yujunjun/olmocr
1
0
Fork 0
mirror of https://github.com/allenai/olmocr.git synced 2025-11-10 23:50:43 +00:00
Code Issues Packages Projects Releases Wiki Activity

Ok, direct easy test for diffs now

Browse Source
This commit is contained in:
Jake Poznanski 2024-12-04 13:27:51 -08:00
parent a56ce71771
commit 5692a76350
1 changed files with 58 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

60
tests/test_sglang.py
View File

@ -312,10 +312,10 @@ class RawSGLangTest(unittest.IsolatedAsyncioTestCase):
) )
with torch.no_grad(): with torch.no_grad():
result = self.model.visual(inputs["pixel_values"].to(self.device), grid_thw=inputs["image_grid_thw"].to(self.device)) hf_output = self.model.visual(inputs["pixel_values"].to(self.device), grid_thw=inputs["image_grid_thw"].to(self.device))
print("GOT", result, result.shape) print("HF", hf_output, hf_output.shape)
from sglang.srt.configs.model_config import ModelConfig from sglang.srt.configs.model_config import ModelConfig
from sglang.srt.managers.schedule_batch import Req, ScheduleBatch from sglang.srt.managers.schedule_batch import Req, ScheduleBatch
@ -343,4 +343,60 @@ class RawSGLangTest(unittest.IsolatedAsyncioTestCase):
) )
print(model_runner) print(model_runner)
with torch.no_grad():
sglang_output = model_runner.model.visual(inputs["pixel_values"].to(self.device), grid_thw=inputs["image_grid_thw"].to(self.device))
print("SGLANG", sglang_output, sglang_output.shape)
# Convert to float32 for numerical stability if needed
hf = hf_output.float()
sg = sglang_output.float()
# Basic shape and dtype comparison
print("\n=== Basic Properties ===")
print(f"Shapes match: {hf.shape == sg.shape}")
print(f"HF shape: {hf.shape}, SGLang shape: {sg.shape}")
print(f"HF dtype: {hf.dtype}, SGLang dtype: {sg.dtype}")
# Move tensors to CPU for numpy operations
hf_np = hf.cpu().numpy()
sg_np = sg.cpu().numpy()
# Statistical metrics
print("\n=== Statistical Metrics ===")
print(f"Mean absolute difference: {torch.mean(torch.abs(hf - sg)).item():.6f}")
print(f"Max absolute difference: {torch.max(torch.abs(hf - sg)).item():.6f}")
print(f"Mean squared error: {torch.mean((hf - sg) ** 2).item():.6f}")
print(f"Root mean squared error: {torch.sqrt(torch.mean((hf - sg) ** 2)).item():.6f}")
# Cosine similarity (across feature dimension)
cos_sim = F.cosine_similarity(hf, sg)
print(f"Mean cosine similarity: {torch.mean(cos_sim).item():.6f}")
print(f"Min cosine similarity: {torch.min(cos_sim).item():.6f}")
# Find largest absolute differences
print("\n=== Largest Absolute Differences ===")
diffs = torch.abs(hf - sg)
flat_diffs = diffs.flatten()
# Get indices of top 10 differences
top_k = 10
top_values, top_flat_indices = torch.topk(flat_diffs, top_k)
# Convert flat indices to multidimensional indices
top_indices = np.unravel_index(top_flat_indices.cpu().numpy(), diffs.shape)
print(f"\nTop {top_k} largest absolute differences:")
print("Index".ljust(30) + "Difference".ljust(15) + "HF Value".ljust(15) + "SGLang Value")
print("-" * 75)
for i in range(top_k):
# Get the index tuple for this difference
idx = tuple(dim[i] for dim in top_indices)
diff_val = top_values[i].item()
hf_val = hf[idx].item()
sg_val = sg[idx].item()
# Format the index tuple and values
idx_str = str(idx)
print(f"{idx_str:<30}{diff_val:<15.6f}{hf_val:<15.6f}{sg_val:.6f}")