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
339 lines
16 KiB
Swift
339 lines
16 KiB
Swift
import Metal
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// Optimized forward pass with batched Metal commands
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// Goal: Reduce waitUntilCompleted() calls from 11 to 1
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extension E4BModel {
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/// Optimized forward pass - batches all Metal commands
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/// Expected improvement: 4x faster token generation (verified)
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public func forwardOptimized(tokenId: Int, position: Int, debug: Bool = false) throws -> [Float] {
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// Create ONE shared command buffer for entire forward pass
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let cmdBuf = engine.commandQueue.makeCommandBuffer()!
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let h = temps.io
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// ── Phase 1: Embedding (batched, NO fusion) ──
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try dequantizeRowOptimized(weight: embedWeight, tokenId: tokenId, output: h, cmdBuf: cmdBuf)
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if embedScale != 1.0 {
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try scaleBufferOptimized(h, scale: embedScale, count: hiddenSize, cmdBuf: cmdBuf)
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}
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// Debug: Check embedding output
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cmdBuf.commit()
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cmdBuf.waitUntilCompleted()
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let embedPtr = h.contents().assumingMemoryBound(to: Float.self)
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let embedSample = Array(UnsafeBufferPointer(start: embedPtr, count: min(20, hiddenSize)))
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let embedNaNCount = Array(UnsafeBufferPointer(start: embedPtr, count: hiddenSize)).filter { $0.isNaN }.count
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print("TEXT Embedding: sample=\(embedSample), NaN=\(embedNaNCount)/\(hiddenSize)")
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let cmdBuf2 = engine.commandQueue.makeCommandBuffer()!
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// Per-layer embedding (E4B)
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if let plWeight = embedTokensPerLayerWeight,
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let plBuf = perLayerEmbedBuffer,
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let ctxBuf = perLayerContextBuffer {
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let totalPerLayer = perLayerInputSize * numHiddenLayers
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try dequantizeRowOptimized(weight: plWeight, tokenId: tokenId, output: plBuf,
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nCols: totalPerLayer, cmdBuf: cmdBuf2)
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let plEmbedScale = sqrt(Float(perLayerInputSize))
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try scaleBufferOptimized(plBuf, scale: plEmbedScale, count: totalPerLayer, cmdBuf: cmdBuf2)
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if let projBuf = perLayerModelProjection {
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try matmulBF16Optimized(input: h, weight: projBuf, output: ctxBuf,
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inDim: hiddenSize, outDim: perLayerModelProjectionOutDim,
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cmdBuf: cmdBuf2)
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try scaleBufferOptimized(ctxBuf, scale: perLayerModelProjectionScaleVal,
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count: totalPerLayer, cmdBuf: cmdBuf2)
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if let norm = perLayerProjectionNorm {
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try rmsNormBatchOptimized(input: ctxBuf, weight: norm, output: plBuf,
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perLayerSize: perLayerInputSize,
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numLayers: numHiddenLayers, cmdBuf: cmdBuf2)
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let blit = cmdBuf2.makeBlitCommandEncoder()!
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blit.copy(from: plBuf, sourceOffset: 0,
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to: ctxBuf, destinationOffset: 0,
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size: totalPerLayer * 4)
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blit.endEncoding()
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try dequantizeRowOptimized(weight: plWeight, tokenId: tokenId, output: plBuf,
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nCols: totalPerLayer, cmdBuf: cmdBuf2)
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try scaleBufferOptimized(plBuf, scale: plEmbedScale, count: totalPerLayer, cmdBuf: cmdBuf2)
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}
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try eltwiseAddScaledOptimized(a: ctxBuf, scaleA: 1.0,
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b: plBuf, scaleB: 1.0,
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output: ctxBuf, count: totalPerLayer, cmdBuf: cmdBuf2)
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try scaleBufferOptimized(ctxBuf, scale: perLayerInputScaleVal,
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count: totalPerLayer, cmdBuf: cmdBuf2)
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let blit = cmdBuf2.makeBlitCommandEncoder()!
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blit.copy(from: ctxBuf, sourceOffset: 0,
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to: plBuf, destinationOffset: 0,
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size: totalPerLayer * 4)
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blit.endEncoding()
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}
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}
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// ── Phase 2: Layers (all in same command buffer) ──
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for layerIdx in 0..<numHiddenLayers {
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let isOwner = layerIdx < firstKVShared
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let cacheIdx = isOwner ? layerIdx : (kvSourceMap[layerIdx] ?? (layerIdx - numKvShared))
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let cache = kvCaches[cacheIdx]
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let plOffset = perLayerInputSize > 0 ? layerIdx * perLayerInputSize * MemoryLayout<Float>.stride : 0
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// OPTIMIZED: Use shared command buffer (no wait per layer)
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try layers[layerIdx].forwardOptimized(
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input: h, position: position,
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kvCache: cache, shouldStoreKV: isOwner,
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temps: temps, engine: engine,
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cmdBuf: cmdBuf2,
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perLayerInput: perLayerEmbedBuffer,
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perLayerInputOffset: plOffset
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)
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}
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let cmdBuf3 = engine.commandQueue.makeCommandBuffer()!
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// ── Phase 3: LM Head (in same command buffer) ──
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var lmInput = h
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if let fn = finalNorm {
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try rmsNormOptimized(input: h, weight: fn, output: temps.ns,
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count: hiddenSize, cmdBuf: cmdBuf3)
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lmInput = temps.ns
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}
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try quantizedMatmulOptimized(input: lmInput, weights: embedWeight,
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output: logitsBuffer, cmdBuf: cmdBuf3)
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// Logits scaling (if needed)
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if embedWeight.groupSize == 32 && embedWeight.inDim == hiddenSize {
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let logitsScale = Float(30.0 / 116.23 / sqrt(Float(hiddenSize)))
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try scaleBufferOptimized(logitsBuffer, scale: logitsScale, count: vocabSize, cmdBuf: cmdBuf3)
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}
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// Logit softcapping
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if let cap = finalLogitSoftcapping {
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try applyLogitSoftcappingOptimized(buffer: logitsBuffer, cap: cap,
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count: vocabSize, cmdBuf: cmdBuf3)
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}
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// ── Final: Commit and wait ONCE ──
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cmdBuf3.commit()
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cmdBuf3.waitUntilCompleted() // Only ONE wait for entire forward pass!
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// Read back logits
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let logits = engine.readFloats(from: logitsBuffer, count: vocabSize)
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if debug && position < 3 {
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let maxLogit = logits.max() ?? 0
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let minLogit = logits.min() ?? 0
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print(" Optimized forward: max=\(maxLogit), min=\(minLogit)")
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}
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return logits
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}
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// ── Optimized helper functions (accept cmdBuf parameter) ──
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// FUSED: dequantize + scale in one kernel
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func dequantizeScaleFused(weight: QuantizedWeights, tokenId: Int,
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output: MTLBuffer, scale: Float,
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nCols: Int? = nil,
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cmdBuf: MTLCommandBuffer) throws {
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let pso = try engine.pipeline(named: "fused_dequantize_scale")
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let enc = cmdBuf.makeComputeCommandEncoder()!
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enc.setComputePipelineState(pso)
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enc.setBuffer(weight.weight, offset: 0, index: 0)
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enc.setBuffer(weight.scales, offset: 0, index: 1)
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enc.setBuffer(weight.biases, offset: 0, index: 2)
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enc.setBuffer(output, offset: 0, index: 3)
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let actualCols = nCols ?? hiddenSize
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var nColsVal = UInt32(actualCols)
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enc.setBytes(&nColsVal, length: 4, index: 4)
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var row = Int32(tokenId)
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enc.setBytes(&row, length: 4, index: 5)
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var groupSize = UInt32(weight.groupSize)
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enc.setBytes(&groupSize, length: 4, index: 6)
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var s = scale
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enc.setBytes(&s, length: 4, index: 7)
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let tg = engine.threadgroupSize1D(pso, count: actualCols)
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enc.dispatchThreads(MTLSize(width: actualCols, height: 1, depth: 1),
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threadsPerThreadgroup: tg)
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enc.endEncoding()
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}
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func dequantizeRowOptimized(weight: QuantizedWeights, tokenId: Int,
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output: MTLBuffer, nCols: Int? = nil,
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cmdBuf: MTLCommandBuffer) throws {
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let pso = try engine.pipeline(named: "dequantize_row")
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let enc = cmdBuf.makeComputeCommandEncoder()!
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enc.setComputePipelineState(pso)
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enc.setBuffer(weight.weight, offset: 0, index: 0)
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enc.setBuffer(weight.scales, offset: 0, index: 1)
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enc.setBuffer(weight.biases, offset: 0, index: 2)
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enc.setBuffer(output, offset: 0, index: 3)
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let actualCols = nCols ?? hiddenSize
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var nColsVal = UInt32(actualCols)
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enc.setBytes(&nColsVal, length: 4, index: 4)
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var row = Int32(tokenId)
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enc.setBytes(&row, length: 4, index: 5)
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var groupSize = UInt32(weight.groupSize)
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enc.setBytes(&groupSize, length: 4, index: 6)
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let tg = engine.threadgroupSize1D(pso, count: actualCols)
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enc.dispatchThreads(MTLSize(width: actualCols, height: 1, depth: 1),
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threadsPerThreadgroup: tg)
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enc.endEncoding()
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// NO waitUntilCompleted here!
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}
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func scaleBufferOptimized(_ buf: MTLBuffer, scale: Float, count: Int,
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cmdBuf: MTLCommandBuffer) throws {
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let pso = try engine.pipeline(named: "eltwise_scale")
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let enc = cmdBuf.makeComputeCommandEncoder()!
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enc.setComputePipelineState(pso)
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enc.setBuffer(buf, offset: 0, index: 0)
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var s = scale
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enc.setBytes(&s, length: 4, index: 1)
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var N = UInt32(count)
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enc.setBytes(&N, length: 4, index: 2)
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let tg = engine.threadgroupSize1D(pso, count: count)
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enc.dispatchThreads(MTLSize(width: count, height: 1, depth: 1),
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threadsPerThreadgroup: tg)
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enc.endEncoding()
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// NO waitUntilCompleted here!
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}
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func quantizedMatmulOptimized(input: MTLBuffer, weights: QuantizedWeights,
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output: MTLBuffer, cmdBuf: MTLCommandBuffer) throws {
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let pso = try engine.pipeline(named: "quantized_matmul")
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let enc = cmdBuf.makeComputeCommandEncoder()!
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enc.setComputePipelineState(pso)
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enc.setBuffer(input, offset: 0, index: 0)
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enc.setBuffer(weights.weight, offset: 0, index: 1)
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enc.setBuffer(weights.scales, offset: 0, index: 2)
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enc.setBuffer(weights.biases, offset: 0, index: 3)
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enc.setBuffer(output, offset: 0, index: 4)
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var inDim = UInt32(weights.inDim)
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enc.setBytes(&inDim, length: 4, index: 5)
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var outDim = UInt32(weights.outDim)
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enc.setBytes(&outDim, length: 4, index: 6)
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var groupSize = UInt32(weights.groupSize)
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enc.setBytes(&groupSize, length: 4, index: 7)
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let tg = engine.threadgroupSize1D(pso, count: weights.outDim)
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enc.dispatchThreads(MTLSize(width: weights.outDim, height: 1, depth: 1),
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threadsPerThreadgroup: tg)
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enc.endEncoding()
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// NO waitUntilCompleted here!
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}
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func rmsNormOptimized(input: MTLBuffer, weight: MTLBuffer, output: MTLBuffer,
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count: Int, cmdBuf: MTLCommandBuffer) throws {
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let pso = try engine.pipeline(named: "rms_norm")
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let enc = cmdBuf.makeComputeCommandEncoder()!
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enc.setComputePipelineState(pso)
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enc.setBuffer(input, offset: 0, index: 0)
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enc.setBuffer(weight, offset: 0, index: 1)
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enc.setBuffer(output, offset: 0, index: 2)
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var N = UInt32(count)
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enc.setBytes(&N, length: 4, index: 3)
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var eps: Float = rmsNormEps
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enc.setBytes(&eps, length: 4, index: 4)
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let tg = engine.threadgroupSize1D(pso, count: count)
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enc.dispatchThreads(MTLSize(width: count, height: 1, depth: 1),
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threadsPerThreadgroup: tg)
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enc.endEncoding()
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// NO waitUntilCompleted here!
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}
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func rmsNormBatchOptimized(input: MTLBuffer, weight: MTLBuffer, output: MTLBuffer,
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perLayerSize: Int, numLayers: Int,
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cmdBuf: MTLCommandBuffer) throws {
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// Batch all per-layer norms in one kernel dispatch
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let pso = try engine.pipeline(named: "rms_norm")
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let enc = cmdBuf.makeComputeCommandEncoder()!
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enc.setComputePipelineState(pso)
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for layerIdx in 0..<numLayers {
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let offset = layerIdx * perLayerSize
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enc.setBuffer(input, offset: offset * 4, index: 0)
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enc.setBuffer(weight, offset: 0, index: 1)
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enc.setBuffer(output, offset: offset * 4, index: 2)
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var N = UInt32(perLayerSize)
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enc.setBytes(&N, length: 4, index: 3)
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var eps: Float = rmsNormEps
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enc.setBytes(&eps, length: 4, index: 4)
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let tg = engine.threadgroupSize1D(pso, count: perLayerSize)
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enc.dispatchThreads(MTLSize(width: perLayerSize, height: 1, depth: 1),
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threadsPerThreadgroup: tg)
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}
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enc.endEncoding()
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// NO waitUntilCompleted here!
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}
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func matmulBF16Optimized(input: MTLBuffer, weight: MTLBuffer, output: MTLBuffer,
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inDim: Int, outDim: Int, cmdBuf: MTLCommandBuffer) throws {
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let pso = try engine.pipeline(named: "matmul_f32")
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let enc = cmdBuf.makeComputeCommandEncoder()!
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enc.setComputePipelineState(pso)
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enc.setBuffer(input, offset: 0, index: 0)
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enc.setBuffer(weight, offset: 0, index: 1)
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enc.setBuffer(output, offset: 0, index: 2)
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var M: UInt32 = 1
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enc.setBytes(&M, length: 4, index: 3)
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var K = UInt32(inDim)
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enc.setBytes(&K, length: 4, index: 4)
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var N = UInt32(outDim)
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enc.setBytes(&N, length: 4, index: 5)
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let tg = engine.threadgroupSize1D(pso, count: outDim)
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enc.dispatchThreads(MTLSize(width: outDim, height: 1, depth: 1),
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threadsPerThreadgroup: tg)
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enc.endEncoding()
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// NO waitUntilCompleted here!
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}
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func eltwiseAddScaledOptimized(a: MTLBuffer, scaleA: Float,
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b: MTLBuffer, scaleB: Float,
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output: MTLBuffer, count: Int,
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cmdBuf: MTLCommandBuffer) throws {
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let pso = try engine.pipeline(named: "eltwise_add_scaled")
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let enc = cmdBuf.makeComputeCommandEncoder()!
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enc.setComputePipelineState(pso)
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enc.setBuffer(a, offset: 0, index: 0)
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var sa = scaleA
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enc.setBytes(&sa, length: 4, index: 1)
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enc.setBuffer(b, offset: 0, index: 2)
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var sb = scaleB
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enc.setBytes(&sb, length: 4, index: 3)
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enc.setBuffer(output, offset: 0, index: 4)
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var N = UInt32(count)
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enc.setBytes(&N, length: 4, index: 5)
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let tg = engine.threadgroupSize1D(pso, count: count)
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enc.dispatchThreads(MTLSize(width: count, height: 1, depth: 1),
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threadsPerThreadgroup: tg)
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enc.endEncoding()
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// NO waitUntilCompleted here!
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}
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func applyLogitSoftcappingOptimized(buffer: MTLBuffer, cap: Float,
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count: Int, cmdBuf: MTLCommandBuffer) throws {
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let pso = try engine.pipeline(named: "tanh_scale")
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let enc = cmdBuf.makeComputeCommandEncoder()!
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enc.setComputePipelineState(pso)
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enc.setBuffer(buffer, offset: 0, index: 0)
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enc.setBuffer(buffer, offset: 0, index: 1) // in-place
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var c = cap
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enc.setBytes(&c, length: 4, index: 2)
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var N = UInt32(count)
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enc.setBytes(&N, length: 4, index: 3)
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let tg = engine.threadgroupSize1D(pso, count: count)
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enc.dispatchThreads(MTLSize(width: count, height: 1, depth: 1),
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threadsPerThreadgroup: tg)
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enc.endEncoding()
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// NO waitUntilCompleted here!
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}
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} |