diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..c22958f969d9099843503e9a71a4e4d765b0b44d
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+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt
@@ -0,0 +1,3 @@
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+size 8066685992
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..46fd354455b820442f291ee0398a379ca314d044
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+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt
@@ -0,0 +1,3 @@
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..483c8031ad987b762d74dface79dfb655b85286b
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt
@@ -0,0 +1,3 @@
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..787cf946e95e2fff60660c2009780ea884368348
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/global_step50000/mp_rank_00_model_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/global_step50000/mp_rank_00_model_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..a509fe8dea47a4c66b4bbf0c8388eb7dcf05d7cc
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/global_step50000/mp_rank_00_model_states.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/latest b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/latest
new file mode 100644
index 0000000000000000000000000000000000000000..59a558c0ee32a055bd8fc16bf0d99a5950c59ac1
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/latest
@@ -0,0 +1 @@
+global_step50000
\ No newline at end of file
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/zero_to_fp32.py b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/zero_to_fp32.py
new file mode 100644
index 0000000000000000000000000000000000000000..f40f1f0acbbff30e8e8d29906b56aae4e8b8a138
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000/zero_to_fp32.py
@@ -0,0 +1,790 @@
+#!/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 gc
+import json
+import numpy as np
+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, AUTOEP_LAYERS_KEY,
+                                            AUTOEP_LAYERS_KEY_LEGACY, AUTOEP_ZERO3_EXPERT_STATE_FORMAT_KEY,
+                                            AUTOEP_ZERO3_PARTITIONED_EXPERT_STATE_FORMAT)
+
+
+@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 _has_autoep_zero3_partitioned_metadata(state_dict):
+    autoep_layers = state_dict.get(AUTOEP_LAYERS_KEY)
+    if autoep_layers is None:
+        autoep_layers = state_dict.get(AUTOEP_LAYERS_KEY_LEGACY)
+    if not isinstance(autoep_layers, list):
+        return False
+    return any(
+        isinstance(entry, dict)
+        and entry.get(AUTOEP_ZERO3_EXPERT_STATE_FORMAT_KEY) == AUTOEP_ZERO3_PARTITIONED_EXPERT_STATE_FORMAT
+        for entry in autoep_layers)
+
+
+def _raise_if_autoep_zero3_partitioned_state(state_dict):
+    if _has_autoep_zero3_partitioned_metadata(state_dict):
+        raise NotImplementedError("zero_to_fp32 does not support AutoEP ZeRO-3 partition-native checkpoints. "
+                                  "AutoEP expert parameters are partitioned over expert replica groups, so "
+                                  "global data-parallel consolidation would produce incomplete expert tensors. "
+                                  "Use ds_to_universal.py for expert-aware conversion.")
+
+
+def _raise_if_autoep_zero3_partitioned_checkpoint(model_files):
+    for file in model_files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+        _raise_if_autoep_zero3_partitioned_state(state_dict)
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+        _raise_if_autoep_zero3_partitioned_state(state_dict)
+
+        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 tqdm(files, desc='Loading checkpoint shards'):
+        state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
+        # 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 ZERO_STAGE not 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}")
+
+    fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] 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}'")
+
+    # parse_model_states rejects AutoEP ZeRO-3 partition-native checkpoints
+    # before the expensive optimizer-shard load below.
+    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}')
+
+    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}")
+
+    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")
+
+
+class GatheredTensor:
+    """
+    A pseudo tensor that collects partitioned weights.
+    It is more memory efficient when there are multiple groups.
+    """
+
+    def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
+        self.flat_groups = flat_groups
+        self.flat_groups_offset = flat_groups_offset
+        self.offset = offset
+        self.partitioned_numel = partitioned_numel
+        self.shape = shape
+        self.dtype = self.flat_groups[0][0].dtype
+
+    def contiguous(self):
+        """
+        Merge partitioned weights from flat_groups into a single tensor.
+        """
+        end_idx = self.offset + self.partitioned_numel
+        world_size = len(self.flat_groups)
+        pad_flat_param_chunks = []
+
+        for rank_i in range(world_size):
+            # for each rank, we need to collect weights from related group/groups
+            flat_groups_at_rank_i = self.flat_groups[rank_i]
+            start_group_id = None
+            end_group_id = None
+            for group_id in range(len(self.flat_groups_offset)):
+                if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
+                    start_group_id = group_id
+                if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
+                    end_group_id = group_id
+                    break
+            # collect weights from related group/groups
+            for group_id in range(start_group_id, end_group_id + 1):
+                flat_tensor = flat_groups_at_rank_i[group_id]
+                start_offset = self.offset - self.flat_groups_offset[group_id]
+                end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
+                pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
+
+        # collect weights from all ranks
+        pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
+        param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
+        return param
+
+
+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 = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * 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
+    flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[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}"
+            )
+
+        # memory efficient tensor
+        tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
+        state_dict[name] = tensor
+        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 to_torch_tensor(state_dict, return_empty_tensor=False):
+    """
+    Convert state_dict of GatheredTensor to torch tensor
+    """
+    torch_state_dict = {}
+    converted_tensors = {}
+    for name, tensor in state_dict.items():
+        tensor_id = id(tensor)
+        if tensor_id in converted_tensors:  # shared tensors
+            shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
+            torch_state_dict[name] = shared_tensor
+        else:
+            converted_tensors[tensor_id] = name
+            if return_empty_tensor:
+                torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
+            else:
+                torch_state_dict[name] = tensor.contiguous()
+    return torch_state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                             tag=None,
+                                             exclude_frozen_parameters=False,
+                                             lazy_mode=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
+        - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
+          Convert the pesduo tensor to torch tensor by ``.contiguous()``
+
+    Returns:
+        - pytorch ``state_dict``
+
+    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.
+
+    Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
+    You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
+    the checkpoint. Or you can load state_dict in lazy mode ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
+        for name, lazy_tensor in state_dict.item():
+            tensor = lazy_tensor.contiguous()  # to cpu
+            print(name, tensor)
+            # del tensor to release memory if it no longer in use
+    """
+    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")
+
+    state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
+    if lazy_mode:
+        return state_dict
+    else:
+        return to_torch_tensor(state_dict)
+
+
+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,
+                                                          lazy_mode=True)
+
+    # 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")
+        # an memory-efficient approach for sharding
+        empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
+        state_dict_split = split_torch_state_dict_into_shards(empty_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 by shard
+    os.makedirs(output_dir, exist_ok=True)
+    filename_to_tensors = state_dict_split.filename_to_tensors.items()
+    for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
+        shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
+        shard_state_dict = to_torch_tensor(shard_state_dict)
+        output_path = os.path.join(output_dir, shard_file)
+        if safe_serialization:
+            save_file(shard_state_dict, output_path, metadata={"format": "pt"})
+        else:
+            torch.save(shard_state_dict, output_path)
+        # release the memory of current shard
+        for tensor_name in list(shard_state_dict.keys()):
+            del state_dict[tensor_name]
+            del shard_state_dict[tensor_name]
+        del shard_state_dict
+        gc.collect()
+
+    # 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("Extracting fp32 weights")
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+
+    logger.info("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)
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..c22958f969d9099843503e9a71a4e4d765b0b44d
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e26ef621a6f96c9ccd801372a6cd50cc75821cbae86df7af3e1f2606f12c7e65
+size 8066685992
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..46fd354455b820442f291ee0398a379ca314d044
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt
@@ -0,0 +1,3 @@
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..483c8031ad987b762d74dface79dfb655b85286b
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 8066672104
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..787cf946e95e2fff60660c2009780ea884368348
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt
@@ -0,0 +1,3 @@
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+size 8066671208
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/global_step50000/mp_rank_00_model_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/global_step50000/mp_rank_00_model_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..a509fe8dea47a4c66b4bbf0c8388eb7dcf05d7cc
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/global_step50000/mp_rank_00_model_states.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/latest b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/latest
new file mode 100644
index 0000000000000000000000000000000000000000..59a558c0ee32a055bd8fc16bf0d99a5950c59ac1
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/latest
@@ -0,0 +1 @@
+global_step50000
\ No newline at end of file
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/zero_to_fp32.py b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/zero_to_fp32.py
new file mode 100644
index 0000000000000000000000000000000000000000..f40f1f0acbbff30e8e8d29906b56aae4e8b8a138
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final/zero_to_fp32.py
@@ -0,0 +1,790 @@
+#!/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 gc
+import json
+import numpy as np
+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, AUTOEP_LAYERS_KEY,
+                                            AUTOEP_LAYERS_KEY_LEGACY, AUTOEP_ZERO3_EXPERT_STATE_FORMAT_KEY,
+                                            AUTOEP_ZERO3_PARTITIONED_EXPERT_STATE_FORMAT)
+
+
+@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 _has_autoep_zero3_partitioned_metadata(state_dict):
+    autoep_layers = state_dict.get(AUTOEP_LAYERS_KEY)
+    if autoep_layers is None:
+        autoep_layers = state_dict.get(AUTOEP_LAYERS_KEY_LEGACY)
+    if not isinstance(autoep_layers, list):
+        return False
+    return any(
+        isinstance(entry, dict)
+        and entry.get(AUTOEP_ZERO3_EXPERT_STATE_FORMAT_KEY) == AUTOEP_ZERO3_PARTITIONED_EXPERT_STATE_FORMAT
+        for entry in autoep_layers)
+
+
+def _raise_if_autoep_zero3_partitioned_state(state_dict):
+    if _has_autoep_zero3_partitioned_metadata(state_dict):
+        raise NotImplementedError("zero_to_fp32 does not support AutoEP ZeRO-3 partition-native checkpoints. "
+                                  "AutoEP expert parameters are partitioned over expert replica groups, so "
+                                  "global data-parallel consolidation would produce incomplete expert tensors. "
+                                  "Use ds_to_universal.py for expert-aware conversion.")
+
+
+def _raise_if_autoep_zero3_partitioned_checkpoint(model_files):
+    for file in model_files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+        _raise_if_autoep_zero3_partitioned_state(state_dict)
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+        _raise_if_autoep_zero3_partitioned_state(state_dict)
+
+        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 tqdm(files, desc='Loading checkpoint shards'):
+        state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
+        # 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 ZERO_STAGE not 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}")
+
+    fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] 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}'")
+
+    # parse_model_states rejects AutoEP ZeRO-3 partition-native checkpoints
+    # before the expensive optimizer-shard load below.
+    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}')
+
+    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}")
+
+    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")
+
+
+class GatheredTensor:
+    """
+    A pseudo tensor that collects partitioned weights.
+    It is more memory efficient when there are multiple groups.
+    """
+
+    def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
+        self.flat_groups = flat_groups
+        self.flat_groups_offset = flat_groups_offset
+        self.offset = offset
+        self.partitioned_numel = partitioned_numel
+        self.shape = shape
+        self.dtype = self.flat_groups[0][0].dtype
+
+    def contiguous(self):
+        """
+        Merge partitioned weights from flat_groups into a single tensor.
+        """
+        end_idx = self.offset + self.partitioned_numel
+        world_size = len(self.flat_groups)
+        pad_flat_param_chunks = []
+
+        for rank_i in range(world_size):
+            # for each rank, we need to collect weights from related group/groups
+            flat_groups_at_rank_i = self.flat_groups[rank_i]
+            start_group_id = None
+            end_group_id = None
+            for group_id in range(len(self.flat_groups_offset)):
+                if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
+                    start_group_id = group_id
+                if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
+                    end_group_id = group_id
+                    break
+            # collect weights from related group/groups
+            for group_id in range(start_group_id, end_group_id + 1):
+                flat_tensor = flat_groups_at_rank_i[group_id]
+                start_offset = self.offset - self.flat_groups_offset[group_id]
+                end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
+                pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
+
+        # collect weights from all ranks
+        pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
+        param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
+        return param
+
+
+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 = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * 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
+    flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[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}"
+            )
+
+        # memory efficient tensor
+        tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
+        state_dict[name] = tensor
+        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 to_torch_tensor(state_dict, return_empty_tensor=False):
+    """
+    Convert state_dict of GatheredTensor to torch tensor
+    """
+    torch_state_dict = {}
+    converted_tensors = {}
+    for name, tensor in state_dict.items():
+        tensor_id = id(tensor)
+        if tensor_id in converted_tensors:  # shared tensors
+            shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
+            torch_state_dict[name] = shared_tensor
+        else:
+            converted_tensors[tensor_id] = name
+            if return_empty_tensor:
+                torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
+            else:
+                torch_state_dict[name] = tensor.contiguous()
+    return torch_state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                             tag=None,
+                                             exclude_frozen_parameters=False,
+                                             lazy_mode=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
+        - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
+          Convert the pesduo tensor to torch tensor by ``.contiguous()``
+
+    Returns:
+        - pytorch ``state_dict``
+
+    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.
+
+    Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
+    You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
+    the checkpoint. Or you can load state_dict in lazy mode ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
+        for name, lazy_tensor in state_dict.item():
+            tensor = lazy_tensor.contiguous()  # to cpu
+            print(name, tensor)
+            # del tensor to release memory if it no longer in use
+    """
+    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")
+
+    state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
+    if lazy_mode:
+        return state_dict
+    else:
+        return to_torch_tensor(state_dict)
+
+
+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,
+                                                          lazy_mode=True)
+
+    # 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")
+        # an memory-efficient approach for sharding
+        empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
+        state_dict_split = split_torch_state_dict_into_shards(empty_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 by shard
+    os.makedirs(output_dir, exist_ok=True)
+    filename_to_tensors = state_dict_split.filename_to_tensors.items()
+    for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
+        shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
+        shard_state_dict = to_torch_tensor(shard_state_dict)
+        output_path = os.path.join(output_dir, shard_file)
+        if safe_serialization:
+            save_file(shard_state_dict, output_path, metadata={"format": "pt"})
+        else:
+            torch.save(shard_state_dict, output_path)
+        # release the memory of current shard
+        for tensor_name in list(shard_state_dict.keys()):
+            del state_dict[tensor_name]
+            del shard_state_dict[tensor_name]
+        del shard_state_dict
+        gc.collect()
+
+    # 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("Extracting fp32 weights")
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+
+    logger.info("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)
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/train.log b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..f7573aff51f3c7acae9f221c83314dad2903184c
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/train.log
@@ -0,0 +1,5026 @@
+2026-09-14 13:22:24 | INFO | train rank=0 | Starting training project=codebase_v8_droid_joint_50ksteps_bs128_qwen3vl2b_end_padding_mask save_dir=/data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask distributed=True deepspeed=True world_size=4 device=cuda:0
+2026-09-14 13:22:47 | INFO | train rank=0 | No pretrained checkpoint provided; training from scratch
+2026-09-14 13:22:47 | INFO | train rank=0 | Initializing Droid LeRobot Dataset: path=/data/NTU_slab/draven/data/DROID fps=15.00 use_relabelled_instruction=False
+2026-09-14 13:22:47 | INFO | train rank=0 | Batch config: total_batch_size=128 world_size=4 grad_acc_steps=1 per_device_batch_size=32
+2026-09-14 13:22:47 | INFO | train rank=0 | Dataset image_resize_size=320
+2026-09-14 13:22:47 | INFO | train rank=0 | Droid uses parquet-shard streaming with a per-rank shuffle buffer: batch_size=32 buffer_size=1024 num_workers=8 drop_last=True
+2026-09-14 13:38:53 | INFO | train rank=0 | Starting training project=codebase_v8_droid_joint_50ksteps_bs128_qwen3vl2b_end_padding_mask save_dir=/data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask distributed=True deepspeed=True world_size=4 device=cuda:0
+2026-09-14 13:39:14 | INFO | train rank=0 | No pretrained checkpoint provided; training from scratch
+2026-09-14 13:39:14 | INFO | train rank=0 | Initializing Droid LeRobot Dataset: path=/data/NTU_slab/draven/data/DROID fps=15.00 use_relabelled_instruction=False
+2026-09-14 13:39:14 | INFO | train rank=0 | Batch config: total_batch_size=128 world_size=4 grad_acc_steps=1 per_device_batch_size=32
+2026-09-14 13:39:14 | INFO | train rank=0 | Dataset image_resize_size=320
+2026-09-14 13:39:14 | INFO | train rank=0 | Droid uses parquet-shard streaming with a per-rank shuffle buffer: batch_size=32 buffer_size=1024 num_workers=8 drop_last=True
+2026-09-14 13:43:07 | INFO | train rank=0 | Starting training project=codebase_v8_droid_joint_50ksteps_bs128_qwen3vl2b_end_padding_mask save_dir=/data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask distributed=True deepspeed=True world_size=4 device=cuda:0
+2026-09-14 13:43:30 | INFO | train rank=0 | No pretrained checkpoint provided; training from scratch
+2026-09-14 13:43:30 | INFO | train rank=0 | Initializing Droid LeRobot Dataset: path=/data/NTU_slab/draven/data/DROID fps=15.00 use_relabelled_instruction=False
+2026-09-14 13:43:30 | INFO | train rank=0 | Batch config: total_batch_size=128 world_size=4 grad_acc_steps=1 per_device_batch_size=32
+2026-09-14 13:43:30 | INFO | train rank=0 | Dataset image_resize_size=320
+2026-09-14 13:43:30 | INFO | train rank=0 | Droid uses parquet-shard streaming with a per-rank shuffle buffer: batch_size=32 buffer_size=1024 num_workers=8 drop_last=True
+2026-09-14 13:43:36 | INFO | train rank=0 | Droid streaming layout: parquet_shards=50 active_workers=8 worker_buffer_min=128 worker_buffer_max=128 max_active_shards=2
+2026-09-14 13:43:36 | INFO | train rank=0 | Resolved Droid joint action stats from /data/NTU_slab/draven/data/DROID/meta/joint_delta_stats_h8.json: q01=[-0.21484030783176422, -0.3382192850112915, -0.21358108520507812, -0.3538644015789032, -0.3344809114933014, -0.3184388279914856, -0.3800385296344757] q99=[0.21101456880569458, 0.39177048206329346, 0.21355487406253815, 0.3544503450393677, 0.3296765685081482, 0.3758541941642761, 0.38154837489128113]
+2026-09-14 13:43:36 | INFO | train rank=0 | Trainable params: 2,688,879,624 / 2,688,879,624
+2026-09-14 13:46:27 | INFO | train rank=0 | Starting training start_step=0 max_steps=50000 remaining_steps=50000 log_interval=10 save_interval=50000
+2026-09-14 13:48:29 | INFO | train rank=0 | step=10/50000 progress=0.0% loss=1.188354 lr=3.600000e-07 lr_backbone=3.600000e-07 elapsed=00:02:01 avg_step=12.18s recent_step=12.18s eta=169:12:01
+2026-09-14 13:48:51 | INFO | train rank=0 | step=20/50000 progress=0.0% loss=1.203709 lr=7.600000e-07 lr_backbone=7.600000e-07 elapsed=00:02:24 avg_step=7.23s recent_step=2.28s eta=100:26:03
+2026-09-14 13:49:18 | INFO | train rank=0 | step=30/50000 progress=0.1% loss=1.231849 lr=1.160000e-06 lr_backbone=1.160000e-06 elapsed=00:02:51 avg_step=5.71s recent_step=2.67s eta=79:18:12
+2026-09-14 13:49:41 | INFO | train rank=0 | step=40/50000 progress=0.1% loss=1.213999 lr=1.560000e-06 lr_backbone=1.560000e-06 elapsed=00:03:14 avg_step=4.85s recent_step=2.27s eta=67:19:37
+2026-09-14 13:50:08 | INFO | train rank=0 | step=50/50000 progress=0.1% loss=1.201466 lr=1.960000e-06 lr_backbone=1.960000e-06 elapsed=00:03:40 avg_step=4.42s recent_step=2.68s eta=61:17:42
+2026-09-14 13:50:29 | INFO | train rank=0 | step=60/50000 progress=0.1% loss=1.179175 lr=2.360000e-06 lr_backbone=2.360000e-06 elapsed=00:04:02 avg_step=4.03s recent_step=2.12s eta=55:57:43
+2026-09-14 13:50:48 | INFO | train rank=0 | step=70/50000 progress=0.1% loss=1.157839 lr=2.760000e-06 lr_backbone=2.760000e-06 elapsed=00:04:21 avg_step=3.74s recent_step=1.95s eta=51:48:45
+2026-09-14 13:51:09 | INFO | train rank=0 | step=80/50000 progress=0.2% loss=1.135914 lr=3.160000e-06 lr_backbone=3.160000e-06 elapsed=00:04:42 avg_step=3.53s recent_step=2.09s eta=48:56:47
+2026-09-14 13:51:37 | INFO | train rank=0 | step=90/50000 progress=0.2% loss=1.130946 lr=3.560000e-06 lr_backbone=3.560000e-06 elapsed=00:05:09 avg_step=3.44s recent_step=2.76s eta=47:45:11
+2026-09-14 13:52:02 | INFO | train rank=0 | step=100/50000 progress=0.2% loss=1.124174 lr=3.960000e-06 lr_backbone=3.960000e-06 elapsed=00:05:35 avg_step=3.35s recent_step=2.55s eta=46:30:06
+2026-09-14 13:52:25 | INFO | train rank=0 | step=110/50000 progress=0.2% loss=1.109153 lr=4.360000e-06 lr_backbone=4.360000e-06 elapsed=00:05:57 avg_step=3.25s recent_step=2.24s eta=45:05:26
+2026-09-14 13:52:51 | INFO | train rank=0 | step=120/50000 progress=0.2% loss=1.100060 lr=4.760000e-06 lr_backbone=4.760000e-06 elapsed=00:06:24 avg_step=3.20s recent_step=2.65s eta=44:22:56
+2026-09-14 13:53:23 | INFO | train rank=0 | step=130/50000 progress=0.3% loss=0.979861 lr=5.160000e-06 lr_backbone=5.160000e-06 elapsed=00:06:55 avg_step=3.20s recent_step=3.15s eta=44:18:59
+2026-09-14 13:53:45 | INFO | train rank=0 | step=140/50000 progress=0.3% loss=1.054357 lr=5.560000e-06 lr_backbone=5.560000e-06 elapsed=00:07:18 avg_step=3.13s recent_step=2.29s eta=43:24:12
+2026-09-14 13:54:05 | INFO | train rank=0 | step=150/50000 progress=0.3% loss=1.005468 lr=5.960000e-06 lr_backbone=5.960000e-06 elapsed=00:07:37 avg_step=3.05s recent_step=1.92s eta=42:16:43
+2026-09-14 13:54:27 | INFO | train rank=0 | step=160/50000 progress=0.3% loss=1.036110 lr=6.360000e-06 lr_backbone=6.360000e-06 elapsed=00:08:00 avg_step=3.00s recent_step=2.28s eta=41:36:03
+2026-09-14 13:54:55 | INFO | train rank=0 | step=170/50000 progress=0.3% loss=0.988663 lr=6.760000e-06 lr_backbone=6.760000e-06 elapsed=00:08:28 avg_step=2.99s recent_step=2.74s eta=41:22:47
+2026-09-14 13:55:21 | INFO | train rank=0 | step=180/50000 progress=0.4% loss=0.913105 lr=7.160000e-06 lr_backbone=7.160000e-06 elapsed=00:08:53 avg_step=2.97s recent_step=2.57s eta=41:02:43
+2026-09-14 13:55:50 | INFO | train rank=0 | step=190/50000 progress=0.4% loss=0.900910 lr=7.560000e-06 lr_backbone=7.560000e-06 elapsed=00:09:22 avg_step=2.96s recent_step=2.89s eta=40:59:02
+2026-09-14 13:56:09 | INFO | train rank=0 | step=200/50000 progress=0.4% loss=0.893876 lr=7.960000e-06 lr_backbone=7.960000e-06 elapsed=00:09:42 avg_step=2.91s recent_step=1.96s eta=40:16:55
+2026-09-14 13:56:32 | INFO | train rank=0 | step=210/50000 progress=0.4% loss=0.855501 lr=8.360000e-06 lr_backbone=8.360000e-06 elapsed=00:10:04 avg_step=2.88s recent_step=2.26s eta=39:50:38
+2026-09-14 13:56:55 | INFO | train rank=0 | step=220/50000 progress=0.4% loss=0.747119 lr=8.760000e-06 lr_backbone=8.760000e-06 elapsed=00:10:28 avg_step=2.86s recent_step=2.32s eta=39:29:00
+2026-09-14 13:57:17 | INFO | train rank=0 | step=230/50000 progress=0.5% loss=0.694270 lr=9.160000e-06 lr_backbone=9.160000e-06 elapsed=00:10:49 avg_step=2.83s recent_step=2.17s eta=39:03:49
+2026-09-14 13:57:37 | INFO | train rank=0 | step=240/50000 progress=0.5% loss=0.677010 lr=9.560000e-06 lr_backbone=9.560000e-06 elapsed=00:11:10 avg_step=2.79s recent_step=2.06s eta=38:36:43
+2026-09-14 13:58:06 | INFO | train rank=0 | step=250/50000 progress=0.5% loss=0.561498 lr=9.960000e-06 lr_backbone=9.960000e-06 elapsed=00:11:38 avg_step=2.80s recent_step=2.84s eta=38:37:53
+2026-09-14 13:58:31 | INFO | train rank=0 | step=260/50000 progress=0.5% loss=0.482870 lr=1.036000e-05 lr_backbone=1.036000e-05 elapsed=00:12:03 avg_step=2.78s recent_step=2.50s eta=38:28:04
+2026-09-14 13:58:52 | INFO | train rank=0 | step=270/50000 progress=0.5% loss=0.495736 lr=1.076000e-05 lr_backbone=1.076000e-05 elapsed=00:12:25 avg_step=2.76s recent_step=2.13s eta=38:07:34
+2026-09-14 13:59:16 | INFO | train rank=0 | step=280/50000 progress=0.6% loss=0.412582 lr=1.116000e-05 lr_backbone=1.116000e-05 elapsed=00:12:49 avg_step=2.75s recent_step=2.45s eta=37:57:50
+2026-09-14 13:59:42 | INFO | train rank=0 | step=290/50000 progress=0.6% loss=0.387799 lr=1.156000e-05 lr_backbone=1.156000e-05 elapsed=00:13:15 avg_step=2.74s recent_step=2.58s eta=37:52:34
+2026-09-14 14:00:08 | INFO | train rank=0 | step=300/50000 progress=0.6% loss=0.361962 lr=1.196000e-05 lr_backbone=1.196000e-05 elapsed=00:13:40 avg_step=2.74s recent_step=2.53s eta=37:46:20
+2026-09-14 14:00:30 | INFO | train rank=0 | step=310/50000 progress=0.6% loss=0.372816 lr=1.236000e-05 lr_backbone=1.236000e-05 elapsed=00:14:03 avg_step=2.72s recent_step=2.29s eta=37:33:54
+2026-09-14 14:00:55 | INFO | train rank=0 | step=320/50000 progress=0.6% loss=0.346866 lr=1.276000e-05 lr_backbone=1.276000e-05 elapsed=00:14:28 avg_step=2.71s recent_step=2.49s eta=37:27:21
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+2026-09-16 17:27:16 | INFO | train rank=0 | step=49810/50000 progress=99.6% loss=0.088138 lr=3.947790e-09 lr_backbone=3.947790e-09 elapsed=51:40:49 avg_step=3.74s recent_step=3.58s eta=00:11:49
+2026-09-16 17:27:49 | INFO | train rank=0 | step=49820/50000 progress=99.6% loss=0.117827 lr=3.543173e-09 lr_backbone=3.543173e-09 elapsed=51:41:22 avg_step=3.74s recent_step=3.32s eta=00:11:12
+2026-09-16 17:28:32 | INFO | train rank=0 | step=49830/50000 progress=99.7% loss=0.088866 lr=3.160427e-09 lr_backbone=3.160427e-09 elapsed=51:42:05 avg_step=3.74s recent_step=4.30s eta=00:10:34
+2026-09-16 17:29:13 | INFO | train rank=0 | step=49840/50000 progress=99.7% loss=0.078022 lr=2.799551e-09 lr_backbone=2.799551e-09 elapsed=51:42:45 avg_step=3.74s recent_step=4.07s eta=00:09:57
+2026-09-16 17:29:41 | INFO | train rank=0 | step=49850/50000 progress=99.7% loss=0.076673 lr=2.460546e-09 lr_backbone=2.460546e-09 elapsed=51:43:14 avg_step=3.74s recent_step=2.83s eta=00:09:20
+2026-09-16 17:30:11 | INFO | train rank=0 | step=49860/50000 progress=99.7% loss=0.093227 lr=2.143411e-09 lr_backbone=2.143411e-09 elapsed=51:43:44 avg_step=3.73s recent_step=3.06s eta=00:08:42
+2026-09-16 17:31:06 | INFO | train rank=0 | step=49870/50000 progress=99.7% loss=0.087461 lr=1.848147e-09 lr_backbone=1.848147e-09 elapsed=51:44:39 avg_step=3.74s recent_step=5.46s eta=00:08:05
+2026-09-16 17:31:44 | INFO | train rank=0 | step=49880/50000 progress=99.8% loss=0.072218 lr=1.574754e-09 lr_backbone=1.574754e-09 elapsed=51:45:17 avg_step=3.74s recent_step=3.77s eta=00:07:28
+2026-09-16 17:32:17 | INFO | train rank=0 | step=49890/50000 progress=99.8% loss=0.090955 lr=1.323232e-09 lr_backbone=1.323232e-09 elapsed=51:45:50 avg_step=3.74s recent_step=3.31s eta=00:06:50
+2026-09-16 17:32:54 | INFO | train rank=0 | step=49900/50000 progress=99.8% loss=0.132379 lr=1.093581e-09 lr_backbone=1.093581e-09 elapsed=51:46:27 avg_step=3.74s recent_step=3.69s eta=00:06:13
+2026-09-16 17:33:43 | INFO | train rank=0 | step=49910/50000 progress=99.8% loss=0.066054 lr=8.858012e-10 lr_backbone=8.858012e-10 elapsed=51:47:16 avg_step=3.74s recent_step=4.89s eta=00:05:36
+2026-09-16 17:34:14 | INFO | train rank=0 | step=49920/50000 progress=99.8% loss=0.108818 lr=6.998928e-10 lr_backbone=6.998928e-10 elapsed=51:47:47 avg_step=3.74s recent_step=3.14s eta=00:04:58
+2026-09-16 17:34:49 | INFO | train rank=0 | step=49930/50000 progress=99.9% loss=0.077332 lr=5.358557e-10 lr_backbone=5.358557e-10 elapsed=51:48:22 avg_step=3.74s recent_step=3.53s eta=00:04:21
+2026-09-16 17:35:20 | INFO | train rank=0 | step=49940/50000 progress=99.9% loss=0.086497 lr=3.936901e-10 lr_backbone=3.936901e-10 elapsed=51:48:53 avg_step=3.74s recent_step=3.05s eta=00:03:44
+2026-09-16 17:36:03 | INFO | train rank=0 | step=49950/50000 progress=99.9% loss=0.108402 lr=2.733960e-10 lr_backbone=2.733960e-10 elapsed=51:49:36 avg_step=3.74s recent_step=4.33s eta=00:03:06
+2026-09-16 17:36:36 | INFO | train rank=0 | step=49960/50000 progress=99.9% loss=0.106112 lr=1.749735e-10 lr_backbone=1.749735e-10 elapsed=51:50:09 avg_step=3.74s recent_step=3.25s eta=00:02:29
+2026-09-16 17:37:10 | INFO | train rank=0 | step=49970/50000 progress=99.9% loss=0.082950 lr=9.842262e-11 lr_backbone=9.842262e-11 elapsed=51:50:42 avg_step=3.74s recent_step=3.39s eta=00:01:52
+2026-09-16 17:37:39 | INFO | train rank=0 | step=49980/50000 progress=100.0% loss=0.118901 lr=4.374339e-11 lr_backbone=4.374339e-11 elapsed=51:51:12 avg_step=3.73s recent_step=2.97s eta=00:01:14
+2026-09-16 17:38:17 | INFO | train rank=0 | step=49990/50000 progress=100.0% loss=0.075177 lr=1.093585e-11 lr_backbone=1.093585e-11 elapsed=51:51:50 avg_step=3.73s recent_step=3.76s eta=00:00:37
+2026-09-16 17:38:49 | INFO | train rank=0 | step=50000/50000 progress=100.0% loss=0.105713 lr=0.000000e+00 lr_backbone=0.000000e+00 elapsed=51:52:22 avg_step=3.73s recent_step=3.25s eta=00:00:00
+2026-09-16 17:39:33 | INFO | train rank=0 | Saved DeepSpeed checkpoint to /data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_50000
+2026-09-16 17:39:33 | INFO | train rank=0 | Finished training final_step=50000/50000 elapsed=51:53:06 avg_step=3.74s
+2026-09-16 17:39:33 | INFO | train rank=0 | Training finished
+2026-09-16 17:40:09 | INFO | train rank=0 | Saved final DeepSpeed checkpoint to /data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask/ds_checkpoint_final
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..060936b569900f2496b305c3f444d24733bf9534
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_50000/global_step50000/mp_rank_00_model_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_50000/global_step50000/mp_rank_00_model_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..faf2b439930397d80568ea86ad6f089d8ac1c174
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+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_50000/global_step50000/mp_rank_00_model_states.pt
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_50000/latest b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_50000/latest
new file mode 100644
index 0000000000000000000000000000000000000000..59a558c0ee32a055bd8fc16bf0d99a5950c59ac1
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+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_50000/latest
@@ -0,0 +1 @@
+global_step50000
\ No newline at end of file
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_50000/zero_to_fp32.py b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_50000/zero_to_fp32.py
new file mode 100644
index 0000000000000000000000000000000000000000..f40f1f0acbbff30e8e8d29906b56aae4e8b8a138
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_50000/zero_to_fp32.py
@@ -0,0 +1,790 @@
+#!/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 gc
+import json
+import numpy as np
+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, AUTOEP_LAYERS_KEY,
+                                            AUTOEP_LAYERS_KEY_LEGACY, AUTOEP_ZERO3_EXPERT_STATE_FORMAT_KEY,
+                                            AUTOEP_ZERO3_PARTITIONED_EXPERT_STATE_FORMAT)
+
+
+@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 _has_autoep_zero3_partitioned_metadata(state_dict):
+    autoep_layers = state_dict.get(AUTOEP_LAYERS_KEY)
+    if autoep_layers is None:
+        autoep_layers = state_dict.get(AUTOEP_LAYERS_KEY_LEGACY)
+    if not isinstance(autoep_layers, list):
+        return False
+    return any(
+        isinstance(entry, dict)
+        and entry.get(AUTOEP_ZERO3_EXPERT_STATE_FORMAT_KEY) == AUTOEP_ZERO3_PARTITIONED_EXPERT_STATE_FORMAT
+        for entry in autoep_layers)
+
+
+def _raise_if_autoep_zero3_partitioned_state(state_dict):
+    if _has_autoep_zero3_partitioned_metadata(state_dict):
+        raise NotImplementedError("zero_to_fp32 does not support AutoEP ZeRO-3 partition-native checkpoints. "
+                                  "AutoEP expert parameters are partitioned over expert replica groups, so "
+                                  "global data-parallel consolidation would produce incomplete expert tensors. "
+                                  "Use ds_to_universal.py for expert-aware conversion.")
+
+
+def _raise_if_autoep_zero3_partitioned_checkpoint(model_files):
+    for file in model_files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+        _raise_if_autoep_zero3_partitioned_state(state_dict)
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+        _raise_if_autoep_zero3_partitioned_state(state_dict)
+
+        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 tqdm(files, desc='Loading checkpoint shards'):
+        state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
+        # 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 ZERO_STAGE not 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}")
+
+    fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] 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}'")
+
+    # parse_model_states rejects AutoEP ZeRO-3 partition-native checkpoints
+    # before the expensive optimizer-shard load below.
+    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}')
+
+    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}")
+
+    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")
+
+
+class GatheredTensor:
+    """
+    A pseudo tensor that collects partitioned weights.
+    It is more memory efficient when there are multiple groups.
+    """
+
+    def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
+        self.flat_groups = flat_groups
+        self.flat_groups_offset = flat_groups_offset
+        self.offset = offset
+        self.partitioned_numel = partitioned_numel
+        self.shape = shape
+        self.dtype = self.flat_groups[0][0].dtype
+
+    def contiguous(self):
+        """
+        Merge partitioned weights from flat_groups into a single tensor.
+        """
+        end_idx = self.offset + self.partitioned_numel
+        world_size = len(self.flat_groups)
+        pad_flat_param_chunks = []
+
+        for rank_i in range(world_size):
+            # for each rank, we need to collect weights from related group/groups
+            flat_groups_at_rank_i = self.flat_groups[rank_i]
+            start_group_id = None
+            end_group_id = None
+            for group_id in range(len(self.flat_groups_offset)):
+                if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
+                    start_group_id = group_id
+                if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
+                    end_group_id = group_id
+                    break
+            # collect weights from related group/groups
+            for group_id in range(start_group_id, end_group_id + 1):
+                flat_tensor = flat_groups_at_rank_i[group_id]
+                start_offset = self.offset - self.flat_groups_offset[group_id]
+                end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
+                pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
+
+        # collect weights from all ranks
+        pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
+        param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
+        return param
+
+
+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 = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * 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
+    flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[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}"
+            )
+
+        # memory efficient tensor
+        tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
+        state_dict[name] = tensor
+        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 to_torch_tensor(state_dict, return_empty_tensor=False):
+    """
+    Convert state_dict of GatheredTensor to torch tensor
+    """
+    torch_state_dict = {}
+    converted_tensors = {}
+    for name, tensor in state_dict.items():
+        tensor_id = id(tensor)
+        if tensor_id in converted_tensors:  # shared tensors
+            shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
+            torch_state_dict[name] = shared_tensor
+        else:
+            converted_tensors[tensor_id] = name
+            if return_empty_tensor:
+                torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
+            else:
+                torch_state_dict[name] = tensor.contiguous()
+    return torch_state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                             tag=None,
+                                             exclude_frozen_parameters=False,
+                                             lazy_mode=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
+        - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
+          Convert the pesduo tensor to torch tensor by ``.contiguous()``
+
+    Returns:
+        - pytorch ``state_dict``
+
+    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.
+
+    Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
+    You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
+    the checkpoint. Or you can load state_dict in lazy mode ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
+        for name, lazy_tensor in state_dict.item():
+            tensor = lazy_tensor.contiguous()  # to cpu
+            print(name, tensor)
+            # del tensor to release memory if it no longer in use
+    """
+    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")
+
+    state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
+    if lazy_mode:
+        return state_dict
+    else:
+        return to_torch_tensor(state_dict)
+
+
+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,
+                                                          lazy_mode=True)
+
+    # 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")
+        # an memory-efficient approach for sharding
+        empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
+        state_dict_split = split_torch_state_dict_into_shards(empty_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 by shard
+    os.makedirs(output_dir, exist_ok=True)
+    filename_to_tensors = state_dict_split.filename_to_tensors.items()
+    for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
+        shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
+        shard_state_dict = to_torch_tensor(shard_state_dict)
+        output_path = os.path.join(output_dir, shard_file)
+        if safe_serialization:
+            save_file(shard_state_dict, output_path, metadata={"format": "pt"})
+        else:
+            torch.save(shard_state_dict, output_path)
+        # release the memory of current shard
+        for tensor_name in list(shard_state_dict.keys()):
+            del state_dict[tensor_name]
+            del shard_state_dict[tensor_name]
+        del shard_state_dict
+        gc.collect()
+
+    # 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("Extracting fp32 weights")
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+
+    logger.info("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)
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..76c72c09fdddc84465be9688098b634952617ba6
--- /dev/null
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..060936b569900f2496b305c3f444d24733bf9534
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_final/global_step50000/mp_rank_00_model_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_final/global_step50000/mp_rank_00_model_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..faf2b439930397d80568ea86ad6f089d8ac1c174
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+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_final/global_step50000/mp_rank_00_model_states.pt
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_final/latest b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_final/latest
new file mode 100644
index 0000000000000000000000000000000000000000..59a558c0ee32a055bd8fc16bf0d99a5950c59ac1
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_final/latest
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+global_step50000
\ No newline at end of file
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_final/zero_to_fp32.py b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_final/zero_to_fp32.py
new file mode 100644
index 0000000000000000000000000000000000000000..f40f1f0acbbff30e8e8d29906b56aae4e8b8a138
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_final/zero_to_fp32.py
@@ -0,0 +1,790 @@
+#!/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 gc
+import json
+import numpy as np
+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, AUTOEP_LAYERS_KEY,
+                                            AUTOEP_LAYERS_KEY_LEGACY, AUTOEP_ZERO3_EXPERT_STATE_FORMAT_KEY,
+                                            AUTOEP_ZERO3_PARTITIONED_EXPERT_STATE_FORMAT)
+
+
+@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 _has_autoep_zero3_partitioned_metadata(state_dict):
+    autoep_layers = state_dict.get(AUTOEP_LAYERS_KEY)
+    if autoep_layers is None:
+        autoep_layers = state_dict.get(AUTOEP_LAYERS_KEY_LEGACY)
+    if not isinstance(autoep_layers, list):
+        return False
+    return any(
+        isinstance(entry, dict)
+        and entry.get(AUTOEP_ZERO3_EXPERT_STATE_FORMAT_KEY) == AUTOEP_ZERO3_PARTITIONED_EXPERT_STATE_FORMAT
+        for entry in autoep_layers)
+
+
+def _raise_if_autoep_zero3_partitioned_state(state_dict):
+    if _has_autoep_zero3_partitioned_metadata(state_dict):
+        raise NotImplementedError("zero_to_fp32 does not support AutoEP ZeRO-3 partition-native checkpoints. "
+                                  "AutoEP expert parameters are partitioned over expert replica groups, so "
+                                  "global data-parallel consolidation would produce incomplete expert tensors. "
+                                  "Use ds_to_universal.py for expert-aware conversion.")
+
+
+def _raise_if_autoep_zero3_partitioned_checkpoint(model_files):
+    for file in model_files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+        _raise_if_autoep_zero3_partitioned_state(state_dict)
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+        _raise_if_autoep_zero3_partitioned_state(state_dict)
+
+        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 tqdm(files, desc='Loading checkpoint shards'):
+        state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
+        # 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 ZERO_STAGE not 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}")
+
+    fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] 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}'")
+
+    # parse_model_states rejects AutoEP ZeRO-3 partition-native checkpoints
+    # before the expensive optimizer-shard load below.
+    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}')
+
+    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}")
+
+    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")
+
+
+class GatheredTensor:
+    """
+    A pseudo tensor that collects partitioned weights.
+    It is more memory efficient when there are multiple groups.
+    """
+
+    def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
+        self.flat_groups = flat_groups
+        self.flat_groups_offset = flat_groups_offset
+        self.offset = offset
+        self.partitioned_numel = partitioned_numel
+        self.shape = shape
+        self.dtype = self.flat_groups[0][0].dtype
+
+    def contiguous(self):
+        """
+        Merge partitioned weights from flat_groups into a single tensor.
+        """
+        end_idx = self.offset + self.partitioned_numel
+        world_size = len(self.flat_groups)
+        pad_flat_param_chunks = []
+
+        for rank_i in range(world_size):
+            # for each rank, we need to collect weights from related group/groups
+            flat_groups_at_rank_i = self.flat_groups[rank_i]
+            start_group_id = None
+            end_group_id = None
+            for group_id in range(len(self.flat_groups_offset)):
+                if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
+                    start_group_id = group_id
+                if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
+                    end_group_id = group_id
+                    break
+            # collect weights from related group/groups
+            for group_id in range(start_group_id, end_group_id + 1):
+                flat_tensor = flat_groups_at_rank_i[group_id]
+                start_offset = self.offset - self.flat_groups_offset[group_id]
+                end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
+                pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
+
+        # collect weights from all ranks
+        pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
+        param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
+        return param
+
+
+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 = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * 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
+    flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[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}"
+            )
+
+        # memory efficient tensor
+        tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
+        state_dict[name] = tensor
+        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 to_torch_tensor(state_dict, return_empty_tensor=False):
+    """
+    Convert state_dict of GatheredTensor to torch tensor
+    """
+    torch_state_dict = {}
+    converted_tensors = {}
+    for name, tensor in state_dict.items():
+        tensor_id = id(tensor)
+        if tensor_id in converted_tensors:  # shared tensors
+            shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
+            torch_state_dict[name] = shared_tensor
+        else:
+            converted_tensors[tensor_id] = name
+            if return_empty_tensor:
+                torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
+            else:
+                torch_state_dict[name] = tensor.contiguous()
+    return torch_state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                             tag=None,
+                                             exclude_frozen_parameters=False,
+                                             lazy_mode=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
+        - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
+          Convert the pesduo tensor to torch tensor by ``.contiguous()``
+
+    Returns:
+        - pytorch ``state_dict``
+
+    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.
+
+    Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
+    You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
+    the checkpoint. Or you can load state_dict in lazy mode ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
+        for name, lazy_tensor in state_dict.item():
+            tensor = lazy_tensor.contiguous()  # to cpu
+            print(name, tensor)
+            # del tensor to release memory if it no longer in use
+    """
+    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")
+
+    state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
+    if lazy_mode:
+        return state_dict
+    else:
+        return to_torch_tensor(state_dict)
+
+
+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,
+                                                          lazy_mode=True)
+
+    # 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")
+        # an memory-efficient approach for sharding
+        empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
+        state_dict_split = split_torch_state_dict_into_shards(empty_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 by shard
+    os.makedirs(output_dir, exist_ok=True)
+    filename_to_tensors = state_dict_split.filename_to_tensors.items()
+    for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
+        shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
+        shard_state_dict = to_torch_tensor(shard_state_dict)
+        output_path = os.path.join(output_dir, shard_file)
+        if safe_serialization:
+            save_file(shard_state_dict, output_path, metadata={"format": "pt"})
+        else:
+            torch.save(shard_state_dict, output_path)
+        # release the memory of current shard
+        for tensor_name in list(shard_state_dict.keys()):
+            del state_dict[tensor_name]
+            del shard_state_dict[tensor_name]
+        del shard_state_dict
+        gc.collect()
+
+    # 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("Extracting fp32 weights")
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+
+    logger.info("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)
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/train.log b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..fe9ac21db3b48f90d52526fb50eb00e9c9b9b783
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/train.log
@@ -0,0 +1,5020 @@
+2026-09-14 13:54:59 | INFO | train rank=0 | Starting training project=codebase_v8_droid_joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16 save_dir=/data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16 distributed=True deepspeed=True world_size=4 device=cuda:0
+2026-09-14 13:55:21 | INFO | train rank=0 | No pretrained checkpoint provided; training from scratch
+2026-09-14 13:55:21 | INFO | train rank=0 | Initializing Droid LeRobot Dataset: path=/data/NTU_slab/draven/data/DROID fps=15.00 use_relabelled_instruction=False
+2026-09-14 13:55:21 | INFO | train rank=0 | Batch config: total_batch_size=128 world_size=4 grad_acc_steps=1 per_device_batch_size=32
+2026-09-14 13:55:21 | INFO | train rank=0 | Dataset image_resize_size=320
+2026-09-14 13:55:21 | INFO | train rank=0 | Droid uses parquet-shard streaming with a per-rank shuffle buffer: batch_size=32 buffer_size=1024 num_workers=8 drop_last=True
+2026-09-14 14:25:53 | INFO | train rank=0 | Starting training project=codebase_v8_droid_joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16 save_dir=/data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16 distributed=True deepspeed=True world_size=4 device=cuda:0
+2026-09-14 14:26:15 | INFO | train rank=0 | No pretrained checkpoint provided; training from scratch
+2026-09-14 14:26:15 | INFO | train rank=0 | Initializing Droid LeRobot Dataset: path=/data/NTU_slab/draven/data/DROID fps=15.00 use_relabelled_instruction=False
+2026-09-14 14:26:15 | INFO | train rank=0 | Batch config: total_batch_size=128 world_size=4 grad_acc_steps=1 per_device_batch_size=32
+2026-09-14 14:26:15 | INFO | train rank=0 | Dataset image_resize_size=320
+2026-09-14 14:26:15 | INFO | train rank=0 | Droid uses parquet-shard streaming with a per-rank shuffle buffer: batch_size=32 buffer_size=1024 num_workers=8 drop_last=True
+2026-09-14 14:26:23 | INFO | train rank=0 | Droid streaming layout: parquet_shards=50 active_workers=8 worker_buffer_min=128 worker_buffer_max=128 max_active_shards=2
+2026-09-14 14:26:23 | INFO | train rank=0 | Resolved Droid joint action stats from /data/NTU_slab/draven/data/DROID/meta/joint_delta_stats_h16.json: q01=[-0.3050941824913025, -0.47471314668655396, -0.3020089268684387, -0.493502140045166, -0.4844675362110138, -0.4544465243816376, -0.550822913646698] q99=[0.30148017406463623, 0.5795503258705139, 0.3001617193222046, 0.5117637515068054, 0.4750978946685791, 0.5400477647781372, 0.5597710609436035]
+2026-09-14 14:26:23 | INFO | train rank=0 | Trainable params: 2,688,879,624 / 2,688,879,624
+2026-09-14 14:30:21 | INFO | train rank=0 | Starting training start_step=0 max_steps=50000 remaining_steps=50000 log_interval=10 save_interval=50000
+2026-09-14 14:33:20 | INFO | train rank=0 | step=10/50000 progress=0.0% loss=1.202225 lr=3.600000e-07 lr_backbone=3.600000e-07 elapsed=00:02:58 avg_step=17.90s recent_step=17.90s eta=248:32:41
+2026-09-14 14:33:50 | INFO | train rank=0 | step=20/50000 progress=0.0% loss=1.201931 lr=7.600000e-07 lr_backbone=7.600000e-07 elapsed=00:03:28 avg_step=10.45s recent_step=2.99s eta=145:00:48
+2026-09-14 14:34:28 | INFO | train rank=0 | step=30/50000 progress=0.1% loss=1.214134 lr=1.160000e-06 lr_backbone=1.160000e-06 elapsed=00:04:06 avg_step=8.21s recent_step=3.73s eta=113:54:52
+2026-09-14 14:34:59 | INFO | train rank=0 | step=40/50000 progress=0.1% loss=1.232838 lr=1.560000e-06 lr_backbone=1.560000e-06 elapsed=00:04:37 avg_step=6.95s recent_step=3.17s eta=96:24:14
+2026-09-14 14:35:45 | INFO | train rank=0 | step=50/50000 progress=0.1% loss=1.199577 lr=1.960000e-06 lr_backbone=1.960000e-06 elapsed=00:05:23 avg_step=6.48s recent_step=4.60s eta=89:51:57
+2026-09-14 14:36:15 | INFO | train rank=0 | step=60/50000 progress=0.1% loss=1.156020 lr=2.360000e-06 lr_backbone=2.360000e-06 elapsed=00:05:53 avg_step=5.89s recent_step=2.93s eta=81:39:30
+2026-09-14 14:36:41 | INFO | train rank=0 | step=70/50000 progress=0.1% loss=1.121820 lr=2.760000e-06 lr_backbone=2.760000e-06 elapsed=00:06:19 avg_step=5.42s recent_step=2.60s eta=75:07:38
+2026-09-14 14:37:09 | INFO | train rank=0 | step=80/50000 progress=0.2% loss=1.167464 lr=3.160000e-06 lr_backbone=3.160000e-06 elapsed=00:06:47 avg_step=5.10s recent_step=2.84s eta=70:39:07
+2026-09-14 14:37:55 | INFO | train rank=0 | step=90/50000 progress=0.2% loss=1.170982 lr=3.560000e-06 lr_backbone=3.560000e-06 elapsed=00:07:33 avg_step=5.04s recent_step=4.59s eta=69:51:51
+2026-09-14 14:38:30 | INFO | train rank=0 | step=100/50000 progress=0.2% loss=1.129605 lr=3.960000e-06 lr_backbone=3.960000e-06 elapsed=00:08:08 avg_step=4.88s recent_step=3.46s eta=67:39:51
+2026-09-14 14:39:08 | INFO | train rank=0 | step=110/50000 progress=0.2% loss=1.124686 lr=4.360000e-06 lr_backbone=4.360000e-06 elapsed=00:08:46 avg_step=4.79s recent_step=3.87s eta=66:22:49
+2026-09-14 14:39:41 | INFO | train rank=0 | step=120/50000 progress=0.2% loss=1.121937 lr=4.760000e-06 lr_backbone=4.760000e-06 elapsed=00:09:19 avg_step=4.66s recent_step=3.28s eta=64:37:09
+2026-09-14 14:40:28 | INFO | train rank=0 | step=130/50000 progress=0.3% loss=1.040055 lr=5.160000e-06 lr_backbone=5.160000e-06 elapsed=00:10:06 avg_step=4.66s recent_step=4.68s eta=64:37:14
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+2026-09-16 18:18:04 | INFO | train rank=0 | step=49590/50000 progress=99.2% loss=0.091481 lr=1.838204e-08 lr_backbone=1.838204e-08 elapsed=51:47:42 avg_step=3.76s recent_step=3.01s eta=00:25:41
+2026-09-16 18:18:34 | INFO | train rank=0 | step=49600/50000 progress=99.2% loss=0.084969 lr=1.749634e-08 lr_backbone=1.749634e-08 elapsed=51:48:12 avg_step=3.76s recent_step=2.93s eta=00:25:03
+2026-09-16 18:19:01 | INFO | train rank=0 | step=49610/50000 progress=99.2% loss=0.084136 lr=1.663251e-08 lr_backbone=1.663251e-08 elapsed=51:48:39 avg_step=3.76s recent_step=2.78s eta=00:24:26
+2026-09-16 18:19:28 | INFO | train rank=0 | step=49620/50000 progress=99.2% loss=0.108279 lr=1.579054e-08 lr_backbone=1.579054e-08 elapsed=51:49:06 avg_step=3.76s recent_step=2.63s eta=00:23:48
+2026-09-16 18:20:02 | INFO | train rank=0 | step=49630/50000 progress=99.3% loss=0.068334 lr=1.497043e-08 lr_backbone=1.497043e-08 elapsed=51:49:40 avg_step=3.76s recent_step=3.47s eta=00:23:10
+2026-09-16 18:20:26 | INFO | train rank=0 | step=49640/50000 progress=99.3% loss=0.074883 lr=1.417219e-08 lr_backbone=1.417219e-08 elapsed=51:50:04 avg_step=3.76s recent_step=2.41s eta=00:22:33
+2026-09-16 18:20:54 | INFO | train rank=0 | step=49650/50000 progress=99.3% loss=0.081405 lr=1.339582e-08 lr_backbone=1.339582e-08 elapsed=51:50:32 avg_step=3.76s recent_step=2.75s eta=00:21:55
+2026-09-16 18:21:20 | INFO | train rank=0 | step=49660/50000 progress=99.3% loss=0.091787 lr=1.264131e-08 lr_backbone=1.264131e-08 elapsed=51:50:58 avg_step=3.76s recent_step=2.61s eta=00:21:17
+2026-09-16 18:21:54 | INFO | train rank=0 | step=49670/50000 progress=99.3% loss=0.117428 lr=1.190867e-08 lr_backbone=1.190867e-08 elapsed=51:51:32 avg_step=3.76s recent_step=3.36s eta=00:20:40
+2026-09-16 18:22:22 | INFO | train rank=0 | step=49680/50000 progress=99.4% loss=0.091134 lr=1.119789e-08 lr_backbone=1.119789e-08 elapsed=51:52:00 avg_step=3.76s recent_step=2.84s eta=00:20:02
+2026-09-16 18:22:49 | INFO | train rank=0 | step=49690/50000 progress=99.4% loss=0.090261 lr=1.050898e-08 lr_backbone=1.050898e-08 elapsed=51:52:27 avg_step=3.76s recent_step=2.70s eta=00:19:25
+2026-09-16 18:23:23 | INFO | train rank=0 | step=49700/50000 progress=99.4% loss=0.075158 lr=9.841942e-09 lr_backbone=9.841942e-09 elapsed=51:53:01 avg_step=3.76s recent_step=3.43s eta=00:18:47
+2026-09-16 18:23:52 | INFO | train rank=0 | step=49710/50000 progress=99.4% loss=0.075101 lr=9.196768e-09 lr_backbone=9.196768e-09 elapsed=51:53:30 avg_step=3.76s recent_step=2.84s eta=00:18:09
+2026-09-16 18:24:19 | INFO | train rank=0 | step=49720/50000 progress=99.4% loss=0.072416 lr=8.573461e-09 lr_backbone=8.573461e-09 elapsed=51:53:57 avg_step=3.76s recent_step=2.70s eta=00:17:32
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+2026-09-16 18:25:15 | INFO | train rank=0 | step=49740/50000 progress=99.5% loss=0.091847 lr=7.392452e-09 lr_backbone=7.392452e-09 elapsed=51:54:53 avg_step=3.76s recent_step=3.10s eta=00:16:16
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+2026-09-16 18:27:04 | INFO | train rank=0 | step=49780/50000 progress=99.6% loss=0.082417 lr=5.292858e-09 lr_backbone=5.292858e-09 elapsed=51:56:42 avg_step=3.76s recent_step=2.30s eta=00:13:46
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+2026-09-16 18:29:07 | INFO | train rank=0 | step=49820/50000 progress=99.6% loss=0.100540 lr=3.543173e-09 lr_backbone=3.543173e-09 elapsed=51:58:45 avg_step=3.76s recent_step=2.90s eta=00:11:16
+2026-09-16 18:29:49 | INFO | train rank=0 | step=49830/50000 progress=99.7% loss=0.077648 lr=3.160427e-09 lr_backbone=3.160427e-09 elapsed=51:59:27 avg_step=3.76s recent_step=4.19s eta=00:10:38
+2026-09-16 18:30:17 | INFO | train rank=0 | step=49840/50000 progress=99.7% loss=0.084739 lr=2.799551e-09 lr_backbone=2.799551e-09 elapsed=51:59:55 avg_step=3.76s recent_step=2.77s eta=00:10:00
+2026-09-16 18:30:41 | INFO | train rank=0 | step=49850/50000 progress=99.7% loss=0.088534 lr=2.460546e-09 lr_backbone=2.460546e-09 elapsed=52:00:19 avg_step=3.76s recent_step=2.40s eta=00:09:23
+2026-09-16 18:31:11 | INFO | train rank=0 | step=49860/50000 progress=99.7% loss=0.089569 lr=2.143411e-09 lr_backbone=2.143411e-09 elapsed=52:00:49 avg_step=3.76s recent_step=2.99s eta=00:08:45
+2026-09-16 18:31:43 | INFO | train rank=0 | step=49870/50000 progress=99.7% loss=0.080070 lr=1.848147e-09 lr_backbone=1.848147e-09 elapsed=52:01:21 avg_step=3.76s recent_step=3.28s eta=00:08:08
+2026-09-16 18:32:14 | INFO | train rank=0 | step=49880/50000 progress=99.8% loss=0.075222 lr=1.574754e-09 lr_backbone=1.574754e-09 elapsed=52:01:52 avg_step=3.76s recent_step=3.02s eta=00:07:30
+2026-09-16 18:32:44 | INFO | train rank=0 | step=49890/50000 progress=99.8% loss=0.075180 lr=1.323232e-09 lr_backbone=1.323232e-09 elapsed=52:02:22 avg_step=3.76s recent_step=3.08s eta=00:06:53
+2026-09-16 18:33:09 | INFO | train rank=0 | step=49900/50000 progress=99.8% loss=0.108326 lr=1.093581e-09 lr_backbone=1.093581e-09 elapsed=52:02:47 avg_step=3.75s recent_step=2.50s eta=00:06:15
+2026-09-16 18:33:33 | INFO | train rank=0 | step=49910/50000 progress=99.8% loss=0.066413 lr=8.858012e-10 lr_backbone=8.858012e-10 elapsed=52:03:11 avg_step=3.75s recent_step=2.36s eta=00:05:37
+2026-09-16 18:34:03 | INFO | train rank=0 | step=49920/50000 progress=99.8% loss=0.096761 lr=6.998928e-10 lr_backbone=6.998928e-10 elapsed=52:03:41 avg_step=3.75s recent_step=3.01s eta=00:05:00
+2026-09-16 18:34:30 | INFO | train rank=0 | step=49930/50000 progress=99.9% loss=0.076597 lr=5.358557e-10 lr_backbone=5.358557e-10 elapsed=52:04:08 avg_step=3.75s recent_step=2.73s eta=00:04:22
+2026-09-16 18:34:57 | INFO | train rank=0 | step=49940/50000 progress=99.9% loss=0.082024 lr=3.936901e-10 lr_backbone=3.936901e-10 elapsed=52:04:35 avg_step=3.75s recent_step=2.71s eta=00:03:45
+2026-09-16 18:35:30 | INFO | train rank=0 | step=49950/50000 progress=99.9% loss=0.110681 lr=2.733960e-10 lr_backbone=2.733960e-10 elapsed=52:05:08 avg_step=3.75s recent_step=3.21s eta=00:03:07
+2026-09-16 18:35:56 | INFO | train rank=0 | step=49960/50000 progress=99.9% loss=0.102438 lr=1.749735e-10 lr_backbone=1.749735e-10 elapsed=52:05:34 avg_step=3.75s recent_step=2.61s eta=00:02:30
+2026-09-16 18:36:23 | INFO | train rank=0 | step=49970/50000 progress=99.9% loss=0.080185 lr=9.842262e-11 lr_backbone=9.842262e-11 elapsed=52:06:01 avg_step=3.75s recent_step=2.69s eta=00:01:52
+2026-09-16 18:36:47 | INFO | train rank=0 | step=49980/50000 progress=100.0% loss=0.101819 lr=4.374339e-11 lr_backbone=4.374339e-11 elapsed=52:06:25 avg_step=3.75s recent_step=2.45s eta=00:01:15
+2026-09-16 18:37:19 | INFO | train rank=0 | step=49990/50000 progress=100.0% loss=0.075504 lr=1.093585e-11 lr_backbone=1.093585e-11 elapsed=52:06:57 avg_step=3.75s recent_step=3.15s eta=00:00:37
+2026-09-16 18:37:47 | INFO | train rank=0 | step=50000/50000 progress=100.0% loss=0.090104 lr=0.000000e+00 lr_backbone=0.000000e+00 elapsed=52:07:25 avg_step=3.75s recent_step=2.79s eta=00:00:00
+2026-09-16 18:38:29 | INFO | train rank=0 | Saved DeepSpeed checkpoint to /data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_50000
+2026-09-16 18:38:29 | INFO | train rank=0 | Finished training final_step=50000/50000 elapsed=52:08:07 avg_step=3.75s
+2026-09-16 18:38:29 | INFO | train rank=0 | Training finished
+2026-09-16 18:39:03 | INFO | train rank=0 | Saved final DeepSpeed checkpoint to /data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16/ds_checkpoint_final
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_50000/global_step50000/mp_rank_00_model_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_50000/global_step50000/mp_rank_00_model_states.pt
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_50000/latest b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_50000/latest
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+global_step50000
\ No newline at end of file
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_50000/zero_to_fp32.py b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_50000/zero_to_fp32.py
new file mode 100644
index 0000000000000000000000000000000000000000..f40f1f0acbbff30e8e8d29906b56aae4e8b8a138
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_50000/zero_to_fp32.py
@@ -0,0 +1,790 @@
+#!/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 gc
+import json
+import numpy as np
+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, AUTOEP_LAYERS_KEY,
+                                            AUTOEP_LAYERS_KEY_LEGACY, AUTOEP_ZERO3_EXPERT_STATE_FORMAT_KEY,
+                                            AUTOEP_ZERO3_PARTITIONED_EXPERT_STATE_FORMAT)
+
+
+@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 _has_autoep_zero3_partitioned_metadata(state_dict):
+    autoep_layers = state_dict.get(AUTOEP_LAYERS_KEY)
+    if autoep_layers is None:
+        autoep_layers = state_dict.get(AUTOEP_LAYERS_KEY_LEGACY)
+    if not isinstance(autoep_layers, list):
+        return False
+    return any(
+        isinstance(entry, dict)
+        and entry.get(AUTOEP_ZERO3_EXPERT_STATE_FORMAT_KEY) == AUTOEP_ZERO3_PARTITIONED_EXPERT_STATE_FORMAT
+        for entry in autoep_layers)
+
+
+def _raise_if_autoep_zero3_partitioned_state(state_dict):
+    if _has_autoep_zero3_partitioned_metadata(state_dict):
+        raise NotImplementedError("zero_to_fp32 does not support AutoEP ZeRO-3 partition-native checkpoints. "
+                                  "AutoEP expert parameters are partitioned over expert replica groups, so "
+                                  "global data-parallel consolidation would produce incomplete expert tensors. "
+                                  "Use ds_to_universal.py for expert-aware conversion.")
+
+
+def _raise_if_autoep_zero3_partitioned_checkpoint(model_files):
+    for file in model_files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+        _raise_if_autoep_zero3_partitioned_state(state_dict)
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+        _raise_if_autoep_zero3_partitioned_state(state_dict)
+
+        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 tqdm(files, desc='Loading checkpoint shards'):
+        state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
+        # 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 ZERO_STAGE not 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}")
+
+    fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] 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}'")
+
+    # parse_model_states rejects AutoEP ZeRO-3 partition-native checkpoints
+    # before the expensive optimizer-shard load below.
+    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}')
+
+    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}")
+
+    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")
+
+
+class GatheredTensor:
+    """
+    A pseudo tensor that collects partitioned weights.
+    It is more memory efficient when there are multiple groups.
+    """
+
+    def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
+        self.flat_groups = flat_groups
+        self.flat_groups_offset = flat_groups_offset
+        self.offset = offset
+        self.partitioned_numel = partitioned_numel
+        self.shape = shape
+        self.dtype = self.flat_groups[0][0].dtype
+
+    def contiguous(self):
+        """
+        Merge partitioned weights from flat_groups into a single tensor.
+        """
+        end_idx = self.offset + self.partitioned_numel
+        world_size = len(self.flat_groups)
+        pad_flat_param_chunks = []
+
+        for rank_i in range(world_size):
+            # for each rank, we need to collect weights from related group/groups
+            flat_groups_at_rank_i = self.flat_groups[rank_i]
+            start_group_id = None
+            end_group_id = None
+            for group_id in range(len(self.flat_groups_offset)):
+                if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
+                    start_group_id = group_id
+                if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
+                    end_group_id = group_id
+                    break
+            # collect weights from related group/groups
+            for group_id in range(start_group_id, end_group_id + 1):
+                flat_tensor = flat_groups_at_rank_i[group_id]
+                start_offset = self.offset - self.flat_groups_offset[group_id]
+                end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
+                pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
+
+        # collect weights from all ranks
+        pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
+        param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
+        return param
+
+
+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 = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * 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
+    flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[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}"
+            )
+
+        # memory efficient tensor
+        tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
+        state_dict[name] = tensor
+        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 to_torch_tensor(state_dict, return_empty_tensor=False):
+    """
+    Convert state_dict of GatheredTensor to torch tensor
+    """
+    torch_state_dict = {}
+    converted_tensors = {}
+    for name, tensor in state_dict.items():
+        tensor_id = id(tensor)
+        if tensor_id in converted_tensors:  # shared tensors
+            shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
+            torch_state_dict[name] = shared_tensor
+        else:
+            converted_tensors[tensor_id] = name
+            if return_empty_tensor:
+                torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
+            else:
+                torch_state_dict[name] = tensor.contiguous()
+    return torch_state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                             tag=None,
+                                             exclude_frozen_parameters=False,
+                                             lazy_mode=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
+        - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
+          Convert the pesduo tensor to torch tensor by ``.contiguous()``
+
+    Returns:
+        - pytorch ``state_dict``
+
+    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.
+
+    Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
+    You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
+    the checkpoint. Or you can load state_dict in lazy mode ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
+        for name, lazy_tensor in state_dict.item():
+            tensor = lazy_tensor.contiguous()  # to cpu
+            print(name, tensor)
+            # del tensor to release memory if it no longer in use
+    """
+    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")
+
+    state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
+    if lazy_mode:
+        return state_dict
+    else:
+        return to_torch_tensor(state_dict)
+
+
+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,
+                                                          lazy_mode=True)
+
+    # 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")
+        # an memory-efficient approach for sharding
+        empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
+        state_dict_split = split_torch_state_dict_into_shards(empty_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 by shard
+    os.makedirs(output_dir, exist_ok=True)
+    filename_to_tensors = state_dict_split.filename_to_tensors.items()
+    for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
+        shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
+        shard_state_dict = to_torch_tensor(shard_state_dict)
+        output_path = os.path.join(output_dir, shard_file)
+        if safe_serialization:
+            save_file(shard_state_dict, output_path, metadata={"format": "pt"})
+        else:
+            torch.save(shard_state_dict, output_path)
+        # release the memory of current shard
+        for tensor_name in list(shard_state_dict.keys()):
+            del state_dict[tensor_name]
+            del shard_state_dict[tensor_name]
+        del shard_state_dict
+        gc.collect()
+
+    # 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("Extracting fp32 weights")
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+
+    logger.info("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)
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..c1085572878f587ae35377b1c9673cb24665dc6b
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_4_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_4_mp_rank_00_optim_states.pt
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new file mode 100644
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_final/global_step50000/mp_rank_00_model_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_final/global_step50000/mp_rank_00_model_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..d9b9d9c494ce26bdd8f625c3b1934db25860eaaa
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_final/latest b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_final/latest
new file mode 100644
index 0000000000000000000000000000000000000000..59a558c0ee32a055bd8fc16bf0d99a5950c59ac1
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@@ -0,0 +1 @@
+global_step50000
\ No newline at end of file
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_final/zero_to_fp32.py b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_final/zero_to_fp32.py
new file mode 100644
index 0000000000000000000000000000000000000000..f40f1f0acbbff30e8e8d29906b56aae4e8b8a138
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_final/zero_to_fp32.py
@@ -0,0 +1,790 @@
+#!/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 gc
+import json
+import numpy as np
+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, AUTOEP_LAYERS_KEY,
+                                            AUTOEP_LAYERS_KEY_LEGACY, AUTOEP_ZERO3_EXPERT_STATE_FORMAT_KEY,
+                                            AUTOEP_ZERO3_PARTITIONED_EXPERT_STATE_FORMAT)
+
+
+@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 _has_autoep_zero3_partitioned_metadata(state_dict):
+    autoep_layers = state_dict.get(AUTOEP_LAYERS_KEY)
+    if autoep_layers is None:
+        autoep_layers = state_dict.get(AUTOEP_LAYERS_KEY_LEGACY)
+    if not isinstance(autoep_layers, list):
+        return False
+    return any(
+        isinstance(entry, dict)
+        and entry.get(AUTOEP_ZERO3_EXPERT_STATE_FORMAT_KEY) == AUTOEP_ZERO3_PARTITIONED_EXPERT_STATE_FORMAT
+        for entry in autoep_layers)
+
+
+def _raise_if_autoep_zero3_partitioned_state(state_dict):
+    if _has_autoep_zero3_partitioned_metadata(state_dict):
+        raise NotImplementedError("zero_to_fp32 does not support AutoEP ZeRO-3 partition-native checkpoints. "
+                                  "AutoEP expert parameters are partitioned over expert replica groups, so "
+                                  "global data-parallel consolidation would produce incomplete expert tensors. "
+                                  "Use ds_to_universal.py for expert-aware conversion.")
+
+
+def _raise_if_autoep_zero3_partitioned_checkpoint(model_files):
+    for file in model_files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+        _raise_if_autoep_zero3_partitioned_state(state_dict)
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+        _raise_if_autoep_zero3_partitioned_state(state_dict)
+
+        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 tqdm(files, desc='Loading checkpoint shards'):
+        state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
+        # 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 ZERO_STAGE not 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}")
+
+    fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] 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}'")
+
+    # parse_model_states rejects AutoEP ZeRO-3 partition-native checkpoints
+    # before the expensive optimizer-shard load below.
+    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}')
+
+    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}")
+
+    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")
+
+
+class GatheredTensor:
+    """
+    A pseudo tensor that collects partitioned weights.
+    It is more memory efficient when there are multiple groups.
+    """
+
+    def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
+        self.flat_groups = flat_groups
+        self.flat_groups_offset = flat_groups_offset
+        self.offset = offset
+        self.partitioned_numel = partitioned_numel
+        self.shape = shape
+        self.dtype = self.flat_groups[0][0].dtype
+
+    def contiguous(self):
+        """
+        Merge partitioned weights from flat_groups into a single tensor.
+        """
+        end_idx = self.offset + self.partitioned_numel
+        world_size = len(self.flat_groups)
+        pad_flat_param_chunks = []
+
+        for rank_i in range(world_size):
+            # for each rank, we need to collect weights from related group/groups
+            flat_groups_at_rank_i = self.flat_groups[rank_i]
+            start_group_id = None
+            end_group_id = None
+            for group_id in range(len(self.flat_groups_offset)):
+                if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
+                    start_group_id = group_id
+                if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
+                    end_group_id = group_id
+                    break
+            # collect weights from related group/groups
+            for group_id in range(start_group_id, end_group_id + 1):
+                flat_tensor = flat_groups_at_rank_i[group_id]
+                start_offset = self.offset - self.flat_groups_offset[group_id]
+                end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
+                pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
+
+        # collect weights from all ranks
+        pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
+        param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
+        return param
+
+
+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 = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * 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
+    flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[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}"
+            )
+
+        # memory efficient tensor
+        tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
+        state_dict[name] = tensor
+        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 to_torch_tensor(state_dict, return_empty_tensor=False):
+    """
+    Convert state_dict of GatheredTensor to torch tensor
+    """
+    torch_state_dict = {}
+    converted_tensors = {}
+    for name, tensor in state_dict.items():
+        tensor_id = id(tensor)
+        if tensor_id in converted_tensors:  # shared tensors
+            shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
+            torch_state_dict[name] = shared_tensor
+        else:
+            converted_tensors[tensor_id] = name
+            if return_empty_tensor:
+                torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
+            else:
+                torch_state_dict[name] = tensor.contiguous()
+    return torch_state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                             tag=None,
+                                             exclude_frozen_parameters=False,
+                                             lazy_mode=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
+        - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
+          Convert the pesduo tensor to torch tensor by ``.contiguous()``
+
+    Returns:
+        - pytorch ``state_dict``
+
+    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.
+
+    Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
+    You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
+    the checkpoint. Or you can load state_dict in lazy mode ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
+        for name, lazy_tensor in state_dict.item():
+            tensor = lazy_tensor.contiguous()  # to cpu
+            print(name, tensor)
+            # del tensor to release memory if it no longer in use
+    """
+    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")
+
+    state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
+    if lazy_mode:
+        return state_dict
+    else:
+        return to_torch_tensor(state_dict)
+
+
+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,
+                                                          lazy_mode=True)
+
+    # 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")
+        # an memory-efficient approach for sharding
+        empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
+        state_dict_split = split_torch_state_dict_into_shards(empty_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 by shard
+    os.makedirs(output_dir, exist_ok=True)
+    filename_to_tensors = state_dict_split.filename_to_tensors.items()
+    for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
+        shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
+        shard_state_dict = to_torch_tensor(shard_state_dict)
+        output_path = os.path.join(output_dir, shard_file)
+        if safe_serialization:
+            save_file(shard_state_dict, output_path, metadata={"format": "pt"})
+        else:
+            torch.save(shard_state_dict, output_path)
+        # release the memory of current shard
+        for tensor_name in list(shard_state_dict.keys()):
+            del state_dict[tensor_name]
+            del shard_state_dict[tensor_name]
+        del shard_state_dict
+        gc.collect()
+
+    # 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("Extracting fp32 weights")
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+
+    logger.info("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)
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/train.log b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..73d2047b58a8e219e45de9e29306c90e8c113fcb
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/train.log
@@ -0,0 +1,5014 @@
+2026-09-17 17:25:01 | INFO | train rank=0 | Starting training project=codebase_v8_droid_joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction save_dir=/data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction distributed=True deepspeed=True world_size=8 device=cuda:0
+2026-09-17 17:25:24 | INFO | train rank=0 | No pretrained checkpoint provided; training from scratch
+2026-09-17 17:25:24 | INFO | train rank=0 | Initializing Droid LeRobot Dataset: path=/data/NTU_slab/draven/data/DROID fps=15.00 use_relabelled_instruction=True
+2026-09-17 17:25:24 | INFO | train rank=0 | Batch config: total_batch_size=128 world_size=8 grad_acc_steps=1 per_device_batch_size=16
+2026-09-17 17:25:24 | INFO | train rank=0 | Dataset image_resize_size=320
+2026-09-17 17:25:24 | INFO | train rank=0 | Droid uses parquet-shard streaming with a per-rank shuffle buffer: batch_size=16 buffer_size=1024 num_workers=8 drop_last=True
+2026-09-17 17:25:31 | INFO | train rank=0 | Droid streaming layout: parquet_shards=50 active_workers=8 worker_buffer_min=128 worker_buffer_max=128 max_active_shards=2
+2026-09-17 17:25:31 | INFO | train rank=0 | Resolved Droid joint action stats from /data/NTU_slab/draven/data/DROID/meta/joint_delta_stats_h16.json: q01=[-0.3050941824913025, -0.47471314668655396, -0.3020089268684387, -0.493502140045166, -0.4844675362110138, -0.4544465243816376, -0.550822913646698] q99=[0.30148017406463623, 0.5795503258705139, 0.3001617193222046, 0.5117637515068054, 0.4750978946685791, 0.5400477647781372, 0.5597710609436035]
+2026-09-17 17:25:31 | INFO | train rank=0 | Trainable params: 2,688,879,624 / 2,688,879,624
+2026-09-17 17:28:36 | INFO | train rank=0 | Starting training start_step=0 max_steps=50000 remaining_steps=50000 log_interval=10 save_interval=50000
+2026-09-17 17:33:00 | INFO | train rank=0 | step=10/50000 progress=0.0% loss=1.152303 lr=3.600000e-07 lr_backbone=3.600000e-07 elapsed=00:04:23 avg_step=26.35s recent_step=26.35s eta=365:54:46
+2026-09-17 17:33:26 | INFO | train rank=0 | step=20/50000 progress=0.0% loss=1.212590 lr=7.600000e-07 lr_backbone=7.600000e-07 elapsed=00:04:49 avg_step=14.50s recent_step=2.65s eta=201:18:14
+2026-09-17 17:33:57 | INFO | train rank=0 | step=30/50000 progress=0.1% loss=1.202617 lr=1.160000e-06 lr_backbone=1.160000e-06 elapsed=00:05:20 avg_step=10.69s recent_step=3.07s eta=148:21:37
+2026-09-17 17:34:22 | INFO | train rank=0 | step=40/50000 progress=0.1% loss=1.182447 lr=1.560000e-06 lr_backbone=1.560000e-06 elapsed=00:05:46 avg_step=8.66s recent_step=2.56s eta=120:07:26
+2026-09-17 17:35:11 | INFO | train rank=0 | step=50/50000 progress=0.1% loss=1.161436 lr=1.960000e-06 lr_backbone=1.960000e-06 elapsed=00:06:35 avg_step=7.90s recent_step=4.89s eta=109:38:11
+2026-09-17 17:35:43 | INFO | train rank=0 | step=60/50000 progress=0.1% loss=1.147813 lr=2.360000e-06 lr_backbone=2.360000e-06 elapsed=00:07:07 avg_step=7.12s recent_step=3.20s eta=98:45:04
+2026-09-17 17:36:15 | INFO | train rank=0 | step=70/50000 progress=0.1% loss=1.162725 lr=2.760000e-06 lr_backbone=2.760000e-06 elapsed=00:07:39 avg_step=6.56s recent_step=3.22s eta=91:00:02
+2026-09-17 17:36:47 | INFO | train rank=0 | step=80/50000 progress=0.2% loss=1.171709 lr=3.160000e-06 lr_backbone=3.160000e-06 elapsed=00:08:10 avg_step=6.14s recent_step=3.15s eta=85:04:22
+2026-09-17 17:37:39 | INFO | train rank=0 | step=90/50000 progress=0.2% loss=1.157530 lr=3.560000e-06 lr_backbone=3.560000e-06 elapsed=00:09:02 avg_step=6.03s recent_step=5.21s eta=83:38:17
+2026-09-17 17:38:15 | INFO | train rank=0 | step=100/50000 progress=0.2% loss=1.128714 lr=3.960000e-06 lr_backbone=3.960000e-06 elapsed=00:09:38 avg_step=5.79s recent_step=3.58s eta=80:13:23
+2026-09-17 17:38:44 | INFO | train rank=0 | step=110/50000 progress=0.2% loss=1.114809 lr=4.360000e-06 lr_backbone=4.360000e-06 elapsed=00:10:07 avg_step=5.52s recent_step=2.88s eta=76:32:30
+2026-09-17 17:39:13 | INFO | train rank=0 | step=120/50000 progress=0.2% loss=1.136812 lr=4.760000e-06 lr_backbone=4.760000e-06 elapsed=00:10:36 avg_step=5.30s recent_step=2.89s eta=73:28:55
+2026-09-17 17:40:03 | INFO | train rank=0 | step=130/50000 progress=0.3% loss=1.013976 lr=5.160000e-06 lr_backbone=5.160000e-06 elapsed=00:11:26 avg_step=5.28s recent_step=5.04s eta=73:11:05
+2026-09-17 17:40:36 | INFO | train rank=0 | step=140/50000 progress=0.3% loss=1.045131 lr=5.560000e-06 lr_backbone=5.560000e-06 elapsed=00:11:59 avg_step=5.14s recent_step=3.29s eta=71:11:55
+2026-09-17 17:41:10 | INFO | train rank=0 | step=150/50000 progress=0.3% loss=1.039700 lr=5.960000e-06 lr_backbone=5.960000e-06 elapsed=00:12:33 avg_step=5.03s recent_step=3.42s eta=69:35:35
+2026-09-17 17:41:39 | INFO | train rank=0 | step=160/50000 progress=0.3% loss=1.004089 lr=6.360000e-06 lr_backbone=6.360000e-06 elapsed=00:13:02 avg_step=4.89s recent_step=2.85s eta=67:41:57
+2026-09-17 17:42:23 | INFO | train rank=0 | step=170/50000 progress=0.3% loss=0.967818 lr=6.760000e-06 lr_backbone=6.760000e-06 elapsed=00:13:46 avg_step=4.86s recent_step=4.44s eta=67:19:17
+2026-09-17 17:42:56 | INFO | train rank=0 | step=180/50000 progress=0.4% loss=0.910042 lr=7.160000e-06 lr_backbone=7.160000e-06 elapsed=00:14:20 avg_step=4.78s recent_step=3.33s eta=66:07:57
+2026-09-17 17:43:26 | INFO | train rank=0 | step=190/50000 progress=0.4% loss=0.892380 lr=7.560000e-06 lr_backbone=7.560000e-06 elapsed=00:14:49 avg_step=4.68s recent_step=2.97s eta=64:48:02
+2026-09-17 17:44:00 | INFO | train rank=0 | step=200/50000 progress=0.4% loss=0.949434 lr=7.960000e-06 lr_backbone=7.960000e-06 elapsed=00:15:23 avg_step=4.62s recent_step=3.39s eta=63:53:35
+2026-09-17 17:44:52 | INFO | train rank=0 | step=210/50000 progress=0.4% loss=0.907690 lr=8.360000e-06 lr_backbone=8.360000e-06 elapsed=00:16:15 avg_step=4.65s recent_step=5.17s eta=64:14:39
+2026-09-17 17:45:12 | INFO | train rank=0 | step=220/50000 progress=0.4% loss=0.721573 lr=8.760000e-06 lr_backbone=8.760000e-06 elapsed=00:16:35 avg_step=4.53s recent_step=2.05s eta=62:35:59
+2026-09-17 17:45:42 | INFO | train rank=0 | step=230/50000 progress=0.5% loss=0.762826 lr=9.160000e-06 lr_backbone=9.160000e-06 elapsed=00:17:05 avg_step=4.46s recent_step=2.95s eta=61:38:11
+2026-09-17 17:46:13 | INFO | train rank=0 | step=240/50000 progress=0.5% loss=0.660649 lr=9.560000e-06 lr_backbone=9.560000e-06 elapsed=00:17:36 avg_step=4.40s recent_step=3.10s eta=60:50:26
+2026-09-17 17:47:08 | INFO | train rank=0 | step=250/50000 progress=0.5% loss=0.589996 lr=9.960000e-06 lr_backbone=9.960000e-06 elapsed=00:18:31 avg_step=4.45s recent_step=5.54s eta=61:27:35
+2026-09-17 17:47:41 | INFO | train rank=0 | step=260/50000 progress=0.5% loss=0.504285 lr=1.036000e-05 lr_backbone=1.036000e-05 elapsed=00:19:05 avg_step=4.40s recent_step=3.34s eta=60:51:39
+2026-09-17 17:48:14 | INFO | train rank=0 | step=270/50000 progress=0.5% loss=0.521228 lr=1.076000e-05 lr_backbone=1.076000e-05 elapsed=00:19:38 avg_step=4.36s recent_step=3.30s eta=60:17:04
+2026-09-17 17:48:48 | INFO | train rank=0 | step=280/50000 progress=0.6% loss=0.388657 lr=1.116000e-05 lr_backbone=1.116000e-05 elapsed=00:20:11 avg_step=4.33s recent_step=3.32s eta=59:45:28
+2026-09-17 17:49:23 | INFO | train rank=0 | step=290/50000 progress=0.6% loss=0.372032 lr=1.156000e-05 lr_backbone=1.156000e-05 elapsed=00:20:46 avg_step=4.30s recent_step=3.54s eta=59:22:11
+2026-09-17 17:50:00 | INFO | train rank=0 | step=300/50000 progress=0.6% loss=0.362246 lr=1.196000e-05 lr_backbone=1.196000e-05 elapsed=00:21:24 avg_step=4.28s recent_step=3.72s eta=59:05:35
+2026-09-17 17:50:31 | INFO | train rank=0 | step=310/50000 progress=0.6% loss=0.346144 lr=1.236000e-05 lr_backbone=1.236000e-05 elapsed=00:21:55 avg_step=4.24s recent_step=3.09s eta=58:33:06
+2026-09-17 17:51:03 | INFO | train rank=0 | step=320/50000 progress=0.6% loss=0.412824 lr=1.276000e-05 lr_backbone=1.276000e-05 elapsed=00:22:26 avg_step=4.21s recent_step=3.19s eta=58:05:09
+2026-09-17 17:51:39 | INFO | train rank=0 | step=330/50000 progress=0.7% loss=0.334619 lr=1.316000e-05 lr_backbone=1.316000e-05 elapsed=00:23:03 avg_step=4.19s recent_step=3.61s eta=57:49:24
+2026-09-17 17:52:22 | INFO | train rank=0 | step=340/50000 progress=0.7% loss=0.381252 lr=1.356000e-05 lr_backbone=1.356000e-05 elapsed=00:23:45 avg_step=4.19s recent_step=4.27s eta=57:50:39
+2026-09-17 17:52:53 | INFO | train rank=0 | step=350/50000 progress=0.7% loss=0.260806 lr=1.396000e-05 lr_backbone=1.396000e-05 elapsed=00:24:16 avg_step=4.16s recent_step=3.08s eta=57:23:35
+2026-09-17 17:53:27 | INFO | train rank=0 | step=360/50000 progress=0.7% loss=0.292349 lr=1.436000e-05 lr_backbone=1.436000e-05 elapsed=00:24:50 avg_step=4.14s recent_step=3.42s eta=57:05:48
+2026-09-17 17:53:57 | INFO | train rank=0 | step=370/50000 progress=0.7% loss=0.331474 lr=1.476000e-05 lr_backbone=1.476000e-05 elapsed=00:25:21 avg_step=4.11s recent_step=3.04s eta=56:40:26
+2026-09-17 17:54:46 | INFO | train rank=0 | step=380/50000 progress=0.8% loss=0.267736 lr=1.516000e-05 lr_backbone=1.516000e-05 elapsed=00:26:09 avg_step=4.13s recent_step=4.86s eta=56:56:07
+2026-09-17 17:55:13 | INFO | train rank=0 | step=390/50000 progress=0.8% loss=0.325171 lr=1.556000e-05 lr_backbone=1.556000e-05 elapsed=00:26:36 avg_step=4.09s recent_step=2.71s eta=56:25:25
+2026-09-17 17:55:40 | INFO | train rank=0 | step=400/50000 progress=0.8% loss=0.275442 lr=1.596000e-05 lr_backbone=1.596000e-05 elapsed=00:27:03 avg_step=4.06s recent_step=2.65s eta=55:54:52
+2026-09-17 17:56:28 | INFO | train rank=0 | step=410/50000 progress=0.8% loss=0.301020 lr=1.636000e-05 lr_backbone=1.636000e-05 elapsed=00:27:51 avg_step=4.08s recent_step=4.83s eta=56:09:39
+2026-09-17 17:57:02 | INFO | train rank=0 | step=420/50000 progress=0.8% loss=0.280966 lr=1.676000e-05 lr_backbone=1.676000e-05 elapsed=00:28:26 avg_step=4.06s recent_step=3.45s eta=55:56:41
+2026-09-17 17:57:36 | INFO | train rank=0 | step=430/50000 progress=0.9% loss=0.280025 lr=1.716000e-05 lr_backbone=1.716000e-05 elapsed=00:28:59 avg_step=4.05s recent_step=3.33s eta=55:41:54
+2026-09-17 17:58:04 | INFO | train rank=0 | step=440/50000 progress=0.9% loss=0.228260 lr=1.756000e-05 lr_backbone=1.756000e-05 elapsed=00:29:27 avg_step=4.02s recent_step=2.80s eta=55:17:47
+2026-09-17 17:58:59 | INFO | train rank=0 | step=450/50000 progress=0.9% loss=0.200583 lr=1.796000e-05 lr_backbone=1.796000e-05 elapsed=00:30:23 avg_step=4.05s recent_step=5.59s eta=55:45:57
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+2026-09-19 21:08:35 | INFO | train rank=0 | step=49930/50000 progress=99.9% loss=0.073409 lr=5.358557e-10 lr_backbone=5.358557e-10 elapsed=51:39:58 avg_step=3.73s recent_step=3.14s eta=00:04:20
+2026-09-19 21:09:26 | INFO | train rank=0 | step=49940/50000 progress=99.9% loss=0.076117 lr=3.936901e-10 lr_backbone=3.936901e-10 elapsed=51:40:49 avg_step=3.73s recent_step=5.09s eta=00:03:43
+2026-09-19 21:10:04 | INFO | train rank=0 | step=49950/50000 progress=99.9% loss=0.086401 lr=2.733960e-10 lr_backbone=2.733960e-10 elapsed=51:41:27 avg_step=3.73s recent_step=3.76s eta=00:03:06
+2026-09-19 21:10:38 | INFO | train rank=0 | step=49960/50000 progress=99.9% loss=0.066509 lr=1.749735e-10 lr_backbone=1.749735e-10 elapsed=51:42:01 avg_step=3.73s recent_step=3.45s eta=00:02:29
+2026-09-19 21:11:09 | INFO | train rank=0 | step=49970/50000 progress=99.9% loss=0.077455 lr=9.842262e-11 lr_backbone=9.842262e-11 elapsed=51:42:33 avg_step=3.73s recent_step=3.11s eta=00:01:51
+2026-09-19 21:12:01 | INFO | train rank=0 | step=49980/50000 progress=100.0% loss=0.087184 lr=4.374339e-11 lr_backbone=4.374339e-11 elapsed=51:43:24 avg_step=3.73s recent_step=5.19s eta=00:01:14
+2026-09-19 21:12:34 | INFO | train rank=0 | step=49990/50000 progress=100.0% loss=0.071303 lr=1.093585e-11 lr_backbone=1.093585e-11 elapsed=51:43:57 avg_step=3.73s recent_step=3.26s eta=00:00:37
+2026-09-19 21:13:08 | INFO | train rank=0 | step=50000/50000 progress=100.0% loss=0.069159 lr=0.000000e+00 lr_backbone=0.000000e+00 elapsed=51:44:31 avg_step=3.73s recent_step=3.38s eta=00:00:00
+2026-09-19 21:13:34 | INFO | train rank=0 | Saved DeepSpeed checkpoint to /data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_50000
+2026-09-19 21:13:34 | INFO | train rank=0 | Finished training final_step=50000/50000 elapsed=51:44:57 avg_step=3.73s
+2026-09-19 21:13:34 | INFO | train rank=0 | Training finished
+2026-09-19 21:13:54 | INFO | train rank=0 | Saved final DeepSpeed checkpoint to /data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_new_instruction/ds_checkpoint_final
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..865538cf7e88d1bcf1a8b3a89e07f57d84b9f8e1
--- /dev/null
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..e8b5045ca912eaf505134a2fee1df200066976fd
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_4_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_4_mp_rank_00_optim_states.pt
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_5_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_5_mp_rank_00_optim_states.pt
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_6_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_6_mp_rank_00_optim_states.pt
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_7_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_7_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/global_step50000/mp_rank_00_model_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/global_step50000/mp_rank_00_model_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..8b2f83897249332f321c95b85811ec95e4521661
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/global_step50000/mp_rank_00_model_states.pt
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diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/latest b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/latest
new file mode 100644
index 0000000000000000000000000000000000000000..59a558c0ee32a055bd8fc16bf0d99a5950c59ac1
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+global_step50000
\ No newline at end of file
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/zero_to_fp32.py b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/zero_to_fp32.py
new file mode 100644
index 0000000000000000000000000000000000000000..f40f1f0acbbff30e8e8d29906b56aae4e8b8a138
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000/zero_to_fp32.py
@@ -0,0 +1,790 @@
+#!/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 gc
+import json
+import numpy as np
+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, AUTOEP_LAYERS_KEY,
+                                            AUTOEP_LAYERS_KEY_LEGACY, AUTOEP_ZERO3_EXPERT_STATE_FORMAT_KEY,
+                                            AUTOEP_ZERO3_PARTITIONED_EXPERT_STATE_FORMAT)
+
+
+@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 _has_autoep_zero3_partitioned_metadata(state_dict):
+    autoep_layers = state_dict.get(AUTOEP_LAYERS_KEY)
+    if autoep_layers is None:
+        autoep_layers = state_dict.get(AUTOEP_LAYERS_KEY_LEGACY)
+    if not isinstance(autoep_layers, list):
+        return False
+    return any(
+        isinstance(entry, dict)
+        and entry.get(AUTOEP_ZERO3_EXPERT_STATE_FORMAT_KEY) == AUTOEP_ZERO3_PARTITIONED_EXPERT_STATE_FORMAT
+        for entry in autoep_layers)
+
+
+def _raise_if_autoep_zero3_partitioned_state(state_dict):
+    if _has_autoep_zero3_partitioned_metadata(state_dict):
+        raise NotImplementedError("zero_to_fp32 does not support AutoEP ZeRO-3 partition-native checkpoints. "
+                                  "AutoEP expert parameters are partitioned over expert replica groups, so "
+                                  "global data-parallel consolidation would produce incomplete expert tensors. "
+                                  "Use ds_to_universal.py for expert-aware conversion.")
+
+
+def _raise_if_autoep_zero3_partitioned_checkpoint(model_files):
+    for file in model_files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+        _raise_if_autoep_zero3_partitioned_state(state_dict)
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+        _raise_if_autoep_zero3_partitioned_state(state_dict)
+
+        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 tqdm(files, desc='Loading checkpoint shards'):
+        state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
+        # 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 ZERO_STAGE not 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}")
+
+    fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] 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}'")
+
+    # parse_model_states rejects AutoEP ZeRO-3 partition-native checkpoints
+    # before the expensive optimizer-shard load below.
+    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}')
+
+    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}")
+
+    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")
+
+
+class GatheredTensor:
+    """
+    A pseudo tensor that collects partitioned weights.
+    It is more memory efficient when there are multiple groups.
+    """
+
+    def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
+        self.flat_groups = flat_groups
+        self.flat_groups_offset = flat_groups_offset
+        self.offset = offset
+        self.partitioned_numel = partitioned_numel
+        self.shape = shape
+        self.dtype = self.flat_groups[0][0].dtype
+
+    def contiguous(self):
+        """
+        Merge partitioned weights from flat_groups into a single tensor.
+        """
+        end_idx = self.offset + self.partitioned_numel
+        world_size = len(self.flat_groups)
+        pad_flat_param_chunks = []
+
+        for rank_i in range(world_size):
+            # for each rank, we need to collect weights from related group/groups
+            flat_groups_at_rank_i = self.flat_groups[rank_i]
+            start_group_id = None
+            end_group_id = None
+            for group_id in range(len(self.flat_groups_offset)):
+                if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
+                    start_group_id = group_id
+                if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
+                    end_group_id = group_id
+                    break
+            # collect weights from related group/groups
+            for group_id in range(start_group_id, end_group_id + 1):
+                flat_tensor = flat_groups_at_rank_i[group_id]
+                start_offset = self.offset - self.flat_groups_offset[group_id]
+                end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
+                pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
+
+        # collect weights from all ranks
+        pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
+        param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
+        return param
+
+
+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 = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * 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
+    flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[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}"
+            )
+
+        # memory efficient tensor
+        tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
+        state_dict[name] = tensor
+        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 to_torch_tensor(state_dict, return_empty_tensor=False):
+    """
+    Convert state_dict of GatheredTensor to torch tensor
+    """
+    torch_state_dict = {}
+    converted_tensors = {}
+    for name, tensor in state_dict.items():
+        tensor_id = id(tensor)
+        if tensor_id in converted_tensors:  # shared tensors
+            shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
+            torch_state_dict[name] = shared_tensor
+        else:
+            converted_tensors[tensor_id] = name
+            if return_empty_tensor:
+                torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
+            else:
+                torch_state_dict[name] = tensor.contiguous()
+    return torch_state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                             tag=None,
+                                             exclude_frozen_parameters=False,
+                                             lazy_mode=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
+        - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
+          Convert the pesduo tensor to torch tensor by ``.contiguous()``
+
+    Returns:
+        - pytorch ``state_dict``
+
+    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.
+
+    Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
+    You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
+    the checkpoint. Or you can load state_dict in lazy mode ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
+        for name, lazy_tensor in state_dict.item():
+            tensor = lazy_tensor.contiguous()  # to cpu
+            print(name, tensor)
+            # del tensor to release memory if it no longer in use
+    """
+    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")
+
+    state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
+    if lazy_mode:
+        return state_dict
+    else:
+        return to_torch_tensor(state_dict)
+
+
+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,
+                                                          lazy_mode=True)
+
+    # 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")
+        # an memory-efficient approach for sharding
+        empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
+        state_dict_split = split_torch_state_dict_into_shards(empty_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 by shard
+    os.makedirs(output_dir, exist_ok=True)
+    filename_to_tensors = state_dict_split.filename_to_tensors.items()
+    for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
+        shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
+        shard_state_dict = to_torch_tensor(shard_state_dict)
+        output_path = os.path.join(output_dir, shard_file)
+        if safe_serialization:
+            save_file(shard_state_dict, output_path, metadata={"format": "pt"})
+        else:
+            torch.save(shard_state_dict, output_path)
+        # release the memory of current shard
+        for tensor_name in list(shard_state_dict.keys()):
+            del state_dict[tensor_name]
+            del shard_state_dict[tensor_name]
+        del shard_state_dict
+        gc.collect()
+
+    # 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("Extracting fp32 weights")
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+
+    logger.info("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)
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..865538cf7e88d1bcf1a8b3a89e07f57d84b9f8e1
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e87d11c81d18c4e78991c5dff9b78a25fe8974eeab9c283b6e4d0929d554a331
+size 4033352816
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..e8b5045ca912eaf505134a2fee1df200066976fd
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 4033337776
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..16ab75f268fe8c0770e0f3d6ef0ced9aa0655afa
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 4033334576
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..51161144bb43dec5e5fee1a57474dc9a2da0702d
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:38ce052a74b0f1ea76f96cc5110560122e900dc267f3308d24b5572306e186ff
+size 4033337712
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_4_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_4_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..fdf2b883986bfe70c0272b2b1c154720830d2617
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_4_mp_rank_00_optim_states.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 4033337904
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_5_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_5_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..9c5c612f9bea4de29a2691bf03b470b91a3de8d6
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_5_mp_rank_00_optim_states.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 4033338800
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_6_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_6_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..3e6a72275afa178ecc83b823d4382ec632b25279
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_6_mp_rank_00_optim_states.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e8c2ea3b9109044ae45fddf035a4c231622ec626002cb7b6fa5172f229ce53fc
+size 4033338160
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_7_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_7_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..f2b32b124484daf1a3d8993d871c5d484597f6bc
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_7_mp_rank_00_optim_states.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d2916c687b512eaf7ebba6e8bd88bbe285d9579c94cd1e7bdd3aa511d7954a70
+size 4033337712
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/mp_rank_00_model_states.pt b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/mp_rank_00_model_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..8b2f83897249332f321c95b85811ec95e4521661
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/global_step50000/mp_rank_00_model_states.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b6a76a3a4eb76a0228e956f75589e4b6fe93e736d09ffcf4f4cf309dacf3699c
+size 5378167832
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/latest b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/latest
new file mode 100644
index 0000000000000000000000000000000000000000..59a558c0ee32a055bd8fc16bf0d99a5950c59ac1
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/latest
@@ -0,0 +1 @@
+global_step50000
\ No newline at end of file
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/zero_to_fp32.py b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/zero_to_fp32.py
new file mode 100644
index 0000000000000000000000000000000000000000..f40f1f0acbbff30e8e8d29906b56aae4e8b8a138
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final/zero_to_fp32.py
@@ -0,0 +1,790 @@
+#!/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 gc
+import json
+import numpy as np
+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, AUTOEP_LAYERS_KEY,
+                                            AUTOEP_LAYERS_KEY_LEGACY, AUTOEP_ZERO3_EXPERT_STATE_FORMAT_KEY,
+                                            AUTOEP_ZERO3_PARTITIONED_EXPERT_STATE_FORMAT)
+
+
+@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 _has_autoep_zero3_partitioned_metadata(state_dict):
+    autoep_layers = state_dict.get(AUTOEP_LAYERS_KEY)
+    if autoep_layers is None:
+        autoep_layers = state_dict.get(AUTOEP_LAYERS_KEY_LEGACY)
+    if not isinstance(autoep_layers, list):
+        return False
+    return any(
+        isinstance(entry, dict)
+        and entry.get(AUTOEP_ZERO3_EXPERT_STATE_FORMAT_KEY) == AUTOEP_ZERO3_PARTITIONED_EXPERT_STATE_FORMAT
+        for entry in autoep_layers)
+
+
+def _raise_if_autoep_zero3_partitioned_state(state_dict):
+    if _has_autoep_zero3_partitioned_metadata(state_dict):
+        raise NotImplementedError("zero_to_fp32 does not support AutoEP ZeRO-3 partition-native checkpoints. "
+                                  "AutoEP expert parameters are partitioned over expert replica groups, so "
+                                  "global data-parallel consolidation would produce incomplete expert tensors. "
+                                  "Use ds_to_universal.py for expert-aware conversion.")
+
+
+def _raise_if_autoep_zero3_partitioned_checkpoint(model_files):
+    for file in model_files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+        _raise_if_autoep_zero3_partitioned_state(state_dict)
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+        _raise_if_autoep_zero3_partitioned_state(state_dict)
+
+        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 tqdm(files, desc='Loading checkpoint shards'):
+        state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
+        # 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 ZERO_STAGE not 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}")
+
+    fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] 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}'")
+
+    # parse_model_states rejects AutoEP ZeRO-3 partition-native checkpoints
+    # before the expensive optimizer-shard load below.
+    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}')
+
+    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}")
+
+    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")
+
+
+class GatheredTensor:
+    """
+    A pseudo tensor that collects partitioned weights.
+    It is more memory efficient when there are multiple groups.
+    """
+
+    def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
+        self.flat_groups = flat_groups
+        self.flat_groups_offset = flat_groups_offset
+        self.offset = offset
+        self.partitioned_numel = partitioned_numel
+        self.shape = shape
+        self.dtype = self.flat_groups[0][0].dtype
+
+    def contiguous(self):
+        """
+        Merge partitioned weights from flat_groups into a single tensor.
+        """
+        end_idx = self.offset + self.partitioned_numel
+        world_size = len(self.flat_groups)
+        pad_flat_param_chunks = []
+
+        for rank_i in range(world_size):
+            # for each rank, we need to collect weights from related group/groups
+            flat_groups_at_rank_i = self.flat_groups[rank_i]
+            start_group_id = None
+            end_group_id = None
+            for group_id in range(len(self.flat_groups_offset)):
+                if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
+                    start_group_id = group_id
+                if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
+                    end_group_id = group_id
+                    break
+            # collect weights from related group/groups
+            for group_id in range(start_group_id, end_group_id + 1):
+                flat_tensor = flat_groups_at_rank_i[group_id]
+                start_offset = self.offset - self.flat_groups_offset[group_id]
+                end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
+                pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
+
+        # collect weights from all ranks
+        pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
+        param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
+        return param
+
+
+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 = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * 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
+    flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[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}"
+            )
+
+        # memory efficient tensor
+        tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
+        state_dict[name] = tensor
+        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 to_torch_tensor(state_dict, return_empty_tensor=False):
+    """
+    Convert state_dict of GatheredTensor to torch tensor
+    """
+    torch_state_dict = {}
+    converted_tensors = {}
+    for name, tensor in state_dict.items():
+        tensor_id = id(tensor)
+        if tensor_id in converted_tensors:  # shared tensors
+            shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
+            torch_state_dict[name] = shared_tensor
+        else:
+            converted_tensors[tensor_id] = name
+            if return_empty_tensor:
+                torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
+            else:
+                torch_state_dict[name] = tensor.contiguous()
+    return torch_state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                             tag=None,
+                                             exclude_frozen_parameters=False,
+                                             lazy_mode=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
+        - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
+          Convert the pesduo tensor to torch tensor by ``.contiguous()``
+
+    Returns:
+        - pytorch ``state_dict``
+
+    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.
+
+    Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
+    You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
+    the checkpoint. Or you can load state_dict in lazy mode ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
+        for name, lazy_tensor in state_dict.item():
+            tensor = lazy_tensor.contiguous()  # to cpu
+            print(name, tensor)
+            # del tensor to release memory if it no longer in use
+    """
+    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")
+
+    state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
+    if lazy_mode:
+        return state_dict
+    else:
+        return to_torch_tensor(state_dict)
+
+
+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,
+                                                          lazy_mode=True)
+
+    # 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")
+        # an memory-efficient approach for sharding
+        empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
+        state_dict_split = split_torch_state_dict_into_shards(empty_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 by shard
+    os.makedirs(output_dir, exist_ok=True)
+    filename_to_tensors = state_dict_split.filename_to_tensors.items()
+    for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
+        shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
+        shard_state_dict = to_torch_tensor(shard_state_dict)
+        output_path = os.path.join(output_dir, shard_file)
+        if safe_serialization:
+            save_file(shard_state_dict, output_path, metadata={"format": "pt"})
+        else:
+            torch.save(shard_state_dict, output_path)
+        # release the memory of current shard
+        for tensor_name in list(shard_state_dict.keys()):
+            del state_dict[tensor_name]
+            del shard_state_dict[tensor_name]
+        del shard_state_dict
+        gc.collect()
+
+    # 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("Extracting fp32 weights")
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+
+    logger.info("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)
diff --git a/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/train.log b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..c198b85ce31a0333a8ac465d45a68c8328ca7bb5
--- /dev/null
+++ b/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/train.log
@@ -0,0 +1,5033 @@
+2026-09-20 05:54:07 | INFO | train rank=0 | Starting training project=codebase_v8_droid_joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset save_dir=/data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset distributed=True deepspeed=True world_size=8 device=cuda:0
+2026-09-20 05:54:30 | INFO | train rank=0 | No pretrained checkpoint provided; training from scratch
+2026-09-20 05:54:30 | INFO | train rank=0 | Initializing Droid LeRobot Dataset: path=/data/NTU_slab/draven/data/DROID_dreamzero_train fps=15.00 use_relabelled_instruction=False
+2026-09-20 05:54:30 | INFO | train rank=0 | Batch config: total_batch_size=128 world_size=8 grad_acc_steps=1 per_device_batch_size=16
+2026-09-20 05:54:30 | INFO | train rank=0 | Dataset image_resize_size=320
+2026-09-20 05:54:30 | INFO | train rank=0 | Droid uses parquet-shard streaming with a per-rank shuffle buffer: batch_size=16 buffer_size=1024 num_workers=8 drop_last=True
+2026-09-20 05:59:04 | INFO | train rank=0 | Starting training project=codebase_v8_droid_joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset save_dir=/data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset distributed=True deepspeed=True world_size=8 device=cuda:0
+2026-09-20 05:59:27 | INFO | train rank=0 | No pretrained checkpoint provided; training from scratch
+2026-09-20 05:59:27 | INFO | train rank=0 | Initializing Droid LeRobot Dataset: path=/data/NTU_slab/draven/data/DROID_dreamzero_train fps=15.00 use_relabelled_instruction=False
+2026-09-20 05:59:27 | INFO | train rank=0 | Batch config: total_batch_size=128 world_size=8 grad_acc_steps=1 per_device_batch_size=16
+2026-09-20 05:59:27 | INFO | train rank=0 | Dataset image_resize_size=320
+2026-09-20 05:59:27 | INFO | train rank=0 | Droid uses parquet-shard streaming with a per-rank shuffle buffer: batch_size=16 buffer_size=1024 num_workers=8 drop_last=True
+2026-09-20 06:06:18 | INFO | train rank=0 | Starting training project=codebase_v8_droid_joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset save_dir=/data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset distributed=True deepspeed=True world_size=8 device=cuda:0
+2026-09-20 06:06:41 | INFO | train rank=0 | No pretrained checkpoint provided; training from scratch
+2026-09-20 06:06:41 | INFO | train rank=0 | Initializing Droid LeRobot Dataset: path=/data/NTU_slab/draven/data/DROID_dreamzero_train fps=15.00 use_relabelled_instruction=False
+2026-09-20 06:06:41 | INFO | train rank=0 | Batch config: total_batch_size=128 world_size=8 grad_acc_steps=1 per_device_batch_size=16
+2026-09-20 06:06:41 | INFO | train rank=0 | Dataset image_resize_size=320
+2026-09-20 06:06:41 | INFO | train rank=0 | Droid uses parquet-shard streaming with a per-rank shuffle buffer: batch_size=16 buffer_size=1024 num_workers=8 drop_last=True
+2026-09-20 06:09:33 | INFO | train rank=0 | Starting training project=codebase_v8_droid_joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset save_dir=/data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset distributed=True deepspeed=True world_size=8 device=cuda:0
+2026-09-20 06:31:51 | INFO | train rank=0 | Starting training project=codebase_v8_droid_joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset save_dir=/data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset distributed=True deepspeed=True world_size=8 device=cuda:0
+2026-09-20 06:32:14 | INFO | train rank=0 | No pretrained checkpoint provided; training from scratch
+2026-09-20 06:32:14 | INFO | train rank=0 | Initializing Droid LeRobot Dataset: path=/data/NTU_slab/draven/data/DROID_dreamzero_train fps=15.00 use_relabelled_instruction=False
+2026-09-20 06:32:14 | INFO | train rank=0 | Batch config: total_batch_size=128 world_size=8 grad_acc_steps=1 per_device_batch_size=16
+2026-09-20 06:32:14 | INFO | train rank=0 | Dataset image_resize_size=320
+2026-09-20 06:32:14 | INFO | train rank=0 | Droid uses parquet-shard streaming with a per-rank shuffle buffer: batch_size=16 buffer_size=1024 num_workers=8 drop_last=True
+2026-09-20 06:35:06 | INFO | train rank=0 | Droid streaming layout: parquet_shards=57774 active_workers=8 worker_buffer_min=128 worker_buffer_max=128 max_active_shards=2
+2026-09-20 06:35:06 | INFO | train rank=0 | Resolved Droid joint action stats from /data/NTU_slab/draven/data/DROID_dreamzero_train/meta/joint_delta_stats_h16.json: q01=[-0.30741822719573975, -0.4808177649974823, -0.3037826418876648, -0.4987107813358307, -0.4846664369106293, -0.4581000804901123, -0.5548821091651917] q99=[0.302254855632782, 0.5667306780815125, 0.3019354045391083, 0.5085083246231079, 0.47911858558654785, 0.533471405506134, 0.5613947510719299]
+2026-09-20 06:35:06 | INFO | train rank=0 | Trainable params: 2,688,879,624 / 2,688,879,624
+2026-09-20 06:38:00 | INFO | train rank=0 | Starting training start_step=0 max_steps=50000 remaining_steps=50000 log_interval=10 save_interval=50000
+2026-09-20 06:40:03 | INFO | train rank=0 | step=10/50000 progress=0.0% loss=1.127002 lr=3.600000e-07 lr_backbone=3.600000e-07 elapsed=00:02:02 avg_step=12.29s recent_step=12.29s eta=170:42:57
+2026-09-20 06:40:14 | INFO | train rank=0 | step=20/50000 progress=0.0% loss=1.219113 lr=7.600000e-07 lr_backbone=7.600000e-07 elapsed=00:02:14 avg_step=6.72s recent_step=1.15s eta=93:18:32
+2026-09-20 06:40:30 | INFO | train rank=0 | step=30/50000 progress=0.1% loss=1.207310 lr=1.160000e-06 lr_backbone=1.160000e-06 elapsed=00:02:29 avg_step=5.00s recent_step=1.55s eta=69:20:57
+2026-09-20 06:40:45 | INFO | train rank=0 | step=40/50000 progress=0.1% loss=1.186673 lr=1.560000e-06 lr_backbone=1.560000e-06 elapsed=00:02:44 avg_step=4.12s recent_step=1.49s eta=57:10:46
+2026-09-20 06:41:08 | INFO | train rank=0 | step=50/50000 progress=0.1% loss=1.186347 lr=1.960000e-06 lr_backbone=1.960000e-06 elapsed=00:03:07 avg_step=3.76s recent_step=2.31s eta=52:09:13
+2026-09-20 06:41:19 | INFO | train rank=0 | step=60/50000 progress=0.1% loss=1.192854 lr=2.360000e-06 lr_backbone=2.360000e-06 elapsed=00:03:18 avg_step=3.31s recent_step=1.08s eta=45:56:27
+2026-09-20 06:41:34 | INFO | train rank=0 | step=70/50000 progress=0.1% loss=1.165909 lr=2.760000e-06 lr_backbone=2.760000e-06 elapsed=00:03:34 avg_step=3.06s recent_step=1.54s eta=42:25:27
+2026-09-20 06:41:46 | INFO | train rank=0 | step=80/50000 progress=0.2% loss=1.158024 lr=3.160000e-06 lr_backbone=3.160000e-06 elapsed=00:03:46 avg_step=2.83s recent_step=1.21s eta=39:12:56
+2026-09-20 06:42:06 | INFO | train rank=0 | step=90/50000 progress=0.2% loss=1.136001 lr=3.560000e-06 lr_backbone=3.560000e-06 elapsed=00:04:06 avg_step=2.74s recent_step=2.03s eta=37:58:35
+2026-09-20 06:42:18 | INFO | train rank=0 | step=100/50000 progress=0.2% loss=1.126919 lr=3.960000e-06 lr_backbone=3.960000e-06 elapsed=00:04:18 avg_step=2.58s recent_step=1.15s eta=35:45:52
+2026-09-20 06:42:30 | INFO | train rank=0 | step=110/50000 progress=0.2% loss=1.065790 lr=4.360000e-06 lr_backbone=4.360000e-06 elapsed=00:04:30 avg_step=2.46s recent_step=1.22s eta=34:02:56
+2026-09-20 06:42:42 | INFO | train rank=0 | step=120/50000 progress=0.2% loss=1.122360 lr=4.760000e-06 lr_backbone=4.760000e-06 elapsed=00:04:42 avg_step=2.35s recent_step=1.20s eta=32:35:10
+2026-09-20 06:43:01 | INFO | train rank=0 | step=130/50000 progress=0.3% loss=1.013085 lr=5.160000e-06 lr_backbone=5.160000e-06 elapsed=00:05:01 avg_step=2.32s recent_step=1.91s eta=32:06:40
+2026-09-20 06:43:21 | INFO | train rank=0 | step=140/50000 progress=0.3% loss=1.059067 lr=5.560000e-06 lr_backbone=5.560000e-06 elapsed=00:05:20 avg_step=2.29s recent_step=1.94s eta=31:44:03
+2026-09-20 06:43:32 | INFO | train rank=0 | step=150/50000 progress=0.3% loss=1.028393 lr=5.960000e-06 lr_backbone=5.960000e-06 elapsed=00:05:32 avg_step=2.22s recent_step=1.17s eta=30:41:24
+2026-09-20 06:43:46 | INFO | train rank=0 | step=160/50000 progress=0.3% loss=0.992907 lr=6.360000e-06 lr_backbone=6.360000e-06 elapsed=00:05:45 avg_step=2.16s recent_step=1.33s eta=29:55:08
+2026-09-20 06:44:04 | INFO | train rank=0 | step=170/50000 progress=0.3% loss=1.039169 lr=6.760000e-06 lr_backbone=6.760000e-06 elapsed=00:06:03 avg_step=2.14s recent_step=1.79s eta=29:36:43
+2026-09-20 06:44:17 | INFO | train rank=0 | step=180/50000 progress=0.4% loss=0.924833 lr=7.160000e-06 lr_backbone=7.160000e-06 elapsed=00:06:17 avg_step=2.10s recent_step=1.36s eta=29:00:23
+2026-09-20 06:44:33 | INFO | train rank=0 | step=190/50000 progress=0.4% loss=0.902046 lr=7.560000e-06 lr_backbone=7.560000e-06 elapsed=00:06:32 avg_step=2.07s recent_step=1.56s eta=28:36:45
+2026-09-20 06:44:47 | INFO | train rank=0 | step=200/50000 progress=0.4% loss=0.905121 lr=7.960000e-06 lr_backbone=7.960000e-06 elapsed=00:06:46 avg_step=2.03s recent_step=1.39s eta=28:08:10
+2026-09-20 06:45:03 | INFO | train rank=0 | step=210/50000 progress=0.4% loss=0.832396 lr=8.360000e-06 lr_backbone=8.360000e-06 elapsed=00:07:02 avg_step=2.01s recent_step=1.61s eta=27:51:11
+2026-09-20 06:45:15 | INFO | train rank=0 | step=220/50000 progress=0.4% loss=0.719027 lr=8.760000e-06 lr_backbone=8.760000e-06 elapsed=00:07:15 avg_step=1.98s recent_step=1.22s eta=27:20:44
+2026-09-20 06:45:26 | INFO | train rank=0 | step=230/50000 progress=0.5% loss=0.720426 lr=9.160000e-06 lr_backbone=9.160000e-06 elapsed=00:07:26 avg_step=1.94s recent_step=1.15s eta=26:50:32
+2026-09-20 06:45:46 | INFO | train rank=0 | step=240/50000 progress=0.5% loss=0.657993 lr=9.560000e-06 lr_backbone=9.560000e-06 elapsed=00:07:45 avg_step=1.94s recent_step=1.91s eta=26:49:10
+2026-09-20 06:45:57 | INFO | train rank=0 | step=250/50000 progress=0.5% loss=0.562300 lr=9.960000e-06 lr_backbone=9.960000e-06 elapsed=00:07:57 avg_step=1.91s recent_step=1.18s eta=26:23:47
+2026-09-20 06:46:15 | INFO | train rank=0 | step=260/50000 progress=0.5% loss=0.488119 lr=1.036000e-05 lr_backbone=1.036000e-05 elapsed=00:08:14 avg_step=1.90s recent_step=1.72s eta=26:17:24
+2026-09-20 06:46:39 | INFO | train rank=0 | step=270/50000 progress=0.5% loss=0.494312 lr=1.076000e-05 lr_backbone=1.076000e-05 elapsed=00:08:38 avg_step=1.92s recent_step=2.42s eta=26:32:52
+2026-09-20 06:46:49 | INFO | train rank=0 | step=280/50000 progress=0.6% loss=0.402690 lr=1.116000e-05 lr_backbone=1.116000e-05 elapsed=00:08:49 avg_step=1.89s recent_step=1.06s eta=26:07:02
+2026-09-20 06:47:02 | INFO | train rank=0 | step=290/50000 progress=0.6% loss=0.362849 lr=1.156000e-05 lr_backbone=1.156000e-05 elapsed=00:09:01 avg_step=1.87s recent_step=1.23s eta=25:47:50
+2026-09-20 06:47:13 | INFO | train rank=0 | step=300/50000 progress=0.6% loss=0.384340 lr=1.196000e-05 lr_backbone=1.196000e-05 elapsed=00:09:13 avg_step=1.84s recent_step=1.15s eta=25:27:45
+2026-09-20 06:47:29 | INFO | train rank=0 | step=310/50000 progress=0.6% loss=0.322729 lr=1.236000e-05 lr_backbone=1.236000e-05 elapsed=00:09:29 avg_step=1.84s recent_step=1.59s eta=25:20:38
+2026-09-20 06:47:42 | INFO | train rank=0 | step=320/50000 progress=0.6% loss=0.451901 lr=1.276000e-05 lr_backbone=1.276000e-05 elapsed=00:09:42 avg_step=1.82s recent_step=1.28s eta=25:06:03
+2026-09-20 06:47:57 | INFO | train rank=0 | step=330/50000 progress=0.7% loss=0.314606 lr=1.316000e-05 lr_backbone=1.316000e-05 elapsed=00:09:57 avg_step=1.81s recent_step=1.51s eta=24:58:06
+2026-09-20 06:48:18 | INFO | train rank=0 | step=340/50000 progress=0.7% loss=0.323738 lr=1.356000e-05 lr_backbone=1.356000e-05 elapsed=00:10:18 avg_step=1.82s recent_step=2.14s eta=25:05:51
+2026-09-20 06:48:32 | INFO | train rank=0 | step=350/50000 progress=0.7% loss=0.244703 lr=1.396000e-05 lr_backbone=1.396000e-05 elapsed=00:10:32 avg_step=1.81s recent_step=1.36s eta=24:54:39
+2026-09-20 06:48:48 | INFO | train rank=0 | step=360/50000 progress=0.7% loss=0.256034 lr=1.436000e-05 lr_backbone=1.436000e-05 elapsed=00:10:48 avg_step=1.80s recent_step=1.62s eta=24:50:06
+2026-09-20 06:49:00 | INFO | train rank=0 | step=370/50000 progress=0.7% loss=0.356262 lr=1.476000e-05 lr_backbone=1.476000e-05 elapsed=00:10:59 avg_step=1.78s recent_step=1.13s eta=24:34:50
+2026-09-20 06:49:18 | INFO | train rank=0 | step=380/50000 progress=0.8% loss=0.262284 lr=1.516000e-05 lr_backbone=1.516000e-05 elapsed=00:11:18 avg_step=1.79s recent_step=1.89s eta=24:36:50
+2026-09-20 06:49:37 | INFO | train rank=0 | step=390/50000 progress=0.8% loss=0.327675 lr=1.556000e-05 lr_backbone=1.556000e-05 elapsed=00:11:36 avg_step=1.79s recent_step=1.80s eta=24:36:54
+2026-09-20 06:49:49 | INFO | train rank=0 | step=400/50000 progress=0.8% loss=0.243571 lr=1.596000e-05 lr_backbone=1.596000e-05 elapsed=00:11:48 avg_step=1.77s recent_step=1.22s eta=24:24:56
+2026-09-20 06:50:01 | INFO | train rank=0 | step=410/50000 progress=0.8% loss=0.269397 lr=1.636000e-05 lr_backbone=1.636000e-05 elapsed=00:12:00 avg_step=1.76s recent_step=1.20s eta=24:13:02
+2026-09-20 06:50:19 | INFO | train rank=0 | step=420/50000 progress=0.8% loss=0.277618 lr=1.676000e-05 lr_backbone=1.676000e-05 elapsed=00:12:18 avg_step=1.76s recent_step=1.81s eta=24:13:48
+2026-09-20 06:50:36 | INFO | train rank=0 | step=430/50000 progress=0.9% loss=0.287273 lr=1.716000e-05 lr_backbone=1.716000e-05 elapsed=00:12:36 avg_step=1.76s recent_step=1.73s eta=24:12:59
+2026-09-20 06:50:49 | INFO | train rank=0 | step=440/50000 progress=0.9% loss=0.236167 lr=1.756000e-05 lr_backbone=1.756000e-05 elapsed=00:12:49 avg_step=1.75s recent_step=1.33s eta=24:04:40
+2026-09-20 06:51:04 | INFO | train rank=0 | step=450/50000 progress=0.9% loss=0.201279 lr=1.796000e-05 lr_backbone=1.796000e-05 elapsed=00:13:03 avg_step=1.74s recent_step=1.43s eta=23:58:36
+2026-09-20 06:51:23 | INFO | train rank=0 | step=460/50000 progress=0.9% loss=0.197422 lr=1.836000e-05 lr_backbone=1.836000e-05 elapsed=00:13:23 avg_step=1.75s recent_step=1.93s eta=24:01:39
+2026-09-20 06:51:36 | INFO | train rank=0 | step=470/50000 progress=0.9% loss=0.227404 lr=1.876000e-05 lr_backbone=1.876000e-05 elapsed=00:13:36 avg_step=1.74s recent_step=1.29s eta=23:53:25
+2026-09-20 06:51:47 | INFO | train rank=0 | step=480/50000 progress=1.0% loss=0.200343 lr=1.916000e-05 lr_backbone=1.916000e-05 elapsed=00:13:47 avg_step=1.72s recent_step=1.11s eta=23:42:25
+2026-09-20 06:52:02 | INFO | train rank=0 | step=490/50000 progress=1.0% loss=0.218990 lr=1.956000e-05 lr_backbone=1.956000e-05 elapsed=00:14:01 avg_step=1.72s recent_step=1.46s eta=23:37:38
+2026-09-20 06:52:16 | INFO | train rank=0 | step=500/50000 progress=1.0% loss=0.202294 lr=1.996000e-05 lr_backbone=1.996000e-05 elapsed=00:14:16 avg_step=1.71s recent_step=1.46s eta=23:33:10
+2026-09-20 06:52:34 | INFO | train rank=0 | step=510/50000 progress=1.0% loss=0.223754 lr=2.036000e-05 lr_backbone=2.036000e-05 elapsed=00:14:33 avg_step=1.71s recent_step=1.73s eta=23:33:13
+2026-09-20 06:52:50 | INFO | train rank=0 | step=520/50000 progress=1.0% loss=0.245861 lr=2.076000e-05 lr_backbone=2.076000e-05 elapsed=00:14:50 avg_step=1.71s recent_step=1.65s eta=23:31:52
+2026-09-20 06:53:04 | INFO | train rank=0 | step=530/50000 progress=1.1% loss=0.217831 lr=2.116000e-05 lr_backbone=2.116000e-05 elapsed=00:15:04 avg_step=1.71s recent_step=1.39s eta=23:26:39
+2026-09-20 06:53:16 | INFO | train rank=0 | step=540/50000 progress=1.1% loss=0.214818 lr=2.156000e-05 lr_backbone=2.156000e-05 elapsed=00:15:15 avg_step=1.70s recent_step=1.18s eta=23:18:17
+2026-09-20 06:53:38 | INFO | train rank=0 | step=550/50000 progress=1.1% loss=0.199835 lr=2.196000e-05 lr_backbone=2.196000e-05 elapsed=00:15:38 avg_step=1.71s recent_step=2.23s eta=23:26:03
+2026-09-20 06:53:50 | INFO | train rank=0 | step=560/50000 progress=1.1% loss=0.235483 lr=2.236000e-05 lr_backbone=2.236000e-05 elapsed=00:15:49 avg_step=1.70s recent_step=1.14s eta=23:17:24
+2026-09-20 06:54:02 | INFO | train rank=0 | step=570/50000 progress=1.1% loss=0.200940 lr=2.276000e-05 lr_backbone=2.276000e-05 elapsed=00:16:01 avg_step=1.69s recent_step=1.20s eta=23:09:59
+2026-09-20 06:54:21 | INFO | train rank=0 | step=580/50000 progress=1.2% loss=0.190732 lr=2.316000e-05 lr_backbone=2.316000e-05 elapsed=00:16:21 avg_step=1.69s recent_step=1.93s eta=23:13:09
+2026-09-20 06:54:34 | INFO | train rank=0 | step=590/50000 progress=1.2% loss=0.232949 lr=2.356000e-05 lr_backbone=2.356000e-05 elapsed=00:16:34 avg_step=1.68s recent_step=1.31s eta=23:07:34
+2026-09-20 06:54:53 | INFO | train rank=0 | step=600/50000 progress=1.2% loss=0.212342 lr=2.396000e-05 lr_backbone=2.396000e-05 elapsed=00:16:52 avg_step=1.69s recent_step=1.85s eta=23:09:33
+2026-09-20 06:55:06 | INFO | train rank=0 | step=610/50000 progress=1.2% loss=0.204488 lr=2.436000e-05 lr_backbone=2.436000e-05 elapsed=00:17:06 avg_step=1.68s recent_step=1.35s eta=23:04:39
+2026-09-20 06:55:20 | INFO | train rank=0 | step=620/50000 progress=1.2% loss=0.187001 lr=2.476000e-05 lr_backbone=2.476000e-05 elapsed=00:17:19 avg_step=1.68s recent_step=1.37s eta=23:00:16
+2026-09-20 06:55:40 | INFO | train rank=0 | step=630/50000 progress=1.3% loss=0.193425 lr=2.516000e-05 lr_backbone=2.516000e-05 elapsed=00:17:40 avg_step=1.68s recent_step=2.04s eta=23:04:46
+2026-09-20 06:55:52 | INFO | train rank=0 | step=640/50000 progress=1.3% loss=0.248708 lr=2.556000e-05 lr_backbone=2.556000e-05 elapsed=00:17:52 avg_step=1.68s recent_step=1.20s eta=22:58:17
+2026-09-20 06:56:04 | INFO | train rank=0 | step=650/50000 progress=1.3% loss=0.186248 lr=2.596000e-05 lr_backbone=2.596000e-05 elapsed=00:18:03 avg_step=1.67s recent_step=1.16s eta=22:51:26
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+2026-09-21 04:39:15 | INFO | train rank=0 | step=49970/50000 progress=99.9% loss=0.088223 lr=9.842262e-11 lr_backbone=9.842262e-11 elapsed=22:01:14 avg_step=1.59s recent_step=1.57s eta=00:00:47
+2026-09-21 04:39:27 | INFO | train rank=0 | step=49980/50000 progress=100.0% loss=0.082739 lr=4.374339e-11 lr_backbone=4.374339e-11 elapsed=22:01:26 avg_step=1.59s recent_step=1.19s eta=00:00:31
+2026-09-21 04:39:45 | INFO | train rank=0 | step=49990/50000 progress=100.0% loss=0.074667 lr=1.093585e-11 lr_backbone=1.093585e-11 elapsed=22:01:45 avg_step=1.59s recent_step=1.88s eta=00:00:15
+2026-09-21 04:40:00 | INFO | train rank=0 | step=50000/50000 progress=100.0% loss=0.064867 lr=0.000000e+00 lr_backbone=0.000000e+00 elapsed=22:02:00 avg_step=1.59s recent_step=1.47s eta=00:00:00
+2026-09-21 04:40:24 | INFO | train rank=0 | Saved DeepSpeed checkpoint to /data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_50000
+2026-09-21 04:40:24 | INFO | train rank=0 | Finished training final_step=50000/50000 elapsed=22:02:23 avg_step=1.59s
+2026-09-21 04:40:24 | INFO | train rank=0 | Training finished
+2026-09-21 04:40:43 | INFO | train rank=0 | Saved final DeepSpeed checkpoint to /data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_qwen3vl2b_end_padding_mask_chunk16_using_dreamzero_dataset/ds_checkpoint_final
diff --git a/joint_50ksteps_bs128_wan5b_end_padding_mask_chunk16_downsample2_new_instruction/train.log b/joint_50ksteps_bs128_wan5b_end_padding_mask_chunk16_downsample2_new_instruction/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..e1a2b41b227ac91b5a6f50d20ed2aa62d8891c99
--- /dev/null
+++ b/joint_50ksteps_bs128_wan5b_end_padding_mask_chunk16_downsample2_new_instruction/train.log
@@ -0,0 +1,37 @@
+2026-09-17 17:45:01 | INFO | train rank=0 | Starting training project=codebase_v8_droid_joint_50ksteps_bs128_wan5b_end_padding_mask_chunk16_downsample2_new_instruction save_dir=/data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_wan5b_end_padding_mask_chunk16_downsample2_new_instruction distributed=True deepspeed=True world_size=8 device=cuda:0
+2026-09-18 01:54:04 | INFO | train rank=0 | Starting training project=codebase_v8_droid_joint_50ksteps_bs128_wan5b_end_padding_mask_chunk16_downsample2_new_instruction save_dir=/data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_wan5b_end_padding_mask_chunk16_downsample2_new_instruction distributed=True deepspeed=True world_size=8 device=cuda:0
+2026-09-18 02:00:10 | INFO | train rank=0 | No pretrained checkpoint provided; training from scratch
+2026-09-18 02:00:10 | INFO | train rank=0 | Initializing Droid LeRobot Dataset: path=/data/NTU_slab/draven/data/DROID fps=15.00 use_relabelled_instruction=True
+2026-09-18 02:00:10 | INFO | train rank=0 | Batch config: total_batch_size=128 world_size=8 grad_acc_steps=1 per_device_batch_size=16
+2026-09-18 02:00:10 | INFO | train rank=0 | Dataset image_resize_size=320
+2026-09-18 02:00:10 | INFO | train rank=0 | Droid uses parquet-shard streaming with a per-rank shuffle buffer: batch_size=16 buffer_size=1024 num_workers=8 drop_last=True
+2026-09-18 02:00:18 | INFO | train rank=0 | Droid streaming layout: parquet_shards=50 active_workers=8 worker_buffer_min=128 worker_buffer_max=128 max_active_shards=2
+2026-09-18 02:00:18 | INFO | train rank=0 | Resolved Droid joint action stats from /data/NTU_slab/draven/data/DROID/meta/joint_delta_stats_h16.json: q01=[-0.3050941824913025, -0.47471314668655396, -0.3020089268684387, -0.493502140045166, -0.4844675362110138, -0.4544465243816376, -0.550822913646698] q99=[0.30148017406463623, 0.5795503258705139, 0.3001617193222046, 0.5117637515068054, 0.4750978946685791, 0.5400477647781372, 0.5597710609436035]
+2026-09-18 02:00:18 | INFO | train rank=0 | Trainable params: 5,624,093,896 / 12,009,742,052
+2026-09-18 02:03:12 | INFO | train rank=0 | Starting training start_step=0 max_steps=50000 remaining_steps=50000 log_interval=10 save_interval=50000
+2026-09-20 05:45:21 | INFO | train rank=0 | Starting training project=codebase_v8_droid_joint_50ksteps_bs128_wan5b_end_padding_mask_chunk16_downsample2_new_instruction save_dir=/data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_wan5b_end_padding_mask_chunk16_downsample2_new_instruction distributed=True deepspeed=True world_size=8 device=cuda:0
+2026-09-20 05:51:25 | INFO | train rank=0 | No pretrained checkpoint provided; training from scratch
+2026-09-20 05:51:25 | INFO | train rank=0 | Initializing Droid LeRobot Dataset: path=/data/NTU_slab/draven/data/DROID fps=15.00 use_relabelled_instruction=True
+2026-09-20 05:51:25 | INFO | train rank=0 | Batch config: total_batch_size=128 world_size=8 grad_acc_steps=1 per_device_batch_size=16
+2026-09-20 05:51:25 | INFO | train rank=0 | Dataset image_resize_size=320
+2026-09-20 05:51:25 | INFO | train rank=0 | Droid uses parquet-shard streaming with a per-rank shuffle buffer: batch_size=16 buffer_size=1024 num_workers=8 drop_last=True
+2026-09-20 05:51:32 | INFO | train rank=0 | Droid streaming layout: parquet_shards=50 active_workers=8 worker_buffer_min=128 worker_buffer_max=128 max_active_shards=2
+2026-09-20 05:51:32 | INFO | train rank=0 | Resolved Droid joint action stats from /data/NTU_slab/draven/data/DROID/meta/joint_delta_stats_h16.json: q01=[-0.3050941824913025, -0.47471314668655396, -0.3020089268684387, -0.493502140045166, -0.4844675362110138, -0.4544465243816376, -0.550822913646698] q99=[0.30148017406463623, 0.5795503258705139, 0.3001617193222046, 0.5117637515068054, 0.4750978946685791, 0.5400477647781372, 0.5597710609436035]
+2026-09-20 05:51:33 | INFO | train rank=0 | Trainable params: 5,624,093,896 / 12,009,742,052
+2026-09-20 05:54:41 | INFO | train rank=0 | Starting training start_step=0 max_steps=50000 remaining_steps=50000 log_interval=10 save_interval=50000
+2026-09-20 05:59:44 | INFO | train rank=0 | step=10/50000 progress=0.0% loss=1.228930 lr=3.600000e-07 lr_backbone=3.600000e-07 elapsed=00:05:02 avg_step=30.26s recent_step=30.26s eta=420:09:53
+2026-09-20 06:00:28 | INFO | train rank=0 | step=20/50000 progress=0.0% loss=1.166950 lr=7.600000e-07 lr_backbone=7.600000e-07 elapsed=00:05:47 avg_step=17.36s recent_step=4.46s eta=240:59:28
+2026-09-20 06:01:12 | INFO | train rank=0 | step=30/50000 progress=0.1% loss=1.228521 lr=1.160000e-06 lr_backbone=1.160000e-06 elapsed=00:06:30 avg_step=13.03s recent_step=4.37s eta=180:51:57
+2026-09-20 06:01:55 | INFO | train rank=0 | step=40/50000 progress=0.1% loss=1.176345 lr=1.560000e-06 lr_backbone=1.560000e-06 elapsed=00:07:14 avg_step=10.86s recent_step=4.34s eta=150:41:07
+2026-09-20 06:02:38 | INFO | train rank=0 | step=50/50000 progress=0.1% loss=1.151849 lr=1.960000e-06 lr_backbone=1.960000e-06 elapsed=00:07:57 avg_step=9.55s recent_step=4.30s eta=132:27:12
+2026-09-20 06:03:22 | INFO | train rank=0 | step=60/50000 progress=0.1% loss=1.175675 lr=2.360000e-06 lr_backbone=2.360000e-06 elapsed=00:08:40 avg_step=8.68s recent_step=4.33s eta=120:22:29
+2026-09-20 06:04:05 | INFO | train rank=0 | step=70/50000 progress=0.1% loss=1.227936 lr=2.760000e-06 lr_backbone=2.760000e-06 elapsed=00:09:23 avg_step=8.05s recent_step=4.32s eta=111:42:28
+2026-09-20 06:04:48 | INFO | train rank=0 | step=80/50000 progress=0.2% loss=1.194378 lr=3.160000e-06 lr_backbone=3.160000e-06 elapsed=00:10:07 avg_step=7.59s recent_step=4.33s eta=105:14:15
+2026-09-20 06:05:31 | INFO | train rank=0 | step=90/50000 progress=0.2% loss=1.212808 lr=3.560000e-06 lr_backbone=3.560000e-06 elapsed=00:10:50 avg_step=7.23s recent_step=4.34s eta=100:12:21
+2026-09-20 06:06:14 | INFO | train rank=0 | step=100/50000 progress=0.2% loss=1.163769 lr=3.960000e-06 lr_backbone=3.960000e-06 elapsed=00:11:33 avg_step=6.93s recent_step=4.28s eta=96:06:02
+2026-09-20 06:06:56 | INFO | train rank=0 | step=110/50000 progress=0.2% loss=1.167954 lr=4.360000e-06 lr_backbone=4.360000e-06 elapsed=00:12:15 avg_step=6.68s recent_step=4.18s eta=92:36:45
+2026-09-20 06:07:38 | INFO | train rank=0 | step=120/50000 progress=0.2% loss=1.150114 lr=4.760000e-06 lr_backbone=4.760000e-06 elapsed=00:12:56 avg_step=6.47s recent_step=4.17s eta=89:41:29
+2026-09-20 06:08:20 | INFO | train rank=0 | step=130/50000 progress=0.3% loss=1.135506 lr=5.160000e-06 lr_backbone=5.160000e-06 elapsed=00:13:39 avg_step=6.30s recent_step=4.23s eta=87:17:00
+2026-09-20 06:09:03 | INFO | train rank=0 | step=140/50000 progress=0.3% loss=1.071019 lr=5.560000e-06 lr_backbone=5.560000e-06 elapsed=00:14:21 avg_step=6.16s recent_step=4.26s eta=85:15:01
+2026-09-20 06:09:45 | INFO | train rank=0 | step=150/50000 progress=0.3% loss=1.083817 lr=5.960000e-06 lr_backbone=5.960000e-06 elapsed=00:15:04 avg_step=6.03s recent_step=4.24s eta=83:28:08
+2026-09-20 06:10:28 | INFO | train rank=0 | step=160/50000 progress=0.3% loss=0.975133 lr=6.360000e-06 lr_backbone=6.360000e-06 elapsed=00:15:46 avg_step=5.92s recent_step=4.25s eta=81:54:48
diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt
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diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_4_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_4_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_5_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_5_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_6_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_6_mp_rank_00_optim_states.pt
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diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_7_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/global_step50000/bf16_zero_pp_rank_7_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/global_step50000/mp_rank_00_model_states.pt b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/global_step50000/mp_rank_00_model_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..debf6ca49db8fba7569e9c58ace096ce4c5882eb
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+++ b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/global_step50000/mp_rank_00_model_states.pt
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diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/latest b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/latest
new file mode 100644
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+global_step50000
\ No newline at end of file
diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/zero_to_fp32.py b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/zero_to_fp32.py
new file mode 100644
index 0000000000000000000000000000000000000000..f40f1f0acbbff30e8e8d29906b56aae4e8b8a138
--- /dev/null
+++ b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000/zero_to_fp32.py
@@ -0,0 +1,790 @@
+#!/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 gc
+import json
+import numpy as np
+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, AUTOEP_LAYERS_KEY,
+                                            AUTOEP_LAYERS_KEY_LEGACY, AUTOEP_ZERO3_EXPERT_STATE_FORMAT_KEY,
+                                            AUTOEP_ZERO3_PARTITIONED_EXPERT_STATE_FORMAT)
+
+
+@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 _has_autoep_zero3_partitioned_metadata(state_dict):
+    autoep_layers = state_dict.get(AUTOEP_LAYERS_KEY)
+    if autoep_layers is None:
+        autoep_layers = state_dict.get(AUTOEP_LAYERS_KEY_LEGACY)
+    if not isinstance(autoep_layers, list):
+        return False
+    return any(
+        isinstance(entry, dict)
+        and entry.get(AUTOEP_ZERO3_EXPERT_STATE_FORMAT_KEY) == AUTOEP_ZERO3_PARTITIONED_EXPERT_STATE_FORMAT
+        for entry in autoep_layers)
+
+
+def _raise_if_autoep_zero3_partitioned_state(state_dict):
+    if _has_autoep_zero3_partitioned_metadata(state_dict):
+        raise NotImplementedError("zero_to_fp32 does not support AutoEP ZeRO-3 partition-native checkpoints. "
+                                  "AutoEP expert parameters are partitioned over expert replica groups, so "
+                                  "global data-parallel consolidation would produce incomplete expert tensors. "
+                                  "Use ds_to_universal.py for expert-aware conversion.")
+
+
+def _raise_if_autoep_zero3_partitioned_checkpoint(model_files):
+    for file in model_files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+        _raise_if_autoep_zero3_partitioned_state(state_dict)
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+        _raise_if_autoep_zero3_partitioned_state(state_dict)
+
+        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 tqdm(files, desc='Loading checkpoint shards'):
+        state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
+        # 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 ZERO_STAGE not 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}")
+
+    fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] 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}'")
+
+    # parse_model_states rejects AutoEP ZeRO-3 partition-native checkpoints
+    # before the expensive optimizer-shard load below.
+    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}')
+
+    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}")
+
+    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")
+
+
+class GatheredTensor:
+    """
+    A pseudo tensor that collects partitioned weights.
+    It is more memory efficient when there are multiple groups.
+    """
+
+    def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
+        self.flat_groups = flat_groups
+        self.flat_groups_offset = flat_groups_offset
+        self.offset = offset
+        self.partitioned_numel = partitioned_numel
+        self.shape = shape
+        self.dtype = self.flat_groups[0][0].dtype
+
+    def contiguous(self):
+        """
+        Merge partitioned weights from flat_groups into a single tensor.
+        """
+        end_idx = self.offset + self.partitioned_numel
+        world_size = len(self.flat_groups)
+        pad_flat_param_chunks = []
+
+        for rank_i in range(world_size):
+            # for each rank, we need to collect weights from related group/groups
+            flat_groups_at_rank_i = self.flat_groups[rank_i]
+            start_group_id = None
+            end_group_id = None
+            for group_id in range(len(self.flat_groups_offset)):
+                if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
+                    start_group_id = group_id
+                if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
+                    end_group_id = group_id
+                    break
+            # collect weights from related group/groups
+            for group_id in range(start_group_id, end_group_id + 1):
+                flat_tensor = flat_groups_at_rank_i[group_id]
+                start_offset = self.offset - self.flat_groups_offset[group_id]
+                end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
+                pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
+
+        # collect weights from all ranks
+        pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
+        param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
+        return param
+
+
+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 = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * 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
+    flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[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}"
+            )
+
+        # memory efficient tensor
+        tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
+        state_dict[name] = tensor
+        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 to_torch_tensor(state_dict, return_empty_tensor=False):
+    """
+    Convert state_dict of GatheredTensor to torch tensor
+    """
+    torch_state_dict = {}
+    converted_tensors = {}
+    for name, tensor in state_dict.items():
+        tensor_id = id(tensor)
+        if tensor_id in converted_tensors:  # shared tensors
+            shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
+            torch_state_dict[name] = shared_tensor
+        else:
+            converted_tensors[tensor_id] = name
+            if return_empty_tensor:
+                torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
+            else:
+                torch_state_dict[name] = tensor.contiguous()
+    return torch_state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                             tag=None,
+                                             exclude_frozen_parameters=False,
+                                             lazy_mode=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
+        - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
+          Convert the pesduo tensor to torch tensor by ``.contiguous()``
+
+    Returns:
+        - pytorch ``state_dict``
+
+    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.
+
+    Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
+    You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
+    the checkpoint. Or you can load state_dict in lazy mode ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
+        for name, lazy_tensor in state_dict.item():
+            tensor = lazy_tensor.contiguous()  # to cpu
+            print(name, tensor)
+            # del tensor to release memory if it no longer in use
+    """
+    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")
+
+    state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
+    if lazy_mode:
+        return state_dict
+    else:
+        return to_torch_tensor(state_dict)
+
+
+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,
+                                                          lazy_mode=True)
+
+    # 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")
+        # an memory-efficient approach for sharding
+        empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
+        state_dict_split = split_torch_state_dict_into_shards(empty_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 by shard
+    os.makedirs(output_dir, exist_ok=True)
+    filename_to_tensors = state_dict_split.filename_to_tensors.items()
+    for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
+        shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
+        shard_state_dict = to_torch_tensor(shard_state_dict)
+        output_path = os.path.join(output_dir, shard_file)
+        if safe_serialization:
+            save_file(shard_state_dict, output_path, metadata={"format": "pt"})
+        else:
+            torch.save(shard_state_dict, output_path)
+        # release the memory of current shard
+        for tensor_name in list(shard_state_dict.keys()):
+            del state_dict[tensor_name]
+            del shard_state_dict[tensor_name]
+        del shard_state_dict
+        gc.collect()
+
+    # 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("Extracting fp32 weights")
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+
+    logger.info("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)
diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..24cc8ef44d1979ee11af1fa385e74694eae653fc
--- /dev/null
+++ b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt
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+size 8436160936
diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..5c3e5c78ffc9aa3d8040cba1770a3201f14b2e6d
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diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..3ce8cd95917ac6d81c80dcc84d678d0367cd0b9c
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diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_4_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_4_mp_rank_00_optim_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..44a04bdf5eaab1484a92adba9065a71749dbea97
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diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_5_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_5_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_6_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_6_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_7_mp_rank_00_optim_states.pt b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/global_step50000/bf16_zero_pp_rank_7_mp_rank_00_optim_states.pt
new file mode 100644
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diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/global_step50000/mp_rank_00_model_states.pt b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/global_step50000/mp_rank_00_model_states.pt
new file mode 100644
index 0000000000000000000000000000000000000000..debf6ca49db8fba7569e9c58ace096ce4c5882eb
--- /dev/null
+++ b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/global_step50000/mp_rank_00_model_states.pt
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+size 24020131416
diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/latest b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/latest
new file mode 100644
index 0000000000000000000000000000000000000000..59a558c0ee32a055bd8fc16bf0d99a5950c59ac1
--- /dev/null
+++ b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/latest
@@ -0,0 +1 @@
+global_step50000
\ No newline at end of file
diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/zero_to_fp32.py b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/zero_to_fp32.py
new file mode 100644
index 0000000000000000000000000000000000000000..f40f1f0acbbff30e8e8d29906b56aae4e8b8a138
--- /dev/null
+++ b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final/zero_to_fp32.py
@@ -0,0 +1,790 @@
+#!/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 gc
+import json
+import numpy as np
+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, AUTOEP_LAYERS_KEY,
+                                            AUTOEP_LAYERS_KEY_LEGACY, AUTOEP_ZERO3_EXPERT_STATE_FORMAT_KEY,
+                                            AUTOEP_ZERO3_PARTITIONED_EXPERT_STATE_FORMAT)
+
+
+@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 _has_autoep_zero3_partitioned_metadata(state_dict):
+    autoep_layers = state_dict.get(AUTOEP_LAYERS_KEY)
+    if autoep_layers is None:
+        autoep_layers = state_dict.get(AUTOEP_LAYERS_KEY_LEGACY)
+    if not isinstance(autoep_layers, list):
+        return False
+    return any(
+        isinstance(entry, dict)
+        and entry.get(AUTOEP_ZERO3_EXPERT_STATE_FORMAT_KEY) == AUTOEP_ZERO3_PARTITIONED_EXPERT_STATE_FORMAT
+        for entry in autoep_layers)
+
+
+def _raise_if_autoep_zero3_partitioned_state(state_dict):
+    if _has_autoep_zero3_partitioned_metadata(state_dict):
+        raise NotImplementedError("zero_to_fp32 does not support AutoEP ZeRO-3 partition-native checkpoints. "
+                                  "AutoEP expert parameters are partitioned over expert replica groups, so "
+                                  "global data-parallel consolidation would produce incomplete expert tensors. "
+                                  "Use ds_to_universal.py for expert-aware conversion.")
+
+
+def _raise_if_autoep_zero3_partitioned_checkpoint(model_files):
+    for file in model_files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+        _raise_if_autoep_zero3_partitioned_state(state_dict)
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+        _raise_if_autoep_zero3_partitioned_state(state_dict)
+
+        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 tqdm(files, desc='Loading checkpoint shards'):
+        state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
+        # 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 ZERO_STAGE not 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}")
+
+    fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] 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}'")
+
+    # parse_model_states rejects AutoEP ZeRO-3 partition-native checkpoints
+    # before the expensive optimizer-shard load below.
+    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}')
+
+    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}")
+
+    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")
+
+
+class GatheredTensor:
+    """
+    A pseudo tensor that collects partitioned weights.
+    It is more memory efficient when there are multiple groups.
+    """
+
+    def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
+        self.flat_groups = flat_groups
+        self.flat_groups_offset = flat_groups_offset
+        self.offset = offset
+        self.partitioned_numel = partitioned_numel
+        self.shape = shape
+        self.dtype = self.flat_groups[0][0].dtype
+
+    def contiguous(self):
+        """
+        Merge partitioned weights from flat_groups into a single tensor.
+        """
+        end_idx = self.offset + self.partitioned_numel
+        world_size = len(self.flat_groups)
+        pad_flat_param_chunks = []
+
+        for rank_i in range(world_size):
+            # for each rank, we need to collect weights from related group/groups
+            flat_groups_at_rank_i = self.flat_groups[rank_i]
+            start_group_id = None
+            end_group_id = None
+            for group_id in range(len(self.flat_groups_offset)):
+                if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
+                    start_group_id = group_id
+                if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
+                    end_group_id = group_id
+                    break
+            # collect weights from related group/groups
+            for group_id in range(start_group_id, end_group_id + 1):
+                flat_tensor = flat_groups_at_rank_i[group_id]
+                start_offset = self.offset - self.flat_groups_offset[group_id]
+                end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
+                pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
+
+        # collect weights from all ranks
+        pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
+        param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
+        return param
+
+
+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 = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * 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
+    flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[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}"
+            )
+
+        # memory efficient tensor
+        tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
+        state_dict[name] = tensor
+        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 to_torch_tensor(state_dict, return_empty_tensor=False):
+    """
+    Convert state_dict of GatheredTensor to torch tensor
+    """
+    torch_state_dict = {}
+    converted_tensors = {}
+    for name, tensor in state_dict.items():
+        tensor_id = id(tensor)
+        if tensor_id in converted_tensors:  # shared tensors
+            shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
+            torch_state_dict[name] = shared_tensor
+        else:
+            converted_tensors[tensor_id] = name
+            if return_empty_tensor:
+                torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
+            else:
+                torch_state_dict[name] = tensor.contiguous()
+    return torch_state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                             tag=None,
+                                             exclude_frozen_parameters=False,
+                                             lazy_mode=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
+        - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
+          Convert the pesduo tensor to torch tensor by ``.contiguous()``
+
+    Returns:
+        - pytorch ``state_dict``
+
+    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.
+
+    Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
+    You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
+    the checkpoint. Or you can load state_dict in lazy mode ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
+        for name, lazy_tensor in state_dict.item():
+            tensor = lazy_tensor.contiguous()  # to cpu
+            print(name, tensor)
+            # del tensor to release memory if it no longer in use
+    """
+    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")
+
+    state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
+    if lazy_mode:
+        return state_dict
+    else:
+        return to_torch_tensor(state_dict)
+
+
+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,
+                                                          lazy_mode=True)
+
+    # 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")
+        # an memory-efficient approach for sharding
+        empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
+        state_dict_split = split_torch_state_dict_into_shards(empty_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 by shard
+    os.makedirs(output_dir, exist_ok=True)
+    filename_to_tensors = state_dict_split.filename_to_tensors.items()
+    for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
+        shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
+        shard_state_dict = to_torch_tensor(shard_state_dict)
+        output_path = os.path.join(output_dir, shard_file)
+        if safe_serialization:
+            save_file(shard_state_dict, output_path, metadata={"format": "pt"})
+        else:
+            torch.save(shard_state_dict, output_path)
+        # release the memory of current shard
+        for tensor_name in list(shard_state_dict.keys()):
+            del state_dict[tensor_name]
+            del shard_state_dict[tensor_name]
+        del shard_state_dict
+        gc.collect()
+
+    # 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("Extracting fp32 weights")
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+
+    logger.info("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)
diff --git a/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/train.log b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..f196744631480550bdcedfbe9470da4716e1ffe4
--- /dev/null
+++ b/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/train.log
@@ -0,0 +1,5014 @@
+2026-09-20 06:18:03 | INFO | train rank=0 | Starting training project=codebase_v8_droid_joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2 save_dir=/data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2 distributed=True deepspeed=True world_size=8 device=cuda:0
+2026-09-20 06:23:37 | INFO | train rank=0 | No pretrained checkpoint provided; training from scratch
+2026-09-20 06:23:37 | INFO | train rank=0 | Initializing Droid LeRobot Dataset: path=/data/NTU_slab/draven/data/DROID fps=15.00 use_relabelled_instruction=False
+2026-09-20 06:23:37 | INFO | train rank=0 | Batch config: total_batch_size=128 world_size=8 grad_acc_steps=1 per_device_batch_size=16
+2026-09-20 06:23:37 | INFO | train rank=0 | Dataset image_resize_size=320
+2026-09-20 06:23:37 | INFO | train rank=0 | Droid uses parquet-shard streaming with a per-rank shuffle buffer: batch_size=16 buffer_size=1024 num_workers=8 drop_last=True
+2026-09-20 06:23:43 | INFO | train rank=0 | Droid streaming layout: parquet_shards=50 active_workers=8 worker_buffer_min=128 worker_buffer_max=128 max_active_shards=2
+2026-09-20 06:23:43 | INFO | train rank=0 | Resolved Droid joint action stats from /data/NTU_slab/draven/data/DROID/meta/joint_delta_stats_h16.json: q01=[-0.3050941824913025, -0.47471314668655396, -0.3020089268684387, -0.493502140045166, -0.4844675362110138, -0.4544465243816376, -0.550822913646698] q99=[0.30148017406463623, 0.5795503258705139, 0.3001617193222046, 0.5117637515068054, 0.4750978946685791, 0.5400477647781372, 0.5597710609436035]
+2026-09-20 06:23:43 | INFO | train rank=0 | Trainable params: 5,624,093,896 / 12,009,742,052
+2026-09-20 06:26:36 | INFO | train rank=0 | Starting training start_step=0 max_steps=50000 remaining_steps=50000 log_interval=10 save_interval=50000
+2026-09-20 06:31:21 | INFO | train rank=0 | step=10/50000 progress=0.0% loss=1.853691 lr=3.600000e-07 lr_backbone=3.600000e-07 elapsed=00:04:45 avg_step=28.51s recent_step=28.51s eta=395:54:58
+2026-09-20 06:32:05 | INFO | train rank=0 | step=20/50000 progress=0.0% loss=1.781186 lr=7.600000e-07 lr_backbone=7.600000e-07 elapsed=00:05:28 avg_step=16.43s recent_step=4.35s eta=228:08:01
+2026-09-20 06:32:48 | INFO | train rank=0 | step=30/50000 progress=0.1% loss=1.640488 lr=1.160000e-06 lr_backbone=1.160000e-06 elapsed=00:06:11 avg_step=12.38s recent_step=4.28s eta=171:51:35
+2026-09-20 06:33:31 | INFO | train rank=0 | step=40/50000 progress=0.1% loss=1.506737 lr=1.560000e-06 lr_backbone=1.560000e-06 elapsed=00:06:54 avg_step=10.36s recent_step=4.32s eta=143:50:32
+2026-09-20 06:34:14 | INFO | train rank=0 | step=50/50000 progress=0.1% loss=1.496737 lr=1.960000e-06 lr_backbone=1.960000e-06 elapsed=00:07:37 avg_step=9.15s recent_step=4.28s eta=126:56:14
+2026-09-20 06:34:56 | INFO | train rank=0 | step=60/50000 progress=0.1% loss=1.467937 lr=2.360000e-06 lr_backbone=2.360000e-06 elapsed=00:08:19 avg_step=8.32s recent_step=4.20s eta=115:28:04
+2026-09-20 06:35:37 | INFO | train rank=0 | step=70/50000 progress=0.1% loss=1.485005 lr=2.760000e-06 lr_backbone=2.760000e-06 elapsed=00:09:00 avg_step=7.72s recent_step=4.13s eta=107:08:28
+2026-09-20 06:36:18 | INFO | train rank=0 | step=80/50000 progress=0.2% loss=1.476600 lr=3.160000e-06 lr_backbone=3.160000e-06 elapsed=00:09:42 avg_step=7.28s recent_step=4.14s eta=100:54:31
+2026-09-20 06:37:00 | INFO | train rank=0 | step=90/50000 progress=0.2% loss=1.523742 lr=3.560000e-06 lr_backbone=3.560000e-06 elapsed=00:10:23 avg_step=6.93s recent_step=4.14s eta=96:03:06
+2026-09-20 06:37:41 | INFO | train rank=0 | step=100/50000 progress=0.2% loss=1.458957 lr=3.960000e-06 lr_backbone=3.960000e-06 elapsed=00:11:04 avg_step=6.65s recent_step=4.13s eta=92:08:54
+2026-09-20 06:38:22 | INFO | train rank=0 | step=110/50000 progress=0.2% loss=1.461770 lr=4.360000e-06 lr_backbone=4.360000e-06 elapsed=00:11:45 avg_step=6.42s recent_step=4.10s eta=88:55:13
+2026-09-20 06:39:03 | INFO | train rank=0 | step=120/50000 progress=0.2% loss=1.461744 lr=4.760000e-06 lr_backbone=4.760000e-06 elapsed=00:12:26 avg_step=6.22s recent_step=4.10s eta=86:13:46
+2026-09-20 06:39:44 | INFO | train rank=0 | step=130/50000 progress=0.3% loss=1.378855 lr=5.160000e-06 lr_backbone=5.160000e-06 elapsed=00:13:08 avg_step=6.06s recent_step=4.13s eta=83:58:54
+2026-09-20 06:40:26 | INFO | train rank=0 | step=140/50000 progress=0.3% loss=1.338836 lr=5.560000e-06 lr_backbone=5.560000e-06 elapsed=00:13:49 avg_step=5.92s recent_step=4.12s eta=82:02:50
+2026-09-20 06:41:07 | INFO | train rank=0 | step=150/50000 progress=0.3% loss=1.352456 lr=5.960000e-06 lr_backbone=5.960000e-06 elapsed=00:14:30 avg_step=5.80s recent_step=4.12s eta=80:22:10
+2026-09-20 06:41:48 | INFO | train rank=0 | step=160/50000 progress=0.3% loss=1.231435 lr=6.360000e-06 lr_backbone=6.360000e-06 elapsed=00:15:11 avg_step=5.70s recent_step=4.13s eta=78:54:11
+2026-09-20 06:42:29 | INFO | train rank=0 | step=170/50000 progress=0.3% loss=1.247363 lr=6.760000e-06 lr_backbone=6.760000e-06 elapsed=00:15:52 avg_step=5.61s recent_step=4.11s eta=77:35:24
+2026-09-20 06:43:10 | INFO | train rank=0 | step=180/50000 progress=0.4% loss=1.114667 lr=7.160000e-06 lr_backbone=7.160000e-06 elapsed=00:16:34 avg_step=5.52s recent_step=4.13s eta=76:26:27
+2026-09-20 06:43:52 | INFO | train rank=0 | step=190/50000 progress=0.4% loss=1.045280 lr=7.560000e-06 lr_backbone=7.560000e-06 elapsed=00:17:15 avg_step=5.45s recent_step=4.15s eta=75:25:33
+2026-09-20 06:44:33 | INFO | train rank=0 | step=200/50000 progress=0.4% loss=0.980774 lr=7.960000e-06 lr_backbone=7.960000e-06 elapsed=00:17:57 avg_step=5.39s recent_step=4.14s eta=74:30:06
+2026-09-20 06:45:15 | INFO | train rank=0 | step=210/50000 progress=0.4% loss=1.024153 lr=8.360000e-06 lr_backbone=8.360000e-06 elapsed=00:18:38 avg_step=5.33s recent_step=4.15s eta=73:40:23
+2026-09-20 06:45:56 | INFO | train rank=0 | step=220/50000 progress=0.4% loss=0.925906 lr=8.760000e-06 lr_backbone=8.760000e-06 elapsed=00:19:20 avg_step=5.27s recent_step=4.15s eta=72:55:13
+2026-09-20 06:46:37 | INFO | train rank=0 | step=230/50000 progress=0.5% loss=0.836113 lr=9.160000e-06 lr_backbone=9.160000e-06 elapsed=00:20:00 avg_step=5.22s recent_step=4.07s eta=72:11:02
+2026-09-20 06:47:19 | INFO | train rank=0 | step=240/50000 progress=0.5% loss=0.769162 lr=9.560000e-06 lr_backbone=9.560000e-06 elapsed=00:20:42 avg_step=5.18s recent_step=4.15s eta=71:33:11
+2026-09-20 06:48:00 | INFO | train rank=0 | step=250/50000 progress=0.5% loss=0.694512 lr=9.960000e-06 lr_backbone=9.960000e-06 elapsed=00:21:23 avg_step=5.13s recent_step=4.13s eta=70:57:28
+2026-09-20 06:48:41 | INFO | train rank=0 | step=260/50000 progress=0.5% loss=0.667187 lr=1.036000e-05 lr_backbone=1.036000e-05 elapsed=00:22:04 avg_step=5.10s recent_step=4.13s eta=70:24:26
+2026-09-20 06:49:22 | INFO | train rank=0 | step=270/50000 progress=0.5% loss=0.645457 lr=1.076000e-05 lr_backbone=1.076000e-05 elapsed=00:22:46 avg_step=5.06s recent_step=4.12s eta=69:53:38
+2026-09-20 06:50:04 | INFO | train rank=0 | step=280/50000 progress=0.6% loss=0.673773 lr=1.116000e-05 lr_backbone=1.116000e-05 elapsed=00:23:27 avg_step=5.03s recent_step=4.13s eta=69:25:13
+2026-09-20 06:50:45 | INFO | train rank=0 | step=290/50000 progress=0.6% loss=0.611383 lr=1.156000e-05 lr_backbone=1.156000e-05 elapsed=00:24:08 avg_step=4.99s recent_step=4.11s eta=68:58:08
+2026-09-20 06:51:26 | INFO | train rank=0 | step=300/50000 progress=0.6% loss=0.617745 lr=1.196000e-05 lr_backbone=1.196000e-05 elapsed=00:24:49 avg_step=4.97s recent_step=4.10s eta=68:32:41
+2026-09-20 06:52:07 | INFO | train rank=0 | step=310/50000 progress=0.6% loss=0.645761 lr=1.236000e-05 lr_backbone=1.236000e-05 elapsed=00:25:30 avg_step=4.94s recent_step=4.14s eta=68:09:48
+2026-09-20 06:52:48 | INFO | train rank=0 | step=320/50000 progress=0.6% loss=0.521389 lr=1.276000e-05 lr_backbone=1.276000e-05 elapsed=00:26:12 avg_step=4.91s recent_step=4.14s eta=67:48:23
+2026-09-20 06:53:30 | INFO | train rank=0 | step=330/50000 progress=0.7% loss=0.596575 lr=1.316000e-05 lr_backbone=1.316000e-05 elapsed=00:26:53 avg_step=4.89s recent_step=4.14s eta=67:28:10
+2026-09-20 06:54:11 | INFO | train rank=0 | step=340/50000 progress=0.7% loss=0.571438 lr=1.356000e-05 lr_backbone=1.356000e-05 elapsed=00:27:35 avg_step=4.87s recent_step=4.14s eta=67:09:11
+2026-09-20 06:54:52 | INFO | train rank=0 | step=350/50000 progress=0.7% loss=0.549368 lr=1.396000e-05 lr_backbone=1.396000e-05 elapsed=00:28:16 avg_step=4.85s recent_step=4.10s eta=66:50:12
+2026-09-20 06:55:34 | INFO | train rank=0 | step=360/50000 progress=0.7% loss=0.556392 lr=1.436000e-05 lr_backbone=1.436000e-05 elapsed=00:28:57 avg_step=4.83s recent_step=4.15s eta=66:33:29
+2026-09-20 06:56:15 | INFO | train rank=0 | step=370/50000 progress=0.7% loss=0.537916 lr=1.476000e-05 lr_backbone=1.476000e-05 elapsed=00:29:38 avg_step=4.81s recent_step=4.11s eta=66:16:38
+2026-09-20 06:56:56 | INFO | train rank=0 | step=380/50000 progress=0.8% loss=0.558400 lr=1.516000e-05 lr_backbone=1.516000e-05 elapsed=00:30:20 avg_step=4.79s recent_step=4.14s eta=66:01:15
+2026-09-20 06:57:38 | INFO | train rank=0 | step=390/50000 progress=0.8% loss=0.598571 lr=1.556000e-05 lr_backbone=1.556000e-05 elapsed=00:31:01 avg_step=4.77s recent_step=4.15s eta=65:46:47
+2026-09-20 06:58:19 | INFO | train rank=0 | step=400/50000 progress=0.8% loss=0.490158 lr=1.596000e-05 lr_backbone=1.596000e-05 elapsed=00:31:43 avg_step=4.76s recent_step=4.16s eta=65:33:14
+2026-09-20 06:59:00 | INFO | train rank=0 | step=410/50000 progress=0.8% loss=0.543594 lr=1.636000e-05 lr_backbone=1.636000e-05 elapsed=00:32:24 avg_step=4.74s recent_step=4.11s eta=65:19:19
+2026-09-20 06:59:42 | INFO | train rank=0 | step=420/50000 progress=0.8% loss=0.508080 lr=1.676000e-05 lr_backbone=1.676000e-05 elapsed=00:33:05 avg_step=4.73s recent_step=4.13s eta=65:06:31
+2026-09-20 07:00:23 | INFO | train rank=0 | step=430/50000 progress=0.9% loss=0.578401 lr=1.716000e-05 lr_backbone=1.716000e-05 elapsed=00:33:46 avg_step=4.71s recent_step=4.11s eta=64:53:54
+2026-09-20 07:01:04 | INFO | train rank=0 | step=440/50000 progress=0.9% loss=0.515183 lr=1.756000e-05 lr_backbone=1.756000e-05 elapsed=00:34:28 avg_step=4.70s recent_step=4.13s eta=64:42:15
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+2026-09-20 07:02:27 | INFO | train rank=0 | step=460/50000 progress=0.9% loss=0.513573 lr=1.836000e-05 lr_backbone=1.836000e-05 elapsed=00:35:50 avg_step=4.68s recent_step=4.13s eta=64:20:12
+2026-09-20 07:03:08 | INFO | train rank=0 | step=470/50000 progress=0.9% loss=0.527036 lr=1.876000e-05 lr_backbone=1.876000e-05 elapsed=00:36:31 avg_step=4.66s recent_step=4.09s eta=64:09:10
+2026-09-20 07:03:49 | INFO | train rank=0 | step=480/50000 progress=1.0% loss=0.550929 lr=1.916000e-05 lr_backbone=1.916000e-05 elapsed=00:37:12 avg_step=4.65s recent_step=4.14s eta=63:59:20
+2026-09-20 07:04:30 | INFO | train rank=0 | step=490/50000 progress=1.0% loss=0.477840 lr=1.956000e-05 lr_backbone=1.956000e-05 elapsed=00:37:54 avg_step=4.64s recent_step=4.11s eta=63:49:27
+2026-09-20 07:05:12 | INFO | train rank=0 | step=500/50000 progress=1.0% loss=0.529581 lr=1.996000e-05 lr_backbone=1.996000e-05 elapsed=00:38:35 avg_step=4.63s recent_step=4.15s eta=63:40:32
+2026-09-20 07:05:53 | INFO | train rank=0 | step=510/50000 progress=1.0% loss=0.469864 lr=2.036000e-05 lr_backbone=2.036000e-05 elapsed=00:39:16 avg_step=4.62s recent_step=4.13s eta=63:31:40
+2026-09-20 07:06:34 | INFO | train rank=0 | step=520/50000 progress=1.0% loss=0.456457 lr=2.076000e-05 lr_backbone=2.076000e-05 elapsed=00:39:58 avg_step=4.61s recent_step=4.15s eta=63:23:29
+2026-09-20 07:07:15 | INFO | train rank=0 | step=530/50000 progress=1.1% loss=0.452002 lr=2.116000e-05 lr_backbone=2.116000e-05 elapsed=00:40:39 avg_step=4.60s recent_step=4.10s eta=63:14:45
+2026-09-20 07:07:57 | INFO | train rank=0 | step=540/50000 progress=1.1% loss=0.414246 lr=2.156000e-05 lr_backbone=2.156000e-05 elapsed=00:41:20 avg_step=4.59s recent_step=4.15s eta=63:07:00
+2026-09-20 07:08:39 | INFO | train rank=0 | step=550/50000 progress=1.1% loss=0.435968 lr=2.196000e-05 lr_backbone=2.196000e-05 elapsed=00:42:02 avg_step=4.59s recent_step=4.17s eta=62:59:50
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+2026-09-20 07:10:01 | INFO | train rank=0 | step=570/50000 progress=1.1% loss=0.403414 lr=2.276000e-05 lr_backbone=2.276000e-05 elapsed=00:43:25 avg_step=4.57s recent_step=4.14s eta=62:45:11
+2026-09-20 07:10:43 | INFO | train rank=0 | step=580/50000 progress=1.2% loss=0.494365 lr=2.316000e-05 lr_backbone=2.316000e-05 elapsed=00:44:06 avg_step=4.56s recent_step=4.16s eta=62:38:35
+2026-09-20 07:11:24 | INFO | train rank=0 | step=590/50000 progress=1.2% loss=0.462918 lr=2.356000e-05 lr_backbone=2.356000e-05 elapsed=00:44:47 avg_step=4.56s recent_step=4.08s eta=62:31:08
+2026-09-20 07:12:05 | INFO | train rank=0 | step=600/50000 progress=1.2% loss=0.462079 lr=2.396000e-05 lr_backbone=2.396000e-05 elapsed=00:45:28 avg_step=4.55s recent_step=4.15s eta=62:24:47
+2026-09-20 07:12:46 | INFO | train rank=0 | step=610/50000 progress=1.2% loss=0.433341 lr=2.436000e-05 lr_backbone=2.436000e-05 elapsed=00:46:10 avg_step=4.54s recent_step=4.13s eta=62:18:23
+2026-09-20 07:13:28 | INFO | train rank=0 | step=620/50000 progress=1.2% loss=0.488325 lr=2.476000e-05 lr_backbone=2.476000e-05 elapsed=00:46:51 avg_step=4.53s recent_step=4.11s eta=62:11:51
+2026-09-20 07:14:09 | INFO | train rank=0 | step=630/50000 progress=1.3% loss=0.461653 lr=2.516000e-05 lr_backbone=2.516000e-05 elapsed=00:47:32 avg_step=4.53s recent_step=4.14s eta=62:05:54
+2026-09-20 07:14:50 | INFO | train rank=0 | step=640/50000 progress=1.3% loss=0.477958 lr=2.556000e-05 lr_backbone=2.556000e-05 elapsed=00:48:14 avg_step=4.52s recent_step=4.14s eta=62:00:08
+2026-09-20 07:15:31 | INFO | train rank=0 | step=650/50000 progress=1.3% loss=0.386365 lr=2.596000e-05 lr_backbone=2.596000e-05 elapsed=00:48:55 avg_step=4.52s recent_step=4.11s eta=61:54:07
+2026-09-20 07:16:13 | INFO | train rank=0 | step=660/50000 progress=1.3% loss=0.420818 lr=2.636000e-05 lr_backbone=2.636000e-05 elapsed=00:49:36 avg_step=4.51s recent_step=4.13s eta=61:48:36
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+2026-09-22 16:35:09 | INFO | train rank=0 | step=49970/50000 progress=99.9% loss=0.308110 lr=9.842262e-11 lr_backbone=9.842262e-11 elapsed=58:08:33 avg_step=4.19s recent_step=4.13s eta=00:02:05
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+2026-09-22 16:37:14 | INFO | train rank=0 | step=50000/50000 progress=100.0% loss=0.349198 lr=0.000000e+00 lr_backbone=0.000000e+00 elapsed=58:10:38 avg_step=4.19s recent_step=4.20s eta=00:00:00
+2026-09-22 16:38:16 | INFO | train rank=0 | Saved DeepSpeed checkpoint to /data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_50000
+2026-09-22 16:38:16 | INFO | train rank=0 | Finished training final_step=50000/50000 elapsed=58:11:39 avg_step=4.19s
+2026-09-22 16:38:16 | INFO | train rank=0 | Training finished
+2026-09-22 16:39:17 | INFO | train rank=0 | Saved final DeepSpeed checkpoint to /data/NTU_slab/draven/checkpoints/codebase/codebase_v8_droid/joint_50ksteps_bs128_wan5b_videogen_fast_end_padding_mask_chunk16_downsample2/ds_checkpoint_final