Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

perf(magic_pdf): optimize batch ratio calculation for GPU #1593

Merged
merged 3 commits into from
Jan 21, 2025
Merged

perf(magic_pdf): optimize batch ratio calculation for GPU #1593

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
49d140c
perf(model): adjust batch size for layout and formula detection
myhloli Jan 21, 2025
052a4d7
perf(magic_pdf): optimize batch ratio calculation for GPU
myhloli Jan 21, 2025
e74a296
refactor(magic_pdf): adjust VRAM allocation and MFR batch size- Updat…
myhloli Jan 21, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 5 additions & 3 deletions magic_pdf/model/batch_analyze.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@
get_adjusted_mfdetrec_res, get_ocr_result_list)
# from magic_pdf.operators.models import InferenceResult

YOLO_LAYOUT_BASE_BATCH_SIZE = 4
YOLO_LAYOUT_BASE_BATCH_SIZE = 1
MFD_BASE_BATCH_SIZE = 1
MFR_BASE_BATCH_SIZE = 16

Expand Down Expand Up @@ -56,7 +56,8 @@ def __call__(self, images: list) -> list:
layout_images.append(pil_img)

images_layout_res += self.model.layout_model.batch_predict(
layout_images, self.batch_ratio * YOLO_LAYOUT_BASE_BATCH_SIZE
# layout_images, self.batch_ratio * YOLO_LAYOUT_BASE_BATCH_SIZE
layout_images, YOLO_LAYOUT_BASE_BATCH_SIZE
)

for image_index, useful_list in modified_images:
Expand All @@ -78,7 +79,8 @@ def __call__(self, images: list) -> list:
# 公式检测
mfd_start_time = time.time()
images_mfd_res = self.model.mfd_model.batch_predict(
images, self.batch_ratio * MFD_BASE_BATCH_SIZE
# images, self.batch_ratio * MFD_BASE_BATCH_SIZE
images, MFD_BASE_BATCH_SIZE
)
logger.info(
f'mfd time: {round(time.time() - mfd_start_time, 2)}, image num: {len(images)}'
Expand Down
10 changes: 3 additions & 7 deletions magic_pdf/model/doc_analyze_by_custom_model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -175,13 +175,9 @@ def doc_analyze(
npu_support = True

if torch.cuda.is_available() and device != 'cpu' or npu_support:
gpu_memory = get_vram(device)
if gpu_memory is not None and gpu_memory >= 7:
# batch_ratio = int((gpu_memory-3) // 1.5)
batch_ratio = 2
if 8 < gpu_memory:
batch_ratio = 4

gpu_memory = int(os.getenv("VIRTUAL_VRAM_SIZE", round(get_vram(device))))
if gpu_memory is not None and gpu_memory >= 8:
batch_ratio = int(gpu_memory-5)
if batch_ratio >= 1:
logger.info(f'gpu_memory: {gpu_memory} GB, batch_ratio: {batch_ratio}')
batch_model = BatchAnalyze(model=custom_model, batch_ratio=batch_ratio)
Expand Down
2 changes: 1 addition & 1 deletion magic_pdf/model/sub_modules/mfr/unimernet/Unimernet.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -89,7 +89,7 @@ def predict(self, mfd_res, image):
mf_image_list.append(bbox_img)

dataset = MathDataset(mf_image_list, transform=self.mfr_transform)
dataloader = DataLoader(dataset, batch_size=64, num_workers=0)
dataloader = DataLoader(dataset, batch_size=32, num_workers=0)
mfr_res = []
for mf_img in dataloader:
mf_img = mf_img.to(self.device)
Expand Down
Loading