82f6a969d4
- document the tokenizer used (https://github.com/huggingface/swift-transformers) - provide a hook for tokenizer configuration, prompt augmentation - this isn't as rich as the python equivalents but it helps a little
96 lines
3.0 KiB
Swift
96 lines
3.0 KiB
Swift
// Copyright © 2024 Apple Inc.
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import AsyncAlgorithms
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import Foundation
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import Hub
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import MLX
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import MLXNN
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import MLXRandom
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import Tokenizers
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struct LLMError: Error {
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let message: String
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}
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/// Load and return the model and tokenizer
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public func load(
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hub: HubApi = HubApi(), configuration: ModelConfiguration, progressHandler: @escaping (Progress) -> Void = { _ in }
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) async throws -> (LLMModel, Tokenizer) {
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// note: this doesn't have a way to pass the HubApi
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let tokenizer = try await loadTokenizer(configuration: configuration)
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// download the model weights and config
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let repo = Hub.Repo(id: configuration.id)
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let modelFiles = ["config.json", "*.safetensors"]
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let modelDirectory = try await hub.snapshot(
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from: repo, matching: modelFiles, progressHandler: progressHandler)
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// create the model (no weights loaded)
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let configurationURL = modelDirectory.appending(component: "config.json")
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let baseConfig = try JSONDecoder().decode(
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BaseConfiguration.self, from: Data(contentsOf: configurationURL))
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let model = try baseConfig.modelType.createModel(configuration: configurationURL)
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// load the weights
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var weights = [String: MLXArray]()
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let enumerator = FileManager.default.enumerator(
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at: modelDirectory, includingPropertiesForKeys: nil)!
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for case let url as URL in enumerator {
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if url.pathExtension == "safetensors" {
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let w = try loadArrays(url: url)
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for (key, value) in w {
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weights[key] = value
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}
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}
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}
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// quantize if needed
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if let quantization = baseConfig.quantization {
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quantizeIfNeeded(model: model, weights: weights, quantization: quantization)
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}
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// apply the loaded weights
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let parameters = ModuleParameters.unflattened(weights)
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try model.update(parameters: parameters, verify: [.all])
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eval(model)
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return (model, tokenizer)
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}
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// MARK: - Quantization
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private func quantizeIfNeeded(
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model: LLMModel, weights: [String: MLXArray], quantization: BaseConfiguration.Quantization
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) {
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func linearPredicate(layer: Module) -> Bool {
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if let layer = layer as? Linear {
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// avoid quantizing gate layers, otherwise we have to re-quant and upload all the mixtral models
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return layer.weight.dim(0) != 8
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}
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return false
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}
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var predicate = linearPredicate(layer:)
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// for legacy models that don't have lm_head quant due to non-32 dims
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if weights["lm_head.scales"] == nil {
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let vocabularySize = model.vocabularySize
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func vocabularySizePredicate(layer: Module) -> Bool {
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if let layer = layer as? Linear {
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return layer.weight.dim(0) != 8 && layer.weight.dim(0) != vocabularySize
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}
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return false
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}
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predicate = vocabularySizePredicate(layer:)
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}
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QuantizedLinear.quantize(
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model: model, groupSize: quantization.groupSize, bits: quantization.bits,
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predicate: predicate)
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}
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