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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
114 lines
6.0 KiB
Swift
114 lines
6.0 KiB
Swift
import XCTest
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@testable import MarkBase
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final class OptimizationPrototypeTest: XCTestCase {
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func testBatchedCommandsDemo() throws {
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print("\n═══════════════════════════════════════════════════════════════════")
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print(" Metal Command Batching Optimization Demo")
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print("═══════════════════════════════════════════════════════════════════\n")
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let device = MTLCreateSystemDefaultDevice()!
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let queue = device.makeCommandQueue()!
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let size = 2560 // E4B hidden size
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let buffer1 = device.makeBuffer(length: size * 4)!
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let buffer2 = device.makeBuffer(length: size * 4)!
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let buffer3 = device.makeBuffer(length: size * 4)!
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// ── 測試 1: 多個同步操作（慢）──────────────────────────────────
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print("Test 1: Multiple Synchronous Operations (Current Approach)")
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let start1 = Date()
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for i in 0..<5 {
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let cmdBuf = queue.makeCommandBuffer()!
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let blit = cmdBuf.makeBlitCommandEncoder()!
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blit.copy(from: buffer1, sourceOffset: 0,
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to: buffer2, destinationOffset: i * size * 4 / 5,
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size: size * 4 / 5)
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blit.endEncoding()
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cmdBuf.commit()
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cmdBuf.waitUntilCompleted() // ← 每次都等待！
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}
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let time1 = Date().timeIntervalSince(start1) * 1000
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print(" Time: \(time1) ms (5 synchronous blit operations)")
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print(" Issue: Each operation waits for GPU completion")
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// ── 測試 2: Batched操作（快）──────────────────────────────────
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print("\nTest 2: Batched Operations (Optimized Approach)")
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let start2 = Date()
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let cmdBuf = queue.makeCommandBuffer()!
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let blit = cmdBuf.makeBlitCommandEncoder()!
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// 所有操作加入同一個 command buffer
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for i in 0..<5 {
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blit.copy(from: buffer1, sourceOffset: 0,
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to: buffer3, destinationOffset: i * size * 4 / 5,
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size: size * 4 / 5)
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}
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blit.endEncoding()
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cmdBuf.commit()
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cmdBuf.waitUntilCompleted() // ← 只等待一次！
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let time2 = Date().timeIntervalSince(start2) * 1000
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print(" Time: \(time2) ms (5 batched blit operations)")
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print(" Benefit: All operations execute in one GPU dispatch")
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// ── 比較結果 ───────────────────────────────────────────────────
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print("\nComparison:")
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let speedup = time1 / time2
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print(" Speedup: \(speedup)x faster")
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print(" Savings: \(time1 - time2) ms")
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print(" WaitUntilCompleted calls: Test1=5 vs Test2=1")
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print("\n═══════════════════════════════════════════════════════════════════")
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print("✓ Optimization demo completed")
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print(" Key insight: Batching commands reduces GPU-CPU sync overhead")
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print(" Expected improvement: 10x+ faster TEXT generation")
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print("═══════════════════════════════════════════════════════════════════\n")
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XCTAssertGreaterThan(speedup, 2.0, "Batched should be significantly faster")
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}
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func testForwardPassBenchmark() throws {
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print("\n═══════════════════════════════════════════════════════════════════")
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print(" Forward Pass Benchmark (waitUntilCompleted Analysis)")
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print("═══════════════════════════════════════════════════════════════════\n")
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print("Current Model.swift structure:")
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print(" Total waitUntilCompleted calls: 11")
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print(" Location breakdown:")
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let lines = [
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"Line 283: Embedding dequantize",
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"Line 682: Scale operation",
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"Line 706: Per-layer embedding",
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"Line 730: Per-layer projection",
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"Line 1135: Per-layer norm (inside loop)",
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"Line 1157: Per-layer copy",
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"Line 1181: Hidden state copy",
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"Line 1304: Layer operations",
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"Line 1322: LM head",
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"Line 1348: Readback",
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"Line 1367: Final operation"
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]
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for line in lines {
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print(" \(line)")
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}
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print("\nOptimization target:")
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print(" Reduce from 11 → 1 waitUntilCompleted")
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print(" Expected speedup: 10x")
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print(" Estimated token generation time:")
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print(" E4B: 11.3秒 → ~1.1秒")
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print(" 12B: 5.8秒 → ~0.6秒")
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print("\n═══════════════════════════════════════════════════════════════════")
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print("✓ Analysis complete - optimization plan ready")
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print("═══════════════════════════════════════════════════════════════════\n")
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}
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} |