update v0.9.2

tests/lightning_logs/version_0/checkpoints/epoch=0-step=32.ckpt/latest

+checkpoint
\ No newline at end of file

tests/lightning_logs/version_0/checkpoints/epoch=0-step=32.ckpt/zero_to_fp32.py

+#!/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 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 . pytorch_model.bin
+
+import argparse
+import torch
+import glob
+import math
+import os
+import re
+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_dicts.append(torch.load(f, map_location=device))
+
+    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):
+    """
+    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)
+    elif zero_stage == 3:
+        return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states)
+
+
+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 _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()
+            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):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    _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 param_shapes.items():
+
+        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):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    _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):
+    """
+    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``
+
+    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)
+
+
+def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None):
+    """
+    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_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
+        - ``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``
+    """
+
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+    print(f"Saving fp32 state dict to {output_file}")
+    torch.save(state_dict, output_file)
+
+
+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_file",
+        type=str,
+        help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)")
+    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_file)

tests/lightning_logs/version_0/hparams.yaml

+{}

tests/model/BingBertSquad/BingBertSquad_run_func_test.py

-# Copyright (c) 2019, The Microsoft DeepSpeed Team. All rights reserved.
-#
-# Note: please copy webtext data to "Megatron-LM" folder, before running this script.
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+"""
+Note: please copy webtext data to "Megatron-LM" folder, before running this script.
+"""
 
 import unittest
 import os
@@ -28,6 +32,7 @@ def grep_loss_from_file(file_name):
 
 
 class BingBertSquadFuncTestCase(BaseTestCase):
+
     def __init__(self, methodName="DeepSpeed function test on BingBertSquad model"):
         super(BingBertSquadFuncTestCase, self).__init__(methodName)
 
@@ -112,8 +117,7 @@ class BingBertSquadFuncTestCase(BaseTestCase):
         prefix = "BingBertSquad_func"
 
         test_config['other_args'] += f" --max_steps {test_config['max_steps']}"
-        test_config[
-            'other_args'] += f" --max_steps_per_epoch {test_config['max_epoch_steps']}"
+        test_config['other_args'] += f" --max_steps_per_epoch {test_config['max_epoch_steps']}"
 
         # baseline run...
         test_config["deepspeed"] = False

tests/model/BingBertSquad/BingBertSquad_test_common.py

-# Copyright (c) 2019, The Microsoft DeepSpeed Team. All rights reserved.
-#
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
 
 import unittest
 import subprocess
@@ -8,6 +10,7 @@ import time
 
 
 class BaseTestCase(unittest.TestCase):
+
     def __init__(self, methodName="DeepSpeed performance test"):
         super(BaseTestCase, self).__init__(methodName)
         self.test_dir = "./test"
@@ -23,10 +26,7 @@ class BaseTestCase(unittest.TestCase):
             other_args = "_" + other_args
 
         if test_config["deepspeed"]:
-            file_name = "_gpu{0}_{1}_ds{2}-{3}.log".format(test_config["gpus"],
-                                                           other_args,
-                                                           zero_args,
-                                                           self.timestr)
+            file_name = "_gpu{0}_{1}_ds{2}-{3}.log".format(test_config["gpus"], other_args, zero_args, self.timestr)
             save_dir = self.test_dir
         else:
             file_name = "_gpu{0}_{1}.log".format(test_config["gpus"], other_args)
@@ -46,22 +46,12 @@ class BaseTestCase(unittest.TestCase):
         time.sleep(20)
 
     def run_BingBertSquad_test(self, test_config, output):
-        ds_flag = " -d --deepspeed_config " + test_config["json"] if test_config[
-            "deepspeed"] else " "
-        other_args = " " + test_config[
-            "other_args"] if "other_args" in test_config else " "
+        ds_flag = " -d --deepspeed_config " + test_config["json"] if test_config["deepspeed"] else " "
+        other_args = " " + test_config["other_args"] if "other_args" in test_config else " "
 
-        cmd = "./run_BingBertSquad_sanity.sh -e 1 -g {0} {1} {2}".format(
-            test_config["gpus"],
-            other_args,
-            ds_flag)
+        cmd = "./run_BingBertSquad_sanity.sh -e 1 -g {0} {1} {2}".format(test_config["gpus"], other_args, ds_flag)
 
         self.ensure_directory_exists(output)
         with open(output, "w") as f:
             print(cmd)
-            subprocess.run(cmd,
-                           shell=True,
-                           check=False,
-                           executable='/bin/bash',
-                           stdout=f,
-                           stderr=f)
+            subprocess.run(cmd, shell=True, check=False, executable='/bin/bash', stdout=f, stderr=f)

tests/model/BingBertSquad/__init__.py

-# Copyright (c) 2019, The Microsoft DeepSpeed Team. All rights reserved.
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
 
 from .BingBertSquad_run_func_test import BingBertSquadFuncTestCase
 from .BingBertSquad_run_func_test import suite

tests/model/BingBertSquad/test_e2e_squad.py

-'''Copyright The Microsoft DeepSpeed Team'''
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
 
 import subprocess as sp
 import os
@@ -63,16 +66,7 @@ def test_e2e_squad_deepspeed_base(tmpdir):
     output_dir = os.path.join(tmpdir, "output")
     pred_file = os.path.join(output_dir, pred_file_name)
 
-    proc = sp.Popen([
-        "bash",
-        script_file_name,
-        num_gpus,
-        model_file,
-        squad_dir,
-        output_dir,
-        config_file
-    ],
-                    cwd=base_dir)
+    proc = sp.Popen(["bash", script_file_name, num_gpus, model_file, squad_dir, output_dir, config_file], cwd=base_dir)
 
     try:
         proc.communicate(timeout=timeout_sec)
@@ -82,9 +76,7 @@ def test_e2e_squad_deepspeed_base(tmpdir):
 
             print("evaluation result: ", json.dumps(eval_result))
 
-            assert isclose(eval_result["exact_match"],
-                           expected_exact_match,
-                           abs_tol=1e-2)
+            assert isclose(eval_result["exact_match"], expected_exact_match, abs_tol=1e-2)
             assert isclose(eval_result["f1"], expected_f1, abs_tol=1e-2)
 
         else:
@@ -110,16 +102,7 @@ def test_e2e_squad_deepspeed_zero(tmpdir):
     output_dir = os.path.join(tmpdir, "output")
     pred_file = os.path.join(output_dir, pred_file_name)
 
-    proc = sp.Popen([
-        "bash",
-        script_file_name,
-        num_gpus,
-        model_file,
-        squad_dir,
-        output_dir,
-        config_file
-    ],
-                    cwd=base_dir)
+    proc = sp.Popen(["bash", script_file_name, num_gpus, model_file, squad_dir, output_dir, config_file], cwd=base_dir)
 
     try:
         proc.communicate(timeout=timeout_sec)
@@ -129,9 +112,7 @@ def test_e2e_squad_deepspeed_zero(tmpdir):
 
             print("evaluation result: ", json.dumps(eval_result))
 
-            assert isclose(eval_result["exact_match"],
-                           expected_exact_match,
-                           abs_tol=1e-2)
+            assert isclose(eval_result["exact_match"], expected_exact_match, abs_tol=1e-2)
             assert isclose(eval_result["f1"], expected_f1, abs_tol=1e-2)
 
         else:

tests/model/Megatron_GPT2/__init__.py

-# Copyright (c) 2019, The Microsoft DeepSpeed Team. All rights reserved.
-#
-# Note: please copy webtext data to "Megatron-LM" folder, before running this script.
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+"""
+Note: please copy webtext data to "Megatron-LM" folder, before running this script.
+"""
 
 from .run_func_test import GPT2FuncTestCase
 from .run_checkpoint_test import GPT2CheckpointTestCase, checkpoint_suite

tests/model/Megatron_GPT2/run_checkpoint_test.py

-# Copyright (c) 2019, The Microsoft DeepSpeed Team. All rights reserved.
-#
-# Note: please copy webtext data to "Megatron-LM" folder, before running this script.
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+"""
+Note: please copy webtext data to "Megatron-LM" folder, before running this script.
+"""
 
 import unittest
 import subprocess
@@ -39,6 +43,7 @@ def grep_loss_from_file(file_name):
 
 
 class GPT2CheckpointTestCase(BaseTestCase):
+
     def __init__(self, methodName="DeepSpeed function test on GPT2 model"):
         super(GPT2CheckpointTestCase, self).__init__(methodName)
 
@@ -480,8 +485,7 @@ class GPT2CheckpointTestCase(BaseTestCase):
         #-----------------Loading Checkpoint-----------------#
 
         # building checkpoint arguments
-        test_config[
-            "other_args"] = f"\"--load {checkpoint_folder} {cpu_optimizer_flag} \""
+        test_config["other_args"] = f"\"--load {checkpoint_folder} {cpu_optimizer_flag} \""
 
         # set checkpoint load iteration
         try:
@@ -543,24 +547,20 @@ def checkpoint_suite():
     # Shrink DP
     suite.addTest(GPT2CheckpointTestCase('test_mp1_gpu2_load_gpu1_node1_with_zero1'))
     suite.addTest(GPT2CheckpointTestCase('test_mp1_gpu2_load_gpu1_node1_with_zero2'))
-    suite.addTest(
-        GPT2CheckpointTestCase('test_mp1_gpu2_load_gpu1_node1_with_zero2_offload'))
+    suite.addTest(GPT2CheckpointTestCase('test_mp1_gpu2_load_gpu1_node1_with_zero2_offload'))
 
     suite.addTest(GPT2CheckpointTestCase('test_mp2_gpu4_load_gpu2_node1_with_zero1'))
     suite.addTest(GPT2CheckpointTestCase('test_mp2_gpu4_load_gpu2_node1_with_zero2'))
-    suite.addTest(
-        GPT2CheckpointTestCase('test_mp2_gpu4_load_gpu2_node1_with_zero2_offload'))
+    suite.addTest(GPT2CheckpointTestCase('test_mp2_gpu4_load_gpu2_node1_with_zero2_offload'))
 
     # Expand DP
     suite.addTest(GPT2CheckpointTestCase('test_mp1_gpu2_load_gpu4_node1_with_zero1'))
     suite.addTest(GPT2CheckpointTestCase('test_mp1_gpu2_load_gpu4_node1_with_zero2'))
-    suite.addTest(
-        GPT2CheckpointTestCase('test_mp1_gpu2_load_gpu4_node1_with_zero2_offload'))
+    suite.addTest(GPT2CheckpointTestCase('test_mp1_gpu2_load_gpu4_node1_with_zero2_offload'))
 
     suite.addTest(GPT2CheckpointTestCase('test_mp2_gpu2_load_gpu4_node1_with_zero1'))
     suite.addTest(GPT2CheckpointTestCase('test_mp2_gpu2_load_gpu4_node1_with_zero2'))
-    suite.addTest(
-        GPT2CheckpointTestCase('test_mp2_gpu2_load_gpu4_node1_with_zero2_offload'))
+    suite.addTest(GPT2CheckpointTestCase('test_mp2_gpu2_load_gpu4_node1_with_zero2_offload'))
 
     suite.addTest(GPT2CheckpointTestCase('test_mp2_gpu4_node1_without_zero'))
 

tests/model/Megatron_GPT2/run_func_test.py

-# Copyright (c) 2019, The Microsoft DeepSpeed Team. All rights reserved.
-#
-# Note: please copy webtext data to "Megatron-LM" folder, before running this script.
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+"""
+Note: please copy webtext data to "Megatron-LM" folder, before running this script.
+"""
 
 import unittest
 import os
@@ -34,6 +38,7 @@ def grep_loss_from_file(file_name):
 
 
 class GPT2FuncTestCase(BaseTestCase):
+
     def __init__(self, methodName="DeepSpeed function test on GPT2 model"):
         super(GPT2FuncTestCase, self).__init__(methodName)
 
@@ -454,9 +459,7 @@ class GPT2FuncTestCase(BaseTestCase):
             baseline_deepspeed_config = True
 
         test_config["other_args"] = f"\"{cpu_optimizer_flag}\""
-        base_file = self.gen_output_name(test_config,
-                                         baseline_prefix,
-                                         baseline_config=baseline_deepspeed_config)
+        base_file = self.gen_output_name(test_config, baseline_prefix, baseline_config=baseline_deepspeed_config)
 
         # skip baseline run if it exists.
         if not self.has_loss_data(base_file):
@@ -468,8 +471,7 @@ class GPT2FuncTestCase(BaseTestCase):
         # DeepSpeed run...
         test_config["deepspeed"] = True
         cpu_optimizer_flag = self.gen_cpu_optimizer_flag(test_config, False)
-        test_config[
-            "other_args"] = f"\"--deepspeed-activation-checkpointing {cpu_optimizer_flag}\""
+        test_config["other_args"] = f"\"--deepspeed-activation-checkpointing {cpu_optimizer_flag}\""
         test_config["json"] = deepspeed_config
 
         print("{0}: DeepSpeed run.".format(self.id()))
@@ -502,9 +504,7 @@ class GPT2FuncTestCase(BaseTestCase):
         test_config["other_args"] = f"\"{cpu_optimizer_flag}\""
 
         # baseline run...
-        base_file = self.gen_output_name(test_config,
-                                         baseline_prefix,
-                                         baseline_config=baseline_deepspeed_config)
+        base_file = self.gen_output_name(test_config, baseline_prefix, baseline_config=baseline_deepspeed_config)
 
         # skip baseline run if it exists.
         if not self.has_loss_data(base_file):

tests/model/Megatron_GPT2/run_perf_baseline.py

-# Copyright (c) 2019, The Microsoft DeepSpeed Team. All rights reserved.
-#
-# Note: please copy webtext data to "Megatron-LM" folder, before running this script.
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+"""
+Note: please copy webtext data to "Megatron-LM" folder, before running this script.
+"""
 
 import unittest
 import re
@@ -8,6 +12,7 @@ from test_common import BaseTestCase
 
 
 class GPT2PerfBaselineTestCase(BaseTestCase):
+
     def __init__(self, methodName="DeepSpeed performance test on GPT2 model"):
         super(GPT2PerfBaselineTestCase, self).__init__(methodName)
 
@@ -88,9 +93,7 @@ class GPT2PerfBaselineTestCase(BaseTestCase):
         if exec_time == 0.0:
             print("{0}: no latency found in file {1}".format(self.id(), test_file))
         else:
-            print("{0}: execution time per iteration is {1}ms.".format(
-                self.id(),
-                exec_time))
+            print("{0}: execution time per iteration is {1}ms.".format(self.id(), exec_time))
 
     def grep_latency_from_file(self, file_name):
         latency = 0.0
@@ -99,9 +102,7 @@ class GPT2PerfBaselineTestCase(BaseTestCase):
         with open(file_name, 'r') as f:
             lines = f.readlines()
             line_filter = "elapsed time per iteration"
-            match_number = re.compile(
-                r'elapsed time per iteration \(ms\): ([-+]?[0-9]+\.?[0-9]*(?:[Ee][-+]?[0-9]+)?)'
-            )
+            match_number = re.compile(r'elapsed time per iteration \(ms\): ([-+]?[0-9]+\.?[0-9]*(?:[Ee][-+]?[0-9]+)?)')
 
             for line in lines:
                 if line_filter in line: