unsloth/studio/backend/core
Daniel Han 77e1a9edc9
feat(studio): architecture-aware KV cache VRAM estimation (#4757)
* feat(studio): architecture-aware KV cache VRAM estimation

Replace the single legacy formula (2 * n_kv_heads * head_dim * n_layers
* n_ctx * bpe) with 5-path estimation that reads 8 additional GGUF
metadata fields:

  1. MLA (DeepSeek-V2/V3, GLM-4.7, GLM-5, Kimi-K2.5) -- K-only cache
     using compressed KV latent + RoPE; no separate V allocation
  2. Hybrid Mamba (Qwen3.5-27B, Qwen3.5-35B-A3B) -- only attention
     layers (1 in N) carry KV; Mamba layers have none
  3. Sliding Window (Gemma-3, gpt-oss) -- SWA layers cache
     min(ctx, window) tokens instead of the full context
  4. Standard GQA -- uses explicit key_length/value_length from GGUF
     instead of embed // n_heads (which is wrong for many models)
  5. Legacy fallback -- identical to old formula for old GGUFs

New GGUF fields parsed: attention.key_length, attention.value_length,
attention.sliding_window, full_attention_interval,
attention.kv_lora_rank, attention.key_length_mla, ssm.inner_size,
ssm.state_size.

Validated against 9 real GGUF files (72/72 field checks pass).
The legacy formula was off by +682% for Gemma-3 and -81% for
DeepSeek-V3.1.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Fix MLA fallback and SWA global/local ratio heuristic

Two fixes based on review findings:

1. MLA fallback now uses key_length_mla from GGUF metadata instead of
   hardcoded rope_dim=64. Falls back to 64 only when key_length_mla is
   absent. This ensures correct estimates for MLA variants that use
   rope dimensions other than 64.

2. SWA global/local layer ratio changed from 50/50 to 1/4 (25% global,
   75% SWA). Most sliding window architectures have predominantly local
   layers (Gemma-3 uses ~17% global, gpt-oss uses ~50%). The 1/4
   heuristic is closer to the common case and still a large improvement
   over the legacy formula which ignores SWA entirely.

* Tighten _can_estimate_kv gate and treat sliding_window=0 as disabled

Two additional fixes from review round 1 (5/8 and 4/8 reviewer consensus):

1. _can_estimate_kv now requires BOTH key_length AND value_length for
   the explicit-dims path. Previously key_length alone was enough,
   which could cause silent fallthrough to the legacy formula with
   fabricated defaults (n_kv=1, head_dim=128) when value_length was
   absent from the GGUF.

2. SWA path now requires sliding_window > 0. Some GGUFs use 0 as a
   disabled sentinel. Without this guard, min(ctx, 0) would zero out
   all SWA layer contributions, severely underestimating KV cache.

* Fix MLA n_kv safety and use ceiling division for hybrid path

Addresses Gemini Code Assist review findings:

1. MLA path now uses n_kv_mla = n_kv_heads or 1 (not n_heads). This
   prevents a 128x overestimate for DeepSeek-V3 if head_count_kv is
   absent from the GGUF (n_heads=128 would have been used instead).

2. Hybrid path now uses ceiling division for attention layer count.
   This prevents undercounting by 1 when n_layers is not perfectly
   divisible by full_attention_interval.

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
2026-04-01 06:04:12 -07:00
..
data_recipe build(deps): bump oxc-parser (#4571) 2026-03-25 02:44:38 -07:00
export studio: add HF/local model selection UI for GGUF export (#4365) 2026-03-28 22:18:25 +04:00
inference feat(studio): architecture-aware KV cache VRAM estimation (#4757) 2026-04-01 06:04:12 -07:00
training fix(studio): correct default weight_decay and learning rate (#4695) 2026-03-31 13:50:25 +04:00
__init__.py Combine studio setup fixes: frontend caching, venv isolation, Windows CPU support (#4413) 2026-03-18 03:52:25 -07:00