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Add tgs metrics #286

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Nov 14, 2023
Merged

Add tgs metrics #286

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5 changes: 5 additions & 0 deletions megatron/training.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1032,13 +1032,17 @@ def training_log(loss_dict, total_loss_dict, learning_rate, iteration,
samples_per_sec_per_replica = samples_per_sec / args.data_parallel_size
tokens_per_sec = samples_per_sec * seq_len
tokens_per_sec_per_replica = tokens_per_sec / args.data_parallel_size
tokens_per_gpu_per_second = tokens_per_sec / args.world_size
tokens_per_gpu_per_second_per_replica = tokens_per_gpu_per_second / args.data_parallel_size
if wandb is not None and getattr(wandb, 'run', None) is not None:
tput = {
'throughput/iteration-time': elapsed_time_per_iteration, # 1000 ms / s
'throughput/samples_per_sec': samples_per_sec,
'throughput/samples_per_sec_per_replica': samples_per_sec_per_replica,
'throughput/tokens_per_sec': tokens_per_sec,
'throughput/tokens_per_sec_per_replica': tokens_per_sec_per_replica,
'throughput/tokens_per_gpu_per_sec': tokens_per_gpu_per_second,
'throughput/tokens_per_gpu_per_sec_per_replica': tokens_per_gpu_per_second_per_replica,
'throughput/tflops': tflops,
'throughput/approx_params_in_billions': approx_parameters_in_billions,
'throughput/elapsed_ms_per_iteration': elapsed_time_per_iteration,
Expand Down Expand Up @@ -1088,6 +1092,7 @@ def training_log(loss_dict, total_loss_dict, learning_rate, iteration,
log_string += ' number of nan iterations: {:3d} |'.format(
total_loss_dict[nan_iters_key])
log_string += ' samples per second: {:.3f} |'.format(samples_per_sec)
log_string += ' tokens per gpu per second (tgs): {:.3f} |'.format(tokens_per_gpu_per_second)
log_string += ' TFLOPs: {:.2f} |'.format(tflops)
total_loss_dict[advanced_iters_key] = 0
total_loss_dict[skipped_iters_key] = 0
Expand Down
3 changes: 0 additions & 3 deletions megatron/utils.py
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Expand Up @@ -251,10 +251,7 @@ def get_parameters_in_billions(model):
return approx_parameters_in_billions*gpus_per_model/(1e9)

def throughput_calculator(model, args, iteration_time, total_iterations):
gpus_per_model = torch.distributed.get_world_size(group = mpu.get_model_parallel_group())
batch_size = args.micro_batch_size * get_num_microbatches() * args.data_parallel_size
samples_per_model = batch_size * args.seq_length
model_replica_count = torch.distributed.get_world_size() / gpus_per_model
approx_parameters_in_billions = None if (model is None) else get_parameters_in_billions(model)
elapsed_time_per_iter = iteration_time/total_iterations
samples_per_second = batch_size / elapsed_time_per_iter
Expand Down