glm52: allocate the MLA absorbed pair before the dequant scratch (stacks on #146)#148
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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>
The absorb path materializes W_UK_T/W_UV after the per-layer dequant scratch has churned, interleaving long-lived tensors with transient allocations. Allocating the pair first lets each layer's scratch free into clean segments: measured allocator reserved-but-unallocated drops 0.577 -> 0.225 GiB/GPU at GLM-5.2 TP4 geometry, with identical tensors served to decode. Validated on 4x96GB TP4+DCP4: graduated prefill ladder (2k/16k/60k/ 157,932 tokens) green, decode 106.7 t/s and MTP acceptance 92.8% unchanged, and the recovered headroom seats a 740,255-token KV pool that passes the same ladder. Teacher-forced prefill KLD re-certified across three 5-run fresh-boot campaigns: pooled 15 runs 0.1366+-0.0042 vs the standing 0.1356+-0.0054 cert. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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📝 WalkthroughWalkthroughAdds GLM-5.2 NVFP4 MLA outer-scale artifacts and launch configuration, clamps speculative draft context limits, and updates MLA absorbed-weight storage and cleanup behavior. ChangesNVFP4 MLA outer scales
Runtime configuration and MLA storage
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
🧹 Nitpick comments (1)
kv-scales/README.md (1)
28-36: 📐 Maintainability & Code Quality | 🔵 Trivial | 💤 Low valueConsider updating the results to reflect the latest validation runs.
The PR objectives indicate that the latest evaluation across 15 fresh-cache runs yielded a KLD of
0.1366 +/- 0.0042, whereas the table currently documents the standing certification of 5 fresh boots with0.1356 +/- 0.0054. Consider updating the documentation to reflect the most recent validation results if they supersede the standing certification.🤖 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 `@kv-scales/README.md` around lines 28 - 36, Update the Results table entry for nvfp4_ds_mla with scales and fp8 rope to reflect the latest 15 fresh-cache validation result, using KLD 0.1366 +/- 0.0042 if it supersedes the documented 5-boot certification. Preserve the existing configuration label and max-context value.
🤖 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/model_executor/layers/attention/mla_attention.py`:
- Around line 1288-1293: Update the device selection for both temporary tensors
in the affected initialization flow, replacing direct access to
self.kv_b_proj.weight.device with the device obtained safely from
self.kv_b_proj’s parameters. Preserve the existing shapes and dtype while
supporting quantized modules that lack a weight attribute.
- Around line 1375-1380: Update the replace_parameter calls for W_UV and W_UK_T
in the initialization flow to use prefer_copy=True instead of prefer_copy=False.
Preserve the existing preallocated tensor preparation and parameter names so
initial loading reuses those tensors while subsequent updates copy in place and
retain data_ptr stability for CUDA graphs.
---
Nitpick comments:
In `@kv-scales/README.md`:
- Around line 28-36: Update the Results table entry for nvfp4_ds_mla with scales
and fp8 rope to reflect the latest 15 fresh-cache validation result, using KLD
0.1366 +/- 0.0042 if it supersedes the documented 5-boot certification. Preserve
the existing configuration label and max-context value.
🪄 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
Review profile: CHILL
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Run ID: d869cb96-085d-4c67-b688-1f255aa63565
📒 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
| pre_w_uv = torch.empty( | ||
| (self.num_heads, self.kv_lora_rank, self.v_head_dim), | ||
| dtype=act_dtype, device=self.kv_b_proj.weight.device) | ||
| pre_w_uk_t = torch.empty( | ||
| (self.num_heads, self.qk_nope_head_dim, self.kv_lora_rank), | ||
| dtype=act_dtype, device=self.kv_b_proj.weight.device) |
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🎯 Functional Correctness | 🔴 Critical | ⚡ Quick win
Prevent AttributeError on quantized layers missing a weight attribute.
If the model is quantized (e.g., AWQ, GPTQ), self.kv_b_proj might not have a weight attribute, using qweight instead. Accessing self.kv_b_proj.weight.device will crash in these scenarios. As evidenced by the hasattr(self.kv_b_proj, "weight") check in __init__, the attribute's existence is not guaranteed.
Retrieve the device safely from the module's parameters.
🐛 Proposed fix
- pre_w_uv = torch.empty(
- (self.num_heads, self.kv_lora_rank, self.v_head_dim),
- dtype=act_dtype, device=self.kv_b_proj.weight.device)
- pre_w_uk_t = torch.empty(
- (self.num_heads, self.qk_nope_head_dim, self.kv_lora_rank),
- dtype=act_dtype, device=self.kv_b_proj.weight.device)
+ device = (
+ self.kv_b_proj.weight.device
+ if hasattr(self.kv_b_proj, "weight")
+ else next(self.kv_b_proj.parameters()).device
+ )
+ pre_w_uv = torch.empty(
+ (self.num_heads, self.kv_lora_rank, self.v_head_dim),
+ dtype=act_dtype,
+ device=device,
+ )
+ pre_w_uk_t = torch.empty(
+ (self.num_heads, self.qk_nope_head_dim, self.kv_lora_rank),
+ dtype=act_dtype,
+ device=device,
+ )📝 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.
| pre_w_uv = torch.empty( | |
| (self.num_heads, self.kv_lora_rank, self.v_head_dim), | |
| dtype=act_dtype, device=self.kv_b_proj.weight.device) | |
| pre_w_uk_t = torch.empty( | |
| (self.num_heads, self.qk_nope_head_dim, self.kv_lora_rank), | |
| dtype=act_dtype, device=self.kv_b_proj.weight.device) | |
| device = ( | |
| self.kv_b_proj.weight.device | |
| if hasattr(self.kv_b_proj, "weight") | |
| else next(self.kv_b_proj.parameters()).device | |
| ) | |
| pre_w_uv = torch.empty( | |
| (self.num_heads, self.kv_lora_rank, self.v_head_dim), | |
| dtype=act_dtype, | |
| device=device, | |
| ) | |
| pre_w_uk_t = torch.empty( | |
| (self.num_heads, self.qk_nope_head_dim, self.kv_lora_rank), | |
| dtype=act_dtype, | |
| device=device, | |
| ) |
🤖 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 1288 -
1293, Update the device selection for both temporary tensors in the affected
initialization flow, replacing direct access to self.kv_b_proj.weight.device
with the device obtained safely from self.kv_b_proj’s parameters. Preserve the
existing shapes and dtype while supporting quantized modules that lack a weight
attribute.
| pre_w_uv.copy_(W_UV.transpose(0, 1)) | ||
| replace_parameter(self, "W_UV", pre_w_uv, prefer_copy=False) | ||
| # Convert from (L, N, P) to (N, P, L) | ||
| replace_parameter(self, "W_UK_T", W_UK.permute(1, 2, 0), prefer_copy=True) | ||
| pre_w_uk_t.copy_(W_UK.permute(1, 2, 0)) | ||
| replace_parameter(self, "W_UK_T", pre_w_uk_t, prefer_copy=False) | ||
|
|
There was a problem hiding this comment.
🩺 Stability & Availability | 🟠 Major | ⚡ Quick win
Enable in-place updates to preserve data_ptr for CUDA graphs.
Explicitly setting prefer_copy=False forces replace_parameter to re-register the parameter. In workflows that perform in-place weight updates (such as RL training loops), this re-registration changes the underlying storage address (data_ptr) and breaks captured CUDA graphs.
Changing this to prefer_copy=True achieves both goals: during the initial load (when the parameter doesn't exist), it wraps the pre-allocated pre_w_uv tensor directly to avoid allocator fragmentation. During subsequent weight updates, it copies the new values into the existing parameter in-place, keeping the data_ptr intact.
🔄 Proposed fix
# Convert from (L, N, V) to (N, L, V)
pre_w_uv.copy_(W_UV.transpose(0, 1))
- replace_parameter(self, "W_UV", pre_w_uv, prefer_copy=False)
+ replace_parameter(self, "W_UV", pre_w_uv, prefer_copy=True)
# Convert from (L, N, P) to (N, P, L)
pre_w_uk_t.copy_(W_UK.permute(1, 2, 0))
- replace_parameter(self, "W_UK_T", pre_w_uk_t, prefer_copy=False)
+ replace_parameter(self, "W_UK_T", pre_w_uk_t, prefer_copy=True)📝 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.
| pre_w_uv.copy_(W_UV.transpose(0, 1)) | |
| replace_parameter(self, "W_UV", pre_w_uv, prefer_copy=False) | |
| # Convert from (L, N, P) to (N, P, L) | |
| replace_parameter(self, "W_UK_T", W_UK.permute(1, 2, 0), prefer_copy=True) | |
| pre_w_uk_t.copy_(W_UK.permute(1, 2, 0)) | |
| replace_parameter(self, "W_UK_T", pre_w_uk_t, prefer_copy=False) | |
| pre_w_uv.copy_(W_UV.transpose(0, 1)) | |
| replace_parameter(self, "W_UV", pre_w_uv, prefer_copy=True) | |
| # Convert from (L, N, P) to (N, P, L) | |
| pre_w_uk_t.copy_(W_UK.permute(1, 2, 0)) | |
| replace_parameter(self, "W_UK_T", pre_w_uk_t, prefer_copy=True) |
🤖 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 1375 -
1380, Update the replace_parameter calls for W_UV and W_UK_T in the
initialization flow to use prefer_copy=True instead of prefer_copy=False.
Preserve the existing preallocated tensor preparation and parameter names so
initial loading reuses those tensors while subsequent updates copy in place and
retain data_ptr stability for CUDA graphs.
|
Superseded by #156, which is a one-commit PR directly against the current |
2 participants
One-commit follow-up to #146 (branch includes #145+#146; review the tip commit only). AI-assisted (Claude); the submitter has reviewed every changed line. Not a duplicate of any open PR (searched
absorb,W_UK_T,fragmentationon the repo).The MLA absorb materializes
W_UK_T/W_UVafter the per-layer dequant scratch has churned, so the long-lived pair lands interleaved with transients. Allocating the pair first lets each layer's scratch free into clean segments: allocator reserved-but-unallocated drops 0.577 → 0.225 GiB/GPU (GLM-5.2 TP4 geometry), with identical tensors served to decode. This is the general MLA absorb path — every MLA model pays some version of this churn.Tests/validation on 4×96GB TP4+DCP4 (
nvfp4_ds_mla+ fp8 rope + #145 scales), servingGLM-5.2viaserve-glm52.sh:glm52-kld-evaluation, fresh caches): three 5-run campaigns pooled = 0.1366 ± 0.0042 over 15 runs vs the standing 0.1356 ± 0.0054 cert — statistically identical (the capture rig runs none of the serving-side changes by construction).🤖 Generated with Claude Code
Summary by CodeRabbit
New Features
VLLM_NVFP4_MLA_SCALES_FILE.Bug Fixes