code stringlengths 3 6.57k |
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value.new_zeros(value.size() |
range(self.dp_world_size) |
dist.all_gather(tensor_list, value, group=self.data_parallel_group) |
module_state_dict(self, destination=None, prefix='', keep_vars=False) |
self.module.state_dict(destination, prefix, keep_vars) |
load_module_state_dict(self, state_dict, strict=True) |
self.module.load_state_dict(state_dict, strict=strict) |
_get_rank_zero_ckpt_name(self, checkpoints_path, tag, mp_rank, dp_rank) |
format(dp_rank) |
str(tag) |
format(mp_rank) |
_get_zero_ckpt_name(self, checkpoints_path, tag) |
self.mpu.get_model_parallel_rank() |
torch.distributed.get_rank(group=self.optimizer.dp_process_group) |
self._get_rank_zero_ckpt_name(checkpoints_path, tag, mp_rank, pp_rank) |
_get_ckpt_name(self, checkpoints_path, tag) |
self.mpu.get_model_parallel_rank() |
self.zero_optimization_partition_weights() |
torch.distributed.get_rank(group=self.optimizer.dp_process_group) |
str(tag) |
format(mp_rank) |
str(tag) |
format(mp_rank) |
os.path.join(load_dir, 'latest') |
os.path.isfile(latest_path) |
open(latest_path, 'r') |
fd.read() |
strip() |
self.zero_optimization() |
self._get_ckpt_name(load_dir, tag) |
os.path.exists(load_path) |
format(load_path) |
logger.info(f'rank: {self.global_rank} loading checkpoint: {load_path}') |
torch.load(load_path, map_location=lambda storage, loc: storage) |
isinstance(self.module, PipelineModule) |
os.path.join(load_dir, tag) |
self.zero_optimization() |
self.fp16_enabled() |
self.optimizer.load_state_dict(checkpoint['optimizer']) |
self.lr_scheduler.load_state_dict(checkpoint['lr_scheduler']) |
self.train_batch_size() |
checkpoint.items() |
_load_zero_checkpoint(self, load_dir, tag, load_optimizer_states=True) |
self._get_all_zero_checkpoints(load_dir, tag) |
self.zero_load_from_fp32_weights() |
len(zero_sd_list) |
_get_mp_rank_zero_checkpoint_names(self, load_dir, tag, mp_rank, dp_world_size) |
range(dp_world_size) |
zero_ckpt_names.append(ckpt_name) |
range(mp_world_size) |
_get_all_zero_checkpoints(self, load_dir, tag) |
self.mpu.get_model_parallel_rank() |
enumerate(zero_ckpt_names) |
os.path.exists(ckpt_name) |
os.path.exists(ckpt_name_try) |
invalid_zero_ckpt_paths.append(ckpt_name) |
len(invalid_zero_ckpt_paths) |
zero_sd_list.append(torch.load(ckpt_name, map_location='cpu') |
len(zero_optimizer_sd) |
_checkpoint_tag_validation(self, tag) |
self.checkpoint_tag_validation_enabled() |
hashlib.sha1(tag.encode() |
torch.ByteTensor([s_hash.digest() |
flatten() |
to(self.device) |
bhash.clone() |
bhash.clone() |
dist.all_reduce(max_bhash, op=torch.distributed.ReduceOp.MAX) |
dist.all_reduce(min_bhash, op=torch.distributed.ReduceOp.MIN) |
all(min_bhash == bhash) |
all(max_bhash == bhash) |
dist.get_rank() |
self.checkpoint_tag_validation_fail() |
logger.warning(msg) |
save_checkpoint(self, save_dir, tag=None, client_state={}, save_latest=True) |
self.zero_optimization_partition_weights() |
state_dict() |
self.optimizer.save_checkpoint_prologue() |
os.makedirs(save_dir, exist_ok=True) |
str(tag) |
self._checkpoint_tag_validation(tag) |
self._create_checkpoint_file(save_dir, tag, False) |
self._save_checkpoint(save_dir, tag, client_state=client_state) |
self._create_zero_checkpoint_files(save_dir, tag) |
self._save_zero_checkpoint(save_dir, tag) |
open(os.path.join(save_dir, 'latest') |
fd.write(tag) |
self.zero_optimization_partition_weights() |
self.optimizer.save_checkpoint_epilogue() |
_create_checkpoint_file(self, save_dir, tag, zero_checkpoint) |
name_function(save_dir, tag) |
ensure_directory_exists(checkpoint_name) |
logger.error(f'Failed saving model checkpoint to {save_dir} with tag {tag}') |
_create_zero_checkpoint_files(self, save_dir, tag) |
range(self.world_size) |
self._create_checkpoint_file(save_dir, tag, True) |
dist.barrier() |
_save_checkpoint(self, save_dir, tag, client_state={}) |
self._get_ckpt_name(save_dir, tag) |
module_state_dict() |
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