mirror of https://github.com/commaai/tinygrad.git
update bert epoch logging (#6940)
* update bert epoch logging epoch for bert is simply number of examples seen (which is used for RCP check) * update total steps too * more changes
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chenyu
GitHub
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0498e846a5
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@ -649,7 +649,7 @@ def train_bert():
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EVAL_BS = config["EVAL_BS"] = getenv("EVAL_BS", 1 * len(GPUS))
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max_lr = config["OPT_BASE_LEARNING_RATE"] = getenv("OPT_BASE_LEARNING_RATE", 0.0001 * math.sqrt(BS/66))
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train_steps = config["TRAIN_STEPS"] = getenv("TRAIN_STEPS", 3000000 // BS)
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train_steps = config["TRAIN_STEPS"] = getenv("TRAIN_STEPS", 3300000 // BS)
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warmup_steps = config["NUM_WARMUP_STEPS"] = getenv("NUM_WARMUP_STEPS", 1)
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max_eval_steps = config["MAX_EVAL_STEPS"] = getenv("MAX_EVAL_STEPS", (10000 + EVAL_BS - 1) // EVAL_BS) # EVAL_BS * MAX_EVAL_STEPS >= 10000
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eval_step_freq = config["EVAL_STEP_FREQ"] = getenv("EVAL_STEP_FREQ", int((math.floor(0.05 * (230.23 * BS + 3000000) / 25000) * 25000) / BS)) # Round down
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@ -749,15 +749,12 @@ def train_bert():
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if RUNMLPERF:
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# only load real data with RUNMLPERF
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i, train_data = start_step, get_data_bert(GPUS, train_it)
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if MLLOGGER:
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MLLOGGER.start(key=mllog_constants.EPOCH_START, value=i*BS, metadata={"epoch_num": i*BS})
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else:
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i, train_data = start_step, get_fake_data_bert(GPUS, BS)
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epoch_started = False
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while train_data is not None and i < train_steps and not achieved:
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if not epoch_started and MLLOGGER and RUNMLPERF:
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MLLOGGER.start(key=mllog_constants.EPOCH_START, value=i+1, metadata=dict(epoch_num=i+1))
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epoch_started = True
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Tensor.training = True
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BEAM.value = TRAIN_BEAM
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st = time.perf_counter()
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@ -791,7 +788,7 @@ def train_bert():
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if WANDB:
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wandb.log({"lr": optimizer_wd.lr.numpy(), "train/loss": loss, "train/step_time": cl - st,
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"train/python_time": pt - st, "train/data_time": dt - pt, "train/cl_time": cl - dt,
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"train/GFLOPS": GlobalCounters.global_ops * 1e-9 / (cl - st)})
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"train/GFLOPS": GlobalCounters.global_ops * 1e-9 / (cl - st), "epoch": (i+1)*BS})
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train_data, next_data = next_data, None
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i += 1
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@ -806,9 +803,7 @@ def train_bert():
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# ** eval loop **
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if i % eval_step_freq == 0 or (BENCHMARK and i == BENCHMARK):
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if MLLOGGER and RUNMLPERF:
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epoch_started = False
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MLLOGGER.event(key=mllog_constants.EPOCH_STOP, value=i+1, metadata=dict(epoch_num=i+1))
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MLLOGGER.start(key=mllog_constants.EVAL_START, value=None, metadata={"epoch_num": 1, "epoch_count": 1, "step_num": i})
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MLLOGGER.start(key=mllog_constants.EVAL_START, value=None, metadata={"epoch_num": i*BS, "step_num": i})
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if getenv("RESET_STEP", 1): train_step_bert.reset()
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eval_lm_losses = []
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eval_clsf_losses = []
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@ -863,12 +858,13 @@ def train_bert():
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"eval/clsf_accuracy": avg_clsf_acc, "eval/forward_time": avg_fw_time})
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if MLLOGGER and RUNMLPERF:
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MLLOGGER.end(key=mllog_constants.EVAL_STOP, value=i, metadata={"epoch_count": 1, "step_num": i, "samples_count": config["EVAL_BS"] * config["MAX_EVAL_STEPS"]})
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MLLOGGER.event(key=mllog_constants.EVAL_ACCURACY, value=avg_lm_acc, metadata={"epoch_num": 1, "masked_lm_accuracy": avg_lm_acc})
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MLLOGGER.end(key=mllog_constants.EVAL_STOP, value=i*BS, metadata={"epoch_count": i*BS, "step_num": i, "samples_count": config["EVAL_BS"] * config["MAX_EVAL_STEPS"]})
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MLLOGGER.event(key=mllog_constants.EVAL_ACCURACY, value=avg_lm_acc, metadata={"epoch_num": i*BS, "masked_lm_accuracy": avg_lm_acc})
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# save model if achieved target
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if not achieved and avg_lm_acc >= target:
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wc_end = time.perf_counter()
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if getenv("CKPT"):
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if not os.path.exists(ckpt_dir := save_ckpt_dir): os.mkdir(ckpt_dir)
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fn = f"{ckpt_dir}/bert-large.safe"
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safe_save(get_state_dict(model), fn)
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@ -881,6 +877,7 @@ def train_bert():
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print(f"Reference Convergence point reached after {i * BS} datasamples and {hours}h{minutes}m{seconds:.2f}s.")
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achieved = True
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if MLLOGGER and RUNMLPERF:
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MLLOGGER.event(key=mllog_constants.EPOCH_STOP, value=i*BS, metadata={"epoch_num": i*BS})
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MLLOGGER.end(key=mllog_constants.RUN_STOP, metadata=dict(status=mllog_constants.SUCCESS))
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# stop once hitting the target
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break
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@ -6,7 +6,8 @@
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"number_of_nodes": "1",
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"host_processors_per_node": "1",
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"host_processor_model_name": "AMD EPYC 7532 32-Core Processor",
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"host_processor_core_count": "64",
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"host_processor_core_count": "32",
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"host_processor_vcpu_count": "64",
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"host_processor_frequency": "",
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"host_processor_caches": "",
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"host_processor_interconnect": "",
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@ -6,7 +6,8 @@
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"number_of_nodes": "1",
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"host_processors_per_node": "1",
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"host_processor_model_name": "AMD EPYC 7532 32-Core Processor",
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"host_processor_core_count": "64",
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"host_processor_core_count": "32",
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"host_processor_vcpu_count": "64",
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"host_processor_frequency": "",
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"host_processor_caches": "",
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"host_processor_interconnect": "",
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