From e028f2a63b77da77985a8cc25efabe93b75e3674 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Thu, 9 Jul 2026 23:32:33 +0200 Subject: [PATCH 01/34] chore: gitignore .worktrees/ --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index 290e727..63944e9 100644 --- a/.gitignore +++ b/.gitignore @@ -155,3 +155,6 @@ cython_debug/ .vscode/ .DS_Store + +# git worktrees for isolated feature work +.worktrees/ From 2b09b11d9e7a48ad4a4fb07601225db4ac85162b Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Fri, 10 Jul 2026 09:12:36 +0200 Subject: [PATCH 02/34] refactor: move model zoo (DiT, MLP, edm, rfm, samplers) from blaxbird package to examples/_common --- README.md | 9 +- .../experimental/consistency_distillation.py | 51 - blaxbird/_src/experimental/nn/unet.py | 289 -- blaxbird/experimental.py | 32 - .../_common}/__init__.py | 0 .../experimental => examples/_common}/edm.py | 4 +- .../_common}/nn/__init__.py | 0 .../_common}/nn/dit.py | 4 +- .../_common}/nn/embedding.py | 0 .../_common}/nn/mlp.py | 0 .../_common}/parameterizations.py | 0 .../experimental => examples/_common}/rfm.py | 4 +- .../_common}/samplers.py | 2 +- examples/cifar10_flow_matching/main.py | 9 +- examples/mnist_classification/main.py | 3 + pyproject.toml | 6 +- uv.lock | 3417 +++++++++-------- 17 files changed, 1734 insertions(+), 2096 deletions(-) delete mode 100644 blaxbird/_src/experimental/consistency_distillation.py delete mode 100644 blaxbird/_src/experimental/nn/unet.py delete mode 100644 blaxbird/experimental.py rename {blaxbird/_src/experimental => examples/_common}/__init__.py (100%) rename {blaxbird/_src/experimental => examples/_common}/edm.py (94%) rename {blaxbird/_src/experimental => examples/_common}/nn/__init__.py (100%) rename {blaxbird/_src/experimental => examples/_common}/nn/dit.py (98%) rename {blaxbird/_src/experimental => examples/_common}/nn/embedding.py (100%) rename {blaxbird/_src/experimental => examples/_common}/nn/mlp.py (100%) rename {blaxbird/_src/experimental => examples/_common}/parameterizations.py (100%) rename {blaxbird/_src/experimental => examples/_common}/rfm.py (92%) rename {blaxbird/_src/experimental => examples/_common}/samplers.py (98%) diff --git a/README.md b/README.md index 0f19504..6e8ced0 100644 --- a/README.md +++ b/README.md @@ -15,11 +15,10 @@ Using `blaxbird` one can - distribute data and model weights over multiple processes or GPUs, - define hooks that are periodically called during training. -In addition, `blaxbird` offers high-quality implementations of common neural network modules and algorithms, such as: - -- MLPs, DiTs, UNets, -- Flow Matching and Denoising Score Matching (EDM schedules) models with Euler and Heun samplers, -- Consistency Distillation/Matching models. +`blaxbird` is a training framework, not a model zoo -- it doesn't ship +neural network architectures. See [examples](examples) for worked +end-to-end examples (including a DiT trained with flow matching on +CIFAR-10) that define their own models and hand them to `blaxbird`. ## Example diff --git a/blaxbird/_src/experimental/consistency_distillation.py b/blaxbird/_src/experimental/consistency_distillation.py deleted file mode 100644 index df87fc5..0000000 --- a/blaxbird/_src/experimental/consistency_distillation.py +++ /dev/null @@ -1,51 +0,0 @@ -import numpy as np -from flax import nnx -from jax import numpy as jnp -from jax import random as jr - -from blaxbird._src.experimental import samplers -from blaxbird._src.experimental.parameterizations import RFMConfig - - -def _forward_process(inputs, times, noise): - new_shape = (-1,) + tuple(np.ones(inputs.ndim - 1, dtype=np.int32).tolist()) - times = times.reshape(new_shape) - inputs_t = times * inputs + (1.0 - times) * noise - return inputs_t - - -def rfm(config: RFMConfig = RFMConfig()): - """Construct rectified flow matching functions. - - Args: - config: a FlowMatchingConfig object - - Returns: - returns a tuple consisting of train_step, val_step and sampling functions - """ - parameterization = config.parameterization - - def _loss_fn(model, rng_key, batch): - inputs = batch["inputs"] - time_key, rng_key = jr.split(rng_key) - times = jr.uniform(time_key, shape=(inputs.shape[0],)) - times = ( - times * (parameterization.t_max - parameterization.t_eps) - + parameterization.t_eps - ) - noise_key, rng_key = jr.split(rng_key) - noise = jr.normal(noise_key, inputs.shape) - inputs_t = _forward_process(inputs, times, noise) - vt = model(inputs=inputs_t, times=times, context=batch.get("context")) - ut = inputs - noise - loss = jnp.mean(jnp.square(ut - vt)) - return loss - - def train_step(model, rng_key, batch, **kwargs): - return nnx.value_and_grad(_loss_fn)(model, rng_key, batch) - - def val_step(model, rng_key, batch, **kwargs): - return _loss_fn(model, rng_key, batch) - - sampler = getattr(samplers, config.sampler + "_sample_fn")(config) - return train_step, val_step, sampler diff --git a/blaxbird/_src/experimental/nn/unet.py b/blaxbird/_src/experimental/nn/unet.py deleted file mode 100644 index 3c11dbf..0000000 --- a/blaxbird/_src/experimental/nn/unet.py +++ /dev/null @@ -1,289 +0,0 @@ -import jax -from einops import rearrange -from flax import nnx -from jax import numpy as jnp - -from blaxbird._src.experimental.nn.embedding import timestep_embedding -from blaxbird._src.experimental.nn.mlp import MLP - - -def _modulate(inputs, shift, scale): # noqa: ANN001, ANN202 - return inputs * (1.0 + scale[:, None]) + shift[:, None] - - -def get_sinusoidal_embedding_1d(length, embedding_dim): # noqa: ANN001, ANN202 - emb = timestep_embedding(length.reshape(-1), embedding_dim) - return emb - - -def sinusoidal_init(shape, dtype): # noqa: ANN001, ANN202 - def get_sinusoidal_embedding_2d(grid, embedding_dim): # noqa: ANN001, ANN202 - emb_h = get_sinusoidal_embedding_1d(grid[0], embedding_dim // 2) - emb_w = get_sinusoidal_embedding_1d(grid[1], embedding_dim // 2) - emb = jnp.concatenate([emb_h, emb_w], axis=1) - return emb - - _, n_h_patches, n_w_patches, embedding_dim = shape - grid_h = jnp.arange(n_h_patches, dtype=jnp.float32) - grid_w = jnp.arange(n_w_patches, dtype=jnp.float32) - grid = jnp.meshgrid(grid_w, grid_h) - - grid = jnp.stack(grid, axis=0) - grid = grid.reshape([2, 1, n_w_patches, n_h_patches]) - pos_embed = get_sinusoidal_embedding_2d(grid, embedding_dim) - - return jnp.expand_dims(pos_embed, 0) # (1, H*W, D) - - -class OutProjection(nnx.Module): - def __init__( # noqa: PLR0913 - self, hidden_size, n_embedding_features, patch_size, out_channels, *, rngs - ): - super().__init__() - self.ada = nnx.Sequential( - nnx.silu, nnx.Linear(n_embedding_features, 2 * hidden_size, rngs=rngs) - ) - self.norm = nnx.LayerNorm(hidden_size, rngs=rngs) - self.out = nnx.Linear( - hidden_size, patch_size * patch_size * out_channels, rngs=rngs - ) - - def __call__(self, inputs, context): - shift, scale = jnp.split(self.ada(context), 2, -1) - outs = self.out(_modulate(self.norm(inputs), shift, scale)) - return outs - - -class DiTBlock(nnx.Module): - def __init__( # noqa: PLR0913 - self, - hidden_size: int, - n_embedding_features: int, - *, - n_heads: int, - dropout_rate: float = 0.1, - rngs: nnx.rnglib.Rngs, - ): - """Diffusion-Transformer block. - - Args: - hidden_size: number of features of the hidden layers - n_embedding_features: number o features of time embedding - n_heads: number of transformer heads - dropout_rate: float - rngs: random keys - """ - super().__init__() - self.ada = nnx.Sequential( - nnx.silu, nnx.Linear(n_embedding_features, hidden_size * 6, rngs=rngs) - ) - - self.layer_norm1 = nnx.LayerNorm( - hidden_size, use_scale=False, use_bias=False, rngs=rngs - ) - self.self_attn = nnx.MultiHeadAttention( - num_heads=n_heads, in_features=hidden_size, rngs=rngs, decode=False - ) - self.layer_norm2 = nnx.LayerNorm( - hidden_size, use_scale=False, use_bias=False, rngs=rngs - ) - self.mlp = MLP( - hidden_size, - (hidden_size * 4, hidden_size), - dropout_rate=dropout_rate, - rngs=rngs, - ) - - def __call__(self, inputs: jax.Array, context: jax.Array) -> jax.Array: - """Transform inputs through the DiT block. - - Args: - inputs: input array - context: values to condition on - - Returns: - returns a jax.Array - """ - hidden = inputs - adaln_norm = self.ada(context) - attn, gate = jnp.split(adaln_norm, 2, axis=-1) - - pre_shift, pre_scale, post_scale = jnp.split(attn, 3, -1) - intermediate = _modulate(self.layer_norm1(hidden), pre_shift, pre_scale) - intermediate = self.self_attn(intermediate) - hidden = hidden + post_scale[:, None] * intermediate - - pre_shift, pre_scale, post_scale = jnp.split(gate, 3, -1) - intermediate = _modulate(self.layer_norm2(hidden), pre_shift, pre_scale) - intermediate = self.mlp(intermediate) - outputs = hidden + post_scale[:, None] * intermediate - - return outputs - - -class DiT(nnx.Module): - def __init__( # noqa: PLR0913 - self, - image_size: tuple[int, int, int], - n_hidden_channels: int, - patch_size: int, - n_layers: int, - n_heads: int, - n_embedding_features=256, - dropout_rate=0.0, - *, - rngs: nnx.rnglib.Rngs, - ): - """Diffusion-Transformer. - - Args: - image_size: size of the image, e.g., (32, 32, 3) - n_hidden_channels: number if hidden channels - patch_size: size of each path - n_layers: integer - n_heads: integer - n_embedding_features: integer - dropout_rate: float - rngs: random keys - """ - self.image_size = image_size - self.n_in_channels = image_size[-1] - self.n_embedding_features = n_embedding_features - self.patch_size = patch_size - self.time_embedding = nnx.Sequential( - nnx.Linear(n_embedding_features, n_embedding_features, rngs=rngs), - nnx.swish, - nnx.Linear(n_embedding_features, n_embedding_features, rngs=rngs), - nnx.swish, - ) - self.patchify = nnx.Conv( - self.n_in_channels, - n_hidden_channels, - (patch_size, patch_size), - (patch_size, patch_size), - padding="VALID", - kernel_init=nnx.initializers.xavier_uniform(), - rngs=rngs, - ) - self.patch_embedding = nnx.Param( - sinusoidal_init( - ( - 1, - image_size[0] // patch_size, - image_size[1] // patch_size, - n_hidden_channels, - ), - None, - ), - ) - self.dit_blocks = tuple( - [ - DiTBlock( - n_hidden_channels, - n_embedding_features, - n_heads=n_heads, - dropout_rate=dropout_rate, - rngs=rngs, - ) - for _ in range(n_layers) - ] - ) - self.out_projection = OutProjection( - n_hidden_channels, - n_embedding_features, - patch_size, - self.n_in_channels, - rngs=rngs, - ) - - def _patchify(self, inputs): - n_h_patches = self.image_size[0] // self.patch_size - n_w_patches = self.image_size[1] // self.patch_size - hidden = self.patchify(inputs) - outputs = rearrange( - hidden, "b h w c -> b (h w) c", h=n_h_patches, w=n_w_patches - ) - return outputs - - def _unpatchify(self, inputs): - H = self.image_size[0] // self.patch_size - W = self.image_size[1] // self.patch_size - P = Q = self.patch_size - hidden = jnp.reshape(inputs, (-1, H, W, P, Q, self.n_in_channels)) - outputs = rearrange( - hidden, "b h w p q c -> b (h p) (w q) c", h=H, w=W, p=P, q=Q - ) - return outputs - - def _embed(self, inputs): - return inputs + jax.lax.stop_gradient(self.patch_embedding.value) - - def __call__( - self, inputs: jax.Array, times: jax.Array, context: jax.Array = None - ): - """Transform inputs through the DiT. - - Args: - inputs: input in image form - times: one-dimensional array - context: conditioning variable in image form - - Returns: - returns a jax - """ - hidden = self._patchify(inputs) - hidden = self._embed(hidden) - times = self.time_embedding( - timestep_embedding(times, self.n_embedding_features) - ) - - for block in self.dit_blocks: - hidden = block(hidden, context=times) - - hidden = self.out_projection(hidden, times) - outputs = self._unpatchify(hidden) - return outputs - - -def SmallDiT(image_size, patch_size=2, **kwargs): - return DiT( - image_size, - n_hidden_channels=384, - patch_size=patch_size, - n_layers=12, - n_heads=6, - **kwargs, - ) - - -def BaseDiT(image_size, patch_size=2, **kwargs): - return DiT( - image_size, - n_hidden_channels=768, - patch_size=patch_size, - n_layers=12, - n_heads=12, - **kwargs, - ) - - -def LargeDiT(image_size, patch_size=2, **kwargs): - return DiT( - image_size, - n_hidden_channels=1024, - patch_size=patch_size, - n_layers=24, - n_heads=16, - **kwargs, - ) - - -def XtraLargeDiT(image_size, patch_size=2, **kwargs): - return DiT( - image_size, - n_hidden_channels=1152, - patch_size=patch_size, - n_layers=28, - n_heads=16, - **kwargs, - ) diff --git a/blaxbird/experimental.py b/blaxbird/experimental.py deleted file mode 100644 index 73e0cfa..0000000 --- a/blaxbird/experimental.py +++ /dev/null @@ -1,32 +0,0 @@ -"""Experimental models that might be moved to the main code base.""" - -from blaxbird._src.experimental.edm import EDMConfig, edm -from blaxbird._src.experimental.nn.dit import ( - BaseDiT, - DiT, - DiTBlock, - LargeDiT, - SmallDiT, - XtraLargeDiT, -) -from blaxbird._src.experimental.nn.mlp import MLP -from blaxbird._src.experimental.rfm import ( - RFMConfig, - rfm, -) - -__all__ = [ - "edm", - "EDMConfig", - "rfm", - "RFMConfig", - # - "DiT", - "DiTBlock", - "SmallDiT", - "BaseDiT", - "LargeDiT", - "XtraLargeDiT", - # - "MLP", -] diff --git a/blaxbird/_src/experimental/__init__.py b/examples/_common/__init__.py similarity index 100% rename from blaxbird/_src/experimental/__init__.py rename to examples/_common/__init__.py diff --git a/blaxbird/_src/experimental/edm.py b/examples/_common/edm.py similarity index 94% rename from blaxbird/_src/experimental/edm.py rename to examples/_common/edm.py index cb06227..5f9330b 100644 --- a/blaxbird/_src/experimental/edm.py +++ b/examples/_common/edm.py @@ -3,8 +3,8 @@ from jax import numpy as jnp from jax import random as jr -from blaxbird._src.experimental import samplers -from blaxbird._src.experimental.parameterizations import EDMConfig +from _common import samplers +from _common.parameterizations import EDMConfig def edm(config: EDMConfig): diff --git a/blaxbird/_src/experimental/nn/__init__.py b/examples/_common/nn/__init__.py similarity index 100% rename from blaxbird/_src/experimental/nn/__init__.py rename to examples/_common/nn/__init__.py diff --git a/blaxbird/_src/experimental/nn/dit.py b/examples/_common/nn/dit.py similarity index 98% rename from blaxbird/_src/experimental/nn/dit.py rename to examples/_common/nn/dit.py index 3c11dbf..95e9ac1 100644 --- a/blaxbird/_src/experimental/nn/dit.py +++ b/examples/_common/nn/dit.py @@ -3,8 +3,8 @@ from flax import nnx from jax import numpy as jnp -from blaxbird._src.experimental.nn.embedding import timestep_embedding -from blaxbird._src.experimental.nn.mlp import MLP +from _common.nn.embedding import timestep_embedding +from _common.nn.mlp import MLP def _modulate(inputs, shift, scale): # noqa: ANN001, ANN202 diff --git a/blaxbird/_src/experimental/nn/embedding.py b/examples/_common/nn/embedding.py similarity index 100% rename from blaxbird/_src/experimental/nn/embedding.py rename to examples/_common/nn/embedding.py diff --git a/blaxbird/_src/experimental/nn/mlp.py b/examples/_common/nn/mlp.py similarity index 100% rename from blaxbird/_src/experimental/nn/mlp.py rename to examples/_common/nn/mlp.py diff --git a/blaxbird/_src/experimental/parameterizations.py b/examples/_common/parameterizations.py similarity index 100% rename from blaxbird/_src/experimental/parameterizations.py rename to examples/_common/parameterizations.py diff --git a/blaxbird/_src/experimental/rfm.py b/examples/_common/rfm.py similarity index 92% rename from blaxbird/_src/experimental/rfm.py rename to examples/_common/rfm.py index df87fc5..a352283 100644 --- a/blaxbird/_src/experimental/rfm.py +++ b/examples/_common/rfm.py @@ -3,8 +3,8 @@ from jax import numpy as jnp from jax import random as jr -from blaxbird._src.experimental import samplers -from blaxbird._src.experimental.parameterizations import RFMConfig +from _common import samplers +from _common.parameterizations import RFMConfig def _forward_process(inputs, times, noise): diff --git a/blaxbird/_src/experimental/samplers.py b/examples/_common/samplers.py similarity index 98% rename from blaxbird/_src/experimental/samplers.py rename to examples/_common/samplers.py index f220567..7d1f427 100644 --- a/blaxbird/_src/experimental/samplers.py +++ b/examples/_common/samplers.py @@ -5,7 +5,7 @@ from jax import numpy as jnp from jax import random as jr -from blaxbird._src.experimental.parameterizations import EDMConfig, RFMConfig +from _common.parameterizations import EDMConfig, RFMConfig def euler_sample_fn(config: RFMConfig): diff --git a/examples/cifar10_flow_matching/main.py b/examples/cifar10_flow_matching/main.py index 7a94859..d098fc9 100644 --- a/examples/cifar10_flow_matching/main.py +++ b/examples/cifar10_flow_matching/main.py @@ -1,5 +1,8 @@ import argparse import os +import sys + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) import dataloader import jax @@ -12,9 +15,9 @@ from jax import random as jr from jax.experimental import mesh_utils -import blaxbird from blaxbird import get_default_checkpointer, train_fn -from blaxbird.experimental import rfm +from _common import rfm +from _common.nn import dit # for getattr(dit, dit_type, ...) below def get_optimizer(model, lr=1e-4): @@ -97,7 +100,7 @@ def run(n_steps, eval_every_n_steps, n_eval_batches, dit_type, log_to_wandb): jr.key(0), os.path.join(outfolder, "data") ) - model = getattr(blaxbird.experimental, dit_type)( + model = getattr(dit, dit_type)( image_size=(32, 32, 3), rngs=nnx.rnglib.Rngs(jr.key(1)) ) train_step, val_step, sample_fn = rfm() diff --git a/examples/mnist_classification/main.py b/examples/mnist_classification/main.py index b0df4c5..f513351 100644 --- a/examples/mnist_classification/main.py +++ b/examples/mnist_classification/main.py @@ -1,5 +1,8 @@ import argparse import os +import sys + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) import dataloader import jax diff --git a/pyproject.toml b/pyproject.toml index 75376e8..60c7b50 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -19,9 +19,7 @@ classifiers = [ ] requires-python = ">=3.10" dependencies = [ - "einops>=0.8.1", "jax-ai-stack", - "ml-collections>=1.1.0", ] dynamic = ["version"] @@ -30,6 +28,7 @@ all = [] [dependency-groups] dev = [ + "chex", "gitlint", "jupyter", "pre-commit", @@ -44,6 +43,9 @@ examples = [ "protobuf==3.20.3", "matplotlib==3.1.0", "wandb>=0.21.1", + "einops>=0.8.1", + "chex", + "ml-collections>=1.1.0", ] [project.urls] diff --git a/uv.lock b/uv.lock index e90eabc..3ff1c5a 100644 --- a/uv.lock +++ b/uv.lock @@ -1,4 +1,5 @@ version = 1 +revision = 3 requires-python = ">=3.10" resolution-markers = [ "python_full_version < '3.11' and sys_platform != 'linux'", @@ 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registry and get_sampler_fn --- examples/_common/edm.py | 2 +- examples/_common/rfm.py | 2 +- examples/_common/samplers.py | 30 +++++++++++++++++++++++++++++- examples/_common/test_edm.py | 9 +++++++++ examples/_common/test_rfm.py | 9 +++++++++ examples/_common/test_samplers.py | 18 ++++++++++++++++++ 6 files changed, 67 insertions(+), 3 deletions(-) create mode 100644 examples/_common/test_edm.py create mode 100644 examples/_common/test_rfm.py create mode 100644 examples/_common/test_samplers.py diff --git a/examples/_common/edm.py b/examples/_common/edm.py index 5f9330b..12d0472 100644 --- a/examples/_common/edm.py +++ b/examples/_common/edm.py @@ -62,5 +62,5 @@ def train_step(model, rng_key, batch, **kwargs): def val_step(model, rng_key, batch, **kwargs): return loss_fn(model, rng_key, batch) - sampler = getattr(samplers, config.sampler + "sample_fn")(config) + sampler = samplers.get_sampler_fn(config.sampler)(config) return train_step, val_step, sampler diff --git a/examples/_common/rfm.py b/examples/_common/rfm.py index a352283..7336ba1 100644 --- a/examples/_common/rfm.py +++ b/examples/_common/rfm.py @@ -47,5 +47,5 @@ def train_step(model, rng_key, batch, **kwargs): def val_step(model, rng_key, batch, **kwargs): return _loss_fn(model, rng_key, batch) - sampler = getattr(samplers, config.sampler + "_sample_fn")(config) + sampler = samplers.get_sampler_fn(config.sampler)(config) return train_step, val_step, sampler diff --git a/examples/_common/samplers.py b/examples/_common/samplers.py index 7d1f427..8c30ab0 100644 --- a/examples/_common/samplers.py +++ b/examples/_common/samplers.py @@ -1,3 +1,5 @@ +from collections.abc import Callable + import chex import jax import numpy as np @@ -54,7 +56,7 @@ def sample_fn( return sample_fn -def heun_sampler_fn(config: EDMConfig): +def heun_sample_fn(config: EDMConfig): """Construct a Heun sampler for denoising score matching. Args: @@ -135,3 +137,29 @@ def sample_fn( return samples return sample_fn + + +SAMPLERS: dict[str, Callable] = { + "euler": euler_sample_fn, + "heun": heun_sample_fn, +} + + +def get_sampler_fn(name: str) -> Callable: + """Look up a sampler constructor by name. + + Args: + name: one of the registered sampler names, currently "euler" or "heun". + + Returns: + the sampler-constructor callable registered under `name`. Calling it + with a config object returns the actual `sample_fn`. + + Raises: + ValueError: if `name` is not a registered sampler. + """ + if name not in SAMPLERS: + raise ValueError( + f"unknown sampler {name!r}, expected one of {sorted(SAMPLERS)}" + ) + return SAMPLERS[name] diff --git a/examples/_common/test_edm.py b/examples/_common/test_edm.py new file mode 100644 index 0000000..8cb3579 --- /dev/null +++ b/examples/_common/test_edm.py @@ -0,0 +1,9 @@ +from _common.edm import edm +from _common.parameterizations import EDMConfig + + +def test_edm_default_config_constructs_without_error(): + train_step, val_step, sample_fn = edm(EDMConfig()) + assert callable(train_step) + assert callable(val_step) + assert callable(sample_fn) diff --git a/examples/_common/test_rfm.py b/examples/_common/test_rfm.py new file mode 100644 index 0000000..6c43a75 --- /dev/null +++ b/examples/_common/test_rfm.py @@ -0,0 +1,9 @@ +from _common.parameterizations import RFMConfig +from _common.rfm import rfm + + +def test_rfm_default_config_constructs_without_error(): + train_step, val_step, sample_fn = rfm(RFMConfig()) + assert callable(train_step) + assert callable(val_step) + assert callable(sample_fn) diff --git a/examples/_common/test_samplers.py b/examples/_common/test_samplers.py new file mode 100644 index 0000000..fbdb85a --- /dev/null +++ b/examples/_common/test_samplers.py @@ -0,0 +1,18 @@ +import pytest + +from _common import samplers + + +def test_get_sampler_fn_returns_euler(): + fn = samplers.get_sampler_fn("euler") + assert fn is samplers.euler_sample_fn + + +def test_get_sampler_fn_returns_heun(): + fn = samplers.get_sampler_fn("heun") + assert fn is samplers.heun_sample_fn + + +def test_get_sampler_fn_unknown_name_raises(): + with pytest.raises(ValueError, match="unknown sampler"): + samplers.get_sampler_fn("not-a-sampler") From 26d2cf1aee5f1454c4f4a2845698974cd4dd18a6 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Fri, 10 Jul 2026 20:31:52 +0200 Subject: [PATCH 04/34] refactor(parameterizations): add EDMParameterization.denoise --- examples/_common/edm.py | 18 +--------------- examples/_common/parameterizations.py | 24 ++++++++++++++++++++++ examples/_common/samplers.py | 23 ++------------------- examples/_common/test_edm.py | 21 +++++++++++++++++++ examples/_common/test_parameterizations.py | 21 +++++++++++++++++++ examples/_common/test_samplers.py | 21 +++++++++++++++++++ 6 files changed, 90 insertions(+), 38 deletions(-) create mode 100644 examples/_common/test_parameterizations.py diff --git a/examples/_common/edm.py b/examples/_common/edm.py index 12d0472..7c8a388 100644 --- a/examples/_common/edm.py +++ b/examples/_common/edm.py @@ -20,20 +20,6 @@ def edm(config: EDMConfig): """ parameterization = config.parameterization - def denoise(model, rng_key, inputs, sigma, context): - new_shape = (-1,) + tuple(np.ones(inputs.ndim - 1, dtype=np.int32).tolist()) - inputs_t = inputs * parameterization.in_scaling(sigma).reshape(new_shape) - noise_cond = parameterization.noise_conditioning(sigma) - outputs = model( - inputs=inputs_t, - context=context, - times=noise_cond, - ) - skip = inputs * parameterization.skip_scaling(sigma).reshape(new_shape) - outputs = outputs * parameterization.out_scaling(sigma).reshape(new_shape) - outputs = skip + outputs - return outputs - def loss_fn(model, rng_key, batch): inputs = batch["inputs"] new_shape = (-1,) + tuple(np.ones(inputs.ndim - 1, dtype=np.int32).tolist()) @@ -43,10 +29,8 @@ def loss_fn(model, rng_key, batch): sigma = parameterization.sigma(epsilon) noise = jr.normal(noise_key, inputs.shape) * sigma.reshape(new_shape) - denoise_key, rng_key = jr.split(rng_key) - target_hat = denoise( + target_hat = parameterization.denoise( model, - denoise_key, inputs=inputs + noise, sigma=sigma, context=batch.get("context"), diff --git a/examples/_common/parameterizations.py b/examples/_common/parameterizations.py index acf575e..a5e086c 100644 --- a/examples/_common/parameterizations.py +++ b/examples/_common/parameterizations.py @@ -1,5 +1,7 @@ import dataclasses +import jax +import numpy as np from jax import numpy as jnp @@ -56,6 +58,28 @@ def sigma_hat(self, sigma, num_steps): ) return sigma + gamma * sigma + def denoise( + self, model, inputs: jax.Array, sigma: jax.Array, context: jax.Array | None + ) -> jax.Array: + """Run the EDM denoising forward pass. + + Args: + model: a nnx.Module callable as model(inputs=, context=, times=). + inputs: noised inputs to denoise. + sigma: per-example noise level, shape (batch,). + context: conditioning variable, or None. + + Returns: + the denoised prediction, same shape as `inputs`. + """ + new_shape = (-1,) + tuple(np.ones(inputs.ndim - 1, dtype=np.int32).tolist()) + inputs_t = inputs * self.in_scaling(sigma).reshape(new_shape) + noise_cond = self.noise_conditioning(sigma) + outputs = model(inputs=inputs_t, context=context, times=noise_cond) + skip = inputs * self.skip_scaling(sigma).reshape(new_shape) + outputs = outputs * self.out_scaling(sigma).reshape(new_shape) + return skip + outputs + @dataclasses.dataclass class EDMConfig: diff --git a/examples/_common/samplers.py b/examples/_common/samplers.py index 8c30ab0..d38698b 100644 --- a/examples/_common/samplers.py +++ b/examples/_common/samplers.py @@ -2,7 +2,6 @@ import chex import jax -import numpy as np from flax import nnx from jax import numpy as jnp from jax import random as jr @@ -67,21 +66,6 @@ def heun_sample_fn(config: EDMConfig): """ params = config.parameterization - # ruff: noqa: ANN001, ANN202, ANN003 - def _denoise(model, rng_key, inputs, sigma, context): - new_shape = (-1,) + tuple(np.ones(inputs.ndim - 1, dtype=np.int32).tolist()) - inputs_t = inputs * params.in_scaling(sigma).reshape(new_shape) - noise_cond = params.noise_conditioning(sigma) - outputs = model( - inputs=inputs_t, - context=context, - times=noise_cond, - ) - skip = inputs * params.skip_scaling(sigma).reshape(new_shape) - outputs = outputs * params.out_scaling(sigma).reshape(new_shape) - outputs = skip + outputs - return outputs - def sample_fn( model: nnx.Module, rng_key: jax.Array, @@ -110,11 +94,9 @@ def sample_fn( samples = jr.normal(rng_key, sample_shape) * sigmas[0] for i, (sigma, sigma_next) in enumerate(zip(sigmas[:-1], sigmas[1:])): - pred_key1, pred_key2, rng_key = jr.split(rng_key, 3) sample_curr = samples - pred_curr = _denoise( + pred_curr = params.denoise( model, - pred_key1, inputs=sample_curr, sigma=jnp.repeat(sigma, n), context=context, @@ -123,9 +105,8 @@ def sample_fn( samples = sample_curr + d_cur * (sigma_next - sigma) # second order correction if i < config.n_sampling_steps - 1: - pred_next = _denoise( + pred_next = params.denoise( model, - pred_key2, inputs=samples, sigma=jnp.repeat(sigma_next, n), context=context, diff --git a/examples/_common/test_edm.py b/examples/_common/test_edm.py index 8cb3579..6ce118e 100644 --- a/examples/_common/test_edm.py +++ b/examples/_common/test_edm.py @@ -1,3 +1,7 @@ +import jax.numpy as jnp +from flax import nnx +from jax import random as jr + from _common.edm import edm from _common.parameterizations import EDMConfig @@ -7,3 +11,20 @@ def test_edm_default_config_constructs_without_error(): assert callable(train_step) assert callable(val_step) assert callable(sample_fn) + + +class _DummyModel(nnx.Module): + def __init__(self, *, rngs): + self.linear = nnx.Linear(4, 4, rngs=rngs) + + def __call__(self, inputs, context, times): + del context, times + return self.linear(inputs) + + +def test_edm_train_step_runs(): + model = _DummyModel(rngs=nnx.rnglib.Rngs(jr.key(0))) + train_step, _, _ = edm(EDMConfig()) + batch = {"inputs": jnp.ones((2, 4))} + loss, grads = train_step(model, jr.key(1), batch) + assert loss.shape == () diff --git a/examples/_common/test_parameterizations.py b/examples/_common/test_parameterizations.py new file mode 100644 index 0000000..c47515d --- /dev/null +++ b/examples/_common/test_parameterizations.py @@ -0,0 +1,21 @@ +import jax.numpy as jnp + +from _common.parameterizations import EDMParameterization + + +def test_denoise_composes_skip_and_out_scaling(): + params = EDMParameterization(sigma_data=0.5) + inputs = jnp.ones((2, 4)) + sigma = jnp.array([1.0, 2.0]) + + def fake_model(inputs, context, times): + del context, times + return inputs * 0.0 + 3.0 # constant model output + + out = params.denoise(fake_model, inputs, sigma, context=None) + + new_shape = (-1, 1) + expected_skip = inputs * params.skip_scaling(sigma).reshape(new_shape) + expected_out = 3.0 * params.out_scaling(sigma).reshape(new_shape) + expected = expected_skip + expected_out + assert jnp.allclose(out, expected) diff --git a/examples/_common/test_samplers.py b/examples/_common/test_samplers.py index fbdb85a..ce35913 100644 --- a/examples/_common/test_samplers.py +++ b/examples/_common/test_samplers.py @@ -1,6 +1,9 @@ import pytest +from flax import nnx +from jax import random as jr from _common import samplers +from _common.parameterizations import EDMConfig def test_get_sampler_fn_returns_euler(): @@ -16,3 +19,21 @@ def test_get_sampler_fn_returns_heun(): def test_get_sampler_fn_unknown_name_raises(): with pytest.raises(ValueError, match="unknown sampler"): samplers.get_sampler_fn("not-a-sampler") + + +class _DummyModel(nnx.Module): + def __init__(self, *, rngs): + self.linear = nnx.Linear(4, 4, rngs=rngs) + + def __call__(self, inputs, context, times): + del context, times + return self.linear(inputs) + + +def test_heun_sample_fn_runs(): + model = _DummyModel(rngs=nnx.rnglib.Rngs(jr.key(0))) + config = EDMConfig(n_sampling_steps=3) + sample_fn = samplers.get_sampler_fn("heun")(config) + context = jr.normal(jr.key(1), (2, 4)) + samples = sample_fn(model, jr.key(2), sample_shape=(2, 4), context=context) + assert samples.shape == (2, 4) From a64c8d1ebe9aae9ef963faafd70f2a0dff52ee8b Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Fri, 10 Jul 2026 20:56:42 +0200 Subject: [PATCH 05/34] feat(types): add ObjectiveFns contract, wire edm/rfm to return it --- blaxbird/_src/_types.py | 21 +++++++++++++++++++++ blaxbird/_src/test_types.py | 22 ++++++++++++++++++++++ examples/_common/edm.py | 5 ++++- examples/_common/rfm.py | 5 ++++- examples/_common/test_edm.py | 8 ++++++++ examples/_common/test_rfm.py | 8 ++++++++ 6 files changed, 67 insertions(+), 2 deletions(-) create mode 100644 blaxbird/_src/_types.py create mode 100644 blaxbird/_src/test_types.py diff --git a/blaxbird/_src/_types.py b/blaxbird/_src/_types.py new file mode 100644 index 0000000..80dafdb --- /dev/null +++ b/blaxbird/_src/_types.py @@ -0,0 +1,21 @@ +"""Shared structural types for blaxbird's objective factories.""" + +from collections.abc import Callable +from typing import NamedTuple + + +class ObjectiveFns(NamedTuple): + """Functions returned by an objective factory (e.g. edm(), rfm()). + + Attributes: + train_step: gradient-step function with signature + (model, rng_key, batch, **kwargs) -> (loss, grads). + val_step: validation function with signature + (model, rng_key, batch, **kwargs) -> loss. + sample_fn: sampling function with signature + (model, rng_key, sample_shape, *, context=None) -> samples. + """ + + train_step: Callable + val_step: Callable + sample_fn: Callable diff --git a/blaxbird/_src/test_types.py b/blaxbird/_src/test_types.py new file mode 100644 index 0000000..f7b720d --- /dev/null +++ b/blaxbird/_src/test_types.py @@ -0,0 +1,22 @@ +from blaxbird._src._types import ObjectiveFns + + +def test_objective_fns_fields_are_named(): + def train_step(): + pass + + def val_step(): + pass + + def sample_fn(): + pass + + fns = ObjectiveFns( + train_step=train_step, val_step=val_step, sample_fn=sample_fn + ) + assert fns.train_step is train_step + assert fns.val_step is val_step + assert fns.sample_fn is sample_fn + # still unpacks positionally like a plain tuple + a, b, c = fns + assert (a, b, c) == (train_step, val_step, sample_fn) diff --git a/examples/_common/edm.py b/examples/_common/edm.py index 7c8a388..b90a6c7 100644 --- a/examples/_common/edm.py +++ b/examples/_common/edm.py @@ -5,6 +5,7 @@ from _common import samplers from _common.parameterizations import EDMConfig +from blaxbird._src._types import ObjectiveFns def edm(config: EDMConfig): @@ -47,4 +48,6 @@ def val_step(model, rng_key, batch, **kwargs): return loss_fn(model, rng_key, batch) sampler = samplers.get_sampler_fn(config.sampler)(config) - return train_step, val_step, sampler + return ObjectiveFns( + train_step=train_step, val_step=val_step, sample_fn=sampler + ) diff --git a/examples/_common/rfm.py b/examples/_common/rfm.py index 7336ba1..be6eabe 100644 --- a/examples/_common/rfm.py +++ b/examples/_common/rfm.py @@ -5,6 +5,7 @@ from _common import samplers from _common.parameterizations import RFMConfig +from blaxbird._src._types import ObjectiveFns def _forward_process(inputs, times, noise): @@ -48,4 +49,6 @@ def val_step(model, rng_key, batch, **kwargs): return _loss_fn(model, rng_key, batch) sampler = samplers.get_sampler_fn(config.sampler)(config) - return train_step, val_step, sampler + return ObjectiveFns( + train_step=train_step, val_step=val_step, sample_fn=sampler + ) diff --git a/examples/_common/test_edm.py b/examples/_common/test_edm.py index 6ce118e..1898c6c 100644 --- a/examples/_common/test_edm.py +++ b/examples/_common/test_edm.py @@ -28,3 +28,11 @@ def test_edm_train_step_runs(): batch = {"inputs": jnp.ones((2, 4))} loss, grads = train_step(model, jr.key(1), batch) assert loss.shape == () + + +def test_edm_returns_objective_fns(): + from blaxbird._src._types import ObjectiveFns + + fns = edm(EDMConfig()) + assert isinstance(fns, ObjectiveFns) + assert fns.sample_fn is fns[2] diff --git a/examples/_common/test_rfm.py b/examples/_common/test_rfm.py index 6c43a75..b1782e2 100644 --- a/examples/_common/test_rfm.py +++ b/examples/_common/test_rfm.py @@ -7,3 +7,11 @@ def test_rfm_default_config_constructs_without_error(): assert callable(train_step) assert callable(val_step) assert callable(sample_fn) + + +def test_rfm_returns_objective_fns(): + from blaxbird._src._types import ObjectiveFns + + fns = rfm(RFMConfig()) + assert isinstance(fns, ObjectiveFns) + assert fns.sample_fn is fns[2] From c3930b2e6eab7c3759037d9e9c521298c1679c3d Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Fri, 10 Jul 2026 21:11:33 +0200 Subject: [PATCH 06/34] refactor(trainer): derive model from optimizer.model, drop redundant param --- blaxbird/_src/test_trainer.py | 46 +++++++++++++++++++++++++++++++++++ blaxbird/_src/trainer.py | 7 +++--- 2 files changed, 50 insertions(+), 3 deletions(-) create mode 100644 blaxbird/_src/test_trainer.py diff --git a/blaxbird/_src/test_trainer.py b/blaxbird/_src/test_trainer.py new file mode 100644 index 0000000..45c098f --- /dev/null +++ b/blaxbird/_src/test_trainer.py @@ -0,0 +1,46 @@ +import itertools + +import jax.numpy as jnp +import optax +from flax import nnx +from jax import random as jr + +from blaxbird._src.trainer import train_fn + + +class _Linear(nnx.Module): + def __init__(self, *, rngs): + self.linear = nnx.Linear(2, 2, rngs=rngs) + + def __call__(self, x): + return self.linear(x) + + +def _dummy_step(model, rng_key, batch, **kwargs): + del rng_key, kwargs + + def loss_fn(model): + return jnp.mean((model(batch["x"]) - batch["y"]) ** 2) + + return nnx.value_and_grad(loss_fn)(model) + + +def _dummy_val(model, rng_key, batch, **kwargs): + del rng_key, kwargs + return jnp.mean((model(batch["x"]) - batch["y"]) ** 2) + + +def test_train_fn_takes_optimizer_only_no_model_arg(): + model = _Linear(rngs=nnx.rnglib.Rngs(jr.key(0))) + optimizer = nnx.Optimizer(model, tx=optax.sgd(1e-2)) + batch = {"x": jnp.ones((4, 2)), "y": jnp.zeros((4, 2))} + itr = itertools.cycle([batch]) + + train = train_fn( + fns=(_dummy_step, _dummy_val), + n_steps=2, + eval_every_n_steps=1, + n_eval_batches=1, + ) + # signature is (rng_key, optimizer, train_itr, val_itr) -- no model arg + train(jr.key(1), optimizer, itr, itr) diff --git a/blaxbird/_src/trainer.py b/blaxbird/_src/trainer.py index 06a9aee..df812e5 100644 --- a/blaxbird/_src/trainer.py +++ b/blaxbird/_src/trainer.py @@ -58,7 +58,6 @@ def train_fn( def train( rng_key: jax.Array, - model: nnx.Module, optimizer: nnx.Optimizer, train_itr: Iterable, val_itr: Iterable, @@ -67,8 +66,9 @@ def train( Args: rng_key: a jax.random.key object - model: a NNX model - optimizer: a nnx.Optimizer object + optimizer: a nnx.Optimizer object. The wrapped model (optimizer.model) + is trained in place -- there is no separate model argument, since + nnx.Optimizer already owns the model it wraps. train_itr: an infinite data loader, i.e., an iteratlor that keeps running. You can, for instance, construct this as a tfds.NumpyIterator or a grain.DataLoader. @@ -76,6 +76,7 @@ def train( You can, for instance, construct this as a tfds.NumpyIterator or a grain.DataLoader. """ + model = optimizer.model # get train and val fns step_fn, eval_fn = _step_and_val_fns(fns) # get model and replicate From 3dd82a8853da84bb9bfeaa8bdb570151974ec70c Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Fri, 10 Jul 2026 21:14:35 +0200 Subject: [PATCH 07/34] refactor(checkpointer): restore_best_fn/restore_last_fn take/return optimizer only --- blaxbird/_src/checkpointer.py | 30 ++++++++++----------- blaxbird/_src/test_checkpointer.py | 36 ++++++++++++++++++++++++++ examples/cifar10_flow_matching/main.py | 14 +++++----- examples/mnist_classification/main.py | 4 +-- 4 files changed, 59 insertions(+), 25 deletions(-) create mode 100644 blaxbird/_src/test_checkpointer.py diff --git a/blaxbird/_src/checkpointer.py b/blaxbird/_src/checkpointer.py index 64e9dd5..6edb1d1 100644 --- a/blaxbird/_src/checkpointer.py +++ b/blaxbird/_src/checkpointer.py @@ -76,16 +76,15 @@ def save_fn( logging.error(f"could not last checkpoint because of: {e}") logging.error("resuming nonetheless") - def restore_best_fn( - model: nnx.Module, optimizer: nnx.Optimizer - ) -> tuple[nnx.Module, nnx.Optimizer]: + def restore_best_fn(optimizer: nnx.Optimizer) -> nnx.Optimizer: """Restore the best checkpoint. Args: - model: a nnx.Model object - optimizer: a nnx.Optimizer object + optimizer: a nnx.Optimizer object. Its wrapped model (optimizer.model) + and opt_state are both updated in place and returned on the same + optimizer instance. """ - graph_def, state = nnx.split(model) + graph_def, state = nnx.split(optimizer.model) opt_def, opt_state = nnx.split(optimizer.opt_state) try: logging.info("trying to restore best checkpoint") @@ -96,22 +95,21 @@ def restore_best_fn( opt_state=ocp.args.StandardRestore(nnx.eval_shape(lambda: opt_state)), ), ) - model = nnx.merge(graph_def, restored["state"]) + optimizer.model = nnx.merge(graph_def, restored["state"]) optimizer.opt_state = nnx.merge(opt_def, restored["opt_state"]) except FileNotFoundError: logging.warning("could not find checkpoint. resuming with blank state") - return model, optimizer + return optimizer - def restore_last_fn( - model: nnx.Module, optimizer: nnx.Optimizer - ) -> tuple[nnx.Module, nnx.Optimizer]: + def restore_last_fn(optimizer: nnx.Optimizer) -> nnx.Optimizer: """Restore the latest training checkpoint. Args: - model: a nnx.Model object - optimizer: a nnx.Optimizer object + optimizer: a nnx.Optimizer object. Its wrapped model (optimizer.model) + and opt_state are both updated in place and returned on the same + optimizer instance. """ - graphdef, state = nnx.split(model) + graphdef, state = nnx.split(optimizer.model) optdef, opt_state = nnx.split(optimizer.opt_state) state = nnx.eval_shape(lambda: state) opt_state = nnx.eval_shape(lambda: opt_state) @@ -120,10 +118,10 @@ def restore_last_fn( restored = checkpointer.restore( os.path.join(outfolder, "last"), (state, opt_state) ) - model = nnx.merge(graphdef, restored[0]) + optimizer.model = nnx.merge(graphdef, restored[0]) optimizer.opt_state = nnx.merge(optdef, restored[1]) except FileNotFoundError: logging.warning("could not find checkpoint. resuming with blank state") - return model, optimizer + return optimizer return save_fn, restore_best_fn, restore_last_fn diff --git a/blaxbird/_src/test_checkpointer.py b/blaxbird/_src/test_checkpointer.py new file mode 100644 index 0000000..3329ab1 --- /dev/null +++ b/blaxbird/_src/test_checkpointer.py @@ -0,0 +1,36 @@ +import optax +from flax import nnx +from jax import numpy as jnp +from jax import random as jr + +from blaxbird._src.checkpointer import get_default_checkpointer + + +class _Linear(nnx.Module): + def __init__(self, *, rngs): + self.linear = nnx.Linear(2, 2, rngs=rngs) + + def __call__(self, x): + return self.linear(x) + + +def test_save_and_restore_last_roundtrip_optimizer_only(tmp_path): + model = _Linear(rngs=nnx.rnglib.Rngs(jr.key(0))) + optimizer = nnx.Optimizer(model, tx=optax.sgd(1e-2)) + + save_fn, _, restore_last_fn = get_default_checkpointer( + str(tmp_path), save_every_n_steps=1 + ) + save_fn( + 1, model=optimizer.model, optimizer=optimizer, metrics={"val/loss": 0.5} + ) + + new_model = _Linear(rngs=nnx.rnglib.Rngs(jr.key(1))) + new_optimizer = nnx.Optimizer(new_model, tx=optax.sgd(1e-2)) + # restore_last_fn takes/returns optimizer only -- no separate model arg + restored_optimizer = restore_last_fn(new_optimizer) + assert isinstance(restored_optimizer, nnx.Optimizer) + assert jnp.allclose( + restored_optimizer.model.linear.kernel.value, + optimizer.model.linear.kernel.value, + ) diff --git a/examples/cifar10_flow_matching/main.py b/examples/cifar10_flow_matching/main.py index d098fc9..11a74d1 100644 --- a/examples/cifar10_flow_matching/main.py +++ b/examples/cifar10_flow_matching/main.py @@ -103,22 +103,22 @@ def run(n_steps, eval_every_n_steps, n_eval_batches, dit_type, log_to_wandb): model = getattr(dit, dit_type)( image_size=(32, 32, 3), rngs=nnx.rnglib.Rngs(jr.key(1)) ) - train_step, val_step, sample_fn = rfm() + objective = rfm() optimizer = get_optimizer(model) save_fn, _, restore_last_fn = get_default_checkpointer( os.path.join(outfolder, "checkpoints"), save_every_n_steps=eval_every_n_steps, ) - hooks = get_hooks(sample_fn, val_itr, eval_every_n_steps, log_to_wandb) + [ - save_fn - ] + hooks = get_hooks( + objective.sample_fn, val_itr, eval_every_n_steps, log_to_wandb + ) + [save_fn] model_sharding, data_sharding = get_sharding() - model, optimizer = restore_last_fn(model, optimizer) + optimizer = restore_last_fn(optimizer) train = train_fn( - fns=(train_step, val_step), + fns=(objective.train_step, objective.val_step), n_steps=n_steps, eval_every_n_steps=eval_every_n_steps, n_eval_batches=n_eval_batches, @@ -126,7 +126,7 @@ def run(n_steps, eval_every_n_steps, n_eval_batches, dit_type, log_to_wandb): hooks=hooks, log_to_wandb=False, ) - train(jr.key(2), model, optimizer, train_itr, val_itr) + train(jr.key(2), optimizer, train_itr, val_itr) if __name__ == "__main__": diff --git a/examples/mnist_classification/main.py b/examples/mnist_classification/main.py index f513351..669fe6e 100644 --- a/examples/mnist_classification/main.py +++ b/examples/mnist_classification/main.py @@ -91,7 +91,7 @@ def run(n_steps, eval_every_n_steps, n_eval_batches): hooks = get_hooks(val_itr, eval_every_n_steps) + [save_fn] model_sharding, data_sharding = get_sharding() - model, optimizer = restore_last_fn(model, optimizer) + optimizer = restore_last_fn(optimizer) train = train_fn( fns=(train_step, val_step), @@ -102,7 +102,7 @@ def run(n_steps, eval_every_n_steps, n_eval_batches): hooks=hooks, log_to_wandb=False, ) - train(jr.key(2), model, optimizer, train_itr, val_itr) + train(jr.key(2), optimizer, train_itr, val_itr) if __name__ == "__main__": From c0b71eb0a71b99ad6bfe04f0efadf10dba689682 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Fri, 10 Jul 2026 22:02:42 +0200 Subject: [PATCH 08/34] feat(checkpointer): make checkpoint criterion_key and best_mode configurable --- blaxbird/_src/checkpointer.py | 12 ++++++++++-- blaxbird/_src/test_checkpointer.py | 29 +++++++++++++++++++++++++++++ 2 files changed, 39 insertions(+), 2 deletions(-) diff --git a/blaxbird/_src/checkpointer.py b/blaxbird/_src/checkpointer.py index 6edb1d1..36a3977 100644 --- a/blaxbird/_src/checkpointer.py +++ b/blaxbird/_src/checkpointer.py @@ -11,6 +11,8 @@ def get_default_checkpointer( *, save_every_n_steps: int, max_to_keep: int = 5, + criterion_key: str = "val/loss", + best_mode: str = "min", ) -> tuple[Callable, Callable, Callable]: """Construct functions for checkpointing functionality. @@ -18,6 +20,12 @@ def get_default_checkpointer( outfolder: a path specifying where checkpoints are stored save_every_n_steps: how often to store checkpoints max_to_keep: number of checkpoints to store before they get deleted + criterion_key: the key into the `metrics` dict passed to `save_fn` used + to decide which checkpoint is "best". Must match a key produced by + whatever metrics dict the caller's training loop passes in -- for + `blaxbird.train_fn`, that's `f"val/{metric_name}"`. + best_mode: "min" or "max" -- whether a lower or higher `criterion_key` + value is better. Returns: returns function to saev and restore checkpoints @@ -26,8 +34,8 @@ def get_default_checkpointer( options = ocp.CheckpointManagerOptions( max_to_keep=max_to_keep, create=True, - best_mode="min", - best_fn=lambda x: x["val/loss"], + best_mode=best_mode, + best_fn=lambda x: x[criterion_key], ) checkpoint_manager = ocp.CheckpointManager( os.path.join(outfolder, "best"), diff --git a/blaxbird/_src/test_checkpointer.py b/blaxbird/_src/test_checkpointer.py index 3329ab1..89422ce 100644 --- a/blaxbird/_src/test_checkpointer.py +++ b/blaxbird/_src/test_checkpointer.py @@ -34,3 +34,32 @@ def test_save_and_restore_last_roundtrip_optimizer_only(tmp_path): restored_optimizer.model.linear.kernel.value, optimizer.model.linear.kernel.value, ) + + +def test_custom_criterion_key_and_best_mode(tmp_path): + model = _Linear(rngs=nnx.rnglib.Rngs(jr.key(0))) + optimizer = nnx.Optimizer(model, tx=optax.sgd(1e-2)) + + save_fn, restore_best_fn, _ = get_default_checkpointer( + str(tmp_path), + save_every_n_steps=1, + criterion_key="val/accuracy", + best_mode="max", + ) + save_fn( + 1, + model=optimizer.model, + optimizer=optimizer, + metrics={"val/accuracy": 0.7}, + ) + save_fn( + 2, + model=optimizer.model, + optimizer=optimizer, + metrics={"val/accuracy": 0.9}, + ) + + new_model = _Linear(rngs=nnx.rnglib.Rngs(jr.key(1))) + new_optimizer = nnx.Optimizer(new_model, tx=optax.sgd(1e-2)) + restored_optimizer = restore_best_fn(new_optimizer) + assert isinstance(restored_optimizer, nnx.Optimizer) From 2322ebb6cd26ba1783a5e6b6b9b61985953e8460 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Fri, 10 Jul 2026 22:16:22 +0200 Subject: [PATCH 09/34] fix(dit): actually use context for class conditioning instead of silently dropping it --- examples/_common/nn/dit.py | 38 +++++++++++++++++--- examples/_common/nn/test_dit.py | 61 +++++++++++++++++++++++++++++++++ 2 files changed, 94 insertions(+), 5 deletions(-) create mode 100644 examples/_common/nn/test_dit.py diff --git a/examples/_common/nn/dit.py b/examples/_common/nn/dit.py index 95e9ac1..063f29a 100644 --- a/examples/_common/nn/dit.py +++ b/examples/_common/nn/dit.py @@ -131,6 +131,7 @@ def __init__( # noqa: PLR0913 n_heads: int, n_embedding_features=256, dropout_rate=0.0, + n_classes: int | None = None, *, rngs: nnx.rnglib.Rngs, ): @@ -144,12 +145,21 @@ def __init__( # noqa: PLR0913 n_heads: integer n_embedding_features: integer dropout_rate: float + n_classes: number of classes to condition on, or None for an + unconditional model. When set, __call__ requires a `context` + argument of integer class labels; when None, __call__ requires + `context=None`. rngs: random keys """ self.image_size = image_size self.n_in_channels = image_size[-1] self.n_embedding_features = n_embedding_features self.patch_size = patch_size + self.n_classes = n_classes + if n_classes is not None: + self.class_embedding = nnx.Embed( + n_classes, n_embedding_features, rngs=rngs + ) self.time_embedding = nnx.Sequential( nnx.Linear(n_embedding_features, n_embedding_features, rngs=rngs), nnx.swish, @@ -226,21 +236,39 @@ def __call__( Args: inputs: input in image form times: one-dimensional array - context: conditioning variable in image form + context: integer class labels, shape (batch,), required if this DiT + was constructed with n_classes set; must be None otherwise. Returns: - returns a jax + returns a jax.Array, same shape as `inputs` + + Raises: + ValueError: if `context` is None but n_classes was set, or if + `context` is given but n_classes was not set. """ + if self.n_classes is not None and context is None: + raise ValueError( + "this DiT was constructed with n_classes set, so context " + "(integer class labels) must be provided" + ) + if self.n_classes is None and context is not None: + raise ValueError( + "this DiT was constructed without n_classes, so context must " + "be None -- pass n_classes at construction to condition on it" + ) + hidden = self._patchify(inputs) hidden = self._embed(hidden) - times = self.time_embedding( + embedding = self.time_embedding( timestep_embedding(times, self.n_embedding_features) ) + if context is not None: + embedding = embedding + self.class_embedding(context) for block in self.dit_blocks: - hidden = block(hidden, context=times) + hidden = block(hidden, context=embedding) - hidden = self.out_projection(hidden, times) + hidden = self.out_projection(hidden, embedding) outputs = self._unpatchify(hidden) return outputs diff --git a/examples/_common/nn/test_dit.py b/examples/_common/nn/test_dit.py new file mode 100644 index 0000000..a705bb5 --- /dev/null +++ b/examples/_common/nn/test_dit.py @@ -0,0 +1,61 @@ +import jax.numpy as jnp +import pytest +from flax import nnx +from jax import random as jr + +from _common.nn.dit import DiT + + +def _make_dit(n_classes=None): + return DiT( + image_size=(8, 8, 3), + n_hidden_channels=16, + patch_size=4, + n_layers=1, + n_heads=2, + n_embedding_features=16, + n_classes=n_classes, + rngs=nnx.rnglib.Rngs(jr.key(0)), + ) + + +def test_unconditional_dit_runs_without_context(): + model = _make_dit(n_classes=None) + inputs = jnp.ones((2, 8, 8, 3)) + times = jnp.array([0.1, 0.2]) + out = model(inputs, times, context=None) + assert out.shape == inputs.shape + + +def test_conditional_dit_runs_with_context(): + model = _make_dit(n_classes=5) + inputs = jnp.ones((2, 8, 8, 3)) + times = jnp.array([0.1, 0.2]) + context = jnp.array([0, 3]) + out = model(inputs, times, context=context) + assert out.shape == inputs.shape + + +def test_conditional_dit_changes_output_per_class(): + model = _make_dit(n_classes=5) + inputs = jnp.ones((1, 8, 8, 3)) + times = jnp.array([0.1]) + out_class_0 = model(inputs, times, context=jnp.array([0])) + out_class_1 = model(inputs, times, context=jnp.array([1])) + assert not jnp.allclose(out_class_0, out_class_1) + + +def test_missing_context_with_n_classes_raises(): + model = _make_dit(n_classes=5) + inputs = jnp.ones((2, 8, 8, 3)) + times = jnp.array([0.1, 0.2]) + with pytest.raises(ValueError, match="context"): + model(inputs, times, context=None) + + +def test_unexpected_context_without_n_classes_raises(): + model = _make_dit(n_classes=None) + inputs = jnp.ones((2, 8, 8, 3)) + times = jnp.array([0.1, 0.2]) + with pytest.raises(ValueError, match="context"): + model(inputs, times, context=jnp.array([0, 1])) From 745c521ba35a01b1f6916c385bd301b6df6829b4 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Fri, 10 Jul 2026 22:16:48 +0200 Subject: [PATCH 10/34] feat(examples): class-conditional CIFAR-10 generation, exercising the DiT context fix --- examples/cifar10_flow_matching/main.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/examples/cifar10_flow_matching/main.py b/examples/cifar10_flow_matching/main.py index 11a74d1..cd8c0c0 100644 --- a/examples/cifar10_flow_matching/main.py +++ b/examples/cifar10_flow_matching/main.py @@ -65,7 +65,10 @@ def fn(step, *, model, **kwargs): all_samples = [] for i, batch in enumerate(val_iter): samples = sample_fn( - model, jr.fold_in(jr.key(step), i), sample_shape=batch["inputs"].shape + model, + jr.fold_in(jr.key(step), i), + sample_shape=batch["inputs"].shape, + context=batch["context"], ) all_samples.append(samples) if len(all_samples) * all_samples[0].shape[0] >= n_row * n_col: @@ -101,7 +104,7 @@ def run(n_steps, eval_every_n_steps, n_eval_batches, dit_type, log_to_wandb): ) model = getattr(dit, dit_type)( - image_size=(32, 32, 3), rngs=nnx.rnglib.Rngs(jr.key(1)) + image_size=(32, 32, 3), n_classes=10, rngs=nnx.rnglib.Rngs(jr.key(1)) ) objective = rfm() optimizer = get_optimizer(model) From bf6f55394f14ef0684450d283f9553a424d4e2bc Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Fri, 10 Jul 2026 22:26:25 +0200 Subject: [PATCH 11/34] feat(unet): add ResBlock, AttentionBlock, Downsample, Upsample building blocks --- examples/_common/nn/test_unet.py | 43 +++++++++ examples/_common/nn/unet.py | 149 +++++++++++++++++++++++++++++++ 2 files changed, 192 insertions(+) create mode 100644 examples/_common/nn/test_unet.py create mode 100644 examples/_common/nn/unet.py diff --git a/examples/_common/nn/test_unet.py b/examples/_common/nn/test_unet.py new file mode 100644 index 0000000..5442087 --- /dev/null +++ b/examples/_common/nn/test_unet.py @@ -0,0 +1,43 @@ +import jax.numpy as jnp +from flax import nnx +from jax import random as jr + +from _common.nn.unet import AttentionBlock, Downsample, ResBlock, Upsample + + +def test_res_block_changes_channels(): + block = ResBlock(8, 16, 32, rngs=nnx.rnglib.Rngs(jr.key(0))) + inputs = jnp.ones((2, 8, 8, 8)) + embedding = jnp.ones((2, 32)) + out = block(inputs, embedding) + assert out.shape == (2, 8, 8, 16) + + +def test_res_block_same_channels_uses_identity_skip(): + block = ResBlock(8, 8, 32, rngs=nnx.rnglib.Rngs(jr.key(0))) + assert block.skip is None + inputs = jnp.ones((2, 8, 8, 8)) + embedding = jnp.ones((2, 32)) + out = block(inputs, embedding) + assert out.shape == (2, 8, 8, 8) + + +def test_attention_block_preserves_shape(): + block = AttentionBlock(16, n_heads=4, rngs=nnx.rnglib.Rngs(jr.key(0))) + inputs = jnp.ones((2, 4, 4, 16)) + out = block(inputs) + assert out.shape == inputs.shape + + +def test_downsample_halves_spatial_dims(): + block = Downsample(8, rngs=nnx.rnglib.Rngs(jr.key(0))) + inputs = jnp.ones((2, 16, 16, 8)) + out = block(inputs) + assert out.shape == (2, 8, 8, 8) + + +def test_upsample_doubles_spatial_dims(): + block = Upsample(8, rngs=nnx.rnglib.Rngs(jr.key(0))) + inputs = jnp.ones((2, 8, 8, 8)) + out = block(inputs) + assert out.shape == (2, 16, 16, 8) diff --git a/examples/_common/nn/unet.py b/examples/_common/nn/unet.py new file mode 100644 index 0000000..4742c7b --- /dev/null +++ b/examples/_common/nn/unet.py @@ -0,0 +1,149 @@ +import jax +from flax import nnx +from jax import numpy as jnp + + +class ResBlock(nnx.Module): + """Residual block with GroupNorm, SiLU, and AdaGN-style conditioning.""" + + def __init__( + self, + in_channels, + out_channels, + n_embedding_features, + *, + dropout_rate=0.0, + rngs, + ): + """Construct a residual block. + + Args: + in_channels: number of input channels + out_channels: number of output channels + n_embedding_features: dimensionality of the conditioning embedding + passed to __call__ + dropout_rate: float + rngs: random keys + """ + self.norm1 = nnx.GroupNorm(in_channels, num_groups=8, rngs=rngs) + self.conv1 = nnx.Conv( + in_channels, out_channels, (3, 3), padding="SAME", rngs=rngs + ) + self.emb_proj = nnx.Linear( + n_embedding_features, out_channels * 2, rngs=rngs + ) + self.norm2 = nnx.GroupNorm(out_channels, num_groups=8, rngs=rngs) + self.dropout = nnx.Dropout(dropout_rate, rngs=rngs) + self.conv2 = nnx.Conv( + out_channels, out_channels, (3, 3), padding="SAME", rngs=rngs + ) + self.skip = ( + None + if in_channels == out_channels + else nnx.Conv(in_channels, out_channels, (1, 1), rngs=rngs) + ) + + def __call__(self, inputs: jax.Array, embedding: jax.Array) -> jax.Array: + """Transform inputs through the residual block. + + Args: + inputs: input array, shape (batch, H, W, in_channels) + embedding: conditioning embedding, shape (batch, n_embedding_features) + + Returns: + returns a jax.Array, shape (batch, H, W, out_channels) + """ + hidden = self.conv1(jax.nn.silu(self.norm1(inputs))) + scale, shift = jnp.split(self.emb_proj(jax.nn.silu(embedding)), 2, axis=-1) + hidden = self.norm2(hidden) * (1 + scale[:, None, None, :]) + hidden = hidden + shift[:, None, None, :] + hidden = jax.nn.silu(hidden) + hidden = self.dropout(hidden) + hidden = self.conv2(hidden) + skip = inputs if self.skip is None else self.skip(inputs) + return skip + hidden + + +class AttentionBlock(nnx.Module): + """Self-attention over spatial positions, with a residual connection.""" + + def __init__(self, channels, n_heads, *, rngs): + """Construct an attention block. + + Args: + channels: number of channels (must be divisible by n_heads) + n_heads: number of attention heads + rngs: random keys + """ + self.norm = nnx.GroupNorm(channels, num_groups=8, rngs=rngs) + self.attn = nnx.MultiHeadAttention( + num_heads=n_heads, in_features=channels, rngs=rngs, decode=False + ) + + def __call__(self, inputs: jax.Array) -> jax.Array: + """Apply self-attention across the H*W spatial positions. + + Args: + inputs: input array, shape (batch, H, W, channels) + + Returns: + returns a jax.Array, same shape as inputs + """ + b, h, w, c = inputs.shape + hidden = self.norm(inputs) + hidden = hidden.reshape(b, h * w, c) + hidden = self.attn(hidden) + hidden = hidden.reshape(b, h, w, c) + return inputs + hidden + + +class Downsample(nnx.Module): + """Halve spatial resolution via a stride-2 convolution.""" + + def __init__(self, channels, *, rngs): + """Construct a downsampling block. + + Args: + channels: number of channels (unchanged by downsampling) + rngs: random keys + """ + self.conv = nnx.Conv( + channels, channels, (3, 3), strides=(2, 2), padding="SAME", rngs=rngs + ) + + def __call__(self, inputs: jax.Array) -> jax.Array: + """Downsample inputs by 2x. + + Args: + inputs: input array, shape (batch, H, W, channels) + + Returns: + returns a jax.Array, shape (batch, H // 2, W // 2, channels) + """ + return self.conv(inputs) + + +class Upsample(nnx.Module): + """Double spatial resolution via nearest-neighbor resize + convolution.""" + + def __init__(self, channels, *, rngs): + """Construct an upsampling block. + + Args: + channels: number of channels (unchanged by upsampling) + rngs: random keys + """ + self.conv = nnx.Conv(channels, channels, (3, 3), padding="SAME", rngs=rngs) + + def __call__(self, inputs: jax.Array) -> jax.Array: + """Upsample inputs by 2x. + + Args: + inputs: input array, shape (batch, H, W, channels) + + Returns: + returns a jax.Array, shape (batch, H * 2, W * 2, channels) + """ + b, h, w, c = inputs.shape + resized = jax.image.resize(inputs, (b, h * 2, w * 2, c), method="nearest") + return self.conv(resized) From 6f5bc66c80c1ce82871ac314002487d331060ac6 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Fri, 10 Jul 2026 22:34:08 +0200 Subject: [PATCH 12/34] feat(unet): assemble full UNet with down/mid/up path and Small/Base/Large presets --- examples/_common/nn/test_unet.py | 42 +++++- examples/_common/nn/unet.py | 248 +++++++++++++++++++++++++++++++ 2 files changed, 289 insertions(+), 1 deletion(-) diff --git a/examples/_common/nn/test_unet.py b/examples/_common/nn/test_unet.py index 5442087..975b532 100644 --- a/examples/_common/nn/test_unet.py +++ b/examples/_common/nn/test_unet.py @@ -1,8 +1,9 @@ import jax.numpy as jnp +import pytest from flax import nnx from jax import random as jr -from _common.nn.unet import AttentionBlock, Downsample, ResBlock, Upsample +from _common.nn.unet import AttentionBlock, Downsample, ResBlock, UNet, Upsample def test_res_block_changes_channels(): @@ -41,3 +42,42 @@ def test_upsample_doubles_spatial_dims(): inputs = jnp.ones((2, 8, 8, 8)) out = block(inputs) assert out.shape == (2, 16, 16, 8) + + +def _make_unet(n_classes=None): + return UNet( + image_size=(32, 32, 3), + n_hidden_channels=32, + channel_mults=(1, 2, 2), + n_res_blocks=2, + attention_resolutions=(16,), + n_embedding_features=64, + n_heads=4, + n_classes=n_classes, + rngs=nnx.rnglib.Rngs(jr.key(0)), + ) + + +def test_unconditional_unet_preserves_input_shape(): + model = _make_unet(n_classes=None) + inputs = jnp.ones((2, 32, 32, 3)) + times = jnp.array([0.1, 0.5]) + out = model(inputs, times, context=None) + assert out.shape == inputs.shape + + +def test_conditional_unet_preserves_input_shape(): + model = _make_unet(n_classes=10) + inputs = jnp.ones((2, 32, 32, 3)) + times = jnp.array([0.1, 0.5]) + context = jnp.array([0, 3]) + out = model(inputs, times, context=context) + assert out.shape == inputs.shape + + +def test_conditional_unet_missing_context_raises(): + model = _make_unet(n_classes=10) + inputs = jnp.ones((2, 32, 32, 3)) + times = jnp.array([0.1, 0.5]) + with pytest.raises(ValueError, match="context"): + model(inputs, times, context=None) diff --git a/examples/_common/nn/unet.py b/examples/_common/nn/unet.py index 4742c7b..6ecaa1a 100644 --- a/examples/_common/nn/unet.py +++ b/examples/_common/nn/unet.py @@ -2,6 +2,8 @@ from flax import nnx from jax import numpy as jnp +from _common.nn.embedding import timestep_embedding + class ResBlock(nnx.Module): """Residual block with GroupNorm, SiLU, and AdaGN-style conditioning.""" @@ -147,3 +149,249 @@ def __call__(self, inputs: jax.Array) -> jax.Array: b, h, w, c = inputs.shape resized = jax.image.resize(inputs, (b, h * 2, w * 2, c), method="nearest") return self.conv(resized) + + +class UNet(nnx.Module): + """ADM/EDM-style UNet: ResBlocks, attention, skip connections.""" + + def __init__( # noqa: PLR0913 + self, + image_size, + n_hidden_channels, + channel_mults=(1, 2, 2, 2), + n_res_blocks=2, + attention_resolutions=(16,), + n_embedding_features=256, + dropout_rate=0.0, + n_heads=4, + n_classes=None, + *, + rngs, + ): + """Construct a UNet. + + Args: + image_size: size of the image, e.g., (32, 32, 3) + n_hidden_channels: base number of hidden channels; each resolution + level uses n_hidden_channels * channel_mults[level] + channel_mults: per-level channel multiplier, one entry per + resolution level (levels after the first are downsampled by 2x) + n_res_blocks: number of ResBlocks per resolution level + attention_resolutions: spatial resolutions (H == W) at which to + insert an AttentionBlock after each ResBlock + n_embedding_features: dimensionality of the time/class embedding + dropout_rate: float + n_heads: number of attention heads + n_classes: number of classes to condition on, or None for an + unconditional model. Same contract as DiT: __call__ requires + context (integer class labels) iff n_classes is set. + rngs: random keys + """ + self.image_size = image_size + self.n_in_channels = image_size[-1] + self.n_embedding_features = n_embedding_features + self.n_classes = n_classes + if n_classes is not None: + self.class_embedding = nnx.Embed( + n_classes, n_embedding_features, rngs=rngs + ) + self.time_embedding = nnx.Sequential( + nnx.Linear(n_embedding_features, n_embedding_features, rngs=rngs), + nnx.swish, + nnx.Linear(n_embedding_features, n_embedding_features, rngs=rngs), + nnx.swish, + ) + self.in_conv = nnx.Conv( + self.n_in_channels, n_hidden_channels, (3, 3), padding="SAME", rngs=rngs + ) + + down_res_blocks = [] + down_attn_blocks = [] + downsamples = [] + channels = n_hidden_channels + resolution = image_size[0] + skip_channels = [channels] + for level, mult in enumerate(channel_mults): + out_channels = n_hidden_channels * mult + level_res_blocks = [] + level_attn_blocks = [] + for _ in range(n_res_blocks): + level_res_blocks.append( + ResBlock( + channels, + out_channels, + n_embedding_features, + dropout_rate=dropout_rate, + rngs=rngs, + ) + ) + channels = out_channels + if resolution in attention_resolutions: + level_attn_blocks.append(AttentionBlock(channels, n_heads, rngs=rngs)) + else: + level_attn_blocks.append(None) + skip_channels.append(channels) + down_res_blocks.append(tuple(level_res_blocks)) + down_attn_blocks.append(tuple(level_attn_blocks)) + if level < len(channel_mults) - 1: + downsamples.append(Downsample(channels, rngs=rngs)) + resolution //= 2 + skip_channels.append(channels) + else: + downsamples.append(None) + self.down_res_blocks = tuple(down_res_blocks) + self.down_attn_blocks = tuple(down_attn_blocks) + self.downsamples = tuple(downsamples) + + self.mid_res_block1 = ResBlock( + channels, + channels, + n_embedding_features, + dropout_rate=dropout_rate, + rngs=rngs, + ) + self.mid_attn = AttentionBlock(channels, n_heads, rngs=rngs) + self.mid_res_block2 = ResBlock( + channels, + channels, + n_embedding_features, + dropout_rate=dropout_rate, + rngs=rngs, + ) + + up_res_blocks = [] + up_attn_blocks = [] + upsamples = [] + for level, mult in reversed(list(enumerate(channel_mults))): + out_channels = n_hidden_channels * mult + level_res_blocks = [] + level_attn_blocks = [] + for _ in range(n_res_blocks + 1): + skip_ch = skip_channels.pop() + level_res_blocks.append( + ResBlock( + channels + skip_ch, + out_channels, + n_embedding_features, + dropout_rate=dropout_rate, + rngs=rngs, + ) + ) + channels = out_channels + if resolution in attention_resolutions: + level_attn_blocks.append(AttentionBlock(channels, n_heads, rngs=rngs)) + else: + level_attn_blocks.append(None) + up_res_blocks.append(tuple(level_res_blocks)) + up_attn_blocks.append(tuple(level_attn_blocks)) + if level > 0: + upsamples.append(Upsample(channels, rngs=rngs)) + resolution *= 2 + else: + upsamples.append(None) + self.up_res_blocks = tuple(up_res_blocks) + self.up_attn_blocks = tuple(up_attn_blocks) + self.upsamples = tuple(upsamples) + + self.out_norm = nnx.GroupNorm(channels, num_groups=8, rngs=rngs) + self.out_conv = nnx.Conv( + channels, self.n_in_channels, (3, 3), padding="SAME", rngs=rngs + ) + + def __call__( + self, inputs: jax.Array, times: jax.Array, context: jax.Array = None + ) -> jax.Array: + """Transform inputs through the UNet. + + Args: + inputs: input in image form, shape (batch, H, W, C) + times: one-dimensional array, shape (batch,) + context: integer class labels, shape (batch,), required if this + UNet was constructed with n_classes set; must be None otherwise. + + Returns: + returns a jax.Array, same shape as inputs + + Raises: + ValueError: if context is None but n_classes was set, or if context + is given but n_classes was not set. + """ + if self.n_classes is not None and context is None: + raise ValueError( + "this UNet was constructed with n_classes set, so context " + "(integer class labels) must be provided" + ) + if self.n_classes is None and context is not None: + raise ValueError( + "this UNet was constructed without n_classes, so context must " + "be None -- pass n_classes at construction to condition on it" + ) + + embedding = self.time_embedding( + timestep_embedding(times, self.n_embedding_features) + ) + if context is not None: + embedding = embedding + self.class_embedding(context) + + hidden = self.in_conv(inputs) + skips = [hidden] + for level in range(len(self.down_res_blocks)): + for res_block, attn_block in zip( + self.down_res_blocks[level], self.down_attn_blocks[level] + ): + hidden = res_block(hidden, embedding) + if attn_block is not None: + hidden = attn_block(hidden) + skips.append(hidden) + if self.downsamples[level] is not None: + hidden = self.downsamples[level](hidden) + skips.append(hidden) + + hidden = self.mid_res_block1(hidden, embedding) + hidden = self.mid_attn(hidden) + hidden = self.mid_res_block2(hidden, embedding) + + for level in range(len(self.up_res_blocks)): + for res_block, attn_block in zip( + self.up_res_blocks[level], self.up_attn_blocks[level] + ): + skip = skips.pop() + hidden = res_block(jnp.concatenate([hidden, skip], axis=-1), embedding) + if attn_block is not None: + hidden = attn_block(hidden) + if self.upsamples[level] is not None: + hidden = self.upsamples[level](hidden) + + hidden = jax.nn.silu(self.out_norm(hidden)) + outputs = self.out_conv(hidden) + return outputs + + +def SmallUNet(image_size, **kwargs): + return UNet( + image_size, + n_hidden_channels=64, + channel_mults=(1, 2, 2), + n_res_blocks=2, + **kwargs, + ) + + +def BaseUNet(image_size, **kwargs): + return UNet( + image_size, + n_hidden_channels=128, + channel_mults=(1, 2, 2, 2), + n_res_blocks=2, + **kwargs, + ) + + +def LargeUNet(image_size, **kwargs): + return UNet( + image_size, + n_hidden_channels=192, + channel_mults=(1, 1, 2, 2, 4), + n_res_blocks=3, + **kwargs, + ) From 54fd6ab0a3edefb0d9da7539295f741f7e2f432d Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Fri, 10 Jul 2026 23:01:01 +0200 Subject: [PATCH 13/34] feat(_common): export UNet and presets from _common --- examples/_common/__init__.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/examples/_common/__init__.py b/examples/_common/__init__.py index e69de29..3d9e4cc 100644 --- a/examples/_common/__init__.py +++ b/examples/_common/__init__.py @@ -0,0 +1,8 @@ +from _common.nn.unet import BaseUNet, LargeUNet, SmallUNet, UNet + +__all__ = [ + "BaseUNet", + "LargeUNet", + "SmallUNet", + "UNet", +] From f02a77bfb4611d64044df59d4b2f20d5a23c6ff4 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Fri, 10 Jul 2026 23:01:03 +0200 Subject: [PATCH 14/34] test(unet): verify gradients flow through the full network --- examples/_common/nn/test_unet.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/examples/_common/nn/test_unet.py b/examples/_common/nn/test_unet.py index 975b532..1e2e766 100644 --- a/examples/_common/nn/test_unet.py +++ b/examples/_common/nn/test_unet.py @@ -1,3 +1,4 @@ +import jax import jax.numpy as jnp import pytest from flax import nnx @@ -81,3 +82,16 @@ def test_conditional_unet_missing_context_raises(): times = jnp.array([0.1, 0.5]) with pytest.raises(ValueError, match="context"): model(inputs, times, context=None) + + +def test_unet_gradients_are_nonzero(): + model = _make_unet(n_classes=None) + inputs = jnp.ones((2, 32, 32, 3)) + times = jnp.array([0.1, 0.5]) + + def loss_fn(model): + return jnp.mean(model(inputs, times, context=None) ** 2) + + grads = nnx.grad(loss_fn)(model) + leaves = jax.tree_util.tree_leaves(grads) + assert all(jnp.any(leaf != 0) for leaf in leaves if leaf.size > 0) From 1b3f418dba783e02a3db6e210dfe4c1255fcc868 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Fri, 10 Jul 2026 23:48:06 +0200 Subject: [PATCH 15/34] feat(hooks): add get_ema_hook, first entry in the new blaxbird.hooks module --- blaxbird/_src/hooks.py | 57 +++++++++++++++++++++++++++++++++++++ blaxbird/_src/test_hooks.py | 57 +++++++++++++++++++++++++++++++++++++ 2 files changed, 114 insertions(+) create mode 100644 blaxbird/_src/hooks.py create mode 100644 blaxbird/_src/test_hooks.py diff --git a/blaxbird/_src/hooks.py b/blaxbird/_src/hooks.py new file mode 100644 index 0000000..0bb7af6 --- /dev/null +++ b/blaxbird/_src/hooks.py @@ -0,0 +1,57 @@ +"""Built-in training hooks for use with train_fn's hooks= argument. + +Every hook here matches train_fn's calling convention: +hook(step, *, model, optimizer, metrics) -> None, called every training +step. Hooks that don't need optimizer/metrics accept **kwargs to ignore +them. + +Note: EMA state is not integrated with get_default_checkpointer -- saving +and restoring EMA state alongside model checkpoints is a separate, +larger change to checkpointer.py's save/restore item structure. Not +covered here. +""" + +from collections.abc import Callable + +import jax +from flax import nnx + + +def get_ema_hook( + model: nnx.Module, decay: float = 0.999 +) -> tuple[Callable, Callable]: + """Construct an exponential-moving-average-of-weights hook. + + Args: + model: the model whose parameter structure the EMA state is + initialized from (its current values seed the EMA state; the + model object itself is not retained or mutated). + decay: EMA decay rate -- ema = decay*ema + (1-decay)*current, applied + every step the hook is called. Higher decay tracks more slowly. + + Returns: + a tuple (hook_fn, get_ema_model_fn): + hook_fn(step, *, model, **kwargs) -> None: updates the tracked EMA + state from `model`'s current parameter values. Call this every + step (e.g. by including it in train_fn's hooks=). + get_ema_model_fn(model: nnx.Module) -> nnx.Module: returns a new, + independent nnx.Module with the same structure as `model` but + with the tracked EMA parameter values. + """ + _, ema_state = nnx.split(model) + box = {"state": ema_state} + + def hook_fn(step, *, model, **kwargs): + del step, kwargs + _, state = nnx.split(model) + box["state"] = jax.tree_util.tree_map( + lambda ema, current: decay * ema + (1 - decay) * current, + box["state"], + state, + ) + + def get_ema_model_fn(model: nnx.Module) -> nnx.Module: + graphdef, _ = nnx.split(model) + return nnx.merge(graphdef, box["state"]) + + return hook_fn, get_ema_model_fn diff --git a/blaxbird/_src/test_hooks.py b/blaxbird/_src/test_hooks.py new file mode 100644 index 0000000..957799d --- /dev/null +++ b/blaxbird/_src/test_hooks.py @@ -0,0 +1,57 @@ +import jax.numpy as jnp +from flax import nnx +from jax import random as jr + +from blaxbird._src.hooks import get_ema_hook + + +class _Linear(nnx.Module): + def __init__(self, *, rngs): + self.linear = nnx.Linear(4, 4, rngs=rngs) + + def __call__(self, x): + return self.linear(x) + + +def test_ema_hook_tracks_toward_but_lags_current_params(): + model = _Linear(rngs=nnx.rnglib.Rngs(jr.key(0))) + original = model.linear.kernel.value.copy() + hook_fn, get_ema_model = get_ema_hook(model, decay=0.9) + + for step in range(3): + model.linear.kernel.value = model.linear.kernel.value + 1.0 + hook_fn(step, model=model) + + ema_model = get_ema_model(model) + assert jnp.all(original < ema_model.linear.kernel.value) + assert jnp.all(ema_model.linear.kernel.value < model.linear.kernel.value) + + +def test_ema_model_does_not_alias_live_model(): + model = _Linear(rngs=nnx.rnglib.Rngs(jr.key(0))) + hook_fn, get_ema_model = get_ema_hook(model, decay=0.9) + hook_fn(0, model=model) + + ema_model = get_ema_model(model) + kernel_before = ema_model.linear.kernel.value.copy() + model.linear.kernel.value = model.linear.kernel.value + 100.0 + assert jnp.array_equal(ema_model.linear.kernel.value, kernel_before) + + +def test_ema_model_is_usable(): + model = _Linear(rngs=nnx.rnglib.Rngs(jr.key(0))) + hook_fn, get_ema_model = get_ema_hook(model, decay=0.9) + hook_fn(0, model=model) + ema_model = get_ema_model(model) + out = ema_model(jnp.ones((1, 4))) + assert out.shape == (1, 4) + + +def test_ema_hook_ignores_extra_trainer_kwargs(): + """trainer.py calls every hook as h(step, model=, optimizer=, + metrics=) -- hook_fn must accept and ignore the kwargs it doesn't + use.""" + model = _Linear(rngs=nnx.rnglib.Rngs(jr.key(0))) + optimizer_stub = object() + hook_fn, _ = get_ema_hook(model, decay=0.9) + hook_fn(0, model=model, optimizer=optimizer_stub, metrics={"train/loss": 1.0}) From 95e355677f6d9278a7fbeec6ba4a6b1bc6a06d06 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Fri, 10 Jul 2026 23:48:26 +0200 Subject: [PATCH 16/34] feat: export get_ema_hook from blaxbird --- blaxbird/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/blaxbird/__init__.py b/blaxbird/__init__.py index 3dc7f3f..df4a01f 100644 --- a/blaxbird/__init__.py +++ b/blaxbird/__init__.py @@ -3,6 +3,7 @@ __version__ = "0.1.1" from blaxbird._src.checkpointer import get_default_checkpointer +from blaxbird._src.hooks import get_ema_hook from blaxbird._src.trainer import train_fn -__all__ = ["get_default_checkpointer", "train_fn"] +__all__ = ["get_default_checkpointer", "get_ema_hook", "train_fn"] From 9af82e7c204635b3de93a176d0826e9d9f1214e5 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Fri, 10 Jul 2026 23:48:58 +0200 Subject: [PATCH 17/34] test(hooks): verify get_ema_hook integrates with train_fn's hooks= --- blaxbird/_src/test_hooks.py | 46 +++++++++++++++++++++++++++++++++++-- 1 file changed, 44 insertions(+), 2 deletions(-) diff --git a/blaxbird/_src/test_hooks.py b/blaxbird/_src/test_hooks.py index 957799d..5973d90 100644 --- a/blaxbird/_src/test_hooks.py +++ b/blaxbird/_src/test_hooks.py @@ -1,8 +1,12 @@ +import itertools + import jax.numpy as jnp +import optax from flax import nnx from jax import random as jr from blaxbird._src.hooks import get_ema_hook +from blaxbird._src.trainer import train_fn class _Linear(nnx.Module): @@ -48,10 +52,48 @@ def test_ema_model_is_usable(): def test_ema_hook_ignores_extra_trainer_kwargs(): - """trainer.py calls every hook as h(step, model=, optimizer=, + """Verify hook_fn tolerates trainer.py's full kwarg set. + + trainer.py calls every hook as h(step, model=, optimizer=, metrics=) -- hook_fn must accept and ignore the kwargs it doesn't - use.""" + use. + """ model = _Linear(rngs=nnx.rnglib.Rngs(jr.key(0))) optimizer_stub = object() hook_fn, _ = get_ema_hook(model, decay=0.9) hook_fn(0, model=model, optimizer=optimizer_stub, metrics={"train/loss": 1.0}) + + +def _dummy_step(model, rng_key, batch, **kwargs): + del rng_key, kwargs + + def loss_fn(model): + return jnp.mean((model(batch["x"]) - batch["y"]) ** 2) + + return nnx.value_and_grad(loss_fn)(model) + + +def _dummy_val(model, rng_key, batch, **kwargs): + del rng_key, kwargs + return jnp.mean((model(batch["x"]) - batch["y"]) ** 2) + + +def test_ema_hook_integrates_with_train_fn(): + model = _Linear(rngs=nnx.rnglib.Rngs(jr.key(0))) + optimizer = nnx.Optimizer(model, tx=optax.sgd(1e-2)) + hook_fn, get_ema_model = get_ema_hook(model, decay=0.9) + + batch = {"x": jnp.ones((4, 4)), "y": jnp.zeros((4, 4))} + itr = itertools.cycle([batch]) + + train = train_fn( + fns=(_dummy_step, _dummy_val), + n_steps=3, + eval_every_n_steps=1, + n_eval_batches=1, + hooks=[hook_fn], + ) + train(jr.key(1), optimizer, itr, itr) + + ema_model = get_ema_model(optimizer.model) + assert ema_model.linear.kernel.value.shape == (4, 4) From efd2170121f97efe5da22152159c94795d7e30dc Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Fri, 10 Jul 2026 23:56:03 +0200 Subject: [PATCH 18/34] feat(examples): add LR warmup/cosine-decay schedule and gradient clipping to cifar10 optimizer --- examples/cifar10_flow_matching/main.py | 24 +++++++++++++++++++----- examples/mnist_classification/main.py | 24 +++++++++++++++++++----- 2 files changed, 38 insertions(+), 10 deletions(-) diff --git a/examples/cifar10_flow_matching/main.py b/examples/cifar10_flow_matching/main.py index cd8c0c0..8808dac 100644 --- a/examples/cifar10_flow_matching/main.py +++ b/examples/cifar10_flow_matching/main.py @@ -20,10 +20,24 @@ from _common.nn import dit # for getattr(dit, dit_type, ...) below -def get_optimizer(model, lr=1e-4): - tx = optax.adamw(lr) - tx = nnx.Optimizer(model, tx=tx) - return tx +def get_optimizer( + model, *, peak_lr=1e-4, n_steps, warmup_steps=1000, grad_clip_norm=1.0 +): + # warmup_cosine_decay_schedule requires decay_steps > warmup_steps (the + # cosine phase runs over decay_steps - warmup_steps); clamp so a short + # n_steps (e.g. a smoke run) doesn't raise ValueError from optax. + warmup_steps = min(warmup_steps, n_steps // 2) + schedule = optax.warmup_cosine_decay_schedule( + init_value=0.0, + peak_value=peak_lr, + warmup_steps=warmup_steps, + decay_steps=n_steps, + end_value=peak_lr * 0.01, + ) + tx = optax.chain( + optax.clip_by_global_norm(grad_clip_norm), optax.adamw(schedule) + ) + return nnx.Optimizer(model, tx=tx) def get_sharding(): @@ -107,7 +121,7 @@ def run(n_steps, eval_every_n_steps, n_eval_batches, dit_type, log_to_wandb): image_size=(32, 32, 3), n_classes=10, rngs=nnx.rnglib.Rngs(jr.key(1)) ) objective = rfm() - optimizer = get_optimizer(model) + optimizer = get_optimizer(model, n_steps=n_steps) save_fn, _, restore_last_fn = get_default_checkpointer( os.path.join(outfolder, "checkpoints"), diff --git a/examples/mnist_classification/main.py b/examples/mnist_classification/main.py index 669fe6e..1846d08 100644 --- a/examples/mnist_classification/main.py +++ b/examples/mnist_classification/main.py @@ -16,10 +16,24 @@ from blaxbird import get_default_checkpointer, train_fn -def get_optimizer(model, lr=1e-4): - tx = optax.adamw(lr) - tx = nnx.Optimizer(model, tx=tx) - return tx +def get_optimizer( + model, *, peak_lr=1e-4, n_steps, warmup_steps=1000, grad_clip_norm=1.0 +): + # warmup_cosine_decay_schedule requires decay_steps > warmup_steps (the + # cosine phase runs over decay_steps - warmup_steps); clamp so a short + # n_steps (e.g. a smoke run) doesn't raise ValueError from optax. + warmup_steps = min(warmup_steps, n_steps // 2) + schedule = optax.warmup_cosine_decay_schedule( + init_value=0.0, + peak_value=peak_lr, + warmup_steps=warmup_steps, + decay_steps=n_steps, + end_value=peak_lr * 0.01, + ) + tx = optax.chain( + optax.clip_by_global_norm(grad_clip_norm), optax.adamw(schedule) + ) + return nnx.Optimizer(model, tx=tx) def get_sharding(): @@ -82,7 +96,7 @@ def run(n_steps, eval_every_n_steps, n_eval_batches): ) model = CNN(rngs=nnx.rnglib.Rngs(jr.key(1))) - optimizer = get_optimizer(model) + optimizer = get_optimizer(model, n_steps=n_steps) save_fn, _, restore_last_fn = get_default_checkpointer( os.path.join(outfolder, "checkpoints"), From dc5559eee826ec2706f305584f4950c96d494a5b Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Sat, 11 Jul 2026 10:02:01 +0200 Subject: [PATCH 19/34] feat(trainer): derive sharding from nnx.with_partitioning via mesh, replacing single blanket shardings arg --- blaxbird/_src/test_trainer.py | 35 ++++++++++++++++++++++++ blaxbird/_src/trainer.py | 38 ++++++++++++++++++-------- examples/cifar10_flow_matching/main.py | 12 ++++---- examples/mnist_classification/main.py | 12 ++++---- 4 files changed, 72 insertions(+), 25 deletions(-) diff --git a/blaxbird/_src/test_trainer.py b/blaxbird/_src/test_trainer.py index 45c098f..377469e 100644 --- a/blaxbird/_src/test_trainer.py +++ b/blaxbird/_src/test_trainer.py @@ -1,9 +1,12 @@ import itertools +import jax import jax.numpy as jnp import optax from flax import nnx from jax import random as jr +from jax.experimental import mesh_utils +from jax.sharding import PartitionSpec as P from blaxbird._src.trainer import train_fn @@ -44,3 +47,35 @@ def test_train_fn_takes_optimizer_only_no_model_arg(): ) # signature is (rng_key, optimizer, train_itr, val_itr) -- no model arg train(jr.key(1), optimizer, itr, itr) + + +def test_train_fn_shards_across_mesh(): + """Verifies train_fn shards state and batches across a device mesh. + + Requires XLA_FLAGS=--xla_force_host_platform_device_count=4 to exercise + real multi-device sharding; degrades to a 1-device no-op sharding + otherwise (still exercises the mesh code path, just not the + multi-shard assertion below). + """ + n_devices = jax.local_device_count() + mesh = jax.sharding.Mesh( + mesh_utils.create_device_mesh((n_devices,)), ("data",) + ) + model = _Linear(rngs=nnx.rnglib.Rngs(jr.key(0))) + optimizer = nnx.Optimizer(model, tx=optax.sgd(1e-2)) + batch = {"x": jnp.ones((8, 2)), "y": jnp.zeros((8, 2))} + itr = itertools.cycle([batch]) + + train = train_fn( + fns=(_dummy_step, _dummy_val), + n_steps=2, + eval_every_n_steps=1, + n_eval_batches=1, + mesh=mesh, + data_partition_spec=P("data"), + ) + train(jr.key(1), optimizer, itr, itr) + + if n_devices > 1: + sharded_x = jax.device_put(batch["x"], jax.NamedSharding(mesh, P("data"))) + assert len(sharded_x.addressable_shards) == n_devices diff --git a/blaxbird/_src/trainer.py b/blaxbird/_src/trainer.py index df812e5..ab8f49f 100644 --- a/blaxbird/_src/trainer.py +++ b/blaxbird/_src/trainer.py @@ -31,7 +31,10 @@ def _eval_step(model, rng_key, metrics, batch, **kwargs): def train_fn( *, fns: tuple[Callable, Callable], - shardings: tuple[jax.NamedSharding, jax.NamedSharding] | None = None, + mesh: jax.sharding.Mesh | None = None, + data_partition_spec: jax.sharding.PartitionSpec = ( + jax.sharding.PartitionSpec() + ), n_steps: int, eval_every_n_steps: int, n_eval_batches: int, @@ -44,8 +47,16 @@ def train_fn( fns: a tuple of two callables. The first one is used as a step function , i.e., function to do gradient steps. The second one is used as an validation function. - shardings: a tuple of shardings, the first one for the model, the second - one for the data. + mesh: a jax.sharding.Mesh to shard training over, or None to run + unsharded on a single device. Per-parameter sharding is derived + from each parameter's own nnx.with_partitioning annotation (see + flax.nnx docs) via nnx.get_named_sharding -- parameters without + such an annotation default to fully replicated, so passing a mesh + with no annotated parameters gives plain data parallelism. + data_partition_spec: how to shard each training/eval batch across + `mesh`. Defaults to PartitionSpec() (fully replicated); pass e.g. + PartitionSpec("data") to shard the batch dimension across a mesh + axis named "data". n_steps: number of training/gradient steps eval_every_n_steps: specified how often to compute validation statistics. n_eval_batches: number of batches to use for validation @@ -79,10 +90,11 @@ def train( model = optimizer.model # get train and val fns step_fn, eval_fn = _step_and_val_fns(fns) - # get model and replicate - state = nnx.state((model, optimizer)) - if shardings is not None: - state = jax.device_put(state, shardings[0]) + # get model and shard + if mesh is not None: + state = nnx.state((model, optimizer)) + sharding = nnx.get_named_sharding(state, mesh) + state = jax.device_put(state, sharding) nnx.update((model, optimizer), state) # metrics metrics = nnx.MultiMetric(loss=nnx.metrics.Average("loss")) @@ -91,8 +103,10 @@ def train( step_key, rng_key = jr.split(rng_key) for step, batch in zip(range(1, n_steps + 1), train_itr): train_key, val_key = jr.split(jr.fold_in(step_key, step)) - if shardings is not None: - batch = jax.device_put(batch, shardings[1]) + if mesh is not None: + batch = jax.device_put( + batch, jax.NamedSharding(mesh, data_partition_spec) + ) # do a gradient step step_fn( model=model, @@ -110,8 +124,10 @@ def train( metrics_history[f"train/{metric}"] = float(value) # do evaluation loop for val_idx, batch in zip(range(n_eval_batches), val_itr): - if shardings is not None: - batch = jax.device_put(batch, shardings[1]) + if mesh is not None: + batch = jax.device_put( + batch, jax.NamedSharding(mesh, data_partition_spec) + ) eval_fn( model=model, rng_key=jr.fold_in(val_key, val_idx), diff --git a/examples/cifar10_flow_matching/main.py b/examples/cifar10_flow_matching/main.py index 8808dac..98509b1 100644 --- a/examples/cifar10_flow_matching/main.py +++ b/examples/cifar10_flow_matching/main.py @@ -40,14 +40,11 @@ def get_optimizer( return nnx.Optimizer(model, tx=tx) -def get_sharding(): +def get_mesh(): num_devices = jax.local_device_count() - mesh = jax.sharding.Mesh( + return jax.sharding.Mesh( mesh_utils.create_device_mesh((num_devices,)), ("data",) ) - model_sharding = jax.NamedSharding(mesh, jax.sharding.PartitionSpec()) - data_sharding = jax.NamedSharding(mesh, jax.sharding.PartitionSpec("data")) - return model_sharding, data_sharding def visualize_hook(sample_fn, val_iter, hook_every_n_steps, log_to_wandb): @@ -131,7 +128,7 @@ def run(n_steps, eval_every_n_steps, n_eval_batches, dit_type, log_to_wandb): objective.sample_fn, val_itr, eval_every_n_steps, log_to_wandb ) + [save_fn] - model_sharding, data_sharding = get_sharding() + mesh = get_mesh() optimizer = restore_last_fn(optimizer) train = train_fn( @@ -139,7 +136,8 @@ def run(n_steps, eval_every_n_steps, n_eval_batches, dit_type, log_to_wandb): n_steps=n_steps, eval_every_n_steps=eval_every_n_steps, n_eval_batches=n_eval_batches, - shardings=(model_sharding, data_sharding), + mesh=mesh, + data_partition_spec=jax.sharding.PartitionSpec("data"), hooks=hooks, log_to_wandb=False, ) diff --git a/examples/mnist_classification/main.py b/examples/mnist_classification/main.py index 1846d08..d2c6fb9 100644 --- a/examples/mnist_classification/main.py +++ b/examples/mnist_classification/main.py @@ -36,14 +36,11 @@ def get_optimizer( return nnx.Optimizer(model, tx=tx) -def get_sharding(): +def get_mesh(): num_devices = jax.local_device_count() - mesh = jax.sharding.Mesh( + return jax.sharding.Mesh( mesh_utils.create_device_mesh((num_devices,)), ("data",) ) - model_sharding = jax.NamedSharding(mesh, jax.sharding.PartitionSpec()) - data_sharding = jax.NamedSharding(mesh, jax.sharding.PartitionSpec("data")) - return model_sharding, data_sharding def metrics_hook(val_iter, hook_every_n_steps): @@ -104,7 +101,7 @@ def run(n_steps, eval_every_n_steps, n_eval_batches): ) hooks = get_hooks(val_itr, eval_every_n_steps) + [save_fn] - model_sharding, data_sharding = get_sharding() + mesh = get_mesh() optimizer = restore_last_fn(optimizer) train = train_fn( @@ -112,7 +109,8 @@ def run(n_steps, eval_every_n_steps, n_eval_batches): n_steps=n_steps, eval_every_n_steps=eval_every_n_steps, n_eval_batches=n_eval_batches, - shardings=(model_sharding, data_sharding), + mesh=mesh, + data_partition_spec=jax.sharding.PartitionSpec("data"), hooks=hooks, log_to_wandb=False, ) From b1cb0f943b77d284774da3d3d25a91d91f62ca15 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Sat, 11 Jul 2026 11:52:44 +0200 Subject: [PATCH 20/34] feat(examples): add FSDP+TP 2D mesh sharding demo --- examples/fsdp_tp_demo/README.md | 16 ++++++++ examples/fsdp_tp_demo/main.py | 62 +++++++++++++++++++++++++++++ examples/fsdp_tp_demo/model.py | 51 ++++++++++++++++++++++++ examples/fsdp_tp_demo/test_model.py | 41 +++++++++++++++++++ 4 files changed, 170 insertions(+) create mode 100644 examples/fsdp_tp_demo/README.md create mode 100644 examples/fsdp_tp_demo/main.py create mode 100644 examples/fsdp_tp_demo/model.py create mode 100644 examples/fsdp_tp_demo/test_model.py diff --git a/examples/fsdp_tp_demo/README.md b/examples/fsdp_tp_demo/README.md new file mode 100644 index 0000000..91809d7 --- /dev/null +++ b/examples/fsdp_tp_demo/README.md @@ -0,0 +1,16 @@ +# FSDP + TP sharding demo + +Demonstrates real 2D-mesh FSDP+tensor-parallel sharding via +`flax.nnx.with_partitioning` + `nnx.get_named_sharding`, wired through +`blaxbird.train_fn`'s `mesh=`/`data_partition_spec=` parameters. + +`ShardedMLP` (`model.py`) annotates its up-projection kernel with +`("fsdp", "tp")` and its down-projection kernel with `("tp", "fsdp")` -- +standard Megatron-style column-parallel-then-row-parallel sharding, +combined with FSDP on the same two axes. + +No real multi-GPU/TPU hardware needed to see actual multi-device +sharding: run with +`XLA_FLAGS="--xla_force_host_platform_device_count=4" python main.py` to +simulate 4 CPU devices arranged as a (2, 2) fsdp x tp mesh. Without that +env var this still runs (single device, degenerate/unsharded). diff --git a/examples/fsdp_tp_demo/main.py b/examples/fsdp_tp_demo/main.py new file mode 100644 index 0000000..48f18d4 --- /dev/null +++ b/examples/fsdp_tp_demo/main.py @@ -0,0 +1,62 @@ +"""Train ShardedMLP on random data under a 2D FSDP+TP mesh. + +Run with XLA_FLAGS="--xla_force_host_platform_device_count=4" to see real +sharding across 4 simulated CPU devices; runs (unsharded, degenerate) +without it too. +""" + +import itertools + +import jax +import optax +from flax import nnx +from jax import numpy as jnp +from jax import random as jr +from jax.experimental import mesh_utils + +from blaxbird import train_fn +from model import ShardedMLP + + +def dummy_step(model, rng_key, batch, **kwargs): + del rng_key, kwargs + + def loss_fn(model): + return jnp.mean((model(batch["x"]) - batch["y"]) ** 2) + + return nnx.value_and_grad(loss_fn)(model) + + +def dummy_val(model, rng_key, batch, **kwargs): + del rng_key, kwargs + return jnp.mean((model(batch["x"]) - batch["y"]) ** 2) + + +def run(n_steps: int) -> None: + n_devices = jax.local_device_count() + fsdp = 2 if n_devices >= 4 else 1 + tp = n_devices // fsdp + mesh = jax.sharding.Mesh( + mesh_utils.create_device_mesh((fsdp, tp)), ("fsdp", "tp") + ) + with mesh: + model = ShardedMLP(64, 256, rngs=nnx.rnglib.Rngs(jr.key(0))) + optimizer = nnx.Optimizer(model, tx=optax.sgd(1e-2)) + + batch = {"x": jnp.ones((16, 64)), "y": jnp.zeros((16, 64))} + itr = itertools.cycle([batch]) + + train = train_fn( + fns=(dummy_step, dummy_val), + n_steps=n_steps, + eval_every_n_steps=max(1, n_steps // 2), + n_eval_batches=1, + mesh=mesh, + data_partition_spec=jax.sharding.PartitionSpec("fsdp"), + ) + train(jr.key(1), optimizer, itr, itr) + print("done. up.kernel sharding:", optimizer.model.up.kernel.value.sharding) + + +if __name__ == "__main__": + run(n_steps=4) diff --git a/examples/fsdp_tp_demo/model.py b/examples/fsdp_tp_demo/model.py new file mode 100644 index 0000000..8c1a715 --- /dev/null +++ b/examples/fsdp_tp_demo/model.py @@ -0,0 +1,51 @@ +"""A tiny MLP with explicit FSDP+TP sharding annotations, demonstrating +flax.nnx's with_partitioning / get_named_sharding mechanism on a 2D +device mesh. Verified live (see the plan doc) with a simulated 4-device +mesh reshaped to (2, 2): the up-projection kernel shards ("fsdp", "tp"), +the down-projection kernel shards ("tp", "fsdp") -- standard +Megatron-style column-parallel-then-row-parallel MLP sharding, combined +with FSDP on the same two axes. +""" + +import jax +from flax import nnx + + +class ShardedMLP(nnx.Module): + """Two-layer MLP with FSDP+TP-annotated kernels.""" + + def __init__(self, d_model, d_ff, *, rngs): + """Construct a sharded MLP. + + Args: + d_model: input/output dimensionality + d_ff: hidden (expansion) dimensionality + rngs: random keys + """ + self.up = nnx.Linear( + d_model, + d_ff, + rngs=rngs, + kernel_init=nnx.with_partitioning( + nnx.initializers.lecun_normal(), ("fsdp", "tp") + ), + ) + self.down = nnx.Linear( + d_ff, + d_model, + rngs=rngs, + kernel_init=nnx.with_partitioning( + nnx.initializers.lecun_normal(), ("tp", "fsdp") + ), + ) + + def __call__(self, x: jax.Array) -> jax.Array: + """Apply the MLP. + + Args: + x: input array, shape (batch, d_model) + + Returns: + jax.Array, shape (batch, d_model) + """ + return self.down(jax.nn.relu(self.up(x))) diff --git a/examples/fsdp_tp_demo/test_model.py b/examples/fsdp_tp_demo/test_model.py new file mode 100644 index 0000000..479f188 --- /dev/null +++ b/examples/fsdp_tp_demo/test_model.py @@ -0,0 +1,41 @@ +import jax +import pytest +from flax import nnx +from jax import random as jr +from jax.experimental import mesh_utils + +from model import ShardedMLP + + +@pytest.mark.skipif( + jax.local_device_count() < 4, + reason="needs XLA_FLAGS=--xla_force_host_platform_device_count=4", +) +def test_sharded_mlp_splits_across_2d_mesh(): + mesh = jax.sharding.Mesh( + mesh_utils.create_device_mesh((2, 2)), ("fsdp", "tp") + ) + with mesh: + model = ShardedMLP(8, 32, rngs=nnx.rnglib.Rngs(jr.key(0))) + graphdef, state = nnx.split(model) + sharding = nnx.get_named_sharding(state, mesh) + state = jax.device_put(state, sharding) + nnx.update(model, state) + + up_kernel = model.up.kernel.value + assert up_kernel.shape == (8, 32) + assert up_kernel.addressable_shards[0].data.shape == (4, 16) + + down_kernel = model.down.kernel.value + assert down_kernel.shape == (32, 8) + assert down_kernel.addressable_shards[0].data.shape == (16, 4) + + +def test_sharded_mlp_forward_pass_shape(): + """Runs on any device count -- proves the model works, not that it's + actually sharded (see the skipif test above for that).""" + import jax.numpy as jnp + + model = ShardedMLP(8, 32, rngs=nnx.rnglib.Rngs(jr.key(0))) + out = model(jnp.ones((4, 8))) + assert out.shape == (4, 8) From fdec12b03a1019506abdb09c9bc67d0470172b32 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Sat, 11 Jul 2026 12:57:31 +0200 Subject: [PATCH 21/34] docs: update README for mesh-based sharding and optimizer-only train()/restore APIs --- README.md | 55 +++++++++++++++++++++++++++++++++++++++---------------- 1 file changed, 39 insertions(+), 16 deletions(-) diff --git a/README.md b/README.md index 6e8ced0..0fb9708 100644 --- a/README.md +++ b/README.md @@ -61,7 +61,7 @@ train = train_fn( eval_every_n_steps=10, n_eval_batches=10 ) -train(jr.key(2), model, optimizer, train_itr, val_itr) +train(jr.key(2), optimizer, train_itr, val_itr) ``` See the entire self-contained example in [examples/mnist_classification](examples/mnist_classification). @@ -74,7 +74,8 @@ See the entire self-contained example in [examples/mnist_classification](example def train_fn( *, fns: tuple[Callable, Callable], - shardings: Optional[tuple[jax.NamedSharding, jax.NamedSharding]] = None, + mesh: jax.sharding.Mesh | None = None, + data_partition_spec: jax.sharding.PartitionSpec = jax.sharding.PartitionSpec(), n_steps: int, eval_every_n_steps: int, n_eval_batches: int, @@ -84,6 +85,8 @@ def train_fn( ... ``` +The returned `train` callable has signature `train(rng_key, optimizer, train_itr, val_itr) -> None` -- it derives the model from `optimizer.model`, so there is no separate `model` argument. + We briefly explain the more ambiguous argument types below. ### `fns` @@ -108,26 +111,40 @@ The loss function that is called by both computes a *scalar* loss value. B While `train_step` returns has to return the loss and gradients, `val_step` only needs to return the loss. -### `shardings` +### `mesh` and `data_partition_spec` To specify how data and model weights are distributed over devices and processes, `blaxbird` uses JAX' [sharding](https://docs.jax.dev/en/latest/notebooks/Distributed_arrays_and_automatic_parallelization.html) functionality. -`shardings` is again specified by a tuple, one for the model sharding, the other for the data sharding. -An example is shown below, where we only distributed the data over `num_devices` devices. -You can, if you don't want to distribute anything, just set the argument to `None` or not specify it. +`mesh` is a `jax.sharding.Mesh` describing your device topology. Per-parameter +sharding is derived from each parameter's own `nnx.with_partitioning` +annotation (see the [flax.nnx docs](https://flax.readthedocs.io/en/latest/)) via +`nnx.get_named_sharding` -- parameters without such an annotation default to +fully replicated, so a mesh with no annotated parameters gives plain data +parallelism. `data_partition_spec` controls how each training/eval batch is +sharded across `mesh` (defaults to `PartitionSpec()`, fully replicated). +You can, if you don't want to distribute anything, just leave `mesh` as `None` +or not specify it. + +An example is shown below, sharding only the data over `num_devices` devices +(the model has no `with_partitioning` annotations, so it stays fully +replicated): ```python -def get_sharding(): +def get_mesh(): num_devices = jax.local_device_count() - mesh = jax.sharding.Mesh( + return jax.sharding.Mesh( mesh_utils.create_device_mesh((num_devices,)), ("data",) ) - model_sharding = jax.NamedSharding(mesh, jax.sharding.PartitionSpec()) - data_sharding = jax.NamedSharding(mesh, jax.sharding.PartitionSpec("data")) - return model_sharding, data_sharding + +mesh = get_mesh() ``` +Pass `mesh=mesh, data_partition_spec=jax.sharding.PartitionSpec("data")` to +`train_fn`. For real FSDP/tensor-parallel sharding, annotate your model's +layers with `nnx.with_partitioning` -- see +[examples/fsdp_tp_demo](examples/fsdp_tp_demo) for a worked 2D-mesh example. + ### `hooks` `hooks` is a list of callables which are periodically called during training. @@ -224,13 +241,18 @@ You can then do either of: model = CNN(rngs=nnx.rnglib.Rngs(jr.key(1))) optimizer = nnx.Optimizer(model, optax.adam(1e-4)) -model, optimizer = restore_best(model, optimizer) -model, optimizer = restore_last(model, optimizer) +optimizer = restore_best(optimizer) +optimizer = restore_last(optimizer) ``` +`restore_best`/`restore_last` take and return `optimizer` only -- the wrapped +model (`optimizer.model`) and `opt_state` are both updated in place on the +same optimizer instance, since `nnx.Optimizer` already owns the model it +wraps. + ### Doing training -After having defined train functions, hooks and shardings, you can train your model like this: +After having defined train functions, hooks and a mesh, you can train your model like this: ```python train = train_fn( @@ -238,11 +260,12 @@ train = train_fn( n_steps=n_steps, eval_every_n_steps=eval_every_n_steps, n_eval_batches=n_eval_batches, - shardings=(model_sharding, data_sharding), + mesh=mesh, + data_partition_spec=jax.sharding.PartitionSpec("data"), hooks=hooks, log_to_wandb=False, ) -train(jr.key(1), model, optimizer, train_itr, val_itr) +train(jr.key(1), optimizer, train_itr, val_itr) ``` Self-contained examples that also explain how the data loaders should look like can be found From 76ba10365f092268b04602a710b8042439b2ea1c Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Sat, 11 Jul 2026 16:17:09 +0200 Subject: [PATCH 22/34] feat(llm_reference): add shared RoPE, masking, RMSNorm, TP-sharded GQAAttention/GeGLU --- examples/llm_reference/layers.py | 251 ++++++++++++++++++++++++++ examples/llm_reference/test_layers.py | 97 ++++++++++ 2 files changed, 348 insertions(+) create mode 100644 examples/llm_reference/layers.py create mode 100644 examples/llm_reference/test_layers.py diff --git a/examples/llm_reference/layers.py b/examples/llm_reference/layers.py new file mode 100644 index 0000000..7462c53 --- /dev/null +++ b/examples/llm_reference/layers.py @@ -0,0 +1,251 @@ +"""Shared primitives for the llm_reference example suite (Gemma-4-style, +DeepSeek-MLA-style, Mixtral-style decoder-only transformers). + +TP-sharded projections use nnx.with_partitioning on their kernel_init so +blaxbird.train_fn's mesh= argument can shard them via +nnx.get_named_sharding -- unannotated parameters (e.g. RMSNorm weights) +default to fully replicated. No mesh is threaded into any __call__: all +sharding here is construction-time weight annotation only, matching this +repo's existing sharding idiom (see examples/fsdp_tp_demo). +""" + +import jax +from flax import nnx +from jax import numpy as jnp + + +def rope_freqs(head_dim: int, theta: float = 10_000.0) -> jax.Array: + """Compute RoPE inverse frequencies. + + Args: + head_dim: dimensionality to rotate (must be even). + theta: RoPE base frequency. + + Returns: + jax.Array, shape (head_dim // 2,). + """ + return 1.0 / (theta ** (jnp.arange(0, head_dim, 2) / head_dim)) + + +def apply_rope( + x: jax.Array, positions: jax.Array, inv_freq: jax.Array +) -> jax.Array: + """Apply rotary position embeddings. + + Args: + x: input array, shape (batch, seq, n_heads, dim) where dim == + 2 * inv_freq.shape[0]. + positions: integer position ids, shape (batch, seq). + inv_freq: RoPE inverse frequencies from rope_freqs. + + Returns: + jax.Array, same shape as x. + """ + freqs = positions[:, :, None] * inv_freq[None, None, :] + cos = jnp.cos(freqs)[:, :, None, :] + sin = jnp.sin(freqs)[:, :, None, :] + x1, x2 = jnp.split(x, 2, axis=-1) + return jnp.concatenate([x1 * cos - x2 * sin, x2 * cos + x1 * sin], axis=-1) + + +def repeat_kv(x: jax.Array, n_rep: int) -> jax.Array: + """Broadcast grouped-query-attention key/value heads to n_heads. + + Args: + x: input array, shape (batch, seq, n_kv_heads, head_dim). + n_rep: number of query heads sharing each kv head + (n_heads // n_kv_heads). + + Returns: + jax.Array, shape (batch, seq, n_kv_heads * n_rep, head_dim). + """ + if n_rep == 1: + return x + b, s, kvh, hd = x.shape + x = jnp.broadcast_to(x[:, :, :, None, :], (b, s, kvh, n_rep, hd)) + return x.reshape(b, s, kvh * n_rep, hd) + + +def make_causal_mask(seq_len: int, window: int | None = None) -> jax.Array: + """Build a causal (optionally sliding-window / "local") attention mask. + + Args: + seq_len: sequence length. + window: if given, restrict attention to the last `window` positions + (Gemma-style "local" attention layers); if None, full causal + ("global") attention -- always the case for DeepSeek and Mixtral in + this suite. + + Returns: + bool jax.Array, shape (seq_len, seq_len), True where attention is + allowed (query position i may attend to key position j). + """ + i = jnp.arange(seq_len)[:, None] + j = jnp.arange(seq_len)[None, :] + causal = j <= i + if window is not None: + causal = causal & (j > i - window) + return causal + + +class RMSNorm(nnx.Module): + """Root-mean-square layer normalization (no mean-centering, no bias).""" + + def __init__(self, dim, *, rngs, eps=1e-6): + """Construct an RMSNorm layer. + + Args: + dim: feature dimensionality. + rngs: random keys (unused -- weight is initialized to ones -- kept + for interface consistency with every other block in this suite). + eps: numerical-stability constant. + """ + del rngs + self.weight = nnx.Param(jnp.ones((dim,))) + self.eps = eps + + def __call__(self, x: jax.Array) -> jax.Array: + """Normalize the last axis of x by its RMS, then scale. + + Args: + x: input array, shape (..., dim). + + Returns: + jax.Array, same shape as x. + """ + var = jnp.mean(jnp.square(x), axis=-1, keepdims=True) + x = x * jax.lax.rsqrt(var + self.eps) + return x * self.weight.value + + +def tp_linear(d_in, d_out, partition_spec, *, rngs, use_bias=False): + """Construct a nnx.Linear whose kernel carries a sharding annotation. + + Args: + d_in: input feature dimensionality. + d_out: output feature dimensionality. + partition_spec: a 2-tuple of mesh-axis names (or None) passed to + nnx.with_partitioning on the kernel initializer -- e.g. + ("fsdp", "tp") for column-parallel, ("tp", "fsdp") for row-parallel. + Resolved to a real per-device shard only when the returned module's + state is passed through nnx.get_named_sharding(state, mesh) inside + blaxbird.train_fn; constructing this module standalone (no mesh) is + unaffected -- same pattern as examples/fsdp_tp_demo's ShardedMLP. + rngs: random keys. + use_bias: whether to include a bias term. Every projection in this + suite uses use_bias=False, matching the real + Gemma/DeepSeek/Mixtral architectures. + + Returns: + a nnx.Linear with a with_partitioning-annotated kernel_init. + """ + return nnx.Linear( + d_in, + d_out, + use_bias=use_bias, + kernel_init=nnx.with_partitioning( + nnx.initializers.lecun_normal(), partition_spec + ), + rngs=rngs, + ) + + +class GQAAttention(nnx.Module): + """Grouped-query attention with RoPE, TP-sharded (column-parallel qkv, + row-parallel output projection -- standard Megatron-style tensor + parallelism).""" + + def __init__(self, d_model, n_heads, n_kv_heads, head_dim, *, rngs): + """Construct a GQA attention block. + + Args: + d_model: model (residual stream) dimensionality. + n_heads: number of query heads. + n_kv_heads: number of key/value heads (n_heads must be a multiple + of n_kv_heads; n_kv_heads == n_heads recovers standard MHA, + n_kv_heads == 1 recovers MQA). + head_dim: dimensionality of each attention head. + rngs: random keys. + """ + self.n_heads = n_heads + self.n_kv_heads = n_kv_heads + self.head_dim = head_dim + self.n_rep = n_heads // n_kv_heads + self.q_proj = tp_linear( + d_model, n_heads * head_dim, ("fsdp", "tp"), rngs=rngs + ) + self.k_proj = tp_linear( + d_model, n_kv_heads * head_dim, ("fsdp", "tp"), rngs=rngs + ) + self.v_proj = tp_linear( + d_model, n_kv_heads * head_dim, ("fsdp", "tp"), rngs=rngs + ) + self.o_proj = tp_linear( + n_heads * head_dim, d_model, ("tp", "fsdp"), rngs=rngs + ) + self.inv_freq = nnx.Variable(rope_freqs(head_dim)) + + def __call__( + self, x: jax.Array, positions: jax.Array, mask: jax.Array + ) -> jax.Array: + """Apply grouped-query self-attention. + + Args: + x: input array, shape (batch, seq, d_model). + positions: integer position ids, shape (batch, seq). + mask: bool attention mask, shape (seq, seq), True = attend, from + make_causal_mask. + + Returns: + jax.Array, same shape as x. + """ + b, s, _ = x.shape + q = self.q_proj(x).reshape(b, s, self.n_heads, self.head_dim) + k = self.k_proj(x).reshape(b, s, self.n_kv_heads, self.head_dim) + v = self.v_proj(x).reshape(b, s, self.n_kv_heads, self.head_dim) + + q = apply_rope(q, positions, self.inv_freq.value) + k = apply_rope(k, positions, self.inv_freq.value) + k = repeat_kv(k, self.n_rep) + v = repeat_kv(v, self.n_rep) + + q = jnp.transpose(q, (0, 2, 1, 3)) + k = jnp.transpose(k, (0, 2, 1, 3)) + v = jnp.transpose(v, (0, 2, 1, 3)) + + scores = jnp.einsum("bhqd,bhkd->bhqk", q, k) / jnp.sqrt(self.head_dim) + scores = jnp.where(mask[None, None, :, :], scores, -jnp.inf) + weights = jax.nn.softmax(scores, axis=-1) + out = jnp.einsum("bhqk,bhkd->bhqd", weights, v) + out = jnp.transpose(out, (0, 2, 1, 3)).reshape( + b, s, self.n_heads * self.head_dim + ) + return self.o_proj(out) + + +class GeGLU(nnx.Module): + """Gated GELU MLP, TP-sharded (column-parallel gate/up, row-parallel + down -- same pattern as GQAAttention's projections).""" + + def __init__(self, d_model, d_ff, *, rngs): + """Construct a GeGLU feed-forward block. + + Args: + d_model: model (residual stream) dimensionality. + d_ff: hidden (expansion) dimensionality. + rngs: random keys. + """ + self.gate = tp_linear(d_model, d_ff, ("fsdp", "tp"), rngs=rngs) + self.up = tp_linear(d_model, d_ff, ("fsdp", "tp"), rngs=rngs) + self.down = tp_linear(d_ff, d_model, ("tp", "fsdp"), rngs=rngs) + + def __call__(self, x: jax.Array) -> jax.Array: + """Apply the GeGLU transform. + + Args: + x: input array, shape (..., d_model). + + Returns: + jax.Array, same shape as x. + """ + return self.down(jax.nn.gelu(self.gate(x)) * self.up(x)) diff --git a/examples/llm_reference/test_layers.py b/examples/llm_reference/test_layers.py new file mode 100644 index 0000000..838402e --- /dev/null +++ b/examples/llm_reference/test_layers.py @@ -0,0 +1,97 @@ +import jax.numpy as jnp +from flax import nnx +from jax import random as jr + +from layers import ( + GQAAttention, + GeGLU, + RMSNorm, + apply_rope, + make_causal_mask, + repeat_kv, + rope_freqs, +) + + +def test_apply_rope_preserves_shape(): + x = jnp.ones((2, 6, 4, 8)) + positions = jnp.broadcast_to(jnp.arange(6), (2, 6)) + out = apply_rope(x, positions, rope_freqs(8)) + assert out.shape == x.shape + + +def test_repeat_kv_broadcasts_heads(): + x = jnp.ones((2, 6, 2, 4)) + out = repeat_kv(x, n_rep=3) + assert out.shape == (2, 6, 6, 4) + + +def test_make_causal_mask_full_is_lower_triangular(): + mask = make_causal_mask(4) + expected = jnp.array( + [ + [True, False, False, False], + [True, True, False, False], + [True, True, True, False], + [True, True, True, True], + ] + ) + assert jnp.array_equal(mask, expected) + + +def test_make_causal_mask_window_restricts_to_local(): + mask = make_causal_mask(4, window=2) + # position 3 may attend to positions 2,3 only (window=2, excludes 0,1) + assert jnp.array_equal(mask[3], jnp.array([False, False, True, True])) + + +def test_rmsnorm_preserves_shape(): + norm = RMSNorm(32, rngs=nnx.rnglib.Rngs(jr.key(0))) + x = jnp.ones((2, 6, 32)) + assert norm(x).shape == x.shape + + +def test_geglu_preserves_shape(): + mlp = GeGLU(32, 64, rngs=nnx.rnglib.Rngs(jr.key(0))) + x = jnp.ones((2, 6, 32)) + assert mlp(x).shape == x.shape + + +def test_gqa_preserves_shape_under_global_mask(): + d_model, n_heads, n_kv_heads, head_dim, seq_len = 32, 4, 2, 8, 6 + attn = GQAAttention( + d_model, n_heads, n_kv_heads, head_dim, rngs=nnx.rnglib.Rngs(jr.key(0)) + ) + x = jnp.ones((2, seq_len, d_model)) + positions = jnp.broadcast_to(jnp.arange(seq_len), (2, seq_len)) + mask = make_causal_mask(seq_len) + out = attn(x, positions, mask) + assert out.shape == x.shape + + +def test_local_and_global_masks_produce_different_outputs(): + d_model, n_heads, n_kv_heads, head_dim, seq_len = 32, 4, 2, 8, 6 + attn = GQAAttention( + d_model, n_heads, n_kv_heads, head_dim, rngs=nnx.rnglib.Rngs(jr.key(0)) + ) + x = jnp.ones((2, seq_len, d_model)) + positions = jnp.broadcast_to(jnp.arange(seq_len), (2, seq_len)) + out_global = attn(x, positions, make_causal_mask(seq_len)) + out_local = attn(x, positions, make_causal_mask(seq_len, window=3)) + assert not jnp.allclose(out_global, out_local) + + +def test_causal_mask_blocks_future_positions(): + """The defining correctness property of causal attention: changing a + future token must not change any earlier position's output.""" + d_model, n_heads, n_kv_heads, head_dim, seq_len = 32, 4, 2, 8, 6 + attn = GQAAttention( + d_model, n_heads, n_kv_heads, head_dim, rngs=nnx.rnglib.Rngs(jr.key(0)) + ) + x = jnp.ones((2, seq_len, d_model)) + x_perturbed = x.at[:, -1, :].set(x[:, -1, :] * 100.0) + positions = jnp.broadcast_to(jnp.arange(seq_len), (2, seq_len)) + mask = make_causal_mask(seq_len) + out = attn(x, positions, mask) + out_perturbed = attn(x_perturbed, positions, mask) + assert jnp.allclose(out[:, :-1], out_perturbed[:, :-1], atol=1e-5) From 58f6662ba1f319986552e63a80167db333f62653 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Sat, 11 Jul 2026 16:48:53 +0200 Subject: [PATCH 23/34] feat(llm_reference): assemble GemmaDense with interleaved local/global attention --- examples/llm_reference/gemma.py | 143 +++++++++++++++++++++++++++ examples/llm_reference/test_gemma.py | 77 +++++++++++++++ 2 files changed, 220 insertions(+) create mode 100644 examples/llm_reference/gemma.py create mode 100644 examples/llm_reference/test_gemma.py diff --git a/examples/llm_reference/gemma.py b/examples/llm_reference/gemma.py new file mode 100644 index 0000000..8d0ef12 --- /dev/null +++ b/examples/llm_reference/gemma.py @@ -0,0 +1,143 @@ +"""Gemma-4-style decoder-only transformer: GQA + RoPE + interleaved +local/global attention + dense GeGLU FFN, TP+FSDP-sharded. +""" + +import jax +from flax import nnx +from jax import numpy as jnp + +from layers import GQAAttention, GeGLU, RMSNorm, make_causal_mask + + +class GemmaTransformerBlock(nnx.Module): + """Pre-norm transformer block: GQA attention + dense GeGLU FFN.""" + + def __init__(self, d_model, n_heads, n_kv_heads, head_dim, d_ff, *, rngs): + """Construct a Gemma transformer block. + + Args: + d_model: model (residual stream) dimensionality. + n_heads: number of query heads. + n_kv_heads: number of key/value heads. + head_dim: dimensionality of each attention head. + d_ff: feed-forward hidden dimensionality. + rngs: random keys. + """ + self.attn_norm = RMSNorm(d_model, rngs=rngs) + self.attn = GQAAttention(d_model, n_heads, n_kv_heads, head_dim, rngs=rngs) + self.ffn_norm = RMSNorm(d_model, rngs=rngs) + self.ffn = GeGLU(d_model, d_ff, rngs=rngs) + + def __call__( + self, x: jax.Array, positions: jax.Array, mask: jax.Array + ) -> jax.Array: + """Apply the block. + + Args: + x: input array, shape (batch, seq, d_model). + positions: integer position ids, shape (batch, seq). + mask: bool attention mask, shape (seq, seq). + + Returns: + jax.Array, same shape as x. + """ + x = x + self.attn(self.attn_norm(x), positions, mask) + x = x + self.ffn(self.ffn_norm(x)) + return x + + +class GemmaLLM(nnx.Module): + """Decoder-only transformer in the Gemma-4 architectural family. + + Interleaves "local" (sliding-window) and "global" (full causal) + attention layers -- every `global_every`-th layer is global, the rest + are local, matching Gemma 2/3/4's actual design choice. Dense FFN + only (no MoE -- that's MixtralSMoE's role in this suite). + """ + + def __init__( # noqa: PLR0913 + self, + vocab_size, + d_model, + n_layers, + n_heads, + n_kv_heads, + head_dim, + d_ff, + local_window, + *, + global_every=4, + rngs, + ): + """Construct a GemmaLLM. + + Args: + vocab_size: token vocabulary size. + d_model: model (residual stream) dimensionality. + n_layers: number of transformer blocks. + n_heads: number of query heads. + n_kv_heads: number of key/value heads. + head_dim: dimensionality of each attention head. + d_ff: feed-forward hidden dimensionality. + local_window: sliding-window size for "local" attention layers. + global_every: every global_every-th layer (1-indexed) is a full + causal ("global") attention layer; the rest are local. + rngs: random keys. + """ + self.local_window = local_window + self.global_every = global_every + self.embed = nnx.Embed( + vocab_size, + d_model, + embedding_init=nnx.with_partitioning( + nnx.initializers.normal(), ("fsdp", None) + ), + rngs=rngs, + ) + self.blocks = tuple( + GemmaTransformerBlock(d_model, n_heads, n_kv_heads, head_dim, d_ff, rngs=rngs) + for _ in range(n_layers) + ) + self.final_norm = RMSNorm(d_model, rngs=rngs) + self.lm_head = nnx.Linear( + d_model, + vocab_size, + use_bias=False, + kernel_init=nnx.with_partitioning( + nnx.initializers.lecun_normal(), ("fsdp", None) + ), + rngs=rngs, + ) + + def __call__( + self, token_ids: jax.Array, positions: jax.Array + ) -> tuple[jax.Array, jax.Array]: + """Compute next-token logits for a batch of token sequences. + + Args: + token_ids: integer token ids, shape (batch, seq). + positions: integer position ids, shape (batch, seq). + + Returns: + a tuple (logits, aux_loss): logits has shape + (batch, seq, vocab_size); aux_loss is always jnp.array(0.0) (dense + model, no MoE) -- kept for interface uniformity with MixtralSMoE so + objective.py's causal_lm works unmodified across this suite. + """ + seq_len = token_ids.shape[1] + global_mask = make_causal_mask(seq_len) + local_mask = make_causal_mask(seq_len, window=self.local_window) + + hidden = self.embed(token_ids) + for i, block in enumerate(self.blocks): + is_global = (i + 1) % self.global_every == 0 + mask = global_mask if is_global else local_mask + hidden = block(hidden, positions, mask) + + hidden = self.final_norm(hidden) + logits = self.lm_head(hidden) + return logits, jnp.array(0.0) + + +def GemmaDense(vocab_size, **kwargs): + return GemmaLLM(vocab_size, **kwargs) diff --git a/examples/llm_reference/test_gemma.py b/examples/llm_reference/test_gemma.py new file mode 100644 index 0000000..2db594d --- /dev/null +++ b/examples/llm_reference/test_gemma.py @@ -0,0 +1,77 @@ +import jax +import jax.numpy as jnp +import pytest +from flax import nnx +from jax import random as jr +from jax.experimental import mesh_utils + +from gemma import GemmaDense + + +def _tiny_kwargs(): + return dict( + d_model=32, + n_layers=4, + n_heads=4, + n_kv_heads=2, + head_dim=8, + d_ff=64, + local_window=4, + global_every=2, + rngs=nnx.rnglib.Rngs(jr.key(0)), + ) + + +def test_gemma_dense_produces_correct_logit_shape(): + vocab_size, seq_len, batch = 100, 6, 2 + model = GemmaDense(vocab_size, **_tiny_kwargs()) + token_ids = jnp.zeros((batch, seq_len), dtype=jnp.int32) + positions = jnp.broadcast_to(jnp.arange(seq_len), (batch, seq_len)) + logits, aux_loss = model(token_ids, positions) + assert logits.shape == (batch, seq_len, vocab_size) + assert aux_loss == 0.0 + + +def test_gemma_dense_gradients_are_nonzero(): + model = GemmaDense(vocab_size=50, **_tiny_kwargs()) + token_ids = jnp.zeros((2, 6), dtype=jnp.int32) + positions = jnp.broadcast_to(jnp.arange(6), (2, 6)) + + def loss_fn(model): + logits, aux_loss = model(token_ids, positions) + return jnp.mean(logits**2) + aux_loss + + grads = nnx.grad(loss_fn)(model) + + leaves = jax.tree_util.tree_leaves(grads) + assert all(jnp.any(leaf != 0) for leaf in leaves if leaf.size > 0) + + +@pytest.mark.skipif( + jax.local_device_count() < 4, + reason="needs XLA_FLAGS=--xla_force_host_platform_device_count=4", +) +def test_gemma_dense_shards_across_2d_mesh(): + mesh = jax.sharding.Mesh( + mesh_utils.create_device_mesh((2, 2)), ("fsdp", "tp") + ) + with mesh: + model = GemmaDense(vocab_size=100, **_tiny_kwargs()) + graphdef, state = nnx.split(model) + sharding = nnx.get_named_sharding(state, mesh) + state = jax.device_put(state, sharding) + nnx.update(model, state) + + # q_proj: TP-sharded column-parallel, ("fsdp", "tp") + q_kernel = model.blocks[0].attn.q_proj.kernel.value + assert q_kernel.shape == (32, 32) # d_model=32, n_heads*head_dim=4*8=32 + assert q_kernel.addressable_shards[0].data.shape == (16, 16) + + # o_proj: TP-sharded row-parallel, ("tp", "fsdp") + o_kernel = model.blocks[0].attn.o_proj.kernel.value + assert o_kernel.addressable_shards[0].data.shape == (16, 16) + + token_ids = jnp.zeros((2, 6), dtype=jnp.int32) + positions = jnp.broadcast_to(jnp.arange(6), (2, 6)) + logits, aux_loss = model(token_ids, positions) + assert logits.shape == (2, 6, 100) From 75140a476a16cdf63758f164136b411b20452928 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Sat, 11 Jul 2026 21:52:49 +0200 Subject: [PATCH 24/34] feat(llm_reference): add MLAAttention with decoupled RoPE --- examples/llm_reference/deepseek.py | 136 ++++++++++++++++++++++++ examples/llm_reference/test_deepseek.py | 41 +++++++ 2 files changed, 177 insertions(+) create mode 100644 examples/llm_reference/deepseek.py create mode 100644 examples/llm_reference/test_deepseek.py diff --git a/examples/llm_reference/deepseek.py b/examples/llm_reference/deepseek.py new file mode 100644 index 0000000..af7e588 --- /dev/null +++ b/examples/llm_reference/deepseek.py @@ -0,0 +1,136 @@ +"""DeepSeek-V2-style Multi-head Latent Attention (MLA): K/V are jointly +compressed into a low-rank latent (much smaller than uncompressed KV +width) and decompressed per-head at attention time. RoPE cannot be +applied to the compressed latent directly -- rotating then compressing +is not equivalent to compressing then rotating, which breaks RoPE's +relative-position dot-product identity -- so positional information is +carried by a separate, small "decoupled RoPE" projection computed +directly from the uncompressed input and concatenated onto the +compressed "content" (no-positional-encoding, "nope") part before the +attention dot product. Verified live (see the plan doc): the decoupled +split produces a numerically different, causally-correct result; +skipping it (naively RoPE-rotating the shared latent) diverges and is +wrong. Value vectors carry no positional component and are sized +head_dim_nope only, not head_dim_nope + head_dim_rope. + +This module does not implement the KV-cache memory savings MLA exists +to provide in production -- this repo's generate() is +full-prefix-recompute for every model in this suite (see the design +doc's Out of Scope section). MLA is architecturally faithful here, but +nothing exploits its smaller cache. +""" + +import jax +from flax import nnx +from jax import numpy as jnp + +from layers import RMSNorm, apply_rope, rope_freqs, tp_linear + + +class MLAAttention(nnx.Module): + """Multi-head Latent Attention with decoupled RoPE.""" + + def __init__( + self, d_model, n_heads, d_latent, head_dim_nope, head_dim_rope, *, rngs + ): + """Construct an MLA attention block. + + Args: + d_model: model (residual stream) dimensionality. + n_heads: number of attention heads (MLA has no separate kv-head + count -- all heads share one compressed KV latent, which is the + whole point of the compression, replacing GQA's coarser + kv-head-sharing with a much more aggressive shared latent). + d_latent: compressed latent dimensionality, shared across all + heads. Should be substantially smaller than + n_heads * head_dim_nope (the real DeepSeek-V2 point) -- guidance: + roughly (n_heads * head_dim_nope) / 4. + head_dim_nope: per-head "content" (no positional encoding) + dimensionality, decompressed from the shared latent. + head_dim_rope: per-head decoupled-RoPE dimensionality, computed + directly from the uncompressed input, not from the latent. + rngs: random keys. + """ + self.n_heads = n_heads + self.head_dim_nope = head_dim_nope + self.head_dim_rope = head_dim_rope + + # KV path: compress (replicated, small bottleneck) -> norm -> + # decompress per-head (TP-sharded by head). + self.down_kv = nnx.Linear(d_model, d_latent, use_bias=False, rngs=rngs) + self.norm_kv = RMSNorm(d_latent, rngs=rngs) + self.up_k = tp_linear( + d_latent, n_heads * head_dim_nope, ("fsdp", "tp"), rngs=rngs + ) + self.up_v = tp_linear( + d_latent, n_heads * head_dim_nope, ("fsdp", "tp"), rngs=rngs + ) + self.rope_k = tp_linear( + d_model, n_heads * head_dim_rope, ("fsdp", "tp"), rngs=rngs + ) + + # Query path: same compress/decompress + decoupled-RoPE split. + self.down_q = nnx.Linear(d_model, d_latent, use_bias=False, rngs=rngs) + self.norm_q = RMSNorm(d_latent, rngs=rngs) + self.up_q = tp_linear( + d_latent, n_heads * head_dim_nope, ("fsdp", "tp"), rngs=rngs + ) + self.rope_q = tp_linear( + d_model, n_heads * head_dim_rope, ("fsdp", "tp"), rngs=rngs + ) + + # Output projection: value vectors carry head_dim_nope only (no + # positional component), so this is sized from n_heads * head_dim_nope. + self.o_proj = tp_linear( + n_heads * head_dim_nope, d_model, ("tp", "fsdp"), rngs=rngs + ) + # nnx.Variable wrap required -- see the identical note on + # GQAAttention.inv_freq in layers.py (Task 1): bare jax.Array module + # attributes are rejected by nnx.split/nnx.grad's graph flattening, + # and a plain nnx.Variable (not nnx.Param) is correctly excluded + # from the gradient pytree entirely. + self.inv_freq = nnx.Variable(rope_freqs(head_dim_rope)) + + def __call__( + self, x: jax.Array, positions: jax.Array, mask: jax.Array + ) -> jax.Array: + """Apply multi-head latent attention. + + Args: + x: input array, shape (batch, seq, d_model). + positions: integer position ids, shape (batch, seq). + mask: bool attention mask, shape (seq, seq), True = attend. MLA is + always used with full causal masking in this suite (no local + windowing -- that's Gemma-specific). + + Returns: + jax.Array, same shape as x. + """ + b, s, _ = x.shape + + latent_kv = self.norm_kv(self.down_kv(x)) + k_nope = self.up_k(latent_kv).reshape(b, s, self.n_heads, self.head_dim_nope) + v = self.up_v(latent_kv).reshape(b, s, self.n_heads, self.head_dim_nope) + + k_rope = self.rope_k(x).reshape(b, s, self.n_heads, self.head_dim_rope) + k_rope = apply_rope(k_rope, positions, self.inv_freq.value) + k = jnp.concatenate([k_nope, k_rope], axis=-1) + + latent_q = self.norm_q(self.down_q(x)) + q_nope = self.up_q(latent_q).reshape(b, s, self.n_heads, self.head_dim_nope) + q_rope = self.rope_q(x).reshape(b, s, self.n_heads, self.head_dim_rope) + q_rope = apply_rope(q_rope, positions, self.inv_freq.value) + q = jnp.concatenate([q_nope, q_rope], axis=-1) + + head_dim = self.head_dim_nope + self.head_dim_rope + q = jnp.transpose(q, (0, 2, 1, 3)) + k = jnp.transpose(k, (0, 2, 1, 3)) + v = jnp.transpose(v, (0, 2, 1, 3)) + scores = jnp.einsum("bhqd,bhkd->bhqk", q, k) / jnp.sqrt(head_dim) + scores = jnp.where(mask[None, None, :, :], scores, -jnp.inf) + weights = jax.nn.softmax(scores, axis=-1) + out = jnp.einsum("bhqk,bhkd->bhqd", weights, v) + out = jnp.transpose(out, (0, 2, 1, 3)).reshape( + b, s, self.n_heads * self.head_dim_nope + ) + return self.o_proj(out) diff --git a/examples/llm_reference/test_deepseek.py b/examples/llm_reference/test_deepseek.py new file mode 100644 index 0000000..d0405be --- /dev/null +++ b/examples/llm_reference/test_deepseek.py @@ -0,0 +1,41 @@ +import jax.numpy as jnp +from flax import nnx +from jax import random as jr + +from deepseek import MLAAttention +from layers import make_causal_mask + + +def _make_mla(): + return MLAAttention( + d_model=32, + n_heads=4, + d_latent=8, + head_dim_nope=6, + head_dim_rope=2, + rngs=nnx.rnglib.Rngs(jr.key(0)), + ) + + +def test_mla_preserves_shape(): + attn = _make_mla() + x = jnp.ones((2, 6, 32)) + positions = jnp.broadcast_to(jnp.arange(6), (2, 6)) + mask = make_causal_mask(6) + out = attn(x, positions, mask) + assert out.shape == x.shape + + +def test_mla_causal_mask_blocks_future_positions(): + """Same defining correctness property as GQAAttention: perturbing a + future token must not change any earlier position's output. This is + the one place a subtle bug in the decoupled content/RoPE split could + leak future positions into the past.""" + attn = _make_mla() + x = jnp.ones((2, 6, 32)) + x_perturbed = x.at[:, -1, :].set(x[:, -1, :] * 100.0) + positions = jnp.broadcast_to(jnp.arange(6), (2, 6)) + mask = make_causal_mask(6) + out = attn(x, positions, mask) + out_perturbed = attn(x_perturbed, positions, mask) + assert jnp.allclose(out[:, :-1], out_perturbed[:, :-1], atol=1e-5) From e6f7bd6571845254f1ba47895aec55219b1346c7 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Sat, 11 Jul 2026 22:22:53 +0200 Subject: [PATCH 25/34] feat(llm_reference): assemble DeepSeekMLA, verify 2D fsdp+tp sharding --- examples/llm_reference/deepseek.py | 126 +++++++++++++++++++++++- examples/llm_reference/test_deepseek.py | 68 ++++++++++++- 2 files changed, 192 insertions(+), 2 deletions(-) diff --git a/examples/llm_reference/deepseek.py b/examples/llm_reference/deepseek.py index af7e588..647a191 100644 --- a/examples/llm_reference/deepseek.py +++ b/examples/llm_reference/deepseek.py @@ -24,7 +24,7 @@ from flax import nnx from jax import numpy as jnp -from layers import RMSNorm, apply_rope, rope_freqs, tp_linear +from layers import GeGLU, RMSNorm, apply_rope, make_causal_mask, rope_freqs, tp_linear class MLAAttention(nnx.Module): @@ -134,3 +134,127 @@ def __call__( b, s, self.n_heads * self.head_dim_nope ) return self.o_proj(out) + + +class DeepSeekTransformerBlock(nnx.Module): + """Pre-norm transformer block: MLA attention + dense GeGLU FFN.""" + + def __init__(self, d_model, n_heads, d_latent, head_dim_nope, head_dim_rope, d_ff, *, rngs): + """Construct a DeepSeek transformer block. + + Args: + d_model: model (residual stream) dimensionality. + n_heads: number of attention heads. + d_latent: compressed KV/Q latent dimensionality. + head_dim_nope: per-head content dimensionality. + head_dim_rope: per-head decoupled-RoPE dimensionality. + d_ff: feed-forward hidden dimensionality. + rngs: random keys. + """ + self.attn_norm = RMSNorm(d_model, rngs=rngs) + self.attn = MLAAttention( + d_model, n_heads, d_latent, head_dim_nope, head_dim_rope, rngs=rngs + ) + self.ffn_norm = RMSNorm(d_model, rngs=rngs) + self.ffn = GeGLU(d_model, d_ff, rngs=rngs) + + def __call__( + self, x: jax.Array, positions: jax.Array, mask: jax.Array + ) -> jax.Array: + """Apply the block. + + Args: + x: input array, shape (batch, seq, d_model). + positions: integer position ids, shape (batch, seq). + mask: bool attention mask, shape (seq, seq). + + Returns: + jax.Array, same shape as x. + """ + x = x + self.attn(self.attn_norm(x), positions, mask) + x = x + self.ffn(self.ffn_norm(x)) + return x + + +class DeepSeekLLM(nnx.Module): + """Decoder-only transformer using DeepSeek-V2-style Multi-head Latent + Attention. Full causal attention only (no local/global interleaving -- + that's a Gemma-specific trait, not part of MLA). Dense FFN only (no + MoE -- that's MixtralSMoE's role in this suite).""" + + def __init__( # noqa: PLR0913 + self, + vocab_size, + d_model, + n_layers, + n_heads, + d_latent, + head_dim_nope, + head_dim_rope, + d_ff, + *, + rngs, + ): + """Construct a DeepSeekLLM. + + Args: + vocab_size: token vocabulary size. + d_model: model (residual stream) dimensionality. + n_layers: number of transformer blocks. + n_heads: number of attention heads. + d_latent: compressed KV/Q latent dimensionality. + head_dim_nope: per-head content dimensionality. + head_dim_rope: per-head decoupled-RoPE dimensionality. + d_ff: feed-forward hidden dimensionality. + rngs: random keys. + """ + self.embed = nnx.Embed( + vocab_size, + d_model, + embedding_init=nnx.with_partitioning( + nnx.initializers.normal(), ("fsdp", None) + ), + rngs=rngs, + ) + self.blocks = tuple( + DeepSeekTransformerBlock( + d_model, n_heads, d_latent, head_dim_nope, head_dim_rope, d_ff, rngs=rngs + ) + for _ in range(n_layers) + ) + self.final_norm = RMSNorm(d_model, rngs=rngs) + self.lm_head = nnx.Linear( + d_model, + vocab_size, + use_bias=False, + kernel_init=nnx.with_partitioning( + nnx.initializers.lecun_normal(), ("fsdp", None) + ), + rngs=rngs, + ) + + def __call__( + self, token_ids: jax.Array, positions: jax.Array + ) -> tuple[jax.Array, jax.Array]: + """Compute next-token logits for a batch of token sequences. + + Args: + token_ids: integer token ids, shape (batch, seq). + positions: integer position ids, shape (batch, seq). + + Returns: + a tuple (logits, aux_loss): logits has shape + (batch, seq, vocab_size); aux_loss is always jnp.array(0.0) (dense + model, no MoE). + """ + mask = make_causal_mask(token_ids.shape[1]) + hidden = self.embed(token_ids) + for block in self.blocks: + hidden = block(hidden, positions, mask) + hidden = self.final_norm(hidden) + logits = self.lm_head(hidden) + return logits, jnp.array(0.0) + + +def DeepSeekMLA(vocab_size, **kwargs): + return DeepSeekLLM(vocab_size, **kwargs) diff --git a/examples/llm_reference/test_deepseek.py b/examples/llm_reference/test_deepseek.py index d0405be..dc59e8f 100644 --- a/examples/llm_reference/test_deepseek.py +++ b/examples/llm_reference/test_deepseek.py @@ -1,8 +1,11 @@ +import jax import jax.numpy as jnp +import pytest from flax import nnx from jax import random as jr +from jax.experimental import mesh_utils -from deepseek import MLAAttention +from deepseek import DeepSeekMLA, MLAAttention from layers import make_causal_mask @@ -39,3 +42,66 @@ def test_mla_causal_mask_blocks_future_positions(): out = attn(x, positions, mask) out_perturbed = attn(x_perturbed, positions, mask) assert jnp.allclose(out[:, :-1], out_perturbed[:, :-1], atol=1e-5) + + +def _tiny_kwargs(): + return dict( + d_model=32, + n_layers=4, + n_heads=4, + d_latent=8, + head_dim_nope=6, + head_dim_rope=2, + d_ff=64, + rngs=nnx.rnglib.Rngs(jr.key(0)), + ) + + +def test_deepseek_mla_produces_correct_logit_shape(): + vocab_size, seq_len, batch = 100, 6, 2 + model = DeepSeekMLA(vocab_size, **_tiny_kwargs()) + token_ids = jnp.zeros((batch, seq_len), dtype=jnp.int32) + positions = jnp.broadcast_to(jnp.arange(seq_len), (batch, seq_len)) + logits, aux_loss = model(token_ids, positions) + assert logits.shape == (batch, seq_len, vocab_size) + assert aux_loss == 0.0 + + +def test_deepseek_mla_gradients_are_nonzero(): + model = DeepSeekMLA(vocab_size=50, **_tiny_kwargs()) + token_ids = jnp.zeros((2, 6), dtype=jnp.int32) + positions = jnp.broadcast_to(jnp.arange(6), (2, 6)) + + def loss_fn(model): + logits, aux_loss = model(token_ids, positions) + return jnp.mean(logits**2) + aux_loss + + grads = nnx.grad(loss_fn)(model) + + leaves = jax.tree_util.tree_leaves(grads) + assert all(jnp.any(leaf != 0) for leaf in leaves if leaf.size > 0) + + +@pytest.mark.skipif( + jax.local_device_count() < 4, + reason="needs XLA_FLAGS=--xla_force_host_platform_device_count=4", +) +def test_deepseek_mla_shards_across_2d_mesh(): + mesh = jax.sharding.Mesh( + mesh_utils.create_device_mesh((2, 2)), ("fsdp", "tp") + ) + with mesh: + model = DeepSeekMLA(vocab_size=100, **_tiny_kwargs()) + graphdef, state = nnx.split(model) + sharding = nnx.get_named_sharding(state, mesh) + state = jax.device_put(state, sharding) + nnx.update(model, state) + + up_k_kernel = model.blocks[0].attn.up_k.kernel.value + assert up_k_kernel.shape == (8, 24) # d_latent=8, n_heads*head_dim_nope=4*6=24 + assert up_k_kernel.addressable_shards[0].data.shape == (4, 12) + + token_ids = jnp.zeros((2, 6), dtype=jnp.int32) + positions = jnp.broadcast_to(jnp.arange(6), (2, 6)) + logits, aux_loss = model(token_ids, positions) + assert logits.shape == (2, 6, 100) From 26ef1f3cb5fbe7bc393e3fc4151d7aa03cbeed01 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Sat, 11 Jul 2026 22:26:29 +0200 Subject: [PATCH 26/34] feat(llm_reference): add SparseMoEFFN with capacity-based dispatch/combine routing --- examples/llm_reference/mixtral.py | 128 +++++++++++++++++++++++++ examples/llm_reference/test_mixtral.py | 86 +++++++++++++++++ 2 files changed, 214 insertions(+) create mode 100644 examples/llm_reference/mixtral.py create mode 100644 examples/llm_reference/test_mixtral.py diff --git a/examples/llm_reference/mixtral.py b/examples/llm_reference/mixtral.py new file mode 100644 index 0000000..435e316 --- /dev/null +++ b/examples/llm_reference/mixtral.py @@ -0,0 +1,128 @@ +"""Mixtral-style decoder-only transformer: GQA + RoPE + full causal +attention + real sparse top-k expert routing via capacity-based +dispatch/combine einsums (not dense-compute-then-select). + +The dispatch/combine formulation lets JAX's SPMD partitioner (GSPMD) +insert the cross-device communication automatically when the expert +axis is sharded -- no hand-written jax.lax.all_to_all. Verified live +before writing this plan (see plan header): sharding the token axis +along one mesh axis and the expert axis along a DIFFERENT mesh axis +produces output numerically identical to the unsharded computation, with +real collective ops (all-gather, all-reduce) present in the compiled +HLO, and this holds with no explicit with_sharding_constraint calls and +no mesh threaded into __call__ -- plain nnx.with_partitioning weight +annotations are sufficient, matching examples/fsdp_tp_demo's pattern. +""" + +import jax +from flax import nnx +from jax import numpy as jnp + + +class SparseMoEFFN(nnx.Module): + """Mixtral-style top-k-routed mixture-of-experts feed-forward block + with real capacity-based dispatch/combine (not dense-compute-then- + select). Expert weights are stacked into single tensors with a leading + n_experts axis so that axis can be sharded across a mesh's "expert" + axis.""" + + def __init__( + self, d_model, d_ff, n_experts, n_active, *, rngs, capacity_factor=1.25 + ): + """Construct a sparse MoE feed-forward block. + + Args: + d_model: model (residual stream) dimensionality. + d_ff: hidden (expansion) dimensionality of each expert. + n_experts: total number of experts. + n_active: number of experts activated per token (top-k). + rngs: random keys. + capacity_factor: per-expert buffer capacity multiplier. Per-expert + capacity = ceil(capacity_factor * n_active * n_tokens / + n_experts). Standard Switch-Transformer value is 1.25. Tokens + beyond an expert's capacity in a batch are dropped (their + contribution from that slot is zeroed, not misrouted). + """ + self.n_experts = n_experts + self.n_active = n_active + self.capacity_factor = capacity_factor + self.router = nnx.Linear(d_model, n_experts, use_bias=False, rngs=rngs) + + expert_partitioning = nnx.with_partitioning( + nnx.initializers.lecun_normal(), ("expert", None, None) + ) + key = rngs.params() + k1, k2, k3 = jax.random.split(key, 3) + self.gate = nnx.Param( + expert_partitioning(k1, (n_experts, d_model, d_ff)) + ) + self.up = nnx.Param( + expert_partitioning(k2, (n_experts, d_model, d_ff)) + ) + self.down = nnx.Param( + expert_partitioning(k3, (n_experts, d_ff, d_model)) + ) + + def __call__(self, x: jax.Array) -> tuple[jax.Array, jax.Array]: + """Route tokens to the top-k experts via capacity-based dispatch/ + combine and combine their outputs. + + Args: + x: input array, shape (batch, seq, d_model). + + Returns: + a tuple (output, aux_loss): output has the same shape as x; + aux_loss is a scalar Switch-Transformer-style load-balancing loss. + """ + b, s, d = x.shape + flat = x.reshape(b * s, d) + n_tok = flat.shape[0] + logits = self.router(flat) + probs = jax.nn.softmax(logits, axis=-1) + top_probs, top_idx = jax.lax.top_k(probs, self.n_active) + top_probs = top_probs / jnp.sum(top_probs, axis=-1, keepdims=True) + + capacity = int( + jnp.ceil(self.capacity_factor * self.n_active * n_tok / self.n_experts) + ) + + expert_onehot = jax.nn.one_hot(top_idx, self.n_experts) + flat_onehot = expert_onehot.reshape(-1, self.n_experts) + position_in_expert = ( + jnp.cumsum(flat_onehot, axis=0) * flat_onehot - flat_onehot + ) + position_in_expert = jnp.sum(position_in_expert, axis=-1) + within_capacity = position_in_expert < capacity + position_in_expert = position_in_expert.reshape(n_tok, self.n_active) + within_capacity = within_capacity.reshape(n_tok, self.n_active) + + capacity_onehot = jax.nn.one_hot( + position_in_expert.astype(jnp.int32), capacity + ) + dispatch_mask = jnp.sum( + expert_onehot[..., None] + * capacity_onehot[:, :, None, :] + * within_capacity[:, :, None, None], + axis=1, + ) # (n_tok, n_experts, capacity) + combine_weight = jnp.sum( + expert_onehot[..., None] + * capacity_onehot[:, :, None, :] + * within_capacity[:, :, None, None] + * top_probs[:, :, None, None], + axis=1, + ) # (n_tok, n_experts, capacity) + + dispatched = jnp.einsum("td,tec->ecd", flat, dispatch_mask) + g = jnp.einsum("ecd,edf->ecf", dispatched, self.gate.value) + u = jnp.einsum("ecd,edf->ecf", dispatched, self.up.value) + h = jax.nn.silu(g) * u + expert_out = jnp.einsum("ecf,efd->ecd", h, self.down.value) + combined = jnp.einsum("ecd,tec->td", expert_out, combine_weight) + + density = jnp.mean(probs, axis=0) + chosen_mask = jax.nn.one_hot(top_idx, self.n_experts).sum(axis=1) + chosen_frac = jnp.mean(chosen_mask, axis=0) + aux_loss = self.n_experts * jnp.sum(density * chosen_frac) + + return combined.reshape(b, s, d), aux_loss diff --git a/examples/llm_reference/test_mixtral.py b/examples/llm_reference/test_mixtral.py new file mode 100644 index 0000000..9f11f91 --- /dev/null +++ b/examples/llm_reference/test_mixtral.py @@ -0,0 +1,86 @@ +import jax +import jax.numpy as jnp +from flax import nnx +from jax import random as jr + +from mixtral import SparseMoEFFN + + +def test_sparse_moe_preserves_shape_and_returns_scalar_aux_loss(): + moe = SparseMoEFFN( + d_model=8, d_ff=16, n_experts=4, n_active=2, rngs=nnx.rnglib.Rngs(jr.key(0)) + ) + x = jnp.ones((2, 6, 8)) + out, aux_loss = moe(x) + assert out.shape == x.shape + assert aux_loss.shape == () + assert aux_loss > 0 + + +def test_sparse_moe_matches_naive_per_token_reference_within_capacity(): + """The correctness check for the whole dispatch/combine mechanism: + with a generous capacity_factor (no drops), the output must exactly + match a naive reference that gathers each token's top-k experts + directly, with no einsum dispatch trick. Verified live before writing + this plan (see plan header) -- this test locks that verification in as + a regression test.""" + d_model, d_ff, n_experts, n_active = 8, 16, 4, 2 + moe = SparseMoEFFN( + d_model, d_ff, n_experts, n_active, + capacity_factor=10.0, + rngs=nnx.rnglib.Rngs(jr.key(0)), + ) + x = jr.normal(jr.key(1), (2, 6, d_model)) + out, aux_loss = moe(x) + + # naive reference: gather each token's top-k experts directly + flat = x.reshape(-1, d_model) + logits = flat @ moe.router.kernel.value + probs = jax.nn.softmax(logits, axis=-1) + top_probs, top_idx = jax.lax.top_k(probs, n_active) + top_probs = top_probs / jnp.sum(top_probs, axis=-1, keepdims=True) + + def expert_ffn(x_token, e): + g = x_token @ moe.gate.value[e] + u = x_token @ moe.up.value[e] + h = jax.nn.silu(g) * u + return h @ moe.down.value[e] + + naive_out = jnp.zeros_like(flat) + for slot in range(n_active): + for tok in range(flat.shape[0]): + e = int(top_idx[tok, slot]) + naive_out = naive_out.at[tok].add( + top_probs[tok, slot] * expert_ffn(flat[tok], e) + ) + naive_out = naive_out.reshape(x.shape) + + assert jnp.allclose(out, naive_out, atol=1e-4) + + +def test_sparse_moe_drops_tokens_cleanly_under_tiny_capacity(): + """Capacity overflow must not crash or corrupt other tokens -- dropped + tokens simply don't contribute from that slot.""" + moe = SparseMoEFFN( + d_model=8, d_ff=16, n_experts=4, n_active=2, + capacity_factor=0.01, + rngs=nnx.rnglib.Rngs(jr.key(0)), + ) + x = jr.normal(jr.key(1), (2, 6, 8)) + out, aux_loss = moe(x) + assert out.shape == x.shape + assert jnp.all(jnp.isfinite(out)) + + +def test_sparse_moe_router_receives_nonzero_gradient(): + moe = SparseMoEFFN( + d_model=8, d_ff=16, n_experts=4, n_active=2, rngs=nnx.rnglib.Rngs(jr.key(0)) + ) + x = jnp.ones((2, 6, 8)) + + def loss_fn(moe): + out, aux = moe(x) + return jnp.mean(out**2) + aux + + grads = nnx.grad(loss_fn)(moe) + assert jnp.any(grads.router.kernel.value != 0) From 5c1356fc2a683664ecb96b934c5b63b3e2570767 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Sat, 11 Jul 2026 22:53:42 +0200 Subject: [PATCH 27/34] feat(llm_reference): assemble MixtralSMoE, verify 3D fsdp+tp+expert sharding matches unsharded reference --- examples/llm_reference/mixtral.py | 135 +++++++++++++++++++++++++ examples/llm_reference/test_mixtral.py | 117 ++++++++++++++++++++- 2 files changed, 251 insertions(+), 1 deletion(-) diff --git a/examples/llm_reference/mixtral.py b/examples/llm_reference/mixtral.py index 435e316..02097f8 100644 --- a/examples/llm_reference/mixtral.py +++ b/examples/llm_reference/mixtral.py @@ -18,6 +18,8 @@ from flax import nnx from jax import numpy as jnp +from layers import GQAAttention, RMSNorm, make_causal_mask + class SparseMoEFFN(nnx.Module): """Mixtral-style top-k-routed mixture-of-experts feed-forward block @@ -126,3 +128,136 @@ def __call__(self, x: jax.Array) -> tuple[jax.Array, jax.Array]: aux_loss = self.n_experts * jnp.sum(density * chosen_frac) return combined.reshape(b, s, d), aux_loss + + +class MixtralTransformerBlock(nnx.Module): + """Pre-norm transformer block: GQA attention (full causal only) + + sparse MoE FFN.""" + + def __init__( # noqa: PLR0913 + self, d_model, n_heads, n_kv_heads, head_dim, d_ff, n_experts, n_active, *, rngs + ): + """Construct a Mixtral transformer block. + + Args: + d_model: model (residual stream) dimensionality. + n_heads: number of query heads. + n_kv_heads: number of key/value heads. + head_dim: dimensionality of each attention head. + d_ff: feed-forward hidden dimensionality of each expert. + n_experts: total experts. + n_active: active experts per token (top-k). + rngs: random keys. + """ + self.attn_norm = RMSNorm(d_model, rngs=rngs) + self.attn = GQAAttention(d_model, n_heads, n_kv_heads, head_dim, rngs=rngs) + self.ffn_norm = RMSNorm(d_model, rngs=rngs) + self.ffn = SparseMoEFFN(d_model, d_ff, n_experts, n_active, rngs=rngs) + + def __call__( + self, x: jax.Array, positions: jax.Array, mask: jax.Array + ) -> tuple[jax.Array, jax.Array]: + """Apply the block. + + Args: + x: input array, shape (batch, seq, d_model). + positions: integer position ids, shape (batch, seq). + mask: bool attention mask, shape (seq, seq). + + Returns: + a tuple (output, aux_loss): output has the same shape as x. + """ + x = x + self.attn(self.attn_norm(x), positions, mask) + ffn_out, aux_loss = self.ffn(self.ffn_norm(x)) + return x + ffn_out, aux_loss + + +class MixtralLLM(nnx.Module): + """Decoder-only transformer with real sparse top-k expert routing. + Full causal attention only (no local/global interleaving -- that's a + Gemma-specific trait).""" + + def __init__( # noqa: PLR0913 + self, + vocab_size, + d_model, + n_layers, + n_heads, + n_kv_heads, + head_dim, + d_ff, + n_experts, + n_active, + *, + aux_loss_coef=0.01, + rngs, + ): + """Construct a MixtralLLM. + + Args: + vocab_size: token vocabulary size. + d_model: model (residual stream) dimensionality. + n_layers: number of transformer blocks. + n_heads: number of query heads. + n_kv_heads: number of key/value heads. + head_dim: dimensionality of each attention head. + d_ff: feed-forward hidden dimensionality of each expert. + n_experts: total experts per block. + n_active: active experts per token (top-k) per block. + aux_loss_coef: weight applied to each block's load-balancing + aux_loss before summing across blocks. + rngs: random keys. + """ + self.aux_loss_coef = aux_loss_coef + self.embed = nnx.Embed( + vocab_size, + d_model, + embedding_init=nnx.with_partitioning( + nnx.initializers.normal(), ("fsdp", None) + ), + rngs=rngs, + ) + self.blocks = tuple( + MixtralTransformerBlock( + d_model, n_heads, n_kv_heads, head_dim, d_ff, n_experts, n_active, rngs=rngs + ) + for _ in range(n_layers) + ) + self.final_norm = RMSNorm(d_model, rngs=rngs) + self.lm_head = nnx.Linear( + d_model, + vocab_size, + use_bias=False, + kernel_init=nnx.with_partitioning( + nnx.initializers.lecun_normal(), ("fsdp", None) + ), + rngs=rngs, + ) + + def __call__( + self, token_ids: jax.Array, positions: jax.Array + ) -> tuple[jax.Array, jax.Array]: + """Compute next-token logits for a batch of token sequences. + + Args: + token_ids: integer token ids, shape (batch, seq). + positions: integer position ids, shape (batch, seq). + + Returns: + a tuple (logits, aux_loss): logits has shape + (batch, seq, vocab_size); aux_loss is aux_loss_coef times the + summed per-block load-balancing loss. + """ + mask = make_causal_mask(token_ids.shape[1]) + hidden = self.embed(token_ids) + total_aux_loss = jnp.array(0.0) + for block in self.blocks: + hidden, aux_loss = block(hidden, positions, mask) + total_aux_loss = total_aux_loss + aux_loss + hidden = self.final_norm(hidden) + logits = self.lm_head(hidden) + return logits, self.aux_loss_coef * total_aux_loss + + +def MixtralSMoE(vocab_size, **kwargs): + return MixtralLLM(vocab_size, **kwargs) diff --git a/examples/llm_reference/test_mixtral.py b/examples/llm_reference/test_mixtral.py index 9f11f91..3d990e6 100644 --- a/examples/llm_reference/test_mixtral.py +++ b/examples/llm_reference/test_mixtral.py @@ -1,9 +1,11 @@ import jax import jax.numpy as jnp +import pytest from flax import nnx from jax import random as jr +from jax.experimental import mesh_utils -from mixtral import SparseMoEFFN +from mixtral import MixtralSMoE, SparseMoEFFN def test_sparse_moe_preserves_shape_and_returns_scalar_aux_loss(): @@ -84,3 +86,116 @@ def loss_fn(moe): grads = nnx.grad(loss_fn)(moe) assert jnp.any(grads.router.kernel.value != 0) + + +def _tiny_kwargs(): + return dict( + d_model=32, + n_layers=4, + n_heads=4, + n_kv_heads=2, + head_dim=8, + d_ff=64, + n_experts=4, + n_active=2, + rngs=nnx.rnglib.Rngs(jr.key(0)), + ) + + +def test_mixtral_smoe_produces_correct_logit_shape_and_nonzero_aux_loss(): + vocab_size, seq_len, batch = 100, 6, 2 + model = MixtralSMoE(vocab_size, **_tiny_kwargs()) + token_ids = jnp.zeros((batch, seq_len), dtype=jnp.int32) + positions = jnp.broadcast_to(jnp.arange(seq_len), (batch, seq_len)) + logits, aux_loss = model(token_ids, positions) + assert logits.shape == (batch, seq_len, vocab_size) + assert aux_loss > 0.0 + + +def test_mixtral_smoe_gradients_are_nonzero(): + """Checks that every parameter LEAF (gate/up/down/router/attention + weights, each a single stacked (n_experts, ...) tensor, not one leaf + per expert) receives a nonzero gradient somewhere in it. This is + whole-tensor liveness, not per-expert liveness: with this seed, 2 of + the 4 experts actually receive zero dispatched tokens in this small + batch (confirmed by inspecting dispatch counts) -- capacity_factor + governs token-dropping when an expert is OVERsubscribed, it has no + bearing on whether an expert is starved of tokens in the first place, + so raising it would not change this. A starved expert's own gate/up/ + down slice legitimately gets zero gradient this step; the assertion + still passes because it checks the whole stacked tensor has some + nonzero entry, not that every expert does. Detecting per-expert + starvation would need a per-expert-slice assertion, which this test + does not attempt -- it exists to catch a structurally dead PARAMETER + (e.g. an unused projection), not routing imbalance.""" + model = MixtralSMoE(vocab_size=50, **{**_tiny_kwargs(), "n_layers": 1}) + token_ids = jnp.zeros((4, 8), dtype=jnp.int32) + positions = jnp.broadcast_to(jnp.arange(8), (4, 8)) + + def loss_fn(model): + logits, aux_loss = model(token_ids, positions) + return jnp.mean(logits**2) + aux_loss + + grads = nnx.grad(loss_fn)(model) + leaves = jax.tree_util.tree_leaves(grads) + assert all(jnp.any(leaf != 0) for leaf in leaves if leaf.size > 0) + + +@pytest.mark.skipif( + jax.local_device_count() < 8, + reason="needs XLA_FLAGS=--xla_force_host_platform_device_count=8", +) +def test_mixtral_smoe_sharded_output_matches_unsharded_reference(): + """The critical correctness test for real expert-parallel dispatch: + sharding tokens along one mesh axis and experts along a DIFFERENT mesh + axis must produce output numerically identical to an unsharded + reference computation with the same weights and inputs. This is what + actually proves the dispatch/combine + GSPMD auto-communication claim + -- shape-only tests cannot catch a token routed to the wrong expert if + the wrong expert happens to produce same-shaped output. Verified live + before writing this plan (see plan header): ~1e-9 max diff, with real + collective ops confirmed present in the compiled HLO.""" + kwargs = {**_tiny_kwargs(), "n_layers": 1} + token_ids = jnp.zeros((4, 8), dtype=jnp.int32) + positions = jnp.broadcast_to(jnp.arange(8), (4, 8)) + + ref_model = MixtralSMoE(vocab_size=50, **kwargs) + ref_logits, ref_aux = ref_model(token_ids, positions) + # ref_logits/ref_aux are already concrete values at this point, so + # resharding ref_model in place below cannot retroactively affect them + # -- no need for a separate cloned module. + + mesh = jax.sharding.Mesh( + mesh_utils.create_device_mesh((2, 2, 2)), ("fsdp", "tp", "expert") + ) + with mesh: + graphdef, state = nnx.split(ref_model) + sharding = nnx.get_named_sharding(state, mesh) + state = jax.device_put(state, sharding) + nnx.update(ref_model, state) + + sharded_logits, sharded_aux = ref_model(token_ids, positions) + + max_diff = jnp.abs(ref_logits - sharded_logits).max() + assert max_diff < 1e-3, f"sharded output diverges from reference: {max_diff}" + assert jnp.allclose(ref_aux, sharded_aux, atol=1e-3) + + +@pytest.mark.skipif( + jax.local_device_count() < 8, + reason="needs XLA_FLAGS=--xla_force_host_platform_device_count=8", +) +def test_mixtral_smoe_shards_expert_axis_across_3d_mesh(): + mesh = jax.sharding.Mesh( + mesh_utils.create_device_mesh((2, 2, 2)), ("fsdp", "tp", "expert") + ) + with mesh: + model = MixtralSMoE(vocab_size=100, **{**_tiny_kwargs(), "n_layers": 1}) + graphdef, state = nnx.split(model) + sharding = nnx.get_named_sharding(state, mesh) + state = jax.device_put(state, sharding) + nnx.update(model, state) + + gate = model.blocks[0].ffn.gate.value + assert gate.shape == (4, 32, 64) # n_experts=4, d_model=32, d_ff=64 + assert gate.addressable_shards[0].data.shape == (2, 32, 64) From 22319d276b7d3099936085a10349ec5b00f30254 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Sun, 12 Jul 2026 00:43:39 +0200 Subject: [PATCH 28/34] feat(llm_reference): add causal_lm training objective, shared across all three model families --- examples/llm_reference/objective.py | 53 ++++++++++++++++ examples/llm_reference/test_objective.py | 79 ++++++++++++++++++++++++ 2 files changed, 132 insertions(+) create mode 100644 examples/llm_reference/objective.py create mode 100644 examples/llm_reference/test_objective.py diff --git a/examples/llm_reference/objective.py b/examples/llm_reference/objective.py new file mode 100644 index 0000000..252221f --- /dev/null +++ b/examples/llm_reference/objective.py @@ -0,0 +1,53 @@ +"""Causal-language-model training objective, shared across every model +family in this suite (GemmaDense, DeepSeekMLA, MixtralSMoE) -- works +uniformly since every model's __call__ returns (logits, aux_loss), with +dense models always returning aux_loss=0.0. +""" + +import optax +from flax import nnx +from jax import numpy as jnp + +from blaxbird._src._types import ObjectiveFns + + +def causal_lm(aux_loss_coef: float = 0.01) -> ObjectiveFns: + """Construct next-token-prediction train/val step functions. + + Args: + aux_loss_coef: weight applied to the model's own aux_loss (already + pre-weighted for MixtralSMoE, always 0.0 for the dense models) + before adding it to the cross-entropy loss. For MixtralSMoE this + compounds with MixtralLLM's own aux_loss_coef constructor argument + (default 0.01 there too) -- the net effective weight on the raw + load-balancing loss is this value times that one (0.01 * 0.01 = + 1e-4 with both defaults), not this value alone. Intentionally not + un-compounded here: doing so would require this function to + special-case MixtralSMoE, breaking the point of this objective + being agnostic to which model family it's given. + + Returns: + an ObjectiveFns with sample_fn=None -- generation is a separate loop + (see generate.py), not a fit for the single-shot sample_fn contract + other blaxbird objectives use. + """ + + def _loss_fn(model, rng_key, batch): + del rng_key + seq_len = batch["token_ids"].shape[1] + positions = jnp.broadcast_to(jnp.arange(seq_len), batch["token_ids"].shape) + logits, aux_loss = model(batch["token_ids"], positions) + ce_loss = optax.softmax_cross_entropy_with_integer_labels( + logits=logits, labels=batch["target_ids"] + ).mean() + return ce_loss + aux_loss_coef * aux_loss + + def train_step(model, rng_key, batch, **kwargs): + del kwargs + return nnx.value_and_grad(_loss_fn)(model, rng_key, batch) + + def val_step(model, rng_key, batch, **kwargs): + del kwargs + return _loss_fn(model, rng_key, batch) + + return ObjectiveFns(train_step=train_step, val_step=val_step, sample_fn=None) diff --git a/examples/llm_reference/test_objective.py b/examples/llm_reference/test_objective.py new file mode 100644 index 0000000..72ba49d --- /dev/null +++ b/examples/llm_reference/test_objective.py @@ -0,0 +1,79 @@ +import jax.numpy as jnp +from flax import nnx +from jax import random as jr + +from deepseek import DeepSeekMLA +from gemma import GemmaDense +from mixtral import MixtralSMoE +from objective import causal_lm + + +def _batch(): + return { + "token_ids": jnp.zeros((2, 6), dtype=jnp.int32), + "target_ids": jnp.ones((2, 6), dtype=jnp.int32), + } + + +def test_causal_lm_train_step_runs_for_all_three_model_families(): + fns = causal_lm() + models = [ + GemmaDense( + vocab_size=50, + d_model=32, + n_layers=2, + n_heads=4, + n_kv_heads=2, + head_dim=8, + d_ff=64, + local_window=4, + global_every=2, + rngs=nnx.rnglib.Rngs(jr.key(0)), + ), + DeepSeekMLA( + vocab_size=50, + d_model=32, + n_layers=2, + n_heads=4, + d_latent=8, + head_dim_nope=6, + head_dim_rope=2, + d_ff=64, + rngs=nnx.rnglib.Rngs(jr.key(0)), + ), + MixtralSMoE( + vocab_size=50, + d_model=32, + n_layers=2, + n_heads=4, + n_kv_heads=2, + head_dim=8, + d_ff=64, + n_experts=4, + n_active=2, + rngs=nnx.rnglib.Rngs(jr.key(0)), + ), + ] + batch = _batch() + for model in models: + loss, grads = fns.train_step(model, jr.key(1), batch) + assert loss.shape == () + assert fns.sample_fn is None + + +def test_causal_lm_val_step_runs(): + fns = causal_lm() + model = GemmaDense( + vocab_size=50, + d_model=32, + n_layers=2, + n_heads=4, + n_kv_heads=2, + head_dim=8, + d_ff=64, + local_window=4, + global_every=2, + rngs=nnx.rnglib.Rngs(jr.key(0)), + ) + loss = fns.val_step(model, jr.key(1), _batch()) + assert loss.shape == () From a084e99946bcde97c549f1707f5f2ba4d3fe62ac Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Sun, 12 Jul 2026 01:22:02 +0200 Subject: [PATCH 29/34] feat(llm_reference): add shared autoregressive generation loop --- examples/llm_reference/generate.py | 54 +++++++++++++++++++++++++ examples/llm_reference/test_generate.py | 41 +++++++++++++++++++ 2 files changed, 95 insertions(+) create mode 100644 examples/llm_reference/generate.py create mode 100644 examples/llm_reference/test_generate.py diff --git a/examples/llm_reference/generate.py b/examples/llm_reference/generate.py new file mode 100644 index 0000000..03290f3 --- /dev/null +++ b/examples/llm_reference/generate.py @@ -0,0 +1,54 @@ +"""Greedy/sampled autoregressive generation, shared across every model +family in this suite. Recomputes the full prefix on every decoding step +rather than threading an explicit KV cache -- O(n^2) in sequence length, +the right tradeoff for reference/test code on tiny sequences, not a +production serving path (applies even to DeepSeekMLA, where a real cache +would normally be the point of the architecture -- see the design doc). +""" + +import jax +import jax.numpy as jnp +from jax import random as jr + + +def generate( + model, + rng_key: jax.Array, + prompt_ids: jax.Array, + max_new_tokens: int, + *, + max_seq_len: int, +) -> jax.Array: + """Autoregressively extend prompt_ids by max_new_tokens tokens. + + Args: + model: any model in this suite (or anything with the same + (token_ids, positions) -> (logits, aux_loss) signature). + rng_key: a jax.random.key object. + prompt_ids: integer token ids, shape (batch, prompt_len). + max_new_tokens: number of tokens to generate. + max_seq_len: total sequence length prompt_ids + generated tokens must + not exceed. + + Returns: + jax.Array, shape (batch, prompt_len + max_new_tokens). + """ + batch, prompt_len = prompt_ids.shape + total_len = prompt_len + max_new_tokens + if total_len > max_seq_len: + raise ValueError( + f"prompt_len + max_new_tokens ({total_len}) exceeds " + f"max_seq_len ({max_seq_len})" + ) + + tokens = prompt_ids + for step in range(max_new_tokens): + seq_len = tokens.shape[1] + positions = jnp.broadcast_to(jnp.arange(seq_len), (batch, seq_len)) + logits, _ = model(tokens, positions) + next_logits = logits[:, -1, :] + step_key = jr.fold_in(rng_key, step) + next_token = jr.categorical(step_key, next_logits, axis=-1) + tokens = jnp.concatenate([tokens, next_token[:, None]], axis=1) + + return tokens diff --git a/examples/llm_reference/test_generate.py b/examples/llm_reference/test_generate.py new file mode 100644 index 0000000..fe9f7b9 --- /dev/null +++ b/examples/llm_reference/test_generate.py @@ -0,0 +1,41 @@ +import jax.numpy as jnp +from flax import nnx +from jax import random as jr + +from gemma import GemmaDense +from generate import generate + + +def _model(): + return GemmaDense( + vocab_size=50, + d_model=32, + n_layers=2, + n_heads=4, + n_kv_heads=2, + head_dim=8, + d_ff=64, + local_window=4, + global_every=2, + rngs=nnx.rnglib.Rngs(jr.key(0)), + ) + + +def test_generate_extends_prompt_by_max_new_tokens(): + model = _model() + prompt_ids = jnp.array([[1, 2, 3]], dtype=jnp.int32) + out = generate(model, jr.key(1), prompt_ids, max_new_tokens=4, max_seq_len=16) + assert out.shape == (1, 3 + 4) + assert jnp.array_equal(out[:, :3], prompt_ids) + + +def test_generate_is_deterministic_given_same_key(): + model = _model() + prompt_ids = jnp.array([[1, 2, 3]], dtype=jnp.int32) + out1 = generate( + model, jr.key(1), prompt_ids, max_new_tokens=4, max_seq_len=16 + ) + out2 = generate( + model, jr.key(1), prompt_ids, max_new_tokens=4, max_seq_len=16 + ) + assert jnp.array_equal(out1, out2) From 5451aa08de10b6ec618568f5c19a50d6d6ec3da7 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Sun, 12 Jul 2026 09:13:27 +0200 Subject: [PATCH 30/34] docs(llm_reference): add multi-mesh smoke-test script and README --- examples/llm_reference/README.md | 42 ++++++ examples/llm_reference/generate.py | 4 +- examples/llm_reference/main.py | 181 ++++++++++++++++++++++++ examples/llm_reference/test_deepseek.py | 8 +- examples/llm_reference/test_gemma.py | 8 +- 5 files changed, 239 insertions(+), 4 deletions(-) create mode 100644 examples/llm_reference/README.md create mode 100644 examples/llm_reference/main.py diff --git a/examples/llm_reference/README.md b/examples/llm_reference/README.md new file mode 100644 index 0000000..bf3d7f3 --- /dev/null +++ b/examples/llm_reference/README.md @@ -0,0 +1,42 @@ +# LLM reference suite + +Three decoder-only transformer reference implementations, each +demonstrating a genuinely different attention/FFN mechanism and each +real-sharded (not just API-compatible) via `blaxbird.train_fn`'s mesh +API: + +- **`GemmaDense`** (`gemma.py`) — Gemma-4-style: GQA + RoPE + + interleaved local/global attention + dense GeGLU FFN. FSDP+TP (2D + mesh). +- **`DeepSeekMLA`** (`deepseek.py`) — DeepSeek-V2-style: Multi-head + Latent Attention (low-rank KV compression + decoupled RoPE) + dense + GeGLU FFN, full causal attention only. FSDP+TP (2D mesh). +- **`MixtralSMoE`** (`mixtral.py`) — Mixtral-style: GQA + RoPE + full + causal attention + real sparse top-2-of-8 expert routing via + capacity-based dispatch/combine (not dense-compute-then-select). + FSDP+TP+Expert (3D mesh) — the dispatch/combine einsum formulation + lets JAX's SPMD partitioner insert the cross-device communication + automatically when the expert axis is sharded, no hand-written + `jax.lax.all_to_all`. + +This is reference code, not a trained model: `main.py` runs a handful of +training steps on random token ids to prove each architecture, the +shared `causal_lm` training objective, and the shared (full-prefix- +recompute) generation loop all compose correctly under +`blaxbird.train_fn` -- none of them learn anything meaningful, since +there's no tokenizer or real text dataset wired up. + +Not included (out of scope for this reference): a tokenizer, a real text +dataset/dataloader, loading real Gemma-4/DeepSeek-V2/Mixtral weights, +and a production KV-cache for any of the three (including DeepSeekMLA, +where a real cache would normally be the point of the architecture -- +the generation loop recomputes the full prefix every step for all three +model families). + +Run: `uv run --active python main.py` (single device, degenerate +sharding) or +`XLA_FLAGS="--xla_force_host_platform_device_count=8" uv run --active python main.py` +(real sharding: Gemma/DeepSeek on a simulated `(2,4)` fsdp+tp mesh, +Mixtral on the full simulated `(2,2,2)` fsdp+tp+expert mesh). Loss is +logged via `absl.logging` at INFO level (not raised by default here); +the printed generated-token-id line is the visible completion signal. diff --git a/examples/llm_reference/generate.py b/examples/llm_reference/generate.py index 03290f3..171471e 100644 --- a/examples/llm_reference/generate.py +++ b/examples/llm_reference/generate.py @@ -1,5 +1,5 @@ -"""Greedy/sampled autoregressive generation, shared across every model -family in this suite. Recomputes the full prefix on every decoding step +"""Sampled autoregressive generation, shared across every model family in +this suite. Recomputes the full prefix on every decoding step rather than threading an explicit KV cache -- O(n^2) in sequence length, the right tradeoff for reference/test code on tiny sequences, not a production serving path (applies even to DeepSeekMLA, where a real cache diff --git a/examples/llm_reference/main.py b/examples/llm_reference/main.py new file mode 100644 index 0000000..5544b12 --- /dev/null +++ b/examples/llm_reference/main.py @@ -0,0 +1,181 @@ +"""Smoke-test all three llm_reference model families on random data, +each under its own real simulated multi-device mesh. + +No tokenizer, no real dataset, no actual training run beyond a handful +of steps on random tokens -- this proves each architecture, the shared +causal_lm objective, the shared generation loop, and (for the Mixtral +variant especially) real sharded dispatch/combine routing all compose +correctly under blaxbird.train_fn, not that any model learns anything. + +Run with XLA_FLAGS="--xla_force_host_platform_device_count=8" to +exercise real multi-device sharding for all three (Gemma/DeepSeek use a +(2,4) fsdp+tp mesh built from all 8 simulated devices; Mixtral uses the +full (2,2,2) 3D mesh). Runs (unsharded, degenerate) on a single device +too, just without meaningfully exercising the sharding. + +Note: train_fn logs train/val loss via absl.logging.info, which this +script does not raise verbosity for -- the printed generated-token-id +line per model family is the visible completion signal, not a loss line. +""" + +import jax +import optax +from flax import nnx +from jax import numpy as jnp +from jax import random as jr +from jax.experimental import mesh_utils +from jax.sharding import PartitionSpec as P + +from blaxbird import train_fn +from deepseek import DeepSeekMLA +from gemma import GemmaDense +from generate import generate +from mixtral import MixtralSMoE +from objective import causal_lm + + +def _random_batch_iter(vocab_size, batch, seq_len): + key = jr.key(1) + while True: + key, batch_key = jr.split(key) + token_ids = jr.randint(batch_key, (batch, seq_len + 1), 0, vocab_size) + yield {"token_ids": token_ids[:, :-1], "target_ids": token_ids[:, 1:]} + + +def run_gemma(n_steps: int) -> None: + n_devices = jax.local_device_count() + fsdp = 2 if n_devices >= 4 else 1 + tp = n_devices // fsdp + mesh = jax.sharding.Mesh( + mesh_utils.create_device_mesh((fsdp, tp)), ("fsdp", "tp") + ) + vocab_size = 100 + with mesh: + model = GemmaDense( + vocab_size, + d_model=32, + n_layers=4, + n_heads=4, + n_kv_heads=2, + head_dim=8, + d_ff=64, + local_window=4, + global_every=2, + rngs=nnx.rnglib.Rngs(jr.key(0)), + ) + optimizer = nnx.Optimizer(model, tx=optax.adamw(1e-3)) + fns = causal_lm() + train = train_fn( + fns=(fns.train_step, fns.val_step), + n_steps=n_steps, + eval_every_n_steps=max(1, n_steps // 2), + n_eval_batches=1, + mesh=mesh, + data_partition_spec=P("fsdp"), + ) + train( + jr.key(2), + optimizer, + _random_batch_iter(vocab_size, 8, 16), + _random_batch_iter(vocab_size, 8, 16), + ) + prompt_ids = jnp.zeros((1, 4), dtype=jnp.int32) + generated = generate( + optimizer.model, jr.key(3), prompt_ids, max_new_tokens=8, max_seq_len=32 + ) + print(f"gemma: generated token ids {generated.tolist()}") + + +def run_deepseek(n_steps: int) -> None: + n_devices = jax.local_device_count() + fsdp = 2 if n_devices >= 4 else 1 + tp = n_devices // fsdp + mesh = jax.sharding.Mesh( + mesh_utils.create_device_mesh((fsdp, tp)), ("fsdp", "tp") + ) + vocab_size = 100 + with mesh: + model = DeepSeekMLA( + vocab_size, + d_model=32, + n_layers=4, + n_heads=4, + d_latent=8, + head_dim_nope=6, + head_dim_rope=2, + d_ff=64, + rngs=nnx.rnglib.Rngs(jr.key(0)), + ) + optimizer = nnx.Optimizer(model, tx=optax.adamw(1e-3)) + fns = causal_lm() + train = train_fn( + fns=(fns.train_step, fns.val_step), + n_steps=n_steps, + eval_every_n_steps=max(1, n_steps // 2), + n_eval_batches=1, + mesh=mesh, + data_partition_spec=P("fsdp"), + ) + train( + jr.key(2), + optimizer, + _random_batch_iter(vocab_size, 8, 16), + _random_batch_iter(vocab_size, 8, 16), + ) + prompt_ids = jnp.zeros((1, 4), dtype=jnp.int32) + generated = generate( + optimizer.model, jr.key(3), prompt_ids, max_new_tokens=8, max_seq_len=32 + ) + print(f"deepseek: generated token ids {generated.tolist()}") + + +def run_mixtral(n_steps: int) -> None: + n_devices = jax.local_device_count() + if n_devices >= 8: + fsdp, tp, expert = 2, 2, 2 + else: + fsdp, tp, expert = 1, 1, n_devices + mesh = jax.sharding.Mesh( + mesh_utils.create_device_mesh((fsdp, tp, expert)), ("fsdp", "tp", "expert") + ) + vocab_size = 100 + with mesh: + model = MixtralSMoE( + vocab_size, + d_model=32, + n_layers=4, + n_heads=4, + n_kv_heads=2, + head_dim=8, + d_ff=64, + n_experts=4, + n_active=2, + rngs=nnx.rnglib.Rngs(jr.key(0)), + ) + optimizer = nnx.Optimizer(model, tx=optax.adamw(1e-3)) + fns = causal_lm() + train = train_fn( + fns=(fns.train_step, fns.val_step), + n_steps=n_steps, + eval_every_n_steps=max(1, n_steps // 2), + n_eval_batches=1, + mesh=mesh, + data_partition_spec=P("fsdp"), + ) + train( + jr.key(2), + optimizer, + _random_batch_iter(vocab_size, 8, 16), + _random_batch_iter(vocab_size, 8, 16), + ) + prompt_ids = jnp.zeros((1, 4), dtype=jnp.int32) + generated = generate( + optimizer.model, jr.key(3), prompt_ids, max_new_tokens=8, max_seq_len=32 + ) + print(f"mixtral: generated token ids {generated.tolist()}") + + +if __name__ == "__main__": + run_gemma(n_steps=4) + run_deepseek(n_steps=4) + run_mixtral(n_steps=4) diff --git a/examples/llm_reference/test_deepseek.py b/examples/llm_reference/test_deepseek.py index dc59e8f..709daf8 100644 --- a/examples/llm_reference/test_deepseek.py +++ b/examples/llm_reference/test_deepseek.py @@ -87,8 +87,14 @@ def loss_fn(model): reason="needs XLA_FLAGS=--xla_force_host_platform_device_count=4", ) def test_deepseek_mla_shards_across_2d_mesh(): + # explicit devices=jax.devices()[:4]: create_device_mesh requires the + # mesh_shape's product to equal the device count exactly, but the + # skipif above only guarantees >= 4 -- slicing to exactly 4 keeps this + # test passing under XLA_FLAGS=...device_count=8 too (used by other + # tests in this suite), not just exactly 4. mesh = jax.sharding.Mesh( - mesh_utils.create_device_mesh((2, 2)), ("fsdp", "tp") + mesh_utils.create_device_mesh((2, 2), devices=jax.devices()[:4]), + ("fsdp", "tp"), ) with mesh: model = DeepSeekMLA(vocab_size=100, **_tiny_kwargs()) diff --git a/examples/llm_reference/test_gemma.py b/examples/llm_reference/test_gemma.py index 2db594d..6f96484 100644 --- a/examples/llm_reference/test_gemma.py +++ b/examples/llm_reference/test_gemma.py @@ -52,8 +52,14 @@ def loss_fn(model): reason="needs XLA_FLAGS=--xla_force_host_platform_device_count=4", ) def test_gemma_dense_shards_across_2d_mesh(): + # explicit devices=jax.devices()[:4]: create_device_mesh requires the + # mesh_shape's product to equal the device count exactly, but the + # skipif above only guarantees >= 4 -- slicing to exactly 4 keeps this + # test passing under XLA_FLAGS=...device_count=8 too (used by other + # tests in this suite), not just exactly 4. mesh = jax.sharding.Mesh( - mesh_utils.create_device_mesh((2, 2)), ("fsdp", "tp") + mesh_utils.create_device_mesh((2, 2), devices=jax.devices()[:4]), + ("fsdp", "tp"), ) with mesh: model = GemmaDense(vocab_size=100, **_tiny_kwargs()) From f6b6bbd1e1cb3f40df435275cedd8f3b88c0fae9 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Tue, 14 Jul 2026 06:04:26 +0200 Subject: [PATCH 31/34] feat(llm): add Gemma4/DeepSeek4/Qwen3Next reference architectures --- examples/llm/README.md | 30 +++ examples/llm/dataloader.py | 58 +++++ examples/llm/main.py | 176 ++++++++++++++ examples/llm/nn/__init__.py | 0 examples/llm/nn/deepseek.py | 339 ++++++++++++++++++++++++++ examples/llm/nn/gemma.py | 275 +++++++++++++++++++++ examples/llm/nn/layers.py | 201 +++++++++++++++ examples/llm/nn/qwen.py | 473 ++++++++++++++++++++++++++++++++++++ examples/llm/objective.py | 83 +++++++ 9 files changed, 1635 insertions(+) create mode 100644 examples/llm/README.md create mode 100644 examples/llm/dataloader.py create mode 100644 examples/llm/main.py create mode 100644 examples/llm/nn/__init__.py create mode 100644 examples/llm/nn/deepseek.py create mode 100644 examples/llm/nn/gemma.py create mode 100644 examples/llm/nn/layers.py create mode 100644 examples/llm/nn/qwen.py create mode 100644 examples/llm/objective.py diff --git a/examples/llm/README.md b/examples/llm/README.md new file mode 100644 index 0000000..d2da482 --- /dev/null +++ b/examples/llm/README.md @@ -0,0 +1,30 @@ +# Distributed language model training in `blaxbird` + +Implements multiple LLM architectures to highlight their different design choices, and to demonstrate distributed training using `blaxbird`. + +| Design axis | Gemma4 | DeepSeek4 | Qwen3Next | +|---|---|---|---| +| Attention mechanism | GQA | GQA + hybrid CSA/HCA block-compressed attention | Gated DeltaNet (linear, 75% of layers) + GQA (25%) | +| Attention pattern | Interleaved local/global, sliding window | Full causal, block-compressed (CSA: top-k selective; HCA: dense over heavier-compressed blocks) | Full causal (GQA layers only -- DeltaNet layers have no explicit attention pattern, it's a recurrence) | +| Positional encoding | Dual-frequency p-RoPE (theta=1M/rotary 25% on global layers, theta=10k/full rotation on local layers) | RoPE | Partial RoPE (first 25% of head, GQA layers only; DeltaNet layers carry no explicit position embedding) | +| KV-cache trick | Key/value reuse on global layers (no separate value projection) | -- | -- | +| Attention sinks | No | Yes -- learnable per-head, per-branch sink logit lets softmax mass sum to <1 | No | +| Normalization | RMSNorm, pre-norm | RMSNorm, pre-norm | RMSNorm, pre-norm | +| FFN activation | GeGLU | GeGLU | SwiGLU (via SparseMoEFFN) | +| Expert routing | -- (dense FFN) | -- (dense FFN) | Top-2-of-8, capacity-based dispatch, Switch-style aux loss (every layer -- both DeltaNet and GQA layers route through it) | +| Embedding tying | Untied | Untied | Untied | +| Sharding | 2D (FSDP+TP) | 2D (FSDP+TP) | 3D (FSDP+TP+expert) | + +Data is byte-level `tiny_shakespeare` (TFDS), vocab_size=256, no +subword tokenizer -- raw bytes in, raw bytes out. + +Run + +```shell +XLA_FLAGS="--xla_force_host_platform_device_count=8" uv run python main.py --model {gemma4,deepseek4,qwen3next} +``` + +to train a tiny LM on `tiny_shakespeare`, or omit `--model` to run all +three in turn. `--n-steps` (default 100) controls step count. Meshes: +- Gemma4/DeepSeek4 `(2,4)` fsdp+tp mesh, +- Qwen3Next `(2,2,2)` fsdp+tp+expert mesh. diff --git a/examples/llm/dataloader.py b/examples/llm/dataloader.py new file mode 100644 index 0000000..956e854 --- /dev/null +++ b/examples/llm/dataloader.py @@ -0,0 +1,58 @@ +"""Tiny_shakespeare data loader. + +Tokenization is raw bytes (vocab_size=256). Each TFDS split is a +text blob which is chunked by the loader it into fixed-length +windows. +""" + +import tensorflow as tf +import tensorflow_datasets as tfds +from jax import numpy as jnp +from jax import random as jr + +VOCAB_SIZE = 256 + + +def _load_byte_ids(data_dir, split): + ds = tfds.load( + "tiny_shakespeare", try_gcs=False, split=split, data_dir=data_dir + ) + text = next(iter(tfds.as_numpy(ds)))["text"] + return tf.io.decode_raw(text, tf.uint8) + + +def data_loaders( + rng_key, + data_dir, + *, + seq_len=128, + batch_size=8, + buffer_size=1024, + prefetch_size=1, + splits=("train", "validation"), +): + + itrs = [] + for split in splits: + itr_key, rng_key = jr.split(rng_key) + byte_ids = tf.cast(_load_byte_ids(data_dir, split), tf.int32) + chunk_len = seq_len + 1 + n_chunks = tf.shape(byte_ids)[0] // chunk_len + chunks = tf.reshape(byte_ids[: n_chunks * chunk_len], (n_chunks, chunk_len)) + + max_int32 = jnp.iinfo(jnp.int32).max + seed = jr.randint(itr_key, shape=(), minval=0, maxval=max_int32) + itr = ( + tf.data.Dataset.from_tensor_slices(chunks) + .repeat() + .shuffle(buffer_size, reshuffle_each_iteration=True, seed=int(seed)) + .batch(batch_size, drop_remainder=True) + .map( + lambda x: {"token_ids": x[:, :-1], "target_ids": x[:, 1:]}, + num_parallel_calls=tf.data.experimental.AUTOTUNE, + ) + .prefetch(prefetch_size) + .as_numpy_iterator() + ) + itrs.append(itr) + return itrs diff --git a/examples/llm/main.py b/examples/llm/main.py new file mode 100644 index 0000000..7d8d1ce --- /dev/null +++ b/examples/llm/main.py @@ -0,0 +1,176 @@ +import argparse +import os + +import dataloader +import jax +import optax +from examples.llm.nn.deepseek import DeepSeek4 +from flax import nnx +from examples.llm.nn.gemma import GemmaDense +from jax import numpy as jnp +from jax import random as jr +from jax.experimental import mesh_utils +from jax.sharding import PartitionSpec as P +from examples.llm.nn.qwen import Qwen3NextHybrid +from objective import get_training_and_eval_fns, sample + +from blaxbird import train_fn + +_DATA_DIR = os.path.join(os.path.dirname(__file__), "workdir", "data") + + +def _get_train_and_val_itrs(rng_key, *, seq_len, batch_size): + return dataloader.data_loaders( + rng_key, _DATA_DIR, seq_len=seq_len, batch_size=batch_size + ) + + +def run_gemma(n_steps: int) -> None: + n_devices = jax.local_device_count() + fsdp = 2 if n_devices >= 4 else 1 + tp = n_devices // fsdp + mesh = jax.sharding.Mesh( + mesh_utils.create_device_mesh((fsdp, tp)), ("fsdp", "tp") + ) + with mesh: + model = GemmaDense( + dataloader.VOCAB_SIZE, + d_model=32, + n_layers=4, + n_heads=4, + n_kv_heads=2, + head_dim=8, + d_ff=64, + local_window=4, + global_every=2, + rngs=nnx.rnglib.Rngs(jr.key(0)), + ) + optimizer = nnx.Optimizer(model, tx=optax.adamw(1e-3)) + fns = get_training_and_eval_fns() + train = train_fn( + fns=fns, + n_steps=n_steps, + eval_every_n_steps=max(1, n_steps // 2), + n_eval_batches=1, + mesh=mesh, + data_partition_spec=P("fsdp"), + ) + train_itr, val_itr = _get_train_and_val_itrs( + jr.key(1), seq_len=32, batch_size=8 + ) + train(jr.key(2), optimizer, train_itr, val_itr) + prompt_ids = jnp.zeros((1, 4), dtype=jnp.int32) + generated = sample( + optimizer.model, jr.key(3), prompt_ids, max_new_tokens=8, max_seq_len=32 + ) + text = bytes(int(b) for b in generated[0]).decode("utf-8", errors="replace") + print(f"gemma: generated text {text!r}") + + +def run_deepseek4(n_steps: int) -> None: + n_devices = jax.local_device_count() + fsdp = 2 if n_devices >= 4 else 1 + tp = n_devices // fsdp + mesh = jax.sharding.Mesh( + mesh_utils.create_device_mesh((fsdp, tp)), ("fsdp", "tp") + ) + with mesh: + model = DeepSeek4( + dataloader.VOCAB_SIZE, + d_model=32, + n_layers=4, + n_heads=4, + n_kv_heads=2, + head_dim=8, + d_ff=64, + csa_block_size=2, + csa_top_k=4, + hca_block_size=4, + rngs=nnx.rnglib.Rngs(jr.key(0)), + ) + optimizer = nnx.Optimizer(model, tx=optax.adamw(1e-3)) + fns = get_training_and_eval_fns() + train = train_fn( + fns=fns, + n_steps=n_steps, + eval_every_n_steps=max(1, n_steps // 2), + n_eval_batches=1, + mesh=mesh, + data_partition_spec=P("fsdp"), + ) + train_itr, val_itr = _get_train_and_val_itrs( + jr.key(1), seq_len=32, batch_size=8 + ) + train(jr.key(2), optimizer, train_itr, val_itr) + prompt_ids = jnp.zeros((1, 4), dtype=jnp.int32) + generated = sample( + optimizer.model, jr.key(3), prompt_ids, max_new_tokens=8, max_seq_len=32 + ) + text = bytes(int(b) for b in generated[0]).decode("utf-8", errors="replace") + print(f"deepseek4: generated text {text!r}") + + +def run_qwen3_next(n_steps: int) -> None: + n_devices = jax.local_device_count() + if n_devices >= 8: + fsdp, tp, expert = 2, 2, 2 + else: + fsdp, tp, expert = 1, 1, n_devices + mesh = jax.sharding.Mesh( + mesh_utils.create_device_mesh((fsdp, tp, expert)), ("fsdp", "tp", "expert") + ) + with mesh: + model = Qwen3NextHybrid( + dataloader.VOCAB_SIZE, + d_model=32, + n_layers=4, + n_heads=4, + n_kv_heads=2, + head_dim=8, + d_ff=64, + n_experts=8, + n_active=2, + rngs=nnx.rnglib.Rngs(jr.key(0)), + ) + optimizer = nnx.Optimizer(model, tx=optax.adamw(1e-3)) + fns = get_training_and_eval_fns() + train = train_fn( + fns=fns, + n_steps=n_steps, + eval_every_n_steps=max(1, n_steps // 2), + n_eval_batches=1, + mesh=mesh, + data_partition_spec=P("fsdp"), + ) + train_itr, val_itr = _get_train_and_val_itrs( + jr.key(1), seq_len=32, batch_size=8 + ) + train(jr.key(2), optimizer, train_itr, val_itr) + prompt_ids = jnp.zeros((1, 4), dtype=jnp.int32) + generated = sample( + optimizer.model, jr.key(3), prompt_ids, max_new_tokens=8, max_seq_len=32 + ) + text = bytes(int(b) for b in generated[0]).decode("utf-8", errors="replace") + print(f"qwen3_next: generated text {text!r}") + + +_RUNS = { + "gemma4": run_gemma, + "deepseek4": run_deepseek4, + "qwen3next": run_qwen3_next, +} + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--model", choices=sorted(_RUNS), default=None) + parser.add_argument("--n-steps", type=int, default=100) + args = parser.parse_args() + + runs = [_RUNS[args.model]] if args.model else list(_RUNS.values()) + for run in runs: + run(n_steps=args.n_steps) + + +if __name__ == "__main__": + main() diff --git a/examples/llm/nn/__init__.py b/examples/llm/nn/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/examples/llm/nn/deepseek.py b/examples/llm/nn/deepseek.py new file mode 100644 index 0000000..aac0033 --- /dev/null +++ b/examples/llm/nn/deepseek.py @@ -0,0 +1,339 @@ +"""DeepSeek-V4-style decoder-only transformer: hybrid Compressed Sparse +Attention (CSA) + Heavily Compressed Attention (HCA) with learnable +attention sinks, RMSNorm, pre-norm, dense GeGLU FFN. + +Real DeepSeek-V4 pairs this attention mechanism with MLA-style latent +KV compression (see DeepSeekMLA in this suite for that axis) and a +1.6T-parameter DeepSeekMoE FFN with shared experts (see MixtralSMoE for +the MoE-routing axis in this suite). This reference implementation +isolates just the new attention mechanism -- plain GQA projections, +dense FFN -- so each axis is demonstrated independently elsewhere in +this suite rather than combined into one file. + +CSA pools keys/values into small blocks (light compression) and +attends to only the top-k most relevant blocks per query (real, +selective sparse attention -- not dense-then-mask). HCA pools into +larger blocks (heavy compression) and attends densely to all of them, +giving a cheap global view that complements CSA's sharper, selective +one. Both branches restrict attention to fully-completed blocks (mean- +pooled here, not the real softmax-gated pooling + FP4 lightning +indexer) -- the most recent < block_size tokens are only visible once +their block completes. Real V4 compensates with an explicit raw +sliding-window branch over recent tokens; omitted here since this +suite's toy sequence lengths (~16-32 tokens) would make that window +cover almost the whole sequence anyway. +""" + +import jax +from flax import nnx +from jax import numpy as jnp +from layers import GeGLU, RMSNorm, apply_rope, repeat_kv, rope_freqs, tp_linear + + +def pool_kv_blocks(x: jax.Array, block_size: int) -> jax.Array: + """Mean-pool (batch, seq, heads, dim) into non-overlapping blocks + along seq, dropping any trailing partial block. + + Args: + x: input array, shape (batch, seq, heads, dim). + block_size: number of positions per block. + + Returns: + jax.Array, shape (batch, seq // block_size, heads, dim). + """ + b, s, h, d = x.shape + n_blocks = s // block_size + x = x[:, : n_blocks * block_size] + return x.reshape(b, n_blocks, block_size, h, d).mean(axis=2) + + +def make_block_causal_mask(seq_len: int, block_size: int) -> jax.Array: + """Build a causal mask from query positions to fully-completed blocks. + + Args: + seq_len: sequence length (number of queries). + block_size: number of positions per key/value block. + + Returns: + bool jax.Array, shape (seq_len, seq_len // block_size), True where + the block lies entirely at or before the query position. + """ + n_blocks = seq_len // block_size + query_pos = jnp.arange(seq_len)[:, None] + block_last_pos = (jnp.arange(n_blocks) + 1) * block_size - 1 + return block_last_pos[None, :] <= query_pos + + +class DeepSeek4Attention(nnx.Module): + """Hybrid CSA + HCA attention with learnable attention sinks.""" + + def __init__( # noqa: PLR0913 + self, + d_model, + n_heads, + n_kv_heads, + head_dim, + *, + csa_block_size, + csa_top_k, + hca_block_size, + rngs, + ): + """Construct a DeepSeek-V4-style hybrid attention block. + + Args: + d_model: model (residual stream) dimensionality. + n_heads: number of query heads. + n_kv_heads: number of key/value heads. + head_dim: dimensionality of each attention head. + csa_block_size: key/value pooling block size for the Compressed + Sparse Attention branch (light compression, top-k selective). + csa_top_k: number of CSA blocks attended to per query. + hca_block_size: key/value pooling block size for the Heavily + Compressed Attention branch (heavy compression, dense). + rngs: random keys. + """ + self.n_heads = n_heads + self.n_kv_heads = n_kv_heads + self.head_dim = head_dim + self.n_rep = n_heads // n_kv_heads + self.csa_block_size = csa_block_size + self.csa_top_k = csa_top_k + self.hca_block_size = hca_block_size + self.q_proj = tp_linear( + d_model, n_heads * head_dim, ("fsdp", "tp"), rngs=rngs + ) + self.k_proj = tp_linear( + d_model, n_kv_heads * head_dim, ("fsdp", "tp"), rngs=rngs + ) + self.v_proj = tp_linear( + d_model, n_kv_heads * head_dim, ("fsdp", "tp"), rngs=rngs + ) + self.o_proj = tp_linear( + 2 * n_heads * head_dim, d_model, ("tp", "fsdp"), rngs=rngs + ) + self.inv_freq = nnx.Variable(rope_freqs(head_dim)) + self.csa_sink = nnx.Param(jnp.zeros((n_heads,))) + self.hca_sink = nnx.Param(jnp.zeros((n_heads,))) + + def _branch(self, q, k_blocks, v_blocks, block_mask, sink, top_k): + """Compute one compressed-attention branch (CSA if top_k is given, + HCA -- dense over all blocks -- otherwise), with a learnable + attention sink competing for softmax probability mass so the real + block weights can sum to less than one. + """ + b, h, s, d = q.shape + n_blocks = k_blocks.shape[2] + scores = jnp.einsum("bhsd,bhnd->bhsn", q, k_blocks) / jnp.sqrt(d) + scores = jnp.where(block_mask[None, None, :, :], scores, -jnp.inf) + sink_logit = jnp.broadcast_to(sink[None, :, None, None], (b, h, s, 1)) + + if top_k is not None and top_k < n_blocks: + top_scores, top_idx = jax.lax.top_k(scores, top_k) + combined = jnp.concatenate([top_scores, sink_logit], axis=-1) + weights = jax.nn.softmax(combined, axis=-1)[..., :-1] + v_full = jnp.broadcast_to(v_blocks[:, :, None], (b, h, s, n_blocks, d)) + v_sel = jnp.take_along_axis(v_full, top_idx[..., None], axis=3) + return jnp.einsum("bhsk,bhskd->bhsd", weights, v_sel) + + combined = jnp.concatenate([scores, sink_logit], axis=-1) + weights = jax.nn.softmax(combined, axis=-1)[..., :-1] + return jnp.einsum("bhsn,bhnd->bhsd", weights, v_blocks) + + def __call__(self, x: jax.Array, positions: jax.Array) -> jax.Array: + """Apply hybrid CSA+HCA self-attention. + + Args: + x: input array, shape (batch, seq, d_model). + positions: integer position ids, shape (batch, seq). + + Returns: + jax.Array, same shape as x. + """ + b, s, _ = x.shape + q = self.q_proj(x).reshape(b, s, self.n_heads, self.head_dim) + k = self.k_proj(x).reshape(b, s, self.n_kv_heads, self.head_dim) + v = self.v_proj(x).reshape(b, s, self.n_kv_heads, self.head_dim) + + q = apply_rope(q, positions, self.inv_freq.value) + k = apply_rope(k, positions, self.inv_freq.value) + k = repeat_kv(k, self.n_rep) + v = repeat_kv(v, self.n_rep) + + csa_k = jnp.transpose(pool_kv_blocks(k, self.csa_block_size), (0, 2, 1, 3)) + csa_v = jnp.transpose(pool_kv_blocks(v, self.csa_block_size), (0, 2, 1, 3)) + hca_k = jnp.transpose(pool_kv_blocks(k, self.hca_block_size), (0, 2, 1, 3)) + hca_v = jnp.transpose(pool_kv_blocks(v, self.hca_block_size), (0, 2, 1, 3)) + q = jnp.transpose(q, (0, 2, 1, 3)) + + csa_mask = make_block_causal_mask(s, self.csa_block_size) + hca_mask = make_block_causal_mask(s, self.hca_block_size) + + csa_out = self._branch( + q, csa_k, csa_v, csa_mask, self.csa_sink.value, self.csa_top_k + ) + hca_out = self._branch(q, hca_k, hca_v, hca_mask, self.hca_sink.value, None) + + out = jnp.concatenate([csa_out, hca_out], axis=-1) + out = jnp.transpose(out, (0, 2, 1, 3)).reshape( + b, s, self.n_heads * 2 * self.head_dim + ) + return self.o_proj(out) + + +class DeepSeek4Block(nnx.Module): + """Pre-norm transformer block: hybrid CSA+HCA attention + dense + GeGLU FFN. + """ + + def __init__( # noqa: PLR0913 + self, + d_model, + n_heads, + n_kv_heads, + head_dim, + d_ff, + *, + csa_block_size, + csa_top_k, + hca_block_size, + rngs, + ): + """Construct a DeepSeek-V4 transformer block. + + Args: + d_model: model (residual stream) dimensionality. + n_heads: number of query heads. + n_kv_heads: number of key/value heads. + head_dim: dimensionality of each attention head. + d_ff: feed-forward hidden dimensionality. + csa_block_size: CSA branch pooling block size. + csa_top_k: number of CSA blocks attended to per query. + hca_block_size: HCA branch pooling block size. + rngs: random keys. + """ + self.attn_norm = RMSNorm(d_model, rngs=rngs) + self.attn = DeepSeek4Attention( + d_model, + n_heads, + n_kv_heads, + head_dim, + csa_block_size=csa_block_size, + csa_top_k=csa_top_k, + hca_block_size=hca_block_size, + rngs=rngs, + ) + self.ffn_norm = RMSNorm(d_model, rngs=rngs) + self.ffn = GeGLU(d_model, d_ff, rngs=rngs) + + def __call__(self, x: jax.Array, positions: jax.Array) -> jax.Array: + """Apply the block. + + Args: + x: input array, shape (batch, seq, d_model). + positions: integer position ids, shape (batch, seq). + + Returns: + jax.Array, same shape as x. + """ + x = x + self.attn(self.attn_norm(x), positions) + x = x + self.ffn(self.ffn_norm(x)) + return x + + +class DeepSeek4LLM(nnx.Module): + """Decoder-only transformer in the DeepSeek-V4 architectural family: + hybrid CSA+HCA attention with learnable attention sinks (see module + docstring for the axes this reference implementation isolates versus + real V4). Dense FFN only (no MoE -- that's MixtralSMoE's role in this + suite; DeepSeekMLA covers the latent-KV-compression axis). + """ + + def __init__( # noqa: PLR0913 + self, + vocab_size, + d_model, + n_layers, + n_heads, + n_kv_heads, + head_dim, + d_ff, + *, + csa_block_size=2, + csa_top_k=4, + hca_block_size=4, + rngs, + ): + """Construct a DeepSeek4LLM. + + Args: + vocab_size: token vocabulary size. + d_model: model (residual stream) dimensionality. + n_layers: number of transformer blocks. + n_heads: number of query heads. + n_kv_heads: number of key/value heads. + head_dim: dimensionality of each attention head. + d_ff: feed-forward hidden dimensionality. + csa_block_size: CSA branch pooling block size. + csa_top_k: number of CSA blocks attended to per query. + hca_block_size: HCA branch pooling block size. + rngs: random keys. + """ + self.embed = nnx.Embed( + vocab_size, + d_model, + embedding_init=nnx.with_partitioning( + nnx.initializers.normal(), ("fsdp", None) + ), + rngs=rngs, + ) + self.blocks = tuple( + DeepSeek4Block( + d_model, + n_heads, + n_kv_heads, + head_dim, + d_ff, + csa_block_size=csa_block_size, + csa_top_k=csa_top_k, + hca_block_size=hca_block_size, + rngs=rngs, + ) + for _ in range(n_layers) + ) + self.final_norm = RMSNorm(d_model, rngs=rngs) + self.lm_head = nnx.Linear( + d_model, + vocab_size, + use_bias=False, + kernel_init=nnx.with_partitioning( + nnx.initializers.lecun_normal(), ("fsdp", None) + ), + rngs=rngs, + ) + + def __call__( + self, token_ids: jax.Array, positions: jax.Array + ) -> tuple[jax.Array, jax.Array]: + """Compute next-token logits for a batch of token sequences. + + Args: + token_ids: integer token ids, shape (batch, seq). + positions: integer position ids, shape (batch, seq). + + Returns: + a tuple (logits, aux_loss): logits has shape + (batch, seq, vocab_size); aux_loss is always jnp.array(0.0) (dense + model, no MoE) -- kept for interface uniformity with MixtralSMoE + so objective.py's causal_lm works unmodified across this suite. + """ + hidden = self.embed(token_ids) + for block in self.blocks: + hidden = block(hidden, positions) + hidden = self.final_norm(hidden) + logits = self.lm_head(hidden) + return logits, jnp.array(0.0) + + +def DeepSeek4(vocab_size, **kwargs): + return DeepSeek4LLM(vocab_size, **kwargs) diff --git a/examples/llm/nn/gemma.py b/examples/llm/nn/gemma.py new file mode 100644 index 0000000..42a0d7d --- /dev/null +++ b/examples/llm/nn/gemma.py @@ -0,0 +1,275 @@ +"""Gemma-4-style decoder-only transformer: GQA + dual-frequency p-RoPE ++ key/value reuse on global layers + interleaved local/global attention ++ dense GeGLU FFN, TP+FSDP-sharded. + +Local (sliding-window) layers rotate the full head with RoPE theta=10k. +Global (full-causal) layers rotate only the leading 25% of the head +("p-RoPE", theta=1M) and skip a separate value projection, reusing the +(pre-rotation) key projection as values -- both real Gemma-4 tricks +that shrink the global-layer KV cache. +""" + +import jax +from flax import nnx +from jax import numpy as jnp +from layers import ( + GeGLU, + RMSNorm, + apply_partial_rope, + make_causal_mask, + repeat_kv, + rope_freqs, + tp_linear, +) + + +class GemmaAttention(nnx.Module): + """Grouped-query attention with Gemma-4-style partial RoPE and, + optionally, key/value reuse (no separate value projection). + """ + + def __init__( + self, + d_model, + n_heads, + n_kv_heads, + head_dim, + *, + theta, + rotary_fraction, + share_kv, + rngs, + ): + """Construct a Gemma-4 attention block. + + Args: + d_model: model (residual stream) dimensionality. + n_heads: number of query heads. + n_kv_heads: number of key/value heads. + head_dim: dimensionality of each attention head. + theta: RoPE base frequency for this layer (1M on global layers, + 10k on local layers). + rotary_fraction: fraction of head_dim rotated by RoPE (0.25 on + global layers, 1.0 -- full head -- on local layers). + share_kv: if True, skip the value projection and reuse the + (pre-rotation) key projection as values (global layers only). + rngs: random keys. + """ + self.n_heads = n_heads + self.n_kv_heads = n_kv_heads + self.head_dim = head_dim + self.n_rep = n_heads // n_kv_heads + self.share_kv = share_kv + self.rotary_dim = max(2, int(rotary_fraction * head_dim) // 2 * 2) + self.q_proj = tp_linear( + d_model, n_heads * head_dim, ("fsdp", "tp"), rngs=rngs + ) + self.k_proj = tp_linear( + d_model, n_kv_heads * head_dim, ("fsdp", "tp"), rngs=rngs + ) + if not share_kv: + self.v_proj = tp_linear( + d_model, n_kv_heads * head_dim, ("fsdp", "tp"), rngs=rngs + ) + self.o_proj = tp_linear( + n_heads * head_dim, d_model, ("tp", "fsdp"), rngs=rngs + ) + self.inv_freq = nnx.Variable(rope_freqs(self.rotary_dim, theta=theta)) + + def __call__( + self, x: jax.Array, positions: jax.Array, mask: jax.Array + ) -> jax.Array: + """Apply grouped-query self-attention. + + Args: + x: input array, shape (batch, seq, d_model). + positions: integer position ids, shape (batch, seq). + mask: bool attention mask, shape (seq, seq), True = attend, from + make_causal_mask. + + Returns: + jax.Array, same shape as x. + """ + b, s, _ = x.shape + q = self.q_proj(x).reshape(b, s, self.n_heads, self.head_dim) + k_content = self.k_proj(x).reshape(b, s, self.n_kv_heads, self.head_dim) + v = ( + k_content + if self.share_kv + else self.v_proj(x).reshape(b, s, self.n_kv_heads, self.head_dim) + ) + + q = apply_partial_rope(q, positions, self.inv_freq.value, self.rotary_dim) + k = apply_partial_rope( + k_content, positions, self.inv_freq.value, self.rotary_dim + ) + k = repeat_kv(k, self.n_rep) + v = repeat_kv(v, self.n_rep) + + q = jnp.transpose(q, (0, 2, 1, 3)) + k = jnp.transpose(k, (0, 2, 1, 3)) + v = jnp.transpose(v, (0, 2, 1, 3)) + + scores = jnp.einsum("bhqd,bhkd->bhqk", q, k) / jnp.sqrt(self.head_dim) + scores = jnp.where(mask[None, None, :, :], scores, -jnp.inf) + weights = jax.nn.softmax(scores, axis=-1) + out = jnp.einsum("bhqk,bhkd->bhqd", weights, v) + out = jnp.transpose(out, (0, 2, 1, 3)).reshape( + b, s, self.n_heads * self.head_dim + ) + return self.o_proj(out) + + +class GemmaTransformerBlock(nnx.Module): + """Pre-norm transformer block: GQA attention + dense GeGLU FFN.""" + + def __init__( # noqa: PLR0913 + self, d_model, n_heads, n_kv_heads, head_dim, d_ff, *, is_global, rngs + ): + """Construct a Gemma transformer block. + + Args: + d_model: model (residual stream) dimensionality. + n_heads: number of query heads. + n_kv_heads: number of key/value heads. + head_dim: dimensionality of each attention head. + d_ff: feed-forward hidden dimensionality. + is_global: whether this is a "global" (full-causal, p-RoPE, + shared-kv) layer or a "local" (sliding-window, full-RoPE) one. + rngs: random keys. + """ + self.is_global = is_global + self.attn_norm = RMSNorm(d_model, rngs=rngs) + self.attn = GemmaAttention( + d_model, + n_heads, + n_kv_heads, + head_dim, + theta=1_000_000.0 if is_global else 10_000.0, + rotary_fraction=0.25 if is_global else 1.0, + share_kv=is_global, + rngs=rngs, + ) + self.ffn_norm = RMSNorm(d_model, rngs=rngs) + self.ffn = GeGLU(d_model, d_ff, rngs=rngs) + + def __call__( + self, x: jax.Array, positions: jax.Array, mask: jax.Array + ) -> jax.Array: + """Apply the block. + + Args: + x: input array, shape (batch, seq, d_model). + positions: integer position ids, shape (batch, seq). + mask: bool attention mask, shape (seq, seq). + + Returns: + jax.Array, same shape as x. + """ + x = x + self.attn(self.attn_norm(x), positions, mask) + x = x + self.ffn(self.ffn_norm(x)) + return x + + +class GemmaLLM(nnx.Module): + """Decoder-only transformer in the Gemma-4 architectural family. + + Interleaves "local" (sliding-window, full-RoPE) and "global" + (full-causal, p-RoPE, shared-kv) attention layers -- every + `global_every`-th layer is global, the rest are local, matching + Gemma 4's actual design choice. Dense FFN only (no MoE -- that's + MixtralSMoE's role in this suite). + """ + + def __init__( # noqa: PLR0913 + self, + vocab_size, + d_model, + n_layers, + n_heads, + n_kv_heads, + head_dim, + d_ff, + local_window, + *, + global_every=4, + rngs, + ): + """Construct a GemmaLLM. + + Args: + vocab_size: token vocabulary size. + d_model: model (residual stream) dimensionality. + n_layers: number of transformer blocks. + n_heads: number of query heads. + n_kv_heads: number of key/value heads. + head_dim: dimensionality of each attention head. + d_ff: feed-forward hidden dimensionality. + local_window: sliding-window size for "local" attention layers. + global_every: every global_every-th layer (1-indexed) is a full + causal ("global") attention layer; the rest are local. + rngs: random keys. + """ + self.local_window = local_window + self.embed = nnx.Embed( + vocab_size, + d_model, + embedding_init=nnx.with_partitioning( + nnx.initializers.normal(), ("fsdp", None) + ), + rngs=rngs, + ) + self.blocks = tuple( + GemmaTransformerBlock( + d_model, + n_heads, + n_kv_heads, + head_dim, + d_ff, + is_global=((i + 1) % global_every == 0), + rngs=rngs, + ) + for i in range(n_layers) + ) + self.final_norm = RMSNorm(d_model, rngs=rngs) + self.lm_head = nnx.Linear( + d_model, + vocab_size, + use_bias=False, + kernel_init=nnx.with_partitioning( + nnx.initializers.lecun_normal(), ("fsdp", None) + ), + rngs=rngs, + ) + + def __call__( + self, token_ids: jax.Array, positions: jax.Array + ) -> tuple[jax.Array, jax.Array]: + """Compute next-token logits for a batch of token sequences. + + Args: + token_ids: integer token ids, shape (batch, seq). + positions: integer position ids, shape (batch, seq). + + Returns: + a tuple (logits, aux_loss): logits has shape + (batch, seq, vocab_size); aux_loss is always jnp.array(0.0) (dense + model, no MoE) -- kept for interface uniformity with MixtralSMoE so + objective.py's causal_lm works unmodified across this suite. + """ + seq_len = token_ids.shape[1] + global_mask = make_causal_mask(seq_len) + local_mask = make_causal_mask(seq_len, window=self.local_window) + + hidden = self.embed(token_ids) + for block in self.blocks: + mask = global_mask if block.is_global else local_mask + hidden = block(hidden, positions, mask) + + hidden = self.final_norm(hidden) + logits = self.lm_head(hidden) + return logits, jnp.array(0.0) + + +def GemmaDense(vocab_size, **kwargs): + return GemmaLLM(vocab_size, **kwargs) diff --git a/examples/llm/nn/layers.py b/examples/llm/nn/layers.py new file mode 100644 index 0000000..a99a190 --- /dev/null +++ b/examples/llm/nn/layers.py @@ -0,0 +1,201 @@ +"""Shared primitives for the llm_reference example suite (Gemma-4- +style, DeepSeek-V4-style, Qwen3-Next-style decoder-only transformers). + +TP-sharded projections use nnx.with_partitioning on their kernel_init so +blaxbird.train_fn's mesh= argument can shard them via +nnx.get_named_sharding -- unannotated parameters (e.g. RMSNorm weights) +default to fully replicated. No mesh is threaded into any __call__: all +sharding here is construction-time weight annotation only, matching this +repo's existing sharding idiom (see examples/fsdp_tp_demo). +""" + +import jax +from flax import nnx +from jax import numpy as jnp + + +def rope_freqs(head_dim: int, theta: float = 10_000.0) -> jax.Array: + """Compute RoPE inverse frequencies. + + Args: + head_dim: dimensionality to rotate (must be even). + theta: RoPE base frequency. + + Returns: + jax.Array, shape (head_dim // 2,). + """ + return 1.0 / (theta ** (jnp.arange(0, head_dim, 2) / head_dim)) + + +def apply_rope( + x: jax.Array, positions: jax.Array, inv_freq: jax.Array +) -> jax.Array: + """Apply rotary position embeddings. + + Args: + x: input array, shape (batch, seq, n_heads, dim) where dim == + 2 * inv_freq.shape[0]. + positions: integer position ids, shape (batch, seq). + inv_freq: RoPE inverse frequencies from rope_freqs. + + Returns: + jax.Array, same shape as x. + """ + freqs = positions[:, :, None] * inv_freq[None, None, :] + cos = jnp.cos(freqs)[:, :, None, :] + sin = jnp.sin(freqs)[:, :, None, :] + x1, x2 = jnp.split(x, 2, axis=-1) + return jnp.concatenate([x1 * cos - x2 * sin, x2 * cos + x1 * sin], axis=-1) + + +def apply_partial_rope( + x: jax.Array, positions: jax.Array, inv_freq: jax.Array, rotary_dim: int +) -> jax.Array: + """Apply RoPE to only the leading `rotary_dim` of the head axis, + leaving the remainder unrotated (Gemma-4-style "p-RoPE" on global + attention layers; Qwen3-Next-style partial RoPE on standard attention + layers). + + Args: + x: input array, shape (batch, seq, n_heads, dim). + positions: integer position ids, shape (batch, seq). + inv_freq: RoPE inverse frequencies, shape (rotary_dim // 2,). + rotary_dim: number of leading dims to rotate (must be even, <= dim). + + Returns: + jax.Array, same shape as x. + """ + x_rot, x_pass = x[..., :rotary_dim], x[..., rotary_dim:] + return jnp.concatenate( + [apply_rope(x_rot, positions, inv_freq), x_pass], axis=-1 + ) + + +def repeat_kv(x: jax.Array, n_rep: int) -> jax.Array: + """Broadcast grouped-query-attention key/value heads to n_heads. + + Args: + x: input array, shape (batch, seq, n_kv_heads, head_dim). + n_rep: number of query heads sharing each kv head + (n_heads // n_kv_heads). + + Returns: + jax.Array, shape (batch, seq, n_kv_heads * n_rep, head_dim). + """ + if n_rep == 1: + return x + b, s, kvh, hd = x.shape + x = jnp.broadcast_to(x[:, :, :, None, :], (b, s, kvh, n_rep, hd)) + return x.reshape(b, s, kvh * n_rep, hd) + + +def make_causal_mask(seq_len: int, window: int | None = None) -> jax.Array: + """Build a causal (optionally sliding-window / "local") attention mask. + + Args: + seq_len: sequence length. + window: if given, restrict attention to the last `window` positions + (Gemma-style "local" attention layers); if None, full causal + ("global") attention. + + Returns: + bool jax.Array, shape (seq_len, seq_len), True where attention is + allowed (query position i may attend to key position j). + """ + i = jnp.arange(seq_len)[:, None] + j = jnp.arange(seq_len)[None, :] + causal = j <= i + if window is not None: + causal = causal & (j > i - window) + return causal + + +class RMSNorm(nnx.Module): + """Root-mean-square layer normalization (no mean-centering, no bias).""" + + def __init__(self, dim, *, rngs, eps=1e-6): + """Construct an RMSNorm layer. + + Args: + dim: feature dimensionality. + rngs: random keys (unused -- weight is initialized to ones -- kept + for interface consistency with every other block in this suite). + eps: numerical-stability constant. + """ + del rngs + self.weight = nnx.Param(jnp.ones((dim,))) + self.eps = eps + + def __call__(self, x: jax.Array) -> jax.Array: + """Normalize the last axis of x by its RMS, then scale. + + Args: + x: input array, shape (..., dim). + + Returns: + jax.Array, same shape as x. + """ + var = jnp.mean(jnp.square(x), axis=-1, keepdims=True) + x = x * jax.lax.rsqrt(var + self.eps) + return x * self.weight.value + + +def tp_linear(d_in, d_out, partition_spec, *, rngs, use_bias=False): + """Construct a nnx.Linear whose kernel carries a sharding annotation. + + Args: + d_in: input feature dimensionality. + d_out: output feature dimensionality. + partition_spec: a 2-tuple of mesh-axis names (or None) passed to + nnx.with_partitioning on the kernel initializer -- e.g. + ("fsdp", "tp") for column-parallel, ("tp", "fsdp") for row-parallel. + Resolved to a real per-device shard only when the returned module's + state is passed through nnx.get_named_sharding(state, mesh) inside + blaxbird.train_fn; constructing this module standalone (no mesh) is + unaffected -- same pattern as examples/fsdp_tp_demo's ShardedMLP. + rngs: random keys. + use_bias: whether to include a bias term. Every projection in this + suite uses use_bias=False, matching the real + Gemma/DeepSeek/Qwen3-Next architectures. + + Returns: + a nnx.Linear with a with_partitioning-annotated kernel_init. + """ + return nnx.Linear( + d_in, + d_out, + use_bias=use_bias, + kernel_init=nnx.with_partitioning( + nnx.initializers.lecun_normal(), partition_spec + ), + rngs=rngs, + ) + + +class GeGLU(nnx.Module): + """Gated GELU MLP, TP-sharded (column-parallel gate/up, row-parallel + down -- same pattern as this suite's attention projections). + """ + + def __init__(self, d_model, d_ff, *, rngs): + """Construct a GeGLU feed-forward block. + + Args: + d_model: model (residual stream) dimensionality. + d_ff: hidden (expansion) dimensionality. + rngs: random keys. + """ + self.gate = tp_linear(d_model, d_ff, ("fsdp", "tp"), rngs=rngs) + self.up = tp_linear(d_model, d_ff, ("fsdp", "tp"), rngs=rngs) + self.down = tp_linear(d_ff, d_model, ("tp", "fsdp"), rngs=rngs) + + def __call__(self, x: jax.Array) -> jax.Array: + """Apply the GeGLU transform. + + Args: + x: input array, shape (..., d_model). + + Returns: + jax.Array, same shape as x. + """ + return self.down(jax.nn.gelu(self.gate(x)) * self.up(x)) diff --git a/examples/llm/nn/qwen.py b/examples/llm/nn/qwen.py new file mode 100644 index 0000000..1f65cf7 --- /dev/null +++ b/examples/llm/nn/qwen.py @@ -0,0 +1,473 @@ +"""Qwen3-Next-style decoder-only transformer: linear-attention/ +Transformer hybrid + sparse MoE FFN. + +75% of layers are Gated DeltaNet -- a linear (constant-memory-per-step) +recurrent attention computed via the delta rule, no softmax, no +quadratic seq x seq score matrix -- and the remaining 25% are standard +GQA attention with partial RoPE (leading 25% of the head) and an output +gate. This is the axis none of this suite's other models cover: every +other model here is quadratic self-attention throughout; Qwen3-Next +interleaves it with a genuinely sub-quadratic sequence mixer. + +The Gated DeltaNet recurrence is implemented as a plain `jax.lax.scan` +over time steps, which is O(seq_len) sequential steps -- correct and +easy to follow, but not the real chunked/parallel-form kernel used in +production Qwen3-Next; fine at this suite's toy sequence lengths. +Multi-token prediction (a real Qwen3-Next feature) is out of scope +here -- see DeepSeekV4Dense for this suite's other new-attention- +mechanism reference instead. +""" + +import jax +from flax import nnx +from jax import numpy as jnp +from layers import RMSNorm, apply_partial_rope, repeat_kv, rope_freqs, tp_linear + + +class SparseMoEFFN(nnx.Module): + """Top-k-routed mixture-of-experts feed-forward block with real + capacity-based dispatch/combine (not dense-compute-then-select). + Expert weights are stacked into single tensors with a leading + n_experts axis so that axis can be sharded across a mesh's "expert" + axis. + """ + + def __init__( + self, d_model, d_ff, n_experts, n_active, *, rngs, capacity_factor=1.25 + ): + """Construct a sparse MoE feed-forward block. + + Args: + d_model: model (residual stream) dimensionality. + d_ff: hidden (expansion) dimensionality of each expert. + n_experts: total number of experts. + n_active: number of experts activated per token (top-k). + rngs: random keys. + capacity_factor: per-expert buffer capacity multiplier. Per-expert + capacity = ceil(capacity_factor * n_active * n_tokens / + n_experts). Standard Switch-Transformer value is 1.25. Tokens + beyond an expert's capacity in a batch are dropped (their + contribution from that slot is zeroed, not misrouted). + """ + self.n_experts = n_experts + self.n_active = n_active + self.capacity_factor = capacity_factor + self.router = nnx.Linear(d_model, n_experts, use_bias=False, rngs=rngs) + + expert_partitioning = nnx.with_partitioning( + nnx.initializers.lecun_normal(), ("expert", None, None) + ) + key = rngs.params() + k1, k2, k3 = jax.random.split(key, 3) + self.gate = nnx.Param(expert_partitioning(k1, (n_experts, d_model, d_ff))) + self.up = nnx.Param(expert_partitioning(k2, (n_experts, d_model, d_ff))) + self.down = nnx.Param(expert_partitioning(k3, (n_experts, d_ff, d_model))) + + def __call__(self, x: jax.Array) -> tuple[jax.Array, jax.Array]: + """Route tokens to the top-k experts via capacity-based dispatch/ + combine and combine their outputs. + + Args: + x: input array, shape (batch, seq, d_model). + + Returns: + a tuple (output, aux_loss): output has the same shape as x; + aux_loss is a scalar Switch-Transformer-style load-balancing loss. + """ + b, s, d = x.shape + flat = x.reshape(b * s, d) + n_tok = flat.shape[0] + logits = self.router(flat) + probs = jax.nn.softmax(logits, axis=-1) + top_probs, top_idx = jax.lax.top_k(probs, self.n_active) + top_probs = top_probs / jnp.sum(top_probs, axis=-1, keepdims=True) + + capacity = int( + jnp.ceil(self.capacity_factor * self.n_active * n_tok / self.n_experts) + ) + + expert_onehot = jax.nn.one_hot(top_idx, self.n_experts) + flat_onehot = expert_onehot.reshape(-1, self.n_experts) + position_in_expert = ( + jnp.cumsum(flat_onehot, axis=0) * flat_onehot - flat_onehot + ) + position_in_expert = jnp.sum(position_in_expert, axis=-1) + within_capacity = position_in_expert < capacity + position_in_expert = position_in_expert.reshape(n_tok, self.n_active) + within_capacity = within_capacity.reshape(n_tok, self.n_active) + + capacity_onehot = jax.nn.one_hot( + position_in_expert.astype(jnp.int32), capacity + ) + dispatch_mask = jnp.sum( + expert_onehot[..., None] + * capacity_onehot[:, :, None, :] + * within_capacity[:, :, None, None], + axis=1, + ) # (n_tok, n_experts, capacity) + combine_weight = jnp.sum( + expert_onehot[..., None] + * capacity_onehot[:, :, None, :] + * within_capacity[:, :, None, None] + * top_probs[:, :, None, None], + axis=1, + ) # (n_tok, n_experts, capacity) + + dispatched = jnp.einsum("td,tec->ecd", flat, dispatch_mask) + g = jnp.einsum("ecd,edf->ecf", dispatched, self.gate.value) + u = jnp.einsum("ecd,edf->ecf", dispatched, self.up.value) + h = jax.nn.silu(g) * u + expert_out = jnp.einsum("ecf,efd->ecd", h, self.down.value) + combined = jnp.einsum("ecd,tec->td", expert_out, combine_weight) + + density = jnp.mean(probs, axis=0) + chosen_mask = jax.nn.one_hot(top_idx, self.n_experts).sum(axis=1) + chosen_frac = jnp.mean(chosen_mask, axis=0) + aux_loss = self.n_experts * jnp.sum(density * chosen_frac) + + return combined.reshape(b, s, d), aux_loss + + +def gated_delta_net( + q: jax.Array, + k: jax.Array, + v: jax.Array, + alpha: jax.Array, + beta: jax.Array, +) -> jax.Array: + """Sequential Gated DeltaNet recurrence. + + Maintains a (head_dim x head_dim) associative-memory state per head, + decayed each step by a data-dependent gate `alpha` and corrected + toward the true value via the delta rule, scaled by a data-dependent + write-rate `beta`: state_t = alpha_t * state_{t-1} + beta_t * (v_t - + state_{t-1} @ k_t) (outer) k_t. Output is state_t @ q_t. + + Args: + q: queries, shape (batch, seq, heads, head_dim). + k: keys, shape (batch, seq, heads, head_dim). + v: values, shape (batch, seq, heads, head_dim). + alpha: decay gate in (0, 1), shape (batch, seq, heads). + beta: write-rate gate in (0, 1), shape (batch, seq, heads). + + Returns: + jax.Array, shape (batch, seq, heads, head_dim). + """ + b, _, h, d = q.shape + + def step(state, inputs): + q_t, k_t, v_t, a_t, beta_t = inputs + predicted = jnp.einsum("bhde,bhe->bhd", state, k_t) + delta = beta_t[..., None, None] * jnp.einsum( + "bhd,bhe->bhde", v_t - predicted, k_t + ) + state = a_t[..., None, None] * state + delta + out = jnp.einsum("bhde,bhe->bhd", state, q_t) + return state, out + + init_state = jnp.zeros((b, h, d, d)) + xs = ( + jnp.moveaxis(q, 1, 0), + jnp.moveaxis(k, 1, 0), + jnp.moveaxis(v, 1, 0), + jnp.moveaxis(alpha, 1, 0), + jnp.moveaxis(beta, 1, 0), + ) + _, outs = jax.lax.scan(step, init_state, xs) + return jnp.moveaxis(outs, 0, 1) + + +class GatedDeltaNetLayer(nnx.Module): + """Linear-attention sequence mixer via the (gated) delta rule.""" + + def __init__(self, d_model, n_heads, head_dim, *, rngs): + """Construct a Gated DeltaNet layer. + + Args: + d_model: model (residual stream) dimensionality. + n_heads: number of heads. + head_dim: dimensionality of each head. + rngs: random keys. + """ + self.n_heads = n_heads + self.head_dim = head_dim + self.q_proj = tp_linear( + d_model, n_heads * head_dim, ("fsdp", "tp"), rngs=rngs + ) + self.k_proj = tp_linear( + d_model, n_heads * head_dim, ("fsdp", "tp"), rngs=rngs + ) + self.v_proj = tp_linear( + d_model, n_heads * head_dim, ("fsdp", "tp"), rngs=rngs + ) + self.gate_proj = nnx.Linear(d_model, 2 * n_heads, rngs=rngs) + self.o_proj = tp_linear( + n_heads * head_dim, d_model, ("tp", "fsdp"), rngs=rngs + ) + + def __call__(self, x: jax.Array) -> jax.Array: + """Apply the Gated DeltaNet layer. + + Args: + x: input array, shape (batch, seq, d_model). + + Returns: + jax.Array, same shape as x. + """ + b, s, _ = x.shape + q = self.q_proj(x).reshape(b, s, self.n_heads, self.head_dim) + k = self.k_proj(x).reshape(b, s, self.n_heads, self.head_dim) + v = self.v_proj(x).reshape(b, s, self.n_heads, self.head_dim) + gates = jax.nn.sigmoid(self.gate_proj(x)) + alpha, beta = gates[..., : self.n_heads], gates[..., self.n_heads :] + + out = gated_delta_net(q, k, v, alpha, beta) + return self.o_proj(out.reshape(b, s, self.n_heads * self.head_dim)) + + +class Qwen3NextAttention(nnx.Module): + """Standard GQA attention with partial RoPE and an output gate.""" + + def __init__(self, d_model, n_heads, n_kv_heads, head_dim, *, rngs): + """Construct a standard-attention layer. + + Args: + d_model: model (residual stream) dimensionality. + n_heads: number of query heads. + n_kv_heads: number of key/value heads. + head_dim: dimensionality of each attention head. + rngs: random keys. + """ + self.n_heads = n_heads + self.n_kv_heads = n_kv_heads + self.head_dim = head_dim + self.n_rep = n_heads // n_kv_heads + self.rotary_dim = max(2, head_dim // 4 // 2 * 2) + self.q_proj = tp_linear( + d_model, n_heads * head_dim, ("fsdp", "tp"), rngs=rngs + ) + self.k_proj = tp_linear( + d_model, n_kv_heads * head_dim, ("fsdp", "tp"), rngs=rngs + ) + self.v_proj = tp_linear( + d_model, n_kv_heads * head_dim, ("fsdp", "tp"), rngs=rngs + ) + self.gate_proj = nnx.Linear(d_model, n_heads * head_dim, rngs=rngs) + self.o_proj = tp_linear( + n_heads * head_dim, d_model, ("tp", "fsdp"), rngs=rngs + ) + self.inv_freq = nnx.Variable(rope_freqs(self.rotary_dim)) + + def __call__( + self, x: jax.Array, positions: jax.Array, mask: jax.Array + ) -> jax.Array: + """Apply gated grouped-query self-attention. + + Args: + x: input array, shape (batch, seq, d_model). + positions: integer position ids, shape (batch, seq). + mask: bool attention mask, shape (seq, seq). + + Returns: + jax.Array, same shape as x. + """ + b, s, _ = x.shape + q = self.q_proj(x).reshape(b, s, self.n_heads, self.head_dim) + k = self.k_proj(x).reshape(b, s, self.n_kv_heads, self.head_dim) + v = self.v_proj(x).reshape(b, s, self.n_kv_heads, self.head_dim) + + q = apply_partial_rope(q, positions, self.inv_freq.value, self.rotary_dim) + k = apply_partial_rope(k, positions, self.inv_freq.value, self.rotary_dim) + k = repeat_kv(k, self.n_rep) + v = repeat_kv(v, self.n_rep) + + q = jnp.transpose(q, (0, 2, 1, 3)) + k = jnp.transpose(k, (0, 2, 1, 3)) + v = jnp.transpose(v, (0, 2, 1, 3)) + + scores = jnp.einsum("bhqd,bhkd->bhqk", q, k) / jnp.sqrt(self.head_dim) + scores = jnp.where(mask[None, None, :, :], scores, -jnp.inf) + weights = jax.nn.softmax(scores, axis=-1) + out = jnp.einsum("bhqk,bhkd->bhqd", weights, v) + out = jnp.transpose(out, (0, 2, 1, 3)).reshape( + b, s, self.n_heads * self.head_dim + ) + + gate = jax.nn.sigmoid(self.gate_proj(x)) + return self.o_proj(gate * out) + + +class Qwen3NextBlock(nnx.Module): + """Pre-norm transformer block: Gated DeltaNet or standard attention, + plus a sparse MoE FFN. + """ + + def __init__( # noqa: PLR0913 + self, + d_model, + n_heads, + n_kv_heads, + head_dim, + d_ff, + n_experts, + n_active, + *, + is_linear, + rngs, + ): + """Construct a Qwen3-Next transformer block. + + Args: + d_model: model (residual stream) dimensionality. + n_heads: number of attention/DeltaNet heads. + n_kv_heads: number of key/value heads (standard-attention layers + only). + head_dim: dimensionality of each head. + d_ff: feed-forward hidden dimensionality of each expert. + n_experts: total experts. + n_active: active experts per token (top-k). + is_linear: whether this is a Gated DeltaNet layer (True, 75% of + layers) or a standard-attention layer (False, 25%). + rngs: random keys. + """ + self.is_linear = is_linear + self.attn_norm = RMSNorm(d_model, rngs=rngs) + self.attn = ( + GatedDeltaNetLayer(d_model, n_heads, head_dim, rngs=rngs) + if is_linear + else Qwen3NextAttention(d_model, n_heads, n_kv_heads, head_dim, rngs=rngs) + ) + self.ffn_norm = RMSNorm(d_model, rngs=rngs) + self.ffn = SparseMoEFFN(d_model, d_ff, n_experts, n_active, rngs=rngs) + + def __call__( + self, x: jax.Array, positions: jax.Array, mask: jax.Array + ) -> tuple[jax.Array, jax.Array]: + """Apply the block. + + Args: + x: input array, shape (batch, seq, d_model). + positions: integer position ids, shape (batch, seq). + mask: bool attention mask, shape (seq, seq) (standard-attention + layers only -- unused on Gated DeltaNet layers). + + Returns: + a tuple (output, aux_loss): output has the same shape as x. + """ + normed = self.attn_norm(x) + attn_out = ( + self.attn(normed) if self.is_linear else self.attn(normed, positions, mask) + ) + x = x + attn_out + ffn_out, aux_loss = self.ffn(self.ffn_norm(x)) + return x + ffn_out, aux_loss + + +class Qwen3NextLLM(nnx.Module): + """Decoder-only transformer in the Qwen3-Next architectural family: + linear-attention (Gated DeltaNet) / standard-attention hybrid with a + sparse MoE FFN. + """ + + def __init__( # noqa: PLR0913 + self, + vocab_size, + d_model, + n_layers, + n_heads, + n_kv_heads, + head_dim, + d_ff, + n_experts, + n_active, + *, + linear_every=4, + aux_loss_coef=0.01, + rngs, + ): + """Construct a Qwen3NextLLM. + + Args: + vocab_size: token vocabulary size. + d_model: model (residual stream) dimensionality. + n_layers: number of transformer blocks. + n_heads: number of attention/DeltaNet heads. + n_kv_heads: number of key/value heads (standard-attention layers). + head_dim: dimensionality of each head. + d_ff: feed-forward hidden dimensionality of each expert. + n_experts: total experts per block. + n_active: active experts per token (top-k) per block. + linear_every: every linear_every-th layer (1-indexed) is a + standard-attention layer; the rest are Gated DeltaNet (3:1 + ratio at the default of 4, matching real Qwen3-Next). + aux_loss_coef: weight applied to each block's load-balancing + aux_loss before summing across blocks. + rngs: random keys. + """ + self.aux_loss_coef = aux_loss_coef + self.embed = nnx.Embed( + vocab_size, + d_model, + embedding_init=nnx.with_partitioning( + nnx.initializers.normal(), ("fsdp", None) + ), + rngs=rngs, + ) + self.blocks = tuple( + Qwen3NextBlock( + d_model, + n_heads, + n_kv_heads, + head_dim, + d_ff, + n_experts, + n_active, + is_linear=((i + 1) % linear_every != 0), + rngs=rngs, + ) + for i in range(n_layers) + ) + self.final_norm = RMSNorm(d_model, rngs=rngs) + self.lm_head = nnx.Linear( + d_model, + vocab_size, + use_bias=False, + kernel_init=nnx.with_partitioning( + nnx.initializers.lecun_normal(), ("fsdp", None) + ), + rngs=rngs, + ) + + def __call__( + self, token_ids: jax.Array, positions: jax.Array + ) -> tuple[jax.Array, jax.Array]: + """Compute next-token logits for a batch of token sequences. + + Args: + token_ids: integer token ids, shape (batch, seq). + positions: integer position ids, shape (batch, seq). + + Returns: + a tuple (logits, aux_loss): logits has shape + (batch, seq, vocab_size); aux_loss is aux_loss_coef times the + summed per-block load-balancing loss (standard-attention layers' + MoE FFNs only -- Gated DeltaNet layers still route through a + SparseMoEFFN, same as standard-attention layers). + """ + seq_len = token_ids.shape[1] + i = jnp.arange(seq_len)[:, None] + j = jnp.arange(seq_len)[None, :] + mask = j <= i + + hidden = self.embed(token_ids) + total_aux_loss = jnp.array(0.0) + for block in self.blocks: + hidden, aux_loss = block(hidden, positions, mask) + total_aux_loss = total_aux_loss + aux_loss + hidden = self.final_norm(hidden) + logits = self.lm_head(hidden) + return logits, self.aux_loss_coef * total_aux_loss + + +def Qwen3NextHybrid(vocab_size, **kwargs): + return Qwen3NextLLM(vocab_size, **kwargs) diff --git a/examples/llm/objective.py b/examples/llm/objective.py new file mode 100644 index 0000000..12b798a --- /dev/null +++ b/examples/llm/objective.py @@ -0,0 +1,83 @@ +"""LM training objectives. + +Contains train_step, eval_step and generate functions. +""" + +import optax +from flax import nnx +import jax +from jax import numpy as jnp + +import jax.numpy as jnp +from jax import random as jr + + +def get_training_and_eval_fns(aux_loss_coef: float = 0.01): + """Construct causal LM train/val step functions. + + Args: + aux_loss_coef: weight applied to the model's own aux_loss + + Returns: + train_step and eval_step functions + """ + + def _loss_fn(model, rng_key, batch): + del rng_key + seq_len = batch["token_ids"].shape[1] + positions = jnp.broadcast_to(jnp.arange(seq_len), batch["token_ids"].shape) + logits, aux_loss = model(batch["token_ids"], positions) + ce_loss = optax.softmax_cross_entropy_with_integer_labels( + logits=logits, labels=batch["target_ids"] + ).mean() + return ce_loss + aux_loss_coef * aux_loss + + def train_step(model, rng_key, batch, **kwargs): + del kwargs + return nnx.value_and_grad(_loss_fn)(model, rng_key, batch) + + def val_step(model, rng_key, batch, **kwargs): + del kwargs + return _loss_fn(model, rng_key, batch) + + return train_step, val_step + +def sample( + model, + rng_key: jax.Array, + prompt: jax.Array, + max_new_tokens: int, + *, + max_seq_len: int, +) -> jax.Array: + """Generate new tokens. + + Args: + model: a LM + rng_key: a jax.random.key object + prompt: integer token ids, shape (batch, prompt_len) + max_new_tokens: number of tokens to generate + max_seq_len: total sequence length prompt + generated tokens must + not exceed + + Returns: + jax.Array, shape (batch, prompt_len + max_new_tokens) + """ + batch, prompt_len = prompt.shape + total_len = prompt_len + max_new_tokens + if total_len > max_seq_len: + raise ValueError( + f"prompt_len + max_new_tokens ({total_len}) exceeds " + f"max_seq_len ({max_seq_len})" + ) + + tokens = prompt + for step in range(max_new_tokens): + seq_len = tokens.shape[1] + positions = jnp.broadcast_to(jnp.arange(seq_len), (batch, seq_len)) + logits, _ = model(tokens, positions) + next_logits = logits[:, -1, :] + next_token = jr.categorical(jr.fold_in(rng_key, step), next_logits, axis=-1) + tokens = jnp.concatenate([tokens, next_token[:, None]], axis=1) + + return tokens From f9e9c1d75f152b821b9a164662664fc5618cc258 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Tue, 14 Jul 2026 06:06:38 +0200 Subject: [PATCH 32/34] chore: reorg examples, drop unused code, make wandb optional --- .github/workflows/ci.yaml | 16 +- .github/workflows/release.yaml | 2 +- .gitlint | 9 +- .pre-commit-config.yaml | 2 +- .python-version | 2 +- Makefile | 11 - README.md | 125 ++- blaxbird/_src/_types.py | 21 - blaxbird/_src/hooks.py | 15 +- blaxbird/_src/test_blaxbird.py | 5 - blaxbird/_src/test_hooks.py | 6 - blaxbird/_src/test_trainer.py | 32 +- blaxbird/_src/test_types.py | 1 - blaxbird/_src/trainer.py | 78 +- examples/_common/__init__.py | 8 - examples/_common/edm.py | 53 -- examples/_common/nn/__init__.py | 0 examples/_common/nn/embedding.py | 9 - examples/_common/nn/mlp.py | 59 -- examples/_common/nn/test_dit.py | 61 -- examples/_common/nn/test_unet.py | 97 --- examples/_common/nn/unet.py | 397 --------- examples/_common/parameterizations.py | 129 --- examples/_common/rfm.py | 54 -- examples/_common/samplers.py | 146 ---- examples/_common/test_edm.py | 38 - examples/_common/test_parameterizations.py | 21 - examples/_common/test_rfm.py | 17 - examples/_common/test_samplers.py | 39 - examples/cifar10_flow_matching/README.md | 9 + examples/cifar10_flow_matching/main.py | 12 +- .../dit.py => cifar10_flow_matching/model.py} | 96 ++- examples/cifar10_flow_matching/objective.py | 117 +++ examples/fsdp_tp_demo/README.md | 16 - examples/fsdp_tp_demo/main.py | 62 -- examples/fsdp_tp_demo/model.py | 51 -- examples/fsdp_tp_demo/test_model.py | 41 - examples/llm_reference/README.md | 42 - examples/llm_reference/deepseek.py | 260 ------ examples/llm_reference/gemma.py | 143 ---- examples/llm_reference/generate.py | 54 -- examples/llm_reference/layers.py | 251 ------ examples/llm_reference/main.py | 181 ----- examples/llm_reference/mixtral.py | 263 ------ examples/llm_reference/objective.py | 53 -- examples/llm_reference/test_deepseek.py | 113 --- examples/llm_reference/test_gemma.py | 83 -- examples/llm_reference/test_generate.py | 41 - examples/llm_reference/test_layers.py | 97 --- examples/llm_reference/test_mixtral.py | 201 ----- examples/llm_reference/test_objective.py | 79 -- examples/mnist_classification/README.md | 8 + examples/mnist_classification/main.py | 6 - pyproject.toml | 70 +- uv.lock | 751 +++++++++++++++--- 55 files changed, 1099 insertions(+), 3454 deletions(-) delete mode 100644 Makefile delete mode 100644 blaxbird/_src/_types.py delete mode 100644 blaxbird/_src/test_blaxbird.py delete mode 100644 examples/_common/__init__.py delete mode 100644 examples/_common/edm.py delete mode 100644 examples/_common/nn/__init__.py delete mode 100644 examples/_common/nn/embedding.py delete mode 100644 examples/_common/nn/mlp.py delete mode 100644 examples/_common/nn/test_dit.py delete mode 100644 examples/_common/nn/test_unet.py delete mode 100644 examples/_common/nn/unet.py delete mode 100644 examples/_common/parameterizations.py delete mode 100644 examples/_common/rfm.py delete mode 100644 examples/_common/samplers.py delete mode 100644 examples/_common/test_edm.py delete mode 100644 examples/_common/test_parameterizations.py delete mode 100644 examples/_common/test_rfm.py delete mode 100644 examples/_common/test_samplers.py create mode 100644 examples/cifar10_flow_matching/README.md rename examples/{_common/nn/dit.py => cifar10_flow_matching/model.py} (77%) create mode 100644 examples/cifar10_flow_matching/objective.py delete mode 100644 examples/fsdp_tp_demo/README.md delete mode 100644 examples/fsdp_tp_demo/main.py delete mode 100644 examples/fsdp_tp_demo/model.py delete mode 100644 examples/fsdp_tp_demo/test_model.py delete mode 100644 examples/llm_reference/README.md delete mode 100644 examples/llm_reference/deepseek.py delete mode 100644 examples/llm_reference/gemma.py delete mode 100644 examples/llm_reference/generate.py delete mode 100644 examples/llm_reference/layers.py delete mode 100644 examples/llm_reference/main.py delete mode 100644 examples/llm_reference/mixtral.py delete mode 100644 examples/llm_reference/objective.py delete mode 100644 examples/llm_reference/test_deepseek.py delete mode 100644 examples/llm_reference/test_gemma.py delete mode 100644 examples/llm_reference/test_generate.py delete mode 100644 examples/llm_reference/test_layers.py delete mode 100644 examples/llm_reference/test_mixtral.py delete mode 100644 examples/llm_reference/test_objective.py create mode 100644 examples/mnist_classification/README.md diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 885ae59..2ffd82a 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -20,7 +20,7 @@ jobs: - precommit strategy: matrix: - python-version: [ 3.12 ] + python-version: [ 3.13 ] steps: - uses: actions/checkout@v3 - name: Set up Python ${{ matrix.python-version }} @@ -32,10 +32,10 @@ jobs: version: "latest" - name: Install dependencies run: | - uv sync --dev + uv sync --all-groups --all-extras - name: Run lints run: | - make lints + uv run ruff check blaxbird examples tests: runs-on: ubuntu-latest @@ -43,7 +43,7 @@ jobs: - lints strategy: matrix: - python-version: [ 3.12 ] + python-version: [ 3.13 ] steps: - uses: actions/checkout@v3 - name: Set up Python ${{ matrix.python-version }} @@ -55,11 +55,11 @@ jobs: version: "latest" - name: Install dependencies run: | - uv sync --dev + uv sync --all-extras - name: Run tests run: | - make tests + uv run pytest - name: Upload coverage reports to Codecov - uses: codecov/codecov-action@v5 + uses: codecov/codecov-action@v3 env: - CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} + CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} \ No newline at end of file diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index d1b137c..6a6f1ad 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -10,7 +10,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.11] + python-version: [ 3.13 ] steps: - uses: actions/checkout@v3 - name: Set up Python ${{ matrix.python-version }} diff --git a/.gitlint b/.gitlint index 1abf77d..2aabde4 100644 --- a/.gitlint +++ b/.gitlint @@ -1,8 +1,15 @@ [general] ignore=body-is-missing +contrib=contrib-title-conventional-commits [title-min-length] min-length=10 +[title-max-length] +line-length=72 + +[body-max-line-length] +line-length=72 + [title-must-not-contain-word] -words=wip,todo +words=wip,todo \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 76bceb2..9940edd 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -18,7 +18,7 @@ repos: hooks: - id: mypy args: ["--ignore-missing-imports"] - files: "(blaxbird)" + files: "(blaxbird|examples)" - repo: https://github.com/astral-sh/ruff-pre-commit rev: v0.3.0 diff --git a/.python-version b/.python-version index 2419ad5..3b2cfc0 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.11.9 +3.12.10 diff --git a/Makefile b/Makefile deleted file mode 100644 index 06755d5..0000000 --- a/Makefile +++ /dev/null @@ -1,11 +0,0 @@ -.PHONY: tests, lints, docs, format - -tests: - uv run pytest - -lints: - uv run ruff check blaxbird examples - -format: - uv run ruff check --select I --fix blaxbird examples - uv run ruff format blaxbird examples diff --git a/README.md b/README.md index 0fb9708..2bfb204 100644 --- a/README.md +++ b/README.md @@ -5,8 +5,6 @@ > A high-level API to build and train NNX models -## About - `Blaxbird` [blækbɜːd] is a high-level API to easily build NNX models and train them on CPU or GPU. Using `blaxbird` one can @@ -15,12 +13,7 @@ Using `blaxbird` one can - distribute data and model weights over multiple processes or GPUs, - define hooks that are periodically called during training. -`blaxbird` is a training framework, not a model zoo -- it doesn't ship -neural network architectures. See [examples](examples) for worked -end-to-end examples (including a DiT trained with flow matching on -CIFAR-10) that define their own models and hand them to `blaxbird`. - -## Example +## Quickstart To use `blaxbird`, one only needs to define a model, a loss function, and train and validation step functions: ```python @@ -64,9 +57,25 @@ train = train_fn( train(jr.key(2), optimizer, train_itr, val_itr) ``` -See the entire self-contained example in [examples/mnist_classification](examples/mnist_classification). +## Examples + +Full self-contained examples can be found in [examples](examples/). + +## Installation + +To install the package from PyPI, call: + +```bash +pip install blaxbird +``` + +To install the latest GitHub , just call the following on the command line: + +```bash +pip install git+https://github.com/dirmeier/blaxbird@ +``` -## Usage +## API `train_fn` is a higher order function with the following signature: @@ -116,15 +125,8 @@ to return the loss. To specify how data and model weights are distributed over devices and processes, `blaxbird` uses JAX' [sharding](https://docs.jax.dev/en/latest/notebooks/Distributed_arrays_and_automatic_parallelization.html) functionality. -`mesh` is a `jax.sharding.Mesh` describing your device topology. Per-parameter -sharding is derived from each parameter's own `nnx.with_partitioning` -annotation (see the [flax.nnx docs](https://flax.readthedocs.io/en/latest/)) via -`nnx.get_named_sharding` -- parameters without such an annotation default to -fully replicated, so a mesh with no annotated parameters gives plain data -parallelism. `data_partition_spec` controls how each training/eval batch is -sharded across `mesh` (defaults to `PartitionSpec()`, fully replicated). -You can, if you don't want to distribute anything, just leave `mesh` as `None` -or not specify it. +`mesh` is a `jax.sharding.Mesh` describing your device topology. Per-parameter sharding is derived from each parameter's own `nnx.with_partitioning` +annotation (see the [flax.nnx docs](https://flax.readthedocs.io/en/latest/)) via `nnx.get_named_sharding` -- parameters without such an annotation default to fully replicated, so a mesh with no annotated parameters gives plain data parallelism. `data_partition_spec` controls how each training/eval batch is sharded across `mesh` (defaults to `PartitionSpec()`, fully replicated). You can, if you don't want to distribute anything, just leave `mesh` as `None` or not specify it. An example is shown below, sharding only the data over `num_devices` devices (the model has no `with_partitioning` annotations, so it stays fully @@ -221,6 +223,53 @@ hook_save, *_ = get_default_checkpointer( ) ``` +#### An EMA `hook` + +We provide a hook for tracking an exponential moving average (EMA) of a +model's weights, constructed via `get_ema_hook`. + +The signature is: + +```python +def get_ema_hook( + model: nnx.Module, decay: float = 0.999 +) -> tuple[Callable, Callable] +``` + +Its arguments are: +- `model`: the model from which the EMA state is initialized. +- `decay`: the EMA decay rate. + +It returns a tuple `(hook_fn, get_ema_model_fn)`: +- `hook_fn(step, *, model, **kwargs) -> None`: updates the tracked EMA + weights every training step. +- `get_ema_model_fn(model: nnx.Module) -> nnx.Module`: returns a new, + independent `nnx.Module` with the same structure as `model` but with the + tracked EMA parameter values. + +For instance, you would construct and use the EMA hook like this: + +```python +from blaxbird import get_ema_hook + +ema_hook, get_ema_model = get_ema_hook(model, decay=0.999) + +train = train_fn( + fns=(train_step, val_step), + n_steps=n_steps, + eval_every_n_steps=eval_every_n_steps, + n_eval_batches=n_eval_batches, + hooks=[ema_hook], +) +train(jr.key(1), optimizer, train_itr, val_itr) + +ema_model = get_ema_model(optimizer.model) +``` + +Note: EMA state is not integrated with `get_default_checkpointer` -- +saving and restoring EMA state alongside model checkpoints is not +covered here. + ### Restoring a run You can also use `get_default_checkpointer` to restart the run where you left off. @@ -268,23 +317,35 @@ train = train_fn( train(jr.key(1), optimizer, train_itr, val_itr) ``` -Self-contained examples that also explain how the data loaders should look like can be found -in [examples](examples). +## Contributing -## Installation +Contributions in the form of pull requests are more than welcome. A good way to +start is to check out issues labelled +[good first issue](https://github.com/dirmeier/surjectors/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22). -To install the package from PyPI, call: +In order to contribute: -```bash -pip install blaxbird -``` +1) Clone `surjectors` and install `uv` from [here](https://docs.astral.sh/uv/getting-started/installation/). +2) Install all dependencies using `uv sync --all-groups`. +3) Install the Git hooks: -To install the latest GitHub , just call the following on the command line: + ```bash + uv run pre-commit install -t pre-commit -t commit-msg + ``` +4) Create a new branch locally, e.g. `git checkout -b feature/my-new-feature`. +5) Implement your contribution and ideally a test case. +6) Check your work (see below). +7) Submit a PR 🙂. -```bash -pip install git+https://github.com/dirmeier/blaxbird@ -``` +### Development commands -## Author +The project uses `uv` for everything: -Simon Dirmeier simd@mailbox.org +```bash +uv sync --all-groups +uv run pytest +uv run ruff format blaxbird examples +uv run ruff check --fix blaxbird examples +uv run mypy blaxbird examples +uv run pre-commit run --all-files +``` \ No newline at end of file diff --git a/blaxbird/_src/_types.py b/blaxbird/_src/_types.py deleted file mode 100644 index 80dafdb..0000000 --- a/blaxbird/_src/_types.py +++ /dev/null @@ -1,21 +0,0 @@ -"""Shared structural types for blaxbird's objective factories.""" - -from collections.abc import Callable -from typing import NamedTuple - - -class ObjectiveFns(NamedTuple): - """Functions returned by an objective factory (e.g. edm(), rfm()). - - Attributes: - train_step: gradient-step function with signature - (model, rng_key, batch, **kwargs) -> (loss, grads). - val_step: validation function with signature - (model, rng_key, batch, **kwargs) -> loss. - sample_fn: sampling function with signature - (model, rng_key, sample_shape, *, context=None) -> samples. - """ - - train_step: Callable - val_step: Callable - sample_fn: Callable diff --git a/blaxbird/_src/hooks.py b/blaxbird/_src/hooks.py index 0bb7af6..1ac6af0 100644 --- a/blaxbird/_src/hooks.py +++ b/blaxbird/_src/hooks.py @@ -20,23 +20,18 @@ def get_ema_hook( model: nnx.Module, decay: float = 0.999 ) -> tuple[Callable, Callable]: - """Construct an exponential-moving-average-of-weights hook. + """Construct an exponential-moving-average hook for weights. Args: - model: the model whose parameter structure the EMA state is - initialized from (its current values seed the EMA state; the - model object itself is not retained or mutated). - decay: EMA decay rate -- ema = decay*ema + (1-decay)*current, applied - every step the hook is called. Higher decay tracks more slowly. + model: the model from which the EMA state is initialized from + decay: EMA decay rate Returns: a tuple (hook_fn, get_ema_model_fn): - hook_fn(step, *, model, **kwargs) -> None: updates the tracked EMA - state from `model`'s current parameter values. Call this every - step (e.g. by including it in train_fn's hooks=). + hook_fn(step, *, model, **kwargs) -> None: updates the weights get_ema_model_fn(model: nnx.Module) -> nnx.Module: returns a new, independent nnx.Module with the same structure as `model` but - with the tracked EMA parameter values. + with the tracked EMA parameter values """ _, ema_state = nnx.split(model) box = {"state": ema_state} diff --git a/blaxbird/_src/test_blaxbird.py b/blaxbird/_src/test_blaxbird.py deleted file mode 100644 index 25e7b3d..0000000 --- a/blaxbird/_src/test_blaxbird.py +++ /dev/null @@ -1,5 +0,0 @@ -import chex - - -def test_blaxbird(): - chex.assert_equal(1, 1) diff --git a/blaxbird/_src/test_hooks.py b/blaxbird/_src/test_hooks.py index 5973d90..cc99db9 100644 --- a/blaxbird/_src/test_hooks.py +++ b/blaxbird/_src/test_hooks.py @@ -52,12 +52,6 @@ def test_ema_model_is_usable(): def test_ema_hook_ignores_extra_trainer_kwargs(): - """Verify hook_fn tolerates trainer.py's full kwarg set. - - trainer.py calls every hook as h(step, model=, optimizer=, - metrics=) -- hook_fn must accept and ignore the kwargs it doesn't - use. - """ model = _Linear(rngs=nnx.rnglib.Rngs(jr.key(0))) optimizer_stub = object() hook_fn, _ = get_ema_hook(model, decay=0.9) diff --git a/blaxbird/_src/test_trainer.py b/blaxbird/_src/test_trainer.py index 377469e..ce6fc16 100644 --- a/blaxbird/_src/test_trainer.py +++ b/blaxbird/_src/test_trainer.py @@ -1,4 +1,5 @@ import itertools +import types import jax import jax.numpy as jnp @@ -8,6 +9,7 @@ from jax.experimental import mesh_utils from jax.sharding import PartitionSpec as P +from blaxbird._src import trainer as trainer_module from blaxbird._src.trainer import train_fn @@ -50,13 +52,6 @@ def test_train_fn_takes_optimizer_only_no_model_arg(): def test_train_fn_shards_across_mesh(): - """Verifies train_fn shards state and batches across a device mesh. - - Requires XLA_FLAGS=--xla_force_host_platform_device_count=4 to exercise - real multi-device sharding; degrades to a 1-device no-op sharding - otherwise (still exercises the mesh code path, just not the - multi-shard assertion below). - """ n_devices = jax.local_device_count() mesh = jax.sharding.Mesh( mesh_utils.create_device_mesh((n_devices,)), ("data",) @@ -79,3 +74,26 @@ def test_train_fn_shards_across_mesh(): if n_devices > 1: sharded_x = jax.device_put(batch["x"], jax.NamedSharding(mesh, P("data"))) assert len(sharded_x.addressable_shards) == n_devices + + +def test_train_fn_logs_to_wandb_when_enabled(monkeypatch): + calls = [] + fake_wandb = types.ModuleType("wandb") + fake_wandb.log = lambda data, step: calls.append((data, step)) + monkeypatch.setattr(trainer_module, "wandb", fake_wandb) + + model = _Linear(rngs=nnx.rnglib.Rngs(jr.key(0))) + optimizer = nnx.Optimizer(model, tx=optax.sgd(1e-2)) + batch = {"x": jnp.ones((4, 2)), "y": jnp.zeros((4, 2))} + itr = itertools.cycle([batch]) + + train = train_fn( + fns=(_dummy_step, _dummy_val), + n_steps=1, + eval_every_n_steps=1, + n_eval_batches=1, + log_to_wandb=True, + ) + train(jr.key(1), optimizer, itr, itr) + + assert len(calls) == 1 diff --git a/blaxbird/_src/test_types.py b/blaxbird/_src/test_types.py index f7b720d..bf5697f 100644 --- a/blaxbird/_src/test_types.py +++ b/blaxbird/_src/test_types.py @@ -17,6 +17,5 @@ def sample_fn(): assert fns.train_step is train_step assert fns.val_step is val_step assert fns.sample_fn is sample_fn - # still unpacks positionally like a plain tuple a, b, c = fns assert (a, b, c) == (train_step, val_step, sample_fn) diff --git a/blaxbird/_src/trainer.py b/blaxbird/_src/trainer.py index ab8f49f..e681a6a 100644 --- a/blaxbird/_src/trainer.py +++ b/blaxbird/_src/trainer.py @@ -1,26 +1,32 @@ +import types from collections.abc import Callable, Iterable import jax -import wandb from absl import logging from flax import nnx from jax import random as jr +wandb: types.ModuleType | None +try: + import wandb +except ImportError: + wandb = None + # ruff: noqa: ANN001, ANN202, ANN003 def _step_and_val_fns(fns): - step, eval = fns + step_fn, eval_fn = fns def _train_step(model, rng_key, optimizer, metrics, batch, **kwargs): model.train() - loss, grads = step(model, rng_key, batch, **kwargs) + loss, grads = step_fn(model, rng_key, batch, **kwargs) optimizer.update(grads) metrics.update(loss=loss) return {"loss": loss} def _eval_step(model, rng_key, metrics, batch, **kwargs): model.eval() - loss = eval(model, rng_key, batch, **kwargs) + loss = eval_fn(model, rng_key, batch, **kwargs) metrics.update(loss=loss) return {"loss": loss} @@ -32,9 +38,7 @@ def train_fn( *, fns: tuple[Callable, Callable], mesh: jax.sharding.Mesh | None = None, - data_partition_spec: jax.sharding.PartitionSpec = ( - jax.sharding.PartitionSpec() - ), + data_partition_spec: jax.sharding.PartitionSpec | None = None, n_steps: int, eval_every_n_steps: int, n_eval_batches: int, @@ -60,12 +64,45 @@ def train_fn( n_steps: number of training/gradient steps eval_every_n_steps: specified how often to compute validation statistics. n_eval_batches: number of batches to use for validation - log_to_wandb: whether to log results to wandb or not + log_to_wandb: whether to log results to wandb or not. Requires the + optional `wandb` package to be installed. hooks: iterable of hooks + Example: + ```python + import optax + from flax import nnx + from jax import random as jr + + model = CNN(rngs=nnx.rnglib.Rngs(jr.key(1))) + optimizer = nnx.Optimizer(model, optax.adam(1e-4)) + + train = train_fn( + fns=(train_step, val_step), + n_steps=100, + eval_every_n_steps=10, + n_eval_batches=10, + ) + train(jr.key(2), optimizer, train_itr, val_itr) + ``` + + Raises: + ImportError: if log_to_wandb is True but wandb is not installed. + Returns: returns a callable for training """ + if log_to_wandb and wandb is None: + raise ImportError( + "log_to_wandb=True requires the 'wandb' package. Install it with " + "`pip install wandb`." + ) + _wandb = wandb + _data_partition_spec = ( + data_partition_spec + if data_partition_spec is not None + else jax.sharding.PartitionSpec() + ) def train( rng_key: jax.Array, @@ -101,11 +138,11 @@ def train( metrics_history = {} # run training step_key, rng_key = jr.split(rng_key) - for step, batch in zip(range(1, n_steps + 1), train_itr): + for step, batch in zip(range(1, n_steps + 1), train_itr, strict=False): train_key, val_key = jr.split(jr.fold_in(step_key, step)) if mesh is not None: batch = jax.device_put( - batch, jax.NamedSharding(mesh, data_partition_spec) + batch, jax.NamedSharding(mesh, _data_partition_spec) ) # do a gradient step step_fn( @@ -121,12 +158,18 @@ def train( if step % eval_every_n_steps == 0 or is_first_or_last_step: # store training losses for metric, value in metrics.compute().items(): - metrics_history[f"train/{metric}"] = float(value) + # nnx.MultiMetric.compute()'s Metric return type is a stub + # imprecision -- the runtime value is a scalar array. + metrics_history[f"train/{metric}"] = float( + value # type: ignore[arg-type] + ) # do evaluation loop - for val_idx, batch in zip(range(n_eval_batches), val_itr): + for val_idx, batch in zip( + range(n_eval_batches), val_itr, strict=False + ): if mesh is not None: batch = jax.device_put( - batch, jax.NamedSharding(mesh, data_partition_spec) + batch, jax.NamedSharding(mesh, _data_partition_spec) ) eval_fn( model=model, @@ -136,7 +179,11 @@ def train( ) # store val losses for metric, value in metrics.compute().items(): - metrics_history[f"val/{metric}"] = float(value) + # nnx.MultiMetric.compute()'s Metric return type is a stub + # imprecision -- the runtime value is a scalar array. + metrics_history[f"val/{metric}"] = float( + value # type: ignore[arg-type] + ) metrics.reset() # log losses after each val round if jax.process_index() == 0: @@ -146,7 +193,8 @@ def train( f"{metrics_history['val/loss']}" ) if log_to_wandb and jax.process_index() == 0: - wandb.log(metrics_history, step=step) + assert _wandb is not None # validated in train_fn above + _wandb.log(metrics_history, step=step) for h in hooks: h(step, model=model, optimizer=optimizer, metrics=metrics_history) diff --git a/examples/_common/__init__.py b/examples/_common/__init__.py deleted file mode 100644 index 3d9e4cc..0000000 --- a/examples/_common/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -from _common.nn.unet import BaseUNet, LargeUNet, SmallUNet, UNet - -__all__ = [ - "BaseUNet", - "LargeUNet", - "SmallUNet", - "UNet", -] diff --git a/examples/_common/edm.py b/examples/_common/edm.py deleted file mode 100644 index b90a6c7..0000000 --- a/examples/_common/edm.py +++ /dev/null @@ -1,53 +0,0 @@ -import numpy as np -from flax import nnx -from jax import numpy as jnp -from jax import random as jr - -from _common import samplers -from _common.parameterizations import EDMConfig -from blaxbird._src._types import ObjectiveFns - - -def edm(config: EDMConfig): - """Construct denoising score-matching functions. - - Uses the EDM parameterization. - - Args: - config: a EDMConfig object - - Returns: - returns a tuple consisting of train_step, val_step and sampling functions - """ - parameterization = config.parameterization - - def loss_fn(model, rng_key, batch): - inputs = batch["inputs"] - new_shape = (-1,) + tuple(np.ones(inputs.ndim - 1, dtype=np.int32).tolist()) - - epsilon_key, noise_key, rng_key = jr.split(rng_key, 3) - epsilon = jr.normal(epsilon_key, (inputs.shape[0],)) - sigma = parameterization.sigma(epsilon) - - noise = jr.normal(noise_key, inputs.shape) * sigma.reshape(new_shape) - target_hat = parameterization.denoise( - model, - inputs=inputs + noise, - sigma=sigma, - context=batch.get("context"), - ) - - loss = jnp.square(inputs - target_hat) - loss = parameterization.loss_weight(sigma).reshape(new_shape) * loss - return loss.mean() - - def train_step(model, rng_key, batch, **kwargs): - return nnx.value_and_grad(loss_fn)(model, rng_key, batch) - - def val_step(model, rng_key, batch, **kwargs): - return loss_fn(model, rng_key, batch) - - sampler = samplers.get_sampler_fn(config.sampler)(config) - return ObjectiveFns( - train_step=train_step, val_step=val_step, sample_fn=sampler - ) diff --git a/examples/_common/nn/__init__.py b/examples/_common/nn/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/examples/_common/nn/embedding.py b/examples/_common/nn/embedding.py deleted file mode 100644 index c500b99..0000000 --- a/examples/_common/nn/embedding.py +++ /dev/null @@ -1,9 +0,0 @@ -from jax import numpy as jnp - - -def timestep_embedding(timesteps, embedding_dim: int, dtype=jnp.float32): - half = embedding_dim // 2 - freqs = jnp.exp(-jnp.log(10_000) * jnp.arange(0, half) / half) - emb = timesteps.astype(dtype)[:, None] * freqs[None, ...] - emb = jnp.concatenate([jnp.sin(emb), jnp.cos(emb)], axis=1) - return emb diff --git a/examples/_common/nn/mlp.py b/examples/_common/nn/mlp.py deleted file mode 100644 index 0afcec2..0000000 --- a/examples/_common/nn/mlp.py +++ /dev/null @@ -1,59 +0,0 @@ -from collections.abc import Callable - -import jax -from flax import nnx - - -class MLP(nnx.Module): - def __init__( - self, - in_features: int, - output_features: tuple[int, ...], - *, - kernel_init: nnx.initializers.Initializer = nnx.initializers.lecun_normal(), - bias_init: nnx.initializers.Initializer = nnx.initializers.zeros_init(), - use_bias: bool = True, - dropout_rate: float | None = None, - activation: Callable[[jax.Array], jax.Array] = jax.nn.silu, - activate_last: bool = False, - rngs: nnx.rnglib.Rngs, - ): - features = [in_features] + list(output_features) - layers = [] - for index, (din, dout) in enumerate(zip(features[:-1], features[1:])): - layers.append( - nnx.Linear( - in_features=din, - out_features=dout, - kernel_init=kernel_init, - bias_init=bias_init, - use_bias=use_bias, - rngs=rngs, - ) - ) - self.layers = tuple(layers) - self.dropout_rate = dropout_rate - self.activate_last = activate_last - self.activation = activation - if dropout_rate is not None: - self.dropout_layer = nnx.Dropout(dropout_rate, rngs=rngs) - - def __call__(self, inputs: jax.Array): - """Project inputs through the MLP. - - Args: - inputs: jax.Array - - Returns: - jax.Array - """ - num_layers = len(self.layers) - - out = inputs - for i, layer in enumerate(self.layers): - out = layer(out) - if i < num_layers - 1 or self.activate_last: - if self.dropout_rate is not None: - out = self.dropout_layer(out) - out = self.activation(out) - return out diff --git a/examples/_common/nn/test_dit.py b/examples/_common/nn/test_dit.py deleted file mode 100644 index a705bb5..0000000 --- a/examples/_common/nn/test_dit.py +++ /dev/null @@ -1,61 +0,0 @@ -import jax.numpy as jnp -import pytest -from flax import nnx -from jax import random as jr - -from _common.nn.dit import DiT - - -def _make_dit(n_classes=None): - return DiT( - image_size=(8, 8, 3), - n_hidden_channels=16, - patch_size=4, - n_layers=1, - n_heads=2, - n_embedding_features=16, - n_classes=n_classes, - rngs=nnx.rnglib.Rngs(jr.key(0)), - ) - - -def test_unconditional_dit_runs_without_context(): - model = _make_dit(n_classes=None) - inputs = jnp.ones((2, 8, 8, 3)) - times = jnp.array([0.1, 0.2]) - out = model(inputs, times, context=None) - assert out.shape == inputs.shape - - -def test_conditional_dit_runs_with_context(): - model = _make_dit(n_classes=5) - inputs = jnp.ones((2, 8, 8, 3)) - times = jnp.array([0.1, 0.2]) - context = jnp.array([0, 3]) - out = model(inputs, times, context=context) - assert out.shape == inputs.shape - - -def test_conditional_dit_changes_output_per_class(): - model = _make_dit(n_classes=5) - inputs = jnp.ones((1, 8, 8, 3)) - times = jnp.array([0.1]) - out_class_0 = model(inputs, times, context=jnp.array([0])) - out_class_1 = model(inputs, times, context=jnp.array([1])) - assert not jnp.allclose(out_class_0, out_class_1) - - -def test_missing_context_with_n_classes_raises(): - model = _make_dit(n_classes=5) - inputs = jnp.ones((2, 8, 8, 3)) - times = jnp.array([0.1, 0.2]) - with pytest.raises(ValueError, match="context"): - model(inputs, times, context=None) - - -def test_unexpected_context_without_n_classes_raises(): - model = _make_dit(n_classes=None) - inputs = jnp.ones((2, 8, 8, 3)) - times = jnp.array([0.1, 0.2]) - with pytest.raises(ValueError, match="context"): - model(inputs, times, context=jnp.array([0, 1])) diff --git a/examples/_common/nn/test_unet.py b/examples/_common/nn/test_unet.py deleted file mode 100644 index 1e2e766..0000000 --- a/examples/_common/nn/test_unet.py +++ /dev/null @@ -1,97 +0,0 @@ -import jax -import jax.numpy as jnp -import pytest -from flax import nnx -from jax import random as jr - -from _common.nn.unet import AttentionBlock, Downsample, ResBlock, UNet, Upsample - - -def test_res_block_changes_channels(): - block = ResBlock(8, 16, 32, rngs=nnx.rnglib.Rngs(jr.key(0))) - inputs = jnp.ones((2, 8, 8, 8)) - embedding = jnp.ones((2, 32)) - out = block(inputs, embedding) - assert out.shape == (2, 8, 8, 16) - - -def test_res_block_same_channels_uses_identity_skip(): - block = ResBlock(8, 8, 32, rngs=nnx.rnglib.Rngs(jr.key(0))) - assert block.skip is None - inputs = jnp.ones((2, 8, 8, 8)) - embedding = jnp.ones((2, 32)) - out = block(inputs, embedding) - assert out.shape == (2, 8, 8, 8) - - -def test_attention_block_preserves_shape(): - block = AttentionBlock(16, n_heads=4, rngs=nnx.rnglib.Rngs(jr.key(0))) - inputs = jnp.ones((2, 4, 4, 16)) - out = block(inputs) - assert out.shape == inputs.shape - - -def test_downsample_halves_spatial_dims(): - block = Downsample(8, rngs=nnx.rnglib.Rngs(jr.key(0))) - inputs = jnp.ones((2, 16, 16, 8)) - out = block(inputs) - assert out.shape == (2, 8, 8, 8) - - -def test_upsample_doubles_spatial_dims(): - block = Upsample(8, rngs=nnx.rnglib.Rngs(jr.key(0))) - inputs = jnp.ones((2, 8, 8, 8)) - out = block(inputs) - assert out.shape == (2, 16, 16, 8) - - -def _make_unet(n_classes=None): - return UNet( - image_size=(32, 32, 3), - n_hidden_channels=32, - channel_mults=(1, 2, 2), - n_res_blocks=2, - attention_resolutions=(16,), - n_embedding_features=64, - n_heads=4, - n_classes=n_classes, - rngs=nnx.rnglib.Rngs(jr.key(0)), - ) - - -def test_unconditional_unet_preserves_input_shape(): - model = _make_unet(n_classes=None) - inputs = jnp.ones((2, 32, 32, 3)) - times = jnp.array([0.1, 0.5]) - out = model(inputs, times, context=None) - assert out.shape == inputs.shape - - -def test_conditional_unet_preserves_input_shape(): - model = _make_unet(n_classes=10) - inputs = jnp.ones((2, 32, 32, 3)) - times = jnp.array([0.1, 0.5]) - context = jnp.array([0, 3]) - out = model(inputs, times, context=context) - assert out.shape == inputs.shape - - -def test_conditional_unet_missing_context_raises(): - model = _make_unet(n_classes=10) - inputs = jnp.ones((2, 32, 32, 3)) - times = jnp.array([0.1, 0.5]) - with pytest.raises(ValueError, match="context"): - model(inputs, times, context=None) - - -def test_unet_gradients_are_nonzero(): - model = _make_unet(n_classes=None) - inputs = jnp.ones((2, 32, 32, 3)) - times = jnp.array([0.1, 0.5]) - - def loss_fn(model): - return jnp.mean(model(inputs, times, context=None) ** 2) - - grads = nnx.grad(loss_fn)(model) - leaves = jax.tree_util.tree_leaves(grads) - assert all(jnp.any(leaf != 0) for leaf in leaves if leaf.size > 0) diff --git a/examples/_common/nn/unet.py b/examples/_common/nn/unet.py deleted file mode 100644 index 6ecaa1a..0000000 --- a/examples/_common/nn/unet.py +++ /dev/null @@ -1,397 +0,0 @@ -import jax -from flax import nnx -from jax import numpy as jnp - -from _common.nn.embedding import timestep_embedding - - -class ResBlock(nnx.Module): - """Residual block with GroupNorm, SiLU, and AdaGN-style conditioning.""" - - def __init__( - self, - in_channels, - out_channels, - n_embedding_features, - *, - dropout_rate=0.0, - rngs, - ): - """Construct a residual block. - - Args: - in_channels: number of input channels - out_channels: number of output channels - n_embedding_features: dimensionality of the conditioning embedding - passed to __call__ - dropout_rate: float - rngs: random keys - """ - self.norm1 = nnx.GroupNorm(in_channels, num_groups=8, rngs=rngs) - self.conv1 = nnx.Conv( - in_channels, out_channels, (3, 3), padding="SAME", rngs=rngs - ) - self.emb_proj = nnx.Linear( - n_embedding_features, out_channels * 2, rngs=rngs - ) - self.norm2 = nnx.GroupNorm(out_channels, num_groups=8, rngs=rngs) - self.dropout = nnx.Dropout(dropout_rate, rngs=rngs) - self.conv2 = nnx.Conv( - out_channels, out_channels, (3, 3), padding="SAME", rngs=rngs - ) - self.skip = ( - None - if in_channels == out_channels - else nnx.Conv(in_channels, out_channels, (1, 1), rngs=rngs) - ) - - def __call__(self, inputs: jax.Array, embedding: jax.Array) -> jax.Array: - """Transform inputs through the residual block. - - Args: - inputs: input array, shape (batch, H, W, in_channels) - embedding: conditioning embedding, shape (batch, n_embedding_features) - - Returns: - returns a jax.Array, shape (batch, H, W, out_channels) - """ - hidden = self.conv1(jax.nn.silu(self.norm1(inputs))) - scale, shift = jnp.split(self.emb_proj(jax.nn.silu(embedding)), 2, axis=-1) - hidden = self.norm2(hidden) * (1 + scale[:, None, None, :]) - hidden = hidden + shift[:, None, None, :] - hidden = jax.nn.silu(hidden) - hidden = self.dropout(hidden) - hidden = self.conv2(hidden) - skip = inputs if self.skip is None else self.skip(inputs) - return skip + hidden - - -class AttentionBlock(nnx.Module): - """Self-attention over spatial positions, with a residual connection.""" - - def __init__(self, channels, n_heads, *, rngs): - """Construct an attention block. - - Args: - channels: number of channels (must be divisible by n_heads) - n_heads: number of attention heads - rngs: random keys - """ - self.norm = nnx.GroupNorm(channels, num_groups=8, rngs=rngs) - self.attn = nnx.MultiHeadAttention( - num_heads=n_heads, in_features=channels, rngs=rngs, decode=False - ) - - def __call__(self, inputs: jax.Array) -> jax.Array: - """Apply self-attention across the H*W spatial positions. - - Args: - inputs: input array, shape (batch, H, W, channels) - - Returns: - returns a jax.Array, same shape as inputs - """ - b, h, w, c = inputs.shape - hidden = self.norm(inputs) - hidden = hidden.reshape(b, h * w, c) - hidden = self.attn(hidden) - hidden = hidden.reshape(b, h, w, c) - return inputs + hidden - - -class Downsample(nnx.Module): - """Halve spatial resolution via a stride-2 convolution.""" - - def __init__(self, channels, *, rngs): - """Construct a downsampling block. - - Args: - channels: number of channels (unchanged by downsampling) - rngs: random keys - """ - self.conv = nnx.Conv( - channels, channels, (3, 3), strides=(2, 2), padding="SAME", rngs=rngs - ) - - def __call__(self, inputs: jax.Array) -> jax.Array: - """Downsample inputs by 2x. - - Args: - inputs: input array, shape (batch, H, W, channels) - - Returns: - returns a jax.Array, shape (batch, H // 2, W // 2, channels) - """ - return self.conv(inputs) - - -class Upsample(nnx.Module): - """Double spatial resolution via nearest-neighbor resize + convolution.""" - - def __init__(self, channels, *, rngs): - """Construct an upsampling block. - - Args: - channels: number of channels (unchanged by upsampling) - rngs: random keys - """ - self.conv = nnx.Conv(channels, channels, (3, 3), padding="SAME", rngs=rngs) - - def __call__(self, inputs: jax.Array) -> jax.Array: - """Upsample inputs by 2x. - - Args: - inputs: input array, shape (batch, H, W, channels) - - Returns: - returns a jax.Array, shape (batch, H * 2, W * 2, channels) - """ - b, h, w, c = inputs.shape - resized = jax.image.resize(inputs, (b, h * 2, w * 2, c), method="nearest") - return self.conv(resized) - - -class UNet(nnx.Module): - """ADM/EDM-style UNet: ResBlocks, attention, skip connections.""" - - def __init__( # noqa: PLR0913 - self, - image_size, - n_hidden_channels, - channel_mults=(1, 2, 2, 2), - n_res_blocks=2, - attention_resolutions=(16,), - n_embedding_features=256, - dropout_rate=0.0, - n_heads=4, - n_classes=None, - *, - rngs, - ): - """Construct a UNet. - - Args: - image_size: size of the image, e.g., (32, 32, 3) - n_hidden_channels: base number of hidden channels; each resolution - level uses n_hidden_channels * channel_mults[level] - channel_mults: per-level channel multiplier, one entry per - resolution level (levels after the first are downsampled by 2x) - n_res_blocks: number of ResBlocks per resolution level - attention_resolutions: spatial resolutions (H == W) at which to - insert an AttentionBlock after each ResBlock - n_embedding_features: dimensionality of the time/class embedding - dropout_rate: float - n_heads: number of attention heads - n_classes: number of classes to condition on, or None for an - unconditional model. Same contract as DiT: __call__ requires - context (integer class labels) iff n_classes is set. - rngs: random keys - """ - self.image_size = image_size - self.n_in_channels = image_size[-1] - self.n_embedding_features = n_embedding_features - self.n_classes = n_classes - if n_classes is not None: - self.class_embedding = nnx.Embed( - n_classes, n_embedding_features, rngs=rngs - ) - self.time_embedding = nnx.Sequential( - nnx.Linear(n_embedding_features, n_embedding_features, rngs=rngs), - nnx.swish, - nnx.Linear(n_embedding_features, n_embedding_features, rngs=rngs), - nnx.swish, - ) - self.in_conv = nnx.Conv( - self.n_in_channels, n_hidden_channels, (3, 3), padding="SAME", rngs=rngs - ) - - down_res_blocks = [] - down_attn_blocks = [] - downsamples = [] - channels = n_hidden_channels - resolution = image_size[0] - skip_channels = [channels] - for level, mult in enumerate(channel_mults): - out_channels = n_hidden_channels * mult - level_res_blocks = [] - level_attn_blocks = [] - for _ in range(n_res_blocks): - level_res_blocks.append( - ResBlock( - channels, - out_channels, - n_embedding_features, - dropout_rate=dropout_rate, - rngs=rngs, - ) - ) - channels = out_channels - if resolution in attention_resolutions: - level_attn_blocks.append(AttentionBlock(channels, n_heads, rngs=rngs)) - else: - level_attn_blocks.append(None) - skip_channels.append(channels) - down_res_blocks.append(tuple(level_res_blocks)) - down_attn_blocks.append(tuple(level_attn_blocks)) - if level < len(channel_mults) - 1: - downsamples.append(Downsample(channels, rngs=rngs)) - resolution //= 2 - skip_channels.append(channels) - else: - downsamples.append(None) - self.down_res_blocks = tuple(down_res_blocks) - self.down_attn_blocks = tuple(down_attn_blocks) - self.downsamples = tuple(downsamples) - - self.mid_res_block1 = ResBlock( - channels, - channels, - n_embedding_features, - dropout_rate=dropout_rate, - rngs=rngs, - ) - self.mid_attn = AttentionBlock(channels, n_heads, rngs=rngs) - self.mid_res_block2 = ResBlock( - channels, - channels, - n_embedding_features, - dropout_rate=dropout_rate, - rngs=rngs, - ) - - up_res_blocks = [] - up_attn_blocks = [] - upsamples = [] - for level, mult in reversed(list(enumerate(channel_mults))): - out_channels = n_hidden_channels * mult - level_res_blocks = [] - level_attn_blocks = [] - for _ in range(n_res_blocks + 1): - skip_ch = skip_channels.pop() - level_res_blocks.append( - ResBlock( - channels + skip_ch, - out_channels, - n_embedding_features, - dropout_rate=dropout_rate, - rngs=rngs, - ) - ) - channels = out_channels - if resolution in attention_resolutions: - level_attn_blocks.append(AttentionBlock(channels, n_heads, rngs=rngs)) - else: - level_attn_blocks.append(None) - up_res_blocks.append(tuple(level_res_blocks)) - up_attn_blocks.append(tuple(level_attn_blocks)) - if level > 0: - upsamples.append(Upsample(channels, rngs=rngs)) - resolution *= 2 - else: - upsamples.append(None) - self.up_res_blocks = tuple(up_res_blocks) - self.up_attn_blocks = tuple(up_attn_blocks) - self.upsamples = tuple(upsamples) - - self.out_norm = nnx.GroupNorm(channels, num_groups=8, rngs=rngs) - self.out_conv = nnx.Conv( - channels, self.n_in_channels, (3, 3), padding="SAME", rngs=rngs - ) - - def __call__( - self, inputs: jax.Array, times: jax.Array, context: jax.Array = None - ) -> jax.Array: - """Transform inputs through the UNet. - - Args: - inputs: input in image form, shape (batch, H, W, C) - times: one-dimensional array, shape (batch,) - context: integer class labels, shape (batch,), required if this - UNet was constructed with n_classes set; must be None otherwise. - - Returns: - returns a jax.Array, same shape as inputs - - Raises: - ValueError: if context is None but n_classes was set, or if context - is given but n_classes was not set. - """ - if self.n_classes is not None and context is None: - raise ValueError( - "this UNet was constructed with n_classes set, so context " - "(integer class labels) must be provided" - ) - if self.n_classes is None and context is not None: - raise ValueError( - "this UNet was constructed without n_classes, so context must " - "be None -- pass n_classes at construction to condition on it" - ) - - embedding = self.time_embedding( - timestep_embedding(times, self.n_embedding_features) - ) - if context is not None: - embedding = embedding + self.class_embedding(context) - - hidden = self.in_conv(inputs) - skips = [hidden] - for level in range(len(self.down_res_blocks)): - for res_block, attn_block in zip( - self.down_res_blocks[level], self.down_attn_blocks[level] - ): - hidden = res_block(hidden, embedding) - if attn_block is not None: - hidden = attn_block(hidden) - skips.append(hidden) - if self.downsamples[level] is not None: - hidden = self.downsamples[level](hidden) - skips.append(hidden) - - hidden = self.mid_res_block1(hidden, embedding) - hidden = self.mid_attn(hidden) - hidden = self.mid_res_block2(hidden, embedding) - - for level in range(len(self.up_res_blocks)): - for res_block, attn_block in zip( - self.up_res_blocks[level], self.up_attn_blocks[level] - ): - skip = skips.pop() - hidden = res_block(jnp.concatenate([hidden, skip], axis=-1), embedding) - if attn_block is not None: - hidden = attn_block(hidden) - if self.upsamples[level] is not None: - hidden = self.upsamples[level](hidden) - - hidden = jax.nn.silu(self.out_norm(hidden)) - outputs = self.out_conv(hidden) - return outputs - - -def SmallUNet(image_size, **kwargs): - return UNet( - image_size, - n_hidden_channels=64, - channel_mults=(1, 2, 2), - n_res_blocks=2, - **kwargs, - ) - - -def BaseUNet(image_size, **kwargs): - return UNet( - image_size, - n_hidden_channels=128, - channel_mults=(1, 2, 2, 2), - n_res_blocks=2, - **kwargs, - ) - - -def LargeUNet(image_size, **kwargs): - return UNet( - image_size, - n_hidden_channels=192, - channel_mults=(1, 1, 2, 2, 4), - n_res_blocks=3, - **kwargs, - ) diff --git a/examples/_common/parameterizations.py b/examples/_common/parameterizations.py deleted file mode 100644 index a5e086c..0000000 --- a/examples/_common/parameterizations.py +++ /dev/null @@ -1,129 +0,0 @@ -import dataclasses - -import jax -import numpy as np -from jax import numpy as jnp - - -@dataclasses.dataclass -class EDMParameterization: - n_sampling_steps: int = 25 - sigma_min: float = 0.002 - sigma_max: float = 80.0 - rho: float = 7.0 - sigma_data: float = 0.5 - P_mean: float = -1.2 - P_std: float = 1.2 - S_churn: float = 40 - S_min: float = 0.05 - S_max: float = 50 - S_noise: float = 1.003 - - def sigma(self, eps): - return jnp.exp(eps * self.P_std + self.P_mean) - - def loss_weight(self, sigma): - return (jnp.square(sigma) + jnp.square(self.sigma_data)) / jnp.square( - sigma * self.sigma_data - ) - - def skip_scaling(self, sigma): - return self.sigma_data**2 / (sigma**2 + self.sigma_data**2) - - def out_scaling(self, sigma): - return sigma * self.sigma_data / (sigma**2 + self.sigma_data**2) ** 0.5 - - def in_scaling(self, sigma): - return 1 / (sigma**2 + self.sigma_data**2) ** 0.5 - - def noise_conditioning(self, sigma): - return 0.25 * jnp.log(sigma) - - def sampling_sigmas(self, num_steps): - rho_inv = 1 / self.rho - step_idxs = jnp.arange(num_steps, dtype=jnp.float32) - sigmas = ( - self.sigma_max**rho_inv - + step_idxs - / (num_steps - 1) - * (self.sigma_min**rho_inv - self.sigma_max**rho_inv) - ) ** self.rho - return jnp.concatenate([sigmas, jnp.zeros_like(sigmas[:1])]) - - def sigma_hat(self, sigma, num_steps): - gamma = ( - jnp.minimum(self.S_churn / num_steps, 2**0.5 - 1) - if self.S_min <= sigma <= self.S_max - else 0 - ) - return sigma + gamma * sigma - - def denoise( - self, model, inputs: jax.Array, sigma: jax.Array, context: jax.Array | None - ) -> jax.Array: - """Run the EDM denoising forward pass. - - Args: - model: a nnx.Module callable as model(inputs=, context=, times=). - inputs: noised inputs to denoise. - sigma: per-example noise level, shape (batch,). - context: conditioning variable, or None. - - Returns: - the denoised prediction, same shape as `inputs`. - """ - new_shape = (-1,) + tuple(np.ones(inputs.ndim - 1, dtype=np.int32).tolist()) - inputs_t = inputs * self.in_scaling(sigma).reshape(new_shape) - noise_cond = self.noise_conditioning(sigma) - outputs = model(inputs=inputs_t, context=context, times=noise_cond) - skip = inputs * self.skip_scaling(sigma).reshape(new_shape) - outputs = outputs * self.out_scaling(sigma).reshape(new_shape) - return skip + outputs - - -@dataclasses.dataclass -class EDMConfig: - n_sampling_steps: int = 25 - sampler: str = "heun" - parameterization: EDMParameterization = dataclasses.field( - default_factory=EDMParameterization - ) - - -@dataclasses.dataclass -class RFMParameterization: - t_eps: float = 1e-5 - t_max: float = 1.0 - - def sigma(self, eps): - return self.t_eps + (self.t_max - self.t_eps) - - def loss_weight(self, t): - return 1.0 - - def skip_scaling(self, t): - return 0.0 - - def out_scaling(self, t): - return 1.0 - - def in_scaling(self, t): - return 1.0 - - def noise_conditioning(self, t): - return t - - def sampling_sigmas(self, num_steps): - return jnp.linspace(self.t_eps, self.t_max, num_steps) - - def sigma_hat(self, t, num_steps): - return t - - -@dataclasses.dataclass -class RFMConfig: - n_sampling_steps: int = 25 - sampler: str = "euler" - parameterization: RFMParameterization = dataclasses.field( - default_factory=RFMParameterization - ) diff --git a/examples/_common/rfm.py b/examples/_common/rfm.py deleted file mode 100644 index be6eabe..0000000 --- a/examples/_common/rfm.py +++ /dev/null @@ -1,54 +0,0 @@ -import numpy as np -from flax import nnx -from jax import numpy as jnp -from jax import random as jr - -from _common import samplers -from _common.parameterizations import RFMConfig -from blaxbird._src._types import ObjectiveFns - - -def _forward_process(inputs, times, noise): - new_shape = (-1,) + tuple(np.ones(inputs.ndim - 1, dtype=np.int32).tolist()) - times = times.reshape(new_shape) - inputs_t = times * inputs + (1.0 - times) * noise - return inputs_t - - -def rfm(config: RFMConfig = RFMConfig()): - """Construct rectified flow matching functions. - - Args: - config: a FlowMatchingConfig object - - Returns: - returns a tuple consisting of train_step, val_step and sampling functions - """ - parameterization = config.parameterization - - def _loss_fn(model, rng_key, batch): - inputs = batch["inputs"] - time_key, rng_key = jr.split(rng_key) - times = jr.uniform(time_key, shape=(inputs.shape[0],)) - times = ( - times * (parameterization.t_max - parameterization.t_eps) - + parameterization.t_eps - ) - noise_key, rng_key = jr.split(rng_key) - noise = jr.normal(noise_key, inputs.shape) - inputs_t = _forward_process(inputs, times, noise) - vt = model(inputs=inputs_t, times=times, context=batch.get("context")) - ut = inputs - noise - loss = jnp.mean(jnp.square(ut - vt)) - return loss - - def train_step(model, rng_key, batch, **kwargs): - return nnx.value_and_grad(_loss_fn)(model, rng_key, batch) - - def val_step(model, rng_key, batch, **kwargs): - return _loss_fn(model, rng_key, batch) - - sampler = samplers.get_sampler_fn(config.sampler)(config) - return ObjectiveFns( - train_step=train_step, val_step=val_step, sample_fn=sampler - ) diff --git a/examples/_common/samplers.py b/examples/_common/samplers.py deleted file mode 100644 index d38698b..0000000 --- a/examples/_common/samplers.py +++ /dev/null @@ -1,146 +0,0 @@ -from collections.abc import Callable - -import chex -import jax -from flax import nnx -from jax import numpy as jnp -from jax import random as jr - -from _common.parameterizations import EDMConfig, RFMConfig - - -def euler_sample_fn(config: RFMConfig): - """Construct an Euler sampler for flow matching. - - Args: - config: a FlowMatchingConfig object - - Returns: - returns a callable that can be used to sample from a flow matching model - """ - - def sample_fn( - model: nnx.Module, - rng_key: jax.Array, - sample_shape: tuple = (), - *, - context: jax.Array = None, - ) -> jax.Array: - """Sample from a flow matching model. - - Args: - model: a nnx.Module that is used as the learned vector field in flow - matching - rng_key: a jax.random.key object - sample_shape: the shape of the data to be generated, where the first axis - is the batch dimension and the other axes are the feature dimensions - context: a conditioning variable (if used) - - Returns: - returns a sample from the model - """ - if context is not None: - chex.assert_equal(sample_shape[0], len(context)) - dt = 1.0 / config.n_sampling_steps - samples = jr.normal(rng_key, sample_shape) - time_steps = config.parameterization.sampling_sigmas( - config.n_sampling_steps - ) - for times in time_steps: - times = jnp.repeat(times, samples.shape[0]) # noqa: PLW2901 - vt = model(inputs=samples, times=times, context=context) - samples = samples + vt * dt - return samples - - return sample_fn - - -def heun_sample_fn(config: EDMConfig): - """Construct a Heun sampler for denoising score matching. - - Args: - config: a EDMConfig object - - Returns: - returns a callable that can be used to sample from a score matching model - """ - params = config.parameterization - - def sample_fn( - model: nnx.Module, - rng_key: jax.Array, - sample_shape: tuple = (), - *, - context: jax.Array = None, - ) -> jax.Array: - """Sample from a score matching model. - - Args: - model: a nnx.Module that is used as the learned score model in score - matching - rng_key: a jax.random.key object - sample_shape: the shape of the data to be generated, where the first axis - is the batch dimension and the other axes are the feature dimensions - context: a conditioning variable (if used) - - Returns: - returns a sample from the model - """ - if context is not None: - chex.assert_equal(sample_shape[0], len(context)) - n = context.shape[0] - noise_key, rng_key = jr.split(rng_key) - sigmas = params.sampling_sigmas(config.n_sampling_steps) - samples = jr.normal(rng_key, sample_shape) * sigmas[0] - - for i, (sigma, sigma_next) in enumerate(zip(sigmas[:-1], sigmas[1:])): - sample_curr = samples - pred_curr = params.denoise( - model, - inputs=sample_curr, - sigma=jnp.repeat(sigma, n), - context=context, - ) - d_cur = (sample_curr - pred_curr) / sigma - samples = sample_curr + d_cur * (sigma_next - sigma) - # second order correction - if i < config.n_sampling_steps - 1: - pred_next = params.denoise( - model, - inputs=samples, - sigma=jnp.repeat(sigma_next, n), - context=context, - ) - d_prime = (samples - pred_next) / sigma_next - samples = sample_curr + (sigma_next - sigma) * ( - 0.5 * d_cur + 0.5 * d_prime - ) - return samples - - return sample_fn - - -SAMPLERS: dict[str, Callable] = { - "euler": euler_sample_fn, - "heun": heun_sample_fn, -} - - -def get_sampler_fn(name: str) -> Callable: - """Look up a sampler constructor by name. - - Args: - name: one of the registered sampler names, currently "euler" or "heun". - - Returns: - the sampler-constructor callable registered under `name`. Calling it - with a config object returns the actual `sample_fn`. - - Raises: - ValueError: if `name` is not a registered sampler. - """ - if name not in SAMPLERS: - raise ValueError( - f"unknown sampler {name!r}, expected one of {sorted(SAMPLERS)}" - ) - return SAMPLERS[name] diff --git a/examples/_common/test_edm.py b/examples/_common/test_edm.py deleted file mode 100644 index 1898c6c..0000000 --- a/examples/_common/test_edm.py +++ /dev/null @@ -1,38 +0,0 @@ -import jax.numpy as jnp -from flax import nnx -from jax import random as jr - -from _common.edm import edm -from _common.parameterizations import EDMConfig - - -def test_edm_default_config_constructs_without_error(): - train_step, val_step, sample_fn = edm(EDMConfig()) - assert callable(train_step) - assert callable(val_step) - assert callable(sample_fn) - - -class _DummyModel(nnx.Module): - def __init__(self, *, rngs): - self.linear = nnx.Linear(4, 4, rngs=rngs) - - def __call__(self, inputs, context, times): - del context, times - return self.linear(inputs) - - -def test_edm_train_step_runs(): - model = _DummyModel(rngs=nnx.rnglib.Rngs(jr.key(0))) - train_step, _, _ = edm(EDMConfig()) - batch = {"inputs": jnp.ones((2, 4))} - loss, grads = train_step(model, jr.key(1), batch) - assert loss.shape == () - - -def test_edm_returns_objective_fns(): - from blaxbird._src._types import ObjectiveFns - - fns = edm(EDMConfig()) - assert isinstance(fns, ObjectiveFns) - assert fns.sample_fn is fns[2] diff --git a/examples/_common/test_parameterizations.py b/examples/_common/test_parameterizations.py deleted file mode 100644 index c47515d..0000000 --- a/examples/_common/test_parameterizations.py +++ /dev/null @@ -1,21 +0,0 @@ -import jax.numpy as jnp - -from _common.parameterizations import EDMParameterization - - -def test_denoise_composes_skip_and_out_scaling(): - params = EDMParameterization(sigma_data=0.5) - inputs = jnp.ones((2, 4)) - sigma = jnp.array([1.0, 2.0]) - - def fake_model(inputs, context, times): - del context, times - return inputs * 0.0 + 3.0 # constant model output - - out = params.denoise(fake_model, inputs, sigma, context=None) - - new_shape = (-1, 1) - expected_skip = inputs * params.skip_scaling(sigma).reshape(new_shape) - expected_out = 3.0 * params.out_scaling(sigma).reshape(new_shape) - expected = expected_skip + expected_out - assert jnp.allclose(out, expected) diff --git a/examples/_common/test_rfm.py b/examples/_common/test_rfm.py deleted file mode 100644 index b1782e2..0000000 --- a/examples/_common/test_rfm.py +++ /dev/null @@ -1,17 +0,0 @@ -from _common.parameterizations import RFMConfig -from _common.rfm import rfm - - -def test_rfm_default_config_constructs_without_error(): - train_step, val_step, sample_fn = rfm(RFMConfig()) - assert callable(train_step) - assert callable(val_step) - assert callable(sample_fn) - - -def test_rfm_returns_objective_fns(): - from blaxbird._src._types import ObjectiveFns - - fns = rfm(RFMConfig()) - assert isinstance(fns, ObjectiveFns) - assert fns.sample_fn is fns[2] diff --git a/examples/_common/test_samplers.py b/examples/_common/test_samplers.py deleted file mode 100644 index ce35913..0000000 --- a/examples/_common/test_samplers.py +++ /dev/null @@ -1,39 +0,0 @@ -import pytest -from flax import nnx -from jax import random as jr - -from _common import samplers -from _common.parameterizations import EDMConfig - - -def test_get_sampler_fn_returns_euler(): - fn = samplers.get_sampler_fn("euler") - assert fn is samplers.euler_sample_fn - - -def test_get_sampler_fn_returns_heun(): - fn = samplers.get_sampler_fn("heun") - assert fn is samplers.heun_sample_fn - - -def test_get_sampler_fn_unknown_name_raises(): - with pytest.raises(ValueError, match="unknown sampler"): - samplers.get_sampler_fn("not-a-sampler") - - -class _DummyModel(nnx.Module): - def __init__(self, *, rngs): - self.linear = nnx.Linear(4, 4, rngs=rngs) - - def __call__(self, inputs, context, times): - del context, times - return self.linear(inputs) - - -def test_heun_sample_fn_runs(): - model = _DummyModel(rngs=nnx.rnglib.Rngs(jr.key(0))) - config = EDMConfig(n_sampling_steps=3) - sample_fn = samplers.get_sampler_fn("heun")(config) - context = jr.normal(jr.key(1), (2, 4)) - samples = sample_fn(model, jr.key(2), sample_shape=(2, 4), context=context) - assert samples.shape == (2, 4) diff --git a/examples/cifar10_flow_matching/README.md b/examples/cifar10_flow_matching/README.md new file mode 100644 index 0000000..a3c9777 --- /dev/null +++ b/examples/cifar10_flow_matching/README.md @@ -0,0 +1,9 @@ +# CIFAR-10 flow matching + +Class-conditional image generation using rectified flow matching on CIFAR-10. Data-parallel only, the mesh shards on a single `data` axis. + +Run via: + +```shell +python main.py +``` \ No newline at end of file diff --git a/examples/cifar10_flow_matching/main.py b/examples/cifar10_flow_matching/main.py index 98509b1..7b5db9b 100644 --- a/examples/cifar10_flow_matching/main.py +++ b/examples/cifar10_flow_matching/main.py @@ -1,10 +1,8 @@ import argparse import os -import sys - -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) import dataloader +import examples.cifar10_flow_matching.model as model import jax import matplotlib.pyplot as plt import numpy as np @@ -14,18 +12,14 @@ from flax import nnx from jax import random as jr from jax.experimental import mesh_utils +from examples.cifar10_flow_matching.objective import rfm from blaxbird import get_default_checkpointer, train_fn -from _common import rfm -from _common.nn import dit # for getattr(dit, dit_type, ...) below def get_optimizer( model, *, peak_lr=1e-4, n_steps, warmup_steps=1000, grad_clip_norm=1.0 ): - # warmup_cosine_decay_schedule requires decay_steps > warmup_steps (the - # cosine phase runs over decay_steps - warmup_steps); clamp so a short - # n_steps (e.g. a smoke run) doesn't raise ValueError from optax. warmup_steps = min(warmup_steps, n_steps // 2) schedule = optax.warmup_cosine_decay_schedule( init_value=0.0, @@ -114,7 +108,7 @@ def run(n_steps, eval_every_n_steps, n_eval_batches, dit_type, log_to_wandb): jr.key(0), os.path.join(outfolder, "data") ) - model = getattr(dit, dit_type)( + model = getattr(model, dit_type)( image_size=(32, 32, 3), n_classes=10, rngs=nnx.rnglib.Rngs(jr.key(1)) ) objective = rfm() diff --git a/examples/_common/nn/dit.py b/examples/cifar10_flow_matching/model.py similarity index 77% rename from examples/_common/nn/dit.py rename to examples/cifar10_flow_matching/model.py index 063f29a..60d0316 100644 --- a/examples/_common/nn/dit.py +++ b/examples/cifar10_flow_matching/model.py @@ -1,25 +1,88 @@ +from collections.abc import Callable + import jax from einops import rearrange from flax import nnx from jax import numpy as jnp -from _common.nn.embedding import timestep_embedding -from _common.nn.mlp import MLP +def timestep_embedding(timesteps, embedding_dim: int, dtype=jnp.float32): + half = embedding_dim // 2 + freqs = jnp.exp(-jnp.log(10_000) * jnp.arange(0, half) / half) + emb = timesteps.astype(dtype)[:, None] * freqs[None, ...] + emb = jnp.concatenate([jnp.sin(emb), jnp.cos(emb)], axis=1) + return emb + + +class MLP(nnx.Module): + def __init__( + self, + in_features: int, + output_features: tuple[int, ...], + *, + kernel_init: nnx.initializers.Initializer = nnx.initializers.lecun_normal(), + bias_init: nnx.initializers.Initializer = nnx.initializers.zeros_init(), + use_bias: bool = True, + dropout_rate: float | None = None, + activation: Callable[[jax.Array], jax.Array] = jax.nn.silu, + activate_last: bool = False, + rngs: nnx.rnglib.Rngs, + ): + features = [in_features] + list(output_features) + layers = [] + for din, dout in zip(features[:-1], features[1:], strict=True): + layers.append( + nnx.Linear( + in_features=din, + out_features=dout, + kernel_init=kernel_init, + bias_init=bias_init, + use_bias=use_bias, + rngs=rngs, + ) + ) + self.layers = tuple(layers) + self.dropout_rate = dropout_rate + self.activate_last = activate_last + self.activation = activation + if dropout_rate is not None: + self.dropout_layer = nnx.Dropout(dropout_rate, rngs=rngs) + + def __call__(self, inputs: jax.Array): + """Project inputs through the MLP. + + Args: + inputs: jax.Array + + Returns: + jax.Array + """ + num_layers = len(self.layers) -def _modulate(inputs, shift, scale): # noqa: ANN001, ANN202 + out = inputs + for i, layer in enumerate(self.layers): + out = layer(out) + if i < num_layers - 1 or self.activate_last: + if self.dropout_rate is not None: + out = self.dropout_layer(out) + out = self.activation(out) + return out + + +def _modulate(inputs, shift, scale): return inputs * (1.0 + scale[:, None]) + shift[:, None] -def get_sinusoidal_embedding_1d(length, embedding_dim): # noqa: ANN001, ANN202 - emb = timestep_embedding(length.reshape(-1), embedding_dim) - return emb +def _get_sinusoidal_embedding_1d(length, embedding_dim): + return timestep_embedding(length.reshape(-1), embedding_dim) -def sinusoidal_init(shape, dtype): # noqa: ANN001, ANN202 - def get_sinusoidal_embedding_2d(grid, embedding_dim): # noqa: ANN001, ANN202 - emb_h = get_sinusoidal_embedding_1d(grid[0], embedding_dim // 2) - emb_w = get_sinusoidal_embedding_1d(grid[1], embedding_dim // 2) +def _sinusoidal_init(shape, dtype): + del dtype + + def get_sinusoidal_embedding_2d(grid, embedding_dim): + emb_h = _get_sinusoidal_embedding_1d(grid[0], embedding_dim // 2) + emb_w = _get_sinusoidal_embedding_1d(grid[1], embedding_dim // 2) emb = jnp.concatenate([emb_h, emb_w], axis=1) return emb @@ -36,7 +99,7 @@ def get_sinusoidal_embedding_2d(grid, embedding_dim): # noqa: ANN001, ANN202 class OutProjection(nnx.Module): - def __init__( # noqa: PLR0913 + def __init__( self, hidden_size, n_embedding_features, patch_size, out_channels, *, rngs ): super().__init__() @@ -55,7 +118,7 @@ def __call__(self, inputs, context): class DiTBlock(nnx.Module): - def __init__( # noqa: PLR0913 + def __init__( self, hidden_size: int, n_embedding_features: int, @@ -122,7 +185,7 @@ def __call__(self, inputs: jax.Array, context: jax.Array) -> jax.Array: class DiT(nnx.Module): - def __init__( # noqa: PLR0913 + def __init__( self, image_size: tuple[int, int, int], n_hidden_channels: int, @@ -176,7 +239,7 @@ def __init__( # noqa: PLR0913 rngs=rngs, ) self.patch_embedding = nnx.Param( - sinusoidal_init( + _sinusoidal_init( ( 1, image_size[0] // patch_size, @@ -229,7 +292,10 @@ def _embed(self, inputs): return inputs + jax.lax.stop_gradient(self.patch_embedding.value) def __call__( - self, inputs: jax.Array, times: jax.Array, context: jax.Array = None + self, + inputs: jax.Array, + times: jax.Array, + context: jax.Array | None = None, ): """Transform inputs through the DiT. diff --git a/examples/cifar10_flow_matching/objective.py b/examples/cifar10_flow_matching/objective.py new file mode 100644 index 0000000..d89bd15 --- /dev/null +++ b/examples/cifar10_flow_matching/objective.py @@ -0,0 +1,117 @@ +"""Rectified flow matching training objective for the CIFAR-10 DiT.""" + +import dataclasses + +import chex +import numpy as np +from typing import NamedTuple, Callable + +from flax import nnx +from jax import numpy as jnp +from jax import random as jr + +__all__ = [ + "RFMParameterization", + "RFMConfig", + "rfm" +] + +class ObjectiveFns(NamedTuple): + """Functions returned by an objective factory (e.g. edm(), rfm()). + + Attributes: + train_step: gradient-step function with signature + (model, rng_key, batch, **kwargs) -> (loss, grads). + val_step: validation function with signature + (model, rng_key, batch, **kwargs) -> loss. + sample_fn: sampling function with signature + (model, rng_key, sample_shape, *, context=None) -> samples. + """ + + train_step: Callable + val_step: Callable + sample_fn: Callable + + +@dataclasses.dataclass +class RFMParameterization: + t_eps: float = 1e-5 + t_max: float = 1.0 + + def sampling_sigmas(self, num_steps): + return jnp.linspace(self.t_eps, self.t_max, num_steps) + + +@dataclasses.dataclass +class RFMConfig: + n_sampling_steps: int = 25 + parameterization: RFMParameterization = dataclasses.field( + default_factory=RFMParameterization + ) + + +def _forward_process(inputs, times, noise): + new_shape = (-1,) + tuple(np.ones(inputs.ndim - 1, dtype=np.int32).tolist()) + times = times.reshape(new_shape) + inputs_t = times * inputs + (1.0 - times) * noise + return inputs_t + + +def _euler_sample_fn(config: RFMConfig): + def sample_fn(model, rng_key, sample_shape=(), *, context=None): + if context is not None: + chex.assert_equal(sample_shape[0], len(context)) + dt = 1.0 / config.n_sampling_steps + samples = jr.normal(rng_key, sample_shape) + time_steps = config.parameterization.sampling_sigmas( + config.n_sampling_steps + ) + for times in time_steps: + times = jnp.repeat(times, samples.shape[0]) # noqa: PLW2901 + vt = model(inputs=samples, times=times, context=context) + samples = samples + vt * dt + return samples + + return sample_fn + + +def rfm(config: RFMConfig = RFMConfig()) -> ObjectiveFns: + """Construct rectified flow matching train/val/sample functions. + + Args: + config: an RFMConfig object + + Returns: + an ObjectiveFns with train_step, val_step and an Euler sample_fn + """ + parameterization = config.parameterization + + def _loss_fn(model, rng_key, batch): + inputs = batch["inputs"] + time_key, rng_key = jr.split(rng_key) + times = jr.uniform(time_key, shape=(inputs.shape[0],)) + times = ( + times * (parameterization.t_max - parameterization.t_eps) + + parameterization.t_eps + ) + noise_key, rng_key = jr.split(rng_key) + noise = jr.normal(noise_key, inputs.shape) + inputs_t = _forward_process(inputs, times, noise) + vt = model(inputs=inputs_t, times=times, context=batch.get("context")) + ut = inputs - noise + loss = jnp.mean(jnp.square(ut - vt)) + return loss + + def train_step(model, rng_key, batch, **kwargs): + del kwargs + return nnx.value_and_grad(_loss_fn)(model, rng_key, batch) + + def val_step(model, rng_key, batch, **kwargs): + del kwargs + return _loss_fn(model, rng_key, batch) + + return ObjectiveFns( + train_step=train_step, + val_step=val_step, + sample_fn=_euler_sample_fn(config), + ) diff --git a/examples/fsdp_tp_demo/README.md b/examples/fsdp_tp_demo/README.md deleted file mode 100644 index 91809d7..0000000 --- a/examples/fsdp_tp_demo/README.md +++ /dev/null @@ -1,16 +0,0 @@ -# FSDP + TP sharding demo - -Demonstrates real 2D-mesh FSDP+tensor-parallel sharding via -`flax.nnx.with_partitioning` + `nnx.get_named_sharding`, wired through -`blaxbird.train_fn`'s `mesh=`/`data_partition_spec=` parameters. - -`ShardedMLP` (`model.py`) annotates its up-projection kernel with -`("fsdp", "tp")` and its down-projection kernel with `("tp", "fsdp")` -- -standard Megatron-style column-parallel-then-row-parallel sharding, -combined with FSDP on the same two axes. - -No real multi-GPU/TPU hardware needed to see actual multi-device -sharding: run with -`XLA_FLAGS="--xla_force_host_platform_device_count=4" python main.py` to -simulate 4 CPU devices arranged as a (2, 2) fsdp x tp mesh. Without that -env var this still runs (single device, degenerate/unsharded). diff --git a/examples/fsdp_tp_demo/main.py b/examples/fsdp_tp_demo/main.py deleted file mode 100644 index 48f18d4..0000000 --- a/examples/fsdp_tp_demo/main.py +++ /dev/null @@ -1,62 +0,0 @@ -"""Train ShardedMLP on random data under a 2D FSDP+TP mesh. - -Run with XLA_FLAGS="--xla_force_host_platform_device_count=4" to see real -sharding across 4 simulated CPU devices; runs (unsharded, degenerate) -without it too. -""" - -import itertools - -import jax -import optax -from flax import nnx -from jax import numpy as jnp -from jax import random as jr -from jax.experimental import mesh_utils - -from blaxbird import train_fn -from model import ShardedMLP - - -def dummy_step(model, rng_key, batch, **kwargs): - del rng_key, kwargs - - def loss_fn(model): - return jnp.mean((model(batch["x"]) - batch["y"]) ** 2) - - return nnx.value_and_grad(loss_fn)(model) - - -def dummy_val(model, rng_key, batch, **kwargs): - del rng_key, kwargs - return jnp.mean((model(batch["x"]) - batch["y"]) ** 2) - - -def run(n_steps: int) -> None: - n_devices = jax.local_device_count() - fsdp = 2 if n_devices >= 4 else 1 - tp = n_devices // fsdp - mesh = jax.sharding.Mesh( - mesh_utils.create_device_mesh((fsdp, tp)), ("fsdp", "tp") - ) - with mesh: - model = ShardedMLP(64, 256, rngs=nnx.rnglib.Rngs(jr.key(0))) - optimizer = nnx.Optimizer(model, tx=optax.sgd(1e-2)) - - batch = {"x": jnp.ones((16, 64)), "y": jnp.zeros((16, 64))} - itr = itertools.cycle([batch]) - - train = train_fn( - fns=(dummy_step, dummy_val), - n_steps=n_steps, - eval_every_n_steps=max(1, n_steps // 2), - n_eval_batches=1, - mesh=mesh, - data_partition_spec=jax.sharding.PartitionSpec("fsdp"), - ) - train(jr.key(1), optimizer, itr, itr) - print("done. up.kernel sharding:", optimizer.model.up.kernel.value.sharding) - - -if __name__ == "__main__": - run(n_steps=4) diff --git a/examples/fsdp_tp_demo/model.py b/examples/fsdp_tp_demo/model.py deleted file mode 100644 index 8c1a715..0000000 --- a/examples/fsdp_tp_demo/model.py +++ /dev/null @@ -1,51 +0,0 @@ -"""A tiny MLP with explicit FSDP+TP sharding annotations, demonstrating -flax.nnx's with_partitioning / get_named_sharding mechanism on a 2D -device mesh. Verified live (see the plan doc) with a simulated 4-device -mesh reshaped to (2, 2): the up-projection kernel shards ("fsdp", "tp"), -the down-projection kernel shards ("tp", "fsdp") -- standard -Megatron-style column-parallel-then-row-parallel MLP sharding, combined -with FSDP on the same two axes. -""" - -import jax -from flax import nnx - - -class ShardedMLP(nnx.Module): - """Two-layer MLP with FSDP+TP-annotated kernels.""" - - def __init__(self, d_model, d_ff, *, rngs): - """Construct a sharded MLP. - - Args: - d_model: input/output dimensionality - d_ff: hidden (expansion) dimensionality - rngs: random keys - """ - self.up = nnx.Linear( - d_model, - d_ff, - rngs=rngs, - kernel_init=nnx.with_partitioning( - nnx.initializers.lecun_normal(), ("fsdp", "tp") - ), - ) - self.down = nnx.Linear( - d_ff, - d_model, - rngs=rngs, - kernel_init=nnx.with_partitioning( - nnx.initializers.lecun_normal(), ("tp", "fsdp") - ), - ) - - def __call__(self, x: jax.Array) -> jax.Array: - """Apply the MLP. - - Args: - x: input array, shape (batch, d_model) - - Returns: - jax.Array, shape (batch, d_model) - """ - return self.down(jax.nn.relu(self.up(x))) diff --git a/examples/fsdp_tp_demo/test_model.py b/examples/fsdp_tp_demo/test_model.py deleted file mode 100644 index 479f188..0000000 --- a/examples/fsdp_tp_demo/test_model.py +++ /dev/null @@ -1,41 +0,0 @@ -import jax -import pytest -from flax import nnx -from jax import random as jr -from jax.experimental import mesh_utils - -from model import ShardedMLP - - -@pytest.mark.skipif( - jax.local_device_count() < 4, - reason="needs XLA_FLAGS=--xla_force_host_platform_device_count=4", -) -def test_sharded_mlp_splits_across_2d_mesh(): - mesh = jax.sharding.Mesh( - mesh_utils.create_device_mesh((2, 2)), ("fsdp", "tp") - ) - with mesh: - model = ShardedMLP(8, 32, rngs=nnx.rnglib.Rngs(jr.key(0))) - graphdef, state = nnx.split(model) - sharding = nnx.get_named_sharding(state, mesh) - state = jax.device_put(state, sharding) - nnx.update(model, state) - - up_kernel = model.up.kernel.value - assert up_kernel.shape == (8, 32) - assert up_kernel.addressable_shards[0].data.shape == (4, 16) - - down_kernel = model.down.kernel.value - assert down_kernel.shape == (32, 8) - assert down_kernel.addressable_shards[0].data.shape == (16, 4) - - -def test_sharded_mlp_forward_pass_shape(): - """Runs on any device count -- proves the model works, not that it's - actually sharded (see the skipif test above for that).""" - import jax.numpy as jnp - - model = ShardedMLP(8, 32, rngs=nnx.rnglib.Rngs(jr.key(0))) - out = model(jnp.ones((4, 8))) - assert out.shape == (4, 8) diff --git a/examples/llm_reference/README.md b/examples/llm_reference/README.md deleted file mode 100644 index bf3d7f3..0000000 --- a/examples/llm_reference/README.md +++ /dev/null @@ -1,42 +0,0 @@ -# LLM reference suite - -Three decoder-only transformer reference implementations, each -demonstrating a genuinely different attention/FFN mechanism and each -real-sharded (not just API-compatible) via `blaxbird.train_fn`'s mesh -API: - -- **`GemmaDense`** (`gemma.py`) — Gemma-4-style: GQA + RoPE + - interleaved local/global attention + dense GeGLU FFN. FSDP+TP (2D - mesh). -- **`DeepSeekMLA`** (`deepseek.py`) — DeepSeek-V2-style: Multi-head - Latent Attention (low-rank KV compression + decoupled RoPE) + dense - GeGLU FFN, full causal attention only. FSDP+TP (2D mesh). -- **`MixtralSMoE`** (`mixtral.py`) — Mixtral-style: GQA + RoPE + full - causal attention + real sparse top-2-of-8 expert routing via - capacity-based dispatch/combine (not dense-compute-then-select). - FSDP+TP+Expert (3D mesh) — the dispatch/combine einsum formulation - lets JAX's SPMD partitioner insert the cross-device communication - automatically when the expert axis is sharded, no hand-written - `jax.lax.all_to_all`. - -This is reference code, not a trained model: `main.py` runs a handful of -training steps on random token ids to prove each architecture, the -shared `causal_lm` training objective, and the shared (full-prefix- -recompute) generation loop all compose correctly under -`blaxbird.train_fn` -- none of them learn anything meaningful, since -there's no tokenizer or real text dataset wired up. - -Not included (out of scope for this reference): a tokenizer, a real text -dataset/dataloader, loading real Gemma-4/DeepSeek-V2/Mixtral weights, -and a production KV-cache for any of the three (including DeepSeekMLA, -where a real cache would normally be the point of the architecture -- -the generation loop recomputes the full prefix every step for all three -model families). - -Run: `uv run --active python main.py` (single device, degenerate -sharding) or -`XLA_FLAGS="--xla_force_host_platform_device_count=8" uv run --active python main.py` -(real sharding: Gemma/DeepSeek on a simulated `(2,4)` fsdp+tp mesh, -Mixtral on the full simulated `(2,2,2)` fsdp+tp+expert mesh). Loss is -logged via `absl.logging` at INFO level (not raised by default here); -the printed generated-token-id line is the visible completion signal. diff --git a/examples/llm_reference/deepseek.py b/examples/llm_reference/deepseek.py deleted file mode 100644 index 647a191..0000000 --- a/examples/llm_reference/deepseek.py +++ /dev/null @@ -1,260 +0,0 @@ -"""DeepSeek-V2-style Multi-head Latent Attention (MLA): K/V are jointly -compressed into a low-rank latent (much smaller than uncompressed KV -width) and decompressed per-head at attention time. RoPE cannot be -applied to the compressed latent directly -- rotating then compressing -is not equivalent to compressing then rotating, which breaks RoPE's -relative-position dot-product identity -- so positional information is -carried by a separate, small "decoupled RoPE" projection computed -directly from the uncompressed input and concatenated onto the -compressed "content" (no-positional-encoding, "nope") part before the -attention dot product. Verified live (see the plan doc): the decoupled -split produces a numerically different, causally-correct result; -skipping it (naively RoPE-rotating the shared latent) diverges and is -wrong. Value vectors carry no positional component and are sized -head_dim_nope only, not head_dim_nope + head_dim_rope. - -This module does not implement the KV-cache memory savings MLA exists -to provide in production -- this repo's generate() is -full-prefix-recompute for every model in this suite (see the design -doc's Out of Scope section). MLA is architecturally faithful here, but -nothing exploits its smaller cache. -""" - -import jax -from flax import nnx -from jax import numpy as jnp - -from layers import GeGLU, RMSNorm, apply_rope, make_causal_mask, rope_freqs, tp_linear - - -class MLAAttention(nnx.Module): - """Multi-head Latent Attention with decoupled RoPE.""" - - def __init__( - self, d_model, n_heads, d_latent, head_dim_nope, head_dim_rope, *, rngs - ): - """Construct an MLA attention block. - - Args: - d_model: model (residual stream) dimensionality. - n_heads: number of attention heads (MLA has no separate kv-head - count -- all heads share one compressed KV latent, which is the - whole point of the compression, replacing GQA's coarser - kv-head-sharing with a much more aggressive shared latent). - d_latent: compressed latent dimensionality, shared across all - heads. Should be substantially smaller than - n_heads * head_dim_nope (the real DeepSeek-V2 point) -- guidance: - roughly (n_heads * head_dim_nope) / 4. - head_dim_nope: per-head "content" (no positional encoding) - dimensionality, decompressed from the shared latent. - head_dim_rope: per-head decoupled-RoPE dimensionality, computed - directly from the uncompressed input, not from the latent. - rngs: random keys. - """ - self.n_heads = n_heads - self.head_dim_nope = head_dim_nope - self.head_dim_rope = head_dim_rope - - # KV path: compress (replicated, small bottleneck) -> norm -> - # decompress per-head (TP-sharded by head). - self.down_kv = nnx.Linear(d_model, d_latent, use_bias=False, rngs=rngs) - self.norm_kv = RMSNorm(d_latent, rngs=rngs) - self.up_k = tp_linear( - d_latent, n_heads * head_dim_nope, ("fsdp", "tp"), rngs=rngs - ) - self.up_v = tp_linear( - d_latent, n_heads * head_dim_nope, ("fsdp", "tp"), rngs=rngs - ) - self.rope_k = tp_linear( - d_model, n_heads * head_dim_rope, ("fsdp", "tp"), rngs=rngs - ) - - # Query path: same compress/decompress + decoupled-RoPE split. - self.down_q = nnx.Linear(d_model, d_latent, use_bias=False, rngs=rngs) - self.norm_q = RMSNorm(d_latent, rngs=rngs) - self.up_q = tp_linear( - d_latent, n_heads * head_dim_nope, ("fsdp", "tp"), rngs=rngs - ) - self.rope_q = tp_linear( - d_model, n_heads * head_dim_rope, ("fsdp", "tp"), rngs=rngs - ) - - # Output projection: value vectors carry head_dim_nope only (no - # positional component), so this is sized from n_heads * head_dim_nope. - self.o_proj = tp_linear( - n_heads * head_dim_nope, d_model, ("tp", "fsdp"), rngs=rngs - ) - # nnx.Variable wrap required -- see the identical note on - # GQAAttention.inv_freq in layers.py (Task 1): bare jax.Array module - # attributes are rejected by nnx.split/nnx.grad's graph flattening, - # and a plain nnx.Variable (not nnx.Param) is correctly excluded - # from the gradient pytree entirely. - self.inv_freq = nnx.Variable(rope_freqs(head_dim_rope)) - - def __call__( - self, x: jax.Array, positions: jax.Array, mask: jax.Array - ) -> jax.Array: - """Apply multi-head latent attention. - - Args: - x: input array, shape (batch, seq, d_model). - positions: integer position ids, shape (batch, seq). - mask: bool attention mask, shape (seq, seq), True = attend. MLA is - always used with full causal masking in this suite (no local - windowing -- that's Gemma-specific). - - Returns: - jax.Array, same shape as x. - """ - b, s, _ = x.shape - - latent_kv = self.norm_kv(self.down_kv(x)) - k_nope = self.up_k(latent_kv).reshape(b, s, self.n_heads, self.head_dim_nope) - v = self.up_v(latent_kv).reshape(b, s, self.n_heads, self.head_dim_nope) - - k_rope = self.rope_k(x).reshape(b, s, self.n_heads, self.head_dim_rope) - k_rope = apply_rope(k_rope, positions, self.inv_freq.value) - k = jnp.concatenate([k_nope, k_rope], axis=-1) - - latent_q = self.norm_q(self.down_q(x)) - q_nope = self.up_q(latent_q).reshape(b, s, self.n_heads, self.head_dim_nope) - q_rope = self.rope_q(x).reshape(b, s, self.n_heads, self.head_dim_rope) - q_rope = apply_rope(q_rope, positions, self.inv_freq.value) - q = jnp.concatenate([q_nope, q_rope], axis=-1) - - head_dim = self.head_dim_nope + self.head_dim_rope - q = jnp.transpose(q, (0, 2, 1, 3)) - k = jnp.transpose(k, (0, 2, 1, 3)) - v = jnp.transpose(v, (0, 2, 1, 3)) - scores = jnp.einsum("bhqd,bhkd->bhqk", q, k) / jnp.sqrt(head_dim) - scores = jnp.where(mask[None, None, :, :], scores, -jnp.inf) - weights = jax.nn.softmax(scores, axis=-1) - out = jnp.einsum("bhqk,bhkd->bhqd", weights, v) - out = jnp.transpose(out, (0, 2, 1, 3)).reshape( - b, s, self.n_heads * self.head_dim_nope - ) - return self.o_proj(out) - - -class DeepSeekTransformerBlock(nnx.Module): - """Pre-norm transformer block: MLA attention + dense GeGLU FFN.""" - - def __init__(self, d_model, n_heads, d_latent, head_dim_nope, head_dim_rope, d_ff, *, rngs): - """Construct a DeepSeek transformer block. - - Args: - d_model: model (residual stream) dimensionality. - n_heads: number of attention heads. - d_latent: compressed KV/Q latent dimensionality. - head_dim_nope: per-head content dimensionality. - head_dim_rope: per-head decoupled-RoPE dimensionality. - d_ff: feed-forward hidden dimensionality. - rngs: random keys. - """ - self.attn_norm = RMSNorm(d_model, rngs=rngs) - self.attn = MLAAttention( - d_model, n_heads, d_latent, head_dim_nope, head_dim_rope, rngs=rngs - ) - self.ffn_norm = RMSNorm(d_model, rngs=rngs) - self.ffn = GeGLU(d_model, d_ff, rngs=rngs) - - def __call__( - self, x: jax.Array, positions: jax.Array, mask: jax.Array - ) -> jax.Array: - """Apply the block. - - Args: - x: input array, shape (batch, seq, d_model). - positions: integer position ids, shape (batch, seq). - mask: bool attention mask, shape (seq, seq). - - Returns: - jax.Array, same shape as x. - """ - x = x + self.attn(self.attn_norm(x), positions, mask) - x = x + self.ffn(self.ffn_norm(x)) - return x - - -class DeepSeekLLM(nnx.Module): - """Decoder-only transformer using DeepSeek-V2-style Multi-head Latent - Attention. Full causal attention only (no local/global interleaving -- - that's a Gemma-specific trait, not part of MLA). Dense FFN only (no - MoE -- that's MixtralSMoE's role in this suite).""" - - def __init__( # noqa: PLR0913 - self, - vocab_size, - d_model, - n_layers, - n_heads, - d_latent, - head_dim_nope, - head_dim_rope, - d_ff, - *, - rngs, - ): - """Construct a DeepSeekLLM. - - Args: - vocab_size: token vocabulary size. - d_model: model (residual stream) dimensionality. - n_layers: number of transformer blocks. - n_heads: number of attention heads. - d_latent: compressed KV/Q latent dimensionality. - head_dim_nope: per-head content dimensionality. - head_dim_rope: per-head decoupled-RoPE dimensionality. - d_ff: feed-forward hidden dimensionality. - rngs: random keys. - """ - self.embed = nnx.Embed( - vocab_size, - d_model, - embedding_init=nnx.with_partitioning( - nnx.initializers.normal(), ("fsdp", None) - ), - rngs=rngs, - ) - self.blocks = tuple( - DeepSeekTransformerBlock( - d_model, n_heads, d_latent, head_dim_nope, head_dim_rope, d_ff, rngs=rngs - ) - for _ in range(n_layers) - ) - self.final_norm = RMSNorm(d_model, rngs=rngs) - self.lm_head = nnx.Linear( - d_model, - vocab_size, - use_bias=False, - kernel_init=nnx.with_partitioning( - nnx.initializers.lecun_normal(), ("fsdp", None) - ), - rngs=rngs, - ) - - def __call__( - self, token_ids: jax.Array, positions: jax.Array - ) -> tuple[jax.Array, jax.Array]: - """Compute next-token logits for a batch of token sequences. - - Args: - token_ids: integer token ids, shape (batch, seq). - positions: integer position ids, shape (batch, seq). - - Returns: - a tuple (logits, aux_loss): logits has shape - (batch, seq, vocab_size); aux_loss is always jnp.array(0.0) (dense - model, no MoE). - """ - mask = make_causal_mask(token_ids.shape[1]) - hidden = self.embed(token_ids) - for block in self.blocks: - hidden = block(hidden, positions, mask) - hidden = self.final_norm(hidden) - logits = self.lm_head(hidden) - return logits, jnp.array(0.0) - - -def DeepSeekMLA(vocab_size, **kwargs): - return DeepSeekLLM(vocab_size, **kwargs) diff --git a/examples/llm_reference/gemma.py b/examples/llm_reference/gemma.py deleted file mode 100644 index 8d0ef12..0000000 --- a/examples/llm_reference/gemma.py +++ /dev/null @@ -1,143 +0,0 @@ -"""Gemma-4-style decoder-only transformer: GQA + RoPE + interleaved -local/global attention + dense GeGLU FFN, TP+FSDP-sharded. -""" - -import jax -from flax import nnx -from jax import numpy as jnp - -from layers import GQAAttention, GeGLU, RMSNorm, make_causal_mask - - -class GemmaTransformerBlock(nnx.Module): - """Pre-norm transformer block: GQA attention + dense GeGLU FFN.""" - - def __init__(self, d_model, n_heads, n_kv_heads, head_dim, d_ff, *, rngs): - """Construct a Gemma transformer block. - - Args: - d_model: model (residual stream) dimensionality. - n_heads: number of query heads. - n_kv_heads: number of key/value heads. - head_dim: dimensionality of each attention head. - d_ff: feed-forward hidden dimensionality. - rngs: random keys. - """ - self.attn_norm = RMSNorm(d_model, rngs=rngs) - self.attn = GQAAttention(d_model, n_heads, n_kv_heads, head_dim, rngs=rngs) - self.ffn_norm = RMSNorm(d_model, rngs=rngs) - self.ffn = GeGLU(d_model, d_ff, rngs=rngs) - - def __call__( - self, x: jax.Array, positions: jax.Array, mask: jax.Array - ) -> jax.Array: - """Apply the block. - - Args: - x: input array, shape (batch, seq, d_model). - positions: integer position ids, shape (batch, seq). - mask: bool attention mask, shape (seq, seq). - - Returns: - jax.Array, same shape as x. - """ - x = x + self.attn(self.attn_norm(x), positions, mask) - x = x + self.ffn(self.ffn_norm(x)) - return x - - -class GemmaLLM(nnx.Module): - """Decoder-only transformer in the Gemma-4 architectural family. - - Interleaves "local" (sliding-window) and "global" (full causal) - attention layers -- every `global_every`-th layer is global, the rest - are local, matching Gemma 2/3/4's actual design choice. Dense FFN - only (no MoE -- that's MixtralSMoE's role in this suite). - """ - - def __init__( # noqa: PLR0913 - self, - vocab_size, - d_model, - n_layers, - n_heads, - n_kv_heads, - head_dim, - d_ff, - local_window, - *, - global_every=4, - rngs, - ): - """Construct a GemmaLLM. - - Args: - vocab_size: token vocabulary size. - d_model: model (residual stream) dimensionality. - n_layers: number of transformer blocks. - n_heads: number of query heads. - n_kv_heads: number of key/value heads. - head_dim: dimensionality of each attention head. - d_ff: feed-forward hidden dimensionality. - local_window: sliding-window size for "local" attention layers. - global_every: every global_every-th layer (1-indexed) is a full - causal ("global") attention layer; the rest are local. - rngs: random keys. - """ - self.local_window = local_window - self.global_every = global_every - self.embed = nnx.Embed( - vocab_size, - d_model, - embedding_init=nnx.with_partitioning( - nnx.initializers.normal(), ("fsdp", None) - ), - rngs=rngs, - ) - self.blocks = tuple( - GemmaTransformerBlock(d_model, n_heads, n_kv_heads, head_dim, d_ff, rngs=rngs) - for _ in range(n_layers) - ) - self.final_norm = RMSNorm(d_model, rngs=rngs) - self.lm_head = nnx.Linear( - d_model, - vocab_size, - use_bias=False, - kernel_init=nnx.with_partitioning( - nnx.initializers.lecun_normal(), ("fsdp", None) - ), - rngs=rngs, - ) - - def __call__( - self, token_ids: jax.Array, positions: jax.Array - ) -> tuple[jax.Array, jax.Array]: - """Compute next-token logits for a batch of token sequences. - - Args: - token_ids: integer token ids, shape (batch, seq). - positions: integer position ids, shape (batch, seq). - - Returns: - a tuple (logits, aux_loss): logits has shape - (batch, seq, vocab_size); aux_loss is always jnp.array(0.0) (dense - model, no MoE) -- kept for interface uniformity with MixtralSMoE so - objective.py's causal_lm works unmodified across this suite. - """ - seq_len = token_ids.shape[1] - global_mask = make_causal_mask(seq_len) - local_mask = make_causal_mask(seq_len, window=self.local_window) - - hidden = self.embed(token_ids) - for i, block in enumerate(self.blocks): - is_global = (i + 1) % self.global_every == 0 - mask = global_mask if is_global else local_mask - hidden = block(hidden, positions, mask) - - hidden = self.final_norm(hidden) - logits = self.lm_head(hidden) - return logits, jnp.array(0.0) - - -def GemmaDense(vocab_size, **kwargs): - return GemmaLLM(vocab_size, **kwargs) diff --git a/examples/llm_reference/generate.py b/examples/llm_reference/generate.py deleted file mode 100644 index 171471e..0000000 --- a/examples/llm_reference/generate.py +++ /dev/null @@ -1,54 +0,0 @@ -"""Sampled autoregressive generation, shared across every model family in -this suite. Recomputes the full prefix on every decoding step -rather than threading an explicit KV cache -- O(n^2) in sequence length, -the right tradeoff for reference/test code on tiny sequences, not a -production serving path (applies even to DeepSeekMLA, where a real cache -would normally be the point of the architecture -- see the design doc). -""" - -import jax -import jax.numpy as jnp -from jax import random as jr - - -def generate( - model, - rng_key: jax.Array, - prompt_ids: jax.Array, - max_new_tokens: int, - *, - max_seq_len: int, -) -> jax.Array: - """Autoregressively extend prompt_ids by max_new_tokens tokens. - - Args: - model: any model in this suite (or anything with the same - (token_ids, positions) -> (logits, aux_loss) signature). - rng_key: a jax.random.key object. - prompt_ids: integer token ids, shape (batch, prompt_len). - max_new_tokens: number of tokens to generate. - max_seq_len: total sequence length prompt_ids + generated tokens must - not exceed. - - Returns: - jax.Array, shape (batch, prompt_len + max_new_tokens). - """ - batch, prompt_len = prompt_ids.shape - total_len = prompt_len + max_new_tokens - if total_len > max_seq_len: - raise ValueError( - f"prompt_len + max_new_tokens ({total_len}) exceeds " - f"max_seq_len ({max_seq_len})" - ) - - tokens = prompt_ids - for step in range(max_new_tokens): - seq_len = tokens.shape[1] - positions = jnp.broadcast_to(jnp.arange(seq_len), (batch, seq_len)) - logits, _ = model(tokens, positions) - next_logits = logits[:, -1, :] - step_key = jr.fold_in(rng_key, step) - next_token = jr.categorical(step_key, next_logits, axis=-1) - tokens = jnp.concatenate([tokens, next_token[:, None]], axis=1) - - return tokens diff --git a/examples/llm_reference/layers.py b/examples/llm_reference/layers.py deleted file mode 100644 index 7462c53..0000000 --- a/examples/llm_reference/layers.py +++ /dev/null @@ -1,251 +0,0 @@ -"""Shared primitives for the llm_reference example suite (Gemma-4-style, -DeepSeek-MLA-style, Mixtral-style decoder-only transformers). - -TP-sharded projections use nnx.with_partitioning on their kernel_init so -blaxbird.train_fn's mesh= argument can shard them via -nnx.get_named_sharding -- unannotated parameters (e.g. RMSNorm weights) -default to fully replicated. No mesh is threaded into any __call__: all -sharding here is construction-time weight annotation only, matching this -repo's existing sharding idiom (see examples/fsdp_tp_demo). -""" - -import jax -from flax import nnx -from jax import numpy as jnp - - -def rope_freqs(head_dim: int, theta: float = 10_000.0) -> jax.Array: - """Compute RoPE inverse frequencies. - - Args: - head_dim: dimensionality to rotate (must be even). - theta: RoPE base frequency. - - Returns: - jax.Array, shape (head_dim // 2,). - """ - return 1.0 / (theta ** (jnp.arange(0, head_dim, 2) / head_dim)) - - -def apply_rope( - x: jax.Array, positions: jax.Array, inv_freq: jax.Array -) -> jax.Array: - """Apply rotary position embeddings. - - Args: - x: input array, shape (batch, seq, n_heads, dim) where dim == - 2 * inv_freq.shape[0]. - positions: integer position ids, shape (batch, seq). - inv_freq: RoPE inverse frequencies from rope_freqs. - - Returns: - jax.Array, same shape as x. - """ - freqs = positions[:, :, None] * inv_freq[None, None, :] - cos = jnp.cos(freqs)[:, :, None, :] - sin = jnp.sin(freqs)[:, :, None, :] - x1, x2 = jnp.split(x, 2, axis=-1) - return jnp.concatenate([x1 * cos - x2 * sin, x2 * cos + x1 * sin], axis=-1) - - -def repeat_kv(x: jax.Array, n_rep: int) -> jax.Array: - """Broadcast grouped-query-attention key/value heads to n_heads. - - Args: - x: input array, shape (batch, seq, n_kv_heads, head_dim). - n_rep: number of query heads sharing each kv head - (n_heads // n_kv_heads). - - Returns: - jax.Array, shape (batch, seq, n_kv_heads * n_rep, head_dim). - """ - if n_rep == 1: - return x - b, s, kvh, hd = x.shape - x = jnp.broadcast_to(x[:, :, :, None, :], (b, s, kvh, n_rep, hd)) - return x.reshape(b, s, kvh * n_rep, hd) - - -def make_causal_mask(seq_len: int, window: int | None = None) -> jax.Array: - """Build a causal (optionally sliding-window / "local") attention mask. - - Args: - seq_len: sequence length. - window: if given, restrict attention to the last `window` positions - (Gemma-style "local" attention layers); if None, full causal - ("global") attention -- always the case for DeepSeek and Mixtral in - this suite. - - Returns: - bool jax.Array, shape (seq_len, seq_len), True where attention is - allowed (query position i may attend to key position j). - """ - i = jnp.arange(seq_len)[:, None] - j = jnp.arange(seq_len)[None, :] - causal = j <= i - if window is not None: - causal = causal & (j > i - window) - return causal - - -class RMSNorm(nnx.Module): - """Root-mean-square layer normalization (no mean-centering, no bias).""" - - def __init__(self, dim, *, rngs, eps=1e-6): - """Construct an RMSNorm layer. - - Args: - dim: feature dimensionality. - rngs: random keys (unused -- weight is initialized to ones -- kept - for interface consistency with every other block in this suite). - eps: numerical-stability constant. - """ - del rngs - self.weight = nnx.Param(jnp.ones((dim,))) - self.eps = eps - - def __call__(self, x: jax.Array) -> jax.Array: - """Normalize the last axis of x by its RMS, then scale. - - Args: - x: input array, shape (..., dim). - - Returns: - jax.Array, same shape as x. - """ - var = jnp.mean(jnp.square(x), axis=-1, keepdims=True) - x = x * jax.lax.rsqrt(var + self.eps) - return x * self.weight.value - - -def tp_linear(d_in, d_out, partition_spec, *, rngs, use_bias=False): - """Construct a nnx.Linear whose kernel carries a sharding annotation. - - Args: - d_in: input feature dimensionality. - d_out: output feature dimensionality. - partition_spec: a 2-tuple of mesh-axis names (or None) passed to - nnx.with_partitioning on the kernel initializer -- e.g. - ("fsdp", "tp") for column-parallel, ("tp", "fsdp") for row-parallel. - Resolved to a real per-device shard only when the returned module's - state is passed through nnx.get_named_sharding(state, mesh) inside - blaxbird.train_fn; constructing this module standalone (no mesh) is - unaffected -- same pattern as examples/fsdp_tp_demo's ShardedMLP. - rngs: random keys. - use_bias: whether to include a bias term. Every projection in this - suite uses use_bias=False, matching the real - Gemma/DeepSeek/Mixtral architectures. - - Returns: - a nnx.Linear with a with_partitioning-annotated kernel_init. - """ - return nnx.Linear( - d_in, - d_out, - use_bias=use_bias, - kernel_init=nnx.with_partitioning( - nnx.initializers.lecun_normal(), partition_spec - ), - rngs=rngs, - ) - - -class GQAAttention(nnx.Module): - """Grouped-query attention with RoPE, TP-sharded (column-parallel qkv, - row-parallel output projection -- standard Megatron-style tensor - parallelism).""" - - def __init__(self, d_model, n_heads, n_kv_heads, head_dim, *, rngs): - """Construct a GQA attention block. - - Args: - d_model: model (residual stream) dimensionality. - n_heads: number of query heads. - n_kv_heads: number of key/value heads (n_heads must be a multiple - of n_kv_heads; n_kv_heads == n_heads recovers standard MHA, - n_kv_heads == 1 recovers MQA). - head_dim: dimensionality of each attention head. - rngs: random keys. - """ - self.n_heads = n_heads - self.n_kv_heads = n_kv_heads - self.head_dim = head_dim - self.n_rep = n_heads // n_kv_heads - self.q_proj = tp_linear( - d_model, n_heads * head_dim, ("fsdp", "tp"), rngs=rngs - ) - self.k_proj = tp_linear( - d_model, n_kv_heads * head_dim, ("fsdp", "tp"), rngs=rngs - ) - self.v_proj = tp_linear( - d_model, n_kv_heads * head_dim, ("fsdp", "tp"), rngs=rngs - ) - self.o_proj = tp_linear( - n_heads * head_dim, d_model, ("tp", "fsdp"), rngs=rngs - ) - self.inv_freq = nnx.Variable(rope_freqs(head_dim)) - - def __call__( - self, x: jax.Array, positions: jax.Array, mask: jax.Array - ) -> jax.Array: - """Apply grouped-query self-attention. - - Args: - x: input array, shape (batch, seq, d_model). - positions: integer position ids, shape (batch, seq). - mask: bool attention mask, shape (seq, seq), True = attend, from - make_causal_mask. - - Returns: - jax.Array, same shape as x. - """ - b, s, _ = x.shape - q = self.q_proj(x).reshape(b, s, self.n_heads, self.head_dim) - k = self.k_proj(x).reshape(b, s, self.n_kv_heads, self.head_dim) - v = self.v_proj(x).reshape(b, s, self.n_kv_heads, self.head_dim) - - q = apply_rope(q, positions, self.inv_freq.value) - k = apply_rope(k, positions, self.inv_freq.value) - k = repeat_kv(k, self.n_rep) - v = repeat_kv(v, self.n_rep) - - q = jnp.transpose(q, (0, 2, 1, 3)) - k = jnp.transpose(k, (0, 2, 1, 3)) - v = jnp.transpose(v, (0, 2, 1, 3)) - - scores = jnp.einsum("bhqd,bhkd->bhqk", q, k) / jnp.sqrt(self.head_dim) - scores = jnp.where(mask[None, None, :, :], scores, -jnp.inf) - weights = jax.nn.softmax(scores, axis=-1) - out = jnp.einsum("bhqk,bhkd->bhqd", weights, v) - out = jnp.transpose(out, (0, 2, 1, 3)).reshape( - b, s, self.n_heads * self.head_dim - ) - return self.o_proj(out) - - -class GeGLU(nnx.Module): - """Gated GELU MLP, TP-sharded (column-parallel gate/up, row-parallel - down -- same pattern as GQAAttention's projections).""" - - def __init__(self, d_model, d_ff, *, rngs): - """Construct a GeGLU feed-forward block. - - Args: - d_model: model (residual stream) dimensionality. - d_ff: hidden (expansion) dimensionality. - rngs: random keys. - """ - self.gate = tp_linear(d_model, d_ff, ("fsdp", "tp"), rngs=rngs) - self.up = tp_linear(d_model, d_ff, ("fsdp", "tp"), rngs=rngs) - self.down = tp_linear(d_ff, d_model, ("tp", "fsdp"), rngs=rngs) - - def __call__(self, x: jax.Array) -> jax.Array: - """Apply the GeGLU transform. - - Args: - x: input array, shape (..., d_model). - - Returns: - jax.Array, same shape as x. - """ - return self.down(jax.nn.gelu(self.gate(x)) * self.up(x)) diff --git a/examples/llm_reference/main.py b/examples/llm_reference/main.py deleted file mode 100644 index 5544b12..0000000 --- a/examples/llm_reference/main.py +++ /dev/null @@ -1,181 +0,0 @@ -"""Smoke-test all three llm_reference model families on random data, -each under its own real simulated multi-device mesh. - -No tokenizer, no real dataset, no actual training run beyond a handful -of steps on random tokens -- this proves each architecture, the shared -causal_lm objective, the shared generation loop, and (for the Mixtral -variant especially) real sharded dispatch/combine routing all compose -correctly under blaxbird.train_fn, not that any model learns anything. - -Run with XLA_FLAGS="--xla_force_host_platform_device_count=8" to -exercise real multi-device sharding for all three (Gemma/DeepSeek use a -(2,4) fsdp+tp mesh built from all 8 simulated devices; Mixtral uses the -full (2,2,2) 3D mesh). Runs (unsharded, degenerate) on a single device -too, just without meaningfully exercising the sharding. - -Note: train_fn logs train/val loss via absl.logging.info, which this -script does not raise verbosity for -- the printed generated-token-id -line per model family is the visible completion signal, not a loss line. -""" - -import jax -import optax -from flax import nnx -from jax import numpy as jnp -from jax import random as jr -from jax.experimental import mesh_utils -from jax.sharding import PartitionSpec as P - -from blaxbird import train_fn -from deepseek import DeepSeekMLA -from gemma import GemmaDense -from generate import generate -from mixtral import MixtralSMoE -from objective import causal_lm - - -def _random_batch_iter(vocab_size, batch, seq_len): - key = jr.key(1) - while True: - key, batch_key = jr.split(key) - token_ids = jr.randint(batch_key, (batch, seq_len + 1), 0, vocab_size) - yield {"token_ids": token_ids[:, :-1], "target_ids": token_ids[:, 1:]} - - -def run_gemma(n_steps: int) -> None: - n_devices = jax.local_device_count() - fsdp = 2 if n_devices >= 4 else 1 - tp = n_devices // fsdp - mesh = jax.sharding.Mesh( - mesh_utils.create_device_mesh((fsdp, tp)), ("fsdp", "tp") - ) - vocab_size = 100 - with mesh: - model = GemmaDense( - vocab_size, - d_model=32, - n_layers=4, - n_heads=4, - n_kv_heads=2, - head_dim=8, - d_ff=64, - local_window=4, - global_every=2, - rngs=nnx.rnglib.Rngs(jr.key(0)), - ) - optimizer = nnx.Optimizer(model, tx=optax.adamw(1e-3)) - fns = causal_lm() - train = train_fn( - fns=(fns.train_step, fns.val_step), - n_steps=n_steps, - eval_every_n_steps=max(1, n_steps // 2), - n_eval_batches=1, - mesh=mesh, - data_partition_spec=P("fsdp"), - ) - train( - jr.key(2), - optimizer, - _random_batch_iter(vocab_size, 8, 16), - _random_batch_iter(vocab_size, 8, 16), - ) - prompt_ids = jnp.zeros((1, 4), dtype=jnp.int32) - generated = generate( - optimizer.model, jr.key(3), prompt_ids, max_new_tokens=8, max_seq_len=32 - ) - print(f"gemma: generated token ids {generated.tolist()}") - - -def run_deepseek(n_steps: int) -> None: - n_devices = jax.local_device_count() - fsdp = 2 if n_devices >= 4 else 1 - tp = n_devices // fsdp - mesh = jax.sharding.Mesh( - mesh_utils.create_device_mesh((fsdp, tp)), ("fsdp", "tp") - ) - vocab_size = 100 - with mesh: - model = DeepSeekMLA( - vocab_size, - d_model=32, - n_layers=4, - n_heads=4, - d_latent=8, - head_dim_nope=6, - head_dim_rope=2, - d_ff=64, - rngs=nnx.rnglib.Rngs(jr.key(0)), - ) - optimizer = nnx.Optimizer(model, tx=optax.adamw(1e-3)) - fns = causal_lm() - train = train_fn( - fns=(fns.train_step, fns.val_step), - n_steps=n_steps, - eval_every_n_steps=max(1, n_steps // 2), - n_eval_batches=1, - mesh=mesh, - data_partition_spec=P("fsdp"), - ) - train( - jr.key(2), - optimizer, - _random_batch_iter(vocab_size, 8, 16), - _random_batch_iter(vocab_size, 8, 16), - ) - prompt_ids = jnp.zeros((1, 4), dtype=jnp.int32) - generated = generate( - optimizer.model, jr.key(3), prompt_ids, max_new_tokens=8, max_seq_len=32 - ) - print(f"deepseek: generated token ids {generated.tolist()}") - - -def run_mixtral(n_steps: int) -> None: - n_devices = jax.local_device_count() - if n_devices >= 8: - fsdp, tp, expert = 2, 2, 2 - else: - fsdp, tp, expert = 1, 1, n_devices - mesh = jax.sharding.Mesh( - mesh_utils.create_device_mesh((fsdp, tp, expert)), ("fsdp", "tp", "expert") - ) - vocab_size = 100 - with mesh: - model = MixtralSMoE( - vocab_size, - d_model=32, - n_layers=4, - n_heads=4, - n_kv_heads=2, - head_dim=8, - d_ff=64, - n_experts=4, - n_active=2, - rngs=nnx.rnglib.Rngs(jr.key(0)), - ) - optimizer = nnx.Optimizer(model, tx=optax.adamw(1e-3)) - fns = causal_lm() - train = train_fn( - fns=(fns.train_step, fns.val_step), - n_steps=n_steps, - eval_every_n_steps=max(1, n_steps // 2), - n_eval_batches=1, - mesh=mesh, - data_partition_spec=P("fsdp"), - ) - train( - jr.key(2), - optimizer, - _random_batch_iter(vocab_size, 8, 16), - _random_batch_iter(vocab_size, 8, 16), - ) - prompt_ids = jnp.zeros((1, 4), dtype=jnp.int32) - generated = generate( - optimizer.model, jr.key(3), prompt_ids, max_new_tokens=8, max_seq_len=32 - ) - print(f"mixtral: generated token ids {generated.tolist()}") - - -if __name__ == "__main__": - run_gemma(n_steps=4) - run_deepseek(n_steps=4) - run_mixtral(n_steps=4) diff --git a/examples/llm_reference/mixtral.py b/examples/llm_reference/mixtral.py deleted file mode 100644 index 02097f8..0000000 --- a/examples/llm_reference/mixtral.py +++ /dev/null @@ -1,263 +0,0 @@ -"""Mixtral-style decoder-only transformer: GQA + RoPE + full causal -attention + real sparse top-k expert routing via capacity-based -dispatch/combine einsums (not dense-compute-then-select). - -The dispatch/combine formulation lets JAX's SPMD partitioner (GSPMD) -insert the cross-device communication automatically when the expert -axis is sharded -- no hand-written jax.lax.all_to_all. Verified live -before writing this plan (see plan header): sharding the token axis -along one mesh axis and the expert axis along a DIFFERENT mesh axis -produces output numerically identical to the unsharded computation, with -real collective ops (all-gather, all-reduce) present in the compiled -HLO, and this holds with no explicit with_sharding_constraint calls and -no mesh threaded into __call__ -- plain nnx.with_partitioning weight -annotations are sufficient, matching examples/fsdp_tp_demo's pattern. -""" - -import jax -from flax import nnx -from jax import numpy as jnp - -from layers import GQAAttention, RMSNorm, make_causal_mask - - -class SparseMoEFFN(nnx.Module): - """Mixtral-style top-k-routed mixture-of-experts feed-forward block - with real capacity-based dispatch/combine (not dense-compute-then- - select). Expert weights are stacked into single tensors with a leading - n_experts axis so that axis can be sharded across a mesh's "expert" - axis.""" - - def __init__( - self, d_model, d_ff, n_experts, n_active, *, rngs, capacity_factor=1.25 - ): - """Construct a sparse MoE feed-forward block. - - Args: - d_model: model (residual stream) dimensionality. - d_ff: hidden (expansion) dimensionality of each expert. - n_experts: total number of experts. - n_active: number of experts activated per token (top-k). - rngs: random keys. - capacity_factor: per-expert buffer capacity multiplier. Per-expert - capacity = ceil(capacity_factor * n_active * n_tokens / - n_experts). Standard Switch-Transformer value is 1.25. Tokens - beyond an expert's capacity in a batch are dropped (their - contribution from that slot is zeroed, not misrouted). - """ - self.n_experts = n_experts - self.n_active = n_active - self.capacity_factor = capacity_factor - self.router = nnx.Linear(d_model, n_experts, use_bias=False, rngs=rngs) - - expert_partitioning = nnx.with_partitioning( - nnx.initializers.lecun_normal(), ("expert", None, None) - ) - key = rngs.params() - k1, k2, k3 = jax.random.split(key, 3) - self.gate = nnx.Param( - expert_partitioning(k1, (n_experts, d_model, d_ff)) - ) - self.up = nnx.Param( - expert_partitioning(k2, (n_experts, d_model, d_ff)) - ) - self.down = nnx.Param( - expert_partitioning(k3, (n_experts, d_ff, d_model)) - ) - - def __call__(self, x: jax.Array) -> tuple[jax.Array, jax.Array]: - """Route tokens to the top-k experts via capacity-based dispatch/ - combine and combine their outputs. - - Args: - x: input array, shape (batch, seq, d_model). - - Returns: - a tuple (output, aux_loss): output has the same shape as x; - aux_loss is a scalar Switch-Transformer-style load-balancing loss. - """ - b, s, d = x.shape - flat = x.reshape(b * s, d) - n_tok = flat.shape[0] - logits = self.router(flat) - probs = jax.nn.softmax(logits, axis=-1) - top_probs, top_idx = jax.lax.top_k(probs, self.n_active) - top_probs = top_probs / jnp.sum(top_probs, axis=-1, keepdims=True) - - capacity = int( - jnp.ceil(self.capacity_factor * self.n_active * n_tok / self.n_experts) - ) - - expert_onehot = jax.nn.one_hot(top_idx, self.n_experts) - flat_onehot = expert_onehot.reshape(-1, self.n_experts) - position_in_expert = ( - jnp.cumsum(flat_onehot, axis=0) * flat_onehot - flat_onehot - ) - position_in_expert = jnp.sum(position_in_expert, axis=-1) - within_capacity = position_in_expert < capacity - position_in_expert = position_in_expert.reshape(n_tok, self.n_active) - within_capacity = within_capacity.reshape(n_tok, self.n_active) - - capacity_onehot = jax.nn.one_hot( - position_in_expert.astype(jnp.int32), capacity - ) - dispatch_mask = jnp.sum( - expert_onehot[..., None] - * capacity_onehot[:, :, None, :] - * within_capacity[:, :, None, None], - axis=1, - ) # (n_tok, n_experts, capacity) - combine_weight = jnp.sum( - expert_onehot[..., None] - * capacity_onehot[:, :, None, :] - * within_capacity[:, :, None, None] - * top_probs[:, :, None, None], - axis=1, - ) # (n_tok, n_experts, capacity) - - dispatched = jnp.einsum("td,tec->ecd", flat, dispatch_mask) - g = jnp.einsum("ecd,edf->ecf", dispatched, self.gate.value) - u = jnp.einsum("ecd,edf->ecf", dispatched, self.up.value) - h = jax.nn.silu(g) * u - expert_out = jnp.einsum("ecf,efd->ecd", h, self.down.value) - combined = jnp.einsum("ecd,tec->td", expert_out, combine_weight) - - density = jnp.mean(probs, axis=0) - chosen_mask = jax.nn.one_hot(top_idx, self.n_experts).sum(axis=1) - chosen_frac = jnp.mean(chosen_mask, axis=0) - aux_loss = self.n_experts * jnp.sum(density * chosen_frac) - - return combined.reshape(b, s, d), aux_loss - - -class MixtralTransformerBlock(nnx.Module): - """Pre-norm transformer block: GQA attention (full causal only) + - sparse MoE FFN.""" - - def __init__( # noqa: PLR0913 - self, d_model, n_heads, n_kv_heads, head_dim, d_ff, n_experts, n_active, *, rngs - ): - """Construct a Mixtral transformer block. - - Args: - d_model: model (residual stream) dimensionality. - n_heads: number of query heads. - n_kv_heads: number of key/value heads. - head_dim: dimensionality of each attention head. - d_ff: feed-forward hidden dimensionality of each expert. - n_experts: total experts. - n_active: active experts per token (top-k). - rngs: random keys. - """ - self.attn_norm = RMSNorm(d_model, rngs=rngs) - self.attn = GQAAttention(d_model, n_heads, n_kv_heads, head_dim, rngs=rngs) - self.ffn_norm = RMSNorm(d_model, rngs=rngs) - self.ffn = SparseMoEFFN(d_model, d_ff, n_experts, n_active, rngs=rngs) - - def __call__( - self, x: jax.Array, positions: jax.Array, mask: jax.Array - ) -> tuple[jax.Array, jax.Array]: - """Apply the block. - - Args: - x: input array, shape (batch, seq, d_model). - positions: integer position ids, shape (batch, seq). - mask: bool attention mask, shape (seq, seq). - - Returns: - a tuple (output, aux_loss): output has the same shape as x. - """ - x = x + self.attn(self.attn_norm(x), positions, mask) - ffn_out, aux_loss = self.ffn(self.ffn_norm(x)) - return x + ffn_out, aux_loss - - -class MixtralLLM(nnx.Module): - """Decoder-only transformer with real sparse top-k expert routing. - Full causal attention only (no local/global interleaving -- that's a - Gemma-specific trait).""" - - def __init__( # noqa: PLR0913 - self, - vocab_size, - d_model, - n_layers, - n_heads, - n_kv_heads, - head_dim, - d_ff, - n_experts, - n_active, - *, - aux_loss_coef=0.01, - rngs, - ): - """Construct a MixtralLLM. - - Args: - vocab_size: token vocabulary size. - d_model: model (residual stream) dimensionality. - n_layers: number of transformer blocks. - n_heads: number of query heads. - n_kv_heads: number of key/value heads. - head_dim: dimensionality of each attention head. - d_ff: feed-forward hidden dimensionality of each expert. - n_experts: total experts per block. - n_active: active experts per token (top-k) per block. - aux_loss_coef: weight applied to each block's load-balancing - aux_loss before summing across blocks. - rngs: random keys. - """ - self.aux_loss_coef = aux_loss_coef - self.embed = nnx.Embed( - vocab_size, - d_model, - embedding_init=nnx.with_partitioning( - nnx.initializers.normal(), ("fsdp", None) - ), - rngs=rngs, - ) - self.blocks = tuple( - MixtralTransformerBlock( - d_model, n_heads, n_kv_heads, head_dim, d_ff, n_experts, n_active, rngs=rngs - ) - for _ in range(n_layers) - ) - self.final_norm = RMSNorm(d_model, rngs=rngs) - self.lm_head = nnx.Linear( - d_model, - vocab_size, - use_bias=False, - kernel_init=nnx.with_partitioning( - nnx.initializers.lecun_normal(), ("fsdp", None) - ), - rngs=rngs, - ) - - def __call__( - self, token_ids: jax.Array, positions: jax.Array - ) -> tuple[jax.Array, jax.Array]: - """Compute next-token logits for a batch of token sequences. - - Args: - token_ids: integer token ids, shape (batch, seq). - positions: integer position ids, shape (batch, seq). - - Returns: - a tuple (logits, aux_loss): logits has shape - (batch, seq, vocab_size); aux_loss is aux_loss_coef times the - summed per-block load-balancing loss. - """ - mask = make_causal_mask(token_ids.shape[1]) - hidden = self.embed(token_ids) - total_aux_loss = jnp.array(0.0) - for block in self.blocks: - hidden, aux_loss = block(hidden, positions, mask) - total_aux_loss = total_aux_loss + aux_loss - hidden = self.final_norm(hidden) - logits = self.lm_head(hidden) - return logits, self.aux_loss_coef * total_aux_loss - - -def MixtralSMoE(vocab_size, **kwargs): - return MixtralLLM(vocab_size, **kwargs) diff --git a/examples/llm_reference/objective.py b/examples/llm_reference/objective.py deleted file mode 100644 index 252221f..0000000 --- a/examples/llm_reference/objective.py +++ /dev/null @@ -1,53 +0,0 @@ -"""Causal-language-model training objective, shared across every model -family in this suite (GemmaDense, DeepSeekMLA, MixtralSMoE) -- works -uniformly since every model's __call__ returns (logits, aux_loss), with -dense models always returning aux_loss=0.0. -""" - -import optax -from flax import nnx -from jax import numpy as jnp - -from blaxbird._src._types import ObjectiveFns - - -def causal_lm(aux_loss_coef: float = 0.01) -> ObjectiveFns: - """Construct next-token-prediction train/val step functions. - - Args: - aux_loss_coef: weight applied to the model's own aux_loss (already - pre-weighted for MixtralSMoE, always 0.0 for the dense models) - before adding it to the cross-entropy loss. For MixtralSMoE this - compounds with MixtralLLM's own aux_loss_coef constructor argument - (default 0.01 there too) -- the net effective weight on the raw - load-balancing loss is this value times that one (0.01 * 0.01 = - 1e-4 with both defaults), not this value alone. Intentionally not - un-compounded here: doing so would require this function to - special-case MixtralSMoE, breaking the point of this objective - being agnostic to which model family it's given. - - Returns: - an ObjectiveFns with sample_fn=None -- generation is a separate loop - (see generate.py), not a fit for the single-shot sample_fn contract - other blaxbird objectives use. - """ - - def _loss_fn(model, rng_key, batch): - del rng_key - seq_len = batch["token_ids"].shape[1] - positions = jnp.broadcast_to(jnp.arange(seq_len), batch["token_ids"].shape) - logits, aux_loss = model(batch["token_ids"], positions) - ce_loss = optax.softmax_cross_entropy_with_integer_labels( - logits=logits, labels=batch["target_ids"] - ).mean() - return ce_loss + aux_loss_coef * aux_loss - - def train_step(model, rng_key, batch, **kwargs): - del kwargs - return nnx.value_and_grad(_loss_fn)(model, rng_key, batch) - - def val_step(model, rng_key, batch, **kwargs): - del kwargs - return _loss_fn(model, rng_key, batch) - - return ObjectiveFns(train_step=train_step, val_step=val_step, sample_fn=None) diff --git a/examples/llm_reference/test_deepseek.py b/examples/llm_reference/test_deepseek.py deleted file mode 100644 index 709daf8..0000000 --- a/examples/llm_reference/test_deepseek.py +++ /dev/null @@ -1,113 +0,0 @@ -import jax -import jax.numpy as jnp -import pytest -from flax import nnx -from jax import random as jr -from jax.experimental import mesh_utils - -from deepseek import DeepSeekMLA, MLAAttention -from layers import make_causal_mask - - -def _make_mla(): - return MLAAttention( - d_model=32, - n_heads=4, - d_latent=8, - head_dim_nope=6, - head_dim_rope=2, - rngs=nnx.rnglib.Rngs(jr.key(0)), - ) - - -def test_mla_preserves_shape(): - attn = _make_mla() - x = jnp.ones((2, 6, 32)) - positions = jnp.broadcast_to(jnp.arange(6), (2, 6)) - mask = make_causal_mask(6) - out = attn(x, positions, mask) - assert out.shape == x.shape - - -def test_mla_causal_mask_blocks_future_positions(): - """Same defining correctness property as GQAAttention: perturbing a - future token must not change any earlier position's output. This is - the one place a subtle bug in the decoupled content/RoPE split could - leak future positions into the past.""" - attn = _make_mla() - x = jnp.ones((2, 6, 32)) - x_perturbed = x.at[:, -1, :].set(x[:, -1, :] * 100.0) - positions = jnp.broadcast_to(jnp.arange(6), (2, 6)) - mask = make_causal_mask(6) - out = attn(x, positions, mask) - out_perturbed = attn(x_perturbed, positions, mask) - assert jnp.allclose(out[:, :-1], out_perturbed[:, :-1], atol=1e-5) - - -def _tiny_kwargs(): - return dict( - d_model=32, - n_layers=4, - n_heads=4, - d_latent=8, - head_dim_nope=6, - head_dim_rope=2, - d_ff=64, - rngs=nnx.rnglib.Rngs(jr.key(0)), - ) - - -def test_deepseek_mla_produces_correct_logit_shape(): - vocab_size, seq_len, batch = 100, 6, 2 - model = DeepSeekMLA(vocab_size, **_tiny_kwargs()) - token_ids = jnp.zeros((batch, seq_len), dtype=jnp.int32) - positions = jnp.broadcast_to(jnp.arange(seq_len), (batch, seq_len)) - logits, aux_loss = model(token_ids, positions) - assert logits.shape == (batch, seq_len, vocab_size) - assert aux_loss == 0.0 - - -def test_deepseek_mla_gradients_are_nonzero(): - model = DeepSeekMLA(vocab_size=50, **_tiny_kwargs()) - token_ids = jnp.zeros((2, 6), dtype=jnp.int32) - positions = jnp.broadcast_to(jnp.arange(6), (2, 6)) - - def loss_fn(model): - logits, aux_loss = model(token_ids, positions) - return jnp.mean(logits**2) + aux_loss - - grads = nnx.grad(loss_fn)(model) - - leaves = jax.tree_util.tree_leaves(grads) - assert all(jnp.any(leaf != 0) for leaf in leaves if leaf.size > 0) - - -@pytest.mark.skipif( - jax.local_device_count() < 4, - reason="needs XLA_FLAGS=--xla_force_host_platform_device_count=4", -) -def test_deepseek_mla_shards_across_2d_mesh(): - # explicit devices=jax.devices()[:4]: create_device_mesh requires the - # mesh_shape's product to equal the device count exactly, but the - # skipif above only guarantees >= 4 -- slicing to exactly 4 keeps this - # test passing under XLA_FLAGS=...device_count=8 too (used by other - # tests in this suite), not just exactly 4. - mesh = jax.sharding.Mesh( - mesh_utils.create_device_mesh((2, 2), devices=jax.devices()[:4]), - ("fsdp", "tp"), - ) - with mesh: - model = DeepSeekMLA(vocab_size=100, **_tiny_kwargs()) - graphdef, state = nnx.split(model) - sharding = nnx.get_named_sharding(state, mesh) - state = jax.device_put(state, sharding) - nnx.update(model, state) - - up_k_kernel = model.blocks[0].attn.up_k.kernel.value - assert up_k_kernel.shape == (8, 24) # d_latent=8, n_heads*head_dim_nope=4*6=24 - assert up_k_kernel.addressable_shards[0].data.shape == (4, 12) - - token_ids = jnp.zeros((2, 6), dtype=jnp.int32) - positions = jnp.broadcast_to(jnp.arange(6), (2, 6)) - logits, aux_loss = model(token_ids, positions) - assert logits.shape == (2, 6, 100) diff --git a/examples/llm_reference/test_gemma.py b/examples/llm_reference/test_gemma.py deleted file mode 100644 index 6f96484..0000000 --- a/examples/llm_reference/test_gemma.py +++ /dev/null @@ -1,83 +0,0 @@ -import jax -import jax.numpy as jnp -import pytest -from flax import nnx -from jax import random as jr -from jax.experimental import mesh_utils - -from gemma import GemmaDense - - -def _tiny_kwargs(): - return dict( - d_model=32, - n_layers=4, - n_heads=4, - n_kv_heads=2, - head_dim=8, - d_ff=64, - local_window=4, - global_every=2, - rngs=nnx.rnglib.Rngs(jr.key(0)), - ) - - -def test_gemma_dense_produces_correct_logit_shape(): - vocab_size, seq_len, batch = 100, 6, 2 - model = GemmaDense(vocab_size, **_tiny_kwargs()) - token_ids = jnp.zeros((batch, seq_len), dtype=jnp.int32) - positions = jnp.broadcast_to(jnp.arange(seq_len), (batch, seq_len)) - logits, aux_loss = model(token_ids, positions) - assert logits.shape == (batch, seq_len, vocab_size) - assert aux_loss == 0.0 - - -def test_gemma_dense_gradients_are_nonzero(): - model = GemmaDense(vocab_size=50, **_tiny_kwargs()) - token_ids = jnp.zeros((2, 6), dtype=jnp.int32) - positions = jnp.broadcast_to(jnp.arange(6), (2, 6)) - - def loss_fn(model): - logits, aux_loss = model(token_ids, positions) - return jnp.mean(logits**2) + aux_loss - - grads = nnx.grad(loss_fn)(model) - - leaves = jax.tree_util.tree_leaves(grads) - assert all(jnp.any(leaf != 0) for leaf in leaves if leaf.size > 0) - - -@pytest.mark.skipif( - jax.local_device_count() < 4, - reason="needs XLA_FLAGS=--xla_force_host_platform_device_count=4", -) -def test_gemma_dense_shards_across_2d_mesh(): - # explicit devices=jax.devices()[:4]: create_device_mesh requires the - # mesh_shape's product to equal the device count exactly, but the - # skipif above only guarantees >= 4 -- slicing to exactly 4 keeps this - # test passing under XLA_FLAGS=...device_count=8 too (used by other - # tests in this suite), not just exactly 4. - mesh = jax.sharding.Mesh( - mesh_utils.create_device_mesh((2, 2), devices=jax.devices()[:4]), - ("fsdp", "tp"), - ) - with mesh: - model = GemmaDense(vocab_size=100, **_tiny_kwargs()) - graphdef, state = nnx.split(model) - sharding = nnx.get_named_sharding(state, mesh) - state = jax.device_put(state, sharding) - nnx.update(model, state) - - # q_proj: TP-sharded column-parallel, ("fsdp", "tp") - q_kernel = model.blocks[0].attn.q_proj.kernel.value - assert q_kernel.shape == (32, 32) # d_model=32, n_heads*head_dim=4*8=32 - assert q_kernel.addressable_shards[0].data.shape == (16, 16) - - # o_proj: TP-sharded row-parallel, ("tp", "fsdp") - o_kernel = model.blocks[0].attn.o_proj.kernel.value - assert o_kernel.addressable_shards[0].data.shape == (16, 16) - - token_ids = jnp.zeros((2, 6), dtype=jnp.int32) - positions = jnp.broadcast_to(jnp.arange(6), (2, 6)) - logits, aux_loss = model(token_ids, positions) - assert logits.shape == (2, 6, 100) diff --git a/examples/llm_reference/test_generate.py b/examples/llm_reference/test_generate.py deleted file mode 100644 index fe9f7b9..0000000 --- a/examples/llm_reference/test_generate.py +++ /dev/null @@ -1,41 +0,0 @@ -import jax.numpy as jnp -from flax import nnx -from jax import random as jr - -from gemma import GemmaDense -from generate import generate - - -def _model(): - return GemmaDense( - vocab_size=50, - d_model=32, - n_layers=2, - n_heads=4, - n_kv_heads=2, - head_dim=8, - d_ff=64, - local_window=4, - global_every=2, - rngs=nnx.rnglib.Rngs(jr.key(0)), - ) - - -def test_generate_extends_prompt_by_max_new_tokens(): - model = _model() - prompt_ids = jnp.array([[1, 2, 3]], dtype=jnp.int32) - out = generate(model, jr.key(1), prompt_ids, max_new_tokens=4, max_seq_len=16) - assert out.shape == (1, 3 + 4) - assert jnp.array_equal(out[:, :3], prompt_ids) - - -def test_generate_is_deterministic_given_same_key(): - model = _model() - prompt_ids = jnp.array([[1, 2, 3]], dtype=jnp.int32) - out1 = generate( - model, jr.key(1), prompt_ids, max_new_tokens=4, max_seq_len=16 - ) - out2 = generate( - model, jr.key(1), prompt_ids, max_new_tokens=4, max_seq_len=16 - ) - assert jnp.array_equal(out1, out2) diff --git a/examples/llm_reference/test_layers.py b/examples/llm_reference/test_layers.py deleted file mode 100644 index 838402e..0000000 --- a/examples/llm_reference/test_layers.py +++ /dev/null @@ -1,97 +0,0 @@ -import jax.numpy as jnp -from flax import nnx -from jax import random as jr - -from layers import ( - GQAAttention, - GeGLU, - RMSNorm, - apply_rope, - make_causal_mask, - repeat_kv, - rope_freqs, -) - - -def test_apply_rope_preserves_shape(): - x = jnp.ones((2, 6, 4, 8)) - positions = jnp.broadcast_to(jnp.arange(6), (2, 6)) - out = apply_rope(x, positions, rope_freqs(8)) - assert out.shape == x.shape - - -def test_repeat_kv_broadcasts_heads(): - x = jnp.ones((2, 6, 2, 4)) - out = repeat_kv(x, n_rep=3) - assert out.shape == (2, 6, 6, 4) - - -def test_make_causal_mask_full_is_lower_triangular(): - mask = make_causal_mask(4) - expected = jnp.array( - [ - [True, False, False, False], - [True, True, False, False], - [True, True, True, False], - [True, True, True, True], - ] - ) - assert jnp.array_equal(mask, expected) - - -def test_make_causal_mask_window_restricts_to_local(): - mask = make_causal_mask(4, window=2) - # position 3 may attend to positions 2,3 only (window=2, excludes 0,1) - assert jnp.array_equal(mask[3], jnp.array([False, False, True, True])) - - -def test_rmsnorm_preserves_shape(): - norm = RMSNorm(32, rngs=nnx.rnglib.Rngs(jr.key(0))) - x = jnp.ones((2, 6, 32)) - assert norm(x).shape == x.shape - - -def test_geglu_preserves_shape(): - mlp = GeGLU(32, 64, rngs=nnx.rnglib.Rngs(jr.key(0))) - x = jnp.ones((2, 6, 32)) - assert mlp(x).shape == x.shape - - -def test_gqa_preserves_shape_under_global_mask(): - d_model, n_heads, n_kv_heads, head_dim, seq_len = 32, 4, 2, 8, 6 - attn = GQAAttention( - d_model, n_heads, n_kv_heads, head_dim, rngs=nnx.rnglib.Rngs(jr.key(0)) - ) - x = jnp.ones((2, seq_len, d_model)) - positions = jnp.broadcast_to(jnp.arange(seq_len), (2, seq_len)) - mask = make_causal_mask(seq_len) - out = attn(x, positions, mask) - assert out.shape == x.shape - - -def test_local_and_global_masks_produce_different_outputs(): - d_model, n_heads, n_kv_heads, head_dim, seq_len = 32, 4, 2, 8, 6 - attn = GQAAttention( - d_model, n_heads, n_kv_heads, head_dim, rngs=nnx.rnglib.Rngs(jr.key(0)) - ) - x = jnp.ones((2, seq_len, d_model)) - positions = jnp.broadcast_to(jnp.arange(seq_len), (2, seq_len)) - out_global = attn(x, positions, make_causal_mask(seq_len)) - out_local = attn(x, positions, make_causal_mask(seq_len, window=3)) - assert not jnp.allclose(out_global, out_local) - - -def test_causal_mask_blocks_future_positions(): - """The defining correctness property of causal attention: changing a - future token must not change any earlier position's output.""" - d_model, n_heads, n_kv_heads, head_dim, seq_len = 32, 4, 2, 8, 6 - attn = GQAAttention( - d_model, n_heads, n_kv_heads, head_dim, rngs=nnx.rnglib.Rngs(jr.key(0)) - ) - x = jnp.ones((2, seq_len, d_model)) - x_perturbed = x.at[:, -1, :].set(x[:, -1, :] * 100.0) - positions = jnp.broadcast_to(jnp.arange(seq_len), (2, seq_len)) - mask = make_causal_mask(seq_len) - out = attn(x, positions, mask) - out_perturbed = attn(x_perturbed, positions, mask) - assert jnp.allclose(out[:, :-1], out_perturbed[:, :-1], atol=1e-5) diff --git a/examples/llm_reference/test_mixtral.py b/examples/llm_reference/test_mixtral.py deleted file mode 100644 index 3d990e6..0000000 --- a/examples/llm_reference/test_mixtral.py +++ /dev/null @@ -1,201 +0,0 @@ -import jax -import jax.numpy as jnp -import pytest -from flax import nnx -from jax import random as jr -from jax.experimental import mesh_utils - -from mixtral import MixtralSMoE, SparseMoEFFN - - -def test_sparse_moe_preserves_shape_and_returns_scalar_aux_loss(): - moe = SparseMoEFFN( - d_model=8, d_ff=16, n_experts=4, n_active=2, rngs=nnx.rnglib.Rngs(jr.key(0)) - ) - x = jnp.ones((2, 6, 8)) - out, aux_loss = moe(x) - assert out.shape == x.shape - assert aux_loss.shape == () - assert aux_loss > 0 - - -def test_sparse_moe_matches_naive_per_token_reference_within_capacity(): - """The correctness check for the whole dispatch/combine mechanism: - with a generous capacity_factor (no drops), the output must exactly - match a naive reference that gathers each token's top-k experts - directly, with no einsum dispatch trick. Verified live before writing - this plan (see plan header) -- this test locks that verification in as - a regression test.""" - d_model, d_ff, n_experts, n_active = 8, 16, 4, 2 - moe = SparseMoEFFN( - d_model, d_ff, n_experts, n_active, - capacity_factor=10.0, - rngs=nnx.rnglib.Rngs(jr.key(0)), - ) - x = jr.normal(jr.key(1), (2, 6, d_model)) - out, aux_loss = moe(x) - - # naive reference: gather each token's top-k experts directly - flat = x.reshape(-1, d_model) - logits = flat @ moe.router.kernel.value - probs = jax.nn.softmax(logits, axis=-1) - top_probs, top_idx = jax.lax.top_k(probs, n_active) - top_probs = top_probs / jnp.sum(top_probs, axis=-1, keepdims=True) - - def expert_ffn(x_token, e): - g = x_token @ moe.gate.value[e] - u = x_token @ moe.up.value[e] - h = jax.nn.silu(g) * u - return h @ moe.down.value[e] - - naive_out = jnp.zeros_like(flat) - for slot in range(n_active): - for tok in range(flat.shape[0]): - e = int(top_idx[tok, slot]) - naive_out = naive_out.at[tok].add( - top_probs[tok, slot] * expert_ffn(flat[tok], e) - ) - naive_out = naive_out.reshape(x.shape) - - assert jnp.allclose(out, naive_out, atol=1e-4) - - -def test_sparse_moe_drops_tokens_cleanly_under_tiny_capacity(): - """Capacity overflow must not crash or corrupt other tokens -- dropped - tokens simply don't contribute from that slot.""" - moe = SparseMoEFFN( - d_model=8, d_ff=16, n_experts=4, n_active=2, - capacity_factor=0.01, - rngs=nnx.rnglib.Rngs(jr.key(0)), - ) - x = jr.normal(jr.key(1), (2, 6, 8)) - out, aux_loss = moe(x) - assert out.shape == x.shape - assert jnp.all(jnp.isfinite(out)) - - -def test_sparse_moe_router_receives_nonzero_gradient(): - moe = SparseMoEFFN( - d_model=8, d_ff=16, n_experts=4, n_active=2, rngs=nnx.rnglib.Rngs(jr.key(0)) - ) - x = jnp.ones((2, 6, 8)) - - def loss_fn(moe): - out, aux = moe(x) - return jnp.mean(out**2) + aux - - grads = nnx.grad(loss_fn)(moe) - assert jnp.any(grads.router.kernel.value != 0) - - -def _tiny_kwargs(): - return dict( - d_model=32, - n_layers=4, - n_heads=4, - n_kv_heads=2, - head_dim=8, - d_ff=64, - n_experts=4, - n_active=2, - rngs=nnx.rnglib.Rngs(jr.key(0)), - ) - - -def test_mixtral_smoe_produces_correct_logit_shape_and_nonzero_aux_loss(): - vocab_size, seq_len, batch = 100, 6, 2 - model = MixtralSMoE(vocab_size, **_tiny_kwargs()) - token_ids = jnp.zeros((batch, seq_len), dtype=jnp.int32) - positions = jnp.broadcast_to(jnp.arange(seq_len), (batch, seq_len)) - logits, aux_loss = model(token_ids, positions) - assert logits.shape == (batch, seq_len, vocab_size) - assert aux_loss > 0.0 - - -def test_mixtral_smoe_gradients_are_nonzero(): - """Checks that every parameter LEAF (gate/up/down/router/attention - weights, each a single stacked (n_experts, ...) tensor, not one leaf - per expert) receives a nonzero gradient somewhere in it. This is - whole-tensor liveness, not per-expert liveness: with this seed, 2 of - the 4 experts actually receive zero dispatched tokens in this small - batch (confirmed by inspecting dispatch counts) -- capacity_factor - governs token-dropping when an expert is OVERsubscribed, it has no - bearing on whether an expert is starved of tokens in the first place, - so raising it would not change this. A starved expert's own gate/up/ - down slice legitimately gets zero gradient this step; the assertion - still passes because it checks the whole stacked tensor has some - nonzero entry, not that every expert does. Detecting per-expert - starvation would need a per-expert-slice assertion, which this test - does not attempt -- it exists to catch a structurally dead PARAMETER - (e.g. an unused projection), not routing imbalance.""" - model = MixtralSMoE(vocab_size=50, **{**_tiny_kwargs(), "n_layers": 1}) - token_ids = jnp.zeros((4, 8), dtype=jnp.int32) - positions = jnp.broadcast_to(jnp.arange(8), (4, 8)) - - def loss_fn(model): - logits, aux_loss = model(token_ids, positions) - return jnp.mean(logits**2) + aux_loss - - grads = nnx.grad(loss_fn)(model) - leaves = jax.tree_util.tree_leaves(grads) - assert all(jnp.any(leaf != 0) for leaf in leaves if leaf.size > 0) - - -@pytest.mark.skipif( - jax.local_device_count() < 8, - reason="needs XLA_FLAGS=--xla_force_host_platform_device_count=8", -) -def test_mixtral_smoe_sharded_output_matches_unsharded_reference(): - """The critical correctness test for real expert-parallel dispatch: - sharding tokens along one mesh axis and experts along a DIFFERENT mesh - axis must produce output numerically identical to an unsharded - reference computation with the same weights and inputs. This is what - actually proves the dispatch/combine + GSPMD auto-communication claim - -- shape-only tests cannot catch a token routed to the wrong expert if - the wrong expert happens to produce same-shaped output. Verified live - before writing this plan (see plan header): ~1e-9 max diff, with real - collective ops confirmed present in the compiled HLO.""" - kwargs = {**_tiny_kwargs(), "n_layers": 1} - token_ids = jnp.zeros((4, 8), dtype=jnp.int32) - positions = jnp.broadcast_to(jnp.arange(8), (4, 8)) - - ref_model = MixtralSMoE(vocab_size=50, **kwargs) - ref_logits, ref_aux = ref_model(token_ids, positions) - # ref_logits/ref_aux are already concrete values at this point, so - # resharding ref_model in place below cannot retroactively affect them - # -- no need for a separate cloned module. - - mesh = jax.sharding.Mesh( - mesh_utils.create_device_mesh((2, 2, 2)), ("fsdp", "tp", "expert") - ) - with mesh: - graphdef, state = nnx.split(ref_model) - sharding = nnx.get_named_sharding(state, mesh) - state = jax.device_put(state, sharding) - nnx.update(ref_model, state) - - sharded_logits, sharded_aux = ref_model(token_ids, positions) - - max_diff = jnp.abs(ref_logits - sharded_logits).max() - assert max_diff < 1e-3, f"sharded output diverges from reference: {max_diff}" - assert jnp.allclose(ref_aux, sharded_aux, atol=1e-3) - - -@pytest.mark.skipif( - jax.local_device_count() < 8, - reason="needs XLA_FLAGS=--xla_force_host_platform_device_count=8", -) -def test_mixtral_smoe_shards_expert_axis_across_3d_mesh(): - mesh = jax.sharding.Mesh( - mesh_utils.create_device_mesh((2, 2, 2)), ("fsdp", "tp", "expert") - ) - with mesh: - model = MixtralSMoE(vocab_size=100, **{**_tiny_kwargs(), "n_layers": 1}) - graphdef, state = nnx.split(model) - sharding = nnx.get_named_sharding(state, mesh) - state = jax.device_put(state, sharding) - nnx.update(model, state) - - gate = model.blocks[0].ffn.gate.value - assert gate.shape == (4, 32, 64) # n_experts=4, d_model=32, d_ff=64 - assert gate.addressable_shards[0].data.shape == (2, 32, 64) diff --git a/examples/llm_reference/test_objective.py b/examples/llm_reference/test_objective.py deleted file mode 100644 index 72ba49d..0000000 --- a/examples/llm_reference/test_objective.py +++ /dev/null @@ -1,79 +0,0 @@ -import jax.numpy as jnp -from flax import nnx -from jax import random as jr - -from deepseek import DeepSeekMLA -from gemma import GemmaDense -from mixtral import MixtralSMoE -from objective import causal_lm - - -def _batch(): - return { - "token_ids": jnp.zeros((2, 6), dtype=jnp.int32), - "target_ids": jnp.ones((2, 6), dtype=jnp.int32), - } - - -def test_causal_lm_train_step_runs_for_all_three_model_families(): - fns = causal_lm() - models = [ - GemmaDense( - vocab_size=50, - d_model=32, - n_layers=2, - n_heads=4, - n_kv_heads=2, - head_dim=8, - d_ff=64, - local_window=4, - global_every=2, - rngs=nnx.rnglib.Rngs(jr.key(0)), - ), - DeepSeekMLA( - vocab_size=50, - d_model=32, - n_layers=2, - n_heads=4, - d_latent=8, - head_dim_nope=6, - head_dim_rope=2, - d_ff=64, - rngs=nnx.rnglib.Rngs(jr.key(0)), - ), - MixtralSMoE( - vocab_size=50, - d_model=32, - n_layers=2, - n_heads=4, - n_kv_heads=2, - head_dim=8, - d_ff=64, - n_experts=4, - n_active=2, - rngs=nnx.rnglib.Rngs(jr.key(0)), - ), - ] - batch = _batch() - for model in models: - loss, grads = fns.train_step(model, jr.key(1), batch) - assert loss.shape == () - assert fns.sample_fn is None - - -def test_causal_lm_val_step_runs(): - fns = causal_lm() - model = GemmaDense( - vocab_size=50, - d_model=32, - n_layers=2, - n_heads=4, - n_kv_heads=2, - head_dim=8, - d_ff=64, - local_window=4, - global_every=2, - rngs=nnx.rnglib.Rngs(jr.key(0)), - ) - loss = fns.val_step(model, jr.key(1), _batch()) - assert loss.shape == () diff --git a/examples/mnist_classification/README.md b/examples/mnist_classification/README.md new file mode 100644 index 0000000..6168380 --- /dev/null +++ b/examples/mnist_classification/README.md @@ -0,0 +1,8 @@ +# MNIST classification + +Minimal classification example using a small CNN trained with +softmax cross-entropy on MNIST. Data-parallel only, the mesh shards on a single `data` axis. Run via: + +```shell +python main.py +``` diff --git a/examples/mnist_classification/main.py b/examples/mnist_classification/main.py index d2c6fb9..6237771 100644 --- a/examples/mnist_classification/main.py +++ b/examples/mnist_classification/main.py @@ -1,8 +1,5 @@ import argparse import os -import sys - -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) import dataloader import jax @@ -19,9 +16,6 @@ def get_optimizer( model, *, peak_lr=1e-4, n_steps, warmup_steps=1000, grad_clip_norm=1.0 ): - # warmup_cosine_decay_schedule requires decay_steps > warmup_steps (the - # cosine phase runs over decay_steps - warmup_steps); clamp so a short - # n_steps (e.g. a smoke run) doesn't raise ValueError from optax. warmup_steps = min(warmup_steps, n_steps // 2) schedule = optax.warmup_cosine_decay_schedule( init_value=0.0, diff --git a/pyproject.toml b/pyproject.toml index 60c7b50..27e9f30 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,10 +5,10 @@ build-backend = "setuptools.build_meta" [project] name = "blaxbird" description = "A high-level API for building and training Flax NNX models." -authors = [{name = "Simon Dirmeier", email = "simd@mailbox.org"}] +authors = [{name = "Simon Dirmeier", email = "simd23@pm.me"}] readme = "README.md" license = {file = "LICENSE"} -keywords = [] +keywords = ["flax", "nnx", "high-level api"] classifiers = [ "Development Status :: 1 - Planning", "Intended Audience :: Science/Research", @@ -16,6 +16,7 @@ classifiers = [ "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", ] requires-python = ">=3.10" dependencies = [ @@ -24,28 +25,30 @@ dependencies = [ dynamic = ["version"] [project.optional-dependencies] -all = [] +all = [ + "wandb>=0.21.1", +] [dependency-groups] dev = [ "chex", - "gitlint", + "gitlint>=0.19.1", "jupyter", + "mypy>=2.2.0", "pre-commit", - "pytest>=7.2.0", - "pytest-cov>=4.0.0", - "ruff", - "wandb>=0.21.1", + "pytest>=9.0.2", + "pytest-cov>=7.0.0", + "ruff>=0.15.6", ] examples = [ - "tensorflow-datasets==4.9.6", - "tensorflow==2.17.1", - "protobuf==3.20.3", - "matplotlib==3.1.0", - "wandb>=0.21.1", - "einops>=0.8.1", "chex", + "einops>=0.8.1", + "matplotlib>=3.9", "ml-collections>=1.1.0", + "protobuf==3.20.3", + "tensorflow-datasets==4.9.6", + "tensorflow==2.17.1", + "wandb>=0.21.1", ] [project.urls] @@ -62,10 +65,24 @@ skips = ["B101", "B310"] [tool.mypy] show_error_codes = true +warn_unused_configs = true no_implicit_optional = true +disallow_untyped_defs = false +ignore_missing_imports = true +disallow_untyped_calls = false +explicit_package_bases = true + [tool.pytest.ini_options] -addopts = "-v --doctest-modules --cov=./blaxbird --cov-report=xml" +addopts = [ + "-v", + "--doctest-modules", + "--cov=blaxbird", + "--cov-report=xml", + "--strict-markers", + "--strict-config", + "-ra" +] testpaths = [ "blaxbird" ] @@ -73,15 +90,28 @@ testpaths = [ [tool.ruff] indent-width = 2 line-length = 80 +extend-exclude = ["examples/**"] [tool.ruff.lint] -select = ["D", "E", "F", "W", "I001"] extend-select = [ - "UP", "I", "PL", "S" + "A", # Builtins shadowing + "ARG", # Unused arguments (New) + #"ANN", # Type annotations + "B", # Bugbear (common bugs) + "C4", # List/dict/set comprehensions + "D", # Docstrings + "I", # Isort (import sorting) + # "N", # PEP8 Naming conventions + "PL", # Pylint rules + # "PTH", # Pathlib usage (New) + #"RET", # Return statements (New) + # "RUF", # Ruff-specific checks (New) + "S", # Security checks + "SIM", # Code simplification + "TCH", # Type-checking blocks + "UP", # Pyupgrade (modern syntax) ] -ignore = ["S101", "ANN101", "PLR2044", "PLR0913"] -exclude = ["*_test.py", "docs/**", "examples/**"] - +ignore=["S101", "F841", "PLR2004", "S610", "ARG001"] [tool.ruff.lint.pydocstyle] convention= 'google' diff --git a/uv.lock b/uv.lock index 3ff1c5a..970a706 100644 --- a/uv.lock +++ b/uv.lock @@ -6,12 +6,14 @@ resolution-markers = [ "python_full_version == '3.11.*' and sys_platform != 'linux'", "python_full_version == '3.12.*' and sys_platform != 'linux'", "python_full_version == '3.13.*' and sys_platform != 'linux'", - "python_full_version >= '3.14' and sys_platform != 'linux'", + "python_full_version >= '3.15' and sys_platform != 'linux'", + "python_full_version == '3.14.*' and sys_platform != 'linux'", "python_full_version < '3.11' and sys_platform == 'linux'", "python_full_version == '3.11.*' and sys_platform == 'linux'", "python_full_version == '3.12.*' and sys_platform == 'linux'", "python_full_version == '3.13.*' and sys_platform == 'linux'", - "python_full_version >= '3.14' and sys_platform == 'linux'", + "python_full_version >= '3.15' and sys_platform == 'linux'", + "python_full_version == '3.14.*' and sys_platform == 'linux'", ] [[package]] @@ -74,8 +76,10 @@ name = "argon2-cffi-bindings" version = "21.2.0" source = { registry = "https://pypi.org/simple" } 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examples/llm/objective.py | 2 +- examples/mnist_classification/dataloader.py | 53 ++- pyproject.toml | 6 +- uv.lock | 60 +++- 18 files changed, 320 insertions(+), 826 deletions(-) delete mode 100644 blaxbird/_src/test_types.py delete mode 100644 examples/llm/nn/deepseek.py diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 2ffd82a..52516a4 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -62,4 +62,4 @@ jobs: - name: Upload coverage reports to Codecov uses: codecov/codecov-action@v3 env: - CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} \ No newline at end of file + CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} diff --git a/.gitlint b/.gitlint index 2aabde4..f74be6f 100644 --- a/.gitlint +++ b/.gitlint @@ -12,4 +12,4 @@ line-length=72 line-length=72 [title-must-not-contain-word] -words=wip,todo \ No newline at end of file +words=wip,todo diff --git a/README.md b/README.md index 2bfb204..d18ae51 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ > A high-level API to build and train NNX models -`Blaxbird` [blækbɜːd] is a high-level API to easily build NNX models and train them on CPU or GPU. +`Blaxbird` [blæksbɜːd] is a high-level API to easily build NNX models and train them on CPU or GPU. Using `blaxbird` one can - concisely define models and loss functions without the usual JAX/Flax verbosity, @@ -59,7 +59,7 @@ train(jr.key(2), optimizer, train_itr, val_itr) ## Examples -Full self-contained examples can be found in [examples](examples/). +Full self-contained examples (flow matching, small LMs training, ...), can be found in [examples](examples/). ## Installation @@ -348,4 +348,4 @@ uv run ruff format blaxbird examples uv run ruff check --fix blaxbird examples uv run mypy blaxbird examples uv run pre-commit run --all-files -``` \ No newline at end of file +``` diff --git a/blaxbird/_src/test_types.py b/blaxbird/_src/test_types.py deleted file mode 100644 index bf5697f..0000000 --- a/blaxbird/_src/test_types.py +++ /dev/null @@ -1,21 +0,0 @@ -from blaxbird._src._types import ObjectiveFns - - -def test_objective_fns_fields_are_named(): - def train_step(): - pass - - def val_step(): - pass - - def sample_fn(): - pass - - fns = ObjectiveFns( - train_step=train_step, val_step=val_step, sample_fn=sample_fn - ) - assert fns.train_step is train_step - assert fns.val_step is val_step - assert fns.sample_fn is sample_fn - a, b, c = fns - assert (a, b, c) == (train_step, val_step, sample_fn) diff --git a/blaxbird/_src/trainer.py b/blaxbird/_src/trainer.py index e681a6a..321fc59 100644 --- a/blaxbird/_src/trainer.py +++ b/blaxbird/_src/trainer.py @@ -1,4 +1,3 @@ -import types from collections.abc import Callable, Iterable import jax @@ -6,7 +5,6 @@ from flax import nnx from jax import random as jr -wandb: types.ModuleType | None try: import wandb except ImportError: @@ -164,9 +162,7 @@ def train( value # type: ignore[arg-type] ) # do evaluation loop - for val_idx, batch in zip( - range(n_eval_batches), val_itr, strict=False - ): + for val_idx, batch in zip(range(n_eval_batches), val_itr, strict=False): if mesh is not None: batch = jax.device_put( batch, jax.NamedSharding(mesh, _data_partition_spec) diff --git a/examples/cifar10_flow_matching/README.md b/examples/cifar10_flow_matching/README.md index a3c9777..7a31b9b 100644 --- a/examples/cifar10_flow_matching/README.md +++ b/examples/cifar10_flow_matching/README.md @@ -6,4 +6,4 @@ Run via: ```shell python main.py -``` \ No newline at end of file +``` diff --git a/examples/cifar10_flow_matching/dataloader.py b/examples/cifar10_flow_matching/dataloader.py index 8c7ed93..009a3b5 100644 --- a/examples/cifar10_flow_matching/dataloader.py +++ b/examples/cifar10_flow_matching/dataloader.py @@ -1,4 +1,5 @@ -import tensorflow as tf +import grain +import numpy as np import tensorflow_datasets as tfds from jax import numpy as jnp from jax import random as jr @@ -9,16 +10,15 @@ def data_loaders( outfolder, *, batch_size=128, - buffer_size=1, - prefetch_size=1, shuffle=True, split="train", ): - datasets = tfds.load( + datasets = tfds.data_source( "cifar10", try_gcs=False, split=split, data_dir=outfolder, + builder_kwargs={"file_format": "array_record"}, ) if isinstance(split, str): datasets = [datasets] @@ -28,30 +28,21 @@ def data_loaders( assert len(datasets) == len(shuffle) for dataset, shuffle_me in zip(datasets, shuffle): itr_key, rng_key = jr.split(rng_key) - itr = as_iterable( - itr_key, dataset, batch_size, buffer_size, prefetch_size, shuffle_me - ) + itr = as_iterable(itr_key, dataset, batch_size, shuffle_me) itrs.append(itr) return itrs -def as_iterable(rng_key, itr, batch_size, buffer_size, prefetch_size, shuffle): - def process_fn(batch): - img = tf.cast(batch["image"], tf.float32) / 255.0 +def as_iterable(rng_key, dataset, batch_size, shuffle): + def process_fn(example): + img = example["image"].astype(np.float32) / 255.0 img = 2.0 * img - 1.0 - return {"inputs": img, "context": batch["label"]} + return {"inputs": img, "context": example["label"]} max_int32 = jnp.iinfo(jnp.int32).max - seed = jr.randint(rng_key, shape=(), minval=0, maxval=max_int32) - return ( - itr.repeat() - .shuffle( - buffer_size, - reshuffle_each_iteration=shuffle, - seed=int(seed), - ) - .map(process_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE) - .batch(batch_size, drop_remainder=True) - .prefetch(prefetch_size) - .as_numpy_iterator() - ) + seed = int(jr.randint(rng_key, shape=(), minval=0, maxval=max_int32)) + ds = grain.MapDataset.source(dataset).map(process_fn) + if shuffle: + ds = ds.shuffle(seed=seed) + ds = ds.repeat().batch(batch_size, drop_remainder=True) + return iter(ds) diff --git a/examples/llm/README.md b/examples/llm/README.md index d2da482..d15e6eb 100644 --- a/examples/llm/README.md +++ b/examples/llm/README.md @@ -2,29 +2,24 @@ Implements multiple LLM architectures to highlight their different design choices, and to demonstrate distributed training using `blaxbird`. -| Design axis | Gemma4 | DeepSeek4 | Qwen3Next | -|---|---|---|---| -| Attention mechanism | GQA | GQA + hybrid CSA/HCA block-compressed attention | Gated DeltaNet (linear, 75% of layers) + GQA (25%) | -| Attention pattern | Interleaved local/global, sliding window | Full causal, block-compressed (CSA: top-k selective; HCA: dense over heavier-compressed blocks) | Full causal (GQA layers only -- DeltaNet layers have no explicit attention pattern, it's a recurrence) | -| Positional encoding | Dual-frequency p-RoPE (theta=1M/rotary 25% on global layers, theta=10k/full rotation on local layers) | RoPE | Partial RoPE (first 25% of head, GQA layers only; DeltaNet layers carry no explicit position embedding) | -| KV-cache trick | Key/value reuse on global layers (no separate value projection) | -- | -- | -| Attention sinks | No | Yes -- learnable per-head, per-branch sink logit lets softmax mass sum to <1 | No | -| Normalization | RMSNorm, pre-norm | RMSNorm, pre-norm | RMSNorm, pre-norm | -| FFN activation | GeGLU | GeGLU | SwiGLU (via SparseMoEFFN) | -| Expert routing | -- (dense FFN) | -- (dense FFN) | Top-2-of-8, capacity-based dispatch, Switch-style aux loss (every layer -- both DeltaNet and GQA layers route through it) | -| Embedding tying | Untied | Untied | Untied | -| Sharding | 2D (FSDP+TP) | 2D (FSDP+TP) | 3D (FSDP+TP+expert) | - -Data is byte-level `tiny_shakespeare` (TFDS), vocab_size=256, no -subword tokenizer -- raw bytes in, raw bytes out. +| | Gemma4 | Qwen3Next | +|---|---|---| +| Attention mechanism | GQA | Gated DeltaNet + GQA | +| Attention pattern | Interleaved local/global, sliding window | Full causal for GQA layers | +| Positional encoding | Dual-frequency p-RoPE | Partial RoPE | +| KV-cache trick | Key/value reuse on global layers | -- | +| Normalization | RMSNorm, pre-norm, QK-norm | RMSNorm, pre-norm, QK-norm | +| FFN activation | GeGLU | SwiGLU | +| Expert routing | -- | Top-2-of-8 + 1 shared | +| Embedding tying | Untied | Untied | +| Sharding | 2D (FSDP+TP) | 3D (FSDP+TP+expert) | Run ```shell -XLA_FLAGS="--xla_force_host_platform_device_count=8" uv run python main.py --model {gemma4,deepseek4,qwen3next} +XLA_FLAGS="--xla_force_host_platform_device_count=8" uv run python main.py --model {gemma4,qwen3next} ``` -to train a tiny LM on `tiny_shakespeare`, or omit `--model` to run all -three in turn. `--n-steps` (default 100) controls step count. Meshes: -- Gemma4/DeepSeek4 `(2,4)` fsdp+tp mesh, +to train a tiny LM on `tiny_shakespeare` with sharding: +- Gemma4 `(2,4)` fsdp+tp mesh, - Qwen3Next `(2,2,2)` fsdp+tp+expert mesh. diff --git a/examples/llm/dataloader.py b/examples/llm/dataloader.py index 956e854..88864b1 100644 --- a/examples/llm/dataloader.py +++ b/examples/llm/dataloader.py @@ -5,7 +5,8 @@ windows. """ -import tensorflow as tf +import grain +import numpy as np import tensorflow_datasets as tfds from jax import numpy as jnp from jax import random as jr @@ -18,7 +19,7 @@ def _load_byte_ids(data_dir, split): "tiny_shakespeare", try_gcs=False, split=split, data_dir=data_dir ) text = next(iter(tfds.as_numpy(ds)))["text"] - return tf.io.decode_raw(text, tf.uint8) + return np.frombuffer(text, dtype=np.uint8) def data_loaders( @@ -27,32 +28,24 @@ def data_loaders( *, seq_len=128, batch_size=8, - buffer_size=1024, - prefetch_size=1, splits=("train", "validation"), ): - itrs = [] for split in splits: itr_key, rng_key = jr.split(rng_key) - byte_ids = tf.cast(_load_byte_ids(data_dir, split), tf.int32) + byte_ids = _load_byte_ids(data_dir, split).astype(np.int32) chunk_len = seq_len + 1 - n_chunks = tf.shape(byte_ids)[0] // chunk_len - chunks = tf.reshape(byte_ids[: n_chunks * chunk_len], (n_chunks, chunk_len)) + n_chunks = byte_ids.shape[0] // chunk_len + chunks = byte_ids[: n_chunks * chunk_len].reshape(n_chunks, chunk_len) max_int32 = jnp.iinfo(jnp.int32).max - seed = jr.randint(itr_key, shape=(), minval=0, maxval=max_int32) - itr = ( - tf.data.Dataset.from_tensor_slices(chunks) + seed = int(jr.randint(itr_key, shape=(), minval=0, maxval=max_int32)) + itr = iter( + grain.MapDataset.source(chunks) + .shuffle(seed=seed) .repeat() - .shuffle(buffer_size, reshuffle_each_iteration=True, seed=int(seed)) .batch(batch_size, drop_remainder=True) - .map( - lambda x: {"token_ids": x[:, :-1], "target_ids": x[:, 1:]}, - num_parallel_calls=tf.data.experimental.AUTOTUNE, - ) - .prefetch(prefetch_size) - .as_numpy_iterator() + .map(lambda x: {"token_ids": x[:, :-1], "target_ids": x[:, 1:]}) ) itrs.append(itr) return itrs diff --git a/examples/llm/main.py b/examples/llm/main.py index 7d8d1ce..29c45dd 100644 --- a/examples/llm/main.py +++ b/examples/llm/main.py @@ -4,14 +4,13 @@ import dataloader import jax import optax -from examples.llm.nn.deepseek import DeepSeek4 from flax import nnx -from examples.llm.nn.gemma import GemmaDense +from nn.gemma import Gemma4 from jax import numpy as jnp from jax import random as jr from jax.experimental import mesh_utils from jax.sharding import PartitionSpec as P -from examples.llm.nn.qwen import Qwen3NextHybrid +from nn.qwen import Qwen3Next from objective import get_training_and_eval_fns, sample from blaxbird import train_fn @@ -33,24 +32,25 @@ def run_gemma(n_steps: int) -> None: mesh_utils.create_device_mesh((fsdp, tp)), ("fsdp", "tp") ) with mesh: - model = GemmaDense( + model = Gemma4( dataloader.VOCAB_SIZE, - d_model=32, + din=32, n_layers=4, n_heads=4, n_kv_heads=2, head_dim=8, - d_ff=64, + dhid=64, local_window=4, global_every=2, rngs=nnx.rnglib.Rngs(jr.key(0)), ) optimizer = nnx.Optimizer(model, tx=optax.adamw(1e-3)) fns = get_training_and_eval_fns() + eval_every = max(1, n_steps // 20) train = train_fn( fns=fns, n_steps=n_steps, - eval_every_n_steps=max(1, n_steps // 2), + eval_every_n_steps=eval_every, n_eval_batches=1, mesh=mesh, data_partition_spec=P("fsdp"), @@ -67,49 +67,6 @@ def run_gemma(n_steps: int) -> None: print(f"gemma: generated text {text!r}") -def run_deepseek4(n_steps: int) -> None: - n_devices = jax.local_device_count() - fsdp = 2 if n_devices >= 4 else 1 - tp = n_devices // fsdp - mesh = jax.sharding.Mesh( - mesh_utils.create_device_mesh((fsdp, tp)), ("fsdp", "tp") - ) - with mesh: - model = DeepSeek4( - dataloader.VOCAB_SIZE, - d_model=32, - n_layers=4, - n_heads=4, - n_kv_heads=2, - head_dim=8, - d_ff=64, - csa_block_size=2, - csa_top_k=4, - hca_block_size=4, - rngs=nnx.rnglib.Rngs(jr.key(0)), - ) - optimizer = nnx.Optimizer(model, tx=optax.adamw(1e-3)) - fns = get_training_and_eval_fns() - train = train_fn( - fns=fns, - n_steps=n_steps, - eval_every_n_steps=max(1, n_steps // 2), - n_eval_batches=1, - mesh=mesh, - data_partition_spec=P("fsdp"), - ) - train_itr, val_itr = _get_train_and_val_itrs( - jr.key(1), seq_len=32, batch_size=8 - ) - train(jr.key(2), optimizer, train_itr, val_itr) - prompt_ids = jnp.zeros((1, 4), dtype=jnp.int32) - generated = sample( - optimizer.model, jr.key(3), prompt_ids, max_new_tokens=8, max_seq_len=32 - ) - text = bytes(int(b) for b in generated[0]).decode("utf-8", errors="replace") - print(f"deepseek4: generated text {text!r}") - - def run_qwen3_next(n_steps: int) -> None: n_devices = jax.local_device_count() if n_devices >= 8: @@ -120,24 +77,25 @@ def run_qwen3_next(n_steps: int) -> None: mesh_utils.create_device_mesh((fsdp, tp, expert)), ("fsdp", "tp", "expert") ) with mesh: - model = Qwen3NextHybrid( + model = Qwen3Next( dataloader.VOCAB_SIZE, - d_model=32, + din=32, n_layers=4, n_heads=4, n_kv_heads=2, head_dim=8, - d_ff=64, + dhid=64, n_experts=8, n_active=2, rngs=nnx.rnglib.Rngs(jr.key(0)), ) optimizer = nnx.Optimizer(model, tx=optax.adamw(1e-3)) fns = get_training_and_eval_fns() + eval_every = max(1, n_steps // 20) train = train_fn( fns=fns, n_steps=n_steps, - eval_every_n_steps=max(1, n_steps // 2), + eval_every_n_steps=eval_every, n_eval_batches=1, mesh=mesh, data_partition_spec=P("fsdp"), @@ -156,14 +114,13 @@ def run_qwen3_next(n_steps: int) -> None: _RUNS = { "gemma4": run_gemma, - "deepseek4": run_deepseek4, "qwen3next": run_qwen3_next, } def main() -> None: parser = argparse.ArgumentParser() - parser.add_argument("--model", choices=sorted(_RUNS), default=None) + parser.add_argument("--model", choices=sorted(_RUNS), default="gemma4") parser.add_argument("--n-steps", type=int, default=100) args = parser.parse_args() diff --git a/examples/llm/nn/deepseek.py b/examples/llm/nn/deepseek.py deleted file mode 100644 index aac0033..0000000 --- a/examples/llm/nn/deepseek.py +++ /dev/null @@ -1,339 +0,0 @@ -"""DeepSeek-V4-style decoder-only transformer: hybrid Compressed Sparse -Attention (CSA) + Heavily Compressed Attention (HCA) with learnable -attention sinks, RMSNorm, pre-norm, dense GeGLU FFN. - -Real DeepSeek-V4 pairs this attention mechanism with MLA-style latent -KV compression (see DeepSeekMLA in this suite for that axis) and a -1.6T-parameter DeepSeekMoE FFN with shared experts (see MixtralSMoE for -the MoE-routing axis in this suite). This reference implementation -isolates just the new attention mechanism -- plain GQA projections, -dense FFN -- so each axis is demonstrated independently elsewhere in -this suite rather than combined into one file. - -CSA pools keys/values into small blocks (light compression) and -attends to only the top-k most relevant blocks per query (real, -selective sparse attention -- not dense-then-mask). HCA pools into -larger blocks (heavy compression) and attends densely to all of them, -giving a cheap global view that complements CSA's sharper, selective -one. Both branches restrict attention to fully-completed blocks (mean- -pooled here, not the real softmax-gated pooling + FP4 lightning -indexer) -- the most recent < block_size tokens are only visible once -their block completes. Real V4 compensates with an explicit raw -sliding-window branch over recent tokens; omitted here since this -suite's toy sequence lengths (~16-32 tokens) would make that window -cover almost the whole sequence anyway. -""" - -import jax -from flax import nnx -from jax import numpy as jnp -from layers import GeGLU, RMSNorm, apply_rope, repeat_kv, rope_freqs, tp_linear - - -def pool_kv_blocks(x: jax.Array, block_size: int) -> jax.Array: - """Mean-pool (batch, seq, heads, dim) into non-overlapping blocks - along seq, dropping any trailing partial block. - - Args: - x: input array, shape (batch, seq, heads, dim). - block_size: number of positions per block. - - Returns: - jax.Array, shape (batch, seq // block_size, heads, dim). - """ - b, s, h, d = x.shape - n_blocks = s // block_size - x = x[:, : n_blocks * block_size] - return x.reshape(b, n_blocks, block_size, h, d).mean(axis=2) - - -def make_block_causal_mask(seq_len: int, block_size: int) -> jax.Array: - """Build a causal mask from query positions to fully-completed blocks. - - Args: - seq_len: sequence length (number of queries). - block_size: number of positions per key/value block. - - Returns: - bool jax.Array, shape (seq_len, seq_len // block_size), True where - the block lies entirely at or before the query position. - """ - n_blocks = seq_len // block_size - query_pos = jnp.arange(seq_len)[:, None] - block_last_pos = (jnp.arange(n_blocks) + 1) * block_size - 1 - return block_last_pos[None, :] <= query_pos - - -class DeepSeek4Attention(nnx.Module): - """Hybrid CSA + HCA attention with learnable attention sinks.""" - - def __init__( # noqa: PLR0913 - self, - d_model, - n_heads, - n_kv_heads, - head_dim, - *, - csa_block_size, - csa_top_k, - hca_block_size, - rngs, - ): - """Construct a DeepSeek-V4-style hybrid attention block. - - Args: - d_model: model (residual stream) dimensionality. - n_heads: number of query heads. - n_kv_heads: number of key/value heads. - head_dim: dimensionality of each attention head. - csa_block_size: key/value pooling block size for the Compressed - Sparse Attention branch (light compression, top-k selective). - csa_top_k: number of CSA blocks attended to per query. - hca_block_size: key/value pooling block size for the Heavily - Compressed Attention branch (heavy compression, dense). - rngs: random keys. - """ - self.n_heads = n_heads - self.n_kv_heads = n_kv_heads - self.head_dim = head_dim - self.n_rep = n_heads // n_kv_heads - self.csa_block_size = csa_block_size - self.csa_top_k = csa_top_k - self.hca_block_size = hca_block_size - self.q_proj = tp_linear( - d_model, n_heads * head_dim, ("fsdp", "tp"), rngs=rngs - ) - self.k_proj = tp_linear( - d_model, n_kv_heads * head_dim, ("fsdp", "tp"), rngs=rngs - ) - self.v_proj = tp_linear( - d_model, n_kv_heads * head_dim, ("fsdp", "tp"), rngs=rngs - ) - self.o_proj = tp_linear( - 2 * n_heads * head_dim, d_model, ("tp", "fsdp"), rngs=rngs - ) - self.inv_freq = nnx.Variable(rope_freqs(head_dim)) - self.csa_sink = nnx.Param(jnp.zeros((n_heads,))) - self.hca_sink = nnx.Param(jnp.zeros((n_heads,))) - - def _branch(self, q, k_blocks, v_blocks, block_mask, sink, top_k): - """Compute one compressed-attention branch (CSA if top_k is given, - HCA -- dense over all blocks -- otherwise), with a learnable - attention sink competing for softmax probability mass so the real - block weights can sum to less than one. - """ - b, h, s, d = q.shape - n_blocks = k_blocks.shape[2] - scores = jnp.einsum("bhsd,bhnd->bhsn", q, k_blocks) / jnp.sqrt(d) - scores = jnp.where(block_mask[None, None, :, :], scores, -jnp.inf) - sink_logit = jnp.broadcast_to(sink[None, :, None, None], (b, h, s, 1)) - - if top_k is not None and top_k < n_blocks: - top_scores, top_idx = jax.lax.top_k(scores, top_k) - combined = jnp.concatenate([top_scores, sink_logit], axis=-1) - weights = jax.nn.softmax(combined, axis=-1)[..., :-1] - v_full = jnp.broadcast_to(v_blocks[:, :, None], (b, h, s, n_blocks, d)) - v_sel = jnp.take_along_axis(v_full, top_idx[..., None], axis=3) - return jnp.einsum("bhsk,bhskd->bhsd", weights, v_sel) - - combined = jnp.concatenate([scores, sink_logit], axis=-1) - weights = jax.nn.softmax(combined, axis=-1)[..., :-1] - return jnp.einsum("bhsn,bhnd->bhsd", weights, v_blocks) - - def __call__(self, x: jax.Array, positions: jax.Array) -> jax.Array: - """Apply hybrid CSA+HCA self-attention. - - Args: - x: input array, shape (batch, seq, d_model). - positions: integer position ids, shape (batch, seq). - - Returns: - jax.Array, same shape as x. - """ - b, s, _ = x.shape - q = self.q_proj(x).reshape(b, s, self.n_heads, self.head_dim) - k = self.k_proj(x).reshape(b, s, self.n_kv_heads, self.head_dim) - v = self.v_proj(x).reshape(b, s, self.n_kv_heads, self.head_dim) - - q = apply_rope(q, positions, self.inv_freq.value) - k = apply_rope(k, positions, self.inv_freq.value) - k = repeat_kv(k, self.n_rep) - v = repeat_kv(v, self.n_rep) - - csa_k = jnp.transpose(pool_kv_blocks(k, self.csa_block_size), (0, 2, 1, 3)) - csa_v = jnp.transpose(pool_kv_blocks(v, self.csa_block_size), (0, 2, 1, 3)) - hca_k = jnp.transpose(pool_kv_blocks(k, self.hca_block_size), (0, 2, 1, 3)) - hca_v = jnp.transpose(pool_kv_blocks(v, self.hca_block_size), (0, 2, 1, 3)) - q = jnp.transpose(q, (0, 2, 1, 3)) - - csa_mask = make_block_causal_mask(s, self.csa_block_size) - hca_mask = make_block_causal_mask(s, self.hca_block_size) - - csa_out = self._branch( - q, csa_k, csa_v, csa_mask, self.csa_sink.value, self.csa_top_k - ) - hca_out = self._branch(q, hca_k, hca_v, hca_mask, self.hca_sink.value, None) - - out = jnp.concatenate([csa_out, hca_out], axis=-1) - out = jnp.transpose(out, (0, 2, 1, 3)).reshape( - b, s, self.n_heads * 2 * self.head_dim - ) - return self.o_proj(out) - - -class DeepSeek4Block(nnx.Module): - """Pre-norm transformer block: hybrid CSA+HCA attention + dense - GeGLU FFN. - """ - - def __init__( # noqa: PLR0913 - self, - d_model, - n_heads, - n_kv_heads, - head_dim, - d_ff, - *, - csa_block_size, - csa_top_k, - hca_block_size, - rngs, - ): - """Construct a DeepSeek-V4 transformer block. - - Args: - d_model: model (residual stream) dimensionality. - n_heads: number of query heads. - n_kv_heads: number of key/value heads. - head_dim: dimensionality of each attention head. - d_ff: feed-forward hidden dimensionality. - csa_block_size: CSA branch pooling block size. - csa_top_k: number of CSA blocks attended to per query. - hca_block_size: HCA branch pooling block size. - rngs: random keys. - """ - self.attn_norm = RMSNorm(d_model, rngs=rngs) - self.attn = DeepSeek4Attention( - d_model, - n_heads, - n_kv_heads, - head_dim, - csa_block_size=csa_block_size, - csa_top_k=csa_top_k, - hca_block_size=hca_block_size, - rngs=rngs, - ) - self.ffn_norm = RMSNorm(d_model, rngs=rngs) - self.ffn = GeGLU(d_model, d_ff, rngs=rngs) - - def __call__(self, x: jax.Array, positions: jax.Array) -> jax.Array: - """Apply the block. - - Args: - x: input array, shape (batch, seq, d_model). - positions: integer position ids, shape (batch, seq). - - Returns: - jax.Array, same shape as x. - """ - x = x + self.attn(self.attn_norm(x), positions) - x = x + self.ffn(self.ffn_norm(x)) - return x - - -class DeepSeek4LLM(nnx.Module): - """Decoder-only transformer in the DeepSeek-V4 architectural family: - hybrid CSA+HCA attention with learnable attention sinks (see module - docstring for the axes this reference implementation isolates versus - real V4). Dense FFN only (no MoE -- that's MixtralSMoE's role in this - suite; DeepSeekMLA covers the latent-KV-compression axis). - """ - - def __init__( # noqa: PLR0913 - self, - vocab_size, - d_model, - n_layers, - n_heads, - n_kv_heads, - head_dim, - d_ff, - *, - csa_block_size=2, - csa_top_k=4, - hca_block_size=4, - rngs, - ): - """Construct a DeepSeek4LLM. - - Args: - vocab_size: token vocabulary size. - d_model: model (residual stream) dimensionality. - n_layers: number of transformer blocks. - n_heads: number of query heads. - n_kv_heads: number of key/value heads. - head_dim: dimensionality of each attention head. - d_ff: feed-forward hidden dimensionality. - csa_block_size: CSA branch pooling block size. - csa_top_k: number of CSA blocks attended to per query. - hca_block_size: HCA branch pooling block size. - rngs: random keys. - """ - self.embed = nnx.Embed( - vocab_size, - d_model, - embedding_init=nnx.with_partitioning( - nnx.initializers.normal(), ("fsdp", None) - ), - rngs=rngs, - ) - self.blocks = tuple( - DeepSeek4Block( - d_model, - n_heads, - n_kv_heads, - head_dim, - d_ff, - csa_block_size=csa_block_size, - csa_top_k=csa_top_k, - hca_block_size=hca_block_size, - rngs=rngs, - ) - for _ in range(n_layers) - ) - self.final_norm = RMSNorm(d_model, rngs=rngs) - self.lm_head = nnx.Linear( - d_model, - vocab_size, - use_bias=False, - kernel_init=nnx.with_partitioning( - nnx.initializers.lecun_normal(), ("fsdp", None) - ), - rngs=rngs, - ) - - def __call__( - self, token_ids: jax.Array, positions: jax.Array - ) -> tuple[jax.Array, jax.Array]: - """Compute next-token logits for a batch of token sequences. - - Args: - token_ids: integer token ids, shape (batch, seq). - positions: integer position ids, shape (batch, seq). - - Returns: - a tuple (logits, aux_loss): logits has shape - (batch, seq, vocab_size); aux_loss is always jnp.array(0.0) (dense - model, no MoE) -- kept for interface uniformity with MixtralSMoE - so objective.py's causal_lm works unmodified across this suite. - """ - hidden = self.embed(token_ids) - for block in self.blocks: - hidden = block(hidden, positions) - hidden = self.final_norm(hidden) - logits = self.lm_head(hidden) - return logits, jnp.array(0.0) - - -def DeepSeek4(vocab_size, **kwargs): - return DeepSeek4LLM(vocab_size, **kwargs) diff --git a/examples/llm/nn/gemma.py b/examples/llm/nn/gemma.py index 42a0d7d..0e53150 100644 --- a/examples/llm/nn/gemma.py +++ b/examples/llm/nn/gemma.py @@ -1,18 +1,51 @@ -"""Gemma-4-style decoder-only transformer: GQA + dual-frequency p-RoPE -+ key/value reuse on global layers + interleaved local/global attention -+ dense GeGLU FFN, TP+FSDP-sharded. +"""Gemma4-style LM. -Local (sliding-window) layers rotate the full head with RoPE theta=10k. -Global (full-causal) layers rotate only the leading 25% of the head -("p-RoPE", theta=1M) and skip a separate value projection, reusing the -(pre-rotation) key projection as values -- both real Gemma-4 tricks -that shrink the global-layer KV cache. +Architecture design: + - Attention: grouped-query attention (GQA), n_kv_heads query groups + broadcast to n_heads via repeat_kv, softmax over head_dim ** -0.5 + scaled scores. Queries and keys are RMS-normed per head before RoPE + ("QK-norm"). + - Attention pattern: interleaved local/global layers. Every + global_every-th layer (1-indexed, default 4) is "global"; the rest + are "local". + - local layers: sliding-window causal attention (last local_window + positions), full RoPE (rotary_fraction=1.0) at base theta=10k. + - global layers: full-causal attention, dual-frequency partial RoPE + ("p-RoPE": only the leading 25% of head_dim rotated) at base + theta=1M. + - KV-cache trick: key/value reuse on global layers -- the value + projection is dropped and the (pre-rotation) key projection is reused + as values (share_kv), shrinking the global-layer KV cache. + - Normalization: RMSNorm (no mean-centering, no bias), pre-norm, with + a separate norm before attention and FFN plus a final norm before the + head. + - FFN: dense GeGLU (gated GELU, column-parallel gate/up, row-parallel + down). + - Embeddings: untied -- a separate input nnx.Embed and output lm_head. + - Residual structure: standard x = x + sublayer(norm(x)). + - Sharding: 2D FSDP+TP -- column-parallel q/k/v/gate/up, row-parallel + o/down projections; embedding and lm_head kernels FSDP-sharded on the + vocab axis. + + Faithful to the real model: + - interleaved local/global attention + - dual-frequency p-RoPE + - key-as-value reuse on global layers + - QK-norm + + Divergences from the real model (real / implemented): + - sandwich normalization (post-attn/post-FFN norms) / pre-norm only + - sqrt(din) embedding scaling / unscaled embeddings + - tied input/output embeddings / untied (separate lm_head) + - per-layer embeddings (E2B/E4B) / one shared embedding table + - cross-layer KV-cache sharing / each layer keeps its own KV + - illustrative layer sizes, not any real variant's config """ import jax from flax import nnx from jax import numpy as jnp -from layers import ( +from nn.layers import ( GeGLU, RMSNorm, apply_partial_rope, @@ -24,13 +57,11 @@ class GemmaAttention(nnx.Module): - """Grouped-query attention with Gemma-4-style partial RoPE and, - optionally, key/value reuse (no separate value projection). - """ + """Grouped-query attention.""" def __init__( self, - d_model, + din, n_heads, n_kv_heads, head_dim, @@ -40,10 +71,10 @@ def __init__( share_kv, rngs, ): - """Construct a Gemma-4 attention block. + """Construct a Gemma4 attention block. Args: - d_model: model (residual stream) dimensionality. + din: model (residual stream) dimensionality. n_heads: number of query heads. n_kv_heads: number of key/value heads. head_dim: dimensionality of each attention head. @@ -62,18 +93,20 @@ def __init__( self.share_kv = share_kv self.rotary_dim = max(2, int(rotary_fraction * head_dim) // 2 * 2) self.q_proj = tp_linear( - d_model, n_heads * head_dim, ("fsdp", "tp"), rngs=rngs + din, n_heads * head_dim, ("fsdp", "tp"), rngs=rngs ) self.k_proj = tp_linear( - d_model, n_kv_heads * head_dim, ("fsdp", "tp"), rngs=rngs + din, n_kv_heads * head_dim, ("fsdp", "tp"), rngs=rngs ) if not share_kv: self.v_proj = tp_linear( - d_model, n_kv_heads * head_dim, ("fsdp", "tp"), rngs=rngs + din, n_kv_heads * head_dim, ("fsdp", "tp"), rngs=rngs ) self.o_proj = tp_linear( - n_heads * head_dim, d_model, ("tp", "fsdp"), rngs=rngs + n_heads * head_dim, din, ("tp", "fsdp"), rngs=rngs ) + self.q_norm = RMSNorm(head_dim, rngs=rngs) + self.k_norm = RMSNorm(head_dim, rngs=rngs) self.inv_freq = nnx.Variable(rope_freqs(self.rotary_dim, theta=theta)) def __call__( @@ -82,7 +115,7 @@ def __call__( """Apply grouped-query self-attention. Args: - x: input array, shape (batch, seq, d_model). + x: input array, shape (batch, seq, din). positions: integer position ids, shape (batch, seq). mask: bool attention mask, shape (seq, seq), True = attend, from make_causal_mask. @@ -99,9 +132,10 @@ def __call__( else self.v_proj(x).reshape(b, s, self.n_kv_heads, self.head_dim) ) + q = self.q_norm(q) q = apply_partial_rope(q, positions, self.inv_freq.value, self.rotary_dim) k = apply_partial_rope( - k_content, positions, self.inv_freq.value, self.rotary_dim + self.k_norm(k_content), positions, self.inv_freq.value, self.rotary_dim ) k = repeat_kv(k, self.n_rep) v = repeat_kv(v, self.n_rep) @@ -121,27 +155,27 @@ def __call__( class GemmaTransformerBlock(nnx.Module): - """Pre-norm transformer block: GQA attention + dense GeGLU FFN.""" + """Pre-norm transformer block.""" def __init__( # noqa: PLR0913 - self, d_model, n_heads, n_kv_heads, head_dim, d_ff, *, is_global, rngs + self, din, n_heads, n_kv_heads, head_dim, dhid, *, is_global, rngs ): """Construct a Gemma transformer block. Args: - d_model: model (residual stream) dimensionality. + din: model (residual stream) dimensionality. n_heads: number of query heads. n_kv_heads: number of key/value heads. head_dim: dimensionality of each attention head. - d_ff: feed-forward hidden dimensionality. + dhid: feed-forward hidden dimensionality. is_global: whether this is a "global" (full-causal, p-RoPE, shared-kv) layer or a "local" (sliding-window, full-RoPE) one. rngs: random keys. """ self.is_global = is_global - self.attn_norm = RMSNorm(d_model, rngs=rngs) + self.attn_norm = RMSNorm(din, rngs=rngs) self.attn = GemmaAttention( - d_model, + din, n_heads, n_kv_heads, head_dim, @@ -150,8 +184,8 @@ def __init__( # noqa: PLR0913 share_kv=is_global, rngs=rngs, ) - self.ffn_norm = RMSNorm(d_model, rngs=rngs) - self.ffn = GeGLU(d_model, d_ff, rngs=rngs) + self.ffn_norm = RMSNorm(din, rngs=rngs) + self.ffn = GeGLU(din, dhid, rngs=rngs) def __call__( self, x: jax.Array, positions: jax.Array, mask: jax.Array @@ -159,7 +193,7 @@ def __call__( """Apply the block. Args: - x: input array, shape (batch, seq, d_model). + x: input array, shape (batch, seq, din). positions: integer position ids, shape (batch, seq). mask: bool attention mask, shape (seq, seq). @@ -171,25 +205,18 @@ def __call__( return x -class GemmaLLM(nnx.Module): - """Decoder-only transformer in the Gemma-4 architectural family. - - Interleaves "local" (sliding-window, full-RoPE) and "global" - (full-causal, p-RoPE, shared-kv) attention layers -- every - `global_every`-th layer is global, the rest are local, matching - Gemma 4's actual design choice. Dense FFN only (no MoE -- that's - MixtralSMoE's role in this suite). - """ +class Gemma4(nnx.Module): + """Gemma4-style LM.""" def __init__( # noqa: PLR0913 self, vocab_size, - d_model, + din, n_layers, n_heads, n_kv_heads, head_dim, - d_ff, + dhid, local_window, *, global_every=4, @@ -199,12 +226,12 @@ def __init__( # noqa: PLR0913 Args: vocab_size: token vocabulary size. - d_model: model (residual stream) dimensionality. + din: model (residual stream) dimensionality. n_layers: number of transformer blocks. n_heads: number of query heads. n_kv_heads: number of key/value heads. head_dim: dimensionality of each attention head. - d_ff: feed-forward hidden dimensionality. + dhid: feed-forward hidden dimensionality. local_window: sliding-window size for "local" attention layers. global_every: every global_every-th layer (1-indexed) is a full causal ("global") attention layer; the rest are local. @@ -213,7 +240,7 @@ def __init__( # noqa: PLR0913 self.local_window = local_window self.embed = nnx.Embed( vocab_size, - d_model, + din, embedding_init=nnx.with_partitioning( nnx.initializers.normal(), ("fsdp", None) ), @@ -221,19 +248,19 @@ def __init__( # noqa: PLR0913 ) self.blocks = tuple( GemmaTransformerBlock( - d_model, + din, n_heads, n_kv_heads, head_dim, - d_ff, + dhid, is_global=((i + 1) % global_every == 0), rngs=rngs, ) for i in range(n_layers) ) - self.final_norm = RMSNorm(d_model, rngs=rngs) + self.final_norm = RMSNorm(din, rngs=rngs) self.lm_head = nnx.Linear( - d_model, + din, vocab_size, use_bias=False, kernel_init=nnx.with_partitioning( @@ -252,10 +279,7 @@ def __call__( positions: integer position ids, shape (batch, seq). Returns: - a tuple (logits, aux_loss): logits has shape - (batch, seq, vocab_size); aux_loss is always jnp.array(0.0) (dense - model, no MoE) -- kept for interface uniformity with MixtralSMoE so - objective.py's causal_lm works unmodified across this suite. + a tuple of logits, aux_loss """ seq_len = token_ids.shape[1] global_mask = make_causal_mask(seq_len) @@ -269,7 +293,3 @@ def __call__( hidden = self.final_norm(hidden) logits = self.lm_head(hidden) return logits, jnp.array(0.0) - - -def GemmaDense(vocab_size, **kwargs): - return GemmaLLM(vocab_size, **kwargs) diff --git a/examples/llm/nn/layers.py b/examples/llm/nn/layers.py index a99a190..441d9e3 100644 --- a/examples/llm/nn/layers.py +++ b/examples/llm/nn/layers.py @@ -1,13 +1,4 @@ -"""Shared primitives for the llm_reference example suite (Gemma-4- -style, DeepSeek-V4-style, Qwen3-Next-style decoder-only transformers). - -TP-sharded projections use nnx.with_partitioning on their kernel_init so -blaxbird.train_fn's mesh= argument can shard them via -nnx.get_named_sharding -- unannotated parameters (e.g. RMSNorm weights) -default to fully replicated. No mesh is threaded into any __call__: all -sharding here is construction-time weight annotation only, matching this -repo's existing sharding idiom (see examples/fsdp_tp_demo). -""" +"""Shared LM primitives.""" import jax from flax import nnx @@ -51,10 +42,7 @@ def apply_rope( def apply_partial_rope( x: jax.Array, positions: jax.Array, inv_freq: jax.Array, rotary_dim: int ) -> jax.Array: - """Apply RoPE to only the leading `rotary_dim` of the head axis, - leaving the remainder unrotated (Gemma-4-style "p-RoPE" on global - attention layers; Qwen3-Next-style partial RoPE on standard attention - layers). + """Apply RoPE to only the leading `rotary_dim` of the head axis. Args: x: input array, shape (batch, seq, n_heads, dim). @@ -111,7 +99,7 @@ def make_causal_mask(seq_len: int, window: int | None = None) -> jax.Array: class RMSNorm(nnx.Module): - """Root-mean-square layer normalization (no mean-centering, no bias).""" + """Root-mean-square layer normalization.""" def __init__(self, dim, *, rngs, eps=1e-6): """Construct an RMSNorm layer. @@ -140,12 +128,12 @@ def __call__(self, x: jax.Array) -> jax.Array: return x * self.weight.value -def tp_linear(d_in, d_out, partition_spec, *, rngs, use_bias=False): - """Construct a nnx.Linear whose kernel carries a sharding annotation. +def tp_linear(din, dout, partition_spec, *, rngs, use_bias=False): + """Linear layer with sharding annotation. Args: - d_in: input feature dimensionality. - d_out: output feature dimensionality. + din: input feature dimensionality. + dout: output feature dimensionality. partition_spec: a 2-tuple of mesh-axis names (or None) passed to nnx.with_partitioning on the kernel initializer -- e.g. ("fsdp", "tp") for column-parallel, ("tp", "fsdp") for row-parallel. @@ -155,15 +143,15 @@ def tp_linear(d_in, d_out, partition_spec, *, rngs, use_bias=False): unaffected -- same pattern as examples/fsdp_tp_demo's ShardedMLP. rngs: random keys. use_bias: whether to include a bias term. Every projection in this - suite uses use_bias=False, matching the real - Gemma/DeepSeek/Qwen3-Next architectures. + suite uses use_bias=False, matching the real Gemma/Qwen3-Next + architectures. Returns: a nnx.Linear with a with_partitioning-annotated kernel_init. """ return nnx.Linear( - d_in, - d_out, + din, + dout, use_bias=use_bias, kernel_init=nnx.with_partitioning( nnx.initializers.lecun_normal(), partition_spec @@ -173,27 +161,25 @@ def tp_linear(d_in, d_out, partition_spec, *, rngs, use_bias=False): class GeGLU(nnx.Module): - """Gated GELU MLP, TP-sharded (column-parallel gate/up, row-parallel - down -- same pattern as this suite's attention projections). - """ + """Gated GELU MLP, TP-sharded (column-parallel gate/up, row-parallel down.""" - def __init__(self, d_model, d_ff, *, rngs): + def __init__(self, din, dhid, *, rngs): """Construct a GeGLU feed-forward block. Args: - d_model: model (residual stream) dimensionality. - d_ff: hidden (expansion) dimensionality. + din: input/output (residual stream) dimensionality. + dhid: hidden (expansion) dimensionality. rngs: random keys. """ - self.gate = tp_linear(d_model, d_ff, ("fsdp", "tp"), rngs=rngs) - self.up = tp_linear(d_model, d_ff, ("fsdp", "tp"), rngs=rngs) - self.down = tp_linear(d_ff, d_model, ("tp", "fsdp"), rngs=rngs) + self.gate = tp_linear(din, dhid, ("fsdp", "tp"), rngs=rngs) + self.up = tp_linear(din, dhid, ("fsdp", "tp"), rngs=rngs) + self.down = tp_linear(dhid, din, ("tp", "fsdp"), rngs=rngs) def __call__(self, x: jax.Array) -> jax.Array: """Apply the GeGLU transform. Args: - x: input array, shape (..., d_model). + x: input array, shape (..., din). Returns: jax.Array, same shape as x. diff --git a/examples/llm/nn/qwen.py b/examples/llm/nn/qwen.py index 1f65cf7..12303c6 100644 --- a/examples/llm/nn/qwen.py +++ b/examples/llm/nn/qwen.py @@ -1,79 +1,69 @@ -"""Qwen3-Next-style decoder-only transformer: linear-attention/ -Transformer hybrid + sparse MoE FFN. - -75% of layers are Gated DeltaNet -- a linear (constant-memory-per-step) -recurrent attention computed via the delta rule, no softmax, no -quadratic seq x seq score matrix -- and the remaining 25% are standard -GQA attention with partial RoPE (leading 25% of the head) and an output -gate. This is the axis none of this suite's other models cover: every -other model here is quadratic self-attention throughout; Qwen3-Next -interleaves it with a genuinely sub-quadratic sequence mixer. - -The Gated DeltaNet recurrence is implemented as a plain `jax.lax.scan` -over time steps, which is O(seq_len) sequential steps -- correct and -easy to follow, but not the real chunked/parallel-form kernel used in -production Qwen3-Next; fine at this suite's toy sequence lengths. -Multi-token prediction (a real Qwen3-Next feature) is out of scope -here -- see DeepSeekV4Dense for this suite's other new-attention- -mechanism reference instead. +"""Qwen3Next-style LM. + +Architecture design: + - Attention: 3:1 linear/standard hybrid. Every linear_every-th layer + (1-indexed, default 4) is standard GQA attention; the other ~75% are + Gated DeltaNet linear-attention layers. + - Gated DeltaNet: linear recurrent sequence mixer via the delta rule + with data-dependent decay (alpha) and write-rate (beta) gates over + L2-normalized queries/keys; O(seq) sequential scan. + - standard attention: GQA with QK-norm, partial RoPE (leading 25% of + head_dim), and a sigmoid output gate. + - Positional encoding: partial RoPE on standard-attention layers; + DeltaNet layers are position-implicit (recurrent). + - Normalization: RMSNorm (no mean-centering, no bias), pre-norm, with + a separate norm before the mixer and FFN plus a final norm before + the head; QK-norm on standard-attention layers. + - FFN: sparse MoE -- top-k routed experts (capacity-based dispatch/ + combine) plus one always-on shared expert; SwiGLU experts and a + Switch-style load-balancing aux loss. + - Embeddings: untied -- a separate input nnx.Embed and output lm_head. + - Sharding: 3D FSDP+TP+expert (routed-expert axis sharded). + + Faithful to the real model: + - 3:1 Gated DeltaNet / standard-attention hybrid + - delta-rule recurrence with decay + write-rate gates, L2-normed Q/K + - output-gated, QK-normed, partial-RoPE GQA + - top-k routed MoE with a shared expert + + Divergences from the real model (real / implemented): + - chunked/parallel-form DeltaNet kernel / an O(seq) sequential scan + - short causal Conv1D before the recurrence / q/k/v feed it directly + - dropless routing / Switch-style capacity dropping + - zero-centered RMSNorm / standard RMSNorm + - multi-token prediction / single next-token objective + - illustrative layer sizes, not any real variant's config """ import jax from flax import nnx from jax import numpy as jnp -from layers import RMSNorm, apply_partial_rope, repeat_kv, rope_freqs, tp_linear +from nn.layers import RMSNorm, apply_partial_rope, repeat_kv, rope_freqs, tp_linear class SparseMoEFFN(nnx.Module): - """Top-k-routed mixture-of-experts feed-forward block with real - capacity-based dispatch/combine (not dense-compute-then-select). - Expert weights are stacked into single tensors with a leading - n_experts axis so that axis can be sharded across a mesh's "expert" - axis. - """ - def __init__( - self, d_model, d_ff, n_experts, n_active, *, rngs, capacity_factor=1.25 + self, din, dhid, n_experts, n_active, *, rngs, capacity_factor=1.25 ): - """Construct a sparse MoE feed-forward block. - - Args: - d_model: model (residual stream) dimensionality. - d_ff: hidden (expansion) dimensionality of each expert. - n_experts: total number of experts. - n_active: number of experts activated per token (top-k). - rngs: random keys. - capacity_factor: per-expert buffer capacity multiplier. Per-expert - capacity = ceil(capacity_factor * n_active * n_tokens / - n_experts). Standard Switch-Transformer value is 1.25. Tokens - beyond an expert's capacity in a batch are dropped (their - contribution from that slot is zeroed, not misrouted). - """ self.n_experts = n_experts self.n_active = n_active self.capacity_factor = capacity_factor - self.router = nnx.Linear(d_model, n_experts, use_bias=False, rngs=rngs) + self.router = nnx.Linear(din, n_experts, use_bias=False, rngs=rngs) expert_partitioning = nnx.with_partitioning( nnx.initializers.lecun_normal(), ("expert", None, None) ) key = rngs.params() k1, k2, k3 = jax.random.split(key, 3) - self.gate = nnx.Param(expert_partitioning(k1, (n_experts, d_model, d_ff))) - self.up = nnx.Param(expert_partitioning(k2, (n_experts, d_model, d_ff))) - self.down = nnx.Param(expert_partitioning(k3, (n_experts, d_ff, d_model))) - - def __call__(self, x: jax.Array) -> tuple[jax.Array, jax.Array]: - """Route tokens to the top-k experts via capacity-based dispatch/ - combine and combine their outputs. + self.gate = nnx.Param(expert_partitioning(k1, (n_experts, din, dhid))) + self.up = nnx.Param(expert_partitioning(k2, (n_experts, din, dhid))) + self.down = nnx.Param(expert_partitioning(k3, (n_experts, dhid, din))) - Args: - x: input array, shape (batch, seq, d_model). + self.shared_gate = tp_linear(din, dhid, ("fsdp", "tp"), rngs=rngs) + self.shared_up = tp_linear(din, dhid, ("fsdp", "tp"), rngs=rngs) + self.shared_down = tp_linear(dhid, din, ("tp", "fsdp"), rngs=rngs) - Returns: - a tuple (output, aux_loss): output has the same shape as x; - aux_loss is a scalar Switch-Transformer-style load-balancing loss. - """ + def __call__(self, x: jax.Array) -> tuple[jax.Array, jax.Array]: b, s, d = x.shape flat = x.reshape(b * s, d) n_tok = flat.shape[0] @@ -120,6 +110,11 @@ def __call__(self, x: jax.Array) -> tuple[jax.Array, jax.Array]: expert_out = jnp.einsum("ecf,efd->ecd", h, self.down.value) combined = jnp.einsum("ecd,tec->td", expert_out, combine_weight) + shared = self.shared_down( + jax.nn.silu(self.shared_gate(flat)) * self.shared_up(flat) + ) + combined = combined + shared + density = jnp.mean(probs, axis=0) chosen_mask = jax.nn.one_hot(top_idx, self.n_experts).sum(axis=1) chosen_frac = jnp.mean(chosen_mask, axis=0) @@ -135,24 +130,6 @@ def gated_delta_net( alpha: jax.Array, beta: jax.Array, ) -> jax.Array: - """Sequential Gated DeltaNet recurrence. - - Maintains a (head_dim x head_dim) associative-memory state per head, - decayed each step by a data-dependent gate `alpha` and corrected - toward the true value via the delta rule, scaled by a data-dependent - write-rate `beta`: state_t = alpha_t * state_{t-1} + beta_t * (v_t - - state_{t-1} @ k_t) (outer) k_t. Output is state_t @ q_t. - - Args: - q: queries, shape (batch, seq, heads, head_dim). - k: keys, shape (batch, seq, heads, head_dim). - v: values, shape (batch, seq, heads, head_dim). - alpha: decay gate in (0, 1), shape (batch, seq, heads). - beta: write-rate gate in (0, 1), shape (batch, seq, heads). - - Returns: - jax.Array, shape (batch, seq, heads, head_dim). - """ b, _, h, d = q.shape def step(state, inputs): @@ -178,46 +155,30 @@ def step(state, inputs): class GatedDeltaNetLayer(nnx.Module): - """Linear-attention sequence mixer via the (gated) delta rule.""" - - def __init__(self, d_model, n_heads, head_dim, *, rngs): - """Construct a Gated DeltaNet layer. - - Args: - d_model: model (residual stream) dimensionality. - n_heads: number of heads. - head_dim: dimensionality of each head. - rngs: random keys. - """ + def __init__(self, din, n_heads, head_dim, *, rngs): self.n_heads = n_heads self.head_dim = head_dim self.q_proj = tp_linear( - d_model, n_heads * head_dim, ("fsdp", "tp"), rngs=rngs + din, n_heads * head_dim, ("fsdp", "tp"), rngs=rngs ) self.k_proj = tp_linear( - d_model, n_heads * head_dim, ("fsdp", "tp"), rngs=rngs + din, n_heads * head_dim, ("fsdp", "tp"), rngs=rngs ) self.v_proj = tp_linear( - d_model, n_heads * head_dim, ("fsdp", "tp"), rngs=rngs + din, n_heads * head_dim, ("fsdp", "tp"), rngs=rngs ) - self.gate_proj = nnx.Linear(d_model, 2 * n_heads, rngs=rngs) + self.gate_proj = nnx.Linear(din, 2 * n_heads, rngs=rngs) self.o_proj = tp_linear( - n_heads * head_dim, d_model, ("tp", "fsdp"), rngs=rngs + n_heads * head_dim, din, ("tp", "fsdp"), rngs=rngs ) def __call__(self, x: jax.Array) -> jax.Array: - """Apply the Gated DeltaNet layer. - - Args: - x: input array, shape (batch, seq, d_model). - - Returns: - jax.Array, same shape as x. - """ b, s, _ = x.shape q = self.q_proj(x).reshape(b, s, self.n_heads, self.head_dim) k = self.k_proj(x).reshape(b, s, self.n_heads, self.head_dim) v = self.v_proj(x).reshape(b, s, self.n_heads, self.head_dim) + q = q * jax.lax.rsqrt(jnp.sum(q * q, axis=-1, keepdims=True) + 1e-6) + k = k * jax.lax.rsqrt(jnp.sum(k * k, axis=-1, keepdims=True) + 1e-6) gates = jax.nn.sigmoid(self.gate_proj(x)) alpha, beta = gates[..., : self.n_heads], gates[..., self.n_heads :] @@ -226,56 +187,39 @@ def __call__(self, x: jax.Array) -> jax.Array: class Qwen3NextAttention(nnx.Module): - """Standard GQA attention with partial RoPE and an output gate.""" - - def __init__(self, d_model, n_heads, n_kv_heads, head_dim, *, rngs): - """Construct a standard-attention layer. - - Args: - d_model: model (residual stream) dimensionality. - n_heads: number of query heads. - n_kv_heads: number of key/value heads. - head_dim: dimensionality of each attention head. - rngs: random keys. - """ + def __init__(self, din, n_heads, n_kv_heads, head_dim, *, rngs): self.n_heads = n_heads self.n_kv_heads = n_kv_heads self.head_dim = head_dim self.n_rep = n_heads // n_kv_heads self.rotary_dim = max(2, head_dim // 4 // 2 * 2) self.q_proj = tp_linear( - d_model, n_heads * head_dim, ("fsdp", "tp"), rngs=rngs + din, n_heads * head_dim, ("fsdp", "tp"), rngs=rngs ) self.k_proj = tp_linear( - d_model, n_kv_heads * head_dim, ("fsdp", "tp"), rngs=rngs + din, n_kv_heads * head_dim, ("fsdp", "tp"), rngs=rngs ) self.v_proj = tp_linear( - d_model, n_kv_heads * head_dim, ("fsdp", "tp"), rngs=rngs + din, n_kv_heads * head_dim, ("fsdp", "tp"), rngs=rngs ) - self.gate_proj = nnx.Linear(d_model, n_heads * head_dim, rngs=rngs) + self.gate_proj = nnx.Linear(din, n_heads * head_dim, rngs=rngs) self.o_proj = tp_linear( - n_heads * head_dim, d_model, ("tp", "fsdp"), rngs=rngs + n_heads * head_dim, din, ("tp", "fsdp"), rngs=rngs ) + self.q_norm = RMSNorm(head_dim, rngs=rngs) + self.k_norm = RMSNorm(head_dim, rngs=rngs) self.inv_freq = nnx.Variable(rope_freqs(self.rotary_dim)) def __call__( self, x: jax.Array, positions: jax.Array, mask: jax.Array ) -> jax.Array: - """Apply gated grouped-query self-attention. - - Args: - x: input array, shape (batch, seq, d_model). - positions: integer position ids, shape (batch, seq). - mask: bool attention mask, shape (seq, seq). - - Returns: - jax.Array, same shape as x. - """ b, s, _ = x.shape q = self.q_proj(x).reshape(b, s, self.n_heads, self.head_dim) k = self.k_proj(x).reshape(b, s, self.n_kv_heads, self.head_dim) v = self.v_proj(x).reshape(b, s, self.n_kv_heads, self.head_dim) + q = self.q_norm(q) + k = self.k_norm(k) q = apply_partial_rope(q, positions, self.inv_freq.value, self.rotary_dim) k = apply_partial_rope(k, positions, self.inv_freq.value, self.rotary_dim) k = repeat_kv(k, self.n_rep) @@ -298,62 +242,32 @@ def __call__( class Qwen3NextBlock(nnx.Module): - """Pre-norm transformer block: Gated DeltaNet or standard attention, - plus a sparse MoE FFN. - """ - def __init__( # noqa: PLR0913 self, - d_model, + din, n_heads, n_kv_heads, head_dim, - d_ff, + dhid, n_experts, n_active, *, is_linear, rngs, ): - """Construct a Qwen3-Next transformer block. - - Args: - d_model: model (residual stream) dimensionality. - n_heads: number of attention/DeltaNet heads. - n_kv_heads: number of key/value heads (standard-attention layers - only). - head_dim: dimensionality of each head. - d_ff: feed-forward hidden dimensionality of each expert. - n_experts: total experts. - n_active: active experts per token (top-k). - is_linear: whether this is a Gated DeltaNet layer (True, 75% of - layers) or a standard-attention layer (False, 25%). - rngs: random keys. - """ self.is_linear = is_linear - self.attn_norm = RMSNorm(d_model, rngs=rngs) + self.attn_norm = RMSNorm(din, rngs=rngs) self.attn = ( - GatedDeltaNetLayer(d_model, n_heads, head_dim, rngs=rngs) + GatedDeltaNetLayer(din, n_heads, head_dim, rngs=rngs) if is_linear - else Qwen3NextAttention(d_model, n_heads, n_kv_heads, head_dim, rngs=rngs) + else Qwen3NextAttention(din, n_heads, n_kv_heads, head_dim, rngs=rngs) ) - self.ffn_norm = RMSNorm(d_model, rngs=rngs) - self.ffn = SparseMoEFFN(d_model, d_ff, n_experts, n_active, rngs=rngs) + self.ffn_norm = RMSNorm(din, rngs=rngs) + self.ffn = SparseMoEFFN(din, dhid, n_experts, n_active, rngs=rngs) def __call__( self, x: jax.Array, positions: jax.Array, mask: jax.Array ) -> tuple[jax.Array, jax.Array]: - """Apply the block. - - Args: - x: input array, shape (batch, seq, d_model). - positions: integer position ids, shape (batch, seq). - mask: bool attention mask, shape (seq, seq) (standard-attention - layers only -- unused on Gated DeltaNet layers). - - Returns: - a tuple (output, aux_loss): output has the same shape as x. - """ normed = self.attn_norm(x) attn_out = ( self.attn(normed) if self.is_linear else self.attn(normed, positions, mask) @@ -363,21 +277,18 @@ def __call__( return x + ffn_out, aux_loss -class Qwen3NextLLM(nnx.Module): - """Decoder-only transformer in the Qwen3-Next architectural family: - linear-attention (Gated DeltaNet) / standard-attention hybrid with a - sparse MoE FFN. - """ +class Qwen3Next(nnx.Module): + """Qwen3Next-style LM.""" def __init__( # noqa: PLR0913 self, vocab_size, - d_model, + din, n_layers, n_heads, n_kv_heads, head_dim, - d_ff, + dhid, n_experts, n_active, *, @@ -385,29 +296,10 @@ def __init__( # noqa: PLR0913 aux_loss_coef=0.01, rngs, ): - """Construct a Qwen3NextLLM. - - Args: - vocab_size: token vocabulary size. - d_model: model (residual stream) dimensionality. - n_layers: number of transformer blocks. - n_heads: number of attention/DeltaNet heads. - n_kv_heads: number of key/value heads (standard-attention layers). - head_dim: dimensionality of each head. - d_ff: feed-forward hidden dimensionality of each expert. - n_experts: total experts per block. - n_active: active experts per token (top-k) per block. - linear_every: every linear_every-th layer (1-indexed) is a - standard-attention layer; the rest are Gated DeltaNet (3:1 - ratio at the default of 4, matching real Qwen3-Next). - aux_loss_coef: weight applied to each block's load-balancing - aux_loss before summing across blocks. - rngs: random keys. - """ self.aux_loss_coef = aux_loss_coef self.embed = nnx.Embed( vocab_size, - d_model, + din, embedding_init=nnx.with_partitioning( nnx.initializers.normal(), ("fsdp", None) ), @@ -415,11 +307,11 @@ def __init__( # noqa: PLR0913 ) self.blocks = tuple( Qwen3NextBlock( - d_model, + din, n_heads, n_kv_heads, head_dim, - d_ff, + dhid, n_experts, n_active, is_linear=((i + 1) % linear_every != 0), @@ -427,9 +319,9 @@ def __init__( # noqa: PLR0913 ) for i in range(n_layers) ) - self.final_norm = RMSNorm(d_model, rngs=rngs) + self.final_norm = RMSNorm(din, rngs=rngs) self.lm_head = nnx.Linear( - d_model, + din, vocab_size, use_bias=False, kernel_init=nnx.with_partitioning( @@ -441,19 +333,6 @@ def __init__( # noqa: PLR0913 def __call__( self, token_ids: jax.Array, positions: jax.Array ) -> tuple[jax.Array, jax.Array]: - """Compute next-token logits for a batch of token sequences. - - Args: - token_ids: integer token ids, shape (batch, seq). - positions: integer position ids, shape (batch, seq). - - Returns: - a tuple (logits, aux_loss): logits has shape - (batch, seq, vocab_size); aux_loss is aux_loss_coef times the - summed per-block load-balancing loss (standard-attention layers' - MoE FFNs only -- Gated DeltaNet layers still route through a - SparseMoEFFN, same as standard-attention layers). - """ seq_len = token_ids.shape[1] i = jnp.arange(seq_len)[:, None] j = jnp.arange(seq_len)[None, :] @@ -467,7 +346,3 @@ def __call__( hidden = self.final_norm(hidden) logits = self.lm_head(hidden) return logits, self.aux_loss_coef * total_aux_loss - - -def Qwen3NextHybrid(vocab_size, **kwargs): - return Qwen3NextLLM(vocab_size, **kwargs) diff --git a/examples/llm/objective.py b/examples/llm/objective.py index 12b798a..5059b98 100644 --- a/examples/llm/objective.py +++ b/examples/llm/objective.py @@ -76,7 +76,7 @@ def sample( seq_len = tokens.shape[1] positions = jnp.broadcast_to(jnp.arange(seq_len), (batch, seq_len)) logits, _ = model(tokens, positions) - next_logits = logits[:, -1, :] + next_logits = logits[:, -1, :] next_token = jr.categorical(jr.fold_in(rng_key, step), next_logits, axis=-1) tokens = jnp.concatenate([tokens, next_token[:, None]], axis=1) diff --git a/examples/mnist_classification/dataloader.py b/examples/mnist_classification/dataloader.py index cf9d8a3..857272b 100644 --- a/examples/mnist_classification/dataloader.py +++ b/examples/mnist_classification/dataloader.py @@ -1,4 +1,6 @@ -import tensorflow as tf +import grain +import jax.image +import numpy as np import tensorflow_datasets as tfds from jax import numpy as jnp from jax import random as jr @@ -9,16 +11,15 @@ def data_loaders( outfolder, *, batch_size=128, - buffer_size=1, - prefetch_size=1, shuffle=True, split="train", ): - datasets = tfds.load( + datasets = tfds.data_source( "mnist", try_gcs=False, split=split, data_dir=outfolder, + builder_kwargs={"file_format": "array_record"}, ) if isinstance(split, str): datasets = [datasets] @@ -28,46 +29,34 @@ def data_loaders( assert len(datasets) == len(shuffle) for dataset, shuffle_me in zip(datasets, shuffle): itr_key, rng_key = jr.split(rng_key) - itr = as_iterable( - itr_key, dataset, batch_size, buffer_size, prefetch_size, shuffle_me - ) + itr = as_iterable(itr_key, dataset, batch_size, shuffle_me) itrs.append(itr) return itrs def _crop_resize(image, resolution): - h, w = tf.shape(image)[0], tf.shape(image)[1] - crop = tf.minimum(h, w) + h, w = image.shape[0], image.shape[1] + crop = min(h, w) image = image[ (h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2 ] - image = tf.image.resize( - image, - size=(resolution, resolution), - antialias=True, - method=tf.image.ResizeMethod.BICUBIC, + image = jax.image.resize( + image, shape=(resolution, resolution, image.shape[-1]), method="bicubic" ) - return tf.cast(image, tf.float32) + return image -def as_iterable(rng_key, itr, batch_size, buffer_size, prefetch_size, shuffle): - def process_fn(batch): - img = tf.cast(batch["image"], tf.float32) / 255.0 +def as_iterable(rng_key, dataset, batch_size, shuffle): + def process_fn(example): + img = example["image"].astype(np.float32) / 255.0 img = _crop_resize(img, 32) img = 2.0 * img - 1.0 - return {"image": img, "label": batch["label"]} + return {"image": img, "label": example["label"]} max_int32 = jnp.iinfo(jnp.int32).max - seed = jr.randint(rng_key, shape=(), minval=0, maxval=max_int32) - return ( - itr.repeat() - .shuffle( - buffer_size, - reshuffle_each_iteration=shuffle, - seed=int(seed), - ) - .map(process_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE) - .batch(batch_size, drop_remainder=True) - .prefetch(prefetch_size) - .as_numpy_iterator() - ) + seed = int(jr.randint(rng_key, shape=(), minval=0, maxval=max_int32)) + ds = grain.MapDataset.source(dataset).map(process_fn) + if shuffle: + ds = ds.shuffle(seed=seed) + ds = ds.repeat().batch(batch_size, drop_remainder=True) + return iter(ds) diff --git a/pyproject.toml 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