From 8e9d0b6b200aa8f57e9f944d764db5df4e724125 Mon Sep 17 00:00:00 2001 From: quyongxiu1 Date: Thu, 18 Jun 2020 20:34:05 +0800 Subject: [PATCH] update warn infos and add interfaces for prompt infos and get ms api name --- mindinsight/mindconverter/config.py | 204 ++++++++++++++++++---------- 1 file changed, 136 insertions(+), 68 deletions(-) diff --git a/mindinsight/mindconverter/config.py b/mindinsight/mindconverter/config.py index 840e4cf..98c0a10 100644 --- a/mindinsight/mindconverter/config.py +++ b/mindinsight/mindconverter/config.py @@ -327,6 +327,48 @@ def load_json_file(file_path): return info +def get_corresponding_ms_name(pt_name): + """ + Get corresponding MindSpore op name for PyTorch name according to the mappings in mindconverter. + + Args: + pt_name: PyTorch op name, whether shortened form or full name is available. + + Returns: + str, full MindSpore op name, None if the op is not supported in mindconverter. + + Raises: + ValueError， if get shortened form of MindSpore name not starts with `P` or 'nn', which means it is wrong in + the mappings file. + """ + helper = ALL_MAPPING.get(pt_name) + if helper is None: + return None + ms_name = helper.ms_api.name + if ms_name.startswith('nn.'): + full_ms_name = 'mindspore.' + ms_name + elif ms_name.startswith('P.'): + full_ms_name = 'mindspore.ops.operations.' + ms_name[len('P.'):] + else: + raise ValueError('check your mapping infos, the corresponding mindspore op name may wrong for torch op : ' + '{}'.format(pt_name)) + return full_ms_name + + +def get_prompt_info(pt_name): + """ + Get prompt info for PyTorch op name. + + Args: + pt_name: PyTorch op name, whether shortened form or full name is available. + + Returns: + str, prompt info on the op, None if no prompt info for the op. + """ + prompt_dict = {**UNSUPPORTED_WARN_INFOS, **SUPPORTED_WARN_INFOS} + return prompt_dict.get(pt_name) + + # ---------------------------- mappings ---------------------------- NN_MAPPING_PATH = os.path.realpath(os.path.join(os.path.dirname(__file__), 'mappings/nn_mappings.json')) NN_MAPPING = get_mapping_from_file(NN_MAPPING_PATH) @@ -390,72 +432,98 @@ ALL_UNSUPPORTED = NN_UNSUPPORTED + F_UNSUPPORTED + TORCH_DOT_UNSUPPORTED + TENSO UNSUPPORTED_WARN_INFOS = { - "nn.AdaptiveAvgPool2d": "maybe could convert to P.ReduceMean", - "nn.AvgPool1d": "maybe could convert to nn.AvgPool1d", - "nn.ConvTranspose2d": "maybe could convert to nn.Conv2dTranspose", - "nn.CrossEntropyLoss": "maybe could convert to nn.SoftmaxCrossEntropyWithLogits", - "nn.Embedding": "maybe could convert to nn.Embedding", - "nn.GroupNorm": "maybe could convert to nn.GroupNorm", - "nn.MSELoss": "maybe could convert to nn.MSELoss", - "nn.LSTM": "maybe could convert to nn.LSTM", - "nn.LSTMCell": "maybe could convert to nn.LSTMCell", - "nn.ModuleList": "maybe could convert to nn.CellList", - "nn.SmoothL1Loss": "maybe could convert to nn.SmoothL1Loss", - "nn.Tanh": "maybe could convert to nn.Tanh", - "nn.Upsample": "maybe could convert to P.ResizeBilinear", - "nn.L1Loss": "maybe could convert to nn.L1Loss", - "nn.Parameter": "maybe could convert to mindspore.Parameter", - "nn.ParameterList": "maybe could convert to mindspore.ParameterTuple", - "nn.Unfold": "maybe could convert to nn.Unfold", - "nn.PixelShuffle": "maybe could convert to P.DepthToSpace", - "F.adaptive_avg_pool2d": "maybe could convert to P.ReduceMean", - "F.conv2d": "maybe could convert to mindspore.ops.operations.Conv2D", - "F.dropout": "please use nn.Dropout in __init__()", - "F.interpolate": "maybe could convert to P.ResizeBilinear", - "torch.bmm": "maybe could convert to P.BatchMatMul", - "torch.cumsum": "maybe could convert to P.CumSum", - "F.relu": "maybe could convert to P.ReLU", - "F.pad": "maybe could convert to P.Pad", - "F.softmax": "maybe could convert to P.Softmax", - "torch.clamp": "maybe could convert to mindspore.ops.composite.clip_by_value", - "torch.eq": "maybe could convert to P.Equal", - "torch.load": "maybe could convert to mindspore.train.serialization.load_checkpoint", - "torch.matmul": "maybe could convert to P.MatMul", - "torch.max": "try to use P.ArgMaxWithValue, notice that two values are returned by P.ArgMaxWithValue", - "torch.mean": "maybe could convert to P.ReduceMean", - "torch.min": "try to use P.ArgMinWithValue, notice that two values are returned by P.ArgMinWithValue", - "torch.mm": "maybe could convert to P.MatMul", - "torch.mul": "maybe could convert to P.Mul", - "torch.norm": "maybe could convert to nn.Norm", - "torch.numel": "maybe could convert to P.Size", - "F.one_hot": "maybe could convert to P.OneHot", - "torch.ones_like": "maybe could convert to P.OnesLike", - "torch.randn": "maybe could convert to P.TruncatedNormal", - "torch.round": "maybe could convert to P.Round", - "torch.save": "maybe could convert to mindspore.train.serialization.save_checkpoint", - "torch.sigmoid": "maybe could convert to P.Sigmoid", - "torch.split": "maybe could convert to P.Split", - "torch.squeeze": "maybe could convert to P.Squeeze", - "torch.stack": "maybe could convert to P.Pack", - "torch.sum": "maybe could convert to mindspore.ops.operations.ReduceSum", - "torch.tanh": "maybe could convert to mindspore.ops.operations.Tanh", - "torch.tensor": "maybe could convert to mindspore.Tensor", - "torch.transpose": "maybe could convert to P.Transpose", - "torch.unsqueeze": "maybe could convert to P.ExpandDims", - "torch.zeros_like": "maybe could convert to P.ZerosLike", - ".chunk": "maybe could convert to P.Split", - ".fill_": "maybe could convert to P.Fill", - ".float": "maybe could convert to P.Cast", - ".mm": "maybe could convert to P.MatMul", - "mul": "maybe could convert to P.Mul", - ".pow": "maybe could convert to P.Pow", - ".round": "maybe could convert to P.Round", - ".scatter": "maybe could convert to P.ScatterNd", - "sigmoid": "maybe could convert to nn.Sigmoid", - ".sign": "maybe could convert to P.Sign", - ".sqrt": "maybe could convert to P.Sqrt", - ".sub": "maybe could convert to P.Sub", - ".transpose": "maybe could convert to P.Transpose", - ".unsqueeze": "maybe could convert to P.ExpandDims", - ".zero_": "maybe could convert to P.ZerosLike", + "nn.AdaptiveAvgPool2d": "Maybe could convert to mindspore.ops.operations.ReduceMean.", + "nn.AvgPool1d": "Maybe could convert to mindspore.nn.AvgPool1d.", + "nn.ConvTranspose2d": "Maybe could convert to mindspore.nn.Conv2dTranspose.", + "nn.CrossEntropyLoss": "Maybe could convert to mindspore.nn.SoftmaxCrossEntropyWithLogits.", + "nn.Embedding": "Maybe could convert to mindspore.nn.Embedding.", + "nn.GroupNorm": "Maybe could convert to mindspore.nn.GroupNorm.", + "nn.MSELoss": "Maybe could convert to mindspore.nn.MSELoss.", + "nn.LSTM": "Maybe could convert to mindspore.nn.LSTM.", + "nn.LSTMCell": "Maybe could convert to mindspore.nn.LSTMCell.", + "nn.ModuleList": "Maybe could convert to mindspore.nn.CellList.", + "nn.SmoothL1Loss": "Maybe could convert to mindspore.nn.SmoothL1Loss.", + "nn.Tanh": "Maybe could convert to mindspore.nn.Tanh.", + "nn.Upsample": "Maybe could convert to mindspore.ops.operations.ResizeBilinear.", + "nn.L1Loss": "Maybe could convert to mindspore.nn.L1Loss.", + "nn.Parameter": "Maybe could convert to mindspore.Parameter.", + "nn.ParameterList": "Maybe could convert to mindspore.ParameterTuple.", + "nn.Unfold": "Maybe could convert to mindspore.nn.Unfold.", + "nn.PixelShuffle": "Maybe could convert to mindspore.ops.operations.DepthToSpace.", + "F.adaptive_avg_pool2d": "Maybe could convert to mindspore.ops.operations.ReduceMean.", + "F.conv2d": "Maybe could convert to mindspore.ops.operations.Conv2D.", + "F.dropout": "please use mindspore.nn.Dropout in __init__().", + "F.interpolate": "Maybe could convert to mindspore.ops.operations.ResizeBilinear.", + "F.one_hot": "Maybe could convert to mindspore.ops.operations.OneHot.", + "torch.bmm": "Maybe could convert to mindspore.ops.operations.BatchMatMul.", + "torch.cumsum": "Maybe could convert to mindspore.ops.operations.CumSum.", + "F.relu": "Maybe could convert to mindspore.ops.operations.ReLU.", + "F.pad": "Maybe could convert to mindspore.ops.operations.Pad.", + "F.softmax": "Maybe could convert to mindspore.ops.operations.Softmax.", + "torch.clamp": "Maybe could convert to mindspore.ops.composite.clip_by_value.", + "torch.eq": "Maybe could convert to mindspore.ops.operations.Equal.", + "torch.load": "Maybe could convert to mindspore.train.serialization.load_checkpoint.", + "torch.matmul": "Maybe could convert to mindspore.ops.operations.MatMul.", + "torch.max": "try to use P.ArgMaxWithValue, notice that two values are returned by mindspore.ops.operations." + "ArgMaxWithValue.", + "torch.mean": "Maybe could convert to mindspore.ops.operations.ReduceMean.", + "torch.min": "try to use P.ArgMinWithValue, notice that two values are returned by mindspore.ops.operations." + "ArgMinWithValue.", + "torch.mm": "Maybe could convert to mindspore.ops.operations.MatMul.", + "torch.mul": "Maybe could convert to mindspore.ops.operations.Mul.", + "torch.norm": "Maybe could convert to mindspore.nn.Norm.", + "torch.numel": "Maybe could convert to mindspore.ops.operations.Size.", + "torch.ones_like": "Maybe could convert to mindspore.ops.operations.OnesLike.", + "torch.randn": "Maybe could convert to mindspore.ops.operations.TruncatedNormal.", + "torch.round": "Maybe could convert to mindspore.ops.operations.Round.", + "torch.save": "Maybe could convert to mindspore.train.serialization.save_checkpoint.", + "torch.sigmoid": "Maybe could convert to mindspore.ops.operations.Sigmoid.", + "torch.split": "Maybe could convert to mindspore.ops.operations.Split.", + "torch.squeeze": "Maybe could convert to mindspore.ops.operations.Squeeze.", + "torch.stack": "Maybe could convert to mindspore.ops.operations.Pack.", + "torch.sum": "Maybe could convert to mindspore.ops.operations.ReduceSum.", + "torch.tanh": "Maybe could convert to mindspore.ops.operations.Tanh.", + "torch.tensor": "Maybe could convert to mindspore.Tensor.", + "torch.transpose": "Maybe could convert to mindspore.ops.operations.Transpose.", + "torch.unsqueeze": "Maybe could convert to mindspore.ops.operations.ExpandDims.", + "torch.zeros_like": "Maybe could convert to mindspore.ops.operations.ZerosLike.", + ".chunk": "Maybe could convert to mindspore.ops.operations.Split.", + ".fill_": "Maybe could convert to mindspore.ops.operations.Fill.", + ".float": "Maybe could convert to mindspore.ops.operations.Cast.", + ".mm": "Maybe could convert to mindspore.ops.operations.MatMul.", + ".mul": "Maybe could convert to mindspore.ops.operations.Mul.", + ".pow": "Maybe could convert to mindspore.ops.operations.Pow.", + ".round": "Maybe could convert to mindspore.ops.operations.Round.", + ".scatter": "Maybe could convert to mindspore.ops.operations.ScatterNd.", + ".sigmoid": "Maybe could convert to mindspore.nn.Sigmoid.", + ".sign": "Maybe could convert to mindspore.ops.operations.Sign.", + ".sqrt": "Maybe could convert to mindspore.ops.operations.Sqrt.", + ".sub": "Maybe could convert to mindspore.ops.operations.Sub.", + ".transpose": "Maybe could convert to mindspore.ops.operations.Transpose.", + ".unsqueeze": "Maybe could convert to mindspore.ops.operations.ExpandDims.", + ".zero_": "Maybe could convert to mindspore.ops.operations.ZerosLike.", +} + +NN_UNSUPPORTED_INFOS = {k: v for k, v in UNSUPPORTED_WARN_INFOS.items() if k.startswith('nn.')} +TORCH_NN_UNSUPPORTED_INFOS = {('torch.' + k): v for k, v in NN_UNSUPPORTED_INFOS.items()} + +F_UNSUPPORTED_INFOS = {k: v for k, v in UNSUPPORTED_WARN_INFOS.items() if k.startswith('F.')} +NN_FUNCTIONAL_UNSUPPORTED_INFOS = {'nn.functional.' + k[len('F.'):]: v for k, v in F_UNSUPPORTED_INFOS.items()} +TORCH_NN_FUNCTIONAL_UNSUPPORTED_INFOS = {'torch.nn.functional.' + k[len('F.'):]: v for k, v in + F_UNSUPPORTED_INFOS.items()} + +UNSUPPORTED_WARN_INFOS.update(TORCH_NN_UNSUPPORTED_INFOS) +UNSUPPORTED_WARN_INFOS.update(NN_FUNCTIONAL_UNSUPPORTED_INFOS) +UNSUPPORTED_WARN_INFOS.update(TORCH_NN_FUNCTIONAL_UNSUPPORTED_INFOS) + +SUPPORTED_WARN_INFOS = { + "torch.eye": "Pay attention to use right mindspore data type.", + "nn.Linear": "Pay attention to reshape the input to 2 dims if it is 3 dims before, because MindSpore.nn.Dense only " + "support 2-dim input.", + ".view": "Only float Tensor is supported in mindspore.ops.operations.Reshape.", + ".reshape": "Only float Tensor is supported in mindspore.ops.operations.Reshape." } + +NN_SUPPORTED_INFOS = {k: v for k, v in SUPPORTED_WARN_INFOS.items() if k.startswith('nn.')} +TORCH_NN_SUPPORTED_INFOS = {('torch.' + k): v for k, v in NN_SUPPORTED_INFOS.items()} +SUPPORTED_WARN_INFOS.update(TORCH_NN_SUPPORTED_INFOS) -- GitLab