m4/models/vgpt2/modeling_vgpt2.py

# coding=utf-8
# Copyright 2018 The OpenAI Team Authors and 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.
"""PyTorch OpenAI GPT-2 model."""

import math
import os
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.cuda.amp import autocast
from torch.nn import CrossEntropyLoss
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
from transformers.pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer
from transformers.utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.utils.model_parallel_utils import assert_device_map, get_device_map

from m4.models import DecoupledEmbedding, DecoupledLinear
from m4.models.common import (
    expand_inputs_for_generation,
    prepare_inputs_for_generation,
    update_model_kwargs_for_generation,
)
from m4.models.custom_modules import VLOOMPreTrainedModelBase
from m4.models.perceiver.perceiver import PerceiverResampler
from m4.models.vgpt2.configuration_vgpt2 import VGPT2Config
from m4.training.utils import (
    compute_perceiver_tflops_per_batch_per_gpu,
    compute_tflops_per_batch_per_gpu,
    freeze_model,
)
from m4.utils import logging


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "gpt2"
_CONFIG_FOR_DOC = "VGPT2Config"
_TOKENIZER_FOR_DOC = "GPT2Tokenizer"

GPT2_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "gpt2",
    "gpt2-medium",
    "gpt2-large",
    "gpt2-xl",
    "distilgpt2",
    # See all GPT-2 models at https://huggingface.co/models?filter=gpt2
]


def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path):
    """Load tf checkpoints in a pytorch model"""
    try:
        import re

        import tensorflow as tf
    except ImportError:
        logger.error(
            "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
            "https://www.tensorflow.org/install/ for installation instructions."
        )
        raise
    tf_path = os.path.abspath(gpt2_checkpoint_path)
    logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
    # Load weights from TF model
    init_vars = tf.train.list_variables(tf_path)
    names = []
    arrays = []
    for name, shape in init_vars:
        logger.info(f"Loading TF weight {name} with shape {shape}")
        array = tf.train.load_variable(tf_path, name)
        names.append(name)
        arrays.append(array.squeeze())

    for name, array in zip(names, arrays):
        name = name[6:]  # skip "model/"
        name = name.split("/")
        pointer = model
        for m_name in name:
            if re.fullmatch(r"[A-Za-z]+\d+", m_name):
                scope_names = re.split(r"(\d+)", m_name)
            else:
                scope_names = [m_name]
            if scope_names[0] == "w" or scope_names[0] == "g":
                pointer = getattr(pointer, "weight")
            elif scope_names[0] == "b":
                pointer = getattr(pointer, "bias")
            elif scope_names[0] == "wpe" or scope_names[0] == "wte":
                pointer = getattr(pointer, scope_names[0])
                pointer = getattr(pointer, "weight")
            else:
                pointer = getattr(pointer, scope_names[0])
            if len(scope_names) >= 2:
                num = int(scope_names[1])
                pointer = pointer[num]
        try:
            assert (
                pointer.shape == array.shape
            ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
        except AssertionError as e:
            e.args += (pointer.shape, array.shape)
            raise
        logger.info(f"Initialize PyTorch weight {name}")
        pointer.data = torch.from_numpy(array)
    return model


class GPT2Attention(nn.Module):
    def __init__(self, config, is_cross_attention=False, layer_idx=None):
        super().__init__()

        max_positions = config.max_position_embeddings
        self.register_buffer(
            "bias",
            torch.tril(torch.ones((max_positions, max_positions), dtype=torch.uint8)).view(
                1, 1, max_positions, max_positions
            ),
        )
        self.register_buffer("masked_bias", torch.tensor(-1e4))

        self.embed_dim = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.embed_dim // self.num_heads
        self.split_size = self.embed_dim
        if self.head_dim * self.num_heads != self.embed_dim:
            raise ValueError(
                f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
                f" {self.num_heads})."
            )

        self.scale_attn_weights = config.scale_attn_weights
        self.is_cross_attention = is_cross_attention

        # Layer-wise attention scaling, reordering, and upcasting
        self.scale_attn_by_inverse_layer_idx = config.scale_attn_by_inverse_layer_idx
        self.layer_idx = layer_idx
        self.reorder_and_upcast_attn = config.reorder_and_upcast_attn

        if self.is_cross_attention:
            in_dim = self.embed_dim if not hasattr(config, "vision_embed_dim") else config.vision_embed_dim
            self.c_attn = Conv1D(2 * self.embed_dim, in_dim)
            self.q_attn = Conv1D(self.embed_dim, self.embed_dim)
        else:
            self.c_attn = Conv1D(3 * self.embed_dim, self.embed_dim)
        self.c_proj = Conv1D(self.embed_dim, self.embed_dim)

        self.attn_dropout = nn.Dropout(config.attn_pdrop)
        self.resid_dropout = nn.Dropout(config.resid_pdrop)

        self.pruned_heads = set()

    def prune_heads(self, heads):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, self.head_dim, self.pruned_heads)
        index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)])

        # Prune conv1d layers
        self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1)
        self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0)

        # Update hyper params
        self.split_size = (self.split_size // self.num_heads) * (self.num_heads - len(heads))
        self.num_heads = self.num_heads - len(heads)
        self.pruned_heads = self.pruned_heads.union(heads)

    def _attn(self, query, key, value, attention_mask=None, head_mask=None):
        attn_weights = torch.matmul(query, key.transpose(-1, -2))

        if self.scale_attn_weights:
            attn_weights = attn_weights / torch.tensor(
                value.size(-1) ** 0.5, dtype=attn_weights.dtype, device=attn_weights.device
            )

        # Layer-wise attention scaling
        if self.scale_attn_by_inverse_layer_idx:
            attn_weights = attn_weights / float(self.layer_idx + 1)

        if not self.is_cross_attention:
            # if only "normal" attention layer implements causal mask
            query_length, key_length = query.size(-2), key.size(-2)
            causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length].to(torch.bool)
            mask_value = torch.finfo(attn_weights.dtype).min
            # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
            # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
            mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
            attn_weights = torch.where(causal_mask, attn_weights, mask_value)

        if attention_mask is not None:
            # Apply the attention mask
            attn_weights = attn_weights + attention_mask

        attn_weights = nn.functional.softmax(attn_weights, dim=-1)

        # Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op otherwise
        attn_weights = attn_weights.type(value.dtype)
        attn_weights = self.attn_dropout(attn_weights)

        # Mask heads if we want to
        if head_mask is not None:
            attn_weights = attn_weights * head_mask

        attn_output = torch.matmul(attn_weights, value)

        return attn_output, attn_weights

    def _upcast_and_reordered_attn(self, query, key, value, attention_mask=None, head_mask=None):
        # Use `torch.baddbmm` (a bit more efficient w/ alpha param for scaling -- from Megatron-LM)
        bsz, num_heads, q_seq_len, dk = query.size()
        _, _, k_seq_len, _ = key.size()

        # Preallocate attn_weights for `baddbmm`
        attn_weights = torch.empty(bsz * num_heads, q_seq_len, k_seq_len, dtype=torch.float32, device=query.device)

        # Compute Scale Factor
        scale_factor = 1.0
        if self.scale_attn_weights:
            scale_factor /= float(value.size(-1)) ** 0.5

        if self.scale_attn_by_inverse_layer_idx:
            scale_factor /= float(self.layer_idx + 1)

        # Upcast (turn off autocast) and reorder (Scale K by 1 / root(dk))
        with autocast(enabled=False):
            q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape(-1, dk, k_seq_len)
            attn_weights = torch.baddbmm(attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor)
            attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len)

        if not self.is_cross_attention:
            # if only "normal" attention layer implements causal mask
            query_length, key_length = query.size(-2), key.size(-2)
            causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length].bool()
            mask_value = torch.finfo(attn_weights.dtype).min
            # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
            # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
            mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
            attn_weights = torch.where(causal_mask, attn_weights, mask_value)

        if attention_mask is not None:
            # Apply the attention mask
            attn_weights = attn_weights + attention_mask

        attn_weights = nn.functional.softmax(attn_weights, dim=-1)

        # Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op if otherwise
        if attn_weights.dtype != torch.float32:
            raise RuntimeError("Error with upcasting, attn_weights does not have dtype torch.float32")
        attn_weights = attn_weights.type(value.dtype)
        attn_weights = self.attn_dropout(attn_weights)

        # Mask heads if we want to
        if head_mask is not None:
            attn_weights = attn_weights * head_mask

        attn_output = torch.matmul(attn_weights, value)

        return attn_output, attn_weights

    def _split_heads(self, tensor, num_heads, attn_head_size):
        """
        Splits hidden_size dim into attn_head_size and num_heads
        """
        new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
        tensor = tensor.view(new_shape)
        return tensor.permute(0, 2, 1, 3)  # (batch, head, seq_length, head_features)

    def _merge_heads(self, tensor, num_heads, attn_head_size):
        """
        Merges attn_head_size dim and num_attn_heads dim into hidden_size
        """
        tensor = tensor.permute(0, 2, 1, 3).contiguous()
        new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
        return tensor.view(new_shape)

    def forward(
        self,
        hidden_states: Optional[Tuple[torch.FloatTensor]],
        layer_past: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = False,
        output_attentions: Optional[bool] = False,
    ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]:
        if encoder_hidden_states is not None:
            if not hasattr(self, "q_attn"):
                raise ValueError(
                    "If class is used as cross attention, the weights `q_attn` have to be defined. "
                    "Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`."
                )

            query = self.q_attn(hidden_states)
            key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2)
            attention_mask = encoder_attention_mask
        else:
            query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2)

        query = self._split_heads(query, self.num_heads, self.head_dim)
        key = self._split_heads(key, self.num_heads, self.head_dim)
        value = self._split_heads(value, self.num_heads, self.head_dim)

        if layer_past is not None:
            past_key, past_value = layer_past
            key = torch.cat((past_key, key), dim=-2)
            value = torch.cat((past_value, value), dim=-2)

        if use_cache is True:
            present = (key, value)
        else:
            present = None

        if self.reorder_and_upcast_attn:
            attn_output, attn_weights = self._upcast_and_reordered_attn(query, key, value, attention_mask, head_mask)
        else:
            attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)

        attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim)
        attn_output = self.c_proj(attn_output)
        attn_output = self.resid_dropout(attn_output)

        outputs = (attn_output, present)
        if output_attentions:
            outputs += (attn_weights,)

        return outputs  # a, present, (attentions)


class GPT2MLP(nn.Module):
    def __init__(self, intermediate_size, config):
        super().__init__()
        embed_dim = config.hidden_size
        self.c_fc = Conv1D(intermediate_size, embed_dim)
        self.c_proj = Conv1D(embed_dim, intermediate_size)
        self.act = ACT2FN[config.activation_function]
        self.dropout = nn.Dropout(config.resid_pdrop)

    def forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.FloatTensor:
        hidden_states = self.c_fc(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.c_proj(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states


class GPT2Block(nn.Module):
    def __init__(self, config, layer_idx=None):
        super().__init__()
        hidden_size = config.hidden_size
        inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size

        self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        self.attn = GPT2Attention(config, layer_idx=layer_idx)
        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)

        if config.add_cross_attention:
            self.crossattention = GPT2Attention(config, is_cross_attention=True, layer_idx=layer_idx)
            self.ln_cross_attn = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)

        self.mlp = GPT2MLP(inner_dim, config)

    def forward(
        self,
        hidden_states: Optional[Tuple[torch.FloatTensor]],
        layer_past: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = False,
        output_attentions: Optional[bool] = False,
    ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
        residual = hidden_states
        hidden_states = self.ln_1(hidden_states)
        attn_outputs = self.attn(
            hidden_states,
            layer_past=layer_past,
            attention_mask=attention_mask,
            head_mask=head_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
        )
        attn_output = attn_outputs[0]  # output_attn: a, present, (attentions)
        outputs = attn_outputs[1:]
        # residual connection
        hidden_states = attn_output + residual

        if encoder_hidden_states is not None:
            # add one self-attention block for cross-attention
            if not hasattr(self, "crossattention"):
                raise ValueError(
                    f"If `encoder_hidden_states` are passed, {self} has to be instantiated with "
                    "cross-attention layers by setting `config.add_cross_attention=True`"
                )
            residual = hidden_states
            hidden_states = self.ln_cross_attn(hidden_states)
            cross_attn_outputs = self.crossattention(
                hidden_states,
                attention_mask=attention_mask,
                head_mask=head_mask,
                encoder_hidden_states=encoder_hidden_states,
                encoder_attention_mask=encoder_attention_mask,
                output_attentions=output_attentions,
            )
            attn_output = cross_attn_outputs[0]
            # residual connection
            hidden_states = residual + attn_output
            outputs = outputs + cross_attn_outputs[2:]  # add cross attentions if we output attention weights

        residual = hidden_states
        hidden_states = self.ln_2(hidden_states)
        feed_forward_hidden_states = self.mlp(hidden_states)
        # residual connection
        hidden_states = residual + feed_forward_hidden_states

        if use_cache:
            outputs = (hidden_states,) + outputs
        else:
            outputs = (hidden_states,) + outputs[1:]

        return outputs  # hidden_states, present, (attentions, cross_attentions)


class VGPT2GatedCrossAttentionBlock(nn.Module):
    def __init__(self, config, layer_idx=None):
        super().__init__()
        hidden_size = config.hidden_size
        inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size

        self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        self.cross_attn = GPT2Attention(config, is_cross_attention=True, layer_idx=layer_idx)
        self.mlp = GPT2MLP(inner_dim, config)
        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        self.act = nn.Tanh()

        if config.alpha_initializer == "zeros":
            if config.alpha_type == "vector":
                self.alpha_cross_attn = nn.Parameter(torch.zeros(1, 1, hidden_size))
                self.alpha_dense = nn.Parameter(torch.zeros(1, 1, hidden_size))
            elif config.alpha_type == "float":
                self.alpha_cross_attn = nn.Parameter(torch.zeros(1))
                self.alpha_dense = nn.Parameter(torch.zeros(1))
            else:
                raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})")

        elif config.alpha_initializer == "ones":
            if config.alpha_type == "vector":
                self.alpha_cross_attn = nn.Parameter(torch.ones(1, 1, hidden_size))
                self.alpha_dense = nn.Parameter(torch.ones(1, 1, hidden_size))
            elif config.alpha_type == "float":
                self.alpha_cross_attn = nn.Parameter(torch.ones(1))
                self.alpha_dense = nn.Parameter(torch.ones(1))
            else:
                raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})")

        elif config.alpha_initializer in {"normal", "gaussian", "random"}:
            if config.alpha_type == "vector":
                self.alpha_cross_attn = nn.Parameter(
                    torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1, 1, hidden_size))
                )
                self.alpha_dense = nn.Parameter(
                    torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1, 1, hidden_size))
                )
            elif config.alpha_type == "float":
                self.alpha_cross_attn = nn.Parameter(
                    torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1))
                )
                self.alpha_dense = nn.Parameter(torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1)))
            else:
                raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})")

        else:
            raise NotImplementedError(f"Alpha initialization scheme {config.alpha_initializer} not yet implemented!")

    def forward(
        self,
        hidden_states: Optional[Tuple[torch.FloatTensor]],
        layer_past: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        image_hidden_states: Optional[torch.Tensor] = None,
        image_attention_mask: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = False,
        output_attentions: Optional[bool] = False,
    ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
        if image_hidden_states is None:
            raise ValueError(
                "`image_hidden_states` is required for VGPT2 cross attention module which are visual features to be"
                " conditioned on."
            )
            # add one self-attention block for cross-attention

        # TODO(aps): Handle cross attention in the outputs
        # if not hasattr(self, "crossattention"):
        #     raise ValueError(
        #         f"If `image_hidden_states` are passed, {self} has to be instantiated with "
        #         "cross-attention layers by setting `config.add_cross_attention=True`"
        #     )
        residual = hidden_states

        hidden_states = self.ln_1(hidden_states)
        cross_attn_outputs = self.cross_attn(
            hidden_states,
            attention_mask=attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=image_hidden_states,
            encoder_attention_mask=image_attention_mask,
            output_attentions=output_attentions,
        )
        attn_output = cross_attn_outputs[0]
        outputs = cross_attn_outputs[1:]
        # residual connection
        hidden_states = residual + self.act(self.alpha_cross_attn) * attn_output
        outputs = outputs + cross_attn_outputs[2:]  # add cross attentions if we output attention weights

        residual = hidden_states
        hidden_states = self.ln_2(hidden_states)
        feed_forward_hidden_states = self.mlp(hidden_states)
        # residual connection
        hidden_states = residual + self.act(self.alpha_dense) * feed_forward_hidden_states

        if use_cache:
            outputs = (hidden_states,) + outputs
        else:
            outputs = (hidden_states,) + outputs[1:]

        return outputs  # hidden_states, present, (attentions, cross_attentions)


class VGPT2PreTrainedModel(VLOOMPreTrainedModelBase):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = VGPT2Config
    load_tf_weights = load_tf_weights_in_gpt2
    base_model_prefix = "transformer"
    is_parallelizable = True
    supports_gradient_checkpointing = True
    _no_split_modules = ["GPT2Block"]

    def __init__(self, *inputs, **kwargs):
        super().__init__(*inputs, **kwargs)

    def _init_weights(self, module):
        """Initialize the weights."""
        if isinstance(module, (nn.Linear, Conv1D)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)

        # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
        #   > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
        #   > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
        #   >   -- GPT-2 :: https://openai.com/blog/better-language-models/
        #
        # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
        for name, p in module.named_parameters():
            if name == "c_proj.weight":
                # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
                p.data.normal_(mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer)))

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, VGPT2Model):
            module.gradient_checkpointing = value

    @classmethod
    def override_vision_model_wrapper(cls, model, config, vision_model_name, vision_model_params, torch_dtype):
        # this can be called via from_pretrained from a class w/ head or w/o head so we extract the beheaded model version
        beheaded_model = model.transformer if hasattr(model, "transformer") else model
        cls.override_vision_model(beheaded_model, vision_model_name, vision_model_params, torch_dtype)
        beheaded_model.freeze_relevant_params(config)


GPT2_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)
    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.
    Parameters:
        config ([`VGPT2Config`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

GPT2_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
            `input_ids_length` = `sequence_length` if `past_key_values` is `None` else
            `past_key_values[0][0].shape[-2]` (`sequence_length` of input past key value states). Indices of input
            sequence tokens in the vocabulary.
            If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
            `input_ids`.
            Indices can be obtained using [`GPT2Tokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.n_layers`):
            Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
            `past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have
            their past given to this model should not be passed as `input_ids` as they have already been computed.
        attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
            If `past_key_values` is used, `attention_mask` needs to contain the masking strategy that was used for
            `past_key_values`. In other words, the `attention_mask` always has to have the length:
            `len(past_key_values) + len(input_ids)`
            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:
            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.
            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.
            [What are position IDs?](../glossary#position-ids)
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
            If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see
            `past_key_values`).
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
PARALLELIZE_DOCSTRING = r"""
    This is an experimental feature and is a subject to change at a moment's notice.
    Uses a device map to distribute attention modules of the model across several devices. If no device map is given,
    it will evenly distribute blocks across all devices.
    Args:
        device_map (`Dict[int, list]`, optional, defaults to None):
            A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always
            automatically mapped to the first device (for esoteric reasons). That means that the first device should
            have fewer attention modules mapped to it than other devices. For reference, the gpt2 models have the
            following number of attention modules:
                - gpt2: 12
                - gpt2-medium: 24
                - gpt2-large: 36
                - gpt2-xl: 48
    Example:
    ```python
    # Here is an example of a device map on a machine with 4 GPUs using gpt2-xl, which has a total of 48 attention modules:
    model = GPT2LMHeadModel.from_pretrained("gpt2-xl")
    device_map = {
        0: [0, 1, 2, 3, 4, 5, 6, 7, 8],
        1: [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21],
        2: [22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34],
        3: [35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],
    }
    model.parallelize(device_map)
    ```
"""
DEPARALLELIZE_DOCSTRING = r"""
    Moves the model to cpu from a model parallel state.
    Example:
    ```python
    # On a 4 GPU machine with gpt2-large:
    model = GPT2LMHeadModel.from_pretrained("gpt2-large")
    device_map = {
        0: [0, 1, 2, 3, 4, 5, 6, 7],
        1: [8, 9, 10, 11, 12, 13, 14, 15],
        2: [16, 17, 18, 19, 20, 21, 22, 23],
        3: [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35],
    }
    model.parallelize(device_map)  # Splits the model across several devices
    model.deparallelize()  # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache()
    ```
"""


@add_start_docstrings(
    "The bare GPT2 Model transformer outputting raw hidden-states without any specific head on top.",
    GPT2_START_DOCSTRING,
)
class VGPT2Model(VGPT2PreTrainedModel):
    _keys_to_ignore_on_load_missing = ["attn.masked_bias"]

    def __init__(self, config, vision_model=None):
        super().__init__(config)

        self.embed_dim = config.hidden_size
        self.config = config

        self.wte = DecoupledEmbedding(
            num_embeddings=config.vocab_size,
            num_additional_embeddings=config.additional_vocab_size,
            embedding_dim=self.embed_dim,
            partially_freeze=config.freeze_text_layers,
        )
        self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)

        self.drop = nn.Dropout(config.embd_pdrop)
        self.h = nn.ModuleList([GPT2Block(config, layer_idx=i) for i in range(config.num_hidden_layers)])
        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)

        self.cross_layer_interval = config.cross_layer_interval
        num_cross_layers = config.num_hidden_layers // self.cross_layer_interval
        self.gated_cross_attn_layers = nn.ModuleList(
            [VGPT2GatedCrossAttentionBlock(config, layer_idx=i) for i in range(num_cross_layers)]
        )

        # Perceiver Resampler
        if config.use_resampler:
            self.perceiver_resampler = PerceiverResampler(
                self.config,
                self.config.vision_embed_dim,
                config.resampler_depth,
                config.resampler_n_heads,
                config.resampler_head_dim,
                config.resampler_n_latents,
            )
        # Model parallel
        self.model_parallel = False
        self.device_map = None
        self.gradient_checkpointing = False
        # will be vocab_size because of indices starting from 0
        self.image_token_idx = config.image_token_index

        # Load an uninitialized model and later in from_pretrained will load the pre-trained model -
        # this solves the losing of weights in `from_pretrained` on the main model
        self.vision_model = vision_model

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

        self.freeze_relevant_params(config)

    def freeze_relevant_params(self, config=None):
        if config is None:
            config = self.config

        if config.freeze_text_layers:
            self.freeze_text_layers()

        if config.freeze_vision_layers:
            freeze_model(self.vision_model)

    def freeze_text_layers(self):
        for module in [self.wpe, self.h, self.ln_f]:
            freeze_model(module)

    @add_start_docstrings(PARALLELIZE_DOCSTRING)
    # TODO(aps): Implement later for VGPT2
    def parallelize(self, device_map=None):
        # Check validity of device_map
        self.device_map = (
            get_device_map(len(self.h), range(torch.cuda.device_count())) if device_map is None else device_map
        )
        assert_device_map(self.device_map, len(self.h))
        self.model_parallel = True
        self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys()))
        self.last_device = "cuda:" + str(max(self.device_map.keys()))
        self.wte = self.wte.to(self.first_device)
        self.wpe = self.wpe.to(self.first_device)
        # Load onto devices
        for k, v in self.device_map.items():
            for block in v:
                cuda_device = "cuda:" + str(k)
                self.h[block] = self.h[block].to(cuda_device)
        # ln_f to last
        self.ln_f = self.ln_f.to(self.last_device)

    @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
    # TODO(aps): Implement later for VGPT2
    def deparallelize(self):
        self.model_parallel = False
        self.device_map = None
        self.first_device = "cpu"
        self.last_device = "cpu"
        self.wte = self.wte.to("cpu")
        self.wpe = self.wpe.to("cpu")
        for index in range(len(self.h)):
            self.h[index] = self.h[index].to("cpu")
        self.ln_f = self.ln_f.to("cpu")
        torch.cuda.empty_cache()

    def get_input_embeddings(self):
        return self.wte

    def set_input_embeddings(self, new_embeddings):
        self.wte = 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}
        """
        for layer, heads in heads_to_prune.items():
            self.h[layer].attn.prune_heads(heads)

    @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPastAndCrossAttentions,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        pixel_values: Optional[torch.FloatTensor] = None,
        image_embeddings: Optional[torch.FloatTensor] = None,
        image_attention_mask: Optional[torch.Tensor] = None,
        crossblock_head_mask: 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, BaseModelOutputWithPastAndCrossAttentions]:
        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
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        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:
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
            batch_size = input_ids.shape[0]
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
            batch_size = inputs_embeds.shape[0]
        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 token_type_ids is not None:
            token_type_ids = token_type_ids.view(-1, input_shape[-1])
        if position_ids is not None:
            position_ids = position_ids.view(-1, input_shape[-1])

        if past_key_values is None:
            past_length = 0
            past_key_values = tuple([None] * len(self.h))
        else:
            past_length = past_key_values[0][0].size(-2)
        if position_ids is None:
            position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
            position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])

        # GPT2Attention mask.
        if attention_mask is not None:
            if batch_size <= 0:
                raise ValueError("batch_size has to be defined and > 0")
            attention_mask = attention_mask.view(batch_size, -1)
            # We create a 3D attention mask from a 2D tensor mask.
            # Sizes are [batch_size, 1, 1, to_seq_length]
            # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
            # this attention mask is more simple than the triangular masking of causal attention
            # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
            attention_mask = attention_mask[:, None, None, :]

            # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
            # masked positions, this operation will create a tensor which is 0.0 for
            # positions we want to attend and the dtype's smallest value for masked positions.
            # Since we are adding it to the raw scores before the softmax, this is
            # effectively the same as removing these entirely.
            attention_mask = attention_mask.to(dtype=self.dtype)  # fp16 compatibility
            attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min

        # 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 pixel_values is not None and image_embeddings is not None:
            raise ValueError("You cannot specify both pixel_values and image_embeddings at the same time")
        elif pixel_values is not None:
            pixel_values = pixel_values.to(dtype=self.dtype, device=input_ids.device)  # fp16 compatibility
            batch_size, num_images = pixel_values.size(0), pixel_values.size(1)
            pixel_values = pixel_values.contiguous().view(batch_size * num_images, *pixel_values.shape[2:])
            # Get sequence from the vision encoder
            image_hidden_states = self.vision_model(pixel_values=pixel_values).last_hidden_state
        elif image_embeddings is not None:
            batch_size, num_images, image_seq_len, image_hidden_size = image_embeddings.size()
            image_hidden_states = image_embeddings.to(dtype=self.dtype, device=input_ids.device)
            image_hidden_states = image_hidden_states.view(batch_size * num_images, image_seq_len, image_hidden_size)

        if self.config.use_resampler:
            image_hidden_states = self.perceiver_resampler(image_hidden_states)
        image_seq_len, image_hidden_size = image_hidden_states.size(1), image_hidden_states.size(2)
        image_hidden_states = image_hidden_states.view(batch_size, num_images * image_seq_len, image_hidden_size)

        # Make image_attention_mask compatible with hidden states
        text_seq_len = image_attention_mask.size(1)
        image_attention_mask = image_attention_mask.unsqueeze(-1)
        image_attention_mask = image_attention_mask.repeat(1, 1, 1, image_seq_len)
        image_attention_mask = image_attention_mask.view(batch_size, text_seq_len, num_images * image_seq_len)
        if image_hidden_states is not None:
            image_batch_size, image_sequence_length, _ = image_hidden_states.size()
            image_hidden_shape = (image_batch_size, image_sequence_length)
            if image_attention_mask is None:
                image_attention_mask = torch.ones(image_hidden_shape, device=device)
            image_attention_mask = self.invert_attention_mask(image_attention_mask)
        else:
            image_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
        # head_mask has shape n_layer x batch x n_heads x N x N
        head_mask = self.get_head_mask(head_mask, self.config.n_layer)

        if inputs_embeds is None:
            inputs_embeds = self.wte(input_ids)

        position_embeds = self.wpe(position_ids)
        hidden_states = inputs_embeds + position_embeds

        if token_type_ids is not None:
            token_type_embeds = self.wte(token_type_ids)
            hidden_states = hidden_states + token_type_embeds

        hidden_states = self.drop(hidden_states)

        output_shape = input_shape + (hidden_states.size(-1),)

        presents = () if use_cache else None
        all_self_attentions = () if output_attentions else None
        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
        all_hidden_states = () if output_hidden_states else None
        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
            # Model parallel
            if self.model_parallel:
                torch.cuda.set_device(hidden_states.device)
                # Ensure layer_past is on same device as hidden_states (might not be correct)
                if layer_past is not None:
                    layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past)
                # Ensure that attention_mask is always on the same device as hidden_states
                if attention_mask is not None:
                    attention_mask = attention_mask.to(hidden_states.device)
                if isinstance(head_mask, torch.Tensor):
                    head_mask = head_mask.to(hidden_states.device)
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            def vblock(
                main_block,
                hidden_states,
                layer_past,
                attention_mask,
                layer_head_mask,
                use_cache,
                output_attentions,
                image_hidden_states,
                image_attention_mask,
                layer_idx,
                cross_layer_interval,
                gated_cross_attn_layers,
            ):
                # TODO(aps): Add cross attention values to respective lists
                # TODO(aps): Add xblock head mask support
                if layer_idx % cross_layer_interval == 0:
                    xblock = gated_cross_attn_layers[layer_idx // cross_layer_interval]
                    outputs = xblock(
                        hidden_states,
                        attention_mask=attention_mask,
                        image_hidden_states=image_hidden_states,
                        image_attention_mask=image_attention_mask,
                        use_cache=use_cache,
                        output_attentions=output_attentions,
                    )
                    hidden_states = outputs[0]

                outputs = main_block(
                    hidden_states,
                    layer_past=layer_past,
                    attention_mask=attention_mask,
                    head_mask=layer_head_mask,
                    use_cache=use_cache,
                    output_attentions=output_attentions,
                )

                return outputs

            if self.gradient_checkpointing and self.training:
                layer_past = None
                if use_cache:
                    logger.warning_once(
                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                    )
                    use_cache = False

                outputs = torch.utils.checkpoint.checkpoint(
                    vblock,
                    block,
                    hidden_states,
                    layer_past,
                    attention_mask,
                    head_mask[i],
                    use_cache,
                    output_attentions,
                    image_hidden_states,
                    image_attention_mask,
                    i,
                    self.cross_layer_interval,
                    self.gated_cross_attn_layers,
                )
            else:
                outputs = vblock(
                    block,
                    hidden_states,
                    layer_past=layer_past,
                    attention_mask=attention_mask,
                    layer_head_mask=head_mask[i],
                    use_cache=use_cache,
                    output_attentions=output_attentions,
                    image_hidden_states=image_hidden_states,
                    image_attention_mask=image_attention_mask,
                    layer_idx=i,
                    cross_layer_interval=self.cross_layer_interval,
                    gated_cross_attn_layers=self.gated_cross_attn_layers,
                )

            hidden_states = outputs[0]
            if use_cache is True:
                presents = presents + (outputs[1],)

            if output_attentions:
                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
                if self.config.add_cross_attention:
                    all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],)

            # Model Parallel: If it's the last layer for that device, put things on the next device
            if self.model_parallel:
                for k, v in self.device_map.items():
                    if i == v[-1] and "cuda:" + str(k) != self.last_device:
                        hidden_states = hidden_states.to("cuda:" + str(k + 1))

        hidden_states = self.ln_f(hidden_states)

        hidden_states = hidden_states.view(output_shape)
        # Add last hidden state
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(
                v
                for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions]
                if v is not None
            )

        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=presents,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
            cross_attentions=all_cross_attentions,
        )


@add_start_docstrings(
    """
    The GPT2 Model transformer with a language modeling head on top (linear layer with weights tied to the input
    embeddings).
    """,
    GPT2_START_DOCSTRING,
)
class VGPT2LMHeadModel(VGPT2PreTrainedModel):
    _keys_to_ignore_on_load_missing = [r"attn.masked_bias", r"attn.bias", r"lm_head.weight"]

    def __init__(self, config, vision_model=None):
        super().__init__(config)
        self.transformer = VGPT2Model(config, vision_model=vision_model)
        self.lm_head = DecoupledLinear(
            in_features=config.n_embd,
            out_features=config.vocab_size,
            out_additional_features=config.additional_vocab_size,
            bias=False,
            partially_freeze=config.freeze_lm_head,
        )

        # Model parallel
        self.model_parallel = False
        self.device_map = None

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

    @add_start_docstrings(PARALLELIZE_DOCSTRING)
    def parallelize(self, device_map=None):
        self.device_map = (
            get_device_map(len(self.transformer.h), range(torch.cuda.device_count()))
            if device_map is None
            else device_map
        )
        assert_device_map(self.device_map, len(self.transformer.h))
        self.transformer.parallelize(self.device_map)
        self.lm_head = self.lm_head.to(self.transformer.first_device)
        self.model_parallel = True

    @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
    def deparallelize(self):
        self.transformer.deparallelize()
        self.transformer = self.transformer.to("cpu")
        self.lm_head = self.lm_head.to("cpu")
        self.model_parallel = False
        torch.cuda.empty_cache()

    def get_output_embeddings(self):
        return self.lm_head

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

    def tie_weights(self):
        """
        Overwrite `transformers.modeling_utils.PreTrainedModel.tie_weights` to handle the case of DecoupledLinear and DecoupledEmbedding.
        """
        output_embeddings = self.get_output_embeddings()
        input_embeddings = self.get_input_embeddings()

        if getattr(self.config, "tie_word_embeddings", True):
            output_embeddings.weight = input_embeddings.weight
            if input_embeddings.num_additional_embeddings > 0:
                assert output_embeddings.out_additional_features == input_embeddings.num_additional_embeddings
                output_embeddings.additional_fc.weight = input_embeddings.additional_embedding.weight

        if hasattr(output_embeddings, "out_features") and hasattr(input_embeddings, "num_embeddings"):
            output_embeddings.out_features = input_embeddings.num_embeddings
            if hasattr(output_embeddings, "out_additional_features") and hasattr(
                input_embeddings, "num_additional_embeddings"
            ):
                output_embeddings.out_additional_features = input_embeddings.num_additional_embeddings

    def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
        return prepare_inputs_for_generation(input_ids, past=past, **kwargs)

    @staticmethod
    def _expand_inputs_for_generation(
        *args,
        **model_kwargs,
    ):
        return expand_inputs_for_generation(*args, **model_kwargs)

    @staticmethod
    def _update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=False):
        return update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder)

    @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=CausalLMOutputWithCrossAttentions,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        pixel_values: Optional[torch.FloatTensor] = None,
        image_embeddings: Optional[torch.FloatTensor] = None,
        image_attention_mask: Optional[torch.Tensor] = None,
        crossblock_head_mask: Optional[torch.Tensor] = 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, CausalLMOutputWithCrossAttentions]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        transformer_outputs = self.transformer(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            pixel_values=pixel_values,
            image_embeddings=image_embeddings,
            image_attention_mask=image_attention_mask,
            crossblock_head_mask=crossblock_head_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = transformer_outputs[0]

        # Set device for model parallelism
        if self.model_parallel:
            torch.cuda.set_device(self.transformer.first_device)
            hidden_states = hidden_states.to(self.lm_head.weight.device)

        lm_logits = self.lm_head(hidden_states)

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            if attention_mask is not None:
                shift_attention_mask = attention_mask[..., 1:]
                shift_logits = lm_logits[..., :-1, :][shift_attention_mask != 0].contiguous()
                shift_labels = labels[..., 1:][shift_attention_mask != 0].contiguous()
            else:
                shift_logits = lm_logits[..., :-1, :].contiguous()
                shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))

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

        return CausalLMOutputWithCrossAttentions(
            loss=loss,
            logits=lm_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
            cross_attentions=transformer_outputs.cross_attentions,
        )

    @staticmethod
    def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
        """
        This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
        [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
        beam_idx at every generation step.
        """
        return tuple(
            tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
            for layer_past in past
        )

    def get_model_tflops_per_batch_per_gpu(self, hparams, data_param, tokenizer, max_num_images):
        config_vl_model = self.config

        language_embed_size = config_vl_model.n_embd
        num_language_layers = config_vl_model.n_layer
        ffn_inner_size = config_vl_model.n_inner

        vision_config = self.transformer.vision_model.config
        if hasattr(vision_config, "vision_config"):
            vision_config = vision_config.vision_config

        # Get vision model blocks infos
        vision_patch_size = vision_config.patch_size
        vision_hidden_size = vision_config.hidden_size
        num_vision_layers = vision_config.num_hidden_layers
        # The +1 is for the CLS token
        single_image_seq_len = (vision_config.image_size // vision_patch_size) ** 2 + 1
        vision_exp_factor = vision_config.intermediate_size // vision_hidden_size

        # Get language and cross-att blocks infos
        num_cross_attn_layers = num_language_layers // config_vl_model.cross_layer_interval
        language_seq_len = data_param.max_seq_len
        language_exp_factor = (ffn_inner_size // language_embed_size) if ffn_inner_size is not None else 4
        cross_att_exp_factor = (ffn_inner_size // language_embed_size) if ffn_inner_size is not None else 4
        k_v_cross_attn_seq_len = (
            (self.config.resampler_n_latents * max_num_images)
            if self.config.use_resampler
            else (single_image_seq_len * max_num_images)
        )

        language_tflops_per_batch_per_gpu = compute_tflops_per_batch_per_gpu(
            num_layers=num_language_layers,
            batch_size=hparams.batch_size_per_gpu,
            q_seq_len=language_seq_len,
            k_seq_len=language_seq_len,
            hidden_size=language_embed_size,
            kv_in_dim=language_embed_size,
            ff_exp_factor=language_exp_factor,
            grad_acc_size=hparams.grad_acc_size,
            swiglu=False,
            vocab_size=tokenizer.vocab_size,
            count_backward=True,  # Always True regardless of freezing, because gradients are computed for cross-attentions
            use_grad_checkpointing=hparams.gradient_checkpointing,
        )
        cross_attention_tflops_per_batch_per_gpu = compute_tflops_per_batch_per_gpu(
            num_layers=num_cross_attn_layers,
            batch_size=hparams.batch_size_per_gpu,
            q_seq_len=language_seq_len,
            k_seq_len=k_v_cross_attn_seq_len,
            hidden_size=language_embed_size,
            kv_in_dim=vision_hidden_size,
            ff_exp_factor=cross_att_exp_factor,
            grad_acc_size=hparams.grad_acc_size,
            swiglu=False,
            vocab_size=None,
            count_backward=True,
            use_grad_checkpointing=hparams.gradient_checkpointing,
        )
        vision_tflops_per_batch_per_gpu = compute_tflops_per_batch_per_gpu(
            num_layers=num_vision_layers,
            batch_size=hparams.batch_size_per_gpu * max_num_images,
            q_seq_len=single_image_seq_len,
            k_seq_len=single_image_seq_len,
            hidden_size=vision_hidden_size,
            kv_in_dim=vision_hidden_size,
            ff_exp_factor=vision_exp_factor,
            grad_acc_size=hparams.grad_acc_size,
            swiglu=False,
            vocab_size=None,
            count_backward=not hparams.model_params["freeze_vision_layers"],
            use_grad_checkpointing=hparams.gradient_checkpointing,
        )
        if self.config.use_resampler:
            perceiver_tflops_per_batch_per_gpu = compute_perceiver_tflops_per_batch_per_gpu(
                num_layers=self.config.resampler_depth,
                batch_size=hparams.batch_size_per_gpu * max_num_images,
                q_seq_len=self.config.resampler_n_latents,
                vision_embed_seq_len=single_image_seq_len,
                q_k_v_input_dim=vision_hidden_size,
                attention_hidden_size=self.config.resampler_n_heads * self.config.resampler_head_dim,
                ff_exp_factor=cross_att_exp_factor,
                count_backward=True,
                use_grad_checkpointing=hparams.gradient_checkpointing,
            )
            flop_count = (
                language_tflops_per_batch_per_gpu
                + cross_attention_tflops_per_batch_per_gpu
                + vision_tflops_per_batch_per_gpu
                + perceiver_tflops_per_batch_per_gpu
            )
        else:
            flop_count = (
                language_tflops_per_batch_per_gpu
                + cross_attention_tflops_per_batch_per_gpu
                + vision_tflops_per_batch_per_gpu
            )
        return flop_count