Created
December 10, 2020 08:49
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2 ways to batch inference
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import jax | |
""" 'Loop over vmaps' | |
This is what most PPLs do. We synchronize chains at the step level, each step | |
ends when the `num_chains` have completed their step. We iterate for `num_samples` | |
steps. | |
""" | |
def one_step(rng_key, states): | |
chain_keys = jax.random.split(rng_key, num_chains) | |
new_states, _ = jax.vmap(kernel)(chain_keys, states) | |
return new_states, states | |
step_keys = jax.random.split(rng_key, num_samples) | |
_, states = jax.lax.scan(one_step, initial_states, step_keys) | |
""" 'vmap the loops' | |
We run `num_chains` independent chains. This ends when all chains | |
have run `num_steps`. | |
""" | |
def chain(rng_key, initial_state): | |
"""Moves one chain `num_steps` time.""" | |
def one_step(state, key): | |
new_state, _ = kernel(key, state) | |
return new_state, state | |
step_keys = jax.random.split(rng_key, num_samples) | |
_, states = jax.lax.scan(one_step, initial_state, step_keys) | |
return states | |
chain_keys = jax.random.split(rng_key, num_chains) | |
run = jax.vmap(chain)(keys, initial_states) |
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