[Feature] Support EAGLE3 for Sarvam - #53052
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Enable EAGLE-3 speculative decoding for the Sarvam MLA architecture by adopting the consolidated SupportsEagle interface from vllm-project#36063. SarvamMLAModel mixes in EagleModelMixin and collects auxiliary hidden states, using absolute layer indices so the capture points stay correct under pipeline parallelism. SarvamMLAForCausalLM declares SupportsEagle3 and relies on the protocol defaults for set_aux_hidden_state_layers and get_eagle3_default_aux_hidden_state_layers. Signed-off-by: mohit-sarvam <mohit@sarvam.ai> Co-authored-by: Cursor <cursoragent@cursor.com>
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Cover the four things that can regress silently: the SupportsEagle3 declaration, the protocol hooks reaching the inner SarvamMLAModel, the captured tensors being complete layer outputs, and forward still returning a bare tensor when no aux layers are configured. The tests build SarvamMLAModel via object.__new__ so they need neither weights nor an initialized distributed environment. Signed-off-by: mohit-sarvam <mohit@sarvam.ai> Co-authored-by: Cursor <cursoragent@cursor.com>
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@claude review |
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WalkthroughSarvam MLA now supports Eagle3 capability detection and auxiliary hidden-state capture. It captures configured embedding and layer outputs, uses absolute pipeline layer indices, and preserves tensor-only output when auxiliary capture is disabled. ChangesSarvam Eagle3 support
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: ⚪ Minimal · up to Sarvam MLA gains EAGLE3 auxiliary hidden-state support while retaining ordinary inference behavior. EAGLE3 is explicitly limited to single-stage pipeline operation, eliminating the prior cross-stage output concern; no merge-blocking risk remains. Sequence Diagram(s)sequenceDiagram
participant Caller
participant SarvamMLAForCausalLM
participant SarvamMLAModel
participant TransformerLayers
Caller->>SarvamMLAForCausalLM: Send forward request
SarvamMLAForCausalLM->>SarvamMLAModel: Run with Eagle3 layer configuration
SarvamMLAModel->>TransformerLayers: Execute layers and capture states
TransformerLayers-->>SarvamMLAModel: Return hidden states and residuals
SarvamMLAModel-->>SarvamMLAForCausalLM: Return final and auxiliary states
SarvamMLAForCausalLM-->>Caller: Return model output
🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 78.57% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 14 functions across 2 files. (1 skipped: 1 unsupported.)
✨ Finishing Touches 💡 1🛠️ Fix failing CI checks 💡
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Inline comments:
In `@vllm/model_executor/models/sarvam.py`:
- Around line 530-532: Update _maybe_add_hidden_state and the
IntermediateTensors pipeline transport so auxiliary states captured on non-final
stages are preserved and merged with states captured on the final stage before
the final tuple return. Add a regression test covering capture points on both a
non-final and final pipeline stage.
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tests/model_executor/test_sarvam_eagle3.pyvllm/model_executor/models/sarvam.py
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Co-authored-by: Codex <noreply@openai.com> Signed-off-by: mohit-sarvam <mohit@sarvam.ai>
Co-authored-by: Codex <noreply@openai.com> Signed-off-by: mohit-sarvam <mohit@sarvam.ai>
…rvam-eagle3 Signed-off-by: mohit-sarvam <mohit@sarvam.ai>
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Documentation preview: https://vllm--53052.org.readthedocs.build/en/53052/ |
Keep the EAGLE3 pipeline scope in the PR description and model docstrings. Co-authored-by: Codex <noreply@openai.com> Signed-off-by: mohit-sarvam <mohit@sarvam.ai>
The PR author tested the Sarvam MLA model end to end with DSpark and confirmed that inference works. Co-authored-by: Codex <noreply@openai.com> Signed-off-by: mohit-sarvam <mohit@sarvam.ai>
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/ci run |
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✅ Triggered Buildkite CI #87571 for commit |
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✅ @mohit-sarvam, CI is now available for this PR.
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mohit-sarvam
changed the title
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/ci retry |
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/ci retry |
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✅ Queued 3 failed job(s) for retry in Buildkite CI #87571. |
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/ci run |
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✅ Triggered Buildkite CI #87591 for commit |
Signed-off-by: mohit-sarvam <mohit@sarvam.ai> Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: Codex <noreply@openai.com> Signed-off-by: Jyotirmoy Roy <jyotirmoyroy649@gmail.com>
4 participants
Purpose
Sarvam MLA (
SarvamMLAForCausalLM) previously did not expose the EAGLE3 interface or capture the auxiliary hidden states required by the drafter. This adds EAGLE3 support with a compatible draft checkpoint on a single pipeline stage.SarvamMLAModelusesEagleModelMixinto capture embeddings and complete layer outputs (hidden_states + residual).SarvamMLAForCausalLMdeclaresSupportsEagle3and inherits the protocol's auxiliary-layer configuration hooks.qwen3_moe.py.The default auxiliary layers are
(2, n // 2, n - 3). Draft checkpoints trained on other layers can seteagle_aux_hidden_state_layer_idsin the draft config.SarvamMoEForCausalLMis unchanged.Pipeline parallelism
Ordinary Sarvam MLA pipeline parallelism remains supported with EAGLE3 disabled: stages transport
hidden_statesandresidual.EAGLE3 for Sarvam MLA requires
--pipeline-parallel-size 1. This model does not transport auxiliary captures across pipeline stages and does not opt intosupports_aux_hidden_states_over_pp.Upstream #50514 added EAGLE3 pipeline transport infrastructure and a per-model capability check in the newer GPU runner. Qwen3 dense inherits that support through
Qwen2Model; Qwen3 MoE and Sarvam MLA have not opted in. The runner therefore rejects EAGLE3 + PP for Sarvam. Adding that support is outside this PR's scope.Validation
The PR author tested the Sarvam MLA model end to end with DSpark and confirmed that inference works.
AI assistance was used for this PR revision.