def ode_sampler(model, z=None):
"""The probability flow ODE sampler with black-box ODE solver.
Args:
model: A score model.
z: If present, generate samples from latent code `z`.
Returns:
samples, number of function evaluations.
"""
with torch.no_grad():
# Initial sample
if z is None:
# If not represent, sample the latent code from the prior distibution of the SDE.
x = sde.prior_sampling(shape).to(device)
else:
x = z
def ode_func(t, x):
x = from_flattened_numpy(x, shape).to(device).type(torch.float32)
vec_t = torch.ones(shape[0], device=x.device) * t
drift = drift_fn(model, x, vec_t)
return to_flattened_numpy(drift)
# Black-box ODE solver for the probability flow ODE
solution = integrate.solve_ivp(ode_func, (sde.T, eps), to_flattened_numpy(x),
rtol=rtol, atol=atol, method=method)
nfe = solution.nfev
x = torch.tensor(solution.y[:, -1]).reshape(shape).to(device).type(torch.float32)
# Denoising is equivalent to running one predictor step without adding noise
if denoise:
x = denoise_update_fn(model, x)
x = inverse_scaler(x)
return x, nfe
def likelihood_fn(prng, pstate, data):
"""Compute an unbiased estimate to the log-likelihood in bits/dim.
Args:
prng: An array of random states. The list dimension equals the number of devices.
pstate: Replicated training state for running on multiple devices.
data: A JAX array of shape [#devices, batch size, ...].
Returns:
bpd: A JAX array of shape [#devices, batch size]. The log-likelihoods on `data` in bits/dim.
z: A JAX array of the same shape as `data`. The latent representation of `data` under the
probability flow ODE.
nfe: An integer. The number of function evaluations used for running the black-box ODE solver.
"""
rng, step_rng = jax.random.split(flax.jax_utils.unreplicate(prng))
shape = data.shape
if hutchinson_type == 'Gaussian':
epsilon = jax.random.normal(step_rng, shape)
elif hutchinson_type == 'Rademacher':
epsilon = jax.random.randint(step_rng, shape,
minval=0, maxval=2).astype(jnp.float32) * 2 - 1
else:
raise NotImplementedError(f"Hutchinson type {hutchinson_type} unknown.")
def ode_func(t, x):
sample = mutils.from_flattened_numpy(x[:-shape[0] * shape[1]], shape)
vec_t = jnp.ones((sample.shape[0], sample.shape[1])) * t
drift = mutils.to_flattened_numpy(p_drift_fn(pstate, sample, vec_t))
logp_grad = mutils.to_flattened_numpy(p_div_fn(pstate, sample, vec_t, epsilon))
return np.concatenate([drift, logp_grad], axis=0)
init = jnp.concatenate([mutils.to_flattened_numpy(data), np.zeros((shape[0] * shape[1],))], axis=0)
solution = integrate.solve_ivp(ode_func, (eps, sde.T), init, rtol=rtol, atol=atol, method=method)
nfe = solution.nfev
zp = jnp.asarray(solution.y[:, -1])
z = mutils.from_flattened_numpy(zp[:-shape[0] * shape[1]], shape)
delta_logp = zp[-shape[0] * shape[1]:].reshape((shape[0], shape[1]))
prior_logp = p_prior_logp_fn(z)
bpd = -(prior_logp + delta_logp) / np.log(2)
N = np.prod(shape[2:])
bpd = bpd / N
# A hack to convert log-likelihoods to bits/dim
# based on the gradient of the inverse data normalizer.
offset = jnp.log2(jax.grad(inverse_scaler)(0.)) + 8.
bpd += offset
return bpd, z, nfe
def ode_sampler(prng, pstate, z=None):
"""The probability flow ODE sampler with black-box ODE solver.
Args:
prng: An array of random state. The leading dimension equals the number of devices.
pstate: Replicated training state for running on multiple devices.
z: If present, generate samples from latent code `z`.
Returns:
Samples, and the number of function evaluations.
"""
# Initial sample
rng = flax.jax_utils.unreplicate(prng)
rng, step_rng = random.split(rng)
if z is None:
# If not represent, sample the latent code from the prior distibution of the SDE.
x = sde.prior_sampling(step_rng,
(jax.local_device_count(), ) + shape)
else:
x = z
def ode_func(t, x):
x = from_flattened_numpy(x, (jax.local_device_count(), ) + shape)
vec_t = jnp.ones((x.shape[0], x.shape[1])) * t
drift = drift_fn(pstate, x, vec_t)
return to_flattened_numpy(drift)
# Black-box ODE solver for the probability flow ODE
solution = integrate.solve_ivp(ode_func, (sde.T, eps),
to_flattened_numpy(x),
rtol=rtol,
atol=atol,
method=method)
nfe = solution.nfev
x = jnp.asarray(
solution.y[:, -1]).reshape((jax.local_device_count(), ) + shape)
# Denoising is equivalent to running one predictor step without adding noise
if denoise:
rng, *step_rng = random.split(rng, jax.local_device_count() + 1)
step_rng = jnp.asarray(step_rng)
x = denoise_update_fn(step_rng, pstate, x)
x = inverse_scaler(x)
return x, nfe
def likelihood_fn(model, data):
"""Compute an unbiased estimate to the log-likelihood in bits/dim.
Args:
model: A score model.
data: A PyTorch tensor.
Returns:
bpd: A PyTorch tensor of shape [batch size]. The log-likelihoods on `data` in bits/dim.
z: A PyTorch tensor of the same shape as `data`. The latent representation of `data` under the
probability flow ODE.
nfe: An integer. The number of function evaluations used for running the black-box ODE solver.
"""
with torch.no_grad():
shape = data.shape
if hutchinson_type == 'Gaussian':
epsilon = torch.randn_like(data)
elif hutchinson_type == 'Rademacher':
epsilon = torch.randint_like(data, low=0, high=2).float() * 2 - 1.
else:
raise NotImplementedError(f"Hutchinson type {hutchinson_type} unknown.")
def ode_func(t, x):
sample = mutils.from_flattened_numpy(x[:-shape[0]], shape).to(data.device).type(torch.float32)
vec_t = torch.ones(sample.shape[0], device=sample.device) * t
drift = mutils.to_flattened_numpy(drift_fn(model, sample, vec_t))
logp_grad = mutils.to_flattened_numpy(div_fn(model, sample, vec_t, epsilon))
return np.concatenate([drift, logp_grad], axis=0)
init = np.concatenate([mutils.to_flattened_numpy(data), np.zeros((shape[0],))], axis=0)
solution = integrate.solve_ivp(ode_func, (eps, sde.T), init, rtol=rtol, atol=atol, method=method)
nfe = solution.nfev
zp = solution.y[:, -1]
z = mutils.from_flattened_numpy(zp[:-shape[0]], shape).to(data.device).type(torch.float32)
delta_logp = mutils.from_flattened_numpy(zp[-shape[0]:], (shape[0],)).to(data.device).type(torch.float32)
prior_logp = sde.prior_logp(z)
bpd = -(prior_logp + delta_logp) / np.log(2)
N = np.prod(shape[1:])
bpd = bpd / N
# A hack to convert log-likelihoods to bits/dim
offset = 7. - inverse_scaler(-1.)
bpd = bpd + offset
return bpd, z, nfe
def ode_func(t, x): sample = mutils.from_flattened_numpy(x[:-shape[0] * shape[1]], shape) vec_t = jnp.ones((sample.shape[0], sample.shape[1])) * t drift = mutils.to_flattened_numpy(p_drift_fn(pstate, sample, vec_t)) logp_grad = mutils.to_flattened_numpy(p_div_fn(pstate, sample, vec_t, epsilon)) return np.concatenate([drift, logp_grad], axis=0)
def ode_func(t, x):
x = from_flattened_numpy(x, (jax.local_device_count(), ) + shape)
vec_t = jnp.ones((x.shape[0], x.shape[1])) * t
drift = drift_fn(pstate, x, vec_t)
return to_flattened_numpy(drift)
def ode_func(t, x): x = from_flattened_numpy(x, shape).to(device).type(torch.float32) vec_t = torch.ones(shape[0], device=x.device) * t drift = drift_fn(model, x, vec_t) return to_flattened_numpy(drift)
def ode_func(t, x): sample = mutils.from_flattened_numpy(x[:-shape[0]], shape).to(data.device).type(torch.float32) vec_t = torch.ones(sample.shape[0], device=sample.device) * t drift = mutils.to_flattened_numpy(drift_fn(model, sample, vec_t)) logp_grad = mutils.to_flattened_numpy(div_fn(model, sample, vec_t, epsilon)) return np.concatenate([drift, logp_grad], axis=0)