feat: add RavenForCausalLM (Huginn) depth-recurrent adapter#1520
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feat: add RavenForCausalLM (Huginn) depth-recurrent adapter#1520mukund1985 wants to merge 3 commits into
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@jlarson4 ready for review. Rebased against dev — conflicts resolved. |
Adds a TransformerBridge architecture adapter for RavenForCausalLM (tomg-group-umd/huginn-0125), a depth-recurrent decoder with a prelude / weight-tied recurrent core / coda structure, resolving TransformerLensOrg#1469. - raven.py delegates the recurrence to the remote-code HF forward and maps the three physical block lists (prelude / core_block / coda) via SSMBlockBridge, with combined-QKV native attention and a gated MLP. Sets applicable_phases=[] (a random initial latent state and post-residual sandwich norms diverge from the verify_models phases) and supports_fold_ln=False (ln_f is reused mid-network). prepare_loading patches Huginn's remote code for transformers v5 (tied-weights-keys dict form + a weight re-init guard). - Registers in the adapter factory, the model registry (canonical author and description) and supported_models.json; surfaces the recurrence-shape config via both _HF_PASSTHROUGH_ATTRS lists. - Adds synthetic-config unit tests and CI-gated integration tests.
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Summary
Adds a TransformerBridge adapter for
RavenForCausalLM(tomg-group-umd/huginn-0125), a depth-recurrent transformer that performs latent reasoning by iterating a weight-tied core block a variable number of times at inference.Closes #1469.
Architecture
Huginn has a three-phase structure under
model.transformer:num_stepstimes at runtime (defaultmean_recurrence=32). Each step re-injects the prelude output via a learnedadapterlinearSandwichBlock uses a post-residual norm pattern (
x = norm_2(attn(norm_1(x)) + x)).BlockBridgeenforces a pre-norm hook flow that does not exist here, soSSMBlockBridgeis used for all three block lists to delegate the full forward unchanged.Key implementation notes
transformers v5 compatibility. The remote code targets transformers 4.44 where
_tied_weights_keysis a list. Under v5,tie_weights()expects a dict. Fixed inprepare_loading()by rewriting it to{"lm_head.weight": "transformer.wte.weight"}, plus the standard_init_weightsre-randomization guard.Non-deterministic forward.
iterate_forwardseeds the recurrence withtorch.randn_like. Integration tests pin the RNG identically before bridge and HF calls.Hook exposure.
core_block.{i}.hook_in/hook_outfire once per recurrence step, enabling cross-step logit lens, fixed-point analysis, and cross-step activation patching.Registration
All four required sites updated per AGENTS.md:
supported_architectures/__init__.py,factories/architecture_adapter_factory.py(key:RavenForCausalLM),tools/model_registry/__init__.py,tools/model_registry/generate_report.py,tools/model_registry/data/supported_models.json.Tests
test_raven_adapter.py): 58 tests, synthetic config, no weight loadingtest_raven_adapter.py): 16/16 pass locally against the full fp32 checkpoint, including forward logit parity vs HF eager, hook shapes across all three phases, and recurrent-core hook firing count. CI-gated on network/memory budget.TestRegistrySyncedWithFactory(4 invariants): all pass