Source code for modelzoo.transformers.pytorch.bert.utils

# Copyright 2022 Cerebras Systems.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import logging
import os


[docs]def set_defaults(params): """ Update any missing parameters in the params dictionary with default values Args: params: The dictionary containing the params """ for section in ["train_input", "eval_input"]: for key in ["vocab_file"]: if params.get(section, {}).get(key): params[section][key] = os.path.abspath(params[section][key]) model_params = params["model"] params["model"]["disable_nsp"] = model_params.get("disable_nsp", False) # Pass settings into data loader. for model_key in ( "disable_nsp", "vocab_size", "mixed_precision", ): for input_key in ("train_input", "eval_input"): params[input_key][model_key] = model_params.get(model_key) params["model"]["max_position_embeddings"] = model_params.get( "max_position_embeddings", params["train_input"]["max_sequence_length"], ) params["model"]["to_float16"] = model_params.get("to_float16", False) params["model"]["fp16_type"] = model_params.get("fp16_type", "float16") params["optimizer"]["log_summaries"] = params["optimizer"].get( "log_summaries", False ) # Attention softmax is fp32 by default. params["model"]["attention_softmax_fp32"] = True # Attention softmax is bf16 for precision_opt_level: 2 if params["runconfig"].get("precision_opt_level", 1) == 2: params["model"]["attention_softmax_fp32"] = False if ( params["model"].get("fp16_type", "bfloat16") == "cbfloat16" and params["runconfig"].get("precision_opt_level", 1) == 1 ): params["model"]["attention_softmax_fp32"] = False
[docs]def check_unused_model_params(model_params): """ While setting up the model, we pop used settings from model_params. This function sends a warning about any unused parameters. """ model_params.pop("to_float16", None) model_params.pop("mixed_precision", None) unused_params = [ key for key in model_params.keys() if key not in ["fp16_type"] ] if unused_params: logging.warning( "The following model params are unused: " + ", ".join(unused_params) )