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Add samples per second #332

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Add samples per second #332

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11 changes: 7 additions & 4 deletions nmt/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -199,7 +199,8 @@ def run_full_eval(model_dir, infer_model, infer_sess, eval_model, eval_sess,
def init_stats():
"""Initialize statistics that we want to accumulate."""
return {"step_time": 0.0, "loss": 0.0, "predict_count": 0.0,
"total_count": 0.0, "grad_norm": 0.0}
"total_count": 0.0, "grad_norm": 0.0,
"checkpoint_total_samples": 0.0}


def update_stats(stats, start_time, step_result):
Expand All @@ -213,16 +214,17 @@ def update_stats(stats, start_time, step_result):
stats["predict_count"] += step_predict_count
stats["total_count"] += float(step_word_count)
stats["grad_norm"] += grad_norm
stats["checkpoint_total_samples"] += float(batch_size)

return global_step, learning_rate, step_summary


def print_step_info(prefix, global_step, info, result_summary, log_f):
"""Print all info at the current global step."""
utils.print_out(
"%sstep %d lr %g step-time %.2fs wps %.2fK ppl %.2f gN %.2f %s, %s" %
"%sstep %d lr %g step-time %.2fs wps %.2fK sps %5.2f ppl %.2f gN %.2f %s, %s" %
(prefix, global_step, info["learning_rate"], info["avg_step_time"],
info["speed"], info["train_ppl"], info["avg_grad_norm"], result_summary,
info["speed"], info["speed_samples"], info["train_ppl"], info["avg_grad_norm"], result_summary,
time.ctime()),
log_f)

Expand All @@ -234,6 +236,7 @@ def process_stats(stats, info, global_step, steps_per_stats, log_f):
info["avg_grad_norm"] = stats["grad_norm"] / steps_per_stats
info["train_ppl"] = utils.safe_exp(stats["loss"] / stats["predict_count"])
info["speed"] = stats["total_count"] / (1000 * stats["step_time"])
info["speed_samples"] = stats["checkpoint_total_samples"] / stats["step_time"]

# Check for overflow
is_overflow = False
Expand All @@ -253,7 +256,7 @@ def before_train(loaded_train_model, train_model, train_sess, global_step,
info = {"train_ppl": 0.0, "speed": 0.0, "avg_step_time": 0.0,
"avg_grad_norm": 0.0,
"learning_rate": loaded_train_model.learning_rate.eval(
session=train_sess)}
session=train_sess), "speed_samples": 0.0}
start_train_time = time.time()
utils.print_out("# Start step %d, lr %g, %s" %
(global_step, info["learning_rate"], time.ctime()), log_f)
Expand Down