#!/usr/bin/env python # Copyright (c) Microsoft Corporation. # SPDX-License-Identifier: Apache-2.0 # DeepSpeed Team # This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets # copied into the top level checkpoint dir, so the user can easily do the conversion at any point in # the future. Once extracted, the weights don't require DeepSpeed and can be used in any # application. # # example: # python zero_to_fp32.py . output_dir/ # or # python zero_to_fp32.py . output_dir/ --safe_serialization import argparse import torch import glob import math import os import re import json from tqdm import tqdm from collections import OrderedDict from dataclasses import dataclass # while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with # DeepSpeed data structures it has to be available in the current python environment. from deepspeed.utils import logger from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS, FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES, FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS) @dataclass class zero_model_state: buffers: dict() param_shapes: dict() shared_params: list ds_version: int frozen_param_shapes: dict() frozen_param_fragments: dict() debug = 0 # load to cpu device = torch.device('cpu') def atoi(text): return int(text) if text.isdigit() else text def natural_keys(text): ''' alist.sort(key=natural_keys) sorts in human order http://nedbatchelder.com/blog/200712/human_sorting.html (See Toothy's implementation in the comments) ''' return [atoi(c) for c in re.split(r'(\d+)', text)] def get_model_state_file(checkpoint_dir, zero_stage): if not os.path.isdir(checkpoint_dir): raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist") # there should be only one file if zero_stage <= 2: file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt") elif zero_stage == 3: file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt") if not os.path.exists(file): raise FileNotFoundError(f"can't find model states file at '{file}'") return file def get_checkpoint_files(checkpoint_dir, glob_pattern): # XXX: need to test that this simple glob rule works for multi-node setup too ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys) if len(ckpt_files) == 0: raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'") return ckpt_files def get_optim_files(checkpoint_dir): return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt") def get_model_state_files(checkpoint_dir): return get_checkpoint_files(checkpoint_dir, "*_model_states.pt") def parse_model_states(files): zero_model_states = [] for file in files: state_dict = torch.load(file, map_location=device) if BUFFER_NAMES not in state_dict: raise ValueError(f"{file} is not a model state checkpoint") buffer_names = state_dict[BUFFER_NAMES] if debug: print("Found buffers:", buffer_names) # recover just the buffers while restoring them to fp32 if they were saved in fp16 buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names} param_shapes = state_dict[PARAM_SHAPES] # collect parameters that are included in param_shapes param_names = [] for s in param_shapes: for name in s.keys(): param_names.append(name) # update with frozen parameters frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None) if frozen_param_shapes is not None: if debug: print(f"Found frozen_param_shapes: {frozen_param_shapes}") param_names += list(frozen_param_shapes.keys()) # handle shared params shared_params = [[k, v] for k, v in state_dict["shared_params"].items()] ds_version = state_dict.get(DS_VERSION, None) frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None) z_model_state = zero_model_state(buffers=buffers, param_shapes=param_shapes, shared_params=shared_params, ds_version=ds_version, frozen_param_shapes=frozen_param_shapes, frozen_param_fragments=frozen_param_fragments) zero_model_states.append(z_model_state) return zero_model_states def parse_optim_states(files, ds_checkpoint_dir): total_files = len(files) state_dicts = [] for f in files: state_dict = torch.load(f, map_location=device) # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights # and also handle the case where it was already removed by another helper script state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None) state_dicts.append(state_dict) if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]: raise ValueError(f"{files[0]} is not a zero checkpoint") zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE] world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT] # For ZeRO-2 each param group can have different partition_count as data parallelism for expert # parameters can be different from data parallelism for non-expert parameters. So we can just # use the max of the partition_count to get the dp world_size. if type(world_size) is list: world_size = max(world_size) if world_size != total_files: raise ValueError( f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. " "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes." ) # the groups are named differently in each stage if zero_stage <= 2: fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS elif zero_stage == 3: fp32_groups_key = FP32_FLAT_GROUPS else: raise ValueError(f"unknown zero stage {zero_stage}") if zero_stage <= 2: fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))] elif zero_stage == 3: # if there is more than one param group, there will be multiple flattened tensors - one # flattened tensor per group - for simplicity merge them into a single tensor # # XXX: could make the script more memory efficient for when there are multiple groups - it # will require matching the sub-lists of param_shapes for each param group flattened tensor fp32_flat_groups = [ torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts)) ] return zero_stage, world_size, fp32_flat_groups def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters): """ Returns fp32 state_dict reconstructed from ds checkpoint Args: - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are) """ print(f"Processing zero checkpoint '{ds_checkpoint_dir}'") optim_files = get_optim_files(ds_checkpoint_dir) zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir) print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}") model_files = get_model_state_files(ds_checkpoint_dir) zero_model_states = parse_model_states(model_files) print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}') if zero_stage <= 2: return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states, exclude_frozen_parameters) elif zero_stage == 3: return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states, exclude_frozen_parameters) def _zero2_merge_frozen_params(state_dict, zero_model_states): if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: return frozen_param_shapes = zero_model_states[0].frozen_param_shapes frozen_param_fragments = zero_model_states[0].frozen_param_fragments if debug: num_elem = sum(s.numel() for s in frozen_param_shapes.values()) print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') wanted_params = len(frozen_param_shapes) wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) avail_numel = sum([p.numel() for p in frozen_param_fragments.values()]) print(f'Frozen params: Have {avail_numel} numels to process.') print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') total_params = 0 total_numel = 0 for name, shape in frozen_param_shapes.items(): total_params += 1 unpartitioned_numel = shape.numel() total_numel += unpartitioned_numel state_dict[name] = frozen_param_fragments[name] if debug: print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") def _has_callable(obj, fn): attr = getattr(obj, fn, None) return callable(attr) def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): param_shapes = zero_model_states[0].param_shapes # Reconstruction protocol: # # XXX: document this if debug: for i in range(world_size): for j in range(len(fp32_flat_groups[0])): print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}") # XXX: memory usage doubles here (zero2) num_param_groups = len(fp32_flat_groups[0]) merged_single_partition_of_fp32_groups = [] for i in range(num_param_groups): merged_partitions = [sd[i] for sd in fp32_flat_groups] full_single_fp32_vector = torch.cat(merged_partitions, 0) merged_single_partition_of_fp32_groups.append(full_single_fp32_vector) avail_numel = sum( [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups]) if debug: wanted_params = sum([len(shapes) for shapes in param_shapes]) wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes]) # not asserting if there is a mismatch due to possible padding print(f"Have {avail_numel} numels to process.") print(f"Need {wanted_numel} numels in {wanted_params} params.") # params # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support # out-of-core computing solution total_numel = 0 total_params = 0 for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups): offset = 0 avail_numel = full_single_fp32_vector.numel() for name, shape in shapes.items(): unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape) total_numel += unpartitioned_numel total_params += 1 if debug: print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape) offset += unpartitioned_numel # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex # paddings performed in the code it's almost impossible to predict the exact numbers w/o the # live optimizer object, so we are checking that the numbers are within the right range align_to = 2 * world_size def zero2_align(x): return align_to * math.ceil(x / align_to) if debug: print(f"original offset={offset}, avail_numel={avail_numel}") offset = zero2_align(offset) avail_numel = zero2_align(avail_numel) if debug: print(f"aligned offset={offset}, avail_numel={avail_numel}") # Sanity check if offset != avail_numel: raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements") def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states, exclude_frozen_parameters): state_dict = OrderedDict() # buffers buffers = zero_model_states[0].buffers state_dict.update(buffers) if debug: print(f"added {len(buffers)} buffers") if not exclude_frozen_parameters: _zero2_merge_frozen_params(state_dict, zero_model_states) _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) # recover shared parameters for pair in zero_model_states[0].shared_params: if pair[1] in state_dict: state_dict[pair[0]] = state_dict[pair[1]] return state_dict def zero3_partitioned_param_info(unpartitioned_numel, world_size): remainder = unpartitioned_numel % world_size padding_numel = (world_size - remainder) if remainder else 0 partitioned_numel = math.ceil(unpartitioned_numel / world_size) return partitioned_numel, padding_numel def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states): if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: return if debug: for i in range(world_size): num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values()) print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') frozen_param_shapes = zero_model_states[0].frozen_param_shapes wanted_params = len(frozen_param_shapes) wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size print(f'Frozen params: Have {avail_numel} numels to process.') print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') total_params = 0 total_numel = 0 for name, shape in zero_model_states[0].frozen_param_shapes.items(): total_params += 1 unpartitioned_numel = shape.numel() total_numel += unpartitioned_numel param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states) state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape) partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) if debug: print( f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" ) print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): param_shapes = zero_model_states[0].param_shapes avail_numel = fp32_flat_groups[0].numel() * world_size # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each # param, re-consolidating each param, while dealing with padding if any # merge list of dicts, preserving order param_shapes = {k: v for d in param_shapes for k, v in d.items()} if debug: for i in range(world_size): print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}") wanted_params = len(param_shapes) wanted_numel = sum(shape.numel() for shape in param_shapes.values()) # not asserting if there is a mismatch due to possible padding avail_numel = fp32_flat_groups[0].numel() * world_size print(f"Trainable params: Have {avail_numel} numels to process.") print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.") # params # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support # out-of-core computing solution offset = 0 total_numel = 0 total_params = 0 for name, shape in tqdm(param_shapes.items(), desc='Gathering Sharded Weights'): unpartitioned_numel = shape.numel() total_numel += unpartitioned_numel total_params += 1 partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) if debug: print( f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" ) # XXX: memory usage doubles here state_dict[name] = torch.cat( tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)), 0).narrow(0, 0, unpartitioned_numel).view(shape) offset += partitioned_numel offset *= world_size # Sanity check if offset != avail_numel: raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements") def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states, exclude_frozen_parameters): state_dict = OrderedDict() # buffers buffers = zero_model_states[0].buffers state_dict.update(buffers) if debug: print(f"added {len(buffers)} buffers") if not exclude_frozen_parameters: _zero3_merge_frozen_params(state_dict, world_size, zero_model_states) _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) # recover shared parameters for pair in zero_model_states[0].shared_params: if pair[1] in state_dict: state_dict[pair[0]] = state_dict[pair[1]] return state_dict def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None, exclude_frozen_parameters=False): """ Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example via a model hub. Args: - ``checkpoint_dir``: path to the desired checkpoint folder - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14`` - ``exclude_frozen_parameters``: exclude frozen parameters Returns: - pytorch ``state_dict`` Note: this approach may not work if your application doesn't have sufficient free CPU memory and you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with the checkpoint. A typical usage might be :: from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint # do the training and checkpoint saving state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu model = model.cpu() # move to cpu model.load_state_dict(state_dict) # submit to model hub or save the model to share with others In this example the ``model`` will no longer be usable in the deepspeed context of the same application. i.e. you will need to re-initialize the deepspeed engine, since ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead. """ if tag is None: latest_path = os.path.join(checkpoint_dir, 'latest') if os.path.isfile(latest_path): with open(latest_path, 'r') as fd: tag = fd.read().strip() else: raise ValueError(f"Unable to find 'latest' file at {latest_path}") ds_checkpoint_dir = os.path.join(checkpoint_dir, tag) if not os.path.isdir(ds_checkpoint_dir): raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist") return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters) def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_dir, max_shard_size="5GB", safe_serialization=False, tag=None, exclude_frozen_parameters=False): """ Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed. Args: - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) - ``output_dir``: directory to the pytorch fp32 state_dict output files - ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB - ``safe_serialization``: whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`). - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` - ``exclude_frozen_parameters``: exclude frozen parameters """ # Dependency pre-check if safe_serialization: try: from safetensors.torch import save_file except ImportError: print('If you want to use `safe_serialization`, please `pip install safetensors`') raise if max_shard_size is not None: try: from huggingface_hub import split_torch_state_dict_into_shards except ImportError: print('If you want to use `max_shard_size`, please `pip install huggingface_hub`') raise # Convert zero checkpoint to state_dict state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag, exclude_frozen_parameters) # Shard the model if it is too big. weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin" if max_shard_size is not None: filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors") state_dict_split = split_torch_state_dict_into_shards(state_dict, filename_pattern=filename_pattern, max_shard_size=max_shard_size) else: from collections import namedtuple StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"]) state_dict_split = StateDictSplit(is_sharded=False, filename_to_tensors={weights_name: list(state_dict.keys())}) # Save the model filename_to_tensors = state_dict_split.filename_to_tensors.items() for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"): shard = {tensor: state_dict[tensor].contiguous() for tensor in tensors} output_path = os.path.join(output_dir, shard_file) if safe_serialization: save_file(shard, output_path, metadata={"format": "pt"}) else: torch.save(shard, output_path) # Save index if sharded if state_dict_split.is_sharded: index = { "metadata": state_dict_split.metadata, "weight_map": state_dict_split.tensor_to_filename, } save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json" save_index_file = os.path.join(output_dir, save_index_file) with open(save_index_file, "w", encoding="utf-8") as f: content = json.dumps(index, indent=2, sort_keys=True) + "\n" f.write(content) def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None): """ 1. Put the provided model to cpu 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` 3. Load it into the provided model Args: - ``model``: the model object to update - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` Returns: - ``model`: modified model Make sure you have plenty of CPU memory available before you call this function. If you don't have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it conveniently placed for you in the checkpoint folder. A typical usage might be :: from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir) # submit to model hub or save the model to share with others Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context of the same application. i.e. you will need to re-initialize the deepspeed engine, since ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. """ logger.info(f"Extracting fp32 weights") state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag) logger.info(f"Overwriting model with fp32 weights") model = model.cpu() model.load_state_dict(state_dict, strict=False) return model if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("checkpoint_dir", type=str, help="path to the desired checkpoint folder, e.g., path/checkpoint-12") parser.add_argument("output_dir", type=str, help="directory to the pytorch fp32 state_dict output files" "(e.g. path/checkpoint-12-output/)") parser.add_argument( "--max_shard_size", type=str, default="5GB", help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size" "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`" "We default it to 5GB in order for models to be able to run easily on free-tier google colab instances" "without CPU OOM issues.") parser.add_argument( "--safe_serialization", default=False, action='store_true', help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).") parser.add_argument("-t", "--tag", type=str, default=None, help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1") parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters") parser.add_argument("-d", "--debug", action='store_true', help="enable debug") args = parser.parse_args() debug = args.debug convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, args.output_dir, max_shard_size=args.max_shard_size, safe_serialization=args.safe_serialization, tag=args.tag, exclude_frozen_parameters=args.exclude_frozen_parameters)