File size: 3,120 Bytes
cab2ef1 | 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 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | #!/usr/bin/env python3
"""Integration Test for StreamingMoELayer with Quantized Weights on Apple Silicon MLX."""
import time
from pathlib import Path
import mlx.core as mx
from expert_registry import SafetensorsIndex
from moe_stream_layer import StreamingMoELayer
def run_test():
model_dir = Path("/Volumes/NVMe2TB/AI-LLM/LLMjp/Pyrite/pyrite-mlx/results/models/deepseek_coder_v2_lite_pyrite_final_mlx")
shard1 = model_dir / "model-00001-of-00002.safetensors"
print("=" * 70)
print(" Streaming MoE Layer Integration Test (Layer 1)")
print("=" * 70)
idx = SafetensorsIndex(shard1)
idx.open()
# 1. Load resident gate & shared expert weights
print("Loading resident weights (Gate + Shared Experts)...")
gate_w = idx.get_tensor("model.layers.1.mlp.gate.weight")
shared = {
"gate_w": idx.get_tensor("model.layers.1.mlp.shared_experts.gate_proj.weight"),
"gate_s": idx.get_tensor("model.layers.1.mlp.shared_experts.gate_proj.scales"),
"gate_b": idx.get_tensor("model.layers.1.mlp.shared_experts.gate_proj.biases"),
"up_w": idx.get_tensor("model.layers.1.mlp.shared_experts.up_proj.weight"),
"up_s": idx.get_tensor("model.layers.1.mlp.shared_experts.up_proj.scales"),
"up_b": idx.get_tensor("model.layers.1.mlp.shared_experts.up_proj.biases"),
"down_w": idx.get_tensor("model.layers.1.mlp.shared_experts.down_proj.weight"),
"down_s": idx.get_tensor("model.layers.1.mlp.shared_experts.down_proj.scales"),
"down_b": idx.get_tensor("model.layers.1.mlp.shared_experts.down_proj.biases"),
}
print(f"Gate weight shape: {gate_w.shape}")
print(f"Shared experts: gate_w={shared['gate_w'].shape}, down_w={shared['down_w'].shape}")
# 2. Instantiate StreamingMoELayer with LRU cache (max 8 experts in RAM)
layer = StreamingMoELayer(
layer_idx=1,
gate_weight=gate_w,
shared_experts=shared,
index_reader=idx,
top_k=6,
max_cache_experts=8,
)
# 3. Simulate forward passes with synthetic activations
hidden_dim = 2048
seq_len = 4
x = mx.random.normal((1, seq_len, hidden_dim)).astype(mx.bfloat16)
print("\n--- Token Step 1 (Cold Start: Streaming from NVMe SSD) ---")
t0 = time.perf_counter()
out1 = layer(x)
mx.eval(out1)
elapsed_cold = (time.perf_counter() - t0) * 1000
print(f"Step 1 Output shape: {out1.shape}")
print(f"Cold Start Time: {elapsed_cold:.2f} ms")
print(f"Current Cached Experts in RAM: {list(layer.cache.cache.keys())}")
print("\n--- Token Step 2 (Subsequent Token: Testing Cache Hits) ---")
t0 = time.perf_counter()
out2 = layer(x)
mx.eval(out2)
elapsed_warm = (time.perf_counter() - t0) * 1000
print(f"Warm Step Time: {elapsed_warm:.2f} ms")
print(f"Current Cached Experts in RAM: {list(layer.cache.cache.keys())}")
idx.close()
print("\n" + "=" * 70)
print(" Test Passed! Dynamic streaming & LRU caching verified on Apple Metal! ")
print("=" * 70)
if __name__ == "__main__":
run_test()
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