<gh_stars>100-1000
# 
# Copyright (c) 2021, NVIDIA CORPORATION.
#
# 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.
#

from hugectr2onnx.hugectr_loader import HugeCTRLoader, LayerParams
from hugectr2onnx.graph_builder import GraphBuilder
import argparse

def convert(onnx_model_path, graph_config, dense_model, convert_embedding = False, sparse_models = None, ntp_file = None, graph_name = "hugectr"):
    """Convert a HugeCTR model to an ONNX model
    Args:
        onnx_model_path: the path to store the ONNX model
        graph_config: the graph configuration JSON file of the HugeCTR model
        dense_model: the file of the dense weights for the HugeCTR model
        convert_embedding: whether to convert the sparse embeddings for the HugeCTR model (optional)
        sparse_models: the files of the sparse embeddings for the HugeCTR model (optional)
        ntp_file: the file of the non-trainable parameters for the HugeCTR model (optional)
        graph_name: the graph name for the ONNX model (optional)
    """    
    loader = HugeCTRLoader(graph_config, dense_model, convert_embedding, sparse_models, ntp_file)
    builder = GraphBuilder(convert_embedding)
    for _ in range(loader.layers):
        layer_params, weights_dict, dimensions = loader.load_layer()
        print(f"[HUGECTR2ONNX][INFO]: Converting {layer_params.layer_type} layer to ONNX")
        builder.add_layer(layer_params, weights_dict, dimensions)
    builder.create_graph(graph_name)
    builder.save_model(onnx_model_path)

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Convert HugeCTR model to ONNX.")
    parser.add_argument("--onnx_model_path", type=str, required=True,
                        help="Output onnx model path")
    parser.add_argument("--graph_config", type=str, required=True,
                        help="HugeCTR graph configuration file")
    parser.add_argument("--dense_model", type=str, required=True,
                        help="HugeCTR dense model")
    parser.add_argument("--convert_embedding", action="store_true",
                        help="Convert sparse embedding or not (optional)")
    parser.add_argument("--sparse_models", nargs='*',
                        help="HugeCTR sparse models (optional)")
    parser.add_argument("--ntp_file", type=str, default=None,
                        help="HugeCTR non-trainable parameters (optional)")
    parser.add_argument("--graph_name", type=str, default="hugectr",
                        help="Graph name for the ONNX model (optional)")
    args = parser.parse_args()
    print(args)
    convert(args.onnx_model_path, args.graph_config, args.dense_model,
            args.convert_embedding, args.sparse_models, args.ntp_file, args.graph_name)
# 
# Copyright (c) 2021, NVIDIA CORPORATION.
#
# 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.
#

from hugectr2onnx.hugectr_loader import HugeCTRLoader, LayerParams
from hugectr2onnx.graph_builder import GraphBuilder
import argparse

def convert(onnx_model_path, graph_config, dense_model, convert_embedding = False, sparse_models = None, ntp_file = None, graph_name = "hugectr"):
    """Convert a HugeCTR model to an ONNX model
    Args:
        onnx_model_path: the path to store the ONNX model
        graph_config: the graph configuration JSON file of the HugeCTR model
        dense_model: the file of the dense weights for the HugeCTR model
        convert_embedding: whether to convert the sparse embeddings for the HugeCTR model (optional)
        sparse_models: the files of the sparse embeddings for the HugeCTR model (optional)
        ntp_file: the file of the non-trainable parameters for the HugeCTR model (optional)
        graph_name: the graph name for the ONNX model (optional)
    """    
    loader = HugeCTRLoader(graph_config, dense_model, convert_embedding, sparse_models, ntp_file)
    builder = GraphBuilder(convert_embedding)
    for _ in range(loader.layers):
        layer_params, weights_dict, dimensions = loader.load_layer()
        print(f"[HUGECTR2ONNX][INFO]: Converting {layer_params.layer_type} layer to ONNX")
        builder.add_layer(layer_params, weights_dict, dimensions)
    builder.create_graph(graph_name)
    builder.save_model(onnx_model_path)

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Convert HugeCTR model to ONNX.")
    parser.add_argument("--onnx_model_path", type=str, required=True,
                        help="Output onnx model path")
    parser.add_argument("--graph_config", type=str, required=True,
                        help="HugeCTR graph configuration file")
    parser.add_argument("--dense_model", type=str, required=True,
                        help="HugeCTR dense model")
    parser.add_argument("--convert_embedding", action="store_true",
                        help="Convert sparse embedding or not (optional)")
    parser.add_argument("--sparse_models", nargs='*',
                        help="HugeCTR sparse models (optional)")
    parser.add_argument("--ntp_file", type=str, default=None,
                        help="HugeCTR non-trainable parameters (optional)")
    parser.add_argument("--graph_name", type=str, default="hugectr",
                        help="Graph name for the ONNX model (optional)")
    args = parser.parse_args()
    print(args)
    convert(args.onnx_model_path, args.graph_config, args.dense_model,
            args.convert_embedding, args.sparse_models, args.ntp_file, args.graph_name)
# # Copyright (c) 2021, NVIDIA CORPORATION. # # 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. # Convert a HugeCTR model to an ONNX model     Args:         onnx_model_path: the path to store the ONNX model         graph_config: the graph configuration JSON file of the HugeCTR model         dense_model: the file of the dense weights for the HugeCTR model         convert_embedding: whether to convert the sparse embeddings for the HugeCTR model (optional)         sparse_models: the files of the sparse embeddings for the HugeCTR model (optional)         ntp_file: the file of the non-trainable parameters for the HugeCTR model (optional)         graph_name: the graph name for the ONNX model (optional)