mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-07-04 04:07:22 +00:00
update transformers required package versions (#6315)
This commit is contained in:
Tianlei Wu
GitHub
parent
c43ca45c4f
commit
a038924bee
6 changed files with 66 additions and 69 deletions
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@ -53,10 +53,6 @@ def create_onnxruntime_session(onnx_model_path,
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if num_threads > 0:
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sess_options.intra_op_num_threads = num_threads
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logger.debug(f"Session option: intra_op_num_threads={sess_options.intra_op_num_threads}")
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elif (not use_gpu) and (version.parse(onnxruntime_version) < version.parse('1.3.0')):
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# Set intra_op_num_threads = 1 to enable OpenMP for onnxruntime 1.2.0 (cpu)
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# onnxruntime-gpu is not built with openmp so it is better to use default (0) or cpu_count instead.
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sess_options.intra_op_num_threads = 1
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if verbose:
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sess_options.log_severity_level = 0
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@ -98,9 +94,10 @@ def prepare_environment(cache_dir, output_dir, use_gpu):
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logger.info(f'Transformers Version:{transformers.__version__}')
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logger.info(f'Onnxruntime Version:{onnxruntime.__version__}')
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# Support three major versions of PyTorch and OnnxRuntime, and up to 6 months of transformers.
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from packaging import version
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assert version.parse(torch.__version__) >= version.parse('1.4.0')
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assert version.parse(transformers.__version__) >= version.parse('2.11.0')
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assert version.parse(torch.__version__) >= version.parse('1.5.0')
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assert version.parse(transformers.__version__) >= version.parse('3.0.0')
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assert version.parse(onnxruntime.__version__) >= version.parse('1.4.0')
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@ -223,8 +220,8 @@ def inference_ort_with_io_binding(ort_session,
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allocateOutputBuffers(output_buffers, output_buffer_max_sizes, device)
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for i in range(len(ort_output_names)):
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io_binding.bind_output(ort_output_names[i], output_buffers[i].device.type, 0, numpy.float32, ort_outputs[i].shape,
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output_buffers[i].data_ptr())
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io_binding.bind_output(ort_output_names[i], output_buffers[i].device.type, 0, numpy.float32,
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ort_outputs[i].shape, output_buffers[i].data_ptr())
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runtimes = timeit.repeat(lambda: ort_session.run_with_iobinding(io_binding), number=1, repeat=repeat_times)
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result.update(result_template)
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result.update({"io_binding": True})
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@ -132,7 +132,6 @@ class BertOnnxModel(OnnxModel):
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new_graph_inputs = []
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casted_bert_graph_inputs = self.get_graph_inputs_from_fused_nodes(casted=True)
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for input in graph.input:
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if input.name in casted_bert_graph_inputs:
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@ -178,13 +177,13 @@ class BertOnnxModel(OnnxModel):
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def preprocess(self):
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self.adjust_reshape_and_expand()
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return
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def adjust_reshape_and_expand(self):
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nodes_to_remove = []
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for node in self.nodes():
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if node.op_type == 'Reshape':
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if node.op_type == 'Reshape':
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# Clean up unneccessary reshape nodes.
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# Find reshape nodes with no actually data in "shape" attribute and remove.
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# Find reshape nodes with no actually data in "shape" attribute and remove.
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reshape_shape = self.get_constant_value(node.input[1])
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if reshape_shape is not None and reshape_shape.size == 0:
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nodes_to_remove.extend([node])
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@ -192,9 +191,9 @@ class BertOnnxModel(OnnxModel):
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continue
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# Find path "Slice" -> "Reshape" -> "Expand" -> "Expand" -> current "Reshape", simplify the graph by
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# changing current reshape's input to output of slice.
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# changing current reshape's input to output of slice.
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reshape_path = self.match_parent_path(node, ['Expand', 'Expand', 'Reshape', 'Slice'], [0, 0, 0, 0],
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self.output_name_to_node())
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self.output_name_to_node())
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if reshape_path is not None:
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expand_node = reshape_path[-3]
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expand_shape_value = self.get_constant_value(expand_node.input[1])
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@ -203,8 +202,9 @@ class BertOnnxModel(OnnxModel):
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shape_value = self.get_constant_value(reshape_before_expand.input[1])
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slice_node = reshape_path[-1]
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if expand_shape_value is not None and shape_value is not None and len(expand_shape_value) is 2 and len(
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shape_value) is 1 and expand_shape_value[1] == shape_value[0]:
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if expand_shape_value is not None and shape_value is not None and len(
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expand_shape_value) is 2 and len(
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shape_value) is 1 and expand_shape_value[1] == shape_value[0]:
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node.input[0] = slice_node.output[0]
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self.remove_nodes(nodes_to_remove)
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logger.info(f"Removed Reshape and Expand count: {len(nodes_to_remove)}")
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@ -340,6 +340,6 @@ class BertOnnxModel(OnnxModel):
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logger.debug("Embed Layer not fused")
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if attention == 0:
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logger.debug("Attention not fused")
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logger.warning("Attention not fused")
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return is_perfect
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@ -214,7 +214,10 @@ def _parse_arguments():
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default=0,
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help="onnxruntime optimization level. 0 will disable onnxruntime.")
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parser.add_argument('--use_external_data_format', required=False, action='store_true', help="use external data format")
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parser.add_argument('--use_external_data_format',
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required=False,
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action='store_true',
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help="use external data format")
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parser.set_defaults(use_external_data_format=False)
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args = parser.parse_args()
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@ -306,8 +309,9 @@ def optimize_model(input,
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os.remove(temp_model_path)
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logger.debug("Remove tempoary model: {}".format(temp_model_path))
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optimizer.model.producer_name = "onnxruntime_tools"
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optimizer.model.producer_version = "1.5.2"
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optimizer.model.producer_name = "onnxruntime.transformers"
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from onnxruntime import __version__ as onnxruntime_version
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optimizer.model.producer_version = onnxruntime_version
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return optimizer
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@ -325,9 +329,7 @@ def main():
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_setup_logger(args.verbose)
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if os.path.realpath(args.input) == os.path.realpath(args.output):
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logger.warning(
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f"Specified the same input and output path. Note that this may overwrite the original model"
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)
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logger.warning(f"Specified the same input and output path. Note that this may overwrite the original model")
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optimization_options = _get_optimization_options(args)
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@ -75,7 +75,7 @@ def parse_arguments(argv=None):
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required=False,
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default='default',
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choices=['bert', 'gpt2', 'longformer', 'default'],
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help="Way to create dummy inputs. If your model is not aa")
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help="Type of dummy inputs. The default will create inputs with ones.")
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parser.add_argument('-g', '--use_gpu', required=False, action='store_true', help="use GPU")
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parser.set_defaults(use_gpu=False)
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@ -96,8 +96,7 @@ def parse_arguments(argv=None):
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parser.add_argument('-v', '--verbose', required=False, action='store_true')
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parser.set_defaults(verbose=False)
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args = parser.parse_args(argv)
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return args
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return parser.parse_args(argv)
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def create_bert_inputs(model, batch_size, sequence_length, samples, input_ids_name, segment_ids_name, input_mask_name):
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@ -116,7 +115,7 @@ def create_bert_inputs(model, batch_size, sequence_length, samples, input_ids_na
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return all_inputs
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def run_profile(onnx_model_path, use_gpu, basic_optimization, thread_num, batch_size, sequence_length, all_inputs):
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def run_profile(onnx_model_path, use_gpu, basic_optimization, thread_num, all_inputs):
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from benchmark_helper import create_onnxruntime_session
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session = create_onnxruntime_session(onnx_model_path,
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@ -135,8 +134,8 @@ def run_profile(onnx_model_path, use_gpu, basic_optimization, thread_num, batch_
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def load_profile_json(profile_file):
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print(f"loading profile output {profile_file} ...")
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with open(profile_file, "r") as f:
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sess_time = json.load(f)
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with open(profile_file, "r") as opened_file:
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sess_time = json.load(opened_file)
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assert isinstance(sess_time, list)
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return sess_time
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@ -167,18 +166,18 @@ def parse_profile_results(sess_time, kernel_time_only=False, threshold=0):
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node_time[item["name"]] = item["dur"]
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total += item["dur"]
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results = []
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lines = []
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if (threshold > 0):
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results.append(f"Threshold of Percentage > {threshold:.2f}%")
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lines.append(f"Threshold of Percentage > {threshold:.2f}%")
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results.append(f"Duration\tPercentage\tProvider\tName")
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lines.append("Duration\tPercentage\tProvider\tName")
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for k, v in sorted(node_time.items(), key=lambda x: x[1], reverse=True):
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provider = node_provider[k] if k in node_provider else ""
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ratio = v / total
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if ratio > threshold:
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results.append(f"{v}\t{ratio * 100.0:5.2f}\t{provider}\t{k}")
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lines.append(f"{v}\t{ratio * 100.0:5.2f}\t{provider}\t{k}")
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return results
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return lines
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def group_profile_results(sess_time, kernel_time_only=False, threshold=0):
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@ -215,15 +214,15 @@ def group_profile_results(sess_time, kernel_time_only=False, threshold=0):
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op_cpu_time[op_name] = item["dur"]
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op_cpu_records[op_name] = 1
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results = [f"Duration\tPercentage\tCalls\tCpu_Duration\tCpu_Calls\tName"]
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lines = ["Duration\tPercentage\tCalls\tCpu_Duration\tCpu_Calls\tName"]
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for k, v in sorted(op_time.items(), key=lambda x: x[1], reverse=True):
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calls = op_records[k]
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cpu_time = op_cpu_time[k] if k in op_cpu_time else 0
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cpu_calls = op_cpu_records[k] if k in op_cpu_records else 0
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ratio = v / total
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if ratio > threshold:
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results.append(f"{v}\t{ratio * 100.0:5.2f}\t{calls}\t{cpu_time}\t{cpu_calls}\t{k}")
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return results
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lines.append(f"{v}\t{ratio * 100.0:5.2f}\t{calls}\t{cpu_time}\t{cpu_calls}\t{k}")
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return lines
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def get_dim_from_type_proto(dim):
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@ -240,11 +239,11 @@ def create_dummy_inputs(onnx_model_path, batch_size, sequence_length, samples):
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onnx_model = OnnxModel(onnx.load(onnx_model_path))
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dummy_inputs = {}
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for input in onnx_model.get_graph_inputs_excluding_initializers():
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shape = get_shape_from_type_proto(input.type)
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for graph_input in onnx_model.get_graph_inputs_excluding_initializers():
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shape = get_shape_from_type_proto(graph_input.type)
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symbol_dims = []
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for i, dim in enumerate(shape):
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if type(dim) == str:
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if isinstance(dim, str):
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symbol_dims.append(i)
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# allowed symbolic dimensions: batch_size and sequence_length
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@ -255,12 +254,12 @@ def create_dummy_inputs(onnx_model_path, batch_size, sequence_length, samples):
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if len(symbol_dims) > 1:
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shape[symbol_dims[1]] = sequence_length
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elem_type = input.type.tensor_type.elem_type
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elem_type = graph_input.type.tensor_type.elem_type
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assert elem_type in [TensorProto.FLOAT, TensorProto.INT32, TensorProto.INT64]
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data_type = numpy.float32 if elem_type == TensorProto.FLOAT else (
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numpy.int64 if elem_type == TensorProto.INT64 else numpy.int32)
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data = numpy.ones(shape, dtype=data_type)
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dummy_inputs[input.name] = data
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dummy_inputs[graph_input.name] = data
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all_inputs = [dummy_inputs for _ in range(samples)]
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return all_inputs
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@ -280,20 +279,20 @@ def create_gpt2_inputs(onnx_model_path, batch_size, sequence_length, past_sequen
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}
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dummy_inputs = {}
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for input in onnx_model.get_graph_inputs_excluding_initializers():
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shape = get_shape_from_type_proto(input.type)
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for graph_input in onnx_model.get_graph_inputs_excluding_initializers():
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shape = get_shape_from_type_proto(graph_input.type)
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for i, dim in enumerate(shape):
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if type(dim) == str and dim not in symbols.keys():
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if isinstance(dim, str) and dim not in symbols.keys():
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raise RuntimeError(f"symbol is not supported: {dim}")
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else:
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shape[i] = symbols[dim]
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elem_type = input.type.tensor_type.elem_type
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elem_type = graph_input.type.tensor_type.elem_type
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assert elem_type in [TensorProto.FLOAT, TensorProto.INT32, TensorProto.INT64]
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data_type = numpy.float32 if elem_type == TensorProto.FLOAT else (
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numpy.int64 if elem_type == TensorProto.INT64 else numpy.int32)
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data = numpy.ones(shape, dtype=data_type)
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dummy_inputs[input.name] = data
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dummy_inputs[graph_input.name] = data
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all_inputs = [dummy_inputs for _ in range(samples)]
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return all_inputs
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@ -307,25 +306,25 @@ def create_longformer_inputs(onnx_model_path, batch_size, sequence_length, globa
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symbols = {'batch_size': batch_size, 'sequence_length': sequence_length}
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dummy_inputs = {}
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for input in onnx_model.get_graph_inputs_excluding_initializers():
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shape = get_shape_from_type_proto(input.type)
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for graph_input in onnx_model.get_graph_inputs_excluding_initializers():
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shape = get_shape_from_type_proto(graph_input.type)
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for i, dim in enumerate(shape):
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if type(dim) == str and dim not in symbols.keys():
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if isinstance(dim, str) and dim not in symbols.keys():
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raise RuntimeError(f"symbol is not supported: {dim}")
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else:
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shape[i] = symbols[dim]
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elem_type = input.type.tensor_type.elem_type
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elem_type = graph_input.type.tensor_type.elem_type
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assert elem_type in [TensorProto.FLOAT, TensorProto.INT32, TensorProto.INT64]
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data_type = numpy.float32 if elem_type == TensorProto.FLOAT else (
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numpy.int64 if elem_type == TensorProto.INT64 else numpy.int32)
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if "global" in input.name:
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if "global" in graph_input.name:
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data = numpy.zeros(shape, dtype=data_type)
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data[:, :global_length] = 1
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else:
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data = numpy.ones(shape, dtype=data_type)
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dummy_inputs[input.name] = data
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dummy_inputs[graph_input.name] = data
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all_inputs = [dummy_inputs for _ in range(samples)]
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return all_inputs
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@ -351,8 +350,7 @@ def run(args):
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else: # default
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all_inputs = create_dummy_inputs(args.model, args.batch_size, args.sequence_length, args.samples)
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profile_file = run_profile(args.model, args.use_gpu, args.basic_optimization, args.thread_num, args.batch_size,
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args.sequence_length, all_inputs)
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profile_file = run_profile(args.model, args.use_gpu, args.basic_optimization, args.thread_num, all_inputs)
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profile_records = load_profile_json(profile_file)
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@ -365,13 +363,13 @@ def run(args):
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if __name__ == '__main__':
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args = parse_arguments()
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print("Arguments", args)
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arguments = parse_arguments()
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print("Arguments", arguments)
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from benchmark_helper import setup_logger
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setup_logger(args.verbose)
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setup_logger(arguments.verbose)
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results = run(args)
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results = run(arguments)
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print("Results:")
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print("-" * 64)
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@ -5,10 +5,10 @@ psutil
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py-cpuinfo
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py3nvml
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packaging
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# Gpt2 and Albert models need a change in transformers (https://github.com/huggingface/transformers/pull/4244) for exporting ONNX models.
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# The change does not exist in 2.9.0 so we install from source instead.
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git+https://github.com/huggingface/transformers.git
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transformers
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onnxruntime
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onnxconverter_common
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--find-links https://download.pytorch.org/whl/torch_stable.html
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torch==1.5.0+cpu
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torchvision==0.6.0+cpu
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torch==1.7.1+cpu
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torchvision==0.8.2+cpu
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torchaudio===0.7.2
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@ -5,10 +5,10 @@ psutil
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py-cpuinfo
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py3nvml
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packaging
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# Gpt2 and Albert models need a change in transformers (https://github.com/huggingface/transformers/pull/4244) for exporting ONNX models.
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# The change does not exist in 2.9.0 so we install from source instead.
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git+https://github.com/huggingface/transformers.git
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transformers
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onnxruntime-gpu
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onnxconverter_common
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--find-links https://download.pytorch.org/whl/torch_stable.html
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torch==1.5.0+cu101
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torchvision==0.6.0+cu101
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torch===1.7.1
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torchvision===0.8.2
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torchaudio===0.7.2
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