ColossalAI/colossalai/shardformer/modeling/whisper.py

import logging
import random
from typing import List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers.modeling_outputs import (
    BaseModelOutput,
    BaseModelOutputWithPastAndCrossAttentions,
    Seq2SeqLMOutput,
    Seq2SeqModelOutput,
    SequenceClassifierOutput,
)
from transformers.models.whisper.modeling_whisper import (
    WhisperEncoder,
    WhisperForAudioClassification,
    WhisperForConditionalGeneration,
    WhisperModel,
)
from transformers.utils import logging

from colossalai.pipeline.stage_manager import PipelineStageManager


def get_whisper_flash_attention_forward():
    from transformers.models.whisper.modeling_whisper import WhisperAttention

    from colossalai.kernel.cuda_native import AttnMaskType, ColoAttention

    def shape(tensor: torch.Tensor, seq_len: int, bsz: int, num_heads: int, head_dim: int):
        return tensor.view(bsz, seq_len, num_heads, head_dim).contiguous()

    def forward(
        self: WhisperAttention,
        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, _ = hidden_states.size()

        # get key, value proj
        # `past_key_value[0].shape[2] == key_value_states.shape[1]`
        # is checking that the `sequence_length` of the `past_key_value` is the same as
        # the provided `key_value_states` to support prefix tuning
        if (
            is_cross_attention
            and past_key_value is not None
            and past_key_value[0].shape[1] == key_value_states.shape[1]
        ):
            # 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 = shape(self.k_proj(key_value_states), -1, bsz, self.num_heads, self.head_dim)
            value_states = shape(self.v_proj(key_value_states), -1, bsz, self.num_heads, self.head_dim)
        elif past_key_value is not None:
            # reuse k, v, self_attention
            key_states = shape(self.k_proj(hidden_states), -1, bsz, self.num_heads, self.head_dim)
            value_states = shape(self.v_proj(hidden_states), -1, bsz, self.num_heads, self.head_dim)
            key_states = torch.cat([past_key_value[0], key_states], dim=1)
            value_states = torch.cat([past_key_value[1], value_states], dim=1)
        else:
            # self_attention
            key_states = shape(self.k_proj(hidden_states), -1, bsz, self.num_heads, self.head_dim)
            value_states = shape(self.v_proj(hidden_states), -1, bsz, self.num_heads, self.head_dim)

        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)

        # get query proj
        query_states = shape(self.q_proj(hidden_states), tgt_len, bsz, self.num_heads, self.head_dim)

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

        attn_type = None
        flash_attention_mask = None

        if self.is_decoder:
            if attention_mask is not None:
                if attention_mask.size() != (bsz, 1, tgt_len, src_len):
                    raise ValueError(
                        f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
                    )
                flash_attention_mask = ~(attention_mask[:, :, -1].squeeze(1).to(torch.bool).contiguous())
                attn_type = AttnMaskType.paddedcausal

        attention = ColoAttention(
            embed_dim=self.embed_dim, num_heads=self.num_heads, dropout=self.dropout, scale=self.scaling
        )
        attn_output = attention(
            query_states, key_states, value_states, attn_mask=flash_attention_mask, attn_mask_type=attn_type
        )

        attn_output = self.out_proj(attn_output)

        return attn_output, None, past_key_value

    return forward


def get_jit_fused_whisper_encoder_layer_forward():
    from transformers.models.whisper.modeling_whisper import WhisperEncoderLayer

    def forward(
        self: WhisperEncoderLayer,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        layer_head_mask: torch.Tensor,
        output_attentions: bool = False,
    ) -> torch.Tensor:
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
                `(encoder_attention_heads,)`.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
        residual = hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)
        hidden_states, attn_weights, _ = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            layer_head_mask=layer_head_mask,
            output_attentions=output_attentions,
        )
        hidden_states = self.dropout_add(hidden_states, residual, self.dropout, self.training)

        residual = hidden_states
        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
        hidden_states = self.fc2(hidden_states)
        hidden_states = self.dropout_add(hidden_states, residual, self.dropout, self.training)

        if hidden_states.dtype == torch.float16 and (
            torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
        ):
            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs

    return forward


def get_jit_fused_whisper_decoder_layer_forward():
    from transformers.models.whisper.modeling_whisper import WhisperDecoderLayer

    def forward(
        self: WhisperDecoderLayer,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        layer_head_mask: Optional[torch.Tensor] = None,
        cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = True,
    ) -> torch.Tensor:
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            encoder_hidden_states (`torch.FloatTensor`):
                cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
            encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
                `(encoder_attention_heads,)`.
            cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
                size `(decoder_attention_heads,)`.
            past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
        residual = hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)

        # Self Attention
        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        # add present self-attn cache to positions 1,2 of present_key_value tuple
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            past_key_value=self_attn_past_key_value,
            attention_mask=attention_mask,
            layer_head_mask=layer_head_mask,
            output_attentions=output_attentions,
        )
        hidden_states = self.dropout_add(hidden_states, residual, self.dropout, self.training)

        # Cross-Attention Block
        cross_attn_present_key_value = None
        cross_attn_weights = None
        if encoder_hidden_states is not None:
            residual = hidden_states
            hidden_states = self.encoder_attn_layer_norm(hidden_states)

            # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
            hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
                hidden_states=hidden_states,
                key_value_states=encoder_hidden_states,
                attention_mask=encoder_attention_mask,
                layer_head_mask=cross_attn_layer_head_mask,
                past_key_value=cross_attn_past_key_value,
                output_attentions=output_attentions,
            )
            hidden_states = self.dropout_add(hidden_states, residual, self.dropout, self.training)

            # add cross-attn to positions 3,4 of present_key_value tuple
            present_key_value = present_key_value + cross_attn_present_key_value

        # Fully Connected
        residual = hidden_states
        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
        hidden_states = self.fc2(hidden_states)
        hidden_states = self.dropout_add(hidden_states, residual, self.dropout, self.training)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights, cross_attn_weights)

        if use_cache:
            outputs += (present_key_value,)

        return outputs

    return forward


class WhisperPipelineForwards:
    """
    This class serves as a micro library for forward function substitution of Llama models
    under pipeline setting.
    """

    @staticmethod
    def whisper_encoder_forward(
        self: WhisperEncoder,
        input_features,
        attention_mask=None,
        head_mask=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        encoder_states=None,
        all_attentions=None,
        stage_index: Optional[List[int]] = None,
        decoder_starting_stage: Optional[int] = None,
    ):
        r"""
        Args:
            input_features (`torch.LongTensor` of shape `(batch_size, feature_size, sequence_length)`):
                Float values of mel features extracted from the raw speech waveform. Raw speech waveform can be
                obtained by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a
                `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
                `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the mel features, padding
                and conversion into a tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
            attention_mask (`torch.Tensor`)`, *optional*):
                Whisper does not support masking of the `input_features`, this argument is preserved for compatibility,
                but it is not used. By default the silence in the input log mel spectrogram are ignored.
            head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

                - 1 indicates the head is **not masked**,
                - 0 indicates the head is **masked**.
            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.
        """
        logging.get_logger(__name__)

        stage = stage_manager.stage
        at_first_stage = stage == 0
        at_last_stage = stage == decoder_starting_stage - 1

        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

        # Process inputs if at the first stage of encoder.
        if at_first_stage:
            inputs_embeds = nn.functional.gelu(self.conv1(input_features))
            inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds))

            inputs_embeds = inputs_embeds.permute(0, 2, 1)
            embed_pos = self.embed_positions.weight

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

            encoder_states = () if output_hidden_states else None
            all_attentions = () if output_attentions else None

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

        else:
            if hidden_states is None:
                raise ValueError(
                    "hidden_states shouldn't be None for stages other than the first stage of encoder/decoder."
                )

        start_idx, end_idx = stage_index[0], stage_index[1]

        for idx in range(start_idx, end_idx):
            encoder_layer = self.layers[idx]

            if output_hidden_states:
                encoder_states = encoder_states + (hidden_states,)
            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            dropout_probability = random.uniform(0, 1)
            if self.training and (dropout_probability < self.layerdrop):  # skip the layer
                layer_outputs = (None, None)
            else:
                if self.gradient_checkpointing and self.training:

                    def create_custom_forward(module):
                        def custom_forward(*inputs):
                            return module(*inputs, output_attentions)

                        return custom_forward

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

                hidden_states = layer_outputs[0]

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

        if at_last_stage:
            hidden_states = self.layer_norm(hidden_states)
            if output_hidden_states:
                encoder_states = encoder_states + (hidden_states,)

            if not return_dict:
                return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
            return BaseModelOutput(
                last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
            )

        else:
            return {"hidden_states": hidden_states, "head_mask": head_mask}

    @staticmethod
    def whisper_decoder_forward(
        self,
        input_ids=None,
        attention_mask=None,
        encoder_hidden_states=None,
        head_mask=None,
        cross_attn_head_mask=None,
        past_key_values=None,
        inputs_embeds=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        stage_index: Optional[List[int]] = None,
        decoder_starting_stage: Optional[int] = None,
    ):
        r"""
        Args:
            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
                provide it.

                Indices can be obtained using [`WhisperTokenizer`]. See [`PreTrainedTokenizer.encode`] and
                [`PreTrainedTokenizer.__call__`] for details.

                [What are input IDs?](../glossary#input-ids)
            attention_mask (`torch.Tensor` 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**.

                [What are attention masks?](../glossary#attention-mask)
            encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
                Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
                of the decoder.
            head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

                - 1 indicates the head is **not masked**,
                - 0 indicates the head is **masked**.

            cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
                Mask to nullify selected heads of the attention modules in encoder to avoid performing cross-attention
                on hidden heads. Mask values selected in `[0, 1]`:

                - 1 indicates the head is **not masked**,
                - 0 indicates the head is **masked**.

            past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
                Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
                shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
                shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

                Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
                cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

                If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
                all `decoder_input_ids` of shape `(batch_size, sequence_length)`. 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.
            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.
        """
        logger = logging.get_logger(__name__)
        stage = stage_manager.stage
        at_first_stage = stage == decoder_starting_stage
        at_last_stage = stage == stage_manager.num_stages - 1

        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 layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
        next_decoder_cache = () if use_cache else None

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

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

        if at_first_stage:
            # 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")

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

            # embed positions
            if input_ids is not None:
                positions = self.embed_positions(input_ids, past_key_values_length=past_key_values_length)
            else:
                positions = self.embed_positions(inputs_embeds, past_key_values_length=past_key_values_length)

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

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

            if self.gradient_checkpointing and self.training:
                if use_cache:
                    logger.warning_once(
                        "`use_cache = True` is incompatible with gradient checkpointing. Setting `use_cache = False`..."
                    )
                    use_cache = False

        else:
            if hidden_states is None:
                raise ValueError(
                    "hidden_states shouldn't be None for stages other than the first stage of encoder/decoder."
                )
            input_shape = hidden_states.size()[:-1]

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

        start_idx, end_idx = stage_index[0], stage_index[1]

        for idx in range(start_idx, end_idx):
            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            decoder_layer = self.layers[idx]

            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:

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

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(decoder_layer),
                    hidden_states,
                    attention_mask,
                    encoder_hidden_states,
                    None,  # encoder attention mask
                    head_mask[idx] if head_mask is not None else None,
                    cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
                    None,  # past_key_value
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    encoder_hidden_states=encoder_hidden_states,
                    layer_head_mask=(head_mask[idx] if head_mask is not None else None),
                    cross_attn_layer_head_mask=(
                        cross_attn_head_mask[idx] if cross_attn_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[3 if output_attentions else 1],)

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

                if encoder_hidden_states is not None:
                    all_cross_attentions += (layer_outputs[2],)

        if at_last_stage:
            hidden_states = self.layer_norm(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:
                return tuple(
                    v
                    for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
                    if v is not None
                )
            return BaseModelOutputWithPastAndCrossAttentions(
                last_hidden_state=hidden_states,
                past_key_values=next_cache,
                hidden_states=all_hidden_states,
                attentions=all_self_attns,
                cross_attentions=all_cross_attentions,
            )

        else:
            return {
                "head_mask": head_mask,
                "cross_attn_head_mask": cross_attn_head_mask,
                "hidden_states": hidden_states,
            }

    @staticmethod
    def whisper_model_forward(
        self: WhisperModel,
        input_features: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        decoder_head_mask: Optional[torch.Tensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        decoder_inputs_embeds: Optional[Tuple[torch.FloatTensor]] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        stage_index: Optional[List[int]] = None,
        decoder_starting_stage: Optional[int] = None,
    ):
        r"""
        Returns:

        Example:
         ```python
         >>> import torch
         >>> from transformers import AutoFeatureExtractor, WhisperModel
         >>> from datasets import load_dataset

         >>> model = WhisperModel.from_pretrained("openai/whisper-base")
         >>> feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base")
         >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt")
         >>> input_features = inputs.input_features
         >>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
         >>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
         >>> list(last_hidden_state.shape)
         [1, 2, 512]
         ```"""
        # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
        if past_key_values:
            logger.warning_once("Non-empty past_key_values is not supported for pipeline models at the moment.")
            past_key_values = None
        if output_attentions:
            logger.warning_once("output_attentions=True is not supported for pipeline models at the moment.")
            output_attentions = False
        if output_hidden_states:
            logger.warning_once("output_hidden_states=True is not supported for pipeline models at the moment.")
            output_hidden_states = False
        if use_cache:
            logger.warning_once("use_cache=True is not supported for pipeline models at the moment.")
            use_cache = False

        logging.get_logger(__name__)

        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
        in_decoder = stage_manager.stage >= decoder_starting_stage
        if not in_decoder:
            if encoder_outputs is None:
                input_features = self._mask_input_features(input_features, attention_mask=attention_mask)

                encoder_outputs = WhisperPipelineForwards.whisper_encoder_forward(
                    self.encoder,
                    input_features,
                    head_mask=head_mask,
                    output_attentions=output_attentions,
                    output_hidden_states=output_hidden_states,
                    return_dict=return_dict,
                    stage_manager=stage_manager,
                    hidden_states=hidden_states,
                    stage_index=stage_index,
                    decoder_starting_stage=decoder_starting_stage,
                )

                if stage_manager.stage == decoder_starting_stage - 1:
                    # last stage of encoder
                    return {"encoder_hidden_states": encoder_outputs[0]}
                else:
                    return encoder_outputs
            # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
            elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
                encoder_outputs = BaseModelOutput(
                    last_hidden_state=encoder_outputs[0],
                    hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
                    attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
                )

        at_last_decoder_stage = stage_manager.is_last_stage()
        at_first_decoder_stage = stage_manager.stage == decoder_starting_stage
        if encoder_outputs is not None:
            encoder_hidden_states = encoder_outputs[0]
        elif encoder_hidden_states is None:
            raise ValueError("Non-empty encoder_hidden_states should be passed in at decoder stages.")

        if not at_first_decoder_stage and hidden_states is None:
            raise ValueError("If not at the first layer of decoder, non-empty hidden_states must be provided.")

        # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
        decoder_outputs = WhisperPipelineForwards.whisper_decoder_forward(
            self.decoder,
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            past_key_values=past_key_values,
            inputs_embeds=decoder_inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            stage_manager=stage_manager,
            hidden_states=hidden_states,
            stage_index=stage_index,
            decoder_starting_stage=decoder_starting_stage,
        )

        # Directly return outputs of overloaded Whisper forward if not at last stage.
        if not at_last_decoder_stage:
            # encoder_hidden_states should be passed to the next stage
            decoder_outputs["encoder_hidden_states"] = encoder_hidden_states
            return decoder_outputs

        if not return_dict:
            return decoder_outputs + encoder_outputs

        return Seq2SeqModelOutput(
            last_hidden_state=decoder_outputs.last_hidden_state,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_hidden_states,
        )

    @staticmethod
    def whisper_for_conditional_generation_forward(
        self: WhisperForConditionalGeneration,
        input_features: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        decoder_head_mask: Optional[torch.Tensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        decoder_inputs_embeds: Optional[Tuple[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,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        stage_index: Optional[List[int]] = None,
        decoder_starting_stage: Optional[int] = None,
    ) -> Union[Tuple[torch.Tensor], Seq2SeqLMOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the language modeling loss. Indices should either be in `[0, ..., config.vocab_size]`
            or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is
            only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

        Returns:

        Example:

        ```python
        >>> import torch
        >>> from transformers import AutoProcessor, WhisperForConditionalGeneration
        >>> from datasets import load_dataset

        >>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en")
        >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")

        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")

        >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="pt")
        >>> input_features = inputs.input_features

        >>> generated_ids = model.generate(inputs=input_features)

        >>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
        >>> transcription
        ' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if labels is not None:
            if decoder_input_ids is None and decoder_inputs_embeds is None:
                decoder_input_ids = shift_tokens_right(
                    labels, self.config.pad_token_id, self.config.decoder_start_token_id
                )
        in_decoder = stage_manager.stage >= decoder_starting_stage
        at_last_decoder_stage = stage_manager.is_last_stage()
        outputs = WhisperPipelineForwards.whisper_model_forward(
            self.model,
            input_features,
            attention_mask=attention_mask,
            decoder_input_ids=decoder_input_ids,
            encoder_outputs=encoder_outputs,
            decoder_attention_mask=decoder_attention_mask,
            head_mask=head_mask,
            decoder_head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            past_key_values=past_key_values,
            decoder_inputs_embeds=decoder_inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            stage_manager=stage_manager,
            hidden_states=hidden_states,
            encoder_hidden_states=encoder_hidden_states,
            stage_index=stage_index,
            decoder_starting_stage=decoder_starting_stage,
        )
        if not in_decoder:
            return outputs

        if not at_last_decoder_stage:
            # encoder_hidden_states should be passed to the next stage
            outputs["encoder_hidden_states"] = encoder_hidden_states
            return outputs

        lm_logits = self.proj_out(outputs[0])

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            # move labels to correct device to enable PP
            labels = labels.to(lm_logits.device)
            loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.reshape(-1))

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

        return Seq2SeqLMOutput(
            loss=loss,
            logits=lm_logits,
            past_key_values=outputs.past_key_values,
            decoder_hidden_states=outputs.decoder_hidden_states,
            decoder_attentions=outputs.decoder_attentions,
            cross_attentions=outputs.cross_attentions,
            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
            encoder_hidden_states=outputs.encoder_hidden_states,
            encoder_attentions=outputs.encoder_attentions,
        )

    @staticmethod
    def whisper_for_audio_classification_forward(
        self: WhisperForAudioClassification,
        input_features: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        encoder_states=None,
        all_attentions=None,
        stage_index: Optional[List[int]] = None,
        decoder_starting_stage: Optional[int] = None,
    ):
        r"""
        This function is modified on the basis of transformers.models.whisper.modeling_whisper.WhisperForAudioClassification.forward.
        Please refer to original code of transformers for more details.
        """
        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

        # audio_classification only holds encoder
        encoder_outputs = WhisperPipelineForwards.whisper_encoder_forward(
            self.encoder,
            input_features,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            stage_manager=stage_manager,
            hidden_states=hidden_states,
            stage_index=stage_index,
            decoder_starting_stage=decoder_starting_stage,
        )

        if not stage_manager.is_last_stage():
            return encoder_outputs

        if self.config.use_weighted_layer_sum:
            hidden_states = torch.stack(encoder_outputs, dim=1)
            norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
            hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
        else:
            hidden_states = encoder_outputs[0]

        hidden_states = self.projector(hidden_states)
        pooled_output = hidden_states.mean(dim=1)

        logits = self.classifier(pooled_output)

        loss = None

        if labels is not None:
            loss_fct = CrossEntropyLoss()
            # move labels to correct device to enable PP
            labels = labels.to(logits.device)
            loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))

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

        return SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )