AMD-SHARK-Studio/tank/examples/opt/shark_opt_wrapper_train.py

# coding=utf-8
# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. 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 OPT model."""
import random
from typing import List, Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import OPTConfig, PreTrainedModel
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
)

_CHECKPOINT_FOR_DOC = "facebook/opt-350m"
_CONFIG_FOR_DOC = "OPTConfig"
_TOKENIZER_FOR_DOC = "GPT2Tokenizer"

# Base model docstring
_EXPECTED_OUTPUT_SHAPE = [1, 8, 1024]


OPT_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "facebook/opt-125m",
    "facebook/opt-350m",
    "facebook/opt-1.3b",
    "facebook/opt-2.7b",
    "facebook/opt-6.7b",
    "facebook/opt-13b",
    "facebook/opt-30b",
    # See all OPT models at https://huggingface.co/models?filter=opt
]


def _make_causal_mask(
    input_ids_shape: torch.Size,
    dtype: torch.dtype,
    past_key_values_length: int = 0,
):
    """
    Make causal mask used for bi-directional self-attention.
    """
    bsz, tgt_len = input_ids_shape
    mask = torch.full((tgt_len, tgt_len), float("-inf"))
    mask_cond = torch.arange(int(mask.size(-1)))
    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
    # mask = mask.to(dtype)

    if past_key_values_length > 0:
        mask = torch.cat(
            [torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask],
            dim=-1,
        )
    return mask[None, None, :, :].expand(
        bsz, 1, tgt_len, tgt_len + past_key_values_length
    )


def _expand_mask(
    mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None
):
    """
    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
    """
    bsz, src_len = map(int, mask.size())
    tgt_len = tgt_len if tgt_len is not None else src_len

    expanded_mask = (
        mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
    )

    inverted_mask = 1.0 - expanded_mask

    return inverted_mask.masked_fill(
        inverted_mask.to(torch.bool), torch.finfo(dtype).min
    )
    # return inverted_mask.masked_fill(inverted_mask, torch.finfo(dtype).min)


class OPTLearnedPositionalEmbedding(nn.Embedding):
    """
    This module learns positional embeddings up to a fixed maximum size.
    """

    def __init__(self, num_embeddings: int, embedding_dim: int):
        # OPT is set up so that if padding_idx is specified then offset the embedding ids by 2
        # and adjust num_embeddings appropriately. Other models don't have this hack
        self.offset = 2
        super().__init__(num_embeddings + self.offset, embedding_dim)

    def forward(
        self,
        attention_mask: torch.LongTensor,
        past_key_values_length: int = 0,
    ):
        """`input_ids_shape` is expected to be [bsz x seqlen]."""
        attention_mask = attention_mask.long()

        # create positions depending on attention_mask
        positions = (
            torch.cumsum(attention_mask, dim=1).type_as(attention_mask)
            * attention_mask
        ).long() - 1

        # cut positions if `past_key_values_length` is > 0
        positions = positions[:, past_key_values_length:]

        return super().forward(positions + self.offset)


# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->OPT
class OPTAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        dropout: float = 0.0,
        is_decoder: bool = False,
        bias: bool = True,
    ):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads

        if (self.head_dim * num_heads) != self.embed_dim:
            raise ValueError(
                "embed_dim must be divisible by num_heads (got `embed_dim`:"
                f" {self.embed_dim} and `num_heads`: {num_heads})."
            )
        self.scaling = self.head_dim**-0.5
        self.is_decoder = is_decoder

        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)

    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
        return (
            tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
            .transpose(1, 2)
            .contiguous()
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        key_value_states: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        layer_head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> Tuple[
        torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]
    ]:
        """Input shape: Batch x Time x Channel"""

        # if key_value_states are provided this layer is used as a cross-attention layer
        # for the decoder
        is_cross_attention = key_value_states is not None

        # bsz, tgt_len, _ = map(int, hidden_states.size())
        bsz, tgt_len, _ = hidden_states.size()

        # get query proj
        query_states = self.q_proj(hidden_states) * self.scaling
        # get key, value proj
        if is_cross_attention and past_key_value is not None:
            # reuse k,v, cross_attentions
            key_states = past_key_value[0]
            value_states = past_key_value[1]
        elif is_cross_attention:
            # cross_attentions
            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
        elif past_key_value is not None:
            # reuse k, v, self_attention
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)
        else:
            # self_attention
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)

        if self.is_decoder:
            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
            # Further calls to cross_attention layer can then reuse all cross-attention
            # key/value_states (first "if" case)
            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
            # all previous decoder key/value_states. Further calls to uni-directional self-attention
            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
            # if encoder bi-directional self-attention `past_key_value` is always `None`
            past_key_value = (key_states, value_states)

        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
        query_states = self._shape(query_states, tgt_len, bsz).view(
            *proj_shape
        )
        key_states = key_states.view(*proj_shape)
        value_states = value_states.view(*proj_shape)

        src_len = key_states.size(1)
        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))

        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
            raise ValueError(
                "Attention weights should be of size"
                f" {(bsz * self.num_heads, tgt_len, src_len)}, but is"
                f" {attn_weights.size()}"
            )

        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
                raise ValueError(
                    "Attention mask should be of size"
                    f" {(bsz, 1, tgt_len, src_len)}, but is"
                    f" {attention_mask.size()}"
                )
            attn_weights = (
                attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
                + attention_mask
            )
            attn_weights = attn_weights.view(
                bsz * self.num_heads, tgt_len, src_len
            )

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

        if layer_head_mask is not None:
            if layer_head_mask.size() != (self.num_heads,):
                raise ValueError(
                    "Head mask for a single layer should be of size"
                    f" {(self.num_heads,)}, but is {layer_head_mask.size()}"
                )
            attn_weights = layer_head_mask.view(
                1, -1, 1, 1
            ) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            attn_weights = attn_weights.view(
                bsz * self.num_heads, tgt_len, src_len
            )

        if output_attentions:
            # this operation is a bit awkward, but it's required to
            # make sure that attn_weights keeps its gradient.
            # In order to do so, attn_weights have to be reshaped
            # twice and have to be reused in the following
            attn_weights_reshaped = attn_weights.view(
                bsz, self.num_heads, tgt_len, src_len
            )
            attn_weights = attn_weights_reshaped.view(
                bsz * self.num_heads, tgt_len, src_len
            )
        else:
            attn_weights_reshaped = None

        attn_probs = nn.functional.dropout(
            attn_weights, p=self.dropout, training=self.training
        )

        attn_output = torch.bmm(attn_probs, value_states)
        if attn_output.size() != (
            bsz * self.num_heads,
            tgt_len,
            self.head_dim,
        ):
            raise ValueError(
                "`attn_output` should be of size"
                f" {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.view(
            bsz, self.num_heads, tgt_len, self.head_dim
        )
        attn_output = attn_output.transpose(1, 2)

        # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
        # partitioned aross GPUs when using tensor-parallelism.
        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)

        attn_output = self.out_proj(attn_output)

        return attn_output, attn_weights_reshaped, past_key_value


class OPTDecoderLayer(nn.Module):
    def __init__(self, config: OPTConfig):
        super().__init__()
        self.embed_dim = config.hidden_size
        self.self_attn = OPTAttention(
            embed_dim=self.embed_dim,
            num_heads=config.num_attention_heads,
            dropout=config.attention_dropout,
            is_decoder=True,
            bias=config.enable_bias,
        )
        self.do_layer_norm_before = config.do_layer_norm_before
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]

        self.activation_dropout = config.activation_dropout

        self.self_attn_layer_norm = nn.LayerNorm(
            self.embed_dim,
            elementwise_affine=config.layer_norm_elementwise_affine,
        )
        self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim)
        self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim)
        self.final_layer_norm = nn.LayerNorm(
            self.embed_dim,
            elementwise_affine=config.layer_norm_elementwise_affine,
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        layer_head_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
    ) -> Tuple[
        torch.FloatTensor,
        Optional[Tuple[torch.FloatTensor, torch.FloatTensor]],
    ]:
        # TODO: Refactor this function

        residual = hidden_states

        # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
        if self.do_layer_norm_before:
            hidden_states = self.self_attn_layer_norm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            past_key_value=past_key_value,
            attention_mask=attention_mask,
            layer_head_mask=layer_head_mask,
            output_attentions=output_attentions,
        )
        hidden_states = nn.functional.dropout(
            hidden_states, p=self.dropout, training=self.training
        )
        hidden_states = residual + hidden_states

        # 350m applies layer norm AFTER attention
        if not self.do_layer_norm_before:
            hidden_states = self.self_attn_layer_norm(hidden_states)

        # Fully Connected
        hidden_states_shape = hidden_states.shape
        hidden_states = hidden_states.reshape(-1, hidden_states.size(-1))
        residual = hidden_states

        # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
        if self.do_layer_norm_before:
            hidden_states = self.final_layer_norm(hidden_states)

        hidden_states = self.fc1(hidden_states)
        hidden_states = self.activation_fn(hidden_states)

        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(
            hidden_states, p=self.dropout, training=self.training
        )

        hidden_states = (residual + hidden_states).view(hidden_states_shape)

        # 350m applies layer norm AFTER attention
        if not self.do_layer_norm_before:
            hidden_states = self.final_layer_norm(hidden_states)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        if use_cache:
            outputs += (present_key_value,)

        return outputs


class OPTPreTrainedModel(PreTrainedModel):
    config_class = OPTConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["OPTDecoderLayer"]
    _keys_to_ignore_on_load_unexpected = [r"decoder.version"]

    def _init_weights(self, module):
        std = self.config.init_std
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

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


class OPTDecoder(OPTPreTrainedModel):
    def __init__(self, config: OPTConfig):
        super().__init__(config)
        self.dropout = config.dropout
        self.layerdrop = config.layerdrop
        self.padding_idx = config.pad_token_id
        self.max_target_positions = config.max_position_embeddings
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(
            config.vocab_size, config.word_embed_proj_dim, self.padding_idx
        )
        self.embed_positions = OPTLearnedPositionalEmbedding(
            config.max_position_embeddings, config.hidden_size
        )

        if config.word_embed_proj_dim != config.hidden_size:
            self.project_out = nn.Linear(
                config.hidden_size, config.word_embed_proj_dim, bias=False
            )
        else:
            self.project_out = None

        if config.word_embed_proj_dim != config.hidden_size:
            self.project_in = nn.Linear(
                config.word_embed_proj_dim, config.hidden_size, bias=False
            )
        else:
            self.project_in = None

        if config.do_layer_norm_before and not config._remove_final_layer_norm:
            self.final_layer_norm = nn.LayerNorm(
                config.hidden_size,
                elementwise_affine=config.layer_norm_elementwise_affine,
            )
        else:
            self.final_layer_norm = None

        self.layers = nn.ModuleList(
            [OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)]
        )

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

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
    def _prepare_decoder_attention_mask(
        self,
        attention_mask,
        input_shape,
        inputs_embeds,
        past_key_values_length,
    ):
        # create causal mask
        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
        combined_attention_mask = None
        if input_shape[-1] > 1:
            combined_attention_mask = _make_causal_mask(
                input_shape,
                inputs_embeds.dtype,
                past_key_values_length=past_key_values_length,
            )  # .to(inputs_embeds.device)

        if attention_mask is not None:
            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
            expanded_attn_mask = _expand_mask(
                attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
            )
            combined_attention_mask = (
                expanded_attn_mask
                if combined_attention_mask is None
                else expanded_attn_mask + combined_attention_mask
            )

        return combined_attention_mask

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        # TODO: Refactor this function

        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
        )

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError(
                "You cannot specify both decoder_input_ids and"
                " decoder_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])
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError(
                "You have to specify either decoder_input_ids or"
                " decoder_inputs_embeds"
            )

        past_key_values_length = (
            past_key_values[0][0].shape[2]
            if past_key_values is not None
            else 0
        )

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

        # embed positions
        if attention_mask is None:
            attention_mask = torch.ones(
                inputs_embeds.shape[:2],
                dtype=torch.bool,
                device=inputs_embeds.device,
            )
        pos_embeds = self.embed_positions(
            attention_mask, past_key_values_length
        )

        attention_mask = self._prepare_decoder_attention_mask(
            attention_mask, input_shape, inputs_embeds, past_key_values_length
        )

        if self.project_in is not None:
            inputs_embeds = self.project_in(inputs_embeds)

        hidden_states = inputs_embeds + pos_embeds
        hidden_states = nn.functional.dropout(
            hidden_states, p=self.dropout, training=self.training
        )

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = () if use_cache else None

        # check if head_mask has a correct number of layers specified if desired
        for attn_mask, mask_name in zip([head_mask], ["head_mask"]):
            if attn_mask is not None:
                if attn_mask.size()[0] != (len(self.layers)):
                    raise ValueError(
                        f"The `{mask_name}` should be specified for"
                        f" {len(self.layers)} layers, but it is for"
                        f" {head_mask.size()[0]}."
                    )

        for idx, decoder_layer in enumerate(self.layers):
            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            dropout_probability = random.uniform(0, 1)
            if self.training and (dropout_probability < self.layerdrop):
                continue

            past_key_value = (
                past_key_values[idx] if past_key_values is not None else None
            )

            if self.gradient_checkpointing and self.training:
                if use_cache:
                    use_cache = False

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        # None for past_key_value
                        return module(*inputs, output_attentions, None)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(decoder_layer),
                    hidden_states,
                    attention_mask,
                    head_mask[idx] if head_mask is not None else None,
                    None,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    layer_head_mask=(
                        head_mask[idx] if head_mask is not None else None
                    ),
                    past_key_value=past_key_value,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                )

            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache += (
                    layer_outputs[2 if output_attentions else 1],
                )

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

        if self.final_layer_norm is not None:
            hidden_states = self.final_layer_norm(hidden_states)

        if self.project_out is not None:
            hidden_states = self.project_out(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = next_decoder_cache if use_cache else None
        if not return_dict:
            # TODO: This tuple needs to be a static list (of tensors)
            # return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
            return hidden_states
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )


class OPTModel(OPTPreTrainedModel):
    def __init__(self, config: OPTConfig):
        super().__init__(config)
        self.decoder = OPTDecoder(config)

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

    def get_input_embeddings(self):
        return self.decoder.embed_tokens

    def set_input_embeddings(self, value):
        self.decoder.embed_tokens = value

    def get_decoder(self):
        return self.decoder

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        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
        )

        # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
        decoder_outputs = self.decoder(
            input_ids=input_ids,
            attention_mask=attention_mask,
            head_mask=head_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            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

        # return BaseModelOutputWithPast(
        #     last_hidden_state=decoder_outputs.last_hidden_state,
        #     past_key_values=decoder_outputs.past_key_values,
        #     hidden_states=decoder_outputs.hidden_states,
        #     attentions=decoder_outputs.attentions,
        # )
        return decoder_outputs.last_hidden_state


class OPTForCausalLM(OPTPreTrainedModel):
    _keys_to_ignore_on_load_missing = [r"lm_head.weight"]

    def __init__(self, config):
        super().__init__(config)
        self.model = OPTModel(config)

        # the lm_head weight is automatically tied to the embed tokens weight
        self.lm_head = nn.Linear(
            config.word_embed_proj_dim, config.vocab_size, bias=False
        )

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

    def get_input_embeddings(self):
        return self.model.decoder.embed_tokens

    def set_input_embeddings(self, value):
        self.model.decoder.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

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

    def set_decoder(self, decoder):
        self.model.decoder = decoder

    def get_decoder(self):
        return self.model.decoder

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        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, CausalLMOutputWithPast]:
        # TODO: Refactor this function

        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
        )

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model.decoder(
            input_ids=input_ids,
            attention_mask=attention_mask,
            head_mask=head_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        logits = self.lm_head(outputs[0]).contiguous()

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(
                shift_logits.view(-1, self.config.vocab_size),
                shift_labels.view(-1),
            )

        if not return_dict:
            if isinstance(outputs[1:], tuple):
                output = (logits,) + outputs[1:]
            else:
                output = (logits, outputs[1:])
            return (loss,) + output if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past=None,
        attention_mask=None,
        use_cache=None,
        **kwargs,
    ):
        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
        if attention_mask is None:
            attention_mask = input_ids.new_ones(input_ids.shape)

        if past:
            input_ids = input_ids[:, -1:]
        # first step, decoder_cached_states are empty
        return {
            "input_ids": input_ids,  # encoder_outputs is defined. input_ids not needed
            "attention_mask": attention_mask,
            "past_key_values": past,
            "use_cache": use_cache,
        }

    @staticmethod
    def _reorder_cache(past, beam_idx):
        reordered_past = ()
        for layer_past in past:
            reordered_past += (
                tuple(
                    past_state.index_select(0, beam_idx)
                    for past_state in layer_past
                ),
            )
        return reordered_past