text stringlengths 1 93.6k |
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save_summary_steps=params["iterations"],
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tpu_config=tpu_config.TPUConfig(
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num_shards=mesh_shape.size,
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iterations_per_loop=params["iterations"],
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num_cores_per_replica=1,
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per_host_input_for_training=tpu_config.InputPipelineConfig.BROADCAST))
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estimator = tpu_estimator.TPUEstimator(
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use_tpu=params["use_tpu"],
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model_fn=model_fn,
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config=config,
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train_batch_size=params["train_batch_size"],
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eval_batch_size=params["train_batch_size"],
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predict_batch_size=params["predict_batch_size"],
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params=params)
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def _make_task_estimator(task):
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task_params = params.copy()
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task_params["eval_task"] = task
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return tpu_estimator.TPUEstimator(
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use_tpu=params["use_tpu"],
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model_fn=model_fn,
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config=config,
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train_batch_size=params["train_batch_size"],
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eval_batch_size=params["eval_batch_size"],
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predict_batch_size=params["predict_batch_size"],
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params=task_params)
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eval_task_estimators = {
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task: _make_task_estimator(task)
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for task in eval_tasks
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}
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if args.export:
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export_model(estimator, "export", params)
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return
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if args.predict:
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# Predict
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predictions = estimator.predict(input_fn=pred_input_fn)
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logger.info("Predictions generated")
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enc = fetch_encoder(params)
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handle_pred_output_fn(predictions, logger, enc, params, out_name=f"predictions_{args.sacred_id}_{current_step}")
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return
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def save_eval_results(task, eval_results):
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def as_python(x):
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if isinstance(x, numpy.generic):
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return x.item()
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return x
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eval_results = {k: as_python(v) for k, v in eval_results.items()}
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with open(f'eval_{args.sacred_id}.jsonl', 'a') as fh:
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json.dump({'task': task, 'current_step': current_step, **eval_results}, fh)
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fh.write('\n')
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def run_eval():
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logger.info("Running evaluation...")
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eval_results = estimator.evaluate(
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input_fn=partial(input_fn, eval=True),
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steps=params["eval_steps"])
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logger.info(f"Eval results: {eval_results}")
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save_eval_results('validation', eval_results)
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def run_eval_tasks():
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for task in eval_tasks:
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logger.info(f"Starting evaluation task '{task}'")
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task_info = task_descriptors[task]["get_task_info_fn"](params)
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task_estimator = eval_task_estimators[task]
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task_input_fn = task_descriptors[task]["input_fn"]
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eval_results = task_estimator.evaluate(
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input_fn=task_input_fn,
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steps=task_info["n_steps"],
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name=task)
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logger.info(f"Eval task '{task}' results: {eval_results}")
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save_eval_results(task, eval_results)
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if args.eval:
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run_eval_tasks()
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if params["eval_steps"] > 0:
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run_eval()
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return
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elif has_predict_or_eval_steps_or_eval_tasks:
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# Eval and train - stop and predict and/or eval every checkpoint
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while current_step < params["train_steps"]:
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next_checkpoint = min(current_step + args.steps_per_checkpoint,
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params["train_steps"])
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estimator.train(input_fn=partial(input_fn, global_step=current_step, eval=False), max_steps=next_checkpoint)
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current_step = next_checkpoint
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if params["predict_steps"] > 0:
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logger.info("Running prediction...")
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predictions = estimator.predict(input_fn=pred_input_fn)
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enc = fetch_encoder(params)
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handle_pred_output_fn(predictions, logger, enc, params, out_name=f"predictions_{args.sacred_id}_{current_step}")
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if params["eval_steps"] > 0:
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run_eval()
|
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