image-match/python/py/Lib/site-packages/transformers/models/phimoe/modeling_phimoe.py

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# Copyright 2024 Microsoft 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.


from collections.abc import Callable
from typing import Optional

import torch
from torch import nn

from ... import initialization as init
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache
from ...generation import GenerationMixin
from ...integrations import use_experts_implementation, use_kernel_func_from_hub, use_kernelized_func
from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
from ...modeling_layers import GenericForSequenceClassification, GradientCheckpointingLayer
from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
from ...utils.generic import maybe_autocast, merge_with_config_defaults
from ...utils.output_capturing import OutputRecorder, capture_outputs
from .configuration_phimoe import PhimoeConfig


class PhimoeRotaryEmbedding(nn.Module):
    inv_freq: torch.Tensor  # fix linting for `register_buffer`

    def __init__(self, config: PhimoeConfig, device=None):
        super().__init__()
        self.max_seq_len_cached = config.max_position_embeddings
        self.original_max_seq_len = config.max_position_embeddings

        self.config = config

        self.rope_type = self.config.rope_parameters["rope_type"]
        self.rope_init_fn: Callable = self.compute_default_rope_parameters
        if self.rope_type != "default":
            self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)

        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)

    @staticmethod
    def compute_default_rope_parameters(
        config: PhimoeConfig | None = None,
        device: Optional["torch.device"] = None,
        seq_len: int | None = None,
    ) -> tuple["torch.Tensor", float]:
        """
        Computes the inverse frequencies according to the original RoPE implementation
        Args:
            config ([`~transformers.PreTrainedConfig`]):
                The model configuration.
            device (`torch.device`):
                The device to use for initialization of the inverse frequencies.
            seq_len (`int`, *optional*):
                The current sequence length. Unused for this type of RoPE.
        Returns:
            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
        """
        base = config.rope_parameters["rope_theta"]
        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads

        attention_factor = 1.0  # Unused in this type of RoPE

        # Compute the inverse frequencies
        inv_freq = 1.0 / (
            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
        )
        return inv_freq, attention_factor

    @torch.no_grad()
    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
    def forward(self, x, position_ids=None, layer_type=None):
        if layer_type is not None:
            raise ValueError(
                f"{self.__class__.__name__} does not support layer types, but got `layer_type={layer_type}`"
            )

        mscale = None
        seq_len = torch.max(position_ids) + 1
        if self.config.rope_parameters["rope_type"] != "default" and seq_len:
            mscale = (
                self.config.rope_parameters["long_mscale"]
                if seq_len > self.config.rope_parameters["original_max_position_embeddings"]
                else self.config.rope_parameters["short_mscale"]
            )
        inv_freq, attention_scaling = self.rope_init_fn(self.config, x.device, seq_len)
        mscale = attention_scaling if mscale is None else mscale
        inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
        position_ids_expanded = position_ids[:, None, :].float()

        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
        with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            emb = torch.cat((freqs, freqs), dim=-1)
            cos = emb.cos() * mscale
            sin = emb.sin() * mscale
        return cos.to(x.dtype), sin.to(x.dtype)


def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)


@use_kernel_func_from_hub("rotary_pos_emb")
def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    """
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)


def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: torch.Tensor | None,
    scaling: float,
    dropout: float = 0.0,
    **kwargs: Unpack[TransformersKwargs],
):
    key_states = repeat_kv(key, module.num_key_value_groups)
    value_states = repeat_kv(value, module.num_key_value_groups)

    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
    if attention_mask is not None:
        attn_weights = attn_weights + attention_mask

    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
    attn_output = torch.matmul(attn_weights, value_states)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attn_weights


@use_kernelized_func(apply_rotary_pos_emb)
class PhimoeAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: PhimoeConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.scaling = self.head_dim**-0.5
        self.attention_dropout = config.attention_dropout
        self.is_causal = True

        self.q_proj = nn.Linear(
            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
        )
        self.k_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.v_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.o_proj = nn.Linear(
            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
        attention_mask: torch.Tensor | None = None,
        past_key_values: Cache | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor, torch.Tensor]:
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)

        cos, sin = position_embeddings
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_values is not None:
            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx)

        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
            self.config._attn_implementation, eager_attention_forward
        )

        attn_output, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            attention_mask,
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=self.scaling,
            **kwargs,
        )

        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
        attn_output = self.o_proj(attn_output)
        return attn_output, attn_weights


class PhimoeMultiplier(torch.autograd.Function):
    @staticmethod
    def forward(
        ctx,
        scores: torch.Tensor,
        multiplier: torch.Tensor,
        selected_experts: torch.Tensor,
        masked_gates: torch.Tensor,
        mask_for_one: torch.Tensor,
    ):
        """
        Forward pass for the custom autograd function.

        Args:
            ctx: Context object to save information for backward computation.
            scores (torch.Tensor): Input scores tensor.
            multiplier (torch.Tensor): Multiplier tensor.
            selected_experts (torch.Tensor): Tensor of selected experts.
            masked_gates (torch.Tensor): Masked gates tensor.
            mask_for_one (torch.Tensor): Mask for one tensor.

        Returns:
            torch.Tensor: Result of the forward pass.
        """
        ctx.save_for_backward(multiplier, selected_experts, masked_gates)
        return multiplier * mask_for_one

    @staticmethod
    def backward(
        ctx,
        grad_at_output: torch.Tensor,
    ):
        """
        Backward pass for the custom autograd function.

        Args:
            ctx: Context object with saved tensors from the forward pass.
            grad_at_output (torch.Tensor): Gradient at the output.

        Returns:
            tuple[torch.Tensor, None, None, None, None]: Gradients for the inputs.
        """
        multiplier, selected_experts, masked_gates = ctx.saved_tensors

        grad_at_output = grad_at_output * multiplier

        grad_at_scores_expanded = masked_gates * grad_at_output.mul(-1)
        grad_at_scores_expanded.scatter_add_(
            dim=-1,
            index=selected_experts,
            src=grad_at_output,
        )

        return (
            grad_at_scores_expanded,
            None,
            None,
            None,
            None,
        )


@use_experts_implementation
class PhimoeExperts(nn.Module):
    """Collection of expert weights stored as 3D tensors."""

    def __init__(self, config: PhimoeConfig):
        super().__init__()
        self.num_experts = config.num_local_experts
        self.hidden_dim = config.hidden_size
        self.intermediate_dim = config.intermediate_size
        self.gate_up_proj = nn.Parameter(torch.empty(self.num_experts, 2 * self.intermediate_dim, self.hidden_dim))
        self.down_proj = nn.Parameter(torch.empty(self.num_experts, self.hidden_dim, self.intermediate_dim))
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(
        self,
        hidden_states: torch.Tensor,
        top_k_index: torch.Tensor,
        top_k_weights: torch.Tensor,
    ) -> torch.Tensor:
        final_hidden_states = torch.zeros_like(hidden_states)
        with torch.no_grad():
            expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts)
            expert_mask = expert_mask.permute(2, 1, 0)
            expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()

        for expert_idx in expert_hit:
            expert_idx = expert_idx[0]
            if expert_idx == self.num_experts:
                continue
            top_k_pos, token_idx = torch.where(expert_mask[expert_idx])
            current_state = hidden_states[token_idx]
            gate, up = nn.functional.linear(current_state, self.gate_up_proj[expert_idx]).chunk(2, dim=-1)
            current_hidden_states = self.act_fn(gate) * up
            current_hidden_states = nn.functional.linear(current_hidden_states, self.down_proj[expert_idx])
            current_hidden_states = current_hidden_states * top_k_weights[token_idx, top_k_pos, None]
            final_hidden_states.index_add_(0, token_idx, current_hidden_states.to(final_hidden_states.dtype))

        return final_hidden_states


def sparsemixer(scores, jitter_eps, training, top_k=2):
    """
    Sparse mixer function to select top-k experts and compute multipliers.
    Based on the paper: https://huggingface.co/papers/2409.12136
    We first replace the TopK(·) function as random sampling of discrete variables
    in model training. Then, following Liu et al. (2023a) and Liu et al. (2023b), we apply Heun's
    third order method to approximate the expert routing gradient and construct a modified
    back-propagation to give a mathematically sound gradient estimation for expert routing.

    Args:
        scores (torch.Tensor): Input scores tensor.
        jitter_eps (float): Jitter epsilon for numerical stability.
        training (bool): Flag indicating if the model is in training mode.
        top_k (int): Number of top experts to select.

    Returns:
        tuple[torch.Tensor, torch.Tensor]: Multiplier and selected experts tensors.
    """
    with torch.no_grad():
        # Compute mask for sparsity
        mask_logits_threshold, max_ind = scores.max(dim=-1, keepdim=True)
        factor = scores.abs().clamp(min=mask_logits_threshold)
        mask_logits_threshold = ((mask_logits_threshold - scores) / factor) > (2 * jitter_eps)

    # Apply mask
    masked_gates = scores.masked_fill(mask_logits_threshold, float("-inf"))
    if training:
        selected_experts = (
            (
                masked_gates
                - torch.empty_like(masked_gates, memory_format=torch.legacy_contiguous_format).exponential_().log()
            )
            .max(dim=-1)[1]
            .unsqueeze(-1)
        )  # Gumbel sampling, more robust than the multinomial method
    else:
        selected_experts = max_ind

    # Compute scores for gradients
    masked_gates = torch.softmax(masked_gates, dim=-1)
    multiplier_o = masked_gates.gather(dim=-1, index=selected_experts)

    if training:
        # Compute midpoint mask
        max_scores, max_ind = masked_gates.max(dim=-1, keepdim=True)
        mask_for_one = torch.logical_or(
            selected_experts == max_ind,
            torch.rand_like(max_scores) > 0.75,  # Heun's third-order method
        )
        # 1 -> 1.0 & 0 -> 1./3: lambda x: (x + 0.5) / 1.5
        mask_for_one = torch.add(0.3333, mask_for_one, alpha=0.6667).type_as(masked_gates)

        multiplier = PhimoeMultiplier.apply(
            scores,
            multiplier_o,
            selected_experts,
            masked_gates,
            mask_for_one,
        )
    else:
        multiplier = multiplier_o

    # Masked out first expert
    masked_scores = torch.scatter(
        scores,
        -1,
        selected_experts,
        float("-inf"),
    )
    with torch.no_grad():
        # Compute mask for sparsity
        mask_logits_threshold, max_ind = masked_scores.max(dim=-1, keepdim=True)
        factor = scores.abs().clamp(min=mask_logits_threshold)
        mask_logits_threshold = ((mask_logits_threshold - scores) / factor) > (2 * jitter_eps)

    # Apply mask
    masked_gates_top2 = masked_scores.masked_fill(mask_logits_threshold, float("-inf"))
    if training:
        selected_experts_top2 = (
            (
                masked_gates_top2
                - torch.empty_like(masked_gates_top2, memory_format=torch.legacy_contiguous_format)
                .exponential_()
                .log()
            )
            .max(dim=-1)[1]
            .unsqueeze(-1)
        )  # Gumbel sampling, more robust than the multinomial method
    else:
        selected_experts_top2 = max_ind
    # Compute scores for gradients
    masked_gates_top2 = torch.softmax(masked_gates_top2, dim=-1)
    multiplier_top2_o = masked_gates_top2.gather(dim=-1, index=selected_experts_top2)

    if training:
        # Compute midpoint mask
        max_scores, max_ind = masked_gates_top2.max(dim=-1, keepdim=True)
        mask_for_one_top2 = torch.logical_or(
            selected_experts_top2 == max_ind,
            torch.rand_like(max_scores).uniform_() > 0.75,  # Heun's third-order method
        )
        # 1 -> 1.0 & 0 -> 1./3: lambda x: (x + 0.5) / 1.5
        mask_for_one_top2 = torch.add(0.3333, mask_for_one_top2, alpha=0.6667).type_as(masked_gates_top2)

        multiplier_top2 = PhimoeMultiplier.apply(
            scores,
            multiplier_top2_o,
            selected_experts_top2,
            masked_gates_top2,
            mask_for_one_top2,
        )
    else:
        multiplier_top2 = multiplier_top2_o

    multiplier = torch.concat((multiplier, multiplier_top2), dim=-1)
    selected_experts = torch.concat((selected_experts, selected_experts_top2), dim=-1)

    return (
        multiplier,
        selected_experts,
    )


class PhimoeTopKRouter(nn.Linear):
    def __init__(self, config: PhimoeConfig):
        super().__init__(config.hidden_size, config.num_local_experts, bias=False)
        self.router_jitter_noise = config.router_jitter_noise
        self.input_jitter_noise = config.input_jitter_noise
        self.top_k = config.num_experts_per_tok

    def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
        if self.training and self.input_jitter_noise > 0:
            hidden_states *= torch.empty_like(hidden_states).uniform_(
                1.0 - self.input_jitter_noise, 1.0 + self.input_jitter_noise
            )
        router_logits = super().forward(hidden_states)
        routing_weights, selected_experts = sparsemixer(
            router_logits, jitter_eps=self.router_jitter_noise, training=self.training, top_k=self.top_k
        )
        return router_logits, routing_weights, selected_experts


class PhimoeSparseMoeBlock(nn.Module):
    """
    This implementation is
    strictly equivalent to standard MoE with full capacity (no
    dropped tokens). It's faster since it formulates MoE operations
    in terms of block-sparse operations to accommodate imbalanced
    assignments of tokens to experts, whereas standard MoE either
    (1) drop tokens at the cost of reduced performance or (2) set
    capacity factor to number of experts and thus waste computation
    and memory on padding.
    """

    def __init__(self, config):
        super().__init__()
        self.hidden_dim = config.hidden_size
        self.ffn_dim = config.intermediate_size
        self.num_experts = config.num_local_experts
        self.top_k = config.num_experts_per_tok
        self.router = PhimoeTopKRouter(config)
        self.experts = PhimoeExperts(config)
        self.input_jitter_noise = config.input_jitter_noise

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        batch_size, sequence_length, hidden_dim = hidden_states.shape
        if self.training and self.input_jitter_noise > 0:
            hidden_states *= torch.empty_like(hidden_states).uniform_(
                1.0 - self.input_jitter_noise, 1.0 + self.input_jitter_noise
            )

        batch_size, sequence_length, hidden_dim = hidden_states.shape
        hidden_states = hidden_states.reshape(-1, hidden_dim)
        _, routing_weights, selected_experts = self.router(hidden_states)
        final_hidden_states = self.experts(hidden_states, selected_experts, routing_weights)
        return final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)


class PhimoeDecoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: PhimoeConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size

        self.self_attn = PhimoeAttention(config, layer_idx)

        self.mlp = PhimoeSparseMoeBlock(config)

        # Phimoe uses nn.LayerNorm
        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True)
        self.post_attention_layernorm = nn.LayerNorm(
            config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.Tensor:
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        hidden_states, _ = self.self_attn(
            hidden_states=hidden_states,
            position_embeddings=position_embeddings,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            **kwargs,
        )
        hidden_states = residual + hidden_states
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states
        return hidden_states


@auto_docstring
class PhimoePreTrainedModel(PreTrainedModel):
    config: PhimoeConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["PhimoeDecoderLayer"]
    _skip_keys_device_placement = ["past_key_values"]
    _supports_flash_attn = True
    _supports_sdpa = True
    _supports_flex_attn = True

    _can_compile_fullgraph = True
    _supports_attention_backend = True
    _can_record_outputs = {
        "router_logits": OutputRecorder(PhimoeTopKRouter, index=0),
        "hidden_states": PhimoeDecoderLayer,
        "attentions": PhimoeAttention,
    }

    @torch.no_grad()
    def _init_weights(self, module):
        super()._init_weights(module)
        std = self.config.initializer_range
        if isinstance(module, PhimoeExperts):
            init.normal_(module.gate_up_proj, mean=0.0, std=std)
            init.normal_(module.down_proj, mean=0.0, std=std)
        elif isinstance(module, PhimoeTopKRouter):
            init.normal_(module.weight, mean=0.0, std=std)


@auto_docstring
class PhimoeModel(PhimoePreTrainedModel):
    def __init__(self, config: PhimoeConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList(
            [PhimoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.norm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True)
        self.rotary_emb = PhimoeRotaryEmbedding(config=config)
        self.gradient_checkpointing = False

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

    @merge_with_config_defaults
    @capture_outputs
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        use_cache: bool | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> MoeModelOutputWithPast:
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if use_cache and past_key_values is None:
            past_key_values = DynamicCache(config=self.config)

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

        if position_ids is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
            position_ids = position_ids.unsqueeze(0)

        mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask
        causal_mask = mask_function(
            config=self.config,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            past_key_values=past_key_values,
            position_ids=position_ids,
        )

        hidden_states = inputs_embeds
        position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids)

        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
            hidden_states = decoder_layer(
                hidden_states,
                attention_mask=causal_mask,
                position_ids=position_ids,
                past_key_values=past_key_values,
                use_cache=use_cache,
                position_embeddings=position_embeddings,
                **kwargs,
            )

        hidden_states = self.norm(hidden_states)

        return MoeModelOutputWithPast(  # only diff with Mistral is the output type, we need MoE
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
        )


def load_balancing_loss_func(
    gate_logits: torch.Tensor | tuple[torch.Tensor] | None,
    num_experts: int | None = None,
    top_k=2,
    attention_mask: torch.Tensor | None = None,
) -> torch.Tensor | int:
    r"""
    Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.

    See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss
    function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
    experts is too unbalanced.

    Args:
        gate_logits:
            Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
            shape [batch_size X sequence_length, num_experts].
        num_experts:
            Number of experts
        top_k:
            The number of experts to route per-token, can be also interpreted as the `top-k` routing
            parameter.
        attention_mask (`torch.Tensor`, *optional*):
            The attention_mask used in forward function
            shape [batch_size X sequence_length] if not None.

    Returns:
        The auxiliary loss.
    """
    if gate_logits is None or not isinstance(gate_logits, tuple):
        return 0

    if isinstance(gate_logits, tuple):
        compute_device = gate_logits[0].device
        concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)

    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)

    _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)

    expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)

    if attention_mask is None:
        # Compute the percentage of tokens routed to each experts
        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)

        # Compute the average probability of routing to these experts
        router_prob_per_expert = torch.mean(routing_weights, dim=0)
    else:
        batch_size, sequence_length = attention_mask.shape
        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)

        # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
        expert_attention_mask = (
            attention_mask[None, :, :, None, None]
            .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
            .reshape(-1, top_k, num_experts)
            .to(compute_device)
        )

        # Compute the percentage of tokens routed to each experts
        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
            expert_attention_mask, dim=0
        )

        # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
        router_per_expert_attention_mask = (
            attention_mask[None, :, :, None]
            .expand((num_hidden_layers, batch_size, sequence_length, num_experts))
            .reshape(-1, num_experts)
            .to(compute_device)
        )

        # Compute the average probability of routing to these experts
        router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
            router_per_expert_attention_mask, dim=0
        )

    overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
    return overall_loss * num_experts


@auto_docstring
class PhimoeForCausalLM(PhimoePreTrainedModel, GenerationMixin):
    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
    _tp_plan = {"lm_head": "colwise_gather_output"}
    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}

    def __init__(self, config):
        super().__init__(config)
        self.model = PhimoeModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=self.config.lm_head_bias)
        self.router_aux_loss_coef = config.router_aux_loss_coef
        self.num_experts = config.num_local_experts
        self.num_experts_per_tok = config.num_experts_per_tok

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

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        labels: torch.LongTensor | None = None,
        use_cache: bool | None = None,
        output_router_logits: bool | None = None,
        logits_to_keep: int | torch.Tensor = 0,
        **kwargs: Unpack[TransformersKwargs],
    ) -> MoeCausalLMOutputWithPast:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked 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]`.

        Example:

        ```python
        >>> from transformers import AutoTokenizer, PhimoeForCausalLM

        >>> model = PhimoeForCausalLM.from_pretrained("mistralai/Phimoe-8x7B-v0.1")
        >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Phimoe-8x7B-v0.1")

        >>> prompt = "Hey, are you conscious? Can you talk to me?"
        >>> inputs = tokenizer(prompt, return_tensors="pt")

        >>> # Generate
        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
        ```"""

        output_router_logits = (
            output_router_logits if output_router_logits is not None else self.config.output_router_logits
        )

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs: MoeModelOutputWithPast = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_router_logits=output_router_logits,
            **kwargs,
        )

        hidden_states = outputs.last_hidden_state
        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
        logits = self.lm_head(hidden_states[:, slice_indices, :])

        loss = None
        if labels is not None:
            loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)

        aux_loss = None
        if output_router_logits:
            aux_loss = load_balancing_loss_func(
                outputs.router_logits,
                self.num_experts,
                self.num_experts_per_tok,
                attention_mask,
            )
            if labels is not None:
                loss += self.router_aux_loss_coef * aux_loss.to(loss.device)  # make sure to reside in the same device

        return MoeCausalLMOutputWithPast(
            loss=loss,
            aux_loss=aux_loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            router_logits=outputs.router_logits,
        )

    # Copied from transformers.models.phi3.modeling_phi3.Phi3ForCausalLM.prepare_inputs_for_generation
    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        attention_mask=None,
        inputs_embeds=None,
        position_ids=None,
        use_cache=True,
        logits_to_keep=None,
        **kwargs,
    ):
        # Overwritten -- this model may need to switch between short and long rope, invalidating the cache in the
        # process

        # When the first time input length reached long and short factor switching point, enforce re-compute cache
        # It will cause downside of slower at this single token position, however, better than current failure.
        if (
            past_key_values
            and hasattr(self.config, "original_max_position_embeddings")
            and input_ids.shape[1] >= self.config.original_max_position_embeddings + 1
        ):
            past_length = past_key_values.get_seq_length()
            if past_length <= self.config.original_max_position_embeddings:
                past_key_values = None

        model_inputs = super().prepare_inputs_for_generation(
            input_ids=input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            position_ids=position_ids,
            use_cache=use_cache,
            logits_to_keep=logits_to_keep,
            **kwargs,
        )
        return model_inputs


class PhimoeForSequenceClassification(GenericForSequenceClassification, PhimoePreTrainedModel): ...


__all__ = ["PhimoePreTrainedModel", "PhimoeModel", "PhimoeForCausalLM", "PhimoeForSequenceClassification"]