Delete modeling_qwen3sa.py

modeling_qwen3sa.py

@@ -1,1587 +0,0 @@
-#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-#           This file was automatically generated from src/transformers/models/qwen3/modular_qwen3.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_qwen3.py file directly. One of our CI enforces this.
-#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-# coding=utf-8
-# Copyright 2025 The Qwen team, Alibaba Group 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 typing import Any, Callable, Optional, Union
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from torch.nn.attention import SDPBackend, sdpa_kernel
-from flash_attn import flash_attn_func
-
-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 (
-    GenericForQuestionAnswering,
-    GenericForSequenceClassification,
-    GenericForTokenClassification,
-    GradientCheckpointingLayer,
-)
-from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
-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.deprecation import deprecate_kwarg
-from transformers.utils.generic import check_model_inputs
-from transformers.models.qwen3.configuration_qwen3 import Qwen3Config
-
-from .summary_context import SummaryBatchContext, build_summary_context, build_summary_sliding_context
-from summary_attn import summary_attn_func
-
-
-def _parse_config_pattern(val):
-    """Parse a config value that may be an int, list, or Python pattern string like '([4096]*1+[128]*3)*9'."""
-    if isinstance(val, list):
-        return val
-    if isinstance(val, str):
-        return eval(val)
-    return val
-
-
-@use_kernel_forward_from_hub("RMSNorm")
-class Qwen3RMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps: float = 1e-6) -> None:
-        """
-        Qwen3RMSNorm is equivalent to T5LayerNorm
-        """
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        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 Qwen3RingBufferCache:
-    """
-    Ring buffer KV cache with summary support.
-
-    Two strategies based on per-layer sliding_chunk_num:
-    - Large window layers (is_large_window=True): append-only buffer storing only text KV.
-      Summary KV is NOT stored since text tokens attend to all text KV directly.
-    - Small window layers (is_large_window=False): single buffer layout:
-      [ scratch (S) | chunk_text (C) ←fill | ring_text (ws) | summaries →fill | headroom ]
-       ^0            ^S          ^S+C-cbl   ^S+C     ^S+C+ws  ^S+C+ws+n_sum
-      scratch: temp area for summary self-KV at chunk boundaries (avoids cat).
-      chunk_text fills right-to-left; summaries append left-to-right.
-      get_attention_kv returns a single contiguous slice.
-      get_summary_attention_kv writes summary KV to scratch, returns [0 : S+C].
-
-    RoPE position information is baked into KV, so physical order doesn't matter.
-    """
-
-    is_compileable = False
-    _SUMMARY_INIT_CAP = 512
-    _APPEND_HEADROOM = 1024
-
-    def __init__(self, config: Qwen3Config, sliding_chunk_nums: list[int]):
-        super().__init__()
-        self.summary_chunk_size = getattr(config, "summary_chunk_size", 0)
-        self.summary_token_num = getattr(config, "summary_token_num", 0)
-        self.num_hidden_layers = config.num_hidden_layers
-
-        self.sliding_chunk_nums = sliding_chunk_nums
-        large_window_threshold = min(sliding_chunk_nums) * self.summary_chunk_size
-        self.is_large_window = [sv * self.summary_chunk_size > large_window_threshold for sv in sliding_chunk_nums]
-        self.window_sizes = [sv * self.summary_chunk_size for sv in sliding_chunk_nums]
-
-        self.key_cache = [None for _ in range(config.num_hidden_layers)]
-        self.value_cache = [None for _ in range(config.num_hidden_layers)]
-
-        # Large window: append-only
-        self._text_len = [0] * config.num_hidden_layers
-        self._capacity = [0] * config.num_hidden_layers
-
-        # Small window: [ scratch (S) | chunk_text (C) ←fill | ring_text (ws) | summaries →fill | headroom ]
-        self._window_write_ptr = [0] * config.num_hidden_layers
-        self._n_valid_window = [0] * config.num_hidden_layers
-        self._chunk_buf_len = [0] * config.num_hidden_layers
-        self._n_summaries = [0] * config.num_hidden_layers    # number of summaries stored
-
-        # Common
-        self.cur_chunk_sizes = [0] * config.num_hidden_layers
-        self.true_tokens = [0] * config.num_hidden_layers
-        self._total_chunks = [0] * config.num_hidden_layers  # completed chunks count
-        self._reorganized = False
-
-    def __len__(self):
-        return self.num_hidden_layers
-
-    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
-        """Returns nonzero when cache is populated (used to detect prefill vs decode)."""
-        if layer_idx >= self.num_hidden_layers:
-            return 0
-        if self.is_large_window[layer_idx]:
-            return self._text_len[layer_idx]
-        else:
-            return self._n_valid_window[layer_idx] + self._chunk_buf_len[layer_idx] + self._n_summaries[layer_idx]
-
-    def get_cur_chunk_size(self, layer_idx: Optional[int] = None) -> int:
-        if layer_idx is None:
-            layer_idx = self.num_hidden_layers - 1
-        return self.cur_chunk_sizes[layer_idx]
-
-    def get_true_token_num(self, layer_idx: Optional[int] = None) -> int:
-        if layer_idx is None:
-            layer_idx = self.num_hidden_layers - 1
-        return self.true_tokens[layer_idx]
-
-    # ── Prefill: standard append (before reorganize) ──
-
-    def update(
-        self,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        layer_idx: int,
-        cache_kwargs: Optional[dict[str, Any]] = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        """Append KV during prefill (before reorganize). Returns full KV for prefill attention."""
-        add_len = key_states.shape[-2]
-        cur_len = self._text_len[layer_idx]
-        new_len = cur_len + add_len
-
-        if self.key_cache[layer_idx] is None:
-            cap = new_len + self._APPEND_HEADROOM
-            bsz, heads, _, head_dim = key_states.shape
-            self.key_cache[layer_idx] = torch.empty(
-                bsz, heads, cap, head_dim, dtype=key_states.dtype, device=key_states.device)
-            self.value_cache[layer_idx] = torch.empty(
-                bsz, heads, cap, head_dim, dtype=value_states.dtype, device=value_states.device)
-            self._capacity[layer_idx] = cap
-        elif new_len > self._capacity[layer_idx]:
-            cap = max(new_len + self._APPEND_HEADROOM, self._capacity[layer_idx] * 2)
-            old_k, old_v = self.key_cache[layer_idx], self.value_cache[layer_idx]
-            bsz, heads, _, head_dim = old_k.shape
-            new_k = torch.empty(bsz, heads, cap, head_dim, dtype=old_k.dtype, device=old_k.device)
-            new_v = torch.empty(bsz, heads, cap, head_dim, dtype=old_v.dtype, device=old_v.device)
-            new_k[:, :, :cur_len, :].copy_(old_k[:, :, :cur_len, :])
-            new_v[:, :, :cur_len, :].copy_(old_v[:, :, :cur_len, :])
-            self.key_cache[layer_idx] = new_k
-            self.value_cache[layer_idx] = new_v
-            self._capacity[layer_idx] = cap
-
-        self.key_cache[layer_idx][:, :, cur_len:new_len, :].copy_(key_states)
-        self.value_cache[layer_idx][:, :, cur_len:new_len, :].copy_(value_states)
-        self._text_len[layer_idx] = new_len
-
-        if self.summary_chunk_size > 0:
-            if cache_kwargs and 'summary_mask' in cache_kwargs:
-                text_count = add_len - cache_kwargs['summary_mask'][0].sum().item()
-            else:
-                text_count = add_len
-            self.cur_chunk_sizes[layer_idx] += add_len
-            self.true_tokens[layer_idx] += text_count
-
-        return self.key_cache[layer_idx][:, :, :new_len, :], self.value_cache[layer_idx][:, :, :new_len, :]
-
-    # ── Reorganize after prefill ──
-
-    def reorganize_after_prefill(self, summary_mask: torch.Tensor):
-        """Reorganize all layers from prefill block layout to ring buffer layout.
-
-        Args:
-            summary_mask: bool tensor [bsz, prefill_seq_len] where True = summary position.
-        """
-        if self._reorganized:
-            return
-        self._reorganized = True
-
-        text_mask = ~summary_mask[0]
-
-        for layer_idx in range(self.num_hidden_layers):
-            prefill_len = self._text_len[layer_idx]
-            prefill_k = self.key_cache[layer_idx][:, :, :prefill_len, :]
-            prefill_v = self.value_cache[layer_idx][:, :, :prefill_len, :]
-            bsz, heads, _, head_dim = prefill_k.shape
-            device, dtype = prefill_k.device, prefill_k.dtype
-
-            text_k = prefill_k[:, :, text_mask, :]
-            text_v = prefill_v[:, :, text_mask, :]
-            n_text = text_k.shape[2]
-
-            if self.is_large_window[layer_idx]:
-                # Large window: keep only text KV
-                cap = n_text + self._APPEND_HEADROOM
-                new_k = torch.empty(bsz, heads, cap, head_dim, dtype=dtype, device=device)
-                new_v = torch.empty(bsz, heads, cap, head_dim, dtype=dtype, device=device)
-                new_k[:, :, :n_text, :].copy_(text_k)
-                new_v[:, :, :n_text, :].copy_(text_v)
-                self.key_cache[layer_idx] = new_k
-                self.value_cache[layer_idx] = new_v
-                self._text_len[layer_idx] = n_text
-                self._capacity[layer_idx] = cap
-            else:
-                # Small window: [ scratch (S) | chunk_text (C) ←fill | ring_text (ws) | summaries →fill | headroom ]
-                summary_k = prefill_k[:, :, summary_mask[0], :]
-                summary_v = prefill_v[:, :, summary_mask[0], :]
-                n_summary = summary_k.shape[2]
-
-                C = self.summary_chunk_size
-                S = self.summary_token_num
-                ws = self.window_sizes[layer_idx]
-                scn = self.sliding_chunk_nums[layer_idx]
-
-                # Split text into complete chunks + partial remainder
-                n_complete_chunks = n_text // C
-                n_partial = n_text % C
-                n_complete_text = n_complete_chunks * C
-
-                # Window: last scn complete chunks (or all if fewer)
-                n_window_chunks = min(scn, n_complete_chunks)
-                n_window_text = n_window_chunks * C
-                window_start = n_complete_text - n_window_text
-
-                # Layout: [ scratch (S) | chunk_text (C) | ring_text (ws) | summaries | headroom ]
-                summary_headroom = max(self._SUMMARY_INIT_CAP, n_summary + 256)
-                total_cap = S + C + ws + n_summary + summary_headroom
-
-                new_k = torch.empty(bsz, heads, total_cap, head_dim, dtype=dtype, device=device)
-                new_v = torch.empty(bsz, heads, total_cap, head_dim, dtype=dtype, device=device)
-
-                # Copy partial chunk text to [S+C-n_partial : S+C] (left-filled)
-                if n_partial > 0:
-                    new_k[:, :, S + C - n_partial:S + C, :].copy_(
-                        text_k[:, :, n_complete_text:, :])
-                    new_v[:, :, S + C - n_partial:S + C, :].copy_(
-                        text_v[:, :, n_complete_text:, :])
-
-                # Copy window text to [S+C : S+C+n_window_text]
-                if n_window_text > 0:
-                    new_k[:, :, S + C:S + C + n_window_text, :].copy_(
-                        text_k[:, :, window_start:n_complete_text, :])
-                    new_v[:, :, S + C:S + C + n_window_text, :].copy_(
-                        text_v[:, :, window_start:n_complete_text, :])
-                self._n_valid_window[layer_idx] = n_window_text
-                self._window_write_ptr[layer_idx] = n_window_text % ws
-
-                # Copy summaries to [S+C+ws : S+C+ws+n_summary]
-                if n_summary > 0:
-                    new_k[:, :, S + C + ws:S + C + ws + n_summary, :].copy_(summary_k)
-                    new_v[:, :, S + C + ws:S + C + ws + n_summary, :].copy_(summary_v)
-
-                self.key_cache[layer_idx] = new_k
-                self.value_cache[layer_idx] = new_v
-                self._n_summaries[layer_idx] = n_summary
-                self._capacity[layer_idx] = total_cap
-                self._text_len[layer_idx] = 0
-                self._chunk_buf_len[layer_idx] = n_partial
-
-        block = self.summary_chunk_size + self.summary_token_num
-        for layer_idx in range(self.num_hidden_layers):
-            self.cur_chunk_sizes[layer_idx] = self.cur_chunk_sizes[layer_idx] % block
-            self._total_chunks[layer_idx] = self._n_summaries[layer_idx] if not self.is_large_window[layer_idx] else (self.true_tokens[layer_idx] // self.summary_chunk_size)
-
-    # ── Decode: text token update ──
-
-    def update_text(self, key_states: torch.Tensor, value_states: torch.Tensor, layer_idx: int):
-        """Write a single text token KV during decode."""
-        if self.is_large_window[layer_idx]:
-            cur = self._text_len[layer_idx]
-            new_len = cur + 1
-            if new_len > self._capacity[layer_idx]:
-                cap = max(new_len + self._APPEND_HEADROOM, self._capacity[layer_idx] * 2)
-                old_k, old_v = self.key_cache[layer_idx], self.value_cache[layer_idx]
-                bsz, heads, _, head_dim = old_k.shape
-                new_k = torch.empty(bsz, heads, cap, head_dim, dtype=old_k.dtype, device=old_k.device)
-                new_v = torch.empty(bsz, heads, cap, head_dim, dtype=old_v.dtype, device=old_v.device)
-                new_k[:, :, :cur, :].copy_(old_k[:, :, :cur, :])
-                new_v[:, :, :cur, :].copy_(old_v[:, :, :cur, :])
-                self.key_cache[layer_idx] = new_k
-                self.value_cache[layer_idx] = new_v
-                self._capacity[layer_idx] = cap
-            self.key_cache[layer_idx][:, :, cur:new_len, :].copy_(key_states)
-            self.value_cache[layer_idx][:, :, cur:new_len, :].copy_(value_states)
-            self._text_len[layer_idx] = new_len
-        else:
-            # Write to chunk_text region, left-filled: position S+C-1-cbl
-            C = self.summary_chunk_size
-            S = self.summary_token_num
-            cbl = self._chunk_buf_len[layer_idx]
-            dst = S + C - 1 - cbl
-            self.key_cache[layer_idx][:, :, dst:dst+1, :].copy_(key_states)
-            self.value_cache[layer_idx][:, :, dst:dst+1, :].copy_(value_states)
-            self._chunk_buf_len[layer_idx] = cbl + 1
-
-        self.cur_chunk_sizes[layer_idx] += 1
-        self.true_tokens[layer_idx] += 1
-
-    # ── Decode: summary token update ──
-
-    def update_summary(self, key_states: torch.Tensor, value_states: torch.Tensor, layer_idx: int):
-        """Write summary token KV during decode (chunk boundary).
-
-        Large window: skip. Small window: flush chunk to ring, append summary rightward.
-        """
-        n_summary = key_states.shape[2]
-
-        if self.is_large_window[layer_idx]:
-            self.cur_chunk_sizes[layer_idx] += n_summary
-            self._total_chunks[layer_idx] += n_summary
-            return
-
-        # ── Small window: boundary processing ──
-        C = self.summary_chunk_size
-        S = self.summary_token_num
-        ws = self.window_sizes[layer_idx]
-        cbl = self._chunk_buf_len[layer_idx]
-        ptr = self._window_write_ptr[layer_idx]
-
-        # Step A: Flush chunk_text to ring_text
-        # chunk_text lives at [S+C-cbl : S+C], left-filled
-        chunk_src = S + C - cbl
-        if cbl > 0:
-            ring_dst = S + C + ptr
-            if ptr + cbl <= ws:
-                self.key_cache[layer_idx][:, :, ring_dst:ring_dst + cbl, :].copy_(
-                    self.key_cache[layer_idx][:, :, chunk_src:chunk_src + cbl, :])
-                self.value_cache[layer_idx][:, :, ring_dst:ring_dst + cbl, :].copy_(
-                    self.value_cache[layer_idx][:, :, chunk_src:chunk_src + cbl, :])
-            else:
-                first = ws - ptr
-                self.key_cache[layer_idx][:, :, ring_dst:ring_dst + first, :].copy_(
-                    self.key_cache[layer_idx][:, :, chunk_src:chunk_src + first, :])
-                self.value_cache[layer_idx][:, :, ring_dst:ring_dst + first, :].copy_(
-                    self.value_cache[layer_idx][:, :, chunk_src:chunk_src + first, :])
-                rest = cbl - first
-                self.key_cache[layer_idx][:, :, S + C:S + C + rest, :].copy_(
-                    self.key_cache[layer_idx][:, :, chunk_src + first:chunk_src + cbl, :])
-                self.value_cache[layer_idx][:, :, S + C:S + C + rest, :].copy_(
-                    self.value_cache[layer_idx][:, :, chunk_src + first:chunk_src + cbl, :])
-
-        self._window_write_ptr[layer_idx] = (ptr + cbl) % ws
-        if self._n_valid_window[layer_idx] < ws:
-            self._n_valid_window[layer_idx] = min(ws, self._n_valid_window[layer_idx] + cbl)
-        self._chunk_buf_len[layer_idx] = 0
-
-        # Step B: Append summary rightward
-        n_sum = self._n_summaries[layer_idx]
-        sum_dst = S + C + ws + n_sum
-        if sum_dst + n_summary > self._capacity[layer_idx]:
-            self._grow_buffer_right(layer_idx)
-        sum_dst = S + C + ws + self._n_summaries[layer_idx]
-
-        self.key_cache[layer_idx][:, :, sum_dst:sum_dst + n_summary, :].copy_(key_states)
-        self.value_cache[layer_idx][:, :, sum_dst:sum_dst + n_summary, :].copy_(value_states)
-        self._n_summaries[layer_idx] += n_summary
-
-        self.cur_chunk_sizes[layer_idx] += n_summary
-        self._total_chunks[layer_idx] += n_summary
-
-    # ── Decode: get KV for attention ──
-
-    def get_attention_kv(self, layer_idx: int) -> tuple[torch.Tensor, torch.Tensor]:
-        """Get full KV for text token attention.
-
-        Large window: buffer[:text_len]
-        Small window: always a single contiguous slice.
-          - ring full:  [S+C-cbl : S+C+ws+n_old_sum]
-          - ring not full: [S+C-cbl : S+C+nv]
-        """
-        if self.is_large_window[layer_idx]:
-            tl = self._text_len[layer_idx]
-            return (self.key_cache[layer_idx][:, :, :tl, :],
-                    self.value_cache[layer_idx][:, :, :tl, :])
-
-        C = self.summary_chunk_size
-        S = self.summary_token_num
-        ws = self.window_sizes[layer_idx]
-        nv = self._n_valid_window[layer_idx]
-        cbl = self._chunk_buf_len[layer_idx]
-
-        start = S + C - cbl
-
-        if nv >= ws:
-            # Ring full: include old summaries (skip in-window ones)
-            scn = self.sliding_chunk_nums[layer_idx]
-            n_summaries = self._n_summaries[layer_idx]
-            skip = min(scn * S, n_summaries)
-            end = S + C + ws + (n_summaries - skip)
-        else:
-            # Ring not full: all summaries are in-window, skip them all
-            end = S + C + nv
-
-        return (self.key_cache[layer_idx][:, :, start:end, :],
-                self.value_cache[layer_idx][:, :, start:end, :])
-
-    def get_current_chunk_kv(self, layer_idx: int) -> tuple[torch.Tensor, torch.Tensor]:
-        """Get KV of the current chunk's C text tokens for summary token attention."""
-        C = self.summary_chunk_size
-        if self.is_large_window[layer_idx]:
-            tl = self._text_len[layer_idx]
-            return (self.key_cache[layer_idx][:, :, tl - C:tl, :],
-                    self.value_cache[layer_idx][:, :, tl - C:tl, :])
-        else:
-            S = self.summary_token_num
-            cbl = self._chunk_buf_len[layer_idx]
-            return (self.key_cache[layer_idx][:, :, S + C - cbl:S + C, :],
-                    self.value_cache[layer_idx][:, :, S + C - cbl:S + C, :])
-
-    def get_summary_attention_kv(
-        self,
-        layer_idx: int,
-        k_summary: torch.Tensor,
-        v_summary: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        """Write summary KV to scratch area [0:S], return contiguous [0 : S+C] for summary attention.
-
-        This avoids a torch.cat by using the pre-reserved scratch region.
-        For large window layers, falls back to cat (no scratch area).
-        """
-        C = self.summary_chunk_size
-        S = self.summary_token_num
-        if self.is_large_window[layer_idx]:
-            tl = self._text_len[layer_idx]
-            k_chunk = self.key_cache[layer_idx][:, :, tl - C:tl, :]
-            v_chunk = self.value_cache[layer_idx][:, :, tl - C:tl, :]
-            return (torch.cat([k_chunk, k_summary], dim=2),
-                    torch.cat([v_chunk, v_summary], dim=2))
-        else:
-            # Write summary KV into scratch area [0:S]
-            self.key_cache[layer_idx][:, :, 0:S, :].copy_(k_summary)
-            self.value_cache[layer_idx][:, :, 0:S, :].copy_(v_summary)
-            # Return contiguous [scratch | chunk_text] = [0 : S+C]
-            return (self.key_cache[layer_idx][:, :, 0:S + C, :],
-                    self.value_cache[layer_idx][:, :, 0:S + C, :])
-
-    def _grow_buffer_right(self, layer_idx: int):
-        """Grow buffer rightward when summary headroom is exhausted (doubling strategy).
-
-        Only the tail (headroom) is extended; chunk_text and ring_text positions are unchanged.
-        """
-        old_k = self.key_cache[layer_idx]
-        old_v = self.value_cache[layer_idx]
-        bsz, heads, old_cap, head_dim = old_k.shape
-
-        extra = max(self._SUMMARY_INIT_CAP, self._n_summaries[layer_idx])
-        new_cap = max(old_cap + extra, old_cap * 2)
-
-        new_k = torch.empty(bsz, heads, new_cap, head_dim, dtype=old_k.dtype, device=old_k.device)
-        new_v = torch.empty(bsz, heads, new_cap, head_dim, dtype=old_v.dtype, device=old_v.device)
-
-        # Copy all existing data in place — positions unchanged
-        new_k[:, :, :old_cap, :].copy_(old_k)
-        new_v[:, :, :old_cap, :].copy_(old_v)
-
-        self.key_cache[layer_idx] = new_k
-        self.value_cache[layer_idx] = new_v
-        self._capacity[layer_idx] = new_cap
-
-    def reset_chunk_counter(self):
-        """Reset chunk counters after a chunk boundary step completes."""
-        block = self.summary_chunk_size + self.summary_token_num
-        for layer_idx in range(self.num_hidden_layers):
-            if self.cur_chunk_sizes[layer_idx] >= block:
-                self.cur_chunk_sizes[layer_idx] %= block
-
-
-class Qwen3MLP(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 apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, 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.
-        position_ids (`torch.Tensor`, *optional*):
-            Deprecated and unused.
-        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: Optional[torch.Tensor],
-    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
-
-
-def _sdpa_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: Unpack[TransformersKwargs],
-):
-    key_states = repeat_kv(key, module.num_key_value_groups)
-    value_states = repeat_kv(value, module.num_key_value_groups)
-    attn_output = F.scaled_dot_product_attention(
-            query,
-            key_states,
-            value_states,
-            attn_mask=None,
-            dropout_p=dropout,
-            is_causal=False,
-    )
-    attn_output = attn_output.transpose(1, 2).contiguous()
-    return attn_output, None
-    
-
-
-class Qwen3Attention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: Qwen3Config, 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.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.q_norm = Qwen3RMSNorm(self.head_dim, eps=config.rms_norm_eps)  # unlike olmo, only on the head dim!
-        self.k_norm = Qwen3RMSNorm(self.head_dim, eps=config.rms_norm_eps)  # thus post q_norm does not need reshape
-        self.sliding_window = config.sliding_window if config.layer_types[layer_idx] == "sliding_attention" else None
-        if getattr(config, "_attn_implementation", None) == "eager":
-            self._decode_attn_fn = eager_attention_forward
-        else:
-            self._decode_attn_fn = _sdpa_attention_forward
-
-    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_embeddings: tuple[torch.Tensor, torch.Tensor],
-        attention_mask: Optional[torch.Tensor],
-        past_key_values: Optional[Cache] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
-        input_shape = hidden_states.shape[:-1]
-        hidden_shape = (*input_shape, -1, self.head_dim)
-
-
-        query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
-        key_states = self.k_norm(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:
-            # sin and cos are specific to RoPE models; cache_position needed for the static cache
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        attn_output, attn_weights = self._decode_attn_fn(
-            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,  # 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 Qwen3SummaryAttention(Qwen3Attention):
-    """
-    Summary-aware variant of Qwen3Attention: uses a sliding summary mask.
-    """
-
-    def __init__(self, config: Qwen3Config, layer_idx: int):
-        super().__init__(config, layer_idx)
-        self.summary_chunk_size = getattr(self.config, "summary_chunk_size", 0)
-        self.summary_token_num = getattr(self.config, "summary_token_num", 0)
-
-        # Cache sliding_chunk_num to avoid eval() on every forward call
-        val = getattr(config, "summary_sliding_chunk_num", 0) or 0
-        val = _parse_config_pattern(val)
-        if isinstance(val, list):
-            self._sliding_chunk_num = val[layer_idx]
-        else:
-            self._sliding_chunk_num = int(val)
-
-        if config.summary_independent_parameters and config.mix_coeff > 0:
-            self.q_proj_summary = nn.Linear(
-                config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
-            )
-            self.k_proj_summary = nn.Linear(
-                config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
-            )
-            self.v_proj_summary = nn.Linear(
-                config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
-            )
-
-    def _get_sliding_chunk_num(self):
-        return self._sliding_chunk_num
-
-    def get_query_key_value_tensors(self, hidden_states):
-        input_shape = hidden_states.shape[:-1]
-        hidden_shape = (*input_shape, -1, self.head_dim)
-        query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
-        key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
-        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-
-        return query_states, key_states, value_states
-
-    def get_query_key_value_tensors_summary(self, hidden_states):
-        input_shape = hidden_states.shape[:-1]
-        hidden_shape = (*input_shape, -1, self.head_dim)
-        query_states = self.q_norm(self.q_proj_summary(hidden_states).view(hidden_shape)).transpose(1, 2)
-        key_states = self.k_norm(self.k_proj_summary(hidden_states).view(hidden_shape)).transpose(1, 2)
-        value_states = self.v_proj_summary(hidden_states).view(hidden_shape).transpose(1, 2)
-
-        return query_states, key_states, value_states
-
-    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_embeddings: tuple[torch.Tensor, torch.Tensor],
-        attention_mask: Optional[torch.Tensor] = None,
-        past_key_values: Optional[Cache] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        summary_ctx: Optional[SummaryBatchContext] = None,
-        **kwargs,
-    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
-        input_shape = hidden_states.shape[:-1]
-        if hidden_states.size(0) != 1:
-            raise ValueError("Summary sliding attention only supports batch size=1.")
-
-        # Compute q/k/v for the full sequence once.
-        if self.config.summary_independent_parameters:
-            if summary_ctx is None:
-                raise ValueError("summary_ctx is required when using summary_independent_parameters.")
-            summary_mask = summary_ctx.summary_mask
-            summary_pos = summary_mask[0]
-            assert (summary_mask == summary_mask[0:1]).all()
-
-            if self.config.mix_coeff == 0:
-                # When mix_coeff=0, summary projections have no effect — skip clone + extra linear
-                query_states, key_states, value_states = self.get_query_key_value_tensors(hidden_states)
-            else:
-                query, key, value = self.get_query_key_value_tensors(hidden_states)
-
-                query_states = query.clone()
-                key_states = key.clone()
-                value_states = value.clone()
-
-                hs_summary = hidden_states[:, summary_pos, :]
-                if hs_summary.size(1) > 0:
-                    base_q_summary = query[:, :, summary_pos, :]
-                    base_k_summary = key[:, :, summary_pos, :]
-                    base_v_summary = value[:, :, summary_pos, :]
-
-                    q_s, k_s, v_s = self.get_query_key_value_tensors_summary(hs_summary)
-
-                    q_s = self.config.mix_coeff * q_s + (1.0 - self.config.mix_coeff) * base_q_summary
-                    k_s = self.config.mix_coeff * k_s + (1.0 - self.config.mix_coeff) * base_k_summary
-                    v_s = self.config.mix_coeff * v_s + (1.0 - self.config.mix_coeff) * base_v_summary
-
-                    query_states[:, :, summary_pos, :] = q_s
-                    key_states[:, :, summary_pos, :] = k_s
-                    value_states[:, :, summary_pos, :] = v_s
-        else:
-            query_states, key_states, value_states = self.get_query_key_value_tensors(hidden_states)
-
-        cos, sin = position_embeddings
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-
-        query_len = query_states.shape[2]
-        is_prefill = past_key_values is None or not past_key_values._reorganized
-
-        if is_prefill:
-            # Prefill: use standard append and summary_attn_func
-            if past_key_values is not None:
-                cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-                if summary_ctx is not None:
-                    cache_kwargs["summary_mask"] = summary_ctx.summary_mask
-                key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-            with torch.cuda.device(query_states.device):
-                attn_output, attn_weights = summary_attn_func(
-                    query_states.transpose(1,2).contiguous(),
-                    key_states.transpose(1,2).contiguous(),
-                    value_states.transpose(1,2).contiguous(),
-                    self.summary_chunk_size,
-                    self.summary_token_num,
-                    self._get_sliding_chunk_num(),
-                    summary_pos=summary_ctx.summary_mask.squeeze()
-                )
-        elif query_len == 1:
-            # Single text token decode: write to cache, attend to full buffer
-            past_key_values.update_text(key_states, value_states, self.layer_idx)
-            k_full, v_full = past_key_values.get_attention_kv(self.layer_idx)
-            attn_output, attn_weights = self._decode_attn_fn(
-                self,
-                query_states,
-                k_full,
-                v_full,
-                None,
-                dropout=0.0 if not self.training else self.attention_dropout,
-                scaling=self.scaling,
-                sliding_window=self.sliding_window,
-                **kwargs,
-            )
-        else:
-            # Chunk boundary: query = [text_token, summary_token(s)]
-            # Split into text (first token) and summary (remaining tokens)
-            q_text = query_states[:, :, :1, :]
-            q_summary = query_states[:, :, 1:, :]
-            k_text = key_states[:, :, :1, :]
-            v_text = value_states[:, :, :1, :]
-            k_summary = key_states[:, :, 1:, :]
-            v_summary = value_states[:, :, 1:, :]
-
-            # 1. Write text token to cache, get full KV, run text attention
-            past_key_values.update_text(k_text, v_text, self.layer_idx)
-            k_full, v_full = past_key_values.get_attention_kv(self.layer_idx)
-            text_out, _ = self._decode_attn_fn(
-                self,
-                q_text,
-                k_full,
-                v_full,
-                None,
-                dropout=0.0 if not self.training else self.attention_dropout,
-                scaling=self.scaling,
-                sliding_window=self.sliding_window,
-                **kwargs,
-            )
-
-            # 2. Summary attention: attend to current chunk's C text tokens + own KV (self-attention)
-            #    Uses scratch area [0:S] for summary self-KV, contiguous with chunk [S:S+C].
-            k_chunk_with_self, v_chunk_with_self = past_key_values.get_summary_attention_kv(
-                self.layer_idx, k_summary, v_summary)
-            summary_out, _ = self._decode_attn_fn(
-                self,
-                q_summary,
-                k_chunk_with_self,
-                v_chunk_with_self,
-                None,
-                dropout=0.0 if not self.training else self.attention_dropout,
-                scaling=self.scaling,
-                sliding_window=self.sliding_window,
-                **kwargs,
-            )
-
-            # 3. Write summary KV to cache
-            past_key_values.update_summary(k_summary, v_summary, self.layer_idx)
-
-            attn_output = torch.cat([text_out, summary_out], dim=2)
-            attn_weights = None
-
-        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
-        attn_output = self.o_proj(attn_output)
-        return attn_output, attn_weights
-
-
-class Qwen3DecoderLayer(GradientCheckpointingLayer):
-    def __init__(self, config: Qwen3Config, layer_idx: int):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-
-        # Use SummaryAttention if enabled in config
-        if getattr(config, "use_summary_attention", False) is True and config.summary_layer_freq[layer_idx] == 1:
-            self.self_attn = Qwen3SummaryAttention(config=config, layer_idx=layer_idx)
-        elif getattr(config, "use_summary_attention", False) is False and config.summary_layer_freq[layer_idx] == 0:
-            self.self_attn = Qwen3Attention(config=config, layer_idx=layer_idx)
-        else:
-            raise ValueError(f'Check config.summary_layer_freq {config.summary_layer_freq} and config.use_summary_attention {config.use_summary_attention}')
-
-        self.mlp = Qwen3MLP(config)
-        self.input_layernorm = Qwen3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        if getattr(config, "summary_independent_attention_layernorm", False):
-            self.input_layernorm_summary = Qwen3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = Qwen3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.attention_type = config.layer_types[layer_idx]
-
-    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: 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
-        summary_ctx: Optional[SummaryBatchContext] = None,
-        **kwargs: Unpack[TransformersKwargs],
-    ) -> torch.Tensor:
-        residual = hidden_states
-        if getattr(self.config, "summary_independent_attention_layernorm", False):
-            summary_mask = summary_ctx.summary_mask
-            assert (summary_mask == summary_mask[0:1]).all(), \
-                "summary_mask must be identical across batch"
-            hidden_states = self.input_layernorm(hidden_states)
-            if summary_mask.any():
-                hidden_summary = residual[:, summary_mask[0].to(residual.device), :]
-                hidden_summary = self.input_layernorm_summary(hidden_summary)
-                hidden_states[:, summary_mask[0], :] = hidden_summary
-        else:
-            hidden_states = self.input_layernorm(hidden_states)
-        
-        # Self Attention - pass summary_ctx if using summary attention
-        attn_kwargs = {
-            "hidden_states": hidden_states,
-            "attention_mask": attention_mask,
-            "position_ids": position_ids,
-            "past_key_values": past_key_values,
-            "use_cache": use_cache,
-            "cache_position": cache_position,
-            "position_embeddings": position_embeddings,
-            **kwargs,
-        }
-        if isinstance(self.self_attn, Qwen3SummaryAttention):
-            attn_kwargs["summary_ctx"] = summary_ctx
-        
-        hidden_states, _ = self.self_attn(**attn_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)
-        hidden_states = residual + hidden_states
-        return hidden_states
-
-
-@auto_docstring
-class Qwen3PreTrainedModel(PreTrainedModel):
-    config: Qwen3Config
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["Qwen3DecoderLayer"]
-    _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 = {
-        "hidden_states": Qwen3DecoderLayer,
-        "attentions": Qwen3Attention,
-    }
-
-
-class Qwen3RotaryEmbedding(nn.Module):
-    inv_freq: torch.Tensor  # fix linting for `register_buffer`
-
-    def __init__(self, config: Qwen3Config, 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"]
-        rope_init_fn: Callable = self.compute_default_rope_parameters
-        if self.rope_type != "default":
-            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
-        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
-
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.original_inv_freq = inv_freq
-
-    @staticmethod
-    def compute_default_rope_parameters(
-        config: Optional[Qwen3Config] = None,
-        device: Optional["torch.device"] = None,
-        seq_len: Optional[int] = 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):
-        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)
-
-
-@auto_docstring
-class Qwen3Model(Qwen3PreTrainedModel):
-    def __init__(self, config: Qwen3Config):
-        super().__init__(config)
-        self.padding_idx = config.pad_token_id
-        self.vocab_size = config.vocab_size
-        if not getattr(config, "summary_layer_freq", False):
-            if config.use_summary_attention:
-                config.summary_layer_freq = [1]*config.num_hidden_layers
-            else:
-                config.summary_layer_freq = [0]*config.num_hidden_layers
-            Warning(f'Please set config.summary_layer_freq, temp set summary_layer_freq = {config.num_hidden_layers}')
-        else:
-            config.summary_layer_freq = _parse_config_pattern(config.summary_layer_freq)
-
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
-        self.layers = nn.ModuleList(
-            [Qwen3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
-        )
-        self.norm = Qwen3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.rotary_emb = Qwen3RotaryEmbedding(config=config)
-        self.gradient_checkpointing = False
-        self.has_sliding_layers = "sliding_attention" in self.config.layer_types
-
-        # Cache per-layer sliding_chunk_nums for KV cache eviction
-        _sv = _parse_config_pattern(getattr(config, "summary_sliding_chunk_num", 0) or 0)
-        if isinstance(_sv, list):
-            self._sliding_chunk_nums = [int(v) for v in _sv]
-        else:
-            self._sliding_chunk_nums = [int(_sv)] * config.num_hidden_layers
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def _expand_input_with_summary_tokens(self, input_ids):
-        """Expand input_ids with summary tokens for prefill phase (vectorized).
-
-        Returns:
-            Tuple of (expanded_input_ids, position_ids, text_only_mask)
-        """
-        summary_chunk = self.config.summary_chunk_size
-        summary_num = self.config.summary_token_num
-        summary_begin = self.config.summary_token_begin
-
-        if summary_chunk == 0 or summary_num == 0:
-            return input_ids, None, None
-
-        batch_size, seq_len = input_ids.shape
-        device = input_ids.device
-        dtype = input_ids.dtype
-        block = summary_chunk + summary_num
-
-        # Number of full chunks and remainder
-        n_full_chunks = seq_len // summary_chunk
-        remainder = seq_len % summary_chunk
-        has_remainder = remainder > 0
-
-        # Total expanded length: full_chunks * block + remainder
-        expanded_len = n_full_chunks * block + (remainder if has_remainder else 0)
-
-        # --- Build expanded_input_ids ---
-        expanded_ids = torch.empty((batch_size, expanded_len), dtype=dtype, device=device)
-        text_only_mask = torch.zeros((batch_size, expanded_len), dtype=torch.bool, device=device)
-
-        # Compute text positions: for chunk i, text goes to [i*block, i*block+summary_chunk)
-        # Summary positions: [i*block+summary_chunk, (i+1)*block)
-        if n_full_chunks > 0:
-            chunk_indices = torch.arange(n_full_chunks, device=device)
-            # Text source positions in original input_ids
-            text_src_offsets = (chunk_indices * summary_chunk).unsqueeze(1) + torch.arange(summary_chunk, device=device).unsqueeze(0)  # [n_full_chunks, summary_chunk]
-            # Text dest positions in expanded
-            text_dst_offsets = (chunk_indices * block).unsqueeze(1) + torch.arange(summary_chunk, device=device).unsqueeze(0)  # [n_full_chunks, summary_chunk]
-            # Summary dest positions
-            summary_dst_offsets = (chunk_indices * block + summary_chunk).unsqueeze(1) + torch.arange(summary_num, device=device).unsqueeze(0)  # [n_full_chunks, summary_num]
-
-            text_src_flat = text_src_offsets.reshape(-1)
-            text_dst_flat = text_dst_offsets.reshape(-1)
-            summary_dst_flat = summary_dst_offsets.reshape(-1)
-
-            # Copy text tokens
-            expanded_ids[:, text_dst_flat] = input_ids[:, text_src_flat]
-            text_only_mask[:, text_dst_flat] = True
-
-            # Fill summary tokens
-            summary_ids_val = torch.arange(summary_num, device=device, dtype=dtype) + summary_begin
-            expanded_ids[:, summary_dst_flat] = summary_ids_val.repeat(n_full_chunks).unsqueeze(0).expand(batch_size, -1)
-
-        # Handle remainder (last partial chunk, no summary tokens)
-        if has_remainder:
-            rem_start_src = n_full_chunks * summary_chunk
-            rem_start_dst = n_full_chunks * block
-            rem_offsets = torch.arange(remainder, device=device)
-            expanded_ids[:, rem_start_dst + rem_offsets] = input_ids[:, rem_start_src + rem_offsets]
-            text_only_mask[:, rem_start_dst + rem_offsets] = True
-
-        # --- Build position_ids ---
-        position_ids = torch.empty((batch_size, expanded_len), dtype=torch.long, device=device)
-
-        if n_full_chunks > 0:
-            # Text position IDs
-            if self.config.summary_chunk_position_ids_type == 'origin':
-                text_pos = text_src_flat.unsqueeze(0).expand(batch_size, -1)
-            elif self.config.summary_chunk_position_ids_type == 'inner_chunk':
-                inner_pos = torch.arange(summary_chunk, device=device).repeat(n_full_chunks)
-                text_pos = inner_pos.unsqueeze(0).expand(batch_size, -1)
-            else:
-                raise ValueError(f'Check config.summary_chunk_position_ids_type: {self.config.summary_chunk_position_ids_type}')
-            position_ids[:, text_dst_flat] = text_pos
-
-            # Summary position IDs
-            if self.config.summary_token_position_ids_type == 'zeros':
-                position_ids[:, summary_dst_flat] = 0
-            elif self.config.summary_token_position_ids_type in ('last_chunk_slice_left', 'last_chunk_slice_right'):
-                # Vectorized slice_ends computation for all chunks at once
-                if self.config.summary_token_position_ids_type == 'last_chunk_slice_left':
-                    idx = torch.arange(0, summary_num, device=device, dtype=torch.long)
-                else:
-                    idx = torch.arange(1, summary_num + 1, device=device, dtype=torch.long)
-                # For each chunk i: prev_text_end = i * summary_chunk
-                prev_ends = (chunk_indices * summary_chunk).unsqueeze(1)  # [n_full_chunks, 1]
-                slice_ends = prev_ends + (idx.unsqueeze(0) * summary_chunk) // summary_num - 1  # [n_full_chunks, summary_num]
-                slice_ends = slice_ends.clamp(min=0)
-                # Clamp per-chunk: min is prev_text_end for that chunk
-                slice_ends = torch.max(slice_ends, prev_ends)
-                position_ids[:, summary_dst_flat] = slice_ends.reshape(-1).unsqueeze(0).expand(batch_size, -1)
-            else:
-                raise ValueError(f'Unknown summary_token_position_ids_type: {self.config.summary_token_position_ids_type}')
-
-        # Remainder position IDs
-        if has_remainder:
-            if self.config.summary_chunk_position_ids_type == 'origin':
-                rem_pos = (rem_start_src + rem_offsets).unsqueeze(0).expand(batch_size, -1)
-            elif self.config.summary_chunk_position_ids_type == 'inner_chunk':
-                rem_pos = rem_offsets.unsqueeze(0).expand(batch_size, -1)
-            else:
-                raise ValueError(f'Check config.summary_chunk_position_ids_type: {self.config.summary_chunk_position_ids_type}')
-            position_ids[:, rem_start_dst + rem_offsets] = rem_pos
-
-        return expanded_ids, position_ids, text_only_mask
-    
-    def _build_summary_context(self, input_ids, position_ids, is_prefill, use_cache):
-        """Build summary context for attention layers."""
-        summary_chunk = self.config.summary_chunk_size
-        summary_num = self.config.summary_token_num
-        summary_begin = self.config.summary_token_begin
-
-        if summary_chunk > 0 and summary_num > 0:
-            return build_summary_sliding_context(
-                input_ids=input_ids,
-                position_ids=position_ids,
-                summary_token_num=summary_num,
-                summary_token_begin=summary_begin,
-            )
-        return None
-    
-    def _filter_summary_tokens(self, hidden_states, text_only_mask, use_summary, is_decode):
-        """Filter out summary tokens from output hidden states."""
-        if text_only_mask is not None:
-            # Prefill: vectorized filtering using boolean mask
-            batch_size, _, hidden_size = hidden_states.shape
-            text_length = text_only_mask[0].sum().item()
-            return hidden_states[text_only_mask.to(hidden_states.device)].reshape(batch_size, text_length, hidden_size)
-        elif use_summary and is_decode and hidden_states.size(1) > 1:
-            # Decode: if we have multiple tokens, only return the first (text token)
-            return hidden_states[:, :1, :]
-        return hidden_states
-
-    @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[Cache] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        summary_ctx: Optional[SummaryBatchContext] = None,
-        **kwargs: Unpack[TransformersKwargs],
-    ) -> BaseModelOutputWithPast:
-        if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-        use_summary = getattr(self.config, "use_summary_attention", False)
-        is_prefill = past_key_values is None or past_key_values.get_seq_length() == 0
-        
-        # Prefill phase with summary attention: expand input_ids with summary tokens
-        text_only_mask = None
-        if use_summary and input_ids is not None and inputs_embeds is None and is_prefill:
-            input_ids, position_ids, text_only_mask = self._expand_input_with_summary_tokens(input_ids)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-
-        # Initialize cache
-        if use_cache and past_key_values is None:
-            if use_summary:
-                past_key_values = Qwen3RingBufferCache(
-                    config=self.config, sliding_chunk_nums=self._sliding_chunk_nums)
-            else:
-                past_key_values = DynamicCache(config=self.config)
-
-        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)
-
-        # Build summary context if needed
-        if use_summary and summary_ctx is None and input_ids is not None:
-            summary_ctx = self._build_summary_context(input_ids, position_ids, is_prefill, use_cache)
-
-        causal_mask_mapping = attention_mask
-        if not isinstance(causal_mask_mapping, (dict, list)):
-            if summary_ctx and summary_ctx.enabled:
-                seq_len = inputs_embeds.shape[1]
-                # During prefill, Qwen3SummaryAttention uses summary_attn_func
-                # which does not need a dense mask. Skip expensive mask construction.
-                # During decode, prepare_inputs_for_generation already computed
-                # per-layer keep_indices and passed them as attention_mask (list).
-                # If we reach here with a non-list, it means no mask is needed.
-                causal_mask_mapping = None
-            else:
-                # Prepare mask arguments
-                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,
-                }
-                # Create the masks - disable causal mask when summary context is enabled
-                causal_mask_mapping = {
-                    "full_attention": create_causal_mask(**mask_kwargs),
-                }
-                # The sliding window alternating layers are not always activated depending on the config
-                if self.has_sliding_layers:
-                    causal_mask_mapping["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)
-
-        for layer_idx, decoder_layer in enumerate(self.layers[: self.config.num_hidden_layers]):
-            if causal_mask_mapping is None:
-                layer_mask = None
-            elif isinstance(causal_mask_mapping, list):
-                layer_mask = causal_mask_mapping[layer_idx]
-            else:
-                layer_mask = causal_mask_mapping[decoder_layer.attention_type]
-            hidden_states = decoder_layer(
-                hidden_states,
-                attention_mask=layer_mask,
-                position_ids=position_ids,
-                past_key_values=past_key_values,
-                use_cache=use_cache,
-                cache_position=cache_position,
-                position_embeddings=position_embeddings,
-                summary_ctx=summary_ctx,
-                **kwargs,
-            )
-
-        hidden_states = self.norm(hidden_states)
-
-        # After prefill: reorganize cache to ring buffer layout
-        if use_cache and use_summary and past_key_values is not None and is_prefill:
-            if hasattr(past_key_values, 'reorganize_after_prefill') and summary_ctx is not None:
-                past_key_values.reorganize_after_prefill(summary_ctx.summary_mask)
-
-        # After chunk boundary decode: reset chunk counters
-        if use_cache and use_summary and past_key_values is not None and not is_prefill:
-            if hasattr(past_key_values, 'reset_chunk_counter'):
-                past_key_values.reset_chunk_counter()
-            
-        # Filter out summary tokens from output
-        hidden_states = self._filter_summary_tokens(hidden_states, text_only_mask, use_summary, 
-                                                      past_key_values is not None and past_key_values.get_seq_length() > 0)
-
-        return BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=past_key_values if use_cache else None,
-        )
-
-
-@auto_docstring
-class Qwen3ForCausalLM(Qwen3PreTrainedModel, GenerationMixin):
-    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
-    _tp_plan = {"lm_head": "colwise_rep"}
-    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = Qwen3Model(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()
-
-    @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,
-        cache_position: Optional[torch.LongTensor] = None,
-        logits_to_keep: Union[int, torch.Tensor] = 0,
-        summary_ctx: Optional[SummaryBatchContext] = None,
-        **kwargs: Unpack[TransformersKwargs],
-    ) -> 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, Qwen3ForCausalLM
-
-        >>> model = Qwen3ForCausalLM.from_pretrained("Qwen/Qwen3-8B")
-        >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
-
-        >>> 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."
-        ```"""
-        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,
-            cache_position=cache_position,
-            summary_ctx=summary_ctx,
-            **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
-        if isinstance(logits_to_keep, int) and logits_to_keep == 0 and labels is None:
-            # Inference: only need last token's logits to avoid OOM from [seq_len, vocab_size]
-            logits = self.lm_head(hidden_states[:, -1:, :])
-        else:
-            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, :])
-
-        truncate_n = getattr(self.config, "truncate_predict_nums", 151936)
-        if truncate_n > 0:
-            logits = logits[..., :truncate_n]
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(logits=logits, labels=labels, vocab_size=logits.shape[-1], **kwargs)
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-    def _build_summary_attention_mask_for_generation(
-        self,
-        *,
-        input_ids: torch.LongTensor,
-        past_key_values: Optional[Cache],
-        attention_mask: Optional[torch.Tensor],
-    ) -> Optional[torch.Tensor]:
-        """Ring buffer cache handles attention internally — no mask needed for decode."""
-        if isinstance(past_key_values, Qwen3RingBufferCache):
-            return None
-        return attention_mask
-
-    def prepare_inputs_for_generation(
-        self,
-        input_ids: torch.LongTensor,
-        past_key_values: Optional[Cache] = None,
-        attention_mask: Optional[torch.LongTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        **kwargs,
-    ):
-        use_summary = getattr(self.config, "use_summary_attention", False)
-
-        # If not using summary attention, use standard behavior
-        if not use_summary:
-            return super().prepare_inputs_for_generation(
-                input_ids=input_ids,
-                past_key_values=past_key_values,
-                attention_mask=attention_mask,
-                inputs_embeds=inputs_embeds,
-                cache_position=cache_position,
-                position_ids=position_ids,
-                **kwargs,
-            )
-
-        # For summary attention: handle cache-based input slicing
-        summary_chunk_size = getattr(self.config, "summary_chunk_size", 0)
-        summary_token_num = getattr(self.config, "summary_token_num", 0)
-        summary_token_begin = getattr(self.config, "summary_token_begin", 0)
-        
-        # Prefill phase: pass full sequence, forward() will handle summary token insertion
-        if past_key_values is None or past_key_values.get_seq_length() == 0:
-            if cache_position is None:
-                cache_position = torch.arange(0, input_ids.shape[1], device=input_ids.device)
-            
-            return {
-                "input_ids": input_ids,
-                "attention_mask": attention_mask,
-                "position_ids": position_ids,
-                "past_key_values": past_key_values,
-                "cache_position": cache_position,
-                "use_cache": kwargs.get("use_cache"),
-            }
-        
-        # Decode phase: only pass new tokens not in cache
-        # Get current chunk size (number of text tokens in current chunk)
-        cur_chunk = past_key_values.get_cur_chunk_size() if hasattr(past_key_values, "get_cur_chunk_size") else 0
-        true_token_num = past_key_values.get_true_token_num()
-        
-        # Only take the new tokens that haven't been processed
-        if input_ids.shape[1] > 1:
-            # Slice to get only new tokens
-            new_token_count = input_ids.shape[1] - true_token_num
-            assert new_token_count > 0, f'new_token_count={new_token_count} should be greater than 0'
-            input_ids = input_ids[:, -new_token_count:]
-        device = input_ids.device
-        # Check if we need to insert summary tokens
-        # If cur_chunk >= summary_chunk_size, we need to generate summary tokens
-        if cur_chunk == summary_chunk_size - 1:
-            # Insert summary tokens
-            batch_size = input_ids.shape[0]
-            summary_ids = (
-                torch.arange(summary_token_num, device=device, dtype=input_ids.dtype)
-                + summary_token_begin
-            ).unsqueeze(0).repeat(batch_size, 1)
-            
-            # Concatenate: [text_token, summary_tokens]
-            input_ids = torch.cat([input_ids, summary_ids], dim=1)
-            
-            # Position IDs: text token uses cur_chunk, summary tokens use 0
-            if self.config.summary_chunk_position_ids_type == 'origin':
-                text_pos = torch.full((batch_size, 1), past_key_values.get_true_token_num(), device=device, dtype=torch.long)
-            elif self.config.summary_chunk_position_ids_type == 'inner_chunk':
-                text_pos = torch.full((batch_size, 1), cur_chunk, device=device, dtype=torch.long)
-            else:
-                raise ValueError(f'Check config.summary_chunk_position_ids_type: {self.config.summary_chunk_position_ids_type}')
-            
-            if self.config.summary_token_position_ids_type == 'zeros':
-                summary_pos = torch.zeros((batch_size, summary_token_num), device=device, dtype=torch.long)
-            elif self.config.summary_token_position_ids_type == 'last_chunk_slice_left':
-                # 等分成 summary_num 份，每个 summary token 取对应 slice 的末尾
-                prev_text_end = past_key_values.get_true_token_num()+1-summary_chunk_size
-                cur_text_end = past_key_values.get_true_token_num()+1
-                chunk_len = cur_text_end - prev_text_end
-
-                idx = torch.arange(0, summary_token_num, device=device, dtype=torch.long,)
-
-                # 每一份的末尾（全局 position）
-                slice_ends = prev_text_end + (idx * chunk_len) // summary_token_num - 1
-                slice_ends = slice_ends.clamp(min=prev_text_end)
-
-                summary_pos = slice_ends.to(dtype=torch.long, device=device).unsqueeze(0)
-            elif self.config.summary_token_position_ids_type == 'last_chunk_slice_right':
-                # 等分成 summary_num 份，每个 summary token 取对应 slice 的末尾
-                prev_text_end = past_key_values.get_true_token_num()+1-summary_chunk_size
-                cur_text_end = past_key_values.get_true_token_num()+1
-                chunk_len = cur_text_end - prev_text_end
-
-                idx = torch.arange(1, summary_token_num + 1, device=device, dtype=torch.long,)
-
-                # 每一份的末尾（全局 position）
-                slice_ends = prev_text_end + (idx * chunk_len) // summary_token_num - 1
-                slice_ends = slice_ends.clamp(min=prev_text_end)
-
-                summary_pos = slice_ends.to(dtype=torch.long, device=device).unsqueeze(0)
-
-            else:
-                raise ValueError('')
-
-            position_ids = torch.cat([text_pos, summary_pos], dim=1)
-        else:
-            # Normal decode: just the new text token with position = cur_chunk
-            if position_ids is None:
-                batch_size = input_ids.shape[0]
-                if self.config.summary_chunk_position_ids_type == 'origin':
-                    position_ids = torch.full((batch_size, input_ids.shape[1]), past_key_values.get_true_token_num(), device=input_ids.device, dtype=torch.long)
-                elif self.config.summary_chunk_position_ids_type == 'inner_chunk':
-                    position_ids = torch.full((batch_size, input_ids.shape[1]), cur_chunk, device=input_ids.device, dtype=torch.long)
-                else:
-                    raise ValueError(f'Check config.summary_chunk_position_ids_type: {self.config.summary_chunk_position_ids_type}')
-        return {
-            "input_ids": input_ids,
-            "attention_mask": self._build_summary_attention_mask_for_generation(
-                input_ids=input_ids,
-                past_key_values=past_key_values,
-                attention_mask=attention_mask,
-            ),
-            "position_ids": position_ids,
-            "past_key_values": past_key_values,
-            "cache_position": cache_position,
-            "use_cache": kwargs.get("use_cache"),
-        }
-
-
-class Qwen3ForSequenceClassification(GenericForSequenceClassification, Qwen3PreTrainedModel):
-    pass
-
-
-class Qwen3ForTokenClassification(GenericForTokenClassification, Qwen3PreTrainedModel):
-    pass
-
-
-class Qwen3ForQuestionAnswering(GenericForQuestionAnswering, Qwen3PreTrainedModel):
-    base_model_prefix = "transformer"  # For BC, where `transformer` was used instead of `model`
-
-
-__all__ = [
-    "Qwen3ForCausalLM",
-    "Qwen3ForQuestionAnswering",
-    "Qwen3PreTrainedModel",
-    "Qwen3Model",
-    "Qwen3ForSequenceClassification",
-    "Qwen3ForTokenClassification",
-    "Qwen3RingBufferCache",
-    "Qwen3SummaryAttention",
-    "SummaryBatchContext",
-    "build_summary_context",
-    "build_summary_sliding_context",
-]