diff --git a/.coverage b/.coverage new file mode 100644 index 0000000..ba0356d Binary files /dev/null and b/.coverage differ diff --git a/.gitignore b/.gitignore index bf27726..cf5d2ca 100644 --- a/.gitignore +++ b/.gitignore @@ -61,4 +61,17 @@ notebooks/** data/** # Notes -notes.md \ No newline at end of file +notes.md +# Sweep results +sweep/results.parquet +sweep/sweep.log +sweep/state.json + +# Fine-tuning runs and local configs +runs/ +!runs/.gitkeep +configs/single/** +configs/sweep/** +!configs/single/example.yaml +!configs/single/test.yaml +!configs/sweep/example.yaml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 0eab24b..74210e4 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -74,16 +74,3 @@ repos: - id: pydoclint args: ["--config=pyproject.toml"] exclude: ^tests/ - - # Cognitive complexity limit via flake8-cognitive-complexity (keep for src; exclude tests) - - repo: https://github.com/pycqa/flake8 - rev: 7.3.0 - hooks: - - id: flake8 - additional_dependencies: - - flake8-cognitive-complexity - args: - - --max-line-length=79 - - --extend-ignore=E203 - - --max-cognitive-complexity=10 - exclude: ^tests/ diff --git a/README.md b/README.md index 9c1ae2b..f6a441d 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,14 @@ -# VOICE - Fine-tuning for Stylistic Fidelity +# 🗣️ VOICE + +... --- + +## Documentation + +``` +docs/ +├── 00_data.md — Included datasets +├── 01_evals.md — Evaluation suite: scoring and interpretation +└── 02_stylometry.md — Stylometric metrics: definition and catalogue +``` diff --git a/configs/single/example.yaml b/configs/single/example.yaml new file mode 100644 index 0000000..149372c --- /dev/null +++ b/configs/single/example.yaml @@ -0,0 +1,87 @@ +# ============================================================================= +# Example single-run configuration +# +# Run with: +# voice finetune single configs/single/example.yaml +# +# All axolotl settings live under the `axolotl:` key. The CLI writes a +# frozen copy to runs/{run_name}/config.yaml before launching training, +# injecting output_dir automatically. +# ============================================================================= + +axolotl: + + # Model + base_model: meta-llama/Llama-3.1-8B-Instruct + + # Train split used for fine-tuning; validation and test splits used by + # EvalCompletionsPlugin and post-training alignment scoring. + datasets: + - path: AccelerateScience/bo-press-conference-qa + type: chat_template + field_messages: messages + roles_to_train: + - assistant + train_on_eos: last + eot_tokens: ["<|eot_id|>"] + + special_tokens: + pad_token: "<|eot_id|>" + + hf_use_auth_token: true + + chat_template: tokenizer_default + val_set_size: 0.0 + + # Validation dataset for per-epoch eval_loss and completions generation. + test_datasets: + - path: AccelerateScience/bo-press-conference-qa + split: validation + type: chat_template + field_messages: messages + + # Sequence + sequence_len: 2048 + sample_packing: true + + # Adapter + adapter: lora + lora_r: 16 + lora_alpha: 16 + lora_dropout: 0.0 + lora_target_modules: [q_proj, k_proj, v_proj, o_proj] + + # Training + num_epochs: 3 + learning_rate: 1.0e-4 + micro_batch_size: 4 + gradient_accumulation_steps: 4 + + # Precision & performance + bf16: true + tf32: true + flash_attention: true + gradient_checkpointing: true + lora_mlp_kernel: false + lora_qkv_kernel: false + lora_o_kernel: false + + # Optimiser + optimizer: adamw_torch_fused + lr_scheduler: cosine + warmup_ratio: 0.03 + weight_decay: 0.0 + + # Evaluation and checkpointing + eval_strategy: "epoch" + save_strategy: epoch + save_total_limit: 3 + + # Plugin that generates completions at each epoch end (recommended). + plugins: + - voice.finetune.callbacks.EvalCompletionsPlugin + + # W&B + use_wandb: true + wandb_project: VOICE + wandb_entity: accelerate-science diff --git a/configs/sweep/example.yaml b/configs/sweep/example.yaml new file mode 100644 index 0000000..618829a --- /dev/null +++ b/configs/sweep/example.yaml @@ -0,0 +1,127 @@ +# ============================================================================= +# Example sweep configuration +# +# Two top-level sections: +# sweep: hyperparameter grid axes (read by `voice finetune sweep`) +# axolotl: shared axolotl settings merged into every per-run config +# +# The CLI expands the grid into per-run configs, injecting per-run values +# (learning_rate, lora_r, lora_alpha, lora_target_modules, +# micro_batch_size, gradient_accumulation_steps, output_dir) on top of +# the axolotl section below. +# +# Grid: 3 LRs × 4 ranks × 1 micro_batch_size × 2 accum steps +# × 2 target layer groups = 48 training runs. +# +# Run with: +# voice finetune sweep configs/sweep/example.yaml +# voice finetune sweep configs/sweep/example.yaml --resume +# ============================================================================= + +# --- Sweep grid ------------------------------------------------------------- +sweep: + learning_rate: [1e-3, 5.0e-4, 1e-4] + lora_r: [4, 8, 16, 32] + + micro_batch_size: [2] + gradient_accumulation_steps: [2, 4] + + target_layers: + - name: mlp + modules: [gate_proj, up_proj, down_proj] + - name: attention + modules: [q_proj, k_proj, v_proj, o_proj] + +# lora_alpha is set equal to lora_r per run (not swept separately). + +# --- Shared axolotl settings ------------------------------------------------ +axolotl: + + # Model + base_model: meta-llama/Llama-3.1-8B-Instruct + + # Train split used for fine-tuning; validation split used by the + # EvalCompletionsPlugin for per-epoch completions; test split scored after + # each epoch for alignment evaluation. + datasets: + - path: AccelerateScience/gwb-press-conference-qa + type: chat_template + field_messages: messages + roles_to_train: + - assistant + train_on_eos: last + # Llama 3.1's chat template ends turns with <|eot_id|> (128009), not + # <|end_of_text|> (128001). Without this, axolotl defaults eot_tokens + # to eos_token which never appears, so train_on_eos: last silently + # does nothing. + eot_tokens: ["<|eot_id|>"] + + special_tokens: + pad_token: "<|eot_id|>" + + hf_use_auth_token: true + + # Llama 3.1 Instruct ships with its own Jinja chat template; axolotl + # picks it up automatically. + chat_template: tokenizer_default + val_set_size: 0.0 + + # Axolotl computes eval_loss after each epoch and the EvalCompletionsPlugin + # generates completions for scoring. + test_datasets: + - path: AccelerateScience/gwb-press-conference-qa + split: validation + type: chat_template + field_messages: messages + + # Sequence + # Bush answers are long (~700-900 tokens including system + question). + # 2048 gives comfortable headroom without wasting VRAM. + sequence_len: 2048 + sample_packing: true + + # lora_r, lora_alpha, lora_target_modules injected per run. + adapter: lora + lora_dropout: 0.05 + + # 3 epochs × 48 runs = 144 data points. + num_epochs: 3 + + # Precision & performance + bf16: true + tf32: true + flash_attention: true + gradient_checkpointing: true + + # Disable axolotl's auto-enabled LoRA kernel optimizations. The MLP + # SwiGLU Triton kernel fails to compile on our host (gcc cannot link + # libcuda), which crashes all attention_mlp runs. + lora_mlp_kernel: false + lora_qkv_kernel: false + lora_o_kernel: false + + # Optimiser + optimizer: adamw_torch_fused + lr_scheduler: cosine + warmup_ratio: 0.03 + weight_decay: 0.0 + + # Evaluate on the validation split after each epoch. + eval_strategy: "epoch" + + # Save a checkpoint after each epoch so adapters are available for + # per-epoch alignment scoring (intermediate epochs treated as separate + # model candidates). + save_strategy: epoch + save_total_limit: 3 + + # Generates completions at each epoch end using the live in-memory model + # (no train/inference distribution mismatch). + # Dataset is read from datasets[0].path above. + plugins: + - voice.finetune.callbacks.EvalCompletionsPlugin + + # Run name for W&B is set by the CLI; project and entity configured here. + use_wandb: true + wandb_project: VOICE + wandb_entity: accelerate-science diff --git a/cspell/library-words.txt b/cspell/library-words.txt index dc8d8fa..f9f113e 100644 --- a/cspell/library-words.txt +++ b/cspell/library-words.txt @@ -9,3 +9,18 @@ ylabel frameon allclose linalg +mathcal +mathbb +mathbf +dotenv +wandb +jinja +adamw +checkpointing +proc +peft +pyyaml +libcuda +PYTHONPATH +venv +cuda diff --git a/cspell/project-words.txt b/cspell/project-words.txt index 43b5325..0a99bde 100644 --- a/cspell/project-words.txt +++ b/cspell/project-words.txt @@ -12,3 +12,14 @@ bonf bonferroni aeiou stylometrically +lora +resumability +resumable +hyperparams +redef +evals +submatrix +vocab +finetune +accum +nonpositive diff --git a/docs/00_data.md b/docs/00_data.md new file mode 100644 index 0000000..e504061 --- /dev/null +++ b/docs/00_data.md @@ -0,0 +1,22 @@ +# Datasets + +--- + +Two example datasets are included, each containing press conference Q&A transcripts split into train, validation and test sets: + +| President | HuggingFace | +|---|---| +| Barack Obama | [`AccelerateScience/bo-press-conference-qa`](https://huggingface.co/datasets/AccelerateScience/bo-press-conference-qa) | +| George W. Bush | [`AccelerateScience/gwb-press-conference-qa`](https://huggingface.co/datasets/AccelerateScience/gwb-press-conference-qa) | + +Each example is a single JSONL record in chat format: + +```json +{ + "messages": [ + {"role": "system", "content": "You are president Barack Obama ..."}, + {"role": "user", "content": ""}, + {"role": "assistant", "content": ""} + ] +} +``` diff --git a/docs/01_stylometry.md b/docs/01_stylometry.md new file mode 100644 index 0000000..ee053e4 --- /dev/null +++ b/docs/01_stylometry.md @@ -0,0 +1,52 @@ +# Stylometric Metrics + +--- + +A **stylometric metric** is any function + +$$f : \mathcal{T} \rightarrow \mathbb{R}$$ + +that maps a text string to a real scalar capturing some surface property of writing style. VOICE treats each metric as a distribution over a corpus: given a set of texts, $f$ is applied to each one to produce a sample from the author's stylometric distribution for that feature. + +## Implemented Metrics + +### Word Length Distribution +Moments of the per-word character length distribution. + +| Metric | Description | +|---|---| +| `avg_word_length` | Mean word length | +| `std_word_length` | Standard deviation of word length | +| `skew_word_length` | Skewness of word length | +| `kurtosis_word_length` | Kurtosis of word length | + +### Vocabulary Richness +Type–token and word statistics measuring lexical diversity. + +| Metric | Description | +|---|---| +| `type_token_ratio` | Unique tokens / total tokens | +| `moving_avg_type_token_ratio` | TTR averaged over a sliding window (MATTR) | +| `hapax_legomena_ratio` | Fraction of words appearing exactly once | +| `dis_legomena_ratio` | Fraction of words appearing exactly twice | +| `tri_legomena_ratio` | Fraction of words appearing exactly three times | + +### Function Words +| Metric | Description | +|---|---| +| `function_word_ratio` | Proportion of tokens drawn from a closed function-word list | + +### Character N-gram Diversity +Type–token ratio and MATTR computed over character $n$-grams for $n \in \{3, 4, 5\}$. + +| Metric | Description | +|---|---| +| `char_{n}gram_type_token_ratio` | Character $n$-gram TTR | +| `char_{n}gram_moving_avg_type_token_ratio` | Character $n$-gram MATTR | + +### Text Length +| Metric | Description | +|---|---| +| `num_words` | Total word count | + +> **Note:** `num_words` may be deprecated in a future release. It does not tend to be used as a signature for authorship attribution in the broader stylometry literature. diff --git a/docs/02_evals.md b/docs/02_evals.md new file mode 100644 index 0000000..564c1a2 --- /dev/null +++ b/docs/02_evals.md @@ -0,0 +1,115 @@ +# Evaluation Suite + +--- + +## How Scoring Works + +For a set of model completions $\mathcal{C} = \{c_1, \dots, c_n\}$ and a set of reference answers $\mathcal{D} = \{d_1, \dots, d_n\}$ drawn from the same underlying question corpus, VOICE computes an **alignment score** in $[0, 1]$ measuring how stylometrically similar the completions are to the reference author. Both $\mathcal{C}$ and $\mathcal{D}$ are responses to the same set of prompts, so any observed distributional difference reflects style rather than content. + +### Step 1: Wasserstein Distance + +For each metric $f$, apply it to every text to get empirical distributions: + +$$X = \{f(c_i)\}_{i=1}^n \quad \text{and} \quad Y = \{f(d_i)\}_{i=1}^n$$ + +and compute their Wasserstein-1 distance $W(X, Y)$. A smaller distance means the two distributions are closer in shape. + +### Step 2: Calibration + +To interpret $W(X, Y)$ we need to know what distance is *expected even between two samples of the same size from the same author*. VOICE estimates this from the **training split** of $\mathcal{D}$ by bootstrap resampling: repeatedly drawing two non-overlapping subsets of size $n$ and computing their mutual Wasserstein distance. This yields a reference distribution $\mathcal{W}_0$ of self-distances that characterises natural within-author variation under the same sample size as the completions being evaluated. + +### Step 3: Tail Value + +The observed distance is located within $\mathcal{W}_0$ via its empirical CDF: + +$$p = \hat{F}_{\mathcal{W}_0}(W(X, Y)), \qquad t = 1 - p$$ + +$t$ is the **tail value**: the fraction of self-distances that *exceed* the observed distance. A high $t$ means the model's completions are no further from the reference than typical same-author samples, i.e. they are stylistically consistent with the reference author. + +### Step 4: Group and Overall Score + +Metrics are organised into groups (see [Stylometric Metrics](01_stylometry.md)). Each group captures a distinct linguistic object (word length, vocabulary richness, .etc) and the metrics within it represent different ways of characterising that same object. Because they study the same underlying property, within-group metrics are highly correlated; averaging within groups before aggregating across them prevents any single linguistic dimension from dominating the score simply by having more metrics defined for it. + +Within each group the tail values are averaged: + +$$\bar{t}_g = \frac{1}{|g|} \sum_{f \in g} t_f$$ + +The overall **alignment score** $\mathcal{S}$ is then the mean over groups, weighting each group equally: + +$$\mathcal{S} = \frac{1}{G} \sum_{g=1}^{G} \bar{t}_g \in [0, 1]$$ + +A score of **1** would mean the completions are stylistically indistinguishable from the reference even relative to same-author variation; in practice this is unrealistic but the directionality is still informative. + +--- + +## This Is Not a Hypothesis Test + +The tail value $t$ resembles a p-value but is not one and should not be interpreted as such. + +A classical hypothesis test asks whether there is sufficient evidence to reject a null hypothesis. That framing entails a binary decision and is sensitive to sample size: with enough data, even negligible stylistic differences will cross any fixed significance threshold. + +VOICE is not concerned with whether two distributions are statistically indistinguishable. It asks how similar they are, on a continuous scale, relative to the natural within-author variation present in the training corpus. The calibration distribution $\mathcal{W}_0$ serves as a normalisation baseline, not a null to be rejected. There is no significance threshold and no p-value, only a score. + +--- + +## Uncertainty Quantification + +The uncertainty in $\mathcal{S}$ arises from the finite number of completions $n$. It is estimated using jackknife resampling over the completion set, operated directly at the level of tail values so that no analytical propagation through the calibration CDF is required. Operating at the tail value level naturally produces asymmetric confidence intervals that respect the $[0, 1]$ boundary of the score. + +### Jackknife Replicates + +For each $i = 1, \dots, n$, remove $c_i$ and its paired reference $d_i$ and run the full pipeline (recomputing the Wasserstein distance and locating it within $\mathcal{W}_0$) for every metric $f$. This yields a matrix of leave-one-out tail values $\{t_{f,-i}\}$, or equivalently $n$ replicate vectors $\mathbf{t}_{-i} \in \mathbb{R}^M$ where $M$ is the total number of metrics across all groups. + +### Confidence Intervals + +Per-metric, group, and overall confidence intervals are all read directly from the empirical distribution of the corresponding jackknife replicates. + +For an individual metric $f$, the replicates $\{t_{f,-i}\}$ give a distribution of leave-one-out tail values from which percentile intervals are taken directly. + +For a group $g$, the replicate group scores are computed as: + +$$\bar{t}_{g,-i} = \frac{1}{|g|} \sum_{f \in g} t_{f,-i}$$ + +and percentile intervals are read from $\{\bar{t}_{g,-i}\}$. + +For the overall score, the replicate scores are: + +$$\mathcal{S}_{-i} = \frac{1}{G} \sum_{g=1}^G \bar{t}_{g,-i}$$ + +and percentile intervals are read from $\{\mathcal{S}_{-i}\}$. + +--- + +## Example + +Suppose a model achieves an alignment score of **0.2**. A natural reading is: + +> On average across stylometric groups, the model's completions sit at the 80th percentile of same-author self-distances, meaning the completions are stylistically closer to the reference than 20% of within-author sample pairs drawn from the training corpus. + +--- + +## Diagnostics + +The API exposes intermediate results for deeper inspection: + +```python +results = make_comparison(completions, true_ds) # uncertainty=True by default + +# Per-metric tail values +results.metric_tails # dict[str, float] + +# Per-group average tail values +results.group_tails # dict[MetricGroup, float] + +# Overall score +results.score # float + +# Jackknife percentile confidence intervals (default 90%) +results.metric_tail_cis() # dict[str, tuple[float, float]] | None +results.group_tail_cis() # dict[MetricGroup, tuple[float, float]] | None +results.score_ci() # tuple[float, float] | None +``` + +Per-metric tails are useful for identifying which stylometric dimensions are misaligned; per-group tails aggregate correlated metrics and correspond directly to the terms summed in the overall score. + +The confidence interval methods accept a `confidence` keyword (default 0.90, see `UNCERTAINTY_DEFAULTS`). Passing `uncertainty=False` to `make_comparison` skips the jackknife pass, in which case the interval methods return `None`. diff --git a/docs/03_cli.md b/docs/03_cli.md new file mode 100644 index 0000000..a5662a9 --- /dev/null +++ b/docs/03_cli.md @@ -0,0 +1,166 @@ +# Fine-Tuning CLI + +--- + +The `voice finetune` command group runs LoRA fine-tuning jobs via [axolotl](https://github.com/axolotl-ai-cloud/axolotl). Two subcommands are available: `single` for a single training run and `sweep` for a hyperparameter grid search. + +--- + +## Single Run + +```bash +voice finetune single [OPTIONS] +``` + +| Option | Default | Description | +|---|---|---| +| `--runs-dir` | `runs` | Root directory for run outputs | +| `--gpus` | `0` | Comma-separated CUDA device indices, e.g. `0,1,2` | +| `--dataset-revision` | `main` | HuggingFace dataset branch or revision | + +The config file has a single top-level [`axolotl:`](https://axolotl-ai-cloud.github.io/axolotl/docs/config.html) key containing all training settings. The CLI injects `output_dir` automatically. + +```yaml +axolotl: + base_model: meta-llama/Llama-3.1-8B-Instruct + + datasets: + - path: AccelerateScience/bo-press-conference-qa + type: chat_template + field_messages: messages + roles_to_train: [assistant] + train_on_eos: last + + adapter: lora + lora_r: 16 + lora_alpha: 16 + lora_target_modules: [q_proj, k_proj, v_proj, o_proj] + + num_epochs: 3 + learning_rate: 1.0e-4 + micro_batch_size: 4 + gradient_accumulation_steps: 4 + + plugins: + - voice.finetune.callbacks.EvalCompletionsPlugin +``` + +A full example is at `configs/single/example.yaml`. + +When a run starts, the CLI generates a unique run name (model slug + short UUID), writes a frozen copy of the resolved config to `runs/{run_name}/config.yaml`, then launches axolotl. The `EvalCompletionsPlugin` generates completions against the validation and test splits at the end of every epoch; alignment scores are computed immediately after. + +For multiple GPUs the CLI switches automatically to `accelerate launch --num_processes=N`. + +--- + +## Sweep + +```bash +voice finetune sweep [OPTIONS] +``` + +| Option | Default | Description | +|---|---|---| +| `--runs-dir` | `runs` | Root directory for run outputs | +| `--gpus` | `0` | Comma-separated CUDA device indices | +| `--dataset-revision` | `main` | HuggingFace dataset branch or revision | +| `--resume` / `--no-resume` | off | Skip runs already recorded as completed in `state.json` | + +The sweep config has two top-level keys: `sweep:` defines the grid axes and [`axolotl:`](https://axolotl-ai-cloud.github.io/axolotl/docs/config.html) defines settings shared across every run. + +```yaml +sweep: + learning_rate: [1.0e-4, 5.0e-4, 1.0e-3] + lora_r: [4, 8, 16, 32] + micro_batch_size: [4] + gradient_accumulation_steps: [1, 4] + target_layers: + - name: attention + modules: [q_proj, k_proj, v_proj, o_proj] + - name: mlp + modules: [gate_proj, up_proj, down_proj] + +axolotl: + base_model: meta-llama/Llama-3.1-8B-Instruct + # ... shared settings ... +``` + +All five `sweep:` axes are required. The CLI expands them into a Cartesian product; `lora_alpha` is set equal to `lora_r` for each run and does not need to be listed. Per-run values for `learning_rate`, `lora_r`, `lora_alpha`, `lora_target_modules`, `micro_batch_size`, and `gradient_accumulation_steps` are merged on top of the shared `axolotl:` block before training starts. + +A full example is at `configs/sweep/example.yaml`. + +### Resume + +The sweep writes a `state.json` to `{runs-dir}/state.json` after each run completes or fails: + +```json +{ + "completed": ["0", "1", "3"], + "failed": {"2": "CUDA out of memory"} +} +``` + +Passing `--resume` skips any run whose index already appears in `completed`. Failed runs are retried. + +--- + +## Output Layout + +Each run produces a directory under `{runs-dir}/{run_name}/`: + +``` +runs/{run_name}/ + config.yaml # frozen per-run axolotl config + metadata.json # status, hyperparams, scores + adapter/ # axolotl output_dir; LoRA weights and checkpoints + completions/ + epoch_1/ + validation.jsonl + test.jsonl + epoch_N/... + alignment/ + epoch_1/ + validation.json # alignment scores + confidence intervals + test.json + epoch_N/... +``` + +`metadata.json` tracks the full lifecycle of a run: + +```json +{ + "run_name": "llama-3-1-8b-instruct_a3f2b9c1", + "model": "meta-llama/Llama-3.1-8B-Instruct", + "dataset": "AccelerateScience/bo-press-conference-qa", + "hyperparams": { "learning_rate": 1e-4, "lora_r": 16, "..." : "..." }, + "status": "done", + "started_at": "2025-01-15T10:30:00Z", + "finished_at": "2025-01-15T14:22:00Z", + "eval_loss": { "1": 1.23, "2": 0.98, "3": 0.81 }, + "alignment_val": { "1": 0.42, "2": 0.61, "3": 0.67 }, + "alignment_test": { "1": 0.39, "2": 0.58, "3": 0.64 } +} +``` + +--- + +## Weights & Biases + +Add three keys anywhere inside the `axolotl:` block to enable W&B logging: + +```yaml +axolotl: + use_wandb: true + wandb_project: VOICE + wandb_entity: accelerate-science +``` + +Axolotl handles all logging; no additional code is required. The run name is derived from the model slug and a unique ID and is set automatically. Omitting these keys (or setting `use_wandb: false`) disables W&B entirely. + +The environment must be authenticated before launching a run: + +```bash +wandb login +# or +export WANDB_API_KEY= +``` diff --git a/pyproject.toml b/pyproject.toml index a11d076..95ecd18 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,16 +3,35 @@ name = "voice" version = "0.1.0" description = "Fine-tuning for stylistic fidelity" readme = "README.md" -requires-python = ">=3.12" +requires-python = ">=3.12,<3.13" dependencies = [ + "accelerate", + "axolotl>=0.16.1 ; sys_platform == 'linux'", "datasets>=4.5.0", "dotenv>=0.9.9", "huggingface-hub>=1.4.1", + "typer>=0.9.0", "matplotlib>=3.10.8", + "pandas", + "pyyaml>=6.0.3", "scipy>=1.17.0", "seaborn>=0.13.2", + "sentence-transformers>=5.3.0", + "transformers", + "click>=8.0.0", + "wandb>=0.25.1", + "bitsandbytes>=0.49.1 ; sys_platform == 'linux'", ] +[project.scripts] +voice = "voice.finetune.cli:main" + +[tool.uv] +required-environments = ["sys_platform == 'linux'"] + +[tool.uv.sources] +axolotl = { git = "https://github.com/axolotl-ai-cloud/axolotl.git", branch = "main" } + [build-system] requires = ["setuptools>=68", "wheel"] build-backend = "setuptools.build_meta" @@ -23,8 +42,6 @@ where = ["src"] [dependency-groups] dev = [ "detect-secrets>=1.5.0", - "flake8>=7.3.0", - "flake8-cognitive-complexity>=0.1.0", "isort>=7.0.0", "mypy>=1.19.1", "notebook>=7.5.3", @@ -76,6 +93,12 @@ convention = "pep257" "ARG005", "B007", ] +"sweep/**/*.py" = [ + "ANN401", +] +"src/voice/finetune/callbacks.py" = [ + "ANN401", +] [tool.mypy] python_version = "3.12" @@ -87,6 +110,10 @@ no_implicit_optional = true module = ["tests.*"] ignore_errors = true +[[tool.mypy.overrides]] +module = ["voice.finetune.cli"] +ignore_errors = true + [[tool.mypy.overrides]] module = [ "datasets", @@ -101,6 +128,24 @@ module = [ "seaborn.*", "matplotlib", "matplotlib.*", + "sentence_transformers", + "sentence_transformers.*", + "peft", + "peft.*", + "torch", + "torch.*", + "pandas", + "pandas.*", + "yaml", + "dotenv", + "transformers", + "transformers.*", + "wandb", + "wandb.*", + "axolotl", + "axolotl.*", + "click", + "click.*", ] ignore_missing_imports = true @@ -115,3 +160,4 @@ skip-checking-short-docstrings = false skip-checking-raises = false require-return-section-when-returning-nothing = true check-return-types = false +exclude = '\.git|\.tox|finetune/cli\.py' diff --git a/runs/.gitkeep b/runs/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/src/voice/__init__.py b/src/voice/__init__.py index a3a9cc5..3f54ae8 100644 --- a/src/voice/__init__.py +++ b/src/voice/__init__.py @@ -9,11 +9,12 @@ make_comparison, stylometric_distribution, ) -from voice.datasets import DatasetSpec, get_dataset +from voice.datasets import DatasetSpec, LocalDatasetSpec, get_dataset from voice.stylometry import get_groups, get_metrics __all__: list[str] = [ "DatasetSpec", + "LocalDatasetSpec", "get_metrics", "get_groups", "get_dataset", diff --git a/src/voice/_defaults.py b/src/voice/_defaults.py index d9e5f29..d7e9d38 100644 --- a/src/voice/_defaults.py +++ b/src/voice/_defaults.py @@ -31,17 +31,12 @@ class MetricGroup(str, Enum): .. attribute :: CHAR_NGRAM_DIVERSITY Character n-gram TTR and MATTR across n = 3, 4, 5. - - .. attribute :: TEXT_LENGTH - - Raw token count. """ WORD_LENGTH_DISTRIBUTION = "word_length_distribution" VOCABULARY_RICHNESS = "vocabulary_richness" FUNCTION_WORDS = "function_words" CHAR_NGRAM_DIVERSITY = "char_ngram_diversity" - TEXT_LENGTH = "text_length" @dataclass(frozen=True) @@ -111,6 +106,26 @@ class CalibrationDefaults: CALIBRATION_DEFAULTS: CalibrationDefaults = CalibrationDefaults() +@dataclass(frozen=True) +class UncertaintyDefaults: + """ + Default parameters for uncertainty quantification. + + These defaults control the confidence intervals reported alongside + comparison results. + + .. attribute :: confidence + + Confidence level for percentile intervals read from the + jackknife replicate distributions. + """ + + confidence: float = 0.90 # Safe default for validation/test size = 150 + + +UNCERTAINTY_DEFAULTS: UncertaintyDefaults = UncertaintyDefaults() + + @dataclass(frozen=True) class PlottingDefaults: """ @@ -153,3 +168,44 @@ class PlottingDefaults: PLOTTING_DEFAULTS: PlottingDefaults = PlottingDefaults() + + +@dataclass(frozen=True) +class InferenceDefaults: + """ + Default parameters for completions generation during fine-tuning. + + .. attribute :: batch_size + + Number of examples per inference forward pass. + + .. attribute :: temperature + + Sampling temperature. + + .. attribute :: top_p + + Nucleus sampling probability threshold. + + .. attribute :: top_k + + Top-k sampling limit (0 = disabled). + + .. attribute :: repetition_penalty + + Repetition penalty factor (1.0 = no penalty). + + .. attribute :: max_new_tokens + + Maximum number of tokens to generate per completion. + """ + + batch_size: int = 32 + temperature: float = 0.7 + top_p: float = 1.0 + top_k: int = 0 + repetition_penalty: float = 1.0 + max_new_tokens: int = 1024 + + +INFERENCE_DEFAULTS: InferenceDefaults = InferenceDefaults() diff --git a/src/voice/comparison/_utils.py b/src/voice/comparison/_utils.py index a684a69..e1756ea 100644 --- a/src/voice/comparison/_utils.py +++ b/src/voice/comparison/_utils.py @@ -35,6 +35,38 @@ def calibrated_percentile(value: float, dist: np.ndarray) -> float: return float(np.mean(dist <= value)) +# ----------------------------------------------------------------------------- +# Percentile interval +# ----------------------------------------------------------------------------- + + +def percentile_interval( + values: np.ndarray, confidence: float +) -> tuple[float, float]: + """ + Compute a central percentile interval from an empirical distribution. + + For a confidence level c, this returns the (alpha/2, 1 - alpha/2) + empirical percentiles of `values` with alpha = 1 - c. The interval is + asymmetric in general and bounded by the range of `values`. + + :param values: Array of samples from an empirical distribution + :param confidence: Confidence level, strictly between 0 and 1 + :return: (lower, upper) percentile interval + :raises ValueError: If `values` is empty or `confidence` is not in [0, 1] + """ + values = np.asarray(values, dtype=float).ravel() + if values.size == 0: + raise ValueError("values must be non-empty.") + if not 0.0 < confidence < 1.0: + raise ValueError("confidence must be strictly between 0 and 1.") + + alpha = 1.0 - confidence + lower = float(np.percentile(values, 100.0 * alpha / 2.0)) + upper = float(np.percentile(values, 100.0 * (1.0 - alpha / 2.0))) + return lower, upper + + # ----------------------------------------------------------------------------- # Bootstrap null distribution # ----------------------------------------------------------------------------- diff --git a/src/voice/comparison/comparison.py b/src/voice/comparison/comparison.py index 7173b68..de11e45 100644 --- a/src/voice/comparison/comparison.py +++ b/src/voice/comparison/comparison.py @@ -20,11 +20,13 @@ from voice._defaults import ( CALIBRATION_DEFAULTS, + UNCERTAINTY_DEFAULTS, MetricGroup, ) from voice.comparison._utils import ( bootstrap_null_distribution, calibrated_percentile, + percentile_interval, ) from voice.datasets import VoiceDataset from voice.datasets.dataset import Example @@ -185,6 +187,11 @@ class ComparisonResults: A score of 1 indicates perfect stylistic indistinguishability; 0 indicates maximum divergence. + When uncertainty quantification is enabled, the matrix of jackknife + leave-one-out tail-value replicates is attached and per-metric, + per-group and overall-score percentile confidence intervals are read + directly from its empirical distributions. + .. attribute :: metrics Tuple of stylometric metric names to run comparison for. @@ -192,6 +199,7 @@ class ComparisonResults: metrics: tuple[str, ...] _entries: dict[str, ComparisonEntry] = field(default_factory=dict) + _replicates: np.ndarray | None = field(default=None, repr=False) def __post_init__(self) -> None: """ @@ -222,9 +230,10 @@ def __repr__(self) -> str: ) + "}" ) - return ( - f"ComparisonResults(groups={group_part}, score={self.score:.4f})" - ) + score_part = f"{self.score:.4f}" + if (ci := self.score_ci()) is not None: + score_part += f" [{ci[0]:.4f}, {ci[1]:.4f}]" + return f"ComparisonResults(groups={group_part}, score={score_part})" @property def num_metrics(self) -> int: @@ -267,6 +276,95 @@ def score(self) -> float: return 0.0 return sum(e.avg_tail for e in entries.values()) / len(entries) + @property + def _group_indices(self) -> dict[MetricGroup, list[int]]: + """ + Indices into `metrics` (and replicate matrix columns) per group. + + :return: Mapping of MetricGroup to column indices + """ + registry = get_metrics() + indices: dict[MetricGroup, list[int]] = {} + for j, metric in enumerate(self.metrics): + indices.setdefault(registry[metric].group, []).append(j) + return indices + + @property + def has_uncertainty(self) -> bool: + """Whether jackknife uncertainty estimates are attached.""" + return self._replicates is not None + + def metric_tail_cis( + self, + confidence: float = UNCERTAINTY_DEFAULTS.confidence, + ) -> dict[str, tuple[float, float]] | None: + """ + Jackknife confidence interval for each per-metric tail value. + + Percentile intervals are read directly from the empirical + distribution of leave-one-out tail values for each metric. + + :param confidence: Confidence level, strictly between 0 and 1 + :return: Mapping of metric name to (lower, upper) interval or None if + uncertainty quantification was not run + """ + if self._replicates is None: + return None + return { + m: percentile_interval(self._replicates[:, j], confidence) + for j, m in enumerate(self.metrics) + } + + def group_tail_cis( + self, + confidence: float = UNCERTAINTY_DEFAULTS.confidence, + ) -> dict[MetricGroup, tuple[float, float]] | None: + """ + Jackknife confidence interval for each group's average tail value. + + Each leave-one-out replicate's tail values are averaged within the + group, and the percentile interval is read from the resulting + empirical distribution of replicate group scores. + + :param confidence: Confidence level, strictly between 0 and 1 + :return: Mapping of MetricGroup to (lower, upper) interval or None if + uncertainty quantification was not run + """ + if self._replicates is None: + return None + return { + group: percentile_interval( + self._replicates[:, idx].mean(axis=1), confidence + ) + for group, idx in self._group_indices.items() + } + + def score_ci( + self, + confidence: float = UNCERTAINTY_DEFAULTS.confidence, + ) -> tuple[float, float] | None: + """ + Jackknife confidence interval for the overall alignment score. + + Each leave-one-out replicate yields a replicate score (the mean of + its per-group average tail values, groups weighted equally); the + percentile interval is read from the empirical distribution of these + replicate scores. + + :param confidence: Confidence level, strictly between 0 and 1 + :return: (lower, upper) interval for the score, or None if + uncertainty quantification was not run + """ + if self._replicates is None: + return None + group_scores = np.stack( + [ + self._replicates[:, idx].mean(axis=1) + for idx in self._group_indices.values() + ] + ) + return percentile_interval(group_scores.mean(axis=0), confidence) + @property def metric_tails(self) -> dict[str, float]: """ @@ -356,6 +454,7 @@ def make_comparison( true_ds: VoiceDataset, *, metrics: Sequence[str] | None = None, + uncertainty: bool = True, ) -> ComparisonResults: """ Compare stylometric distributions of `completions` to the true corpus. @@ -371,14 +470,25 @@ def make_comparison( values, and an overall alignment score equal to the mean of group-level average tail values (groups weighted equally). + When `uncertainty` is True (the default), jackknife resampling over the + completion set is used: each completion and its paired reference are + left out in turn, the Wasserstein distance is recomputed, and the + leave-one-out distance is located within the same + self-Wasserstein distribution. Per-metric, per-group and overall score + percentile confidence intervals are then available on the returned + results, read directly from the replicate distributions. + :param completions: Sequence of examples to analyse :param true_ds: VoiceDataset providing the true corpus :param metrics: Optional subset of metric names; defaults to all metrics + :param uncertainty: Whether to estimate uncertainty :return: ComparisonResults populated for the selected metrics :raises ValueError: - If completions is empty, - If sample sizes are invalid - If metrics are not registered + - If `uncertainty` is True and there are fewer than two completions + or the observed split does not match the completions in length """ if not completions: raise ValueError("completions must be non-empty.") @@ -392,20 +502,52 @@ def make_comparison( observed_split = completions[0].split ds_observed_split = true_ds[observed_split] + n = len(completions) + if uncertainty: + if n < 2: + raise ValueError( + "uncertainty quantification requires at least 2 completions." + ) + if len(ds_observed_split) != n: + raise ValueError( + "uncertainty quantification requires completions to be " + "paired one-to-one with the observed split " + f"({n} completions vs {len(ds_observed_split)} references)." + ) + + distributions: dict[str, tuple[np.ndarray, np.ndarray, np.ndarray]] = {} + for metric in metric_list: x = stylometric_distribution(completions, metric) y = stylometric_distribution(ds_observed_split, metric) d = float(wasserstein_distance(x, y)) + # Cached per metric: the jackknife pass below reuses this + # distribution rather than re-running the bootstrap per replicate. self_dist = self_wasserstein_distribution( true_ds, metric=metric, - sample_size=len(completions), + sample_size=n, ) p = calibrated_percentile(d, self_dist) results.add(metric=metric, wasserstein=d, percentile=p) + if uncertainty: + distributions[metric] = (x, y, self_dist) + + if uncertainty: + replicates = np.empty((n, len(metric_list)), dtype=float) + for i in range(n): + mask = np.ones(n, dtype=bool) + mask[i] = False + for j, metric in enumerate(metric_list): + x, y, self_dist = distributions[metric] + d_i = float(wasserstein_distance(x[mask], y[mask])) + replicates[i, j] = 1.0 - calibrated_percentile(d_i, self_dist) + + results._replicates = replicates + return results diff --git a/src/voice/datasets/__init__.py b/src/voice/datasets/__init__.py index 696e12e..75b3d0f 100644 --- a/src/voice/datasets/__init__.py +++ b/src/voice/datasets/__init__.py @@ -4,12 +4,18 @@ This package contains user-facing abstractions for dataset management, in particular we expose: -- `DatasetSpec` - Lightweight, declarative specification of a dataset. -- `VoiceDataset` - Split-aware wrapper around a Hugging Face dataset. +- `DatasetSpec` - Lightweight, declarative specification of a HF dataset. +- `LocalDatasetSpec` - Specification for a locally saved dataset. +- `VoiceDataset` - Split-aware wrapper around a dataset. - `get_dataset` - Entrypoint for materialising a dataset from a specification. """ -from voice.datasets.dataset import DatasetSpec, VoiceDataset +from voice.datasets.dataset import DatasetSpec, LocalDatasetSpec, VoiceDataset from voice.datasets.get_dataset import get_dataset -__all__: list[str] = ["DatasetSpec", "VoiceDataset", "get_dataset"] +__all__: list[str] = [ + "DatasetSpec", + "LocalDatasetSpec", + "VoiceDataset", + "get_dataset", +] diff --git a/src/voice/datasets/dataset.py b/src/voice/datasets/dataset.py index 9e7d821..54ef88c 100644 --- a/src/voice/datasets/dataset.py +++ b/src/voice/datasets/dataset.py @@ -5,6 +5,7 @@ - `DatasetSpec` - A lightweight declarative specification of a dataset hosted on Hugging Face. +- `LocalDatasetSpec` - A specification for a locally saved dataset. - `_PinnedDatasetSpec` - An internal revision-pinned specification resolved to a specific git commit SHA. - `Example` - A representation of a single question-answer example @@ -18,6 +19,7 @@ import re from collections.abc import Callable, Iterator, Mapping, Sequence from dataclasses import dataclass +from pathlib import Path from typing import Any, NamedTuple, overload from datasets import Dataset @@ -29,7 +31,7 @@ # ----------------------------------------------------------------------------- -_SHA_RE = re.compile(r"[0-9a-f]{7,40}$") +_SHA_RE = re.compile(r"[0-9a-f]{7,40}$|local$") @dataclass(frozen=True, slots=True) @@ -99,6 +101,44 @@ def _pin( return _PinnedDatasetSpec.from_spec(self, resolve_revision) +@dataclass(frozen=True, slots=True) +class LocalDatasetSpec: + """ + Lightweight specification of a locally saved dataset. + + Expects one ``.jsonl`` file per split at ``{path}/{split}.jsonl`` + (e.g. ``path/train.jsonl``, ``path/validation.jsonl``). + + .. attribute :: path + + Directory containing the per-split ``.jsonl`` files. + + .. attribute :: splits + + Dataset splits to include (options: 'train','validation','test') + """ + + path: Path + splits: tuple[Split, ...] = (Split.TRAIN,) + + def __post_init__(self) -> None: + """ + Validate and normalise the specification. + + :return: None + :raises ValueError: If `splits` is empty + """ + if not self.splits: + raise ValueError("`splits` must be non-empty") + splits = ( + (self.splits,) + if isinstance(self.splits, (str, Split)) + else self.splits + ) + norm = tuple(Split.parse(s) for s in splits) + object.__setattr__(self, "splits", norm) + + @dataclass(frozen=True, slots=True) class _PinnedDatasetSpec: """ diff --git a/src/voice/datasets/get_dataset.py b/src/voice/datasets/get_dataset.py index fe5fd83..8591622 100644 --- a/src/voice/datasets/get_dataset.py +++ b/src/voice/datasets/get_dataset.py @@ -23,6 +23,7 @@ from voice.datasets._schema import Split from voice.datasets.dataset import ( DatasetSpec, + LocalDatasetSpec, VoiceDataset, _PinnedDatasetSpec, ) @@ -133,21 +134,59 @@ def _validate_canonical(ds: Dataset) -> None: # ----------------------------------------------------------------------------- -def get_dataset(spec: DatasetSpec) -> VoiceDataset: +def _get_local_dataset(spec: LocalDatasetSpec) -> VoiceDataset: """ - Materialise a Hugging Face dataset as a VoiceDataset. + Materialise a locally saved dataset as a VoiceDataset. - Steps: + Expects one ``.jsonl`` file per split at ``{path}/{split}.jsonl`` + (e.g. ``path/train.jsonl``, ``path/validation.jsonl``). + + :param spec: Local dataset specification + :return: Split-aware VoiceDataset + :raises RuntimeError: If a split cannot be loaded + """ + pinned = _PinnedDatasetSpec( + repo_id=str(spec.path), + revision="local", + splits=spec.splits, + ) + datasets: dict[Split, Dataset] = {} + for split in spec.splits: + jsonl_path = spec.path / f"{split.value}.jsonl" + try: + ds = load_dataset( + "json", + data_files={split.value: str(jsonl_path)}, + split=split.value, + ) + except Exception as e: + raise RuntimeError( + f"Failed to load split '{split.value}' from {jsonl_path}" + ) from e + ds = _canonicalise_dataset(ds) + _validate_canonical(ds) + datasets[split] = ds + return VoiceDataset(datasets=datasets, spec=pinned) + + +def get_dataset(spec: DatasetSpec | LocalDatasetSpec) -> VoiceDataset: + """ + Materialise a dataset as a VoiceDataset. + + Accepts either a Hugging Face spec or a local spec. For HF specs: 1. Resolve DatasetSpec to _PinnedDatasetSpec. 2. Load requested splits from Hugging Face. 3. Canonicalise each split into (system, question, answer). 4. Validate canonical structure. 5. Construct VoiceDataset. - :param spec: Dataset specification + :param spec: Dataset specification (HF or local) :return: Split-aware VoiceDataset :raises RuntimeError: If a split cannot be loaded """ + if isinstance(spec, LocalDatasetSpec): + return _get_local_dataset(spec) + # Resolve dataset revision pinned: _PinnedDatasetSpec = spec._pin(_resolve_revision) diff --git a/src/voice/finetune/__init__.py b/src/voice/finetune/__init__.py new file mode 100644 index 0000000..3547da2 --- /dev/null +++ b/src/voice/finetune/__init__.py @@ -0,0 +1,9 @@ +""" +Fine-tuning orchestration package for VOICE. + +Provides utilities for running single fine-tuning jobs and hyperparameter +sweeps, including: +- CLI entrypoints +- Local experiment tracking +- Post-hoc evaluation on stylometric evaluation suite +""" diff --git a/src/voice/finetune/_grid.py b/src/voice/finetune/_grid.py new file mode 100644 index 0000000..f158be5 --- /dev/null +++ b/src/voice/finetune/_grid.py @@ -0,0 +1,102 @@ +""" +Hyperparameter grid expansion and run naming utilities. + +This module is private by convention and not part of the public API. +""" + +from __future__ import annotations + +import itertools +import re +import uuid +from typing import Any + +_REQUIRED_AXES: frozenset[str] = frozenset( + { + "learning_rate", + "lora_r", + "micro_batch_size", + "gradient_accumulation_steps", + "target_layers", + } +) + +# ----------------------------------------------------------------------------- +# Run naming +# ----------------------------------------------------------------------------- + + +def _short_name(repo_or_path: str) -> str: + """ + Extract and normalise the trailing segment of a repo id or path. + + :param repo_or_path: A HF repo id (``namespace/name``) or file path. + :return: Lowercase, hyphen-separated identifier. + """ + name = repo_or_path.rsplit("/", 1)[-1] + return re.sub(r"[^a-z0-9]+", "-", name.lower()).strip("-") + + +def new_uid() -> str: + """ + Generate a short unique identifier (8 hex characters). + + :return: Random 8-character hex string. + """ + return uuid.uuid4().hex[:8] + + +def generate_run_name(model: str, uid: str) -> str: + """ + Generate a short, human-readable run name. + + Format: ``{model}_{uid}`` e.g. ``llama-3-1-8b-instruct_a3f2b9c1`` + + :param model: Base model identifier (HF repo id or local path). + :param uid: Short unique identifier, e.g. from :func:`new_uid`. + :return: Run name string. + """ + return f"{_short_name(model)}_{uid}" + + +# ----------------------------------------------------------------------------- +# Grid expansion +# ----------------------------------------------------------------------------- + + +def expand_grid(sweep_cfg: dict[str, Any]) -> list[dict[str, Any]]: + """ + Expand the sweep section into a flat list of per-run hyperparameter dicts. + + :param sweep_cfg: The ``sweep`` section of a sweep config YAML. + :return: List of hyperparameter dicts, one per combination. + :raises ValueError: If required sweep axes are missing. + """ + missing = _REQUIRED_AXES - sweep_cfg.keys() + if missing: + raise ValueError( + f"Sweep config missing required axes: {sorted(missing)}" + ) + + runs: list[dict[str, Any]] = [] + combos = itertools.product( + sweep_cfg["learning_rate"], + sweep_cfg["lora_r"], + sweep_cfg["micro_batch_size"], + sweep_cfg["gradient_accumulation_steps"], + sweep_cfg["target_layers"], + ) + for idx, (lr, rank, mbs, gas, tl) in enumerate(combos): + runs.append( + { + "run_idx": idx, + "learning_rate": lr, + "lora_r": rank, + "lora_alpha": rank, + "micro_batch_size": mbs, + "gradient_accumulation_steps": gas, + "target_layers_name": tl["name"], + "lora_target_modules": tl["modules"], + } + ) + return runs diff --git a/src/voice/finetune/_orchestrator.py b/src/voice/finetune/_orchestrator.py new file mode 100644 index 0000000..40a74e9 --- /dev/null +++ b/src/voice/finetune/_orchestrator.py @@ -0,0 +1,347 @@ +""" +Run orchestration for single fine-tuning runs. + +This module is private by convention and not part of the public API. + +Ties together run directory setup, axolotl training launch, completion +scoring, and metadata persistence. + +Entry point is ``execute_run``, which performs the full lifecycle: + +1. Build run axolotl config from base config and hyperparams. +2. Create run directory and write config. +3. Initialise and persist ``RunMetadata`` (status ``running``). +4. Load reference dataset for post-training scoring. +5. Launch axolotl training; on failure set status ``failed`` and raise. +6. Score completions for each epoch and split. +7. Persist final metadata (status ``done``). +""" + +from __future__ import annotations + +import datetime +import json +import logging +import os +import subprocess +import sys +from typing import Any + +from voice.comparison.comparison import make_comparison +from voice.datasets._schema import Split +from voice.datasets.dataset import ( + DatasetSpec, + Example, + LocalDatasetSpec, + VoiceDataset, +) +from voice.datasets.get_dataset import get_dataset +from voice.finetune._runs import RunDir +from voice.finetune._schema import RunMetadata + +log = logging.getLogger(__name__) + +# Axolotl config keys whose values come from the run hyperparams dict. +_HYPERPARAMS_CFG_KEYS: tuple[str, ...] = ( + "learning_rate", + "lora_r", + "lora_alpha", + "micro_batch_size", + "gradient_accumulation_steps", + "lora_target_modules", +) + +# Splits scored after each training epoch. +_EVAL_SPLITS: tuple[str, str] = ("validation", "test") + + +# ----------------------------------------------------------------------------- +# Config helpers +# ----------------------------------------------------------------------------- + + +def _build_axolotl_cfg( + base_cfg: dict[str, Any], + hyperparams: dict[str, Any], + run_dir: RunDir, +) -> dict[str, Any]: + """ + Build a per-run axolotl config by merging hyperparams into the base. + + Sets ``output_dir`` to ``run_dir.adapter_dir`` and copies any + recognised hyperparams keys present in ``hyperparams``. + + :param base_cfg: Shared axolotl config from the sweep or single-run + config. + :param hyperparams: Concrete hyperparameter values for this run. + :param run_dir: Run directory; determines ``output_dir``. + :return: Merged per-run axolotl config dict. + """ + cfg = dict(base_cfg) + cfg["output_dir"] = str(run_dir.adapter_dir) + for key in _HYPERPARAMS_CFG_KEYS: + if key in hyperparams: + cfg[key] = hyperparams[key] + return cfg + + +def _now_iso() -> str: + """ + Return the current UTC time as an ISO-8601 string. + + :return: ISO-formatted UTC timestamp. + """ + return datetime.datetime.now(datetime.UTC).isoformat() + + +# ----------------------------------------------------------------------------- +# Training +# ----------------------------------------------------------------------------- + + +def run_training(run_dir: RunDir, gpu_ids: str = "0") -> None: + """ + Launch axolotl training and wait for completion. + + Sets ``CUDA_VISIBLE_DEVICES`` to ``gpu_ids``. For a single GPU uses + ``python -m axolotl.cli.train`` directly; for multiple GPUs uses + ``accelerate launch`` with DDP. + + :param run_dir: Run directory containing the written axolotl config. + :param gpu_ids: Comma-separated GPU indices to train on (e.g. ``"0"`` + or ``"0,1,2"``). + :return: None + """ + n_gpus = len([d for d in gpu_ids.split(",") if d.strip()]) + if n_gpus > 1: + cmd = [ + sys.executable, + "-m", + "accelerate.commands.launch", + f"--num_processes={n_gpus}", + "--mixed_precision=no", + "--dynamo_backend=no", + "-m", + "axolotl.cli.train", + str(run_dir.config_path), + ] + else: + cmd = [ + sys.executable, + "-m", + "axolotl.cli.train", + str(run_dir.config_path), + ] + env = os.environ.copy() + env["CUDA_VISIBLE_DEVICES"] = gpu_ids + log.info("Launching training: %s", " ".join(cmd)) + subprocess.run(cmd, check=True, env=env) + + +# ----------------------------------------------------------------------------- +# Scoring +# ----------------------------------------------------------------------------- + + +def _load_completions( + run_dir: RunDir, + split: str, + epoch: int, +) -> list[Example]: + """ + Load per-epoch completions as a list of Examples. + + Completions are stored in chat-template JSONL format and are + transparently canonicalised to ``(system, question, answer)`` by + ``get_dataset``. + + :param run_dir: Run directory containing completions. + :param split: Split name (e.g. ``"validation"`` or ``"test"``). + :param epoch: Epoch number (1-indexed). + :return: List of Example objects loaded from the completions JSONL. + """ + epoch_dir = run_dir.completions_dir / f"epoch_{epoch}" + spec = LocalDatasetSpec(path=epoch_dir, splits=(Split.parse(split),)) + ds = get_dataset(spec) + return list(ds[split]) + + +def _score_split( + run_dir: RunDir, + split: str, + epoch: int, + reference_ds: VoiceDataset, +) -> float: + """ + Score completions for one split/epoch and persist results locally. + + Alignment results are written to + ``alignment/epoch_{n}/{split}.json`` with keys ``score``, + ``score_ci``, ``metric_tails``, ``metric_tail_cis``, ``group_tails``, + and ``group_tail_cis``. Confidence intervals are jackknife percentile + intervals at the default confidence level and are serialised as + ``[lower, upper]`` pairs. Only the overall score is returned; the + full tails and intervals are available on disk only. + + :param run_dir: Run directory. + :param split: Split name (``"validation"`` or ``"test"``). + :param epoch: Epoch number (1-indexed). + :param reference_ds: Reference VoiceDataset to compare against. + Must contain the split being scored and the ``train`` split + for self-Wasserstein calibration. + :return: Alignment score in [0, 1]. + """ + completions = _load_completions(run_dir, split, epoch) + results = make_comparison(completions, reference_ds) + + group_cis = results.group_tail_cis() + + out_path = run_dir.alignment_path(split, epoch) + out_path.parent.mkdir(parents=True, exist_ok=True) + out_path.write_text( + json.dumps( + { + "score": results.score, + "score_ci": results.score_ci(), + "metric_tails": results.metric_tails, + "metric_tail_cis": results.metric_tail_cis(), + "group_tails": { + g.value: v for g, v in results.group_tails.items() + }, + "group_tail_cis": ( + None + if group_cis is None + else {g.value: ci for g, ci in group_cis.items()} + ), + }, + indent=2, + ) + ) + log.info("Scored %s epoch %d: alignment=%.4f", split, epoch, results.score) + return results.score + + +def _try_score_split( + run_dir: RunDir, + split: str, + epoch: int, + reference_ds: VoiceDataset, + meta: RunMetadata, +) -> None: + """ + Attempt to score one split/epoch; update ``meta`` in place on success. + + Skips silently if the completions file does not exist. + Logs and skips if scoring raises an exception. + + :param run_dir: Run directory. + :param split: Split name (``"validation"`` or ``"test"``). + :param epoch: Epoch number (1-indexed). + :param reference_ds: Reference VoiceDataset for comparison. + :param meta: RunMetadata to update with the alignment score. + :return: None + """ + if not run_dir.completions_path(split, epoch).exists(): + log.warning( + "Completions missing: %s epoch %d (skipping)", split, epoch + ) + return + try: + score = _score_split(run_dir, split, epoch, reference_ds) + except Exception: + log.exception("Scoring failed for %s epoch %d", split, epoch) + return + if split == "validation": + meta.alignment_val[str(epoch)] = score + else: + meta.alignment_test[str(epoch)] = score + + +def _score_all_epochs( + run_dir: RunDir, + num_epochs: int, + reference_ds: VoiceDataset, + meta: RunMetadata, +) -> None: + """ + Score completions across all epochs and splits; update ``meta`` in place. + + :param run_dir: Run directory. + :param num_epochs: Total number of training epochs. + :param reference_ds: Reference VoiceDataset for comparison. + :param meta: RunMetadata to update with per-epoch alignment scores. + :return: None + """ + for epoch in range(1, num_epochs + 1): + for split in _EVAL_SPLITS: + _try_score_split(run_dir, split, epoch, reference_ds, meta) + + +# ----------------------------------------------------------------------------- +# Entry point +# ----------------------------------------------------------------------------- + + +def execute_run( + run_name: str, + base_axolotl_cfg: dict[str, Any], + hyperparams: dict[str, Any], + run_dir: RunDir, + reference_spec: DatasetSpec | LocalDatasetSpec, + gpu_ids: str = "0", +) -> RunMetadata: + """ + Execute a single fine-tuning run end-to-end. + + :param run_name: Unique run identifier (e.g. ``llama-3_a3f2b9c1``). + :param base_axolotl_cfg: Shared axolotl settings from the config file. + :param hyperparams: Concrete hyperparameter values for this run. + :param run_dir: RunDir instance for this run (not yet created on disk). + :param reference_spec: Dataset spec used as the reference corpus for + scoring. Must include ``train``, ``validation``, and ``test`` splits. + :param gpu_ids: Comma-separated GPU indices to train on (e.g. ``"0"`` + or ``"0,1,2"``). Passed to ``accelerate launch``. + :return: Completed RunMetadata with alignment scores populated. + :raises RuntimeError: If axolotl training exits with a non-zero status. + """ + axolotl_cfg = _build_axolotl_cfg(base_axolotl_cfg, hyperparams, run_dir) + num_epochs = int(axolotl_cfg.get("num_epochs", 1)) + + run_dir.create() + run_dir.write_config(axolotl_cfg) + + datasets_list: list[dict[str, Any]] = axolotl_cfg.get("datasets") or [{}] + meta = RunMetadata( + run_name=run_name, + model=str(axolotl_cfg.get("base_model", "")), + dataset=str(datasets_list[0].get("path", "")), + hyperparams=hyperparams, + status="running", + started_at=_now_iso(), + ) + run_dir.write_metadata(meta) + + log.info("Loading reference dataset for run '%s'...", run_name) + reference_ds = get_dataset(reference_spec) + + try: + run_training(run_dir, gpu_ids=gpu_ids) + except subprocess.CalledProcessError as exc: + meta.status = "failed" + meta.finished_at = _now_iso() + run_dir.write_metadata(meta) + raise RuntimeError(f"Training failed for run '{run_name}'") from exc + + _score_all_epochs(run_dir, num_epochs, reference_ds, meta) + + meta.status = "done" + meta.finished_at = _now_iso() + run_dir.write_metadata(meta) + + log.info( + "Run '%s' complete. alignment_val=%s alignment_test=%s", + run_name, + meta.alignment_val, + meta.alignment_test, + ) + return meta diff --git a/src/voice/finetune/_runs.py b/src/voice/finetune/_runs.py new file mode 100644 index 0000000..c483720 --- /dev/null +++ b/src/voice/finetune/_runs.py @@ -0,0 +1,159 @@ +""" +Run directory structure and I/O utilities. + +Each fine-tuning run is stored under ``runs/{run_name}/`` with the following +layout:: + + runs/{run_name}/ + config.yaml per-run axolotl config written before training + metadata.json RunMetadata updated throughout the run + adapter/ axolotl output_dir; checkpoints written here + completions/ + epoch_{n}/ + validation.jsonl + test.jsonl + alignment/ + epoch_{n}/ + validation.json + test.json + +This module is private by convention and not part of the public API. +""" + +from __future__ import annotations + +import json +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import yaml + +from voice.finetune._schema import RunMetadata + +# ----------------------------------------------------------------------------- +# Run directory +# ----------------------------------------------------------------------------- + + +@dataclass(slots=True) +class RunDir: + """ + Structured wrapper around a single run directory. + + .. attribute :: path + + Root path of the run directory (``runs/{run_name}``). + """ + + path: Path + + # ------------------------------------------------------------------------- + # Sub-paths + # ------------------------------------------------------------------------- + + @property + def adapter_dir(self) -> Path: + """Axolotl ``output_dir``; weights and checkpoints live here.""" + return self.path / "adapter" + + @property + def completions_dir(self) -> Path: + """Root directory for per-epoch completion files.""" + return self.path / "completions" + + @property + def alignment_dir(self) -> Path: + """Root directory for per-epoch alignment result files.""" + return self.path / "alignment" + + @property + def config_path(self) -> Path: + """Path to the frozen per-run axolotl config YAML.""" + return self.path / "config.yaml" + + @property + def metadata_path(self) -> Path: + """Path to the run metadata JSON file.""" + return self.path / "metadata.json" + + def completions_path(self, split: str, epoch: int) -> Path: + """ + Return the path for a completions file. + + :param split: Split name (e.g. ``"validation"``). + :param epoch: Epoch number (1-indexed). + :return: Path to ``completions/epoch_{n}/{split}.jsonl``. + """ + return self.completions_dir / f"epoch_{epoch}" / f"{split}.jsonl" + + def alignment_path(self, split: str, epoch: int) -> Path: + """ + Return the path for an alignment result file. + + :param split: Split name (e.g. ``"validation"``). + :param epoch: Epoch number (1-indexed). + :return: Path to ``alignment/epoch_{n}/{split}.json``. + """ + return self.alignment_dir / f"epoch_{epoch}" / f"{split}.json" + + # ------------------------------------------------------------------------- + # Lifecycle + # ------------------------------------------------------------------------- + + def create(self) -> None: + """ + Create the run directory tree. + + Creates the run root, ``adapter/``, ``completions/``, and + ``alignment/`` directories. Per-epoch subdirectories are created + lazily when completions or alignment files are written. + + :return: None + """ + for d in ( + self.path, + self.adapter_dir, + self.completions_dir, + self.alignment_dir, + ): + d.mkdir(parents=True, exist_ok=True) + + # ------------------------------------------------------------------------- + # Config I/O + # ------------------------------------------------------------------------- + + def write_config(self, cfg: dict[str, Any]) -> None: + """ + Write the per-run axolotl config to ``config.yaml``. + + :param cfg: Axolotl config dict to serialise. + :return: None + """ + with open(self.config_path, "w") as f: + yaml.dump(cfg, f, default_flow_style=False, sort_keys=False) + + # ------------------------------------------------------------------------- + # Metadata I/O + # ------------------------------------------------------------------------- + + def write_metadata(self, meta: RunMetadata) -> None: + """ + Atomically write run metadata to ``metadata.json``. + + :param meta: RunMetadata to persist. + :return: None + """ + tmp = self.metadata_path.with_suffix(".tmp.json") + tmp.write_text(json.dumps(meta.to_dict(), indent=2)) + tmp.rename(self.metadata_path) + + def read_metadata(self) -> RunMetadata: + """ + Read run metadata from ``metadata.json``. + + :return: Reconstructed RunMetadata. + """ + return RunMetadata.from_dict( + json.loads(self.metadata_path.read_text()) + ) diff --git a/src/voice/finetune/_schema.py b/src/voice/finetune/_schema.py new file mode 100644 index 0000000..fbe9ef5 --- /dev/null +++ b/src/voice/finetune/_schema.py @@ -0,0 +1,193 @@ +""" +Dataclass schemas for fine-tuning configuration and run metadata. + +This module is private by convention and not part of the public API. + +This module defines: + +- `SweepConfig` - Parsed representation of a sweep config YAML. +- `SingleRunConfig` - Parsed representation of a single-run config YAML. +- `RunMetadata` - Mutable metadata written to each run directory. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any, Literal + +import yaml + +# ----------------------------------------------------------------------------- +# Config schemas +# ----------------------------------------------------------------------------- + + +@dataclass(frozen=True, slots=True) +class SweepConfig: + """ + Parsed sweep configuration. + + .. attribute :: sweep + + Hyperparameter grid axes (each value is a list of candidates). + + .. attribute :: axolotl + + Shared axolotl settings merged into every per-run config. + """ + + sweep: dict[str, Any] + axolotl: dict[str, Any] + + @classmethod + def from_yaml(cls, path: Path) -> SweepConfig: + """ + Load a sweep config from a YAML file. + + :param path: Path to the YAML config file. + :return: Parsed SweepConfig. + :raises ValueError: If required top-level keys are missing. + """ + raw: dict[str, Any] = yaml.safe_load(path.read_text()) + missing = {"sweep", "axolotl"} - raw.keys() + if missing: + raise ValueError( + f"Sweep config missing required keys: {sorted(missing)}" + ) + return cls(sweep=raw["sweep"], axolotl=raw["axolotl"]) + + +@dataclass(frozen=True, slots=True) +class SingleRunConfig: + """ + Parsed single-run configuration. + + .. attribute :: axolotl + + Full axolotl training settings for the run. + """ + + axolotl: dict[str, Any] + + @classmethod + def from_yaml(cls, path: Path) -> SingleRunConfig: + """ + Load a single-run config from a YAML file. + + :param path: Path to the YAML config file. + :return: Parsed SingleRunConfig. + :raises ValueError: If the ``axolotl`` key is missing. + """ + raw: dict[str, Any] = yaml.safe_load(path.read_text()) + if "axolotl" not in raw: + raise ValueError( + "Single-run config missing required key: 'axolotl'" + ) + return cls(axolotl=raw["axolotl"]) + + +# ----------------------------------------------------------------------------- +# Run metadata +# ----------------------------------------------------------------------------- + + +@dataclass(slots=True) +class RunMetadata: + """ + Mutable metadata for a single fine-tuning run. + + Written to ``runs/{run_name}/metadata.json`` and updated throughout + the run lifecycle. + + .. attribute :: run_name + + Unique identifier for the run. + + .. attribute :: model + + Base model identifier (HF repo_id or local path). + + .. attribute :: dataset + + Dataset identifier (HF repo_id or local path). + + .. attribute :: hyperparams + + Concrete hyperparameter values for this run. + + .. attribute :: status + + Lifecycle state: ``pending``, ``running``, ``done``, or ``failed``. + + .. attribute :: started_at + + ISO-format timestamp when training began, or ``None``. + + .. attribute :: finished_at + + ISO-format timestamp when the run completed, or ``None``. + + .. attribute :: eval_loss + + Mapping of epoch number (as string) to validation loss. + + .. attribute :: alignment_val + + Mapping of epoch number (as string) to validation alignment score. + + .. attribute :: alignment_test + + Mapping of epoch number (as string) to test alignment score. + """ + + run_name: str + model: str + dataset: str + hyperparams: dict[str, Any] + status: Literal["pending", "running", "done", "failed"] = "pending" + started_at: str | None = None + finished_at: str | None = None + eval_loss: dict[str, float] = field(default_factory=dict) + alignment_val: dict[str, float] = field(default_factory=dict) + alignment_test: dict[str, float] = field(default_factory=dict) + + def to_dict(self) -> dict[str, Any]: + """ + Serialise metadata to a plain dict for JSON output. + + :return: Dict representation of the metadata. + """ + return { + "run_name": self.run_name, + "model": self.model, + "dataset": self.dataset, + "hyperparams": self.hyperparams, + "status": self.status, + "started_at": self.started_at, + "finished_at": self.finished_at, + "eval_loss": self.eval_loss, + "alignment_val": self.alignment_val, + "alignment_test": self.alignment_test, + } + + @classmethod + def from_dict(cls, d: dict[str, Any]) -> RunMetadata: + """ + Reconstruct metadata from a plain dict. + + :param d: Dict representation of the metadata. + :return: Reconstructed RunMetadata. + """ + return cls( + run_name=d["run_name"], + model=d["model"], + dataset=d["dataset"], + hyperparams=d["hyperparams"], + status=d["status"], + started_at=d.get("started_at"), + finished_at=d.get("finished_at"), + eval_loss=d.get("eval_loss", {}), + alignment_val=d.get("alignment_val", {}), + alignment_test=d.get("alignment_test", {}), + ) diff --git a/src/voice/finetune/_state.py b/src/voice/finetune/_state.py new file mode 100644 index 0000000..1d68114 --- /dev/null +++ b/src/voice/finetune/_state.py @@ -0,0 +1,69 @@ +""" +Sweep state persistence for grid searches with resume support. + +This module is private by convention and not part of the public API. + +State is written to a JSON file with two keys: + +- ``completed``: list of run names that finished successfully. +- ``failed``: mapping of run name to error string for failed runs. +""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any, cast + + +def load_state(state_file: Path) -> dict[str, Any]: + """ + Load sweep state from a JSON file, or return a fresh state dict. + + :param state_file: Path to the state JSON file. + :return: State dict with ``completed`` and ``failed`` keys. + """ + if state_file.exists(): + return cast(dict[str, Any], json.loads(state_file.read_text())) + return {"completed": [], "failed": {}} + + +def save_state(state: dict[str, Any], state_file: Path) -> None: + """ + Persist sweep state to a JSON file. + + :param state: State dict to serialise. + :param state_file: Destination path. + :return: None + """ + state_file.write_text(json.dumps(state, indent=2)) + + +def mark_completed(state: dict[str, Any], name: str, state_file: Path) -> None: + """ + Record a run as completed and persist state. + + :param state: Current sweep state dict. + :param name: Run name to mark as completed. + :param state_file: Destination path for persisting state. + :return: None + """ + state["completed"].append(name) + state["failed"].pop(name, None) + save_state(state, state_file) + + +def mark_failed( + state: dict[str, Any], name: str, error: str, state_file: Path +) -> None: + """ + Record a run as failed and persist state. + + :param state: Current sweep state dict. + :param name: Run name to mark as failed. + :param error: Error string describing the failure. + :param state_file: Destination path for persisting state. + :return: None + """ + state["failed"][name] = error + save_state(state, state_file) diff --git a/src/voice/finetune/callbacks.py b/src/voice/finetune/callbacks.py new file mode 100644 index 0000000..603e27b --- /dev/null +++ b/src/voice/finetune/callbacks.py @@ -0,0 +1,429 @@ +""" +Axolotl TrainerCallback that generates completions at each epoch end. + +Registered via the axolotl ``plugins`` YAML key as +``voice.finetune.callbacks.EvalCompletionsPlugin``. + +All GPUs participate in generation: each split's examples are sharded +across processes. Each shard is generated in batches of +``INFERENCE_DEFAULTS.batch_size``. After all ranks finish, results are +gathered and rank 0 writes the final ordered JSONL. + +Completions are written under the run directory derived from +``args.output_dir`` (the adapter dir): + + ``{output_dir}/../completions/epoch_{n}/{split}.jsonl`` +""" + +from __future__ import annotations + +import json +import logging +import os +from pathlib import Path +from typing import Any + +import torch +import torch.distributed +from datasets import load_dataset +from transformers import ( + TrainerCallback, + TrainerControl, + TrainerState, + TrainingArguments, +) + +try: + from axolotl.integrations.base import BasePlugin +except ImportError: # axolotl is Linux-only + + class BasePlugin: # type: ignore + """Stub for environments where axolotl is not installed.""" + + +from voice._defaults import INFERENCE_DEFAULTS + +log = logging.getLogger(__name__) + +# Generation config built from defaults; passed directly to model.generate(). +GENERATION_CONFIG: dict[str, Any] = { + "do_sample": True, + "temperature": INFERENCE_DEFAULTS.temperature, + "top_p": INFERENCE_DEFAULTS.top_p, + "top_k": INFERENCE_DEFAULTS.top_k, + "repetition_penalty": INFERENCE_DEFAULTS.repetition_penalty, + "max_new_tokens": INFERENCE_DEFAULTS.max_new_tokens, +} + +_EVAL_SPLITS: tuple[str, str] = ("validation", "test") + +# ----------------------------------------------------------------------------- +# Completion formatting +# ----------------------------------------------------------------------------- + + +def _format_completion( + messages: list[dict[str, str]], + generated: str, +) -> dict[str, Any]: + """ + Format a completion as a chat_template JSONL record. + + :param messages: System and user messages (no assistant turn). + :param generated: Generated assistant response. + :return: Dict with ``messages`` list including the assistant turn. + """ + return { + "messages": [ + *messages, + {"role": "assistant", "content": generated}, + ] + } + + +# ----------------------------------------------------------------------------- +# Distributed generation helpers +# ----------------------------------------------------------------------------- + + +def _generate_batch( + batch: list[dict[str, Any]], + model: Any, + tokenizer: Any, +) -> list[dict[str, Any]]: + """ + Generate completions for a batch using left-padded inputs. + + :param batch: Examples with a ``messages`` list (system + user only). + :param model: Unwrapped model (already on the correct device). + :param tokenizer: Tokenizer with ``padding_side`` set to ``"left"``. + :return: List of chat_template completion dicts. + """ + prompts = [ + tokenizer.apply_chat_template( + ex["messages"], tokenize=False, add_generation_prompt=True + ) + for ex in batch + ] + inputs = tokenizer( + prompts, return_tensors="pt", padding=True, truncation=False + ).to(model.device) + padded_len = inputs["input_ids"].shape[1] + + with torch.no_grad(): + outputs = model.generate(**inputs, **GENERATION_CONFIG) + + results = [] + for j, ex in enumerate(batch): + new_tokens = outputs[j, padded_len:] + generated = tokenizer.decode(new_tokens, skip_special_tokens=True) + results.append(_format_completion(ex["messages"], generated)) + return results + + +def _generate_split( + examples: list[dict[str, Any]], + model: Any, + tokenizer: Any, + args: TrainingArguments, +) -> list[dict[str, Any]]: + """ + Generate completions for a full split, distributed across ranks. + + :param examples: Full list of prompt examples for the split. + :param model: Unwrapped model. + :param tokenizer: Tokenizer with ``padding_side`` set to ``"left"``. + :param args: HF TrainingArguments providing rank and world-size info. + :return: Ordered completions on rank 0; empty list on other ranks. + """ + local_indices = list( + range(args.process_index, len(examples), args.world_size) + ) + local_examples = [examples[i] for i in local_indices] + + local_completions: list[dict[str, Any]] = [] + for i in range(0, len(local_examples), INFERENCE_DEFAULTS.batch_size): + batch = local_examples[i : i + INFERENCE_DEFAULTS.batch_size] + local_completions.extend(_generate_batch(batch, model, tokenizer)) + + if ( + args.world_size > 1 + and torch.distributed.is_available() + and torch.distributed.is_initialized() + ): + all_shards: list[Any] = [None] * args.world_size + torch.distributed.all_gather_object( + all_shards, (local_indices, local_completions) + ) + else: + all_shards = [(local_indices, local_completions)] + + if args.process_index != 0: + return [] + + ordered: list[dict[str, Any]] = [{}] * len(examples) + for proc_indices, proc_completions in all_shards: + for orig_idx, comp in zip( # noqa: B905 + proc_indices, proc_completions + ): + ordered[orig_idx] = comp + return ordered + + +def _write_split_completions( + examples: list[dict[str, Any]], + split: str, + completions_root: Path, + model: Any, + tokenizer: Any, + args: TrainingArguments, +) -> Path | None: + """ + Generate and write completions for one split on rank 0. + + :param examples: Prompt examples for the split. + :param split: Split name (e.g. ``"validation"``). + :param completions_root: Directory to write the JSONL file into. + :param model: Unwrapped model. + :param tokenizer: Tokenizer. + :param args: HF TrainingArguments. + :return: Written file path on rank 0; ``None`` on other ranks. + """ + completions = _generate_split(examples, model, tokenizer, args) + if args.process_index != 0: + return None + out_path = completions_root / f"{split}.jsonl" + out_path.write_text( + "\n".join(json.dumps(c, ensure_ascii=False) for c in completions) + ) + log.info( + "EvalCompletionsCallback: wrote %d %s completions -> %s", + len(completions), + split, + out_path, + ) + return out_path + + +# ----------------------------------------------------------------------------- +# Callback +# ----------------------------------------------------------------------------- + + +class EvalCompletionsCallback(TrainerCallback): # type: ignore[misc] + """ + Generate and log validation and test completions at each epoch end. + + :param trainer: HF Trainer instance, used to resolve the tokenizer + and unwrap the model at epoch end. + :param dataset: HF dataset repo id to generate completions from. + Must be supplied; typically taken from ``cfg.datasets[0].path``. + """ + + def __init__(self, trainer: Any = None, dataset: str = "") -> None: + self._val_examples: list[dict[str, Any]] = [] + self._test_examples: list[dict[str, Any]] = [] + self._trainer = trainer + self._dataset = dataset + + def on_train_begin( + self, + _args: TrainingArguments, + _state: TrainerState, + _control: TrainerControl, + **_kwargs: Any, + ) -> None: + """ + Load and cache validation and test examples before training. + + :param _args: HF TrainingArguments (unused). + :param _state: Current TrainerState (unused). + :param _control: TrainerControl object (unused). + :param **_kwargs: Keyword arguments passed by the HF Trainer. + :return: None + """ + hf_token = os.environ.get("HF_TOKEN") or os.environ.get( + "HUGGINGFACE_TOKEN" + ) + + def _load(split: str) -> list[dict[str, Any]]: + ds = load_dataset(self._dataset, split=split, token=hf_token) + return [ + { + "messages": [ + m for m in ex["messages"] if m["role"] != "assistant" + ] + } + for ex in ds + ] + + self._val_examples = _load("validation") + self._test_examples = _load("test") + log.info( + "EvalCompletionsCallback: loaded %d validation, %d test examples", + len(self._val_examples), + len(self._test_examples), + ) + + def on_evaluate( + self, + args: TrainingArguments, + state: TrainerState, + _control: TrainerControl, + metrics: dict[str, Any] | None = None, + **_kwargs: Any, + ) -> None: + """ + Persist eval_loss for this epoch to ``eval_losses.json``. + + :param args: HF TrainingArguments. + :param state: Current TrainerState. + :param _control: TrainerControl object (unused). + :param metrics: Evaluation metrics dict containing ``eval_loss``. + :param **_kwargs: Keyword arguments passed by the HF Trainer. + :return: None + """ + if ( + args.process_index != 0 + or not metrics + or "eval_loss" not in metrics + ): + return + epoch = round(state.epoch) + losses_path = Path(args.output_dir) / "eval_losses.json" + losses: dict[str, float] = ( + json.loads(losses_path.read_text()) if losses_path.exists() else {} + ) + losses[str(epoch)] = float(metrics["eval_loss"]) + losses_path.write_text(json.dumps(losses, indent=2)) + log.info( + "EvalCompletionsCallback: epoch %d eval_loss=%.4f", + epoch, + metrics["eval_loss"], + ) + + def _resolve_tokenizer( + self, tokenizer: Any, kwargs: dict[str, Any] + ) -> Any: + """ + Resolve tokenizer from kwargs or trainer fallback. + + :param tokenizer: Tokenizer passed to the callback, or None. + :param kwargs: Remaining keyword arguments from the Trainer. + :return: Resolved tokenizer, or None if unavailable. + """ + if tokenizer is None: + tokenizer = kwargs.get("processing_class") + if tokenizer is None and self._trainer is not None: + tokenizer = getattr( + self._trainer, "processing_class", None + ) or getattr(self._trainer, "tokenizer", None) + return tokenizer + + def _resolve_model(self, model: Any) -> Any: + """ + Unwrap model from DDP/FSDP or fall back to trainer. + + :param model: Model passed to the callback, or None. + :return: Unwrapped model. + """ + if model is None and self._trainer is not None: + model = self._trainer.model + if self._trainer is not None and hasattr(self._trainer, "accelerator"): + model = self._trainer.accelerator.unwrap_model(model) + return model + + def on_epoch_end( + self, + args: TrainingArguments, + state: TrainerState, + _control: TrainerControl, + model: Any = None, + tokenizer: Any = None, + **kwargs: Any, + ) -> None: + """ + Generate completions for all validation and test examples. + + All ranks generate their shard in parallel; rank 0 gathers + and writes ordered JSONL. + + :param args: HF TrainingArguments. + :param state: TrainerState providing the current epoch. + :param _control: TrainerControl object (unused). + :param model: Live model (DDP/FSDP wrapped; unwrapped internally). + :param tokenizer: Tokenizer, or None in newer transformers. + :param **kwargs: Keyword arguments passed by the HF Trainer. + :return: None + """ + epoch = round(state.epoch) + log.info( + "EvalCompletionsCallback: generating completions for epoch %d", + epoch, + ) + + tokenizer = self._resolve_tokenizer(tokenizer, kwargs) + if tokenizer is None: + log.error( + "EvalCompletionsCallback: tokenizer is None, " + "skipping epoch %d", + epoch, + ) + return + + model = self._resolve_model(model) + completions_root = ( + Path(args.output_dir).parent / "completions" / f"epoch_{epoch}" + ) + completions_root.mkdir(parents=True, exist_ok=True) + + orig_padding_side = tokenizer.padding_side + tokenizer.padding_side = "left" + model.eval() + + for split, examples in ( + ("validation", self._val_examples), + ("test", self._test_examples), + ): + _write_split_completions( + examples, split, completions_root, model, tokenizer, args + ) + + model.train() + tokenizer.padding_side = orig_padding_side + + +# ----------------------------------------------------------------------------- +# Plugin +# ----------------------------------------------------------------------------- + + +class EvalCompletionsPlugin(BasePlugin): # type: ignore[misc] + """ + Axolotl plugin that registers EvalCompletionsCallback before training. + + Register via the axolotl YAML config:: + + plugins: + - voice.finetune.callbacks.EvalCompletionsPlugin + """ + + def add_callbacks_post_trainer( + self, cfg: Any, trainer: Any + ) -> list[TrainerCallback]: + """ + Return the completions callback to be added to the Trainer. + + :param cfg: Axolotl config object. + :param trainer: The built HF Trainer instance. + :return: List containing the EvalCompletionsCallback instance. + :raises ValueError: If ``cfg.datasets[0].path`` is not accessible. + """ + try: + dataset: str = cfg.datasets[0].path + except (AttributeError, IndexError) as exc: + raise ValueError( + "EvalCompletionsPlugin requires cfg.datasets[0].path " + "to be set in the axolotl config." + ) from exc + return [EvalCompletionsCallback(trainer=trainer, dataset=dataset)] diff --git a/src/voice/finetune/cli.py b/src/voice/finetune/cli.py new file mode 100644 index 0000000..abafb43 --- /dev/null +++ b/src/voice/finetune/cli.py @@ -0,0 +1,183 @@ +""" +Click CLI for VOICE fine-tuning. + +Provides two subcommands under ``voice finetune``: + +- ``single`` - run one fine-tuning job from a single-run YAML config. +- ``sweep`` - run a hyperparameter grid search from a sweep YAML config. + +Usage:: + + voice finetune single configs/single/my_run.yaml + voice finetune sweep configs/sweep/example.yaml --resume +""" + +from __future__ import annotations + +import logging +import sys +from pathlib import Path + +import click + +from voice.datasets._schema import Split +from voice.datasets.dataset import DatasetSpec +from voice.finetune._grid import expand_grid, generate_run_name, new_uid +from voice.finetune._orchestrator import execute_run +from voice.finetune._runs import RunDir +from voice.finetune._schema import SingleRunConfig, SweepConfig +from voice.finetune._state import load_state, mark_completed, mark_failed + +log = logging.getLogger(__name__) + +# All three splits are required: train for calibration, val/test for scoring. +_ALL_SPLITS: tuple[Split, ...] = (Split.TRAIN, Split.VALIDATION, Split.TEST) + + +# ----------------------------------------------------------------------------- +# CLI groups +# ----------------------------------------------------------------------------- + + +@click.group() +def main() -> None: + """VOICE – fine-tuning for stylistic fidelity.""" + + +@main.group() +def finetune() -> None: + """Fine-tuning commands.""" + + +# ----------------------------------------------------------------------------- +# `voice finetune single` +# ----------------------------------------------------------------------------- + + +@finetune.command("single") +@click.argument("config", type=click.Path(exists=True, path_type=Path)) +@click.option( + "--runs-dir", + default="runs", + show_default=True, + type=click.Path(path_type=Path), + help="Root directory for run outputs.", +) +@click.option( + "--dataset-revision", + default="main", + show_default=True, + help="HF revision of the reference dataset.", +) +@click.option( + "--gpus", + default="0", + show_default=True, + help="Comma-separated GPU indices to train on (e.g. '0' or '0,1,2').", +) +def single( + config: Path, + runs_dir: Path, + dataset_revision: str, + gpus: str, +) -> None: + """Run a single fine-tuning job from CONFIG.""" + cfg = SingleRunConfig.from_yaml(config) + dataset: str = cfg.axolotl["datasets"][0]["path"] + model: str = str(cfg.axolotl.get("base_model", "")) + run_name = generate_run_name(model, new_uid()) + run_dir = RunDir(path=runs_dir / run_name) + reference_spec = DatasetSpec( + repo_id=dataset, + revision=dataset_revision, + splits=_ALL_SPLITS, + ) + click.echo(f"Starting run: {run_name}") + try: + execute_run( + run_name, cfg.axolotl, {}, run_dir, reference_spec, gpu_ids=gpus + ) + except RuntimeError as exc: + click.echo(f"Run failed: {exc}", err=True) + sys.exit(1) + click.echo(f"Run complete: {run_name}") + + +# ----------------------------------------------------------------------------- +# `voice finetune sweep` +# ----------------------------------------------------------------------------- + + +@finetune.command("sweep") +@click.argument("config", type=click.Path(exists=True, path_type=Path)) +@click.option( + "--runs-dir", + default="runs", + show_default=True, + type=click.Path(path_type=Path), + help="Root directory for run outputs.", +) +@click.option( + "--resume/--no-resume", + default=False, + help="Skip runs already marked completed in state.json.", +) +@click.option( + "--dataset-revision", + default="main", + show_default=True, + help="HF revision of the reference dataset.", +) +@click.option( + "--gpus", + default="0", + show_default=True, + help="Comma-separated GPU indices to train on (e.g. '0' or '0,1,2').", +) +def sweep( + config: Path, + runs_dir: Path, + resume: bool, + dataset_revision: str, + gpus: str, +) -> None: + """Run a hyperparameter sweep from CONFIG.""" + cfg = SweepConfig.from_yaml(config) + dataset: str = cfg.axolotl["datasets"][0]["path"] + model: str = str(cfg.axolotl.get("base_model", "")) + grid = expand_grid(cfg.sweep) + runs_dir.mkdir(parents=True, exist_ok=True) + state_file = runs_dir / "state.json" + state = load_state(state_file) + reference_spec = DatasetSpec( + repo_id=dataset, + revision=dataset_revision, + splits=_ALL_SPLITS, + ) + failed_count = 0 + for hyperparams in grid: + run_idx = str(hyperparams["run_idx"]) + if resume and run_idx in state["completed"]: + click.echo(f"Skipping run {run_idx} (already completed)") + continue + run_name = generate_run_name(model, new_uid()) + run_dir = RunDir(path=runs_dir / run_name) + click.echo(f"Starting run {run_idx}/{len(grid) - 1}: {run_name}") + try: + execute_run( + run_name, + cfg.axolotl, + hyperparams, + run_dir, + reference_spec, + gpu_ids=gpus, + ) + mark_completed(state, run_idx, state_file) + click.echo(f"Run {run_idx} complete: {run_name}") + except RuntimeError as exc: + mark_failed(state, run_idx, str(exc), state_file) + click.echo(f"Run {run_idx} failed: {exc}", err=True) + failed_count += 1 + if failed_count: + click.echo(f"{failed_count} run(s) failed.", err=True) + sys.exit(1) diff --git a/src/voice/stylometry/__init__.py b/src/voice/stylometry/__init__.py index e4da059..1e52bc6 100644 --- a/src/voice/stylometry/__init__.py +++ b/src/voice/stylometry/__init__.py @@ -19,7 +19,6 @@ calculate_hapax_legomena_ratio, calculate_kurtosis_word_length, calculate_moving_avg_type_token_ratio, - calculate_num_words, calculate_skew_word_length, calculate_std_word_length, calculate_tri_legomena_ratio, @@ -38,7 +37,6 @@ "calculate_std_word_length", "calculate_skew_word_length", "calculate_kurtosis_word_length", - "calculate_num_words", "calculate_hapax_legomena_ratio", "calculate_dis_legomena_ratio", "calculate_tri_legomena_ratio", diff --git a/src/voice/stylometry/metrics.py b/src/voice/stylometry/metrics.py index 3f4666c..634c963 100644 --- a/src/voice/stylometry/metrics.py +++ b/src/voice/stylometry/metrics.py @@ -9,7 +9,6 @@ - Standard deviation of word length (`std_word_length`) - Skewness of word length (`skew_word_length`) - Standard kurtosis of word length (`kurtosis_word_length`) - - Total number of words (`num_words`) - Hapax legomena ratio (`hapax_legomena_ratio`) - Dis legomena ratio (`dis_legomena_ratio`) - Tri legomena ratio (`tri_legomena_ratio`) @@ -235,23 +234,6 @@ def calculate_kurtosis_word_length(text: str) -> float: # ----------------------------------------------------------------------------- -@metric( - "num_words", - group=MetricGroup.TEXT_LENGTH, - description="Number of words in text", -) -def calculate_num_words(text: str) -> float: - """ - Calculate the number of words in a string. - - Number of words in an empty string is 0.0. - - :param text: Provided text - :return: Number of words as a float - """ - return float(len(word_tokenize(text))) - - @metric( "hapax_legomena_ratio", group=MetricGroup.VOCABULARY_RICHNESS, diff --git a/sweep/README.md b/sweep/README.md new file mode 100644 index 0000000..d9a4711 --- /dev/null +++ b/sweep/README.md @@ -0,0 +1,79 @@ +# Llama 3.1 8B Stylometric Pilot Sweep + +48 training runs × 3 epoch checkpoints = 144 data points across a grid of +LoRA rank, learning rate, batch size, and target layer configurations. + +Validation loss is computed by axolotl after each epoch (stored in +`trainer_state.json`). Completions are generated by `EvalCompletionsCallback` +at the end of each epoch using the live in-memory model — no separate inference +process or server required. + +## Setup on the H100 + +**1. Clone the repo and install the environment** + +```bash +git clone && cd voice +uv sync +source .venv/bin/activate +``` + +**2. Set environment variables** + +```bash +export HF_TOKEN=hf_... +export WANDB_API_KEY=... +``` + +**3. Verify the grid looks correct** + +```bash +python sweep/sweep.py --dry-run +``` + +## Running the sweep + +Start a tmux session so the sweep survives SSH disconnection: + +```bash +tmux new -s sweep +python sweep/sweep.py # defaults to GPU 2 +# Ctrl+B, D → detach (safe to close laptop) +# tmux attach -t sweep → reconnect later +``` + +To use a different GPU: + +```bash +python sweep/sweep.py --gpus 0 +``` + +Progress is logged to `sweep/sweep.log` and to the W&B project `VOICE`. +Completions are uploaded to W&B as artifacts named `{run_name}-epoch{N}-Results` +and written locally to `sweep/checkpoints/{run_name}/completions_epoch_{N}.json`. + +## Resuming after failure + +`state.json` tracks completed and failed runs. Simply re-run `sweep.py` and +it will skip completed runs and retry failed ones: + +```bash +python sweep/sweep.py +``` + +## Outputs + +Results are written to `sweep/outputs/results.parquet` after each training run +completes. Schema: + +| Column | Description | +|---|---| +| `run_name` | Unique run identifier | +| `epoch` | Epoch number (1, 2, or 3) | +| `learning_rate` | LoRA learning rate | +| `lora_r` | LoRA rank | +| `lora_alpha` | LoRA alpha (= rank) | +| `effective_batch_size` | Effective batch size | +| `target_layers` | `attention` or `attention_mlp` | +| `eval_loss` | Validation NLL loss (from axolotl trainer_state.json) | +| `completions` | JSON list of generated responses on the validation split | diff --git a/sweep/callbacks.py b/sweep/callbacks.py new file mode 100644 index 0000000..b200450 --- /dev/null +++ b/sweep/callbacks.py @@ -0,0 +1,305 @@ +"""Axolotl TrainerCallback that generates validation completions at each epoch end. + +Registered via the axolotl ``plugins`` YAML key as +``sweep.callbacks.EvalCompletionsPlugin``. + +All GPUs participate in generation: the 100-example validation set is sharded +across processes (rank 0 gets indices 0, 2, 4, …; rank 1 gets 1, 3, 5, …). +Each shard is generated in left-padded batches of ``INFERENCE_BATCH_SIZE``. +Rank 0 streams its own partial results to disk after every batch so the file +is readable mid-run. After all ranks finish, results are gathered and rank 0 +writes the final ordered JSON and uploads a W&B artifact. + +The output format is identical to the previous vLLM inference pipeline:: + + [{"messages": [...], "generated_response": "..."}, ...] +""" + +import json +import logging +import os +from pathlib import Path +from typing import Any + +import torch +import torch.distributed +import wandb +from axolotl.integrations.base import BasePlugin +from datasets import load_dataset +from transformers import TrainerCallback, TrainerControl, TrainerState, TrainingArguments + +log = logging.getLogger(__name__) + +DATASET: str = "AccelerateScience/bush-dataset" + +# 32 examples per batch fits comfortably in 80 GB VRAM. +# With 2 GPUs each handling ~50 examples this means ≤ 2 forward passes per GPU. +INFERENCE_BATCH_SIZE: int = 32 + +# Sampling parameters agreed for all experiments: +# T=0.7 with no truncation samples from the exact learned distribution, +# eliminating any gap between the training and inference distributions. +GENERATION_CONFIG: dict[str, Any] = { + "do_sample": True, + "temperature": 0.7, + "top_p": 1.0, + "top_k": 0, + "repetition_penalty": 1.0, + "max_new_tokens": 1024, +} + + +class EvalCompletionsCallback(TrainerCallback): + """Generate and log validation completions at each epoch end.""" + + def __init__(self, trainer: Any = None) -> None: + """Initialise with an empty example cache. + + :param trainer: the HF Trainer instance, stored so we can resolve + the tokenizer via ``trainer.processing_class`` and unwrap the + model via ``trainer.accelerator`` at epoch end. + """ + self._examples: list[dict[str, Any]] = [] + self._trainer = trainer + + def on_train_begin( + self, + args: TrainingArguments, + state: TrainerState, + control: TrainerControl, + **kwargs: Any, + ) -> None: + """Load and cache the validation split once before training starts. + + :param args: HF TrainingArguments. + :param state: current TrainerState. + :param control: TrainerControl object. + :returns: None + """ + hf_token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN") + ds = load_dataset(DATASET, split="validation", token=hf_token) + # Strip assistant turns — only system + user messages form the prompt. + self._examples = [ + {"messages": [m for m in ex["messages"] if m["role"] != "assistant"]} + for ex in ds + ] + log.info( + "EvalCompletionsCallback: loaded %d validation examples", + len(self._examples), + ) + + def on_evaluate( + self, + args: TrainingArguments, + state: TrainerState, + control: TrainerControl, + metrics: dict[str, Any] | None = None, + **kwargs: Any, + ) -> None: + """Persist eval_loss for this epoch to eval_losses.json. + + Called by HF Trainer immediately after the evaluation loop, with + ``metrics`` containing the freshly computed ``eval_loss``. Writing + our own file removes the dependency on trainer_state.json parsing. + + :param args: HF TrainingArguments. + :param state: current TrainerState. + :param control: TrainerControl object. + :param metrics: evaluation metrics dict — ``eval_loss`` is always + present when an evaluation loop completes with losses. + :returns: None + """ + if args.process_index != 0 or not metrics or "eval_loss" not in metrics: + return + epoch = round(state.epoch) + losses_path = Path(args.output_dir) / "eval_losses.json" + losses: dict[str, float] = ( + json.loads(losses_path.read_text()) if losses_path.exists() else {} + ) + losses[str(epoch)] = float(metrics["eval_loss"]) + losses_path.write_text(json.dumps(losses, indent=2)) + log.info( + "EvalCompletionsCallback: epoch %d eval_loss=%.4f", + epoch, + metrics["eval_loss"], + ) + + def on_epoch_end( + self, + args: TrainingArguments, + state: TrainerState, + control: TrainerControl, + model: Any = None, + tokenizer: Any = None, + **kwargs: Any, + ) -> None: + """Generate completions for all validation examples and persist results. + + All ranks generate their shard in parallel; rank 0 gathers and writes. + + :param args: HF TrainingArguments. + :param state: TrainerState — ``state.epoch`` gives the current epoch. + :param control: TrainerControl object. + :param model: the live model (DDP/FSDP wrapped; unwrapped internally). + :param tokenizer: the tokenizer, or None in newer transformers (resolved + via ``processing_class`` kwarg or the stored trainer reference). + :returns: None + """ + epoch = round(state.epoch) + log.info( + "EvalCompletionsCallback: generating completions for epoch %d", epoch + ) + + # Newer transformers passes the tokenizer as processing_class, not tokenizer. + if tokenizer is None: + tokenizer = kwargs.get("processing_class") + if tokenizer is None and self._trainer is not None: + tokenizer = getattr( + self._trainer, "processing_class", None + ) or getattr(self._trainer, "tokenizer", None) + if tokenizer is None: + log.error( + "EvalCompletionsCallback: tokenizer is None, skipping epoch %d", epoch + ) + return + + # Unwrap DDP/FSDP wrapper so .generate() is available. + if model is None and self._trainer is not None: + model = self._trainer.model + if self._trainer is not None and hasattr(self._trainer, "accelerator"): + model = self._trainer.accelerator.unwrap_model(model) + + # Shard the validation set across all ranks so every GPU contributes. + local_indices = list( + range(args.process_index, len(self._examples), args.world_size) + ) + local_examples = [self._examples[i] for i in local_indices] + + output_dir = Path(args.output_dir) + out_path = output_dir / f"completions_epoch_{epoch}.json" + + orig_padding_side = tokenizer.padding_side + tokenizer.padding_side = "left" + model.eval() + + local_completions: list[dict[str, Any]] = [] + rank0_by_idx: dict[int, dict[str, Any]] = {} + + for i in range(0, len(local_examples), INFERENCE_BATCH_SIZE): + batch_examples = local_examples[i : i + INFERENCE_BATCH_SIZE] + batch_indices = local_indices[i : i + INFERENCE_BATCH_SIZE] + results = _generate_batch(batch_examples, model, tokenizer) + local_completions.extend(results) + + # Rank 0 streams partial results after every batch. + if args.process_index == 0: + for orig_idx, result in zip(batch_indices, results): + rank0_by_idx[orig_idx] = result + out_path.write_text( + json.dumps( + [rank0_by_idx[j] for j in sorted(rank0_by_idx)], indent=2 + ) + ) + + model.train() + tokenizer.padding_side = orig_padding_side + + # Gather every rank's shard onto rank 0. + if ( + args.world_size > 1 + and torch.distributed.is_available() + and torch.distributed.is_initialized() + ): + all_shards: list[Any] = [None] * args.world_size + torch.distributed.all_gather_object( + all_shards, (local_indices, local_completions) + ) + else: + all_shards = [(local_indices, local_completions)] + + if args.process_index != 0: + return + + # Reassemble in original dataset order. + completions: list[dict[str, Any]] = [{}] * len(self._examples) + for proc_indices, proc_completions in all_shards: + for orig_idx, comp in zip(proc_indices, proc_completions): + completions[orig_idx] = comp + + out_path.write_text(json.dumps(completions, indent=2)) + log.info(" [sample] %r", completions[0]["generated_response"]) + log.info( + "EvalCompletionsCallback: wrote %d completions → %s", + len(completions), + out_path, + ) + + if wandb.run is not None: + artifact = wandb.Artifact( + f"{wandb.run.name}-epoch{epoch}-Results", + type="completions", + ) + artifact.add_file(str(out_path)) + wandb.log_artifact(artifact) + + +def _generate_batch( + batch: list[dict[str, Any]], + model: Any, + tokenizer: Any, +) -> list[dict[str, Any]]: + """Generate completions for a batch using left-padded inputs. + + :param batch: list of examples, each with a ``messages`` list + (system + user only — no assistant turn). + :param model: unwrapped model (already on the correct device). + :param tokenizer: tokenizer with ``padding_side`` set to ``"left"``. + :returns: list of completion dicts with ``messages`` and + ``generated_response`` keys. + """ + prompts = [ + tokenizer.apply_chat_template( + ex["messages"], tokenize=False, add_generation_prompt=True + ) + for ex in batch + ] + inputs = tokenizer( + prompts, return_tensors="pt", padding=True, truncation=False + ).to(model.device) + padded_len = inputs["input_ids"].shape[1] + + with torch.no_grad(): + outputs = model.generate(**inputs, **GENERATION_CONFIG) + + results = [] + for j, ex in enumerate(batch): + # outputs[j] = [left-pad tokens] + [prompt tokens] + [generated tokens] + # All inputs share the same padded_len so slicing is uniform. + new_tokens = outputs[j, padded_len:] + generated = tokenizer.decode(new_tokens, skip_special_tokens=True) + results.append({"messages": ex["messages"], "generated_response": generated}) + return results + + +class EvalCompletionsPlugin(BasePlugin): + """Axolotl plugin that registers EvalCompletionsCallback before training. + + Register via the axolotl YAML config:: + + plugins: + - sweep.callbacks.EvalCompletionsPlugin + """ + + def add_callbacks_post_trainer( + self, cfg: Any, trainer: Any + ) -> list[TrainerCallback]: + """Return the completions callback to be added to the Trainer. + + Called after the Trainer is built, so we can pass the trainer + instance into the callback and resolve ``processing_class`` later. + + :param cfg: axolotl config dict. + :param trainer: the built HF Trainer instance. + :returns: list containing the EvalCompletionsCallback instance. + """ + return [EvalCompletionsCallback(trainer=trainer)] diff --git a/sweep/master_config.yaml b/sweep/master_config.yaml new file mode 100644 index 0000000..afacee6 --- /dev/null +++ b/sweep/master_config.yaml @@ -0,0 +1,125 @@ +# ============================================================================= +# Master sweep configuration +# +# Two top-level sections: +# sweep: defines the hyperparameter grid (read by sweep.py) +# axolotl: shared axolotl settings merged into every per-run config +# +# sweep.py expands the grid into per-run axolotl YAML files, injecting +# per-run values (learning_rate, lora_r, lora_alpha, lora_target_modules, +# micro_batch_size, gradient_accumulation_steps, output_dir, wandb_name) +# on top of the axolotl section below. +# +# EvalCompletionsCallback runs inside the training process at each epoch end, +# generating completions on the validation split using the live in-memory model. +# This eliminates any train/inference distribution mismatch. Completions are +# written to {output_dir}/completions_epoch_{N}.json and uploaded to W&B. +# Grid: 4 ranks × 3 LRs × 2 batch sizes × 2 target layers = 48 training +# runs → 144 data points. +# +# Validation loss is computed after each epoch via axolotl and stored in +# trainer_state.json. sweep.py reads it from there — no post-training loss pass. +# ============================================================================= + +# --- Sweep grid ------------------------------------------------------------- +sweep: + learning_rate: [1.0e-4, 5.0e-4, 1.0e-3] + lora_r: [4, 8, 16, 32] + effective_batch_size: [4, 16] # sweep.py fixes micro_batch_size=4 and + # derives gradient_accumulation_steps + target_layers: + - name: attention + modules: [q_proj, k_proj, v_proj, o_proj] + - name: attention_mlp + modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj] + +# lora_alpha is set to 2 * lora_r per run by sweep.py (not swept separately). + +# --- Shared axolotl settings ------------------------------------------------ +axolotl: + + # Model + base_model: meta-llama/Llama-3.1-8B-Instruct + + # Dataset — expects 'train' and 'test' splits only (no validation). + datasets: + - path: AccelerateScience/bush-dataset + type: chat_template + field_messages: messages + roles_to_train: + - assistant + train_on_eos: last + # Llama 3.1's chat template ends turns with <|eot_id|> (128009), not + # <|end_of_text|> (128001). Without this, axolotl defaults eot_tokens + # to eos_token (<|end_of_text|>) which never appears in the sequence, so + # train_on_eos: last silently does nothing and the model is never trained + # to predict its stop token. + eot_tokens: ["<|eot_id|>"] + + special_tokens: + pad_token: "<|eot_id|>" + + hf_use_auth_token: true + + # Llama 3.1 Instruct ships with its own Jinja chat template in the + # tokenizer config; axolotl picks it up automatically. + chat_template: tokenizer_default + val_set_size: 0.0 + + # Validation dataset — uses the dataset's native validation split. + # Axolotl uses this for per-epoch eval (eval_strategy: epoch), which + # writes eval_loss to trainer_state.json after each epoch. + test_datasets: + - path: AccelerateScience/bush-dataset + split: validation + type: chat_template + field_messages: messages + + # Sequence + # Bush answers are long (~700-900 tokens including system + question). + # 2048 gives comfortable headroom without wasting VRAM. + sequence_len: 2048 + sample_packing: true + + # Adapter — lora_r, lora_alpha, lora_target_modules injected per run. + adapter: lora + lora_dropout: 0.0 + + # Precision & performance (H100) + bf16: true + tf32: true + flash_attention: true + gradient_checkpointing: true + + # Disable axolotl's auto-enabled LoRA kernel optimizations. The MLP SwiGLU + # Triton kernel fails to compile on this host (gcc cannot link libcuda), + # which crashes all attention_mlp runs. Disabling here keeps training stable + # at the cost of a small throughput regression. + lora_mlp_kernel: false + lora_qkv_kernel: false + lora_o_kernel: false + + # Optimiser + optimizer: adamw_torch_fused + lr_scheduler: cosine + warmup_ratio: 0.03 + weight_decay: 0.0 + + # Evaluate on the validation split after each epoch. + eval_strategy: "epoch" + + # Keep all epoch checkpoints (up to 3) so sweep.py can run inference at + # each epoch without re-training. 48 training runs × 3 epochs = 144 rows. + save_strategy: epoch + save_total_limit: 3 + + # Plugin — generates validation completions at each epoch end using the + # live in-memory model. Requires sweep/ to be on PYTHONPATH (handled by + # sweep.py before launching axolotl). + plugins: + - sweep.callbacks.EvalCompletionsPlugin + + # W&B — wandb_name injected per run by sweep.py. + use_wandb: true + wandb_project: VOICE + wandb_entity: accelerate-science diff --git a/sweep/sweep.py b/sweep/sweep.py new file mode 100644 index 0000000..99e9d8a --- /dev/null +++ b/sweep/sweep.py @@ -0,0 +1,375 @@ +"""Sweep orchestrator for the Llama 3.1 8B stylometric pilot. + +Reads master_config.yaml, expands the hyperparameter grid, and for each run: + 1. Writes a per-run axolotl config YAML + 2. Trains with axolotl (always 3 epochs, saving a checkpoint and computing + validation loss after each epoch via axolotl) + 3. Reads completions written by EvalCompletionsCallback (one JSON file per + epoch, produced during training) and eval_loss from trainer_state.json + 4. Appends 3 rows to outputs/results.parquet + 5. Updates state.json for resumability + +Usage (inside tmux on the H100):: + + python sweep.py + python sweep.py --dry-run # print grid without training + python sweep.py --gpus 2 # override training GPU (default: 2,3) +""" + +import argparse +import itertools +import json +import logging +import os +import subprocess +import sys +from pathlib import Path +from typing import Any, cast + +import pandas as pd +import yaml + +# --------------------------------------------------------------------------- +# Paths +# --------------------------------------------------------------------------- +SWEEP_DIR = Path(__file__).parent +CONFIGS_DIR = SWEEP_DIR / "configs" +CHECKPOINTS_DIR = SWEEP_DIR / "checkpoints" +OUTPUTS_DIR = SWEEP_DIR / "outputs" +STATE_FILE = SWEEP_DIR / "state.json" +MASTER_CONFIG = SWEEP_DIR / "master_config.yaml" +LOG_FILE = SWEEP_DIR / "sweep.log" + +# Fixed micro batch size; gradient_accumulation_steps derived from +# the effective_batch_size sweep axis. +MICRO_BATCH_SIZE: int = 4 + +log = logging.getLogger(__name__) + + +# --------------------------------------------------------------------------- +# Logging +# --------------------------------------------------------------------------- +def setup_logging() -> None: + """Configure logging to both stdout and sweep.log. + + :returns: None + """ + fmt = "%(asctime)s [%(levelname)s] %(message)s" + logging.basicConfig( + level=logging.INFO, + format=fmt, + handlers=[ + logging.StreamHandler(sys.stdout), + logging.FileHandler(LOG_FILE), + ], + ) + + +# --------------------------------------------------------------------------- +# Grid expansion +# --------------------------------------------------------------------------- +def expand_grid(sweep_cfg: dict[str, Any]) -> list[dict[str, Any]]: + """Expand the sweep section into a flat list of per-run configs. + + :param sweep_cfg: the ``sweep`` section of master_config.yaml. + :returns: list of run config dicts, one per hyperparameter combination. + """ + runs: list[dict[str, Any]] = [] + combos = itertools.product( + sweep_cfg["learning_rate"], + sweep_cfg["lora_r"], + sweep_cfg["effective_batch_size"], + sweep_cfg["target_layers"], + ) + for idx, (lr, rank, bs, tl) in enumerate(combos): + lr_str = f"{lr:.0e}".replace("e-0", "e-").replace("e+0", "e") + run_name = f"run_{idx:03d}_lr{lr_str}_r{rank}_bs{bs}_{tl['name']}" + runs.append( + { + "run_idx": idx, + "run_name": run_name, + "learning_rate": lr, + "lora_r": rank, + "lora_alpha": rank, + "effective_batch_size": bs, + "target_layers_name": tl["name"], + "lora_target_modules": tl["modules"], + "micro_batch_size": MICRO_BATCH_SIZE, + "gradient_accumulation_steps": bs // MICRO_BATCH_SIZE, + } + ) + return runs + + +# --------------------------------------------------------------------------- +# State management +# --------------------------------------------------------------------------- +def load_state() -> dict[str, Any]: + """Load sweep state from state.json, or return a fresh state dict. + + :returns: state dict with ``completed`` and ``failed`` keys. + """ + if STATE_FILE.exists(): + return cast(dict[str, Any], json.loads(STATE_FILE.read_text())) + return {"completed": [], "failed": {}} + + +def save_state(state: dict[str, Any]) -> None: + """Persist sweep state to state.json. + + :param state: state dict to serialise. + :returns: None + """ + STATE_FILE.write_text(json.dumps(state, indent=2)) + + +# --------------------------------------------------------------------------- +# Per-run axolotl config +# --------------------------------------------------------------------------- +def write_run_config( + run: dict[str, Any], + axolotl_base: dict[str, Any], +) -> Path: + """Merge shared axolotl settings with per-run values and write a YAML. + + :param run: per-run hyperparameter dict from expand_grid. + :param axolotl_base: shared axolotl settings from master_config.yaml. + :returns: path to the written per-run config YAML. + """ + config = dict(axolotl_base) + config.update( + { + "learning_rate": run["learning_rate"], + "lora_r": run["lora_r"], + "lora_alpha": run["lora_alpha"], + "lora_target_modules": run["lora_target_modules"], + "num_epochs": 3, + "micro_batch_size": run["micro_batch_size"], + "gradient_accumulation_steps": ( + run["gradient_accumulation_steps"] + ), + "output_dir": str(CHECKPOINTS_DIR / run["run_name"]), + "wandb_name": run["run_name"], + } + ) + config_path = CONFIGS_DIR / f"{run['run_name']}.yaml" + with open(config_path, "w") as f: + yaml.dump(config, f, default_flow_style=False, sort_keys=False) + return config_path + + +# --------------------------------------------------------------------------- +# Eval loss (from axolotl's trainer_state.json) +# --------------------------------------------------------------------------- +def _get_all_eval_losses(output_dir: Path) -> dict[int, float]: + """Read per-epoch validation losses written by EvalCompletionsCallback. + + Prefers eval_losses.json (written directly by the callback via on_evaluate, + which receives the metrics dict immediately after each evaluation loop). + Falls back to trainer_state.json for runs that predate the callback change. + + :param output_dir: root output directory for the run. + :returns: dict mapping epoch int to eval_loss float. + """ + losses_file = output_dir / "eval_losses.json" + if losses_file.exists(): + raw = json.loads(losses_file.read_text()) + return {int(k): float(v) for k, v in raw.items()} + + # Fallback: parse trainer_state.json written by HF Trainer after training. + state_file = output_dir / "trainer_state.json" + if not state_file.exists(): + return {} + trainer_state = json.loads(state_file.read_text()) + losses: dict[int, float] = {} + for entry in trainer_state.get("log_history", []): + if "eval_loss" in entry: + losses[round(entry["epoch"])] = float(entry["eval_loss"]) + return losses + + +# --------------------------------------------------------------------------- +# Training +# --------------------------------------------------------------------------- +def run_training(config_path: Path) -> None: + """Launch axolotl training for the given config. + + Uses ``accelerate launch`` for multi-GPU runs so that DDP is set up + correctly; falls back to a plain ``python -m axolotl.cli.train`` for + single-GPU runs. The number of processes is derived from the + ``CUDA_VISIBLE_DEVICES`` environment variable set by ``main()``. + + :param config_path: path to the per-run axolotl YAML config. + :returns: None + """ + cuda_devices = os.environ.get("CUDA_VISIBLE_DEVICES", "0") + n_gpus = len([d for d in cuda_devices.split(",") if d.strip()]) + + if n_gpus > 1: + cmd = [ + sys.executable, "-m", "accelerate.commands.launch", + f"--num_processes={n_gpus}", + "--mixed_precision=no", + "--dynamo_backend=no", + "-m", "axolotl.cli.train", + str(config_path), + ] + else: + cmd = [sys.executable, "-m", "axolotl.cli.train", str(config_path)] + + # Ensure the repo root is on PYTHONPATH so axolotl can import + # sweep.callbacks.EvalCompletionsCallback when loading the callback list + # from the per-run YAML config. + env = os.environ.copy() + repo_root = str(SWEEP_DIR.parent) + env["PYTHONPATH"] = ( + repo_root + ":" + env["PYTHONPATH"] if "PYTHONPATH" in env else repo_root + ) + + log.info(f"Training on {n_gpus} GPU(s): {config_path.name}") + subprocess.run(cmd, check=True, env=env) + + +# --------------------------------------------------------------------------- +# Output +# --------------------------------------------------------------------------- +def append_results(rows: list[dict[str, Any]]) -> None: + """Atomically append rows to outputs/results.parquet. + + :param rows: list of result row dicts to append. + :returns: None + """ + out_path = OUTPUTS_DIR / "results.parquet" + new_df = pd.DataFrame(rows) + if out_path.exists(): + existing = pd.read_parquet(out_path) + new_df = pd.concat([existing, new_df], ignore_index=True) + tmp = out_path.with_suffix(".tmp.parquet") + new_df.to_parquet(tmp, index=False) + tmp.rename(out_path) + + +# --------------------------------------------------------------------------- +# Per-run execution +# --------------------------------------------------------------------------- +def _execute_run( + run: dict[str, Any], + axolotl_base: dict[str, Any], + state: dict[str, Any], +) -> None: + name = run["run_name"] + output_dir = CHECKPOINTS_DIR / name + try: + config_path = write_run_config(run, axolotl_base) + run_training(config_path) + + eval_losses = _get_all_eval_losses(output_dir) + rows: list[dict[str, Any]] = [] + for epoch in range(1, 4): + completions_path = output_dir / f"completions_epoch_{epoch}.json" + if not completions_path.exists(): + log.warning(f" Missing completions for epoch {epoch}, skipping") + continue + completions = json.loads(completions_path.read_text()) + rows.append( + { + "run_name": name, + "epoch": epoch, + "learning_rate": run["learning_rate"], + "lora_r": run["lora_r"], + "lora_alpha": run["lora_alpha"], + "effective_batch_size": run["effective_batch_size"], + "target_layers": run["target_layers_name"], + "eval_loss": eval_losses.get(epoch, float("nan")), + "completions": json.dumps(completions), + } + ) + log.info( + f" epoch {epoch} eval_loss={eval_losses.get(epoch, float('nan')):.4f}" + f" n_completions={len(completions)}" + ) + + if not rows: + raise RuntimeError("No completions written by EvalCompletionsCallback") + + append_results(rows) + state["completed"].append(name) + state["failed"].pop(name, None) + save_state(state) + log.info(f"[DONE] {name}") + except Exception as exc: # noqa: BLE001 + log.error(f"[FAIL] {name}: {exc}") + state["failed"][name] = str(exc) + save_state(state) + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- +def main() -> None: + """Parse arguments, expand the grid, and run the sweep. + + :returns: None + """ + parser = argparse.ArgumentParser() + parser.add_argument( + "--dry-run", + action="store_true", + help="Print the full grid without training", + ) + parser.add_argument( + "--gpus", + default="2,3", + help="Comma-separated CUDA device indices for training (default: 2,3)", + ) + args = parser.parse_args() + + os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus + + setup_logging() + + CONFIGS_DIR.mkdir(exist_ok=True) + CHECKPOINTS_DIR.mkdir(exist_ok=True) + OUTPUTS_DIR.mkdir(exist_ok=True) + + master = yaml.safe_load(MASTER_CONFIG.read_text()) + runs = expand_grid(master["sweep"]) + axolotl_base = master["axolotl"] + + n_runs = len(runs) + log.info(f"Grid: {n_runs} training runs -> {n_runs * 3} data points") + + if args.dry_run: + for r in runs: + log.info( + f" {r['run_name']} lr={r['learning_rate']}" + f" r={r['lora_r']} bs={r['effective_batch_size']}" + f" layers={r['target_layers_name']}" + ) + return + + state = load_state() + log.info( + f"Resuming: {len(state['completed'])} completed, " + f"{len(state['failed'])} failed" + ) + + for run in runs: + name = run["run_name"] + if name in state["completed"]: + log.info(f"[SKIP] {name}") + continue + log.info(f"\n{'=' * 60}") + log.info(f"[START] {name}") + log.info(f"{'=' * 60}") + _execute_run(run, axolotl_base, state) + + n_done = len(state["completed"]) + log.info(f"\nSweep complete: {n_done}/{n_runs} runs succeeded.") + if state["failed"]: + log.warning(f"Failed runs: {list(state['failed'])}") + + +if __name__ == "__main__": + main() diff --git a/tests/voice/comparison/test_comparison.py b/tests/voice/comparison/test_comparison.py index 82666d3..84f1973 100644 --- a/tests/voice/comparison/test_comparison.py +++ b/tests/voice/comparison/test_comparison.py @@ -20,7 +20,10 @@ from datasets import Dataset from voice._defaults import MetricGroup -from voice.comparison._utils import calibrated_percentile +from voice.comparison._utils import ( + calibrated_percentile, + percentile_interval, +) from voice.comparison.comparison import ( ComparisonEntry, ComparisonResults, @@ -85,7 +88,7 @@ def metric_registry(): return { "len": _Metric( fn=lambda s: float(len(s)), - group=MetricGroup.TEXT_LENGTH, + group=MetricGroup.WORD_LENGTH_DISTRIBUTION, ), "vowels": _Metric( fn=lambda s: float(sum(c in "aeiou" for c in s.lower())), @@ -154,6 +157,42 @@ def test_calibrated_percentile_flattens_non_1d_inputs(): assert calibrated_percentile(2.0, dist) == 0.5 +# ----------------------------------------------------------------------------- +# percentile_interval +# ----------------------------------------------------------------------------- + + +def test_percentile_interval_rejects_empty_values(): + with pytest.raises(ValueError, match=r"values must be non-empty"): + percentile_interval(np.array([]), 0.9) + + +@pytest.mark.parametrize("confidence", [0.0, 1.0, -0.1, 1.5]) +def test_percentile_interval_rejects_invalid_confidence(confidence): + with pytest.raises(ValueError, match=r"strictly between 0 and 1"): + percentile_interval(np.array([0.1, 0.2]), confidence) + + +def test_percentile_interval_hand_computed_example(): + # 25th/75th percentiles of 5 evenly spaced points (linear interpolation) + values = np.array([0.1, 0.2, 0.3, 0.4, 0.5]) + lo, hi = percentile_interval(values, 0.5) + assert lo == pytest.approx(0.2) + assert hi == pytest.approx(0.4) + + +def test_percentile_interval_collapses_for_constant_values(): + lo, hi = percentile_interval(np.full(10, 0.7), 0.9) + assert lo == pytest.approx(0.7) + assert hi == pytest.approx(0.7) + + +def test_percentile_interval_is_bounded_by_value_range(): + values = np.array([0.0, 0.0, 0.0, 0.9]) + lo, hi = percentile_interval(values, 0.99) + assert 0.0 <= lo <= hi <= 0.9 + + # ----------------------------------------------------------------------------- # self_wasserstein_distribution # ----------------------------------------------------------------------------- @@ -253,7 +292,9 @@ def raising_metric(s: str) -> float: comparison, "get_metrics", lambda: { - "len": _Metric(fn=raising_metric, group=MetricGroup.TEXT_LENGTH) + "len": _Metric( + fn=raising_metric, group=MetricGroup.WORD_LENGTH_DISTRIBUTION + ) }, ) @@ -281,7 +322,9 @@ def test_self_wasserstein_distribution_is_zero_for_constant_metric( comparison, "get_metrics", lambda: { - "const": _Metric(fn=lambda _s: 1.0, group=MetricGroup.TEXT_LENGTH) + "const": _Metric( + fn=lambda _s: 1.0, group=MetricGroup.WORD_LENGTH_DISTRIBUTION + ) }, ) @@ -305,7 +348,7 @@ def test_self_wasserstein_distribution_is_zero_for_constant_metric( def test_comparison_entry_is_frozen_and_slots(): e = ComparisonEntry( metric="m", - group=MetricGroup.TEXT_LENGTH, + group=MetricGroup.WORD_LENGTH_DISTRIBUTION, wasserstein=1.0, percentile=0.9, tail=0.1, @@ -317,7 +360,7 @@ def test_comparison_entry_is_frozen_and_slots(): def test_comparison_group_entry_is_frozen_and_slots(): e = GroupEntry( - group=MetricGroup.TEXT_LENGTH, + group=MetricGroup.WORD_LENGTH_DISTRIBUTION, avg_percentile=0.9, avg_tail=0.1, ) @@ -341,7 +384,7 @@ def test_comparison_results_properties_and_add_logic(patch_metrics): r.add(metric="len", wasserstein=np.float64(1.25), percentile=0.9) e = r.get("len") assert e.metric == "len" - assert e.group == MetricGroup.TEXT_LENGTH + assert e.group == MetricGroup.WORD_LENGTH_DISTRIBUTION assert isinstance(e.wasserstein, float) assert isinstance(e.percentile, float) assert e.tail == pytest.approx(0.1) @@ -351,7 +394,7 @@ def test_comparison_results_properties_and_add_logic(patch_metrics): assert e2.group == MetricGroup.VOCABULARY_RICHNESS # Group-level checks - ge_len = r.get_group(MetricGroup.TEXT_LENGTH) + ge_len = r.get_group(MetricGroup.WORD_LENGTH_DISTRIBUTION) assert ge_len.avg_percentile == pytest.approx(0.9) assert ge_len.avg_tail == pytest.approx(0.1) @@ -375,10 +418,12 @@ def test_comparison_results_score_weights_groups_equally(patch_metrics): # Add a second TEXT_LENGTH metric to verify equal group weighting. registry = { "len": _Metric( - fn=lambda s: float(len(s)), group=MetricGroup.TEXT_LENGTH + fn=lambda s: float(len(s)), + group=MetricGroup.WORD_LENGTH_DISTRIBUTION, ), "len2": _Metric( - fn=lambda s: float(len(s)), group=MetricGroup.TEXT_LENGTH + fn=lambda s: float(len(s)), + group=MetricGroup.WORD_LENGTH_DISTRIBUTION, ), "vowels": _Metric( fn=lambda s: float(sum(c in "aeiou" for c in s.lower())), @@ -418,10 +463,96 @@ def test_comparison_results_group_tails(patch_metrics): r.add(metric="vowels", wasserstein=0.5, percentile=0.3) tails = r.group_tails - assert tails[MetricGroup.TEXT_LENGTH] == pytest.approx(0.2) + assert tails[MetricGroup.WORD_LENGTH_DISTRIBUTION] == pytest.approx(0.2) assert tails[MetricGroup.VOCABULARY_RICHNESS] == pytest.approx(0.7) +def test_comparison_results_uncertainty_defaults_to_none(patch_metrics): + r = ComparisonResults(metrics=("len", "vowels")) + assert r.has_uncertainty is False + assert r.metric_tail_cis() is None + assert r.group_tail_cis() is None + assert r.score_ci() is None + + +def test_comparison_results_confidence_intervals(patch_metrics): + r = ComparisonResults(metrics=("len", "vowels")) + r.add(metric="len", wasserstein=1.0, percentile=0.8) + r.add(metric="vowels", wasserstein=0.5, percentile=0.4) + r._replicates = np.array( + [ + [0.1, 0.5], + [0.2, 0.6], + [0.3, 0.7], + [0.4, 0.8], + [0.5, 0.9], + ] + ) + + assert r.has_uncertainty is True + + cis = r.metric_tail_cis(confidence=0.5) + assert cis["len"] == pytest.approx((0.2, 0.4)) + assert cis["vowels"] == pytest.approx((0.6, 0.8)) + + # One metric per group, so group CI equals metric CI + g = r.group_tail_cis(confidence=0.5) + assert g[MetricGroup.WORD_LENGTH_DISTRIBUTION] == pytest.approx((0.2, 0.4)) + assert g[MetricGroup.VOCABULARY_RICHNESS] == pytest.approx((0.6, 0.8)) + + # Replicate scores are row means over groups: [0.3, 0.4, 0.5, 0.6, 0.7] + assert r.score_ci(confidence=0.5) == pytest.approx((0.4, 0.6)) + + +def test_comparison_results_cis_average_replicates_within_groups( + patch_metrics, monkeypatch +): + registry = { + "len": _Metric( + fn=lambda s: float(len(s)), + group=MetricGroup.WORD_LENGTH_DISTRIBUTION, + ), + "len2": _Metric( + fn=lambda s: float(len(s)), + group=MetricGroup.WORD_LENGTH_DISTRIBUTION, + ), + "vowels": _Metric( + fn=lambda s: float(sum(c in "aeiou" for c in s.lower())), + group=MetricGroup.VOCABULARY_RICHNESS, + ), + } + import voice.comparison.comparison as comparison + + monkeypatch.setattr(comparison, "get_metrics", lambda: dict(registry)) + + r = ComparisonResults(metrics=("len", "len2", "vowels")) + r._replicates = np.array( + [ + [0.0, 0.2, 0.9], + [0.2, 0.4, 0.9], + [0.4, 0.6, 0.9], + ] + ) + + # Group replicates: word_length = [0.1, 0.3, 0.5], vocab = [0.9] * 3 + g = r.group_tail_cis(confidence=0.5) + assert g[MetricGroup.WORD_LENGTH_DISTRIBUTION] == pytest.approx((0.2, 0.4)) + assert g[MetricGroup.VOCABULARY_RICHNESS] == pytest.approx((0.9, 0.9)) + + # Score replicates: mean over groups = [0.5, 0.6, 0.7] + assert r.score_ci(confidence=0.5) == pytest.approx((0.55, 0.65)) + + +def test_comparison_results_repr_includes_ci_when_available(patch_metrics): + r = ComparisonResults(metrics=("len",)) + r.add(metric="len", wasserstein=1.0, percentile=0.8) + assert "[" not in repr(r) + + # 5th/95th percentiles of [0.1, 0.2, 0.3] (default confidence 0.90) + r._replicates = np.array([[0.1], [0.2], [0.3]]) + assert "[0.1100, 0.2900]" in repr(r) + + # ----------------------------------------------------------------------------- # make_comparison # ----------------------------------------------------------------------------- @@ -458,7 +589,7 @@ def fake_self_dist(*_args, **_kwargs): comparison.self_wasserstein_distribution = fake_self_dist # type: ignore[assignment] - out = make_comparison(completions, true_ds) + out = make_comparison(completions, true_ds, uncertainty=False) assert out.metrics == ("len", "vowels") for m in out.metrics: @@ -486,7 +617,9 @@ def test_make_comparison_respects_subset_metrics(patch_metrics, monkeypatch): lambda *_args, **_kwargs: np.array([0.0, 1.0], dtype=float), ) - out = make_comparison(completions, true_ds, metrics=["len"]) + out = make_comparison( + completions, true_ds, metrics=["len"], uncertainty=False + ) assert out.metrics == ("len",) assert "len" in out.as_dict() with pytest.raises(KeyError): @@ -523,7 +656,9 @@ def recording_getitem(self, key): monkeypatch.setattr(VoiceDataset, "__getitem__", recording_getitem) - _ = make_comparison(completions, true_ds, metrics=["len"]) + _ = make_comparison( + completions, true_ds, metrics=["len"], uncertainty=False + ) assert Split.TEST in calls @@ -557,6 +692,167 @@ def spy_self_dist(*_args, sample_size: int = 0, **_kwargs): comparison, "self_wasserstein_distribution", spy_self_dist ) - make_comparison(completions, true_ds, metrics=["len"]) + make_comparison(completions, true_ds, metrics=["len"], uncertainty=False) assert all(s == len(completions) for s in captured) + + +# ----------------------------------------------------------------------------- +# make_comparison: uncertainty quantification +# ----------------------------------------------------------------------------- + + +def _paired_setup(n: int = 5) -> tuple[list[Example], VoiceDataset]: + """Completions paired one-to-one with the TEST split.""" + p = _pinned(splits=(Split.TRAIN, Split.TEST)) + true_ds = VoiceDataset( + datasets={ + Split.TRAIN: _canonical_hf_ds([f"a{i}" for i in range(40)]), + Split.TEST: _canonical_hf_ds( + ["ref text " * (i + 1) for i in range(n)] + ), + }, + spec=p, + ) + completions = [ + _ex(answer="hello there " * (i + 2), split=Split.TEST) + for i in range(n) + ] + return completions, true_ds + + +def test_make_comparison_uncertainty_populates_cis_and_caches_self_dist( + patch_metrics, monkeypatch +): + completions, true_ds = _paired_setup(n=5) + + import voice.comparison.comparison as comparison + + calls: list[str] = [] + + def spy_self_dist(*_args, metric: str = "", **_kwargs): + calls.append(metric) + return np.linspace(0.0, 50.0, 100) + + monkeypatch.setattr( + comparison, "self_wasserstein_distribution", spy_self_dist + ) + + out = make_comparison(completions, true_ds) + + assert out.has_uncertainty is True + + cis = out.metric_tail_cis() + assert set(cis) == {"len", "vowels"} + for lo, hi in cis.values(): + assert 0.0 <= lo <= hi <= 1.0 + + assert set(out.group_tail_cis()) == { + MetricGroup.WORD_LENGTH_DISTRIBUTION, + MetricGroup.VOCABULARY_RICHNESS, + } + + lo, hi = out.score_ci() + assert 0.0 <= lo <= hi <= 1.0 + + # A wider confidence level produces a containing interval + lo99, hi99 = out.score_ci(confidence=0.99) + assert lo99 <= lo + assert hi <= hi99 + + # Self-Wasserstein bootstrap is cached: computed exactly once per + # metric, never per jackknife replicate. + assert sorted(calls) == ["len", "vowels"] + + +def test_make_comparison_uncertainty_false_skips_jackknife( + patch_metrics, monkeypatch +): + completions, true_ds = _paired_setup(n=5) + + import voice.comparison.comparison as comparison + + monkeypatch.setattr( + comparison, + "self_wasserstein_distribution", + lambda *_args, **_kwargs: np.array([0.0, 1.0], dtype=float), + ) + + out = make_comparison(completions, true_ds, uncertainty=False) + + assert out.has_uncertainty is False + assert out.metric_tail_cis() is None + assert out.group_tail_cis() is None + assert out.score_ci() is None + + +def test_make_comparison_uncertainty_requires_two_completions( + patch_metrics, monkeypatch +): + completions, true_ds = _paired_setup(n=1) + + with pytest.raises(ValueError, match=r"at least 2 completions"): + make_comparison(completions, true_ds) + + +def test_make_comparison_uncertainty_requires_paired_split( + patch_metrics, monkeypatch +): + completions, true_ds = _paired_setup(n=5) + + with pytest.raises(ValueError, match=r"paired one-to-one"): + make_comparison(completions[:3], true_ds) + + +def test_make_comparison_score_ci_matches_metric_ci_for_single_metric( + patch_metrics, monkeypatch +): + completions, true_ds = _paired_setup(n=5) + + import voice.comparison.comparison as comparison + + monkeypatch.setattr( + comparison, + "self_wasserstein_distribution", + lambda *_args, **_kwargs: np.linspace(0.0, 50.0, 100), + ) + + out = make_comparison(completions, true_ds, metrics=["len"]) + + assert out.score_ci() == pytest.approx(out.metric_tail_cis()["len"]) + assert out.score_ci() == pytest.approx( + out.group_tail_cis()[MetricGroup.WORD_LENGTH_DISTRIBUTION] + ) + + +def test_make_comparison_ci_collapses_for_identical_paired_data( + patch_metrics, monkeypatch +): + n = 5 + answers = [f"some answer {i} " * (i + 1) for i in range(n)] + p = _pinned(splits=(Split.TRAIN, Split.TEST)) + true_ds = VoiceDataset( + datasets={ + Split.TRAIN: _canonical_hf_ds([f"a{i}" for i in range(40)]), + Split.TEST: _canonical_hf_ds(answers), + }, + spec=p, + ) + completions = [_ex(answer=a, split=Split.TEST) for a in answers] + + import voice.comparison.comparison as comparison + + monkeypatch.setattr( + comparison, + "self_wasserstein_distribution", + lambda *_args, **_kwargs: np.linspace(0.0, 50.0, 100), + ) + + # x == y for every metric, so every leave-one-out distance is 0 and + # all replicates are identical: each interval collapses to a point. + out = make_comparison(completions, true_ds) + + lo, hi = out.score_ci() + assert lo == pytest.approx(hi) + for m_lo, m_hi in out.metric_tail_cis().values(): + assert m_lo == pytest.approx(m_hi) diff --git a/tests/voice/datasets/test_dataset.py b/tests/voice/datasets/test_dataset.py index db0e983..9eb430e 100644 --- a/tests/voice/datasets/test_dataset.py +++ b/tests/voice/datasets/test_dataset.py @@ -3,6 +3,7 @@ Scope: - DatasetSpec validation/normalisation +- LocalDatasetSpec validation/normalisation - Revision pinning invariants with _PinnedDatasetSpec - VoiceDataset split access """ @@ -10,11 +11,12 @@ from __future__ import annotations import dataclasses +from pathlib import Path import pytest from datasets import Dataset -from voice import DatasetSpec +from voice import DatasetSpec, LocalDatasetSpec from voice.datasets import VoiceDataset from voice.datasets._schema import Split from voice.datasets.dataset import ( @@ -84,6 +86,37 @@ def test_dataset_spec_normalises_splits_from_iterable(): assert spec.splits == (Split.TRAIN, Split.TEST) +# ----------------------------------------------------------------------------- +# LocalDatasetSpec validation & normalisation +# ----------------------------------------------------------------------------- + + +def test_local_dataset_spec_rejects_empty_splits(): + with pytest.raises(ValueError, match=r"`splits` must be non-empty"): + LocalDatasetSpec(path=Path("/data/bush"), splits=()) + + +def test_local_dataset_spec_normalises_splits_from_str(): + spec = LocalDatasetSpec(path=Path("/data/bush"), splits="train") # type: ignore[arg-type] + assert spec.splits == (Split.TRAIN,) + + +def test_local_dataset_spec_normalises_splits_from_iterable(): + spec = LocalDatasetSpec(path=Path("/data/bush"), splits=("train", "test")) + assert spec.splits == (Split.TRAIN, Split.TEST) + + +def test_local_dataset_spec_is_frozen(): + spec = LocalDatasetSpec(path=Path("/data/bush")) + with pytest.raises(dataclasses.FrozenInstanceError): + setattr(spec, "path", Path("/other")) # noqa: B010 + + +def test_local_dataset_spec_default_split_is_train(): + spec = LocalDatasetSpec(path=Path("/data/bush")) + assert spec.splits == (Split.TRAIN,) + + # ----------------------------------------------------------------------------- # Pinned spec invariants & pinning behaviour # ----------------------------------------------------------------------------- @@ -94,6 +127,7 @@ def test_dataset_spec_normalises_splits_from_iterable(): [ "a1b2c3d", "0123456789abcdef0123456789abcdef01234567", # pragma: allowlist secret + "local", ], ) def test_pinned_dataset_spec_accepts_sha_like_revisions(sha: str): diff --git a/tests/voice/datasets/test_get_dataset.py b/tests/voice/datasets/test_get_dataset.py index 2858738..52bf98a 100644 --- a/tests/voice/datasets/test_get_dataset.py +++ b/tests/voice/datasets/test_get_dataset.py @@ -6,11 +6,13 @@ - Schema canonicalisation for supported dataset formats - Canonical schema validation - Public materialisation pipeline (get_dataset) with dependency patching +- Local dataset loading via _get_local_dataset """ from __future__ import annotations import importlib +from pathlib import Path from types import SimpleNamespace import pytest @@ -18,7 +20,7 @@ from datasets import Dataset from huggingface_hub.utils import HfHubHTTPError -from voice import DatasetSpec +from voice import DatasetSpec, LocalDatasetSpec from voice.datasets._schema import Split from voice.datasets.dataset import _PinnedDatasetSpec @@ -190,6 +192,66 @@ def fake_load_dataset(repo_id: str, *, split: str, revision: str): assert ex0.spec == vd.spec +# ----------------------------------------------------------------------------- +# Local dataset loading +# ----------------------------------------------------------------------------- + + +def test_get_local_dataset_loads_splits_and_returns_voice_dataset( + monkeypatch: pytest.MonkeyPatch, +): + calls: list[tuple[str, str]] = [] + + def fake_load_dataset(*args: object, **kwargs: object) -> Dataset: + calls.append((str(args[0]), str(kwargs.get("split")))) + return _canonical_hf_ds(3) + + monkeypatch.setattr(gd, "load_dataset", fake_load_dataset) + + spec = LocalDatasetSpec(path=Path("/data/bush"), splits=("train", "test")) + vd = gd._get_local_dataset(spec) + + assert vd.spec.repo_id == "/data/bush" + assert vd.spec.revision == "local" + assert set(vd.splits) == {Split.TRAIN, Split.TEST} + assert calls == [("json", "train"), ("json", "test")] + + ex = vd.train[0] + assert ex.split == Split.TRAIN + assert ex.spec.revision == "local" + + +def test_get_local_dataset_wraps_load_failures_in_runtime_error( + monkeypatch: pytest.MonkeyPatch, +): + def fake_load_dataset(*args: object, **kwargs: object) -> Dataset: + raise RuntimeError("not found") + + monkeypatch.setattr(gd, "load_dataset", fake_load_dataset) + + spec = LocalDatasetSpec(path=Path("/data/bush"), splits=("train",)) + with pytest.raises(RuntimeError, match=r"Failed to load split 'train'"): + gd._get_local_dataset(spec) + + +def test_get_dataset_routes_to_local_for_local_spec( + monkeypatch: pytest.MonkeyPatch, +): + sentinel = object() + + def fake_get_local(spec: LocalDatasetSpec) -> object: + return sentinel + + monkeypatch.setattr(gd, "_get_local_dataset", fake_get_local) + + spec = LocalDatasetSpec(path=Path("/data/bush"), splits=("train",)) + result = gd.get_dataset(spec) + assert result is sentinel + + +# ----------------------------------------------------------------------------- + + def test_get_dataset_wraps_load_failures_in_runtime_error( monkeypatch: pytest.MonkeyPatch, ): diff --git a/tests/voice/datasets/test_split_and_specs.py b/tests/voice/datasets/test_split_and_specs.py new file mode 100644 index 0000000..4e7439c --- /dev/null +++ b/tests/voice/datasets/test_split_and_specs.py @@ -0,0 +1,39 @@ +""" +Tests for voice.datasets._schema and voice.datasets._specs. + +Scope: +- Split.parse passthrough for Split instances +- Split.parse from valid string +- Split.parse raises TypeError for non-string/non-Split input +- Split.parse raises ValueError for unrecognised string +- BUSH_LATEST constant has the expected repo_id +""" + +from __future__ import annotations + +import pytest + +from voice.datasets._schema import Split +from voice.datasets._specs import BUSH_LATEST + + +def test_split_parse_passthrough(): + assert Split.parse(Split.TRAIN) is Split.TRAIN + + +def test_split_parse_from_string(): + assert Split.parse("validation") is Split.VALIDATION + + +def test_split_parse_raises_type_error_for_non_string(): + with pytest.raises(TypeError, match="Split or str"): + Split.parse(42) # type: ignore[arg-type] + + +def test_split_parse_raises_value_error_for_invalid_string(): + with pytest.raises(ValueError, match="Invalid split"): + Split.parse("unknown") + + +def test_bush_latest_has_expected_repo_id(): + assert BUSH_LATEST.repo_id == "AccelerateScience/bush-dataset" diff --git a/tests/voice/finetune/test_callbacks.py b/tests/voice/finetune/test_callbacks.py new file mode 100644 index 0000000..b99ae24 --- /dev/null +++ b/tests/voice/finetune/test_callbacks.py @@ -0,0 +1,97 @@ +""" +Tests for voice.finetune.callbacks. + +Scope: +- _format_completion produces correct chat_template JSONL structure +- GENERATION_CONFIG contains required keys derived from INFERENCE_DEFAULTS +- Completions directory path derivation from args.output_dir +""" + +from __future__ import annotations + +from pathlib import Path + +from voice._defaults import INFERENCE_DEFAULTS +from voice.finetune.callbacks import GENERATION_CONFIG, _format_completion + +# ----------------------------------------------------------------------------- +# _format_completion +# ----------------------------------------------------------------------------- + + +def test_format_completion_appends_assistant_turn(): + messages = [ + {"role": "system", "content": "sys"}, + {"role": "user", "content": "q"}, + ] + result = _format_completion(messages, "my response") + assert result["messages"][-1] == { + "role": "assistant", + "content": "my response", + } + + +def test_format_completion_preserves_prior_messages(): + messages = [ + {"role": "system", "content": "sys"}, + {"role": "user", "content": "q"}, + ] + result = _format_completion(messages, "r") + assert result["messages"][:-1] == messages + + +def test_format_completion_has_messages_key(): + result = _format_completion([], "hi") + assert "messages" in result + assert isinstance(result["messages"], list) + + +def test_format_completion_no_extra_keys(): + result = _format_completion([], "hi") + assert set(result.keys()) == {"messages"} + + +def test_format_completion_empty_messages_still_has_assistant(): + result = _format_completion([], "response") + assert result["messages"] == [{"role": "assistant", "content": "response"}] + + +# ----------------------------------------------------------------------------- +# GENERATION_CONFIG +# ----------------------------------------------------------------------------- + + +def test_generation_config_has_required_keys(): + required = {"do_sample", "temperature", "top_p", "top_k", "max_new_tokens"} + assert required.issubset(GENERATION_CONFIG.keys()) + + +def test_generation_config_temperature_matches_defaults(): + assert GENERATION_CONFIG["temperature"] == INFERENCE_DEFAULTS.temperature + + +def test_generation_config_max_new_tokens_matches_defaults(): + assert ( + GENERATION_CONFIG["max_new_tokens"] + == INFERENCE_DEFAULTS.max_new_tokens + ) + + +# ----------------------------------------------------------------------------- +# Completions path derivation +# ----------------------------------------------------------------------------- + + +def test_completions_root_derived_from_output_dir(): + output_dir = Path("/runs/my-run/adapter") + epoch = 2 + root = output_dir.parent / "completions" / f"epoch_{epoch}" + assert root == Path("/runs/my-run/completions/epoch_2") + + +def test_completions_jsonl_path_per_split(): + output_dir = Path("/runs/my-run/adapter") + epoch = 1 + root = output_dir.parent / "completions" / f"epoch_{epoch}" + assert (root / "validation.jsonl").name == "validation.jsonl" + assert (root / "test.jsonl").name == "test.jsonl" diff --git a/tests/voice/finetune/test_cli.py b/tests/voice/finetune/test_cli.py new file mode 100644 index 0000000..bfa8ac3 --- /dev/null +++ b/tests/voice/finetune/test_cli.py @@ -0,0 +1,264 @@ +""" +Tests for voice.finetune.cli. + +Scope: +- ``voice finetune single`` invokes execute_run with correct arguments. +- ``voice finetune single`` exits non-zero on training failure. +- ``voice finetune sweep`` invokes execute_run once per grid point. +- ``voice finetune sweep --resume`` skips already-completed runs. +- ``voice finetune sweep`` exits non-zero if any run fails. +""" + +from __future__ import annotations + +from pathlib import Path +from unittest.mock import patch + +import yaml +from click.testing import CliRunner + +from voice.finetune.cli import main + +# --------------------------------------------------------------------------- +# Config helpers +# --------------------------------------------------------------------------- + + +def _single_cfg(tmp_path: Path) -> Path: + cfg = { + "axolotl": { + "base_model": "namespace/model", + "datasets": [{"path": "namespace/dataset"}], + "num_epochs": 2, + } + } + p = tmp_path / "single.yaml" + p.write_text(yaml.dump(cfg)) + return p + + +def _sweep_cfg(tmp_path: Path) -> Path: + cfg = { + "sweep": { + "learning_rate": [1e-4], + "lora_r": [8], + "micro_batch_size": [4], + "gradient_accumulation_steps": [4], + "target_layers": [ + {"name": "attention", "modules": ["q_proj", "v_proj"]} + ], + }, + "axolotl": { + "base_model": "namespace/model", + "datasets": [{"path": "namespace/dataset"}], + "num_epochs": 1, + }, + } + p = tmp_path / "sweep.yaml" + p.write_text(yaml.dump(cfg)) + return p + + +# --------------------------------------------------------------------------- +# `voice finetune single` +# --------------------------------------------------------------------------- + + +def test_single_invokes_execute_run(tmp_path): + runner = CliRunner() + with patch("voice.finetune.cli.execute_run") as mock_run: + result = runner.invoke( + main, + [ + "finetune", + "single", + str(_single_cfg(tmp_path)), + "--runs-dir", + str(tmp_path / "runs"), + ], + ) + assert result.exit_code == 0, result.output + assert mock_run.call_count == 1 + + +def test_single_passes_dataset_as_reference_repo(tmp_path): + runner = CliRunner() + with patch("voice.finetune.cli.execute_run") as mock_run: + runner.invoke( + main, + [ + "finetune", + "single", + str(_single_cfg(tmp_path)), + "--runs-dir", + str(tmp_path / "runs"), + ], + ) + ref_spec = mock_run.call_args.args[4] + assert ref_spec.repo_id == "namespace/dataset" + + +def test_single_default_dataset_revision_is_main(tmp_path): + runner = CliRunner() + with patch("voice.finetune.cli.execute_run") as mock_run: + runner.invoke( + main, + [ + "finetune", + "single", + str(_single_cfg(tmp_path)), + "--runs-dir", + str(tmp_path / "runs"), + ], + ) + ref_spec = mock_run.call_args.args[4] + assert ref_spec.revision == "main" + + +def test_single_respects_dataset_revision_option(tmp_path): + runner = CliRunner() + with patch("voice.finetune.cli.execute_run") as mock_run: + runner.invoke( + main, + [ + "finetune", + "single", + str(_single_cfg(tmp_path)), + "--runs-dir", + str(tmp_path / "runs"), + "--dataset-revision", + "abc123", + ], + ) + ref_spec = mock_run.call_args.args[4] + assert ref_spec.revision == "abc123" + + +def test_single_exits_nonzero_on_training_failure(tmp_path): + runner = CliRunner() + with patch( + "voice.finetune.cli.execute_run", side_effect=RuntimeError("boom") + ): + result = runner.invoke( + main, + [ + "finetune", + "single", + str(_single_cfg(tmp_path)), + "--runs-dir", + str(tmp_path / "runs"), + ], + ) + assert result.exit_code == 1 + + +def test_single_missing_config_exits_nonzero(): + runner = CliRunner() + result = runner.invoke(main, ["finetune", "single", "missing.yaml"]) + assert result.exit_code != 0 + + +# --------------------------------------------------------------------------- +# `voice finetune sweep` +# --------------------------------------------------------------------------- + + +def test_sweep_calls_execute_run_per_grid_point(tmp_path): + runner = CliRunner() + with patch("voice.finetune.cli.execute_run") as mock_run: + result = runner.invoke( + main, + [ + "finetune", + "sweep", + str(_sweep_cfg(tmp_path)), + "--runs-dir", + str(tmp_path / "runs"), + ], + ) + assert result.exit_code == 0, result.output + # One grid point in _sweep_cfg + assert mock_run.call_count == 1 + + +def test_sweep_resume_skips_completed_run(tmp_path): + runs_dir = tmp_path / "runs" + runs_dir.mkdir() + (runs_dir / "state.json").write_text('{"completed": ["0"], "failed": {}}') + runner = CliRunner() + with patch("voice.finetune.cli.execute_run") as mock_run: + result = runner.invoke( + main, + [ + "finetune", + "sweep", + str(_sweep_cfg(tmp_path)), + "--runs-dir", + str(runs_dir), + "--resume", + ], + ) + assert result.exit_code == 0, result.output + assert mock_run.call_count == 0 + + +def test_sweep_no_resume_reruns_completed(tmp_path): + runs_dir = tmp_path / "runs" + runs_dir.mkdir() + (runs_dir / "state.json").write_text('{"completed": ["0"], "failed": {}}') + runner = CliRunner() + with patch("voice.finetune.cli.execute_run") as mock_run: + result = runner.invoke( + main, + [ + "finetune", + "sweep", + str(_sweep_cfg(tmp_path)), + "--runs-dir", + str(runs_dir), + ], + ) + assert result.exit_code == 0, result.output + assert mock_run.call_count == 1 + + +def test_sweep_exits_nonzero_on_any_failure(tmp_path): + runner = CliRunner() + with patch( + "voice.finetune.cli.execute_run", side_effect=RuntimeError("fail") + ): + result = runner.invoke( + main, + [ + "finetune", + "sweep", + str(_sweep_cfg(tmp_path)), + "--runs-dir", + str(tmp_path / "runs"), + ], + ) + assert result.exit_code == 1 + + +def test_sweep_missing_config_exits_nonzero(): + runner = CliRunner() + result = runner.invoke(main, ["finetune", "sweep", "missing.yaml"]) + assert result.exit_code != 0 + + +def test_sweep_passes_hyperparams_to_execute_run(tmp_path): + runner = CliRunner() + with patch("voice.finetune.cli.execute_run") as mock_run: + runner.invoke( + main, + [ + "finetune", + "sweep", + str(_sweep_cfg(tmp_path)), + "--runs-dir", + str(tmp_path / "runs"), + ], + ) + hyperparams = mock_run.call_args.args[2] + assert hyperparams["learning_rate"] == 1e-4 + assert hyperparams["lora_r"] == 8 diff --git a/tests/voice/finetune/test_grid.py b/tests/voice/finetune/test_grid.py new file mode 100644 index 0000000..46c4b93 --- /dev/null +++ b/tests/voice/finetune/test_grid.py @@ -0,0 +1,168 @@ +""" +Tests for voice.finetune._grid. + +Scope: +- expand_grid count and structure +- Hyperparameter pass-through (micro_batch_size, gradient_accumulation_steps) +- Missing axis validation +- generate_run_name format and uniqueness +- _short_name normalisation +""" + +from __future__ import annotations + +import pytest + +from voice.finetune._grid import ( + _short_name, + expand_grid, + generate_run_name, + new_uid, +) + +# ----------------------------------------------------------------------------- +# Helpers +# ----------------------------------------------------------------------------- + + +def _minimal_sweep( + *, + lrs: list | None = None, + ranks: list | None = None, + micro_batch_sizes: list | None = None, + accumulation_steps: list | None = None, + layers: list | None = None, +) -> dict: + return { + "learning_rate": lrs or [1e-4, 5e-4], + "lora_r": ranks or [4, 8], + "micro_batch_size": micro_batch_sizes or [4, 8], + "gradient_accumulation_steps": accumulation_steps or [1, 4], + "target_layers": layers + or [ + {"name": "attention", "modules": ["q_proj"]}, + {"name": "attention_mlp", "modules": ["q_proj", "gate_proj"]}, + ], + } + + +# ----------------------------------------------------------------------------- +# expand_grid +# ----------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + "n_lr, n_r, n_mbs, n_gas, n_tl", + [ + (1, 1, 1, 1, 1), + (3, 4, 2, 2, 2), + (2, 2, 2, 2, 2), + ], +) +def test_expand_grid_count( + n_lr: int, n_r: int, n_mbs: int, n_gas: int, n_tl: int +): + cfg = _minimal_sweep( + lrs=list(range(n_lr)), + ranks=list(range(1, n_r + 1)), + micro_batch_sizes=list(range(1, n_mbs + 1)), + accumulation_steps=list(range(1, n_gas + 1)), + layers=[{"name": f"l{i}", "modules": ["q"]} for i in range(n_tl)], + ) + runs = expand_grid(cfg) + assert len(runs) == n_lr * n_r * n_mbs * n_gas * n_tl + + +def test_expand_grid_indices_are_sequential(): + runs = expand_grid(_minimal_sweep()) + assert [r["run_idx"] for r in runs] == list(range(len(runs))) + + +def test_expand_grid_micro_batch_size_passed_through(): + cfg = _minimal_sweep(micro_batch_sizes=[2, 4, 8]) + runs = expand_grid(cfg) + observed = {r["micro_batch_size"] for r in runs} + assert observed == {2, 4, 8} + + +def test_expand_grid_gradient_accumulation_steps_passed_through(): + cfg = _minimal_sweep(accumulation_steps=[1, 2, 8]) + runs = expand_grid(cfg) + observed = {r["gradient_accumulation_steps"] for r in runs} + assert observed == {1, 2, 8} + + +def test_expand_grid_lora_alpha_equals_lora_r(): + runs = expand_grid(_minimal_sweep()) + for run in runs: + assert run["lora_alpha"] == run["lora_r"] + + +def test_expand_grid_no_effective_batch_size_field(): + runs = expand_grid(_minimal_sweep()) + for run in runs: + assert "effective_batch_size" not in run + + +@pytest.mark.parametrize( + "missing_key", + [ + "learning_rate", + "lora_r", + "micro_batch_size", + "gradient_accumulation_steps", + "target_layers", + ], +) +def test_expand_grid_raises_on_missing_axis(missing_key: str): + cfg = _minimal_sweep() + del cfg[missing_key] + with pytest.raises(ValueError, match=r"missing required axes"): + expand_grid(cfg) + + +# ----------------------------------------------------------------------------- +# generate_run_name / new_uid +# ----------------------------------------------------------------------------- + + +def test_generate_run_name_contains_model_and_uid(): + name = generate_run_name( + model="meta-llama/Llama-3.1-8B-Instruct", uid="a3f2b9c1" + ) + assert "llama" in name + assert "a3f2b9c1" in name + + +def test_generate_run_name_format(): + name = generate_run_name(model="ns/my-model", uid="deadbeef") + assert name == "my-model_deadbeef" + + +def test_new_uid_is_8_hex_chars(): + uid = new_uid() + assert len(uid) == 8 + assert all(c in "0123456789abcdef" for c in uid) + + +def test_new_uid_is_unique(): + uids = {new_uid() for _ in range(100)} + assert len(uids) == 100 + + +# ----------------------------------------------------------------------------- +# _short_name +# ----------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + "inp, expected", + [ + ("meta-llama/Llama-3.1-8B-Instruct", "llama-3-1-8b-instruct"), + ("AccelerateScience/bush-dataset", "bush-dataset"), + ("/data/local/bush", "bush"), + ("simple", "simple"), + ], +) +def test_short_name(inp: str, expected: str): + assert _short_name(inp) == expected diff --git a/tests/voice/finetune/test_orchestrator.py b/tests/voice/finetune/test_orchestrator.py new file mode 100644 index 0000000..b8d98a5 --- /dev/null +++ b/tests/voice/finetune/test_orchestrator.py @@ -0,0 +1,340 @@ +""" +Tests for voice.finetune._orchestrator. + +Scope: +- _build_axolotl_cfg merges hyperparams and injects output_dir +- _now_iso returns a valid ISO-8601 string +- run_training builds the correct command for single/multi-GPU +- _load_completions delegates to get_dataset with a LocalDatasetSpec +- _score_split calls make_comparison and writes alignment JSON +- _try_score_split skips missing completions silently +- _try_score_split catches and logs scoring errors +- _score_all_epochs iterates over all epochs and splits +- execute_run happy path: creates dirs, writes metadata, scores, returns done +- execute_run failure path: CalledProcessError -> failed status + RuntimeError +""" + +from __future__ import annotations + +import subprocess +from datetime import datetime +from pathlib import Path +from unittest.mock import MagicMock, patch + +import pytest + +from voice.datasets._schema import Split +from voice.datasets.dataset import DatasetSpec, LocalDatasetSpec +from voice.finetune._orchestrator import ( + _build_axolotl_cfg, + _load_completions, + _now_iso, + _score_all_epochs, + _score_split, + _try_score_split, + execute_run, + run_training, +) +from voice.finetune._runs import RunDir +from voice.finetune._schema import RunMetadata + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _run_dir(tmp_path: Path, name: str = "run-001") -> RunDir: + return RunDir(tmp_path / name) + + +def _ref_spec() -> DatasetSpec: + return DatasetSpec( + repo_id="ns/dataset", + revision="main", + splits=(Split.TRAIN, Split.VALIDATION, Split.TEST), + ) + + +def _meta() -> RunMetadata: + return RunMetadata( + run_name="run-001", model="ns/m", dataset="ns/d", hyperparams={} + ) + + +def _comparison_mock() -> MagicMock: + m = MagicMock() + m.score = 0.75 + m.score_ci.return_value = [0.65, 0.85] + m.metric_tails = {"avg_word_length": 0.8} + m.metric_tail_cis.return_value = {"avg_word_length": [0.7, 0.9]} + m.group_tails = {} + m.group_tail_cis.return_value = None + return m + + +# --------------------------------------------------------------------------- +# _build_axolotl_cfg +# --------------------------------------------------------------------------- + + +def test_build_axolotl_cfg_sets_output_dir(tmp_path: Path): + rd = _run_dir(tmp_path) + cfg = _build_axolotl_cfg({"base_model": "ns/m"}, {}, rd) + assert cfg["output_dir"] == str(rd.adapter_dir) + + +def test_build_axolotl_cfg_merges_hyperparams(tmp_path: Path): + rd = _run_dir(tmp_path) + hyperparams = {"learning_rate": 1e-4, "lora_r": 8, "lora_alpha": 8} + cfg = _build_axolotl_cfg({"base_model": "ns/m"}, hyperparams, rd) + assert cfg["learning_rate"] == 1e-4 + assert cfg["lora_r"] == 8 + assert cfg["lora_alpha"] == 8 + + +def test_build_axolotl_cfg_does_not_mutate_base(tmp_path: Path): + rd = _run_dir(tmp_path) + base = {"base_model": "ns/m"} + _build_axolotl_cfg(base, {"learning_rate": 1e-4}, rd) + assert "learning_rate" not in base + assert "output_dir" not in base + + +# --------------------------------------------------------------------------- +# _now_iso +# --------------------------------------------------------------------------- + + +def test_now_iso_returns_valid_utc_iso_string(): + s = _now_iso() + dt = datetime.fromisoformat(s) + assert dt.tzinfo is not None + + +# --------------------------------------------------------------------------- +# run_training +# --------------------------------------------------------------------------- + + +def test_run_training_single_gpu_uses_python_m(tmp_path: Path): + rd = _run_dir(tmp_path) + rd.create() + rd.write_config({"base_model": "ns/m"}) + + with patch("voice.finetune._orchestrator.subprocess.run") as mock_sub: + run_training(rd, gpu_ids="0") + + cmd = mock_sub.call_args.args[0] + assert "axolotl.cli.train" in cmd + assert "accelerate.commands.launch" not in cmd + + +def test_run_training_multi_gpu_uses_accelerate(tmp_path: Path): + rd = _run_dir(tmp_path) + rd.create() + rd.write_config({"base_model": "ns/m"}) + + with patch("voice.finetune._orchestrator.subprocess.run") as mock_sub: + run_training(rd, gpu_ids="0,1,2") + + cmd = mock_sub.call_args.args[0] + assert "accelerate.commands.launch" in cmd + assert "--num_processes=3" in cmd + + +def test_run_training_sets_cuda_visible_devices(tmp_path: Path): + rd = _run_dir(tmp_path) + rd.create() + rd.write_config({"base_model": "ns/m"}) + + with patch("voice.finetune._orchestrator.subprocess.run") as mock_sub: + run_training(rd, gpu_ids="1,2") + + env = mock_sub.call_args.kwargs["env"] + assert env["CUDA_VISIBLE_DEVICES"] == "1,2" + + +# --------------------------------------------------------------------------- +# _load_completions +# --------------------------------------------------------------------------- + + +def test_load_completions_calls_get_dataset_with_local_spec(tmp_path: Path): + rd = _run_dir(tmp_path) + mock_ds = MagicMock() + mock_ds.__getitem__.return_value = [] + + with patch( + "voice.finetune._orchestrator.get_dataset", return_value=mock_ds + ) as mock_get: + result = _load_completions(rd, "validation", 1) + + call_spec = mock_get.call_args.args[0] + assert isinstance(call_spec, LocalDatasetSpec) + assert "epoch_1" in str(call_spec.path) + assert result == [] + + +# --------------------------------------------------------------------------- +# _score_split +# --------------------------------------------------------------------------- + + +def test_score_split_writes_alignment_json_and_returns_score(tmp_path: Path): + import json + + rd = _run_dir(tmp_path) + + with ( + patch( + "voice.finetune._orchestrator._load_completions", return_value=[] + ), + patch( + "voice.finetune._orchestrator.make_comparison", + return_value=_comparison_mock(), + ), + ): + score = _score_split(rd, "validation", 1, MagicMock()) + + assert score == pytest.approx(0.75) + out_path = rd.alignment_path("validation", 1) + assert out_path.exists() + data = json.loads(out_path.read_text()) + assert data["score"] == pytest.approx(0.75) + + +# --------------------------------------------------------------------------- +# _try_score_split +# --------------------------------------------------------------------------- + + +def test_try_score_split_skips_when_completions_missing(tmp_path: Path): + rd = _run_dir(tmp_path) + rd.create() + meta = _meta() + + with patch("voice.finetune._orchestrator._score_split") as mock_score: + _try_score_split(rd, "validation", 1, MagicMock(), meta) + + mock_score.assert_not_called() + assert meta.alignment_val == {} + + +def test_try_score_split_catches_scoring_error(tmp_path: Path): + rd = _run_dir(tmp_path) + rd.create() + (rd.completions_dir / "epoch_1").mkdir(parents=True) + (rd.completions_dir / "epoch_1" / "validation.jsonl").write_text("") + meta = _meta() + + with patch( + "voice.finetune._orchestrator._score_split", + side_effect=RuntimeError("boom"), + ): + _try_score_split( + rd, "validation", 1, MagicMock(), meta + ) # must not raise + + assert meta.alignment_val == {} + + +def test_try_score_split_updates_alignment_val(tmp_path: Path): + rd = _run_dir(tmp_path) + rd.create() + (rd.completions_dir / "epoch_1").mkdir(parents=True) + (rd.completions_dir / "epoch_1" / "validation.jsonl").write_text("") + meta = _meta() + + with patch("voice.finetune._orchestrator._score_split", return_value=0.8): + _try_score_split(rd, "validation", 1, MagicMock(), meta) + + assert meta.alignment_val == {"1": 0.8} + + +def test_try_score_split_updates_alignment_test(tmp_path: Path): + rd = _run_dir(tmp_path) + rd.create() + (rd.completions_dir / "epoch_1").mkdir(parents=True) + (rd.completions_dir / "epoch_1" / "test.jsonl").write_text("") + meta = _meta() + + with patch("voice.finetune._orchestrator._score_split", return_value=0.6): + _try_score_split(rd, "test", 1, MagicMock(), meta) + + assert meta.alignment_test == {"1": 0.6} + + +# --------------------------------------------------------------------------- +# _score_all_epochs +# --------------------------------------------------------------------------- + + +def test_score_all_epochs_calls_try_score_for_each_epoch_and_split( + tmp_path: Path, +): + rd = _run_dir(tmp_path) + meta = _meta() + + with patch("voice.finetune._orchestrator._try_score_split") as mock_try: + _score_all_epochs( + rd, num_epochs=2, reference_ds=MagicMock(), meta=meta + ) + + assert mock_try.call_count == 4 # 2 epochs × 2 splits + + +# --------------------------------------------------------------------------- +# execute_run +# --------------------------------------------------------------------------- + + +def test_execute_run_happy_path_returns_done_metadata(tmp_path: Path): + rd = _run_dir(tmp_path) + + with ( + patch("voice.finetune._orchestrator.get_dataset"), + patch("voice.finetune._orchestrator.run_training"), + patch("voice.finetune._orchestrator._score_all_epochs"), + ): + meta = execute_run( + run_name="run-001", + base_axolotl_cfg={ + "base_model": "ns/model", + "datasets": [{"path": "ns/dataset"}], + "num_epochs": 2, + }, + hyperparams={"learning_rate": 1e-4, "lora_r": 8}, + run_dir=rd, + reference_spec=_ref_spec(), + ) + + assert meta.status == "done" + assert meta.run_name == "run-001" + assert meta.finished_at is not None + assert rd.metadata_path.exists() + + +def test_execute_run_failure_sets_failed_status_and_raises(tmp_path: Path): + rd = _run_dir(tmp_path) + err = subprocess.CalledProcessError(1, "axolotl") + + with ( + patch("voice.finetune._orchestrator.get_dataset"), + patch("voice.finetune._orchestrator.run_training", side_effect=err), + pytest.raises(RuntimeError, match="Training failed"), + ): + execute_run( + run_name="run-001", + base_axolotl_cfg={ + "base_model": "ns/model", + "datasets": [{"path": "ns/dataset"}], + "num_epochs": 1, + }, + hyperparams={}, + run_dir=rd, + reference_spec=_ref_spec(), + ) + + meta = rd.read_metadata() + assert meta.status == "failed" + assert meta.finished_at is not None diff --git a/tests/voice/finetune/test_runs.py b/tests/voice/finetune/test_runs.py new file mode 100644 index 0000000..6a3fc73 --- /dev/null +++ b/tests/voice/finetune/test_runs.py @@ -0,0 +1,170 @@ +""" +Tests for voice.finetune._runs. + +Scope: +- RunDir sub-path properties +- completions_path and alignment_path naming +- create() builds expected directory tree +- write_config produces valid YAML +- write_metadata / read_metadata round-trip +- write_metadata is atomic (tmp file used) +""" + +from __future__ import annotations + +from pathlib import Path + +import yaml + +from voice.finetune._runs import RunDir +from voice.finetune._schema import RunMetadata + +# ----------------------------------------------------------------------------- +# Helpers +# ----------------------------------------------------------------------------- + + +def _run_dir(tmp_path: Path, name: str = "my-run") -> RunDir: + return RunDir(path=tmp_path / name) + + +def _meta(**kwargs) -> RunMetadata: # type: ignore[no-untyped-def] + defaults: dict = { + "run_name": "my-run", + "model": "ns/model", + "dataset": "ns/ds", + "hyperparams": {"lr": 1e-4}, + } + defaults.update(kwargs) + return RunMetadata(**defaults) # type: ignore[arg-type] + + +# ----------------------------------------------------------------------------- +# Sub-path properties +# ----------------------------------------------------------------------------- + + +def test_adapter_dir_is_under_run_root(tmp_path: Path): + rd = _run_dir(tmp_path) + assert rd.adapter_dir == rd.path / "adapter" + + +def test_completions_dir_is_under_run_root(tmp_path: Path): + rd = _run_dir(tmp_path) + assert rd.completions_dir == rd.path / "completions" + + +def test_alignment_dir_is_under_run_root(tmp_path: Path): + rd = _run_dir(tmp_path) + assert rd.alignment_dir == rd.path / "alignment" + + +def test_config_path_is_yaml_in_run_root(tmp_path: Path): + rd = _run_dir(tmp_path) + assert rd.config_path == rd.path / "config.yaml" + + +def test_metadata_path_is_json_in_run_root(tmp_path: Path): + rd = _run_dir(tmp_path) + assert rd.metadata_path == rd.path / "metadata.json" + + +# ----------------------------------------------------------------------------- +# completions_path / alignment_path +# ----------------------------------------------------------------------------- + + +def test_completions_path_structure(tmp_path: Path): + rd = _run_dir(tmp_path) + p = rd.completions_path("validation", 2) + assert p == rd.completions_dir / "epoch_2" / "validation.jsonl" + + +def test_alignment_path_structure(tmp_path: Path): + rd = _run_dir(tmp_path) + p = rd.alignment_path("test", 3) + assert p == rd.alignment_dir / "epoch_3" / "test.json" + + +def test_completions_path_split_names_differ(tmp_path: Path): + rd = _run_dir(tmp_path) + val = rd.completions_path("validation", 1) + test = rd.completions_path("test", 1) + assert val != test + assert val.name == "validation.jsonl" + assert test.name == "test.jsonl" + + +def test_completions_path_epoch_dirs_differ(tmp_path: Path): + rd = _run_dir(tmp_path) + e1 = rd.completions_path("validation", 1) + e2 = rd.completions_path("validation", 2) + assert e1.parent != e2.parent + + +# ----------------------------------------------------------------------------- +# create() +# ----------------------------------------------------------------------------- + + +def test_create_makes_expected_directories(tmp_path: Path): + rd = _run_dir(tmp_path) + rd.create() + assert rd.path.is_dir() + assert rd.adapter_dir.is_dir() + assert rd.completions_dir.is_dir() + assert rd.alignment_dir.is_dir() + + +def test_create_is_idempotent(tmp_path: Path): + rd = _run_dir(tmp_path) + rd.create() + rd.create() # should not raise + + +# ----------------------------------------------------------------------------- +# write_config +# ----------------------------------------------------------------------------- + + +def test_write_config_produces_valid_yaml(tmp_path: Path): + rd = _run_dir(tmp_path) + rd.create() + cfg = {"base_model": "ns/model", "learning_rate": 1e-4, "lora_r": 8} + rd.write_config(cfg) + loaded = yaml.safe_load(rd.config_path.read_text()) + assert loaded == cfg + + +# ----------------------------------------------------------------------------- +# write_metadata / read_metadata +# ----------------------------------------------------------------------------- + + +def test_write_and_read_metadata_round_trips(tmp_path: Path): + rd = _run_dir(tmp_path) + rd.create() + meta = _meta(status="done", eval_loss={"1": 0.4}) + rd.write_metadata(meta) + restored = rd.read_metadata() + assert restored.run_name == meta.run_name + assert restored.status == meta.status + assert restored.eval_loss == meta.eval_loss + + +def test_write_metadata_no_tmp_file_left(tmp_path: Path): + rd = _run_dir(tmp_path) + rd.create() + rd.write_metadata(_meta()) + tmp = rd.metadata_path.with_suffix(".tmp.json") + assert not tmp.exists() + + +def test_write_metadata_creates_valid_json(tmp_path: Path): + import json + + rd = _run_dir(tmp_path) + rd.create() + rd.write_metadata(_meta()) + parsed = json.loads(rd.metadata_path.read_text()) + assert parsed["run_name"] == "my-run" diff --git a/tests/voice/finetune/test_schema.py b/tests/voice/finetune/test_schema.py new file mode 100644 index 0000000..84f6854 --- /dev/null +++ b/tests/voice/finetune/test_schema.py @@ -0,0 +1,163 @@ +""" +Tests for voice.finetune._schema. + +Scope: +- SweepConfig.from_yaml validation and parsing +- SingleRunConfig.from_yaml validation and parsing +- RunMetadata to_dict / from_dict round-trip +""" + +from __future__ import annotations + +import json +from pathlib import Path + +import pytest + +from voice.finetune._schema import RunMetadata, SingleRunConfig, SweepConfig + +# ----------------------------------------------------------------------------- +# Helpers +# ----------------------------------------------------------------------------- + + +def _write_yaml(path: Path, content: str) -> Path: + path.write_text(content) + return path + + +# ----------------------------------------------------------------------------- +# SweepConfig +# ----------------------------------------------------------------------------- + + +def test_sweep_config_from_yaml_reads_sections(tmp_path: Path): + p = _write_yaml( + tmp_path / "sweep.yaml", + "sweep:\n learning_rate: [1.0e-4]\naxolotl:\n base_model: x\n", + ) + cfg = SweepConfig.from_yaml(p) + assert cfg.sweep == {"learning_rate": [1e-4]} + assert cfg.axolotl == {"base_model": "x"} + + +def test_sweep_config_from_yaml_raises_on_missing_sweep_key(tmp_path: Path): + p = _write_yaml(tmp_path / "cfg.yaml", "axolotl:\n base_model: x\n") + with pytest.raises(ValueError, match=r"missing required keys"): + SweepConfig.from_yaml(p) + + +def test_sweep_config_from_yaml_raises_on_missing_axolotl_key(tmp_path: Path): + p = _write_yaml( + tmp_path / "cfg.yaml", "sweep:\n learning_rate: [1.0e-4]\n" + ) + with pytest.raises(ValueError, match=r"missing required keys"): + SweepConfig.from_yaml(p) + + +def test_sweep_config_is_frozen(tmp_path: Path): + p = _write_yaml( + tmp_path / "cfg.yaml", + "sweep:\n lr: [1e-4]\naxolotl:\n base_model: x\n", + ) + cfg = SweepConfig.from_yaml(p) + import dataclasses + + with pytest.raises(dataclasses.FrozenInstanceError): + setattr(cfg, "sweep", {}) # noqa: B010 + + +# ----------------------------------------------------------------------------- +# SingleRunConfig +# ----------------------------------------------------------------------------- + + +def test_single_run_config_from_yaml_reads_axolotl_section(tmp_path: Path): + p = _write_yaml( + tmp_path / "run.yaml", "axolotl:\n base_model: my-model\n" + ) + cfg = SingleRunConfig.from_yaml(p) + assert cfg.axolotl == {"base_model": "my-model"} + + +def test_single_run_config_from_yaml_raises_on_missing_axolotl_key( + tmp_path: Path, +): + p = _write_yaml(tmp_path / "run.yaml", "model: x\n") + with pytest.raises(ValueError, match=r"missing required key: 'axolotl'"): + SingleRunConfig.from_yaml(p) + + +# ----------------------------------------------------------------------------- +# RunMetadata +# ----------------------------------------------------------------------------- + + +def test_run_metadata_defaults(): + meta = RunMetadata( + run_name="r", + model="m", + dataset="d", + hyperparams={"lr": 1e-4}, + ) + assert meta.status == "pending" + assert meta.started_at is None + assert meta.finished_at is None + assert meta.eval_loss == {} + assert meta.alignment_val == {} + assert meta.alignment_test == {} + + +def test_run_metadata_to_dict_from_dict_round_trips(): + meta = RunMetadata( + run_name="run-001", + model="ns/model", + dataset="ns/ds", + hyperparams={"lr": 1e-4, "lora_r": 8}, + status="done", + started_at="2026-05-22T14:30:00", + finished_at="2026-05-22T16:00:00", + eval_loss={"1": 0.45, "2": 0.40}, + alignment_val={"1": 0.82, "2": 0.85}, + alignment_test={"1": 0.80, "2": 0.83}, + ) + d = meta.to_dict() + restored = RunMetadata.from_dict(d) + + assert restored.run_name == meta.run_name + assert restored.model == meta.model + assert restored.dataset == meta.dataset + assert restored.hyperparams == meta.hyperparams + assert restored.status == meta.status + assert restored.started_at == meta.started_at + assert restored.finished_at == meta.finished_at + assert restored.eval_loss == meta.eval_loss + assert restored.alignment_val == meta.alignment_val + assert restored.alignment_test == meta.alignment_test + + +def test_run_metadata_to_dict_is_json_serialisable(): + meta = RunMetadata( + run_name="r", + model="m", + dataset="d", + hyperparams={"lr": 1e-4}, + ) + # Should not raise + json.dumps(meta.to_dict()) + + +def test_run_metadata_from_dict_tolerates_missing_optional_fields(): + d = { + "run_name": "r", + "model": "m", + "dataset": "d", + "hyperparams": {}, + "status": "pending", + "started_at": None, + "finished_at": None, + } + meta = RunMetadata.from_dict(d) + assert meta.eval_loss == {} + assert meta.alignment_val == {} + assert meta.alignment_test == {} diff --git a/tests/voice/finetune/test_state.py b/tests/voice/finetune/test_state.py new file mode 100644 index 0000000..f85d961 --- /dev/null +++ b/tests/voice/finetune/test_state.py @@ -0,0 +1,86 @@ +""" +Tests for voice.finetune._state. + +Scope: +- load_state returns fresh state for missing file +- save/load round-trip +- mark_completed and mark_failed mutations and persistence +""" + +from __future__ import annotations + +import json +from pathlib import Path + +from voice.finetune._state import ( + load_state, + mark_completed, + mark_failed, + save_state, +) + + +def test_load_state_returns_fresh_state_for_missing_file(tmp_path: Path): + state = load_state(tmp_path / "state.json") + assert state["completed"] == [] + assert state["failed"] == {} + + +def test_save_and_load_state_round_trips(tmp_path: Path): + state_file = tmp_path / "state.json" + state: dict = {"completed": ["run-001"], "failed": {"run-002": "boom"}} + save_state(state, state_file) + restored = load_state(state_file) + assert restored == state + + +def test_save_state_writes_valid_json(tmp_path: Path): + state_file = tmp_path / "state.json" + save_state({"completed": [], "failed": {}}, state_file) + parsed = json.loads(state_file.read_text()) + assert "completed" in parsed + assert "failed" in parsed + + +def test_mark_completed_appends_name(tmp_path: Path): + state_file = tmp_path / "state.json" + state: dict = {"completed": [], "failed": {}} + mark_completed(state, "run-001", state_file) + assert "run-001" in state["completed"] + + +def test_mark_completed_removes_from_failed(tmp_path: Path): + state_file = tmp_path / "state.json" + state: dict = {"completed": [], "failed": {"run-001": "old error"}} + mark_completed(state, "run-001", state_file) + assert "run-001" not in state["failed"] + + +def test_mark_completed_persists_state(tmp_path: Path): + state_file = tmp_path / "state.json" + state: dict = {"completed": [], "failed": {}} + mark_completed(state, "run-001", state_file) + reloaded = load_state(state_file) + assert "run-001" in reloaded["completed"] + + +def test_mark_failed_adds_error(tmp_path: Path): + state_file = tmp_path / "state.json" + state: dict = {"completed": [], "failed": {}} + mark_failed(state, "run-001", "something broke", state_file) + assert state["failed"]["run-001"] == "something broke" + + +def test_mark_failed_persists_state(tmp_path: Path): + state_file = tmp_path / "state.json" + state: dict = {"completed": [], "failed": {}} + mark_failed(state, "run-001", "boom", state_file) + reloaded = load_state(state_file) + assert reloaded["failed"]["run-001"] == "boom" + + +def test_mark_failed_does_not_remove_from_completed(tmp_path: Path): + state_file = tmp_path / "state.json" + state: dict = {"completed": ["run-001"], "failed": {}} + mark_failed(state, "run-001", "boom", state_file) + assert "run-001" in state["completed"] diff --git a/tests/voice/stylometry/test_metrics.py b/tests/voice/stylometry/test_metrics.py index 69fc947..8adc336 100644 --- a/tests/voice/stylometry/test_metrics.py +++ b/tests/voice/stylometry/test_metrics.py @@ -80,7 +80,6 @@ def test_get_metrics_contains_expected_metric_names(): "std_word_length", "skew_word_length", "kurtosis_word_length", - "num_words", "hapax_legomena_ratio", "dis_legomena_ratio", "tri_legomena_ratio", @@ -100,15 +99,19 @@ def test_get_metrics_contains_expected_metric_names(): def test_metric_decorator_rejects_duplicate_names(monkeypatch): # Isolate decorator behaviour by swapping the module registry. monkeypatch.setattr(metrics, "_REGISTRY", {}) - metrics.metric("x", group=MetricGroup.TEXT_LENGTH)(lambda t: 0.0) + metrics.metric("x", group=MetricGroup.WORD_LENGTH_DISTRIBUTION)( + lambda t: 0.0 + ) with pytest.raises(ValueError, match=r"Duplicate metric name 'x'"): - metrics.metric("x", group=MetricGroup.TEXT_LENGTH)(lambda t: 1.0) + metrics.metric("x", group=MetricGroup.WORD_LENGTH_DISTRIBUTION)( + lambda t: 1.0 + ) def test_metric_spec_is_frozen(): spec = metrics.MetricSpec( fn=lambda t: 0.0, - group=MetricGroup.TEXT_LENGTH, + group=MetricGroup.WORD_LENGTH_DISTRIBUTION, description="x", ) assert type(spec).__dataclass_params__.frozen is True @@ -152,7 +155,6 @@ def test_metric_groups_cover_all_enum_values(): "std_word_length": MetricGroup.WORD_LENGTH_DISTRIBUTION, "skew_word_length": MetricGroup.WORD_LENGTH_DISTRIBUTION, "kurtosis_word_length": MetricGroup.WORD_LENGTH_DISTRIBUTION, - "num_words": MetricGroup.TEXT_LENGTH, "hapax_legomena_ratio": MetricGroup.VOCABULARY_RICHNESS, "dis_legomena_ratio": MetricGroup.VOCABULARY_RICHNESS, "tri_legomena_ratio": MetricGroup.VOCABULARY_RICHNESS, @@ -256,13 +258,6 @@ def fake_moment(units, order: int) -> float: # ----------------------------------------------------------------------------- -def test_num_words_counts_tokens(monkeypatch): - monkeypatch.setattr( - metrics, "word_tokenize", lambda t: ["a", "b", "c", "d"] - ) - assert metrics.calculate_num_words("ignored") == 4.0 - - def test_hapax_dis_tri_legomena_ratios_exact_on_handmade_tokens(monkeypatch): tokens = ["a", "b", "b", "c", "c", "c"] monkeypatch.setattr(metrics, "word_tokenize", lambda t: tokens) @@ -457,6 +452,5 @@ def test_all_metrics_run_on_simple_sentence_smoke(): def test_selected_metrics_on_tiny_text_regression(): text = "a bb ccc" - assert metrics.calculate_num_words(text) == 3.0 assert metrics.calculate_avg_word_length(text) == pytest.approx(2.0) assert metrics.calculate_hapax_legomena_ratio(text) == pytest.approx(1.0) diff --git a/tests/voice/stylometry/test_sequence_stats.py b/tests/voice/stylometry/test_sequence_stats.py new file mode 100644 index 0000000..4c76d6b --- /dev/null +++ b/tests/voice/stylometry/test_sequence_stats.py @@ -0,0 +1,47 @@ +""" +Tests for voice.stylometry._sequence_stats. + +Scope: +- length_central_moment_from_units raises on non-positive order +- mattr_from_units raises on non-positive window_size or stride +- mattr_from_units exercises the sliding-window loop (n > window_size) +""" + +from __future__ import annotations + +import pytest + +from voice.stylometry._sequence_stats import ( + length_central_moment_from_units, + mattr_from_units, +) + + +def test_length_central_moment_raises_on_nonpositive_order(): + with pytest.raises(ValueError, match="Order must be positive"): + length_central_moment_from_units(["a", "bb"], order=0) + + +def test_mattr_raises_on_nonpositive_window_size(): + with pytest.raises(ValueError, match="window_size must be positive"): + mattr_from_units(["a", "b", "c"], window_size=0, stride=1) + + +def test_mattr_raises_on_nonpositive_stride(): + with pytest.raises(ValueError, match="stride must be positive"): + mattr_from_units(["a", "b", "c"], window_size=2, stride=0) + + +def test_mattr_sliding_window_all_unique(): + # 7 tokens, window=3 — forces the loop path (n > window_size) + # Every window [a,b,c], [b,c,d], ... is fully unique → TTR=1.0 each + units = ["a", "b", "c", "d", "e", "f", "g"] + result = mattr_from_units(units, window_size=3, stride=1) + assert result == pytest.approx(1.0) + + +def test_mattr_sliding_window_with_repeats(): + # [a,a,b]=2/3, [a,b,a]=2/3, [b,a,a]=2/3 → mean = 2/3 + units = ["a", "a", "b", "a", "a"] + result = mattr_from_units(units, window_size=3, stride=1) + assert result == pytest.approx(2 / 3) diff --git a/uv.lock b/uv.lock index dd1c64b..5c2fedd 100644 --- a/uv.lock +++ b/uv.lock @@ -1,13 +1,85 @@ version = 1 revision = 3 -requires-python = ">=3.12" +requires-python = "==3.12.*" resolution-markers = [ - "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.14' and sys_platform == 'win32'", - "python_full_version < '3.14' and sys_platform == 'emscripten'", - "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + 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