"""DeepSeek-inspired efficient inference patterns — interface only. This module defines the SEAMS for V4-Pro / DSpark-style efficiency in openclinical-ai. It does NOT implement fake MoE routing — it defines the contract so real adapters can plug in. Three architectural patterns mirrored here (from DeepSeek-V4-Pro + DSpark): 1. **Hybrid Attention (CSA + HCA)** - Compressed Sparse Attention: compresses long context into compact representations, retaining semantic anchors. - Heavily Compressed Attention: aggressive compression for million- token contexts (full patient history, longitudinal records). - Result: 27% FLOPs, 10% KV cache vs DeepSeek V3.2. 2. **Fine-grained MoE Expert Routing** - V4-Pro: 1.6T total params, 49B (3%) activated per inference. - V4-Flash: 284B total, 13B (4.5%) activated. - For healthcare: medical specialty experts (cardiology, oncology, geriatrics, mental health, pediatrics, pharmacy, etc.). 3. **FP8 Quantized Inference** - 8-bit floating point halves memory bandwidth vs FP16. - 4-bit KV cache for long-context efficiency. - Policy hook lives in runtime.affordability (per-model decision). The interface here lets real adapters plug in. For heuristic adapters (MVP), the routing is trivial (single expert) — but the contract is in place so production adapters slot in without changing the substrate. References: - DeepSeek-V4-Pro: https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro - DeepSeek-V4-Pro-DSpark: https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-DSpark """ from __future__ import annotations import logging from dataclasses import dataclass, field from typing import Any logger = logging.getLogger("openclinical.runtime.efficient") # --- Expert routing (MoE seam) ------------------------------------------- @dataclass class ExpertRoute: """A single expert in a MoE model — what it handles, when it's selected. In V4-Pro, there are thousands of these. For openclinical-ai's first real adapter, expect ~10 medical specialty experts. The router picks which subset handles a given input. """ expert_id: str specialty: str # clinical specialty or model behavior handles: list[str] # input patterns / query types this expert handles estimated_params_b: float = 0.0 # parameter count in billions (for cost) class ExpertRouter: """Selects which expert(s) handle a given input. V4-Pro style: routes among many fine-grained experts (learned router). Heuristic MVP: routes based on keyword/pattern matching against `ExpertRoute.handles`. Future: learned router trained alongside the experts. The MVP returns all experts (no real selection yet). The seam is in place; real selection lands when real adapters ship. """ def __init__(self, experts: list[ExpertRoute]) -> None: self.experts = experts def route(self, inputs: dict[str, Any]) -> list[ExpertRoute]: """Return the experts that should handle this input. MVP heuristic: returns all experts. This is the seam — real routing logic plugs in here when production adapters ship. Production routing will: 1. Score each expert's `handles` against the input content 2. Select top-k experts (typically 1-2) 3. Apply auxiliary-loss-free load balancing (DeepSeek-style) """ # MVP: return all experts. Seams in place, real routing is # a v2 deliverable alongside real adapters. if not self.experts: return [] return list(self.experts) def activated_params_b(self, selected: list[ExpertRoute]) -> float: """Sum activated parameters across the selected experts.""" return sum(e.estimated_params_b for e in selected) # Default medical-specialty expert set (matches what a real clinical MoE # adapter would expose). The MVP heuristic adapter doesn't use these — # they're here so real adapters slot in without changing the substrate. DEFAULT_MEDICAL_EXPERTS: list[ExpertRoute] = [ ExpertRoute( expert_id="clinical-reasoning", specialty="General clinical reasoning", handles=["assessment", "diagnosis support", "care plan"], estimated_params_b=8.0, ), ExpertRoute( expert_id="drug-interaction", specialty="Pharmacology", handles=["medication", "drug interaction", "polypharmacy", "rx"], estimated_params_b=4.0, ), ExpertRoute( expert_id="variant-impact", specialty="Clinical genomics", handles=["variant", "genomic", "rare disease", "vcf"], estimated_params_b=6.0, ), ExpertRoute( expert_id="geriatrics", specialty="Geriatric medicine", handles=["elderly", "dementia", "falls", "ltc", "psw"], estimated_params_b=5.0, ), ExpertRoute( expert_id="mental-health", specialty="Mental health", handles=["depression", "anxiety", "mood", "suicide"], estimated_params_b=4.0, ), ExpertRoute( expert_id="pediatrics", specialty="Pediatrics", handles=["child", "infant", "neonate", "vaccination"], estimated_params_b=4.0, ), ExpertRoute( expert_id="emergency", specialty="Emergency / triage", handles=["triage", "acute", "trauma", "code"], estimated_params_b=5.0, ), ExpertRoute( expert_id="cardiology", specialty="Cardiology", handles=["heart", "bp", "blood pressure", "arrhythmia"], estimated_params_b=4.0, ), ExpertRoute( expert_id="oncology", specialty="Oncology", handles=["cancer", "tumor", "chemo", "metastasis"], estimated_params_b=5.0, ), ExpertRoute( expert_id="infectious-disease", specialty="Infectious disease", handles=["infection", "antibiotic", "sepsis", "outbreak"], estimated_params_b=4.0, ), ] # --- Context compression (CSA + HCA seam) -------------------------------- @dataclass class CompressedContext: """A compressed representation of long context — CSA + HCA inspired. In production, V4-Pro's hybrid attention produces these natively. For MVP heuristic adapters, we provide a simple truncation + key-passage extraction so the cost/affordability story holds even before real models plug in. """ original_tokens: int compressed_tokens: int compression_ratio: float method: str # csa | hca | mixed semantic_anchors: list[str] # key passages preserved verbatim def to_dict(self) -> dict[str, Any]: return { "original_tokens": self.original_tokens, "compressed_tokens": self.compressed_tokens, "compression_ratio": self.compression_ratio, "method": self.method, "semantic_anchors": self.semantic_anchors, } class ContextCompressor: """Compresses long context into a compact representation. In production: V4-Pro's CSA/HCA hybrid attention produces these. For MVP heuristic adapters: simple truncation + key-passage extraction (the contract is right, the implementation is a placeholder until real models plug in). Healthcare use case: - Patient's 5-year medical history = ~500K tokens raw - Compressed to 32K tokens preserves diagnoses, medications, allergies - Inference runs at ~27% the cost of full-context inference """ def compress( self, context: str, max_tokens: int, ) -> CompressedContext: """Compress `context` to <= max_tokens, preserving semantic anchors. MVP implementation: tokenize roughly (split on whitespace), pick the last `max_tokens` tokens plus any line containing clinical keywords (diagnoses, medications, allergies, vitals). """ if not context: return CompressedContext( original_tokens=0, compressed_tokens=0, compression_ratio=1.0, method="csa", semantic_anchors=[], ) tokens = context.split() original_tokens = len(tokens) if original_tokens <= max_tokens: return CompressedContext( original_tokens=original_tokens, compressed_tokens=original_tokens, compression_ratio=1.0, method="csa", semantic_anchors=[], ) # Anchor keywords — lines containing these are preserved verbatim anchor_keywords = [ "diagnosis", "diagnosed", "allergy", "allergic", "medication", "medications", "prescribed", "adverse", "reaction", "code", "code blue", "code white", "fall", "fell", "incident", "emergency", "transfer", "transferred", "discharge", "death", "died", "deceased", ] semantic_anchors = [] for line in context.split("\n"): line_lower = line.lower() if any(kw in line_lower for kw in anchor_keywords): semantic_anchors.append(line.strip()) if len(semantic_anchors) >= 20: break # Reserve space for anchors anchor_tokens = sum(len(a.split()) for a in semantic_anchors) remaining = max(0, max_tokens - anchor_tokens) # Tail-truncate to remaining budget (most recent context is most # relevant for clinical reasoning — last visit, recent vitals) tail = tokens[-remaining:] if remaining > 0 else [] compressed_tokens = anchor_tokens + len(tail) # Method selection: CSA for modest compression, HCA for aggressive ratio = compressed_tokens / original_tokens if original_tokens else 1.0 method = "hca" if ratio < 0.1 else "csa" if ratio < 0.5 else "mixed" return CompressedContext( original_tokens=original_tokens, compressed_tokens=compressed_tokens, compression_ratio=round(ratio, 4), method=method, semantic_anchors=semantic_anchors, ) # --- Tiered model adapter (the substrate seam for V4-Pro / V4-Flash) ----- @dataclass class TieredModelSpec: """Specification for a model at a particular tier. One model can have multiple tiers — each tier maps to a different activated-parameter count, quantization, and pricing. """ model_id: str tier_id: str activated_params_b: float # V4-Pro = 49, V4-Flash = 13 quantization: str # fp8 | fp16 | bf16 context_length: int cost_input_usd_per_m: float cost_output_usd_per_m: float class TieredModelAdapter: """Base class for adapters that support multiple tiers. Real adapters (V4-Pro, V4-Flash) override this. The MVP heuristic PSW adapter doesn't use it — but the contract is in place so production adapters slot in cleanly. The seam: - Each adapter declares its tier specs - The router picks the right tier based on tenant policy + request - Cost is computed from the tier spec, not hardcoded """ def __init__(self, specs: list[TieredModelSpec]) -> None: self.specs = specs self._by_tier: dict[str, TieredModelSpec] = {s.tier_id: s for s in specs} def spec_for(self, tier_id: str) -> TieredModelSpec | None: """Return the spec for a given tier, or None if not supported.""" return self._by_tier.get(tier_id) async def run(self, inputs: dict[str, Any]) -> dict[str, Any]: """Run inference. Override in subclasses. The base class raises — real adapters must implement. """ raise NotImplementedError def default_router() -> ExpertRouter: """Return a router configured with the default medical-specialty set.""" return ExpertRouter(DEFAULT_MEDICAL_EXPERTS) def default_compressor() -> ContextCompressor: """Return a default context compressor (CSA+HCA seam).""" return ContextCompressor()