modeling_gpt_oss.py

import copy
from typing import Callable, Optional, Union

import torch
from accelerate import init_empty_weights
from torch import nn
from torch.nn import functional as F

from transformers.cache_utils import Cache, DynamicCache
from transformers.generation import GenerationMixin
from transformers.integrations.hub_kernels import use_kernel_forward_from_hub
from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask
from transformers.modeling_layers import GradientCheckpointingLayer
from transformers.modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from transformers.processing_utils import Unpack
from transformers.utils import TransformersKwargs, auto_docstring, can_return_tuple
from transformers.utils.generic import OutputRecorder, check_model_inputs

from transformers.configuration_utils import PretrainedConfig, layer_type_validation
from transformers.modeling_rope_utils import rope_config_validation

class GptOssConfig(PretrainedConfig):
    r"""
    This will yield a configuration to that of the BERT
    [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) architecture.

    """

    model_type = "gpt_oss"
    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }
    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise",
        "layers.*.self_attn.k_proj": "colwise",
        "layers.*.self_attn.v_proj": "colwise",
        "layers.*.self_attn.o_proj": "rowwise",
        "layers.*.self_attn.sinks": "local_rowwise",
        "layers.*.mlp.experts": "gather",
        "layers.*.mlp.router": "ep_router",
        "layers.*.mlp.experts.gate_up_proj": "grouped_gemm",
        "layers.*.mlp.experts.gate_up_proj_bias": "grouped_gemm",
        "layers.*.mlp.experts.down_proj": "grouped_gemm",
        "layers.*.mlp.experts.down_proj_bias": "grouped_gemm",
    }

    def __init__(
        self,
        num_hidden_layers: int = 36,
        num_local_experts: int = 128,
        vocab_size: int = 201088,
        hidden_size: int = 2880,
        intermediate_size: int = 2880,
        head_dim: int = 64,
        num_attention_heads: int = 64,
        num_key_value_heads: int = 8,
        sliding_window: int = 128,
        rope_theta: float = 150000.0,
        tie_word_embeddings=False,
        hidden_act: str = "silu",
        initializer_range: float = 0.02,
        max_position_embeddings=131072,
        rms_norm_eps: float = 1e-5,
        rope_scaling={"rope_type": "yarn", "factor": 32.0, "beta_fast": 32.0, "beta_slow": 1.0, "truncate": False},
        attention_dropout: float = 0.0,
        num_experts_per_tok=4,
        router_aux_loss_coef: float = 0.9,
        output_router_logits=False,
        use_cache=True,
        layer_types=None,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.num_local_experts = num_local_experts
        self.sliding_window = sliding_window
        self.num_experts_per_tok = num_experts_per_tok
        # for backward compatibility
        if num_key_value_heads is None:
            num_key_value_heads = num_attention_heads

        self.num_key_value_heads = num_key_value_heads
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.rope_theta = rope_theta
        self.rope_scaling = rope_scaling
        self.attention_dropout = attention_dropout
        self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
        self.layer_types = layer_types
        if self.layer_types is None:
            self.layer_types = [
                "sliding_attention" if bool((i + 1) % 2) else "full_attention" for i in range(self.num_hidden_layers)
            ]
        layer_type_validation(self.layer_types)

        # Validate the correctness of rotary position embeddings parameters
        # BC: if there is a 'type' field, copy it it to 'rope_type'.
        if self.rope_scaling is not None and "type" in self.rope_scaling:
            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
        rope_config_validation(self)

        self.attention_bias = True
        self.max_position_embeddings = max_position_embeddings
        self.router_aux_loss_coef = router_aux_loss_coef
        self.output_router_logits = output_router_logits
        self.use_cache = use_cache
        super().__init__(
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )

@use_kernel_forward_from_hub("RMSNorm")
class GptOssRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        GptOssRMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return (self.weight * hidden_states).to(input_dtype)  # main diff with Llama

    def extra_repr(self):
        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"

#
# class GptOssExperts(nn.Module):
#     def __init__(self, config):
#         super().__init__()
#         self.intermediate_size = config.intermediate_size
#         self.num_experts = config.num_local_experts
#         self.hidden_size = config.hidden_size
#         self.expert_dim = self.intermediate_size
#         self.gate_up_proj = nn.Parameter(torch.empty(self.num_experts, self.hidden_size, 2 * self.expert_dim))
#         self.gate_up_proj_bias = nn.Parameter(torch.empty(self.num_experts, 2 * self.expert_dim))
#         self.down_proj = nn.Parameter(torch.empty((self.num_experts, self.expert_dim, self.hidden_size)))
#         self.down_proj_bias = nn.Parameter(torch.empty(self.num_experts, self.hidden_size))
#         self.alpha = 1.702
#         self.limit = 7.0
#
#
#
#     def forward(self, hidden_states: torch.Tensor, router_indices=None, routing_weights=None) -> torch.Tensor:
#         """
#         When training is is more efficient to just loop over the experts and compute the output for each expert
#         as otherwise the memory would explode.
#
#         For inference we can sacrifice some memory and compute the output for all experts at once. By repeating the inputs.
#
#         Args:
#             hidden_states (torch.Tensor): (batch_size, seq_len, hidden_size)
#             selected_experts (torch.Tensor): (batch_size * token_num, top_k)
#             routing_weights (torch.Tensor): (batch_size * token_num, num_experts)
#         Returns:
#             torch.Tensor
#         """
#         batch_size = hidden_states.shape[0]
#         hidden_states = hidden_states.reshape(-1, self.hidden_size)  # (num_tokens, hidden_size)
#         num_experts = routing_weights.shape[1]
#         if self.training:
#             next_states = torch.zeros_like(hidden_states, dtype=hidden_states.dtype, device=hidden_states.device)
#             with torch.no_grad():
#                 expert_mask = torch.nn.functional.one_hot(router_indices, num_classes=num_experts)
#                 expert_mask = expert_mask.permute(2, 1, 0)
#                 # we sum on the top_k and on the sequence lenght to get which experts
#                 # are hit this time around
#                 expert_hitted = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
#             for expert_idx in expert_hitted[:]:
#                 with torch.no_grad():
#                     _, token_idx = torch.where(expert_mask[expert_idx[0]])
#                 current_state = hidden_states[token_idx]
#                 gate_up = current_state @ self.gate_up_proj[expert_idx] + self.gate_up_proj_bias[expert_idx]
#                 gate, up = gate_up[..., ::2], gate_up[..., 1::2]
#                 gate = gate.clamp(min=None, max=self.limit)
#                 up = up.clamp(min=-self.limit, max=self.limit)
#                 glu = gate * torch.sigmoid(gate * self.alpha)
#                 gated_output = (up + 1) * glu
#                 out = gated_output @ self.down_proj[expert_idx] + self.down_proj_bias[expert_idx]
#                 weighted_output = out[0] * routing_weights[token_idx, expert_idx, None]
#                 next_states.index_add_(0, token_idx, weighted_output.to(hidden_states.dtype))
#             next_states = next_states.view(batch_size, -1, self.hidden_size)
#         else:
#             hidden_states = hidden_states.repeat(num_experts, 1)
#             hidden_states = hidden_states.view(num_experts, -1, self.hidden_size)
#             gate_up = torch.bmm(hidden_states, self.gate_up_proj) + self.gate_up_proj_bias[..., None, :]
#             gate, up = gate_up[..., ::2], gate_up[..., 1::2]
#             gate = gate.clamp(min=None, max=self.limit)
#             up = up.clamp(min=-self.limit, max=self.limit)
#             glu = gate * torch.sigmoid(gate * self.alpha)
#             next_states = torch.bmm(((up + 1) * glu), self.down_proj)
#             next_states = next_states + self.down_proj_bias[..., None, :]
#             next_states = next_states.view(num_experts, batch_size, -1, self.hidden_size)
#             next_states = next_states * routing_weights.transpose(0, 1).view(num_experts, batch_size, -1)[..., None]
#             next_states = next_states.sum(dim=0)
#         return next_states

class GptOssExperts(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.intermediate_size = config.intermediate_size
        self.num_experts = config.num_local_experts
        self.hidden_size = config.hidden_size
        self.expert_dim = self.intermediate_size

        # 使用nn.Linear替代手动矩阵乘法
        self.gate_up_projs = nn.ModuleList([
            nn.Linear(self.hidden_size, 2 * self.expert_dim)
            for _ in range(self.num_experts)
        ])

        self.down_projs = nn.ModuleList([
            nn.Linear(self.expert_dim, self.hidden_size)
            for _ in range(self.num_experts)
        ])

        self.alpha = 1.702
        self.limit = 7.0

    def forward(self, hidden_states: torch.Tensor, router_indices=None, routing_weights=None) -> torch.Tensor:
        batch_size = hidden_states.shape[0]
        hidden_states = hidden_states.reshape(-1, self.hidden_size)  # (num_tokens, hidden_size)
        num_experts = routing_weights.shape[1]

        if self.training:
            next_states = torch.zeros_like(hidden_states, dtype=hidden_states.dtype, device=hidden_states.device)

            with torch.no_grad():
                expert_mask = torch.nn.functional.one_hot(router_indices, num_classes=num_experts)
                expert_mask = expert_mask.permute(2, 1, 0)
                expert_hitted = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()

            for expert_idx in expert_hitted[:]:
                with torch.no_grad():
                    _, token_idx = torch.where(expert_mask[expert_idx[0]])

                current_state = hidden_states[token_idx]

                # 使用Linear层替代手动矩阵乘法
                gate_up = self.gate_up_projs[expert_idx](current_state)
                gate, up = gate_up[..., ::2], gate_up[..., 1::2]
                gate = gate.clamp(min=None, max=self.limit)
                up = up.clamp(min=-self.limit, max=self.limit)

                glu = gate * torch.sigmoid(gate * self.alpha)
                gated_output = (up + 1) * glu

                # 使用Linear层替代手动矩阵乘法
                out = self.down_projs[expert_idx](gated_output)

                weighted_output = out[0] * routing_weights[token_idx, expert_idx, None]
                next_states.index_add_(0, token_idx, weighted_output.to(hidden_states.dtype))

            next_states = next_states.view(batch_size, -1, self.hidden_size)
        else:
            hidden_states = hidden_states.repeat(num_experts, 1)
            hidden_states = hidden_states.view(num_experts, -1, self.hidden_size)

            # 批量处理所有专家
            gate_up = torch.stack([proj(hidden_states[i]) for i, proj in enumerate(self.gate_up_projs)])
            gate, up = gate_up[..., ::2], gate_up[..., 1::2]
            gate = gate.clamp(min=None, max=self.limit)
            up = up.clamp(min=-self.limit, max=self.limit)

            glu = gate * torch.sigmoid(gate * self.alpha)
            next_states = torch.stack([proj((up[i] + 1) * glu[i]) for i, proj in enumerate(self.down_projs)])

            next_states = next_states.view(num_experts, batch_size, -1, self.hidden_size)
            next_states = next_states * routing_weights.transpose(0, 1).view(num_experts, batch_size, -1)[..., None]
            next_states = next_states.sum(dim=0)

        return next_states

# class GptOssTopKRouter(nn.Module):
#     def __init__(self, config):
#         super().__init__()
#         self.top_k = config.num_experts_per_tok
#         self.num_experts = config.num_local_experts
#         self.hidden_dim = config.hidden_size
#         self.weight = nn.Parameter(torch.empty(self.num_experts, self.hidden_dim))
#         self.bias = nn.Parameter(torch.empty(self.num_experts))
#
#     def forward(self, hidden_states):
#         hidden_states = hidden_states.reshape(-1, self.hidden_dim)
#         router_logits = F.linear(hidden_states, self.weight, self.bias)  # (seq_len, num_experts)
#         router_top_value, router_indices = torch.topk(router_logits, self.top_k, dim=-1)  # (seq_len, top_k)
#         router_top_value = torch.nn.functional.softmax(router_top_value, dim=1, dtype=router_top_value.dtype)
#         router_scores = torch.zeros_like(router_logits).scatter_(1, router_indices, router_top_value)
#         return router_scores, router_indices


class GptOssTopKRouter(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.top_k = config.num_experts_per_tok
        self.num_experts = config.num_local_experts
        self.hidden_dim = config.hidden_size

        # 使用nn.Linear替代手动参数
        self.router = nn.Linear(self.hidden_dim, self.num_experts)

    def forward(self, hidden_states):
        # 展平输入 (batch_size * seq_len, hidden_dim)
        hidden_states = hidden_states.reshape(-1, self.hidden_dim)
        router_logits = self.router(hidden_states)  # (num_tokens, num_experts)

        router_top_value, router_indices = torch.topk(
            router_logits,
            self.top_k,
            dim=-1
        )  # (num_tokens, top_k)

        router_top_value = F.softmax(router_top_value, dim=-1, dtype=router_top_value.dtype)

        router_scores = torch.zeros_like(router_logits).scatter_(
            dim=1,
            index=router_indices,
            src=router_top_value
        )

        return router_scores, router_indices




@use_kernel_forward_from_hub("MegaBlocksMoeMLP")
class GptOssMLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.router = GptOssTopKRouter(config)
        self.experts = GptOssExperts(config)

    def forward(self, hidden_states):
        router_scores, router_indices = self.router(hidden_states)  # (num_experts, seq_len)
        routed_out = self.experts(hidden_states, router_indices=router_indices, routing_weights=router_scores)
        return routed_out, router_scores


class GptOssRotaryEmbedding(nn.Module):
    def __init__(self, config: GptOssConfig, device=None):
        super().__init__()
        # BC: "rope_type" was originally "type"
        if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
        else:
            self.rope_type = "default"
        self.max_seq_len_cached = config.max_position_embeddings
        self.original_max_seq_len = config.max_position_embeddings

        self.config = config
        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.original_inv_freq = self.inv_freq

    @torch.no_grad()
    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
    def forward(self, x, position_ids):
        inv_freq_expanded = self.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 torch.autocast(device_type=device_type, enabled=False):  # Force float32
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            emb = freqs
            cos = emb.cos() * self.attention_scaling
            sin = emb.sin() * self.attention_scaling

        return cos.to(x.dtype), sin.to(x.dtype)


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 _apply_rotary_emb(
    x: torch.Tensor,
    cos: torch.Tensor,
    sin: torch.Tensor,
) -> torch.Tensor:
    first_half, second_half = torch.chunk(x, 2, dim=-1)
    first_ = first_half * cos - second_half * sin
    second_ = second_half * cos + first_half * sin
    return torch.cat((first_, second_), dim=-1)


def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = _apply_rotary_emb(q, cos, sin)
    k_embed = _apply_rotary_emb(k, cos, sin)
    return q_embed, k_embed


def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: Optional[torch.Tensor],
    scaling: float,
    dropout: float = 0.0,
    **kwargs,
):
    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:
        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
        attn_weights = attn_weights + causal_mask

    sinks = module.sinks.reshape(1, -1, 1, 1).expand(query.shape[0], -1, query.shape[-2], -1)
    combined_logits = torch.cat([attn_weights, sinks], dim=-1)

    # This was not in the original implementation and slightly affect results; it prevents overflow in BF16/FP16
    # when training with bsz>1 we clamp max values.

    combined_logits = combined_logits - combined_logits.max(dim=-1, keepdim=True).values
    probs = F.softmax(combined_logits, dim=-1, dtype=combined_logits.dtype)
    scores = probs[..., :-1]  # we drop the sink here
    attn_weights = nn.functional.dropout(scores, 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


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

    def __init__(self, config: GptOssConfig, 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
        )
        self.sliding_window = config.sliding_window if config.layer_types[layer_idx] == "sliding_attention" else None
        self.sinks = nn.Parameter(torch.empty(config.num_attention_heads))

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor],
        attention_mask: Optional[torch.Tensor],
        past_key_value: Optional[Cache] = None,
        cache_position: Optional[torch.LongTensor] = 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_value is not None:
            cache_kwargs = {"cache_position": cache_position}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        attention_interface: Callable = eager_attention_forward
        if self.config._attn_implementation != "eager":
            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]

        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,
            sliding_window=self.sliding_window,
            s_aux=self.sinks,  # diff with Llama
            **kwargs,
        )

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


class GptOssDecoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: GptOssConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = GptOssAttention(config=config, layer_idx=layer_idx)
        self.mlp = GptOssMLP(config)
        self.input_layernorm = GptOssRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = GptOssRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.attention_type = config.layer_types[layer_idx]

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        use_cache: Optional[bool] = False,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor]:
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        # Self Attention
        hidden_states, _ = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            use_cache=use_cache,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
            **kwargs,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states, _ = self.mlp(hidden_states)  # diff with llama: router scores
        hidden_states = residual + hidden_states
        return hidden_states


@auto_docstring
class GptOssPreTrainedModel(PreTrainedModel):
    config: GptOssConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["GptOssDecoderLayer"]
    _skip_keys_device_placement = ["past_key_values"]
    _supports_flash_attn = True
    _supports_sdpa = False
    _supports_flex_attn = True

    _can_compile_fullgraph = True
    _supports_attention_backend = True
    _can_record_outputs = {
        "router_logits": OutputRecorder(GptOssTopKRouter, index=0),
        "hidden_states": GptOssDecoderLayer,
        "attentions": GptOssAttention,
    }
    _keep_in_fp32_modules = ["post_attention_layernorm", "input_layernorm", "norm"]
    _supports_flash_attention = False
    _supports_flex_attention = False

    def _init_weights(self, module):
        std = self.config.initializer_range
        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.Parameter):
            module.data.normal_(mean=0.0, std=std)
        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_()
        elif isinstance(module, GptOssRMSNorm):
            module.weight.data.fill_(1.0)
        # elif isinstance(module, GptOssExperts):##too slow
        #     for gate_up_proj in module.gate_up_projs:
        #         gate_up_proj.weight.normal_(mean=0.0, std=std)
        #         gate_up_proj.bias.data.zero_()
        #     for down_proj in module.down_projs:
        #         down_proj.weight.data.normal_(mean=0.0, std=std)
        #         down_proj.bias.data.zero_()
        elif isinstance(module, GptOssAttention):
            module.sinks.data.normal_(mean=0.0, std=std)
        # elif isinstance(module, GptOssTopKRouter):
        #     module.weight.data.normal_(mean=0.0, std=std)
        #     module.bias.data.normal_(mean=0.0, std=std)


@auto_docstring
class GptOssModel(GptOssPreTrainedModel):
    _no_split_modules = ["GptOssDecoderLayer"]

    def __init__(self, config: GptOssConfig):
        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(
            [GptOssDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.norm = GptOssRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = GptOssRotaryEmbedding(config=config)
        self.gradient_checkpointing = False

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

    @check_model_inputs
    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[list[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = 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()

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

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

        # It may already have been prepared by e.g. `generate`
        if not isinstance(causal_mask_mapping := attention_mask, dict):
            mask_kwargs = {
                "config": self.config,
                "input_embeds": inputs_embeds,
                "attention_mask": attention_mask,
                "cache_position": cache_position,
                "past_key_values": past_key_values,
            }
            causal_mask_mapping = {
                "full_attention": create_causal_mask(**mask_kwargs),
                "sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
            }

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

        for decoder_layer in self.layers:
            hidden_states = decoder_layer(
                hidden_states,
                attention_mask=causal_mask_mapping[decoder_layer.attention_type],
                position_ids=position_ids,
                past_key_value=past_key_values,
                use_cache=use_cache,
                cache_position=cache_position,
                position_embeddings=position_embeddings,
                **kwargs,
            )
        hidden_states = self.norm(hidden_states)
        return MoeModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
        )


def load_balancing_loss_func(
    gate_logits: Union[torch.Tensor, tuple[torch.Tensor], None],
    num_experts: Optional[int] = None,
    top_k=2,
    attention_mask: Optional[torch.Tensor] = None,
) -> Union[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 GptOssForCausalLM(GptOssPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]
    _tp_plan = {"lm_head": "colwise_rep"}
    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}

    def __init__(self, config):
        super().__init__(config)
        self.model = GptOssModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        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()

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

    def get_decoder(self):
        return self.model

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_router_logits: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        logits_to_keep: Union[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, GptOssForCausalLM

        >>> model = GptOssForCausalLM.from_pretrained("mistralai/GptOss-8x7B-v0.1")
        >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/GptOss-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,
            cache_position=cache_position,
            **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,
        )


__all__ = ["GptOssForCausalLM", "GptOssModel", "GptOssPreTrainedModel"]