diff --git a/README1.md b/README1.md
deleted file mode 100644
index f81be350848f2e9d42a7b4d602590ea62a799d4d..0000000000000000000000000000000000000000
--- a/README1.md
+++ /dev/null
@@ -1,153 +0,0 @@
----
-license: other
-library_name: peft
-tags:
-- generated_from_trainer
-base_model: google/gemma-7b-it
-model-index:
-- name: out
- results: []
----
-
-
-[
](https://github.com/OpenAccess-AI-Collective/axolotl)
-See axolotl config
-
-axolotl version: `0.4.0`
-```yaml
-# use google/gemma-7b if you have access
-base_model: google/gemma-7b-it
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-# huggingface repo
-datasets:
- - path: ./python-oasst/chunk_1.jsonl
- type: oasst
-val_set_size: 0.1
-output_dir: ./out
-
-adapter: qlora
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-
-sequence_len: 4096
-sample_packing: false
-pad_to_sequence_len: true
-
-wandb_project: gemma-7b-it
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-
-gradient_accumulation_steps: 6
-micro_batch_size: 4
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: true
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed: deepspeed_configs/zero1.json
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-
-```
-
-
-
-# out
-
-This model is a fine-tuned version of [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.1911
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 0.0002
-- train_batch_size: 4
-- eval_batch_size: 4
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 4
-- gradient_accumulation_steps: 6
-- total_train_batch_size: 96
-- total_eval_batch_size: 16
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_steps: 9
-- num_epochs: 4
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss |
-|:-------------:|:-----:|:----:|:---------------:|
-| 5.0474 | 0.01 | 1 | 5.9279 |
-| 1.2191 | 0.26 | 24 | 1.2947 |
-| 1.1165 | 0.51 | 48 | 1.1679 |
-| 1.0711 | 0.77 | 72 | 1.1377 |
-| 0.9546 | 1.02 | 96 | 1.1303 |
-| 0.9309 | 1.28 | 120 | 1.1298 |
-| 0.9588 | 1.54 | 144 | 1.1242 |
-| 0.8553 | 1.79 | 168 | 1.1259 |
-| 0.8231 | 2.05 | 192 | 1.1449 |
-| 0.8154 | 2.31 | 216 | 1.1514 |
-| 0.7354 | 2.56 | 240 | 1.1471 |
-| 0.7577 | 2.82 | 264 | 1.1479 |
-| 0.6647 | 3.07 | 288 | 1.1923 |
-| 0.6928 | 3.33 | 312 | 1.1856 |
-| 0.731 | 3.59 | 336 | 1.1890 |
-| 0.7193 | 3.84 | 360 | 1.1911 |
-
-
-### Framework versions
-
-- PEFT 0.9.0
-- Transformers 4.39.0.dev0
-- Pytorch 2.1.2+cu118
-- Datasets 2.18.0
-- Tokenizers 0.15.0
diff --git a/checkpoint-279/README.md b/checkpoint-279/README.md
deleted file mode 100644
index cc48d049490be40c7b7654441aabee7b5bc360d2..0000000000000000000000000000000000000000
--- a/checkpoint-279/README.md
+++ /dev/null
@@ -1,202 +0,0 @@
----
-library_name: peft
-base_model: google/gemma-7b-it
----
-
-# Model Card for Model ID
-
-
-
-
-
-## Model Details
-
-### Model Description
-
-
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-
-
-### Direct Use
-
-
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-
-
-[More Information Needed]
-
-### Recommendations
-
-
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-
-
-[More Information Needed]
-
-### Training Procedure
-
-
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed]
-
-#### Speeds, Sizes, Times [optional]
-
-
-
-[More Information Needed]
-
-## Evaluation
-
-
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-
-
-[More Information Needed]
-
-#### Factors
-
-
-
-[More Information Needed]
-
-#### Metrics
-
-
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-
-
-[More Information Needed]
-
-## Environmental Impact
-
-
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-### Framework versions
-
-- PEFT 0.9.0
\ No newline at end of file
diff --git a/checkpoint-279/adapter_config.json b/checkpoint-279/adapter_config.json
deleted file mode 100644
index 3c3e2768efa37865b99706584c01866eafc6c45a..0000000000000000000000000000000000000000
--- a/checkpoint-279/adapter_config.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
- "alpha_pattern": {},
- "auto_mapping": null,
- "base_model_name_or_path": "google/gemma-7b-it",
- "bias": "none",
- "fan_in_fan_out": null,
- "inference_mode": true,
- "init_lora_weights": true,
- "layers_pattern": null,
- "layers_to_transform": null,
- "loftq_config": {},
- "lora_alpha": 16,
- "lora_dropout": 0.05,
- "megatron_config": null,
- "megatron_core": "megatron.core",
- "modules_to_save": null,
- "peft_type": "LORA",
- "r": 32,
- "rank_pattern": {},
- "revision": null,
- "target_modules": [
- "down_proj",
- "o_proj",
- "k_proj",
- "q_proj",
- "gate_proj",
- "up_proj",
- "v_proj"
- ],
- "task_type": "CAUSAL_LM",
- "use_dora": false,
- "use_rslora": false
-}
\ No newline at end of file
diff --git a/checkpoint-279/adapter_model.safetensors b/checkpoint-279/adapter_model.safetensors
deleted file mode 100644
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-global_step279
\ No newline at end of file
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-{
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diff --git a/checkpoint-279/training_args.bin b/checkpoint-279/training_args.bin
deleted file mode 100644
index bc11601d02e30533659dbf12ba92faed1ba25b5a..0000000000000000000000000000000000000000
--- a/checkpoint-279/training_args.bin
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c56515a18cd914d4eee44c09952d3a756ea623b0b6e69e8dfaeb0dbc7b665f46
-size 6776
diff --git a/checkpoint-279/zero_to_fp32.py b/checkpoint-279/zero_to_fp32.py
deleted file mode 100644
index 49b846633d6eb1e836e34681e44033581f4edb7b..0000000000000000000000000000000000000000
--- a/checkpoint-279/zero_to_fp32.py
+++ /dev/null
@@ -1,592 +0,0 @@
-#!/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 . pytorch_model.bin
-
-import argparse
-import torch
-import glob
-import math
-import os
-import re
-from collections import OrderedDict
-from dataclasses import dataclass
-
-# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
-# DeepSpeed data structures it has to be available in the current python environment.
-from deepspeed.utils import logger
-from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
- FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
- FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
-
-
-@dataclass
-class zero_model_state:
- buffers: dict()
- param_shapes: dict()
- shared_params: list
- ds_version: int
- frozen_param_shapes: dict()
- frozen_param_fragments: dict()
-
-
-debug = 0
-
-# load to cpu
-device = torch.device('cpu')
-
-
-def atoi(text):
- return int(text) if text.isdigit() else text
-
-
-def natural_keys(text):
- '''
- alist.sort(key=natural_keys) sorts in human order
- http://nedbatchelder.com/blog/200712/human_sorting.html
- (See Toothy's implementation in the comments)
- '''
- return [atoi(c) for c in re.split(r'(\d+)', text)]
-
-
-def get_model_state_file(checkpoint_dir, zero_stage):
- if not os.path.isdir(checkpoint_dir):
- raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
-
- # there should be only one file
- if zero_stage <= 2:
- file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
- elif zero_stage == 3:
- file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
-
- if not os.path.exists(file):
- raise FileNotFoundError(f"can't find model states file at '{file}'")
-
- return file
-
-
-def get_checkpoint_files(checkpoint_dir, glob_pattern):
- # XXX: need to test that this simple glob rule works for multi-node setup too
- ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
-
- if len(ckpt_files) == 0:
- raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
-
- return ckpt_files
-
-
-def get_optim_files(checkpoint_dir):
- return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
-
-
-def get_model_state_files(checkpoint_dir):
- return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
-
-
-def parse_model_states(files):
- zero_model_states = []
- for file in files:
- state_dict = torch.load(file, map_location=device)
-
- if BUFFER_NAMES not in state_dict:
- raise ValueError(f"{file} is not a model state checkpoint")
- buffer_names = state_dict[BUFFER_NAMES]
- if debug:
- print("Found buffers:", buffer_names)
-
- # recover just the buffers while restoring them to fp32 if they were saved in fp16
- buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
- param_shapes = state_dict[PARAM_SHAPES]
-
- # collect parameters that are included in param_shapes
- param_names = []
- for s in param_shapes:
- for name in s.keys():
- param_names.append(name)
-
- # update with frozen parameters
- frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
- if frozen_param_shapes is not None:
- if debug:
- print(f"Found frozen_param_shapes: {frozen_param_shapes}")
- param_names += list(frozen_param_shapes.keys())
-
- # handle shared params
- shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
-
- ds_version = state_dict.get(DS_VERSION, None)
-
- frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
-
- z_model_state = zero_model_state(buffers=buffers,
- param_shapes=param_shapes,
- shared_params=shared_params,
- ds_version=ds_version,
- frozen_param_shapes=frozen_param_shapes,
- frozen_param_fragments=frozen_param_fragments)
- zero_model_states.append(z_model_state)
-
- return zero_model_states
-
-
-def parse_optim_states(files, ds_checkpoint_dir):
-
- total_files = len(files)
- state_dicts = []
- for f in files:
- state_dict = torch.load(f, map_location=device)
- # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
- # and also handle the case where it was already removed by another helper script
- state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
- state_dicts.append(state_dict)
-
- if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
- raise ValueError(f"{files[0]} is not a zero checkpoint")
- zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
- world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
-
- # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
- # parameters can be different from data parallelism for non-expert parameters. So we can just
- # use the max of the partition_count to get the dp world_size.
-
- if type(world_size) is list:
- world_size = max(world_size)
-
- if world_size != total_files:
- raise ValueError(
- f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
- "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
- )
-
- # the groups are named differently in each stage
- if zero_stage <= 2:
- fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
- elif zero_stage == 3:
- fp32_groups_key = FP32_FLAT_GROUPS
- else:
- raise ValueError(f"unknown zero stage {zero_stage}")
-
- if zero_stage <= 2:
- fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
- elif zero_stage == 3:
- # if there is more than one param group, there will be multiple flattened tensors - one
- # flattened tensor per group - for simplicity merge them into a single tensor
- #
- # XXX: could make the script more memory efficient for when there are multiple groups - it
- # will require matching the sub-lists of param_shapes for each param group flattened tensor
-
- fp32_flat_groups = [
- torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts))
- ]
-
- return zero_stage, world_size, fp32_flat_groups
-
-
-def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir):
- """
- Returns fp32 state_dict reconstructed from ds checkpoint
-
- Args:
- - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
-
- """
- print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
-
- optim_files = get_optim_files(ds_checkpoint_dir)
- zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
- print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
-
- model_files = get_model_state_files(ds_checkpoint_dir)
-
- zero_model_states = parse_model_states(model_files)
- print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
-
- if zero_stage <= 2:
- return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states)
- elif zero_stage == 3:
- return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states)
-
-
-def _zero2_merge_frozen_params(state_dict, zero_model_states):
- if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
- return
-
- frozen_param_shapes = zero_model_states[0].frozen_param_shapes
- frozen_param_fragments = zero_model_states[0].frozen_param_fragments
-
- if debug:
- num_elem = sum(s.numel() for s in frozen_param_shapes.values())
- print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
-
- wanted_params = len(frozen_param_shapes)
- wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
- avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
- print(f'Frozen params: Have {avail_numel} numels to process.')
- print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
-
- total_params = 0
- total_numel = 0
- for name, shape in frozen_param_shapes.items():
- total_params += 1
- unpartitioned_numel = shape.numel()
- total_numel += unpartitioned_numel
-
- state_dict[name] = frozen_param_fragments[name]
-
- if debug:
- print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
-
- print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
-
-
-def _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):
- state_dict = OrderedDict()
-
- # buffers
- buffers = zero_model_states[0].buffers
- state_dict.update(buffers)
- if debug:
- print(f"added {len(buffers)} buffers")
-
- _zero2_merge_frozen_params(state_dict, zero_model_states)
-
- _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
-
- # recover shared parameters
- for pair in zero_model_states[0].shared_params:
- if pair[1] in state_dict:
- state_dict[pair[0]] = state_dict[pair[1]]
-
- return state_dict
-
-
-def zero3_partitioned_param_info(unpartitioned_numel, world_size):
- remainder = unpartitioned_numel % world_size
- padding_numel = (world_size - remainder) if remainder else 0
- partitioned_numel = math.ceil(unpartitioned_numel / world_size)
- return partitioned_numel, padding_numel
-
-
-def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
- if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
- return
-
- if debug:
- for i in range(world_size):
- num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
- print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
-
- frozen_param_shapes = zero_model_states[0].frozen_param_shapes
- wanted_params = len(frozen_param_shapes)
- wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
- avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
- print(f'Frozen params: Have {avail_numel} numels to process.')
- print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
-
- total_params = 0
- total_numel = 0
- for name, shape in zero_model_states[0].frozen_param_shapes.items():
- total_params += 1
- unpartitioned_numel = shape.numel()
- total_numel += unpartitioned_numel
-
- param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
- state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
-
- partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
-
- if debug:
- print(
- f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
- )
-
- print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
-
-
-def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
- param_shapes = zero_model_states[0].param_shapes
- avail_numel = fp32_flat_groups[0].numel() * world_size
- # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
- # param, re-consolidating each param, while dealing with padding if any
-
- # merge list of dicts, preserving order
- param_shapes = {k: v for d in param_shapes for k, v in d.items()}
-
- if debug:
- for i in range(world_size):
- print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
-
- wanted_params = len(param_shapes)
- wanted_numel = sum(shape.numel() for shape in param_shapes.values())
- # not asserting if there is a mismatch due to possible padding
- avail_numel = fp32_flat_groups[0].numel() * world_size
- print(f"Trainable params: Have {avail_numel} numels to process.")
- print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
-
- # params
- # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
- # out-of-core computing solution
- offset = 0
- total_numel = 0
- total_params = 0
- for name, shape in param_shapes.items():
-
- unpartitioned_numel = shape.numel()
- total_numel += unpartitioned_numel
- total_params += 1
-
- partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
-
- if debug:
- print(
- f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
- )
-
- # XXX: memory usage doubles here
- state_dict[name] = torch.cat(
- tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)),
- 0).narrow(0, 0, unpartitioned_numel).view(shape)
- offset += partitioned_numel
-
- offset *= world_size
-
- # Sanity check
- if offset != avail_numel:
- raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
-
- print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
-
-
-def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states):
- state_dict = OrderedDict()
-
- # buffers
- buffers = zero_model_states[0].buffers
- state_dict.update(buffers)
- if debug:
- print(f"added {len(buffers)} buffers")
-
- _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
-
- _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
-
- # recover shared parameters
- for pair in zero_model_states[0].shared_params:
- if pair[1] in state_dict:
- state_dict[pair[0]] = state_dict[pair[1]]
-
- return state_dict
-
-
-def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None):
- """
- Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
- ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
- via a model hub.
-
- Args:
- - ``checkpoint_dir``: path to the desired checkpoint folder
- - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
-
- Returns:
- - pytorch ``state_dict``
-
- Note: this approach may not work if your application doesn't have sufficient free CPU memory and
- you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
- the checkpoint.
-
- A typical usage might be ::
-
- from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
- # do the training and checkpoint saving
- state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
- model = model.cpu() # move to cpu
- model.load_state_dict(state_dict)
- # submit to model hub or save the model to share with others
-
- In this example the ``model`` will no longer be usable in the deepspeed context of the same
- application. i.e. you will need to re-initialize the deepspeed engine, since
- ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
-
- If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
-
- """
- if tag is None:
- latest_path = os.path.join(checkpoint_dir, 'latest')
- if os.path.isfile(latest_path):
- with open(latest_path, 'r') as fd:
- tag = fd.read().strip()
- else:
- raise ValueError(f"Unable to find 'latest' file at {latest_path}")
-
- ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
-
- if not os.path.isdir(ds_checkpoint_dir):
- raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
-
- return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir)
-
-
-def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None):
- """
- Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
- loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
-
- Args:
- - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
- - ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
- - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
- """
-
- state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
- print(f"Saving fp32 state dict to {output_file}")
- torch.save(state_dict, output_file)
-
-
-def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
- """
- 1. Put the provided model to cpu
- 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
- 3. Load it into the provided model
-
- Args:
- - ``model``: the model object to update
- - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
- - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
-
- Returns:
- - ``model`: modified model
-
- Make sure you have plenty of CPU memory available before you call this function. If you don't
- have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
- conveniently placed for you in the checkpoint folder.
-
- A typical usage might be ::
-
- from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
- model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
- # submit to model hub or save the model to share with others
-
- Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
- of the same application. i.e. you will need to re-initialize the deepspeed engine, since
- ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
-
- """
- logger.info(f"Extracting fp32 weights")
- state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
-
- logger.info(f"Overwriting model with fp32 weights")
- model = model.cpu()
- model.load_state_dict(state_dict, strict=False)
-
- return model
-
-
-if __name__ == "__main__":
-
- parser = argparse.ArgumentParser()
- parser.add_argument("checkpoint_dir",
- type=str,
- help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
- parser.add_argument(
- "output_file",
- type=str,
- help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)")
- parser.add_argument("-t",
- "--tag",
- type=str,
- default=None,
- help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
- parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
- args = parser.parse_args()
-
- debug = args.debug
-
- convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, args.output_file, tag=args.tag)
diff --git a/checkpoint-336/README.md b/checkpoint-336/README.md
deleted file mode 100644
index 1d1a367c446e3a5f9a585222f3bda62c8b677d2b..0000000000000000000000000000000000000000
--- a/checkpoint-336/README.md
+++ /dev/null
@@ -1,202 +0,0 @@
----
-library_name: peft
-base_model: google/gemma-2b
----
-
-# Model Card for Model ID
-
-
-
-
-
-## Model Details
-
-### Model Description
-
-
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-
-
-### Direct Use
-
-
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-
-
-[More Information Needed]
-
-### Recommendations
-
-
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-
-
-[More Information Needed]
-
-### Training Procedure
-
-
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed]
-
-#### Speeds, Sizes, Times [optional]
-
-
-
-[More Information Needed]
-
-## Evaluation
-
-
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-
-
-[More Information Needed]
-
-#### Factors
-
-
-
-[More Information Needed]
-
-#### Metrics
-
-
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-
-
-[More Information Needed]
-
-## Environmental Impact
-
-
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-### Framework versions
-
-- PEFT 0.9.0
\ No newline at end of file
diff --git a/checkpoint-336/adapter_config.json b/checkpoint-336/adapter_config.json
deleted file mode 100644
index 9a11027e1f94ce0414cb1f976775603b02ca0a82..0000000000000000000000000000000000000000
--- a/checkpoint-336/adapter_config.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
- "alpha_pattern": {},
- "auto_mapping": null,
- "base_model_name_or_path": "google/gemma-2b",
- "bias": "none",
- "fan_in_fan_out": null,
- "inference_mode": true,
- "init_lora_weights": true,
- "layers_pattern": null,
- "layers_to_transform": null,
- "loftq_config": {},
- "lora_alpha": 16,
- "lora_dropout": 0.05,
- "megatron_config": null,
- "megatron_core": "megatron.core",
- "modules_to_save": null,
- "peft_type": "LORA",
- "r": 32,
- "rank_pattern": {},
- "revision": null,
- "target_modules": [
- "up_proj",
- "q_proj",
- "v_proj",
- "down_proj",
- "gate_proj",
- "k_proj",
- "o_proj"
- ],
- "task_type": "CAUSAL_LM",
- "use_dora": false,
- "use_rslora": false
-}
\ No newline at end of file
diff --git a/checkpoint-336/adapter_model.safetensors b/checkpoint-336/adapter_model.safetensors
deleted file mode 100644
index 88007c5ab9f91dae1bc4ebc890c4c4c690721391..0000000000000000000000000000000000000000
--- a/checkpoint-336/adapter_model.safetensors
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5ce5c9479f7b4e2f4f1c71ed29d0ec95f79e1731de4be9d3f7759abe3043fcdc
-size 78480320
diff --git a/checkpoint-336/global_step336/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt b/checkpoint-336/global_step336/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt
deleted file mode 100644
index 0f616a6d85e21a06963728d0c146c064a0bb10e0..0000000000000000000000000000000000000000
--- a/checkpoint-336/global_step336/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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-size 58886928
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deleted file mode 100644
index e801efc107d09178bacb23707472c4f4cc14c46e..0000000000000000000000000000000000000000
--- a/checkpoint-336/global_step336/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt
+++ /dev/null
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-oid sha256:bd427c55f17c0510ec2ed53fe5e319eb0a2c4761d4083df28d11ba7aa84e5a15
-size 58885968
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deleted file mode 100644
index fa0bcde701b60ab4d2f01a933057b3ea69842e19..0000000000000000000000000000000000000000
--- a/checkpoint-336/global_step336/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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-size 58886992
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deleted file mode 100644
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+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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-size 58886032
diff --git a/checkpoint-336/global_step336/mp_rank_00_model_states.pt b/checkpoint-336/global_step336/mp_rank_00_model_states.pt
deleted file mode 100644
index 5348de636f4e1ff9b365c04b03562adcc211d530..0000000000000000000000000000000000000000
--- a/checkpoint-336/global_step336/mp_rank_00_model_states.pt
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
-oid sha256:8dbda4b13cb1e71570782ac3ce184727dbacb34070d7b08deeb937890375555c
-size 1159049922
diff --git a/checkpoint-336/latest b/checkpoint-336/latest
deleted file mode 100644
index 2318a7b72334dbaaeb3296df1453d0694267c1df..0000000000000000000000000000000000000000
--- a/checkpoint-336/latest
+++ /dev/null
@@ -1 +0,0 @@
-global_step336
\ No newline at end of file
diff --git a/checkpoint-336/rng_state_0.pth b/checkpoint-336/rng_state_0.pth
deleted file mode 100644
index 1eae54b3e473b47c36ca4eb73a37d8438e940599..0000000000000000000000000000000000000000
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-version https://git-lfs.github.com/spec/v1
-oid sha256:fe5c5388f4cf688aa51717160bed97071e825a07ba7d9a22897241c258de91d9
-size 15024
diff --git a/checkpoint-336/scheduler.pt b/checkpoint-336/scheduler.pt
deleted file mode 100644
index a68d06deacf6d087bdaf7905fa20eeaa6368b72f..0000000000000000000000000000000000000000
--- a/checkpoint-336/scheduler.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:033700c231840b794630147afe6dca04265ec61bb681c241b2e3012bcb9cc8a3
-size 1064
diff --git a/checkpoint-336/trainer_state.json b/checkpoint-336/trainer_state.json
deleted file mode 100644
index 66c93d537aceb5a86d3b8101462f232903cd385e..0000000000000000000000000000000000000000
--- a/checkpoint-336/trainer_state.json
+++ /dev/null
@@ -1,2477 +0,0 @@
-{
- "best_metric": 1.203959345817566,
- "best_model_checkpoint": "./out/checkpoint-112",
- "epoch": 2.991097922848665,
- "eval_steps": 28,
- "global_step": 336,
- "is_hyper_param_search": false,
- "is_local_process_zero": true,
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diff --git a/checkpoint-336/training_args.bin b/checkpoint-336/training_args.bin
deleted file mode 100644
index 4992a3b445f8065303289f8a30d4fedc43285059..0000000000000000000000000000000000000000
--- a/checkpoint-336/training_args.bin
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d400c16f982c36b10268ff7e69e878c44d11f5fb692a61770a8e1efb50d4491c
-size 6776
diff --git a/checkpoint-336/zero_to_fp32.py b/checkpoint-336/zero_to_fp32.py
deleted file mode 100644
index 49b846633d6eb1e836e34681e44033581f4edb7b..0000000000000000000000000000000000000000
--- a/checkpoint-336/zero_to_fp32.py
+++ /dev/null
@@ -1,592 +0,0 @@
-#!/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 . pytorch_model.bin
-
-import argparse
-import torch
-import glob
-import math
-import os
-import re
-from collections import OrderedDict
-from dataclasses import dataclass
-
-# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
-# DeepSpeed data structures it has to be available in the current python environment.
-from deepspeed.utils import logger
-from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
- FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
- FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
-
-
-@dataclass
-class zero_model_state:
- buffers: dict()
- param_shapes: dict()
- shared_params: list
- ds_version: int
- frozen_param_shapes: dict()
- frozen_param_fragments: dict()
-
-
-debug = 0
-
-# load to cpu
-device = torch.device('cpu')
-
-
-def atoi(text):
- return int(text) if text.isdigit() else text
-
-
-def natural_keys(text):
- '''
- alist.sort(key=natural_keys) sorts in human order
- http://nedbatchelder.com/blog/200712/human_sorting.html
- (See Toothy's implementation in the comments)
- '''
- return [atoi(c) for c in re.split(r'(\d+)', text)]
-
-
-def get_model_state_file(checkpoint_dir, zero_stage):
- if not os.path.isdir(checkpoint_dir):
- raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
-
- # there should be only one file
- if zero_stage <= 2:
- file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
- elif zero_stage == 3:
- file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
-
- if not os.path.exists(file):
- raise FileNotFoundError(f"can't find model states file at '{file}'")
-
- return file
-
-
-def get_checkpoint_files(checkpoint_dir, glob_pattern):
- # XXX: need to test that this simple glob rule works for multi-node setup too
- ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
-
- if len(ckpt_files) == 0:
- raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
-
- return ckpt_files
-
-
-def get_optim_files(checkpoint_dir):
- return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
-
-
-def get_model_state_files(checkpoint_dir):
- return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
-
-
-def parse_model_states(files):
- zero_model_states = []
- for file in files:
- state_dict = torch.load(file, map_location=device)
-
- if BUFFER_NAMES not in state_dict:
- raise ValueError(f"{file} is not a model state checkpoint")
- buffer_names = state_dict[BUFFER_NAMES]
- if debug:
- print("Found buffers:", buffer_names)
-
- # recover just the buffers while restoring them to fp32 if they were saved in fp16
- buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
- param_shapes = state_dict[PARAM_SHAPES]
-
- # collect parameters that are included in param_shapes
- param_names = []
- for s in param_shapes:
- for name in s.keys():
- param_names.append(name)
-
- # update with frozen parameters
- frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
- if frozen_param_shapes is not None:
- if debug:
- print(f"Found frozen_param_shapes: {frozen_param_shapes}")
- param_names += list(frozen_param_shapes.keys())
-
- # handle shared params
- shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
-
- ds_version = state_dict.get(DS_VERSION, None)
-
- frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
-
- z_model_state = zero_model_state(buffers=buffers,
- param_shapes=param_shapes,
- shared_params=shared_params,
- ds_version=ds_version,
- frozen_param_shapes=frozen_param_shapes,
- frozen_param_fragments=frozen_param_fragments)
- zero_model_states.append(z_model_state)
-
- return zero_model_states
-
-
-def parse_optim_states(files, ds_checkpoint_dir):
-
- total_files = len(files)
- state_dicts = []
- for f in files:
- state_dict = torch.load(f, map_location=device)
- # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
- # and also handle the case where it was already removed by another helper script
- state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
- state_dicts.append(state_dict)
-
- if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
- raise ValueError(f"{files[0]} is not a zero checkpoint")
- zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
- world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
-
- # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
- # parameters can be different from data parallelism for non-expert parameters. So we can just
- # use the max of the partition_count to get the dp world_size.
-
- if type(world_size) is list:
- world_size = max(world_size)
-
- if world_size != total_files:
- raise ValueError(
- f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
- "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
- )
-
- # the groups are named differently in each stage
- if zero_stage <= 2:
- fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
- elif zero_stage == 3:
- fp32_groups_key = FP32_FLAT_GROUPS
- else:
- raise ValueError(f"unknown zero stage {zero_stage}")
-
- if zero_stage <= 2:
- fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
- elif zero_stage == 3:
- # if there is more than one param group, there will be multiple flattened tensors - one
- # flattened tensor per group - for simplicity merge them into a single tensor
- #
- # XXX: could make the script more memory efficient for when there are multiple groups - it
- # will require matching the sub-lists of param_shapes for each param group flattened tensor
-
- fp32_flat_groups = [
- torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts))
- ]
-
- return zero_stage, world_size, fp32_flat_groups
-
-
-def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir):
- """
- Returns fp32 state_dict reconstructed from ds checkpoint
-
- Args:
- - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
-
- """
- print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
-
- optim_files = get_optim_files(ds_checkpoint_dir)
- zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
- print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
-
- model_files = get_model_state_files(ds_checkpoint_dir)
-
- zero_model_states = parse_model_states(model_files)
- print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
-
- if zero_stage <= 2:
- return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states)
- elif zero_stage == 3:
- return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states)
-
-
-def _zero2_merge_frozen_params(state_dict, zero_model_states):
- if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
- return
-
- frozen_param_shapes = zero_model_states[0].frozen_param_shapes
- frozen_param_fragments = zero_model_states[0].frozen_param_fragments
-
- if debug:
- num_elem = sum(s.numel() for s in frozen_param_shapes.values())
- print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
-
- wanted_params = len(frozen_param_shapes)
- wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
- avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
- print(f'Frozen params: Have {avail_numel} numels to process.')
- print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
-
- total_params = 0
- total_numel = 0
- for name, shape in frozen_param_shapes.items():
- total_params += 1
- unpartitioned_numel = shape.numel()
- total_numel += unpartitioned_numel
-
- state_dict[name] = frozen_param_fragments[name]
-
- if debug:
- print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
-
- print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
-
-
-def _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):
- state_dict = OrderedDict()
-
- # buffers
- buffers = zero_model_states[0].buffers
- state_dict.update(buffers)
- if debug:
- print(f"added {len(buffers)} buffers")
-
- _zero2_merge_frozen_params(state_dict, zero_model_states)
-
- _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
-
- # recover shared parameters
- for pair in zero_model_states[0].shared_params:
- if pair[1] in state_dict:
- state_dict[pair[0]] = state_dict[pair[1]]
-
- return state_dict
-
-
-def zero3_partitioned_param_info(unpartitioned_numel, world_size):
- remainder = unpartitioned_numel % world_size
- padding_numel = (world_size - remainder) if remainder else 0
- partitioned_numel = math.ceil(unpartitioned_numel / world_size)
- return partitioned_numel, padding_numel
-
-
-def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
- if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
- return
-
- if debug:
- for i in range(world_size):
- num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
- print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
-
- frozen_param_shapes = zero_model_states[0].frozen_param_shapes
- wanted_params = len(frozen_param_shapes)
- wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
- avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
- print(f'Frozen params: Have {avail_numel} numels to process.')
- print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
-
- total_params = 0
- total_numel = 0
- for name, shape in zero_model_states[0].frozen_param_shapes.items():
- total_params += 1
- unpartitioned_numel = shape.numel()
- total_numel += unpartitioned_numel
-
- param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
- state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
-
- partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
-
- if debug:
- print(
- f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
- )
-
- print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
-
-
-def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
- param_shapes = zero_model_states[0].param_shapes
- avail_numel = fp32_flat_groups[0].numel() * world_size
- # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
- # param, re-consolidating each param, while dealing with padding if any
-
- # merge list of dicts, preserving order
- param_shapes = {k: v for d in param_shapes for k, v in d.items()}
-
- if debug:
- for i in range(world_size):
- print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
-
- wanted_params = len(param_shapes)
- wanted_numel = sum(shape.numel() for shape in param_shapes.values())
- # not asserting if there is a mismatch due to possible padding
- avail_numel = fp32_flat_groups[0].numel() * world_size
- print(f"Trainable params: Have {avail_numel} numels to process.")
- print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
-
- # params
- # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
- # out-of-core computing solution
- offset = 0
- total_numel = 0
- total_params = 0
- for name, shape in param_shapes.items():
-
- unpartitioned_numel = shape.numel()
- total_numel += unpartitioned_numel
- total_params += 1
-
- partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
-
- if debug:
- print(
- f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
- )
-
- # XXX: memory usage doubles here
- state_dict[name] = torch.cat(
- tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)),
- 0).narrow(0, 0, unpartitioned_numel).view(shape)
- offset += partitioned_numel
-
- offset *= world_size
-
- # Sanity check
- if offset != avail_numel:
- raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
-
- print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
-
-
-def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states):
- state_dict = OrderedDict()
-
- # buffers
- buffers = zero_model_states[0].buffers
- state_dict.update(buffers)
- if debug:
- print(f"added {len(buffers)} buffers")
-
- _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
-
- _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
-
- # recover shared parameters
- for pair in zero_model_states[0].shared_params:
- if pair[1] in state_dict:
- state_dict[pair[0]] = state_dict[pair[1]]
-
- return state_dict
-
-
-def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None):
- """
- Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
- ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
- via a model hub.
-
- Args:
- - ``checkpoint_dir``: path to the desired checkpoint folder
- - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
-
- Returns:
- - pytorch ``state_dict``
-
- Note: this approach may not work if your application doesn't have sufficient free CPU memory and
- you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
- the checkpoint.
-
- A typical usage might be ::
-
- from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
- # do the training and checkpoint saving
- state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
- model = model.cpu() # move to cpu
- model.load_state_dict(state_dict)
- # submit to model hub or save the model to share with others
-
- In this example the ``model`` will no longer be usable in the deepspeed context of the same
- application. i.e. you will need to re-initialize the deepspeed engine, since
- ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
-
- If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
-
- """
- if tag is None:
- latest_path = os.path.join(checkpoint_dir, 'latest')
- if os.path.isfile(latest_path):
- with open(latest_path, 'r') as fd:
- tag = fd.read().strip()
- else:
- raise ValueError(f"Unable to find 'latest' file at {latest_path}")
-
- ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
-
- if not os.path.isdir(ds_checkpoint_dir):
- raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
-
- return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir)
-
-
-def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None):
- """
- Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
- loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
-
- Args:
- - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
- - ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
- - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
- """
-
- state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
- print(f"Saving fp32 state dict to {output_file}")
- torch.save(state_dict, output_file)
-
-
-def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
- """
- 1. Put the provided model to cpu
- 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
- 3. Load it into the provided model
-
- Args:
- - ``model``: the model object to update
- - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
- - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
-
- Returns:
- - ``model`: modified model
-
- Make sure you have plenty of CPU memory available before you call this function. If you don't
- have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
- conveniently placed for you in the checkpoint folder.
-
- A typical usage might be ::
-
- from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
- model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
- # submit to model hub or save the model to share with others
-
- Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
- of the same application. i.e. you will need to re-initialize the deepspeed engine, since
- ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
-
- """
- logger.info(f"Extracting fp32 weights")
- state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
-
- logger.info(f"Overwriting model with fp32 weights")
- model = model.cpu()
- model.load_state_dict(state_dict, strict=False)
-
- return model
-
-
-if __name__ == "__main__":
-
- parser = argparse.ArgumentParser()
- parser.add_argument("checkpoint_dir",
- type=str,
- help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
- parser.add_argument(
- "output_file",
- type=str,
- help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)")
- parser.add_argument("-t",
- "--tag",
- type=str,
- default=None,
- help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
- parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
- args = parser.parse_args()
-
- debug = args.debug
-
- convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, args.output_file, tag=args.tag)
diff --git a/checkpoint-372/README.md b/checkpoint-372/README.md
deleted file mode 100644
index cc48d049490be40c7b7654441aabee7b5bc360d2..0000000000000000000000000000000000000000
--- a/checkpoint-372/README.md
+++ /dev/null
@@ -1,202 +0,0 @@
----
-library_name: peft
-base_model: google/gemma-7b-it
----
-
-# Model Card for Model ID
-
-
-
-
-
-## Model Details
-
-### Model Description
-
-
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-
-
-### Direct Use
-
-
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-
-
-[More Information Needed]
-
-### Recommendations
-
-
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-
-
-[More Information Needed]
-
-### Training Procedure
-
-
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed]
-
-#### Speeds, Sizes, Times [optional]
-
-
-
-[More Information Needed]
-
-## Evaluation
-
-
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-
-
-[More Information Needed]
-
-#### Factors
-
-
-
-[More Information Needed]
-
-#### Metrics
-
-
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-
-
-[More Information Needed]
-
-## Environmental Impact
-
-
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-### Framework versions
-
-- PEFT 0.9.0
\ No newline at end of file
diff --git a/checkpoint-372/adapter_config.json b/checkpoint-372/adapter_config.json
deleted file mode 100644
index 3c3e2768efa37865b99706584c01866eafc6c45a..0000000000000000000000000000000000000000
--- a/checkpoint-372/adapter_config.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
- "alpha_pattern": {},
- "auto_mapping": null,
- "base_model_name_or_path": "google/gemma-7b-it",
- "bias": "none",
- "fan_in_fan_out": null,
- "inference_mode": true,
- "init_lora_weights": true,
- "layers_pattern": null,
- "layers_to_transform": null,
- "loftq_config": {},
- "lora_alpha": 16,
- "lora_dropout": 0.05,
- "megatron_config": null,
- "megatron_core": "megatron.core",
- "modules_to_save": null,
- "peft_type": "LORA",
- "r": 32,
- "rank_pattern": {},
- "revision": null,
- "target_modules": [
- "down_proj",
- "o_proj",
- "k_proj",
- "q_proj",
- "gate_proj",
- "up_proj",
- "v_proj"
- ],
- "task_type": "CAUSAL_LM",
- "use_dora": false,
- "use_rslora": false
-}
\ No newline at end of file
diff --git a/checkpoint-372/adapter_model.safetensors b/checkpoint-372/adapter_model.safetensors
deleted file mode 100644
index 0015bf12d8aa10457cc3eafcef7b9515dd2b63c3..0000000000000000000000000000000000000000
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+++ /dev/null
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-size 200068904
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deleted file mode 100644
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-size 1896781286
diff --git a/checkpoint-372/latest b/checkpoint-372/latest
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index 58718163630dae3a397bab9fcebd1e5516bfae6b..0000000000000000000000000000000000000000
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+++ /dev/null
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-global_step372
\ No newline at end of file
diff --git a/checkpoint-372/rng_state_0.pth b/checkpoint-372/rng_state_0.pth
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diff --git a/checkpoint-372/trainer_state.json b/checkpoint-372/trainer_state.json
deleted file mode 100644
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-{
- "best_metric": null,
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- "epoch": 3.9715302491103204,
- "eval_steps": 24,
- "global_step": 372,
- "is_hyper_param_search": false,
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- "is_world_process_zero": true,
- "log_history": [
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- "eval_runtime": 117.3665,
- "eval_samples_per_second": 8.512,
- "eval_steps_per_second": 0.537,
- "step": 1
- },
- {
- "epoch": 0.02,
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- "learning_rate": 4.4444444444444447e-05,
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- },
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diff --git a/checkpoint-372/training_args.bin b/checkpoint-372/training_args.bin
deleted file mode 100644
index bc11601d02e30533659dbf12ba92faed1ba25b5a..0000000000000000000000000000000000000000
--- a/checkpoint-372/training_args.bin
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c56515a18cd914d4eee44c09952d3a756ea623b0b6e69e8dfaeb0dbc7b665f46
-size 6776
diff --git a/checkpoint-372/zero_to_fp32.py b/checkpoint-372/zero_to_fp32.py
deleted file mode 100644
index 49b846633d6eb1e836e34681e44033581f4edb7b..0000000000000000000000000000000000000000
--- a/checkpoint-372/zero_to_fp32.py
+++ /dev/null
@@ -1,592 +0,0 @@
-#!/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 . pytorch_model.bin
-
-import argparse
-import torch
-import glob
-import math
-import os
-import re
-from collections import OrderedDict
-from dataclasses import dataclass
-
-# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
-# DeepSpeed data structures it has to be available in the current python environment.
-from deepspeed.utils import logger
-from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
- FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
- FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
-
-
-@dataclass
-class zero_model_state:
- buffers: dict()
- param_shapes: dict()
- shared_params: list
- ds_version: int
- frozen_param_shapes: dict()
- frozen_param_fragments: dict()
-
-
-debug = 0
-
-# load to cpu
-device = torch.device('cpu')
-
-
-def atoi(text):
- return int(text) if text.isdigit() else text
-
-
-def natural_keys(text):
- '''
- alist.sort(key=natural_keys) sorts in human order
- http://nedbatchelder.com/blog/200712/human_sorting.html
- (See Toothy's implementation in the comments)
- '''
- return [atoi(c) for c in re.split(r'(\d+)', text)]
-
-
-def get_model_state_file(checkpoint_dir, zero_stage):
- if not os.path.isdir(checkpoint_dir):
- raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
-
- # there should be only one file
- if zero_stage <= 2:
- file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
- elif zero_stage == 3:
- file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
-
- if not os.path.exists(file):
- raise FileNotFoundError(f"can't find model states file at '{file}'")
-
- return file
-
-
-def get_checkpoint_files(checkpoint_dir, glob_pattern):
- # XXX: need to test that this simple glob rule works for multi-node setup too
- ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
-
- if len(ckpt_files) == 0:
- raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
-
- return ckpt_files
-
-
-def get_optim_files(checkpoint_dir):
- return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
-
-
-def get_model_state_files(checkpoint_dir):
- return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
-
-
-def parse_model_states(files):
- zero_model_states = []
- for file in files:
- state_dict = torch.load(file, map_location=device)
-
- if BUFFER_NAMES not in state_dict:
- raise ValueError(f"{file} is not a model state checkpoint")
- buffer_names = state_dict[BUFFER_NAMES]
- if debug:
- print("Found buffers:", buffer_names)
-
- # recover just the buffers while restoring them to fp32 if they were saved in fp16
- buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
- param_shapes = state_dict[PARAM_SHAPES]
-
- # collect parameters that are included in param_shapes
- param_names = []
- for s in param_shapes:
- for name in s.keys():
- param_names.append(name)
-
- # update with frozen parameters
- frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
- if frozen_param_shapes is not None:
- if debug:
- print(f"Found frozen_param_shapes: {frozen_param_shapes}")
- param_names += list(frozen_param_shapes.keys())
-
- # handle shared params
- shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
-
- ds_version = state_dict.get(DS_VERSION, None)
-
- frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
-
- z_model_state = zero_model_state(buffers=buffers,
- param_shapes=param_shapes,
- shared_params=shared_params,
- ds_version=ds_version,
- frozen_param_shapes=frozen_param_shapes,
- frozen_param_fragments=frozen_param_fragments)
- zero_model_states.append(z_model_state)
-
- return zero_model_states
-
-
-def parse_optim_states(files, ds_checkpoint_dir):
-
- total_files = len(files)
- state_dicts = []
- for f in files:
- state_dict = torch.load(f, map_location=device)
- # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
- # and also handle the case where it was already removed by another helper script
- state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
- state_dicts.append(state_dict)
-
- if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
- raise ValueError(f"{files[0]} is not a zero checkpoint")
- zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
- world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
-
- # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
- # parameters can be different from data parallelism for non-expert parameters. So we can just
- # use the max of the partition_count to get the dp world_size.
-
- if type(world_size) is list:
- world_size = max(world_size)
-
- if world_size != total_files:
- raise ValueError(
- f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
- "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
- )
-
- # the groups are named differently in each stage
- if zero_stage <= 2:
- fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
- elif zero_stage == 3:
- fp32_groups_key = FP32_FLAT_GROUPS
- else:
- raise ValueError(f"unknown zero stage {zero_stage}")
-
- if zero_stage <= 2:
- fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
- elif zero_stage == 3:
- # if there is more than one param group, there will be multiple flattened tensors - one
- # flattened tensor per group - for simplicity merge them into a single tensor
- #
- # XXX: could make the script more memory efficient for when there are multiple groups - it
- # will require matching the sub-lists of param_shapes for each param group flattened tensor
-
- fp32_flat_groups = [
- torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts))
- ]
-
- return zero_stage, world_size, fp32_flat_groups
-
-
-def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir):
- """
- Returns fp32 state_dict reconstructed from ds checkpoint
-
- Args:
- - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
-
- """
- print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
-
- optim_files = get_optim_files(ds_checkpoint_dir)
- zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
- print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
-
- model_files = get_model_state_files(ds_checkpoint_dir)
-
- zero_model_states = parse_model_states(model_files)
- print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
-
- if zero_stage <= 2:
- return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states)
- elif zero_stage == 3:
- return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states)
-
-
-def _zero2_merge_frozen_params(state_dict, zero_model_states):
- if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
- return
-
- frozen_param_shapes = zero_model_states[0].frozen_param_shapes
- frozen_param_fragments = zero_model_states[0].frozen_param_fragments
-
- if debug:
- num_elem = sum(s.numel() for s in frozen_param_shapes.values())
- print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
-
- wanted_params = len(frozen_param_shapes)
- wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
- avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
- print(f'Frozen params: Have {avail_numel} numels to process.')
- print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
-
- total_params = 0
- total_numel = 0
- for name, shape in frozen_param_shapes.items():
- total_params += 1
- unpartitioned_numel = shape.numel()
- total_numel += unpartitioned_numel
-
- state_dict[name] = frozen_param_fragments[name]
-
- if debug:
- print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
-
- print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
-
-
-def _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):
- state_dict = OrderedDict()
-
- # buffers
- buffers = zero_model_states[0].buffers
- state_dict.update(buffers)
- if debug:
- print(f"added {len(buffers)} buffers")
-
- _zero2_merge_frozen_params(state_dict, zero_model_states)
-
- _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
-
- # recover shared parameters
- for pair in zero_model_states[0].shared_params:
- if pair[1] in state_dict:
- state_dict[pair[0]] = state_dict[pair[1]]
-
- return state_dict
-
-
-def zero3_partitioned_param_info(unpartitioned_numel, world_size):
- remainder = unpartitioned_numel % world_size
- padding_numel = (world_size - remainder) if remainder else 0
- partitioned_numel = math.ceil(unpartitioned_numel / world_size)
- return partitioned_numel, padding_numel
-
-
-def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
- if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
- return
-
- if debug:
- for i in range(world_size):
- num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
- print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
-
- frozen_param_shapes = zero_model_states[0].frozen_param_shapes
- wanted_params = len(frozen_param_shapes)
- wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
- avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
- print(f'Frozen params: Have {avail_numel} numels to process.')
- print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
-
- total_params = 0
- total_numel = 0
- for name, shape in zero_model_states[0].frozen_param_shapes.items():
- total_params += 1
- unpartitioned_numel = shape.numel()
- total_numel += unpartitioned_numel
-
- param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
- state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
-
- partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
-
- if debug:
- print(
- f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
- )
-
- print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
-
-
-def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
- param_shapes = zero_model_states[0].param_shapes
- avail_numel = fp32_flat_groups[0].numel() * world_size
- # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
- # param, re-consolidating each param, while dealing with padding if any
-
- # merge list of dicts, preserving order
- param_shapes = {k: v for d in param_shapes for k, v in d.items()}
-
- if debug:
- for i in range(world_size):
- print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
-
- wanted_params = len(param_shapes)
- wanted_numel = sum(shape.numel() for shape in param_shapes.values())
- # not asserting if there is a mismatch due to possible padding
- avail_numel = fp32_flat_groups[0].numel() * world_size
- print(f"Trainable params: Have {avail_numel} numels to process.")
- print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
-
- # params
- # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
- # out-of-core computing solution
- offset = 0
- total_numel = 0
- total_params = 0
- for name, shape in param_shapes.items():
-
- unpartitioned_numel = shape.numel()
- total_numel += unpartitioned_numel
- total_params += 1
-
- partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
-
- if debug:
- print(
- f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
- )
-
- # XXX: memory usage doubles here
- state_dict[name] = torch.cat(
- tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)),
- 0).narrow(0, 0, unpartitioned_numel).view(shape)
- offset += partitioned_numel
-
- offset *= world_size
-
- # Sanity check
- if offset != avail_numel:
- raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
-
- print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
-
-
-def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states):
- state_dict = OrderedDict()
-
- # buffers
- buffers = zero_model_states[0].buffers
- state_dict.update(buffers)
- if debug:
- print(f"added {len(buffers)} buffers")
-
- _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
-
- _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
-
- # recover shared parameters
- for pair in zero_model_states[0].shared_params:
- if pair[1] in state_dict:
- state_dict[pair[0]] = state_dict[pair[1]]
-
- return state_dict
-
-
-def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None):
- """
- Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
- ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
- via a model hub.
-
- Args:
- - ``checkpoint_dir``: path to the desired checkpoint folder
- - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
-
- Returns:
- - pytorch ``state_dict``
-
- Note: this approach may not work if your application doesn't have sufficient free CPU memory and
- you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
- the checkpoint.
-
- A typical usage might be ::
-
- from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
- # do the training and checkpoint saving
- state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
- model = model.cpu() # move to cpu
- model.load_state_dict(state_dict)
- # submit to model hub or save the model to share with others
-
- In this example the ``model`` will no longer be usable in the deepspeed context of the same
- application. i.e. you will need to re-initialize the deepspeed engine, since
- ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
-
- If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
-
- """
- if tag is None:
- latest_path = os.path.join(checkpoint_dir, 'latest')
- if os.path.isfile(latest_path):
- with open(latest_path, 'r') as fd:
- tag = fd.read().strip()
- else:
- raise ValueError(f"Unable to find 'latest' file at {latest_path}")
-
- ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
-
- if not os.path.isdir(ds_checkpoint_dir):
- raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
-
- return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir)
-
-
-def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None):
- """
- Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
- loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
-
- Args:
- - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
- - ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
- - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
- """
-
- state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
- print(f"Saving fp32 state dict to {output_file}")
- torch.save(state_dict, output_file)
-
-
-def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
- """
- 1. Put the provided model to cpu
- 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
- 3. Load it into the provided model
-
- Args:
- - ``model``: the model object to update
- - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
- - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
-
- Returns:
- - ``model`: modified model
-
- Make sure you have plenty of CPU memory available before you call this function. If you don't
- have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
- conveniently placed for you in the checkpoint folder.
-
- A typical usage might be ::
-
- from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
- model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
- # submit to model hub or save the model to share with others
-
- Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
- of the same application. i.e. you will need to re-initialize the deepspeed engine, since
- ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
-
- """
- logger.info(f"Extracting fp32 weights")
- state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
-
- logger.info(f"Overwriting model with fp32 weights")
- model = model.cpu()
- model.load_state_dict(state_dict, strict=False)
-
- return model
-
-
-if __name__ == "__main__":
-
- parser = argparse.ArgumentParser()
- parser.add_argument("checkpoint_dir",
- type=str,
- help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
- parser.add_argument(
- "output_file",
- type=str,
- help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)")
- parser.add_argument("-t",
- "--tag",
- type=str,
- default=None,
- help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
- parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
- args = parser.parse_args()
-
- debug = args.debug
-
- convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, args.output_file, tag=args.tag)
diff --git a/checkpoint-448/README.md b/checkpoint-448/README.md
deleted file mode 100644
index cf214dc8241cd3fa9db952d247cbfcba94fa04e6..0000000000000000000000000000000000000000
--- a/checkpoint-448/README.md
+++ /dev/null
@@ -1,1091 +0,0 @@
----
-library_name: peft
-base_model: google/gemma-2b
----
-
-# Model Card for Model ID
-
-Fine-tuned on python
-
-
-## Model Details
-
-### Model Description
-
-
-### Model Sources [optional]
-Gemma-2b trained on python-oasst dataset
-
-## Uses
-
-
-## Training Details
-
-### Training Data
-
-{
- "_timestamp": 1711018613.433522,
- "train/grad_norm": 0.1240904619429259,
- "train/global_step": 232,
- "eval/steps_per_second": 2.894,
- "_step": 232,
- "_runtime": 2545.4226660728455,
- "eval/loss": 1.189491629600525,
- "eval/runtime": 1805.8574,
- "train/learning_rate": 0.000014800637958532697,
- "eval/samples_per_second": 23.152,
- "_wandb.runtime": 2547,
- "train/loss": 1.0436,
- "train/epoch": 0.01
-}
-
-### Results
-
-
-#### Summary
-
-
-
-
-## Technical Specifications [optional]
-{
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- "fsdp_transformer_layer_cls_to_wrap": {
- "desc": null,
- "value": null
- }
-}
-### Model Architecture and Objective
-
-## Citation [optional]
-
-
-## Glossary [optional]
-### Framework versions
-
-- PEFT 0.9.0
diff --git a/checkpoint-448/adapter_config.json b/checkpoint-448/adapter_config.json
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--- a/checkpoint-448/adapter_config.json
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-{
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- "use_dora": false,
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\ No newline at end of file
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-global_step448
\ No newline at end of file
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diff --git a/checkpoint-448/training_args.bin b/checkpoint-448/training_args.bin
deleted file mode 100644
index 4992a3b445f8065303289f8a30d4fedc43285059..0000000000000000000000000000000000000000
--- a/checkpoint-448/training_args.bin
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d400c16f982c36b10268ff7e69e878c44d11f5fb692a61770a8e1efb50d4491c
-size 6776
diff --git a/checkpoint-448/zero_to_fp32.py b/checkpoint-448/zero_to_fp32.py
deleted file mode 100644
index 49b846633d6eb1e836e34681e44033581f4edb7b..0000000000000000000000000000000000000000
--- a/checkpoint-448/zero_to_fp32.py
+++ /dev/null
@@ -1,592 +0,0 @@
-#!/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 . pytorch_model.bin
-
-import argparse
-import torch
-import glob
-import math
-import os
-import re
-from collections import OrderedDict
-from dataclasses import dataclass
-
-# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
-# DeepSpeed data structures it has to be available in the current python environment.
-from deepspeed.utils import logger
-from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
- FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
- FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
-
-
-@dataclass
-class zero_model_state:
- buffers: dict()
- param_shapes: dict()
- shared_params: list
- ds_version: int
- frozen_param_shapes: dict()
- frozen_param_fragments: dict()
-
-
-debug = 0
-
-# load to cpu
-device = torch.device('cpu')
-
-
-def atoi(text):
- return int(text) if text.isdigit() else text
-
-
-def natural_keys(text):
- '''
- alist.sort(key=natural_keys) sorts in human order
- http://nedbatchelder.com/blog/200712/human_sorting.html
- (See Toothy's implementation in the comments)
- '''
- return [atoi(c) for c in re.split(r'(\d+)', text)]
-
-
-def get_model_state_file(checkpoint_dir, zero_stage):
- if not os.path.isdir(checkpoint_dir):
- raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
-
- # there should be only one file
- if zero_stage <= 2:
- file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
- elif zero_stage == 3:
- file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
-
- if not os.path.exists(file):
- raise FileNotFoundError(f"can't find model states file at '{file}'")
-
- return file
-
-
-def get_checkpoint_files(checkpoint_dir, glob_pattern):
- # XXX: need to test that this simple glob rule works for multi-node setup too
- ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
-
- if len(ckpt_files) == 0:
- raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
-
- return ckpt_files
-
-
-def get_optim_files(checkpoint_dir):
- return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
-
-
-def get_model_state_files(checkpoint_dir):
- return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
-
-
-def parse_model_states(files):
- zero_model_states = []
- for file in files:
- state_dict = torch.load(file, map_location=device)
-
- if BUFFER_NAMES not in state_dict:
- raise ValueError(f"{file} is not a model state checkpoint")
- buffer_names = state_dict[BUFFER_NAMES]
- if debug:
- print("Found buffers:", buffer_names)
-
- # recover just the buffers while restoring them to fp32 if they were saved in fp16
- buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
- param_shapes = state_dict[PARAM_SHAPES]
-
- # collect parameters that are included in param_shapes
- param_names = []
- for s in param_shapes:
- for name in s.keys():
- param_names.append(name)
-
- # update with frozen parameters
- frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
- if frozen_param_shapes is not None:
- if debug:
- print(f"Found frozen_param_shapes: {frozen_param_shapes}")
- param_names += list(frozen_param_shapes.keys())
-
- # handle shared params
- shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
-
- ds_version = state_dict.get(DS_VERSION, None)
-
- frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
-
- z_model_state = zero_model_state(buffers=buffers,
- param_shapes=param_shapes,
- shared_params=shared_params,
- ds_version=ds_version,
- frozen_param_shapes=frozen_param_shapes,
- frozen_param_fragments=frozen_param_fragments)
- zero_model_states.append(z_model_state)
-
- return zero_model_states
-
-
-def parse_optim_states(files, ds_checkpoint_dir):
-
- total_files = len(files)
- state_dicts = []
- for f in files:
- state_dict = torch.load(f, map_location=device)
- # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
- # and also handle the case where it was already removed by another helper script
- state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
- state_dicts.append(state_dict)
-
- if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
- raise ValueError(f"{files[0]} is not a zero checkpoint")
- zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
- world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
-
- # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
- # parameters can be different from data parallelism for non-expert parameters. So we can just
- # use the max of the partition_count to get the dp world_size.
-
- if type(world_size) is list:
- world_size = max(world_size)
-
- if world_size != total_files:
- raise ValueError(
- f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
- "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
- )
-
- # the groups are named differently in each stage
- if zero_stage <= 2:
- fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
- elif zero_stage == 3:
- fp32_groups_key = FP32_FLAT_GROUPS
- else:
- raise ValueError(f"unknown zero stage {zero_stage}")
-
- if zero_stage <= 2:
- fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
- elif zero_stage == 3:
- # if there is more than one param group, there will be multiple flattened tensors - one
- # flattened tensor per group - for simplicity merge them into a single tensor
- #
- # XXX: could make the script more memory efficient for when there are multiple groups - it
- # will require matching the sub-lists of param_shapes for each param group flattened tensor
-
- fp32_flat_groups = [
- torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts))
- ]
-
- return zero_stage, world_size, fp32_flat_groups
-
-
-def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir):
- """
- Returns fp32 state_dict reconstructed from ds checkpoint
-
- Args:
- - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
-
- """
- print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
-
- optim_files = get_optim_files(ds_checkpoint_dir)
- zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
- print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
-
- model_files = get_model_state_files(ds_checkpoint_dir)
-
- zero_model_states = parse_model_states(model_files)
- print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
-
- if zero_stage <= 2:
- return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states)
- elif zero_stage == 3:
- return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states)
-
-
-def _zero2_merge_frozen_params(state_dict, zero_model_states):
- if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
- return
-
- frozen_param_shapes = zero_model_states[0].frozen_param_shapes
- frozen_param_fragments = zero_model_states[0].frozen_param_fragments
-
- if debug:
- num_elem = sum(s.numel() for s in frozen_param_shapes.values())
- print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
-
- wanted_params = len(frozen_param_shapes)
- wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
- avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
- print(f'Frozen params: Have {avail_numel} numels to process.')
- print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
-
- total_params = 0
- total_numel = 0
- for name, shape in frozen_param_shapes.items():
- total_params += 1
- unpartitioned_numel = shape.numel()
- total_numel += unpartitioned_numel
-
- state_dict[name] = frozen_param_fragments[name]
-
- if debug:
- print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
-
- print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
-
-
-def _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):
- state_dict = OrderedDict()
-
- # buffers
- buffers = zero_model_states[0].buffers
- state_dict.update(buffers)
- if debug:
- print(f"added {len(buffers)} buffers")
-
- _zero2_merge_frozen_params(state_dict, zero_model_states)
-
- _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
-
- # recover shared parameters
- for pair in zero_model_states[0].shared_params:
- if pair[1] in state_dict:
- state_dict[pair[0]] = state_dict[pair[1]]
-
- return state_dict
-
-
-def zero3_partitioned_param_info(unpartitioned_numel, world_size):
- remainder = unpartitioned_numel % world_size
- padding_numel = (world_size - remainder) if remainder else 0
- partitioned_numel = math.ceil(unpartitioned_numel / world_size)
- return partitioned_numel, padding_numel
-
-
-def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
- if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
- return
-
- if debug:
- for i in range(world_size):
- num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
- print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
-
- frozen_param_shapes = zero_model_states[0].frozen_param_shapes
- wanted_params = len(frozen_param_shapes)
- wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
- avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
- print(f'Frozen params: Have {avail_numel} numels to process.')
- print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
-
- total_params = 0
- total_numel = 0
- for name, shape in zero_model_states[0].frozen_param_shapes.items():
- total_params += 1
- unpartitioned_numel = shape.numel()
- total_numel += unpartitioned_numel
-
- param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
- state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
-
- partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
-
- if debug:
- print(
- f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
- )
-
- print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
-
-
-def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
- param_shapes = zero_model_states[0].param_shapes
- avail_numel = fp32_flat_groups[0].numel() * world_size
- # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
- # param, re-consolidating each param, while dealing with padding if any
-
- # merge list of dicts, preserving order
- param_shapes = {k: v for d in param_shapes for k, v in d.items()}
-
- if debug:
- for i in range(world_size):
- print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
-
- wanted_params = len(param_shapes)
- wanted_numel = sum(shape.numel() for shape in param_shapes.values())
- # not asserting if there is a mismatch due to possible padding
- avail_numel = fp32_flat_groups[0].numel() * world_size
- print(f"Trainable params: Have {avail_numel} numels to process.")
- print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
-
- # params
- # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
- # out-of-core computing solution
- offset = 0
- total_numel = 0
- total_params = 0
- for name, shape in param_shapes.items():
-
- unpartitioned_numel = shape.numel()
- total_numel += unpartitioned_numel
- total_params += 1
-
- partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
-
- if debug:
- print(
- f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
- )
-
- # XXX: memory usage doubles here
- state_dict[name] = torch.cat(
- tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)),
- 0).narrow(0, 0, unpartitioned_numel).view(shape)
- offset += partitioned_numel
-
- offset *= world_size
-
- # Sanity check
- if offset != avail_numel:
- raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
-
- print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
-
-
-def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states):
- state_dict = OrderedDict()
-
- # buffers
- buffers = zero_model_states[0].buffers
- state_dict.update(buffers)
- if debug:
- print(f"added {len(buffers)} buffers")
-
- _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
-
- _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
-
- # recover shared parameters
- for pair in zero_model_states[0].shared_params:
- if pair[1] in state_dict:
- state_dict[pair[0]] = state_dict[pair[1]]
-
- return state_dict
-
-
-def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None):
- """
- Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
- ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
- via a model hub.
-
- Args:
- - ``checkpoint_dir``: path to the desired checkpoint folder
- - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
-
- Returns:
- - pytorch ``state_dict``
-
- Note: this approach may not work if your application doesn't have sufficient free CPU memory and
- you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
- the checkpoint.
-
- A typical usage might be ::
-
- from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
- # do the training and checkpoint saving
- state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
- model = model.cpu() # move to cpu
- model.load_state_dict(state_dict)
- # submit to model hub or save the model to share with others
-
- In this example the ``model`` will no longer be usable in the deepspeed context of the same
- application. i.e. you will need to re-initialize the deepspeed engine, since
- ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
-
- If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
-
- """
- if tag is None:
- latest_path = os.path.join(checkpoint_dir, 'latest')
- if os.path.isfile(latest_path):
- with open(latest_path, 'r') as fd:
- tag = fd.read().strip()
- else:
- raise ValueError(f"Unable to find 'latest' file at {latest_path}")
-
- ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
-
- if not os.path.isdir(ds_checkpoint_dir):
- raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
-
- return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir)
-
-
-def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None):
- """
- Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
- loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
-
- Args:
- - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
- - ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
- - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
- """
-
- state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
- print(f"Saving fp32 state dict to {output_file}")
- torch.save(state_dict, output_file)
-
-
-def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
- """
- 1. Put the provided model to cpu
- 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
- 3. Load it into the provided model
-
- Args:
- - ``model``: the model object to update
- - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
- - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
-
- Returns:
- - ``model`: modified model
-
- Make sure you have plenty of CPU memory available before you call this function. If you don't
- have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
- conveniently placed for you in the checkpoint folder.
-
- A typical usage might be ::
-
- from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
- model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
- # submit to model hub or save the model to share with others
-
- Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
- of the same application. i.e. you will need to re-initialize the deepspeed engine, since
- ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
-
- """
- logger.info(f"Extracting fp32 weights")
- state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
-
- logger.info(f"Overwriting model with fp32 weights")
- model = model.cpu()
- model.load_state_dict(state_dict, strict=False)
-
- return model
-
-
-if __name__ == "__main__":
-
- parser = argparse.ArgumentParser()
- parser.add_argument("checkpoint_dir",
- type=str,
- help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
- parser.add_argument(
- "output_file",
- type=str,
- help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)")
- parser.add_argument("-t",
- "--tag",
- type=str,
- default=None,
- help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
- parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
- args = parser.parse_args()
-
- debug = args.debug
-
- convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, args.output_file, tag=args.tag)
diff --git a/merged/config.json b/merged/config.json
deleted file mode 100644
index 5cf635c5516ca060a95e347a8fd1d8b0e1719cbf..0000000000000000000000000000000000000000
--- a/merged/config.json
+++ /dev/null
@@ -1,28 +0,0 @@
-{
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- "architectures": [
- "GemmaForCausalLM"
- ],
- "attention_bias": false,
- "attention_dropout": 0.0,
- "bos_token_id": 2,
- "eos_token_id": 1,
- "head_dim": 256,
- "hidden_act": "gelu",
- "hidden_size": 2048,
- "initializer_range": 0.02,
- "intermediate_size": 16384,
- "max_position_embeddings": 8192,
- "model_type": "gemma",
- "num_attention_heads": 8,
- "num_hidden_layers": 18,
- "num_key_value_heads": 1,
- "pad_token_id": 0,
- "rms_norm_eps": 1e-06,
- "rope_scaling": null,
- "rope_theta": 10000.0,
- "torch_dtype": "bfloat16",
- "transformers_version": "4.39.0.dev0",
- "use_cache": false,
- "vocab_size": 256000
-}
diff --git a/merged/generation_config.json b/merged/generation_config.json
deleted file mode 100644
index 6315b520e1541d5ac8d7c9fa201d06bdc5cc22ce..0000000000000000000000000000000000000000
--- a/merged/generation_config.json
+++ /dev/null
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-{
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- "bos_token_id": 2,
- "do_sample": true,
- "eos_token_id": 1,
- "pad_token_id": 0,
- "transformers_version": "4.39.0.dev0"
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index f6119589e367b2de0fc8cbd2f1217667532e3174..0000000000000000000000000000000000000000
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-{
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index 7d526fa4a2fc483e768773df90a944b54094e367..0000000000000000000000000000000000000000
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-{
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