feat: Qwen2 support
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John Mai
263
Libraries/LLM/Qwen2.swift
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263
Libraries/LLM/Qwen2.swift
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//
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// Qwen2.swift
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// LLM
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//
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// Created by John Mai on 2024/3/3.
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//
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import Foundation
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import MLX
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import MLXNN
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// port of https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/models/qwen2.py
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private class Attention: Module {
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let args: Qwen2Configuration
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let repeats: Int
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let scale: Float
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@ModuleInfo(key: "q_proj") var wq: Linear
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@ModuleInfo(key: "k_proj") var wk: Linear
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@ModuleInfo(key: "v_proj") var wv: Linear
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@ModuleInfo(key: "o_proj") var wo: Linear
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let rope: RoPE
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public init(_ args: Qwen2Configuration) {
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self.args = args
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let dim = args.hiddenSize
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let heads = args.attentionHeads
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let kvHeads = args.kvHeads
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self.repeats = heads / kvHeads
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let headDim = args.hiddenSize / heads
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self.scale = pow(Float(headDim), -0.5)
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_wq.wrappedValue = Linear(dim, heads * headDim, bias: true)
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_wk.wrappedValue = Linear(dim, kvHeads * headDim, bias: true)
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_wv.wrappedValue = Linear(dim, kvHeads * headDim, bias: true)
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_wo.wrappedValue = Linear(heads * headDim, dim, bias: false)
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let ropeScale: Float
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if let ropeScaling = args.ropeScaling, ropeScaling["type"] == .string("linear"),
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let factor = ropeScaling["factor"]
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{
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switch factor {
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case .string:
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fatalError("ropeScaling.factor must be a float")
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case .float(let v):
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ropeScale = 1 / v
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}
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} else {
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ropeScale = 1
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}
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self.rope = RoPE(
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dimensions: headDim, traditional: args.ropeTraditional, base: args.ropeTheta,
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scale: ropeScale)
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}
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public func callAsFunction(
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_ x: MLXArray, mask: MLXArray? = nil, cache: (MLXArray, MLXArray)? = nil) -> (MLXArray, (MLXArray, MLXArray))
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{
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let (B, L) = (x.dim(0), x.dim(1))
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var queries = wq(x)
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var keys = wk(x)
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var values = wv(x)
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// prepare the queries, keys and values for the attention computation
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queries = queries.reshaped(B, L, args.attentionHeads, -1).transposed(0, 2, 1, 3)
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keys = keys.reshaped(B, L, args.kvHeads, -1).transposed(0, 2, 1, 3)
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values = values.reshaped(B, L, args.kvHeads, -1).transposed(0, 2, 1, 3)
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if repeats > 1 {
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keys = MLXArray.repeat(keys, count: repeats, axis: 1)
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values = MLXArray.repeat(values, count: repeats, axis: 1)
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}
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if let (keyCache, valueCache) = cache {
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queries = rope(queries, offset: keyCache.dim(2))
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keys = rope(keys, offset: keyCache.dim(2))
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keys = concatenated([keyCache, keys], axis: 2)
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values = concatenated([valueCache, values], axis: 2)
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} else {
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queries = rope(queries)
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keys = rope(keys)
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}
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var scores = (queries * scale).matmul(keys.transposed(0, 1, 3, 2))
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if let mask {
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scores = scores + mask
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}
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scores = softMax(scores.asType(.float32), axis: -1).asType(scores.dtype)
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let output = matmul(scores, values).transposed(0, 2, 1, 3).reshaped(B, L, -1)
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return (wo(output), (keys, values))
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}
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}
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private class MLP: Module, UnaryLayer {
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@ModuleInfo(key: "gate_proj") var gate: Linear
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@ModuleInfo(key: "down_proj") var down: Linear
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@ModuleInfo(key: "up_proj") var up: Linear
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public init(dimensions: Int, hiddenDimensions: Int) {
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_gate.wrappedValue = Linear(dimensions, hiddenDimensions, bias: false)
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_down.wrappedValue = Linear(hiddenDimensions, dimensions, bias: false)
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_up.wrappedValue = Linear(dimensions, hiddenDimensions, bias: false)
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}
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public func callAsFunction(_ x: MLXArray) -> MLXArray {
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down(silu(gate(x)) * up(x))
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}
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}
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private class TransformerBlock: Module {
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@ModuleInfo(key: "self_attn") var attention: Attention
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let mlp: MLP
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@ModuleInfo(key: "input_layernorm") var inputLayerNorm: RMSNorm
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@ModuleInfo(key: "post_attention_layernorm") var postAttentionLayerNorm: RMSNorm
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public init(_ args: Qwen2Configuration) {
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_attention.wrappedValue = Attention(args)
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self.mlp = MLP(dimensions: args.hiddenSize, hiddenDimensions: args.intermediateSize)
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_inputLayerNorm.wrappedValue = RMSNorm(
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dimensions: args.hiddenSize, eps: args.rmsNormEps)
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_postAttentionLayerNorm.wrappedValue = RMSNorm(
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dimensions: args.hiddenSize, eps: args.rmsNormEps)
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}
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public func callAsFunction(
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_ x: MLXArray, mask: MLXArray? = nil, cache: (MLXArray, MLXArray)? = nil) -> (MLXArray, (MLXArray, MLXArray))
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{
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var (r, cache) = attention(inputLayerNorm(x), mask: mask, cache: cache)
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let h = x + r
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r = mlp(postAttentionLayerNorm(h))
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let out = h + r
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return (out, cache)
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}
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}
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public class Qwen2ModelInner: Module {
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@ModuleInfo(key: "embed_tokens") var embedTokens: Embedding
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fileprivate let layers: [TransformerBlock]
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let norm: RMSNorm
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public init(_ args: Qwen2Configuration) {
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precondition(args.vocabularySize > 0)
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_embedTokens.wrappedValue = Embedding(
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embeddingCount: args.vocabularySize, dimensions: args.hiddenSize)
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self.layers = (0 ..< args.hiddenLayers)
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.map { _ in
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TransformerBlock(args)
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}
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self.norm = RMSNorm(dimensions: args.hiddenSize, eps: args.rmsNormEps)
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}
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public func callAsFunction(_ inputs: MLXArray, cache: [(MLXArray, MLXArray)]? = nil) -> (
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MLXArray, [(MLXArray, MLXArray)])
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{
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var h = embedTokens(inputs)
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var mask: MLXArray? = nil
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if h.dim(1) > 1 {
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mask = MultiHeadAttention.createAdditiveCausalMask(h.dim(1))
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mask = mask?.asType(h.dtype)
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}
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var newCache = [(MLXArray, MLXArray)]()
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for (i, layer) in layers.enumerated() {
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var cacheUpdate: (MLXArray, MLXArray)
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(h, cacheUpdate) = layer(h, mask: mask, cache: cache?[i])
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newCache.append(cacheUpdate)
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}
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return (norm(h), newCache)
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}
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}
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public class Qwen2Model: Module, LLMModel {
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public let vocabularySize: Int
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let model: Qwen2ModelInner
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@ModuleInfo(key: "lm_head") var lmHead: Linear
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public init(_ args: Qwen2Configuration) {
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self.vocabularySize = args.vocabularySize
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self.model = Qwen2ModelInner(args)
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_lmHead.wrappedValue = Linear(args.hiddenSize, args.vocabularySize, bias: false)
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}
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public func callAsFunction(_ inputs: MLXArray, cache: [(MLXArray, MLXArray)]?) -> (
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MLXArray, [(MLXArray, MLXArray)])
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{
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let (out, cache) = model(inputs, cache: cache)
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return (lmHead(out), cache)
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}
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}
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public struct Qwen2Configuration: Codable {
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var hiddenSize: Int
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var hiddenLayers: Int
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var intermediateSize: Int
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var attentionHeads: Int
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var rmsNormEps: Float
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var vocabularySize: Int
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var kvHeads: Int
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var ropeTheta: Float = 1_000_000
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var ropeTraditional: Bool = false
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var ropeScaling: [String: StringOrNumber]? = nil
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enum CodingKeys: String, CodingKey {
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case hiddenSize = "hidden_size"
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case hiddenLayers = "num_hidden_layers"
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case intermediateSize = "intermediate_size"
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case attentionHeads = "num_attention_heads"
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case rmsNormEps = "rms_norm_eps"
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case vocabularySize = "vocab_size"
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case kvHeads = "num_key_value_heads"
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case ropeTheta = "rope_theta"
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case ropeTraditional = "rope_traditional"
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case ropeScaling = "rope_scaling"
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}
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public init(from decoder: Decoder) throws {
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// custom implementation to handle optional keys with required values
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let container: KeyedDecodingContainer<Qwen2Configuration.CodingKeys> =
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try decoder.container(
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keyedBy: Qwen2Configuration.CodingKeys.self)
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self.hiddenSize = try container.decode(
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Int.self, forKey: Qwen2Configuration.CodingKeys.hiddenSize)
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self.hiddenLayers = try container.decode(
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Int.self, forKey: Qwen2Configuration.CodingKeys.hiddenLayers)
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self.intermediateSize = try container.decode(
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Int.self, forKey: Qwen2Configuration.CodingKeys.intermediateSize)
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self.attentionHeads = try container.decode(
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Int.self, forKey: Qwen2Configuration.CodingKeys.attentionHeads)
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self.rmsNormEps = try container.decode(
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Float.self, forKey: Qwen2Configuration.CodingKeys.rmsNormEps)
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self.vocabularySize = try container.decode(
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Int.self, forKey: Qwen2Configuration.CodingKeys.vocabularySize)
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self.kvHeads = try container.decode(Int.self, forKey: Qwen2Configuration.CodingKeys.kvHeads)
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self.ropeTheta =
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try container.decodeIfPresent(
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Float.self, forKey: Qwen2Configuration.CodingKeys.ropeTheta)
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?? 1_000_000
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self.ropeTraditional =
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try container.decodeIfPresent(
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Bool.self, forKey: Qwen2Configuration.CodingKeys.ropeTraditional) ?? false
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self.ropeScaling = try container.decodeIfPresent(
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[String: StringOrNumber].self, forKey: Qwen2Configuration.CodingKeys.ropeScaling)
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}
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}
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