Initial commit: E4B-MarkBase model integration with passing tests
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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
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MarkBase Admin
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import Metal
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// Optimized E4BLayer forward pass - accepts shared command buffer
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// Goal: Eliminate per-layer waitUntilCompleted calls
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extension E4BLayer {
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/// Optimized forward pass - batches operations with shared command buffer
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/// No waitUntilCompleted at end - caller handles that
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public func forwardOptimized(input: MTLBuffer, position: Int,
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kvCache: KVCache,
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shouldStoreKV: Bool,
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temps: ForwardTemps,
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engine: MarkBaseEngine,
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cmdBuf: MTLCommandBuffer,
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perLayerInput: MTLBuffer? = nil,
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perLayerInputOffset: Int = 0) throws {
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self.attnBuf = temps.attn
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if useMoE {
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// ── MoE path: GPU mega kernel eliminates CPU dependency ──
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// All operations use shared command buffer (NO waits)
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// Attention + MoE + post-FFN all use shared command buffer
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try attentionForwardOptimized(input: input, position: position,
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kvCache: kvCache, shouldStoreKV: shouldStoreKV,
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temps: temps, engine: engine, cmdBuf: cmdBuf)
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try moeForwardOptimized(input: input, ns: temps.ns, temps: temps,
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cmdBuf: cmdBuf, engine: engine)
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try postFfnForwardOptimized(input: input, temps: temps, engine: engine,
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cmdBuf: cmdBuf,
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perLayerInput: perLayerInput,
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perLayerInputOffset: perLayerInputOffset)
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if layerScalar != 1.0 {
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try eltwiseAddScaled(engine: engine, cmdBuf: cmdBuf,
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a: input, scaleA: layerScalar,
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b: input, scaleB: 0,
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output: input, count: config.hiddenSize)
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}
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// NO waitUntilCompleted - mega kernel does ALL work on GPU!
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} else {
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// ── Dense path: all operations in shared command buffer (NO wait) ──
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try attentionForwardOptimized(input: input, position: position,
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kvCache: kvCache, shouldStoreKV: shouldStoreKV,
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temps: temps, engine: engine, cmdBuf: cmdBuf)
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// FFN: gate+up fused → down → residual
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try fusedGateUp(engine: engine, cmdBuf: cmdBuf,
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input: temps.ns, output: temps.gate)
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try quantizedMatmul(engine: engine, cmdBuf: cmdBuf,
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input: temps.gate, weights: downProj, output: temps.h)
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try eltwiseAdd(engine: engine, cmdBuf: cmdBuf,
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a: input, b: temps.h,
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output: input, count: config.hiddenSize)
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try postFfnForwardOptimized(input: input, temps: temps, engine: engine,
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cmdBuf: cmdBuf,
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perLayerInput: perLayerInput,
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perLayerInputOffset: perLayerInputOffset)
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if layerScalar != 1.0 {
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try eltwiseAddScaled(engine: engine, cmdBuf: cmdBuf,
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a: input, scaleA: layerScalar,
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b: input, scaleB: 0,
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output: input, count: config.hiddenSize)
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}
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// NO waitUntilCompleted - caller handles that!
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}
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}
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// ── Optimized attention forward (reuses existing functions) ──
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private func attentionForwardOptimized(input: MTLBuffer, position: Int,
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kvCache: KVCache,
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shouldStoreKV: Bool,
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temps: ForwardTemps,
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engine: MarkBaseEngine,
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cmdBuf: MTLCommandBuffer) throws {
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// Same logic as attentionForward, but using passed cmdBuf
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// Steps 1-13 from original implementation
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// ── 1. input_layernorm(x) → temps.attnH ──
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try rmsNorm(engine: engine, cmdBuf: cmdBuf,
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input: input, weight: inputLayernorm,
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output: temps.attnH, count: config.hiddenSize, eps: rmsNormEps)
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// ── 2. Q = q_proj(temps.attnH) → temps.q ──
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try quantizedMatmul(engine: engine, cmdBuf: cmdBuf,
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input: temps.attnH, weights: qProj, output: temps.q)
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// ── 3. Q = q_norm(Q) → ns (per-head RMSNorm) ──
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try groupedRmsNorm(engine: engine, cmdBuf: cmdBuf,
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input: temps.q, weight: qNorm,
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output: temps.ns,
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count: config.nHeads * config.headDim,
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groupSize: config.headDim, eps: rmsNormEps)
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// ── 4. RoPE(Q) on ns ──
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try applyRoPEQ(engine: engine, cmdBuf: cmdBuf,
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q: temps.ns, position: position)
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// ── 5. K,V projections ──
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try quantizedMatmul(engine: engine, cmdBuf: cmdBuf,
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input: temps.attnH, weights: kProj, output: temps.k)
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if let vp = vProj {
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try quantizedMatmul(engine: engine, cmdBuf: cmdBuf,
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input: temps.attnH, weights: vp, output: temps.v)
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} else if kEqualsV {
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let blit = cmdBuf.makeBlitCommandEncoder()!
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let copyBytes = config.nKvHeads * config.headDim * MemoryLayout<Float>.stride
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blit.copy(from: temps.k, sourceOffset: 0,
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to: temps.v, destinationOffset: 0,
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size: copyBytes)
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blit.endEncoding()
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}
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// ── 6. K,V norms ──
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try groupedRmsNorm(engine: engine, cmdBuf: cmdBuf,
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input: temps.k, weight: kNorm,
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output: temps.up,
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count: config.nKvHeads * config.headDim,
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groupSize: config.headDim, eps: rmsNormEps)
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if let vn = vNorm {
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try groupedRmsNorm(engine: engine, cmdBuf: cmdBuf,
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input: temps.v, weight: vn,
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output: temps.gate,
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count: config.nKvHeads * config.headDim,
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groupSize: config.headDim, eps: rmsNormEps)
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}
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// ── 7. RoPE(K) ──
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try applyRoPEK(engine: engine, cmdBuf: cmdBuf,
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k: temps.up, position: position)
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// ── 8. Store K,V ──
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if shouldStoreKV {
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let valueBuf = vNorm != nil ? temps.gate : temps.v
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kvCache.store(key: temps.up, keySrcOffset: 0,
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value: valueBuf, valueSrcOffset: 0,
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position: position, commandBuffer: cmdBuf)
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}
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// ── 9. Attention ──
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let curK = temps.up
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let curV = vNorm != nil ? temps.gate : temps.v
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if config.isSliding {
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if shouldStoreKV {
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try slidingAttention(engine: engine, cmdBuf: cmdBuf,
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q: temps.ns, cache: kvCache, position: position)
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} else {
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try slidingAttentionWithCurrent(engine: engine, cmdBuf: cmdBuf,
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q: temps.ns, cache: kvCache,
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curK: curK, curV: curV,
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position: position)
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}
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} else {
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if shouldStoreKV {
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try fullAttention(engine: engine, cmdBuf: cmdBuf,
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q: temps.ns, cache: kvCache, position: position)
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} else {
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try fullAttentionWithCurrent(engine: engine, cmdBuf: cmdBuf,
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q: temps.ns, cache: kvCache,
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curK: curK, curV: curV,
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position: position)
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}
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}
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// ── 10. O projection ──
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try quantizedMatmul(engine: engine, cmdBuf: cmdBuf,
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input: temps.attn, weights: oProj, output: temps.attnH)
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// ── 11. Residual 1 ──
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try eltwiseAdd(engine: engine, cmdBuf: cmdBuf,
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a: input, b: temps.attnH,
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output: input, count: config.hiddenSize)
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// ── 12. post_attention_layernorm → temps.attnH ──
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try rmsNorm(engine: engine, cmdBuf: cmdBuf,
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input: input, weight: postAttentionLayernorm,
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output: temps.attnH, count: config.hiddenSize, eps: rmsNormEps)
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// ── 13. pre_feedforward_layernorm → ns ──
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try rmsNorm(engine: engine, cmdBuf: cmdBuf,
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input: temps.attnH, weight: preFeedforwardLayernorm,
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output: temps.ns, count: config.hiddenSize, eps: rmsNormEps)
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}
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// ── Optimized MoE forward ──
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private func moeForwardOptimized(input: MTLBuffer, ns: MTLBuffer, temps: ForwardTemps,
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cmdBuf: MTLCommandBuffer, engine: MarkBaseEngine) throws {
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// Call existing moeForward with shared cmdBuf
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try moeForward(input: input, ns: ns, temps: temps,
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cmdBuf: cmdBuf, engine: engine)
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}
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// ── Optimized post-FFN forward ──
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private func postFfnForwardOptimized(input: MTLBuffer, temps: ForwardTemps,
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engine: MarkBaseEngine,
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cmdBuf: MTLCommandBuffer,
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perLayerInput: MTLBuffer?,
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perLayerInputOffset: Int) throws {
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// Duplicate logic from postFfnForward (it's private in E4BLayer)
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// ── 17. post_feedforward_layernorm → temps.h ──
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try rmsNorm(engine: engine, cmdBuf: cmdBuf,
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input: input, weight: postFeedforwardLayernorm,
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output: temps.h, count: config.hiddenSize, eps: rmsNormEps)
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// ── 18. Per-layer gating (optional) ──
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if let pg = perLayerGate, let pp = perLayerProjection, let pl = perLayerInput {
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try quantizedMatmul(engine: engine, cmdBuf: cmdBuf,
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input: temps.h, weights: pg,
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output: temps.gating)
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try gelu(engine: engine, cmdBuf: cmdBuf,
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input: temps.gating, output: temps.gating, count: 256)
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try eltwiseMul(engine: engine, cmdBuf: cmdBuf,
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a: temps.gating, aOffset: 0,
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b: pl, bOffset: perLayerInputOffset,
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output: temps.gating, outputOffset: 0,
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count: 256)
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try quantizedMatmul(engine: engine, cmdBuf: cmdBuf,
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input: temps.gating, weights: pp,
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output: temps.h)
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if let ppn = postPerLayerInputNorm {
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try rmsNorm(engine: engine, cmdBuf: cmdBuf,
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input: temps.h, weight: ppn,
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output: temps.h, count: config.hiddenSize, eps: rmsNormEps)
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}
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try eltwiseAddScaled(engine: engine, cmdBuf: cmdBuf,
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a: temps.h, scaleA: 1.0,
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b: temps.h, scaleB: 0.0,
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output: input, count: config.hiddenSize)
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} else {
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try eltwiseAddScaled(engine: engine, cmdBuf: cmdBuf,
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a: temps.h, scaleA: 1.0,
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b: temps.h, scaleB: 0.0,
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output: input, count: config.hiddenSize)
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}
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}
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}
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