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---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- multimodal
- action
- agent
---
# Holo1-7B
## Model Description
Holo1 is an Action Vision-Language Model (VLM) developed by [HCompany](https://www.hcompany.ai/) for use in the Surfer-H web agent system. It is designed to interact with web interfaces like a human user.
As part of a broader agentic architecture, Holo1 acts as a policy, localizer, or validator, helping the agent understand and act in digital environments.
Trained on a mix of open-access, synthetic, and self-generated data, Holo1 enables state-of-the-art (SOTA) performance on the [WebVoyager](https://arxiv.org/pdf/2401.13919) benchmark, offering the best accuracy/cost tradeoff among current models.
It also excels in UI localization tasks such as [Screenspot](https://huggingface.co/datasets/rootsautomation/ScreenSpot), [Screenspot-V2](https://huggingface.co/datasets/HongxinLi/ScreenSpot_v2), [Screenspot-Pro](https://huggingface.co/datasets/likaixin/ScreenSpot-Pro), [GroundUI-Web](https://huggingface.co/datasets/agent-studio/GroundUI-1K), and our own newly introduced
benchmark [WebClick](https://huggingface.co/datasets/Hcompany/WebClick).
Holo1 is optimized for both accuracy and cost-efficiency, making it a strong open-source alternative to existing VLMs.
For more details, check our paper and our blog post.
- **Developed by:** [HCompany](https://www.hcompany.ai/)
- **Model type:** Action Vision-Language Model
- **Finetuned from model:** Qwen/Qwen2.5-VL-7B-Instruct
- **Paper:** https://arxiv.org/abs/2506.02865
- **Blog Post:** https://www.hcompany.ai/surfer-h
- **License:** Apache 2.0
## Results
### Surfer-H: Pareto-Optimal Performance on [WebVoyager](https://arxiv.org/pdf/2401.13919)
Surfer-H is designed to be flexible and modular. It is composed of three independent components:
- A Policy model that plans, decides, and drives the agent's behavior
- A Localizer model that sees and understands visual UIs to drive precise interactions
- A Validator model that checks whether the answer is valid
The agent thinks before acting, takes notes, and can retry if its answer is rejected. It can operate with different models for each module, allowing for tradeoffs between accuracy, speed, and cost.
We evaluated Surfer-H on the [WebVoyager](https://arxiv.org/pdf/2401.13919) benchmark: 643 real-world web tasks ranging from retrieving prices to finding news or scheduling events.
<div style="text-align: center;">
<img src="https://cdn-uploads.huggingface.co/production/uploads/682c3e22650f6bbe33bb9d94/kO_4DlW_O45Wi7eK9-r8v.png" width="800"/>
</div>
We’ve tested multiple configurations, from GPT-4-powered agents to 100% open Holo1 setups. Among them, the fully Holo1-based agents offered the strongest tradeoff between accuracy and cost:
- Surfer-H + Holo1-7B: 92.2% accuracy at $0.13 per task
- Surfer-H + GPT-4.1: 92.0% at $0.54 per task
- Surfer-H + Holo1-3B: 89.7% at $0.11 per task
- Surfer-H + GPT-4.1-mini: 88.8% at $0.26 per task
This places Holo1-powered agents on the Pareto frontier, delivering the best accuracy per dollar.
Unlike other agents that rely on custom APIs or brittle wrappers, Surfer-H operates purely through the browser — just like a real user. Combined with Holo1, it becomes a powerful, general-purpose, cost-efficient web automation system.
### Holo1: State-of-the-Art UI Localization
A key skill for the real-world utility of our VLMs within agents is localization: the ability to identify precise
coordinates on a user interface (UI) to interact with to complete a task or follow an instruction. To assess
this capability, we evaluated our Holo1 models on several established localization benchmarks, including
[Screenspot](https://huggingface.co/datasets/rootsautomation/ScreenSpot), [Screenspot-V2](https://huggingface.co/datasets/HongxinLi/ScreenSpot_v2), [Screenspot-Pro](https://huggingface.co/datasets/likaixin/ScreenSpot-Pro), [GroundUI-Web](https://huggingface.co/datasets/agent-studio/GroundUI-1K), and our own newly introduced
benchmark [WebClick](https://huggingface.co/datasets/Hcompany/WebClick).
<div style="text-align: center;">
<img src="https://cdn-uploads.huggingface.co/production/uploads/682c3e22650f6bbe33bb9d94/UutD2Meevd5Xw0_mhX2wK.png" width="600"/>
</div>
<div style="text-align: center;">
<img src="https://cdn-uploads.huggingface.co/production/uploads/682c3e22650f6bbe33bb9d94/NhzkB8xnEQYMqiGxPnJSt.png" width="600"/>
</div>
## Get Started with the Model
We provide 2 spaces to experiment with Localization and Navigation:
- https://huggingface.co/spaces/Hcompany/Holo1-Navigation
- https://huggingface.co/spaces/Hcompany/Holo1-Localization
We provide starter code for the localization task: i.e. image + instruction -> click coordinates
We also provide code to reproduce screenspot evaluations: screenspot_eval.py
### Prepare model, processor
Holo1 models are based on Qwen2.5-VL architecture, which comes with transformers support. Here we provide a simple usage example.
You can load the model and the processor as follows:
```python
import json
import os
from typing import Any, Literal
from transformers import AutoModelForImageTextToText, AutoProcessor
# default: Load the model on the available device(s)
# We recommend enabling flash_attention_2 for better acceleration and memory saving.
model = AutoModelForImageTextToText.from_pretrained(
"Hcompany/Holo1-7B",
torch_dtype="auto",
# torch_dtype=torch.bfloat16,
# attn_implementation="flash_attention_2",
device_map="auto",
)
# default processor
processor = AutoProcessor.from_pretrained("Hcompany/Holo1-7B")
# The default range for the number of visual tokens per image in the model is 4-1280.
# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
# processor = AutoProcessor.from_pretrained(model_dir, min_pixels=min_pixels, max_pixels=max_pixels)
# Helper function to run inference
def run_inference(messages: list[dict[str, Any]]) -> str:
# Preparation for inference
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(
text=[text],
images=image,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
return processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)
```
### Prepare image and instruction
WARNING: Holo1 is using absolute coordinates (number of pixels) and HuggingFace processor is doing image resize. To have matching coordinates, one needs to smart_resize the image.
```python
from PIL import Image
from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize
# Prepare image and instruction
image_url = "https://huggingface.co/Hcompany/Holo1-7B/resolve/main/calendar_example.jpg"
image = Image.open(requests.get(image_url, stream=True).raw)
# Resize the image so that predicted absolute coordinates match the size of the image.
image_processor = processor.image_processor
resized_height, resized_width = smart_resize(
image.height,
image.width,
factor=image_processor.patch_size * image_processor.merge_size,
min_pixels=image_processor.min_pixels,
max_pixels=image_processor.max_pixels,
)
image = image.resize(size=(resized_width, resized_height), resample=None) # type: ignore
```
### Navigation with Structured Output
```python
import json
from . import navigation
task = "Book a hotel in Paris on August 3rd for 3 nights"
prompt = navigation.get_navigation_prompt(task, image, step=1)
navigation_str = run_inference(prompt)[0]
navigation = navigation.NavigationStep(**json.loads(navigation_str))
print(navigation)
# Expected NavigationStep(note='', thought='I need to select the check-out date as August 3rd and then proceed to search for hotels.', action=ClickElementAction(action='click_element', element='August 3rd on the calendar', x=777, y=282))
```
### Localization with click(x, y)
```python
from . import localization
instruction = "Select July 14th as the check-out date"
prompt = localization.get_localization_prompt(image, instruction)
coordinates = run_inference(prompt)[0]
print(coordinates)
# Expected Click(352, 348)
```
### Localization with Structured Output
We trained Holo1 as an Action VLM with extensive use of json and tool calls. Therefore, it can be queried reliably with structured output:
```python
import json
from . import localization
instruction = "Select July 14th as the check-out date"
prompt = localization.get_localization_prompt_structured_output(image, instruction)
coordinates_structured_str = run_inference(prompt)[0]
coordinates_structured = localization.ClickAction(**json.loads(coordinates_structured_str))
print(coordinates_structured)
# Expected ClickAction(action='click', x=352, y=340)
```
## Citation
**BibTeX:**
```
@misc{andreux2025surferhmeetsholo1costefficient,
title={Surfer-H Meets Holo1: Cost-Efficient Web Agent Powered by Open Weights},
author={Mathieu Andreux and Breno Baldas Skuk and Hamza Benchekroun and Emilien Biré and Antoine Bonnet and Riaz Bordie and Matthias Brunel and Pierre-Louis Cedoz and Antoine Chassang and Mickaël Chen and Alexandra D. Constantinou and Antoine d'Andigné and Hubert de La Jonquière and Aurélien Delfosse and Ludovic Denoyer and Alexis Deprez and Augustin Derupti and Michael Eickenberg and Mathïs Federico and Charles Kantor and Xavier Koegler and Yann Labbé and Matthew C. H. Lee and Erwan Le Jumeau de Kergaradec and Amir Mahla and Avshalom Manevich and Adrien Maret and Charles Masson and Rafaël Maurin and Arturo Mena and Philippe Modard and Axel Moyal and Axel Nguyen Kerbel and Julien Revelle and Mats L. Richter and María Santos and Laurent Sifre and Maxime Theillard and Marc Thibault and Louis Thiry and Léo Tronchon and Nicolas Usunier and Tony Wu},
year={2025},
eprint={2506.02865},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2506.02865},
}
```

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"Qwen2_5_VLForConditionalGeneration"
],
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import json
from typing import Any, Literal
from pydantic import BaseModel
def get_localization_prompt(image, instruction: str) -> list[dict[str, Any]]:
guidelines: str = "Localize an element on the GUI image according to my instructions and output a click position as Click(x, y) with x num pixels from the left edge and y num pixels from the top edge."
return [
{
"role": "user",
"content": [
{
"type": "image",
"image": image,
},
{"type": "text", "text": f"{guidelines}\n{instruction}"},
],
}
]
class ClickAction(BaseModel):
"""Click at specific coordinates on the screen."""
action: Literal["click"] = "click"
x: int
"""The x coordinate, number of pixels from the left edge."""
y: int
"""The y coordinate, number of pixels from the top edge."""
def get_localization_prompt_structured_output(image, instruction: str) -> list[dict[str, Any]]:
guidelines: str = "Localize an element on the GUI image according to my instructions and output a click position. You must output a valid JSON format."
return [
{
"role": "system",
"content": json.dumps([ClickAction.model_json_schema()]),
},
{
"role": "user",
"content": [
{
"type": "image",
"image": image,
},
{"type": "text", "text": f"{guidelines}\n{instruction}"},
],
},
]

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}

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from typing import Literal
from pydantic import BaseModel, Field
SYSTEM_PROMPT: str = """Imagine you are a robot browsing the web, just like humans. Now you need to complete a task.
In each iteration, you will receive an Observation that includes the last screenshots of a web browser and the current memory of the agent.
You have also information about the step that the agent is trying to achieve to solve the task.
Carefully analyze the visual information to identify what to do, then follow the guidelines to choose the following action.
You should detail your thought (i.e. reasoning steps) before taking the action.
Also detail in the notes field of the action the extracted information relevant to solve the task.
Once you have enough information in the notes to answer the task, return an answer action with the detailed answer in the notes field.
This will be evaluated by an evaluator and should match all the criteria or requirements of the task.
Guidelines:
- store in the notes all the relevant information to solve the task that fulfill the task criteria. Be precise
- Use both the task and the step information to decide what to do
- if you want to write in a text field and the text field already has text, designate the text field by the text it contains and its type
- If there is a cookies notice, always accept all the cookies first
- The observation is the screenshot of the current page and the memory of the agent.
- If you see relevant information on the screenshot to answer the task, add it to the notes field of the action.
- If there is no relevant information on the screenshot to answer the task, add an empty string to the notes field of the action.
- If you see buttons that allow to navigate directly to relevant information, like jump to ... or go to ... , use them to navigate faster.
- In the answer action, give as many details a possible relevant to answering the task.
- if you want to write, don't click before. Directly use the write action
- to write, identify the web element which is type and the text it already contains
- If you want to use a search bar, directly write text in the search bar
- Don't scroll too much. Don't scroll if the number of scrolls is greater than 3
- Don't scroll if you are at the end of the webpage
- Only refresh if you identify a rate limit problem
- If you are looking for a single flights, click on round-trip to select 'one way'
- Never try to login, enter email or password. If there is a need to login, then go back.
- If you are facing a captcha on a website, try to solve it.
- if you have enough information in the screenshot and in the notes to answer the task, return an answer action with the detailed answer in the notes field
- The current date is {timestamp}.
# <output_json_format>
# ```json
# {output_format}
# ```
# </output_json_format>
"""
class ClickElementAction(BaseModel):
"""Click at absolute coordinates of a web element with its description"""
action: Literal["click_element"] = Field(description="Click at absolute coordinates of a web element")
element: str = Field(description="text description of the element")
x: int = Field(description="The x coordinate, number of pixels from the left edge.")
y: int = Field(description="The y coordinate, number of pixels from the top edge.")
def log(self):
return f"I have clicked on the element '{self.element}' at absolute coordinates {self.x}, {self.y}"
class WriteElementAction(BaseModel):
"""Write content at absolute coordinates of a web element identified by its description, then press Enter."""
action: Literal["write_element_abs"] = Field(description="Write content at absolute coordinates of a web page")
content: str = Field(description="Content to write")
element: str = Field(description="Text description of the element")
x: int = Field(description="The x coordinate, number of pixels from the left edge.")
y: int = Field(description="The y coordinate, number of pixels from the top edge.")
def log(self):
return f"I have written '{self.content}' in the element '{self.element}' at absolute coordinates {self.x}, {self.y}"
class ScrollAction(BaseModel):
"""Scroll action with no required element"""
action: Literal["scroll"] = Field(description="Scroll the page or a specific element")
direction: Literal["down", "up", "left", "right"] = Field(description="The direction to scroll in")
def log(self):
return f"I have scrolled {self.direction}"
class GoBackAction(BaseModel):
"""Action to navigate back in browser history"""
action: Literal["go_back"] = Field(description="Navigate to the previous page")
def log(self):
return "I have gone back to the previous page"
class RefreshAction(BaseModel):
"""Action to refresh the current page"""
action: Literal["refresh"] = Field(description="Refresh the current page")
def log(self):
return "I have refreshed the page"
class GotoAction(BaseModel):
"""Action to go to a particular URL"""
action: Literal["goto"] = Field(description="Goto a particular URL")
url: str = Field(description="A url starting with http:// or https://")
def log(self):
return f"I have navigated to the URL {self.url}"
class WaitAction(BaseModel):
"""Action to wait for a particular amount of time"""
action: Literal["wait"] = Field(description="Wait for a particular amount of time")
seconds: int = Field(default=2, ge=0, le=10, description="The number of seconds to wait")
def log(self):
return f"I have waited for {self.seconds} seconds"
class RestartAction(BaseModel):
"""Restart the task from the beginning."""
action: Literal["restart"] = "restart"
def log(self):
return "I have restarted the task from the beginning"
class AnswerAction(BaseModel):
"""Return a final answer to the task. This is the last action to call in an episode."""
action: Literal["answer"] = "answer"
content: str = Field(description="The answer content")
def log(self):
return f"I have answered the task with '{self.content}'"
ActionSpace = (
ClickElementAction
| WriteElementAction
| ScrollAction
| GoBackAction
| RefreshAction
| WaitAction
| RestartAction
| AnswerAction
| GotoAction
)
class NavigationStep(BaseModel):
note: str = Field(
default="",
description="Task-relevant information extracted from the previous observation. Keep empty if no new info.",
)
thought: str = Field(description="Reasoning about next steps (<4 lines)")
action: ActionSpace = Field(description="Next action to take")
def get_navigation_prompt(task, image, step=1):
system_prompt = SYSTEM_PROMPT.format(
output_format=NavigationStep.model_json_schema(),
timestamp="2025-06-04 14:16:03",
)
return [
{
"role": "system",
"content": [
{"type": "text", "text": system_prompt},
],
},
{
"role": "user",
"content": [
{"type": "text", "text": f"<task>\n{task}\n</task>\n"},
{"type": "text", "text": f"<observation step={step}>\n"},
{"type": "text", "text": "<screenshot>\n"},
{
"type": "image",
"image": image,
},
{"type": "text", "text": "\n</screenshot>\n"},
{"type": "text", "text": "\n</observation>\n"},
],
},
]

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preprocessor_config.json Normal file
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{
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_processor_type": "Qwen2VLImageProcessor",
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"max_pixels": 1003520,
"merge_size": 2,
"min_pixels": 3136,
"patch_size": 14,
"processor_class": "Qwen2_5_VLProcessor",
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"longest_edge": 12845056,
"shortest_edge": 3136
},
"temporal_patch_size": 2
}

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import argparse
import json
import math
import re
from io import BytesIO
import numpy as np
from datasets import load_dataset
from PIL.Image import Image
from PIL.Image import open as open_img
from tqdm import tqdm
from transformers import AutoModelForImageTextToText, AutoProcessor
from transformers.modeling_utils import PreTrainedModel
from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize
from transformers.processing_utils import ProcessorMixin
INSTRUCTION_LOCALIZATION: str = "Localize an element on the GUI image according to my instructions and output a click position as Click(x, y) with x num pixels from the left edge and y num pixels from the top edge."
INSTRUCTION_LOCALIZATION_TOOLCALL: str = "Localize an element on the GUI image according to my instructions and output a click position. You must output a valid JSON format."
def load_screenspot(dataset_id: str, subset: str = "test"):
dataset = load_dataset(dataset_id)
return dataset[subset]
def l1(dx: float, dy: float) -> float:
"""Return L1 length of a vector"""
return abs(dx) + abs(dy)
def l2(dx: float, dy: float) -> float:
"""Return L2 length of a vector"""
return (dx**2 + dy**2) ** 0.5
def point_to_rectangle_dist(x: float, y: float, rectangle: tuple, distance_type="L2"):
"""Compute the distance of a predicted point to the closest edge of the bbox. If the point is in the bbox, then return 0."""
x1, y1, x2, y2 = rectangle # x1,y1 is top-left, x2,y2 is bottom-right
# Check if the point is inside the rectangle
if x1 <= x <= x2 and y1 <= y <= y2:
return 0
# Calculate the closest point on the rectangle
closest_x = max(x1, min(x, x2))
closest_y = max(y1, min(y, y2))
# Calculate the distance
dx = x - closest_x
dy = y - closest_y
if distance_type == "L1":
return l1(dx, dy)
elif distance_type == "L2":
return l2(dx, dy)
else:
raise ValueError("Invalid distance type. Use 'L1' or 'L2'.")
def is_in_bbox(bbox: tuple, x: float, y: float) -> bool:
"""Check if a point is inside a bounding box."""
x_top_left, y_top_left, x_bottom_right, y_bottom_right = bbox
return x_top_left <= x <= x_bottom_right and y_top_left <= y <= y_bottom_right
def assemble_message(image, instruction, use_tool_call: bool = True):
system_message = {
"role": "system",
"content": '[{"name": "click_action", "description": "Click at specific coordinates on the screen.", "parameters": {"additionalProperties": false, "description": "Click at specific coordinates on the screen.", "properties": {"action": {"const": "click", "default": "click", "title": "Action", "type": "string"}, "x": {"description": "The x coordinate, number of pixels from the left edge.", "title": "X", "type": "integer"}, "y": {"description": "The y coordinate, number of pixels from the top edge.", "title": "Y", "type": "integer"}}, "required": ["action", "x", "y"], "title": "ClickAction", "type": "object"}, "strict": true}]',
}
user_message = {
"role": "user",
"content": [
{
"type": "image",
"image": image,
},
{
"type": "text",
"text": f"{INSTRUCTION_LOCALIZATION_TOOLCALL if use_tool_call else INSTRUCTION_LOCALIZATION}\n{instruction}",
},
],
}
messages = [system_message, user_message] if use_tool_call else [user_message]
return messages
def do_smart_resize(image: Image, image_processor: ProcessorMixin) -> tuple[Image, int, int]:
"""Do a QWEN2.5-VL smart resize using parameters of an image-processor"""
resized_height, resized_width = smart_resize(
image.height,
image.width,
factor=image_processor.patch_size * image_processor.merge_size,
min_pixels=image_processor.min_pixels,
max_pixels=image_processor.max_pixels,
)
return image.resize(size=(resized_width, resized_height), resample=None), resized_height, resized_width
def inference(
model: PreTrainedModel, processor: ProcessorMixin, dataset, smart_resize: bool = True, use_toolcall: bool = True
):
"""Gather raw inference results from the model"""
results = []
for i, sample in enumerate(tqdm(dataset, "running inference requests")):
bbox = sample["bbox"]
instruction = sample["instruction"]
image = sample["image"] # this seems to be a pnd , maybe jpg artifacts cause the difference?
image_shape_raw = (image.height, image.width)
message = assemble_message(image=image, instruction=instruction)
# Preparation for inference
if smart_resize:
image, resized_height, resized_width = do_smart_resize(
image=image, image_processor=processor.image_processor
)
else:
resized_height, resized_width = image_shape_raw
text = processor.apply_chat_template(message, tokenize=False, add_generation_prompt=True)
# compress to JPEG, which is needed for highest possible performance
buffer = BytesIO()
image.convert("RGB").save(buffer, format="JPEG", quality=90)
image = open_img(buffer)
inputs = processor(
text=[text],
images=image,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False)
generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
# print(output_text)
if use_toolcall:
try:
content = json.loads(output_text[0])
prediction_raw = f"Click({content['arguments']['x']}, {content['arguments']['y']})"
except Exception as e:
print(f"Error parsing tool call, using message content instead if available: {repr(e)}")
prediction_raw = output_text[0]
else:
prediction_raw = output_text[0]
results.append(
{
"sample_id": i,
"ground_truth": tuple(bbox),
"prediction_raw": prediction_raw,
"image_shape_raw": image_shape_raw,
"img_shape_processed": (resized_height, resized_width),
}
)
return results
def get_sample_result(result: dict):
"""Postprocess a inference result and compute metrics for this sample."""
raw_height, raw_width = result["image_shape_raw"]
height, width = result["img_shape_processed"]
has_resized_image = height != raw_height or width != raw_width
try:
bbox = result["ground_truth"]
prediction_raw = result["prediction_raw"]
match = re.match(r"Click\((\d+),\s*(\d+)\)", prediction_raw)
assert match is not None
predicted_x = float(match.group(1)) / width
predicted_y = float(match.group(2)) / height
except Exception as e:
sample_metric = {
"sample_id": result["sample_id"],
"has_correct_format": False,
"has_resized_image": has_resized_image,
"click_in_box": False,
"click_l1_dist_to_bbox": 2, # Longest possible L1 distance in the unit square
"click_l2_dist_to_bbox": math.sqrt(2), # Longest possible L2 distance in the unit square
}
sample_metric = {
"sample_id": result["sample_id"],
"has_correct_format": True,
"has_resized_image": has_resized_image,
"click_in_box": True if is_in_bbox(bbox, x=predicted_x, y=predicted_y) else False,
"click_l1_dist_to_bbox": point_to_rectangle_dist(
predicted_x, predicted_y, bbox, "L1"
), # Longest possible L1 distance in the unit square
"click_l2_dist_to_bbox": point_to_rectangle_dist(
predicted_x, predicted_y, bbox, "L2"
), # Longest possible L2 distance in the unit square
}
return sample_metric
def aggregate_metrics(sample_metrics):
"""Aggregate per-sample metrics into metrics for the entire dataset."""
aggregated_metrics = {}
aggregated_metrics["click_accuracy"] = np.mean([r["click_in_box"] for r in sample_metrics])
for threshold in [0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5]:
aggregated_metrics[f"click_accuracy_p{threshold}"] = np.mean(
[r["click_l2_dist_to_bbox"] < threshold for r in sample_metrics]
)
aggregated_metrics["avg_click_l1_dist_to_bbox"] = np.mean([r["click_l1_dist_to_bbox"] for r in sample_metrics])
aggregated_metrics["avg_click_l2_dist_to_bbox"] = np.mean([r["click_l2_dist_to_bbox"] for r in sample_metrics])
aggregated_metrics["format_accuracy"] = np.mean([r["has_correct_format"] for r in sample_metrics])
aggregated_metrics["has_resized_image"] = np.mean([r["has_resized_image"] for r in sample_metrics])
return aggregated_metrics
def evaluate_results(results: list[dict]):
"""Do evaluate based on the raw model outputs."""
per_sample_metrics = []
for result in results:
metric_dict = get_sample_result(result)
per_sample_metrics.append(metric_dict)
aggregated = aggregate_metrics(per_sample_metrics)
return aggregated
def main(
model_id: str = "Hcompany/Holo1-3B",
dataset_id: str = "rootsautomation/ScreenSpot",
outfile: str = "results.json",
use_toolcall: bool = True,
):
model = AutoModelForImageTextToText.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
dataset = load_screenspot(dataset_id)
results = inference(model.cuda(), processor, dataset, use_toolcall=use_toolcall)
metrics = evaluate_results(results)
with open(outfile, "w") as fp:
json.dump(metrics, fp)
for metric, value in metrics.items():
print(f"{metric}:\t{value}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Run the main function with model and dataset IDs.")
parser.add_argument(
"--model_id",
type=str,
default="Hcompany/Holo1-3B",
help="The identifier for the model to use (default: Hcompany/Holo1-3B)",
)
parser.add_argument(
"--dataset_id",
type=str,
default="rootsautomation/ScreenSpot",
help="The identifier for the dataset to use (default: rootsautomation/ScreenSpot)",
)
parser.add_argument(
"--outfile",
type=str,
default="result.json",
help="Output json-file containing the aggregated metrics.",
)
parser.add_argument(
"--use_toolcall",
type=bool,
default=True,
help="Enable or disable tool call prompting",
)
args = parser.parse_args()
main(model_id=args.model_id, dataset_id=args.dataset_id, outfile=args.outfile, use_toolcall=args.use_toolcall)

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{
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"eos_token": {
"content": "<|im_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

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version https://git-lfs.github.com/spec/v1
oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
size 11421896

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{
"add_bos_token": false,
"add_prefix_space": false,
"added_tokens_decoder": {
"151643": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151644": {
"content": "<|im_start|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151645": {
"content": "<|im_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151646": {
"content": "<|object_ref_start|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151647": {
"content": "<|object_ref_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151648": {
"content": "<|box_start|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151649": {
"content": "<|box_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151650": {
"content": "<|quad_start|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151651": {
"content": "<|quad_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151652": {
"content": "<|vision_start|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151653": {
"content": "<|vision_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151654": {
"content": "<|vision_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151655": {
"content": "<|image_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151656": {
"content": "<|video_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151657": {
"content": "<tool_call>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151658": {
"content": "</tool_call>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151659": {
"content": "<|fim_prefix|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151660": {
"content": "<|fim_middle|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151661": {
"content": "<|fim_suffix|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151662": {
"content": "<|fim_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151663": {
"content": "<|repo_name|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151664": {
"content": "<|file_sep|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
}
},
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"bos_token": null,
"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
"clean_up_tokenization_spaces": false,
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": {},
"max_pixels": 1003520,
"min_pixels": 3136,
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"processor_class": "Qwen2_5_VLProcessor",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

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