lib.linux-x86_64-cpython-310/tensorrt_llm/tools/onnx_utils.py

# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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 onnx
import tensorrt as trt
from onnx import TensorProto, helper


def trt_dtype_to_onnx(dtype):
    if dtype == trt.float16:
        return TensorProto.DataType.FLOAT16
    if dtype == trt.bfloat16:
        return TensorProto.DataType.BFLOAT16
    elif dtype == trt.float32:
        return TensorProto.DataType.FLOAT
    elif dtype == trt.int32:
        return TensorProto.DataType.INT32
    elif dtype == trt.int64:
        return TensorProto.DataType.INT64
    elif dtype == trt.bool:
        return TensorProto.DataType.BOOL
    else:
        raise TypeError("%s is not supported" % dtype)


def to_onnx(network, path, name: str = None):
    if name is None:
        name = "debug_graph"
    inputs = []
    for i in range(network.num_inputs):
        network_input = network.get_input(i)
        inputs.append(
            helper.make_tensor_value_info(
                network_input.name, trt_dtype_to_onnx(network_input.dtype),
                list(network_input.shape)))

    outputs = []
    for i in range(network.num_outputs):
        network_output = network.get_output(i)
        outputs.append(
            helper.make_tensor_value_info(
                network_output.name, trt_dtype_to_onnx(network_output.dtype),
                list(network_output.shape)))

    nodes = []
    for i in range(network.num_layers):
        layer = network.get_layer(i)
        layer_inputs = []
        for j in range(layer.num_inputs):
            ipt = layer.get_input(j)
            if ipt is not None:
                layer_inputs.append(layer.get_input(j).name)
        layer_outputs = [
            layer.get_output(j).name for j in range(layer.num_outputs)
        ]
        nodes.append(
            helper.make_node(str(layer.type),
                             name=layer.name,
                             inputs=layer_inputs,
                             outputs=layer_outputs,
                             domain="com.nvidia"))

    onnx_model = helper.make_model(helper.make_graph(nodes,
                                                     name,
                                                     inputs,
                                                     outputs,
                                                     initializer=None),
                                   producer_name='NVIDIA')
    onnx.save(onnx_model, path)