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nnue_dataset.py
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nnue_dataset.py
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import numpy as np
import ctypes
import torch
import os
import sys
import glob
from torch.utils.data import Dataset
local_dllpath = [n for n in glob.glob('./*training_data_loader.*') if n.endswith('.so') or n.endswith('.dll') or n.endswith('.dylib')]
if not local_dllpath:
print('Cannot find data_loader shared library.')
sys.exit(1)
dllpath = os.path.abspath(local_dllpath[0])
dll = ctypes.cdll.LoadLibrary(dllpath)
class SparseBatch(ctypes.Structure):
_fields_ = [
('num_inputs', ctypes.c_int),
('size', ctypes.c_int),
('is_white', ctypes.POINTER(ctypes.c_float)),
('outcome', ctypes.POINTER(ctypes.c_float)),
('score', ctypes.POINTER(ctypes.c_float)),
('num_active_white_features', ctypes.c_int),
('num_active_black_features', ctypes.c_int),
('max_active_features', ctypes.c_int),
('white', ctypes.POINTER(ctypes.c_int)),
('black', ctypes.POINTER(ctypes.c_int)),
('white_values', ctypes.POINTER(ctypes.c_float)),
('black_values', ctypes.POINTER(ctypes.c_float)),
('psqt_indices', ctypes.POINTER(ctypes.c_int)),
('layer_stack_indices', ctypes.POINTER(ctypes.c_int)),
]
def get_tensors(self, device):
white_values = torch.from_numpy(np.ctypeslib.as_array(self.white_values, shape=(self.size, self.max_active_features))).pin_memory().to(device=device, non_blocking=True)
black_values = torch.from_numpy(np.ctypeslib.as_array(self.black_values, shape=(self.size, self.max_active_features))).pin_memory().to(device=device, non_blocking=True)
white_indices = torch.from_numpy(np.ctypeslib.as_array(self.white, shape=(self.size, self.max_active_features))).pin_memory().to(device=device, non_blocking=True)
black_indices = torch.from_numpy(np.ctypeslib.as_array(self.black, shape=(self.size, self.max_active_features))).pin_memory().to(device=device, non_blocking=True)
us = torch.from_numpy(np.ctypeslib.as_array(self.is_white, shape=(self.size, 1))).pin_memory().to(device=device, non_blocking=True)
them = 1.0 - us
outcome = torch.from_numpy(np.ctypeslib.as_array(self.outcome, shape=(self.size, 1))).pin_memory().to(device=device, non_blocking=True)
score = torch.from_numpy(np.ctypeslib.as_array(self.score, shape=(self.size, 1))).pin_memory().to(device=device, non_blocking=True)
psqt_indices = torch.from_numpy(np.ctypeslib.as_array(self.psqt_indices, shape=(self.size,))).long().pin_memory().to(device=device, non_blocking=True)
layer_stack_indices = torch.from_numpy(np.ctypeslib.as_array(self.layer_stack_indices, shape=(self.size,))).long().pin_memory().to(device=device, non_blocking=True)
return us, them, white_indices, white_values, black_indices, black_values, outcome, score, psqt_indices, layer_stack_indices
SparseBatchPtr = ctypes.POINTER(SparseBatch)
class TrainingDataProvider:
def __init__(
self,
feature_set,
create_stream,
destroy_stream,
fetch_next,
destroy_part,
filename,
cyclic,
num_workers,
batch_size=None,
filtered=False,
random_fen_skipping=0,
device='cpu'):
self.feature_set = feature_set.encode('utf-8')
self.create_stream = create_stream
self.destroy_stream = destroy_stream
self.fetch_next = fetch_next
self.destroy_part = destroy_part
self.filename = filename.encode('utf-8')
self.cyclic = cyclic
self.num_workers = num_workers
self.batch_size = batch_size
self.filtered = filtered
self.random_fen_skipping = random_fen_skipping
self.device = device
if batch_size:
self.stream = self.create_stream(self.feature_set, self.num_workers, self.filename, batch_size, cyclic, filtered, random_fen_skipping)
else:
self.stream = self.create_stream(self.feature_set, self.num_workers, self.filename, cyclic, filtered, random_fen_skipping)
def __iter__(self):
return self
def __next__(self):
v = self.fetch_next(self.stream)
if v:
tensors = v.contents.get_tensors(self.device)
self.destroy_part(v)
return tensors
else:
raise StopIteration
def __del__(self):
self.destroy_stream(self.stream)
create_sparse_batch_stream = dll.create_sparse_batch_stream
create_sparse_batch_stream.restype = ctypes.c_void_p
create_sparse_batch_stream.argtypes = [ctypes.c_char_p, ctypes.c_int, ctypes.c_char_p, ctypes.c_int, ctypes.c_bool, ctypes.c_bool]
destroy_sparse_batch_stream = dll.destroy_sparse_batch_stream
destroy_sparse_batch_stream.argtypes = [ctypes.c_void_p]
fetch_next_sparse_batch = dll.fetch_next_sparse_batch
fetch_next_sparse_batch.restype = SparseBatchPtr
fetch_next_sparse_batch.argtypes = [ctypes.c_void_p]
destroy_sparse_batch = dll.destroy_sparse_batch
class SparseBatchProvider(TrainingDataProvider):
def __init__(self, feature_set, filename, batch_size, cyclic=True, num_workers=1, filtered=False, random_fen_skipping=0, device='cpu'):
super(SparseBatchProvider, self).__init__(
feature_set,
create_sparse_batch_stream,
destroy_sparse_batch_stream,
fetch_next_sparse_batch,
destroy_sparse_batch,
filename,
cyclic,
num_workers,
batch_size,
filtered,
random_fen_skipping,
device)
class SparseBatchDataset(torch.utils.data.IterableDataset):
def __init__(self, feature_set, filename, batch_size, cyclic=True, num_workers=1, filtered=False, random_fen_skipping=0, device='cpu'):
super(SparseBatchDataset).__init__()
self.feature_set = feature_set
self.filename = filename
self.batch_size = batch_size
self.cyclic = cyclic
self.num_workers = num_workers
self.filtered = filtered
self.random_fen_skipping = random_fen_skipping
self.device = device
def __iter__(self):
return SparseBatchProvider(self.feature_set, self.filename, self.batch_size, cyclic=self.cyclic, num_workers=self.num_workers, filtered=self.filtered, random_fen_skipping=self.random_fen_skipping, device=self.device)
class FixedNumBatchesDataset(Dataset):
def __init__(self, dataset, num_batches):
super(FixedNumBatchesDataset, self).__init__()
self.dataset = dataset;
self.iter = iter(self.dataset)
self.num_batches = num_batches
def __len__(self):
return self.num_batches
def __getitem__(self, idx):
return next(self.iter)