Upload modeling_openpangu_dense.py with huggingface_hub

modeling_openpangu_dense.py

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+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#                       This file was automatically generated from modular_openpangu_dense.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_openpangu_dense.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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 typing import Callable, Optional, Union
+
+import torch
+import torch.nn.functional as F
+import torch_npu
+from torch_npu.contrib import transfer_to_npu
+
+if "910" in torch.npu.get_device_name():
+    NPU_ATTN_INFR = True
+    print("[INFO] torch_npu detected. Using NPU fused infer attention.")
+else:
+    NPU_ATTN_INFR = False
+from einops import rearrange
+from torch import nn
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.generation import GenerationMixin
+from transformers.integrations import use_kernel_forward_from_hub
+from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from transformers.modeling_layers import GradientCheckpointingLayer
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from transformers.modeling_rope_utils import dynamic_rope_update
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.utils import LossKwargs, auto_docstring, can_return_tuple, logging
+
+from .configuration_openpangu_dense import PanguEmbeddedConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+def aggregate_hidden_through_time(
+    input_hidden, merge_conv, sliding_window=2, decay_coeff=0.5, restore_sliding_window=False, history_cache=None
+):
+    """
+    input_hidden.shape = (B, S, H)
+    return.shape = (B, S, H)
+    """
+    B, S, H = input_hidden.shape
+
+    # concat zeors to the lefe of the first token
+    if history_cache is None:
+        history_cache = torch.zeros((B, H, sliding_window - 1), device=input_hidden.device, dtype=input_hidden.dtype)
+    else:
+        history_cache = history_cache.permute(0, 2, 1)
+
+    conv_input = torch.cat(
+        [history_cache, input_hidden.permute(0, 2, 1)],  # input_hidden (B, S, H) -> (B, H, S)
+        dim=-1,
+    )
+
+    conv_output = merge_conv(conv_input)
+    # (B, H, S) -> (B, S, H)
+    return conv_output.permute(0, 2, 1)
+
+
+class WindowBuffer:
+    def __init__(self, win_size, decay_coeff, use_cache, aggregate_fn):
+        self.win_size = win_size
+        self.decay_coeff = decay_coeff
+        self.use_cache = use_cache
+        self.aggregate_fn = aggregate_fn
+        self.buffer = None
+
+    def get_aggregated_hidden(self, hidden_states):
+        if not self.use_cache:
+            self.buffer = None
+            return aggregate_hidden_through_time(hidden_states, self.aggregate_fn, sliding_window=self.win_size)
+
+        B, S, H = hidden_states.shape
+        if S > 1:
+            # prefill, generate first token
+            win_input = aggregate_hidden_through_time(hidden_states, self.aggregate_fn, sliding_window=self.win_size)
+            self.buffer = hidden_states[:, -(self.win_size - 1) :]
+        else:
+            # decode stage
+            win_input = aggregate_hidden_through_time(
+                hidden_states, self.aggregate_fn, sliding_window=self.win_size, history_cache=self.buffer
+            )
+            if self.win_size > 2:
+                self.buffer = torch.cat([self.buffer[:, -(self.win_size - 2) :], hidden_states], dim=1)
+            else:
+                self.buffer = hidden_states
+
+        return win_input
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class PanguEmbeddedRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        PanguEmbeddedRMSNorm 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)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class PanguEmbeddedRotaryEmbedding(nn.Module):
+    def __init__(self, config: PanguEmbeddedConfig, device=None):
+        super().__init__()
+
+        base_dim = config.head_dim
+
+        rotary_percent = config.rotary_percent
+
+        dim = base_dim
+        if rotary_percent < 1.0:
+            dim = int(dim * rotary_percent)
+            if dim % 2 != 0:
+                dim += 1
+
+        rotary_base = config.rope_theta
+        inv_freq = 1.0 / (rotary_base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+
+        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
+            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.attention_scaling = 1.0
+
+        if device is not None:
+            inv_freq = inv_freq.to(device)
+
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+        self.dim = dim
+
+    @torch.no_grad()
+    @dynamic_rope_update
+    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 = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+class PanguEmbeddedMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+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)
+
+
+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: 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
+
+    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
+
+
+def apply_rotary_pos_emb(
+    q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, unsqueeze_dim: int = 1
+):
+    """
+    Applies Rotary Position Embedding to the query and key tensors,
+    handling cases where rotary_percent < 1.0 by only rotating a subset of the dimensions.
+
+    ATTENTION: This version assumes cos/sin tensors are already the full rotation dimension (D_rot),
+    consistent with some Megatron/Fusion implementations, rather than the standard HF (D_rot/2) format.
+
+    Args:
+        q (`torch.Tensor`): The query tensor [Batch, Heads, Seq, Head_Dim].
+        k (`torch.Tensor`): The key tensor [Batch, Heads, Seq, Head_Dim].
+        cos (`torch.Tensor`): The cosine part of the rotary embedding [Batch, Seq, Head_Dim_Rotary]. <--- FULL D_ROT
+        sin (`torch.Tensor`): The sine part of the rotary embedding [Batch, Seq, Head_Dim_Rotary]. <--- FULL D_ROT
+        unsqueeze_dim (`int`, *optional*, defaults to 1): The dimension to unsqueeze cos/sin for broadcasting (usually the Heads dimension).
+
+    Returns:
+        `tuple(torch.Tensor)` comprising of the rotated query and key tensors.
+    """
+    rot_dim = cos.shape[-1]
+
+    q_rope, q_pass = q[..., :rot_dim], q[..., rot_dim:]
+    k_rope, k_pass = k[..., :rot_dim], k[..., rot_dim:]
+
+    cos_broad = cos.unsqueeze(unsqueeze_dim)  # [B, 1, S, Dim]
+    sin_broad = sin.unsqueeze(unsqueeze_dim)  # [B, 1, S, Dim]
+
+    q_embed_rope = (q_rope * cos_broad) + (rotate_half(q_rope) * sin_broad)
+    k_embed_rope = (k_rope * cos_broad) + (rotate_half(k_rope) * sin_broad)
+
+    q_embed = torch.cat((q_embed_rope, q_pass), dim=-1)
+    k_embed = torch.cat((k_embed_rope, k_pass), dim=-1)
+
+    return q_embed, k_embed
+
+
+class PanguEmbeddedAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: PanguEmbeddedConfig, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = config.head_dim
+        self.num_key_value_groups = config.num_key_value_groups
+        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.bias)
+        self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.bias)
+        self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.bias)
+        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.bias)
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.qk_nope_dim = config.qk_nope_dim
+        self.qk_rope_dim = config.qk_rope_dim
+        self.v_channels = config.v_channels
+        self.num_key_value_heads = config.num_key_value_heads
+
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.attn_groupnorm = config.attn_groupnorm
+        self.attn_elementwise_gate = config.attn_elementwise_gate
+
+        self.param_sink_number = config.param_sink_number
+        self.param_sink_with_value = config.param_sink_with_value
+        self.num_attention_heads = config.num_attention_heads
+
+        self.rotary_emb = PanguEmbeddedRotaryEmbedding(config=config)
+        if self.param_sink_number > 0:
+            self.param_sink_query = torch.zeros(
+                (self.param_sink_number, self.num_heads, self.head_dim), dtype=config.torch_dtype
+            )
+
+            self.param_sink_num_heads_per_partition = self.num_key_value_heads
+            self.param_sink_key = torch.nn.Parameter(
+                torch.empty(
+                    (self.param_sink_number, self.param_sink_num_heads_per_partition, self.head_dim),
+                    dtype=config.torch_dtype,
+                )
+            )
+            if self.param_sink_with_value:
+                self.param_sink_value = torch.nn.Parameter(
+                    torch.empty(
+                        (self.param_sink_number, self.param_sink_num_heads_per_partition, self.v_channels),
+                        dtype=config.torch_dtype,
+                    )
+                )
+            else:
+                self.param_sink_value = torch.zeros(
+                    (self.param_sink_number, self.param_sink_num_heads_per_partition, self.v_channels),
+                    dtype=config.torch_dtype,
+                )
+
+        if self.attn_groupnorm:
+            self.groupnorm = PanguEmbeddedRMSNorm(hidden_size=self.head_dim, eps=config.rms_norm_eps)
+
+        if self.attn_elementwise_gate:
+            self.attention_gate = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        bsz, q_len, _ = hidden_states.size()
+        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)
+
+        if self.attn_elementwise_gate:
+            gate_score = self.attention_gate(hidden_states)
+        else:
+            gate_score = None
+
+        kv_seq_len = q_len
+        is_prefill = past_key_value.get_usable_length(kv_seq_len, self.layer_idx) == 0
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+
+        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:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with key_states/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        kv_seq_len = key_states.shape[-2]
+
+        if self.param_sink_number > 0:
+            batch_size = query_states.shape[0]
+            if is_prefill:
+                param_sink_query = (
+                    self.param_sink_query.permute(1, 0, 2)
+                    .unsqueeze(0)
+                    .expand(batch_size, -1, -1, -1)
+                    .to(query_states.device)
+                )
+                query_states = torch.cat([param_sink_query, query_states], dim=2)
+                q_len += self.param_sink_number
+
+            param_sink_key = (
+                self.param_sink_key.permute(1, 0, 2).unsqueeze(0).expand(batch_size, -1, -1, -1).to(key_states.device)
+            )
+            param_sink_value = (
+                self.param_sink_value.permute(1, 0, 2)
+                .unsqueeze(0)
+                .expand(batch_size, -1, -1, -1)
+                .to(value_states.device)
+            )
+
+            key_states = torch.cat([param_sink_key, key_states], dim=2)
+            value_states = torch.cat([param_sink_value, value_states], dim=2)
+
+            kv_seq_len += self.param_sink_number
+
+        if not self.training and NPU_ATTN_INFR:
+            q_len_current = query_states.shape[2]
+            kv_len_current = key_states.shape[2]
+            param_sink_number = self.config.param_sink_number
+
+            # Causal Mask
+            if is_prefill:
+                causal_mask_npu = (
+                    torch.triu(torch.ones([q_len_current, kv_len_current]), diagonal=1)
+                    .bool()
+                    .unsqueeze(0)
+                    .unsqueeze(0)
+                    .to(query_states.device)
+                )
+                original_mask = ~attention_mask.bool()
+                expanded_mask = F.pad(
+                    original_mask.float(), (param_sink_number, 0, param_sink_number, 0), mode="constant", value=1.0
+                ).bool()
+                attention_mask_npu = (expanded_mask) & (~causal_mask_npu)
+            else:
+                original_mask = ~attention_mask.bool()
+                attention_mask_npu = F.pad(
+                    original_mask.float(), (param_sink_number, 0, 0, 0), mode="constant", value=1.0
+                ).bool()
+
+            attention_mask_npu = ~attention_mask_npu.bool()
+
+            attn_output, _ = torch_npu.npu_fused_infer_attention_score(
+                query_states,
+                key_states,
+                value_states,
+                num_heads=self.num_heads,
+                num_key_value_heads=self.num_key_value_heads,
+                input_layout="BNSD",
+                atten_mask=attention_mask_npu,
+                scale=self.scaling,
+            )
+            attn_output = attn_output.transpose(1, 2)  # (bsz, q_len, num_heads * head_dim)
+            attn_weights = None
+        else:
+            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,
+                position_ids=position_ids,
+            )
+
+        if self.param_sink_number > 0 and is_prefill:
+            # (bsz, q_len_original, hidden_dim)
+            attn_output = attn_output[:, self.param_sink_number :, :]
+
+        if self.attn_groupnorm:
+            attn_output = self.groupnorm(attn_output)
+        if self.attn_elementwise_gate:
+            core_attn_out_reshaped = rearrange(attn_output, "s b h d -> s b (h d)", h=self.num_attention_heads)
+            core_attn_out_reshaped = core_attn_out_reshaped * F.sigmoid(gate_score)
+            attn_output = rearrange(core_attn_out_reshaped, "s b (h d) -> s b h d", h=self.num_attention_heads)
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class PanguEmbeddedDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: PanguEmbeddedConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = PanguEmbeddedAttention(config=config, layer_idx=layer_idx)
+        self.mlp = PanguEmbeddedMLP(config)
+        self.input_layernorm = PanguEmbeddedRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = PanguEmbeddedRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.attention_type = config.layer_types[layer_idx]
+        if layer_idx == 0 or layer_idx == config.num_hidden_layers - 1:
+            self.start_end = True
+        else:
+            self.start_end = False
+        if self.start_end:
+            self.router_sliding_window = config.router_sliding_window
+            self.router_win_decay = config.router_win_decay
+            self.merge_conv = torch.nn.Conv1d(
+                config.hidden_size,
+                config.hidden_size,
+                self.router_sliding_window,
+                groups=config.hidden_size,
+                bias=False,
+            )
+            self.window_buffer = WindowBuffer(
+                self.router_sliding_window, self.router_win_decay, True, self.merge_conv.forward
+            )
+
+    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,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+
+        if self.start_end and self.router_sliding_window:
+            win_input = self.window_buffer.get_aggregated_hidden(hidden_states)
+        else:
+            win_input = hidden_states
+
+        hidden_states = self.post_attention_layernorm(win_input)
+
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        return outputs
+
+
+@auto_docstring
+class PanguEmbeddedPreTrainedModel(PreTrainedModel):
+    config_class = PanguEmbeddedConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["PanguEmbeddedDecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn_3 = True
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _supports_cache_class = True
+    _supports_quantized_cache = True
+    _supports_static_cache = True
+    _supports_attention_backend = True
+    _keys_to_ignore_on_load_unexpected = [r"model\.layers\.27.*"]
+
+    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.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, PanguEmbeddedRMSNorm):
+            module.weight.data.fill_(1.0)
+
+
+@auto_docstring
+class PanguEmbeddedModel(PanguEmbeddedPreTrainedModel):
+    def __init__(self, config: PanguEmbeddedConfig):
+        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(
+            [PanguEmbeddedDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.rotary_emb = PanguEmbeddedRotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+        self.norms = nn.ModuleList(
+            [
+                PanguEmbeddedRMSNorm(config.hidden_size, eps=config.rms_norm_eps),
+                PanguEmbeddedRMSNorm(config.hidden_size, eps=config.rms_norm_eps),
+            ]
+        )
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @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,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
+    ) -> BaseModelOutputWithPast:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+            )
+            use_cache = False
+
+        if not isinstance(past_key_values, (type(None), Cache)):
+            raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache()
+
+        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)
+
+        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,
+                "position_ids": position_ids,
+            }
+            causal_mask_mapping = {
+                "full_attention": create_causal_mask(**mask_kwargs),
+                "sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
+            }
+
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask_mapping[decoder_layer.attention_type],
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cache_position=cache_position,
+                position_embeddings=position_embeddings,
+                **flash_attn_kwargs,
+            )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norms[0](hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values if use_cache else None,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
+
+
+@auto_docstring
+class PanguEmbeddedForCausalLM(PanguEmbeddedPreTrainedModel, 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 = PanguEmbeddedModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    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_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        **kwargs: Unpack[KwargsForCausalLM],
+    ) -> CausalLMOutputWithPast:
+        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, PanguEmbeddedForCausalLM
+
+        >>> model = PanguEmbeddedForCausalLM.from_pretrained("meta-PanguEmbedded/PanguEmbedded-2-7b-hf")
+        >>> tokenizer = AutoTokenizer.from_pretrained("meta-PanguEmbedded/PanguEmbedded-2-7b-hf")
+
+        >>> 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_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
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs: BaseModelOutputWithPast = 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_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            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=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+__all__ = ["PanguEmbeddedForCausalLM", "PanguEmbeddedModel", "PanguEmbeddedPreTrainedModel"]