masahi commented on a change in pull request #4497: [WIP] [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#discussion_r371813536
########## File path: python/tvm/relay/frontend/pytorch.py ########## @@ -0,0 +1,1135 @@ +# 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=import-self, too-many-lines, len-as-condition, no-else-return, unused-variable, too-many-nested-blocks +# pylint: disable=consider-iterating-dictionary, invalid-name, unused-argument, unused-variable, broad-except +"""PT: PyTorch frontend.""" +import numpy as np + +import tvm + +from .. import analysis as _analysis +from .. import expr as _expr +from .. import module as _module +from .. import op as _op +from .common import get_relay_op +from .common import infer_shape as _infer_shape + +__all__ = ['from_pytorch'] + +# operator implementation +def _elemwise(name): + def _impl(inputs, input_types): + data0 = convert_input(inputs[0]) + data1 = convert_input(inputs[1]) + + if not isinstance(data0, (_expr.Call, _expr.TupleGetItem, _expr.Var)): + temp = data0 + data0 = data1 + data1 = temp + + return get_relay_op(name)(data0, data1) + return _impl + +def _unsqueeze(): + def _impl(inputs, input_types): + data = inputs[0] + axis = inputs[1] + + return _op.transform.expand_dims(data, int(axis), 1) + return _impl + +def _concatenate(): + def _impl(inputs, input_types): + data = inputs[0] + axis = inputs[1] + + if isinstance(data, (_expr.Call, _expr.TupleGetItem, _expr.Var)): + data = [data] + + return _op.tensor.concatenate(data, int(axis)) + return _impl + +def _slice(): + def _impl(inputs, input_types): + data = inputs[0] + strides = [] + + inferred_shape = _infer_shape(data) + end = [] + for infer in inferred_shape: + end.append(int(infer)) + if isinstance(data, _expr.Var): + end = _infer_shape(data) + end = list(end) + + begin = [0]*len(end) + dim = int(inputs[1]) + begin[dim] = int(inputs[2]) + + if inputs[3].isdigit(): + end[dim] = min(end[dim], int(inputs[3])) + + strides.append(int(inputs[4])) + return _op.transform.strided_slice(data, begin, end, strides) + return _impl + +def _select(): + def _impl(inputs, input_types): + data = inputs[0] + inferred_shape = _infer_shape(data) + end = [] + + for infer in inferred_shape: + end.append(int(infer)) + + begin = [0]*len(end) + dim = int(inputs[1]) + index = int(inputs[2]) + + end[dim] = index+1 + begin[dim] = index + + strides = [1]*len(end) + + sym = _op.transform.strided_slice(data, begin, end, strides) + axis = [dim] + + return _op.transform.squeeze(sym, axis) + return _impl + +def _convert_data_type(input_type): + if input_type == 'double' or input_type == 'torch.float64': + return 'float64' + elif input_type == 'float' or input_type == 'torch.float32': + return 'float32' + elif input_type == 'half' or input_type == 'torch.float16': + return 'float16' + elif input_type == 'long' or input_type == 'torch.int64': + return 'int64' + elif input_type == 'int' or input_type == 'torch.int32': + return 'int32' + elif input_type == 'short' or input_type == 'torch.int16': + return 'int16' + elif input_type == 'char' or input_type == 'torch.int8': + return 'int8' + elif input_type == 'byte' or input_type == 'torch.uint8': + return 'uint8' + else: + return input_type + +def _ones(): + def _impl(inputs, input_types): + if isinstance(inputs[0], _expr.Var): + shape = _infer_shape(inputs[0]) + elif isinstance(inputs[0], (_expr.Call, _expr.TupleGetItem)): + shape = _infer_shape(inputs[0]) + else: + shape = inputs[0].shape + + fill_value = _get_fill_value(input_types) + + return get_relay_op('full')(fill_value, shape, dtype=_convert_data_type(input_types[0])) + return _impl + +def _zeros(): + def _impl(inputs, input_types): + if isinstance(inputs[0], _expr.Var): + shape = _infer_shape(inputs[0]) + elif isinstance(inputs[0], (_expr.Call, _expr.TupleGetItem)): + shape = _infer_shape(inputs[0]) + else: + shape = inputs[0].shape + + fill_value = _get_fill_value(input_types) + + return _op.full(fill_value, shape, dtype=input_types[0]) + return _impl + +def _get_fill_value(input_types): + if input_types[0] == 'int': + fill_value = _expr.const(1) + elif input_types[0] == 'float': Review comment: this branch is redundant ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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