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| Original file line number | Diff line number | Diff line change |
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| // | ||
| // Starcoder2.swift | ||
| // LLM | ||
| // | ||
| // Created by John Mai on 2024/3/7. | ||
| // | ||
|
|
||
| import Foundation | ||
| import MLX | ||
| import MLXNN | ||
|
|
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| // port of https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/models/starcoder2.py | ||
|
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| private class Attention: Module { | ||
| let args: Starcoder2Configuration | ||
| let repeats: Int | ||
| let scale: Float | ||
|
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| @ModuleInfo(key: "q_proj") var wq: Linear | ||
| @ModuleInfo(key: "k_proj") var wk: Linear | ||
| @ModuleInfo(key: "v_proj") var wv: Linear | ||
| @ModuleInfo(key: "o_proj") var wo: Linear | ||
|
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| let rope: RoPE | ||
|
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| public init(_ args: Starcoder2Configuration) { | ||
| self.args = args | ||
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| let dim = args.hiddenSize | ||
| let heads = args.attentionHeads | ||
| let kvHeads = args.kvHeads | ||
|
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| self.repeats = heads / kvHeads | ||
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| let headDim = args.hiddenSize / heads | ||
| self.scale = pow(Float(headDim), -0.5) | ||
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| _wq.wrappedValue = Linear(dim, heads * headDim, bias: true) | ||
| _wk.wrappedValue = Linear(dim, kvHeads * headDim, bias: true) | ||
| _wv.wrappedValue = Linear(dim, kvHeads * headDim, bias: true) | ||
| _wo.wrappedValue = Linear(heads * headDim, dim, bias: true) | ||
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| self.rope = RoPE(dimensions: headDim, traditional: false, base: args.ropeTheta) | ||
| } | ||
|
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| public func callAsFunction( | ||
| _ x: MLXArray, mask: MLXArray? = nil, cache: (MLXArray, MLXArray)? = nil | ||
| ) -> (MLXArray, (MLXArray, MLXArray)) { | ||
| let (B, L) = (x.dim(0), x.dim(1)) | ||
|
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| var queries = wq(x) | ||
| var keys = wk(x) | ||
| var values = wv(x) | ||
|
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| // prepare the queries, keys and values for the attention computation | ||
| queries = queries.reshaped(B, L, args.attentionHeads, -1).transposed(0, 2, 1, 3) | ||
| keys = keys.reshaped(B, L, args.kvHeads, -1).transposed(0, 2, 1, 3) | ||
| values = values.reshaped(B, L, args.kvHeads, -1).transposed(0, 2, 1, 3) | ||
|
|
||
| if repeats > 1 { | ||
| keys = MLXArray.repeat(keys, count: repeats, axis: 1) | ||
| values = MLXArray.repeat(values, count: repeats, axis: 1) | ||
| } | ||
|
|
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| if let (keyCache, valueCache) = cache { | ||
| queries = rope(queries, offset: keyCache.dim(2)) | ||
| keys = rope(keys, offset: keyCache.dim(2)) | ||
| keys = concatenated([keyCache, keys], axis: 2) | ||
| values = concatenated([valueCache, values], axis: 2) | ||
| } else { | ||
| queries = rope(queries) | ||
| keys = rope(keys) | ||
| } | ||
|
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| var scores = (queries * scale).matmul(keys.transposed(0, 1, 3, 2)) | ||
| if let mask { | ||
| scores = scores + mask | ||
| } | ||
|
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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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| private class MLP: Module, UnaryLayer { | ||
| @ModuleInfo(key: "c_fc") var cFc: Linear | ||
| @ModuleInfo(key: "c_proj") var cProj: Linear | ||
|
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| public init(dimensions: Int, hiddenDimensions: Int) { | ||
| _cFc.wrappedValue = Linear(dimensions, hiddenDimensions, bias: true) | ||
| _cProj.wrappedValue = Linear(hiddenDimensions, dimensions, bias: true) | ||
| } | ||
|
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| public func callAsFunction(_ x: MLXArray) -> MLXArray { | ||
| cProj(gelu(cFc(x))) | ||
| } | ||
| } | ||
|
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| private class TransformerBlock: Module { | ||
| @ModuleInfo(key: "self_attn") var attention: Attention | ||
| let mlp: MLP | ||
|
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| @ModuleInfo(key: "input_layernorm") var inputLayerNorm: LayerNorm | ||
| @ModuleInfo(key: "post_attention_layernorm") var postAttentionLayerNorm: LayerNorm | ||
|
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| public init(_ args: Starcoder2Configuration) { | ||
| _attention.wrappedValue = Attention(args) | ||
| self.mlp = MLP(dimensions: args.hiddenSize, hiddenDimensions: args.intermediateSize) | ||
| _inputLayerNorm.wrappedValue = LayerNorm( | ||
| dimensions: args.hiddenSize, eps: args.normEpsilon) | ||
| _postAttentionLayerNorm.wrappedValue = LayerNorm( | ||
| dimensions: args.hiddenSize, eps: args.normEpsilon) | ||
| } | ||
|
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| public func callAsFunction( | ||
| _ x: MLXArray, mask: MLXArray? = nil, cache: (MLXArray, MLXArray)? = nil | ||
| ) -> (MLXArray, (MLXArray, MLXArray)) { | ||
| var (r, cache) = attention(inputLayerNorm(x), mask: mask, cache: cache) | ||
| let h = x + r | ||
| r = mlp(postAttentionLayerNorm(h)) | ||
| let out = h + r | ||
| return (out, cache) | ||
| } | ||
| } | ||
|
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| public class Starcoder2ModelInner: Module { | ||
| @ModuleInfo(key: "embed_tokens") var embedTokens: Embedding | ||
|
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| fileprivate let layers: [TransformerBlock] | ||
| let norm: LayerNorm | ||
|
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| public init(_ args: Starcoder2Configuration) { | ||
| precondition(args.vocabularySize > 0) | ||
|
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| _embedTokens.wrappedValue = Embedding( | ||
| embeddingCount: args.vocabularySize, dimensions: args.hiddenSize) | ||
|
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| self.layers = (0 ..< args.hiddenLayers) | ||
| .map { _ in | ||
| TransformerBlock(args) | ||
| } | ||
| self.norm = LayerNorm(dimensions: args.hiddenSize, eps: args.normEpsilon) | ||
| } | ||
|
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| public func callAsFunction(_ inputs: MLXArray, cache: [(MLXArray, MLXArray)]? = nil) -> ( | ||
| MLXArray, [(MLXArray, MLXArray)] | ||
| ) { | ||
| var h = embedTokens(inputs) | ||
|
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| var mask: MLXArray? = nil | ||
| if h.dim(1) > 1 { | ||
| mask = MultiHeadAttention.createAdditiveCausalMask(h.dim(1)) | ||
| mask = mask?.asType(h.dtype) | ||
| } | ||
|
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| var newCache = [(MLXArray, MLXArray)]() | ||
|
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| for (i, layer) in layers.enumerated() { | ||
| var cacheUpdate: (MLXArray, MLXArray) | ||
| (h, cacheUpdate) = layer(h, mask: mask, cache: cache?[i]) | ||
| newCache.append(cacheUpdate) | ||
| } | ||
|
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| return (norm(h), newCache) | ||
| } | ||
| } | ||
|
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| public class Starcoder2Model: Module, LLMModel { | ||
| public var vocabularySize: Int | ||
|
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| public let tieWordEmbeddings: Bool | ||
| let model: Starcoder2ModelInner | ||
|
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| @ModuleInfo(key: "lm_head") var lmHead: Linear | ||
|
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| public init(_ args: Starcoder2Configuration) { | ||
| self.vocabularySize = args.vocabularySize | ||
| self.model = Starcoder2ModelInner(args) | ||
| self.tieWordEmbeddings = args.tieWordEmbeddings | ||
| if !self.tieWordEmbeddings { | ||
| _lmHead.wrappedValue = Linear(args.hiddenSize, args.vocabularySize, bias: false) | ||
| } | ||
| } | ||
|
|
||
| public func callAsFunction(_ inputs: MLXArray, cache: [(MLXArray, MLXArray)]?) -> ( | ||
| MLXArray, [(MLXArray, MLXArray)] | ||
| ) { | ||
| var (out, cache) = model(inputs, cache: cache) | ||
|
|
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| if !tieWordEmbeddings { | ||
| return (lmHead(out), cache) | ||
| } else { | ||
| out = matmul(out, model.embedTokens.weight.T) | ||
| return (out, cache) | ||
| } | ||
| } | ||
| } | ||
|
|
||
| public struct Starcoder2Configuration: Codable { | ||
| var hiddenSize: Int | ||
| var hiddenLayers: Int | ||
| var intermediateSize: Int | ||
| var attentionHeads: Int | ||
| var kvHeads: Int | ||
| var maxPositionEmbeddings: Int = 16384 | ||
| var normEpsilon: Float = 1e-5 | ||
| var normType: String = "layer_norm" | ||
| var vocabularySize: Int = 49152 | ||
| var ropeTheta: Float = 100000 | ||
| var tieWordEmbeddings: Bool = true | ||
|
|
||
| enum CodingKeys: String, CodingKey { | ||
| case hiddenSize = "hidden_size" | ||
| case hiddenLayers = "num_hidden_layers" | ||
| case intermediateSize = "intermediate_size" | ||
| case attentionHeads = "num_attention_heads" | ||
| case kvHeads = "num_key_value_heads" | ||
| case maxPositionEmbeddings = "max_position_embeddings" | ||
| case normEpsilon = "norm_epsilon" | ||
| case normType = "norm_type" | ||
| case vocabularySize = "vocab_size" | ||
| case ropeTheta = "rope_theta" | ||
| case tieWordEmbeddings = "tie_word_embeddings" | ||
| } | ||
|
|
||
| public init(from decoder: Decoder) throws { | ||
| // custom implementation to handle optional keys with required values | ||
| let container: KeyedDecodingContainer<Starcoder2Configuration.CodingKeys> = | ||
| try decoder.container( | ||
| keyedBy: Starcoder2Configuration.CodingKeys.self) | ||
|
|
||
| self.hiddenSize = try container.decode( | ||
| Int.self, forKey: Starcoder2Configuration.CodingKeys.hiddenSize) | ||
| self.hiddenLayers = try container.decode( | ||
| Int.self, forKey: Starcoder2Configuration.CodingKeys.hiddenLayers) | ||
| self.intermediateSize = try container.decode( | ||
| Int.self, forKey: Starcoder2Configuration.CodingKeys.intermediateSize) | ||
| self.attentionHeads = try container.decode( | ||
| Int.self, forKey: Starcoder2Configuration.CodingKeys.attentionHeads) | ||
| self.kvHeads = try container.decode( | ||
| Int.self, forKey: Starcoder2Configuration.CodingKeys.kvHeads) | ||
| self.maxPositionEmbeddings = | ||
| try container.decodeIfPresent( | ||
| Int.self, forKey: Starcoder2Configuration.CodingKeys.maxPositionEmbeddings) ?? 16384 | ||
| self.normEpsilon = | ||
| try container.decodeIfPresent( | ||
| Float.self, forKey: Starcoder2Configuration.CodingKeys.normEpsilon) ?? 1e-5 | ||
| self.normType = | ||
| try container.decodeIfPresent( | ||
| String.self, forKey: Starcoder2Configuration.CodingKeys.normType) ?? "layer_norm" | ||
| self.vocabularySize = | ||
| try container.decodeIfPresent( | ||
| Int.self, forKey: Starcoder2Configuration.CodingKeys.vocabularySize) ?? 49152 | ||
| self.ropeTheta = | ||
| try container.decodeIfPresent( | ||
| Float.self, forKey: Starcoder2Configuration.CodingKeys.ropeTheta) | ||
| ?? 100000 | ||
| self.tieWordEmbeddings = | ||
| try container.decodeIfPresent( | ||
| Bool.self, forKey: Starcoder2Configuration.CodingKeys.tieWordEmbeddings) | ||
| ?? true | ||
| } | ||
| } |
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