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_r370061548
########## File path: python/tvm/relay/frontend/pytorch.py ########## @@ -0,0 +1,1138 @@ +# 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': + fill_value = _expr.const(1.0) + else: + fill_value = _expr.const(1.0) + + return fill_value + +def _relu(): + def _impl(inputs, input_types): + data = inputs[0] + return _op.nn.relu(data) + return _impl + +def _adaptive_avg_2d(): + def _impl(inputs, input_types): + data = inputs[0] + output_size = _infer_shape(inputs[1]) + + return _op.contrib.contrib.adaptive_avg_pool2d( + data, + output_size=output_size) + return _impl + +def _adaptive_max_2d(): + def _impl(inputs, input_types): + data = inputs[0] + output_size = _infer_shape(inputs[1]) + + return _op.contrib.contrib.adaptive_max_pool2d( + data, + output_size=output_size) + return _impl + +def _maxpool_2d(): + def _impl(inputs, input_types): + data = inputs[0] + + pool_size = _infer_shape(inputs[1]) + strides = _infer_shape(inputs[2]) + padding = _infer_shape(inputs[3]) + + ceil_mode = int(inputs[5]) + + return _op.nn.max_pool2d(data, pool_size, strides, padding, "NCHW", ceil_mode) + return _impl + +def _hardtanh(): + def _impl(inputs, input_types): + a = inputs[0] + tanh_min = float(inputs[1]) + tanh_max = float(inputs[2]) + return _op.tensor.clip(a, tanh_min, tanh_max) + return _impl + +def _convolution(): + def _impl(inputs, input_types): + # Use transpose or normal + use_transpose = False + if inputs[6] == '1': + use_transpose = True + + use_bias = False + if isinstance(inputs[2], _expr.Var): + use_bias = True + + data = inputs[0] + weight = inputs[1] + bias = inputs[2] + + if isinstance(weight, (_expr.Call, _expr.Var, _expr.TupleGetItem)): + inferred_shape = _infer_shape(weight) + weight_shape = [] + for infer in inferred_shape: + weight_shape.append(infer) + else: + weight_shape = weight.shape + channels = weight_shape[0] + + strides = inputs[3] + padding = inputs[4] + dilation = inputs[5] + + kernel_size = weight_shape[2:] + + else: + data = inputs[0] + weight = inputs[1] + bias = inputs[2] + + if isinstance(weight, (_expr.Call, _expr.Var, _expr.TupleGetItem)): + inferred_shape = _infer_shape(weight) + weight_shape = [] + for infer in inferred_shape: + weight_shape.append(infer) + else: + weight_shape = weight.shape + channels = weight_shape[0] + + strides = inputs[3] + padding = inputs[4] + dilation = inputs[5] + + kernel_size = weight_shape[2:] + + if isinstance(strides, _expr.Var): + strides = _infer_shape(strides) + + if isinstance(padding, _expr.Var): + padding = _infer_shape(padding) + + if isinstance(dilation, _expr.Var): + dilation = _infer_shape(dilation) + + groups = int(inputs[8]) + + if use_transpose: + conv_out = _op.nn.conv2d_transpose(data, + weight, + strides=strides, + padding=padding, + dilation=dilation, + groups=groups, + channels=channels, + kernel_size=kernel_size, + data_layout="NCHW", + kernel_layout="OIHW", + out_layout="", + out_dtype="") + else: + conv_out = _op.nn.conv2d(data, + weight, + strides=strides, + padding=padding, + dilation=dilation, + groups=groups, + channels=channels, + kernel_size=kernel_size, + data_layout="NCHW", + kernel_layout="OIHW", + out_layout="", + out_dtype="") + + if use_bias: + return _op.nn.bias_add(conv_out, bias) + else: + return conv_out + return _impl + +def _softmax(): + def _impl(inputs, input_types): + data = inputs[0] + axis = inputs[1] + if isinstance(axis, str): + axis = int(axis) + + return _op.nn.softmax(data, axis=axis) + return _impl + +def _threshold(): + def _impl(inputs, input_types): + data = inputs[0] + return _op.nn.relu(data) + return _impl + +def _contiguous(): + def _impl(inputs, input_types): + data = inputs[0] + return _op.tensor.copy(data) + return _impl + +def _batch_norm(): + def _impl(inputs, input_types): + data = inputs[0] + data_type = input_types[0] + + channels = _infer_shape(data) + + if isinstance(inputs[1], _expr.Var) and isinstance(inputs[2], _expr.Var): + scale = center = True + weight = inputs[1] + beta = inputs[2] + else: + scale = center = False + + if scale: + gamma = weight + else: + if data_type == 'double': + gamma = _expr.const(np.ones([int(channels[1])]).astype('float64')) + elif data_type == 'float': + gamma = _expr.const(np.ones([int(channels[1])]).astype('float32')) + elif data_type == 'half': + gamma = _expr.const(np.ones([int(channels[1])]).astype('float16')) + elif data_type == 'long': + gamma = _expr.const(np.ones([int(channels[1])]).astype('int64')) + elif data_type == 'int': + gamma = _expr.const(np.ones([int(channels[1])]).astype('int32')) + elif data_type == 'short': + gamma = _expr.const(np.ones([int(channels[1])]).astype('int16')) + elif data_type == 'char': + gamma = _expr.const(np.ones([int(channels[1])]).astype('int8')) + elif data_type == 'byte': + gamma = _expr.const(np.ones([int(channels[1])]).astype('uint8')) + + if center: + beta = beta + else: + if data_type == 'double': + beta = _expr.const(np.zeros([int(channels[1])]).astype('float64')) + elif data_type == 'float': + beta = _expr.const(np.zeros([int(channels[1])]).astype('float32')) + elif data_type == 'half': + beta = _expr.const(np.zeros([int(channels[1])]).astype('float16')) + elif data_type == 'long': + beta = _expr.const(np.zeros([int(channels[1])]).astype('int64')) + elif data_type == 'int': + beta = _expr.const(np.zeros([int(channels[1])]).astype('int32')) + elif data_type == 'short': + beta = _expr.const(np.zeros([int(channels[1])]).astype('int16')) + elif data_type == 'char': + beta = _expr.const(np.zeros([int(channels[1])]).astype('int8')) + elif data_type == 'byte': + beta = _expr.const(np.zeros([int(channels[1])]).astype('uint8')) + + moving_mean = inputs[3] + moving_var = inputs[4] + epsilon = float(inputs[7]) + + center = center + scale = scale + + return _op.nn.batch_norm(data, + gamma, + beta, + moving_mean, + moving_var, + axis=1, + epsilon=epsilon, + center=center, + scale=scale)[0] + return _impl + +def _transpose(): + def _impl(inputs, input_types): + data = inputs[0] + + if isinstance(data, _expr.Var): + ndims = len(_infer_shape(data)) + elif isinstance(data, (_expr.Call, _expr.TupleGetItem)): + ndims = _infer_shape(data) + else: + ndims = data.shape + + if isinstance(data, tvm.ndarray.NDArray): + ndims = len(data.shape) + axes = list(range(ndims)) + + num_inputs = len(inputs) + + if num_inputs == 1: + if ndims >= 2: + axes[-1] = ndims - 2 + axes[-2] = ndims - 1 + if not isinstance(data, _expr.Var): + data = _expr.const(data) + + elif num_inputs == 3: + parse = lambda i: ndims * (i < 0) + i + src, dst = [parse(int(inputs[i])) for i in [1, 2]] + axes[src] = dst + axes[dst] = src + else: + axes = inputs[1] + return _op.transform.transpose(data, axes) + return _impl + +def _flatten(): + def _impl(inputs, input_types): + data = inputs[0] + return _op.nn.batch_flatten(data) + return _impl + +def _dense(): + def _impl(inputs, input_types): + use_bias = False + + if isinstance(inputs[0], _expr.Var): + use_bias = True + + data = inputs[1] + data_type = input_types[1] + weight = inputs[2] + + beta = inputs[3] + alpha = inputs[4] + + if not isinstance(alpha, (_expr.Var, _expr.Call, _expr.TupleGetItem)): + if data_type == 'double': + alpha = _expr.const(np.float64(alpha), dtype='float64') + elif data_type == 'float': + alpha = _expr.const(np.float32(alpha), dtype='float32') + elif data_type == 'half': + alpha = _expr.const(np.float16(alpha), dtype='float16') + elif data_type == 'long': + alpha = _expr.const(np.int64(alpha), dtype='int64') + elif data_type == 'int': + alpha = _expr.const(np.int32(alpha), dtype='int32') + elif data_type == 'short': + alpha = _expr.const(np.int16(alpha), dtype='int16') + elif data_type == 'char': + alpha = _expr.const(np.int8(alpha), dtype='int8') + elif data_type == 'byte': + alpha = _expr.const(np.uint8(alpha), dtype='uint8') + data *= alpha + + if not isinstance(beta, (_expr.Var, _expr.Call, _expr.TupleGetItem)): + if data_type == 'double': + beta = _expr.const(np.float64(beta), dtype='float64') + elif data_type == 'float': + beta = _expr.const(np.float32(beta), dtype='float32') + elif data_type == 'half': + beta = _expr.const(np.float16(beta), dtype='float16') + elif data_type == 'long': + beta = _expr.const(np.int64(beta), dtype='int64') + elif data_type == 'int': + beta = _expr.const(np.int32(beta), dtype='int32') + elif data_type == 'short': + beta = _expr.const(np.int16(beta), dtype='int16') + elif data_type == 'char': + beta = _expr.const(np.int8(beta), dtype='int8') + elif data_type == 'byte': + beta = _expr.const(np.uint8(beta), dtype='uint8') + weight *= beta + + weight_out = _op.transform.transpose(weight, axes=[1, 0]) + + units = _infer_shape(weight_out)[0] + dense_out = _op.nn.dense(data, weight_out, units=units) + + if use_bias: + bias = inputs[0] + return _op.nn.bias_add(dense_out, bias) + else: + return dense_out + return _impl + +def _size(): + def _impl(inputs, input_types): + axis = int(inputs[1]) + if isinstance(inputs[0], _expr.Var): + shape = _infer_shape(inputs[0]) + else: + shape = _infer_shape(inputs[0]) + return shape[axis] + return _impl + +def _numtotensor(): + def _impl(inputs, input_types): + val = inputs[0] + dtype = type(val) + + if isinstance(val, tvm.expr.IntImm): + val = val.__int__() + dtype = int + + arr = val * np.ones([]).astype(dtype) + return arr + return _impl + +def _view(): + def _impl(inputs, input_types): + data = inputs[0] + + if len(inputs) == 3: + new_shape = [inputs[1], _infer_shape(inputs[2])[0]] + else: + if isinstance(inputs[1], list): + new_shape = inputs[1] + else: + new_shape = _infer_shape(inputs[1]) + + return _op.transform.reshape(data, new_shape) + return _impl + +def _clone(): + def _impl(inputs, input_types): + data = inputs[0] + return _op.tensor.copy(data) + return _impl + +def _log_softmax(): + def _impl(inputs, input_types): + data = inputs[0] + axis = int(inputs[1]) + return _op.nn.log_softmax(data, axis) + return _impl + +def _sigmoid(): + def _impl(inputs, input_types): + data = inputs[0] + return _op.tensor.sigmoid(data) + return _impl + +def _avg_pool2d(): + def _impl(inputs, input_types): + data = inputs[0] + + pool_size = _infer_shape(inputs[1]) + strides = _infer_shape(inputs[2]) + padding = _infer_shape(inputs[3]) + + ceil_mode = int(inputs[4]) + count_include_pad = int(inputs[5]) + + return _op.nn.avg_pool2d(data, + pool_size=pool_size, + strides=strides, + padding=padding, + ceil_mode=ceil_mode, + count_include_pad=count_include_pad) + return _impl + +def _dropout(): + def _impl(inputs, input_types): + data = inputs[0] + rate = float(inputs[1]) + + return _op.nn.dropout(data, rate) + return _impl + +def _reduce(name): + def _impl(inputs, attrs, params): + data = inputs[0] + return get_relay_op(name)(data) + return _impl + +def _mean(): + def _impl(inputs, input_types): + data = inputs[0] + axis = _infer_shape(inputs[1]) + + keepdims = int(inputs[2]) + exclude = int(inputs[3]) + + return _op.mean(data, axis, keepdims, exclude) + return _impl + +def _chunk(): + def _impl(inputs, input_types): + data = inputs[0] + + num_chunks = int(inputs[1]) + axis = int(inputs[2]) + + if isinstance(data, _expr.Var): + inferred_shape = _infer_shape(data) + elif isinstance(data, (_expr.Call, _expr.TupleGetItem)): + inferred_shape = _infer_shape(data) + + shape = [] + for infer in inferred_shape: + shape.append(infer) + + dim = int(shape[axis]) + + if dim % num_chunks: + unif_size = int(dim / (num_chunks - 1)) + else: + unif_size = int(dim / num_chunks) + + chunks = [] + for i in range(0, dim, unif_size): + begin = [0] * len(shape) + end = shape[:] + begin[axis] = i + end[axis] = i + unif_size + stride = [1] * len(shape) + + chunk_out = _op.transform.strided_slice(data, begin, end, stride) + chunks.append(chunk_out) + + + if dim % num_chunks: + begin = [0] * len(shape) + end = shape[:] + begin[axis] = unif_size * (num_chunks - 1) + end[axis] = dim + stride = [1] * len(shape) + + chunk_out = _op.transform.strided_slice(data, begin, end, stride) + chunks.append(chunk_out) + + return chunks + return _impl + +def _matmul(): + def _impl(inputs, input_types): + data0 = inputs[0] + data1 = inputs[1] + data1_t = _op.transpose(data1, axes=(1, 0)) + + return _op.nn.dense(data0, data1_t) + return _impl + +def _expand(): + def _impl(inputs, input_types): + data_in = inputs[0] + if isinstance(data_in, _expr.Var): + shape = _infer_shape(data_in) + elif isinstance(data_in, (_expr.Call, _expr.TupleGetItem)): + shape = _infer_shape(data_in) + + ndims = len(shape) + sizes = _infer_shape(inputs[1]) + out = inputs[0] + + for i in range(ndims): + if sizes[i] in {-1, shape[i]}: + continue + data = list() + for temp in range(sizes[i]): + data.append(out) + call = _op.tensor.concatenate(data, i) + + return call + return _impl + +def _int(): + def _impl(inputs, input_types): + if isinstance(inputs[0], _expr.Call): + return inputs[0] + return int(inputs[0]) + return _impl + +def _listunpack(): + def _impl(inputs, input_types): + return inputs[0] + return _impl + +def _to(): + def _impl(inputs, input_types): + return inputs[0] + return _impl + +def _device(): + def _impl(inputs, input_types): + return None + return _impl + +def _pad(): + def _impl(inputs, input_types): + data = inputs[0] + padding = inputs[1] + pad_width = list(zip(padding, padding)) + pad_value = inputs[2] + return _op.nn.pad(data, pad_width, pad_value) + return _impl + +def _sqrt(): + def _impl(inputs, input_types): + data = inputs[0] + return _op.tensor.sqrt(data) + return _impl + +# Helper functions for operator implementation + +def convert_input(data): + """ Handle input conversion for elemwise op """ + if isinstance(data, (_expr.Call, _expr.TupleGetItem, _expr.Var)): + return data + elif isinstance(data, str): + if len(data) == 1: + return _expr.const(int(data), dtype='float32') + else: + if '.' in data: + return _expr.const(float(data[1:-1]), dtype='float32') + else: + return _expr.const(int(data[1:-1]), dtype='float32') + else: + return _expr.const(int(data), dtype='float32') + +# Operator mappings + +_convert_map = { + 'aten::device' : _device(), + 'aten::add' : _elemwise('add'), + 'aten::add_' : _elemwise('add'), + 'aten::sub' : _elemwise('subtract'), + 'aten::sub_' : _elemwise('subtract'), + 'aten::max' : _elemwise('maximum'), + 'aten::min' : _elemwise('minimum'), + 'aten::mul' : _elemwise('multiply'), + 'aten::mul_' : _elemwise('multiply'), + 'aten::pow' : _elemwise('power'), + 'aten::div' : _elemwise('divide'), + 'aten::div_' : _elemwise('divide'), + 'aten::ones' : _ones(), + 'aten::zeros' : _zeros(), + 'aten::to' : _to(), + 'aten::unsqueeze' : _unsqueeze(), + 'aten::cat' : _concatenate(), + 'aten::slice' : _slice(), + 'aten::select' : _select(), + 'aten::relu' : _relu(), + 'aten::relu_' : _relu(), + 'aten::adaptive_avg_pool2d' : _adaptive_avg_2d(), + 'aten::adaptive_max_pool2d' : _adaptive_max_2d(), + 'aten::max_pool2d' : _maxpool_2d(), + 'aten::max_pool2d_with_indices' : _maxpool_2d(), + 'aten::hardtanh' : _hardtanh(), + 'aten::hardtanh_' : _hardtanh(), + 'aten::_convolution' : _convolution(), + 'aten::softmax' : _softmax(), + 'aten::threshold' : _threshold(), + 'aten::threshold_' : _threshold(), + 'aten::contiguous' : _contiguous(), + 'aten::batch_norm' : _batch_norm(), + 'aten::transpose' : _transpose(), + 'aten::transpose_' : _transpose(), + 'aten::t' : _transpose(), + 'aten::flatten' : _flatten(), + 'aten::addmm' : _dense(), + 'aten::size' : _size(), + 'aten::view' : _view(), + 'aten::clone' : _clone(), + 'aten::log_softmax' : _log_softmax(), + 'aten::sigmoid' : _sigmoid(), + 'aten::avg_pool2d' : _avg_pool2d(), + 'aten::dropout' : _dropout(), + 'aten::dropout_' : _dropout(), + 'aten::mean' : _mean(), + 'aten::chunk' : _chunk(), + 'aten::matmul' : _matmul(), + 'aten::expand' : _expand(), + 'aten::Int' : _int(), + 'prim::NumToTensor' : _numtotensor(), + 'prim::ListUnpack' : _listunpack(), + 'aten::constant_pad_nd' : _pad(), + 'aten::permute' : _transpose(), + 'aten::sum' : _reduce('sum'), + 'aten::prod' : _reduce('prod'), + 'aten::sqrt' : _sqrt() +} + +# Internal graph for parsing + +class Graph(object): + """ A helper class for handling relay graph copying from PyTorch trace. """ + + def __init__(self, trace, input_shapes, input_types): + + self._trace = trace + self._inputs_r = {} + self._params = {} + self._param_tensors = {} + self._consts = {} + self._ops = {} + self._op_inputs_r = {} + self._op_inputs_types = {} + self._op_inputs_otypes = {} + self._input_shapes = input_shapes if input_shapes else {} + self._input_types = input_types if input_types else {} + self._fn_param = [] + self._relay_map = {} + self._nid_to_node_name = {} + + def from_pytorch(self): + """ Construct relay nodes from trace of PyTorch graph + + Currently only supports traced PyTorch format which means no control flow. + User must perform torch.jit.trace on a model and pass this in. + Future support should include support scripted models (torch.jit.script) which + preserves control flow. + + Returns + ------- + mod : tvm.relay.Module + The module that optimizations will be performed on. + + params : dict of str to tvm.ndarray + Dict of converted parameters stored in tvm.ndarray format + """ + # Check for missing ops + missing_operators = self._parse_import_prerequisites() + + if missing_operators: + raise NotImplementedError( \ + "The following operators are not implemented: {}".format(missing_operators)) + + # Translate PyTorch graph to by decorating Graph with state dict and inputs into each op + self._parse_inputs() + self._parse_params() + self._parse_ops() + + nid = 0 + for (op_name, operator), op_node in self._ops.items(): + if operator == 'prim::Constant': + pass + elif operator == 'prim::ListConstruct': + if any(inp.debugName() in self._nid_to_node_name.keys() \ + for inp in op_node.inputs()): + listconstr = [] + for i in op_node.inputs(): + if i.debugName() in self._nid_to_node_name.keys(): + listconstr.append( \ + self._relay_map[self._nid_to_node_name[i.debugName()]]) + elif i.node().kind() == 'prim::Constant': + listconstr.append(int(self._consts[i.debugName()])) + elif i.debugName() in self._inputs_r.keys(): + listconstr.append(int(self._inputs_r[i.debugName()])) + + # Unwrap for tensors + if len(listconstr) == 1: + listconstr = listconstr[0] + + self._relay_map[nid] = listconstr + self._nid_to_node_name[op_name] = nid + nid = nid+1 + else: + for i in op_node.inputs(): + if i.debugName() in self._nid_to_node_name.keys(): + for cnt in range(0, len(self._op_inputs_r[(op_name, operator)])): + if isinstance(self._op_inputs_r[(op_name, operator)][cnt], str): + if "call/var" in self._op_inputs_r[(op_name, operator)][cnt]: + self._op_inputs_r[(op_name, operator)][cnt] = \ + self._relay_map[self._nid_to_node_name[i.debugName()]] + break + + call = _convert_map[operator](self._op_inputs_r[(op_name, operator)], + self._op_inputs_types[(op_name, operator)]) + + self._relay_map[nid] = call + self._nid_to_node_name[op_name] = nid + nid = nid+1 + + outputs = [] + + for i in range(nid): + output = self._relay_map[i] + outputs.append(output) + + if len(outputs) == 1: + body = outputs[0] + else: + body = outputs[-1] + + func = tvm.relay.Function(_analysis.free_vars(body), body) + + param = {k: tvm.nd.array(v) for k, v in self._param_tensors.items()} + + return _module.Module.from_expr(func), param + + def _parse_inputs(self): + """ Map inputs to parser and inputs to graph. """ + # Get names and objects of inputs for IR + ir_names = [i.debugName() for i in self._trace.graph.inputs()] + ir_inputs = [i for i in self._trace.graph.inputs()] + + # Create corresponding shape and add to input + for input_name, ir_input in zip(self._input_shapes, ir_inputs[1:]): + input_shape = self._input_shapes[input_name] + ir_input.setDebugName(input_name) + + self._inputs_r[input_name] = _expr.var(input_name, + shape=self._input_shapes[input_name], + dtype=_convert_data_type( + self._input_types[input_name])) + self._fn_param.append(_expr.var(input_name, + shape=self._input_shapes[input_name], + dtype=_convert_data_type( + self._input_types[input_name]))) + + # Add self (first input of a PyTorch graph) to inputs + input_shape = [3] + tensor = tvm.nd.array(np.zeros(input_shape).astype(np.float32)) + input_name = ir_names[0] + self._inputs_r[input_name] = tensor + + def _parse_params(self): + """ Map state dictionary values to corresponding prim::GetAttr op node. """ + # Grab weights, biases, etc. from graph + state_dict = self._trace.state_dict() + param_names = [] + for key, value in state_dict.items(): + param_str = str(key) + param_name = param_str.split('.')[-1] + param_names.append(param_name) + + # Get names of all inputs + input_names = [i for i in self._inputs_r.keys()] + + # Iterate through graph for getAttr nodes and match full state_dict name to nodes + node_weight_map = {} + for node in self._trace.graph.nodes(): + if node.kind() == "prim::GetAttr": + node_str = str(node) + node_assign = (node_str.split(' = ')[0]).split(' : ') + node_name = (node_assign[0])[1:] + node_getattr_name = ((node_str.split(' = ')[1]).split('"')[1::2])[0] + node_arg = (((node_str.split(' = '))[1]).split('(')[1])[1:-2] + + if node_arg in input_names: + node_weight_map[node_name] = node_getattr_name + else: + previous_map = node_weight_map[node_arg[:]] + node_weight_map[node_name] = previous_map+"."+node_getattr_name + + if node_getattr_name in param_names: + + value = state_dict[node_weight_map[node_name]] + tensor = tvm.nd.array(value.cpu().numpy()) + shape = tensor.shape + self._param_tensors[node_name] = tensor + + self._params[node_name] = _expr.var(node_name, + shape=shape, + dtype=_convert_data_type(str(value.dtype))) + + self._fn_param.append(_expr.var(node_name, + shape=shape, + dtype=_convert_data_type(str(value.dtype)))) + + + def _parse_ops(self): + """ Iterate through nodes and decorate graph with constants, operators, + and the inputs to each operator. """ + # Traverse nodes and add to graph + for node in self._trace.graph.nodes(): + + node_str = str(node) + node_assign = (node_str.split(' = ')[0]).split(' : ') + node_name = (node_assign[0])[1:] + node_expr = (node_str.split(' = ')[1]).split(',')[0] + + if node.kind() == "prim::Constant": + node_value = '0' + if "None" not in node_str and node_expr != "prim::Constant()" and \ + "?" not in node_str: + node_value = ((node_str.split(' = ')[1]).split('value=')[1]).split(']')[0] + self._consts[node_name] = node_value + elif node.kind() == "prim::ListConstruct": + list_shape = [] + for input_node in node.inputs(): + if input_node.debugName() in self._inputs_r.keys(): + list_shape.append(int(self._inputs_r[input_node.debugName()])) + elif input_node.debugName() in self._consts.keys(): + list_shape.append(int(self._consts[input_node.debugName()])) + else: + pass + self._inputs_r[node_name] = _expr.var(node_name, shape=list_shape) + elif node.kind() == "prim::GetAttr": + continue + + self._add_op(node_name, node.kind(), node) + + # Graph Helper Functions + + def _add_op(self, op_name, operator, op_node): + """ Add an operator and its operators inputs to the graph and insert placeholders + where an input is a call node. + + Parameters + ---------- + op_name : string + The ID of the op node + + operator : string + The kind of operator + + op_node : PyTorch Node object + The full Node object for the op node + + """ + self._ops[(op_name, operator)] = op_node + input_list_r = [] + input_list_types = [] + for input_node in op_node.inputs(): + if input_node.debugName() in self._inputs_r.keys(): Review comment: input_node -> input_value ``` node_id = input_value.debugName() node = input_value.node() ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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