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import itertools as it | |
import jax | |
import jax.numpy as jnp | |
jax.config.update('jax_enable_x64', True) | |
jax.config.update('jax_platform_name', 'cpu') | |
L = num_stages = 5 | |
N = batch_size = 6 | |
M = num_microbatches = 2 | |
B = microbatch_size = 3 | |
assert N == M * B | |
F = num_feat = 3 | |
params = jax.random.normal(jax.random.PRNGKey(0), (L, F, F)) | |
inputs = jnp.arange(N * F).reshape(N, F) | |
def fn(params, inputs): | |
assert params.ndim == 2 and inputs.ndim == 1 | |
return jnp.tanh(jnp.dot(params, inputs)) | |
state = inputs | |
for i in range(L): | |
state = jax.vmap(fn, (None, 0))(params[i], state) | |
outputs = state | |
print(outputs) | |
def spmd_pipeline(fn, params, inputs): | |
inputs = jnp.pad(inputs, [[0, L-1], [0, 0], [0, 0]]) | |
outputs = jnp.zeros((M+L-1, B, F)) | |
state = jnp.zeros([L, B, F]) | |
for i in range(M + L - 1): | |
state = shift_and_insert(state, inputs[i]) | |
state = jax.vmap(jax.vmap(fn, (None, 0)))(params, state) | |
outputs = outputs.at[i].set(state[-1]) # last layer output | |
return outputs[L-1:] | |
def shift_and_insert(arr, x): | |
padding = [[1, 0]] + [[0, 0]] * (arr.ndim - 1) | |
arr = jnp.pad(arr, padding)[:-1] | |
iota = jax.lax.broadcasted_iota('int32', arr.shape, 0) | |
return jnp.where(iota == 0, x, arr) | |
outputs2 = spmd_pipeline(fn, params, inputs.reshape(M, B, F)).reshape(N, F) | |
print(outputs2) |
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