def create_process(self, vector: jnp.array):
vector.astype(np.float)
left_matrix = jnp.array([[0, 0, 1, 0], [0, 0, 0, 1]])
right_matrix = jnp.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]])
tags = jnp.dot(jnp.dot(left_matrix, vector), right_matrix)
final_tags = tags
final_tags[0][1:3] = tags[1][:, 1:3]
res_tags = jnp.append(tags.reshape(1, length), final_tags)
return res_tags
def read_process(self, vector: jnp.array):
# 2x4 4x4 4x3 -> 2x3
# tags structure
# srcNode: sTag iTag cTag
# desNode: sTag iTag cTag
# print(vector)
for i, l in enumerate(vector):
for j, t in enumerate(l):
if isinstance(t, Tensor):
# print(type(vector))
# print(type(jax.ops.index[i, j]))
vector[i][j] = float(t.cpu().detach().numpy())
# jax.ops.index_update(vector, jax.ops.index[i,j], float(t.cpu().detach().numpy()))
elif isinstance(t, np.ndarray):
vector[i][j] = float(t)
# jax.ops.index_update(vector, jax.ops.index[i,j], t.astype(float))
vector = vector.astype('float64')
# print("vector", vector.dtype)
left_matrix = jnp.array([[0, 0, 1, 0], [0, 0, 0, 1]])
right_matrix = jnp.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]])
tags = jnp.dot(jnp.dot(left_matrix, vector), right_matrix)
final_tags = (jax.ops.index_update(tags, jax.ops.index[0, 1:3],
jnp.min(tags[:, 1:3],
axis=0))).reshape(
1, length)
tags.reshape(1, length)
res_tags = jnp.append(tags, final_tags)
return res_tags
def __call__(self, inp: np.array, axis: int = 0) -> np.array:
return inp.astype(self.prefactors.dtype) * np.expand_dims(
self.prefactors, axis=(axis + 1) % 2)
def write_process(self, vector: jnp.array):
benign_thresh = vector[1][2]
susp_thresh = vector[1][3]
a_b = vector[1][0]
a_e = vector[1][1]
# print(vector)
vector.astype(float)
left_matrix = jnp.array([[0, 0, 1, 0], [0, 0, 0, 1]])
right_matrix = jnp.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]])
# print("left_matrix: ", left_matrix.shape)
# print("vector: ", vector.shape)
# print("right_matrix: ", right_matrix.shape)
# print(vector)
# some values in the vector are torch tensor but not a number, so they are replaced by their data, losing the autograd trace
# print("left_matrix", left_matrix)
# print("vector", vector)
# print("vector", vector)
# vector = jnp.array(vector)
for i, l in enumerate(vector):
for j, t in enumerate(l):
if isinstance(t, Tensor):
# print(type(vector))
# print(type(jax.ops.index[i, j]))
vector[i][j] = float(t.cpu().detach().numpy())
# jax.ops.index_update(vector, jax.ops.index[i,j], float(t.cpu().detach().numpy()))
elif isinstance(t, np.ndarray):
vector[i][j] = float(t)
# jax.ops.index_update(vector, jax.ops.index[i,j], t.astype(float))
# print("left_matrix", left_matrix)
# print("vector", vector)
# print(left_matrix.dtype)
# print(vector.dtype)
# for l in vector:
# for t in l:
# print(t.dtype)
# print(type(vector))
vector = vector.astype('float64')
tmp = jnp.dot(left_matrix, vector)
tags = jnp.dot(tmp, right_matrix)
benign_mul = benign_thresh + susp_thresh
susp_mul = (1 - benign_thresh) + susp_thresh
dangerous_mul = (1 - benign_thresh) + (1 - susp_thresh)
# print("a_b", type(a_b))
# print("a_e", type(a_e))
if isinstance(a_b, Tensor):
a_b = float(a_b.cpu().detach().numpy())
if isinstance(a_e, Tensor):
a_e = float(a_e.cpu().detach().numpy())
attenuation_b = jnp.array([[0, a_b, a_b], [0, 0, 0]])
attenuation_e = jnp.array([[0, a_e, a_e], [0, 0, 0]])
tag_benign = (jax.ops.index_update(
tags, jax.ops.index[0, 1:3],
jnp.min(tags + attenuation_b, axis=0)[1:3])).reshape(1, length)
tag_susp_env = (jax.ops.index_update(
tags, jax.ops.index[0, 1:3],
jnp.min(tags + attenuation_e, axis=0)[1:3])).reshape(1, length)
tag_dangerous = (jax.ops.index_update(tags, jax.ops.index[0, 1:3],
jnp.min(tags[:, 1:3],
axis=0))).reshape(
1, length)
possible_tags = jnp.concatenate(
[tag_benign, tag_susp_env, tag_dangerous])
tags_probability = jax.nn.softmax(
jnp.array([benign_mul, susp_mul, dangerous_mul]))
final_tags = jnp.dot(tags_probability, possible_tags)
# if tags[0][0] >= benign:
# attenuation = jnp.array([[0, a_b, a_b], [0, 0, 0]])
# jax.ops.index_update(tags, jax.ops.index[0, 1:3], jnp.min(tags + attenuation, axis=0)[1:3])
# elif tags[0][0] >= suspect_env:
# attenuation = jnp.array([[0, a_e, a_e], [0, 0, 0]])
# jax.ops.index_update(tags, jax.ops.index[0, 1:3], jnp.min(tags + attenuation, axis=0)[1:3])
# else:
# jax.ops.index_update(tags, jax.ops.index[0, 1:3], jnp.min(tags[:, 1:3], axis=0))
res_tags = jnp.append(tags.reshape(1, length), final_tags)
return res_tags
def get_exec_grad(self, vector: jnp.array):
vector = vector.astype('float64')
grad = self.jrev(vector)
# [12 * 4 * 4]
return grad[:, 1, :]
def get_read_grad(self, vector: jnp.array):
vector = vector.astype('float64')
grad = self.jrev(vector)
# grad = jit(jacrev(EventProcessor.write_process)(vector))
# [12 * 4 * 4]
return grad[:, 1, :]
def __call__(self, inp: np.array, axis: int = 0) -> np.array:
assert self.prefactors.shape[0] == inp.shape[axis]
return inp.astype(self.prefactors.dtype) * np.expand_dims(
self.prefactors, axis=(axis + 1) % 2)