remove stale/AppleDouble files

plugins/sglang_glm5v/patch_sglang_dsa_arch.py

@@ -1,162 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-License-Identifier: Apache-2.0
-"""Register Glm5vForConditionalGeneration as a DSA-capable architecture in SGLang.
-
-WHY THIS PATCH IS NECESSARY (not an environment workaround):
-SGLang gates the DeepSeek-Sparse-Attention path on a hardcoded architecture
-tuple in ``is_deepseek_dsa`` (``srt/configs/model_config.py``). Upstream's own
-DSA vision-language model (Mistral Large 3) is supported by adding its VLM
-arch ``PixtralForConditionalGeneration`` to exactly this tuple in-tree — that
-is the upstream mechanism; no env-var/out-of-tree hook exists for it (unlike
-models/processors/mm-arch, which we register via the shipped
-``SGLANG_EXTERNAL_*`` hooks). Our GLM5V wrapper needs the same one-line entry
-so the DSA attention backend (and the ``server_args`` DSA backend defaults)
-recognize the model whose text backbone is GlmMoeDsa.
-
-Three insertions, each at a place where upstream registered its own DSA/MLA
-VLM archs (KimiK25 / Pixtral precedents):
-
-  1. ``model_config.py``: the ``is_deepseek_dsa`` arch tuple — gates the DSA
-     attention backend;
-  2. ``model_config.py``: the MLA attention-arch condition — without it the
-     KV pool is built as MHA and the DSA backend crashes on
-     ``dsa_kv_cache_store_fp8``;
-  3. ``server_args.py``: the DSA model-adjustments gate list — sets the DSA
-     dense-attn kv-len threshold (= model index_topk) and the prefill/decode
-     DSA backend defaults (trtllm on Blackwell); without it decode dies with
-     ``Unsupported self.dsa_decode_impl = None``.
-
-A 4th insertion (added later) enables MTP/NEXTN speculative decoding: without
-it the draft worker rebuilds the entire ~90GB multimodal model as the "draft"
-and OOMs (our wrapper arch is in none of SGLang's draft-model rewrite lists).
-See the ``DRAFT_ANCHOR`` block below. This is only needed when serving with
-``--speculative-algorithm NEXTN``; it is a no-op otherwise. (Measured: MTP gives
-~1.1-1.16x tok/s at low/moderate batch + ~2.0 accept-len, greedy outputs are
-token-identical to non-spec, but it is graph-capped at batch 256 so plain
-high-batch TP8 wins for throughput-bound RL rollout — MTP is a single-stream
-LATENCY lever, not a throughput one.)
-
-Other in-tree arch lists mentioning GlmMoeDsa/KimiK25 are deliberately NOT
-patched: they gate features we do not use (encoder disaggregation) or pure
-performance defaults we set explicitly (flashinfer allreduce fusion,
-attention-backend fallback).
-
-The same entries will be needed wherever slime launches SGLang rollout engines
-for glm5v (Milestone 1) — candidate for an upstream sglang PR.
-
-Idempotent; run before ``sglang.launch_server`` (serve.sh does this).
-"""
-
-import pathlib
-import subprocess
-import sys
-
-ARCH = "Glm5vForConditionalGeneration"
-DSA_ANCHOR = '"GlmMoeDsaForCausalLM",'
-DSA_INSERT = (
-    DSA_ANCHOR
-    + f'\n            "{ARCH}",  # glm5v: GLM-5.2 (DSA) text + MoonViT vision (sglang_glm5v)'
-)
-MLA_ANCHOR = 'or "KimiK25ForConditionalGeneration" in self.hf_config.architectures'
-MLA_INSERT = (
-    MLA_ANCHOR
-    + f'\n            or "{ARCH}" in self.hf_config.architectures  # glm5v (sglang_glm5v)'
-)
-
-
-# server_args.py DSA adjustments gate: the list containing KimiK25 is unique
-# to that block (other GlmMoeDsa lists in the file lack it).
-SA_ANCHOR = (
-    '"KimiK25ForConditionalGeneration",\n'
-    '            "MistralLarge3ForCausalLM",\n'
-    '            "PixtralForConditionalGeneration",\n'
-    '            "GlmMoeDsaForCausalLM",'
-)
-SA_INSERT = SA_ANCHOR + f'\n            "{ARCH}",  # glm5v (sglang_glm5v)'
-
-
-# --- MTP / speculative NEXTN draft-model support (4th insertion) ---
-# WHY: with --speculative-algorithm NEXTN, SGLang builds a SEPARATE draft model
-# (is_draft_model=True) whose arch it rewrites to a single-layer NextN module in
-# ``_config_draft_model``. That method maps the *text* backbone
-# ``GlmMoeDsaForCausalLM`` -> ``DeepseekV3ForCausalLMNextN`` (correct: our MTP
-# weights live at text layer 78: eh_proj/enorm/hnorm + one block). But our
-# TOP-LEVEL arch is the multimodal wrapper ``Glm5vForConditionalGeneration``,
-# which is in NONE of the rewrite lists -> the draft worker instead rebuilds the
-# ENTIRE multimodal model (MoonViT + full 78-layer 90GB text model) as the
-# "draft" and OOMs (a second ~90GB copy on top of base weights + KV + graphs).
-#
-# FIX: mirror the shipped ``Step3p7ForConditionalGeneration`` precedent (a VLM
-# wrapper whose draft is its text MTP): when is_draft_model and arch is our
-# wrapper, swap hf_config -> hf_text_config and set arch to GlmMoeDsaForCausalLM.
-# Inserted at the TOP of _config_draft_model so the existing
-# GlmMoeDsaForCausalLM -> DeepseekV3ForCausalLMNextN rule then fires on it.
-DRAFT_ANCHOR = (
-    "    def _config_draft_model(self):\n"
-    "        is_draft_model = self.is_draft_model\n"
-)
-DRAFT_INSERT = (
-    DRAFT_ANCHOR
-    + "\n"
-    + "        # glm5v (sglang_glm5v): map the multimodal wrapper draft to its\n"
-    + "        # text backbone so the GlmMoeDsa->NextN rewrite below applies.\n"
-    + f'        if is_draft_model and self.hf_config.architectures[0] == "{ARCH}":\n'
-    + "            self.hf_config = self.hf_text_config\n"
-    + '            self.hf_config.architectures = ["GlmMoeDsaForCausalLM"]\n'
-)
-
-
-def find_module(mod: str) -> pathlib.Path:
-    out = subprocess.run(
-        [sys.executable, "-c", f"import {mod} as m; print(m.__file__)"],
-        capture_output=True,
-        text=True,
-        check=True,
-    )
-    return pathlib.Path(out.stdout.strip())
-
-
-def apply(path: pathlib.Path, anchor: str, insert: str, marker: str, label: str) -> None:
-    src = path.read_text()
-    if marker in src:
-        print(f"{label}: already patched")
-        return
-    assert anchor in src, f"{label}: anchor not found in {path} (sglang version changed?)"
-    path.write_text(src.replace(anchor, insert, 1))
-    print(f"{label}: patched ({path})")
-
-
-def main() -> None:
-    mc = find_module("sglang.srt.configs.model_config")
-    apply(mc, DSA_ANCHOR, DSA_INSERT, f'"{ARCH}",  # glm5v', "DSA tuple")
-    apply(mc, MLA_ANCHOR, MLA_INSERT, f'or "{ARCH}" in self.hf_config.architectures', "MLA condition")
-    apply(
-        mc,
-        DRAFT_ANCHOR,
-        DRAFT_INSERT,
-        f'if is_draft_model and self.hf_config.architectures[0] == "{ARCH}"',
-        "NEXTN draft-model map",
-    )
-    sa = find_module("sglang.srt.server_args")
-    apply(sa, SA_ANCHOR, SA_INSERT, f'"{ARCH}",  # glm5v', "server_args DSA gate")
-
-    # verify the DSA gate end-to-end
-    check = subprocess.run(
-        [
-            sys.executable,
-            "-c",
-            (
-                "from sglang.srt.configs.model_config import is_deepseek_dsa;"
-                f"print(is_deepseek_dsa({{'architectures': ['{ARCH}'], 'index_topk': 2048}}))"
-            ),
-        ],
-        capture_output=True,
-        text=True,
-        check=True,
-    )
-    assert check.stdout.strip() == "True", check.stdout + check.stderr
-    print("verified: is_deepseek_dsa recognizes the arch")
-
-
-if __name__ == "__main__":
-    main()