glm52: free dead kv_b pack post-absorb + share draft rope tables (stacks on #145)#146
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Per-layer outer-scale calibration for nvfp4_ds_mla KV (VLLM_NVFP4_MLA_SCALES_FILE, format v1) with an explicit default-off knob in serve-glm52.sh. Collapses the NVFP4-vs-FP8 KV KLD gap to ~+0.008-0.009 (0.1345/0.1356 vs 0.1263, 5 fresh boots each, rtx6kpro protocol) while raising max context from 373k to 550k/600k+ on 4x96GB. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
With no MHA prefill backend the MLA runtime consumes only the absorbed W_UK_T/W_UV pair, so kv_b_proj's quantized pack is dead after the absorb dequant: free it (~290 MiB/GPU at GLM-5.2 TP4). Draft configs never receive the target's dict hf_overrides, so a shrunken target max_position_embeddings left the draft allocating duplicate full-length rotary tables (128 MiB/GPU): propagate the smaller bound, which is exact because scheduling is limited by the target. Validated on GLM-5.2 4x96GB TP4+DCP4: KV pool 600,255 -> 700,255 tokens at unchanged KLD (5 fresh-boot runs 0.1360+-0.0019 vs 0.1356+-0.0054 cert), 157,932-token prefill and decode throughput unchanged, MTP accept rate 94.1% unchanged. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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📝 WalkthroughWalkthroughAdds GLM-5.2 NVFP4 MLA outer-scale documentation, calibration data, serving configuration, and attention cleanup. It also clamps speculative draft position limits to the target model’s limit. ChangesNVFP4 MLA outer scales
Speculative decoding position limit
Estimated code review effort: 3 (Moderate) | ~20 minutes Possibly related PRs
Suggested reviewers: 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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Actionable comments posted: 2
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Inline comments:
In `@vllm/config/speculative.py`:
- Around line 965-972: Update the max_position_embeddings lookup in the
speculative configuration clamp to read from
self.target_model_config.hf_text_config and
self.draft_model_config.hf_text_config instead of the top-level hf_config
objects. Preserve the existing None checks, identity comparison, and
target-versus-draft limit comparison.
In `@vllm/model_executor/layers/attention/mla_attention.py`:
- Around line 1379-1381: Update the parameter-emptying logic in the shown block
to avoid all param.data access: inspect the parameter tensor’s numel and dtype
directly, then use PyTorch’s native set() method to replace its underlying
storage with an empty tensor of the appropriate type. Preserve the existing
None, non-empty, and non-bfloat16 conditions.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
ℹ️ Review info
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Configuration used: Organization UI
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📒 Files selected for processing (5)
kv-scales/README.mdkv-scales/glm52-nvfp4-nf3-hybrid_mla_outer_scales_v1.jsonserve-glm52.shvllm/config/speculative.pyvllm/model_executor/layers/attention/mla_attention.py
| target_mpe = getattr( | ||
| self.target_model_config.hf_config, | ||
| "max_position_embeddings", None) | ||
| draft_hf = self.draft_model_config.hf_config | ||
| draft_mpe = getattr(draft_hf, "max_position_embeddings", None) | ||
| if (target_mpe is not None and draft_mpe is not None | ||
| and draft_hf is not self.target_model_config.hf_config | ||
| and target_mpe < draft_mpe): |
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🎯 Functional Correctness | 🟠 Major | ⚡ Quick win
🧩 Analysis chain
🏁 Script executed:
#!/bin/bash
set -euo pipefail
# Inspect the relevant section of vllm/config/speculative.py
wc -l vllm/config/speculative.py
sed -n '920,1010p' vllm/config/speculative.py
# Find related text-config accessors/usages in the repo
rg -n "hf_text_config|get_text_config\(\)|max_position_embeddings" vllm/config -S
# Show where hf_text_config is defined/used on the config objects
rg -n "class .*Config|def .*hf_text_config|`@property`.*hf_text_config" vllm -SRepository: local-inference-lab/vllm
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#!/bin/bash
set -euo pipefail
echo '--- speculative.py around the target block ---'
sed -n '940,980p' vllm/config/speculative.py
echo '--- hf_text_config references in vllm/config/*.py ---'
rg -n --glob 'vllm/config/*.py' 'hf_text_config|get_text_config\(\)|max_position_embeddings' vllm/config
echo '--- likely config property definitions ---'
rg -n --glob 'vllm/config/*.py' 'def hf_text_config|`@property`\s+def hf_text_config|class .*Config' vllm/configRepository: local-inference-lab/vllm
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#!/bin/bash
set -euo pipefail
echo '--- vllm/config/model.py: hf_text_config field and init ---'
sed -n '130,190p' vllm/config/model.py
sed -n '540,590p' vllm/config/model.py
echo '--- vllm/transformers_utils/config.py: get_hf_text_config ---'
rg -n "def get_hf_text_config|class .*Config" vllm/transformers_utils/config.py
sed -n '1,220p' vllm/transformers_utils/config.py
echo '--- speculative.py: later draft hf_text_config assignment ---'
sed -n '1270,1310p' vllm/config/speculative.pyRepository: local-inference-lab/vllm
Length of output: 15528
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#!/bin/bash
set -euo pipefail
echo '--- get_hf_text_config definition ---'
sed -n '1040,1125p' vllm/transformers_utils/config.py
echo '--- model config helpers around LongRoPE / max_position_embeddings ---'
sed -n '2200,2275p' vllm/config/model.py
echo '--- where get_text_config() is used in the repo ---'
rg -n "get_text_config\(\)\.max_position_embeddings|get_text_config\(\)|original_max_position_embeddings" vllm -SRepository: local-inference-lab/vllm
Length of output: 18153
Read max_position_embeddings from the text sub-config. hf_config.max_position_embeddings is unset on nested multimodal wrappers, so this clamp can miss the real limit and leave the draft at the checkpoint default. Use self.target_model_config.hf_text_config and self.draft_model_config.hf_text_config here.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@vllm/config/speculative.py` around lines 965 - 972, Update the
max_position_embeddings lookup in the speculative configuration clamp to read
from self.target_model_config.hf_text_config and
self.draft_model_config.hf_text_config instead of the top-level hf_config
objects. Preserve the existing None checks, identity comparison, and
target-versus-draft limit comparison.
| if (param is not None and param.data.numel() > 0 | ||
| and param.data.dtype != torch.bfloat16): | ||
| param.data = param.data.new_empty((0,)) |
There was a problem hiding this comment.
🩺 Stability & Availability | 🔴 Critical | ⚡ Quick win
Avoid .data access on parameters to prevent runtime crashes.
Accessing the .data attribute on tensor-parallel parameters will cause a deterministic runtime crash. As per the provided codebase context, the .data property on SharedWeightParameter is intentionally disabled and explicitly raises a ValueError when accessed.
You can safely check the tensor's properties directly and use PyTorch's native .set_() method to overwrite its underlying storage to an empty tensor without invoking the .data property getter or setter.
🐛 Proposed fix
- if (param is not None and param.data.numel() > 0
- and param.data.dtype != torch.bfloat16):
- param.data = param.data.new_empty((0,))
+ if (param is not None and param.numel() > 0
+ and param.dtype != torch.bfloat16):
+ param.set_(param.new_empty(0))📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| if (param is not None and param.data.numel() > 0 | |
| and param.data.dtype != torch.bfloat16): | |
| param.data = param.data.new_empty((0,)) | |
| if (param is not None and param.numel() > 0 | |
| and param.dtype != torch.bfloat16): | |
| param.set_(param.new_empty(0)) |
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@vllm/model_executor/layers/attention/mla_attention.py` around lines 1379 -
1381, Update the parameter-emptying logic in the shown block to avoid all
param.data access: inspect the parameter tensor’s numel and dtype directly, then
use PyTorch’s native set() method to replace its underlying storage with an
empty tensor of the appropriate type. Preserve the existing None, non-empty, and
non-bfloat16 conditions.
|
Superseded by two independent PRs, both based directly on the current
This PR combined unrelated lifecycle and configuration changes, was stacked on |
2 participants
Two KLD-neutral VRAM reclaims for the GLM-5.2 serving stack. Branch includes #145 (nvfp4 outer scales) as its base commit; review the tip commit only.
kv_b dead pack (−290 MiB/GPU). With no MHA prefill backend (
prefill_backend is None— the B12X_MLA_SPARSE shape), decode and extend both run the MQA lane on the absorbedW_UK_T/W_UVpair;kv_b_proj's quantized weight is only ever read by the absorb dequant inprocess_weights_after_loading. Free the pack right there (bf16-posture guarded, load-time only — runtime frees of this tensor are unsafe while a kernel launch may hold its storage).Draft rope-table duplication (−128 MiB/GPU). Draft ModelConfigs never receive the target's dict
hf_overrides, so a target that shrinksmax_position_embeddings(600k here vs a 1M checkpoint default) leaves the draft allocating full-length bf16 cos/sin tables. Propagating the smaller bound is exact: scheduling is limited by the target's max.Validated on 4×96GB TP4+DCP4 (
nvfp4_ds_mla+ fp8 rope + #145 scales): KV pool 600,255 → 700,255 tokens at the same pin arithmetic headroom, teacher-forced prefill KLD 0.1360 ± 0.0019 over 5 fresh boots (cert band 0.1356 ± 0.0054), 157,932-token prefill 67 s and decode 104-108 t/s unchanged, MTP acceptance 94.1% unchanged.🤖 Generated with Claude Code
Summary by CodeRabbit
New Features
Bug Fixes