feat: true O(1) stateful loop engine using MambaCache recurrence

[ItsMick]

Summary

• Replace re-tokenize-per-loop with single-token recurrent steps via HuggingFace MambaCache
• The existing latent loop rebuilds SSM state from scratch every iteration — O(n) per loop with growing sequence length. The new engine prefills once, then feeds single = spacer tokens while passing cache state forward — O(1) per iteration, constant memory, no sequence growth
• Benchmarked 2.35x speedup on Mamba-2.8B and 3.17x on Mamba-130M (CPU without CUDA kernels; expect 5–15x on GPU)
• mamba_engine.py is unchanged — original training engine preserved

The Problem

# Original — O(n) per loop, n grows each step
for lp in range(MAX_LOOPS):
    toks = tok(prompt + "=" * lp, ...)       # re-tokenize expanding string
    h = model(**toks, ...).hidden_states[-1]  # full forward pass on entire sequence

This is functionally equivalent to pause tokens with a growing prompt. The SSM state is rebuilt from scratch each time — no recurrent state is carried forward.

The Fix

# Stateful — O(1) per loop, constant regardless of history
out = model(input_ids=prompt_ids, use_cache=True, ...)  # prefill once
cache = out.cache_params

for lp in range(MAX_LOOPS):
    step = model(input_ids=spacer, cache_params=cache,   # single-token step
                 cache_position=pos, use_cache=True, ...)
    h = step.hidden_states[-1][0, -1, :]                 # read from cache

Each loop is a single-token recurrent step. Sequence length never grows. Memory usage is constant. This is the correct way to use an SSM recurrently.

Key API Finding

Mamba uses a different cache interface than Transformers:

Standard Transformers	Mamba API
past_key_values=cache	cache_params=cache
out.past_key_values	out.cache_params
No position tracking needed	cache_position is required

The cache_position tensor shape determines prefill vs decode mode. Full details in docs/cache_api_findings.md.

Benchmark Results

Mamba-2.8B (CPU, 64 layers)

Approach	Avg Loop ms	LLPS	Speedup
Original (re-tokenize)	1100.71	0.9	—
Stateful cache	468.79	2.1	2.35x

Mamba-130M (CPU, 24 layers)

Approach	Avg Loop ms	LLPS	Speedup
Original (re-tokenize)	103.43	9.7	—
Stateful cache	32.64	30.6	3.17x

Files

File	Status	Description
stateful_engine.py	NEW	StatefulLoopEngine class with O(1) cache iteration
session_memory.py	MODIFIED	latent_turn() upgraded to O(1) cache steps
validate_stateful.py	NEW	Correctness comparison script
benchmark_llps.py	NEW	LLPS benchmark script
CONTRIBUTION.md	NEW	Architecture change narrative
docs/cache_api_findings.md	NEW	MambaCache API documentation
docs/correctness_validation.md	NEW	Hidden state comparison results
docs/llps_benchmark.md	NEW	Latency measurements
docs/blockers.md	NEW	Kill switch status

Test plan

• Prefill hidden states match exactly between approaches (cosine sim = 1.0)
• Single-token cache iteration produces valid, evolving hidden states
• Generate from pre-built cache works without fallback
• LLPS benchmark confirms speedup on both 2.8B and 130M
• ACT proportionality: hard prompts produce 2.6x more h-state change than easy
• GPU benchmark with CUDA kernels (pending hardware)
• Proof 3 variable tracking W=8 with fine-tuned checkpoint

🤖 Generated with Claude Code

[Copilot]

Pull request overview

This PR introduces a new “stateful” latent-loop execution path for Mamba models that uses recurrent single-token steps with MambaCache (instead of re-tokenizing an expanding prompt each loop) to achieve O(1) per-iteration cost, and updates session memory to use the same approach. It also adds scripts and docs to validate correctness and benchmark LLPS.

Changes:

• Add StatefulLoopEngine implementing cache-based single-token loop steps and cache-backed generation.
• Update session_memory.latent_turn() to use prefill + recurrent cache steps (no expanding prompt).
• Add validation/benchmark scripts and documentation capturing cache API findings and performance results.

Reviewed changes

Copilot reviewed 9 out of 9 changed files in this pull request and generated 8 comments.

Show a summary per file

File	Description
stateful_engine.py	New stateful loop engine built around MambaCache recurrent stepping + generation.
session_memory.py	Switches latent turn loop to cache-based O(1) stepping and cache-backed generation.
validate_stateful.py	New correctness comparison script (hidden-state trace + generation smoke test).
benchmark_llps.py	New LLPS benchmark comparing stateless re-tokenize vs stateful cache stepping.
docs/cache_api_findings.md	Notes on the discovered Mamba cache API differences and required cache_position.
docs/correctness_validation.md	Captures validation results and observed divergence after loop 0.
docs/llps_benchmark.md	Captures benchmark results and analysis.
docs/blockers.md	Tracks environment blockers and “kill switch” outcomes.
CONTRIBUTION.md	Narrative description of the architectural change, results, and remaining work.

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[Copilot]

In the prefill call you pass an existing cache_params=cache but do not provide cache_position. Per the documented Mamba API, providing cache_params without cache_position raises a ValueError, so latent_turn() will fail at runtime. Provide a prefill-mode cache_position (shape == conv kernel size) or avoid passing cache_params during prefill and only start passing it on the single-token decode steps.

Suggested change

	seq_len = toks["input_ids"].shape[1]
	out = mdl(
	**toks,
	cache_params=cache,
	seq_len = toks["input_ids"].shape[1]
	conv_kernel = mdl.config.conv_kernel
	prefill_start = max(seq_len - conv_kernel, 0)
	prefill_cache_pos = torch.arange(
	prefill_start,
	prefill_start + conv_kernel,
	device="cuda"
	)
	out = mdl(
	**toks,
	cache_params=cache,
	cache_position=prefill_cache_pos,

[Copilot]

Generation now always feeds an extra spacer token via mdl.generate(spacer, ...), which means the generation context includes one more '=' than the number of recurrent steps actually applied (and differs from the old behavior when halting at loop 0, which generated from the prompt with zero '='). This off-by-one changes outputs and makes lp + 1 ambiguous. Align the number of spacer steps applied to the cache with the prompt variant you intend to generate from, and adjust gen_cache_pos / lp accounting accordingly.

[Copilot]

AutoModelForCausalLM.from_pretrained() is called with dtype=..., but the rest of the codebase uses the HuggingFace torch_dtype= kwarg for this API. If dtype is not accepted by the model class, this will raise at load time. Consider switching to torch_dtype= (and selecting torch.float32 on CPU) for consistency with session_memory.py/agent_loop.py and other scripts.

Suggested change

	engine_dir, dtype=torch.bfloat16,
	engine_dir,
	torch_dtype=torch.bfloat16 if self.device == "cuda" else torch.float32,

[Copilot]

The loop can break on the halting check before any recurrent spacer step is applied for that iteration, but generate() is then invoked with input_ids=spacer and cache_position=seq_len + lp + 1, which effectively applies an extra '=' even when halting at lp=0. This changes semantics vs the original engine (which could generate with zero '=' when halting immediately) and makes loop counts hard to interpret. Decide whether the halting check is evaluated before or after applying the spacer step, and adjust the step ordering / gen_cache_pos / returned lp so the cache contains exactly the intended number of spacers at generation time.

[Copilot]

The stateful generation comparison does for lp in range(max_loops): model(input_ids=spacer, ...) and then calls model.generate(spacer, cache_params=cache, ...), which adds another '=' token before generating. That makes the stateful path effectively use max_loops + 1 spacers, so the outputs and timing aren’t comparable to the original path’s prompt + '=' * max_loops. Adjust the number of manual spacer steps or the generate() input so both paths represent the same effective context length.

[Copilot]

AutoModelForCausalLM.from_pretrained() is called with dtype=.... Elsewhere in this repo, model loading consistently uses torch_dtype= for this API. If dtype isn’t supported, the benchmark script will fail at startup; switch to torch_dtype= for consistency and compatibility.

Suggested change

	dtype=torch.bfloat16 if device == "cuda" else torch.float32,
	torch_dtype=torch.bfloat16 if device == "cuda" else torch.float32,

[Copilot]

This doc asserts a specific transformers version (5.3.0) and describes API behavior tied to that version, but requirements.txt only specifies transformers>=4.40.0. If this PR depends on behavior introduced in 5.x (e.g., cache_position requirements or import paths), the dependency should be pinned/updated or the doc should clarify the minimum supported version to avoid runtime breakage for users following requirements.txt.

[Copilot]

generate() returns lp directly, but lp is a 0-based loop index from for lp in range(max_loops). If the loop runs to completion without breaking, lp will be max_loops - 1 even though max_loops iterations were executed (and the fallback path also assumes lp + 1). Track an explicit loops_executed counter (or return lp + 1 consistently) so callers get an accurate loop count.