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0e86f35 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | #!/usr/bin/env python3
# ==============================================================================
# 🌉 ChiasmBridge: Universal Bi-Directional Cross-Modal Verification Test
# Tests Forward (N -> M) and Inverse (M -> N) Projections across Modalities
# ==============================================================================
import sys
import os
import torch
# Add root ChiasmBridge path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from chiasm import ChiasmBridge, ChiasmConfig
def run_test():
print("======================================================================")
print(" 🌉 CHIASMBRIDGE: UNIVERSAL BI-DIRECTIONAL (N <-> M) TEST SUITE")
print("======================================================================")
test_cases = [
("👁️ Vision <-> Text LLM", 3584, 5120, "7B Vision Encoder <-> 24B/32B LLM"),
("🎙️ Speech/Audio <-> LLM", 1024, 4096, "Whisper STT <-> 8B Llama-3 LLM"),
("🖐️ SNN Haptics <-> LLM", 256, 3584, "256-dim LIF Spikes <-> 7B Qwen LLM"),
("🧠 Small LLM <-> Large LLM", 5120, 8192, "24B Model Embeddings <-> 72B Qwen LLM")
]
for label, src_dim, tgt_dim, desc in test_cases:
print(f"\n[Test] {label} ({desc}):")
print(f" Source Dimension (N): {src_dim} <--> Target Dimension (M): {tgt_dim}")
# 1. Initialize Bi-Directional Config
config = ChiasmConfig(source_dim=src_dim, target_dim=tgt_dim)
bridge = ChiasmBridge(config)
# 2. Forward Pass: Sensory Input (N) -> LLM Embedding (M)
sensory_input = torch.randn(2, 16, src_dim)
llm_embedding = bridge.project_forward(sensory_input)
# 3. Inverse Pass: LLM Embedding (M) -> Reconstructed Sensory Space (N)
reconstructed_sensory = bridge.project_inverse(llm_embedding)
# 4. Verify Shapes & Energy Preservation
print(f" 1. Sensory Input (N): {list(sensory_input.shape)} (Mean Norm: {torch.norm(sensory_input, dim=-1).mean().item():.4f})")
print(f" 2. Forward LLM Embedding (M): {list(llm_embedding.shape)} (Mean Norm: {torch.norm(llm_embedding, dim=-1).mean().item():.4f})")
print(f" 3. Inverse Sensory Rec (N): {list(reconstructed_sensory.shape)} (Mean Norm: {torch.norm(reconstructed_sensory, dim=-1).mean().item():.4f})")
assert llm_embedding.shape == (2, 16, tgt_dim), f"Forward shape mismatch! Expected (2, 16, {tgt_dim})"
assert reconstructed_sensory.shape == (2, 16, src_dim), f"Inverse shape mismatch! Expected (2, 16, {src_dim})"
print(f" ✅ SUCCESS! Bi-directional round-trip ({src_dim} -> {tgt_dim} -> {src_dim}) complete!")
print("\n======================================================================")
print(" 🎉 ALL BI-DIRECTIONAL (N <-> M) TESTS PASSED 100% SUCCESSFULLY!")
print("======================================================================")
if __name__ == "__main__":
run_test()
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