MarkBase Admin
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- E4B-MarkBase model (42 layers, 4.4GB) loaded successfully - All Phase 1-6 tests passed (model loading, forward pass, vision/audio towers, token generation, performance) - All stress tests passed (5/5 in 127.6s) - Concurrent inference - Memory stress (67.5 tok/s, 0 NaN) - Continuous generation - Batch processing - Long-running stability - Swift Metal inference engine with multimodal support
85 lines
4.2 KiB
Swift
85 lines
4.2 KiB
Swift
import XCTest
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@testable import MarkBase
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final class PerformanceAnalysisTest: XCTestCase {
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func testMetalOperationCount() throws {
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print("\n═══════════════════════════════════════════════════════════════════")
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print(" Metal Operation Count Analysis")
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print("═══════════════════════════════════════════════════════════════════\n")
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let modelDir = "/Users/accusys/MarkBaseEngine/models/E4B-MarkBase"
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let engine = try MarkBaseEngine(autoCompile: true)
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let textModel = try E4BModel(modelDir: modelDir, engine: engine, maxContextLength: 256)
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print("Model: \(textModel.numHiddenLayers) layers")
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print("\nPer-layer operations (estimated):")
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// Count operations per layer (dense path)
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let opsPerLayer = [
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"1. input_layernorm (rmsNorm)",
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"2. q_proj (quantizedMatmul)",
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"3. q_norm (groupedRmsNorm)",
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"4. RoPE Q (applyRoPEQ)",
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"5. k_proj (quantizedMatmul)",
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"6. k_norm (groupedRmsNorm)",
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"7. RoPE K (applyRoPEK)",
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"8. v_proj (quantizedMatmul or blit)",
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"9. v_norm (groupedRmsNorm if present)",
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"10. KV cache store",
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"11. Attention (sliding or full)",
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"12. o_proj (quantizedMatmul)",
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"13. Residual add (eltwiseAdd)",
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"14. post_attention_layernorm (rmsNorm)",
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"15. pre_feedforward_layernorm (rmsNorm)",
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"16. gate+up fused (fusedGateUp)",
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"17. down_proj (quantizedMatmul)",
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"18. Residual add (eltwiseAdd)",
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"19. post_feedforward_layernorm (rmsNorm)",
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"20. Per-layer gating (optional, 4-5 ops)"
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]
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for op in opsPerLayer {
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print(" \(op)")
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}
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let numOps = opsPerLayer.count
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print("\nTotal ops per layer: ~\(numOps)")
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print("Total ops per forward: ~\(numOps * textModel.numHiddenLayers)")
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// Additional embedding/lm head ops
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print("\nEmbedding phase:")
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print(" 1. dequantize embedding")
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print(" 2. embedding scale")
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print(" 3. dequantize per-layer embedding")
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print(" 4. per-layer scale")
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print(" 5-10. per-layer projection (matmul, scale, norm, add, scale)")
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print("\nLM head phase:")
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print(" 11. final norm")
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print(" 12. lm head (quantizedMatmul)")
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print(" 13. logits scaling (if needed)")
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print(" 14. logit softcapping")
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let embedOps = 10
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let lmOps = 4
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let totalOps = embedOps + numOps * textModel.numHiddenLayers + lmOps
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print("\n═══════════════════════════════════════════════════════════════════")
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print("Estimated total Metal operations per forward pass:")
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print(" Embedding: \(embedOps)")
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print(" Layers: \(numOps) × \(textModel.numHiddenLayers) = \(numOps * textModel.numHiddenLayers)")
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print(" LM head: \(lmOps)")
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print(" Total: ~\(totalOps)")
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print("═══════════════════════════════════════════════════════════════════\n")
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print("Optimization analysis:")
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print(" Original: \(totalOps) operations in \(textModel.numHiddenLayers) command buffers")
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print(" Optimized: \(totalOps) operations in 1 command buffer")
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print(" Expected: reduce \(textModel.numHiddenLayers) → 1 waits")
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print(" But: Each Metal operation has kernel launch overhead (~0.1-0.5ms)")
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print(" Total overhead: \(totalOps) × 0.2ms = \(Double(totalOps) * 0.2)ms")
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print(" This explains why we only see 4x instead of 42x!")
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print(" The bottleneck is kernel dispatch overhead, not waitUntilCompleted")
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}
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} |