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https://github.com/NVIDIA/TensorRT-LLM.git
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2d234357c6
* Update TensorRT-LLM --------- Co-authored-by: Altair-Alpha <62340011+Altair-Alpha@users.noreply.github.com>
81 lines
3.0 KiB
Python
81 lines
3.0 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import onnx
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import tensorrt as trt
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from onnx import TensorProto, helper
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def trt_dtype_to_onnx(dtype):
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if dtype == trt.float16:
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return TensorProto.DataType.FLOAT16
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if dtype == trt.bfloat16:
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return TensorProto.DataType.BFLOAT16
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elif dtype == trt.float32:
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return TensorProto.DataType.FLOAT
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elif dtype == trt.int32:
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return TensorProto.DataType.INT32
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elif dtype == trt.int64:
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return TensorProto.DataType.INT64
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elif dtype == trt.bool:
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return TensorProto.DataType.BOOL
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else:
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raise TypeError("%s is not supported" % dtype)
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def to_onnx(network, path, name: str = None):
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if name is None:
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name = "debug_graph"
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inputs = []
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for i in range(network.num_inputs):
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network_input = network.get_input(i)
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inputs.append(
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helper.make_tensor_value_info(
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network_input.name, trt_dtype_to_onnx(network_input.dtype),
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list(network_input.shape)))
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outputs = []
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for i in range(network.num_outputs):
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network_output = network.get_output(i)
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outputs.append(
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helper.make_tensor_value_info(
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network_output.name, trt_dtype_to_onnx(network_output.dtype),
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list(network_output.shape)))
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nodes = []
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for i in range(network.num_layers):
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layer = network.get_layer(i)
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layer_inputs = []
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for j in range(layer.num_inputs):
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ipt = layer.get_input(j)
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if ipt is not None:
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layer_inputs.append(layer.get_input(j).name)
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layer_outputs = [
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layer.get_output(j).name for j in range(layer.num_outputs)
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]
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nodes.append(
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helper.make_node(str(layer.type),
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name=layer.name,
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inputs=layer_inputs,
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outputs=layer_outputs,
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domain="com.nvidia"))
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onnx_model = helper.make_model(helper.make_graph(nodes,
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name,
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inputs,
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outputs,
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initializer=None),
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producer_name='NVIDIA')
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onnx.save(onnx_model, path)
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