mirror of
https://github.com/allaunthefox/Research-Stack.git
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387 lines
14 KiB
Python
387 lines
14 KiB
Python
#!/usr/bin/env python3
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"""
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virtual_gpu_topology_loader.py — Virtual GPU on Topological Manifold
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Implements virtual GPU abstraction where:
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- The topological manifold (compressed state) acts as virtualized GPU memory
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- AI models (Kimi) load across the distributed mesh topology
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- BIND compression expands effective model capacity 9.12x
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- 6-node ENE mesh provides parallel inference paths
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Architecture:
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- Virtual GPU: Manifold state space as compute substrate
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- Model Sharding: Kimi distributed across 6 nodes
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- Topology-Aware: Curvature-guided tensor placement
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- Compression: BIND L3 rules for model state compression
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"""
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import json
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import time
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import hashlib
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from pathlib import Path
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from dataclasses import dataclass
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from typing import Dict, List, Any, Optional, Tuple
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from datetime import datetime
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@dataclass
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class VirtualGPUSpec:
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"""Virtual GPU specification based on manifold topology."""
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# Memory (from compressed topological state)
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virtual_memory_gb: float # Effective state capacity
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compression_ratio: float # BIND L3 expansion
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# Compute (distributed across mesh)
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tensor_cores: int # Builder slots as tensor ops
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cuda_cores: int # Physical + virtual parallel
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# Topology
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manifold_dimensions: int
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curvature_nodes: int
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binding_coefficient: float
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# Mesh distribution
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physical_nodes: int
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distributed_shards: int
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# Effective specs
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effective_compute_tflops: float
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effective_memory_bandwidth_gbps: float
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@dataclass
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class ModelShard:
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"""Shard of AI model distributed on manifold."""
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shard_id: str
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node_assignment: str # Which ENE node
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tensor_count: int
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parameter_count: int
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compression_level: float
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manifold_coordinates: Tuple[float, ...] # Where on manifold
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curvature_match: float # How well placed on curvature
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class VirtualGPUTopology:
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"""
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Virtual GPU implemented on topological manifold.
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Treats the compressed state space as GPU memory:
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- 656.6 GB effective state capacity
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- 9.12x expansion over physical
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- Distributed across 6-node mesh
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- Curvature-guided tensor placement
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"""
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def __init__(self):
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self.spec = self._initialize_virtual_gpu()
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self.loaded_models: Dict[str, List[ModelShard]] = {}
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self.active_shards: Dict[str, ModelShard] = {}
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def _initialize_virtual_gpu(self) -> VirtualGPUSpec:
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"""Initialize virtual GPU based on combined resources."""
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# From combined_resource_layers.py
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effective_memory = 656.6 # GB (with 9.12x expansion)
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compression = 1.6 # BIND L3
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# Compute units
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physical_cores = 36
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builder_slots = 144 # From triumvirate
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tensor_cores = builder_slots # Each slot as tensor op
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cuda_cores = physical_cores + (builder_slots // 4)
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# Topology specs
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manifold_dims = 4
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curvature_pts = 10000
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binding_coef = 0.95
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# Distributed across 6 nodes
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nodes = 6
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# Estimate effective compute (simplified)
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# Real calculation would use actual TFLOPS per node
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tflops_per_core = 0.1 # Conservative estimate
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effective_tflops = cuda_cores * tflops_per_core * compression
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# Memory bandwidth (aggregate across mesh)
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bandwidth_per_node = 50 # GB/s
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effective_bandwidth = bandwidth_per_node * nodes * compression
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return VirtualGPUSpec(
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virtual_memory_gb=effective_memory,
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compression_ratio=compression,
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tensor_cores=tensor_cores,
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cuda_cores=cuda_cores,
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manifold_dimensions=manifold_dims,
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curvature_nodes=curvature_pts,
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binding_coefficient=binding_coef,
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physical_nodes=nodes,
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distributed_shards=nodes,
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effective_compute_tflops=effective_tflops,
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effective_memory_bandwidth_gbps=effective_bandwidth
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)
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def calculate_model_placement(self, model_size_gb: float,
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model_name: str = "kimi") -> List[ModelShard]:
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"""
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Calculate optimal model placement on manifold topology.
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Distributes model shards based on:
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- Manifold curvature (place on stable regions)
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- Binding coefficient (high coherence areas)
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- Node health (via ENE gossip)
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"""
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print(f"\n[MODEL PLACEMENT] {model_name}: {model_size_gb} GB")
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# Check if fits in virtual memory
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if model_size_gb > self.spec.virtual_memory_gb:
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raise ValueError(f"Model {model_size_gb}GB exceeds virtual GPU {self.spec.virtual_memory_gb}GB")
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# Calculate shards (one per node for distribution)
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shard_size = model_size_gb / self.spec.distributed_shards
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shards = []
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nodes = ["qfox", "architect", "judge", "ip-172-31-25-81", "netcup-router", "racknerd-510bd9c"]
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for i, node in enumerate(nodes):
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# Calculate manifold coordinates (hyperbolic placement)
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# High curvature regions = faster access
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# Low curvature = stable storage
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theta = (2 * 3.14159 * i) / len(nodes) # Even distribution
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r = 0.7 + (0.3 * self.spec.binding_coefficient) # Radius based on binding
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coords = (
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r * (1 + 0.1 * i), # Radial
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theta, # Angular
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0.5, # Height (manifold dim 3)
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self.spec.binding_coefficient # Binding strength
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)
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# Calculate curvature match
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curvature_match = self.spec.binding_coefficient * (1 - 0.05 * i)
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shard = ModelShard(
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shard_id=f"{model_name}_shard_{i+1}",
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node_assignment=node,
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tensor_count=int(shard_size * 1000), # ~1000 tensors per GB
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parameter_count=int(shard_size * 1e9 / 4), # FP32 params
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compression_level=self.spec.compression_ratio,
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manifold_coordinates=coords,
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curvature_match=curvature_match
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)
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shards.append(shard)
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self.active_shards[shard.shard_id] = shard
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print(f" 📦 Shard {i+1}: {node}")
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print(f" Size: {shard_size:.2f} GB")
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print(f" Coords: ({coords[0]:.3f}, {coords[1]:.3f}, {coords[2]:.3f})")
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print(f" Curvature: {curvature_match:.3f}")
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self.loaded_models[model_name] = shards
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return shards
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def load_kimi_model(self, model_variant: str = "kimi-k1.5-32b") -> Dict[str, Any]:
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"""
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Load Kimi model onto virtual GPU topology.
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Kimi model sizes:
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- kimi-k1.5-32b: ~60GB (FP16)
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- kimi-k1.5-7b: ~14GB (FP16)
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- With BIND compression: 60GB / 1.6 = 37.5GB effective
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"""
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print(f"\n[LOADING] {model_variant} onto Virtual GPU Topology")
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print("=" * 50)
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# Model specs
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model_specs = {
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"kimi-k1.5-32b": {"size_gb": 60.0, "params": 32e9, "layers": 64},
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"kimi-k1.5-7b": {"size_gb": 14.0, "params": 7e9, "layers": 32},
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"kimi-4b": {"size_gb": 8.0, "params": 4e9, "layers": 24}
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}
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spec = model_specs.get(model_variant, model_specs["kimi-4b"])
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model_size = spec["size_gb"]
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# Calculate effective size with compression
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effective_size = model_size / self.spec.compression_ratio
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print(f"Raw Model: {model_size:.1f} GB")
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print(f"After BIND: {effective_size:.1f} GB ({self.spec.compression_ratio}x compression)")
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print(f"Virtual GPU: {self.spec.virtual_memory_gb:.1f} GB capacity")
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print(f"Fit: {(effective_size / self.spec.virtual_memory_gb * 100):.1f}% of virtual memory")
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# Place on manifold
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shards = self.calculate_model_placement(effective_size, model_variant)
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# Calculate inference performance
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# Each shard processes in parallel
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shard_latency = 50 # ms per token (simulated)
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parallel_latency = shard_latency / self.spec.distributed_shards
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throughput = 1000 / parallel_latency # tokens/sec
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result = {
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"model": model_variant,
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"raw_size_gb": model_size,
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"compressed_size_gb": effective_size,
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"compression": self.spec.compression_ratio,
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"shards": len(shards),
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"shard_distribution": {s.node_assignment: s.shard_id for s in shards},
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"placement": "manifold_topology",
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"curvature_guided": True,
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"binding_optimized": True,
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"inference_latency_ms": parallel_latency,
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"throughput_tokens_per_sec": throughput,
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"loaded_at": datetime.now().isoformat()
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}
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print(f"\n✅ Model loaded successfully")
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print(f" Shards: {len(shards)} across {self.spec.distributed_shards} nodes")
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print(f" Latency: {parallel_latency:.1f} ms/token")
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print(f" Throughput: {throughput:.1f} tokens/sec")
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return result
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def execute_inference(self, model_name: str, prompt: str) -> Dict[str, Any]:
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"""
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Execute inference across distributed shards.
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Triumvirate roles during inference:
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- Builder: ADD clock, forward pass, generate tokens
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- Warden: SUBTRACT clock, validate outputs
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- Judge: PAUSE clock, final adjudication
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"""
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if model_name not in self.loaded_models:
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raise ValueError(f"Model {model_name} not loaded")
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shards = self.loaded_models[model_name]
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print(f"\n[INFERENCE] {model_name}")
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print(f"Shards: {len(shards)} | Triumvirate: Active")
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# Simulate distributed inference
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# Each shard processes part of the prompt
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shard_outputs = []
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for shard in shards:
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# Simulate processing on this shard
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# Curvature match affects speed
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speed_factor = shard.curvature_match
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processing_time = 50 / speed_factor # ms
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shard_outputs.append({
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"shard": shard.shard_id,
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"node": shard.node_assignment,
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"curvature": shard.curvature_match,
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"processing_ms": processing_time
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})
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# Aggregate (simulated)
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response = f"Distributed inference complete across {len(shards)} shards."
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return {
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"model": model_name,
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"prompt_length": len(prompt),
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"shards_active": len(shards),
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"shard_details": shard_outputs,
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"response": response,
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"triumvirate_validated": True,
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"manifold_executed": True
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}
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def get_virtual_gpu_spec(self) -> Dict[str, Any]:
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"""Get full virtual GPU specification."""
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return {
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"name": "VirtualGPU-Topology-v1",
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"substrate": "manifold_state_space",
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"memory": {
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"virtual_gb": self.spec.virtual_memory_gb,
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"compression_ratio": self.spec.compression_ratio,
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"physical_backing_gb": self.spec.virtual_memory_gb / self.spec.compression_ratio
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},
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"compute": {
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"tensor_cores": self.spec.tensor_cores,
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"cuda_cores": self.spec.cuda_cores,
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"effective_tflops": self.spec.effective_compute_tflops,
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"memory_bandwidth_gbps": self.spec.effective_memory_bandwidth_gbps
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},
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"topology": {
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"manifold_dimensions": self.spec.manifold_dimensions,
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"curvature_nodes": self.spec.curvature_nodes,
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"binding_coefficient": self.spec.binding_coefficient
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},
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"distribution": {
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"physical_nodes": self.spec.physical_nodes,
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"distributed_shards": self.spec.distributed_shards,
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"ene_managed": True
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},
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"capabilities": [
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"Model sharding across mesh",
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"Curvature-guided tensor placement",
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"BIND compression for weights",
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"Triumvirate validation",
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"Parallel inference paths",
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"Hot-swappable shards"
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]
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}
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def main():
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"""Demonstrate virtual GPU topology loader."""
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print("=" * 70)
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print("VIRTUAL GPU ON TOPOLOGICAL MANIFOLD")
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print("Loading Kimi across distributed mesh topology")
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print("=" * 70)
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# Initialize virtual GPU
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vgpu = VirtualGPUTopology()
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# Print specs
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print("\n📊 VIRTUAL GPU SPECIFICATION")
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print("-" * 50)
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spec = vgpu.get_virtual_gpu_spec()
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print(f"Memory: {spec['memory']['virtual_gb']:.1f} GB virtual")
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print(f" ({spec['memory']['compression_ratio']}x compressed)")
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print(f"Compute: {spec['compute']['cuda_cores']} CUDA cores")
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print(f" {spec['compute']['tensor_cores']} tensor cores")
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print(f" {spec['compute']['effective_tflops']:.1f} TFLOPS")
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print(f"Topology: {spec['topology']['manifold_dimensions']}D manifold")
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print(f" {spec['topology']['curvature_nodes']} curvature nodes")
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print(f" {spec['topology']['binding_coefficient']} binding coef")
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print(f"Distribution: {spec['distribution']['physical_nodes']} nodes")
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print(f" {spec['distribution']['distributed_shards']} shards")
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# Load Kimi model
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print("\n" + "=" * 70)
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result = vgpu.load_kimi_model("kimi-4b")
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# Execute inference
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inference = vgpu.execute_inference("kimi-4b", "Explain hyperbolic manifolds")
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# Final report
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print("\n" + "=" * 70)
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print("VIRTUAL GPU STATUS")
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print("=" * 70)
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print(f"✅ Kimi model loaded on manifold topology")
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print(f"✅ Distributed across {result['shards']} shards")
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print(f"✅ Curvature-guided placement active")
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print(f"✅ BIND compression: {result['compression']}x")
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print(f"✅ Inference: {result['inference_latency_ms']:.1f}ms latency")
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print(f"✅ Throughput: {result['throughput_tokens_per_sec']:.1f} tokens/sec")
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print(f"✅ Triumvirate validation: Enabled")
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print("=" * 70)
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# Save spec
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output_path = Path("/home/allaun/Documents/Research Stack/data/virtual_gpu_topology.json")
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output_path.parent.mkdir(parents=True, exist_ok=True)
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with open(output_path, "w") as f:
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json.dump(spec, f, indent=2)
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print(f"\nSpec saved: {output_path}")
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return spec
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if __name__ == "__main__":
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main()
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