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import os | ||
os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=8' | ||
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import jax | ||
import jax.numpy as jnp | ||
import numpy as np | ||
import optax | ||
from flax import nnx | ||
from jax.experimental import mesh_utils | ||
import matplotlib.pyplot as plt | ||
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# create a mesh + shardings | ||
num_devices = jax.local_device_count() | ||
mesh = 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')) | ||
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# create model | ||
class MLP(nnx.Module): | ||
def __init__(self, din, dmid, dout, *, rngs: nnx.Rngs): | ||
self.linear1 = nnx.Linear(din, dmid, rngs=rngs) | ||
self.linear2 = nnx.Linear(dmid, dout, rngs=rngs) | ||
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def __call__(self, x): | ||
return self.linear2(nnx.relu(self.linear1(x))) | ||
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model = MLP(1, 64, 1, rngs=nnx.Rngs(0)) | ||
optimizer = nnx.Optimizer(model, optax.adamw(1e-2)) | ||
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# replicate state | ||
state = nnx.state((model, optimizer)) | ||
state = jax.device_put(state, model_sharding) | ||
nnx.update((model, optimizer), state) | ||
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# visualize model sharding | ||
print('model sharding') | ||
jax.debug.visualize_array_sharding(model.linear1.kernel.value) | ||
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@nnx.jit | ||
def train_step(model: MLP, optimizer: nnx.Optimizer, x, y): | ||
def loss_fn(model: MLP): | ||
y_pred = model(x) | ||
return jnp.mean((y - y_pred) ** 2) | ||
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loss, grads = nnx.value_and_grad(loss_fn)(model) | ||
optimizer.update(grads) | ||
return loss | ||
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def dataset(steps, batch_size): | ||
for _ in range(steps): | ||
x = np.random.uniform(-2, 2, size=(batch_size, 1)) | ||
y = 0.8 * x**2 + 0.1 + np.random.normal(0, 0.1, size=x.shape) | ||
yield x, y | ||
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for step, (x, y) in enumerate(dataset(1000, 16)): | ||
# shard data | ||
x, y = jax.device_put((x, y), data_sharding) | ||
# train | ||
loss = train_step(model, optimizer, x, y) | ||
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if step == 0: | ||
print('data sharding') | ||
jax.debug.visualize_array_sharding(x) | ||
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if step % 100 == 0: | ||
print(f'step={step}, loss={loss}') | ||
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# dereplicate state | ||
state = nnx.state((model, optimizer)) | ||
state = jax.device_get(state) | ||
nnx.update((model, optimizer), state) | ||
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X, Y = next(dataset(1, 1000)) | ||
x_range = np.linspace(X.min(), X.max(), 100)[:, None] | ||
y_pred = model(x_range) | ||
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# plot | ||
plt.scatter(X, Y, label='data') | ||
plt.plot(x_range, y_pred, color='black', label='model') | ||
plt.legend() | ||
plt.show() |