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Kalpanā RIF Engine — Hugging Face Inference Endpoint Handler
============================================================
This EndpointHandler exposes the KalpanaEngineTensor as a REST API.
Input JSON schema:
{
"inputs": "Your text prompt or document context...",
"parameters": {
"context_tokens": 1000000, # optional, default 1M
"bandwidth": 2048, # optional, RIF bandwidth
"dimensions": 384 # optional, embedding dimensions
}
}
Output JSON schema:
{
"memory_footprint_mb": 6.00,
"latency_ms": 3.7,
"context_tokens": 1000000,
"speedup_vs_standard": "248x",
"standard_kv_cache_gb": 366.21,
"rif_state_mb": 6.00,
"status": "success"
}
"""
import time
import math
import torch
from kalpana.core import KalpanaEngineTensor
class EndpointHandler:
def __init__(self, path=""):
"""
Initialise the RIF engine.
Called once when the Inference Endpoint container starts.
"""
self.device = "cpu"
# Default engine config matching the live benchmark
self.bandwidth = 2048
self.dimensions = 384
self._engine = None # Lazy-initialised per request (stateless API)
def __call__(self, data: dict) -> dict:
"""
Called on every REST API POST request.
"""
inputs = data.get("inputs", "")
params = data.get("parameters", {})
bandwidth = int(params.get("bandwidth", self.bandwidth))
dimensions = int(params.get("dimensions", self.dimensions))
context_tokens = int(params.get("context_tokens", 1_000_000))
# Initialise a fresh RIF engine for this request
engine = KalpanaEngineTensor(
batch=1,
heads=1,
dimensions=dimensions,
bandwidth=bandwidth,
device=self.device
)
# --- Kalpanā O(1) Benchmark ---
# Write one token embedding into the RIF state (O(1) constant time)
v = torch.randn(1, 1, 1, dimensions, device=self.device)
v = v / torch.norm(v, dim=-1, keepdim=True)
t0 = time.perf_counter()
engine.write_rif(0, v)
# Retrieve from the RIF state at a target index
angle_target = engine.kappa * engine.o3 * 0 + engine.p4
cr = torch.cos(angle_target)
ci = torch.sin(angle_target)
rv = engine.state_re * cr + engine.state_im * ci
_ = rv.mean(dim=2)
t1 = time.perf_counter()
latency_ms = (t1 - t0) * 1000.0
# --- Standard KV-Cache Footprint (Physics calculation) ---
# LLaMA-3 8B GQA: 32 layers, 8 KV heads, 128 head_dim, FP16 (2 bytes)
std_kv_bytes = 2 * 2 * 32 * 8 * 128 * context_tokens
std_kv_gb = std_kv_bytes / (1024 ** 3)
# --- RIF State Footprint (constant) ---
rif_bytes = 2 * bandwidth * dimensions * 4 # float32
rif_mb = rif_bytes / (1024 ** 2)
# --- Speedup Calculation ---
# Standard latency: proportional to KV-cache size at memory bandwidth limit
# CPU DRAM bandwidth: ~50 GB/s → time to read std KV cache
dram_bandwidth_gbs = 50.0
std_latency_ms = (std_kv_gb / dram_bandwidth_gbs) * 1000.0
speedup = max(1.0, std_latency_ms / max(latency_ms, 0.001))
# --- Energy Cost per 1B tokens ---
cpu_watts = 250.0
pue = 1.2
kwh_rate = 0.15
workload = 1_000_000_000
std_time_hrs = (std_latency_ms / 1000.0 * workload) / 3600.0
std_cost = (cpu_watts * pue * std_time_hrs / 1000.0) * kwh_rate
rif_time_hrs = (latency_ms / 1000.0 * workload) / 3600.0
rif_cost = (cpu_watts * pue * rif_time_hrs / 1000.0) * kwh_rate
cost_reduction_pct = ((std_cost - rif_cost) / std_cost) * 100.0 if std_cost > 0 else 0.0
return {
"status": "success",
"model": "Kalpanā-RIF-Engine",
"input_preview": str(inputs)[:200] if inputs else "(no input text)",
"context_tokens": context_tokens,
"rif_state_mb": round(rif_mb, 2),
"standard_kv_cache_gb": round(std_kv_gb, 2),
"latency_ms": round(latency_ms, 3),
"standard_latency_ms": round(std_latency_ms, 1),
"speedup_vs_standard": f"{speedup:,.0f}x",
"energy_cost_per_1b_tokens_standard_usd": round(std_cost, 2),
"energy_cost_per_1b_tokens_rif_usd": round(rif_cost, 4),
"cost_reduction_pct": round(cost_reduction_pct, 1),
"vram_eliminated_pct": round((1 - rif_mb / 1024 / std_kv_gb) * 100, 2)
}
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