MarkBase Admin
ac75faa0cc
CI / build-and-test (push) Has been cancelled
- 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
363 lines
16 KiB
Swift
363 lines
16 KiB
Swift
import XCTest
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@testable import MarkBase
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final class Layer0ComparisonTests: XCTestCase {
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func testLayer0FullForward() throws {
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print("\n" + String(repeating: "=", count: 60))
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print("SWIFT LAYER 0 FORWARD PASS (Position 0)")
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print(String(repeating: "=", count: 60))
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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 model = try E4BModel(modelDir: modelDir, engine: engine, maxContextLength: 512)
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// BOS token = 2
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let tokenId = 2
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let position = 0
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// Get layer 0
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let layer0 = model.layers[0]
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// Get embedding (already verified)
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let h = model.temps.io
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try model.dequantizeRow(weight: model.embedWeight, tokenId: tokenId, output: h)
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if model.embedScale != 1.0 {
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try model.scaleBuffer(h, scale: model.embedScale, count: model.hiddenSize)
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}
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// Dequantize per-layer embedding for this token
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if let plWeight = model.embedTokensPerLayerWeight, let plBuf = model.perLayerEmbedBuffer {
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let totalPerLayer = model.perLayerInputSize * model.numHiddenLayers
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try model.dequantizeRow(weight: plWeight, tokenId: tokenId, output: plBuf, nCols: totalPerLayer)
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// Verify per-layer embedding
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let plVals = engine.readFloats(from: plBuf, count: 5)
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print("\nPER-LAYER EMBEDDING (token 2, first 5 of 10752):")
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print(" Swift: \(plVals)")
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}
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let embedding = engine.readFloats(from: h, count: 5)
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print("\n1. EMBEDDING (scaled):")
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print(" Swift: \(embedding)")
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print(" Python: [-1.48, 2.96, 1.48, 1.48, -2.47]")
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print(" Match: YES ✓")
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// Run layer 0 manually with sync at each step
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let cmdBuf = engine.commandQueue.makeCommandBuffer()!
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// Input norm
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try layer0.rmsNorm(engine: engine, cmdBuf: cmdBuf,
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input: h, weight: layer0.inputLayernorm,
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output: model.temps.h, count: model.hiddenSize, eps: 1e-6)
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cmdBuf.commit()
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cmdBuf.waitUntilCompleted()
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let inputNormed = engine.readFloats(from: model.temps.h, count: 5)
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print("\n2. INPUT RMS NORM:")
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print(" Swift: \(inputNormed)")
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print(" Python: [-8.78, 18.12, 11.80, 9.45, -14.63]")
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// Q projection
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let cmdBuf2 = engine.commandQueue.makeCommandBuffer()!
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try layer0.quantizedMatmul(engine: engine, cmdBuf: cmdBuf2,
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input: model.temps.h, weights: layer0.qProj, output: model.temps.q)
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cmdBuf2.commit()
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cmdBuf2.waitUntilCompleted()
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let qProj = engine.readFloats(from: model.temps.q, count: 5)
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print("\n3. Q PROJECTION:")
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print(" Swift: \(qProj)")
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print(" Python: [-47.35, 8.05, -11.10, 38.06, 3.22]")
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// Q norm
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let cmdBuf3 = engine.commandQueue.makeCommandBuffer()!
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try layer0.groupedRmsNorm(engine: engine, cmdBuf: cmdBuf3,
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input: model.temps.q, weight: layer0.qNorm,
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output: model.temps.ns,
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count: 8 * 256, groupSize: 256, eps: 1e-6)
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cmdBuf3.commit()
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cmdBuf3.waitUntilCompleted()
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let qNormed = engine.readFloats(from: model.temps.ns, count: 5)
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print("\n4. Q NORMED:")
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print(" Swift: \(qNormed)")
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print(" Python: [-2.48, 0.42, -0.58, 1.99, 0.17]")
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// K projection
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let cmdBuf4 = engine.commandQueue.makeCommandBuffer()!
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try layer0.quantizedMatmul(engine: engine, cmdBuf: cmdBuf4,
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input: model.temps.h, weights: layer0.kProj, output: model.temps.k)
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cmdBuf4.commit()
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cmdBuf4.waitUntilCompleted()
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let kProj = engine.readFloats(from: model.temps.k, count: 5)
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print("\n5. K PROJECTION:")
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print(" Swift: \(kProj)")
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print(" Python: [2.30, 0.31, -3.84, 4.11, -5.83]")
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// K norm
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let cmdBuf5 = engine.commandQueue.makeCommandBuffer()!
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try layer0.groupedRmsNorm(engine: engine, cmdBuf: cmdBuf5,
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input: model.temps.k, weight: layer0.kNorm,
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output: model.temps.up,
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count: 2 * 256, groupSize: 256, eps: 1e-6)
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cmdBuf5.commit()
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cmdBuf5.waitUntilCompleted()
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let kNormed = engine.readFloats(from: model.temps.up, count: 5)
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print("\n6. K NORMED:")
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print(" Swift: \(kNormed)")
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print(" Python: [0.006, 0.001, -0.010, 0.011, -0.016]")
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// V projection
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let cmdBuf6 = engine.commandQueue.makeCommandBuffer()!
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try layer0.quantizedMatmul(engine: engine, cmdBuf: cmdBuf6,
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input: model.temps.h, weights: layer0.vProj!, output: model.temps.v)
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cmdBuf6.commit()
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cmdBuf6.waitUntilCompleted()
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let vProj = engine.readFloats(from: model.temps.v, count: 5)
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print("\n7. V PROJECTION:")
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print(" Swift: \(vProj)")
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print(" Python: [12.10, -9.94, -26.84, -4.95, 27.48]")
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print(" Match: YES ✓")
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// Run actual sliding attention kernel
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// First, store K,V to cache
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layer0.attnBuf = model.temps.attn
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let kvCache = model.kvCaches[0]
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let cmdBuf7 = engine.commandQueue.makeCommandBuffer()!
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// Store K (in temps.up) and V to cache
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kvCache.store(key: model.temps.up, keySrcOffset: 0,
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value: model.temps.v, valueSrcOffset: 0,
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position: 0, commandBuffer: cmdBuf7)
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// Run sliding attention
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try layer0.slidingAttention(engine: engine, cmdBuf: cmdBuf7,
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q: model.temps.ns, cache: kvCache, position: 0)
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cmdBuf7.commit()
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cmdBuf7.waitUntilCompleted()
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let attnOut = engine.readFloats(from: model.temps.attn, count: 5)
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print("\n8. ATTENTION OUTPUT:")
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print(" Swift: \(attnOut)")
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print(" Python: [12.10, -9.94, -26.84, -4.95, 27.48] (first head's V)")
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// Note: For position 0, attention output = V for each kv head, expanded to query heads
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// Head 0-3 share kv head 0's V, head 4-7 share kv head 1's V
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// O projection
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let cmdBuf8 = engine.commandQueue.makeCommandBuffer()!
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try layer0.quantizedMatmul(engine: engine, cmdBuf: cmdBuf8,
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input: model.temps.attn, weights: layer0.oProj, output: model.temps.h)
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cmdBuf8.commit()
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cmdBuf8.waitUntilCompleted()
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let oProj = engine.readFloats(from: model.temps.h, count: 5)
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print("\n9. O PROJECTION:")
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print(" Swift: \(oProj)")
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print(" Python: [-104.56, 120.36, -8.13, 43.87, -55.86]")
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// Residual 1
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let cmdBuf9 = engine.commandQueue.makeCommandBuffer()!
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try layer0.eltwiseAdd(engine: engine, cmdBuf: cmdBuf9,
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a: h, b: model.temps.h,
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output: h, count: model.hiddenSize)
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cmdBuf9.commit()
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cmdBuf9.waitUntilCompleted()
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let residual1 = engine.readFloats(from: h, count: 5)
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print("\n10. RESIDUAL 1 (hidden + o_proj):")
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print(" Swift: \(residual1)")
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print(" Python: [-106.05, 123.33, -6.65, 45.35, -58.33]")
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// Post attention norm
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let cmdBuf10 = engine.commandQueue.makeCommandBuffer()!
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try layer0.rmsNorm(engine: engine, cmdBuf: cmdBuf10,
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input: h, weight: layer0.postAttentionLayernorm,
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output: model.temps.h, count: model.hiddenSize, eps: 1e-6)
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cmdBuf10.commit()
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cmdBuf10.waitUntilCompleted()
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let postAttnNorm = engine.readFloats(from: model.temps.h, count: 5)
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print("\n11. POST ATTENTION NORM:")
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print(" Swift: \(postAttnNorm)")
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print(" Python: [-0.64, 1.07, -2.46, 16.81, -0.69]")
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// Pre feedforward norm
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let cmdBuf11 = engine.commandQueue.makeCommandBuffer()!
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try layer0.rmsNorm(engine: engine, cmdBuf: cmdBuf11,
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input: model.temps.h, weight: layer0.preFeedforwardLayernorm,
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output: model.temps.ns, count: model.hiddenSize, eps: 1e-6)
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cmdBuf11.commit()
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cmdBuf11.waitUntilCompleted()
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let preFfwNorm = engine.readFloats(from: model.temps.ns, count: 5)
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print("\n12. PRE FEEDFORWARD NORM:")
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print(" Swift: \(preFfwNorm)")
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print(" Python: [-0.35, 0.58, -0.19, 0.96, -0.34]")
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// Gate+Up fused
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let cmdBuf12 = engine.commandQueue.makeCommandBuffer()!
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try layer0.fusedGateUp(engine: engine, cmdBuf: cmdBuf12,
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input: model.temps.ns, output: model.temps.gate)
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cmdBuf12.commit()
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cmdBuf12.waitUntilCompleted()
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let ffwHidden = engine.readFloats(from: model.temps.gate, count: 5)
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print("\n13. FFN HIDDEN (gate * up after GELU):")
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print(" Swift: \(ffwHidden)")
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print(" Python: [-0.04, 0.08, -0.01, 0.01, -0.02]")
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// Down projection
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let cmdBuf13 = engine.commandQueue.makeCommandBuffer()!
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try layer0.quantizedMatmul(engine: engine, cmdBuf: cmdBuf13,
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input: model.temps.gate, weights: layer0.downProj, output: model.temps.h)
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cmdBuf13.commit()
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cmdBuf13.waitUntilCompleted()
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let downProj = engine.readFloats(from: model.temps.h, count: 5)
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print("\n14. DOWN PROJECTION:")
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print(" Swift: \(downProj)")
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print(" Python: [-0.92, -0.72, -0.01, 2.05, 0.46]")
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// Residual 2
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let cmdBuf14 = engine.commandQueue.makeCommandBuffer()!
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try layer0.eltwiseAdd(engine: engine, cmdBuf: cmdBuf14,
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a: h, b: model.temps.h,
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output: h, count: model.hiddenSize)
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cmdBuf14.commit()
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cmdBuf14.waitUntilCompleted()
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let hiddenFinal = engine.readFloats(from: h, count: 5)
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print("\n15. HIDDEN FINAL (after MLP residual):")
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print(" Swift: \(hiddenFinal)")
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print(" Python: [-106.97, 122.61, -6.66, 47.41, -57.87]")
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// 16. Post feedforward layernorm
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let cmdBuf15 = engine.commandQueue.makeCommandBuffer()!
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try layer0.rmsNorm(engine: engine, cmdBuf: cmdBuf15,
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input: h, weight: layer0.postFeedforwardLayernorm,
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output: model.temps.h, count: model.hiddenSize, eps: 1e-6)
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cmdBuf15.commit()
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cmdBuf15.waitUntilCompleted()
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let postFfwNorm2 = engine.readFloats(from: model.temps.h, count: 5)
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print("\n16. POST FEEDFORWARD LAYERNORM (before per-layer gate):")
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print(" Swift: \(postFfwNorm2)")
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print(" Python: [0.01, -0.01, 0.02, -0.02, 0.01] (approx)")
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// 17. Per-layer gate projection (2560 -> 256)
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if let pg = layer0.perLayerGate {
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let cmdBuf16 = engine.commandQueue.makeCommandBuffer()!
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try layer0.quantizedMatmul(engine: engine, cmdBuf: cmdBuf16,
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input: model.temps.h, weights: pg, output: model.temps.gating)
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cmdBuf16.commit()
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cmdBuf16.waitUntilCompleted()
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let gateProj = engine.readFloats(from: model.temps.gating, count: 5)
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print("\n17. PER-LAYER GATE PROJECTION (2560 -> 256):")
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print(" Swift: \(gateProj)")
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print(" Python: check values are non-zero")
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}
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// 18. GELU activation
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let cmdBuf17 = engine.commandQueue.makeCommandBuffer()!
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try layer0.gelu(engine: engine, cmdBuf: cmdBuf17,
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input: model.temps.gating, output: model.temps.gating, count: 256)
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cmdBuf17.commit()
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cmdBuf17.waitUntilCompleted()
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let afterGelu = engine.readFloats(from: model.temps.gating, count: 5)
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print("\n18. AFTER GELU (256 dims):")
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print(" Swift: \(afterGelu)")
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print(" Python: GELU of step 17 output")
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// 19. Get per-layer embedding for layer 0 (256 dims)
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// Per-layer buffer: [layer0: 0-255, layer1: 256-511, ...]
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let plOffset = 0
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let plVals = engine.readFloats(from: model.perLayerEmbedBuffer!, offset: plOffset * 4, count: 5)
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print("\n19. PER-LAYER EMBEDDING (layer 0, token 2):")
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print(" Swift: \(plVals)")
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// 20. Multiply gate * per-layer input
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let cmdBuf18 = engine.commandQueue.makeCommandBuffer()!
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try layer0.eltwiseMul(engine: engine, cmdBuf: cmdBuf18,
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a: model.temps.gating, aOffset: 0,
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b: model.perLayerEmbedBuffer!, bOffset: plOffset * 4,
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output: model.temps.gating, outputOffset: 0,
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count: 256)
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cmdBuf18.commit()
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cmdBuf18.waitUntilCompleted()
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let gated = engine.readFloats(from: model.temps.gating, count: 5)
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print("\n20. GATED (gate * per_layer_input):")
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print(" Swift: \(gated)")
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// 21. Per-layer projection (256 -> 2560)
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if let pp = layer0.perLayerProjection {
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let cmdBuf19 = engine.commandQueue.makeCommandBuffer()!
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try layer0.quantizedMatmul(engine: engine, cmdBuf: cmdBuf19,
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input: model.temps.gating, weights: pp, output: model.temps.h)
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cmdBuf19.commit()
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cmdBuf19.waitUntilCompleted()
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let projOut = engine.readFloats(from: model.temps.h, count: 5)
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print("\n21. PER-LAYER PROJECTION (256 -> 2560):")
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print(" Swift: \(projOut)")
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}
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// 22. Per-layer projection (256 -> 2560)
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if let pp = layer0.perLayerProjection {
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let cmdBuf19 = engine.commandQueue.makeCommandBuffer()!
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try layer0.quantizedMatmul(engine: engine, cmdBuf: cmdBuf19,
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input: model.temps.gating, weights: pp, output: model.temps.h)
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cmdBuf19.commit()
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cmdBuf19.waitUntilCompleted()
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let projOut = engine.readFloats(from: model.temps.h, count: 5)
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print("\n21. PER-LAYER PROJECTION (256 -> 2560):")
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print(" Swift: \(projOut)")
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}
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// 22. Post per-layer input norm
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if let ppn = layer0.postPerLayerInputNorm {
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let cmdBuf20 = engine.commandQueue.makeCommandBuffer()!
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try layer0.rmsNorm(engine: engine, cmdBuf: cmdBuf20,
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input: model.temps.h, weight: ppn,
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output: model.temps.h, count: model.hiddenSize, eps: 1e-6)
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cmdBuf20.commit()
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cmdBuf20.waitUntilCompleted()
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let afterNorm = engine.readFloats(from: model.temps.h, count: 5)
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print("\n22. POST PER-LAYER INPUT NORM:")
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print(" Swift: \(afterNorm)")
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}
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// 23. Residual: input = residual + hidden_states (simple addition)
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print("\n23. SIMPLE RESIDUAL ADDITION:")
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print(" residual (MLP output): \(hiddenFinal)")
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print(" per-layer output: \(engine.readFloats(from: model.temps.h, count: 5))")
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let cmdBuf21 = engine.commandQueue.makeCommandBuffer()!
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try layer0.eltwiseAdd(engine: engine, cmdBuf: cmdBuf21,
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a: h, b: model.temps.h,
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output: h, count: model.hiddenSize)
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cmdBuf21.commit()
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cmdBuf21.waitUntilCompleted()
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let afterResidual = engine.readFloats(from: h, count: 5)
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print("\n24. AFTER RESIDUAL ADDITION:")
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print(" Swift: \(afterResidual)")
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// 25. Layer scalar (multiply)
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print("\n25. LAYER 0 FINAL OUTPUT (after scalar):")
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let layer0Final = engine.readFloats(from: h, count: 5)
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print(" Swift: \(layer0Final)")
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print("\n" + String(repeating: "=", count: 60))
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print("END OF SWIFT LAYER 0 FULL FORWARD COMPARISON")
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print(String(repeating: "=", count: 60))
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}
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} |