modeling_qwen3.py

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#           This file was automatically generated from src/transformers/models/qwen3/modular_qwen3.py.
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# 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 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 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
# InfinityLM imports for summary attention
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):
      Three buffers:
        1. key_cache: [ring(ws) | old_summaries(growing) | chunk_mirror(≤C)]
           → attention input, steady state is a single contiguous slice
        2. new_summary_buf: ring buffer of size scn, stores summaries whose text
           is still in the window (not needed for attention)
        3. chunk_buf: size C, holds current chunk's text KV

    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: ring buffer + summary
        self._window_write_ptr = [0] * config.num_hidden_layers
        self._n_valid_window = [0] * config.num_hidden_layers
        self._old_summary_len = [0] * config.num_hidden_layers   # old summaries in key_cache
        self._old_summary_cap = [0] * config.num_hidden_layers

        # New summary ring buffer (small window only): summaries whose text is still in window
        self._new_sum_key_buf = [None for _ in range(config.num_hidden_layers)]
        self._new_sum_value_buf = [None for _ in range(config.num_hidden_layers)]
        self._new_sum_len = [0] * config.num_hidden_layers       # how many filled (≤ scn)
        self._new_sum_write_ptr = [0] * config.num_hidden_layers  # ring write pointer

        # Current chunk buffer (small window only): holds partial chunk text KV
        self._chunk_key_buf = [None for _ in range(config.num_hidden_layers)]
        self._chunk_value_buf = [None for _ in range(config.num_hidden_layers)]
        self._chunk_buf_len = [0] * config.num_hidden_layers

        # 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._old_summary_len[layer_idx] + self._new_sum_len[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."""
        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: split summaries into old (evicted) and new (in window)
                all_summary_k = prefill_k[:, :, summary_mask[0], :]
                all_summary_v = prefill_v[:, :, summary_mask[0], :]
                n_summary = all_summary_k.shape[2]

                C = self.summary_chunk_size
                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

                # Split summaries: old (text evicted from ring) vs new (text in ring)
                n_old = max(0, n_summary - n_window_chunks)
                n_new = n_summary - n_old

                # key_cache: [ring(ws) | old_summaries | chunk_mirror(≤C)]
                old_s_cap = max(self._SUMMARY_INIT_CAP, (n_old + 1) * 2)
                total_cap = ws + old_s_cap + C
                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)

                if n_window_text > 0:
                    new_k[:, :, :n_window_text, :].copy_(text_k[:, :, window_start:n_complete_text, :])
                    new_v[:, :, :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

                # Old summaries go into key_cache after ring
                if n_old > 0:
                    new_k[:, :, ws:ws + n_old, :].copy_(all_summary_k[:, :, :n_old, :])
                    new_v[:, :, ws:ws + n_old, :].copy_(all_summary_v[:, :, :n_old, :])
                self._old_summary_len[layer_idx] = n_old
                self._old_summary_cap[layer_idx] = old_s_cap

                # Mirror partial chunk into key_cache after old_summaries
                if n_partial > 0:
                    mirror_start = ws + n_old
                    new_k[:, :, mirror_start:mirror_start + n_partial, :].copy_(
                        text_k[:, :, n_complete_text:, :])
                    new_v[:, :, mirror_start:mirror_start + n_partial, :].copy_(
                        text_v[:, :, n_complete_text:, :])

                self.key_cache[layer_idx] = new_k
                self.value_cache[layer_idx] = new_v
                self._capacity[layer_idx] = total_cap
                self._text_len[layer_idx] = 0

                # New summary ring buffer
                ns_buf_k = torch.empty(bsz, heads, scn, head_dim, dtype=dtype, device=device)
                ns_buf_v = torch.empty(bsz, heads, scn, head_dim, dtype=dtype, device=device)
                if n_new > 0:
                    ns_buf_k[:, :, :n_new, :].copy_(all_summary_k[:, :, n_old:, :])
                    ns_buf_v[:, :, :n_new, :].copy_(all_summary_v[:, :, n_old:, :])
                self._new_sum_key_buf[layer_idx] = ns_buf_k
                self._new_sum_value_buf[layer_idx] = ns_buf_v
                self._new_sum_len[layer_idx] = n_new
                self._new_sum_write_ptr[layer_idx] = n_new % scn

                # Chunk buffer for partial remainder
                chunk_buf_k = torch.empty(bsz, heads, C, head_dim, dtype=dtype, device=device)
                chunk_buf_v = torch.empty(bsz, heads, C, head_dim, dtype=dtype, device=device)
                if n_partial > 0:
                    chunk_buf_k[:, :, :n_partial, :].copy_(text_k[:, :, n_complete_text:, :])
                    chunk_buf_v[:, :, :n_partial, :].copy_(text_v[:, :, n_complete_text:, :])
                self._chunk_key_buf[layer_idx] = chunk_buf_k
                self._chunk_value_buf[layer_idx] = chunk_buf_v
                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._old_summary_len[layer_idx] + self._new_sum_len[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 only to key_cache mirror region (chunk_buf eliminated)
            ws = self.window_sizes[layer_idx]
            n_old = self._old_summary_len[layer_idx]
            pos = self._chunk_buf_len[layer_idx]
            mirror_pos = ws + n_old + pos
            self.key_cache[layer_idx][:, :, mirror_pos:mirror_pos+1, :].copy_(key_states)
            self.value_cache[layer_idx][:, :, mirror_pos:mirror_pos+1, :].copy_(value_states)
            self._chunk_buf_len[layer_idx] = pos + 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 (order matters — flush mirror before evict to avoid clobbering):
          1. Flush mirror region → ring
          2. Evict oldest new_summary → old_summary in key_cache (if full)
          3. Write new summary → new_summary_buf
        """
        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

        C = self.summary_chunk_size
        ws = self.window_sizes[layer_idx]
        scn = self.sliding_chunk_nums[layer_idx]
        cbl = self._chunk_buf_len[layer_idx]
        ptr = self._window_write_ptr[layer_idx]
        n_old = self._old_summary_len[layer_idx]

        # Step 1: Flush mirror region → ring (must happen before evict touches mirror[0])
        mirror_start = ws + n_old
        if ptr + cbl <= ws:
            self.key_cache[layer_idx][:, :, ptr:ptr + cbl, :].copy_(
                self.key_cache[layer_idx][:, :, mirror_start:mirror_start + cbl, :])
            self.value_cache[layer_idx][:, :, ptr:ptr + cbl, :].copy_(
                self.value_cache[layer_idx][:, :, mirror_start:mirror_start + cbl, :])
        else:
            first = ws - ptr
            self.key_cache[layer_idx][:, :, ptr:ws, :].copy_(
                self.key_cache[layer_idx][:, :, mirror_start:mirror_start + first, :])
            self.value_cache[layer_idx][:, :, ptr:ws, :].copy_(
                self.value_cache[layer_idx][:, :, mirror_start:mirror_start + first, :])
            rest = cbl - first
            self.key_cache[layer_idx][:, :, :rest, :].copy_(
                self.key_cache[layer_idx][:, :, mirror_start + first:mirror_start + cbl, :])
            self.value_cache[layer_idx][:, :, :rest, :].copy_(
                self.value_cache[layer_idx][:, :, mirror_start + first:mirror_start + 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 2: Evict oldest new_summary → old_summary (now safe — mirror already flushed)
        if self._new_sum_len[layer_idx] >= scn:
            read_ptr = self._new_sum_write_ptr[layer_idx]
            old_dst = ws + n_old  # == mirror_start, but mirror data is already in ring

            # Check capacity for old_summary growth
            needed = old_dst + 1 + C  # +1 for new old_sum, +C for future chunk mirror
            if needed > self._capacity[layer_idx]:
                new_s_cap = max(self._old_summary_cap[layer_idx] * 2, n_old + self._SUMMARY_INIT_CAP)
                new_total = ws + new_s_cap + C
                old_k, old_v = self.key_cache[layer_idx], self.value_cache[layer_idx]
                bsz, heads, _, head_dim = old_k.shape
                nk = torch.empty(bsz, heads, new_total, head_dim, dtype=old_k.dtype, device=old_k.device)
                nv = torch.empty(bsz, heads, new_total, head_dim, dtype=old_v.dtype, device=old_v.device)
                copy_len = ws + n_old
                nk[:, :, :copy_len, :].copy_(old_k[:, :, :copy_len, :])
                nv[:, :, :copy_len, :].copy_(old_v[:, :, :copy_len, :])
                self.key_cache[layer_idx] = nk
                self.value_cache[layer_idx] = nv
                self._old_summary_cap[layer_idx] = new_s_cap
                self._capacity[layer_idx] = new_total

            self.key_cache[layer_idx][:, :, old_dst:old_dst+1, :].copy_(
                self._new_sum_key_buf[layer_idx][:, :, read_ptr:read_ptr+1, :])
            self.value_cache[layer_idx][:, :, old_dst:old_dst+1, :].copy_(
                self._new_sum_value_buf[layer_idx][:, :, read_ptr:read_ptr+1, :])
            self._old_summary_len[layer_idx] += 1

        # Step 3: Write new summary to new_summary_buf (overwrite oldest slot)
        w_ptr = self._new_sum_write_ptr[layer_idx]
        self._new_sum_key_buf[layer_idx][:, :, w_ptr:w_ptr+1, :].copy_(key_states)
        self._new_sum_value_buf[layer_idx][:, :, w_ptr:w_ptr+1, :].copy_(value_states)
        self._new_sum_write_ptr[layer_idx] = (w_ptr + 1) % scn
        if self._new_sum_len[layer_idx] < scn:
            self._new_sum_len[layer_idx] += 1

        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 (steady state): key_cache[:ws + n_old + cbl] — single slice, zero cat
        """
        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, :])

        ws = self.window_sizes[layer_idx]
        nv = self._n_valid_window[layer_idx]
        cbl = self._chunk_buf_len[layer_idx]
        n_old = self._old_summary_len[layer_idx]

        # Steady state: ring full → [ring(ws) | old_sums(n_old) | chunk_mirror(cbl)] contiguous
        if nv == ws:
            end = ws + n_old + cbl
            return (self.key_cache[layer_idx][:, :, :end, :],
                    self.value_cache[layer_idx][:, :, :end, :])

        # Warmup: ring not full, [nv:ws] is gap → cat
        parts_k, parts_v = [], []
        if nv > 0:
            parts_k.append(self.key_cache[layer_idx][:, :, :nv, :])
            parts_v.append(self.value_cache[layer_idx][:, :, :nv, :])
        if cbl > 0:
            mirror_start = ws + n_old
            parts_k.append(self.key_cache[layer_idx][:, :, mirror_start:mirror_start + cbl, :])
            parts_v.append(self.value_cache[layer_idx][:, :, mirror_start:mirror_start + cbl, :])
        if n_old > 0:
            parts_k.append(self.key_cache[layer_idx][:, :, ws:ws + n_old, :])
            parts_v.append(self.value_cache[layer_idx][:, :, ws:ws + n_old, :])
        if len(parts_k) == 1:
            return parts_k[0], parts_v[0]
        return torch.cat(parts_k, dim=2), torch.cat(parts_v, dim=2)

    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:
            ws = self.window_sizes[layer_idx]
            n_old = self._old_summary_len[layer_idx]
            cbl = self._chunk_buf_len[layer_idx]
            mirror_start = ws + n_old
            return (self.key_cache[layer_idx][:, :, mirror_start:mirror_start + cbl, :],
                    self.value_cache[layer_idx][:, :, mirror_start:mirror_start + cbl, :])

    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 _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

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

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


class 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 = _sdpa_attention_forward(
                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, _ = _sdpa_attention_forward(
                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)
            #    The original model includes the summary token's own KV in its attention
            #    (causal within summary positions). With S=1, this is just self-attention.
            k_chunk, v_chunk = past_key_values.get_current_chunk_kv(self.layer_idx)
            k_chunk_with_self = torch.cat([k_chunk, k_summary], dim=2)
            v_chunk_with_self = torch.cat([v_chunk, v_summary], dim=2)
            summary_out, _ = _sdpa_attention_forward(
                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",
]