# 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.
[docs]def set_attention_params(params):
'''
Set attention related params
:param params: model_params
:return:
'''
# Attention softmax is fp32 by default.
params["model"]["attention_softmax_fp32"] = True
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 set_defaults(params):
"""
Update any missing parameters in the params dictionary with default values
Args:
params: The dictionary containing the params
"""
if (
params.get("train_input", {}).get("data_processor")
== "Gpt2SyntheticDataProcessor"
):
if "train_input" in params:
params["train_input"]["vocab_size"] = params["train_input"].get(
"vocab_size", params["model"]["vocab_size"]
)
assert (
params["train_input"]["vocab_size"]
== params["model"]["vocab_size"]
), f"Found different vocab_size in train_input ({params['train_input']['vocab_size']}) vs. model ({params['model']['vocab_size']})"
params["train_input"]["max_sequence_length"] = params[
"train_input"
].get(
"max_sequence_length",
params["model"]["max_position_embeddings"],
)
if "eval_input" in params:
params["eval_input"]["vocab_size"] = params["eval_input"].get(
"vocab_size", params["model"]["vocab_size"]
)
assert (
params["eval_input"]["vocab_size"]
== params["model"]["vocab_size"]
), f"Found different vocab_size in eval_input ({params['eval_input']['vocab_size']}) vs. model ({params['model']['vocab_size']})"
params["eval_input"]["max_sequence_length"] = params[
"eval_input"
].get(
"max_sequence_length",
params["model"]["max_position_embeddings"],
)
params["model"]["fp16_type"] = params["model"].get("fp16_type", "bfloat16")
params["optimizer"]["loss_scaling_factor"] = params["optimizer"].get(
"loss_scaling_factor", 1.0
)
params["optimizer"]["log_summaries"] = params["optimizer"].get(
"log_summaries", False
)
params["runconfig"]["precision_opt_level"] = params["runconfig"].get(
"precision_opt_level", 1
)
set_attention_params(params)