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[nnx] add some optimizations to graph.py #4377

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3 changes: 2 additions & 1 deletion .gitignore
Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,8 @@ flaxlib_src/build
flaxlib_src/builddir
flaxlib_src/dist
flaxlib_src/subprojects

target/
flaxlib.cpython-*
# used by direnv
.envrc

Expand Down
118 changes: 118 additions & 0 deletions benchmarks/nnx_graph_overhead.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,118 @@
# Copyright 2024 The Flax Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# %%
import jax
import jax.numpy as jnp
import numpy as np
import optax
from time import time

from flax import nnx

from absl import flags
from absl import app

FLAGS = flags.FLAGS
flags.DEFINE_enum('mode', 'all', ['all', 'nnx', 'jax'], 'Mode to run the script in')
flags.DEFINE_integer('total_steps', 100, 'Total number of training steps')
flags.DEFINE_integer('width', 32, 'Hidden layer size')
flags.DEFINE_integer('depth', 5, 'Depth of the model')



class Linear(nnx.Module):
def __init__(self, din: int, dout: int, *, rngs: nnx.Rngs):
self.list = [
nnx.Param(jax.random.uniform(rngs.params(), (din, dout))),
nnx.Param(jnp.zeros((dout,))),
]
self.dict = {
'w': nnx.Param(jax.random.uniform(rngs.params(), (din, dout))),
'b': nnx.Param(jnp.zeros((dout,))),
}



class MLP(nnx.Module):
def __init__(self, depth, *, rngs: nnx.Rngs):
self.intermediates = [
Linear(10, 10, rngs=rngs) for _ in range(depth)
]


def main(argv):
print(argv)
mode: str = FLAGS.mode
total_steps: int = FLAGS.total_steps
width: int = FLAGS.width
depth: int = FLAGS.depth

print(f'{mode=}, {total_steps=}, {width=}')

X = np.linspace(0, 1, 100)[:, None]
Y = 0.8 * X**2 + 0.1 + np.random.normal(0, 0.1, size=X.shape)

model = MLP(depth=depth, rngs=nnx.Rngs(0))
tx = optax.sgd(1e-3)
optimizer = nnx.Optimizer(model, tx)

#------------------------------------------------------------
# NNX
#------------------------------------------------------------
if mode in ['all', 'nnx']:
@nnx.jit
def step_nnx(model: MLP, optimizer: nnx.Optimizer):
pass

t0 = time()
for _ in range(total_steps):
step_nnx(model, optimizer)

total_time = time() - t0
time_per_step = total_time / total_steps
time_per_layer = time_per_step / depth
print("### NNX ###")
print('total time:', total_time)
print(f'time per step: {time_per_step * 1e6:.2f} µs')
print(f'time per layer: {time_per_layer * 1e6:.2f} µs')


#------------------------------------------------------------
# JAX
#------------------------------------------------------------

if mode in ['all', 'jax']:
@jax.jit
def step_jax(graphdef, state):
return graphdef, state

graphdef, state = nnx.split((model, optimizer))
t0 = time()
for _ in range(total_steps):
graphdef, state = step_jax(graphdef, state)

total_time = time() - t0
time_per_step = total_time / total_steps
time_per_layer = time_per_step / depth
print("### JAX ###")
print('total time:', total_time)
print(f'time per step: {time_per_step * 1e6:.2f} µs')
print(f'time per layer: {time_per_layer * 1e6:.2f} µs')
print()



if __name__ == '__main__':
app.run(main)
168 changes: 168 additions & 0 deletions benchmarks/nnx_simple_training.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,168 @@
# Copyright 2024 The Flax Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# %%
import jax
import jax.numpy as jnp
import numpy as np
import optax
from time import time

from flax import nnx

from absl import flags
from absl import app

FLAGS = flags.FLAGS
flags.DEFINE_enum('mode', 'nnx', ['nnx', 'jax'], 'Mode to run the script in')
flags.DEFINE_integer('total_steps', 10_000, 'Total number of training steps')
flags.DEFINE_integer('batch_size', 32, 'Batch size')
flags.DEFINE_integer('width', 32, 'Hidden layer size')
flags.DEFINE_integer('depth', 5, 'Depth of the model')


def dataset(X, Y, batch_size):
while True:
idx = np.random.choice(len(X), size=batch_size)
yield X[idx], Y[idx]


class Linear(nnx.Module):
def __init__(self, din: int, dout: int, *, rngs: nnx.Rngs):
self.w = nnx.Param(jax.random.uniform(rngs.params(), (din, dout)))
self.b = nnx.Param(jnp.zeros((dout,)))

def __call__(self, x):
return x @ self.w + self.b


class Count(nnx.Variable):
pass


class MLP(nnx.Module):
def __init__(self, din, dhidden, dout, depth, *, rngs: nnx.Rngs):
self.count = Count(jnp.array(0))
self.linear_in = Linear(din, dhidden, rngs=rngs)
self.intermediates = [
Linear(dhidden, dhidden, rngs=rngs) for _ in range(depth - 2)
]
self.linear_out = Linear(dhidden, dout, rngs=rngs)

def __call__(self, x):
self.count.value += 1
x = nnx.relu(self.linear_in(x))
for layer in self.intermediates:
x = nnx.relu(layer(x))
x = self.linear_out(x)
return x


def main(argv):
print(argv)
mode: str = FLAGS.mode
total_steps: int = FLAGS.total_steps
batch_size: int = FLAGS.batch_size
width: int = FLAGS.width
depth: int = FLAGS.depth

print(f'{mode=}, {total_steps=}, {batch_size=}, {width=}')

if mode not in ['nnx', 'jax']:
raise ValueError(f'Invalid mode: {mode}')

X = np.linspace(0, 1, 100)[:, None]
Y = 0.8 * X**2 + 0.1 + np.random.normal(0, 0.1, size=X.shape)

model = MLP(din=1, dhidden=width, dout=1, depth=depth, rngs=nnx.Rngs(0))
tx = optax.sgd(1e-3)
optimizer = nnx.Optimizer(model, tx)
t0 = time()

if mode == 'nnx':

@nnx.jit
def train_step_nnx(model: MLP, optimizer: nnx.Optimizer, batch):
x, y = batch

def loss_fn(model: MLP):
y_pred = model(x)
return jnp.mean((y - y_pred) ** 2)

grads: nnx.State = nnx.grad(loss_fn)(model)
optimizer.update(grads)

@nnx.jit
def test_step_nnx(model: MLP, batch):
x, y = batch
y_pred = model(x)
loss = jnp.mean((y - y_pred) ** 2)
return {'loss': loss}

for step, batch in enumerate(dataset(X, Y, batch_size)):
train_step_nnx(model, optimizer, batch)

if step % 1000 == 0:
logs = test_step_nnx(model, (X, Y))
print(f"step: {step}, loss: {logs['loss']}")

if step >= total_steps - 1:
break
else:

@jax.jit
def train_step_jax(graphdef, state, batch):
model, optimizer = nnx.merge(graphdef, state)
x, y = batch

def loss_fn(model: MLP):
y_pred = model(x)
return jnp.mean((y - y_pred) ** 2)

grads = nnx.grad(loss_fn)(model)
optimizer.update(grads)

return nnx.state((model, optimizer))

@jax.jit
def test_step_jax(graphdef, state, batch):
model, optimizer = nnx.merge(graphdef, state)
x, y = batch
y_pred = model(x)
loss = jnp.mean((y - y_pred) ** 2)
state = nnx.state((model, optimizer))
return state, {'loss': loss}

graphdef, state = nnx.split((model, optimizer))

for step, batch in enumerate(dataset(X, Y, batch_size)):
state = train_step_jax(graphdef, state, batch)

if step % 1000 == 0:
state, logs = test_step_jax(graphdef, state, (X, Y))
print(f"step: {step}, loss: {logs['loss']}")

if step >= total_steps - 1:
break

model, optimizer = nnx.merge(graphdef, state)

total_time = time() - t0
print('total time:', total_time)
print(f'time per step: {total_time / total_steps * 1e6:.2f} µs')
print('times called:', model.count.value)


if __name__ == '__main__':
app.run(main)
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