From 0de7e23d9a0c2c25c7b77258a5e42c1cb3527cb4 Mon Sep 17 00:00:00 2001 From: sukru tikves Date: Tue, 25 Aug 2026 17:33:51 -0700 Subject: [PATCH 01/21] Add Qwen3-1.7B iOS and macOS export support MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Register Qwen3-1.7B in the model registry for both macOS (4-bit) and iOS (mixed 4-bit/8-bit palettized) platforms. Add the mixed quantization config yaml and HuggingFace metadata entry. Perplexity: 20.96 (float16) → 21.19 (mixed 4/8-bit, 5.43 BPW). --- models/qwen3/README.md | 4 ++++ models/qwen3/qwen3_1_7b_mixed_4bit_8bit.yaml | 19 +++++++++++++++++++ python/src/coreai_models/export/metadata.py | 8 ++++++++ python/src/coreai_models/model_registry.py | 12 ++++++++++++ 4 files changed, 43 insertions(+) create mode 100644 models/qwen3/qwen3_1_7b_mixed_4bit_8bit.yaml diff --git a/models/qwen3/README.md b/models/qwen3/README.md index bfa50cee..27b71471 100644 --- a/models/qwen3/README.md +++ b/models/qwen3/README.md @@ -7,6 +7,7 @@ Alibaba's Qwen3 models for on-device inference via Core AI. | Model | Parameters | macOS | iOS | | ---------- | ---------- | ----- | --- | | Qwen3 0.6B | 0.6B | Yes | Yes | +| Qwen3 1.7B | 1.7B | Yes | Yes | | Qwen3 4B | 4.0B | Yes | Yes | | Qwen3 8B | 8.0B | Yes | No | @@ -81,6 +82,8 @@ Perplexity score on the [`WikiText-2`](https://huggingface.co/datasets/EleutherA | ---------- | ---------------------------------------------------- | --------------------- | -------- | ---------------- | | Qwen3 0.6B | none (`float16`) | 16.00 | iOS | 26.16 | | Qwen3 0.6B | [Mixed 4-bit/8-bit palettized][mixed-4bit-8bit-yaml] | 5.71\* | iOS | 30.90 | +| Qwen3 1.7B | none (`float16`) | 16.00 | iOS | 20.96 | +| Qwen3 1.7B | [Mixed 4-bit/8-bit palettized][qwen3-1.7b-mixed-yaml] | 5.43\* | iOS | 21.19 | | Qwen3 4B | none (`float16`) | 16.00 | macOS | 16.41 | | Qwen3 4B | [4-bit quantized][presets-info] | 4.50 | macOS | 18.33 | | Qwen3 4B | none (`float16`) | 16.00 | iOS | 16.41 | @@ -92,4 +95,5 @@ Perplexity score on the [`WikiText-2`](https://huggingface.co/datasets/EleutherA [presets-info]: ../README.md#quantization-options [mixed-4bit-8bit-yaml]: qwen3_0_6b_mixed_4bit_8bit.yaml +[qwen3-1.7b-mixed-yaml]: qwen3_1_7b_mixed_4bit_8bit.yaml [qwen3-4b-mixed-yaml]: qwen3_4b_mixed_4bit_8bit.yaml diff --git a/models/qwen3/qwen3_1_7b_mixed_4bit_8bit.yaml b/models/qwen3/qwen3_1_7b_mixed_4bit_8bit.yaml new file mode 100644 index 00000000..718d58f5 --- /dev/null +++ b/models/qwen3/qwen3_1_7b_mixed_4bit_8bit.yaml @@ -0,0 +1,19 @@ +kmeans_palettization_config: + global_config: + op_state_spec: + weight: + n_bits: 4 + granularity: + type: per_grouped_channel + axis: 0 + group_size: 8 + module_type_configs: + torch.nn.modules.sparse.Embedding: null + coreai_models.primitives.ios.embedding.LoadEmbeddings: null + module_name_configs: + 'extend\.model\.layers\.(0|2|14|26|27)\.(self_attn|mlp)\.(q_proj|k_proj|v_proj|o_proj|gate_proj|up_proj|down_proj)': + op_state_spec: + weight: + n_bits: 8 + granularity: + type: per_tensor diff --git a/python/src/coreai_models/export/metadata.py b/python/src/coreai_models/export/metadata.py index d4228a91..891d6d64 100644 --- a/python/src/coreai_models/export/metadata.py +++ b/python/src/coreai_models/export/metadata.py @@ -52,6 +52,14 @@ class AIModelMetadataFields: "family. Source: https://huggingface.co/Qwen/Qwen3-0.6B" ), ), + "Qwen/Qwen3-1.7B": AIModelMetadataFields( + author="Qwen Team", + license="Apache-2.0", + model_description=( + "Qwen3-1.7B is a 1.7B-parameter causal language model from the Qwen3 " + "family. Source: https://huggingface.co/Qwen/Qwen3-1.7B" + ), + ), "Qwen/Qwen3-4B": AIModelMetadataFields( author="Qwen Team", license="Apache-2.0", diff --git a/python/src/coreai_models/model_registry.py b/python/src/coreai_models/model_registry.py index d5b5717a..8d7bbc18 100644 --- a/python/src/coreai_models/model_registry.py +++ b/python/src/coreai_models/model_registry.py @@ -83,6 +83,7 @@ class UtilityModel: 32768, ), ModelPreset("qwen3-0.6b", "Qwen/Qwen3-0.6B", "qwen3", "llm", "macOS", "4bit", "float16", 8192), + ModelPreset("qwen3-1.7b", "Qwen/Qwen3-1.7B", "qwen3", "llm", "macOS", "4bit", "float16", 32768), ModelPreset("qwen3-4b", "Qwen/Qwen3-4B", "qwen3", "llm", "macOS", "4bit", "float16", 40960), ModelPreset("qwen3-8b", "Qwen/Qwen3-8B", "qwen3", "llm", "macOS", "4bit", "float16", 40960), ModelPreset( @@ -143,6 +144,17 @@ class UtilityModel: IOS_DEFAULT_MAX_CONTEXT_LENGTH, compression_config="models/qwen3/qwen3_0_6b_mixed_4bit_8bit.yaml", ), + ModelPreset( + "qwen3-1.7b", + "Qwen/Qwen3-1.7B", + "qwen3", + "llm", + "iOS", + "none", + "float16", + IOS_DEFAULT_MAX_CONTEXT_LENGTH, + compression_config="models/qwen3/qwen3_1_7b_mixed_4bit_8bit.yaml", + ), ModelPreset( "qwen2.5-1.5b-instruct", "Qwen/Qwen2.5-1.5B-Instruct", From cc5e8f196e5be2bf95e5d733d06d947233cc1ed2 Mon Sep 17 00:00:00 2001 From: Lewis300 <34315738+Lewis300@users.noreply.github.com> Date: Sat, 15 Aug 2026 18:13:03 -0700 Subject: [PATCH 02/21] Cap tokenizers dependency at <0.23 to fix SD2.1 export (#174) --- python/pyproject.toml | 2 +- uv.lock | 43 +++++++++++++++++++++---------------------- 2 files changed, 22 insertions(+), 23 deletions(-) diff --git a/python/pyproject.toml b/python/pyproject.toml index 450c8044..35cb4038 100644 --- a/python/pyproject.toml +++ b/python/pyproject.toml @@ -38,7 +38,7 @@ dependencies = [ "huggingface-hub>=1.5.0,<2.0", "safetensors>=0.5,<1.0", "sentencepiece>=0.2,<1.0", - "tokenizers>=0.22,<1.0", + "tokenizers>=0.22,<0.23", "diffusers>=0.37,<1.0", ] diff --git 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RNG/NoiseGeneration.swift. The new function accepts a RandomSourceType parameter (.numPy, .torch, .nvidia) defaulting to .numPy for backwards compatibility, allowing callers to select the appropriate RNG for their model. --- .../Pipelines/Flux2Pipeline.swift | 7 ----- .../Pipelines/SD3Pipeline.swift | 5 ---- .../Pipelines/StableDiffusionPipeline.swift | 5 ---- .../RNG/NoiseGeneration.swift | 26 +++++++++++++++++++ 4 files changed, 26 insertions(+), 17 deletions(-) create mode 100644 swift/Sources/CoreAIDiffusionPipeline/RNG/NoiseGeneration.swift diff --git a/swift/Sources/CoreAIDiffusionPipeline/Pipelines/Flux2Pipeline.swift b/swift/Sources/CoreAIDiffusionPipeline/Pipelines/Flux2Pipeline.swift index 74ccada2..be38ff0b 100644 --- a/swift/Sources/CoreAIDiffusionPipeline/Pipelines/Flux2Pipeline.swift +++ b/swift/Sources/CoreAIDiffusionPipeline/Pipelines/Flux2Pipeline.swift @@ -581,13 +581,6 @@ public struct Flux2Pipeline: DiffusionPipeline { return result } - // MARK: - Noise Generation - - private func generateNoise(count: Int, seed: UInt32) -> [Float] { - var rng = NumPyRandomSource(seed: seed) - return (0.. [Float] { - var rng = NumPyRandomSource(seed: seed) - return (0.. [Float] { - var rng = NumPyRandomSource(seed: seed) - return (0.. [Float] { + switch sourceType { + case .numPy: + var rng = NumPyRandomSource(seed: seed) + return (0.. Date: Mon, 17 Aug 2026 12:34:48 -0700 Subject: [PATCH 04/21] Extract lastSafeIndex into shared free function (#179) * Extract lastSafeIndex into shared free function * Add unit tests for lastSafeIndex streaming marker holdback * Additional unit tests. --- .../LanguageModel/ThinkTagParser.swift | 20 +----- .../StreamingMarkerMatcher.swift | 24 +++++++ .../CoreAILanguageModels/ToolCallParser.swift | 17 +---- .../StreamingMarkerMatcherTests.swift | 63 +++++++++++++++++++ 4 files changed, 89 insertions(+), 35 deletions(-) create mode 100644 swift/Sources/CoreAILanguageModels/StreamingMarkerMatcher.swift create mode 100644 swift/Tests/LanguageModelsTests/StreamingMarkerMatcherTests.swift diff --git a/swift/Sources/CoreAILanguageModels/LanguageModel/ThinkTagParser.swift b/swift/Sources/CoreAILanguageModels/LanguageModel/ThinkTagParser.swift index 95384921..05a173d9 100644 --- a/swift/Sources/CoreAILanguageModels/LanguageModel/ThinkTagParser.swift +++ b/swift/Sources/CoreAILanguageModels/LanguageModel/ThinkTagParser.swift @@ -72,7 +72,7 @@ struct ThinkTagParser { // a partial-marker suffix; emit the entire buffer. Otherwise: // hold back at most `marker.count - 1` characters in case the // next delta completes the marker. - let safe = isFinal ? buffer.endIndex : lastSafeIndex(forTag: marker) + let safe = isFinal ? buffer.endIndex : lastSafeIndex(in: buffer, forTag: marker) if safe > buffer.startIndex { let toEmit = String(buffer[buffer.startIndex.. String.Index { - let maxHold = tag.count - 1 - guard !buffer.isEmpty, maxHold > 0 else { return buffer.endIndex } - let holdStart = buffer.index(buffer.endIndex, offsetBy: -min(maxHold, buffer.count)) - for offset in 0.., so we pass the - // Substring directly — avoids a per-iteration String allocation. - if tag.starts(with: buffer[idx...]) { - return idx - } - } - return buffer.endIndex - } } diff --git a/swift/Sources/CoreAILanguageModels/StreamingMarkerMatcher.swift b/swift/Sources/CoreAILanguageModels/StreamingMarkerMatcher.swift new file mode 100644 index 00000000..408396eb --- /dev/null +++ b/swift/Sources/CoreAILanguageModels/StreamingMarkerMatcher.swift @@ -0,0 +1,24 @@ +// Copyright 2026 Apple Inc. +// +// Use of this source code is governed by a BSD-3-clause license that can +// be found in the LICENSE file or at https://opensource.org/licenses/BSD-3-Clause + +/// Returns the rightmost index in `buffer` such that the suffix from that +/// index to the end is NOT a non-empty prefix of `tag`. +/// +/// Used by streaming parsers to decide how much of a buffer can be safely +/// emitted without cutting off a marker that might span two deltas. At most +/// `tag.count - 1` trailing characters are held back (the longest partial +/// prefix that could still complete on the next delta). +func lastSafeIndex(in buffer: String, forTag tag: String) -> String.Index { + let maxHold = tag.count - 1 + guard !buffer.isEmpty, maxHold > 0 else { return buffer.endIndex } + let holdStart = buffer.index(buffer.endIndex, offsetBy: -min(maxHold, buffer.count)) + for offset in 0.. buffer.startIndex { let toEmit = String(buffer[buffer.startIndex.. String.Index { - let maxHold = tag.count - 1 - guard !buffer.isEmpty, maxHold > 0 else { return buffer.endIndex } - let holdStart = buffer.index(buffer.endIndex, offsetBy: -min(maxHold, buffer.count)) - for offset in 0..") == buffer.endIndex) + } + + @Test("Single-char prefix held back") + func singleCharHoldback() { + let buffer = "hello<" + let expected = buffer.index(buffer.startIndex, offsetBy: 5) + #expect(lastSafeIndex(in: buffer, forTag: "") == expected) + } + + @Test("Multi-char partial prefix held back") + func multiCharHoldback() { + let buffer = "some text") == expected) + } + + @Test("Maximum holdback — entire buffer is a prefix of tag") + func maxHoldback() { + let buffer = "") == buffer.startIndex) + } + + @Test("Empty buffer returns endIndex") + func emptyBuffer() { + let buffer = "" + #expect(lastSafeIndex(in: buffer, forTag: "") == buffer.endIndex) + } + + @Test("Buffer is just '<' — held back as prefix") + func bufferIsSingleOpenAngle() { + let buffer = "<" + #expect(lastSafeIndex(in: buffer, forTag: "") == buffer.startIndex) + } + + @Test("'>' and '!' are not prefixes — safe to emit") + func nonPrefixSpecialChars() { + let gt = ">" + let bang = "!" + #expect(lastSafeIndex(in: gt, forTag: "") == gt.endIndex) + #expect(lastSafeIndex(in: bang, forTag: "") == bang.endIndex) + } + + @Test("Single-char tag — nothing is ever held back") + func singleCharTag() { + let buffer = "abc<" + #expect(lastSafeIndex(in: buffer, forTag: "X") == buffer.endIndex) + } +} From 1d156b517ecd9a4162448f84e3539d352af034f2 Mon Sep 17 00:00:00 2001 From: Sukru Date: Mon, 17 Aug 2026 14:51:03 -0700 Subject: [PATCH 05/21] Extract shared model I/O name-discovery helpers into CoreAIShared (#177) Move findImageInputName, findLogitsOutputName, and findBoxesOutputName into a new ModelIONameResolver enum in CoreAIShared/Runtime, eliminating duplication between ObjectDetector and ImageSegmentationEngine. --- .../ImageSegmentationEngine.swift | 32 +++++-------------- .../CoreAIObjectDetector/ObjectDetector.swift | 26 ++------------- .../Runtime/ModelIONameResolver.swift | 23 +++++++++++++ 3 files changed, 34 insertions(+), 47 deletions(-) create mode 100644 swift/Sources/CoreAIShared/Runtime/ModelIONameResolver.swift diff --git a/swift/Sources/CoreAIImageSegmenter/ImageSegmentationEngine.swift b/swift/Sources/CoreAIImageSegmenter/ImageSegmentationEngine.swift index 94a5cffd..dcfc4afc 100644 --- a/swift/Sources/CoreAIImageSegmenter/ImageSegmentationEngine.swift +++ b/swift/Sources/CoreAIImageSegmenter/ImageSegmentationEngine.swift @@ -168,7 +168,7 @@ public struct CoreAISegmentationEngine { let iouScoresOutputName: String? init(model: AIModel, descriptor: InferenceFunctionDescriptor) async throws { - guard let imageInputName = findImageInputName(in: descriptor.inputNames) else { + guard let imageInputName = ModelIONameResolver.findImageInputName(in: descriptor.inputNames) else { throw SegmentationRuntimeError.invalidConfiguration( "Cannot find image input in model. Inputs: \(descriptor.inputNames)" ) @@ -188,8 +188,8 @@ public struct CoreAISegmentationEngine { throw SegmentationRuntimeError.outputMissing(masksOutputName) } - let boxesOutputName = findBoxesOutputName(in: descriptor.outputNames) - let logitsOutputName = findLogitsOutputName(in: descriptor.outputNames) + let boxesOutputName = ModelIONameResolver.findBoxesOutputName(in: descriptor.outputNames) + let logitsOutputName = ModelIONameResolver.findLogitsOutputName(in: descriptor.outputNames) let presenceLogitsOutputName = findPresenceOutputName(in: descriptor.outputNames) let semanticSegOutputName = findSemanticOutputName(in: descriptor.outputNames) let iouScoresOutputName = findIouScoresOutputName(in: descriptor.outputNames) @@ -284,7 +284,8 @@ public struct CoreAISegmentationEngine { detectDescriptor: InferenceFunctionDescriptor ) async throws { // image_encode: needs an image input + backbone-features output. - guard let imageInputName = findImageInputName(in: imageEncodeDescriptor.inputNames) else { + guard let imageInputName = ModelIONameResolver.findImageInputName(in: imageEncodeDescriptor.inputNames) + else { throw SegmentationRuntimeError.invalidConfiguration( "Cannot find image input in 'image_encode'. Inputs: \(imageEncodeDescriptor.inputNames)" ) @@ -336,12 +337,13 @@ public struct CoreAISegmentationEngine { guard case .ndArray = detectDescriptor.outputDescriptor(of: masksOutputName) else { throw SegmentationRuntimeError.outputMissing(masksOutputName) } - guard let boxesOutputName = findBoxesOutputName(in: detectDescriptor.outputNames) else { + guard let boxesOutputName = ModelIONameResolver.findBoxesOutputName(in: detectDescriptor.outputNames) else { throw SegmentationRuntimeError.invalidConfiguration( "Cannot find boxes output in 'detect'. Outputs: \(detectDescriptor.outputNames)" ) } - guard let logitsOutputName = findLogitsOutputName(in: detectDescriptor.outputNames) else { + guard let logitsOutputName = ModelIONameResolver.findLogitsOutputName(in: detectDescriptor.outputNames) + else { throw SegmentationRuntimeError.invalidConfiguration( "Cannot find logits output in 'detect'. Outputs: \(detectDescriptor.outputNames)" ) @@ -1190,13 +1192,6 @@ public struct CoreAISegmentationEngine { // MARK: - Static name-discovery helpers - static func findImageInputName(in names: [String]) -> String? { - names.first { - let l = $0.lowercased() - return l.contains("pixel") || l.contains("image") - } - } - static func findTextInputName(in names: [String]) -> String? { names.first { let l = $0.lowercased() @@ -1243,17 +1238,6 @@ public struct CoreAISegmentationEngine { names.first { $0.lowercased().contains("mask") } } - static func findBoxesOutputName(in names: [String]) -> String? { - names.first { $0.lowercased().contains("box") } - } - - static func findLogitsOutputName(in names: [String]) -> String? { - names.first { - let l = $0.lowercased() - return l.contains("logit") && !l.contains("presence") - } - } - static func findPresenceOutputName(in names: [String]) -> String? { names.first { $0.lowercased().contains("presence") } } diff --git a/swift/Sources/CoreAIObjectDetector/ObjectDetector.swift b/swift/Sources/CoreAIObjectDetector/ObjectDetector.swift index 9473e7f1..0145ae00 100644 --- a/swift/Sources/CoreAIObjectDetector/ObjectDetector.swift +++ b/swift/Sources/CoreAIObjectDetector/ObjectDetector.swift @@ -40,19 +40,19 @@ public struct ObjectDetector { } // Discover input names - guard let imageInputName = Self.findImageInputName(in: descriptor.inputNames) else { + guard let imageInputName = ModelIONameResolver.findImageInputName(in: descriptor.inputNames) else { throw DetectionRuntimeError.invalidConfiguration( "Cannot find image input in model. Inputs: \(descriptor.inputNames)" ) } // Discover output names - guard let logitsOutputName = Self.findLogitsOutputName(in: descriptor.outputNames) else { + guard let logitsOutputName = ModelIONameResolver.findLogitsOutputName(in: descriptor.outputNames) else { throw DetectionRuntimeError.invalidConfiguration( "Cannot find logits output in model. Outputs: \(descriptor.outputNames)" ) } - guard let boxesOutputName = Self.findBoxesOutputName(in: descriptor.outputNames) else { + guard let boxesOutputName = ModelIONameResolver.findBoxesOutputName(in: descriptor.outputNames) else { throw DetectionRuntimeError.invalidConfiguration( "Cannot find boxes output in model. Outputs: \(descriptor.outputNames)" ) @@ -310,26 +310,6 @@ public struct ObjectDetector { return BatchPlan(batch: imageCount, height: height, width: width) } - - // MARK: - Name Discovery - - static func findImageInputName(in names: [String]) -> String? { - names.first { - let l = $0.lowercased() - return l.contains("pixel") || l.contains("image") - } - } - - static func findLogitsOutputName(in names: [String]) -> String? { - names.first { $0.lowercased().contains("logit") } - } - - static func findBoxesOutputName(in names: [String]) -> String? { - names.first { - let l = $0.lowercased() - return l.contains("box") - } - } } // MARK: - Errors diff --git a/swift/Sources/CoreAIShared/Runtime/ModelIONameResolver.swift b/swift/Sources/CoreAIShared/Runtime/ModelIONameResolver.swift new file mode 100644 index 00000000..4298d28d --- /dev/null +++ b/swift/Sources/CoreAIShared/Runtime/ModelIONameResolver.swift @@ -0,0 +1,23 @@ +/// Shared helpers for discovering model input/output names by substring matching. +public enum ModelIONameResolver { + /// Finds the first name containing "pixel" or "image" (case-insensitive). + public static func findImageInputName(in names: [String]) -> String? { + names.first { + let l = $0.lowercased() + return l.contains("pixel") || l.contains("image") + } + } + + /// Finds the first name containing "logit" but NOT "presence" (case-insensitive). + public static func findLogitsOutputName(in names: [String]) -> String? { + names.first { + let l = $0.lowercased() + return l.contains("logit") && !l.contains("presence") + } + } + + /// Finds the first name containing "box" (case-insensitive). + public static func findBoxesOutputName(in names: [String]) -> String? { + names.first { $0.lowercased().contains("box") } + } +} From 7c65dd6e47899ad1300ec411cad60da921959b1a Mon Sep 17 00:00:00 2001 From: Sukru Date: Tue, 18 Aug 2026 11:13:43 -0700 Subject: [PATCH 06/21] Fix segmentation tests after ModelIONameResolver extraction (#177) (#181) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit findImageInputName and findLogitsOutputName moved to ModelIONameResolver in CoreAIShared — update test references. --- .../ImageSegmentationEngineTests.swift | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/swift/Tests/ImageSegmenterTests/ImageSegmentationEngineTests.swift b/swift/Tests/ImageSegmenterTests/ImageSegmentationEngineTests.swift index bdcd4629..023b65c3 100644 --- a/swift/Tests/ImageSegmenterTests/ImageSegmentationEngineTests.swift +++ b/swift/Tests/ImageSegmenterTests/ImageSegmentationEngineTests.swift @@ -3,6 +3,7 @@ // Use of this source code is governed by a BSD-3-clause license that can // be found in the LICENSE file or at https://opensource.org/licenses/BSD-3-Clause +import CoreAIShared import CoreGraphics import Foundation import Testing @@ -15,9 +16,9 @@ struct CoreAISegmentationEngineTests { @Test("findImageInputName: matches 'pixel' and 'image' variants") func findImageInputName() { - #expect(CoreAISegmentationEngine.findImageInputName(in: ["pixel_values", "input_ids"]) == "pixel_values") - #expect(CoreAISegmentationEngine.findImageInputName(in: ["text_tokens", "image_input"]) == "image_input") - #expect(CoreAISegmentationEngine.findImageInputName(in: ["text_tokens", "input_ids"]) == nil) + #expect(ModelIONameResolver.findImageInputName(in: ["pixel_values", "input_ids"]) == "pixel_values") + #expect(ModelIONameResolver.findImageInputName(in: ["text_tokens", "image_input"]) == "image_input") + #expect(ModelIONameResolver.findImageInputName(in: ["text_tokens", "input_ids"]) == nil) } @Test("findTextInputName: matches 'input_id', 'token', and 'text' variants") @@ -83,10 +84,10 @@ struct CoreAISegmentationEngineTests { @Test("findLogitsOutputName: skips presence_logits, picks pred_logits") func findLogitsOutputNameSkipsPresence() { let outputs = ["pred_masks", "pred_boxes", "pred_logits", "presence_logits", "semantic_seg"] - #expect(CoreAISegmentationEngine.findLogitsOutputName(in: outputs) == "pred_logits") + #expect(ModelIONameResolver.findLogitsOutputName(in: outputs) == "pred_logits") // Order shouldn't matter let reversed = outputs.reversed() as [String] - #expect(CoreAISegmentationEngine.findLogitsOutputName(in: reversed) == "pred_logits") + #expect(ModelIONameResolver.findLogitsOutputName(in: reversed) == "pred_logits") } @Test("findPresenceOutputName: picks presence_logits and not pred_logits") From 09974cabf96e82d2df8d51d94b288fd12e778203 Mon Sep 17 00:00:00 2001 From: Sukru Date: Wed, 19 Aug 2026 08:15:56 -0700 Subject: [PATCH 07/21] Add Muse Glimmer 30B text decoder support (#180) * Add Muse Glimmer 30B text decoder support Meta's on-device agentic model (Apache 2.0). Architecture features: - Local/Global attention: [S,S,S,G] repeating (39+13 layers) - CenteredRMSNorm (1+weight) for layer norms, plain RMSNorm for final norm - Weight-less RMSNorm on embeddings - QK norm (shared RMSNorm on Q/K per-head) + qk_scale_factor on Q - Gated attention: sigmoid(gate_proj(x)) * attn_output - Extreme GQA: 32Q / 2KV (16:1 ratio) - Per-layer RoPE control (global layers skip RoPE) - output_multiplier (0.196) and logit softcapping (20.0) Evaluated: word_ppl = 7.71 (FP16), ~8.4 (INT4). --- models/README.md | 1 + models/muse_glimmer/README.md | 78 ++++ python/src/coreai_models/model_registry.py | 10 + .../models/macos/muse_glimmer.py | 364 ++++++++++++++++++ python/src/coreai_models/models/registry.py | 63 +++ .../test_macos_layers/test_muse_glimmer.py | 219 +++++++++++ 6 files changed, 735 insertions(+) create mode 100644 models/muse_glimmer/README.md create mode 100644 python/src/coreai_models/models/macos/muse_glimmer.py create mode 100644 python/tests/test_model_units/test_models/test_macos_layers/test_muse_glimmer.py diff --git a/models/README.md b/models/README.md index 9d08f361..9f54f57d 100644 --- a/models/README.md +++ b/models/README.md @@ -171,6 +171,7 @@ uv run models//export.py --include-debug-info # embed debug information - [GPT-OSS](gpt_oss) - [Mistral](mistral) - [Mixtral](mixtral) +- [Muse Glimmer](muse_glimmer) - [Qwen2.5](qwen2) - [Qwen3](qwen3) - [Qwen3 MoE](qwen3_moe) diff --git a/models/muse_glimmer/README.md b/models/muse_glimmer/README.md new file mode 100644 index 00000000..545b6eeb --- /dev/null +++ b/models/muse_glimmer/README.md @@ -0,0 +1,78 @@ +# Muse Glimmer + +Meta's Muse Glimmer 30B for on-device agentic tasks via Core AI. Apache 2.0 license. + +## Supported Models + +| Model | Parameters | Context | macOS | iOS | +|------------------|-----------|---------|-------|-----| +| Muse-Glimmer-30B | ~29.6B | 131072 | Yes | No | + +## Setup to export models + +If you haven't installed `uv`, install it by +```bash +brew install uv +``` + +## Export models + +```bash +# Defaults to macOS variant +uv run coreai.llm.export muse-glimmer-30b +``` + +**Options:** + +```bash +# Full precision +uv run coreai.llm.export muse-glimmer-30b --compression none + +# Custom output directory +uv run coreai.llm.export muse-glimmer-30b --output-dir ./my-models/ + +# Preview resolved config without exporting +uv run coreai.llm.export muse-glimmer-30b --dry-run +``` + +## Run a Core AI Language Model + +### In your iOS and macOS applications via Foundation Models + +```swift +import FoundationModels +import CoreAILanguageModels + +let model = try await CoreAILanguageModel(resourcesAt: modelURL) + +let session = LanguageModelSession(model: model) + +let response = try await session.respond(to: "What is quantum computing?") + +print(response) +``` + +### On your Mac using built-in Command Line Tool + +```bash +swift run -c release llm-runner --model path/to/exported_model --prompt "Hello" +``` + +## Benchmark a Core AI Language Model + +```bash +swift run -c release llm-benchmark --model path/to/exported_model +``` + +Defaults: 512 prompt tokens, 1024 generation tokens, 5 trials. Override with `-p`, `-g`, and `-n`. + +## Evaluation + +Perplexity score on the [`WikiText-2`](https://huggingface.co/datasets/EleutherAI/wikitext_document_level) dataset computed using the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/wikitext/README.md) with the Core AI PyTorch models. + +| Model | Compression | Bits Per Weight (BPW) | Platform | Perplexity Score | +|-------|---------------------------|-----------------------|----------|------------------| +| 30B | none (`float16`) | 16.00 | macOS | 7.71 | +| 30B | [4-bit quantized][p-4bit] | 4.50 | macOS | 8.46 | + +[p-4bit]: ../README.md#quantization-options diff --git a/python/src/coreai_models/model_registry.py b/python/src/coreai_models/model_registry.py index 8d7bbc18..6376d362 100644 --- a/python/src/coreai_models/model_registry.py +++ b/python/src/coreai_models/model_registry.py @@ -132,6 +132,16 @@ class UtilityModel: ModelPreset( "gpt-oss-20b", "openai/gpt-oss-20b", "gpt-oss", "llm", "macOS", "none", "bfloat16", 32768 ), + ModelPreset( + "muse-glimmer-30b", + "meta-models/Muse-Glimmer-30B", + "muse_glimmer", + "llm", + "macOS", + "4bit", + "float16", + 131072, + ), # --- iOS (compression = palettized) --- ModelPreset( "qwen3-0.6b", diff --git a/python/src/coreai_models/models/macos/muse_glimmer.py b/python/src/coreai_models/models/macos/muse_glimmer.py new file mode 100644 index 00000000..1dbf0f3b --- /dev/null +++ b/python/src/coreai_models/models/macos/muse_glimmer.py @@ -0,0 +1,364 @@ +# Copyright 2026 Apple Inc. +# +# Use of this source code is governed by a BSD-3-clause license that can +# be found in the LICENSE file or at https://opensource.org/licenses/BSD-3-Clause + +"""Muse Glimmer text decoder for CoreAI model export. + +Meta's 30B on-device agentic model (Apache 2.0). Architecture features: +- Local/Global attention: [S,S,S,G] repeating (39 sliding + 13 full) +- RoPE on local layers only (global layers skip RoPE) +- Extreme GQA: 32Q / 2KV heads +- Gated attention: learned gate_proj on attention output +- Sandwich norm: pre+post norm on both attention and MLP +- qk_scale_factor: custom attention scaling (not 1/sqrt(d)) +- output_multiplier: scales final hidden state before lm_head +- Logit softcapping: tanh(logits/cap) * cap +""" + +import gc +import json +import os +from types import SimpleNamespace + +import torch +import torch.nn as nn +from huggingface_hub import snapshot_download +from typing_extensions import Self, override + +from coreai_models.models.base import ( + BaseForCausalLM, + _load_tensors_for_keys, + _resolve_safetensors_files, +) +from coreai_models.primitives.macos.cache import KVCache +from coreai_models.primitives.macos.mlp import MLP +from coreai_models.primitives.macos.rms_norm import RMSNorm, RMSNormPlusOne +from coreai_models.primitives.macos.rope import RoPE +from coreai_models.primitives.macos.sdpa import SDPA + + +class Attention(nn.Module): + def __init__(self, config, layer_idx: int) -> None: + super().__init__() + self.layer_idx = layer_idx + + dim = config.hidden_size + self.n_heads = n_heads = config.num_attention_heads + self.n_kv_heads = n_kv_heads = config.num_key_value_heads + self.head_dim = head_dim = config.head_dim + + self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=False) + self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False) + self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False) + self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False) + self.gate_proj = nn.Linear(dim, n_heads * head_dim, bias=False) + + self.qk_norm = RMSNorm(head_dim, eps=config.rms_norm_eps) + self.qk_scale_factor = getattr(config, "qk_scale_factor", 1.0) + + layer_types = config.layer_types + self.is_sliding = layer_types[layer_idx] == "sliding_attention" + + layer_rope_theta = config.layer_rope_theta + rope_theta = layer_rope_theta[layer_idx] if layer_rope_theta else 500000.0 + self.has_rope = rope_theta > 0 + + if self.is_sliding: + self.sdpa = SDPA(is_causal=True, window_size=config.sliding_window) + else: + self.sdpa = SDPA(is_causal=True) + + if self.has_rope: + self.rope = RoPE() + with torch.device("cpu"): + self._rope_freqs = 1.0 / ( + rope_theta ** (torch.arange(0, head_dim, 2, dtype=torch.float32) / head_dim) + ) + + def forward( + self, + x: torch.Tensor, + position_ids: torch.IntTensor, + cache: KVCache | None = None, + ) -> torch.Tensor: + batch_size, query_len, _ = x.shape + n_heads, n_kv_heads = self.n_heads, self.n_kv_heads + + query = ( + self.qk_norm( + self.q_proj(x) + .reshape(batch_size, query_len, n_heads, self.head_dim) + .permute(0, 2, 1, 3) + ) + * self.qk_scale_factor + ) + key = self.qk_norm( + self.k_proj(x) + .reshape(batch_size, query_len, n_kv_heads, self.head_dim) + .permute(0, 2, 1, 3) + ) + value = ( + self.v_proj(x) + .reshape(batch_size, query_len, n_kv_heads, self.head_dim) + .permute(0, 2, 1, 3) + ) + + gate = torch.sigmoid(self.gate_proj(x)) + + seq_len = position_ids.shape[-1] + torch._check_is_size(query_len) + torch._check_is_size(seq_len) + offset = seq_len - query_len + torch._check_is_size(offset) + rope_positions = position_ids.narrow(-1, offset, query_len) + + if self.has_rope: + freqs = self._rope_freqs.to(device=query.device) + query = self.rope(query, position_ids=rope_positions, freqs=freqs) + key = self.rope(key, position_ids=rope_positions, freqs=freqs) + + if cache is not None: + key, value = cache.update_and_fetch( + self.layer_idx, offset, key, value, seq_len=seq_len, query_len=query_len + ) + + attn_output = ( + self.sdpa(query, key, value) + .permute(0, 2, 1, 3) + .reshape(batch_size, query_len, self.n_heads * self.head_dim) + ) + + return self.o_proj(attn_output * gate) + + +class TransformerBlock(nn.Module): + def __init__(self, config, layer_idx: int) -> None: + super().__init__() + hidden_size = config.hidden_size + self.self_attn = Attention(config, layer_idx=layer_idx) + self.mlp = MLP(hidden_size, config.intermediate_size) + + post_eps = getattr(config, "post_norm_eps", config.rms_norm_eps) + self.input_layernorm = RMSNormPlusOne(hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = RMSNormPlusOne(hidden_size, eps=post_eps) + self.pre_feedforward_layernorm = RMSNormPlusOne(hidden_size, eps=config.rms_norm_eps) + self.post_feedforward_layernorm = RMSNormPlusOne(hidden_size, eps=post_eps) + + def forward( + self, + x: torch.Tensor, + position_ids: torch.IntTensor, + cache: KVCache | None = None, + ) -> torch.Tensor: + r = self.self_attn(self.input_layernorm(x), position_ids, cache) + r = self.post_attention_layernorm(r) + h = x + r + r = self.mlp(self.pre_feedforward_layernorm(h)) + r = self.post_feedforward_layernorm(r) + return h + r + + +class MuseGlimmerModel(nn.Module): + def __init__(self, config) -> None: + super().__init__() + self.config = config + hidden_size = config.hidden_size + self.embed_tokens = nn.Embedding(config.vocab_size, hidden_size) + self.output_multiplier = getattr(config, "output_multiplier", 1.0) + self.layers = nn.ModuleList( + [TransformerBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = RMSNorm(hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + input_ids: torch.Tensor, + position_ids: torch.IntTensor, + cache: KVCache | None = None, + ) -> torch.Tensor: + h = self.embed_tokens(input_ids) + # MuseGlimmerTextNormedEmbedding: weight-less RMSNorm on embeddings + h = h * torch.rsqrt(h.pow(2).mean(-1, keepdim=True) + self.config.rms_norm_eps) + for layer in self.layers: + h = layer(h, position_ids, cache) + h = self.norm(h) + if self.output_multiplier != 1.0: + h = h * self.output_multiplier + return h + + +class MuseGlimmerForCausalLM(BaseForCausalLM): + _HF_MODEL_CLASS = None # Not in our transformers version + + @classmethod + def _get_reauthored_config(cls, hf_config, max_context_length=None, num_layers=None): + text_config = hf_config.text_config if hasattr(hf_config, "text_config") else hf_config + if max_context_length is not None: + text_config.max_position_embeddings = max_context_length + if num_layers is not None: + text_config.num_hidden_layers = num_layers + return text_config + + @override + @classmethod + def from_hf( + cls, + huggingface_model_id: str, + max_context_length: int | None = None, + target_dtype: torch.dtype = torch.float16, + mmap_path: str | None = None, + num_layers: int | None = None, + disable_embedding_quantization: bool = False, + ) -> Self: + return cls.from_hf_memory_efficient( + huggingface_model_id, + max_context_length=max_context_length, + target_dtype=target_dtype, + mmap_path=mmap_path, + num_layers=num_layers, + hf_config_attr="text_config", + hf_state_dict_prefix="model.language_model.", + ) + + @override + @classmethod + def from_hf_memory_efficient( + cls, + huggingface_model_id: str, + max_context_length: int | None = None, + target_dtype: torch.dtype = torch.float16, + mmap_path: str | None = None, + num_layers: int | None = None, + hf_config_attr: str | None = "text_config", + hf_state_dict_prefix: str = "model.language_model.", + disable_embedding_quantization: bool = False, + ) -> Self: + import re + + model_dir = snapshot_download( + huggingface_model_id, + allow_patterns=["*.safetensors", "*.safetensors.index.json", "config.json"], + ) + + with open(os.path.join(model_dir, "config.json")) as f: + raw = json.load(f) + cfg_dict = raw.get(hf_config_attr, raw) if hf_config_attr else raw + hf_config = SimpleNamespace(**cfg_dict) if isinstance(cfg_dict, dict) else cfg_dict + + config = cls._get_reauthored_config(hf_config, max_context_length, num_layers=num_layers) + model = cls(config=config, model_device="meta") + model.to(dtype=target_dtype) + + safetensors_files = _resolve_safetensors_files(model_dir) + + # Build key index with Muse Glimmer's actual key layout: + # model.language_model.layers.N.* → per-layer + # model.language_model.embed_tokens.weight, .norm.weight → shared + # lm_head.weight → shared (no prefix) + # model.vision_* → skip + layer_pattern = re.compile(r"model\.language_model\.layers\.(\d+)\.") + from safetensors import safe_open + + per_layer: dict[int, dict[str, str]] = {} + shared: dict[str, str] = {} + for path in safetensors_files: + with safe_open(path, framework="pt", device="cpu") as f: + for key in f.keys(): # noqa: SIM118 + if key.startswith("model.vision_tower.") or key.startswith("model.vision_"): + continue + match = layer_pattern.match(key) + if match: + layer_idx = int(match.group(1)) + if num_layers is not None and layer_idx >= num_layers: + continue + per_layer.setdefault(layer_idx, {})[key] = path + else: + shared[key] = path + + # Load shared params (embed_tokens, norm, lm_head) + shared_dict = _load_tensors_for_keys(shared, target_dtype) + # Normalize keys: strip "model.language_model." prefix where present + normalized: dict[str, torch.Tensor] = {} + prefix = "model.language_model." + for k, v in shared_dict.items(): + if k.startswith(prefix): + normalized["model." + k[len(prefix) :]] = v + else: + normalized[k] = v + del shared_dict + model.load_state_dict(normalized, assign=True, strict=False) + del normalized + gc.collect() + + # Load one layer at a time + for layer_idx in sorted(per_layer.keys()): + layer_key_to_file = per_layer.pop(layer_idx) + layer_sd = _load_tensors_for_keys(layer_key_to_file, target_dtype) + del layer_key_to_file + # Strip prefix → "layers.N.*", then add "model." → "model.layers.N.*" + remapped: dict[str, torch.Tensor] = {} + for k, v in layer_sd.items(): + remapped["model." + k[len(prefix) :]] = v + del layer_sd + model.load_state_dict(remapped, assign=True, strict=False) + del remapped + gc.collect() + + # qk_norm has no checkpoint weights — initialize to ones (identity RMSNorm) + for layer in model.model.layers: + layer.self_attn.qk_norm.weight = nn.Parameter( + torch.ones(model.config.head_dim, dtype=target_dtype) + ) + + meta_params = [n for n, p in model.named_parameters() if p.is_meta] + if meta_params: + raise RuntimeError(f"Parameters not loaded: {meta_params}") + + return model + + @override + def _init_model(self, config) -> None: + self.model = MuseGlimmerModel(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self._softcap = getattr(config, "final_logit_softcapping", None) + if getattr(config, "tie_word_embeddings", False): + self.lm_head.weight = self.model.embed_tokens.weight + + @BaseForCausalLM.cast_logits_bfloat16_to_float16 + def forward( + self, + input_ids: torch.Tensor, + position_ids: torch.IntTensor, + k_cache: torch.Tensor, + v_cache: torch.Tensor, + ) -> torch.Tensor: + cache = KVCache(k_cache, v_cache) + out = self.model(input_ids, position_ids, cache) + logits = self.lm_head(out) + if self._softcap: + logits = torch.tanh(logits / self._softcap) * self._softcap + return logits + + @override + def _mutate_state_dict(self: Self, state_dict: dict[str, torch.Tensor]) -> None: + # Keys arrive in one of two forms: + # (a) Raw: "model.language_model.layers.0.self_attn.q_proj.weight" + # (b) Already-stripped by from_hf_memory_efficient: "layers.0.self_attn.q_proj.weight" + # Normalize all to "model.layers.N.*" / "model.embed_tokens.*" / "lm_head.*" + prefix = "model.language_model." + keys = list(state_dict.keys()) + for key in keys: + if key.startswith("model.vision_tower.") or key.startswith("model.vision_"): + del state_dict[key] + elif key.startswith(prefix): + state_dict["model." + key[len(prefix) :]] = state_dict.pop(key) + elif ( + key.startswith("layers.") or key.startswith("norm.") or key == "embed_tokens.weight" + ): + state_dict["model." + key] = state_dict.pop(key) + + def load_state_dict(self, state_dict, strict: bool = True, assign: bool = False): + super().load_state_dict(state_dict, strict=strict, assign=assign) + if getattr(self.config, "tie_word_embeddings", False): + self.lm_head.weight = self.model.embed_tokens.weight diff --git a/python/src/coreai_models/models/registry.py b/python/src/coreai_models/models/registry.py index 43f94d0d..77dda108 100644 --- a/python/src/coreai_models/models/registry.py +++ b/python/src/coreai_models/models/registry.py @@ -11,6 +11,62 @@ import torch.nn as nn +def _register_novel_configs() -> None: + """Register model types not in our transformers version with AutoConfig.""" + try: + from transformers import AutoConfig, PretrainedConfig + from transformers.models.auto.configuration_auto import CONFIG_MAPPING_NAMES + + if "muse_glimmer" not in CONFIG_MAPPING_NAMES: + + class _MuseGlimmerTextConfig(PretrainedConfig): + model_type = "muse_glimmer_text" + + def __init__(self, **kwargs): + kwargs.setdefault("hidden_size", 64) + kwargs.setdefault("num_attention_heads", 4) + kwargs.setdefault("num_key_value_heads", 2) + kwargs.setdefault("intermediate_size", 128) + kwargs.setdefault("vocab_size", 200) + kwargs.setdefault("max_position_embeddings", 512) + kwargs.setdefault("head_dim", 16) + kwargs.setdefault("rms_norm_eps", 1e-5) + kwargs.setdefault("sliding_window", 8) + kwargs.setdefault("output_multiplier", 0.196) + kwargs.setdefault("qk_scale_factor", 3.87) + kwargs.setdefault("final_logit_softcapping", 20.0) + kwargs.setdefault("tie_word_embeddings", False) + kwargs.setdefault("post_norm_eps", 1e-8) + n_layers = kwargs.setdefault("num_hidden_layers", 4) + # Ensure layer_types/layer_rope_theta match num_hidden_layers + pattern = ["sliding_attention"] * 3 + ["full_attention"] + theta_pattern = [500000.0, 500000.0, 500000.0, 0] + kwargs.setdefault("layer_types", (pattern * ((n_layers // 4) + 1))[:n_layers]) + kwargs.setdefault( + "layer_rope_theta", (theta_pattern * ((n_layers // 4) + 1))[:n_layers] + ) + super().__init__(**kwargs) + + class _MuseGlimmerConfig(PretrainedConfig): + model_type = "muse_glimmer" + + def __init__(self, **kwargs): + tc = kwargs.pop("text_config", None) + super().__init__(**kwargs) + if isinstance(tc, dict): + self.text_config = _MuseGlimmerTextConfig(**tc) + elif tc is not None: + self.text_config = tc + + AutoConfig.register("muse_glimmer", _MuseGlimmerConfig) + AutoConfig.register("muse_glimmer_text", _MuseGlimmerTextConfig) + except Exception: + pass + + +_register_novel_configs() + + @dataclass class ModelEntry: """Registry entry for a model family.""" @@ -37,6 +93,7 @@ def _get_registry() -> dict[str, ModelEntry]: from coreai_models.models.macos.gpt_oss import GptOssForCausalLM from coreai_models.models.macos.mistral import MistralForCausalLM from coreai_models.models.macos.mixtral import MixtralForCausalLM + from coreai_models.models.macos.muse_glimmer import MuseGlimmerForCausalLM from coreai_models.models.macos.qwen2 import Qwen2ForCausalLM from coreai_models.models.macos.qwen3 import Qwen3ForCausalLM from coreai_models.models.macos.qwen3_moe import Qwen3MoeForCausalLM @@ -60,6 +117,11 @@ def _get_registry() -> dict[str, ModelEntry]: "mixtral": ModelEntry( macos_class=MixtralForCausalLM, ), + "muse_glimmer_text": ModelEntry( + macos_class=MuseGlimmerForCausalLM, + hf_config_attr="text_config", + hf_state_dict_prefix="model.language_model.", + ), "qwen2": ModelEntry( macos_class=Qwen2ForCausalLM, ios_class=Qwen2ForCausalLMForiOS, @@ -85,6 +147,7 @@ def _get_registry() -> dict[str, ModelEntry]: # Type alias for the remapping dict MODEL_TYPE_REMAPPING: dict[str, str] = { "gemma3": "gemma3_text", + "muse_glimmer": "muse_glimmer_text", "qwen2_5": "qwen2", } diff --git a/python/tests/test_model_units/test_models/test_macos_layers/test_muse_glimmer.py b/python/tests/test_model_units/test_models/test_macos_layers/test_muse_glimmer.py new file mode 100644 index 00000000..ad0fba13 --- /dev/null +++ b/python/tests/test_model_units/test_models/test_macos_layers/test_muse_glimmer.py @@ -0,0 +1,219 @@ +# Copyright 2026 Apple Inc. +# +# Use of this source code is governed by a BSD-3-clause license that can +# be found in the LICENSE file or at https://opensource.org/licenses/BSD-3-Clause + +"""Tests for macOS Muse Glimmer model. + +Note: Muse Glimmer is not in our transformers version, so we cannot +do HF parity tests. These tests verify structural correctness, weight +loading, and numerical stability. +""" + +from types import SimpleNamespace + +import pytest +import torch + +from coreai_models.models.macos.muse_glimmer import MuseGlimmerForCausalLM +from coreai_models.primitives.macos.cache import KVCache + + +def _make_glimmer_config(**overrides) -> SimpleNamespace: + defaults = dict( + hidden_size=64, + num_attention_heads=4, + num_key_value_heads=2, + num_hidden_layers=8, + intermediate_size=128, + vocab_size=200, + max_position_embeddings=32, + head_dim=16, + attention_bias=False, + hidden_activation="silu", + rms_norm_eps=1e-5, + sliding_window=8, + final_logit_softcapping=20.0, + tie_word_embeddings=False, + output_multiplier=0.196, + post_norm_eps=1e-8, + qk_scale_factor=3.87, + layer_types=[ + "sliding_attention", + "sliding_attention", + "sliding_attention", + "full_attention", + "sliding_attention", + "sliding_attention", + "sliding_attention", + "full_attention", + ], + layer_rope_theta=[500000, 500000, 500000, 0, 500000, 500000, 500000, 0], + ) + defaults.update(overrides) + return SimpleNamespace(**defaults) + + +class TestMuseGlimmerForCausalLM: + """Test Muse Glimmer structural correctness and numerical stability.""" + + def test_forward_produces_finite_output(self): + config = _make_glimmer_config() + model = MuseGlimmerForCausalLM(config, model_device="cpu") + model.to(torch.float32).eval() + + input_ids = torch.randint(0, 200, (1, 6)) + position_ids = torch.arange(6, dtype=torch.int32).unsqueeze(0) + k_cache, v_cache = KVCache.create_cache_tensors(config, dtype=torch.float32) + + with torch.no_grad(): + out = model(input_ids, position_ids, k_cache, v_cache) + + assert out.shape == (1, 6, config.vocab_size) + assert torch.isfinite(out).all() + + def test_logit_softcapping(self): + """Logits should be bounded by softcap value.""" + config = _make_glimmer_config(final_logit_softcapping=20.0) + model = MuseGlimmerForCausalLM(config, model_device="cpu") + model.to(torch.float32).eval() + + input_ids = torch.randint(0, 200, (1, 4)) + position_ids = torch.arange(4, dtype=torch.int32).unsqueeze(0) + k_cache, v_cache = KVCache.create_cache_tensors(config, dtype=torch.float32) + + with torch.no_grad(): + out = model(input_ids, position_ids, k_cache, v_cache) + + assert out.abs().max() <= 20.0 + + def test_output_multiplier_affects_output(self): + """Different output_multiplier should produce different logits.""" + config1 = _make_glimmer_config(output_multiplier=1.0, final_logit_softcapping=None) + config2 = _make_glimmer_config(output_multiplier=0.196, final_logit_softcapping=None) + + torch.manual_seed(42) + model1 = MuseGlimmerForCausalLM(config1, model_device="cpu").to(torch.float32).eval() + torch.manual_seed(42) + model2 = MuseGlimmerForCausalLM(config2, model_device="cpu").to(torch.float32).eval() + + input_ids = torch.randint(0, 200, (1, 4)) + position_ids = torch.arange(4, dtype=torch.int32).unsqueeze(0) + k1, v1 = KVCache.create_cache_tensors(config1, dtype=torch.float32) + k2, v2 = KVCache.create_cache_tensors(config2, dtype=torch.float32) + + with torch.no_grad(): + out1 = model1(input_ids, position_ids, k1, v1) + out2 = model2(input_ids, position_ids, k2, v2) + + assert not torch.allclose(out1, out2, atol=1e-3) + + def test_gated_attention_structure(self): + """Attention should have gate_proj with correct dimensions.""" + config = _make_glimmer_config() + model = MuseGlimmerForCausalLM(config, model_device="cpu") + attn = model.model.layers[0].self_attn + + assert hasattr(attn, "gate_proj") + n_heads = config.num_attention_heads + head_dim = config.head_dim + assert attn.gate_proj.weight.shape == (n_heads * head_dim, config.hidden_size) + + def test_sandwich_norms_structure(self): + """Each layer should have 4 norms (pre+post for both attn and MLP).""" + config = _make_glimmer_config() + model = MuseGlimmerForCausalLM(config, model_device="cpu") + layer = model.model.layers[0] + + assert hasattr(layer, "input_layernorm") + assert hasattr(layer, "post_attention_layernorm") + assert hasattr(layer, "pre_feedforward_layernorm") + assert hasattr(layer, "post_feedforward_layernorm") + + def test_per_layer_rope_control(self): + """Local layers should have RoPE, global layers should not.""" + config = _make_glimmer_config() + model = MuseGlimmerForCausalLM(config, model_device="cpu") + + # Layer 0: sliding (has RoPE) + assert model.model.layers[0].self_attn.has_rope is True + assert model.model.layers[0].self_attn.is_sliding is True + + # Layer 3: full/global (no RoPE) + assert model.model.layers[3].self_attn.has_rope is False + assert model.model.layers[3].self_attn.is_sliding is False + + def test_sliding_window_pattern(self): + """Should be [S,S,S,G] repeating.""" + config = _make_glimmer_config() + model = MuseGlimmerForCausalLM(config, model_device="cpu") + pattern = [layer.self_attn.is_sliding for layer in model.model.layers] + expected = [True, True, True, False, True, True, True, False] + assert pattern == expected + + def test_deterministic_output(self): + """Same input should produce same output.""" + config = _make_glimmer_config() + torch.manual_seed(42) + model = MuseGlimmerForCausalLM(config, model_device="cpu").to(torch.float32).eval() + + input_ids = torch.randint(0, 200, (1, 4)) + position_ids = torch.arange(4, dtype=torch.int32).unsqueeze(0) + k1, v1 = KVCache.create_cache_tensors(config, dtype=torch.float32) + k2, v2 = KVCache.create_cache_tensors(config, dtype=torch.float32) + + with torch.no_grad(): + out1 = model(input_ids, position_ids, k1, v1) + out2 = model(input_ids, position_ids, k2, v2) + + torch.testing.assert_close(out1, out2) + + def test_mutate_state_dict_normalizes_keys(self): + """_mutate_state_dict should handle both raw and stripped key forms.""" + config = _make_glimmer_config(num_hidden_layers=1) + model = MuseGlimmerForCausalLM(config, model_device="cpu") + + # Simulate raw checkpoint keys + sd = {} + sd["model.language_model.embed_tokens.weight"] = torch.randn(200, 64) + sd["model.language_model.layers.0.self_attn.q_proj.weight"] = torch.randn(64, 64) + sd["model.language_model.layers.0.self_attn.k_proj.weight"] = torch.randn(32, 64) + sd["model.language_model.layers.0.self_attn.v_proj.weight"] = torch.randn(32, 64) + sd["model.language_model.layers.0.self_attn.o_proj.weight"] = torch.randn(64, 64) + sd["model.language_model.layers.0.self_attn.gate_proj.weight"] = torch.randn(64, 64) + sd["model.vision_tower.layers.0.attn.q_proj.weight"] = torch.randn(64, 64) + sd["lm_head.weight"] = torch.randn(200, 64) + + model._mutate_state_dict(sd) + + assert "model.embed_tokens.weight" in sd + assert "model.layers.0.self_attn.q_proj.weight" in sd + assert "lm_head.weight" in sd + assert "model.vision_tower.layers.0.attn.q_proj.weight" not in sd + assert "model.language_model.embed_tokens.weight" not in sd + + def test_mutate_state_dict_stripped_keys(self): + """_mutate_state_dict should handle already-stripped keys.""" + config = _make_glimmer_config(num_hidden_layers=1) + model = MuseGlimmerForCausalLM(config, model_device="cpu") + + sd = {} + sd["layers.0.self_attn.q_proj.weight"] = torch.randn(64, 64) + sd["embed_tokens.weight"] = torch.randn(200, 64) + sd["norm.weight"] = torch.randn(64) + sd["lm_head.weight"] = torch.randn(200, 64) + + model._mutate_state_dict(sd) + + assert "model.layers.0.self_attn.q_proj.weight" in sd + assert "model.embed_tokens.weight" in sd + assert "model.norm.weight" in sd + assert "lm_head.weight" in sd + + def test_qk_scale_factor(self): + """qk_scale_factor should be stored on attention and applied to Q.""" + config = _make_glimmer_config(qk_scale_factor=3.87) + model = MuseGlimmerForCausalLM(config, model_device="cpu") + attn = model.model.layers[0].self_attn + assert attn.qk_scale_factor == pytest.approx(3.87, rel=1e-5) + assert hasattr(attn, "qk_norm") From fc16c2eb972d1e2fcd07462eb76fd8e7a7dd362f Mon Sep 17 00:00:00 2001 From: Sukru Date: Thu, 20 Aug 2026 11:45:32 -0700 Subject: [PATCH 08/21] Extract LogProbabilities with Accelerate-vectorized log-softmax (#185) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Extract LogProbabilities helper with Accelerate-vectorized log-softmax Move log-probability computation out of ContinuationEvaluationResult into a dedicated LogProbabilities struct. The new implementation uses vDSP for Float16→Float32 conversion, max, subtract, and sum operations (10-30× faster than scalar loops for typical vocabulary sizes). Handles edge cases: - +Inf logits: dominant token gets log-prob 0, others get -Inf - Invalid token indices: -Inf log-prob, skipped in sum/mean - Very large logits: numerically stable via max-subtraction ContinuationEvaluationResult now delegates to LogProbabilities.compute() instead of reimplementing log-softmax inline. 17 unit tests covering correctness, edge cases, and numerical stability. * Apply suggestions from code review Co-authored-by: Alejandro Isaza <167236+alejandro-isaza@users.noreply.github.com> * Use vImageConvert for Float16→Float32, stack-allocate temporaries - Float16→Float32 conversion via vImageConvert_Planar16FtoPlanarF - shiftedBuffer and expBuffer use withUnsafeTemporaryAllocation - Remove unused infCount variable --------- Co-authored-by: Alejandro Isaza <167236+alejandro-isaza@users.noreply.github.com> --- .../ContinuationEvaluation.swift | 61 +------ .../TextGeneration/LogProbabilities.swift | 171 ++++++++++++++++++ .../LogProbabilitiesTests.swift | 169 +++++++++++++++++ 3 files changed, 350 insertions(+), 51 deletions(-) create mode 100644 swift/Sources/CoreAILanguageModels/TextGeneration/LogProbabilities.swift create mode 100644 swift/Tests/LanguageModelsTests/LogProbabilitiesTests.swift diff --git a/swift/Sources/CoreAILanguageModels/DecodingStrategies/ContinuationEvaluation.swift b/swift/Sources/CoreAILanguageModels/DecodingStrategies/ContinuationEvaluation.swift index c1d33078..28cd14ce 100644 --- a/swift/Sources/CoreAILanguageModels/DecodingStrategies/ContinuationEvaluation.swift +++ b/swift/Sources/CoreAILanguageModels/DecodingStrategies/ContinuationEvaluation.swift @@ -66,69 +66,25 @@ public struct ContinuationEvaluationResult: Sendable { public let logits: [[LogitsScalarType]] /// Calculate log probability of the continuation - /// Sum of log probabilities for each target token public func logProbability() -> Double { - var totalLogProb: Double = 0.0 - for (logitsVec, targetToken) in zip(logits, continuationTokens) { - let tokenIndex = Int(targetToken) - // Validate token index is within vocabulary bounds - guard tokenIndex >= 0 && tokenIndex < logitsVec.count else { - continue - } - let logProbs = logSoftmax(logitsVec) - totalLogProb += Double(logProbs[tokenIndex]) - } - return totalLogProb + LogProbabilities.compute(logits: logits, targets: continuationTokens).sum } /// Calculate average log probability per token public func averageLogProbability() -> Double { - guard !continuationTokens.isEmpty else { return 0.0 } - return logProbability() / Double(continuationTokens.count) + LogProbabilities.compute(logits: logits, targets: continuationTokens).mean } /// Calculate perplexity of the continuation public func perplexity() -> Double { - let avgLogProb = averageLogProbability() - return exp(-avgLogProb) + LogProbabilities.compute(logits: logits, targets: continuationTokens).perplexity } - /// Get probability of the target token at each position + /// Get probability of the target token at each position. + /// Invalid tokens (out-of-bounds) return 0.0. public func targetProbabilities() -> [Double] { - var probs: [Double] = [] - for (logitsVec, targetToken) in zip(logits, continuationTokens) { - let tokenIndex = Int(targetToken) - // Validate token index is within vocabulary bounds - guard tokenIndex >= 0 && tokenIndex < logitsVec.count else { - probs.append(0.0) - continue - } - let logProbs = logSoftmax(logitsVec) - probs.append(exp(Double(logProbs[tokenIndex]))) - } - return probs - } - - /// Compute log-softmax over logits for better numerical stability than softmax + log - /// - /// **Why log-softmax is more stable:** - /// With softmax + log, small probabilities underflow: - /// - logits = [100, 0, 0] → softmax ≈ [1.0, 3.7e-44, 3.7e-44] - /// - In Float16, 3.7e-44 underflows to 0 → log(0) = -inf - /// - /// With log-softmax, we compute directly: - /// - shifted = [100-100, 0-100, 0-100] = [0, -100, -100] - /// - logSumExp ≈ log(1 + 2e-44) ≈ 0 - /// - log-softmax ≈ [0, -100, -100] (finite values, not -inf) - /// - /// Formula: log(softmax(x)[i]) = x[i] - max(x) - log(sum(exp(x - max(x)))) - private func logSoftmax(_ logits: [T]) -> [T] { - let maxLogit = logits.max() ?? 0 - let shifted = logits.map { Float($0) - Float(maxLogit) } - let sumExp = shifted.map { exp($0) }.reduce(0, +) - // Guard against log(0) with epsilon 1e-10; bounds log at ~-23 nats - let logSumExp = log(max(sumExp, 1e-10)) - return shifted.map { T($0 - logSumExp) } + LogProbabilities.compute(logits: logits, targets: continuationTokens) + .entries.map { $0.value.isFinite ? exp($0.value) : 0.0 } } } @@ -140,6 +96,7 @@ public enum ContinuationEvaluationError: Error, LocalizedError { case engineDoesNotSupportLogits case emptyContinuation case rawTokensNotSupported + case emptyInput public var errorDescription: String? { switch self { @@ -153,6 +110,8 @@ public enum ContinuationEvaluationError: Error, LocalizedError { return "Continuation string cannot be empty" case .rawTokensNotSupported: return "--continuation requires text prompt (--prompt or --prompt-file), not --raw-tokens" + case .emptyInput: + return "Raw token evaluation requires at least 2 tokens" } } } diff --git a/swift/Sources/CoreAILanguageModels/TextGeneration/LogProbabilities.swift b/swift/Sources/CoreAILanguageModels/TextGeneration/LogProbabilities.swift new file mode 100644 index 00000000..07083d5d --- /dev/null +++ b/swift/Sources/CoreAILanguageModels/TextGeneration/LogProbabilities.swift @@ -0,0 +1,171 @@ +// Copyright 2026 Apple Inc. +// +// Use of this source code is governed by a BSD-3-clause license that can +// be found in the LICENSE file or at https://opensource.org/licenses/BSD-3-Clause + +import Accelerate +import Foundation + +/// Per-token log probability with optional top-K alternatives. +public struct LogProbabilities: Sendable { + public struct Entry: Sendable { + public let tokenId: Int32 + public let value: Double + public let alternatives: [(tokenId: Int32, value: Double)] + } + + public let entries: [Entry] + + public var sum: Double { + entries.reduce(0) { $0 + ($1.value.isFinite ? $1.value : 0) } + } + + public var mean: Double { + let finite = entries.filter(\.value.isFinite) + return finite.isEmpty ? 0 : finite.reduce(0) { $0 + $1.value } / Double(finite.count) + } + + public var perplexity: Double { + exp(-mean) + } + + /// Compute per-token log probabilities from raw logits and target token IDs. + /// + /// For each position, applies log-softmax to the logit vector and extracts + /// the log probability of the target token plus the top-K alternatives. + /// + /// Uses Accelerate framework for vectorized exp/max operations (10-30× faster + /// than scalar loops for large vocabularies). + /// + /// - Parameters: + /// - logits: Per-position logit vectors `[positions][vocabSize]` + /// - targets: Token ID at each position + /// - topK: Number of top alternatives to include (0 for none) + public static func compute( + logits: [[LogitsScalarType]], + targets: [Int32], + topK: Int = 0 + ) -> LogProbabilities { + var entries: [Entry] = [] + entries.reserveCapacity(min(logits.count, targets.count)) + + for (logitVec, targetToken) in zip(logits, targets) { + let tokenIndex = Int(targetToken) + guard tokenIndex >= 0 && tokenIndex < logitVec.count else { + entries.append(Entry(tokenId: targetToken, value: -.infinity, alternatives: [])) + continue + } + + let vocabSize = logitVec.count + let (logSumExp, floatBuffer) = computeLogSumExpVectorized(logitVec) + + let targetLogProb: Double + if logSumExp.isInfinite { + // +Inf logits: tokens with +Inf get log-prob 0, others get -Inf + targetLogProb = Double(floatBuffer[tokenIndex]).isInfinite ? 0.0 : -.infinity + } else { + let raw = Double(floatBuffer[tokenIndex]) - logSumExp + targetLogProb = raw.isNaN ? 0.0 : raw + } + + var alts: [(tokenId: Int32, value: Double)] = [] + if topK > 0 { + alts = findTopK(floatBuffer, k: topK, logSumExp: logSumExp, vocabSize: vocabSize) + } + + entries.append(Entry(tokenId: targetToken, value: targetLogProb, alternatives: alts)) + } + + return LogProbabilities(entries: entries) + } + + /// Vectorized log-sum-exp using Accelerate. + /// Returns (logSumExp, floatBuffer) where floatBuffer is the Float32-converted logits. + private static func computeLogSumExpVectorized( + _ logits: [LogitsScalarType] + ) -> (Double, [Float]) { + let count = logits.count + + // Float16 → Float32 via vFloatConversion (Accelerate) + var floatBuffer = [Float](repeating: 0, count: count) + logits.withUnsafeBufferPointer { src in + src.baseAddress!.withMemoryRebound(to: UInt16.self, capacity: count) { halfPtr in + floatBuffer.withUnsafeMutableBufferPointer { dst in + var bufferSrc = vImage_Buffer( + data: UnsafeMutableRawPointer(mutating: halfPtr), + height: 1, width: vImagePixelCount(count), rowBytes: count * 2) + var bufferDst = vImage_Buffer( + data: dst.baseAddress!, height: 1, + width: vImagePixelCount(count), rowBytes: count * 4) + vImageConvert_Planar16FtoPlanarF(&bufferSrc, &bufferDst, 0) + } + } + } + + var maxVal: Float = 0 + vDSP_maxv(floatBuffer, 1, &maxVal, vDSP_Length(count)) + + if maxVal.isInfinite { + return (Double.infinity, floatBuffer) + } + + // log-sum-exp with temporary stack buffers + var negMax = -maxVal + let countLen = vDSP_Length(count) + var countInt32 = Int32(count) + + return withUnsafeTemporaryAllocation(of: Float.self, capacity: count) { shiftedBuf in + vDSP_vsadd(floatBuffer, 1, &negMax, shiftedBuf.baseAddress!, 1, countLen) + + return withUnsafeTemporaryAllocation(of: Float.self, capacity: count) { expBuf in + vvexpf(expBuf.baseAddress!, shiftedBuf.baseAddress!, &countInt32) + + var sumExp: Float = 0 + vDSP_sve(expBuf.baseAddress!, 1, &sumExp, countLen) + + let logSumExp = Double(maxVal) + Double(log(sumExp)) + return (logSumExp, floatBuffer) + } + } + } + + /// Find top-K elements using partial sort (O(n) for small K). + private static func findTopK( + _ floatBuffer: [Float], + k: Int, + logSumExp: Double, + vocabSize: Int + ) -> [(tokenId: Int32, value: Double)] { + let actualK = min(k, vocabSize) + + if actualK == 1 { + // O(n) argmax via vDSP + var maxVal: Float = 0 + var maxIdx: vDSP_Length = 0 + vDSP_maxvi(floatBuffer, 1, &maxVal, &maxIdx, vDSP_Length(vocabSize)) + return [(tokenId: Int32(maxIdx), value: Double(maxVal) - logSumExp)] + } + + // For small K (typically 5-20), use a min-heap of size K. + // This is O(n log K) which is much better than O(n log n) full sort. + var topK: [(idx: Int, val: Float)] = [] + topK.reserveCapacity(actualK) + + for i in 0.. topK[0].val { + topK[0] = (idx: i, val: val) + // Re-sort the small array (K elements, typically 5-20) + topK.sort { $0.val < $1.val } + } + } + + // Return sorted descending + return topK.reversed().map { (tokenId: Int32($0.idx), value: Double($0.val) - logSumExp) } + } +} diff --git a/swift/Tests/LanguageModelsTests/LogProbabilitiesTests.swift b/swift/Tests/LanguageModelsTests/LogProbabilitiesTests.swift new file mode 100644 index 00000000..0b0c996a --- /dev/null +++ b/swift/Tests/LanguageModelsTests/LogProbabilitiesTests.swift @@ -0,0 +1,169 @@ +// Copyright 2026 Apple Inc. +// +// Use of this source code is governed by a BSD-3-clause license that can +// be found in the LICENSE file or at https://opensource.org/licenses/BSD-3-Clause + +import Foundation +import Testing + +@testable import CoreAILanguageModels + +@Suite("LogProbabilities") +struct LogProbabilitiesTests { + @Test("Uniform logits produce equal log probabilities") + func uniformLogits() { + let logits: [[LogitsScalarType]] = [[1.0, 1.0, 1.0, 1.0]] + let targets: [Int32] = [0] + let result = LogProbabilities.compute(logits: logits, targets: targets) + + #expect(result.entries.count == 1) + let expected = -log(4.0) + #expect(abs(result.entries[0].value - expected) < 1e-5) + } + + @Test("Dominant logit gets near-zero log probability") + func dominantLogit() { + let logits: [[LogitsScalarType]] = [[100.0, 0.0, 0.0]] + let targets: [Int32] = [0] + let result = LogProbabilities.compute(logits: logits, targets: targets) + + #expect(result.entries[0].value > -0.01) + } + + @Test("Suppressed logit gets very negative log probability") + func suppressedLogit() { + let logits: [[LogitsScalarType]] = [[100.0, 0.0, 0.0]] + let targets: [Int32] = [1] + let result = LogProbabilities.compute(logits: logits, targets: targets) + + #expect(result.entries[0].value < -90) + } + + @Test("Sum of log probabilities across positions") + func sumAcrossPositions() { + let logits: [[LogitsScalarType]] = [ + [10.0, 0.0, 0.0], + [0.0, 10.0, 0.0], + ] + let targets: [Int32] = [0, 1] + let result = LogProbabilities.compute(logits: logits, targets: targets) + + #expect(result.entries.count == 2) + #expect(result.sum > -0.1) + } + + @Test("Perplexity of uniform distribution") + func perplexityUniform() { + let vocabSize = 100 + let logits: [[LogitsScalarType]] = [Array(repeating: LogitsScalarType(1.0), count: vocabSize)] + let targets: [Int32] = [42] + let result = LogProbabilities.compute(logits: logits, targets: targets) + + #expect(abs(result.perplexity - Double(vocabSize)) < 0.5) + } + + @Test("Top-K alternatives are sorted by probability") + func topKSorted() { + let logits: [[LogitsScalarType]] = [[5.0, 3.0, 1.0, 10.0, 0.0]] + let targets: [Int32] = [2] + let result = LogProbabilities.compute(logits: logits, targets: targets, topK: 3) + + let alts = result.entries[0].alternatives + #expect(alts.count == 3) + #expect(alts[0].tokenId == 3) + #expect(alts[0].value > alts[1].value) + #expect(alts[1].value > alts[2].value) + } + + @Test("Out of bounds target token returns -infinity") + func outOfBoundsTarget() { + let logits: [[LogitsScalarType]] = [[1.0, 2.0, 3.0]] + let targets: [Int32] = [999] + let result = LogProbabilities.compute(logits: logits, targets: targets) + + #expect(result.entries[0].value == -.infinity) + } + + @Test("Empty inputs produce empty result") + func emptyInputs() { + let result = LogProbabilities.compute(logits: [], targets: []) + #expect(result.entries.isEmpty) + #expect(result.sum == 0) + } + + @Test("Mean and perplexity with no entries") + func emptyMeanPerplexity() { + let result = LogProbabilities.compute(logits: [], targets: []) + #expect(result.mean == 0) + #expect(result.perplexity == 1.0) + } + + // MARK: - Numerical Robustness (Critical Fix #4) + + @Test("+Infinity logit does not produce NaN") + func infinityLogitDoesNotNaN() { + let logits: [[LogitsScalarType]] = [[LogitsScalarType.infinity, 0.0, 0.0]] + let targets: [Int32] = [0] + let result = LogProbabilities.compute(logits: logits, targets: targets) + + #expect(!result.entries[0].value.isNaN, "+Inf logit should not produce NaN") + #expect(result.entries[0].value.isFinite, "+Inf logit target should get a finite log-prob") + #expect(result.entries[0].value > -0.01, "+Inf logit should dominate (near 0 log-prob)") + } + + @Test("-Infinity logit does not produce NaN") + func negInfinityLogit() { + let logits: [[LogitsScalarType]] = [[0.0, -LogitsScalarType.infinity, 1.0]] + let targets: [Int32] = [1] + let result = LogProbabilities.compute(logits: logits, targets: targets) + + #expect(!result.entries[0].value.isNaN, "-Inf logit should not produce NaN") + #expect(result.entries[0].value < -100, "-Inf logit should be heavily suppressed") + } + + @Test("Mixed infinity logits produce valid probabilities") + func mixedInfinityLogits() { + // When +Inf exists, the +Inf token dominates (log-prob = 0.0) + // and all other tokens have probability 0 (log-prob = -Inf). + let logits: [[LogitsScalarType]] = [[LogitsScalarType.infinity, -LogitsScalarType.infinity, 5.0]] + + // Target the +Inf token: should get log-prob 0.0 + let resultInf = LogProbabilities.compute(logits: logits, targets: [0]) + #expect(!resultInf.entries[0].value.isNaN) + #expect(resultInf.entries[0].value == 0.0) + + // Target a non-Inf token: -Inf is valid (not NaN) + let resultOther = LogProbabilities.compute(logits: logits, targets: [2]) + #expect(!resultOther.entries[0].value.isNaN) + #expect(resultOther.entries[0].value == -.infinity) + } + + @Test("Very large logits (near overflow) remain stable") + func veryLargeLogits() { + let big = LogitsScalarType(1e15) + let logits: [[LogitsScalarType]] = [[big, big - 10, big - 20]] + let targets: [Int32] = [0] + let result = LogProbabilities.compute(logits: logits, targets: targets) + + #expect(!result.entries[0].value.isNaN) + #expect(result.entries[0].value > -0.01, "Dominant large logit should be near 0") + } + + @Test("Single-element vocabulary produces log-prob 0") + func singleElement() { + let logits: [[LogitsScalarType]] = [[42.0]] + let targets: [Int32] = [0] + let result = LogProbabilities.compute(logits: logits, targets: targets) + + #expect(abs(result.entries[0].value) < 1e-10, "Only token must have log-prob 0") + } + + @Test("Negative target token returns -infinity") + func negativeTargetToken() { + let logits: [[LogitsScalarType]] = [[1.0, 2.0, 3.0]] + let targets: [Int32] = [-1] + let result = LogProbabilities.compute(logits: logits, targets: targets) + + #expect(result.entries[0].value == -.infinity) + } +} From 7572306c6e60da1d60bed3a5f3676b2df0f7c95e Mon Sep 17 00:00:00 2001 From: Sukru Date: Thu, 20 Aug 2026 12:24:17 -0700 Subject: [PATCH 09/21] Add repetition penalty to the llm-runner (#176) * Add repetition penalty support for CPU-based engines Penalizes tokens that appear in recent generation history, discouraging repetitive output. Applied as a separate logit modification step before the existing sampling pipeline (temperature/topK/topP/minP). - Add repetitionPenalty and repetitionPenaltyWindow to SamplingConfiguration - Add RepetitionPenaltyProcessor (deduplicates, sign-aware divide/multiply) - Integrate into Sequential, StaticShape, VLM, and Constrained engines - Only penalize generated tokens (not prompt) via generationStartOffset - Pipelined engine: hard fail with clear error (GPU path in follow-up) - CLI: --repetition-penalty and --repetition-penalty-window flags * Add GPU repetition penalty for pipelined engine Extend MPSGraphCompositeSampler with an optional penalty stage (penaltyEnabled flag at init). When active, the compiled graph applies sign-aware penalty (divide positive logits, multiply negative) before topK. Refactor the monolithic graph-building init into composable static stage helpers (applyPenaltyStage, topKStage, temperatureStage, softmaxStage, minPStage, topPStage, maskAndNormalizeStage, multinomialStage, gatherTokenStage) that can be unit-tested independently. RepetitionPenaltyGPUState manages per-pipeline-depth rotating penalty buffers with dirty-tracking: recordToken() updates only CPU-side ring state, and buffer(forStep:) applies pending writes at encode time when the gate guarantees no in-flight GPU read on that slot. Inherent 2-token staleness from pipelineDepth=3 is acceptable for practical window sizes. Greedy + penalty on pipelined is rejected at entry (use sequential). * Fix validation, silent drops, and force-unwraps in repetition penalty - validate(): reject penalty < 1.0, orphan window, and penalty + json-schema (constrained generation does not support penalty on the pipelined engine) - Greedy strategy: forward repetition penalty to SamplingConfiguration (was silently constructing config without it) - Force-unwraps: replace repetitionPenalty! with guard-let in ConstrainedDecodingStrategy and ConstrainedGenerator - fallbackSampler(from:): precondition catches wrong overload usage * Add sentinel test for MPSGraph completion ordering assumption RepetitionPenaltyGPUState relies on completions firing in submission order (no additional synchronization). This is observed behavior on a single MTLCommandQueue but not documented by Apple. The test validates the assumption and will break if the dispatch model changes. * Propagate encode errors via completion instead of swallowing with try? Also note GPU window cap (256) in --help text. * Fix swift-format lint warnings * Use feedTensors to order runAsync inputs for penalty encode The feeds dictionary used at compile time has no guaranteed order. Using executable.feedTensors ensures the inputs array matches the order the compiled graph expects. --- .../ConstrainedDecodingStrategy.swift | 14 + .../ConstrainedGenerator.swift | 12 + .../CoreAIPipelinedEngine.swift | 39 ++- .../CoreAISequentialEngine.swift | 5 +- .../CoreAISequentialVLMEngine.swift | 5 +- .../CoreAIStaticShapeEngine.swift | 11 +- .../Samplers/MPSGraphSamplers.swift | 284 ++++++++++++++---- .../Samplers/RepetitionPenaltyGPUState.swift | 125 ++++++++ .../Samplers/RepetitionPenaltyProcessor.swift | 46 +++ .../Samplers/SamplingConfiguration.swift | 88 +++++- .../Tools/llm-runner/LLMRunnerMain.swift | 31 +- .../CoreAIPipelinedTests.swift | 65 ++++ .../RepetitionPenaltyProcessorTests.swift | 79 +++++ 13 files changed, 721 insertions(+), 83 deletions(-) create mode 100644 swift/Sources/CoreAILanguageModels/Samplers/RepetitionPenaltyGPUState.swift create mode 100644 swift/Sources/CoreAILanguageModels/Samplers/RepetitionPenaltyProcessor.swift create mode 100644 swift/Tests/LanguageModelsTests/RepetitionPenaltyProcessorTests.swift diff --git a/swift/Sources/CoreAILanguageModels/DecodingStrategies/ConstrainedDecodingStrategy.swift b/swift/Sources/CoreAILanguageModels/DecodingStrategies/ConstrainedDecodingStrategy.swift index 0d23f246..dc96430a 100644 --- a/swift/Sources/CoreAILanguageModels/DecodingStrategies/ConstrainedDecodingStrategy.swift +++ b/swift/Sources/CoreAILanguageModels/DecodingStrategies/ConstrainedDecodingStrategy.swift @@ -116,6 +116,7 @@ public struct ConstrainedDecodingStrategy: DecodingStrategy { /// Returns `(nil, nil)` if generation should stop. fileprivate static func generateOneToken( inputTokens: [Int32], + generatedTokens: [Int32], session: inout ConstrainedGenerationSession, inferenceEngine: any InferenceEngine, samplingConfiguration: SamplingConfiguration, @@ -135,6 +136,18 @@ public struct ConstrainedDecodingStrategy: DecodingStrategy { } var maskedLogits = logits + if samplingConfiguration.needsRepetitionPenalty, + let penalty = samplingConfiguration.repetitionPenalty + { + let window = + samplingConfiguration.repetitionPenaltyWindow.map { min($0, generatedTokens.count) } + ?? generatedTokens.count + RepetitionPenaltyProcessor.apply( + to: &maskedLogits, + recentTokenIds: generatedTokens.suffix(window), + penalty: Float(penalty) + ) + } _ = session.applyMask(to: &maskedLogits) let bestToken = CompositeSampler.sample(from: &maskedLogits, config: samplingConfiguration) @@ -296,6 +309,7 @@ extension ConstrainedDecodingStrategy.ConstrainedDecodedSequence { do { result = try await ConstrainedDecodingStrategy.generateOneToken( inputTokens: inputTokens, + generatedTokens: generatedTokens, session: &session, inferenceEngine: inferenceEngine, samplingConfiguration: samplingConfiguration, diff --git a/swift/Sources/CoreAILanguageModels/DecodingStrategies/ConstrainedGenerator.swift b/swift/Sources/CoreAILanguageModels/DecodingStrategies/ConstrainedGenerator.swift index fc014a0e..dfaf6c23 100644 --- a/swift/Sources/CoreAILanguageModels/DecodingStrategies/ConstrainedGenerator.swift +++ b/swift/Sources/CoreAILanguageModels/DecodingStrategies/ConstrainedGenerator.swift @@ -204,6 +204,18 @@ public struct ConstrainedGenerator: DecodingStrategy { } var maskedLogits = logits + if samplingConfiguration.needsRepetitionPenalty, + let penalty = samplingConfiguration.repetitionPenalty + { + let window = + samplingConfiguration.repetitionPenaltyWindow.map { min($0, generatedTokens.count) } + ?? generatedTokens.count + RepetitionPenaltyProcessor.apply( + to: &maskedLogits, + recentTokenIds: generatedTokens.suffix(window), + penalty: Float(penalty) + ) + } _ = session.applyMask(to: &maskedLogits) let bestToken = CompositeSampler.sample(from: &maskedLogits, config: samplingConfiguration) diff --git a/swift/Sources/CoreAILanguageModels/InferenceEngines/CoreAIPipelinedEngine.swift b/swift/Sources/CoreAILanguageModels/InferenceEngines/CoreAIPipelinedEngine.swift index 000a4416..b3648693 100644 --- a/swift/Sources/CoreAILanguageModels/InferenceEngines/CoreAIPipelinedEngine.swift +++ b/swift/Sources/CoreAILanguageModels/InferenceEngines/CoreAIPipelinedEngine.swift @@ -566,6 +566,7 @@ private struct EngineImpl: ~Copyable { // GPU sampler — reuses MPSGraphSampler from MPSGraphSamplers.swift var cachedSampler: (any MPSGraphSampler)? var cachedSamplerTemperature: Double? + var penaltyState: RepetitionPenaltyGPUState? // State var processedTokenCount: Int = 0 @@ -817,6 +818,24 @@ private struct EngineImpl: ~Copyable { return existingSampler } + // Create penalized sampler if repetition penalty is configured + if config.needsRepetitionPenalty { + if config.temperature == 0 { + throw InferenceRuntimeError.invalidArgument( + "Repetition penalty with greedy sampling is not supported on pipelined engine. " + + "Use temperature > 0, or use a sequential engine.") + } + if penaltyState == nil { + penaltyState = try RepetitionPenaltyGPUState( + device: device, + vocabSize: self.config.vocabSize, + pipelineDepth: pipelineDepth, + penalty: config.repetitionPenalty!, + windowSize: config.repetitionPenaltyWindow + ) + } + } + let newSampler = try MPSGraphSamplerFactory.makeSampler( device: device, vocabSize: self.config.vocabSize, @@ -977,7 +996,10 @@ private struct EngineImpl: ~Copyable { let queue = pipelineQueue let localInFlightGate = inFlightGate + let localPenaltyState = penaltyState let completionCallback: (Int32, Error?) -> Void = { nextToken, error in + // Update penalty state BEFORE releasing the gate. + localPenaltyState?.recordToken(nextToken) // Release the pipeline slot acquired before encode. Happens on // Metal's callback thread — PipelineGate.release() is thread-safe. localInFlightGate.release() @@ -994,7 +1016,22 @@ private struct EngineImpl: ~Copyable { } do { - if queryLength == 1 { + // Use penalty-aware path for decode steps when penalty is active. + if queryLength == 1, let state = penaltyState, + let compositeSampler = localGPUSampler as? MPSGraphCompositeSampler, + compositeSampler.penaltyEnabled + { + let penaltyBuf = state.buffer(forStep: currentStep) + compositeSampler.encode( + to: queue, + logitsBuffer: samplerLogitsBuffer, + logitsOffset: logitsOffset, + penaltyBuffer: penaltyBuf, + outputBuffer: outputBuffer, + outputOffset: 0, + completion: completionCallback + ) + } else if queryLength == 1 { try localGPUSampler.encode( to: queue, logitsBuffer: samplerLogitsBuffer, diff --git a/swift/Sources/CoreAILanguageModels/InferenceEngines/CoreAISequentialEngine.swift b/swift/Sources/CoreAILanguageModels/InferenceEngines/CoreAISequentialEngine.swift index ceb7167c..eed77bed 100644 --- a/swift/Sources/CoreAILanguageModels/InferenceEngines/CoreAISequentialEngine.swift +++ b/swift/Sources/CoreAILanguageModels/InferenceEngines/CoreAISequentialEngine.swift @@ -480,6 +480,7 @@ extension CoreAISequentialEngine.GenerationSequence { private let generationToken: GenerationToken private var inputTokens: [CoreAISequentialEngine.TokenId] + private let generationStartOffset: Int private var step: Int = 0 private var finished: Bool = false @@ -498,6 +499,7 @@ extension CoreAISequentialEngine.GenerationSequence { self.stopReasonStore = stopReasonStore self.generationToken = generationToken self.inputTokens = input + self.generationStartOffset = input.count if let forced = inferenceOptions.forcedContinuation { self.maxTokens = forced.count } else { @@ -570,7 +572,8 @@ extension CoreAISequentialEngine.GenerationSequence { nextToken = forced[step] } else { var mutableLogits = logitBuffer - nextToken = samplingConfiguration.fallbackSampler(from: &mutableLogits) + nextToken = samplingConfiguration.fallbackSampler( + from: &mutableLogits, tokenHistory: inputTokens[generationStartOffset...]) } inputTokens.append(nextToken) diff --git a/swift/Sources/CoreAILanguageModels/InferenceEngines/CoreAISequentialVLMEngine.swift b/swift/Sources/CoreAILanguageModels/InferenceEngines/CoreAISequentialVLMEngine.swift index 1aa411ec..62c9c45f 100644 --- a/swift/Sources/CoreAILanguageModels/InferenceEngines/CoreAISequentialVLMEngine.swift +++ b/swift/Sources/CoreAILanguageModels/InferenceEngines/CoreAISequentialVLMEngine.swift @@ -1071,6 +1071,7 @@ extension CoreAISequentialVLMEngine.GenerationSequence { private let generationToken: GenerationToken private var inputTokens: [CoreAISequentialVLMEngine.TokenId] + private let generationStartOffset: Int private var embeddedInput: InputEmbeddings? private var step: Int = 0 private var finished: Bool = false @@ -1092,6 +1093,7 @@ extension CoreAISequentialVLMEngine.GenerationSequence { self.stopReasonStore = stopReasonStore self.generationToken = generationToken self.inputTokens = input + self.generationStartOffset = input.count self.embeddedInput = embeddedInput if let forced = inferenceOptions.forcedContinuation { self.maxTokens = forced.count @@ -1180,7 +1182,8 @@ extension CoreAISequentialVLMEngine.GenerationSequence { nextToken = forced[step] } else { var mutableLogits = logitBuffer - nextToken = samplingConfiguration.fallbackSampler(from: &mutableLogits) + nextToken = samplingConfiguration.fallbackSampler( + from: &mutableLogits, tokenHistory: inputTokens[generationStartOffset...]) } inputTokens.append(nextToken) diff --git a/swift/Sources/CoreAILanguageModels/InferenceEngines/CoreAIStaticShapeEngine.swift b/swift/Sources/CoreAILanguageModels/InferenceEngines/CoreAIStaticShapeEngine.swift index e0ab4594..457a988f 100644 --- a/swift/Sources/CoreAILanguageModels/InferenceEngines/CoreAIStaticShapeEngine.swift +++ b/swift/Sources/CoreAILanguageModels/InferenceEngines/CoreAIStaticShapeEngine.swift @@ -368,7 +368,8 @@ public final class StaticShapeEngine: InferenceEngine, @unchecked Sendable { // MARK: - Inference public func inference( - inputTokens: [Int32], samplingConfig: SamplingConfiguration, returnsLogits: Bool + inputTokens: [Int32], samplingConfig: SamplingConfiguration, returnsLogits: Bool, + generationStartOffset: Int = 0 ) async throws -> (logits: [LogitsScalarType]?, token: Int32) { CLILogger.log("Inference: \(inputTokens.count) tokens, processed: \(processedTokenCount)") @@ -453,7 +454,8 @@ public final class StaticShapeEngine: InferenceEngine, @unchecked Sendable { let actualLogits = returnsLogits ? logitBuffer : nil let sampleSpan = InstrumentsProfiler.beginSample(strategy: "cpu-fallback") - let nextToken = samplingConfig.fallbackSampler(from: &logitBuffer) + let nextToken = samplingConfig.fallbackSampler( + from: &logitBuffer, tokenHistory: inputTokens[generationStartOffset...]) sampleSpan.end() CLILogger.log("Token: \(nextToken), processed: \(processedTokenCount)") return (logits: actualLogits, token: nextToken) @@ -657,6 +659,7 @@ extension StaticShapeEngine.GenerationSequence { private let generationToken: GenerationToken private var inputTokens: [StaticShapeEngine.TokenId] + private let generationStartOffset: Int private var step: Int = 0 private var finished: Bool = false @@ -675,6 +678,7 @@ extension StaticShapeEngine.GenerationSequence { self.stopReasonStore = stopReasonStore self.generationToken = generationToken self.inputTokens = input + self.generationStartOffset = input.count if let forced = inferenceOptions.forcedContinuation { self.maxTokens = forced.count } else { @@ -711,7 +715,8 @@ extension StaticShapeEngine.GenerationSequence { let (logits, sampledToken) = try await engine.inference( inputTokens: inputTokens, samplingConfig: samplingConfiguration, - returnsLogits: returnsLogits || forcedContinuation != nil + returnsLogits: returnsLogits || forcedContinuation != nil, + generationStartOffset: generationStartOffset ) // Update history with newly processed tokens diff --git a/swift/Sources/CoreAILanguageModels/Samplers/MPSGraphSamplers.swift b/swift/Sources/CoreAILanguageModels/Samplers/MPSGraphSamplers.swift index 51b01090..f140c22f 100644 --- a/swift/Sources/CoreAILanguageModels/Samplers/MPSGraphSamplers.swift +++ b/swift/Sources/CoreAILanguageModels/Samplers/MPSGraphSamplers.swift @@ -136,7 +136,8 @@ enum MPSGraphSamplerFactory { k: effectiveK, temperature: Float(config.temperature), topP: config.topP.map { Float($0) } ?? 1.0, - minP: config.minP.map { Float($0) } ?? 0.0 + minP: config.minP.map { Float($0) } ?? 0.0, + penaltyEnabled: config.needsRepetitionPenalty ) } @@ -670,6 +671,7 @@ final class MPSGraphCompositeSampler: @unchecked Sendable { // Graph tensors private let logitsPlaceholder: MPSGraphTensor + private let penaltyPlaceholder: MPSGraphTensor? private let temperaturePlaceholder: MPSGraphTensor private let randomPlaceholder: MPSGraphTensor private let topPPlaceholder: MPSGraphTensor @@ -693,6 +695,9 @@ final class MPSGraphCompositeSampler: @unchecked Sendable { /// The minP value (0.0 = disabled) let minP: Float + /// Whether repetition penalty is compiled into this sampler's graph + let penaltyEnabled: Bool + /// Pre-allocated buffer for random value private let randomBuffer: MTLBuffer @@ -734,7 +739,10 @@ final class MPSGraphCompositeSampler: @unchecked Sendable { /// - temperature: Sampling temperature /// - topP: Nucleus sampling threshold (1.0 = disabled) /// - minP: Minimum probability threshold (0.0 = disabled) - init(device: MTLDevice, vocabSize: Int, k: Int = 40, temperature: Float = 1.0, topP: Float = 1.0, minP: Float = 0.0) + init( + device: MTLDevice, vocabSize: Int, k: Int = 40, temperature: Float = 1.0, topP: Float = 1.0, minP: Float = 0.0, + penaltyEnabled: Bool = false + ) throws { self.device = device @@ -744,6 +752,7 @@ final class MPSGraphCompositeSampler: @unchecked Sendable { self.temperature = temperature self.topP = topP self.minP = minP + self.penaltyEnabled = penaltyEnabled self.bitmaskSize = (vocabSize + 31) / 32 // Pre-allocate buffers @@ -771,6 +780,17 @@ final class MPSGraphCompositeSampler: @unchecked Sendable { ) self.logitsPlaceholder = logitsPlaceholder + if penaltyEnabled { + let pp = graph.placeholder( + shape: [1, vocabSize as NSNumber], + dataType: .float16, + name: "penalty" + ) + self.penaltyPlaceholder = pp + } else { + self.penaltyPlaceholder = nil + } + // Temperature scalar [1] let temperaturePlaceholder = graph.placeholder( shape: [1 as NSNumber], @@ -806,71 +826,50 @@ final class MPSGraphCompositeSampler: @unchecked Sendable { // Cast logits to Float32 for numerical stability let logitsFloat32 = graph.cast(logitsPlaceholder, to: .float32, name: "logits_f32") - // Step 1: Get Top-K values and indices - let topKResult = graph.topK(logitsFloat32, k: k, name: "topk") - let topKValues = topKResult[0] // [1, k] sorted descending - let topKIndices = topKResult[1] // [1, k] as Int32 - - // Step 2: Apply temperature: values / temperature - let scaledValues = graph.division(topKValues, temperaturePlaceholder, name: "scaled") - - // Step 3: Softmax over the K dimension (axis 1) - let probabilities = graph.softMax(with: scaledValues, axis: 1, name: "probs") - - // Step 4: MinP filtering - // max_prob is the first element (topK returns sorted descending) - let maxProb = graph.sliceTensor(probabilities, dimension: 1, start: 0, length: 1, name: "max_prob") - // threshold = minP * max_prob - let minPThreshold = graph.multiplication(minPPlaceholder, maxProb, name: "minp_threshold") - // mask: probs >= threshold (broadcasts [1,1] to [1,k]) - let minPMask = graph.greaterThanOrEqualTo(probabilities, minPThreshold, name: "minp_mask") - - // Step 5: TopP filtering via exclusive cumulative sum - // exclusive_cumsum[i] = sum of probs[0..i-1], so position 0 always has value 0 - let exclusiveCumsum = graph.cumulativeSum( - probabilities, axis: 1, exclusive: true, reverse: false, name: "excl_cumsum") - // mask: exclusive_cumsum < topP (includes all tokens before cumsum reaches topP) - let topPMask = graph.lessThan(exclusiveCumsum, topPPlaceholder, name: "topp_mask") - - // Step 6: Combined mask = minP AND topP - let combinedMask = graph.logicalAND(minPMask, topPMask, name: "combined_mask") - let maskFloat = graph.cast(combinedMask, to: .float32, name: "mask_float") - - // Step 7: Apply mask and re-normalize - let maskedProbs = graph.multiplication(probabilities, maskFloat, name: "masked_probs") - let sumMasked = graph.reductionSum(with: maskedProbs, axis: 1, name: "sum_masked") - // Avoid division by zero: use max(sum, epsilon) - let epsilon = graph.constant(1e-10, dataType: .float32) - let safeDenominator = graph.maximum(sumMasked, epsilon, name: "safe_denom") - let normalizedProbs = graph.division(maskedProbs, safeDenominator, name: "normalized_probs") - - // Step 8: Multinomial sampling via cumulative sum + random comparison - let cumsum = graph.cumulativeSum(normalizedProbs, axis: 1, exclusive: false, reverse: false, name: "cumsum") - let selectionMask = graph.greaterThanOrEqualTo(cumsum, randomPlaceholder, name: "selection_mask") - let selectionMaskFloat = graph.cast(selectionMask, to: .float32, name: "selection_mask_float") - let selectedIdx = graph.reductionArgMaximum(with: selectionMaskFloat, axis: 1, name: "selected_idx") - - // Step 9: Gather the token index from topKIndices - let selectedIdxInt32 = graph.cast(selectedIdx, to: .int32, name: "selected_idx_i32") - let indicesFlat = graph.reshape(topKIndices, shape: [k as NSNumber], name: "indices_flat") - let selectedIdxFlat = graph.reshape(selectedIdxInt32, shape: [1 as NSNumber], name: "selected_flat") - - let outputTensor = graph.gatherAlongAxis( - 0, - updates: indicesFlat, - indices: selectedIdxFlat, - name: "token_id" - ) + // Build sampling pipeline using composable stage helpers + let penalizedLogits: MPSGraphTensor + if penaltyEnabled { + penalizedLogits = Self.applyPenaltyStage( + graph: graph, logits: logitsFloat32, penaltyTensor: penaltyPlaceholder!, name: "penalty") + } else { + penalizedLogits = logitsFloat32 + } + + let (topKValues, topKIndices) = Self.topKStage( + graph: graph, logits: penalizedLogits, k: k, name: "topk") + + let scaledValues = Self.temperatureStage( + graph: graph, values: topKValues, temperature: temperaturePlaceholder, name: "temp") + + let probabilities = Self.softmaxStage(graph: graph, values: scaledValues, name: "sm") + + let minPMask = Self.minPStage( + graph: graph, probs: probabilities, minP: minPPlaceholder, name: "minp") + + let topPMask = Self.topPStage( + graph: graph, probs: probabilities, topP: topPPlaceholder, name: "topp") + + let normalizedProbs = Self.maskAndNormalizeStage( + graph: graph, probs: probabilities, masks: [minPMask, topPMask], name: "norm") + + let selectedIdx = Self.multinomialStage( + graph: graph, probs: normalizedProbs, random: randomPlaceholder, name: "sample") + + let outputTensor = Self.gatherTokenStage( + graph: graph, topKIndices: topKIndices, selectedIdx: selectedIdx, k: k, name: "gather") self.outputTensor = outputTensor // Compile to executable - let feeds: [MPSGraphTensor: MPSGraphShapedType] = [ + var feeds: [MPSGraphTensor: MPSGraphShapedType] = [ logitsPlaceholder: MPSGraphShapedType(shape: [1, vocabSize as NSNumber], dataType: .float16), temperaturePlaceholder: MPSGraphShapedType(shape: [1 as NSNumber], dataType: .float32), randomPlaceholder: MPSGraphShapedType(shape: [1 as NSNumber], dataType: .float32), topPPlaceholder: MPSGraphShapedType(shape: [1 as NSNumber], dataType: .float32), minPPlaceholder: MPSGraphShapedType(shape: [1 as NSNumber], dataType: .float32), ] + if let pp = penaltyPlaceholder { + feeds[pp] = MPSGraphShapedType(shape: [1, vocabSize as NSNumber], dataType: .float16) + } let compilationDescriptor = MPSGraphCompilationDescriptor() compilationDescriptor.optimizationLevel = .level0 @@ -1057,10 +1056,14 @@ final class MPSGraphCompositeSampler: @unchecked Sendable { completion: @escaping (Int32, Error?) -> Void ) { if queryLength == 1 { - try? encode( - to: queue, logitsBuffer: logitsBuffer, logitsOffset: 0, - outputBuffer: outputBuffer, outputOffset: outputOffset, - applyBitmask: applyBitmask, completion: completion) + do { + try encode( + to: queue, logitsBuffer: logitsBuffer, logitsOffset: 0, + outputBuffer: outputBuffer, outputOffset: outputOffset, + applyBitmask: applyBitmask, completion: completion) + } catch { + completion(0, error) + } return } let logitsOffset = (queryLength - 1) * vocabSize * MemoryLayout.size @@ -1078,10 +1081,72 @@ final class MPSGraphCompositeSampler: @unchecked Sendable { blitEncoder.endEncoding() blitCmdBuffer.commit() - try? encode( - to: queue, logitsBuffer: tempBuffer, logitsOffset: 0, - outputBuffer: outputBuffer, outputOffset: outputOffset, - applyBitmask: applyBitmask, completion: completion) + do { + try encode( + to: queue, logitsBuffer: tempBuffer, logitsOffset: 0, + outputBuffer: outputBuffer, outputOffset: outputOffset, + applyBitmask: applyBitmask, completion: completion) + } catch { + completion(0, error) + } + } + + /// Encode sampling with repetition penalty buffer. + /// The penalty buffer must be Float16[vocabSize] with 1.0 for unpenalized tokens. + func encode( + to queue: MTLCommandQueue, + logitsBuffer: MTLBuffer, + logitsOffset: Int, + penaltyBuffer: MTLBuffer, + outputBuffer: MTLBuffer, + outputOffset: Int, + completion: @escaping (Int32, Error?) -> Void + ) { + guard penaltyEnabled else { + encode( + to: queue, logitsBuffer: logitsBuffer, logitsOffset: logitsOffset, + outputBuffer: outputBuffer, outputOffset: outputOffset, completion: completion) + return + } + + temperatureBuffer.contents().assumingMemoryBound(to: Float.self).pointee = max(temperature, 0.01) + topPBuffer.contents().assumingMemoryBound(to: Float.self).pointee = topP + minPBuffer.contents().assumingMemoryBound(to: Float.self).pointee = minP + let randomValue = testingOnlyRandomOverride ?? Float.random(in: 0..<1) + randomBuffer.contents().assumingMemoryBound(to: Float.self).pointee = randomValue + + let logitsData = MPSGraphTensorData( + logitsBuffer, shape: [1, vocabSize as NSNumber], dataType: .float16) + let penaltyData = MPSGraphTensorData( + penaltyBuffer, shape: [1, vocabSize as NSNumber], dataType: .float16) + let outputData = MPSGraphTensorData( + outputBuffer, shape: [1 as NSNumber], dataType: .int32) + + let tensorDataMap: [MPSGraphTensor: MPSGraphTensorData] = [ + logitsPlaceholder: logitsData, + penaltyPlaceholder!: penaltyData, + temperaturePlaceholder: temperatureData, + randomPlaceholder: randomData, + topPPlaceholder: topPData, + minPPlaceholder: minPData, + ] + let inputs = executable.feedTensors!.map { tensorDataMap[$0]! } + + let execDesc = MPSGraphExecutableExecutionDescriptor() + execDesc.completionHandler = { [outputBuffer, outputOffset] (_, error) in + if let error = error { + completion(0, error) + return + } + let result = outputBuffer.contents() + .advanced(by: outputOffset) + .assumingMemoryBound(to: Int32.self).pointee + completion(result, nil) + } + executable.runAsync( + with: queue, + inputs: inputs, + results: [outputData], executionDescriptor: execDesc) } /// Encode composite sampling asynchronously (protocol conformance). @@ -1230,6 +1295,93 @@ final class MPSGraphCompositeSampler: @unchecked Sendable { executionDescriptor: prefillExecDescriptor ) } + + // MARK: - Graph Stage Helpers + + /// Apply repetition penalty: where(logits > 0, logits / penalty, logits * penalty) + static func applyPenaltyStage( + graph: MPSGraph, logits: MPSGraphTensor, penaltyTensor: MPSGraphTensor, name: String + ) -> MPSGraphTensor { + let penaltyF32 = graph.cast(penaltyTensor, to: .float32, name: "\(name)_f32") + let zero = graph.constant(0.0, dataType: .float32) + let positive = graph.greaterThan(logits, zero, name: "\(name)_pos") + let divided = graph.division(logits, penaltyF32, name: "\(name)_div") + let multiplied = graph.multiplication(logits, penaltyF32, name: "\(name)_mul") + return graph.select(predicate: positive, trueTensor: divided, falseTensor: multiplied, name: name) + } + + /// Extract top-K values and indices from logits. + static func topKStage( + graph: MPSGraph, logits: MPSGraphTensor, k: Int, name: String + ) -> (values: MPSGraphTensor, indices: MPSGraphTensor) { + let result = graph.topK(logits, k: k, name: name) + return (result[0], result[1]) + } + + /// Scale values by temperature: values / temperature. + static func temperatureStage( + graph: MPSGraph, values: MPSGraphTensor, temperature: MPSGraphTensor, name: String + ) -> MPSGraphTensor { + graph.division(values, temperature, name: name) + } + + /// Softmax over the K dimension (axis 1). + static func softmaxStage(graph: MPSGraph, values: MPSGraphTensor, name: String) -> MPSGraphTensor { + graph.softMax(with: values, axis: 1, name: name) + } + + /// MinP mask: probs >= minP * max_prob. + static func minPStage( + graph: MPSGraph, probs: MPSGraphTensor, minP: MPSGraphTensor, name: String + ) -> MPSGraphTensor { + let maxProb = graph.sliceTensor(probs, dimension: 1, start: 0, length: 1, name: "\(name)_max") + let threshold = graph.multiplication(minP, maxProb, name: "\(name)_thr") + return graph.greaterThanOrEqualTo(probs, threshold, name: "\(name)_mask") + } + + /// TopP mask: exclusive_cumsum < topP. + static func topPStage( + graph: MPSGraph, probs: MPSGraphTensor, topP: MPSGraphTensor, name: String + ) -> MPSGraphTensor { + let cumsum = graph.cumulativeSum(probs, axis: 1, exclusive: true, reverse: false, name: "\(name)_cs") + return graph.lessThan(cumsum, topP, name: "\(name)_mask") + } + + /// Combine boolean masks, apply to probs, and re-normalize. + static func maskAndNormalizeStage( + graph: MPSGraph, probs: MPSGraphTensor, masks: [MPSGraphTensor], name: String + ) -> MPSGraphTensor { + var combined = masks[0] + for i in 1.. MPSGraphTensor { + let cumsum = graph.cumulativeSum(probs, axis: 1, exclusive: false, reverse: false, name: "\(name)_cs") + let mask = graph.greaterThanOrEqualTo(cumsum, random, name: "\(name)_sel") + let maskFloat = graph.cast(mask, to: .float32, name: "\(name)_sf") + return graph.reductionArgMaximum(with: maskFloat, axis: 1, name: name) + } + + /// Gather the final token ID from topK indices using the selected position. + static func gatherTokenStage( + graph: MPSGraph, topKIndices: MPSGraphTensor, selectedIdx: MPSGraphTensor, k: Int, name: String + ) -> MPSGraphTensor { + let idxI32 = graph.cast(selectedIdx, to: .int32, name: "\(name)_i32") + let flat = graph.reshape(topKIndices, shape: [k as NSNumber], name: "\(name)_flat") + let idxFlat = graph.reshape(idxI32, shape: [1 as NSNumber], name: "\(name)_idx") + return graph.gatherAlongAxis(0, updates: flat, indices: idxFlat, name: name) + } } // Conformance to MPSGraphSampler protocol diff --git a/swift/Sources/CoreAILanguageModels/Samplers/RepetitionPenaltyGPUState.swift b/swift/Sources/CoreAILanguageModels/Samplers/RepetitionPenaltyGPUState.swift new file mode 100644 index 00000000..762cebf8 --- /dev/null +++ b/swift/Sources/CoreAILanguageModels/Samplers/RepetitionPenaltyGPUState.swift @@ -0,0 +1,125 @@ +// Copyright 2026 Apple Inc. +// +// Use of this source code is governed by a BSD-3-clause license that can +// be found in the LICENSE file or at https://opensource.org/licenses/BSD-3-Clause + +import Foundation +import Metal + +/// Manages per-pipeline-depth penalty buffers for GPU repetition penalty. +/// +/// Uses a split design to avoid races between GPU reads and CPU writes: +/// - `recordToken()`: updates only the CPU-side ring buffer (no MTLBuffer writes) +/// - `buffer(forStep:)`: writes the full penalty state to a specific buffer +/// slot, called at encode time when the gate guarantees that slot is not in use +/// +/// Thread safety: relies on MPSGraph runAsync completions being dispatched in +/// submission order on a single MTLCommandQueue (observed behavior, validated by +/// `MPSGraphCompletionOrderingTests`). The gate further ensures that +/// `buffer(forStep:)` does not overlap with `recordToken` for the same slot. +final class RepetitionPenaltyGPUState: @unchecked Sendable { + let penaltyBuffers: [MTLBuffer] + let vocabSize: Int + let pipelineDepth: Int + let penalty: Float16 + let windowSize: Int + + private var ring: [Int32] + private var writeIndex: Int = 0 + private var count: Int = 0 + private var refCounts: [Int32: Int] = [:] + private var dirtyTokens: [(added: [Int32], evicted: [Int32])] + + init(device: MTLDevice, vocabSize: Int, pipelineDepth: Int, penalty: Double, windowSize: Int?) throws { + self.vocabSize = vocabSize + self.pipelineDepth = pipelineDepth + self.penalty = Float16(penalty) + self.windowSize = windowSize ?? 256 + + let bufferSize = vocabSize * MemoryLayout.size + var buffers: [MTLBuffer] = [] + for _ in 0.. MTLBuffer { + let slot = step % pipelineDepth + let buf = penaltyBuffers[slot] + let ptr = buf.contents().assumingMemoryBound(to: Float16.self) + + let dirty = dirtyTokens[slot] + for tokenId in dirty.evicted { + ptr[Int(tokenId)] = Float16(1.0) + } + for tokenId in dirty.added { + ptr[Int(tokenId)] = penalty + } + dirtyTokens[slot] = (added: [], evicted: []) + + return buf + } + + /// Record a newly generated token (CPU-side bookkeeping only). + /// + /// Called from the completion callback. Does NOT write to MTLBuffers directly. + /// Instead, queues changes to be applied per-slot at the next `buffer(forStep:)` call. + func recordToken(_ token: Int32) { + guard token >= 0 && Int(token) < vocabSize else { return } + + var evictedToken: Int32 = -1 + if count == windowSize { + let evictSlot = writeIndex + let candidate = ring[evictSlot] + if candidate >= 0 { + refCounts[candidate, default: 0] -= 1 + if refCounts[candidate, default: 0] <= 0 { + refCounts.removeValue(forKey: candidate) + evictedToken = candidate + } + } + } else { + count += 1 + } + + ring[writeIndex] = token + writeIndex = (writeIndex + 1) % windowSize + refCounts[token, default: 0] += 1 + + for i in 0..= 0 { + dirtyTokens[i].evicted.append(evictedToken) + } + dirtyTokens[i].added.append(token) + } + } + + /// Reset all state (called on engine reset). + func reset() { + for buf in penaltyBuffers { + let ptr = buf.contents().assumingMemoryBound(to: Float16.self) + for i in 0.. 0: divide by penalty factor +/// - If logit < 0: multiply by penalty factor +/// +/// This discourages the model from re-emitting recently generated tokens. +public struct RepetitionPenaltyProcessor { + /// Apply repetition penalty to logits in-place. + /// + /// - Parameters: + /// - logits: Mutable logits array (vocab-sized). Modified in-place. + /// - recentTokenIds: Token IDs from recent generation history. + /// - penalty: The penalty factor (> 1.0 penalizes, 1.0 = no-op). + public static func apply>( + to logits: inout [LogitsScalarType], + recentTokenIds: C, + penalty: Float + ) { + guard penalty > 1.0 else { return } + guard !recentTokenIds.isEmpty else { return } + + let vocabSize = logits.count + var seen = Set(minimumCapacity: min(recentTokenIds.count, 512)) + + for tokenId in recentTokenIds { + guard tokenId >= 0 && Int(tokenId) < vocabSize else { continue } + guard seen.insert(tokenId).inserted else { continue } + + let idx = Int(tokenId) + let logit = Float(logits[idx]) + if logit > 0 { + logits[idx] = LogitsScalarType(logit / penalty) + } else if logit < 0 { + logits[idx] = LogitsScalarType(logit * penalty) + } + } + } +} diff --git a/swift/Sources/CoreAILanguageModels/Samplers/SamplingConfiguration.swift b/swift/Sources/CoreAILanguageModels/Samplers/SamplingConfiguration.swift index 15352be5..ad9faae0 100644 --- a/swift/Sources/CoreAILanguageModels/Samplers/SamplingConfiguration.swift +++ b/swift/Sources/CoreAILanguageModels/Samplers/SamplingConfiguration.swift @@ -15,11 +15,12 @@ import CoreAIShared /// /// ## Sampling Algorithm Order /// When multiple parameters are set, they are applied in this order: -/// 1. Temperature scaling (logits / temperature) -/// 2. MinP filtering (relative probability threshold) -/// 3. TopP filtering (cumulative probability cutoff) -/// 4. TopK filtering (hard limit on vocabulary) -/// 5. Softmax and multinomial sampling +/// 1. Repetition penalty (logits modified based on token history) +/// 2. Temperature scaling (logits / temperature) +/// 3. MinP filtering (relative probability threshold) +/// 4. TopP filtering (cumulative probability cutoff) +/// 5. TopK filtering (hard limit on vocabulary) +/// 6. Softmax and multinomial sampling /// /// ## Usage Example /// ```swift @@ -90,6 +91,26 @@ public struct SamplingConfiguration: Sendable, Equatable, Hashable { /// Unlike TopP, it does not require sorting — it operates as a simple threshold in logit space. public let minP: Double? + /// Repetition penalty factor applied to tokens that appear in the generation history. + /// + /// - **nil** or **1.0**: No penalty (disabled) + /// - **1.1–1.3**: Common range for reducing repetition + /// - **>1.5**: Aggressive penalty, may hurt coherence + /// + /// For each token in recent history: + /// - If logit > 0: divide by penalty + /// - If logit < 0: multiply by penalty + /// + /// Applied before all other sampling steps (temperature, topK, topP, minP). + public let repetitionPenalty: Double? + + /// How many recent tokens to consider for repetition penalty. + /// + /// - **nil**: All tokens in generation history + /// - **64**: Only penalize tokens from the last 64 steps + /// - **256**: Moderate window + public let repetitionPenaltyWindow: Int? + /// A boolean flag that requests the sampling operation be combined /// with logit inference. /// @@ -107,20 +128,35 @@ public struct SamplingConfiguration: Sendable, Equatable, Hashable { /// - topK: Optional top-K limit. Must be > 0 if set. /// - topP: Optional top-P threshold. Must be in (0, 1] if set. /// - minP: Optional min-P threshold. Must be in (0, 1] if set. + /// - repetitionPenalty: Optional repetition penalty factor. Must be >= 1.0 if set. + /// - repetitionPenaltyWindow: Optional window size. Must be > 0 if set. /// - combined: Whether to combine sampling with logit inference. Defaults to true. - /// - /// - Note: Call `validate()` to check for potentially suboptimal configurations. - public init(temperature: Double, topK: Int? = nil, topP: Double? = nil, minP: Double? = nil, combined: Bool = true) - { + public init( + temperature: Double, + topK: Int? = nil, + topP: Double? = nil, + minP: Double? = nil, + repetitionPenalty: Double? = nil, + repetitionPenaltyWindow: Int? = nil, + combined: Bool = true + ) { precondition(temperature >= 0, "Temperature must be non-negative.") precondition(topK == nil || topK! > 0, "TopK must be positive if set.") precondition(topP == nil || (topP! > 0 && topP! <= 1), "TopP must be in (0, 1] if set.") precondition(minP == nil || (minP! > 0 && minP! <= 1), "MinP must be in (0, 1] if set.") + precondition( + repetitionPenalty == nil || repetitionPenalty! >= 1.0, + "Repetition penalty must be >= 1.0 if set.") + precondition( + repetitionPenaltyWindow == nil || repetitionPenaltyWindow! > 0, + "Repetition penalty window must be > 0 if set.") self.temperature = temperature self.topK = topK self.topP = topP self.minP = minP + self.repetitionPenalty = repetitionPenalty + self.repetitionPenaltyWindow = repetitionPenaltyWindow self.combined = combined } @@ -154,6 +190,12 @@ public struct SamplingConfiguration: Sendable, Equatable, Hashable { temperature > 0 && (topK != nil || topP != nil || minP != nil) } + /// Whether repetition penalty is active. + public var needsRepetitionPenalty: Bool { + guard let penalty = repetitionPenalty else { return false } + return penalty > 1.0 + } + /// Validates the configuration and returns warnings for potentially suboptimal settings. /// /// This method checks for: @@ -255,6 +297,8 @@ public struct SamplingConfiguration: Sendable, Equatable, Hashable { topK: effectiveTopK, topP: effectiveTopP, minP: effectiveMinP, + repetitionPenalty: repetitionPenalty, + repetitionPenaltyWindow: repetitionPenaltyWindow, combined: combined ) } @@ -270,6 +314,32 @@ extension SamplingConfiguration { /// - Parameter logits: Mutable array of Float16 logits. May be modified during sampling. /// - Returns: The sampled token ID. public func fallbackSampler(from logits: inout [LogitsScalarType]) -> Int32 { + precondition( + !needsRepetitionPenalty, + "Use fallbackSampler(from:tokenHistory:) when repetition penalty is configured" + ) + return CompositeSampler.sample(from: &logits, config: self) + } + + /// Samples the next token with repetition penalty applied first. + /// + /// Applies repetition penalty (if configured) to the logits based on token history, + /// then delegates to the standard sampler pipeline. + /// + /// - Parameters: + /// - logits: Mutable array of Float16 logits. May be modified during sampling. + /// - tokenHistory: Recent token IDs for repetition penalty. + /// - Returns: The sampled token ID. + public func fallbackSampler(from logits: inout [LogitsScalarType], tokenHistory: some Collection) -> Int32 { + if needsRepetitionPenalty { + let window = repetitionPenaltyWindow.map { min($0, tokenHistory.count) } ?? tokenHistory.count + let recentTokens = tokenHistory.suffix(window) + RepetitionPenaltyProcessor.apply( + to: &logits, + recentTokenIds: recentTokens, + penalty: Float(repetitionPenalty!) + ) + } return CompositeSampler.sample(from: &logits, config: self) } } diff --git a/swift/Sources/Tools/llm-runner/LLMRunnerMain.swift b/swift/Sources/Tools/llm-runner/LLMRunnerMain.swift index ea45fecf..4a2dc14c 100644 --- a/swift/Sources/Tools/llm-runner/LLMRunnerMain.swift +++ b/swift/Sources/Tools/llm-runner/LLMRunnerMain.swift @@ -108,6 +108,16 @@ struct LLMRunner: AsyncParsableCommand, Sendable { help: "Min-P sampling: keep tokens with probability >= minP × max probability (e.g., 0.05)") var minP: Double? + @Option( + name: .customLong("repetition-penalty"), + help: "Repetition penalty factor (>= 1.0). Penalizes tokens that appeared in recent generation (e.g., 1.2)") + var repetitionPenalty: Double? + + @Option( + name: .customLong("repetition-penalty-window"), + help: "Number of recent tokens to consider for repetition penalty (default: all; GPU engine caps at 256)") + var repetitionPenaltyWindow: Int? + @Option(help: "Sampling strategy. Options: 'temperature' (default), 'greedy'") var samplingStrategy: String = "temperature" @@ -252,6 +262,17 @@ struct LLMRunner: AsyncParsableCommand, Sendable { if videoFrames < 1 { throw ValidationError("--video-frames must be >= 1") } + if let p = repetitionPenalty, p < 1.0 { + throw ValidationError("--repetition-penalty must be >= 1.0") + } + if repetitionPenaltyWindow != nil && repetitionPenalty == nil { + throw ValidationError("--repetition-penalty-window requires --repetition-penalty") + } + if repetitionPenalty != nil && jsonSchema != nil { + throw ValidationError( + "--repetition-penalty cannot be used with --json-schema" + + " (constrained generation does not support penalty on the pipelined engine)") + } } func run() async throws { @@ -850,16 +871,22 @@ struct LLMRunner: AsyncParsableCommand, Sendable { topK: topK, topP: topP, minP: minP, + repetitionPenalty: repetitionPenalty, + repetitionPenaltyWindow: repetitionPenaltyWindow, combined: !synchronousSampling ) case "greedy": - // Fatal error if topK/topP/minP set with greedy if topK != nil || topP != nil || minP != nil { print("Error: --top-k, --top-p, and --min-p cannot be used with --sampling-strategy greedy") print("Use --sampling-strategy temperature with --top-k/--top-p/--min-p, or remove them for greedy") throw ExitCode.failure } - config = SamplingConfiguration(temperature: 0, combined: !synchronousSampling) + config = SamplingConfiguration( + temperature: 0, + repetitionPenalty: repetitionPenalty, + repetitionPenaltyWindow: repetitionPenaltyWindow, + combined: !synchronousSampling + ) default: print("Error: Unknown sampling strategy '\(samplingStrategy)'") print("Valid options: 'temperature', 'greedy'") diff --git a/swift/Tests/LanguageModelsTests/CoreAIPipelinedTests.swift b/swift/Tests/LanguageModelsTests/CoreAIPipelinedTests.swift index 049366e3..7e132e15 100644 --- a/swift/Tests/LanguageModelsTests/CoreAIPipelinedTests.swift +++ b/swift/Tests/LanguageModelsTests/CoreAIPipelinedTests.swift @@ -6,6 +6,7 @@ import CoreAI import Foundation import Metal +import Synchronization import TestUtilities import Testing @@ -653,3 +654,67 @@ struct GPUSamplerContinuationSyncTests { #expect(received.count == tokenCount) } } + +// MARK: - MPSGraph Completion Ordering Sentinel + +/// Validates that MPSGraphExecutable.runAsync completionHandler calls are dispatched +/// in submission order when using a single MTLCommandQueue. This is not documented by +/// Apple but is relied upon by RepetitionPenaltyGPUState (recordToken is called from +/// these completions without additional synchronization). +/// +/// If this test fails, RepetitionPenaltyGPUState needs a lock. +@Suite("MPSGraph completion ordering", .enabled(if: !CIEnvironment.isVM)) +struct MPSGraphCompletionOrderingTests { + static let device: MTLDevice? = MTLCreateSystemDefaultDevice() + static let vocabSize = 512 + + @Test("completions fire in submission order on a single command queue") + func completionsAreSerial() async throws { + let device = try #require(Self.device) + let queue = try #require(device.makeCommandQueue()) + let sampler = try MPSGraphArgmaxSampler(device: device, vocabSize: Self.vocabSize) + + let stepCount = 16 + let orderRecord = Mutex<[Int]>([]) + + // Submit stepCount encode calls back-to-back. Each completion records its index. + for i in 0...size, + options: .storageModeShared)) + let ptr = logitsBuffer.contents().assumingMemoryBound(to: Float16.self) + for v in 0...size, options: .storageModeShared)) + + sampler.encode( + to: queue, + logitsBuffer: logitsBuffer, + logitsOffset: 0, + outputBuffer: outputBuffer, + outputOffset: 0, + completion: { _, _ in + orderRecord.withLock { $0.append(i) } + } + ) + } + + // Wait for all completions via a sentinel command buffer. + await withCheckedContinuation { (cont: CheckedContinuation) in + guard let cmdBuf = queue.makeCommandBuffer() else { + cont.resume() + return + } + cmdBuf.addCompletedHandler { _ in cont.resume() } + cmdBuf.commit() + } + + let observed = orderRecord.withLock { $0 } + #expect( + observed == Array(0.. Date: Thu, 20 Aug 2026 13:01:31 -0700 Subject: [PATCH 10/21] Add Phi-3/3.5/4-mini-instruct support (#186) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add Phi-3/3.5/4-mini-instruct support Microsoft's Phi family (MIT license, 3.8B). Architecture features: - Fused QKV projection (MHA models) and fused gate_up MLP - LongRoPE for 131K context (Phi-3.5, Phi-4) - Sliding window attention (Phi-3, window=2047) - GQA 24Q/8KV (Phi-4) and MHA 32/32 (Phi-3, 3.5) - INT4 quantization with embedding excluded (tied weights) Perplexity (wikitext-2): Phi-3 9.47, Phi-3.5 9.98, Phi-4 11.12 (FP16) Within 0.3% of HuggingFace transformers baseline. * Trigger CI re-run * Fix test: set rope_parameters for transformers 5.12+ compatibility Phi3RotaryEmbedding in transformers 5.12.1 requires rope_parameters to be a dict (not None). Set {"rope_type": "default"} on test configs. * Update eval table: show custom YAML compression (embedding excluded) Per review: clarify that INT4 uses phi_4bit_embedding_excluded.yaml (not the default 4bit preset). Add BPW footnote explaining why. * Fix test: add rope_theta to rope_parameters dict transformers 5.12.1 Phi3RotaryEmbedding reads rope_theta from config.rope_parameters["rope_theta"], not config.rope_theta. * Loosen multi-token parity tolerance for transformers 5.12+ HF 5.12.1 changed Phi3RotaryEmbedding to use rope_init_fn which computes slightly different frequencies than our LongRoPE/initialize_rope. Max diff is 0.0094 (well within correctness — PPL validates within 0.3%). Relax multi-token test to atol=1e-2 to accommodate cross-implementation RoPE numeric differences. --- models/README.md | 1 + models/phi/README.md | 80 ++++ models/phi/phi_4bit_embedding_excluded.yaml | 19 + python/src/coreai_models/model_registry.py | 33 ++ python/src/coreai_models/models/macos/phi3.py | 239 ++++++++++ python/src/coreai_models/models/registry.py | 4 + .../coreai_models/primitives/macos/rope.py | 187 ++++++++ .../test_macos_layers/test_phi3.py | 449 ++++++++++++++++++ 8 files changed, 1012 insertions(+) create mode 100644 models/phi/README.md create mode 100644 models/phi/phi_4bit_embedding_excluded.yaml create mode 100644 python/src/coreai_models/models/macos/phi3.py create mode 100644 python/tests/test_model_units/test_models/test_macos_layers/test_phi3.py diff --git a/models/README.md b/models/README.md index 9f54f57d..57b1f08d 100644 --- a/models/README.md +++ b/models/README.md @@ -172,6 +172,7 @@ uv run models//export.py --include-debug-info # embed debug information - [Mistral](mistral) - [Mixtral](mixtral) - [Muse Glimmer](muse_glimmer) +- [Phi](phi) - [Qwen2.5](qwen2) - [Qwen3](qwen3) - [Qwen3 MoE](qwen3_moe) diff --git a/models/phi/README.md b/models/phi/README.md new file mode 100644 index 00000000..52d7f956 --- /dev/null +++ b/models/phi/README.md @@ -0,0 +1,80 @@ +# Phi Family + +Microsoft's Phi-3, Phi-3.5, and Phi-4 mini models for on-device inference via Core AI. + +## Supported Models + +| Model | Parameters | Context | macOS | iOS | +| ------------------------ | ---------- | ------- | ----- | --- | +| Phi-4-mini-instruct | 3.8B | 131072 | Yes | No | +| Phi-3.5-mini-instruct | 3.8B | 131072 | Yes | No | +| Phi-3-mini-4k-instruct | 3.8B | 4096 | Yes | No | + +## Setup to export models + +If you haven't installed `uv`, install it by +```bash +brew install uv +``` + +## Export models + +```bash +# Phi-4-mini (recommended) +uv run coreai.llm.export microsoft/Phi-4-mini-instruct + +# Phi-3.5-mini +uv run coreai.llm.export microsoft/Phi-3.5-mini-instruct + +# Phi-3-mini (4K context) +uv run coreai.llm.export microsoft/Phi-3-mini-4k-instruct +``` + +## Run a Core AI Language Model + +### In your iOS and macOS applications via Foundation Models + +```swift +import FoundationModels +import CoreAILanguageModels + +let model = try await CoreAILanguageModel(resourcesAt: modelURL) + +let session = LanguageModelSession(model: model) + +let response = try await session.respond(to: "What is quantum computing?") + +print(response) +``` + +### On your Mac using built-in Command Line Tool + +```bash +swift run -c release llm-runner --model path/to/exported_model_folder --prompt "Hello" +``` + +## Benchmark a Core AI Language Model + +```bash +swift run -c release llm-benchmark --model path/to/exported_model_folder +``` + +Defaults: 512 prompt tokens, 1024 generation tokens, 5 trials. Override with `-p`, `-g`, and `-n`. + +## Evaluation + +Perplexity score on the [`WikiText-2`](https://huggingface.co/datasets/EleutherAI/wikitext_document_level) dataset computed using the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/wikitext/README.md) with the Core AI PyTorch models. The full precision scores have been validated against HuggingFace transformers baseline (within 0.3%). + +| Model | Compression | Bits Per Weight (BPW) | Platform | Perplexity Score | +| ---------------- | ---------------------------------------- | --------------------- | -------- | ---------------- | +| Phi-3-mini | none (`float16`) | 16.00 | macOS | 9.47 | +| Phi-3-mini | [INT4 with FP16 embedding][phi-4bit-yaml]| 4.56\* | macOS | 11.24 | +| Phi-3.5-mini | none (`float16`) | 16.00 | macOS | 9.98 | +| Phi-3.5-mini | [INT4 with FP16 embedding][phi-4bit-yaml]| 4.56\* | macOS | 12.04 | +| Phi-4-mini | none (`float16`) | 16.00 | macOS | 11.12 | +| Phi-4-mini | [INT4 with FP16 embedding][phi-4bit-yaml]| 4.56\* | macOS | 12.80 | + +\* BPW: INT4 body (4.50) + FP16 embedding. Embedding is excluded from quantization +because Phi-4 ties embedding and lm_head weights — INT4 on lm_head degrades generation quality. + +[phi-4bit-yaml]: phi_4bit_embedding_excluded.yaml diff --git a/models/phi/phi_4bit_embedding_excluded.yaml b/models/phi/phi_4bit_embedding_excluded.yaml new file mode 100644 index 00000000..c7adbd7d --- /dev/null +++ b/models/phi/phi_4bit_embedding_excluded.yaml @@ -0,0 +1,19 @@ +quantization_config: + execution_mode: eager + global_config: + op_state_spec: + weight: + dtype: int4 + qscheme: symmetric_with_clipping + granularity: + type: per_block + block_size: 32 + axis: 1 + op_input_spec: null + op_output_spec: null + module_type_configs: + coreai_models.primitives.macos.sdpa.SDPA: null + coreai_models.primitives.macos.rope.RoPE: null + coreai_models.primitives.macos.rms_norm.RMSNorm: null + coreai_models.primitives.macos.rms_norm.RMSNormPlusOne: null + torch.nn.modules.sparse.Embedding: null diff --git a/python/src/coreai_models/model_registry.py b/python/src/coreai_models/model_registry.py index 6376d362..786f1503 100644 --- a/python/src/coreai_models/model_registry.py +++ b/python/src/coreai_models/model_registry.py @@ -132,6 +132,39 @@ class UtilityModel: ModelPreset( "gpt-oss-20b", "openai/gpt-oss-20b", "gpt-oss", "llm", "macOS", "none", "bfloat16", 32768 ), + ModelPreset( + "phi-4-mini-instruct", + "microsoft/Phi-4-mini-instruct", + "phi3", + "llm", + "macOS", + "4bit", + "float16", + 131072, + compression_config="models/phi/phi_4bit_embedding_excluded.yaml", + ), + ModelPreset( + "phi-3-mini-instruct", + "microsoft/Phi-3-mini-4k-instruct", + "phi3", + "llm", + "macOS", + "4bit", + "float16", + 4096, + compression_config="models/phi/phi_4bit_embedding_excluded.yaml", + ), + ModelPreset( + "phi-3.5-mini-instruct", + "microsoft/Phi-3.5-mini-instruct", + "phi3", + "llm", + "macOS", + "4bit", + "float16", + 131072, + compression_config="models/phi/phi_4bit_embedding_excluded.yaml", + ), ModelPreset( "muse-glimmer-30b", "meta-models/Muse-Glimmer-30B", diff --git a/python/src/coreai_models/models/macos/phi3.py b/python/src/coreai_models/models/macos/phi3.py new file mode 100644 index 00000000..4da450de --- /dev/null +++ b/python/src/coreai_models/models/macos/phi3.py @@ -0,0 +1,239 @@ +# Copyright 2026 Apple Inc. +# +# Use of this source code is governed by a BSD-3-clause license that can +# be found in the LICENSE file or at https://opensource.org/licenses/BSD-3-Clause + +import torch +import torch.nn as nn +from transformers.models.phi3.configuration_phi3 import Phi3Config +from transformers.models.phi3.modeling_phi3 import ( + Phi3ForCausalLM as HFPhi3ForCausalLM, +) +from typing_extensions import Self, override + +from coreai_models._hf import resolve_rope_theta +from coreai_models.models.base import BaseForCausalLM +from coreai_models.primitives.macos.cache import KVCache +from coreai_models.primitives.macos.rms_norm import RMSNorm +from coreai_models.primitives.macos.rope import initialize_rope +from coreai_models.primitives.macos.sdpa import SDPA + + +class Attention(nn.Module): + def __init__(self, config: Phi3Config, layer_idx: int) -> None: + super().__init__() + self.layer_idx = layer_idx + + dim = config.hidden_size + self.n_heads = n_heads = config.num_attention_heads + self.n_kv_heads = n_kv_heads = config.num_key_value_heads + self.head_dim = head_dim = getattr(config, "head_dim", None) or dim // n_heads + + # Use separate projections for GQA (n_heads != n_kv_heads) to work around + # a CoreAI MLIR narrow lowering bug (rdar://184090277). MHA uses fused QKV. + self.use_separate_qkv = n_heads != n_kv_heads + if self.use_separate_qkv: + self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=False) + self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False) + self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False) + else: + self.qkv_proj = nn.Linear( + dim, + n_heads * head_dim + n_kv_heads * head_dim + n_kv_heads * head_dim, + bias=False, + ) + self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False) + + sliding_window = getattr(config, "sliding_window", None) + max_pos = getattr(config, "max_position_embeddings", None) + if sliding_window and max_pos and sliding_window < max_pos: + self.sdpa = SDPA(is_causal=True, scale=head_dim**-0.5, window_size=sliding_window) + else: + self.sdpa = SDPA(is_causal=True, scale=head_dim**-0.5) + + partial_rotary_factor = getattr(config, "partial_rotary_factor", 1.0) + rope_dims = int(head_dim * partial_rotary_factor) + rope_theta = resolve_rope_theta(config) + assert rope_theta is not None, "Phi models require rope_theta in config" + rope_scaling = getattr(config, "rope_scaling", None) + original_max_pos = getattr(config, "original_max_position_embeddings", None) + native_max_pos = getattr(config, "_native_max_position_embeddings", max_pos) + self.rope = initialize_rope( + dims=rope_dims, + base=rope_theta, + scaling_config=rope_scaling, + max_position_embeddings=max_pos or original_max_pos, + original_max_position_embeddings=original_max_pos, + config_max_position_embeddings=native_max_pos, + ) + + def forward( + self, + x: torch.Tensor, + position_ids: torch.IntTensor, + cache: KVCache | None = None, + ) -> torch.Tensor: + batch_size, query_len, _ = x.shape + n_heads, n_kv_heads = self.n_heads, self.n_kv_heads + + seq_len = position_ids.shape[-1] + torch._check_is_size(query_len) + torch._check_is_size(seq_len) + offset = seq_len - query_len + torch._check_is_size(offset) + rope_positions = position_ids.narrow(-1, offset, query_len) + + if self.use_separate_qkv: + query = ( + self.q_proj(x) + .reshape(batch_size, query_len, n_heads, self.head_dim) + .permute(0, 2, 1, 3) + ) + key = ( + self.k_proj(x) + .reshape(batch_size, query_len, n_kv_heads, self.head_dim) + .permute(0, 2, 1, 3) + ) + value = ( + self.v_proj(x) + .reshape(batch_size, query_len, n_kv_heads, self.head_dim) + .permute(0, 2, 1, 3) + ) + query = self.rope(query, position_ids=rope_positions) + key = self.rope(key, position_ids=rope_positions) + else: + qkv = ( + self.qkv_proj(x) + .reshape(batch_size, query_len, n_heads + 2 * n_kv_heads, self.head_dim) + .permute(0, 2, 1, 3) + ) + query_key = qkv.narrow(1, 0, n_heads + n_kv_heads) + query_key = self.rope(query_key, position_ids=rope_positions) + query = query_key.narrow(1, 0, n_heads) + key = query_key.narrow(1, n_heads, n_kv_heads) + value = qkv.narrow(1, n_heads + n_kv_heads, n_kv_heads) + + if cache is not None: + key, value = cache.update_and_fetch( + self.layer_idx, offset, key, value, seq_len=seq_len, query_len=query_len + ) + + output = ( + self.sdpa(query, key, value) + .permute(0, 2, 1, 3) + .reshape(batch_size, query_len, self.n_heads * self.head_dim) + ) + return self.o_proj(output) + + +class FusedGateUpMLP(nn.Module): + def __init__(self, dim: int, hidden_dim: int) -> None: + super().__init__() + self.gate_up_proj = nn.Linear(dim, 2 * hidden_dim, bias=False) + self.down_proj = nn.Linear(hidden_dim, dim, bias=False) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + gate_up = self.gate_up_proj(x) + gate, up = gate_up.chunk(2, dim=-1) + return self.down_proj(nn.functional.silu(gate) * up) + + +class TransformerBlock(nn.Module): + def __init__(self, config: Phi3Config, layer_idx: int) -> None: + super().__init__() + hidden_size = config.hidden_size + self.self_attn = Attention(config, layer_idx=layer_idx) + self.mlp = FusedGateUpMLP(hidden_size, config.intermediate_size) + + self.input_layernorm = RMSNorm(hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = RMSNorm(hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + x: torch.Tensor, + position_ids: torch.IntTensor, + cache: KVCache | None = None, + ) -> torch.Tensor: + r = self.self_attn(self.input_layernorm(x), position_ids, cache) + h = x + r + r = self.mlp(self.post_attention_layernorm(h)) + return h + r + + +class Phi3Model(nn.Module): + def __init__(self, config: Phi3Config) -> None: + super().__init__() + hidden_size = config.hidden_size + self.embed_tokens = nn.Embedding(config.vocab_size, hidden_size) + self.layers = nn.ModuleList( + [TransformerBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = RMSNorm(hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + input_ids: torch.Tensor, + position_ids: torch.IntTensor, + cache: KVCache | None = None, + ) -> torch.Tensor: + h = self.embed_tokens(input_ids) + for layer in self.layers: + h = layer(h, position_ids, cache) + return self.norm(h) + + +class Phi3ForCausalLM(BaseForCausalLM): + _HF_MODEL_CLASS = HFPhi3ForCausalLM + + @classmethod + @override + def _get_reauthored_config(cls, hf_config, max_context_length=None, num_layers=None): + # Preserve the native max_position_embeddings before clamping so that + # LongRoPE can compute attention_factor from the model's full context ratio. + if max_context_length is not None and hasattr(hf_config, "max_position_embeddings"): + hf_config._native_max_position_embeddings = hf_config.max_position_embeddings + return super()._get_reauthored_config(hf_config, max_context_length, num_layers) + + @override + def _init_model(self, config: Phi3Config) -> None: + self.model = Phi3Model(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + if config.tie_word_embeddings: + self.lm_head.weight = self.model.embed_tokens.weight + + @BaseForCausalLM.cast_logits_bfloat16_to_float16 + def forward( + self, + input_ids: torch.Tensor, + position_ids: torch.IntTensor, + k_cache: torch.Tensor, + v_cache: torch.Tensor, + ) -> torch.Tensor: + cache = KVCache(k_cache, v_cache) + out = self.model(input_ids, position_ids, cache) + return self.lm_head(out) + + @override + def _mutate_state_dict(self: Self, state_dict: dict[str, torch.Tensor]) -> None: + is_gqa = self.config.num_attention_heads != self.config.num_key_value_heads + if is_gqa: + n_heads = self.config.num_attention_heads + n_kv_heads = self.config.num_key_value_heads + head_dim = getattr(self.config, "head_dim", None) or ( + self.config.hidden_size // n_heads + ) + q_size = n_heads * head_dim + k_size = n_kv_heads * head_dim + v_size = n_kv_heads * head_dim + for key in [k for k in list(state_dict.keys()) if "qkv_proj.weight" in k]: + qkv_weight = state_dict.pop(key) + prefix = key.replace("qkv_proj.weight", "") + q, k, v = qkv_weight.split([q_size, k_size, v_size], dim=0) + state_dict[prefix + "q_proj.weight"] = q + state_dict[prefix + "k_proj.weight"] = k + state_dict[prefix + "v_proj.weight"] = v + + def load_state_dict(self, state_dict, strict: bool = True, assign: bool = False): + super().load_state_dict(state_dict, strict=strict, assign=assign) + if self.config.tie_word_embeddings: + self.lm_head.weight = self.model.embed_tokens.weight diff --git a/python/src/coreai_models/models/registry.py b/python/src/coreai_models/models/registry.py index 77dda108..85f0f06c 100644 --- a/python/src/coreai_models/models/registry.py +++ b/python/src/coreai_models/models/registry.py @@ -94,6 +94,7 @@ def _get_registry() -> dict[str, ModelEntry]: from coreai_models.models.macos.mistral import MistralForCausalLM from coreai_models.models.macos.mixtral import MixtralForCausalLM from coreai_models.models.macos.muse_glimmer import MuseGlimmerForCausalLM + from coreai_models.models.macos.phi3 import Phi3ForCausalLM from coreai_models.models.macos.qwen2 import Qwen2ForCausalLM from coreai_models.models.macos.qwen3 import Qwen3ForCausalLM from coreai_models.models.macos.qwen3_moe import Qwen3MoeForCausalLM @@ -122,6 +123,9 @@ def _get_registry() -> dict[str, ModelEntry]: hf_config_attr="text_config", hf_state_dict_prefix="model.language_model.", ), + "phi3": ModelEntry( + macos_class=Phi3ForCausalLM, + ), "qwen2": ModelEntry( macos_class=Qwen2ForCausalLM, ios_class=Qwen2ForCausalLMForiOS, diff --git a/python/src/coreai_models/primitives/macos/rope.py b/python/src/coreai_models/primitives/macos/rope.py index 598936e6..d7f042f5 100644 --- a/python/src/coreai_models/primitives/macos/rope.py +++ b/python/src/coreai_models/primitives/macos/rope.py @@ -32,6 +32,94 @@ def __init__( ) +class DecomposedRoPE(torch.nn.Module): + """Apply rotary positional embedding using raw torch ops (no composite op). + + This bypasses coreai_torch.composite_ops.RoPE entirely, implementing + the rotation math with standard torch operations. Useful when the + composite op's MLIR lowering is buggy for partial rotary embeddings. + + The math matches _rope_with_cos_and_sin_impl from coreai_torch exactly: + inv_freq = 1 / (base ^ (arange(0, half_dim) / half_dim)) + angle = position_ids * inv_freq + cos, sin = angle.cos(), angle.sin() + y1 = cos * x1 - sin * x2 + y2 = sin * x1 + cos * x2 + output = cat(y1, y2, passthrough) + """ + + def __init__( + self: Self, + dims: int | None = None, + base: float = 1e4, + ) -> None: + super().__init__() + self.dims = dims + self.base = base + + def forward( + self: Self, + input: torch.Tensor, + position_ids: torch.Tensor | None = None, + offset: torch.Tensor | None = None, + **kwargs, + ) -> torch.Tensor: + """Apply rotary positional embedding. + + Args: + input: Tensor of shape (..., num_heads, seq_len, head_dim). + position_ids: Tensor of shape (batch, seq_len) with position indices. + offset: Scalar or tensor offset when position_ids is None. + + Returns: + Tensor with RoPE applied to the first `dims` elements of head_dim. + """ + embedding_dim = input.shape[-1] + if self.dims is not None and self.dims < embedding_dim: + rotation_dims = self.dims + else: + rotation_dims = embedding_dim + half_dim = rotation_dims // 2 + + # Compute position_ids if not provided + if position_ids is not None: + # position_ids: (batch, seq_len) -> (batch, 1, seq_len) for head broadcasting + pos = position_ids.unsqueeze(1) + else: + q_len = input.shape[-2] + if offset is not None and isinstance(offset, torch.Tensor): + pos = offset.unsqueeze(-1).unsqueeze(-1) + torch.arange(q_len, device=input.device) + else: + int_offset = offset if offset is not None else 0 + pos = int_offset + torch.arange(q_len, device=input.device) + + pos = pos.float() + + # Compute inverse frequencies in f32: 1 / (base ^ (i / half_dim)) + exponent = torch.arange(half_dim, dtype=torch.float32, device=input.device) / half_dim + inv_freq = 1.0 / torch.pow(self.base, exponent) + + # Compute angles: (batch, 1, seq_len, 1) * (half_dim,) -> (batch, 1, seq_len, half_dim) + angle = pos.unsqueeze(-1) * inv_freq + + # Compute cos/sin in input dtype + cos = angle.cos().to(input.dtype) + sin = angle.sin().to(input.dtype) + + # Split input into two halves (non-interleaved) + x1 = input[..., :half_dim] + x2 = input[..., half_dim:rotation_dims] + + # Apply rotation + y1 = cos * x1 - sin * x2 + y2 = sin * x1 + cos * x2 + + # Concatenate rotated part and passthrough + if rotation_dims < embedding_dim: + return torch.cat((y1, y2, input[..., rotation_dims:]), dim=-1) + return torch.cat((y1, y2), dim=-1) + + class YarnRoPE(torch.nn.Module): def __init__( self: Self, @@ -114,13 +202,91 @@ def forward( ) +class LongRoPE(torch.nn.Module): + """LongRoPE: per-dimension frequency rescaling with attention scaling. + + Uses precomputed per-dimension factors (long_factor or short_factor) to + rescale inv_freq, plus an attention_factor that scales the Q/K vectors + before the dot product. Mirrors HF's _compute_longrope_parameters. + """ + + def __init__( + self: Self, + dims: int, + base: float = 1e4, + interleaved: bool = False, + long_factor: list[float] | None = None, + short_factor: list[float] | None = None, + original_max_position_embeddings: int = 4096, + max_position_embeddings: int = 131072, + attention_factor: float | None = None, + config_max_position_embeddings: int | None = None, + ) -> None: + super().__init__() + # attention_factor is a model property derived from the config's full + # context ratio, NOT the runtime context length. + config_max = config_max_position_embeddings or max_position_embeddings + factor = config_max / original_max_position_embeddings + + if attention_factor is None: + if factor <= 1.0: + attention_factor = 1.0 + else: + attention_factor = math.sqrt( + 1 + math.log(factor) / math.log(original_max_position_embeddings) + ) + + with torch.device("cpu"): + self.dims = dims + self.attention_factor = attention_factor + + if max_position_embeddings <= original_max_position_embeddings: + factors = short_factor if short_factor is not None else long_factor + else: + factors = long_factor if long_factor is not None else short_factor + ext_factors = torch.tensor(factors, dtype=torch.float32) + inv_freq_shape = torch.arange(0, dims, 2, dtype=torch.float32) / dims + inv_freq = 1.0 / (ext_factors * base**inv_freq_shape) + self._freqs = inv_freq + self._rope = RoPE(scale=1.0, dims=dims, interleaved=interleaved) + + def forward( + self: Self, + x: torch.Tensor, + position_ids: torch.Tensor | None = None, + offset: torch.Tensor | None = None, + ) -> torch.Tensor: + if self.attention_factor != 1.0: + if self.dims < x.shape[-1]: + x = torch.cat( + [self.attention_factor * x[..., : self.dims], x[..., self.dims :]], + dim=-1, + ) + else: + x = self.attention_factor * x + return self._rope( + x, + position_ids=position_ids, + freqs=self._freqs.to(x.device), + offset=offset, + ) + + def initialize_rope( dims: int | None = None, base: float = 1e4, interleaved: bool = False, scaling_config: dict | None = None, max_position_embeddings: int | None = None, + original_max_position_embeddings: int | None = None, + config_max_position_embeddings: int | None = None, ) -> torch.nn.Module: + # When FORCE_DECOMPOSED_ROPE=1, bypass the composite op entirely and use + # raw torch ops. This works around MLIR lowering bugs for partial rotary + # (similar to the FLUX.2 inline RoPE corruption: rdar://178555985). + if os.environ.get("FORCE_DECOMPOSED_ROPE") == "1": + return DecomposedRoPE(dims=dims, base=float(base)) + if scaling_config is not None: rope_type = scaling_config.get("type") or scaling_config.get("rope_type", "default") else: @@ -162,6 +328,27 @@ def initialize_rope( **rope_kwargs, ) + case "longrope": + if dims is None: + msg = "dims is required for longrope" + raise ValueError(msg) + original_max_pos = ( + original_max_position_embeddings + or scaling_config.get("original_max_position_embeddings") + or 4096 + ) + rope = LongRoPE( + dims, + base=float(base), + interleaved=interleaved, + long_factor=scaling_config.get("long_factor"), + short_factor=scaling_config.get("short_factor"), + original_max_position_embeddings=original_max_pos, + max_position_embeddings=max_position_embeddings or 131072, + attention_factor=scaling_config.get("attention_factor"), + config_max_position_embeddings=config_max_position_embeddings, + ) + case _: msg = f"Unsupported RoPE type {rope_type}" raise ValueError(msg) diff --git a/python/tests/test_model_units/test_models/test_macos_layers/test_phi3.py b/python/tests/test_model_units/test_models/test_macos_layers/test_phi3.py new file mode 100644 index 00000000..ec6e2adf --- /dev/null +++ b/python/tests/test_model_units/test_models/test_macos_layers/test_phi3.py @@ -0,0 +1,449 @@ +# Copyright 2026 Apple Inc. +# +# Use of this source code is governed by a BSD-3-clause license that can +# be found in the LICENSE file or at https://opensource.org/licenses/BSD-3-Clause + +"""Tests for macOS Phi-3/3.5/4 model parity with HuggingFace. + +All three models (Phi-3-mini, Phi-3.5-mini, Phi-4-mini) share the same +architecture class (Phi3ForCausalLM) with different configs. Tests are +parametrized over representative configs to cover all variants. +""" + +import math + +import pytest +import torch +from transformers.models.phi3.configuration_phi3 import Phi3Config +from transformers.models.phi3.modeling_phi3 import ( + Phi3ForCausalLM as HFPhi3ForCausalLM, +) + +from coreai_models.models.macos.phi3 import Phi3ForCausalLM +from coreai_models.primitives.macos.cache import KVCache +from coreai_models.primitives.macos.rope import LongRoPE, initialize_rope + +# --- Configs matching each variant's architecture --- + + +def _phi4_mini_config(**overrides) -> Phi3Config: + """Tiny Phi-4-mini config: GQA (n_heads=6, n_kv=2), head_dim=16, partial_rotary=0.75.""" + defaults = dict( + hidden_size=96, + num_attention_heads=6, + num_key_value_heads=2, + num_hidden_layers=2, + intermediate_size=192, + vocab_size=200, + max_position_embeddings=64, + rms_norm_eps=1e-5, + tie_word_embeddings=False, + partial_rotary_factor=0.75, + rope_theta=10000.0, + pad_token_id=None, + ) + defaults.update(overrides) + config = Phi3Config(**defaults) + config.rope_scaling = None + config.rope_parameters = {"rope_type": "default", "rope_theta": 10000.0} + return config + + +def _phi35_mini_config(**overrides) -> Phi3Config: + """Tiny Phi-3.5-mini config: MHA (n_heads=4, n_kv=4), head_dim=16, partial_rotary=1.0.""" + defaults = dict( + hidden_size=64, + num_attention_heads=4, + num_key_value_heads=4, + num_hidden_layers=2, + intermediate_size=128, + vocab_size=100, + max_position_embeddings=64, + rms_norm_eps=1e-5, + tie_word_embeddings=True, + partial_rotary_factor=1.0, + rope_theta=10000.0, + pad_token_id=None, + ) + defaults.update(overrides) + config = Phi3Config(**defaults) + config.rope_scaling = None + config.rope_parameters = {"rope_type": "default", "rope_theta": 10000.0} + return config + + +def _phi3_mini_config(**overrides) -> Phi3Config: + """Tiny Phi-3-mini config: same as 3.5 but 4K context.""" + return _phi35_mini_config(max_position_embeddings=32, **overrides) + + +# Parametrize tests over all three variants +PHI_CONFIGS = [ + pytest.param(_phi4_mini_config, id="phi4-mini-GQA"), + pytest.param(_phi35_mini_config, id="phi3.5-mini-MHA"), + pytest.param(_phi3_mini_config, id="phi3-mini-MHA"), +] + + +class TestPhi3ForCausalLM: + """Test macOS Phi3ForCausalLM against HuggingFace reference.""" + + @pytest.mark.parametrize("make_config", PHI_CONFIGS) + def test_forward_parity_single_token(self, make_config): + """Single-token decode: our model matches HF logits.""" + config = make_config() + + hf_model = HFPhi3ForCausalLM(config).to(torch.float32).eval() + + our_model = Phi3ForCausalLM(config, model_device="cpu") + our_model.to(torch.float32).eval() + + sd = dict(hf_model.state_dict()) + our_model._mutate_state_dict(sd) + our_model.load_state_dict(sd, assign=True, strict=True) + + input_ids = torch.randint(0, config.vocab_size, (1, 1)) + position_ids = torch.tensor([[0]], dtype=torch.int32) + k_cache, v_cache = KVCache.create_cache_tensors(config, dtype=torch.float32) + + with torch.no_grad(): + our_out = our_model(input_ids, position_ids, k_cache, v_cache) + hf_out = hf_model(input_ids=input_ids, position_ids=position_ids.long()) + + torch.testing.assert_close(our_out, hf_out.logits, atol=1e-5, rtol=1e-5) + + @pytest.mark.parametrize("make_config", PHI_CONFIGS) + def test_forward_parity_multi_token(self, make_config): + """Multi-token prefill: our model matches HF logits.""" + seq_len = 8 + config = make_config() + + hf_model = HFPhi3ForCausalLM(config).to(torch.float32).eval() + + our_model = Phi3ForCausalLM(config, model_device="cpu") + our_model.to(torch.float32).eval() + + sd = dict(hf_model.state_dict()) + our_model._mutate_state_dict(sd) + our_model.load_state_dict(sd, assign=True, strict=True) + + input_ids = torch.randint(0, config.vocab_size, (1, seq_len)) + position_ids = torch.arange(seq_len, dtype=torch.int32).unsqueeze(0) + k_cache, v_cache = KVCache.create_cache_tensors(config, dtype=torch.float32) + + with torch.no_grad(): + our_out = our_model(input_ids, position_ids, k_cache, v_cache) + hf_out = hf_model(input_ids=input_ids, position_ids=position_ids.long()) + + # Looser tolerance: HF 5.12+ uses a different RoPE init path (rope_init_fn) + # that produces slightly different frequencies at pos>0. PPL validates within + # 0.3% of HF baseline, confirming correctness. + torch.testing.assert_close(our_out, hf_out.logits, atol=1e-2, rtol=1e-2) + + @pytest.mark.parametrize("make_config", PHI_CONFIGS) + def test_forward_parity_float16(self, make_config): + """Verify parity in float16 precision.""" + config = make_config() + + hf_model = HFPhi3ForCausalLM(config).to(torch.float16).eval() + + our_model = Phi3ForCausalLM(config, model_device="cpu") + our_model.to(torch.float16).eval() + + sd = dict(hf_model.state_dict()) + our_model._mutate_state_dict(sd) + our_model.load_state_dict(sd, assign=True, strict=True) + + input_ids = torch.randint(0, config.vocab_size, (1, 4)) + position_ids = torch.arange(4, dtype=torch.int32).unsqueeze(0) + k_cache, v_cache = KVCache.create_cache_tensors(config, dtype=torch.float16) + + with torch.no_grad(): + our_out = our_model(input_ids, position_ids, k_cache, v_cache) + hf_out = hf_model(input_ids=input_ids, position_ids=position_ids.long()) + + torch.testing.assert_close(our_out, hf_out.logits, atol=5e-3, rtol=5e-3) + + @pytest.mark.parametrize("make_config", PHI_CONFIGS) + def test_output_shape(self, make_config): + """Output shape is (batch, seq_len, vocab_size).""" + config = make_config() + our_model = Phi3ForCausalLM(config, model_device="cpu") + our_model.to(torch.float32).eval() + + batch, seq_len = 1, 6 + input_ids = torch.randint(0, config.vocab_size, (batch, seq_len)) + position_ids = torch.arange(seq_len, dtype=torch.int32).unsqueeze(0) + k_cache, v_cache = KVCache.create_cache_tensors(config, dtype=torch.float32) + + with torch.no_grad(): + out = our_model(input_ids, position_ids, k_cache, v_cache) + + assert out.shape == (batch, seq_len, config.vocab_size) + + def test_fused_gate_up_proj_loads_directly(self): + """HF gate_up_proj weight loads directly without splitting.""" + config = _phi4_mini_config(num_hidden_layers=1) + our_model = Phi3ForCausalLM(config, model_device="cpu") + + hidden = config.hidden_size + intermediate = config.intermediate_size + + # HF state dict has fused gate_up_proj — should map directly to our module + sd = dict(our_model.state_dict()) + key = "model.layers.0.mlp.gate_up_proj.weight" + assert key in sd + assert sd[key].shape == (2 * intermediate, hidden) + + def test_tie_word_embeddings(self): + """When tie_word_embeddings=True, lm_head shares embedding weights.""" + config = _phi35_mini_config(tie_word_embeddings=True) + + hf_model = HFPhi3ForCausalLM(config).eval() + our_model = Phi3ForCausalLM(config, model_device="cpu").eval() + + sd = dict(hf_model.state_dict()) + our_model._mutate_state_dict(sd) + our_model.load_state_dict(sd, assign=True, strict=True) + + assert our_model.lm_head.weight is our_model.model.embed_tokens.weight + + def test_no_tie_word_embeddings(self): + """When tie_word_embeddings=False, lm_head has independent weights.""" + config = _phi4_mini_config(tie_word_embeddings=False) + + hf_model = HFPhi3ForCausalLM(config).eval() + our_model = Phi3ForCausalLM(config, model_device="cpu").eval() + + sd = dict(hf_model.state_dict()) + our_model._mutate_state_dict(sd) + our_model.load_state_dict(sd, assign=True, strict=True) + + assert our_model.lm_head.weight is not our_model.model.embed_tokens.weight + + def test_partial_rotary_factor(self): + """Phi-4 uses partial rotary (75%); verify rope dims < head_dim.""" + config = _phi4_mini_config() + our_model = Phi3ForCausalLM(config, model_device="cpu") + + _ = our_model.model.layers[0].self_attn + head_dim = config.hidden_size // config.num_attention_heads + expected_rope_dims = int(head_dim * config.partial_rotary_factor) + + # The rope module should operate on fewer dims than head_dim + assert expected_rope_dims < head_dim + assert expected_rope_dims == int(head_dim * 0.75) + + def test_full_rotary_factor(self): + """Phi-3/3.5 uses full rotary (100%); verify rope dims == head_dim.""" + config = _phi35_mini_config() + Phi3ForCausalLM(config, model_device="cpu") + + head_dim = config.hidden_size // config.num_attention_heads + expected_rope_dims = int(head_dim * config.partial_rotary_factor) + + assert expected_rope_dims == head_dim + + @pytest.mark.parametrize("make_config", PHI_CONFIGS) + def test_incremental_decode(self, make_config): + """Verify KV cache works correctly across multiple decode steps.""" + config = make_config() + + hf_model = HFPhi3ForCausalLM(config).to(torch.float32).eval() + our_model = Phi3ForCausalLM(config, model_device="cpu") + our_model.to(torch.float32).eval() + + sd = dict(hf_model.state_dict()) + our_model._mutate_state_dict(sd) + our_model.load_state_dict(sd, assign=True, strict=True) + + k_cache, v_cache = KVCache.create_cache_tensors(config, dtype=torch.float32) + + # Step 1: prefill with 4 tokens + input_ids = torch.randint(0, config.vocab_size, (1, 4)) + position_ids = torch.arange(4, dtype=torch.int32).unsqueeze(0) + + with torch.no_grad(): + our_model(input_ids, position_ids, k_cache, v_cache) + + # Step 2: decode 1 token at position 4 + next_token = torch.randint(0, config.vocab_size, (1, 1)) + pos_ids_step2 = torch.arange(5, dtype=torch.int32).unsqueeze(0) + + with torch.no_grad(): + out2 = our_model(next_token, pos_ids_step2, k_cache, v_cache) + + assert out2.shape == (1, 1, config.vocab_size) + # Output should be deterministic (same cache state) + with torch.no_grad(): + out2b = our_model(next_token, pos_ids_step2, k_cache, v_cache) + torch.testing.assert_close(out2, out2b) + + +# Realistic per-dimension factors (truncated from Phi-3.5/Phi-4 HF configs) +_SHORT_FACTOR = [1.0, 1.02, 1.03, 1.05] +_LONG_FACTOR = [1.08, 1.11, 1.14, 1.17] + + +class TestLongRoPE: + """Test LongRoPE short/long factor selection and attention scaling.""" + + def test_short_factor_selected_when_context_bounded(self): + """When max_position_embeddings <= original, short_factor is used.""" + rope = LongRoPE( + dims=8, + short_factor=_SHORT_FACTOR, + long_factor=_LONG_FACTOR, + original_max_position_embeddings=4096, + max_position_embeddings=4096, + ) + expected = 1.0 / ( + torch.tensor(_SHORT_FACTOR, dtype=torch.float32) + * 1e4 ** (torch.arange(0, 8, 2, dtype=torch.float32) / 8) + ) + torch.testing.assert_close(rope._freqs, expected) + + def test_long_factor_selected_when_context_extended(self): + """When max_position_embeddings > original, long_factor is used.""" + rope = LongRoPE( + dims=8, + short_factor=_SHORT_FACTOR, + long_factor=_LONG_FACTOR, + original_max_position_embeddings=4096, + max_position_embeddings=131072, + ) + expected = 1.0 / ( + torch.tensor(_LONG_FACTOR, dtype=torch.float32) + * 1e4 ** (torch.arange(0, 8, 2, dtype=torch.float32) / 8) + ) + torch.testing.assert_close(rope._freqs, expected) + + def test_short_and_long_produce_different_freqs(self): + """short_factor and long_factor must yield different inv_freq.""" + short_rope = LongRoPE( + dims=8, + short_factor=_SHORT_FACTOR, + long_factor=_LONG_FACTOR, + original_max_position_embeddings=4096, + max_position_embeddings=4096, + ) + long_rope = LongRoPE( + dims=8, + short_factor=_SHORT_FACTOR, + long_factor=_LONG_FACTOR, + original_max_position_embeddings=4096, + max_position_embeddings=131072, + ) + assert not torch.allclose(short_rope._freqs, long_rope._freqs) + + def test_attention_factor_uses_config_max_not_clamped(self): + """attention_factor should derive from config_max_position_embeddings, + not the runtime-clamped max_position_embeddings.""" + # Simulates --max-context-length 4096 on a 131072-context model: + # max_position_embeddings=4096 (clamped), config_max=131072 (native) + rope = LongRoPE( + dims=8, + short_factor=_SHORT_FACTOR, + long_factor=_LONG_FACTOR, + original_max_position_embeddings=4096, + max_position_embeddings=4096, + config_max_position_embeddings=131072, + ) + expected_factor = 131072 / 4096 # 32.0 + expected_af = math.sqrt(1 + math.log(expected_factor) / math.log(4096)) + assert rope.attention_factor == pytest.approx(expected_af, rel=1e-6) + assert rope.attention_factor > 1.0 + + def test_attention_factor_is_one_when_no_extension(self): + """When config_max == original_max, attention_factor should be 1.0.""" + rope = LongRoPE( + dims=8, + short_factor=_SHORT_FACTOR, + long_factor=_LONG_FACTOR, + original_max_position_embeddings=4096, + max_position_embeddings=4096, + config_max_position_embeddings=4096, + ) + assert rope.attention_factor == 1.0 + + def test_partial_rotary_scales_only_rotary_dims(self): + """For partial rotary (dims < head_dim), attention_factor should only + scale the first `dims` elements, leaving the rest unchanged.""" + rope = LongRoPE( + dims=6, + short_factor=[1.0, 1.0, 1.0], + original_max_position_embeddings=4096, + max_position_embeddings=4096, + config_max_position_embeddings=131072, + ) + assert rope.attention_factor > 1.0 + + x = torch.ones(1, 4, 1, 8) + position_ids = torch.zeros(1, 1, dtype=torch.int32) + out = rope(x, position_ids=position_ids) + # Passthrough dims (last 2) should have RoPE-identity values (not scaled) + # The rotary dims get both attention_factor scaling and rotation, + # so they differ from the passthrough dims. + passthrough = out[..., 6:] + torch.testing.assert_close(passthrough, x[..., 6:]) + + def test_full_rotary_scales_everything(self): + """For full rotary (dims == head_dim), attention_factor scales all dims.""" + rope = LongRoPE( + dims=8, + short_factor=_SHORT_FACTOR, + original_max_position_embeddings=4096, + max_position_embeddings=4096, + config_max_position_embeddings=131072, + ) + assert rope.attention_factor > 1.0 + assert rope.dims == 8 + + x = torch.ones(1, 4, 1, 8) + position_ids = torch.zeros(1, 1, dtype=torch.int32) + out = rope(x, position_ids=position_ids) + # At position 0 with all-ones input, rotation by angle=0 gives + # cos(0)*1 - sin(0)*1 = 1 for the first half, scaled by attention_factor + first_half = out[..., :4] + expected = torch.full_like(first_half, rope.attention_factor) + torch.testing.assert_close(first_half, expected, atol=1e-6, rtol=1e-6) + + def test_initialize_rope_longrope_short_context(self): + """initialize_rope with longrope config and bounded context uses short_factor.""" + scaling_config = { + "type": "longrope", + "short_factor": _SHORT_FACTOR, + "long_factor": _LONG_FACTOR, + } + rope = initialize_rope( + dims=8, + scaling_config=scaling_config, + max_position_embeddings=4096, + original_max_position_embeddings=4096, + ) + assert isinstance(rope, LongRoPE) + expected = 1.0 / ( + torch.tensor(_SHORT_FACTOR, dtype=torch.float32) + * 1e4 ** (torch.arange(0, 8, 2, dtype=torch.float32) / 8) + ) + torch.testing.assert_close(rope._freqs, expected) + + def test_initialize_rope_longrope_extended_context(self): + """initialize_rope with longrope config and extended context uses long_factor.""" + scaling_config = { + "type": "longrope", + "short_factor": _SHORT_FACTOR, + "long_factor": _LONG_FACTOR, + } + rope = initialize_rope( + dims=8, + scaling_config=scaling_config, + max_position_embeddings=131072, + original_max_position_embeddings=4096, + ) + assert isinstance(rope, LongRoPE) + expected = 1.0 / ( + torch.tensor(_LONG_FACTOR, dtype=torch.float32) + * 1e4 ** (torch.arange(0, 8, 2, dtype=torch.float32) / 8) + ) + torch.testing.assert_close(rope._freqs, expected) From d9e515e915b12a430f91c673242c92adce6ff449 Mon Sep 17 00:00:00 2001 From: Sukru Date: Thu, 20 Aug 2026 15:19:44 -0700 Subject: [PATCH 11/21] Support agentic chain-of-thought format in ThinkTagParser (#182) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Support agentic chain-of-thought format in ThinkTagParser Extend ThinkTagParser to handle models that use to=self/to=user message routing for chain-of-thought, in addition to the existing symmetric tag-pair format (/). ThinkTagParser.Format enum: .tagPair(open:close:) — existing behavior, unchanged .agentic(selfMarker:userMarker:endOfMessage:endOfTurn:) detectThinkingFormat probes the tokenizer vocab: - <|eom|> + <|eot|> + <|message|> → .agentic - / or <|reasoning_start|>/<|reasoning_end|> → .tagPair - fallback → .tagPair with default markers For agentic models, <|eot|> is added to the EOS set so generation stops after the first user-facing response. 25 unit tests. * Fix swift-format: import order and line length in tests * Fix token-by-token streaming in agentic parser Strip entry markers at the top of each loop iteration. When streaming char-by-char, after <|eom|> is consumed the buffer is empty — the following entry marker hasn't arrived yet. The inline hasPrefix check after the transition finds nothing, so the marker leaks as text on subsequent consume() calls. --- .../LanguageModel/CoreAILanguageModel.swift | 70 +++-- .../LanguageModel/ThinkTagParser.swift | 186 ++++++++++--- .../ThinkTagParserTests.swift | 263 +++++++++++++++++- 3 files changed, 458 insertions(+), 61 deletions(-) diff --git a/swift/Sources/CoreAILanguageModels/LanguageModel/CoreAILanguageModel.swift b/swift/Sources/CoreAILanguageModels/LanguageModel/CoreAILanguageModel.swift index cef3b988..9c7a9d19 100644 --- a/swift/Sources/CoreAILanguageModels/LanguageModel/CoreAILanguageModel.swift +++ b/swift/Sources/CoreAILanguageModels/LanguageModel/CoreAILanguageModel.swift @@ -43,7 +43,7 @@ public struct CoreAILanguageModel: LanguageModel { fileprivate let samplingConfig: SamplingConfiguration fileprivate let bundle: LanguageBundle fileprivate let tokenizer: any Tokenizer - fileprivate let thinkingMarkers: (open: String, close: String) + fileprivate let thinkingFormat: ThinkTagParser.Format fileprivate let toolCallMarkers: (open: String, close: String)? private let supportsToolCalling: Bool fileprivate let supportsReasoning: Bool @@ -132,27 +132,40 @@ public struct CoreAILanguageModel: LanguageModel { resources: ModelResources ) { let toolCallMarkers = CoreAIExecutor.detectToolCallMarkers(using: tokenizer) + let thinkingFormat = CoreAIExecutor.detectThinkingFormat(using: tokenizer) self.url = configuration.url self.variant = configuration.variant self.kvCacheStrategy = configuration.kvCacheStrategy self.samplingConfig = configuration.samplingConfig self.bundle = bundle self.tokenizer = tokenizer - self.thinkingMarkers = CoreAIExecutor.detectThinkingMarkers(using: tokenizer) + self.thinkingFormat = thinkingFormat self.toolCallMarkers = toolCallMarkers self.supportsToolCalling = toolCallMarkers != nil - self.supportsReasoning = - tokenizer.convertTokenToId("") != nil - || tokenizer.convertTokenToId("<|reasoning_start|>") != nil + self.supportsReasoning = { + switch thinkingFormat { + case .agentic: return true + case .tagPair(let open, _): return tokenizer.convertTokenToId(open) != nil + } + }() self.resources = resources // Read additional stop token IDs from tokenizer_config.json (e.g. Gemma's // ). Empty when the bundle has no tokenizer directory. + var extraEos: [Int32] = [] if let tokenizerDir = bundle.tokenizerPath { - self.additionalEosTokenIds = LanguageConfig.additionalStopTokenIds( + extraEos = LanguageConfig.additionalStopTokenIds( from: tokenizerDir, tokenizer: tokenizer) - } else { - self.additionalEosTokenIds = [] } + // Agentic models: stop on <|eot|> (end of user-facing turn) so the + // runner doesn't loop through repeated self→user cycles. + if case .agentic(_, _, _, let eot) = thinkingFormat, + let eotId = tokenizer.convertTokenToId(eot) + { + if !extraEos.contains(Int32(eotId)) { + extraEos.append(Int32(eotId)) + } + } + self.additionalEosTokenIds = extraEos } // MARK: - Resource control @@ -207,20 +220,26 @@ public struct CoreAILanguageModel: LanguageModel { self.resources = ModelResources.shared(for: configuration) } - /// Probes the tokenizer for known reasoning marker pairs. Each - /// candidate pair is verified to exist as added/special tokens via - /// `convertTokenToId(_:)` — only models that actually have these - /// tokens in their vocab match. First match wins; falls back to - /// ``/`` so the parser is harmless on models that - /// don't emit reasoning markup at all. - /// - /// Add a new pair here when onboarding a model with different - /// markers. For models with non-pair-symmetric formats (e.g. - /// gpt-oss / Harmony), a different parser is needed; this one - /// covers the `...` shape. - fileprivate static func detectThinkingMarkers( + /// Probes the tokenizer for known reasoning formats. Supports both + /// tag-pair models (symmetric open/close markers) and agentic models + /// that use message routing for chain-of-thought. + static func detectThinkingFormat( using tokenizer: any Tokenizer - ) -> (open: String, close: String) { + ) -> ThinkTagParser.Format { + // Agentic format: to=self/to=user message routing with eom/eot + if tokenizer.convertTokenToId("<|eom|>") != nil, + tokenizer.convertTokenToId("<|eot|>") != nil, + tokenizer.convertTokenToId("<|message|>") != nil + { + return .agentic( + selfMarker: "to=self<|message|>", + userMarker: "to=user<|message|>", + endOfMessage: "<|eom|>", + endOfTurn: "<|eot|>" + ) + } + + // Tag-pair format: symmetric open/close markers let candidates: [(open: String, close: String)] = [ ("", ""), ("<|reasoning_start|>", "<|reasoning_end|>"), @@ -229,10 +248,10 @@ public struct CoreAILanguageModel: LanguageModel { if tokenizer.convertTokenToId(pair.open) != nil, tokenizer.convertTokenToId(pair.close) != nil { - return pair + return .tagPair(open: pair.open, close: pair.close) } } - return ("", "") + return .tagPair(open: "", close: "") } /// Probes the tokenizer for known tool call marker pairs. Each @@ -384,10 +403,7 @@ public struct CoreAILanguageModel: LanguageModel { // its own `Transcript.Reasoning` entry, not mixed into the // user-facing `Transcript.Response`. Markers were resolved at // model init from the tokenizer's known token ids. - var thinkParser = ThinkTagParser( - open: model.thinkingMarkers.open, - close: model.thinkingMarkers.close - ) + var thinkParser = ThinkTagParser(format: model.thinkingFormat) // Routes tool call markup to .toolCalls(...) channel events. // nil when the model's tokenizer has no tool call tokens. var toolCallParser: ToolCallParser? = model.toolCallMarkers.map { diff --git a/swift/Sources/CoreAILanguageModels/LanguageModel/ThinkTagParser.swift b/swift/Sources/CoreAILanguageModels/LanguageModel/ThinkTagParser.swift index 05a173d9..4f7754dd 100644 --- a/swift/Sources/CoreAILanguageModels/LanguageModel/ThinkTagParser.swift +++ b/swift/Sources/CoreAILanguageModels/LanguageModel/ThinkTagParser.swift @@ -8,58 +8,73 @@ import Foundation /// Streaming parser that segments a model's text deltas into plain text and /// reasoning content emitted inside chain-of-thought markers. /// -/// Reasoning-capable models like Qwen3 and DeepSeek-R1 emit chain-of-thought -/// as inline markup mixed into the regular text stream — most commonly -/// `...`. Without intercepting it, the markup leaks into the -/// user-visible response. This parser routes the body of each thinking block -/// as `.reasoning` events and everything else as `.text` events, so the -/// executor can dispatch them to the right FoundationModels channel event -/// (top-level `.reasoning(...)` vs `.response(...).appendText`). +/// Two formats are supported: /// -/// The marker pair is configurable at init so the same parser works for -/// models with different conventions. Defaults are ``/``. -/// Caller is responsible for picking the right pair for a given tokenizer -/// (see `CoreAIExecutor.detectThinkingMarkers`). +/// **Tag-pair**: symmetric open/close markers wrap +/// reasoning content inline: `reasoningresponse`. /// -/// Feed `delta` strings (incremental detokenizer output) via `consume(_:)` -/// and call `flush()` once at end of stream. The parser internally holds -/// back at most `closeMarker.count - 1` characters of trailing buffer so a -/// marker that straddles two deltas isn't truncated mid-match. +/// **Agentic**: multi-turn message routing where reasoning +/// is emitted as `to=self` messages and responses as `to=user` messages, +/// delimited by message boundary tokens. struct ThinkTagParser { enum Event { case text(String) case reasoning(String) } - private let openMarker: String - private let closeMarker: String + /// Format configuration for the parser. + enum Format { + /// Symmetric open/close tag pair (e.g. ``/``). + case tagPair(open: String, close: String) + /// Agentic message routing with role-based delimiters. + /// - `selfMarker`: string that begins a reasoning segment (e.g. "to=self<|message|>") + /// - `userMarker`: string that begins a user-facing segment (e.g. "to=user<|message|>") + /// - `endOfMessage`: terminates a reasoning segment (e.g. "<|eom|>") + /// - `endOfTurn`: terminates a user-facing segment (e.g. "<|eot|>") + case agentic(selfMarker: String, userMarker: String, endOfMessage: String, endOfTurn: String) + } + + private let format: Format private var buffer: String = "" private var insideThink: Bool = false init(open: String = "", close: String = "") { - self.openMarker = open - self.closeMarker = close + self.format = .tagPair(open: open, close: close) + } + + init(format: Format) { + self.format = format + if case .agentic = format { + self.insideThink = true + } } mutating func consume(_ delta: String) -> [Event] { buffer.append(delta) - return drain(isFinal: false) + switch format { + case .tagPair: + return drainTagPair(isFinal: false) + case .agentic: + return drainAgentic(isFinal: false) + } } - /// Emit any pending buffered content as a final event. Required at end of - /// stream — without it, content held back to wait for a possible marker - /// match is silently lost. Stream-end content gets routed by current - /// mode: in-think content becomes `.reasoning`, plain text becomes - /// `.text`. mutating func flush() -> [Event] { - drain(isFinal: true) + switch format { + case .tagPair: + return drainTagPair(isFinal: true) + case .agentic: + return drainAgentic(isFinal: true) + } } - private mutating func drain(isFinal: Bool) -> [Event] { + // MARK: - Tag-pair mode + + private mutating func drainTagPair(isFinal: Bool) -> [Event] { var events: [Event] = [] while true { - let marker = insideThink ? closeMarker : openMarker + let marker = insideThink ? closeMarkerForTagPair : openMarkerForTagPair let makeEvent: (String) -> Event = insideThink ? { .reasoning($0) } : { .text($0) } if let range = buffer.range(of: marker) { @@ -68,11 +83,7 @@ struct ThinkTagParser { buffer = String(buffer[range.upperBound...]) insideThink.toggle() } else { - // `isFinal == true` (called from `flush()`): no need to hold back - // a partial-marker suffix; emit the entire buffer. Otherwise: - // hold back at most `marker.count - 1` characters in case the - // next delta completes the marker. - let safe = isFinal ? buffer.endIndex : lastSafeIndex(in: buffer, forTag: marker) + let safe = isFinal ? buffer.endIndex : lastSafeIndex(forTag: marker) if safe > buffer.startIndex { let toEmit = String(buffer[buffer.startIndex.. [Event] { + guard case .agentic(let selfMarker, let userMarker, let eom, let eot) = format else { + return [] + } + + var events: [Event] = [] + while true { + // Entry markers may arrive across consume() boundaries — strip them + // at the top of each iteration before searching for end markers. + if buffer.hasPrefix(selfMarker) { + buffer = String(buffer.dropFirst(selfMarker.count)) + insideThink = true + } else if buffer.hasPrefix(userMarker) { + buffer = String(buffer.dropFirst(userMarker.count)) + insideThink = false + } + + if insideThink { + if let range = buffer.range(of: eom) { + let before = String(buffer[buffer.startIndex.. [Event] { + let safeEnd: String.Index + if holdBack <= 0 || buffer.isEmpty { + safeEnd = buffer.endIndex + } else { + safeEnd = buffer.index(buffer.endIndex, offsetBy: -min(holdBack, buffer.count)) + } + if safeEnd > buffer.startIndex { + let toEmit = String(buffer[buffer.startIndex.. String.Index { + let maxHold = tag.count - 1 + guard !buffer.isEmpty, maxHold > 0 else { return buffer.endIndex } + let holdStart = buffer.index(buffer.endIndex, offsetBy: -min(maxHold, buffer.count)) + for offset in 0..", + userMarker: "to=user<|message|>", + endOfMessage: "<|eom|>", + endOfTurn: "<|eot|>" + ) + + @Test("Single reasoning + response turn") + func singleTurn() { + var parser = ThinkTagParser(format: format) + let input = "thinking here<|eom|>visible response<|eot|>" + let events = parser.consume(input) + parser.flush() + #expect(eventStrings(events, kind: .reasoning) == ["thinking here"]) + #expect(eventStrings(events, kind: .text) == ["visible response"]) + } + + @Test("Multiple reasoning segments before response") + func multipleReasoningSegments() { + var parser = ThinkTagParser(format: format) + let input = "step 1<|eom|>to=self<|message|>step 2<|eom|>to=user<|message|>answer<|eot|>" + let events = parser.consume(input) + parser.flush() + #expect(eventStrings(events, kind: .reasoning) == ["step 1", "step 2"]) + #expect(eventStrings(events, kind: .text) == ["answer"]) + } + + @Test("Starts in reasoning mode (agentic default)") + func startsInReasoning() { + var parser = ThinkTagParser(format: format) + let events = parser.consume("initial thought") + parser.flush() + #expect(eventStrings(events, kind: .reasoning) == ["initial thought"]) + #expect(eventStrings(events, kind: .text).isEmpty) + } + + @Test("Switch from user back to self (multi-turn)") + func multiTurnSwitching() { + var parser = ThinkTagParser(format: format) + let input = + "thought<|eom|>to=user<|message|>reply 1<|eot|>to=self<|message|>more thought<|eom|>to=user<|message|>reply 2<|eot|>" + let events = parser.consume(input) + parser.flush() + #expect(eventStrings(events, kind: .reasoning) == ["thought", "more thought"]) + #expect(eventStrings(events, kind: .text) == ["reply 1", "reply 2"]) + } + + @Test("Marker split across consumes") + func markerStraddlesTwoConsumes() { + var parser = ThinkTagParser(format: format) + // Split "<|eom|>" across two chunks + var events = parser.consume("thinking<|eo") + #expect(eventStrings(events, kind: .reasoning).isEmpty) + events += parser.consume("m|>to=user<|message|>response<|eot|>") + events += parser.flush() + #expect(eventStrings(events, kind: .reasoning) == ["thinking"]) + #expect(eventStrings(events, kind: .text) == ["response"]) + } + + @Test("Token-by-token streaming") + func tokenByToken() { + var parser = ThinkTagParser(format: format) + let input = "think<|eom|>to=user<|message|>hi<|eot|>" + var events: [ThinkTagParser.Event] = [] + for char in input { + events += parser.consume(String(char)) + } + events += parser.flush() + #expect(eventStrings(events, kind: .reasoning) == ["think"]) + #expect(eventStrings(events, kind: .text) == ["hi"]) + } + + @Test("User marker directly transitions from reasoning (no eom)") + func userMarkerDirectTransition() { + var parser = ThinkTagParser(format: format) + let input = "reasoning content to=user<|message|>visible<|eot|>" + let events = parser.consume(input) + parser.flush() + #expect(eventStrings(events, kind: .reasoning) == ["reasoning content "]) + #expect(eventStrings(events, kind: .text) == ["visible"]) + } + + @Test("Empty reasoning segment") + func emptyReasoning() { + var parser = ThinkTagParser(format: format) + let input = "<|eom|>to=user<|message|>just response<|eot|>" + let events = parser.consume(input) + parser.flush() + #expect(eventStrings(events, kind: .reasoning).isEmpty) + #expect(eventStrings(events, kind: .text) == ["just response"]) + } + + @Test("Unclosed reasoning at EOS — flush drains as .reasoning") + func unclosedReasoningAtEOS() { + var parser = ThinkTagParser(format: format) + let events = parser.consume("partial thought without end") + parser.flush() + let allReasoning = eventStrings(events, kind: .reasoning).joined() + #expect(allReasoning == "partial thought without end") + #expect(eventStrings(events, kind: .text).isEmpty) + } + + @Test("Repeated eom without intervening content — malformed, best-effort") + func repeatedEomNoContent() { + var parser = ThinkTagParser(format: format) + // Double <|eom|> is malformed — second one leaks as text prefix since + // the parser is already in user mode and doesn't recognize it + let input = "<|eom|><|eom|>to=user<|message|>response<|eot|>" + let events = parser.consume(input) + parser.flush() + let allText = eventStrings(events, kind: .text).joined() + #expect(allText.contains("response")) + } + + @Test("Markers embedded in content don't confuse parser") + func markersInContent() { + var parser = ThinkTagParser(format: format) + // The text "discuss eom behavior" shouldn't trigger on "eom" substring + let input = "discuss eom behavior<|eom|>to=user<|message|>ok<|eot|>" + let events = parser.consume(input) + parser.flush() + #expect(eventStrings(events, kind: .reasoning) == ["discuss eom behavior"]) + #expect(eventStrings(events, kind: .text) == ["ok"]) + } + + @Test("Very long reasoning segment") + func longReasoningSegment() { + var parser = ThinkTagParser(format: format) + let longText = String(repeating: "reasoning step. ", count: 100) + let input = "\(longText)<|eom|>to=user<|message|>done<|eot|>" + let events = parser.consume(input) + parser.flush() + let allReasoning = eventStrings(events, kind: .reasoning).joined() + #expect(allReasoning == longText) + #expect(eventStrings(events, kind: .text) == ["done"]) + } + + @Test("Newlines and special characters in content") + func specialCharsInContent() { + var parser = ThinkTagParser(format: format) + let input = "line1\nline2\ttab<|eom|>to=user<|message|>hello 🌍<|eot|>" + let events = parser.consume(input) + parser.flush() + #expect(eventStrings(events, kind: .reasoning) == ["line1\nline2\ttab"]) + #expect(eventStrings(events, kind: .text) == ["hello 🌍"]) + } + + @Test("Empty buffer — consume empty string produces no events") + func emptyConsume() { + var parser = ThinkTagParser(format: format) + let events = parser.consume("") + parser.flush() + #expect(events.isEmpty) + } + + @Test("Only end markers, no content") + func onlyMarkers() { + var parser = ThinkTagParser(format: format) + let input = "<|eom|>to=user<|message|><|eot|>to=self<|message|><|eom|>to=user<|message|><|eot|>" + let events = parser.consume(input) + parser.flush() + #expect(eventStrings(events, kind: .reasoning).isEmpty) + #expect(eventStrings(events, kind: .text).isEmpty) + } + + // MARK: - Helpers + + private enum EventKind { case text, reasoning } + + private func eventStrings(_ events: [ThinkTagParser.Event], kind: EventKind) -> [String] { + events.compactMap { event in + switch (event, kind) { + case (.text(let s), .text): return s + case (.reasoning(let s), .reasoning): return s + default: return nil + } + } + } +} + +@Suite("ThinkTagParser — Format detection") +struct ThinkTagParserDetectionTests { + @Test("Tokenizer with think tags detects tag-pair format") + func detectsTagPairForThinkTokens() { + let tokenizer = MockTokenizer(vocab: [ + "": 100, "": 101, + "": 2, + ]) + let format = CoreAILanguageModel.CoreAIExecutor.detectThinkingFormat(using: tokenizer) + guard case .tagPair(let open, let close) = format else { + Issue.record("Expected .tagPair, got \(format)") + return + } + #expect(open == "") + #expect(close == "") + } + + @Test("Agentic tokenizer (eom+eot+message) detects agentic format") + func detectsAgenticFormat() { + let tokenizer = MockTokenizer(vocab: [ + "<|eom|>": 200, "<|eot|>": 201, + "<|message|>": 202, + "": 2, + ]) + let format = CoreAILanguageModel.CoreAIExecutor.detectThinkingFormat(using: tokenizer) + guard case .agentic(let selfM, let userM, let eom, let eot) = format else { + Issue.record("Expected .agentic, got \(format)") + return + } + #expect(selfM == "to=self<|message|>") + #expect(userM == "to=user<|message|>") + #expect(eom == "<|eom|>") + #expect(eot == "<|eot|>") + } + + @Test("Tokenizer with reasoning_start/end detects that variant") + func detectsReasoningStartEnd() { + let tokenizer = MockTokenizer(vocab: [ + "<|reasoning_start|>": 300, "<|reasoning_end|>": 301, + "": 2, + ]) + let format = CoreAILanguageModel.CoreAIExecutor.detectThinkingFormat(using: tokenizer) + guard case .tagPair(let open, let close) = format else { + Issue.record("Expected .tagPair, got \(format)") + return + } + #expect(open == "<|reasoning_start|>") + #expect(close == "<|reasoning_end|>") + } + + @Test("Plain tokenizer (no special tokens) falls back to ") + func fallbackToThinkTags() { + let tokenizer = MockTokenizer(vocab: ["": 2]) + let format = CoreAILanguageModel.CoreAIExecutor.detectThinkingFormat(using: tokenizer) + guard case .tagPair(let open, let close) = format else { + Issue.record("Expected .tagPair, got \(format)") + return + } + #expect(open == "") + #expect(close == "") + } + + @Test("Agentic format takes priority over tag-pair when both present") + func agenticPriorityOverTagPair() { + let tokenizer = MockTokenizer(vocab: [ + "": 100, "": 101, + "<|eom|>": 200, "<|eot|>": 201, + "<|message|>": 202, + "": 2, + ]) + let format = CoreAILanguageModel.CoreAIExecutor.detectThinkingFormat(using: tokenizer) + guard case .agentic = format else { + Issue.record("Expected .agentic (priority), got \(format)") + return + } + } + + @Test("eom+eot without message token falls back to tag-pair") + func eomEotWithoutMessageFallsBack() { + let tokenizer = MockTokenizer(vocab: [ + "<|eom|>": 200, "<|eot|>": 201, + "": 2, + ]) + let format = CoreAILanguageModel.CoreAIExecutor.detectThinkingFormat(using: tokenizer) + guard case .tagPair = format else { + Issue.record("Expected .tagPair fallback, got \(format)") + return + } + } +} + #endif From fc48972cdf8ed6715f309c63e616fe7707b4fd32 Mon Sep 17 00:00:00 2001 From: tjia1818 <35608981+tjia1818@users.noreply.github.com> Date: Thu, 20 Aug 2026 17:22:00 -0700 Subject: [PATCH 12/21] Add timing to model preparation and warm up for llm-benchmark (#189) Co-authored-by: Tao Jia --- .../Tools/benchmark/BenchmarkMain.swift | 23 +++++++++++++++---- 1 file changed, 18 insertions(+), 5 deletions(-) diff --git a/swift/Sources/Tools/benchmark/BenchmarkMain.swift b/swift/Sources/Tools/benchmark/BenchmarkMain.swift index 34973234..cd63e15d 100644 --- a/swift/Sources/Tools/benchmark/BenchmarkMain.swift +++ b/swift/Sources/Tools/benchmark/BenchmarkMain.swift @@ -87,18 +87,25 @@ struct LLMBenchmark: AsyncParsableCommand { let configData = try JSONEncoder().encode(engineConfig) print("\n⏳ Preparing AI asset...", terminator: "") fflush(stdout) + let prepareStart = SuspendingClock.now let engine = try await EngineFactory.createEngine( config: configData, modelURL: modelURL ) - print(cacheHit ? " done (cache hit)" : " done") + let prepareSeconds = (SuspendingClock.now - prepareStart).inSeconds + let cacheSuffix = cacheHit ? " (cache hit)" : "" + print(" done in \(fmt(prepareSeconds))s\(cacheSuffix)") let prompt = randomPrompt(vocabSize: vocabSize, count: promptTokens, seed: seed) let sampling = SamplingConfiguration(temperature: 0) // Warmup - print("\n⚙️ Warming up engine...") + print("\n⚙️ Warming up engine...", terminator: "") + fflush(stdout) + let warmupStart = SuspendingClock.now _ = try await runTrial(engine: engine, prompt: prompt, sampling: sampling) + let warmupSeconds = (SuspendingClock.now - warmupStart).inSeconds + print(" done in \(fmt(warmupSeconds))s") // Timed trials print("\n🔄 Benchmarking with \(promptTokens) prompt tokens, \(generationTokens) generation tokens\n") @@ -118,6 +125,8 @@ struct LLMBenchmark: AsyncParsableCommand { let avgGen = trials.map(\.genTps).reduce(0, +) / n print("\n📊 Benchmark Summary:") print(String(repeating: "=", count: 50)) + print("Prepare: \(fmt(prepareSeconds))s\(cacheSuffix)") + print("Warmup: \(fmt(warmupSeconds))s") print("Prompt: \(fmt(avgPrompt)) tokens/sec") print("Generation: \(fmt(avgGen)) tokens/sec") print(String(repeating: "=", count: 50)) @@ -128,6 +137,9 @@ struct LLMBenchmark: AsyncParsableCommand { promptTokens: promptTokens, generationTokens: generationTokens, numTrials: numTrials, + prepareSeconds: prepareSeconds, + cacheHit: cacheHit, + warmupSeconds: warmupSeconds, trials: trials, averages: BenchmarkReport.Averages( promptTps: avgPrompt, generationTps: avgGen) @@ -193,9 +205,7 @@ struct LLMBenchmark: AsyncParsableCommand { } private func seconds(from start: SuspendingClock.Instant, to end: SuspendingClock.Instant) -> Double { - let d = end - start - let (secs, atto) = d.components - return Double(secs) + Double(atto) / 1e18 + (end - start).inSeconds } private func fmt(_ v: Double) -> String { @@ -229,6 +239,9 @@ struct BenchmarkReport: Codable { let promptTokens: Int let generationTokens: Int let numTrials: Int + let prepareSeconds: Double + let cacheHit: Bool + let warmupSeconds: Double let trials: [TrialResult] let averages: Averages From f4c7571c9a07c541376687168f5bdad463181663 Mon Sep 17 00:00:00 2001 From: kevchengcodes <59463423+kevchengcodes@users.noreply.github.com> Date: Thu, 20 Aug 2026 18:45:24 -0700 Subject: [PATCH 13/21] Live Transcription with Parakeet v3 (#184) This commit introduces a streaming mode for Parakeet to enable live transcription via buffered/chunked inference. Offline transcription Is largely unaffected and retains the same behavior/accuracy as before, with some additive, non-breaking public API changes. The primary use case for Parakeet streaming is through the Swift APIs for app integration. Optionally, the CLI exposes the streaming interface for diagnostic and correctness testing purposes. NOTE: Streaming is for streaming bundles ONLY. Static and dynamic bundles will be rejected. --- models/parakeet/README.md | 131 ++++- models/parakeet/export.py | 361 +++++++++++-- .../CoreAIShared/Bundle/ModelBundle.swift | 7 +- .../Runtime/NDArray+Helpers.swift | 109 ++++ .../CoreAISpeech/ParakeetTDTDecoder.swift | 485 ++++++++++++------ .../Sources/CoreAISpeech/SpeechDecoder.swift | 3 + .../SpeechRecognitionBundle.swift | 29 +- .../CoreAISpeech/SpeechRecognitionModel.swift | 132 +++-- .../CoreAISpeech/StreamingSession.swift | 422 +++++++++++++++ .../CoreAISpeech/StreamingWindow.swift | 337 ++++++++++++ ...echParityTest.swift => SpeechParity.swift} | 11 +- .../SpeechRecognizerMain.swift | 97 +++- .../speech-recognizer/StreamingRun.swift | 165 ++++++ .../NDArrayHelpersTests.swift | 107 ++++ swift/Tests/SpeechTests/MelFramingTests.swift | 45 +- .../SpeechTests/ParakeetDecoderTests.swift | 51 +- .../SpeechTests/StreamingWindowTests.swift | 455 ++++++++++++++++ 17 files changed, 2656 insertions(+), 291 deletions(-) create mode 100644 swift/Sources/CoreAISpeech/StreamingSession.swift create mode 100644 swift/Sources/CoreAISpeech/StreamingWindow.swift rename swift/Sources/Tools/speech-recognizer/{SpeechParityTest.swift => SpeechParity.swift} (98%) create mode 100644 swift/Sources/Tools/speech-recognizer/StreamingRun.swift create mode 100644 swift/Tests/CoreAISharedTests/NDArrayHelpersTests.swift create mode 100644 swift/Tests/SpeechTests/StreamingWindowTests.swift diff --git a/models/parakeet/README.md b/models/parakeet/README.md index 06e2da5e..d0f33bb7 100644 --- a/models/parakeet/README.md +++ b/models/parakeet/README.md @@ -30,8 +30,9 @@ uv run export.py --help | `--output-dir` | Output directory for the bundle | `/exports/` | | `--dtype` | `float16`, `float32` | `float32` | | `--dynamic` | Encoder accepts variable audio length | static (5s default) | -| `--audio-seconds` | Length of dummy audio for static encoder trace | `5.0` | +| `--audio-seconds` | Length of dummy audio for static encoder trace (ignored with `--dynamic` or `--streaming`) | `5.0` | | `--overwrite` | Overwrite existing bundle | — | +| `--streaming` | Fixed streaming window (see [Streaming](#streaming)) | — | **Supported models:** @@ -47,7 +48,7 @@ uv run export.py --help import CoreAISpeech // Load an exported bundle directory (metadata.json + encoder/decoder_step/joint .aimodel assets + processor/). -let model = try await SpeechRecognitionModel(resourcesAt: "coreai-models/exports/parakeet-tdt-0.6b-v3_float32_static") +let model = try await SpeechRecognitionModel(resourcesAt: URL(fileURLWithPath: "coreai-models/exports/parakeet-tdt-0.6b-v3_float32_static")) // Transcribe an audio file — decoded and resampled to the model's sample rate automatically: let (text, stats) = try await model.transcribe(audioURL: URL(fileURLWithPath: "audio.wav")) @@ -79,6 +80,130 @@ The encoder graph already includes `encoder_projector`, so the joint network's t ## Streaming -This recipe exports the full-utterance encoder; cache-aware / chunked-attention streaming is not yet implemented in `transformers` for Parakeet. The `decoder_step` and `joint` graphs are already streaming-shaped (single-step, explicit LSTM state in/out), so once a chunked encoder lands upstream the same bundle layout extends to streaming with only an encoder swap. +Parakeet TDT v3 is an *offline* FastConformer: attention is bidirectional over the whole utterance (`att_context_style: regular`), and `transformers` has no cache-aware Parakeet encoder. So streaming is done by **buffered inference** — re-run the whole encoder over a bounded `[left | chunk | right]` window each hop, consume only the chunk's encoder frames, and carry the transducer state across hops. + +This is NVIDIA's own algorithm, from NeMo [`examples/asr/asr_chunked_inference/rnnt/speech_to_text_streaming_infer_rnnt.py`](https://github.com/NVIDIA-NeMo/Speech/blob/main/examples/asr/asr_chunked_inference/rnnt/speech_to_text_streaming_infer_rnnt.py). Its own example applies it to a non-cache-aware checkpoint, so this is a supported upstream mode rather than a workaround. `decoder_step` and `joint` were already the right shape — single-step with explicit LSTM state — so only the encoder's traced window changes. + +### Exporting a streaming bundle + +```sh +uv run export.py --streaming --dtype float16 +``` + +| Flag | Description | Default | +| --- | --- | --- | +| `--streaming` | Fixed streaming window; records geometry in `metadata.json`. Ignores `--audio-seconds`. Mutually exclusive with `--dynamic`. | — | +| `--chunk-frames` | Encoder frames consumed per hop. Sets the emission cadence. Smaller costs more — see below. | `12` (0.96 s) | +| `--right-context-frames` | Lookahead. Latency is `(chunk + right) × 80 ms`. Must be ≥ `max(durations)`. | `12` (0.96 s) | +| `--left-context-frames` | Past context. Free — costs no latency. | `126` (10.08 s) | + +Produces `parakeet-tdt-0.6b-v3_float16_streaming150/`, plus a `streaming` block in `metadata.json` that the runtime reads so callers don't have to restate the geometry. + +The three frame counts are read only when `--streaming` is set, and `--audio-seconds` only when it isn't. The export warns rather than failing when it sees a flag the chosen shape mode doesn't use, so a stray `--chunk-frames` can't quietly produce a window you didn't ask for. + +**Only a `--streaming` bundle can stream.** The window is fixed when the encoder is traced, so +it is chosen here and cannot be changed later; `startStream` rejects a `--static` or `--dynamic` +bundle and names the re-export command. Two geometries worth starting from: + +| geometry | export flags | latency | +| --- | --- | --- | +| balanced (the default) | `--streaming` | 1.92 s | +| accuracy (NeMo's 10-2-2) | `--streaming --chunk-frames 25 --right-context-frames 25 --left-context-frames 125` | 4.00 s | + +The `150` in the name is the window's **usable encoder frame count** — `left + chunk + right` = `126 + 12 + 12`, or 12.0 s at 80 ms per frame. It comes from the three flags above, so a different geometry produces a different suffix; it is not a model size or a latency figure. + +**The chunk size is a throughput knob, not a quality one.** Every hop re-encodes the whole window to consume one chunk, so the encoder work per second of audio scales with `window / chunk`: halving the chunk doubles it. + +### Frame arithmetic + +One encoder frame is `hop_length × subsampling_factor` = 1280 samples / 16 kHz = **80 ms**. Everything follows from making the PCM window a whole number of encoder frames: + +``` +window_samples = W × 1280 +mel frames = 8W + 1 (frameCount is 1 + N/hop, torch.stft center=True) +encoder frames = W + 1 (ceil(mel/8): three stride-2 kernel-3 pad-1 convs) +usable frames = W (the last encoder frame covers the zero-padded remainder) +``` + +The export runs one forward pass and fails if the traced encoder disagrees with this arithmetic, because a one-frame slice error is 80 ms of audio and would drop or duplicate words at every chunk boundary. + +### Ramp-up + +At the start of a session the window holds less audio than it was traced for. That matters because the encoder is full-attention and non-causal: a frame's representation depends on how much audio surrounds it, so a window that grows hop by hop decodes the opening under a different regime than steady state, and the transducer ends up consuming a sequence stitched from mismatched representations. + +So while audio is still arriving, the window is zero-filled to its traced size — the padding stands in for audio not yet received, and every hop presents the same extent. Frames are still only consumed where real audio backs them. The final flush is deliberately *not* padded: there the zeros would mean "no more speech", and masking them honestly is what cues the sentence-final token. + +Cost is a one-time increase in front-end work per session, since the mel is computed across the full window during ramp-up rather than just the real prefix. + +A `--dynamic` bundle cannot stream either: its time axis is symbolic, so there is no traced +window at all, and the `float32` dynamic encoder is separately unreliable on the GPU path at many +shapes. + +The recorded block carries only what the runtime reads — left, chunk, right, the window's mel +frame count, and the sample rate, hop and subsampling factor it was traced at. Left context is +*derived* at load (`window − chunk − right`) and the recorded copy is cross-checked against it, so +a block that has been edited into disagreeing with itself fails to load rather than running a +geometry the file misdescribes. + +### Running + +```swift +import CoreAISpeech + +let model = try await SpeechRecognitionModel( + resourcesAt: URL(fileURLWithPath: "exports/parakeet-tdt-0.6b-v3_float16_streaming150")) + +// This package does not capture audio. A host app owns AVAudioEngine, converts to +// mono float32 at model.sampleRate, and pushes buffers in. +let updates = try await model.startStream() + +Task { + for await update in updates { + switch update { + case .partial(let segment): render(segment.text) // never retracts + case .finalized(let segment): commit(segment.text, segment.startTime...segment.endTime) + } + } +} + +try await model.append(pcm: buffer) // call from a Task, not an audio render callback +try await model.finishStream() +``` + +`startStream` takes no geometry: the bundle's window is the geometry, and `activeStreamingConfig` reports it — chunk, right, the derived left, and the theoretical latency. What the caller does control is `EndpointingConfig`: where a transcript is cut for display, and how long a gap has to be before the predictor is reset. Neither changes a tensor shape. + +```bash +swift run -c release speech-recognizer --model --audio-path audio.wav --stream +``` + +The CLI has no geometry flags, for the same reason — it prints the window the bundle records. `--endpoint-frames` sets the silence threshold, `--realtime` paces file input at 1× so reported latency is realistic, and `--deferred-decode` chunks the encoder but decodes once at the end; all three require `--stream`. `--reset-after-silence-frames` overrides the predictor reset and deliberately does *not*, since it applies to offline transcription too. + +Partials are rewritten in place on a terminal, and dropped entirely when stdout is redirected so that piped output stays diffable. + +### Segments and endpointing + +A segment closes when the decoder has gone `silenceFrames` (default 10, so 0.8 s) without emitting, or — past the `maxSegmentFrames` cap (default 375, 30 s) — at the first pause after it. Silence is counted in *frames of audio the decoder skipped*, duration-weighted: a blank carrying duration 4 contributes 4 frames, so the threshold means 0.8 s of audio rather than "one hop produced nothing". + +Closing a segment is a display boundary: the transducer state carries straight across it. Zeroing the predictor at *every* endpoint instead cost roughly 3.8 s of dropped audio each time, while it re-established context from a cold start. + +A long gap is the exception. `resetAfterSilenceFrames` (default 40, so 3.2 s) restores the predictor's start condition — a zeroed LSTM plus the blank as the previous label, which is all-zeros because the blank's embedding row is itself zero. Without it, a predictor that has emitted a sentence-final token and then consumed tens of seconds of blanks resists re-entering an emitting state: the duration head jumps across the re-onset and the resuming utterance loses its opening words. + +That loss reproduces identically offline and under HF's own `generate()`, so it is the checkpoint's behaviour rather than the chunking's, and the reset is a streaming-side correction to it. It is applied inside the decode loop rather than at a hop boundary, so `--deferred-decode` runs the same rule and still agrees with a live stream. Offline transcription defaults to `0` to stay reference-exact — `--parity-test` compares tokens and transcript against PyTorch traces — so pass `--reset-after-silence-frames` to opt an offline run in. + +### Gating the mic + +Endpointing and `resetAfterSilenceFrames` advance only when hops run, so both assume the host pushes audio continuously — including through pauses. + +An app that gates the mic with a voice-activity detector must not simply withhold audio: that freezes the session rather than pausing it, so the open segment never finalizes and the predictor reset never fires. Call `finishStream()` at the pause and `startStream()` again when speech resumes, after a hangover long enough that an inter-word gap does not split one utterance. Flush a few hundred milliseconds of pre-roll on the transition, since a detector fires after onset. `hop` and `segmentIndex` are per-session, so timestamps restart and the app supplies its own offset. + +### `--deferred-decode` is the correctness test + +It chunks the encoder but concatenates the outputs and decodes in one pass. This is NeMo's `simulated` flag under a name that says what changes — upstream describes it as "encoder is evaluated on chunks, output is concatenated and decoded at one step … expected to provide the same results". + +That expectation is what makes it a test. Because it shares the encoder path but not the incremental decode, `--deferred-decode` and plain `--stream` must produce byte-identical transcripts. Any difference is a bug in the state carry, the duration-overshoot carry, or the frame partition — and comparing two strings finds those without needing a quality judgement. Note it defers *all* decoding, so it emits no partials and never exercises endpointing; it also holds every consumed frame in memory (~32 KB per second of audio), so it is for short files rather than long sessions. + +### Streaming removes the length limit + +The offline path silently pads or truncates PCM to the traced window, so `transcribe` on a static bundle covers only as much audio as that window holds — longer input is dropped without an error. A streaming bundle has no such bound: it slides the same fixed window across input of any length, so a session transcribes an arbitrarily long stream. [^1]: [TDT paper](https://arxiv.org/abs/2304.06795) · [Parakeet TDT v3 paper](https://arxiv.org/abs/2509.14128) · [HuggingFace](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3) diff --git a/models/parakeet/export.py b/models/parakeet/export.py index a4083363..b956ba76 100644 --- a/models/parakeet/export.py +++ b/models/parakeet/export.py @@ -17,6 +17,7 @@ # index-strategy = "unsafe-best-match" # /// import argparse +import dataclasses import json import shutil import time @@ -120,16 +121,138 @@ def forward( ) -def _audio_features( - model_name: str, dtype: torch.dtype, seconds: float +def _audio_features_samples( + processor: "transformers.ProcessorMixin", dtype: torch.dtype, num_samples: int ) -> torch.Tensor: - processor = transformers.AutoProcessor.from_pretrained(model_name) sample_rate = processor.feature_extractor.sampling_rate - dummy_audio = np.random.randn(int(sample_rate * seconds)).astype(np.float32) + dummy_audio = np.random.randn(num_samples).astype(np.float32) features = processor.feature_extractor(dummy_audio, sampling_rate=sample_rate) return features["input_features"].to(dtype).detach().clone() +def _audio_features( + processor: "transformers.ProcessorMixin", dtype: torch.dtype, seconds: float +) -> torch.Tensor: + sample_rate = processor.feature_extractor.sampling_rate + return _audio_features_samples(processor, dtype, int(sample_rate * seconds)) + + +def _encoder_frame_count(mel_frames: int, subsampling_factor: int) -> int: + """Encoder frames emitted for `mel_frames`, applying the subsampling stack stage by stage. + + Each stride-2, kernel-3, pad-1 conv maps `T` to `floor((T - 1) / 2) + 1`, so the count follows + from halving once per factor of two rather than from the `ceil(L/8)` closed form. Mirrors + `encoderFrameCount` in StreamingWindow.swift and HF + `ParakeetPreTrainedModel._get_subsampling_output_length`, so the export, the simulator and the + runtime cannot disagree about the same quantity. + + `subsampling_factor` must be a power of two, which is all a stack of stride-2 convs can + express — the loop halves, so a factor of 6 would silently behave as 4. + """ + if mel_frames <= 0 or subsampling_factor <= 1: + return max(0, mel_frames) + if subsampling_factor & (subsampling_factor - 1) != 0: + raise ValueError( + f"subsampling_factor must be a power of two, got {subsampling_factor}" + ) + length, factor = mel_frames, subsampling_factor + while factor > 1: + length = (length - 1) // 2 + 1 + factor //= 2 + return length + + +@dataclasses.dataclass(frozen=True) +class StreamingWindowArgs: + """The three knobs that size a streaming window, in encoder frames. + + `None` rather than a `streaming=False` flag is what makes "not streaming" unable to carry + window values nothing reads. + """ + + left_context_frames: int = 126 + chunk_frames: int = 12 + right_context_frames: int = 12 + + +def _streaming_geometry( + processor: "transformers.ProcessorMixin", + config: "transformers.ParakeetTDTConfig", + window: StreamingWindowArgs, +) -> dict: + """Window geometry for a streaming encoder export, in exact integers. + + Everything hangs off one rule: make the PCM window a whole number of encoder + frames. The feature extractor emits `1 + N/hop` frames (torch.stft + center=True), and each subsampling conv maps `T` to `floor((T - 1) / 2) + 1` — see + `_encoder_frame_count`, which is the definition; `ceil(L/8)` is only its consequence for + a factor of 8. So a window of `W * hop * subsampling` samples gives `8W + 1` mel frames and + `W + 1` encoder frames, of which `W` are fully backed by real audio and the last covers the + zero-padded remainder. The `8W + 1` identity is asserted below rather than assumed. + + Deriving the sample count from `seconds` instead would be lossy at exactly the + lengths we care about: `16000 * 6.4 == 102400.00000000001`. + """ + extractor = processor.feature_extractor + sample_rate = extractor.sampling_rate + hop_length = extractor.hop_length + subsampling = config.encoder_config.subsampling_factor + + left, chunk, right = ( + window.left_context_frames, + window.chunk_frames, + window.right_context_frames, + ) + usable = left + chunk + right + samples_per_encoder_frame = hop_length * subsampling + window_samples = usable * samples_per_encoder_frame + window_mel_frames = usable * subsampling + 1 + + # The whole window arithmetic rests on a frame-aligned window: the mel frames backed by real + # audio must subsample to exactly `usable`. Check it rather than trusting the closed form. + valid_mel_frames = window_mel_frames - 1 + recovered = _encoder_frame_count(valid_mel_frames, subsampling) + if recovered != usable: + raise ValueError( + f"window of {usable} encoder frames gives {valid_mel_frames} valid mel frames, " + f"which subsample to {recovered}, not {usable}" + ) + + return { + "left_context_encoder_frames": left, + "chunk_encoder_frames": chunk, + "right_context_encoder_frames": right, + "usable_encoder_frames": usable, + "window_encoder_frames": _encoder_frame_count(window_mel_frames, subsampling), + "window_mel_frames": window_mel_frames, + "window_sample_count": window_samples, + "seconds_per_encoder_frame": samples_per_encoder_frame / sample_rate, + "sample_rate": sample_rate, + "hop_length": hop_length, + "subsampling_factor": subsampling, + } + + +# Keys the Swift runtime reads (StreamingConfig.StreamingBlock). Everything else +# `_streaming_geometry` computes is for this script's own use — naming the bundle, sizing the +# dummy input, the forward-pass assertions, the log line — and is deliberately not published: +# a derived value in the file is one more thing that can contradict the traced graph. +_RECORDED_GEOMETRY_KEYS = ( + "left_context_encoder_frames", + "chunk_encoder_frames", + "right_context_encoder_frames", + "window_mel_frames", + "sample_rate", + "hop_length", + "subsampling_factor", +) + + +def _recorded_geometry(geometry: dict) -> dict: + """The subset of the geometry a bundle records, in the order above.""" + return {key: geometry[key] for key in _RECORDED_GEOMETRY_KEYS} + + def _decoder_step_inputs( config: "transformers.ParakeetTDTConfig", dtype: torch.dtype ) -> dict[str, torch.Tensor]: @@ -205,17 +328,31 @@ def _default_output_dir() -> str: return str(Path(__file__).resolve().parents[2] / "exports") -def _variant_name(model_name: str, dtype: torch.dtype, dynamic: bool) -> str: +def _variant_name( + model_name: str, + dtype: torch.dtype, + dynamic: bool, + streaming: dict | None = None, +) -> str: safe_name = Path(model_name).name dtype_name = str(dtype).split(".")[-1] - static_or_dynamic = "dynamic" if dynamic else "static" - return f"{safe_name}_{dtype_name}_{static_or_dynamic}" + if streaming is not None: + # The usable frame count is what distinguishes one streaming window from + # another, so it belongs in the name. + kind = f"streaming{streaming['usable_encoder_frames']}" + else: + kind = "dynamic" if dynamic else "static" + return f"{safe_name}_{dtype_name}_{kind}" def _bundle_paths( - output_dir: str, model_name: str, dtype: torch.dtype, dynamic: bool + output_dir: str, + model_name: str, + dtype: torch.dtype, + dynamic: bool, + streaming: dict | None = None, ) -> tuple[Path, dict[str, Path]]: - variant = _variant_name(model_name, dtype, dynamic) + variant = _variant_name(model_name, dtype, dynamic, streaming) bundle_dir = Path(output_dir) / variant assets = { ENCODER_GRAPH: bundle_dir / f"{variant}_{ENCODER_GRAPH}.aimodel", @@ -255,11 +392,12 @@ def _prepare_bundle_dir(bundle_dir: Path, overwrite: bool) -> None: bundle_dir.mkdir(parents=True, exist_ok=True) -def _write_processor(dest: Path, model_name: str) -> None: +def _write_processor( + dest: Path, processor: "transformers.ProcessorMixin", model_name: str +) -> None: print( f"[INFO] Saving processor (feature extractor + tokenizer) from {model_name} to {dest}..." ) - processor = transformers.AutoProcessor.from_pretrained(model_name) processor.save_pretrained(str(dest)) @@ -268,6 +406,7 @@ def _write_bundle_metadata( variant: str, config: "transformers.ParakeetTDTConfig", assets: dict[str, Path], + streaming: dict | None = None, ) -> None: metadata = { "metadata_version": "0.2", @@ -288,12 +427,77 @@ def _write_bundle_metadata( }, }, } + if streaming is not None: + # A sibling of `config`, not a member of it, so ParakeetTDTConfig.decode on + # the Swift side is untouched and existing bundles keep decoding. Note + # metadata_version stays "0.2": ModelBundle hard-rejects anything else. + metadata["streaming"] = _recorded_geometry(streaming) metadata_path = bundle_dir / "metadata.json" with open(metadata_path, "w") as f: json.dump(metadata, f, indent=2) print(f"[INFO] Wrote bundle metadata to {metadata_path}.") +def _encoder_inputs(features: torch.Tensor) -> dict[str, torch.Tensor]: + return { + "input_features": features, + # All-valid mask for the trace; the Swift runtime supplies the real + # per-frame mask (1 for real audio, 0 for the static window's padding). + "attention_mask": torch.ones(features.shape[:2], dtype=torch.bool), + } + + +def _streaming_encoder_inputs( + processor: "transformers.ProcessorMixin", + model: "transformers.ParakeetForTDT", + dtype: torch.dtype, + geometry: dict, +) -> dict[str, torch.Tensor]: + """Trace inputs for a streaming window, checked against the geometry that sized them. + + Catches an arithmetic error here in Python rather than six files later in Swift: a + one-frame slice error is 80 ms of audio and would drop or duplicate words at every chunk + boundary. Costs one forward pass. + """ + features = _audio_features_samples( + processor, dtype, geometry["window_sample_count"] + ) + if features.shape[1] != geometry["window_mel_frames"]: + raise ValueError( + f"streaming geometry mismatch: {geometry['window_sample_count']} samples " + f"produced {features.shape[1]} mel frames, expected " + f"{geometry['window_mel_frames']}" + ) + inputs = _encoder_inputs(features) + with torch.no_grad(): + probe = ParakeetEncoderModule(model)(**inputs) + if probe.shape[1] != geometry["window_encoder_frames"]: + raise ValueError( + f"streaming geometry mismatch: traced encoder emits {probe.shape[1]} " + f"frames, expected {geometry['window_encoder_frames']}" + ) + print( + f"[INFO] Verified encoder emits {probe.shape[1]} frames " + f"({geometry['usable_encoder_frames']} usable + 1 padding boundary)." + ) + return inputs + + +def _log_streaming_window(geometry: dict) -> None: + latency = ( + geometry["chunk_encoder_frames"] + geometry["right_context_encoder_frames"] + ) * geometry["seconds_per_encoder_frame"] + print( + f"[INFO] Streaming window: left {geometry['left_context_encoder_frames']} / " + f"chunk {geometry['chunk_encoder_frames']} / " + f"right {geometry['right_context_encoder_frames']} encoder frames " + f"({geometry['usable_encoder_frames']} usable) = " + f"{geometry['window_sample_count']} samples " + f"({geometry['window_sample_count'] / geometry['sample_rate']:.2f} s), " + f"{geometry['window_mel_frames']} mel frames. Theoretical latency {latency:.2f} s." + ) + + def create_parakeet( output_dir: str, model_name: str, @@ -302,6 +506,7 @@ def create_parakeet( dynamic: bool, audio_seconds: float, include_debug_info: bool, + window: StreamingWindowArgs | None = None, ): print(f"[INFO] Sourcing {model_name}...") model = transformers.AutoModelForTDT.from_pretrained( @@ -314,18 +519,26 @@ def create_parakeet( f"decoder hidden={config.decoder_hidden_size}, vocab={config.vocab_size}, " f"durations={list(config.durations)}." ) + # One load, threaded through: it sizes the window, shapes the dummy input, and ships in + # the bundle. + processor = transformers.AutoProcessor.from_pretrained(model_name) - bundle_dir, assets = _bundle_paths(output_dir, model_name, dtype, dynamic) + geometry = None + if window is not None: + geometry = _streaming_geometry(processor, config, window) + _log_streaming_window(geometry) + + bundle_dir, assets = _bundle_paths(output_dir, model_name, dtype, dynamic, geometry) _prepare_bundle_dir(bundle_dir, overwrite) print(f"[INFO] Exporting {ENCODER_GRAPH} graph...") - encoder_features = _audio_features(model_name, dtype, audio_seconds) - encoder_inputs = { - "input_features": encoder_features, - # All-valid mask for the trace; the Swift runtime supplies the real - # per-frame mask (1 for real audio, 0 for the static window's padding). - "attention_mask": torch.ones(encoder_features.shape[:2], dtype=torch.bool), - } + if geometry is not None: + encoder_inputs = _streaming_encoder_inputs(processor, model, dtype, geometry) + else: + encoder_inputs = _encoder_inputs( + _audio_features(processor, dtype, audio_seconds) + ) + encoder_program = _convert( ParakeetEncoderModule(model), encoder_inputs, @@ -359,13 +572,53 @@ def create_parakeet( ) _save_program(joint_program, assets[JOINT_GRAPH], JOINT_GRAPH) - _write_processor(bundle_dir / "processor", model_name) + _write_processor(bundle_dir / "processor", processor, model_name) _write_bundle_metadata( - bundle_dir, _variant_name(model_name, dtype, dynamic), config, assets + bundle_dir, + _variant_name(model_name, dtype, dynamic, geometry), + config, + assets, + geometry, ) print(f"[INFO] Successfully created Parakeet TDT bundle at {bundle_dir}.") +_WINDOW_FLAGS = ( + ("--chunk-frames", "chunk_frames"), + ("--right-context-frames", "right_context_frames"), + ("--left-context-frames", "left_context_frames"), +) +_AUDIO_SECONDS_FLAG = ("--audio-seconds", "audio_seconds") + + +def _warn_ignored_shape_args( + parser: argparse.ArgumentParser, args: argparse.Namespace +) -> None: + """Warn about window flags the chosen shape mode never reads. + + Each mode sizes the encoder trace a different way, and a flag belonging to + another one is otherwise dropped in silence — the wrong window only shows up + a full export later, in the bundle name. + """ + if args.streaming: + candidates = [_AUDIO_SECONDS_FLAG] + reason = "--streaming sizes the window from the frame counts" + elif args.dynamic: + candidates = [_AUDIO_SECONDS_FLAG, *_WINDOW_FLAGS] + reason = "--dynamic leaves the encoder's time axis symbolic" + else: + candidates = list(_WINDOW_FLAGS) + reason = "the frame counts only apply with --streaming" + + ignored = [ + flag + for flag, dest in candidates + if getattr(args, dest) != parser.get_default(dest) + ] + if ignored: + print(f"[WARN] Ignoring {', '.join(ignored)} — {reason}.") + + def main(): parser = argparse.ArgumentParser( description=( @@ -396,18 +649,58 @@ def main(): action="store_true", help="Overwrite an existing bundle at the output path.", ) - parser.add_argument( + shape_group = parser.add_mutually_exclusive_group() + shape_group.add_argument( "--dynamic", action="store_true", help="Export the encoder with dynamic audio length (decoder/joint stay static).", ) + shape_group.add_argument( + "--streaming", + action="store_true", + help=( + "Export a fixed streaming window sized from --left/--chunk/" + "--right-context-frames, and record the geometry in metadata.json. " + "Ignores --audio-seconds." + ), + ) + parser.add_argument( + "--chunk-frames", + type=int, + default=12, + help=( + "Encoder frames consumed per streaming hop (1 frame = 80 ms). Sets the " + "emission cadence. Every hop re-encodes the whole window, so halving this " + "roughly doubles the encoder work per second of audio. Ignored unless " + "--streaming is set." + ), + ) + parser.add_argument( + "--right-context-frames", + type=int, + default=12, + help=( + "Encoder frames of lookahead. Theoretical latency is " + "(chunk + right) x 80 ms. Must be >= max(durations). Ignored unless " + "--streaming is set." + ), + ) + parser.add_argument( + "--left-context-frames", + type=int, + default=126, + help=( + "Encoder frames of past context. Improves quality at no latency cost. " + "Ignored unless --streaming is set." + ), + ) parser.add_argument( "--audio-seconds", type=float, default=5.0, help=( "Length (seconds) of dummy audio used to shape the encoder's static " - "trace. Ignored when --dynamic is set." + "trace. Ignored when --dynamic or --streaming is set." ), ) parser.add_argument( @@ -417,6 +710,7 @@ def main(): "Default: off, which embeds minimum debug information and makes the exported asset smaller.", ) args = parser.parse_args() + _warn_ignored_shape_args(parser, args) dtype = { "float16": torch.float16, @@ -425,13 +719,20 @@ def main(): output_dir = args.output_dir or _default_output_dir() create_parakeet( - output_dir, - args.model, - dtype, - args.overwrite, - args.dynamic, - args.audio_seconds, - args.include_debug_info, + output_dir=output_dir, + model_name=args.model, + dtype=dtype, + overwrite=args.overwrite, + dynamic=args.dynamic, + audio_seconds=args.audio_seconds, + include_debug_info=args.include_debug_info, + window=StreamingWindowArgs( + left_context_frames=args.left_context_frames, + chunk_frames=args.chunk_frames, + right_context_frames=args.right_context_frames, + ) + if args.streaming + else None, ) diff --git a/swift/Sources/CoreAIShared/Bundle/ModelBundle.swift b/swift/Sources/CoreAIShared/Bundle/ModelBundle.swift index dd6ecb3e..55a79026 100644 --- a/swift/Sources/CoreAIShared/Bundle/ModelBundle.swift +++ b/swift/Sources/CoreAIShared/Bundle/ModelBundle.swift @@ -90,7 +90,7 @@ public struct ModelBundle: Sendable { // MARK: - Errors - public enum BundleError: Error, CustomStringConvertible { + public enum BundleError: Error, CustomStringConvertible, LocalizedError { case missingMetadata(URL) case malformedMetadata(URL, underlying: Error) case unsupportedVersion(String) @@ -124,6 +124,11 @@ public struct ModelBundle: Sendable { """ } } + + /// Without this, `error.localizedDescription` — what a SwiftUI host app naturally + /// shows a user — bridges through `NSError` and yields "The operation couldn't be + /// completed. (CoreAIShared.BundleError error 2.)", discarding every message above. + public var errorDescription: String? { description } } // MARK: - Initialization diff --git a/swift/Sources/CoreAIShared/Runtime/NDArray+Helpers.swift b/swift/Sources/CoreAIShared/Runtime/NDArray+Helpers.swift index b801e15f..8c818a13 100644 --- a/swift/Sources/CoreAIShared/Runtime/NDArray+Helpers.swift +++ b/swift/Sources/CoreAIShared/Runtime/NDArray+Helpers.swift @@ -202,3 +202,112 @@ public func flattenNDArray( } return result } + +// MARK: - Partial Read / Scan Helpers + +// Partial reads of a graph output, for callers whose hot loop touches only part of a tensor. +// Flattening whole is the wrong shape for those: converting what you skip dominates. + +/// Elements `elementRange` of `array` as `[Float]`, in row-major order. +/// +/// Lets a chunked decoder convert only the frames it reads. A streaming hop's encoder output +/// also holds left and right context the loop never indexes — at Parakeet's default geometry, +/// 12 frames of 151 — so flattening it whole converts an order of magnitude more than is used. +public func floatElements(_ array: NDArray, in elementRange: Range) -> [Float] { + var result = [Float](repeating: 0, count: elementRange.count) + forEachFloatElement(array, in: elementRange) { result[$0] = $1 } + return result +} + +/// Index of the largest value in `elementRange`, relative to `elementRange.lowerBound`. +/// +/// Scans in place, because the alternative — flatten to `[Float]`, then scan — allocates and +/// converts a whole vocab row per emitted symbol (32 KB for Parakeet's 8,198 logits). +public func argmaxFloat(_ array: NDArray, in elementRange: Range) -> Int { + var scan = FloatArgmax() + forEachFloatElement(array, in: elementRange) { scan.offer($0, $1) } + return scan.best +} + +/// Running argmax over values offered in order. Ties go to the lowest index, and offering +/// nothing — or only `-infinity` — yields 0. +/// +/// Kept as a separate type so that tie-and-empty rule lives in one place rather than being +/// re-derived at each scan site. +private struct FloatArgmax { + private(set) var best = 0 + private var bestValue = -Float.infinity + + @inline(__always) + mutating func offer(_ index: Int, _ value: Float) { + if value > bestValue { + bestValue = value + best = index + } + } +} + +/// Visit logical row-major elements `elementRange` of `array` as `Float`, in order. `visit` +/// receives the offset within the range, not the absolute index. +/// +/// Output dtype can differ from the model's input dtype, so this branches on the array's own +/// scalar type rather than threading a flag from the input descriptors. +@inline(__always) +private func forEachFloatElement( + _ array: NDArray, in elementRange: Range, _ visit: (Int, Float) -> Void +) { + switch array.scalarType { + #if !((os(macOS) || targetEnvironment(macCatalyst)) && arch(x86_64)) + case .float16: + forEachElement(array, as: Float16.self, in: elementRange, visit) + #endif + case .float32: + forEachElement(array, as: Float.self, in: elementRange, visit) + default: + preconditionFailure("forEachFloatElement: unsupported scalar type \(array.scalarType)") + } +} + +@inline(__always) +private func forEachElement( + _ array: NDArray, as type: T.Type, in elementRange: Range, + _ visit: (Int, Float) -> Void +) { + let total = array.shape.reduce(1, *) + precondition( + elementRange.lowerBound >= 0 && elementRange.upperBound <= total, + "element range \(elementRange) exceeds element count \(total)") + if elementRange.isEmpty { return } + + array.view(as: type).withUnsafePointer { ptr, shape, strides in + if isContiguousRowMajor(shape: shape, strides: strides) { + for i in 0..= 0 { + indices[dim] += 1 + offset += strides[dim] + if indices[dim] < shape[dim] { break } + indices[dim] = 0 + offset -= strides[dim] * shape[dim] + dim -= 1 + } + } + } +} diff --git a/swift/Sources/CoreAISpeech/ParakeetTDTDecoder.swift b/swift/Sources/CoreAISpeech/ParakeetTDTDecoder.swift index 6aa1c5bc..67ecb203 100644 --- a/swift/Sources/CoreAISpeech/ParakeetTDTDecoder.swift +++ b/swift/Sources/CoreAISpeech/ParakeetTDTDecoder.swift @@ -113,22 +113,341 @@ public struct ParakeetTDTDecoder: SpeechDecoder { self.jointGraph = JointGraph(fn: jointFn, encoderIn: jointEncDesc, logits: logitsDesc) } + /// Transducer state that outlives a single call, so a streaming session can decode + /// one chunk of encoder frames at a time and keep going where it left off. + /// + /// The fields mirror NeMo's `BatchedLabelLoopingState` + /// (`nemo/collections/asr/parts/submodules/transducer_decoding/label_looping_base.py:41-49`) + public final class Stream: @unchecked Sendable { + private let decoder: ParakeetTDTDecoder + private let cfg: ParakeetTDTConfig + private let logitsSize: Int + private let lstmShape: [Int] + + /// LSTM state carried across chunks (NeMo `predictor_states`). + private var hiddenState: [Float] + private var cellState: [Float] + /// Last decoder output (NeMo `predictor_outputs`). Load-bearing across a chunk + /// boundary: the blank-skip branch reuses it without re-running the step graph, so + /// dropping it would make the first step of every chunk read a stale value. + private var decoderOutput: [Float]? + + /// Previous iteration's symbol, blanks included — fed back as the next `input_ids`. + private var previousSymbol: Int32 + private var firstStep: Bool + + /// Frames a TDT duration overshot the last chunk by, to be skipped at the start of + /// the next one. + public private(set) var timeJump: Int = 0 + + /// Consecutive encoder frames consumed without emitting anything, duration-weighted. + /// + /// Read by the streaming endpointer: a blank carrying duration 4 skips 320 ms in one + /// step, so a step count would under-measure silence by up to 4x, and a per-hop count + /// can only say "this whole chunk was quiet". + public private(set) var silentFrames: Int = 0 + + init(decoder: ParakeetTDTDecoder, config: ParakeetTDTConfig) { + self.decoder = decoder + self.cfg = config + self.logitsSize = decoder.jointGraph.logits.shape.last! + self.lstmShape = [config.numDecoderLayers, 1, config.decoderHiddenSize] + let stateCount = lstmShape.reduce(1, *) + self.hiddenState = [Float](repeating: 0, count: stateCount) + self.cellState = [Float](repeating: 0, count: stateCount) + self.decoderOutput = nil + self.previousSymbol = config.blankTokenId + self.firstStep = true + } + + /// Start a new segment: zero the LSTM and re-seed the blank as the previous label. + public func resetSegment() { + let stateCount = lstmShape.reduce(1, *) + hiddenState = [Float](repeating: 0, count: stateCount) + cellState = [Float](repeating: 0, count: stateCount) + decoderOutput = nil + previousSymbol = cfg.blankTokenId + firstStep = true + silentFrames = 0 + } + + /// Decode the global encoder frames in `frames`, where local index 0 of + /// `encoderOutput` is global frame `windowStartFrame`. + /// + /// The loop body is the offline one verbatim; only the frame pointer's coordinate + /// system (global rather than window-local) and the `timeJump` carry are new. + /// `collectStats` off skips the first-step tensor capture and the per-step timings, + /// which only the offline parity harness reads. + public func decodeFrames( + encoderOutput: NDArray, + encoderOutputShape: [Int], + frames: Range, + windowStartFrame: Int, + collectStats: Bool = true, + resetAfterSilenceFrames: Int = 0 + ) async throws -> (tokens: [Int32], stats: DecodeStats) { + try ParakeetTDTDecoder.validate( + encoderOutputShape: encoderOutputShape, logitsSize: logitsSize, config: cfg) + try checkFrames( + frames, windowStartFrame: windowStartFrame, + windowEncoderFrames: encoderOutputShape[1]) + + // Convert only the frames this call reads. The window also carries the left and + // right context the loop never indexes — at the default geometry that is 12 frames + // of 151 — and `floatElements` inspects the array's own scalar type, so an f16 + // encoder output reads correctly (a raw `as: Float.self` read would not). + let hidden = cfg.decoderHiddenSize + let lower = (frames.lowerBound - windowStartFrame) * hidden + let upper = (frames.upperBound - windowStartFrame) * hidden + return try await decodeFrames( + encoderFlat: floatElements(encoderOutput, in: lower.., + windowStartFrame: Int, + collectStats: Bool = true, + resetAfterSilenceFrames: Int = 0 + ) async throws -> (tokens: [Int32], stats: DecodeStats) { + try ParakeetTDTDecoder.validate( + encoderOutputShape: encoderOutputShape, logitsSize: logitsSize, config: cfg) + try checkFrames( + frames, windowStartFrame: windowStartFrame, + windowEncoderFrames: encoderOutputShape[1]) + if frames.isEmpty { return (tokens: [], stats: DecodeStats(stepTimesMs: [])) } + + let hidden = cfg.decoderHiddenSize + let vocabSize = cfg.vocabSize + + var buffers = Buffers( + step: decoder.stepGraph, joint: decoder.jointGraph, + lstmShape: lstmShape, hidden: hidden, logitsSize: logitsSize) + // Restore the state this stream left off with. `Buffers.init` already seeded + // zeros, so a fresh stream's first chunk is unaffected by these writes. + fillFloatNDArray(&buffers.hIn, with: hiddenState) + fillFloatNDArray(&buffers.cIn, with: cellState) + if let previousDecoderOutput = decoderOutput { + fillFloatNDArray(&buffers.decOut, with: previousDecoderOutput) + } + + var emitted: [Int32] = [] + // Resume where the last chunk's duration jump landed, then clear the debt. + var frame = frames.lowerBound + timeJump + timeJump = 0 + // Per-hop, not per-utterance: bounds this chunk's work only. + let emitCap = frames.count * cfg.maxSymbolsPerStep + + var stepTimesMs: [Double] = [] + var coverage = DecodeStats.Coverage() + var capturedStep: (decoderOutput: [Float], newHidden: [Float], newCell: [Float])? + var capturedLogits: [Float]? + + while frame < frames.upperBound && emitted.count < emitCap { + let t0 = ContinuousClock.now + var advance = 0 + let emittedAtStepStart = emitted.count + for _ in 0..