573"

        The reason we permute the weights before init is because `kaiming_uniform_` uses the number of in and out
        features for initialization, which are computed as tensor.size(0) and tensor.size(1). However, these
        dimensions do not correspond for my weights.
        """
        if self.groups == self.out_channels:
            nn.init.kaiming_uniform_(self.weight.permute(3, 0, 1, 2), a=math.sqrt(5))
        else:
            nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))

    def forward(self, x: torch.Tensor):
        if self.groups == 1:
            return self.forward_matmul(x)
        else:
            return self.forward_elementwise(x)

    def forward_matmul(self, x: torch.Tensor):

        if self.padding == "same":
            padded_x = self._pad_pre_conv(x)
        else:
            padded_x = x

        bs, _, seq_len = padded_x.size()

        padded_x = padded_x.transpose(-1, -2)
        # Size: (bs, seq_len+pad, hidden)

        out = padded_x.matmul(self.weight.view(self.weight.size(0), -1)).view(bs, seq_len, self.out_channels, -1)
        # Size: (bs, seq_len+pad, hidden, kernel_size)

        return out.permute(0, 2, 3, 1)

    def forward_elementwise(self, x: torch.Tensor):
        assert len(x.size()) == 3
        assert x.size(1) == self.out_channels
        # Size: (bs, hidden, seq_len)

        if self.padding == "same":
            padded_x = self._pad_pre_conv(x)
        else:
            padded_x = x

        # Unsqueeze for broadcasting with the kernel_size dim of the filters
        padded_x = padded_x.transpose(-1, -2).unsqueeze(2)
        # Size: (bs, seq_len, 1, hidden)

        out = padded_x * self.weight
        # Size: (bs, seq_len, kernel_size, hidden)

        return out.transpose(1, 3)

    def _pad_pre_conv(self, inp: torch.Tensor):
        """
        Pad with zeros at the beginning and end just like `nn.Conv1d`.
        """
        return nn.functional.pad(inp, self.pad, "constant", 0.0)

    def extra_repr(self):
        return "in_features={}, out_features={}, kernel_size={}, groups={}".format(
            self.in_channels, self.out_channels, self.kernel_size, self.groups
        )


class MantaCachedConvolutionPooling(nn.Module):
    def __init__(
        self,
        padding_length,
        output_dim,
        kernel_size,
        hidden_dim,
        depthwise_convolution,
        variance_regularization,
        mean_pool,
    ):
        super().__init__()
        self.padding_length = padding_length
        self.output_dim = output_dim
        self.kernel_size = kernel_size
        self.hidden_dim = hidden_dim
        self.depthwise_convolution = depthwise_convolution
        self.variance_regularization = variance_regularization
        self.mean_pool = mean_pool

        if isinstance(self.kernel_size, int):
            self.kernel_size = [[self.kernel_size, hidden_dim]]

        self.conv_output_dim = sum([k_dim[1] for k_dim in self.kernel_size])

        # Since the sum of the hidden dimensions of all the filters might not match the language model hidden size, we
        # specify it here
        self.out_projection = nn.Linear(self.conv_output_dim, self.output_dim, bias=True)

        self.conv_layers = nn.Sequential(
            *[
                MantaConvFeatures(self.hidden_dim, h, k, groups=h if self.depthwise_convolution else 1, padding="same")
                for (k, h) in self.kernel_size
            ]
        )

        self.eps = None
        self.conv_layer = None

    def forward(self, unconstrained_separation_probs: torch.Tensor, byte_embeddings: torch.Tensor):
        device = unconstrained_separation_probs.device
        if self.eps is None:
            self.eps = 5 * torch.finfo(unconstrained_separation_probs.dtype).resolution
            self.variance_regularization = max(self.eps, self.variance_regularization)

        if self.conv_layer is not None:
            self.conv_layer = self.conv_layer.to(device)
        batch_size, seq_len = byte_embeddings.shape[:2]

        # We set the probability of the first token to be 0 therwise the cumsum will not work
        separation_probs = unconstrained_separation_probs.clone()
        separation_probs[:, 0] = 0

        assert separation_probs.shape == (batch_size, seq_len)

        # Compute the moments of the block_id random variable
        block_id_expectation = separation_probs.cumsum(axis=-1)
        block_id_std = torch.sqrt(
            (separation_probs * (1.0 - separation_probs)).cumsum(axis=-1) + self.variance_regularization
        )

        # Get the maximum number of blocks
        max_nb_blocks = min(seq_len, (block_id_expectation + 3 * block_id_std).max().int().item() + 1)
        possible_blocks_id = torch.arange(max_nb_blocks).to(device)

        # Get the block/byte proba using the Gaussian PDF
        log_scale = block_id_std[:, None, :].log()
        log_proba = (
            -((block_id_expectation[:, None, :] - possible_blocks_id[None, :, None]) ** 2)
            / (2 * block_id_std[:, None, :])
            - log_scale
            - math.log((2 * math.pi) ** 0.5)
        )
        block_byte_proba = log_proba.softmax(-2)

        token_size = block_byte_proba.sum(-1, keepdim=True)
        regularized_token_size = torch.maximum(token_size, torch.ones_like(token_size))

        if self.mean_pool:
            block_byte_proba_normalized = block_byte_proba / regularized_token_size
        else:
            # Makes no sense to regularize using sequence length in the max_pooling case.
            block_byte_proba_normalized = block_byte_proba

        block_embeddings = self.pooling(byte_embeddings, block_byte_proba_normalized)

        pad_length = min(self.padding_length, max_nb_blocks)

        block_embeddings = pad_block_embeddings(block_embeddings, pad_length)
        block_embeddings = self.out_projection(block_embeddings)

        return block_embeddings

    def pooling(self, embeddings: torch.Tensor, block_byte_proba: torch.Tensor):
        block_embeddings = []

        for conv_layer in self.conv_layers:
            # First, compute the convolution maps SEPARATELY, i.e. without summing them together, only the element wise multiplication
            # This is similar to a cache that we'll reuse for each block probabilities.
            features = conv_layer(embeddings.transpose(1, 2)).permute(0, 3, 1, 2)
            # Size : (batch_size, seq_len + padding, hidden_dim, kernel_size)

            pad = conv_layer.pad

            for i in range(0, conv_layer.kernel_size):
                # We shift like that to match the padding done inside `conv_layer`
                features[..., i] = features[..., i].roll(pad[0] - i, 1)
            # Cut out the padded vector to obtain the right sequence length at the end
            features = features[:, pad[1] : features.size(1) - pad[0]]
            # Size : (batch_size, seq_len, hidden_dim, kernel_size)

            # Then, artificially sum the convolution features by shifting the input bytes
            padded_block_byte_proba = nn.functional.pad(block_byte_proba, pad, "constant", 0.0)
            expanded_block_byte_proba = []
            for i in range(0, conv_layer.kernel_size):
                rolled_proba = padded_block_byte_proba.clone().roll(pad[0] - i, -1)
                expanded_block_byte_proba.append(rolled_proba)
            expanded_block_byte_proba = torch.stack(expanded_block_byte_proba, -1)
            # We use :tensor.size(2) - pad instead of just :-pad because if pad = 0, we have an undesired behaviour where the whole sequence is removed
            expanded_block_byte_proba = expanded_block_byte_proba[
                :, :, pad[1] : expanded_block_byte_proba.size(2) - pad[0], :
            ]
            # Size : (batch_size, block_size, seq_len, kernel_size)

            if self.mean_pool:
                convolved = torch.einsum("b s h k, b B s k -> b B h", features, expanded_block_byte_proba)
            else:
                convolved = torch.einsum("b s h k, b B s k -> b B s h", features, expanded_block_byte_proba)
                convolved = convolved.max(dim=-2).values

            block_embeddings.append(convolved)

        block_embeddings = torch.cat(block_embeddings, dim=-1)

        return block_embeddings


class MantaPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = MantaConfig
    base_model_prefix = "transformer"
    supports_gradient_checkpointing = True

    def _init_weights(self, module):
        """Initialize the weights"""
        pass

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, (T5Attention, T5Stack)):
            module.gradient_checkpointing = value

    def _shift_right(self, input_ids):
        decoder_start_token_id = self.config.decoder_start_token_id
        pad_token_id = self.config.pad_token_id

        assert decoder_start_token_id is not None, (
            "self.model.config.decoder_start_token_id has to be defined. In T5 it is usually set to the pad_token_id."
            " See T5 docs for more information"
        )

        # shift inputs to the right
        if is_torch_fx_proxy(input_ids):
            # Item assignment is not supported natively for proxies.
            shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), decoder_start_token_id)
            shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1)
        else:
            shifted_input_ids = input_ids.new_zeros(input_ids.shape)
            shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
            shifted_input_ids[..., 0] = decoder_start_token_id

        assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined."
        # replace possible -100 values in labels by `pad_token_id`
        shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)

        return shifted_input_ids


@add_start_docstrings(
    "The bare Manta Model transformer outputting encoder's raw hidden-states without any specific head on top."
)
class MantaEncoderModel(MantaPreTrainedModel):
    authorized_missing_keys = [
        r"encoder.embed_tokens.weight",
    ]

    def __init__(self, config: MantaConfig):
        super().__init__(config)
        self.byte_embeddings = nn.Embedding(config.vocab_size, config.byte_embedding_dim)

        self.frontier_predictor = MantaFrontierPredictor(
            hidden_size=config.byte_embedding_dim,
            num_layers=config.frontier_predictor_num_layers,
            num_attention_heads=config.frontier_predictor_num_attention_heads,
            dropout_rate=config.dropout_rate,
            attention_window=config.frontier_predictor_attention_window,
            max_length=config.max_length_inputs,
        )

        self.pooler = MantaCachedConvolutionPooling(
            padding_length=config.max_length_encoder_decoder,
            output_dim=config.d_model,
            kernel_size=config.pooling_kernel_size,
            hidden_dim=config.byte_embedding_dim,
            depthwise_convolution=config.pooling_depthwise_convolution,
            variance_regularization=config.pooling_variance_regularization,
            mean_pool=config.pooling_mean_pool,
        )

        self.t5_encoder = T5Stack(
            T5Config(
                d_model=config.d_model,
                d_kv=config.d_kv,
                d_ff=config.d_ff,
                num_layers=config.num_layers,
                num_heads=config.num_heads,
                relative_attention_num_buckets=config.relative_attention_num_buckets,
                relative_attention_max_distance=config.relative_attention_max_distance,
                dropout_rate=config.dropout_rate,
                layer_norm_epsilon=config.layer_norm_epsilon,
                initializer_factor=config.initializer_factor,
                feed_forward_proj=config.feed_forward_proj,
                pad_token_id=config.pad_token_id,
                eos_token_id=config.eos_token_id,
                is_decoder=False,
                use_cache=False,
            )
        )

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.byte_embeddings

    def set_input_embeddings(self, new_embeddings):
        self.byte_embeddings = new_embeddings

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.t5_encoder.block[layer].layer[0].SelfAttention.prune_heads(heads)

    def _compute_pooled_representations(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
    ):
        if inputs_embeds is None and input_ids is None:
            return None

        byte_embeddings = inputs_embeds if inputs_embeds is not None else self.byte_embeddings(input_ids)

        frontier_predictions = self.frontier_predictor(byte_embeddings, attention_mask)

        pooled_representations = self.pooler(frontier_predictions, byte_embeddings)

        return pooled_representations, frontier_predictions

    @replace_return_docstrings(output_type=MantaBaseModelOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.FloatTensor], MantaBaseModelOutput]:
        r"""
        Returns:

        Example:

        ```python
        >>> from transformers import ByT5Tokenizer, MantaEncoderModel

        >>> tokenizer = ByT5Tokenizer.from_pretrained("google/byt5-small")
        >>> model = MantaEncoderModel.from_pretrained("nthngdy/manta-small")
        >>> input_ids = tokenizer(
        ...     "Studies have been shown that owning a dog is good for you", return_tensors="pt"
        ... ).input_ids  # Batch size 1
        >>> outputs = model(input_ids=input_ids)
        >>> last_hidden_states = outputs.last_hidden_state
        ```"""
        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

        pooled_representations, frontier_predictions = self._compute_pooled_representations(
            input_ids, attention_mask, inputs_embeds
        )

        encoder_outputs = self.t5_encoder(
            inputs_embeds=pooled_representations,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if not return_dict:
            return encoder_outputs + (frontier_predictions,)

        return MantaBaseModelOutput(frontier_predictions=frontier_predictions, **encoder_outputs)


class MantaModel(MantaPreTrainedModel):
    _keys_to_ignore_on_load_missing = [
        r"encoder_decoder.encoder.embed_tokens.weight",
        r"encoder_decoder.decoder.embed_tokens.weight",
    ]
    _keys_to_ignore_on_load_unexpected = [
        r"encoder_decoder.decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight",
    ]

    def __init__(self, config: MantaConfig):
        super().__init__(config)

        self.encoder = MantaEncoderModel(config)

        self.decoder_embeddings = nn.Embedding(config.vocab_size, config.d_model)
        self.decoder = T5Stack(
            T5Config(
                vocab_size=config.vocab_size,
                d_model=config.d_model,
                d_kv=config.d_kv,
                d_ff=config.d_ff,
                num_layers=config.num_decoder_layers,
                num_heads=config.num_heads,
                relative_attention_num_buckets=config.relative_attention_num_buckets,
                relative_attention_max_distance=config.relative_attention_max_distance,
                dropout_rate=config.dropout_rate,
                layer_norm_epsilon=config.layer_norm_epsilon,
                initializer_factor=config.initializer_factor,
                feed_forward_proj=config.feed_forward_proj,
                use_cache=config.use_cache,
                pad_token_id=config.pad_token_id,
                eos_token_id=config.eos_token_id,
                is_decoder=True,
                is_encoder_decoder=False,
            ),
            self.decoder_embeddings,
        )

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.encoder.get_input_embeddings()

    def set_input_embeddings(self, new_embeddings):
        self.encoder.set_input_embeddings(new_embeddings)

    def get_encoder(self):
        return self.encoder

    def get_decoder(self):
        return self.decoder

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    @replace_return_docstrings(output_type=MantaSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.BoolTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        decoder_head_mask: Optional[torch.FloatTensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        decoder_inputs_embeds: Optional[torch.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.FloatTensor], MantaSeq2SeqLMOutput]:
        r"""
        Returns:

        Example:

        ```python
        >>> from transformers import ByT5Tokenizer, MantaModel

        >>> tokenizer = ByT5Tokenizer.from_pretrained("google/byt5-small")
        >>> model = MantaModel.from_pretrained("nthngdy/manta-small")

        >>> input_ids = tokenizer(
        ...     "Studies have been shown that owning a dog is good for you", return_tensors="pt"
        ... ).input_ids  # Batch size 1
        >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1

        >>> # preprocess: Prepend decoder_input_ids with start token which is pad token for MantaModel.
        >>> # This is not needed for torch's MantaForConditionalGeneration as it does this internally using labels arg.
        >>> decoder_input_ids = model._shift_right(decoder_input_ids)

        >>> # forward pass
        >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
        >>> last_hidden_states = outputs.last_hidden_state
        ```"""
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        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 encoder_outputs is None:
            encoder_outputs = self.encoder(
                input_ids=input_ids,
                attention_mask=attention_mask,
                inputs_embeds=inputs_embeds,
                head_mask=head_mask,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
            )
        elif return_dict and not isinstance(encoder_outputs, MantaBaseModelOutput):
            encoder_outputs = MantaBaseModelOutput(
                last_hidden_state=encoder_outputs[0],
                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
                frontier_predictions=encoder_outputs[3] if len(encoder_outputs) > 3 else None,
            )

        hidden_states = encoder_outputs[0]

        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            encoder_hidden_states=hidden_states,
            encoder_attention_mask=attention_mask,
            inputs_embeds=decoder_inputs_embeds,
            head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if not return_dict:
            return decoder_outputs + encoder_outputs

        return MantaSeq2SeqLMOutput(
            last_hidden_state=decoder_outputs.last_hidden_state,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
            frontier_predictions=encoder_outputs.frontier_predictions,
        )


@add_start_docstrings("""Manta Model with a `language modeling` head on top.""")
class MantaForConditionalGeneration(MantaPreTrainedModel):
    _keys_to_ignore_on_load_missing = [
        r"encoder.embed_tokens.weight",
        r"decoder.embed_tokens.weight",
        r"lm_head.weight",
    ]
    _keys_to_ignore_on_load_unexpected = [
        r"decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight",
    ]

    def __init__(self, config: MantaConfig):
        super().__init__(config)
        self.model_dim = config.d_model

        self.encoder = MantaEncoderModel(config)

        self.decoder_embeddings = nn.Embedding(config.vocab_size, config.d_model)
        self.decoder = T5Stack(
            T5Config(
                vocab_size=config.vocab_size,
                d_model=config.d_model,
                d_kv=config.d_kv,
                d_ff=config.d_ff,
                num_layers=config.num_decoder_layers,
                num_heads=config.num_heads,
                relative_attention_num_buckets=config.relative_attention_num_buckets,
                relative_attention_max_distance=config.relative_attention_max_distance,
                dropout_rate=config.dropout_rate,
                layer_norm_epsilon=config.layer_norm_epsilon,
                initializer_factor=config.initializer_factor,
                feed_forward_proj=config.feed_forward_proj,
                use_cache=config.use_cache,
                pad_token_id=config.pad_token_id,
                eos_token_id=config.eos_token_id,
                is_decoder=True,
                is_encoder_decoder=False,
            ),
            self.decoder_embeddings,
        )

        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.encoder.get_input_embeddings()

    def set_input_embeddings(self, new_embeddings):
        self.encoder.set_input_embeddings(new_embeddings)

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def get_output_embeddings(self):
        return self.lm_head

    def get_encoder(self):
        return self.encoder

    def get_decoder(self):
        return self.decoder

    @replace_return_docstrings(output_type=MantaSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.BoolTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        decoder_head_mask: Optional[torch.FloatTensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.FloatTensor], MantaSeq2SeqLMOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ...,
            config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for
            labels in `[0, ..., config.vocab_size]`

        Returns:

        Examples:

        ```python
        >>> from transformers import ByT5Tokenizer, MantaForConditionalGeneration

        >>> tokenizer = ByT5Tokenizer.from_pretrained("google/byt5-small")
        >>> model = MantaForConditionalGeneration.from_pretrained("nthngdy/manta-small")

        >>> # training
        >>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
        >>> labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id_2>", return_tensors="pt").input_ids
        >>> outputs = model(input_ids=input_ids, labels=labels)
        >>> loss = outputs.loss
        >>> logits = outputs.logits

        >>> # inference
        >>> input_ids = tokenizer(
        ...     "summarize: studies have shown that owning a dog is good for you", return_tensors="pt"
        ... ).input_ids  # Batch size 1
        >>> outputs = model.generate(input_ids)
        >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
        >>> # studies have shown that owning a dog is good for you.
        ```"""
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        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

        # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
        if head_mask is not None and decoder_head_mask is None:
            if self.config.num_layers == self.config.num_decoder_layers:
                warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
                decoder_head_mask = head_mask

        # Encode if needed (training, first prediction pass)
        if encoder_outputs is None:
            encoder_outputs = self.encoder(
                input_ids=input_ids,
                attention_mask=attention_mask,
                inputs_embeds=inputs_embeds,
                head_mask=head_mask,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
            )
        elif return_dict and not isinstance(encoder_outputs, MantaBaseModelOutput):
            encoder_outputs = BaseModelOutput(
                last_hidden_state=encoder_outputs[0],
                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
                frontier_predictions=encoder_outputs[3] if len(encoder_outputs) > 3 else None,
            )

        hidden_states = encoder_outputs[0]

        if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
            # get decoder inputs from shifting lm labels to the right
            decoder_input_ids = self._shift_right(labels)

        # Decode
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            inputs_embeds=decoder_inputs_embeds,
            past_key_values=past_key_values,
            encoder_hidden_states=hidden_states,
            head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = decoder_outputs[0]

        if self.config.tie_word_embeddings:
            # Rescale output before projecting on vocab
            # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
            sequence_output = sequence_output * (self.model_dim**-0.5)

        lm_logits = self.lm_head(sequence_output)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss(ignore_index=-100)
            loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
            # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666

        if not return_dict:
            output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
            return ((loss,) + output) if loss is not None else output

        return MantaSeq2SeqLMOutput(
            loss=loss,
            logits=lm_logits,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
            frontier_predictions=encoder_outputs.frontier_predictions,
        )

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past=None,
        attention_mask=None,
        head_mask=None,
        decoder_head_mask=None,
        cross_attn_head_mask=None,
        use_cache=None,
        encoder_outputs=None,
        **kwargs
    ):

        # cut decoder_input_ids if past is used
        if past is not None:
            input_ids = input_ids[:, -1:]

        return {
            "decoder_input_ids": input_ids,
            "past_key_values": past,
            "encoder_outputs": encoder_outputs,
            "attention_mask": attention_mask,
            "head_mask": head_mask,
            "decoder_head_mask": decoder_head_mask,
            "cross_attn_head_mask": cross_attn_head_mask,
            "use_cache": use_cache,
        }

    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
        return self._shift_right(labels)

    def _reorder_cache(self, past, beam_idx):
        # if decoder past is not included in output
        # speedy decoding is disabled and no need to reorder
        if past is None:
            logger.warning("You might want to consider setting `use_cache=True` to speed up decoding")
            return past

        reordered_decoder_past = ()
        for layer_past_states in past:
            # get the correct batch idx from layer past batch dim
            # batch dim of `past` is at 2nd position
            reordered_layer_past_states = ()
            for layer_past_state in layer_past_states:
                # need to set correct `past` for each of the four key / value states
                reordered_layer_past_states = reordered_layer_past_states + (
                    layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)),
                )

            assert reordered_layer_past_states[0].shape == layer_past_states[0].shape
            assert len(reordered_layer_past_states) == len(layer_past_states)

            reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,)
        return reordered_decoder_past