Some fixes for gemma2 (#99)
* some fixes for gemma2 * format * fixes * format
This commit is contained in:
Awni Hannun
GitHub
@@ -58,7 +58,7 @@ public enum ModelType: String, Codable {
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return GemmaModel(configuration)
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case .gemma2:
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let configuration = try JSONDecoder().decode(
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GemmaConfiguration.self, from: Data(contentsOf: configuration))
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Gemma2Configuration.self, from: Data(contentsOf: configuration))
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return Gemma2Model(configuration)
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case .qwen2:
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let configuration = try JSONDecoder().decode(
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@@ -262,111 +262,3 @@ extension GemmaModel: LoRAModel {
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model.layers.map { ($0.attention, ["q_proj", "v_proj"]) }
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}
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}
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// Gemma 2
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// Port of https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/models/gemma2.py
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// Minimal changes from Gemma TransformerBlock
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private class Gemma2TransformerBlock: 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: "pre_feedforward_layernorm") var preFeedforwardLayerNorm: RMSNorm
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@ModuleInfo(key: "post_feedforward_layernorm") var postFeedforwardLayerNorm: RMSNorm
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@ModuleInfo(key: "post_attention_layernorm") var postAttentionLayerNorm: RMSNorm
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public init(_ args: GemmaConfiguration) {
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self._attention.wrappedValue = Attention(args)
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self.mlp = MLP(dimensions: args.hiddenSize, hiddenDimensions: args.intermediateSize)
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self._inputLayerNorm.wrappedValue = RMSNorm(
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dimensions: args.hiddenSize, eps: args.rmsNormEps)
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self._preFeedforwardLayerNorm.wrappedValue = RMSNorm(
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dimensions: args.hiddenSize, eps: args.rmsNormEps)
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self._postFeedforwardLayerNorm.wrappedValue = RMSNorm(
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dimensions: args.hiddenSize, eps: args.rmsNormEps)
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self._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
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) -> (MLXArray, (MLXArray, MLXArray)) {
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var (r, cache) = attention(inputLayerNorm(x), mask: mask, cache: cache)
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let h = x + postAttentionLayerNorm(r)
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r = mlp(preFeedforwardLayerNorm(h))
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let out = h + postFeedforwardLayerNorm(r)
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return (out, cache)
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}
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}
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// Uses Gemma2TransformerBlock, otherwise same as GemmaModelInner
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public class Gemma2ModelInner: Module {
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@ModuleInfo(key: "embed_tokens") var embedTokens: Embedding
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fileprivate let layers: [Gemma2TransformerBlock]
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fileprivate let norm: RMSNorm
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let hiddenScale: Float
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public init(_ args: GemmaConfiguration) {
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precondition(args.vocabularySize > 0)
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self._embedTokens.wrappedValue = Embedding(
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embeddingCount: args.vocabularySize, dimensions: args.hiddenSize)
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self.hiddenScale = pow(Float(args.hiddenSize), 0.5)
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self.layers = (0 ..< args.hiddenLayers)
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.map { _ in
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Gemma2TransformerBlock(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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h = h * hiddenScale
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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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// Uses Gemma2ModelInner, otherwise same as GemmaModel
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public class Gemma2Model: Module, LLMModel {
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public let vocabularySize: Int
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let model: Gemma2ModelInner
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public init(_ args: GemmaConfiguration) {
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self.vocabularySize = args.vocabularySize
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self.model = Gemma2ModelInner(args)
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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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var (out, cache) = model(inputs, cache: cache)
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out = model.embedTokens.asLinear(out)
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return (out, cache)
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}
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}
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309
Libraries/LLM/Gemma2.swift
Normal file
309
Libraries/LLM/Gemma2.swift
Normal file
@@ -0,0 +1,309 @@
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// Copyright © 2024 Apple Inc.
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import Foundation
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import MLX
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import MLXFast
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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/gemma2.py
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// specialized norm for gemma
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private class RMSNorm: Module, UnaryLayer {
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let weight: MLXArray
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let eps: Float
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public init(dimensions: Int, eps: Float = 1e-5) {
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self.weight = MLXArray.ones([dimensions])
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self.eps = eps
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super.init()
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}
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public func callAsFunction(_ x: MLXArray) -> MLXArray {
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return MLXFast.rmsNorm(x, weight: 1.0 + self.weight, eps: self.eps)
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}
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}
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private class Attention: Module {
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let args: Gemma2Configuration
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let scale: Float
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let logitSoftCap: Float
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let headDim: Int
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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: Gemma2Configuration) {
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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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let headDim = args.headDimensions
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self.headDim = headDim
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self.scale = pow(Float(args.queryPreAttnScalar), -0.5)
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self.logitSoftCap = args.attnLogitSoftcapping
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self._wq.wrappedValue = Linear(dim, heads * headDim, bias: false)
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self._wk.wrappedValue = Linear(dim, kvHeads * headDim, bias: false)
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self._wv.wrappedValue = Linear(dim, kvHeads * headDim, bias: false)
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self._wo.wrappedValue = Linear(heads * headDim, dim, bias: false)
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self.rope = RoPE(
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dimensions: headDim, traditional: args.ropeTraditional, base: args.ropeTheta)
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}
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public func callAsFunction(
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_ x: MLXArray, mask: MLXArray? = nil, cache: (MLXArray, MLXArray)? = nil
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) -> (MLXArray, (MLXArray, MLXArray)) {
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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 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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let newCache = (keys, values)
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let repeats = self.args.attentionHeads / self.args.kvHeads
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if repeats > 1 {
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queries = queries.reshaped(
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[B, self.args.kvHeads, repeats, L, self.headDim]
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)
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keys = expandedDimensions(keys, axes: [2])
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values = expandedDimensions(values, axes: [2])
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}
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var scores = matmul(queries, keys.swappedAxes(-1, -2))
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scores = tanh(scores / self.logitSoftCap) * self.logitSoftCap
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if mask != nil {
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scores = scores + mask!
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}
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scores = softmax(scores, axis: -1, precise: true)
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var output = matmul(scores, values)
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if repeats > 1 {
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output = output.reshaped([B, self.args.attentionHeads, L, self.headDim])
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}
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output = output.transposed(0, 2, 1, 3).reshaped(B, L, -1)
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return (wo(output), newCache)
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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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self._gate.wrappedValue = Linear(dimensions, hiddenDimensions, bias: false)
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self._down.wrappedValue = Linear(hiddenDimensions, dimensions, bias: false)
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self._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(gelu(gate(x)) * up(x))
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}
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}
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// Minimal changes from Gemma TransformerBlock
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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: "pre_feedforward_layernorm") var preFeedforwardLayerNorm: RMSNorm
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@ModuleInfo(key: "post_feedforward_layernorm") var postFeedforwardLayerNorm: RMSNorm
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@ModuleInfo(key: "post_attention_layernorm") var postAttentionLayerNorm: RMSNorm
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public init(_ args: Gemma2Configuration) {
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self._attention.wrappedValue = Attention(args)
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self.mlp = MLP(dimensions: args.hiddenSize, hiddenDimensions: args.intermediateSize)
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self._inputLayerNorm.wrappedValue = RMSNorm(
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dimensions: args.hiddenSize, eps: args.rmsNormEps)
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self._preFeedforwardLayerNorm.wrappedValue = RMSNorm(
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dimensions: args.hiddenSize, eps: args.rmsNormEps)
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self._postFeedforwardLayerNorm.wrappedValue = RMSNorm(
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dimensions: args.hiddenSize, eps: args.rmsNormEps)
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self._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
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) -> (MLXArray, (MLXArray, MLXArray)) {
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var (r, cache) = attention(inputLayerNorm(x), mask: mask, cache: cache)
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let h = x + postAttentionLayerNorm(r)
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r = mlp(preFeedforwardLayerNorm(h))
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let out = h + postFeedforwardLayerNorm(r)
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return (out, cache)
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}
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}
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// Uses Gemma2TransformerBlock, otherwise same as GemmaModelInner
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public class ModelInner: Module {
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@ModuleInfo(key: "embed_tokens") var embedTokens: Embedding
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fileprivate let layers: [TransformerBlock]
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fileprivate let norm: RMSNorm
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let hiddenScale: Float
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public init(_ args: Gemma2Configuration) {
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precondition(args.vocabularySize > 0)
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self._embedTokens.wrappedValue = Embedding(
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embeddingCount: args.vocabularySize, dimensions: args.hiddenSize)
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self.hiddenScale = pow(Float(args.hiddenSize), 0.5)
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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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h = h * hiddenScale
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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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// Uses Gemma2ModelInner, otherwise same as GemmaModel
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public class Gemma2Model: Module, LLMModel {
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public let vocabularySize: Int
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let model: ModelInner
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let logitSoftCap: Float
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public init(_ args: Gemma2Configuration) {
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self.vocabularySize = args.vocabularySize
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self.model = ModelInner(args)
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self.logitSoftCap = args.finalLogitSoftcapping
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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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var (out, cache) = model(inputs, cache: cache)
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out = model.embedTokens.asLinear(out)
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out = tanh(out / self.logitSoftCap) * self.logitSoftCap
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return (out, cache)
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}
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}
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public struct Gemma2Configuration: 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 headDimensions: 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 = 10_000
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var ropeTraditional: Bool = false
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var attnLogitSoftcapping: Float = 50.0
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var finalLogitSoftcapping: Float = 30.0
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var queryPreAttnScalar: Int = 256
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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 headDimensions = "head_dim"
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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 attnLogitSoftcapping = "attn_logit_softcapping"
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case finalLogitSoftcapping = "final_logit_softcapping"
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case queryPreAttnScalar = "query_pre_attn_scalar"
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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<CodingKeys> = try decoder.container(
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keyedBy: CodingKeys.self)
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self.hiddenSize = try container.decode(
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Int.self, forKey: CodingKeys.hiddenSize)
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self.hiddenLayers = try container.decode(
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Int.self, forKey: CodingKeys.hiddenLayers)
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self.intermediateSize = try container.decode(
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Int.self, forKey: CodingKeys.intermediateSize)
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self.attentionHeads = try container.decode(
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Int.self, forKey: CodingKeys.attentionHeads)
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self.headDimensions = try container.decode(
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Int.self, forKey: CodingKeys.headDimensions)
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self.rmsNormEps = try container.decode(
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Float.self, forKey: CodingKeys.rmsNormEps)
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self.vocabularySize = try container.decode(
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Int.self, forKey: CodingKeys.vocabularySize)
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self.kvHeads = try container.decode(Int.self, forKey: CodingKeys.kvHeads)
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self.ropeTheta =
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try container.decodeIfPresent(Float.self, forKey: CodingKeys.ropeTheta)
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?? 10_000
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self.ropeTraditional =
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try container.decodeIfPresent(
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Bool.self, forKey: CodingKeys.ropeTraditional) ?? false
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self.attnLogitSoftcapping = try container.decode(
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Float.self, forKey: CodingKeys.attnLogitSoftcapping)
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self.finalLogitSoftcapping = try container.decode(
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Float.self, forKey: CodingKeys.finalLogitSoftcapping)
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self.queryPreAttnScalar = try container.decode(
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Int.self, forKey: CodingKeys.queryPreAttnScalar)
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}
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}
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// MARK: - LoRA
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extension Gemma2Model: LoRAModel {
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public func loraLinearLayers() -> LoRALinearLayers {
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model.layers.map { ($0.attention, ["q_proj", "v_proj"]) }
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}
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}
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@@ -168,7 +168,7 @@ extension ModelConfiguration {
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defaultPrompt: "what is the difference between lettuce and cabbage?"
|
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) { prompt in
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"<start_of_turn>user \(prompt)<end_of_turn><start_of_turn>model"
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"<start_of_turn>user\n\(prompt)<end_of_turn>\n<start_of_turn>model\n"
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}
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public static let gemma_2_9b_it_4bit = ModelConfiguration(
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@@ -178,6 +178,17 @@ extension ModelConfiguration {
|
||||
// https://www.promptingguide.ai/models/gemma
|
||||
defaultPrompt: "What is the difference between lettuce and cabbage?"
|
||||
|
||||
) { prompt in
|
||||
"<start_of_turn>user\n\(prompt)<end_of_turn>\n<start_of_turn>model\n"
|
||||
}
|
||||
|
||||
public static let gemma_2_2b_it_4bit = ModelConfiguration(
|
||||
id: "mlx-community/gemma-2-2b-it-4bit",
|
||||
overrideTokenizer: "PreTrainedTokenizer",
|
||||
|
||||
// https://www.promptingguide.ai/models/gemma
|
||||
defaultPrompt: "What is the difference between lettuce and cabbage?"
|
||||
|
||||
) { prompt in
|
||||
"<start_of_turn>user \(prompt)<end_of_turn><start_of_turn>model"
|
||||
}
|
||||
|
||||
@@ -8,6 +8,7 @@
|
||||
|
||||
/* Begin PBXBuildFile section */
|
||||
12305EAF2B9D864400C92FEE /* PredictionView.swift in Sources */ = {isa = PBXBuildFile; fileRef = 12305EAE2B9D864400C92FEE /* PredictionView.swift */; };
|
||||
1C55317A2C5AAB4E00B07ECD /* Gemma2.swift in Sources */ = {isa = PBXBuildFile; fileRef = 1C5531792C5AAB4E00B07ECD /* Gemma2.swift */; };
|
||||
1CD79C702BD80DE100B6C06F /* Phi3.swift in Sources */ = {isa = PBXBuildFile; fileRef = 1CD79C6F2BD80DE100B6C06F /* Phi3.swift */; };
|
||||
525C1E9D2B9A011000B5C356 /* Starcoder2.swift in Sources */ = {isa = PBXBuildFile; fileRef = 525C1E9C2B9A010F00B5C356 /* Starcoder2.swift */; };
|
||||
52A776182B94B5EE00AA6E80 /* Qwen2.swift in Sources */ = {isa = PBXBuildFile; fileRef = 52A776172B94B5EE00AA6E80 /* Qwen2.swift */; };
|
||||
@@ -218,6 +219,7 @@
|
||||
|
||||
/* Begin PBXFileReference section */
|
||||
12305EAE2B9D864400C92FEE /* PredictionView.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = PredictionView.swift; sourceTree = "<group>"; };
|
||||
1C5531792C5AAB4E00B07ECD /* Gemma2.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = Gemma2.swift; sourceTree = "<group>"; };
|
||||
1CD79C6F2BD80DE100B6C06F /* Phi3.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = Phi3.swift; sourceTree = "<group>"; };
|
||||
525C1E9C2B9A010F00B5C356 /* Starcoder2.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = Starcoder2.swift; sourceTree = "<group>"; };
|
||||
52A776172B94B5EE00AA6E80 /* Qwen2.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = Qwen2.swift; sourceTree = "<group>"; };
|
||||
@@ -480,6 +482,7 @@
|
||||
C3A8B3AB2B9283150002EFB8 /* Models.swift */,
|
||||
C34E48EE2B696E6500FCB841 /* Llama.swift */,
|
||||
C38935E22B86C0FE0037B833 /* Gemma.swift */,
|
||||
1C5531792C5AAB4E00B07ECD /* Gemma2.swift */,
|
||||
C38935C72B869C7A0037B833 /* LLM.h */,
|
||||
C38935E02B869F420037B833 /* LLMModel.swift */,
|
||||
C38935DE2B869DD00037B833 /* Phi.swift */,
|
||||
@@ -1014,6 +1017,7 @@
|
||||
C38935CE2B869C870037B833 /* Load.swift in Sources */,
|
||||
C3E786AD2B8D4AF50004D037 /* Tokenizer.swift in Sources */,
|
||||
C3A8B3AC2B9283150002EFB8 /* Models.swift in Sources */,
|
||||
1C55317A2C5AAB4E00B07ECD /* Gemma2.swift in Sources */,
|
||||
C3E786AB2B8D1AEC0004D037 /* Evaluate.swift in Sources */,
|
||||
C38935CC2B869C870037B833 /* Llama.swift in Sources */,
|
||||
52A776182B94B5EE00AA6E80 /* Qwen2.swift in Sources */,
|
||||
|
||||
Reference in New Issue
Block a user