diff --git a/dpsynth/text/dp_sft.py b/dpsynth/text/dp_sft.py index 0318b44d..825d4ed7 100644 --- a/dpsynth/text/dp_sft.py +++ b/dpsynth/text/dp_sft.py @@ -24,7 +24,7 @@ num_bands=1, iterations=100, expected_participations=1.0, ) fine_tuner = DPFineTuner( - model_variant=model.GemmaModel.default('gemma3_270m_it'), + model_variant=model.GemmaModel.default('gemma4_e2b_it'), mechanism_config=config, ).configure(budget=0.5) @@ -45,10 +45,9 @@ from dpsynth import api from dpsynth.text import dp_trainer from dpsynth.text import model -from gemma import gm -from gemma import peft +from flax import nnx +import jax.numpy as jnp from jax_privacy import execution_plan -from jax_privacy import training import optax @@ -60,12 +59,10 @@ class FineTuneResult: excluded. Attributes: - model: The LoRA-wrapped model architecture. - params: Pretrained and trained LoRA parameters merged into a single tree. + model: The LoRA-wrapped model with trained parameters merged in. """ - model: gm.nn.LoRA - params: training.Params + model: nnx.Module @dataclasses.dataclass(frozen=True) @@ -145,29 +142,33 @@ def __call__( """ dataset = model.tokenize_texts( data, - model_variant=self.model_variant, max_seq_length=self.max_seq_length, ) lora_config = model.LoraConfig(rank=self.lora_rank) - module, frozen_params, trainable_params = model.load_gemma( + loaded_model = model.load_gemma( self.model_variant, lora_config, ) + graphdef, trainable_params, frozen_params = nnx.split( + loaded_model, nnx.LoRAParam, ... + ) - def loss_fn(trainable_params, batch, prng): + def loss_fn(params, data, prng): del prng # Deterministic forward pass (no dropout during DP training). - full_params = peft.merge_params(frozen_params, trainable_params) - return model.sft_loss_fn(module, full_params, batch) + bf16_params = optax.tree.cast(params, jnp.bfloat16) + merged = nnx.merge(graphdef, bf16_params, frozen_params) + return model.sft_loss_fn(merged, data) trainer = dp_trainer.DPTrainer( mechanism_config=self.mechanism_config, - init_params=trainable_params, - loss_fn=loss_fn, # pyrefly: ignore[bad-argument-type] + init_params=optax.tree.cast(trainable_params, jnp.float32), + loss_fn=loss_fn, optimizer=self.optimizer, performance_flags=self.performance_flags, ) state = trainer(rng=rng, data=dataset) - merged = peft.merge_params(frozen_params, state.params) # pyrefly: ignore[bad-argument-type] - return FineTuneResult(model=module, params=merged) + bf16_params = optax.tree.cast(state.params, jnp.bfloat16) + merged = nnx.merge(graphdef, bf16_params, frozen_params) + return FineTuneResult(model=merged) diff --git a/dpsynth/text/dp_trainer.py b/dpsynth/text/dp_trainer.py index 813daf89..cda861c7 100644 --- a/dpsynth/text/dp_trainer.py +++ b/dpsynth/text/dp_trainer.py @@ -57,7 +57,7 @@ import dp_accounting from dpsynth import api from jax_privacy import execution_plan -from jax_privacy.experimental import training +from jax_privacy import training import optax diff --git a/dpsynth/text/model.py b/dpsynth/text/model.py index 0f053654..2105cfb2 100644 --- a/dpsynth/text/model.py +++ b/dpsynth/text/model.py @@ -24,51 +24,36 @@ from absl import logging from etils import epath -from gemma import gm -from gemma import peft +from flax import nnx import jax import jax.numpy as jnp -from kauldron import kd import numpy as np import optax - -# Type alias for Gemma model params (nested dict of jax.Array). -Params = Any +from orbax import checkpoint as ocp +import qwix +import tunix +from tunix.models.gemma4 import model as gemma4_model +from tunix.models.gemma4 import params as gemma4_params # Supported model names for GemmaModel.default(). ModelName = Literal[ - 'gemma3_270m_it', - 'gemma3_1b_it', - 'gemma3_4b_it', 'gemma4_e2b_it', 'gemma4_e4b_it', ] -_DEFAULTS: dict[str, tuple[Callable[..., Any], str, Callable[..., Any]]] = { - 'gemma3_270m_it': ( - gm.nn.Gemma3_270M, - gm.ckpts.CheckpointPath.GEMMA3_270M_IT, - gm.text.Gemma3Tokenizer, - ), - 'gemma3_1b_it': ( - gm.nn.Gemma3_1B, - gm.ckpts.CheckpointPath.GEMMA3_1B_IT, - gm.text.Gemma3Tokenizer, - ), - 'gemma3_4b_it': ( - gm.nn.Gemma3_4B, - gm.ckpts.CheckpointPath.GEMMA3_4B_IT, - gm.text.Gemma3Tokenizer, - ), +_DEFAULT_LORA_TARGETS = ( + r'.*q_einsum|.*k_einsum|.*kv_einsum|.*qkv_einsum|.*attn_vec_einsum' + r'|.*gate_proj|.*up_proj|.*down_proj' +) + +_DEFAULTS: dict[str, tuple[Callable[..., Any], str]] = { 'gemma4_e2b_it': ( - gm.nn.Gemma4_E2B, - gm.ckpts.CheckpointPath.GEMMA4_E2B_IT, - gm.text.Gemma4Tokenizer, + gemma4_model.ModelConfig.gemma4_e2b, + gemma4_params.GEMMA4_E2B_IT, ), 'gemma4_e4b_it': ( - gm.nn.Gemma4_E4B, - gm.ckpts.CheckpointPath.GEMMA4_E4B_IT, - gm.text.Gemma4Tokenizer, + gemma4_model.ModelConfig.gemma4_e4b, + gemma4_params.GEMMA4_E4B_IT, ), } @@ -77,17 +62,16 @@ class GemmaModel: """Specification for a Gemma model variant.""" - model_class: Callable[..., Any] + name: ModelName checkpoint_path: str - tokenizer_class: Callable[..., Any] @classmethod def default(cls, name: ModelName) -> GemmaModel: """Constructs a GemmaModel from a preset name.""" if name not in _DEFAULTS: raise ValueError(f'Unknown model {name!r}. Options: {list(_DEFAULTS)}') - model_class, checkpoint_path, tokenizer_class = _DEFAULTS[name] - return cls(model_class, checkpoint_path, tokenizer_class) + _, checkpoint_path = _DEFAULTS[name] + return cls(name=name, checkpoint_path=checkpoint_path) @dataclasses.dataclass(frozen=True) @@ -95,7 +79,9 @@ class LoraConfig: """Configuration for LoRA adaptation.""" rank: int = 16 - dtype: Any = jnp.bfloat16 + alpha: float | None = None + targets: str = _DEFAULT_LORA_TARGETS + weight_qtype: str | None = None def load_gemma( @@ -103,8 +89,8 @@ def load_gemma( lora_config: LoraConfig, *, checkpoint_path: epath.PathLike | None = None, - sharding: Any = None, -) -> tuple[gm.nn.LoRA, Params, Params]: + mesh: jax.sharding.Mesh | None = None, +) -> nnx.Module: """Loads a Gemma model with LoRA adapters. Args: @@ -112,36 +98,40 @@ def load_gemma( lora_config: LoRA adapter configuration. checkpoint_path: Checkpoint to restore from. If None, loads pretrained base weights from ``model_variant.checkpoint_path`` and initializes fresh LoRA - adapters. If a checkpoint is provided, restores base and LoRA params. - sharding: Optional sharding tree to constrain parameters across devices. + adapters. If a checkpoint is provided, restores LoRA params. + mesh: Optional sharding mesh to constrain parameters across devices. Returns: - ``(module, base_params, lora_params)`` tuple. + The LoRA-wrapped ``nnx.Module``. """ - base_model = model_variant.model_class() - model = gm.nn.LoRA( + config_fn, _ = _DEFAULTS[model_variant.name] + loaded = gemma4_params.create_model_from_checkpoint( + model_variant.checkpoint_path, + dataclasses.replace(config_fn(), dtype=jnp.bfloat16), + mesh=mesh, + ) + alpha = ( + lora_config.alpha + if lora_config.alpha is not None + else float(lora_config.rank) + ) + lora_provider = qwix.LoraProvider( + module_path=lora_config.targets, rank=lora_config.rank, - model=base_model, - dtype=lora_config.dtype, + alpha=alpha, + weight_qtype=lora_config.weight_qtype, ) + lora_model = qwix.apply_lora_to_model( + loaded, lora_provider, **loaded.get_model_input(), rngs=nnx.Rngs(0) + ) + assert isinstance(lora_model, nnx.Module) if checkpoint_path is not None: - all_params = gm.ckpts.load_params(checkpoint_path, sharding=sharding) - # LoRA params are present when loading from a lora checkpoint - base_params, lora_params = peft.split_params(all_params) # pyrefly: ignore[bad-argument-type] - else: - # When starting from scratch, initialize LoRA params - init_params = model.init( - jax.random.key(0), - tokens=jnp.ones((1, 64), dtype=jnp.int32), - )['params'] - _, lora_params = peft.split_params(init_params) - lora_params = kd.sharding.with_sharding_constraint(lora_params, sharding) - base_params = gm.ckpts.load_params( - model_variant.checkpoint_path, sharding=sharding - ) + load_lora_params(lora_model, checkpoint_path) - num_lora = optax.tree.size(lora_params) - num_base = optax.tree.size(base_params) + lora_state = nnx.state(lora_model, nnx.LoRAParam) + num_lora = sum(x.size for x in jax.tree.leaves(lora_state)) + total = sum(x.size for x in jax.tree.leaves(nnx.state(lora_model))) + num_base = total - num_lora logging.info( 'Loaded Gemma model w/ LoRA (rank=%d): %d lora (%.4f%%), %d base', lora_config.rank, @@ -149,19 +139,33 @@ def load_gemma( 100.0 * num_lora / (num_lora + num_base), num_base, ) - return model, base_params, lora_params + return lora_model + + +def save_lora_params(lora_model: nnx.Module, path: epath.PathLike) -> None: + """Saves trainable LoRA parameters from ``lora_model`` to ``path``.""" + lora_state = nnx.state(lora_model, nnx.LoRAParam) + checkpointer = ocp.StandardCheckpointer() + checkpointer.save(epath.Path(path), lora_state, force=True) + checkpointer.wait_until_finished() + + +def load_lora_params(lora_model: nnx.Module, path: epath.PathLike) -> None: + """Restores trainable LoRA parameters into ``lora_model`` in-place.""" + abstract_lora = nnx.state(lora_model, nnx.LoRAParam) + checkpointer = ocp.StandardCheckpointer() + restored = checkpointer.restore(epath.Path(path), abstract_lora) + nnx.update(lora_model, restored) def sft_loss_fn( - module: Any, - full_params: Params, + module: nnx.Module, data: dict[str, jax.Array], ) -> tuple[jax.Array, dict[str, jax.Array]]: """Cross-entropy next-token-prediction loss for supervised fine-tuning. Args: module: LoRA-wrapped Gemma model. - full_params: Full parameter dict (frozen + trainable, merged). data: Dict with ``'input_tokens'`` and ``'loss_mask'`` (int32 ``[B, L]``). Returns: @@ -170,8 +174,16 @@ def sft_loss_fn( input_tokens = data['input_tokens'] loss_mask = data['loss_mask'] - out = module.apply({'params': full_params}, tokens=input_tokens) - logits = out.logits[:, :-1, :] + pad_mask = input_tokens != 0 + positions = jnp.cumsum(pad_mask, axis=-1) - ( + jnp.cumsum(pad_mask, axis=-1) >= 1 + ) + seq_len = input_tokens.shape[-1] + causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=jnp.bool_)) + attention_mask = pad_mask[..., None, :] * causal_mask[None, :] + + logits, _ = module(input_tokens, positions, None, attention_mask) + logits = logits[:, :-1, :] targets = input_tokens[:, 1:] mask = loss_mask[:, 1:] @@ -180,26 +192,19 @@ def sft_loss_fn( return loss, {'loss': loss} -def format_prompt(prompt: str, tokenizer: Any) -> str: +def format_prompt(prompt: str) -> str: """Formats a prompt string with the Gemma user and model turn tags.""" - sp = tokenizer.special_tokens - sot, eot = ( - tokenizer.tokens[sp.START_OF_TURN], - tokenizer.tokens[sp.END_OF_TURN], - ) # Universal chat prompt format expected by Gemma instruction-tuned models. - return f'{sot}user\n{prompt}{eot}\n{sot}model\n' + return f'user\n{prompt}\nmodel\n' -def format_response(response: str, tokenizer: Any) -> str: +def format_response(response: str) -> str: """Formats a model response string with the end-of-turn tag.""" - eot = tokenizer.tokens[tokenizer.special_tokens.END_OF_TURN] - return f'{response}{eot}' + return f'{response}' def tokenize_texts( examples: Sequence[tuple[str, str]], - model_variant: GemmaModel, max_seq_length: int, ) -> dict[str, np.ndarray]: """Tokenizes (prompt, response) pairs for supervised fine-tuning. @@ -210,13 +215,12 @@ def tokenize_texts( Args: examples: Sequence of ``(prompt, response)`` string pairs. - model_variant: Determines which tokenizer and turn format to use. max_seq_length: Maximum sequence length (including special tokens). Returns: Dict with ``'input_tokens'`` and ``'loss_mask'`` (int32 ``[N, L]``). """ - tokenizer = model_variant.tokenizer_class() + tokenizer = gemma4_params.create_tokenizer() tokens = np.zeros((len(examples), max_seq_length), dtype=np.int32) mask = np.zeros((len(examples), max_seq_length), dtype=np.int32) @@ -224,10 +228,10 @@ def tokenize_texts( for i, (prompt, response) in enumerate(examples): # Embed turn tags as strings so SentencePiece handles tokenization # boundaries correctly (encoding pieces separately can shift BPE merges). - prompt_str = format_prompt(prompt, tokenizer) - response_str = format_response(response, tokenizer) - prompt_ids = tokenizer.encode(prompt_str, add_bos=True) - response_ids = tokenizer.encode(response_str, add_eos=True) + prompt_str = format_prompt(prompt) + response_str = format_response(response) + prompt_ids = [tokenizer.bos_id()] + tokenizer.EncodeAsIds(prompt_str) + response_ids = tokenizer.EncodeAsIds(response_str) + [tokenizer.eos_id()] ids = prompt_ids + response_ids length = min(len(ids), max_seq_length) @@ -251,23 +255,30 @@ class GemmaSampler: def __init__( self, *, - model: Any, - params: Params, + model: nnx.Module, + model_variant: GemmaModel, max_seq_length: int, temperature: float, ): - sampling_method = ( - gm.text.RandomSampling(temperature=temperature) - if temperature > 0 - else gm.text.Greedy() + config_fn, _ = _DEFAULTS[model_variant.name] + cfg = config_fn() + cache_config = tunix.CacheConfig( + cache_size=max_seq_length, + num_layers=cfg.num_layers, + num_kv_heads=cfg.num_kv_heads or cfg.num_heads, + head_dim=cfg.head_dim, ) - self._sampler = gm.text.Sampler( - model=model, - params=params, - cache_length=max_seq_length, - max_out_length=max_seq_length, - sampling=sampling_method, + self._max_seq_length = max_seq_length + self._temperature = temperature + self._sampler = tunix.Sampler( + transformer=model, + tokenizer=gemma4_params.create_tokenizer(), + cache_config=cache_config, ) + self._eos_tokens = [ + self._sampler.tokenizer.eos_id(), + *self._sampler.tokenizer.encode(''), + ] def __call__( self, @@ -288,15 +299,20 @@ def __call__( Returns: List of generated response strings corresponding to each prompt. """ - formatted = [format_prompt(p, self._sampler.tokenizer) for p in prompts] + formatted = [format_prompt(p) for p in prompts] results: list[str] = [] for i, batch_items in enumerate(itertools.batched(formatted, batch_size)): cur_size = len(batch_items) batch = list(batch_items) + [batch_items[-1]] * (batch_size - cur_size) t0 = time.perf_counter() - responses = self._sampler.sample( - batch, sharding=kd.sharding.FIRST_DIM, rng=rng + i + out = self._sampler( + input_strings=batch, + max_generation_steps=self._max_seq_length // 2, + temperature=self._temperature, + top_p=1.0 if self._temperature > 0 else None, + eos_tokens=self._eos_tokens, + seed=rng + i, ) elapsed = time.perf_counter() - t0 logging.info( @@ -306,5 +322,5 @@ def __call__( elapsed, cur_size / elapsed if elapsed > 0 else 0.0, ) - results.extend([str(r) for r in responses[:cur_size]]) + results.extend([str(r) for r in out.text[:cur_size]]) return results diff --git a/examples/finetune_pubmed.py b/examples/finetune_pubmed.py index beffe76b..8ab6daa3 100644 --- a/examples/finetune_pubmed.py +++ b/examples/finetune_pubmed.py @@ -36,20 +36,14 @@ from dpsynth.text import dp_sft from dpsynth.text import model from etils import epath -from gemma import gm -from gemma import peft import jax from jax_privacy import execution_plan -from kauldron import kd import optax _MODEL = flags.DEFINE_enum( 'model', - 'gemma3_270m_it', + 'gemma4_e2b_it', [ - 'gemma3_270m_it', - 'gemma3_1b_it', - 'gemma3_4b_it', 'gemma4_e2b_it', 'gemma4_e4b_it', ], @@ -151,23 +145,19 @@ def load_finetuned() -> dp_sft.FineTuneResult: Returns: A FineTuneResult holding the reconstructed model and loaded params. """ - module, base_params, lora_params = model.load_gemma( + loaded = model.load_gemma( _model_variant(), model.LoraConfig(rank=_LORA_RANK.value), checkpoint_path=_ckpt_dir(), - sharding=kd.sharding.FSDPSharding(), - ) - return dp_sft.FineTuneResult( - model=module, - params=peft.merge_params(base_params, lora_params), ) + return dp_sft.FineTuneResult(model=loaded) def sample(result: dp_sft.FineTuneResult) -> None: """Generates synthetic abstracts and writes them to as JSONL.""" sampler = model.GemmaSampler( model=result.model, - params=result.params, + model_variant=_model_variant(), max_seq_length=_MAX_SEQ_LENGTH.value, temperature=_TEMPERATURE.value, ) @@ -200,11 +190,7 @@ def main(_) -> None: if _TRAIN.value: result = train() - gm.ckpts.save_params( - typing.cast(typing.Mapping[str, typing.Any], result.params), - _ckpt_dir(), - wait_until_finished=True, - ) + model.save_lora_params(result.model, _ckpt_dir()) logging.info('Saved fine-tuned model to %s.', _ckpt_dir()) else: result = 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absltest from dpsynth.text import dp_sft from dpsynth.text import model +from flax import nnx import jax import jax.numpy as jnp from jax_privacy import execution_plan @@ -37,78 +38,71 @@ def _default_config(): ) -def _make_tiny_model_and_params(vocab_size=8, embed_dim=4, seq_len=4): - """Creates a minimal Flax Linen model and random params for testing.""" - import flax.linen as nn # pylint: disable=g-import-not-at-top +def _make_tiny_model(vocab_size=8, embed_dim=4): + """Creates a minimal Flax NNX model for testing.""" - @dataclasses.dataclass - class _Output: - logits: jnp.ndarray + class TinyModel(nnx.Module): - class TinyModel(nn.Module): - vocab_size: int - embed_dim: int + def __init__(self, rngs: nnx.Rngs): + self.embed = nnx.Embed(vocab_size, embed_dim, rngs=rngs) + self.dense = nnx.Linear(embed_dim, vocab_size, rngs=rngs) - @nn.compact - def __call__(self, tokens, **kwargs): - x = nn.Embed(self.vocab_size, self.embed_dim)(tokens) - logits = nn.Dense(self.vocab_size)(x) - return _Output(logits=logits) + def __call__(self, tokens, *args): + del args + return self.dense(self.embed(tokens)), None - module = TinyModel(vocab_size=vocab_size, embed_dim=embed_dim) - dummy = jnp.ones((1, seq_len), dtype=jnp.int32) - params = module.init(jax.random.key(0), dummy)['params'] - return module, params + return TinyModel(nnx.Rngs(0)) class SftLossFnTest(absltest.TestCase): def test_loss_is_finite_and_positive(self): - module, params = _make_tiny_model_and_params() + module = _make_tiny_model() data = { 'input_tokens': jnp.array([[1, 2, 3, 4, 0]], dtype=jnp.int32), 'loss_mask': jnp.array([[1, 1, 1, 1, 0]], dtype=jnp.int32), } - loss, aux = model.sft_loss_fn(module, params, data) + loss, aux = model.sft_loss_fn(module, data) self.assertTrue(jnp.isfinite(loss)) self.assertGreater(float(loss), 0.0) self.assertIn('loss', aux) def test_loss_decreases_with_gradient_descent(self): """Gradient descent on a tiny model should reduce loss.""" - module, params = _make_tiny_model_and_params() + module = _make_tiny_model() tokens = jnp.array([[1, 2, 3, 1]], dtype=jnp.int32) mask = jnp.ones_like(tokens) data = {'input_tokens': tokens, 'loss_mask': mask} - loss_before = float(model.sft_loss_fn(module, params, data)[0]) + loss_before = float(model.sft_loss_fn(module, data)[0]) # Overfit on this single example. + graphdef, params = nnx.split(module) for _ in range(200): def step_fn(p): - return model.sft_loss_fn(module, p, data) + return model.sft_loss_fn(nnx.merge(graphdef, p), data) (_, _), grads = jax.value_and_grad(step_fn, has_aux=True)(params) params = jax.tree.map(lambda p, g: p - 0.1 * g, params, grads) - loss_after = float(model.sft_loss_fn(module, params, data)[0]) + loss_after = float(model.sft_loss_fn(nnx.merge(graphdef, params), data)[0]) self.assertLess(loss_after, loss_before) def test_masked_tokens_do_not_contribute(self): - module, params = _make_tiny_model_and_params() + module = _make_tiny_model() tokens = jnp.array([[1, 2, 3, 4]], dtype=jnp.int32) mask_short = jnp.array([[1, 1, 0, 0]], dtype=jnp.int32) mask_full = jnp.array([[1, 1, 1, 1]], dtype=jnp.int32) loss_short = float( model.sft_loss_fn( - module, params, {'input_tokens': tokens, 'loss_mask': mask_short} + module, {'input_tokens': tokens, 'loss_mask': mask_short} )[0] ) loss_full = float( model.sft_loss_fn( - module, params, {'input_tokens': tokens, 'loss_mask': mask_full} + module, {'input_tokens': tokens, 'loss_mask': mask_full} )[0] ) # Different masks should give different losses. @@ -118,14 +112,13 @@ def test_masked_tokens_do_not_contribute(self): class GemmaModelTest(absltest.TestCase): def test_default_preset(self): - m = model.GemmaModel.default('gemma3_270m_it') + m = model.GemmaModel.default('gemma4_e2b_it') + self.assertEqual(m.name, 'gemma4_e2b_it') self.assertIsNotNone(m.checkpoint_path) - self.assertTrue(callable(m.model_class)) - self.assertTrue(callable(m.tokenizer_class)) def test_custom_checkpoint_path(self): m = dataclasses.replace( - model.GemmaModel.default('gemma3_270m_it'), + model.GemmaModel.default('gemma4_e2b_it'), checkpoint_path='/custom/path', ) self.assertEqual(m.checkpoint_path, '/custom/path') @@ -140,19 +133,21 @@ class LoraConfigTest(absltest.TestCase): def test_defaults(self): cfg = model.LoraConfig() self.assertEqual(cfg.rank, 16) - self.assertEqual(cfg.dtype, jnp.bfloat16) + self.assertIsNone(cfg.alpha) + self.assertIsNone(cfg.weight_qtype) def test_custom_values(self): - cfg = model.LoraConfig(rank=8, dtype=jnp.float32) + cfg = model.LoraConfig(rank=8, alpha=16.0, targets='.*q_einsum') self.assertEqual(cfg.rank, 8) - self.assertEqual(cfg.dtype, jnp.float32) + self.assertEqual(cfg.alpha, 16.0) + self.assertEqual(cfg.targets, '.*q_einsum') class DPFineTunerTest(absltest.TestCase): def test_creates_valid_mechanism_config(self): mechanism = dp_sft.DPFineTuner( - model_variant=model.GemmaModel.default('gemma3_270m_it'), + model_variant=model.GemmaModel.default('gemma4_e2b_it'), mechanism_config=_default_config(), ) self.assertEqual(mechanism.mechanism_config.iterations, 100) @@ -160,7 +155,7 @@ def test_creates_valid_mechanism_config(self): def test_calibrate_sets_noise_multiplier(self): mechanism = dp_sft.DPFineTuner( - model_variant=model.GemmaModel.default('gemma3_270m_it'), + model_variant=model.GemmaModel.default('gemma4_e2b_it'), mechanism_config=_default_config(), ).configure(budget=0.5) self.assertIsNotNone(mechanism.mechanism_config.noise_multiplier) @@ -168,7 +163,7 @@ def test_calibrate_sets_noise_multiplier(self): def test_calibrate_noise_formula(self): mechanism = dp_sft.DPFineTuner( - model_variant=model.GemmaModel.default('gemma3_270m_it'), + model_variant=model.GemmaModel.default('gemma4_e2b_it'), mechanism_config=_default_config(), ).configure(budget=0.5) # Single band: rounds = iterations, sigma = sqrt(T / (2*rho)). @@ -179,7 +174,7 @@ def test_calibrate_noise_formula(self): def test_dp_event_before_calibration_raises(self): mechanism = dp_sft.DPFineTuner( - model_variant=model.GemmaModel.default('gemma3_270m_it'), + model_variant=model.GemmaModel.default('gemma4_e2b_it'), mechanism_config=_default_config(), ) with self.assertRaises(ValueError): @@ -187,7 +182,7 @@ def test_dp_event_before_calibration_raises(self): def test_dp_event_after_calibration(self): mechanism = dp_sft.DPFineTuner( - model_variant=model.GemmaModel.default('gemma3_270m_it'), + model_variant=model.GemmaModel.default('gemma4_e2b_it'), mechanism_config=_default_config(), ).configure(budget=0.5) event = mechanism.dp_event