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neurocuber.py
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neurocuber.py
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import numpy as np
from tensorflow.python.framework import tensor_shape
import tfutil
# from neurosat import *
from train_util import *
from pysat.formula import CNF
from cnf_util import res_graph_idxs, G_cl_of_idxs, clgraph
from config import *
import os
import time
import tensorflow as tf
import numpy as np
default_cfg_path = os.path.join(PROJECT_DIR, "res_models/", "test.json")
default_cfg = ModelCfg_from_file(default_cfg_path)
class DenseWeightNormLayer(tf.keras.layers.Layer):
"""
A dense layer with optional weight reparametrization.
"""
def __init__(self, d_out, activation="relu6", weight_reparam=True, dtype="float32"):
super(DenseWeightNormLayer, self).__init__(dtype=dtype)
self.weight_reparam = weight_reparam
self.d_out = d_out
if activation == "relu": activation = tf.nn.relu
elif activation == "relu6": activation = tf.nn.relu6
elif activation == "tanh": activation = tf.nn.tanh
elif activation == "sig": activation = tf.nn.sigmoid
elif activation == "elu": activation = tf.nn.elu
elif activation == "softmax": activation = tf.nn.softmax
elif activation == None: activation = None
else:
raise Exception(f"Unsupported transfer function {activation}")
self.transfer_fn = activation
def build(self, input_shape):
self.last_dim = tensor_shape.dimension_value(input_shape[-1])
if self.weight_reparam:
self.kernel_w = self.add_weight("w", shape = [self.last_dim, self.d_out], initializer = tf.keras.initializers.he_normal(seed=None))
self.kernel_g = self.add_weight("g", shape = [1, self.d_out], initializer = tf.ones_initializer())
else:
self.kernel = self.add_weight("w", shape = [self.last_dim, self.d_out],initializer = tf.initializers.GlorotNormal())
self.bias = self.add_weight("b", shape = [self.d_out], initializer = tf.zeros_initializer())
def call(self, input_):
if self.weight_reparam:
kernel = tf.nn.l2_normalize(self.kernel_w, axis = 0) * tf.tile(self.kernel_g,[self.last_dim,1])
if self.transfer_fn is not None:
return self.transfer_fn(tf.matmul(input_, kernel) + self.bias)
else:
return tf.matmul(input_, kernel) + self.bias
else:
if self.transfer_fn is not None:
return self.transfer_fn(tf.matmul(input_, self.kernel) + self.bias)
else:
return tf.matmul(input_, self.kernel) + self.bias
class NeuroSAT_MLP(tf.keras.layers.Layer):
def __init__(self, hidden_layers=3, hidden_dim=80,output_dim=80, activation="relu6", weight_reparam=True, dtype="float32", **kwargs):
super(NeuroSAT_MLP, self).__init__(dtype=dtype, **kwargs)
self.hidden_layers=hidden_layers
self.hidden_dim=hidden_dim
self.output_dim=output_dim
self.layer_list = []
for i in list(range(hidden_layers)):
self.layer_list.append(DenseWeightNormLayer(hidden_dim))
self.layer_list.append(DenseWeightNormLayer(output_dim, activation=None))
def call(self, inputs):
z = inputs
for l in self.layer_list:
z = l(z)
return z
def blockify3(Gs, n_row_list, n_col_list, DEBUG=False):
"""
shift columns only
"""
i = tf.constant(0, dtype=tf.int64)
batch_size = len(Gs)
n_col_total = tf.reduce_sum(n_col_list)
def lambda_body(n):
n_cells = Gs[n].indices.shape[0]
shifts = tf.tile(np.array([[0 # tf.reduce_sum(n_row_list[0:n])
, tf.reduce_sum(n_col_list[0:n])]], dtype="int64"), [n_cells,1])
Gs[n] = tf.SparseTensor(indices=tf.add(Gs[n].indices, shifts), values=Gs[n].values, dense_shape=(Gs[n].dense_shape[0], n_col_total))
n = tf.add(n, 1)
return [n]
tf.while_loop(lambda i: i < batch_size, lambda_body, [i])
# print(tf.sparse.to_dense(Gs[0], validate_indices=False))
result = tf.sparse.concat(axis=0, sp_inputs=Gs)
return tf.sparse.reorder(result)
class NeuroCuber(tf.keras.Model):
# This implements the following behavior:
# - default argument of subsequent arguments to cfg is `None`
# - if an argument is None, fall back to the value of the corresponding key of `cfg`, otherwise override `cfg`
def __init__(self, cfg = default_cfg, dtype="float32", # TODO(jesse): define a default ModelCfg
**kwargs):
self.RES_FLAG = False
self.RESL_FLAG = False
self.CUBE_FLAG = False
if "mode" in kwargs:
self.mode = kwargs["mode"]
else:
self.mode = cfg.mode
if self.mode == "res":
raise Exception("res not supported")
self.RES_FLAG = True
elif self.mode == "resl":
raise Exception("resl not supported")
self.RESL_FLAG = True
elif self.mode == "cube":
self.CUBE_FLAG = True
else:
raise Exception("unsupported mode")
if "name" in kwargs:
name = kwargs["name"]
else:
name = cfg.model_id
super(NeuroCuber, self).__init__(name=name, dtype=dtype)
### begin setting hyperparameters
if "d" in kwargs:
self.d = kwargs["d"]
else:
self.d = cfg.d
if "C_res_depth" in kwargs:
self.C_res_depth = kwargs["C_res_depth"]
else:
self.C_res_depth = cfg.C_res_depth
if "C_update_depth" in kwargs:
self.C_update_depth = kwargs["C_update_depth"]
else:
self.C_update_depth = cfg.C_update_depth
if "L_update_depth" in kwargs:
self.L_update_depth = kwargs["L_update_depth"]
else:
self.L_update_depth = cfg.L_update_depth
if "V_proof_depth" in kwargs:
self.V_proof_depth = kwargs["V_proof_depth"]
else:
self.V_proof_depth = cfg.V_proof_depth
if "V_core_depth" in kwargs:
self.V_core_depth = kwargs["V_core_depth"]
else:
self.V_core_depth = cfg.V_core_depth
if "C_core_depth" in kwargs:
self.C_core_depth = kwargs["C_core_depth"]
else:
self.C_core_depth = cfg.C_core_depth
if "n_rounds" in kwargs:
self.n_rounds = kwargs["n_rounds"]
else:
self.n_rounds = cfg.n_rounds
if "weight_reparam" in kwargs:
self.weight_reparam = kwargs["weight_reparam"]
else:
self.weight_reparam = cfg.weight_reparam
if "res_layers" in kwargs:
self.res_layers = kwargs["res_layers"]
else:
self.res_layers = cfg.res_layers
if "norm_axis" in kwargs:
self.norm_axis = kwargs["norm_axis"]
else:
self.norm_axis = cfg.norm_axis
if "norm_eps" in kwargs:
self.norm_eps = kwargs["norm_eps"]
else:
self.norm_eps = cfg.norm_eps
if "CL_scale" in kwargs:
self.CL_scale = kwargs["CL_scale"]
else:
self.CL_scale = cfg.CL_scale
if "LC_scale" in kwargs:
self.LC_scale = kwargs["LC_scale"]
else:
self.LC_scale = cfg.LC_scale
if "activation" in kwargs:
self.activation = kwargs["activation"]
else:
self.activation = cfg.activation
self.L_init_scale = 1.0/np.sqrt(self.d)
self.C_init_scale = 1.0/np.sqrt(self.d)
### end hyperparameters
self.C_update = NeuroSAT_MLP( hidden_layers = self.C_update_depth,
hidden_dim = 2 * self.d,
output_dim = self.d,
activation = self.activation,
weight_reparam = self.weight_reparam,
dtype = "float32",
name = "C_update"
)
self.L_update = NeuroSAT_MLP( hidden_layers = self.L_update_depth,
hidden_dim = 3 * self.d,
output_dim = self.d,
activation = self.activation,
weight_reparam = self.weight_reparam,
dtype = "float32",
name = "L_update"
)
self.V_proof = NeuroSAT_MLP( hidden_layers = self.V_proof_depth,
hidden_dim = 2 * self.d,
output_dim = 1,
activation = self.activation,
weight_reparam = self.weight_reparam,
dtype = "float32",
name = "V_proof"
)
self.V_core = NeuroSAT_MLP( hidden_layers = self.V_core_depth,
hidden_dim = 2 * self.d,
output_dim = 1,
activation = self.activation,
weight_reparam = self.weight_reparam,
dtype = "float32",
name = "V_core"
)
self.C_core = NeuroSAT_MLP( hidden_layers = self.C_core_depth,
hidden_dim = self.d,
output_dim = 1,
activation = self.activation,
weight_reparam = self.weight_reparam,
dtype = "float32",
name = "C_core"
)
def call(self, G_cls, n_clauses_list, n_vars_list):
"""
Accepts:
A list of sparse clause-literal adjacency matrices, a list of n_clause dimensions, and a list of n_var dimensions
"""
n_clauses = tf.reduce_sum(n_clauses_list, axis=-1)
n_vars = tf.reduce_sum(n_vars_list, axis=-1)
n_lits = tf.multiply(2, n_vars)
batch_size = n_clauses_list.shape[0]
G_cl = blockify3(G_cls, n_clauses_list, 2*n_vars_list)
L = tf.ones(shape=[2 * n_vars, self.d], dtype=tf.float32) * self.L_init_scale
C = tf.ones(shape=[n_clauses, self.d], dtype=tf.float32) * self.C_init_scale
def flip(lits):
Ls = tf.split(tf.cast(lits, tf.float32), tf.multiply(2, n_vars_list))
i = tf.constant(0, dtype=tf.int64)
def lambda_body(i):
Ls[i] = tf.concat([Ls[i][tf.cast(Ls[i].shape[0]/2, dtype=tf.int64):,:], Ls[i][0:tf.cast(Ls[i].shape[0]/2, dtype=tf.int64),:]], axis=0)
i = tf.add(i,1)
return [i]
tf.while_loop(lambda i: i < batch_size, lambda_body, [i])
result = tf.concat(Ls, axis=0)
return tf.reshape(result, (n_lits, self.d))
for t in range(self.n_rounds):
if self.res_layers:
C_0, L_0 = C, L
# C = tf.debugging.check_numerics(C, message="C before G_cl update")
C = self.C_update(tf.concat((C, tf.sparse.sparse_dense_matmul(G_cl, L) * self.LC_scale), axis=1))
# C = tf.debugging.check_numerics(C, message="C after G_cl update")
C = tfutil.normalize(C, axis=self.norm_axis, eps=self.norm_eps)
# C = tf.debugging.check_numerics(C, message="C after norm")
C = tfutil.normalize(C, axis=self.norm_axis, eps=self.norm_eps)
# C = tf.debugging.check_numerics(C, message="C after norm")
if self.res_layers:
C = C + C_0
# L = tf.debugging.check_numerics(L, message="L before update")
L = self.L_update(tf.concat((L, tf.sparse.sparse_dense_matmul(tf.sparse.transpose(G_cl), C, adjoint_a = False) * self.CL_scale, flip(L)), axis=1))
# L = tf.debugging.check_numerics(L, message="L after update")
L = tfutil.normalize(L, axis=self.norm_axis, eps=self.norm_eps)
# L = tf.debugging.check_numerics(L, message="L after norm")
if self.res_layers: # TODO(jesse): test res_layers
L = L + L_0
# flop the literals
def flop(lits):
# split the tensors, flip them, then re-stack them
Ls = tf.split(tf.cast(lits, tf.float32), tf.multiply(2, n_vars_list))
i = tf.constant(0, dtype=tf.int64)
def lambda_body(i):
Ls[i] = tf.concat([Ls[i][0:tf.cast(Ls[i].shape[0]/2, dtype=tf.int64):,:], Ls[i][tf.cast(Ls[i].shape[0]/2, dtype=tf.int64):,:]], axis=1)
i = tf.add(i,1)
return [i]
tf.while_loop(lambda i: i < batch_size, lambda_body, [i])
return tf.reshape((tf.concat(Ls, axis=0)), (n_vars, 2*self.d))
V = flop(L)
V_proof_logits = tf.squeeze(self.V_proof(V))
V_core_logits = tf.squeeze(self.V_core(V))
C_core_logits = tf.squeeze(self.C_core(C))
# print(V_proof_logits)
return tf.split(V_proof_logits, n_vars_list), tf.split(V_core_logits, n_vars_list), tf.split(C_core_logits, n_clauses_list)
test_fmla = CNF(from_clauses=[[1,2,3],[-1,2,3],[1,-2,3],[-3]])
G_cl_test = G_cl_of_idxs(len(test_fmla.clauses), test_fmla.nv, tf.cast(clgraph(test_fmla), tf.int64))
def initialize_neurocuber(neurocuber): # TODO(jesse): fix
# print(G_cl_test)
# print(G_cl_test.dense_shape)
return neurocuber([G_cl_test], np.array([len(test_fmla.clauses)]), np.array([test_fmla.nv]))
def mk_neurocuber_loss(loss_fn1, loss_fn2=tfutil.mask_kldiv(), loss_fn3=tfutil.mask_kldiv(), pv_loss_scale = 1.0# , cv_loss_scale = 1.0, cc_loss_scale = 1.0, l2_loss_scale = 1e-6 # TODO(jesse): implement this
):
"""
deprecated
"""
return [loss_fn1, loss_fn2, loss_fn3]
class mk_neurocuber_loss2(tf.keras.losses.Loss):
"""
TODO(jesse): document this hack
"""
def __init__(self, loss_fn1, loss_fn2=tfutil.mask_kldiv(), loss_fn3=tfutil.mask_kldiv(), pv_loss_scale = 1.0, cv_loss_scale = 1.0, cc_loss_scale = 1.0, l2_loss_scale = 1e-6):
self.loss_fn1 = loss_fn1
self.loss_fn2 = loss_fn2
self.loss_fn3 = loss_fn3
self.pv_loss_scale = pv_loss_scale
self.cv_loss_scale = cv_loss_scale
self.cc_loss_scale = cc_loss_scale
self.l2_loss_scale = l2_loss_scale
super(mk_neurocuber_loss2, self).__init__(name="losszilla")
def call(self, y_true, y_pred):
return self.pv_loss_scale * self.loss_fn1(np.array([y_true[0][0]]), np.array([y_pred[0][0]]))+ self.cv_loss_scale * self.loss_fn2(np.array([y_true[0][1]]), np.array([y_pred[0][1]])) + self.cc_loss_scale * self.loss_fn3(np.array([y_true[0][2]]), np.array([y_pred[0][2]]))
def mk_CL_idxs(tfdc):
return tf.concat((np.array([[tfdc.n_clauses, tfdc.n_vars]]), tfdc.CL_idxs), axis=0)
# test datapoint:
# formula
# p cnf 3 4
# 1 2 3
# -2 3
# 2
# -3
# CL_idxs
# [[4,3], [0,0],[0,1],[0,2],[1,4],[1,2],[2,1],[3,5]]
test_CL_idxs = np.array([[4,3],[0,0],[0,1],[0,2],[1,4],[1,2],[2,1],[3,5]])
# resolution graph indices:
# nonsense DRAT lemma count:
# [0,2,1]
test_proof_count = np.array([[0,2,1]])
# var_mask:
# [0,1,1]
test_var_mask = np.array([[0,1,1]])
# core_mask:
# [0,1,1,1]
test_core_mask = np.array([[0,1,1,1]])
def init_neurocuber(cfg, restore=False, restore_from=None, **kwargs):
neurocuber = NeuroCuber(cfg=cfg, **kwargs)
initialize_neurocuber(neurocuber)
if restore:
if restore_from is None:
checkpoint = tf.train.Checkpoint(model=neurocuber)
latest = tf.train.latest_checkpoint(cfg.ckpt_dir)
print("restoring from", latest)
restore_from = latest
# neurocuber.load_weights(restore_from).expect_partial()
checkpoint.restore(latest).expect_partial()
return neurocuber
# def init_core_model(cfg):
# neurocuber = NeuroCore(cfg=cfg)
# x = initialize_neurosat(neurocuber)
# print(x)
# return neurocuber
# input: a list of sparse adjacency matrices which do not need to be resized
# returns: a sparse matrix with the list as diagonal blocks
# @tf.function
# from each G in Gs, get the list of indices
# shift the indices so that both row and column start from n * max_n_row, n * max_n_col for n in range(len_Gs)
# return a new sparse tensor with shape n * max_n_row, n * max_n_col
# TODO(jesse): is this efficient?
@tf.function(experimental_relax_shapes=True)
def reconstitute_CL_idxs(CL_idxs, n_vars, max_n_clauses, max_n_vars):
"""
Args:
CL_idxs: CL_idxs from a TFDCR
n_vars: number of variables for CL_idxs
max_n_clauses: maximum number of clauses in a batch
max_n_vars: maximum number of variables in a batch
Returns:
A sparse clause-literal adjacency matrix, appropriately padded to dimensions max_n_clauses x 2*(max_n_var)
"""
# indices = tf.cast(CL_idxs, dtype="int64")
# start = time.time()
# loop variables: CL_idxs
# operation: CL_idx + shift_indices =
# new_indices = tf.while_loop(lambda i: True, b = lambda i: )
# print((lambda pr: [pr[0], (lambda l_idx: tf.cond(l_idx < n_vars, lambda: l_idx, lambda: tf.add(l_idx, tf.subtract(max_n_vars, n_vars))))(pr[1])])([1,15]))
new_indices = tf.cast(tf.map_fn(lambda pr:tf.stack([pr[0], tf.cond(pr[1] < n_vars, lambda: pr[1], lambda: tf.add(pr[1], tf.subtract(max_n_vars, n_vars)))]), CL_idxs), dtype=tf.int64)
# new_indices = tf.cast(tf.vectorized_map(lambda pr: tf.stack([pr[0], (lambda l_idx: tf.cond(l_idx < n_vars, lambda: l_idx, lambda: tf.add(l_idx, tf.subtract(max_n_vars, n_vars))))(pr[1])]), CL_idxs), dtype=tf.int64)
# raise Exception
# new_indices = tf.cast([[c_idx, tf.cond(l_idx < n_vars, lambda: l_idx, lambda: tf.add(l_idx, tf.subtract(max_n_vars, n_vars)))] for c_idx, l_idx in CL_idxs], dtype="int64")
# print("reconstitution time: ", time.time() - start)
return tf.sparse.reorder(tf.SparseTensor(
indices=new_indices,
values=tf.ones(tf.shape(new_indices)[0]),
dense_shape=tf.cast((max_n_clauses, 2*max_n_vars), tf.int64)
)) # TODO(jesse): test
# @tf.function(experimental_relax_shapes=True)
@tf.function
def transform_CL_idxs(CL_idxs, max_n_clauses, max_n_vars):
"""
combines reconstitute + shift_pad
Args:
CL_idxs: CL_idxs from a TFDCR with a constant value at the end of each row
n_vars: number of variables for CL_idxs
max_n_clauses: maximum number of clauses in a batch
max_n_vars: maximum number of variables in a batch
Returns:
Indices for a sparse adjacency matrix, which should be padded to max_n_clauses x 2*max_n_vars
"""
# indices = tf.cast(CL_idxs, dtype="int64")
# start = time.time()
# loop variables: CL_idxs
# operation: CL_idx + shift_indices =
# new_indices = tf.while_loop(lambda i: True, b = lambda i: )
# print((lambda pr: [pr[0], (lambda l_idx: tf.cond(l_idx < n_vars, lambda: l_idx, lambda: tf.add(l_idx, tf.subtract(max_n_vars, n_vars))))(pr[1])])([1,15]))
n = CL_idxs[0,2]
# print(n)
n_cells = CL_idxs.shape[0]
n_vars = CL_idxs[0,3]
new_indices = tf.cast(tf.map_fn(lambda pr:tf.stack([pr[0], tf.cond(pr[1] < n_vars, lambda: pr[1], lambda: tf.add(pr[1], tf.subtract(max_n_vars, n_vars)))]), CL_idxs), dtype=tf.int64)
# print(tf.stack([tf.multiply(n,max_n_clauses), 2*tf.multiply(n, max_n_vars)]))
# shifts = tf.cast(tf.tile(tf.cast([[n*max_n_clauses, 2*n* max_n_vars]], dtype=tf.int64), [n_cells,1]),dtype=tf.int64)
shifts = tf.tile(tf.cast([[n*max_n_clauses, 2*n*max_n_vars]], dtype="int64"), [n_cells, 1])
# print("HEWWO")
final_indices = tf.add(new_indices, shifts)
# print(final_indices)
return final_indices
# return new_indices
@tf.function
def mk_batch_G_cl2(new_indices, max_n_clauses, max_n_vars, batch_size):
final_indices = tf.reshape(new_indices, shape=(new_indices.shape[0]*new_indices.shape[1], 2))
return tf.sparse.reorder(tf.SparseTensor(
indices=final_indices,
values=tf.ones(tf.shape(final_indices)[0]),
dense_shape=(tf.cast((max_n_clauses * batch_size, 2*max_n_vars*batch_size), tf.int64))
))
# new_indices = tf.cast(tf.vectorized_map(lambda pr: tf.stack([pr[0], (lambda l_idx: tf.cond(l_idx < n_vars, lambda: l_idx, lambda: tf.add(l_idx, tf.subtract(max_n_vars, n_vars))))(pr[1])]), CL_idxs), dtype=tf.int64)
# raise Exception
# new_indices = tf.cast([[c_idx, tf.cond(l_idx < n_vars, lambda: l_idx, lambda: tf.add(l_idx, tf.subtract(max_n_vars, n_vars)))] for c_idx, l_idx in CL_idxs], dtype="int64")
# print("reconstitution time: ", time.time() - start)
# return tf.sparse.reorder(tf.SparseTensor(
# indices=new_indices,
# values=tf.ones(tf.shape(new_indices)[0]),
# dense_shape=tf.cast((max_n_clauses, 2*max_n_vars), tf.int64)
# )) # TODO(jesse): test
# return new_indices
# def reconstitute_shift_pad(G_idxs, max_n_row, max_n_col, batch_size,n, DEBUG=False):
# """
# shift columns only and pad to max width
# """
# if DEBUG:
# start = time.time()
# G = tf.sparse.reset_shape(G, [max_n_row, batch_size * max_n_col])
# if DEBUG:
# print("sparse reset shape time: ", time.time() - start)
# n_cells = G.indices.shape[0]
# if DEBUG:
# start = time.time()
# shifts = tf.tile(np.array([[0, n*max_n_col]], dtype="int64"), [n_cells,1])
# if DEBUG:
# print("shifts creation time: ", time.time() - start)
# if DEBUG:
# start = time.time()
# G = tf.SparseTensor(indices=tf.add(G.indices, shifts), values=G.values, dense_shape=G.dense_shape)
# if DEBUG:
# print("final G creation time: ", time.time() - start)
# return G
# def reconstitute_CL_idxs(CL_idxs, n_vars, max_n_clauses, max_n_vars):
# """
# Args:
# CL_idxs: CL_idxs from a TFDCR
# n_vars: number of variables for CL_idxs
# max_n_clauses: maximum number of clauses in a batch
# max_n_vars: maximum number of variables in a batch
# Returns:
# A sparse clause-literal adjacency matrix, appropriately padded to dimensions max_n_clauses x 2*(max_n_var)
# """
# # indices = tf.cast(CL_idxs, dtype="int64")
# start = time.time()
# new_indices = tf.cast([[c_idx, tf.cond(l_idx < n_vars, lambda: l_idx, lambda: tf.add(l_idx, tf.subtract(max_n_vars, n_vars)))] for c_idx, l_idx in CL_idxs], dtype="int64")
# print("reconstitution time: ", time.time() - start)
# return tf.sparse.reorder(tf.SparseTensor(
# indices=new_indices,
# values=tf.ones(tf.shape(new_indices)[0]),
# dense_shape=tf.cast([max_n_clauses, 2*max_n_vars], tf.int64)
# )) # TODO(jesse): test
@tf.function(experimental_relax_shapes=True)
def reconstitute_res_idxs(res_idxs,max_n_clauses):
"""
Args:
res_idxs: res_idxs from a TFDCR
max_n_clauses: maximum number of clauses in a batch
Returns:
A sparse resolution graph adjacency matrix, reshaped to max_n_clauses x max_n_clauses
"""
# indices = tf.cast(res_idxs, dtype="int64")
new_indices = tf.cast(res_idxs, dtype="int64")
return tf.sparse.reorder(tf.SparseTensor(
indices=new_indices,
values=tf.ones(tf.shape(new_indices)[0]),
dense_shape=tf.cast([max_n_clauses, max_n_clauses], tf.int64)
)) # TODO(jesse): test
def blockify(Gs,max_n_row, max_n_col, batch_size):
result = tf.SparseTensor(indices=np.empty((0,2), dtype=np.int64), values=[], dense_shape=np.array([batch_size*max_n_row, batch_size*max_n_col]))
for G, n in zip(Gs, range(batch_size)): # maybe pass batch dim instead of calculating len?
G = tf.sparse.reset_shape(G, [batch_size * max_n_row, batch_size * max_n_col]) # uniformize shape
n_cells = G.indices.shape[0]
shifts = tf.tile(np.array([[n*max_n_row, n*max_n_col]], dtype="int64"), [n_cells,1])
G = tf.SparseTensor(indices=tf.add(G.indices, shifts), values=G.values, dense_shape=G.dense_shape)
result = tf.sparse.add(result, G)
return tf.sparse.reorder(result)
# if DEBUG:
# start = time.time()
# # G = tf.sparse.reset_shape(G, [max_n_row, batch_size * max_n_col])
# if DEBUG:
# print("sparse reset shape time: ", time.time() - start)
# n_cells = G.indices.shape[0]
# if DEBUG:
# start = time.time()
# shifts = tf.tile(np.array([[0, n*max_n_col]], dtype="int64"), [n_cells,1])
# if DEBUG:
# print("shifts creation time: ", time.time() - start)
# if DEBUG:
# start = time.time()
# G = tf.SparseTensor(indices=tf.add(G.indices, shifts), values=G.values, dense_shape=G.dense_shape)
# if DEBUG:
# print("final G creation time: ", time.time() - start)
# return G
# def shift_pads(Gs, max_n_row, max_n_col, batch_size, DEBUG=False):
# result = []
# for sample_count in range(batch_size):
# result.append(shift_pad(Gs[sample_count], max_n_row, max_n_col, batch_size, sample_count, DEBUG))
# return result
# return [shift_pad(x[0], max_n_row, max_n_col, batch_size, x[1], DEBUG) for x in zip(Gs, range(batch_size))]
# accepts a single sparse tensor whose slices are the Gs
def blockify2(Gs,max_n_row, max_n_col, batch_size, DEBUG=False):
if DEBUG:
start = time.time()
# Gs = shift_pads(Gs, max_n_row, max_n_col, batch_size, DEBUG)
i = tf.constant(0, dtype=tf.int64)
def lambda_body(i):
# i = tf.add(i,1)
Gs[i] = shift_pad(Gs[i], max_n_row, max_n_col, batch_size, i, DEBUG)
i = tf.add(i, 1)
return [i]
tf.while_loop(lambda i: i < batch_size, lambda_body, [i])
# Gs = [shift_pad(x[0], max_n_row, max_n_col, batch_size, x[1], DEBUG) for x in zip(Gs, range(batch_size))]
if DEBUG:
print("total Gs shift_pad time: ", time.time() - start)
if DEBUG:
start = time.time()
result = tf.sparse.concat(axis=0, sp_inputs=Gs)
if DEBUG:
print("total concat time", time.time() - start)
if DEBUG:
start = time.time()
final_result = tf.sparse.reorder(result)
if DEBUG:
print("total sparse_reorder time", time.time() - start)
return final_result
def pad_CL_idxs(CL_idxss, max_n_cells):
result = tf.stack(list(map(lambda CL_idxs: tf.concat((CL_idxs,tf.tile([CL_idxs[-1]], [max_n_cells - CL_idxs.shape[0], 1])),axis=0), CL_idxss)))
return result
@tf.function
def pad_CL_idxs_index_vars(CL_idxss, n_varss,max_n_cells, batch_size):
n_varss = tf.reshape(n_varss, shape=(batch_size, 1, 1))
n_varss = tf.broadcast_to(n_varss, shape=(batch_size, max_n_cells, 1))
x = tf.reshape(tf.range(batch_size), shape=(batch_size, 1, 1) )
x = tf.cast(tf.broadcast_to(x, shape=(batch_size, max_n_cells, 1)), dtype=tf.int64)
return tf.cast(tf.concat((CL_idxss, x, n_varss), axis=-1), dtype=tf.int64)
@tf.function
def map_transform_CL_idxs(CL_idxs, max_n_clauses, max_n_vars):
return tf.map_fn(lambda x: transform_CL_idxs(x,max_n_clauses, max_n_vars), CL_idxs)
if __name__ == "__main__": # for testing
G_cl = G_cl_test
n_vars = test_fmla.nv
n_clauses = len(test_fmla.clauses)
n_vars_list = tf.cast(np.array([n_vars, n_vars, n_vars]), dtype=tf.int64)
n_clauses_list = tf.cast(np.array([n_clauses, n_clauses, n_clauses]), dtype=tf.int64)
G_cls = [G_cl, G_cl, G_cl]
neurocuber = NeuroCuber(d=20, mode="cube")
logits = neurocuber(G_cls, n_clauses_list, n_vars_list)
print(logits)