diff --git a/.lintrunner.toml b/.lintrunner.toml index 86be8d0d0bd38..c44a66200ad1b 100644 --- a/.lintrunner.toml +++ b/.lintrunner.toml @@ -97,33 +97,6 @@ init_command = [ ] is_formatter = true -[[linter]] -code = 'PYLINT' -include_patterns = [ - # TODO: Opt in to pylint by adding paths here -] -exclude_patterns = [ -] -command = [ - 'python', - '-m', - 'lintrunner_adapters', - 'run', - 'pylint_linter', - '--rcfile=pyproject.toml', - '--', - '@{{PATHSFILE}}' -] -init_command = [ - 'python', - '-m', - 'lintrunner_adapters', - 'run', - 'pip_init', - '--dry-run={{DRYRUN}}', - '--requirement=requirements-lintrunner.txt', -] - [[linter]] code = 'RUSTFMT' include_patterns = ['**/*.rs'] diff --git a/VERSION_NUMBER b/VERSION_NUMBER index 4a02d2c3170bd..c807441cfed77 100644 --- a/VERSION_NUMBER +++ b/VERSION_NUMBER @@ -1 +1 @@ -1.16.2 +1.16.3 diff --git a/docs/python/README.rst b/docs/python/README.rst index bcf7c635afd82..bcc9ef2124130 100644 --- a/docs/python/README.rst +++ b/docs/python/README.rst @@ -8,6 +8,11 @@ For more information on ONNX Runtime, please see `aka.ms/onnxruntime `_ or the `Github project `_. """ -__version__ = "1.16.2" +__version__ = "1.16.3" __author__ = "Microsoft" # we need to do device version validation (for example to check Cuda version for an onnxruntime-training package). diff --git a/onnxruntime/core/framework/session_state_utils.cc b/onnxruntime/core/framework/session_state_utils.cc index df3a7afebc176..df11fe8302aef 100644 --- a/onnxruntime/core/framework/session_state_utils.cc +++ b/onnxruntime/core/framework/session_state_utils.cc @@ -455,11 +455,10 @@ common::Status SaveInputOutputNamesToNodeMapping(const onnxruntime::GraphViewer& // utils::CopyOneInputAcrossDevices is happy. auto& input_map = session_state.GetInputNodeInfoMap(); - auto end_map = input_map.cend(); for (const auto& graph_input : graph_inputs) { const auto& name = graph_input->Name(); - if (input_map.find(name) == end_map) { + if (input_map.find(name) == input_map.cend()) { // dummy entry for an input that we didn't find a use of in the graph. log it in case that's a bug. // utils::CopyOneInputAcrossDevices will use the input OrtValue as is given we don't believe it's used anywhere. LOGS(session_state.Logger(), INFO) << (graph.IsSubgraph() ? "Subgraph" : "Graph") << " input with name " diff --git a/onnxruntime/core/providers/tensorrt/tensorrt_execution_provider.cc b/onnxruntime/core/providers/tensorrt/tensorrt_execution_provider.cc index ac92d46ca87fc..6f4c144421104 100644 --- a/onnxruntime/core/providers/tensorrt/tensorrt_execution_provider.cc +++ b/onnxruntime/core/providers/tensorrt/tensorrt_execution_provider.cc @@ -1126,6 +1126,11 @@ TensorrtExecutionProvider::~TensorrtExecutionProvider() { } } + if (external_stream_) { + ORT_IGNORE_RETURN_VALUE(CUBLAS_CALL(cublasDestroy(external_cublas_handle_))); + ORT_IGNORE_RETURN_VALUE(CUDNN_CALL(cudnnDestroy(external_cudnn_handle_))); + } + if (!external_stream_ && stream_) { ORT_IGNORE_RETURN_VALUE(CUDA_CALL(cudaStreamDestroy(stream_))); } diff --git a/onnxruntime/core/session/onnxruntime_c_api.cc b/onnxruntime/core/session/onnxruntime_c_api.cc index 70d2d0fe5d511..85dd517ccf304 100644 --- a/onnxruntime/core/session/onnxruntime_c_api.cc +++ b/onnxruntime/core/session/onnxruntime_c_api.cc @@ -2744,7 +2744,7 @@ static_assert(offsetof(OrtApi, GetBuildInfoString) / sizeof(void*) == 254, "Size static_assert(offsetof(OrtApi, KernelContext_GetResource) / sizeof(void*) == 265, "Size of version 16 API cannot change"); // So that nobody forgets to finish an API version, this check will serve as a reminder: -static_assert(std::string_view(ORT_VERSION) == "1.16.2", +static_assert(std::string_view(ORT_VERSION) == "1.16.3", "ORT_Version change detected, please follow below steps to ensure OrtApi is updated properly"); // 1. Update the hardcoded version string in above static_assert to silence it // 2. If there were any APIs added to ort_api_1_to_16 above: diff --git a/onnxruntime/python/backend/backend.py b/onnxruntime/python/backend/backend.py index 1edae383e93e6..97b7358f2a223 100644 --- a/onnxruntime/python/backend/backend.py +++ b/onnxruntime/python/backend/backend.py @@ -63,7 +63,7 @@ def is_opset_supported(cls, model): error_message = ( "Skipping this test as only released onnx opsets are supported." "To run this test set env variable ALLOW_RELEASED_ONNX_OPSET_ONLY to 0." - " Got Domain '{}' version '{}'.".format(domain, opset.version) + f" Got Domain '{domain}' version '{opset.version}'." ) return False, error_message except AttributeError: @@ -74,7 +74,7 @@ def is_opset_supported(cls, model): error_message = ( "Skipping this test as only released onnx opsets are supported." "To run this test set env variable ALLOW_RELEASED_ONNX_OPSET_ONLY to 0." - " Got Domain '{}' version '{}'.".format(domain, opset.version) + f" Got Domain '{domain}' version '{opset.version}'." ) return False, error_message return True, "" diff --git a/onnxruntime/python/tools/onnxruntime_test.py b/onnxruntime/python/tools/onnxruntime_test.py index c20e055d72720..5605568edaccc 100644 --- a/onnxruntime/python/tools/onnxruntime_test.py +++ b/onnxruntime/python/tools/onnxruntime_test.py @@ -40,7 +40,7 @@ def generate_feeds(sess, symbolic_dims: dict | None = None): if not dim: # unknown dim shape.append(1) - elif type(dim) == str: + elif isinstance(dim, str): # symbolic dim. see if we have a value otherwise use 1 if dim in symbolic_dims: shape.append(int(symbolic_dims[dim])) diff --git a/onnxruntime/python/tools/profile_explorer/profile_explorer.py b/onnxruntime/python/tools/profile_explorer/profile_explorer.py index 78f8805a89076..6e0747883989f 100644 --- a/onnxruntime/python/tools/profile_explorer/profile_explorer.py +++ b/onnxruntime/python/tools/profile_explorer/profile_explorer.py @@ -82,8 +82,8 @@ def _shape_to_string(shape): for dict_obj in shape: if len(dict_obj) > 1: raise ValueError("Unhandled type in _shape_to_string()") - key = list(dict_obj.keys())[0] - value = list(dict_obj.values())[0] + key = next(iter(dict_obj.keys())) + value = next(iter(dict_obj.values())) if len(res) != 0: res += "," res += f'{key}({"x".join(str(v) for v in value)})' diff --git a/onnxruntime/python/tools/quantization/calibrate.py b/onnxruntime/python/tools/quantization/calibrate.py index 26e74a6dfbac9..c8aa610d07e2a 100644 --- a/onnxruntime/python/tools/quantization/calibrate.py +++ b/onnxruntime/python/tools/quantization/calibrate.py @@ -363,9 +363,9 @@ def compute_data(self) -> TensorsData: else: min_value_array = min(merged_added_output_dict[added_output_names[i]]) max_value_array = max(merged_added_output_dict[added_output_names[i + 1]]) - if type(min_value_array) == int or min_value_array.size > 0: + if isinstance(min_value_array, int) or min_value_array.size > 0: min_value = float(min_value_array) - if type(max_value_array) == int or max_value_array.size > 0: + if isinstance(max_value_array, int) or max_value_array.size > 0: max_value = float(max_value_array) if self.symmetric: diff --git a/onnxruntime/python/tools/quantization/onnx_quantizer.py b/onnxruntime/python/tools/quantization/onnx_quantizer.py index bb968d660c30c..3a66051a8e3d0 100644 --- a/onnxruntime/python/tools/quantization/onnx_quantizer.py +++ b/onnxruntime/python/tools/quantization/onnx_quantizer.py @@ -597,7 +597,7 @@ def _get_quantization_params(self, param_name, use_scale=None, use_zeropoint=Non if params is None or len(params) != 2: raise ValueError( "Quantization parameters should contain zero point and scale. " - "Specified values for output {}: {}".format(param_name, params) + f"Specified values for output {param_name}: {params}" ) zero_point_values = [params["zero_point"]] diff --git a/onnxruntime/python/tools/symbolic_shape_infer.py b/onnxruntime/python/tools/symbolic_shape_infer.py index 69f8530dff39a..963ae799dadc5 100755 --- a/onnxruntime/python/tools/symbolic_shape_infer.py +++ b/onnxruntime/python/tools/symbolic_shape_infer.py @@ -24,7 +24,7 @@ def get_attribute(node, attr_name, default_value=None): def get_dim_from_proto(dim): - return getattr(dim, dim.WhichOneof("value")) if type(dim.WhichOneof("value")) == str else None + return getattr(dim, dim.WhichOneof("value")) if type(dim.WhichOneof("value")) is str else None # noqa: E721 def is_sequence(type_proto): @@ -82,7 +82,7 @@ def handle_negative_axis(axis, rank): def get_opset(mp, domain=None): domain = domain or ["", "onnx", "ai.onnx"] - if type(domain) != list: + if type(domain) != list: # noqa: E721 domain = [domain] for opset in mp.opset_import: if opset.domain in domain: @@ -92,7 +92,7 @@ def get_opset(mp, domain=None): def as_scalar(x): - if type(x) == list: + if type(x) == list: # noqa: E721 assert len(x) == 1 return x[0] elif type(x) == np.ndarray: @@ -102,7 +102,7 @@ def as_scalar(x): def as_list(x, keep_none): - if type(x) == list: + if type(x) == list: # noqa: E721 return x elif type(x) == np.ndarray: return list(x) @@ -113,7 +113,7 @@ def as_list(x, keep_none): def sympy_reduce_product(x): - if type(x) == list: + if type(x) == list: # noqa: E721 value = sympy.Integer(1) for v in x: value = value * v @@ -249,7 +249,7 @@ def __init__(self, int_max, auto_merge, guess_output_rank, verbose, prefix=""): self.prefix_ = prefix def _add_suggested_merge(self, symbols, apply=False): - assert all([(type(s) == str and s in self.symbolic_dims_) or is_literal(s) for s in symbols]) + assert all([(type(s) == str and s in self.symbolic_dims_) or is_literal(s) for s in symbols]) # noqa: E721 symbols = set(symbols) for k, v in self.suggested_merge_.items(): if k in symbols: @@ -319,7 +319,7 @@ def _preprocess(self, in_mp): ) def _merge_symbols(self, dims): - if not all([type(d) == str for d in dims]): + if not all([type(d) == str for d in dims]): # noqa: E721 if self.auto_merge_: unique_dims = list(set(dims)) is_int = [is_literal(d) for d in unique_dims] @@ -402,7 +402,7 @@ def _get_shape_rank(self, node, idx): def _get_sympy_shape(self, node, idx): sympy_shape = [] for d in self._get_shape(node, idx): - if type(d) == str: + if type(d) == str: # noqa: E721 sympy_shape.append( self.symbolic_dims_[d] if d in self.symbolic_dims_ @@ -428,7 +428,7 @@ def _try_get_value(self, node, idx): def _update_computed_dims(self, new_sympy_shape): for i, new_dim in enumerate(new_sympy_shape): - if not is_literal(new_dim) and type(new_dim) != str: + if not is_literal(new_dim) and type(new_dim) != str: # noqa: E721 str_dim = str(new_dim) if str_dim in self.suggested_merge_: if is_literal(self.suggested_merge_[str_dim]): @@ -556,7 +556,7 @@ def _onnx_infer_subgraph(self, node, subgraph, use_node_input=True, inc_subgraph # for new symbolic dims from subgraph output, add to main graph symbolic dims subgraph_shapes = [get_shape_from_value_info(o) for o in symbolic_shape_inference.out_mp_.graph.output] subgraph_new_symbolic_dims = { - d for s in subgraph_shapes if s for d in s if type(d) == str and d not in self.symbolic_dims_ + d for s in subgraph_shapes if s for d in s if type(d) == str and d not in self.symbolic_dims_ # noqa: E721 } new_dims = {} for d in subgraph_new_symbolic_dims: @@ -586,14 +586,14 @@ def int_or_float(value, allow_float_values): assert len(v.shape) == 1 new_v = [int_or_float(vv, allow_float_values) for vv in v] values[i] = new_v - values_len = [len(v) if type(v) == list else 0 for v in values] + values_len = [len(v) if isinstance(v, list) else 0 for v in values] max_len = max(values_len) if max_len >= 1 and broadcast: # broadcast for i, v in enumerate(values): if v is None: continue # don't broadcast if value is unknown - if type(v) == list: + if isinstance(v, list): if len(v) < max_len: values[i] = v * max_len else: @@ -614,7 +614,7 @@ def _compute_on_sympy_data(self, node, op_func): values = self._get_int_or_float_values(node, broadcast=True) if all([v is not None for v in values]): - is_list = [type(v) == list for v in values] + is_list = [isinstance(v, list) for v in values] as_list = any(is_list) if as_list: self.sympy_data_[node.output[0]] = [op_func(vs) for vs in zip(*values)] @@ -871,7 +871,7 @@ def _infer_Concat(self, node): # noqa: N802 self.sympy_data_[node.output[0]] = [] for i in range(len(node.input)): value = values[i] - if type(value) == list: + if isinstance(value, list): self.sympy_data_[node.output[0]].extend(value) else: self.sympy_data_[node.output[0]].append(value) @@ -891,7 +891,7 @@ def _infer_Concat(self, node): # noqa: N802 if all([d == dims[0] for d in dims]): continue merged = self._merge_symbols(dims) - if type(merged) == str: + if type(merged) == str: # noqa: E721 sympy_shape[d] = self.symbolic_dims_[merged] if merged else None else: sympy_shape[d] = merged @@ -931,7 +931,7 @@ def _infer_ConstantOfShape(self, node): # noqa: N802 sympy_shape = self._get_int_or_float_values(node)[0] vi = self.known_vi_[node.output[0]] if sympy_shape is not None: - if type(sympy_shape) != list: + if type(sympy_shape) != list: # noqa: E721 sympy_shape = [sympy_shape] self._update_computed_dims(sympy_shape) # update sympy data if output type is int, and shape is known @@ -1002,7 +1002,7 @@ def _infer_Einsum(self, node): # noqa: N802 letter = term[-i] if letter != 46: # letter != b'.' dim = shape[-i] - if letter not in letter_to_dim.keys(): + if letter not in letter_to_dim: letter_to_dim[letter] = dim elif type(dim) != sympy.Symbol: letter_to_dim[letter] = dim @@ -1071,7 +1071,7 @@ def _infer_Gather(self, node): # noqa: N802 idx = self._try_get_value(node, 1) if idx is not None: data = self.sympy_data_[node.input[0]] - if type(data) == list: + if type(data) == list: # noqa: E721 if type(idx) == np.ndarray and len(idx.shape) == 1: self.sympy_data_[node.output[0]] = [data[int(i)] for i in idx] else: @@ -1563,12 +1563,12 @@ def _infer_Reshape(self, node): # noqa: N802 ) else: input_sympy_shape = self._get_sympy_shape(node, 0) - total = int(1) + total = 1 for d in input_sympy_shape: total = total * d new_sympy_shape = [] deferred_dim_idx = -1 - non_deferred_size = int(1) + non_deferred_size = 1 for i, d in enumerate(shape_value): if type(d) == sympy.Symbol: new_sympy_shape.append(d) @@ -1874,7 +1874,7 @@ def handle_negative_index(index, bound): and len(steps) == 1 ): input_sympy_data = self.sympy_data_[node.input[0]] - if type(input_sympy_data) == list or ( + if type(input_sympy_data) == list or ( # noqa: E721 type(input_sympy_data) == np.array and len(input_sympy_data.shape) == 1 ): self.sympy_data_[node.output[0]] = input_sympy_data[starts[0] : ends[0] : steps[0]] @@ -1942,7 +1942,7 @@ def _infer_Squeeze(self, node): # noqa: N802 # For symbolic dimensions we guess they are !=1. output_shape = [s for s in input_shape if s != 1] if self.verbose_ > 0: - symbolic_dimensions = [s for s in input_shape if type(s) != int] + symbolic_dimensions = [s for s in input_shape if type(s) != int] # noqa: E721 if len(symbolic_dimensions) > 0: logger.debug( f"Symbolic dimensions in input shape of op: '{node.op_type}' node: '{node.name}'. " @@ -1955,8 +1955,8 @@ def _infer_Squeeze(self, node): # noqa: N802 if i not in axes: output_shape.append(input_shape[i]) else: - assert input_shape[i] == 1 or type(input_shape[i]) != int - if self.verbose_ > 0 and type(input_shape[i]) != int: + assert input_shape[i] == 1 or type(input_shape[i]) != int # noqa: E721 + if self.verbose_ > 0 and type(input_shape[i]) != int: # noqa: E721 logger.debug( f"Symbolic dimensions in input shape of op: '{node.op_type}' node: '{node.name}'. " f"Assuming the dimension '{input_shape[i]}' at index {i} of the input to be equal to 1." @@ -2458,7 +2458,7 @@ def _propagate_shape_and_type(self, node, input_index=0, output_index=0): vi.CopyFrom(helper.make_tensor_value_info(node.output[output_index], output_dtype, shape)) def _is_none_dim(self, dim_value): - if type(dim_value) != str: + if type(dim_value) != str: # noqa: E721 return False if "unk__" not in dim_value: return False @@ -2492,7 +2492,7 @@ def _infer_impl(self, start_sympy_data=None): # some models use None for symbolic dim in input, replace it with a string input_dims[i_dim].dim_param = str(self._new_symbolic_dim(i.name, i_dim)) - self.input_symbols_.update([d for d in input_shape if type(d) == str]) + self.input_symbols_.update([d for d in input_shape if type(d) == str]) # noqa: E721 for s in self.input_symbols_: if s in self.suggested_merge_: diff --git a/onnxruntime/python/tools/tensorrt/perf/benchmark.py b/onnxruntime/python/tools/tensorrt/perf/benchmark.py index d440cafb23236..9c0ae2386b918 100644 --- a/onnxruntime/python/tools/tensorrt/perf/benchmark.py +++ b/onnxruntime/python/tools/tensorrt/perf/benchmark.py @@ -812,7 +812,7 @@ def write_map_to_file(result, file_name): if os.path.exists(file_name): existed_result = read_map_from_file(file_name) - for model, _ep_list in result.items(): + for model in result: if model in existed_result: existed_result[model] = {**existed_result[model], **result[model]} else: @@ -1122,7 +1122,7 @@ def calculate_gain(value, ep1, ep2): def add_improvement_information(model_to_latency): - for _key, value in model_to_latency.items(): + for value in model_to_latency.values(): if trt in value and cuda in value: gain = calculate_gain(value, trt, cuda) value[trt_cuda_gain] = f"{gain:.2f} %" @@ -1209,13 +1209,13 @@ def add_status_dict(status_dict, model_name, ep, status): def build_status(status_dict, results, is_fail): if is_fail: for model, model_info in results.items(): - for ep, _ep_info in model_info.items(): + for ep in model_info: model_name = model status = "Fail" add_status_dict(status_dict, model_name, ep, status) else: for model, value in results.items(): - for ep, _ep_info in value.items(): + for ep in value: model_name = model status = "Pass" add_status_dict(status_dict, model_name, ep, status) @@ -2270,7 +2270,7 @@ def main(): logger.info(f"\nTotal models: {len(models)}") fail_model_cnt = 0 - for key, _value in models.items(): + for key in models: if key in model_to_fail_ep: fail_model_cnt += 1 logger.info(f"Fail models: {fail_model_cnt}") diff --git a/onnxruntime/python/tools/tensorrt/perf/perf_utils.py b/onnxruntime/python/tools/tensorrt/perf/perf_utils.py index c46cadc2c1752..c639c6c73c82b 100644 --- a/onnxruntime/python/tools/tensorrt/perf/perf_utils.py +++ b/onnxruntime/python/tools/tensorrt/perf/perf_utils.py @@ -279,7 +279,7 @@ def calculate_trt_latency_percentage(trt_op_map): op_map = trt_op_map[ep] total_time = 0 - for _key, value in op_map.items(): + for value in op_map.values(): total_time += int(value) if ep == "TensorrtExecutionProvider": diff --git a/onnxruntime/python/tools/transformers/float16.py b/onnxruntime/python/tools/transformers/float16.py index 95e7437493bc8..f680a15fc2c1b 100644 --- a/onnxruntime/python/tools/transformers/float16.py +++ b/onnxruntime/python/tools/transformers/float16.py @@ -403,7 +403,7 @@ def convert_float_to_float16( queue = next_level - for _key, value in fp32_initializers.items(): + for value in fp32_initializers.values(): # By default, to avoid precision loss, do not convert an initializer to fp16 when it is used only by fp32 nodes. if force_fp16_initializers or value.fp16_nodes: value.initializer = convert_tensor_float_to_float16(value.initializer, min_positive_val, max_finite_val) diff --git a/onnxruntime/python/tools/transformers/fusion_attention.py b/onnxruntime/python/tools/transformers/fusion_attention.py index edaf78edb2021..c1b241aa1a5ec 100644 --- a/onnxruntime/python/tools/transformers/fusion_attention.py +++ b/onnxruntime/python/tools/transformers/fusion_attention.py @@ -673,8 +673,8 @@ def create_multihead_attention_node( else: mha_inputs.extend([q_matmul.output[0], k_matmul.output[0], v_matmul.output[0]]) elif ( - type(k_matmul) == str - and type(v_matmul) == str + type(k_matmul) == str # noqa: E721 + and type(v_matmul) == str # noqa: E721 and k_matmul in graph_input_names and v_matmul in graph_input_names ): diff --git a/onnxruntime/python/tools/transformers/models/stable_diffusion/diffusion_models.py b/onnxruntime/python/tools/transformers/models/stable_diffusion/diffusion_models.py index 4a2e9eb3443da..8b7579653d1b5 100644 --- a/onnxruntime/python/tools/transformers/models/stable_diffusion/diffusion_models.py +++ b/onnxruntime/python/tools/transformers/models/stable_diffusion/diffusion_models.py @@ -448,7 +448,7 @@ def add_hidden_states_graph_output(self, model: ModelProto, optimized_onnx_path, assert self.clip_skip >= 0 and self.clip_skip < hidden_layers - node_output_name = "/text_model/encoder/layers.{}/Add_1_output_0".format(hidden_layers - 1 - self.clip_skip) + node_output_name = f"/text_model/encoder/layers.{hidden_layers - 1 - self.clip_skip}/Add_1_output_0" # search the name in outputs of all node found = False diff --git a/onnxruntime/python/tools/transformers/models/stable_diffusion/engine_builder_tensorrt.py b/onnxruntime/python/tools/transformers/models/stable_diffusion/engine_builder_tensorrt.py index 4a924abfb8600..61a9c0d2c8fa9 100644 --- a/onnxruntime/python/tools/transformers/models/stable_diffusion/engine_builder_tensorrt.py +++ b/onnxruntime/python/tools/transformers/models/stable_diffusion/engine_builder_tensorrt.py @@ -490,7 +490,7 @@ def load_engines( def max_device_memory(self): max_device_memory = 0 - for _model_name, engine in self.engines.items(): + for engine in self.engines.values(): max_device_memory = max(max_device_memory, engine.engine.device_memory_size) return max_device_memory diff --git a/onnxruntime/python/tools/transformers/onnx_model.py b/onnxruntime/python/tools/transformers/onnx_model.py index 5fda3e6d84c1b..7bdbc08cf733a 100644 --- a/onnxruntime/python/tools/transformers/onnx_model.py +++ b/onnxruntime/python/tools/transformers/onnx_model.py @@ -675,7 +675,7 @@ def convert_float_to_float16(self, use_symbolic_shape_infer=True, **kwargs): if vi.name in name_vi: del name_vi[vi.name] for vi in name_vi.values(): - model.graph.value_info.append(vi) # noqa: PERF402 + model.graph.value_info.append(vi) except Exception: logger.warning( "Failed to run symbolic shape inference. Please file an issue in https://github.com/microsoft/onnxruntime." diff --git a/onnxruntime/python/tools/transformers/profiler.py b/onnxruntime/python/tools/transformers/profiler.py index fc2417ea897c3..8e45b149eaf03 100644 --- a/onnxruntime/python/tools/transformers/profiler.py +++ b/onnxruntime/python/tools/transformers/profiler.py @@ -454,7 +454,7 @@ def group_node_results(sess_time, kernel_time_only, use_gpu): def get_dim_from_type_proto(dim): - return getattr(dim, dim.WhichOneof("value")) if type(dim.WhichOneof("value")) == str else None + return getattr(dim, dim.WhichOneof("value")) if type(dim.WhichOneof("value")) == str else None # noqa: E721 def get_shape_from_type_proto(type_proto): @@ -573,7 +573,7 @@ def create_gpt2_inputs(onnx_model, batch_size, sequence_length, past_sequence_le shape = get_shape_from_type_proto(graph_input.type) for i, dim in enumerate(shape): if isinstance(dim, str): - if dim not in symbols.keys(): + if dim not in symbols: raise RuntimeError(f"symbol is not supported: {dim}") else: shape[i] = symbols[dim] @@ -615,7 +615,7 @@ def create_longformer_inputs(onnx_model, batch_size, sequence_length, global_len shape = get_shape_from_type_proto(graph_input.type) for i, dim in enumerate(shape): if isinstance(dim, str): - if dim not in symbols.keys(): + if dim not in symbols: raise RuntimeError(f"symbol is not supported: {dim}") else: shape[i] = symbols[dim] diff --git a/onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc b/onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc index 257ce977700a6..5e746ed0c62d4 100644 --- a/onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc +++ b/onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc @@ -1238,7 +1238,7 @@ TEST(MathOpTest, Sum_8_Test1) { // This test runs fine on CPU Plugin test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider, kOpenVINOExecutionProvider}); #else - test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); // TensorRT: Expected output shape [{3,3,3}] did not match run output shape [{3,1,1}] for sum + test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); // TensorRT: Expected output shape [{3,3,3}] did not match run output shape [{3,1,1}] for sum #endif } @@ -1264,7 +1264,7 @@ TEST(MathOpTest, Sum_8_Test1_double) { // This test runs fine on CPU Plugin test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider, kOpenVINOExecutionProvider}); #else - test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); // TensorRT: Expected output shape [{3,3,3}] did not match run output shape [{3,1,1}] for sum + test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); // TensorRT: Expected output shape [{3,3,3}] did not match run output shape [{3,1,1}] for sum #endif } TEST(MathOpTest, Sum_8_Test2) { diff --git a/onnxruntime/test/providers/cpu/reduction/reduction_ops_test.cc b/onnxruntime/test/providers/cpu/reduction/reduction_ops_test.cc index 1dfaf9b10ee2c..655f2a84d6dda 100644 --- a/onnxruntime/test/providers/cpu/reduction/reduction_ops_test.cc +++ b/onnxruntime/test/providers/cpu/reduction/reduction_ops_test.cc @@ -1086,7 +1086,7 @@ TEST(ReductionOpTest, ReduceMax_int32) { #if defined(OPENVINO_CONFIG_GPU_FP32) || defined(OPENVINO_CONFIG_GPU_FP16) test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider, kOpenVINOExecutionProvider}); // OpenVINO: Disabled temporarily #else - test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); // TensorRT: axis must be 0 + test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); // TensorRT: axis must be 0 #endif } @@ -1107,7 +1107,7 @@ TEST(ReductionOpTest, ReduceMax_int64) { #if defined(OPENVINO_CONFIG_GPU_FP32) || defined(OPENVINO_CONFIG_GPU_FP16) test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider, kOpenVINOExecutionProvider}); // OpenVINO: Disabled temporarily #else - test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); // TensorRT: axis must be 0 + test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); // TensorRT: axis must be 0 #endif } diff --git a/onnxruntime/test/providers/nnapi/nnapi_basic_test.cc b/onnxruntime/test/providers/nnapi/nnapi_basic_test.cc index 0e783a94c5479..b3e1025e7367c 100644 --- a/onnxruntime/test/providers/nnapi/nnapi_basic_test.cc +++ b/onnxruntime/test/providers/nnapi/nnapi_basic_test.cc @@ -556,10 +556,11 @@ TEST(NnapiExecutionProviderTest, ActivationOutsideOfPartition) { constexpr auto* model_file_name = ORT_TSTR("testdata/mnist.basic.ort"); // stop NNAPI partitioning at Relu so NNAPI EP only takes first Conv const auto nnapi_partitioning_stop_ops = "Relu"; - TestModelLoad(model_file_name, std::make_unique(0, nnapi_partitioning_stop_ops), - // expect one NNAPI partition - [](const Graph& graph) { ASSERT_EQ(CountAssignedNodes(graph, kNnapiExecutionProvider), 1) - << "Exactly one node should have been taken by the NNAPI EP"; }); + TestModelLoad( + model_file_name, std::make_unique(0, nnapi_partitioning_stop_ops), + // expect one NNAPI partition + [](const Graph& graph) { ASSERT_EQ(CountAssignedNodes(graph, kNnapiExecutionProvider), 1) + << "Exactly one node should have been taken by the NNAPI EP"; }); } } // namespace test diff --git a/onnxruntime/test/python/onnx_backend_test_series.py b/onnxruntime/test/python/onnx_backend_test_series.py index e8dc93049e18e..ecf4b001eec68 100644 --- a/onnxruntime/test/python/onnx_backend_test_series.py +++ b/onnxruntime/test/python/onnx_backend_test_series.py @@ -73,7 +73,7 @@ def apply_filters(filters, category): opset_version = f"opset{onnx.defs.onnx_opset_version()}" validated_filters = [] for f in filters[category]: - if type(f) is list: + if type(f) is list: # noqa: E721 opset_regex = f[0] filter_regex = f[1] opset_match = re.match(opset_regex, opset_version) diff --git a/onnxruntime/test/python/quantization/test_symmetric_flag.py b/onnxruntime/test/python/quantization/test_symmetric_flag.py index f24daddbbcf83..701da80d543d3 100644 --- a/onnxruntime/test/python/quantization/test_symmetric_flag.py +++ b/onnxruntime/test/python/quantization/test_symmetric_flag.py @@ -74,10 +74,10 @@ def get_next(self): # Extract quantization parameters: scales and zero points for activations, weights, and results model = onnx.load("quantized-model.onnx") - act_zp = [init for init in model.graph.initializer if init.name == "ACT_zero_point"][0].int32_data[0] - act_sc = [init for init in model.graph.initializer if init.name == "ACT_scale"][0].float_data[0] - wgt_zp = [init for init in model.graph.initializer if init.name == "WGT_zero_point"][0].int32_data[0] - wgt_sc = [init for init in model.graph.initializer if init.name == "WGT_scale"][0].float_data[0] + act_zp = next(init for init in model.graph.initializer if init.name == "ACT_zero_point").int32_data[0] + act_sc = next(init for init in model.graph.initializer if init.name == "ACT_scale").float_data[0] + wgt_zp = next(init for init in model.graph.initializer if init.name == "WGT_zero_point").int32_data[0] + wgt_sc = next(init for init in model.graph.initializer if init.name == "WGT_scale").float_data[0] # Return quantization parameters return act_zp, act_sc, wgt_zp, wgt_sc diff --git a/onnxruntime/test/python/transformers/test_optimizer.py b/onnxruntime/test/python/transformers/test_optimizer.py index eedadfd8d4448..c7db636a2f11f 100644 --- a/onnxruntime/test/python/transformers/test_optimizer.py +++ b/onnxruntime/test/python/transformers/test_optimizer.py @@ -122,7 +122,7 @@ def _test_optimizer_on_huggingface_model( "SkipLayerNormalization": expected_fusion_result_list[6], } - for _onnx_path, value in model_fusion_statistics.items(): + for value in model_fusion_statistics.values(): actual_node_count = value for op_type, count in expected_node_count.items(): @@ -354,7 +354,7 @@ def _test_optimizer_on_tf_model(self, model_name, expected_fusion_result_list, i fusion_options, ) - onnx_model = list(model_fusion_statistics.keys())[0] + onnx_model = next(iter(model_fusion_statistics.keys())) fusion_result_list = list(model_fusion_statistics[onnx_model].values()) if validate_model: diff --git a/onnxruntime/test/testdata/CNTK/gen.py b/onnxruntime/test/testdata/CNTK/gen.py index 51ad5e781c243..37241a46808b5 100644 --- a/onnxruntime/test/testdata/CNTK/gen.py +++ b/onnxruntime/test/testdata/CNTK/gen.py @@ -48,10 +48,10 @@ def Save(dir, func, feed, outputs): # noqa: N802 if actual_input_name.startswith(cntk_name): cntk_to_actual_names[cntk_name] = actual_input_name - if type(feed) is not dict: + if type(feed) is not dict: # noqa: E721 feed = {func.arguments[0]: feed} - if type(outputs) is not dict: + if type(outputs) is not dict: # noqa: E721 outputs = {func.outputs[0]: outputs} test_data_dir = os.path.join(dir, data_dir) @@ -213,7 +213,7 @@ def GenScan(): # noqa: N802 for n in out_mp.graph.node: if n.op_type == "Scan": - body = [attr for attr in n.attribute if attr.name == "body"][0] + body = next(attr for attr in n.attribute if attr.name == "body") for vi in list(body.g.input) + list(body.g.output) + list(body.g.value_info): dim = vi.type.tensor_type.shape.dim dim[0].dim_param = "batch" diff --git a/orttraining/orttraining/python/ort_trainer.py b/orttraining/orttraining/python/ort_trainer.py index 7c90054a85dc5..5286c087cfb64 100644 --- a/orttraining/orttraining/python/ort_trainer.py +++ b/orttraining/orttraining/python/ort_trainer.py @@ -65,7 +65,7 @@ def generate_sample(desc, device=None): def get_device_index(device): - if type(device) == str: + if type(device) == str: # noqa: E721 # could be 'cuda:0', 'cuda:1', or 'cpu'. with cpu, set index=0 device = torch.device(device) return 0 if device.index is None else device.index @@ -580,10 +580,10 @@ def _load_single_checkpoint(model, checkpoint_dir, checkpoint_prefix, is_partiti if is_partitioned: assert_msg = ( - "Couldn't find checkpoint file {}." + f"Couldn't find checkpoint file {checkpoint_file}." "Optimizer partitioning is enabled using ZeRO. Please make sure that the " - "checkpoint file exists for rank {} of {}." - ).format(checkpoint_file, model.world_rank, model.world_size) + f"checkpoint file exists for rank {model.world_rank} of {model.world_size}." + ) else: assert_msg = f"Couldn't find checkpoint file {checkpoint_file}." @@ -1005,7 +1005,7 @@ def _prepare_input_and_fetches( self, input_desc_with_, internal_learning_rate, internal_loss_scale, *args, **kwargs ): fetches = None - if type(args) == tuple and len(args) == 1 and type(args[0]) == list: + if type(args) == tuple and len(args) == 1 and type(args[0]) == list: # noqa: E721 input = tuple(args[0]) else: input = args @@ -1174,7 +1174,7 @@ def eval_step(self, *args, **kwargs): ) if len(session_run_results) == 1: - return session_run_results[list(session_run_results.keys())[0]] + return session_run_results[next(iter(session_run_results.keys()))] else: return [session_run_results[output_desc.name_] for output_desc in output_desc] diff --git a/orttraining/orttraining/python/training/onnxblock/model_accessor.py b/orttraining/orttraining/python/training/onnxblock/model_accessor.py index cff435c5626c4..ac7a53a554e0a 100644 --- a/orttraining/orttraining/python/training/onnxblock/model_accessor.py +++ b/orttraining/orttraining/python/training/onnxblock/model_accessor.py @@ -69,7 +69,7 @@ def base(model: onnx.ModelProto): "model from scratch." ) - _GLOBAL_ACCESSOR = ModelAccessor(model_clone) # noqa: PLW0603 + _GLOBAL_ACCESSOR = ModelAccessor(model_clone) try: yield _GLOBAL_ACCESSOR finally: @@ -112,7 +112,7 @@ def empty_base(opset_version: int | None = None): ) ) - _GLOBAL_ACCESSOR = ModelAccessor(model) # noqa: PLW0603 + _GLOBAL_ACCESSOR = ModelAccessor(model) try: yield _GLOBAL_ACCESSOR finally: @@ -144,7 +144,7 @@ def custom_op_library(custom_op_library_path: os.PathLike): if not os.path.exists(custom_op_library_path): raise RuntimeError(f"Custom op library path {custom_op_library_path} does not exist.") - _GLOBAL_CUSTOM_OP_LIBRARY = copy.copy(custom_op_library_path) # noqa: PLW0603 + _GLOBAL_CUSTOM_OP_LIBRARY = copy.copy(custom_op_library_path) try: yield _GLOBAL_CUSTOM_OP_LIBRARY finally: diff --git a/orttraining/orttraining/python/training/optim/_ds_modifier.py b/orttraining/orttraining/python/training/optim/_ds_modifier.py index 6a52550882c25..6b1c98cc02a52 100644 --- a/orttraining/orttraining/python/training/optim/_ds_modifier.py +++ b/orttraining/orttraining/python/training/optim/_ds_modifier.py @@ -55,7 +55,7 @@ def can_be_modified(self): if not get_accelerator().device_name().startswith("cuda"): warnings.warn( "Skip modifying optimizer as device is not supported, " - "device name: {}".format(get_accelerator().device_name()), + f"device name: {get_accelerator().device_name()}", UserWarning, ) return False diff --git a/orttraining/orttraining/python/training/ortmodule/_custom_op_symbolic_registry.py b/orttraining/orttraining/python/training/ortmodule/_custom_op_symbolic_registry.py index 938bc568b6b32..d5bcf16a9ca05 100644 --- a/orttraining/orttraining/python/training/ortmodule/_custom_op_symbolic_registry.py +++ b/orttraining/orttraining/python/training/ortmodule/_custom_op_symbolic_registry.py @@ -156,7 +156,7 @@ def cross_entropy_loss(g, node, logits, target, weight, reduction, ignore_index, output_type = logits_casted.type() else: # For higher version torch we can get node output types - loss_output = list(node.outputs())[0] + loss_output = next(iter(node.outputs())) output_type = loss_output.type() ################################## diff --git a/orttraining/orttraining/python/training/ortmodule/_fallback.py b/orttraining/orttraining/python/training/ortmodule/_fallback.py index 44f96dcff7fb0..56bb45d064d8a 100644 --- a/orttraining/orttraining/python/training/ortmodule/_fallback.py +++ b/orttraining/orttraining/python/training/ortmodule/_fallback.py @@ -175,9 +175,9 @@ def fallback(self, log_level: _logger.LogLevel, *inputs, **kwargs): # This warning will not be raised again if retry is not enabled self._logger.warning( - "Fallback to PyTorch due to exception {} was triggered. " + f"Fallback to PyTorch due to exception {exception_type} was triggered. " "Report this issue with a minimal repro at https://www.github.com/microsoft/onnxruntime. " - "See details below:\n\n{}".format(exception_type, exception_string) + f"See details below:\n\n{exception_string}" ) self._raised_fallback_exception = True diff --git a/orttraining/orttraining/python/training/ortmodule/_training_manager.py b/orttraining/orttraining/python/training/ortmodule/_training_manager.py index 3be4c05797978..e9f383abc888d 100644 --- a/orttraining/orttraining/python/training/ortmodule/_training_manager.py +++ b/orttraining/orttraining/python/training/ortmodule/_training_manager.py @@ -403,7 +403,7 @@ def _create_execution_agent(self): session_options, providers, provider_options = self._get_session_config() fw_feed_names = [input.name for input in self._onnx_models.optimized_model.graph.input] - device_type = self._device if type(self._device) is str else self._device.type.lower() + device_type = self._device if type(self._device) is str else self._device.type.lower() # noqa: E721 if device_type == "ort": fw_outputs_device_info = [C.get_ort_device(self._device.index)] * ( len(self._graph_info.user_output_names) + len(self._graph_info.frontier_node_arg_map) diff --git a/orttraining/orttraining/python/training/orttrainer.py b/orttraining/orttraining/python/training/orttrainer.py index a6c6c8af2723b..d5a488c436a1d 100644 --- a/orttraining/orttraining/python/training/orttrainer.py +++ b/orttraining/orttraining/python/training/orttrainer.py @@ -306,8 +306,8 @@ def _check_model_export(self, input): # Mute the dropout nodes dropout_nodes = [n for n in onnx_model_copy.graph.node if n.op_type == "Dropout"] for node in dropout_nodes: - ratio_node = [n for n in onnx_model_copy.graph.node if node.input[1] in n.output][0] - training_mode_node = [n for n in onnx_model_copy.graph.node if node.input[2] in n.output][0] + ratio_node = next(n for n in onnx_model_copy.graph.node if node.input[1] in n.output) + training_mode_node = next(n for n in onnx_model_copy.graph.node if node.input[2] in n.output) training_mode_node.attribute.pop() ratio_node.attribute.pop() @@ -844,7 +844,7 @@ def _init_session(self, optimizer_state_dict={}, session_options=None, provider_ def _prepare_model_input(self, inputs_desc, lr, loss_scale, *inputs, **kwargs): # Normalize input to tuple of samples - if type(inputs) == tuple and len(inputs) == 1 and type(inputs[0]) == list: + if type(inputs) == tuple and len(inputs) == 1 and type(inputs[0]) == list: # noqa: E721 input = tuple(inputs[0]) else: input = inputs diff --git a/orttraining/orttraining/test/python/orttraining_run_frontend_batch_size_test.py b/orttraining/orttraining/test/python/orttraining_run_frontend_batch_size_test.py index e96b90138c3d5..3e2d1a7154bfd 100644 --- a/orttraining/orttraining/test/python/orttraining_run_frontend_batch_size_test.py +++ b/orttraining/orttraining/test/python/orttraining_run_frontend_batch_size_test.py @@ -60,7 +60,7 @@ def run_with_config(config): cmds.append("--transformer_layer_recompute") # access to azure storage shared disk is much slower so we need a longer timeout. - subprocess.run(cmds, timeout=1200).check_returncode() + subprocess.run(cmds, timeout=1200).check_returncode() # noqa: PLW1510 for config in configs: diff --git a/orttraining/orttraining/test/python/orttraining_test_checkpoint_storage.py b/orttraining/orttraining/test/python/orttraining_test_checkpoint_storage.py index 71d13fdcfd290..21372caaf6779 100644 --- a/orttraining/orttraining/test/python/orttraining_test_checkpoint_storage.py +++ b/orttraining/orttraining/test/python/orttraining_test_checkpoint_storage.py @@ -79,7 +79,7 @@ def _get_dict(separated_key): "int2": 2, "int_list": [1, 2, 3, 5, 6], "dict1": {"np_array": np.arange(100), "dict2": {"int3": 3, "int4": 4}, "str1": "onnxruntime"}, - "bool1": bool(True), + "bool1": True, "int5": 5, "float1": 2.345, "np_array_float": np.array([1.234, 2.345, 3.456]), diff --git a/orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py b/orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py index 0e88ce8e6c2d1..fcc1e9ff668d5 100644 --- a/orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py +++ b/orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py @@ -3904,9 +3904,9 @@ def forward(self, input1, bool_argument, int_argument, float_argument): out = self.relu(out) return out - assert type(bool_argument) is bool - assert type(int_argument) is int - assert type(float_argument) is float + assert type(bool_argument) is bool # noqa: E721 + assert type(int_argument) is int # noqa: E721 + assert type(float_argument) is float # noqa: E721 device = "cuda" N, D_in, H, D_out = 32, 784, 500, 10 # noqa: N806 @@ -3942,8 +3942,8 @@ def forward(self, input1, bool_argument): out = self.relu(out) return out - assert type(bool_arguments[0]) is bool - assert type(bool_arguments[1]) is bool + assert type(bool_arguments[0]) is bool # noqa: E721 + assert type(bool_arguments[1]) is bool # noqa: E721 device = "cuda" N, D_in, H, D_out = 32, 784, 500, 10 # noqa: N806 @@ -5893,7 +5893,7 @@ def generate_inputs(batch_size, max_seq_length, vocab_size): assert len([node.op_type for node in training_model.graph.node if node.op_type == "ShrunkenGather"]) == 2 else: assert len([node.op_type for node in training_model.graph.node if node.op_type == "ShrunkenGather"]) == 1 - gathergrad_node = [node for node in training_model.graph.node if node.op_type == "PadAndUnflatten"][0] + gathergrad_node = next(node for node in training_model.graph.node if node.op_type == "PadAndUnflatten") def find_input_node_type(model, arg): result = [] diff --git a/orttraining/orttraining/test/python/utils_multiple_choice.py b/orttraining/orttraining/test/python/utils_multiple_choice.py index f425cf3d61545..e0febaf2d6334 100644 --- a/orttraining/orttraining/test/python/utils_multiple_choice.py +++ b/orttraining/orttraining/test/python/utils_multiple_choice.py @@ -98,8 +98,6 @@ def __init__( overwrite_cache=False, mode: Split = Split.train, ): - processor = processor - cached_features_file = os.path.join( data_dir, "cached_{}_{}_{}_{}".format( diff --git a/orttraining/tools/ci_test/run_batch_size_test.py b/orttraining/tools/ci_test/run_batch_size_test.py index ba2be03618197..348d490678e9a 100755 --- a/orttraining/tools/ci_test/run_batch_size_test.py +++ b/orttraining/tools/ci_test/run_batch_size_test.py @@ -108,7 +108,7 @@ def main(): if config.enable_mixed_precision: cmds.append("--use_mixed_precision"), - subprocess.run(cmds, timeout=120).check_returncode() + subprocess.run(cmds, timeout=120).check_returncode() # noqa: PLW1510 return 0 diff --git a/orttraining/tools/ci_test/run_bert_perf_test.py b/orttraining/tools/ci_test/run_bert_perf_test.py index fbc1403583ba0..bb15d6f5965b6 100644 --- a/orttraining/tools/ci_test/run_bert_perf_test.py +++ b/orttraining/tools/ci_test/run_bert_perf_test.py @@ -97,7 +97,7 @@ def main(): cmds.append("--use_mixed_precision"), cmds.append("--allreduce_in_fp16"), - subprocess.run(cmds).check_returncode() + subprocess.run(cmds).check_returncode() # noqa: PLW1510 if c.expected_perf > 0.0: json_filename = "onnxruntime_perf_metrics_{}.onnx_bert_{}_{}_Lamb.json".format( model, precision_prefix, c.max_seq_length diff --git a/orttraining/tools/ci_test/run_convergence_test.py b/orttraining/tools/ci_test/run_convergence_test.py index 58250e7f8ae8c..2ec32bca77640 100755 --- a/orttraining/tools/ci_test/run_convergence_test.py +++ b/orttraining/tools/ci_test/run_convergence_test.py @@ -35,7 +35,7 @@ def main(): convergence_test_output_path = os.path.join(output_dir, "convergence_test_out.csv") # run BERT training - subprocess.run( + subprocess.run( # noqa: PLW1510 [ os.path.join(args.binary_dir, "onnxruntime_training_bert"), "--model_name", diff --git a/orttraining/tools/ci_test/run_gpt2_perf_test.py b/orttraining/tools/ci_test/run_gpt2_perf_test.py index e64fc3c7812e3..18e59d275b6b5 100644 --- a/orttraining/tools/ci_test/run_gpt2_perf_test.py +++ b/orttraining/tools/ci_test/run_gpt2_perf_test.py @@ -62,7 +62,7 @@ def main(): if c.use_mixed_precision: cmds.append("--use_mixed_precision"), - subprocess.run(cmds).check_returncode() + subprocess.run(cmds).check_returncode() # noqa: PLW1510 return 0 diff --git a/requirements-lintrunner.txt b/requirements-lintrunner.txt index 2068040443a20..abd6c03f684a3 100644 --- a/requirements-lintrunner.txt +++ b/requirements-lintrunner.txt @@ -1,11 +1,9 @@ # This file is auto updated by dependabot -lintrunner-adapters>=0.8.0 -# RUFF, RUFF-FIX -ruff==0.0.278 +lintrunner-adapters>=0.11.0 +# RUFF +ruff==0.1.4 # BLACK-ISORT -black==23.7.0 +black==23.10.1 isort==5.12.0 -# PYLINT -pylint==2.17.2 # CLANGFORMAT -clang-format==16.0.6 +clang-format==17.0.5 diff --git a/samples/python/training/orttrainer/pytorch_transformer/pt_model.py b/samples/python/training/orttrainer/pytorch_transformer/pt_model.py index 07752f52d7a84..4f2e03192c6cf 100644 --- a/samples/python/training/orttrainer/pytorch_transformer/pt_model.py +++ b/samples/python/training/orttrainer/pytorch_transformer/pt_model.py @@ -22,7 +22,7 @@ def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5): def _generate_square_subsequent_mask(self, sz): mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1) - mask = mask.float().masked_fill(mask == 0, float("-inf")).masked_fill(mask == 1, float(0.0)) + mask = mask.float().masked_fill(mask == 0, float("-inf")).masked_fill(mask == 1, 0.0) return mask def init_weights(self): diff --git a/tools/android_custom_build/build_custom_android_package.py b/tools/android_custom_build/build_custom_android_package.py index aa57cf341942c..35adb41690e98 100755 --- a/tools/android_custom_build/build_custom_android_package.py +++ b/tools/android_custom_build/build_custom_android_package.py @@ -22,7 +22,7 @@ def is_windows(): def run(cmd_arg_list, **kwargs): print(f"Running command:\n {shlex.join(cmd_arg_list)}") kwargs.update({"check": True}) - return subprocess.run(cmd_arg_list, **kwargs) + return subprocess.run(cmd_arg_list, **kwargs) # noqa: PLW1510 def parse_args(): diff --git a/tools/ci_build/build.py b/tools/ci_build/build.py index 638196e73a77b..0b1cd205d8ca0 100644 --- a/tools/ci_build/build.py +++ b/tools/ci_build/build.py @@ -1264,8 +1264,8 @@ def generate_build_tree( args.apple_deploy_target, ] arg_names = [ - "--ios_sysroot " + "", # noqa: ISC003 - "--apple_deploy_target " + "", # noqa: ISC003 + "--ios_sysroot " + "", + "--apple_deploy_target " + "", ] if not all(needed_args): raise BuildError( @@ -1634,9 +1634,7 @@ def run_adb_shell(cmd): # GCOV_PREFIX specifies the root directory # for creating the runtime code coverage files. if args.code_coverage: - adb_shell( - "cd {0} && GCOV_PREFIX={0} GCOV_PREFIX_STRIP={1} {2}".format(device_dir, cwd.count(os.sep) + 1, cmd) - ) + adb_shell(f"cd {device_dir} && GCOV_PREFIX={device_dir} GCOV_PREFIX_STRIP={cwd.count(os.sep) + 1} {cmd}") else: adb_shell(f"cd {device_dir} && {cmd}") @@ -1682,9 +1680,9 @@ def run_adb_shell(cmd): ) if args.use_nnapi: - run_adb_shell("{0}/onnx_test_runner -e nnapi {0}/test".format(device_dir)) + run_adb_shell(f"{device_dir}/onnx_test_runner -e nnapi {device_dir}/test") else: - run_adb_shell("{0}/onnx_test_runner {0}/test".format(device_dir)) + run_adb_shell(f"{device_dir}/onnx_test_runner {device_dir}/test") # run shared_lib_test if necessary if args.build_shared_lib: @@ -1695,9 +1693,9 @@ def run_adb_shell(cmd): adb_push("onnxruntime_customopregistration_test", device_dir, cwd=cwd) adb_shell(f"chmod +x {device_dir}/onnxruntime_shared_lib_test") adb_shell(f"chmod +x {device_dir}/onnxruntime_customopregistration_test") - run_adb_shell("LD_LIBRARY_PATH=$LD_LIBRARY_PATH:{0} {0}/onnxruntime_shared_lib_test".format(device_dir)) + run_adb_shell(f"LD_LIBRARY_PATH=$LD_LIBRARY_PATH:{device_dir} {device_dir}/onnxruntime_shared_lib_test") run_adb_shell( - "LD_LIBRARY_PATH=$LD_LIBRARY_PATH:{0} {0}/onnxruntime_customopregistration_test".format(device_dir) + f"LD_LIBRARY_PATH=$LD_LIBRARY_PATH:{device_dir} {device_dir}/onnxruntime_customopregistration_test" ) diff --git a/tools/ci_build/github/linux/ort_minimal/readelf_utils.py b/tools/ci_build/github/linux/ort_minimal/readelf_utils.py index 43bc107df401b..dec070e3f5c75 100644 --- a/tools/ci_build/github/linux/ort_minimal/readelf_utils.py +++ b/tools/ci_build/github/linux/ort_minimal/readelf_utils.py @@ -23,7 +23,7 @@ def get_section_sizes(binary_path, readelf_path, dump_to_file=None): """ cmd = [readelf_path, "--sections", "--wide", binary_path] - result = subprocess.run(cmd, stdout=subprocess.PIPE) + result = subprocess.run(cmd, stdout=subprocess.PIPE) # noqa: PLW1510 result.check_returncode() output = result.stdout.decode("utf-8") diff --git a/tools/ci_build/upload_python_package_to_azure_storage.py b/tools/ci_build/upload_python_package_to_azure_storage.py index 365cb67381ce7..b7969f02e518e 100755 --- a/tools/ci_build/upload_python_package_to_azure_storage.py +++ b/tools/ci_build/upload_python_package_to_azure_storage.py @@ -55,7 +55,7 @@ def upload_whl(python_wheel_path, final_storage=False): with open(download_path_to_html) as f: lines = f.read().splitlines() - new_line = '{blobname}
'.format(blobname=blob_name_plus_replaced) + new_line = f'{blob_name_plus_replaced}
' if new_line not in lines: lines.append(new_line) lines.sort() diff --git a/tools/python/dump_subgraphs.py b/tools/python/dump_subgraphs.py index a1b9782374ca7..529d798d50149 100644 --- a/tools/python/dump_subgraphs.py +++ b/tools/python/dump_subgraphs.py @@ -19,11 +19,11 @@ def dump_subgraph(model, output_dir, level=0): for node in graph.node: if node.op_type == "Scan" or node.op_type == "Loop": - body_attribute = list(filter(lambda attr: attr.name == "body", node.attribute))[0] + body_attribute = next(iter(filter(lambda attr: attr.name == "body", node.attribute))) export_and_recurse(node, body_attribute, output_dir, level) if node.op_type == "If": - then_attribute = list(filter(lambda attr: attr.name == "then_branch", node.attribute))[0] - else_attribute = list(filter(lambda attr: attr.name == "else_branch", node.attribute))[0] + then_attribute = next(iter(filter(lambda attr: attr.name == "then_branch", node.attribute))) + else_attribute = next(iter(filter(lambda attr: attr.name == "else_branch", node.attribute))) export_and_recurse(node, then_attribute, output_dir, level) export_and_recurse(node, else_attribute, output_dir, level) diff --git a/tools/python/find_optimizer_opset_version_updates_required.py b/tools/python/find_optimizer_opset_version_updates_required.py index 0076d27fe950e..8a5e57b51e38d 100644 --- a/tools/python/find_optimizer_opset_version_updates_required.py +++ b/tools/python/find_optimizer_opset_version_updates_required.py @@ -54,7 +54,7 @@ def get_call_args_from_file(filename: str, function_or_declaration: str) -> typi # TODO: handle automatically by merging lines log.error( "Call/Declaration is split over multiple lines. Please check manually." - "File:{} Line:{}".format(filename, line_num) + f"File:{filename} Line:{line_num}" ) continue diff --git a/tools/python/gen_opkernel_doc.py b/tools/python/gen_opkernel_doc.py index 2d0d16cf9a0de..1075ed8192fdd 100644 --- a/tools/python/gen_opkernel_doc.py +++ b/tools/python/gen_opkernel_doc.py @@ -150,7 +150,7 @@ def main(output_path: pathlib.Path, provider_filter: [str]): tnameindex += 1 tclist = [] for tc in sorted(tcset): - tclist.append(tc) # noqa: PERF402 + tclist.append(tc) fout.write("**" + tname + "** = " + format_type_constraints(tclist)) if tnameindex < len(typemap): fout.write("
") diff --git a/tools/python/ort_test_dir_utils.py b/tools/python/ort_test_dir_utils.py index 2fc4921a7bb67..cd1f5022af526 100644 --- a/tools/python/ort_test_dir_utils.py +++ b/tools/python/ort_test_dir_utils.py @@ -212,8 +212,8 @@ def run_test_dir(model_or_dir): models = onnx_models + ort_models if len(models) > 1: raise ValueError( - "'Multiple .onnx and/or .ort files found in {}. '" - "'Please provide specific .onnx or .ort file as input.".format(model_dir) + f"'Multiple .onnx and/or .ort files found in {model_dir}. '" + "'Please provide specific .onnx or .ort file as input." ) elif len(models) == 0: raise ValueError(f"'No .onnx or .ort files found in {model_dir}.") diff --git a/tools/python/sparsify_initializers.py b/tools/python/sparsify_initializers.py index 8f5034c4ef5cc..f9cc8db38ecff 100644 --- a/tools/python/sparsify_initializers.py +++ b/tools/python/sparsify_initializers.py @@ -78,7 +78,7 @@ def convert_tensor_to_sparse( indices.append(index) nnz_count += 1 - sparsity = float(1.0) - float(nnz_count) / data_len + sparsity = 1.0 - float(nnz_count) / data_len ind_data_type = TensorProto.INT8 ind_dtype = np.int8 @@ -126,7 +126,7 @@ def convert_tensor_to_sparse( # int32 indices are often selected, thus we really want to guard against loosing # rather than winning. if tensor_data_bytes <= total_sparse_bytes: - sparsity = float(1.0) - float(tensor_data_bytes) / total_sparse_bytes + sparsity = 1.0 - float(tensor_data_bytes) / total_sparse_bytes logger.debug(f"initializer={tensor.name}, adjusted_sparsity={sparsity}") return (object(), sparsity) diff --git a/tools/python/util/get_azcopy.py b/tools/python/util/get_azcopy.py index 76c75ad8c60eb..bfcf228a956eb 100644 --- a/tools/python/util/get_azcopy.py +++ b/tools/python/util/get_azcopy.py @@ -27,7 +27,7 @@ def _check_version(azcopy_path): - proc = subprocess.run([azcopy_path, "--version"], stdout=subprocess.PIPE, text=True) + proc = subprocess.run([azcopy_path, "--version"], stdout=subprocess.PIPE, text=True) # noqa: PLW1510 match = re.search(r"\d+(?:\.\d+)+", proc.stdout) if not match: diff --git a/tools/python/util/ort_format_model/operator_type_usage_processors.py b/tools/python/util/ort_format_model/operator_type_usage_processors.py index 5905000a14972..22d7dff3e13b2 100644 --- a/tools/python/util/ort_format_model/operator_type_usage_processors.py +++ b/tools/python/util/ort_format_model/operator_type_usage_processors.py @@ -193,7 +193,7 @@ def process_node(self, node: fbs.Node, value_name_to_typeinfo: dict): def is_typed_registration_needed( self, type_in_registration: str, globally_allowed_types: typing.Optional[typing.Set[str]] ): - if 0 not in self._input_types.keys(): + if 0 not in self._input_types: # currently all standard typed registrations are for input 0. # custom registrations can be handled by operator specific processors (e.g. OneHotProcessor below). raise RuntimeError(f"Expected typed registration to use type from input 0. Node:{self.name}") diff --git a/winml/lib/Api.Image/inc/ImageConversionHelpers.h b/winml/lib/Api.Image/inc/ImageConversionHelpers.h index 8e3dca2ae11e8..5a9c8f21255b5 100644 --- a/winml/lib/Api.Image/inc/ImageConversionHelpers.h +++ b/winml/lib/Api.Image/inc/ImageConversionHelpers.h @@ -52,5 +52,6 @@ bool VideoFramesHaveSameDevice(const wm::IVideoFrame& video_frame_1, const wm::I wgdx::Direct3D11::IDirect3DDevice GetDeviceFromDirect3DSurface(const wgdx::Direct3D11::IDirect3DSurface& d3dSurface); constexpr std::array supportedWinMLFormats = { - DXGI_FORMAT_R8G8B8A8_UNORM, DXGI_FORMAT_B8G8R8A8_UNORM, DXGI_FORMAT_B8G8R8X8_UNORM}; + DXGI_FORMAT_R8G8B8A8_UNORM, DXGI_FORMAT_B8G8R8A8_UNORM, DXGI_FORMAT_B8G8R8X8_UNORM +}; } // namespace _winml::Imaging diff --git a/winml/lib/Api.Image/inc/TensorToVideoFrameConverter.h b/winml/lib/Api.Image/inc/TensorToVideoFrameConverter.h index 12f676459293b..b09046471bdf6 100644 --- a/winml/lib/Api.Image/inc/TensorToVideoFrameConverter.h +++ b/winml/lib/Api.Image/inc/TensorToVideoFrameConverter.h @@ -40,10 +40,7 @@ class TensorToVideoFrameConverter : public ImageConverter { private: GUID _d3d11TextureGUID = { - 0x14bf1054, - 0x6ce7, - 0x4c00, - {0xa1, 0x32, 0xb0, 0xf2, 0x11, 0x5D, 0xE0, 0x7f} + 0x14bf1054, 0x6ce7, 0x4c00, {0xa1, 0x32, 0xb0, 0xf2, 0x11, 0x5D, 0xE0, 0x7f} }; // {14BF1054-6CE7-4C00-A132-B0F2115DE07F} GUID _handleGUID = { 0x700148fc, 0xc0cb, 0x4a7e, {0xa7, 0xc0, 0xe7, 0x43, 0xc1, 0x9, 0x9d, 0x62} diff --git a/winml/lib/Api.Image/inc/VideoFrameToTensorConverter.h b/winml/lib/Api.Image/inc/VideoFrameToTensorConverter.h index e34030bbd6833..3e57af42f4d09 100644 --- a/winml/lib/Api.Image/inc/VideoFrameToTensorConverter.h +++ b/winml/lib/Api.Image/inc/VideoFrameToTensorConverter.h @@ -50,10 +50,7 @@ class VideoFrameToTensorConverter : public ImageConverter { private: GUID d3d11_texture_GUID_ = { - 0x485e4bb3, - 0x3fe8, - 0x497b, - {0x85, 0x9e, 0xc7, 0x5, 0x18, 0xdb, 0x11, 0x2a} + 0x485e4bb3, 0x3fe8, 0x497b, {0x85, 0x9e, 0xc7, 0x5, 0x18, 0xdb, 0x11, 0x2a} }; // {485E4BB3-3FE8-497B-859E-C70518DB112A} GUID handle_GUID_ = { 0xce43264e, 0x41f7, 0x4882, {0x9e, 0x20, 0xfa, 0xa5, 0x1e, 0x37, 0x64, 0xfc} diff --git a/winml/lib/Api.Ort/OnnxruntimeModel.cpp b/winml/lib/Api.Ort/OnnxruntimeModel.cpp index fb8413a897e75..24eb44b73dd3c 100644 --- a/winml/lib/Api.Ort/OnnxruntimeModel.cpp +++ b/winml/lib/Api.Ort/OnnxruntimeModel.cpp @@ -81,7 +81,8 @@ HRESULT ModelInfo::RuntimeClassInitialize(_In_ OnnxruntimeEngineFactory* engine_ winml_adapter_api->ModelGetInputCount, winml_adapter_api->ModelGetInputName, winml_adapter_api->ModelGetInputDescription, - winml_adapter_api->ModelGetInputTypeInfo}; + winml_adapter_api->ModelGetInputTypeInfo + }; // Create inputs std::vector inputs; @@ -93,7 +94,8 @@ HRESULT ModelInfo::RuntimeClassInitialize(_In_ OnnxruntimeEngineFactory* engine_ winml_adapter_api->ModelGetOutputCount, winml_adapter_api->ModelGetOutputName, winml_adapter_api->ModelGetOutputDescription, - winml_adapter_api->ModelGetOutputTypeInfo}; + winml_adapter_api->ModelGetOutputTypeInfo + }; std::vector outputs; RETURN_IF_FAILED(CreateFeatureDescriptors(engine_factory, &output_helpers, ort_model, outputs)); diff --git a/winml/lib/Api/impl/TensorBase.h b/winml/lib/Api/impl/TensorBase.h index c9299a00ddaa2..00176038d574f 100644 --- a/winml/lib/Api/impl/TensorBase.h +++ b/winml/lib/Api/impl/TensorBase.h @@ -217,7 +217,8 @@ struct TensorBase : TBase { } D3D12_HEAP_PROPERTIES heapProperties = { - D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0}; + D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0 + }; D3D12_RESOURCE_DESC resourceDesc = { D3D12_RESOURCE_DIMENSION_BUFFER, 0, diff --git a/winml/test/adapter/AdapterDmlEpTest.cpp b/winml/test/adapter/AdapterDmlEpTest.cpp index 81437f9db2de3..b4220650abb9c 100644 --- a/winml/test/adapter/AdapterDmlEpTest.cpp +++ b/winml/test/adapter/AdapterDmlEpTest.cpp @@ -116,7 +116,8 @@ std::array tensor_values = {}; winrt::com_ptr CreateD3D12Resource(ID3D12Device& device) { constexpr uint64_t buffer_size = tensor_size * sizeof(float); constexpr D3D12_HEAP_PROPERTIES heap_properties = { - D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0}; + D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0 + }; constexpr D3D12_RESOURCE_DESC resource_desc = { D3D12_RESOURCE_DIMENSION_BUFFER, 0, @@ -365,6 +366,7 @@ const AdapterDmlEpTestApi& getapi() { DmlCopyTensor, CreateCustomRegistry, ValueGetDeviceId, - SessionGetInputRequiredDeviceId}; + SessionGetInputRequiredDeviceId + }; return api; } diff --git a/winml/test/adapter/AdapterSessionTest.cpp b/winml/test/adapter/AdapterSessionTest.cpp index 1b1a36004264c..8c9124b2ff4ae 100644 --- a/winml/test/adapter/AdapterSessionTest.cpp +++ b/winml/test/adapter/AdapterSessionTest.cpp @@ -368,7 +368,8 @@ const AdapterSessionTestAPI& getapi() { Profiling, CopyInputAcrossDevices, CopyInputAcrossDevices_DML, - GetNumberOfIntraOpThreads}; + GetNumberOfIntraOpThreads + }; if (SkipGpuTests()) { api.AppendExecutionProvider_DML = SkipTest; diff --git a/winml/test/api/LearningModelAPITest.cpp b/winml/test/api/LearningModelAPITest.cpp index ab45e2414854d..01ca2b8930506 100644 --- a/winml/test/api/LearningModelAPITest.cpp +++ b/winml/test/api/LearningModelAPITest.cpp @@ -247,9 +247,11 @@ static void CheckLearningModelPixelRange() { // Normalized_0_1 and image output L"Add_ImageNet1920WithImageMetadataBgr8_SRGB_0_1.onnx", // Normalized_1_1 and image output - L"Add_ImageNet1920WithImageMetadataBgr8_SRGB_1_1.onnx"}; + L"Add_ImageNet1920WithImageMetadataBgr8_SRGB_1_1.onnx" + }; std::vector pixelRanges = { - LearningModelPixelRange::ZeroTo255, LearningModelPixelRange::ZeroToOne, LearningModelPixelRange::MinusOneToOne}; + LearningModelPixelRange::ZeroTo255, LearningModelPixelRange::ZeroToOne, LearningModelPixelRange::MinusOneToOne + }; for (uint32_t model_i = 0; model_i < modelPaths.size(); model_i++) { LearningModel learningModel = nullptr; WINML_EXPECT_NO_THROW(APITest::LoadModel(modelPaths[model_i], learningModel)); @@ -329,7 +331,8 @@ const LearningModelApiTestsApi& getapi() { CloseModelCheckEval, CloseModelNoNewSessions, CheckMetadataCaseInsensitive, - CreateCorruptModel}; + CreateCorruptModel + }; if (RuntimeParameterExists(L"noVideoFrameTests")) { api.CloseModelCheckEval = SkipTest; diff --git a/winml/test/api/LearningModelBindingAPITest.cpp b/winml/test/api/LearningModelBindingAPITest.cpp index b77421e191020..8279f4f89f0ed 100644 --- a/winml/test/api/LearningModelBindingAPITest.cpp +++ b/winml/test/api/LearningModelBindingAPITest.cpp @@ -669,7 +669,8 @@ const LearningModelBindingAPITestsApi& getapi() { VerifyOutputAfterEvaluateAsyncCalledTwice, VerifyOutputAfterImageBindCalledTwice, SequenceLengthTensorFloat, - SequenceConstructTensorString}; + SequenceConstructTensorString + }; if (SkipGpuTests()) { api.GpuSqueezeNet = SkipTest; diff --git a/winml/test/api/LearningModelSessionAPITest.cpp b/winml/test/api/LearningModelSessionAPITest.cpp index 21cdaa62bc898..4ec79b8a0f4c6 100644 --- a/winml/test/api/LearningModelSessionAPITest.cpp +++ b/winml/test/api/LearningModelSessionAPITest.cpp @@ -793,7 +793,8 @@ static void STFT( auto n_dfts = static_cast(1 + floor((signal_size - dft_size) / hop_size)); auto input_shape = std::vector{1, INT64(signal_size)}; auto output_shape = std::vector{ - INT64(batch_size), INT64(n_dfts), is_onesided ? ((INT64(dft_size) >> 1) + 1) : INT64(dft_size), 2}; + INT64(batch_size), INT64(n_dfts), is_onesided ? ((INT64(dft_size) >> 1) + 1) : INT64(dft_size), 2 + }; auto dft_length = TensorInt64Bit::CreateFromArray({}, {INT64(dft_size)}); auto model = @@ -1372,7 +1373,8 @@ static void ModelBuilding_GridSample_Internal(LearningModelDeviceKind kind) { 5.0000f, 5.0000f, 10.0000f, - 10.0000f}; + 10.0000f + }; input_dims = {1, 1, 3, 2}; grid_dims = {1, 2, 4, 2}; @@ -2312,7 +2314,8 @@ const LearningModelSessionAPITestsApi& getapi() { ModelBuilding_STFT, ModelBuilding_MelSpectrogramOnThreeToneSignal, ModelBuilding_MelWeightMatrix, - SetName}; + SetName + }; if (SkipGpuTests()) { api.CreateSessionDeviceDirectX = SkipTest; diff --git a/winml/test/api/RawApiHelpers.cpp b/winml/test/api/RawApiHelpers.cpp index b6f39f8e88224..e84af6d239799 100644 --- a/winml/test/api/RawApiHelpers.cpp +++ b/winml/test/api/RawApiHelpers.cpp @@ -38,7 +38,8 @@ void RunOnDevice(ml::learning_model& model, ml::learning_model_device& device, I auto channel_buffers_pointers = std::vector{ &input_data.at(0), &input_data.at(0) + channel_buffers_sizes[0], - &input_data.at(0) + channel_buffers_sizes[0] + +channel_buffers_sizes[1]}; + &input_data.at(0) + channel_buffers_sizes[0] + +channel_buffers_sizes[1] + }; WINML_EXPECT_HRESULT_SUCCEEDED(binding->bind_as_references( input_name, diff --git a/winml/test/api/RawApiTestsGpu.cpp b/winml/test/api/RawApiTestsGpu.cpp index 9c1c06a01603f..f12ba0f36cebf 100644 --- a/winml/test/api/RawApiTestsGpu.cpp +++ b/winml/test/api/RawApiTestsGpu.cpp @@ -165,7 +165,8 @@ const RawApiTestsGpuApi& getapi() { CreateDirectXMinPowerDevice, Evaluate, EvaluateNoInputCopy, - EvaluateManyBuffers}; + EvaluateManyBuffers + }; if (SkipGpuTests()) { api.CreateDirectXDevice = SkipTest; diff --git a/winml/test/concurrency/ConcurrencyTests.cpp b/winml/test/concurrency/ConcurrencyTests.cpp index 46528ef70d377..404afbf67ea1c 100644 --- a/winml/test/concurrency/ConcurrencyTests.cpp +++ b/winml/test/concurrency/ConcurrencyTests.cpp @@ -141,7 +141,8 @@ void EvalAsyncDifferentBindings() { std::vector evaluation_units(num_units, EvaluationUnit()); std::vector ifvs = { - FileHelpers::LoadImageFeatureValue(L"kitten_224.png"), FileHelpers::LoadImageFeatureValue(L"fish.png")}; + FileHelpers::LoadImageFeatureValue(L"kitten_224.png"), FileHelpers::LoadImageFeatureValue(L"fish.png") + }; // same session, different binding auto model = LearningModel::LoadFromFilePath(FileHelpers::GetModulePath() + L"model.onnx"); @@ -191,7 +192,8 @@ void MultiThreadMultiSessionOnDevice(const LearningModelDevice& device) { auto path = FileHelpers::GetModulePath() + L"model.onnx"; auto model = LearningModel::LoadFromFilePath(path); std::vector ivfs = { - FileHelpers::LoadImageFeatureValue(L"kitten_224.png"), FileHelpers::LoadImageFeatureValue(L"fish.png")}; + FileHelpers::LoadImageFeatureValue(L"kitten_224.png"), FileHelpers::LoadImageFeatureValue(L"fish.png") + }; std::vector max_indices = { 281, // tabby, tabby cat 0 // tench, Tinca tinca @@ -257,7 +259,8 @@ void MultiThreadSingleSessionOnDevice(const LearningModelDevice& device) { LearningModelSession model_session = nullptr; WINML_EXPECT_NO_THROW(model_session = LearningModelSession(model, device)); std::vector ivfs = { - FileHelpers::LoadImageFeatureValue(L"kitten_224.png"), FileHelpers::LoadImageFeatureValue(L"fish.png")}; + FileHelpers::LoadImageFeatureValue(L"kitten_224.png"), FileHelpers::LoadImageFeatureValue(L"fish.png") + }; std::vector max_indices = { 281, // tabby, tabby cat 0 // tench, Tinca tinca @@ -322,7 +325,8 @@ const ConcurrencyTestsApi& getapi() { MultiThreadSingleSessionGpu, EvalAsyncDifferentModels, EvalAsyncDifferentSessions, - EvalAsyncDifferentBindings}; + EvalAsyncDifferentBindings + }; if (SkipGpuTests()) { api.MultiThreadMultiSessionGpu = SkipTest; diff --git a/winml/test/image/imageTestHelper.cpp b/winml/test/image/imageTestHelper.cpp index b7c1eb42965f8..91eed2a807782 100644 --- a/winml/test/image/imageTestHelper.cpp +++ b/winml/test/image/imageTestHelper.cpp @@ -148,7 +148,8 @@ TensorFloat LoadInputImageFromGPU(SoftwareBitmap softwareBitmap, const std::wstr // 3 is number of channels we use. R G B without alpha. UINT64 bufferbytesize = 3 * sizeof(float) * softwareBitmap.PixelWidth() * softwareBitmap.PixelHeight(); D3D12_HEAP_PROPERTIES heapProperties = { - D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0}; + D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0 + }; D3D12_RESOURCE_DESC resourceDesc = { D3D12_RESOURCE_DIMENSION_BUFFER, 0, diff --git a/winml/test/image/imagetests.cpp b/winml/test/image/imagetests.cpp index 6157520ca96a3..2251954c59e4c 100644 --- a/winml/test/image/imagetests.cpp +++ b/winml/test/image/imagetests.cpp @@ -939,7 +939,8 @@ TEST_F(ImageTests, ImageBindingAsGPUTensor) { UINT64 buffer_byte_size = static_cast(software_bitmap.PixelWidth()) * software_bitmap.PixelHeight() * 3 * sizeof(float); D3D12_HEAP_PROPERTIES heap_properties = { - D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0}; + D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0 + }; D3D12_RESOURCE_DESC resource_desc = { D3D12_RESOURCE_DIMENSION_BUFFER, 0, diff --git a/winml/test/model/model_tests.cpp b/winml/test/model/model_tests.cpp index b2dd331ccef2c..d4cb664c9e564 100644 --- a/winml/test/model/model_tests.cpp +++ b/winml/test/model/model_tests.cpp @@ -231,7 +231,8 @@ static std::vector GetAllTestCases() { ORT_TSTR("tf_resnet_v2_152"), ORT_TSTR("vgg19"), ORT_TSTR("yolov3"), - ORT_TSTR("zfnet512")}; + ORT_TSTR("zfnet512") + }; allDisabledTests.insert(std::begin(x86DisabledTests), std::end(x86DisabledTests)); #endif // Bad onnx test output caused by previously wrong SAME_UPPER/SAME_LOWER for ConvTranspose diff --git a/winml/test/scenario/cppwinrt/CustomNullOp.h b/winml/test/scenario/cppwinrt/CustomNullOp.h index 33709c5f72d3c..b50909548a6bf 100644 --- a/winml/test/scenario/cppwinrt/CustomNullOp.h +++ b/winml/test/scenario/cppwinrt/CustomNullOp.h @@ -69,7 +69,8 @@ struct NullOperatorFactory : winrt::implements allowedEdges{ CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Double), CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float), - CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float16)}; + CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float16) + }; typeConstraint.allowedTypes = allowedEdges.data(); typeConstraint.allowedTypeCount = static_cast(allowedEdges.size()); diff --git a/winml/test/scenario/cppwinrt/CustomOps.cpp b/winml/test/scenario/cppwinrt/CustomOps.cpp index 075bf5ed877a3..58d0fe6e64efc 100644 --- a/winml/test/scenario/cppwinrt/CustomOps.cpp +++ b/winml/test/scenario/cppwinrt/CustomOps.cpp @@ -305,7 +305,8 @@ static void CustomKernelWithBuiltInSchema() { // Register the kernel MLOperatorEdgeDescription floatTensorType = { - MLOperatorEdgeType::Tensor, static_cast(MLOperatorTensorDataType::Float)}; + MLOperatorEdgeType::Tensor, static_cast(MLOperatorTensorDataType::Float) + }; MLOperatorEdgeTypeConstrant constraint = {"T", &floatTensorType, 1}; @@ -318,7 +319,8 @@ static void CustomKernelWithBuiltInSchema() { 1, nullptr, 0, - MLOperatorKernelOptions::AllowDynamicInputShapes}; + MLOperatorKernelOptions::AllowDynamicInputShapes + }; Microsoft::WRL::ComPtr factory = wil::MakeOrThrow(CreateABIFooKernel); @@ -614,7 +616,8 @@ static void CustomKernelWithCustomSchema() { MLOperatorEdgeTypeConstrant kernelConstraint = {"T1", &floatTensorEdgeDesc, 1}; MLOperatorKernelDescription kernelDesc = { - "", "Foo", 7, MLOperatorExecutionType::Cpu, &kernelConstraint, testCases[caseIndex].useTypeLabel ? 1u : 0u}; + "", "Foo", 7, MLOperatorExecutionType::Cpu, &kernelConstraint, testCases[caseIndex].useTypeLabel ? 1u : 0u + }; if (!testCases[caseIndex].attributeDefaultsInSchema) { kernelDesc.defaultAttributes = defaultAttributes; @@ -693,10 +696,8 @@ static void CustomKernelWithCustomSchema() { const CustomOpsTestsApi& getapi() { static CustomOpsTestsApi api = { - CustomOpsScenarioTestsClassSetup, - CustomOperatorFusion, - CustomKernelWithBuiltInSchema, - CustomKernelWithCustomSchema}; + CustomOpsScenarioTestsClassSetup, CustomOperatorFusion, CustomKernelWithBuiltInSchema, CustomKernelWithCustomSchema + }; if (SkipGpuTests()) { api.CustomOperatorFusion = SkipTest; diff --git a/winml/test/scenario/cppwinrt/NoisyReluCpu.h b/winml/test/scenario/cppwinrt/NoisyReluCpu.h index 5f89b20beebb9..5cccbae67407c 100644 --- a/winml/test/scenario/cppwinrt/NoisyReluCpu.h +++ b/winml/test/scenario/cppwinrt/NoisyReluCpu.h @@ -157,7 +157,8 @@ struct NoisyReluOperatorFactory : winrt::implements allowedEdges{ CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Double), CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float), - CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float16)}; + CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float16) + }; typeConstraint.allowedTypes = allowedEdges.data(); typeConstraint.allowedTypeCount = static_cast(allowedEdges.size()); @@ -194,7 +195,8 @@ struct NoisyReluOperatorFactory : winrt::implements attributeDefaultValues{ - noisyReluMeanAttributeValue, noisyReluVarianceAttributeValue}; + noisyReluMeanAttributeValue, noisyReluVarianceAttributeValue + }; noisyReluSchema.defaultAttributes = attributeDefaultValues.data(); noisyReluSchema.defaultAttributeCount = static_cast(attributeDefaultValues.size()); @@ -216,7 +218,8 @@ struct NoisyReluOperatorFactory : winrt::implements allowedEdges{ CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Double), CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float), - CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float16)}; + CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float16) + }; typeConstraint.allowedTypes = allowedEdges.data(); typeConstraint.allowedTypeCount = static_cast(allowedEdges.size()); @@ -239,7 +242,8 @@ struct NoisyReluOperatorFactory : winrt::implements attributeDefaultValues{ - noisyReluMeanAttributeValue, noisyReluVarianceAttributeValue}; + noisyReluMeanAttributeValue, noisyReluVarianceAttributeValue + }; kernelDescription.defaultAttributes = attributeDefaultValues.data(); kernelDescription.defaultAttributeCount = static_cast(attributeDefaultValues.size()); kernelDescription.options = MLOperatorKernelOptions::None; diff --git a/winml/test/scenario/cppwinrt/ReluCpu.h b/winml/test/scenario/cppwinrt/ReluCpu.h index c72285a4de7fb..7bb275f7b399b 100644 --- a/winml/test/scenario/cppwinrt/ReluCpu.h +++ b/winml/test/scenario/cppwinrt/ReluCpu.h @@ -114,7 +114,8 @@ struct ReluOperatorFactory : winrt::implements allowedEdges{ CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Double), CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float), - CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float16)}; + CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float16) + }; typeConstraint.allowedTypes = allowedEdges.data(); typeConstraint.allowedTypeCount = static_cast(allowedEdges.size()); diff --git a/winml/test/scenario/cppwinrt/scenariotestscppwinrt.cpp b/winml/test/scenario/cppwinrt/scenariotestscppwinrt.cpp index 9b389d014c953..9a03172340bf7 100644 --- a/winml/test/scenario/cppwinrt/scenariotestscppwinrt.cpp +++ b/winml/test/scenario/cppwinrt/scenariotestscppwinrt.cpp @@ -510,7 +510,8 @@ static void Scenario9LoadBindEvalInputTensorGPU() { UINT64 bufferbytesize = 720 * 720 * 3 * sizeof(float); D3D12_HEAP_PROPERTIES heapProperties = { - D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0}; + D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0 + }; D3D12_RESOURCE_DESC resourceDesc = { D3D12_RESOURCE_DIMENSION_BUFFER, 0, @@ -983,7 +984,8 @@ static void Scenario22ImageBindingAsGPUTensor() { // 3 is number of channels we use. R G B without alpha. UINT64 bufferbytesize = 3 * sizeof(float) * softwareBitmap.PixelWidth() * softwareBitmap.PixelHeight(); D3D12_HEAP_PROPERTIES heapProperties = { - D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0}; + D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0 + }; D3D12_RESOURCE_DESC resourceDesc = { D3D12_RESOURCE_DIMENSION_BUFFER, 0, @@ -1085,7 +1087,8 @@ static void Scenario23NominalPixelRange() { std::vector modelPaths = {// Normalized_0_1 and image output modulePath + L"Add_ImageNet1920WithImageMetadataBgr8_SRGB_0_1.onnx", // Normalized_1_1 and image output - modulePath + L"Add_ImageNet1920WithImageMetadataBgr8_SRGB_1_1.onnx"}; + modulePath + L"Add_ImageNet1920WithImageMetadataBgr8_SRGB_1_1.onnx" + }; for (uint32_t model_i = 0; model_i < modelPaths.size(); model_i++) { // load model and create session