zhiics commented on a change in pull request #4459: [RUNTIME] Implement TVMDSOOp(TensorFlow custom op) for TVM runtime URL: https://github.com/apache/incubator-tvm/pull/4459#discussion_r360498305
########## File path: python/tvm/contrib/tf_op/module.py ########## @@ -0,0 +1,104 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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 tensorflow as tf +from tensorflow.python.framework import load_library + + +class Module(): + + def __init__(self, lib_path): + self.lib_path = lib_path + + def func(self, name, output_dtype=None, output_shape=None): + return Func(self.lib_path, name, output_dtype, output_shape) + + def __getitem__(self, func_name): + return self.func(func_name) + + +class Func(): + + def __init__(self, lib_path, func_name, output_dtype, output_shape): + self.lib_path = lib_path + self.func_name = func_name + self.output_dtype = output_dtype + + # const(0) indicate invalid dynamic shape + self.dynamic_output_shape = tf.constant(0, tf.int64) + self.static_output_shape = None + self.has_static_output_shape = False # extra flag is required + + if self._is_static_shape(output_shape): + self.static_output_shape = output_shape + self.has_static_output_shape = True + elif output_shape is not None: + self.dynamic_output_shape = self._pack_shape_tensor(output_shape) + + # TODO: support non-xpu device + #self.device = device + # delay initialization to called first time, where num input arguments is known + self.tvm_dso_op = None + self.module = load_library.load_op_library('tvm_dso_op.so') + + def apply(self, *params): + if self.tvm_dso_op is None: + num_inputs = len(params) + self.tvm_dso_op = getattr(self.module, "tvm_dso_op%s" % num_inputs) + + return self.tvm_dso_op(*params, + dynamic_output_shape=self.dynamic_output_shape, + static_output_shape=self.static_output_shape, + has_static_output_shape=self.has_static_output_shape, + lib_path=self.lib_path, + func_name=self.func_name, + output_dtype=self.output_dtype) + + def __call__(self, *params): + return self.apply(*params) + + def _is_static_shape(self, shape): + if shape is None or not isinstance(shape, list): + return False + for d in shape: + if not isinstance(d, int): + return False + if d < 0: + raise Exception("Negative dimension is illegal: %d" % d) + return True + + def _pack_shape_tensor(self, shape): + if isinstance(shape, tf.Tensor): + if shape.dtype == tf.int32: + shape = tf.cast(shape, tf.int64) + return shape + elif isinstance(shape, list): + shape_dims = [] + for d in shape: + if isinstance(d, int): + shape_dims.append(tf.constant(d, tf.int64)) + elif isinstance(d, tf.Tensor) and len(d.shape) == 0: + if d.dtype == tf.int32: + d = tf.cast(d, tf.int64) + shape_dims.append(d) + else: + raise TypeError("Input shape dimension is neither scala tensor nor int") Review comment: scalar ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services
