[example] reorganize for community examples (#3557)

examples/community/roberta/pretraining/utils/exp_util.py

@@ -0,0 +1,114 @@
+import functools
+import os
+import shutil
+
+import psutil
+import torch
+
+from colossalai.core import global_context as gpc
+
+
+def logging(s, log_path, print_=True, log_=True):
+    if print_:
+        print(s)
+    if log_:
+        with open(log_path, 'a+') as f_log:
+            f_log.write(s + '\n')
+
+
+def get_logger(log_path, **kwargs):
+    return functools.partial(logging, log_path=log_path, **kwargs)
+
+
+def create_exp_dir(dir_path, scripts_to_save=None, debug=False):
+    if debug:
+        print('Debug Mode : no experiment dir created')
+        return functools.partial(logging, log_path=None, log_=False)
+
+    if not os.path.exists(dir_path):
+        os.makedirs(dir_path)
+
+    print('Experiment dir : {}'.format(dir_path))
+    if scripts_to_save is not None:
+        script_path = os.path.join(dir_path, 'scripts')
+        if not os.path.exists(script_path):
+            os.makedirs(script_path)
+        for script in scripts_to_save:
+            dst_file = os.path.join(dir_path, 'scripts', os.path.basename(script))
+            shutil.copyfile(script, dst_file)
+
+    return get_logger(log_path=os.path.join(dir_path, 'log.txt'))
+
+
+def get_cpu_mem():
+    return psutil.Process().memory_info().rss / 1024**2
+
+
+def get_gpu_mem():
+    return torch.cuda.memory_allocated() / 1024**2
+
+
+def get_mem_info(prefix=''):
+    return f'{prefix}GPU memory usage: {get_gpu_mem():.2f} MB, CPU memory usage: {get_cpu_mem():.2f} MB'
+
+
+def get_tflops(model_numel, batch_size, seq_len, step_time):
+    return model_numel * batch_size * seq_len * 8 / 1e12 / (step_time + 1e-12)
+
+
+def get_parameters_in_billions(model, world_size=1):
+    gpus_per_model = world_size
+
+    approx_parameters_in_billions = sum([
+        sum([p.ds_numel if hasattr(p, 'ds_id') else p.nelement()
+             for p in model_module.parameters()])
+        for model_module in model
+    ])
+
+    return approx_parameters_in_billions * gpus_per_model / (1e9)
+
+
+def throughput_calculator(numel, args, config, iteration_time, total_iterations, world_size=1):
+    gpus_per_model = 1
+    batch_size = args.train_micro_batch_size_per_gpu
+    samples_per_model = batch_size * args.max_seq_length
+    model_replica_count = world_size / gpus_per_model
+    approx_parameters_in_billions = numel
+    elapsed_time_per_iter = iteration_time / total_iterations
+    samples_per_second = batch_size / elapsed_time_per_iter
+
+    #flops calculator
+    hidden_size = config.hidden_size
+    num_layers = config.num_hidden_layers
+    vocab_size = config.vocab_size
+
+    # General TFLOPs formula (borrowed from Equation 3 in Section 5.1 of
+    # https://arxiv.org/pdf/2104.04473.pdf).
+    # The factor of 4 is when used with activation check-pointing,
+    # otherwise it will be 3.
+    checkpoint_activations_factor = 4 if args.checkpoint_activations else 3
+    flops_per_iteration = (24 * checkpoint_activations_factor * batch_size * args.max_seq_length * num_layers *
+                           (hidden_size**2)) * (1. + (args.max_seq_length / (6. * hidden_size)) +
+                                                (vocab_size / (16. * num_layers * hidden_size)))
+    tflops = flops_per_iteration / (elapsed_time_per_iter * (10**12))
+    return samples_per_second, tflops, approx_parameters_in_billions
+
+
+def synchronize():
+    if not torch.distributed.is_available():
+        return
+    if not torch.distributed.is_intialized():
+        return
+    world_size = torch.distributed.get_world_size()
+    if world_size == 1:
+        return
+    torch.distributed.barrier()
+
+
+def log_args(logger, args):
+    logger.info('--------args----------')
+    message = '\n'.join([f'{k:<30}: {v}' for k, v in vars(args).items()])
+    message += '\n'
+    message += '\n'.join([f'{k:<30}: {v}' for k, v in gpc.config.items()])
+    logger.info(message)
+    logger.info('--------args----------\n')