[lora] refactor - native LoRA by yushengsu-thu · Pull Request #2017 · radixark/miles

yushengsu-thu · 2026-07-31T02:57:19Z

Refactor PR into #1792's branch (zhichen/lora-native), so the native-LoRA extraction ships as a plugin from the start. Adapter math is unchanged — a side-by-side harness against the base monolith shows bit-identical adapter parameters, forward outputs, backward gradients, and HF exports for full-target GQA (TP=1, TP=2 ± SP) and MLA configs. Bridge-mode LoRA is untouched.

Includes #2072 (function-level redesign + fixes from validating four models end-to-end), the lora-native CI suite (#2073), and — after merging main's Inkling LoRA (#2122) — a rewrite of Inkling onto the plugin architecture, replacing the model-specific monolith.

Layout

miles_plugins/lora/
    __init__.py
    lora.py              # Single public entry point and lifecycle orchestration
    config.py            # LoRAConfig: rank, alpha, dropout, and target modules
    registry.py          # model_type -> ModelEntry(spec, status); launcher helpers
    hf_adapter.py        # HF/PEFT adapter naming, import, and export
    serving.py           # SGLang serving export (fused-buffer zero-fill, target expansion)
    checkpointing.py     # Adapter checkpoint state dicts and per-rank shard naming
    distributed.py       # TP/EP/SP communication and gradient handling (ParallelGather)

    spec/
      base.py            # Contracts: ProjectionSpec, AttachContext, protocols, enums
      layout.py          # Declarative layouts: dim resolvers, bindings, attach walk, spec bases
      attention.py       # GQA / MLA / GDN / hybrid attention spec classes
      mlp.py             # Fused gate/up + down spec class
      moe.py             # Routed-expert per-layer policy; Inkling grouped experts

    modules/
      linear.py          # LoRALinear + LoRASplitAdapter (SplitQKV / SplitFC1)
      moe.py             # Grouped-GEMM expert, shared-experts, and lm_head adapters

    kernel/              # Future fused and grouped-expert kernels

Design

Classes carry the taxonomy; every fact is declared once and derived everywhere else.

Declarative layouts (spec/layout.py): an architecture spec is a class holding a ModuleLayout table — which projections exist, on which physical linear, with which shard geometry. One shared attach_layout() implements the existence-check → target-filter → guard → build → hook walk; the dim-resolver section is the only spec-side reader of MCore attribute names. A new layout is ~15 lines of facts.
Spec hierarchy (LayoutSpec → AttentionSpecBase → GQA/MLA/GDN/Inkling, HybridGQAGDN(GQAAttentionSpec), SharedOuterExpertMoESpec(GeneralExpertMoESpec)): supported_targets, the canonical --target-modules order, and SGLang's fused-family expansion all derive from the layout declaration; a new architecture's fused groups reach serving-side target expansion automatically. ShardLayout/AttentionFamily str-enums replace bare strings; no parallel name tables in the exporters.
ModelEntry(spec, status, reason) with VALIDATED / STRUCTURAL / UNSTABLE: support status is one first-class field; UNSTABLE entries carry their reason and warn at attach.
Public export descriptors: adapters expose exports() -> ProjectionExport (with SGLangFusedGroup metadata); serving.py consumes only that API.
Adapter IO hooks: modules whose export shape isn't a per-projection column/row gather override export_plan(gather) / load_plan_custom(take); hf_adapter.py stays generic. ParallelGather batches async all-gathers over the TP or EP group behind one request(tensor, dim, group=...) call.
Escape hatches for non-table structure: ModuleLayout.hf_block_prefix and ProjectionBinding.adapter_class override HF naming and the module class per binding; LoRAArchSpec gained moe.attach(...) and an optional lm_head spec so a fully custom family still routes through the one orchestrator in lora.py.
Launcher helpers: default_target_modules(hf_checkpoint) and preflight_native_lora(...) (registry ∧ mbridge bridge ∧ model-args audit, no GPU) — launch scripts stop re-declaring per-family target strings and fail in seconds when a conversion bridge is missing.

Inkling on the plugin (replaces the #2122 monolith)

miles_plugins/models/inkling/lora.py (759 lines, its own attach/export/IO stack) is deleted; Inkling's specs live in the role files next to the base classes they inherit from (InklingAttentionSpec in spec/attention.py, InklingDenseMLPSpec in spec/mlp.py, InklingMoESpec in spec/moe.py, InklingLMHeadSpec beside its registry entry), registered as inkling_mm_model:

Attention: 4-way fused [q;k;v;r] declared as one FusedAttach — Inkling's fusion is a plain concat (no GQA group-permute), so the split-adapter row table {q,k,v,r} is the entire model-specific code. TML export names (attn.wq_du/wk_dv/wv_dv/wr_du/wo_ud) come straight from the ProjectionSpecs.
Dense MLP: fused gate/up exports as the single TML gate_up_proj projection via an adapter_class override whose export_plan gathers the gate/up halves separately (a plain dim-0 TP gather would interleave ranks).
Routed experts: LoRAGroupedFC1/FC2 — shared-A, per-expert-B grouped-GEMM deltas (F.grouped_mm over the router's token offsets), EP-aware export/load. Shared experts: one adapter with per-sub-expert B slices, hooked into each sub-expert's forward.
lm_head: LoRAOutputHead with muP logit scaling and unpadded-vocab trim.
Serving: TML names are auto-detected by SGLang, so serving passes lora_target_modules=["all"] (sentinel keyed off the checkpoint, not the model list).
normalize_targets maps any request (all-linear or HF-style names) to the full native TML set, matching the provider it replaces.

Fixes found by running the models natively

miles_plugins/mbridge/kimi_k25.py (new): raw-mode K2.5 conversion needs an mbridge bridge (bridge mode uses megatron.bridge's own KimiK25VL bridge and never converts). Thin DeepseekV3Bridge subclass: text_config descent + language_model. prefix remap.
megatron_to_hf kimi dispatch: _convert_to_hf_core matched only kimi_k25, but the direct weight iterator passes kimik25config (dead branch); the converter also lacked the raw text-only provider's unprefixed top-level weight names.
convert_kimi_int4_to_bf16.py strips quantization_config: a surviving one sends SGLang through its CompressedTensors path, which serves the BF16 checkpoint with a degenerate context-free forward → garbage rollouts, train/rollout logprob_abs_diff ~2.2. Verified by a megatron-vs-HF-vs-SGLang three-way logprob comparison.
torch_dsa_topk clamps k to the key length: short sequences (< index_topk keys) crashed torch.topk in raw-mode log-prob compute.
run_glm5_2_744b_a40b_lora_native.py (new): raw-mode GLM-5.2 launcher; its toy conversion must be single-rank (any PP split of the 5-layer toy starts a stage on a DSA skip layer).

CI

tests/e2e/lora_native/ (label lora-native, trigger run-ci-lora-native): qwen3, qwen3.5, glm4.7, glm5.2-5layer, kimi-2.5-2layer, each a 20-rollout native run checking logprob parity and weight-update equality (vision towers and engine-transformed params skip-listed). The pre-existing inkling 4-layer CI runs against the plugin path.

Validation

20-rollout native GRPO runs (wandb ch271828n-team/miles_lora_native), acceptance ≈ 0.01 train/rollout logprob_abs_diff:

model	logprob_abs_diff
Qwen3-8B	0.0099
Qwen3.5-35B-A3B	0.0105 (grad_norm ~1e-2 throughout; the recorded GDN raw-backward divergence did not reproduce → `qwen3_5_moe` VALIDATED)
GLM-5.2 5-layer	0.0096
Kimi-K2.5 2-layer	0.0124 (after the quantization_config fix)
Inkling-Small (4-node LoRA)	running

tests/fast/miles_plugins/lora: 64 passed (includes Inkling spec locks). Behavior-preservation smoke: a 2-rollout Qwen3-8B run reproduces the baseline's first-rollout logprob_abs_diff (0.00967 vs 0.00978). Adapter attribute names are pinned, so checkpoint keys are unchanged.

Moves miles/backends/megatron_utils/lora_native.py (906 lines, one file) into a miles_plugins/lora package, so model-architecture support lives in the plugin layer like mbridge and megatron_bridge already do. Bridge-mode LoRA is untouched: the shared helpers both paths use stay in lora_utils.py, and nothing under miles/ imports the plugin at module level -- core reaches it only through the resolve_lora_provider dotted path, whose default is now miles_plugins.lora.lora. Layout, split along the seams the code already had: * adapter.py -- NativeLoRAAdapter and the _ADAPTER_LAYOUT projection table. Adapters stay self-describing, so export/load never know which spec attached them. * io.py -- HF/PEFT export (TP-gathered) and per-rank load. * naming.py -- the checkpoint-index naming heuristic, plus an optional mbridge cross-check (lazy import, warns on disagreement, no-op when mbridge is absent). * spec/ -- attach paths: attention.py (GQA fused-qkv + MLA, with their guards), mlp.py (gated MLP / shared experts), base.py (per-run _Spec and shared primitives). * registry.py -- new: HF model_type -> spec family, keyed the same way mbridge's register_model is. Unregistered architectures fail at startup listing the supported set and the escape hatches; checkpoints with no config.json (the numerical harnesses) fall back to structural dispatch with a warning. The config x parallelism guards (num_query_groups >= TP, MLA q_lora_rank, replicated down-projections) are unchanged and still apply on top. * lora.py -- the provider entrypoint: apply_native_lora, the run guards, and the three-function provider protocol. Function bodies are unchanged from lora_native.py except apply_native_lora gaining the registry lookup and arch=... in its startup log line. resolve_lora_provider moves to lora_utils.py, next to the other helpers its call sites (model.py, hf_weight_iterator_direct.py, save_lora_checkpoint) already import. Tests move to tests/fast/miles_plugins/lora/ -- the 47 existing tests unchanged apart from imports, plus 17 new registry/naming tests. Verified against the parent branch in the same environment: identical pass/fail sets on the moved suite and on the four lora_utils-dependent suites (test_lora_utils, test_lora_model_branches, test_lora_update_weight, test_lora_hf_weight_iterator); the only local failure either way is megatron-core's triton import on macOS. black / isort / ruff clean. Co-authored-by: Cursor <cursoragent@cursor.com>

gemini-code-assist · 2026-07-31T02:57:23Z

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…fety, API cleanup - MoE without shared experts (qwen3_moe): parser-expanded all-linear MLP targets now skip with a log line instead of asserting at startup, mirroring the MLA generic-qkv normalization; explicit gate/up/down requests still fail closed. - Registry: drop deepseek_v4 (DeepSeek-V4-Flash attention is wq_a/wq_b/wkv, not mcore MLA — docs already declare it out of scope), and report "config.json has no model_type" distinctly from "config.json missing". - Resume safety: load_native_adapter_state_dict now reports missing tensors (shard saved with narrower targets) in addition to unexpected ones, and the optimizer/scheduler-restore decision is agreed across ranks via all_gather_object so per-rank shard differences cannot desync the resume iteration or LR schedule. - Provider path: the package itself now exports the provider protocol, the default becomes miles_plugins.lora (old miles_plugins.lora.lora pins keep working, including for the native save fast path). - Delete all 17 transitional underscore aliases; tests and the numerical harness import the public names. - Extract parse_lora_target_modules out of miles_validate_args so tests/fast/utils/test_lora_arguments.py exercises the production function instead of a hand-copied replica (now also covers MLA all-linear expansion). - New tests/fast/miles_plugins/lora/test_orchestration.py: apply_native_lora attach counts / freeze / grad flow on fake decoders, registry-gated startup failures, _assert_supported_run rejections, and MLA attach for both q_lora_rank branches (previously 0% fast-test coverage on lora.py and the MLA attach path). - docs: VL wrappers resolve via text_config.model_type (vision never adapted; unregistered VL types fail closed), shared-expert-less MoE all-linear behavior, provider default.

- SharedExpertOnlyMoESpec splits into SharedOuterExpertMoESpec (layers with a shared/outer expert: LoRA adapts the shared expert's MLP) and GeneralExpertMoESpec (routed-only layers: all-linear skips, explicit MLP targets fail closed). The shared spec delegates routed-only layers to the general spec, so one registry entry still covers mixed-layer models. Module docstring spells out the distinction from the bridge-only --experts-shared-outer-loras adapter layout (SGLang PR #21466). - SplitQKV -> LoRASplitQKV, SplitFC1 -> LoRASplitFC1, aligning with Megatron-Bridge's LoRALinearSplitQKV / LoRALinearSplitFC1UpGate naming (LoRALinear already matches).

…pported combos Findings from a parallelism audit of the native path. TP-only handling is correct for what the plugin attaches (ETP is moot because MCore builds the MoE shared expert on the attention TP group; CP needs nothing because adapters are ordinary replicated DDP-bucket params reduced over dp_cp), but the surrounding plumbing had two real bugs: - EP>1 resume found no shard. The adapter shard name carried ep_rank while only dense-DP-rank-0 ranks wrote, so under scripts/run_lora_native.py's shipped TP2/EP4 defaults exactly {tp0_pp0.pt, tp1_pp0_ep1.pt} were written and the EP 2/3 ranks found nothing (verified against the pinned fork's RankGenerator). Native adapter state is EP-invariant, so its name is now keyed on (tp, pp) only; bridge shards keep the EP component because bridge PEFT can attach genuinely expert-parallel adapters. Writers are now elected per distinct shard filename, which covers every name some rank will request under any layout and collapses to the old behavior when the name has no EP component. - --overlap-grad-reduce silently corrupted adapter gradients: the TP grad sum runs before finalize_model_grads and writes the same main_grad buffer MCore's per-bucket reduce-scatter is already streaming into. It cannot move after finish_grad_sync (--use-distributed-optimizer leaves only the local shard valid), so it is refused at startup. Guards for combinations that silently did the wrong thing: - --experts-shared-outer-loras now requires bridge mode or a custom provider; native adapts no routed experts, so SGLang was being configured for buffers the export never fills. - --train-backend fsdp + --lora-rank now fails instead of silently full-finetuning while the rollout engine expects an adapter. - qwen3_5/qwen3_6/qwen3_next warn that raw mode's frozen-base backward is known to diverge there (scripts/run_lora_native.py documents grad_norm 1e7-1e10 / NaN), so a run says so instead of blowing up at step 1. Also records, as roadmap notes next to the code that would change, the dimensions Megatron-Bridge PEFT covers and native does not: routed-expert LoRA in three layouts and router LoRA (spec/moe.py), the expert-TP group and the structural reason overlap flags are refused (distributed.py), expert-axis export (codec/hf.py), GDN in_proj and uncompressed-MLA q_proj (spec/attention.py), each with the bridge file:line to port from.

create_lora_instance passed both target_modules and exclude_modules to the bridge LoRA, but Megatron-Bridge treats them as mutually exclusive selection mechanisms and asserts the latter is empty whenever the former is set (peft/module_matcher.py). Since --target-modules is required whenever LoRA is on, any --exclude-modules value made every bridge-mode LoRA run die during the module walk with "exclude_modules should be empty when using target_modules". Reproduced against the pinned radixark/Megatron-Bridge matcher. The forwarded value was redundant anyway: parse_lora_target_modules already subtracts the excluded names from --target-modules, which is the only mechanism both LoRA paths share, and miles can never reach the bridge branch that honors exclude_modules. So the kwarg is dropped (verified against the real matcher: linear_qkv still matches, the excluded linear_proj returns None, no assert), and parse_exclude_modules goes with it — its only caller was that kwarg. Wildcard excludes now fail at startup instead of being silently ignored: exact names are all the subtraction can remove, the native provider matches leaf names, and bridge refuses patterns alongside a target list.

yushengsu-thu

fix them

yushengsu-thu · 2026-08-01T01:00:06Z

To-do

dsk-v4 support: because we have not support dsk-v4 lora on sglang side yet. Thus, I reomve dsk-v4 part and we can add it after sglang side support.
Refer to megatron-bridge to compelete the features at below layouts:
- miles_plugins/lora/spec/moe.py
- miles_plugins/lora/distributed.py
[I added] moe lora: in native branch, it only supports shared moe lora (bth, that's tml special design moe lora). Another general moe lora is non-shared and this is implemented in sglang and megatron-bridge already as well. We can refer to that part when we refactor megatron-bridge lora in miles. (Refer to different type of lora in megatron-bridge).

Applies the style already used in distributed.py and spec/moe.py to the places that still carried long roadmap prose: codec/hf.py, codec/checkpoint.py, modules/linear.py, modules/moe.py, spec/base.py, the GDN and MLA spec docstrings, the plugin __init__, five docstrings and one comment in lora_utils.py, parse_lora_target_modules, two test docstrings, and the docs parallelism section. Assertion and warning text is untouched: those are user-facing diagnostics where naming the reason and the escape hatch is the point. No behavior change; 177 LoRA fast tests pass.

…g/lora-plugin-refactor # Conflicts: # tests/fast/fixtures/generation_fixtures.py # tests/fast/fixtures/rollout_fixtures.py

… / kimi-k2.5-2layer (#2073)

Zhichenzzz · 2026-08-03T04:42:17Z

+
+from miles_plugins.lora.modules.linear import iter_adapters
+
+_MODEL_CHUNK_PREFIX = "_miles_model_chunks."


here we don't need to make the miles-sepcific lora weights, how about aligning with the megatron-bridge naming ways?

use mcore checkpoint and sharding

The ci-test weight checker runs in equality mode; Qwen3.5's visual.* and K2.5's vision_tower./mm_projector. weights are never re-shipped by the text-only frozen-base run and are transformed by the engine at load, so raw equality against the checkpoint cannot hold (all text weights pass). Same pattern as the GLM-5.2 bridge CI's skip list.

Replace the per-rank .pt shard format (and per-rank optimizer pickles) for the native provider with one MCore torch_dist checkpoint holding adapter weights, fully_reshardable optimizer state, and scheduler/iteration: - NativeLoRAAdapter.sharded_state_dict is metadata-gated: invisible to base checkpoint loads as before, and emits real ShardedTensors (TP axes from partition_dim) when the adapter checkpoint walk opts in. - Standard MCore walk supplies global layer keys and PP offsets; the merged dict is keyed by (ShardedTensor.key, global_offset) since VPP chunks share chunk-local state-dict keys. A completeness assert fails loudly if a provider's module tree hides adapters from the walk. - Checkpoints reshard on load across TP/PP/DP changes; optimizer fp32 mains and Adam moments travel with the weights, so resume no longer depends on rank-count-pinned files or post-load main-param refresh ordering. - The miles-specific '_miles_model_chunks.' key namespace is gone; both legacy shard schemes (flat and namespaced) remain readable. Path selection between dist and legacy formats is agreed across ranks before entering the collective load. Bridge/custom providers keep their existing shard + training-state format; HF PEFT export for rollout sync is unchanged.

…imizer roundtrip tests/fast (gloo, CPU): metadata gating, save/load roundtrip with optimizer plumbing, TP2-save -> TP1-load resharding, VPP chunks with identical chunk-local dict keys, both legacy shard schemes, and the loud-failure path for module trees that hide adapters from the sharded walk. A scoped fixture shims two upstream GPU/Linux assumptions in MCore's async writer for CUDA-less lanes and macOS. tests/fast-gpu: real MCore DDP + DistributedOptimizer (bf16 params, fp32 mains, fully_reshardable state) saved under TP2 and reloaded under TP1; one more identical-gradient Adam step on both sides must match bitwise, covering weights, fp32 mains, and Adam moments.

yushengsu-thu

clean here

…st, fix isort - checkpointing.py docstrings cut to abstracts per review. - Drop the kimik25 dispatch comment per review. - tests/fast-gpu/test_native_lora_dist_ckpt_optimizer.py: add the required register_cuda_ci (stage-b-2-gpu-h200, lora-native) — its absence failed the registry sanity check in every CPU suite — and apply isort's import order.

Copilot AI review requested due to automatic review settings July 31, 2026 02:57

yushengsu-thu requested review from Shi-Dong, Zhichenzzz, fzyzcjy, guapisolo, jybsuper, maocheng23 and yueming-yuan as code owners July 31, 2026 02:57

Copilot AI reviewed Jul 31, 2026

yushengsu-thu commented Jul 31, 2026

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Comment thread miles/backends/megatron_utils/lora_utils.py Outdated

yushengsu-thu added 3 commits July 31, 2026 10:56

style: move LoRA provider constant to module scope

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[lora] refactor native plugin into layered architecture

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yushengsu-thu force-pushed the yusheng/lora-plugin-refactor branch from f3c0c73 to dd9d77c Compare July 31, 2026 20:11

yushengsu-thu changed the title ~~[lora] extract native LoRA into the miles_plugins/lora plugin~~ [lora] refactor - native LoRA Jul 31, 2026

yushengsu-thu added 3 commits July 31, 2026 14:21

[lora] fix native plugin correctness and CI regressions

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Comment thread miles_plugins/lora/distributed.py Outdated

Comment thread miles_plugins/lora/spec/moe.py Outdated

yushengsu-thu and others added 4 commits July 31, 2026 18:15

[lora] trim tests and fix session CI fixtures

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Merge PR changes onto zhichen/lora-native

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Merge remote-tracking branch 'origin/zhichen/lora-native' into yushen…

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…g/lora-plugin-refactor # Conflicts: # tests/fast/fixtures/generation_fixtures.py # tests/fast/fixtures/rollout_fixtures.py

Zhichenzzz added run-ci-megatron run-ci-lora run-ci-miles-plugin Run CI tests labeled miles-plugin labels Aug 2, 2026

Zhichenzzz mentioned this pull request Aug 2, 2026

[lora] declarative layouts, support-status registry #2072

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[lora] declarative layouts, support-status registry (#2072)

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[lora] lora-native CI suite: qwen3 / qwen3.5 / glm4.7 / glm5.2-5layer / kimi-k2.5-2layer #2073

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[lora] lora-native CI suite: qwen3 / qwen3.5 / glm4.7 / glm5.2-5layer…

69a5807

… / kimi-k2.5-2layer (#2073)

Zhichenzzz added the run-ci-lora-native Run native (raw-mode) LoRA plugin e2e tests label Aug 2, 2026

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Zhichenzzz and others added 4 commits August 2, 2026 21:48

[lora] ci: drop skip-list comments, apply black

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yushengsu-thu force-pushed the yusheng/lora-plugin-refactor branch from 60c2a0d to 9f173d8 Compare August 3, 2026 08:51

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yushengsu-thu merged commit 11a3d8b into zhichen/lora-native Aug 3, 2026
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yushengsu-thu deleted the yusheng/lora-plugin-refactor branch August 3, 2026 16:14

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[lora] Native LoRA for standard Megatron-core raw mode models #1792

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[lora] refactor - native LoRA - #2017

[lora] refactor - native LoRA#2017
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		from miles_plugins.lora.modules.linear import iter_adapters

		_MODEL_CHUNK_PREFIX = "_miles_model_chunks."

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Layout

Design

Inkling on the plugin (replaces the #2122 monolith)

Fixes found by running the models natively

CI

Validation

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