BBuf commented on a change in pull request #8790: URL: https://github.com/apache/tvm/pull/8790#discussion_r782643076
########## File path: python/tvm/relay/frontend/oneflow.py ########## @@ -0,0 +1,1842 @@ +# 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. +# pylint: disable=invalid-name, import-self, len-as-condition, unused-argument, too-many-lines +# pylint: disable=import-outside-toplevel +"""OF: OneFlow frontend""" +import os +import re +import copy +import warnings + +import numpy as np +import tvm +from tvm.ir import IRModule +from tvm.topi.utils import get_const_tuple + +from .. import analysis +from .. import expr as _expr +from .. import function as _function +from .. import op as _op +from .. import ty as _ty +from .common import ( + AttrCvt, + Renamer, + fold_constant, + get_name, + get_relay_op, + infer_channels, + infer_shape, + infer_type, + infer_value, + new_var, +) + +__all__ = ["from_oneflow"] + +FLOW_2_STR_DTYPE = { + 2: "float32", + 3: "float64", + 6: "int64", + 5: "int32", + 4: "int8", + 7: "uint8", + 9: "float16" +} + +FLOW_2_NP_DTYPE = { + 2: np.float32, + 3: np.float64, + 6: np.int64, + 5: np.int32, + 4: np.int8, + 7: np.uint8, + 9: np.float16 +} + +_identity_list = [] + + +def is_input_op(node): + # Determine if the the node is the input of graph + return node.WhichOneof("op_type") == "input_conf" + + +def is_user_op(node): + # Determine if the the node is the intermediate variables of graph + return node.WhichOneof("op_type") == "user_conf" + + +def is_output_op(node): + # Determine if the the node is the output of graph + return node.WhichOneof("op_type") == "output_conf" + + +def is_param_op(node): + # Determine if the the node is the intermediate variables of model(saved) + return node.WhichOneof("op_type") == "variable_conf" + + +def get_node_info(node): + """ + Get basic information about nodes: shape、data_type + """ + # list->tuple + shape = tuple(node.input_conf.blob_conf.shape.dim) + # get data type + dtype = node.input_conf.blob_conf.data_type + if dtype in list(FLOW_2_NP_DTYPE.keys()): + data_type = FLOW_2_NP_DTYPE[dtype] + else: + raise IndexError('Please check the data type of your node: %s' % node.name) + + return shape, data_type + + +def parse_attr(attr): + # Parse node_attr + attrs = {} + for a in attr: + attr_str = str(attr[a]) + + if attr_str[0:7] == "at_list": + attr_str_ = attr_str.split(" ")[0] + + if attr_str_ == "at_list_float": + attrs[a] = tuple(attr[a].at_list_float.val) + elif attr_str_ == "at_list_int32": + attrs[a] = tuple(attr[a].at_list_int32.val) + elif attr_str_ == "at_list_int64": + attrs[a] = tuple(attr[a].at_list_int64.val) + + elif attr_str.split(":")[0] == "at_string": + attrs[a] = attr[a].at_string + + elif attr_str.split(" ")[0] == "at_shape": + attrs[a] = tuple(list(attr[a].at_shape.dim)) + + else: + attr_str_ = attr_str.split(":")[0] + if attr_str_ == "at_bool": + attrs[a] = attr[a].at_bool + elif attr_str_ == "at_double": + attrs[a] = attr[a].at_double + elif attr_str_ == "at_float": + attrs[a] = attr[a].at_float + elif attr_str_ == "at_int32": + attrs[a] = attr[a].at_int32 + elif attr_str_ == "at_int64": + attrs[a] = attr[a].at_int64 + + return attrs + + +def shape_of(x, dtype="int64"): + ttype = infer_type(x).checked_type + if not _ty.is_dynamic(ttype): + shape = list(ttype.shape) + return _expr.const(shape, dtype) + + return _op.shape_of(x, dtype) + + +def dimension_constraint(): + def _dim_check(attrs): + if len(attrs["kernel_size"]) in [1, 2, 3]: + return True + return False + + return _dim_check, "Only 1d, 2d and 3d kernel supported." + + +class OneFlowOpConverter(object): + """A helper class for holding oneflow op converters.""" + + @classmethod + def get_converter(cls): + """ + Get converter matches given opset. + Parameters + ---------- + None + + Returns + ------- + converter, which should be `_impl_vx`. + """ + version = 1 + if hasattr(cls, "_impl_v{}".format(version)): + return getattr(cls, "_impl_v{}".format(version)) + raise NotImplementedError( + "version {} of {} not implemented".format(version, cls.__name__) + ) + + +class Pool(OneFlowOpConverter): + """A helper class for pool op converters.""" + name = "" Review comment: I think also it is unnecessary， I will solve this. -- This is an automated message from the Apache Git Service. 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