def convert(topology, device=None, extra_config={}): """ This function is used to convert a `onnxconverter_common.topology.Topology` object into a *PyTorch* model. Args: topology: The `onnxconverter_common.topology.Topology` object that will be converted into Pytorch device: Which device the translated model will be run on extra_config: Extra configurations to be used by individual operator converters Returns: A *PyTorch* model """ assert topology is not None, "Cannot convert a Topology object of type None." operator_map = {} for operator in topology.topological_operator_iterator(): try: converter = get_converter(operator.type) operator_map[operator.full_name] = converter( operator, device, extra_config) except ValueError: raise MissingConverter( "Unable to find converter for {} type {} with extra config: {}." .format(operator.type, type(getattr(operator, "raw_model", None)), extra_config)) except Exception as e: raise e operators = list(topology.topological_operator_iterator()) if operator_map[operators[-1].full_name].regression: # We are doing a regression task. pytorch_container = PyTorchBackendModelRegression elif operator_map[operators[-1].full_name].transformer: # We are just transforming the input data. pytorch_container = PyTorchBackendModelTransformer else: # We are doing a classification task. pytorch_container = PyTorchBackendModelClassification pytorch_model = pytorch_container(topology.raw_model.input_names, topology.raw_model.output_names, operator_map, operators, extra_config).eval() if device is not None: pytorch_model = pytorch_model.to(device) return pytorch_model
def _convert_topology(topology, device=None, extra_config={}): """ This function is used to convert a `onnxconverter_common.topology.Topology` object into a *PyTorch* model. Args: topology: The `onnxconverter_common.topology.Topology` object that will be converted into Pytorch device: Which device the translated model will be run on extra_config: Extra configurations to be used by individual operator converters Returns: A *PyTorch* model """ assert topology is not None, "Cannot convert a Topology object of type None." operator_map = {} for operator in topology.topological_operator_iterator(): try: converter = get_converter(operator.type) operator_map[operator.full_name] = converter( operator, device, extra_config) except ValueError: raise MissingConverter( "Unable to find converter for {} type {} with extra config: {}." .format(operator.type, type(getattr(operator, "raw_model", None)), extra_config)) except Exception as e: raise e pytorch_model = PyTorchBackendModel(topology.raw_model.input_names, topology.raw_model.output_names, operator_map, topology, extra_config).eval() if device is not None: pytorch_model = pytorch_model.to(device) return pytorch_model
def convert(topology, backend, device=None, extra_config={}): """ This function is used to convert a `onnxconverter_common.topology.Topology` object into a *backend* model. Args: topology: The `onnxconverter_common.topology.Topology` object that will be converted into a backend model backend: Which backend the model should be run on device: Which device the translated model will be run on extra_config: Extra configurations to be used by individual operator converters Returns: A model implemented in the selected backend """ assert topology is not None, "Cannot convert a Topology object of type None." assert backend is not None, "Cannot convert a Topology object into backend None." operator_map = {} onnx_backend = None if onnx_runtime_installed(): import onnx onnx_backend = onnx.__name__ for operator in topology.topological_operator_iterator(): try: converter = get_converter(operator.type) if backend == onnx_backend: # vers = LooseVersion(torch.__version__) # allowed_min = LooseVersion("1.6.1") # Pytorch <= 1.6.0 has a bug with exporting GEMM into ONNX. # For the moment only tree_trav is enabled for pytorch <= 1.6.0 # if vers < allowed_min: extra_config[constants.TREE_IMPLEMENTATION] = "tree_trav" operator_map[operator.full_name] = converter(operator, device, extra_config) except ValueError: raise MissingConverter( "Unable to find converter for {} type {} with extra config: {}.".format( operator.type, type(getattr(operator, "raw_model", None)), extra_config ) ) except Exception as e: raise e operators = list(topology.topological_operator_iterator()) if operator_map[operators[-1].full_name].regression: # We are doing a regression task. pytorch_container = PyTorchBackendModelRegression elif operator_map[operators[-1].full_name].anomaly_detection: # We are doing anomaly detection. pytorch_container = PyTorchBackendModelAnomalyDetection elif operator_map[operators[-1].full_name].transformer: # We are just transforming the input data. pytorch_container = PyTorchBackendModelTransformer else: # We are doing a classification task. pytorch_container = PyTorchBackendModelClassification pytorch_model = pytorch_container( topology.raw_model.input_names, topology.raw_model.output_names, operator_map, operators, extra_config ).eval() if backend == onnx_backend: onnx_model_name = output_model_name = None target_opset = 11 # Set optional configuration options for ONNX if any. if constants.ONNX_OUTPUT_MODEL_NAME in extra_config: onnx_model_name = extra_config[constants.ONNX_OUTPUT_MODEL_NAME] output_model_name = onnx_model_name + ".onnx" if constants.ONNX_TARGET_OPSET in extra_config: target_opset = extra_config[constants.ONNX_TARGET_OPSET] if output_model_name is None: output_model_name = str(uuid4().hex) + ".onnx" # Generate the ONNX models torch.onnx.export( pytorch_model, torch.from_numpy(extra_config[constants.ONNX_TEST_INPUT]), output_model_name, input_names=topology.raw_model.input_names, output_names=topology.raw_model.output_names, keep_initializers_as_inputs=False, opset_version=target_opset, do_constant_folding=True, ) onnx_model = onnx.load(output_model_name) os.remove(output_model_name) # Set the ONNX model name if any. if onnx_model_name is not None: onnx_model.graph.name = onnx_model_name return onnx_model if device is not None: pytorch_model = pytorch_model.to(device) return pytorch_model
def convert(topology, backend, test_input, device, extra_config={}): """ This function is used to convert a `onnxconverter_common.topology.Topology` object into a *backend* model. Args: topology: The `onnxconverter_common.topology.Topology` object that will be converted into a backend model backend: Which backend the model should be run on test_input: Inputs for PyTorch model tracing device: Which device the translated model will be run on extra_config: Extra configurations to be used by individual operator converters Returns: A model implemented in the selected backend """ assert topology is not None, "Cannot convert a Topology object of type None." assert backend is not None, "Cannot convert a Topology object into backend None." assert device is not None, "Cannot convert a Topology object into device None." tvm_backend = None operator_map = {} if tvm_installed(): import tvm tvm_backend = tvm.__name__ for operator in topology.topological_operator_iterator(): converter = get_converter(operator.type) if convert is None: raise MissingConverter( "Unable to find converter for {} type {} with extra config: {}." .format(operator.type, type(getattr(operator, "raw_model", None)), extra_config)) if backend == onnx.__name__: # vers = LooseVersion(torch.__version__) # allowed_min = LooseVersion("1.6.0") # Pytorch <= 1.6.0 has a bug with exporting GEMM into ONNX. # For the moment only tree_trav is enabled for pytorch <= 1.6.0 # if vers < allowed_min: extra_config[constants.TREE_IMPLEMENTATION] = "tree_trav" operator_map[operator.full_name] = converter(operator, device, extra_config) # Set the parameters for the model / container n_threads = None if constants.N_THREADS not in extra_config else extra_config[ constants.N_THREADS] # We set the number of threads for torch here to avoid errors in case we JIT. # We set intra op concurrency while we force operators to run sequentially. # We can revise this later, but in general we don't have graphs requireing inter-op parallelism. if n_threads is not None: if torch.get_num_interop_threads() != 1: torch.set_num_interop_threads(1) torch.set_num_threads(n_threads) operators = list(topology.topological_operator_iterator()) executor = Executor(topology.raw_model.input_names, topology.raw_model.output_names, operator_map, operators, extra_config).eval() # if constants.REMAINDER_SIZE is present in extra_config, we are in the convert_batch mode. remainder_model = None remainder_size = None if constants.REMAINDER_SIZE not in extra_config else extra_config[ constants.REMAINDER_SIZE] if backend == onnx.__name__: onnx_model_name = output_model_name = None target_opset = 11 # Set optional configuration options for ONNX if any. if constants.ONNX_OUTPUT_MODEL_NAME in extra_config: onnx_model_name = extra_config[constants.ONNX_OUTPUT_MODEL_NAME] output_model_name = onnx_model_name + ".onnx" if constants.ONNX_TARGET_OPSET in extra_config: target_opset = extra_config[constants.ONNX_TARGET_OPSET] if output_model_name is None: output_model_name = str(uuid4().hex) + ".onnx" # Put the tracing test input into the right format. batch_trace_input, _ = _get_trace_input_from_test_input( test_input, remainder_size, extra_config) # Generate the ONNX models torch.onnx.export( executor, batch_trace_input, output_model_name, input_names=topology.raw_model.input_names, output_names=topology.raw_model.output_names, keep_initializers_as_inputs=False, opset_version=target_opset, do_constant_folding=True, ) hb_model = onnx.load(output_model_name) os.remove(output_model_name) # Set the ONNX model name if any. if onnx_model_name is not None: hb_model.graph.name = onnx_model_name # Fix the model to use arbitrary batch dimensions def fix_dim(dim): updated = False if dim.HasField("dim_value"): dim.Clear() updated = True dim.dim_param = "sym" return updated def fix_value_info(value): num_fixed = 0 if value.type.HasField("tensor_type"): shape = value.type.tensor_type.shape if shape: dim = shape.dim[0] if fix_dim(dim): num_fixed += 1 return num_fixed def fix_graph(graph): num_fixed = 0 for input in graph.input: num_fixed += fix_value_info(input) for output in graph.output: num_fixed += fix_value_info(output) for node in graph.node: for attr in node.attribute: if attr.HasField("g"): num_fixed += fix_graph(attr.g) return num_fixed fix_graph(hb_model.graph) elif backend == tvm_backend: # Pick the proper target. if device == "cuda": target = tvm.target.cuda() ctx = tvm.gpu() elif device == "cpu": target = "llvm" ctx = tvm.cpu() elif "llvm" in device: target = device ctx = tvm.cpu() else: raise RuntimeError("Device {} not recognized".format(device)) # Get configuration parameters. # 50 is a good depth for operator fusion. More than that will probably hurt performance. # https://github.com/microsoft/hummingbird/issues/232#issuecomment-697979508 config = {"relay.FuseOps.max_depth": 50} if constants.TVM_MAX_FUSE_DEPTH in extra_config: config["relay.FuseOps.max_depth"] = extra_config[ constants.TVM_MAX_FUSE_DEPTH] # First we need to generate the torchscript model. batch_trace_input, remainder_trace_input = _get_trace_input_from_test_input( test_input, remainder_size, extra_config) tvm_model = _compile_to_tvm(topology, executor, batch_trace_input, target, ctx, config, extra_config) if remainder_trace_input is not None: remainder_model = _compile_to_tvm(topology, executor, remainder_trace_input, target, ctx, config, extra_config) # In the container we will be using the context to properly configure the input tensors. extra_config[constants.TVM_CONTEXT] = ctx extra_config[ constants.TVM_INPUT_NAMES] = topology.raw_model.input_names hb_model = tvm_model else: # Set the device for the model. if device != "cpu": if backend == torch.__name__ or torch.jit.__name__: executor = executor.to(device) # If the backend is tochscript, jit the model. if backend == torch.jit.__name__: trace_input, _ = _get_trace_input_from_test_input( test_input, remainder_size, extra_config) executor = _jit_trace(executor, trace_input, device, extra_config) torch.jit.optimized_execution(executor) hb_model = executor # Return if the container is not needed. if constants.CONTAINER in extra_config and not extra_config[ constants.CONTAINER]: return hb_model # We scan the operators backwards until we find an operator with a defined type. # This is necessary because ONNX models can have arbitrary operators doing casting, reshaping etc. idx = len(operators) - 1 while (idx >= 0 and not operator_map[operators[idx].full_name].regression and not operator_map[operators[idx].full_name].classification and not operator_map[operators[idx].full_name].anomaly_detection and not operator_map[operators[idx].full_name].transformer): idx -= 1 assert idx >= 0, "Cannot detect container type. Please fill an issue at https://github.com/microsoft/hummingbird." # If is a transformer, we need to check whether there is another operator type before. # E.g., normalization after classification. tmp_idx = idx if operator_map[operators[idx].full_name].transformer: while (idx >= 0 and not operator_map[operators[idx].full_name].regression and not operator_map[operators[idx].full_name].classification and not operator_map[operators[idx].full_name].anomaly_detection): idx -= 1 if idx < 0: idx = tmp_idx # Get the proper container type. if operator_map[operators[idx].full_name].regression: # We are doing a regression task. if backend == torch.jit.__name__: container = TorchScriptSklearnContainerRegression elif backend == onnx.__name__: container = ONNXSklearnContainerRegression elif backend == tvm_backend: container = TVMSklearnContainerRegression else: container = PyTorchSklearnContainerRegression elif operator_map[operators[idx].full_name].anomaly_detection: # We are doing anomaly detection. if backend == torch.jit.__name__: container = TorchScriptSklearnContainerAnomalyDetection elif backend == onnx.__name__: container = ONNXSklearnContainerAnomalyDetection elif backend == tvm_backend: container = TVMSklearnContainerAnomalyDetection else: container = PyTorchSklearnContainerAnomalyDetection elif operator_map[operators[idx].full_name].transformer: # We are just transforming the input data. if backend == torch.jit.__name__: container = TorchScriptSklearnContainerTransformer elif backend == onnx.__name__: container = ONNXSklearnContainerTransformer elif backend == tvm_backend: container = TVMSklearnContainerTransformer else: container = PyTorchSklearnContainerTransformer else: # We are doing a classification task. if backend == torch.jit.__name__: container = TorchScriptSklearnContainerClassification elif backend == onnx.__name__: container = ONNXSklearnContainerClassification elif backend == tvm_backend: container = TVMSklearnContainerClassification else: container = PyTorchSklearnContainerClassification n_threads = None if constants.N_THREADS not in extra_config else extra_config[ constants.N_THREADS] batch_size = None if constants.TEST_INPUT not in extra_config else _get_batch_size( test_input) hb_container = container(hb_model, n_threads, batch_size, extra_config=extra_config) if remainder_model: aux_container = container(remainder_model, n_threads, remainder_size, extra_config=extra_config) return BatchContainer(hb_container, aux_container) elif remainder_size is not None and remainder_size > 0: # remainder_size is non zero but remainder_model is not created # -> torch backend case aux_container = container(hb_model, n_threads, remainder_size, extra_config=extra_config) return BatchContainer(hb_container, aux_container) elif remainder_size is not None: # remainder_size is not None but remainder_model is not created # -> remainder_size must be zero (no need to create remainder_model) assert remainder_size == 0, "remainder_size is non zero but no remainder_model has been created" # remainder_size is not None only if called by convert_batch(...), so we return BatchContainer # for this code path, even though there is no remainder_model created. return BatchContainer(hb_container) return hb_container
def convert(topology, backend, device, extra_config={}): """ This function is used to convert a `onnxconverter_common.topology.Topology` object into a *backend* model. Args: topology: The `onnxconverter_common.topology.Topology` object that will be converted into a backend model backend: Which backend the model should be run on device: Which device the translated model will be run on extra_config: Extra configurations to be used by individual operator converters Returns: A model implemented in the selected backend """ assert topology is not None, "Cannot convert a Topology object of type None." assert backend is not None, "Cannot convert a Topology object into backend None." assert device is not None, "Cannot convert a Topology object into device None." operator_map = {} for operator in topology.topological_operator_iterator(): try: converter = get_converter(operator.type) if backend == onnx.__name__: # vers = LooseVersion(torch.__version__) # allowed_min = LooseVersion("1.6.0") # Pytorch <= 1.6.0 has a bug with exporting GEMM into ONNX. # For the moment only tree_trav is enabled for pytorch <= 1.6.0 # if vers < allowed_min: extra_config[constants.TREE_IMPLEMENTATION] = "tree_trav" operator_map[operator.full_name] = converter(operator, device, extra_config) except ValueError: raise MissingConverter( "Unable to find converter for {} type {} with extra config: {}.".format( operator.type, type(getattr(operator, "raw_model", None)), extra_config ) ) except Exception as e: raise e operators = list(topology.topological_operator_iterator()) torch_model = PyTorchBackendModel( topology.raw_model.input_names, topology.raw_model.output_names, operator_map, operators, extra_config ).eval() if backend == onnx.__name__: onnx_model_name = output_model_name = None target_opset = 11 # Set optional configuration options for ONNX if any. if constants.ONNX_OUTPUT_MODEL_NAME in extra_config: onnx_model_name = extra_config[constants.ONNX_OUTPUT_MODEL_NAME] output_model_name = onnx_model_name + ".onnx" if constants.ONNX_TARGET_OPSET in extra_config: target_opset = extra_config[constants.ONNX_TARGET_OPSET] if output_model_name is None: output_model_name = str(uuid4().hex) + ".onnx" # Put the tracing test input into the right format. trace_input = _get_trace_input_from_test_input(extra_config[constants.TEST_INPUT]) # Generate the ONNX models torch.onnx.export( torch_model, trace_input, output_model_name, input_names=topology.raw_model.input_names, output_names=topology.raw_model.output_names, keep_initializers_as_inputs=False, opset_version=target_opset, do_constant_folding=True, ) hb_model = onnx.load(output_model_name) os.remove(output_model_name) # Set the ONNX model name if any. if onnx_model_name is not None: hb_model.graph.name = onnx_model_name # Fix the model to use arbitrary batch dimensions def fix_dim(dim): updated = False if dim.HasField("dim_value"): dim.Clear() updated = True dim.dim_param = "sym" return updated def fix_value_info(value): num_fixed = 0 if value.type.HasField("tensor_type"): shape = value.type.tensor_type.shape if shape: dim = shape.dim[0] if fix_dim(dim): num_fixed += 1 return num_fixed def fix_graph(graph): num_fixed = 0 for input in graph.input: num_fixed += fix_value_info(input) for output in graph.output: num_fixed += fix_value_info(output) for node in graph.node: for attr in node.attribute: if attr.HasField("g"): num_fixed += fix_graph(attr.g) return num_fixed fix_graph(hb_model.graph) else: # Set the device for the model. if device != "cpu": if backend == torch.__name__ or torch.jit.__name__: torch_model = torch_model.to(device) # If the backend is tochscript, jit the model. if backend == torch.jit.__name__: trace_input = _get_trace_input_from_test_input(extra_config[constants.TEST_INPUT]) if device != "cpu": trace_input.to(device) torch_model = torch.jit.trace(torch_model, trace_input).eval() torch.jit.optimized_execution(torch_model) hb_model = torch_model # Return if the container is not needed. if constants.CONTAINER in extra_config and not extra_config[constants.CONTAINER]: return hb_model # We scan the operators backwards until we find an operator with a defined type # This is necessary because ONNX models can have arbitrary operators doing casting, reshaping etc. idx = len(operators) - 1 while ( idx >= 0 and not operator_map[operators[idx].full_name].regression and not operator_map[operators[idx].full_name].classification and not operator_map[operators[idx].full_name].anomaly_detection and not operator_map[operators[idx].full_name].transformer ): idx -= 1 assert idx >= 0, "Cannot detect container type. Please fill an issue at https://github.com/microsoft/hummingbird." # If is a transformer, we need to check whether there is another operator type before. # E.g., normalization after classification. tmp_idx = idx if operator_map[operators[idx].full_name].transformer: while ( idx >= 0 and not operator_map[operators[idx].full_name].regression and not operator_map[operators[idx].full_name].classification and not operator_map[operators[idx].full_name].anomaly_detection ): idx -= 1 if idx < 0: idx = tmp_idx if operator_map[operators[idx].full_name].regression: # We are doing a regression task. if backend == torch.jit.__name__: container = TorchScriptSklearnContainerRegression elif backend == onnx.__name__: container = ONNXSklearnContainerRegression else: container = PyTorchSklearnContainerRegression elif operator_map[operators[idx].full_name].anomaly_detection: # We are doing anomaly detection. if backend == torch.jit.__name__: container = TorchScriptSklearnContainerAnomalyDetection elif backend == onnx.__name__: container = ONNXSklearnContainerAnomalyDetection else: container = PyTorchSklearnContainerAnomalyDetection elif operator_map[operators[idx].full_name].transformer: # We are just transforming the input data. if backend == torch.jit.__name__: container = TorchScriptSklearnContainerTransformer elif backend == onnx.__name__: container = ONNXSklearnContainerTransformer else: container = PyTorchSklearnContainerTransformer else: # We are doing a classification task. if backend == torch.jit.__name__: container = TorchScriptSklearnContainerClassification elif backend == onnx.__name__: container = ONNXSklearnContainerClassification else: container = PyTorchSklearnContainerClassification hb_model = container(hb_model, extra_config) return hb_model