278 lines
9.6 KiB
Python
278 lines
9.6 KiB
Python
"""
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"""
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import unittest
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from io import BytesIO
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from typing import List, Optional
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import numpy as np
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import requests
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import torch
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import torch.nn.functional as F
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from PIL import Image
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from transformers import (
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AutoModel,
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AutoProcessor,
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AutoTokenizer,
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Gemma3ForConditionalGeneration,
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Qwen2_5_VLForConditionalGeneration,
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)
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from sglang import Engine
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from sglang.srt.configs.model_config import ModelConfig
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from sglang.srt.conversation import generate_chat_conv
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from sglang.srt.entrypoints.openai.protocol import ChatCompletionRequest
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from sglang.srt.managers.mm_utils import embed_mm_inputs, init_embedding_cache
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from sglang.srt.managers.multimodal_processors.base_processor import (
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BaseMultimodalProcessor,
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)
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from sglang.srt.managers.schedule_batch import (
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Modality,
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MultimodalDataItem,
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MultimodalInputs,
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)
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from sglang.srt.model_executor.model_runner import ModelRunner
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from sglang.srt.server_args import ServerArgs
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# Test the logits output between HF and SGLang
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class VisionLLMLogitsBase(unittest.IsolatedAsyncioTestCase):
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@classmethod
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def setUpClass(cls):
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cls.image_url = "https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true"
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cls.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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cls.model_path = ""
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cls.chat_template = ""
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cls.processor = ""
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response = requests.get(cls.image_url)
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cls.main_image = Image.open(BytesIO(response.content))
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def compare_outputs(self, sglang_output: torch.Tensor, hf_output: torch.Tensor):
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# Convert to float32 for numerical stability if needed
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hf = hf_output.float()
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sg = sglang_output.float()
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# Basic shape and dtype comparison
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print("\n=== Basic Properties ===")
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print(f"Shapes match: {hf.shape == sg.shape}")
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print(f"HF shape: {hf.shape}, SGLang shape: {sg.shape}")
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print(f"HF dtype: {hf.dtype}, SGLang dtype: {sg.dtype}")
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# Move tensors to CPU for numpy operations
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hf_np = hf.cpu().numpy()
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sg_np = sg.cpu().numpy()
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# Statistical metrics
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print("\n=== Statistical Metrics ===")
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print(f"Mean absolute difference: {torch.mean(torch.abs(hf - sg)).item():.6f}")
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print(f"Max absolute difference: {torch.max(torch.abs(hf - sg)).item():.6f}")
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print(f"Mean squared error: {torch.mean((hf - sg) ** 2).item():.6f}")
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print(
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f"Root mean squared error: {torch.sqrt(torch.mean((hf - sg) ** 2)).item():.6f}"
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)
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# Cosine similarity (across feature dimension)
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cos_sim = F.cosine_similarity(hf, sg)
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print(f"Mean cosine similarity: {torch.mean(cos_sim).item():.6f}")
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print(f"Min cosine similarity: {torch.min(cos_sim).item():.6f}")
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# Find largest absolute differences
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print("\n=== Largest Absolute Differences ===")
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diffs = torch.abs(hf - sg)
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flat_diffs = diffs.flatten()
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# Get indices of top 10 differences
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top_k = 10
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top_values, top_flat_indices = torch.topk(flat_diffs, top_k)
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# Convert flat indices to multidimensional indices
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top_indices = np.unravel_index(top_flat_indices.cpu().numpy(), diffs.shape)
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print(f"\nTop {top_k} largest absolute differences:")
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print(
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"Index".ljust(30)
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+ "Difference".ljust(15)
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+ "HF Value".ljust(15)
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+ "SGLang Value"
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)
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print("-" * 75)
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for i in range(top_k):
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# Get the index tuple for this difference
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idx = tuple(dim[i] for dim in top_indices)
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diff_val = top_values[i].item()
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hf_val = hf[idx].item()
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sg_val = sg[idx].item()
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# Format the index tuple and values
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idx_str = str(idx)
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print(f"{idx_str:<30}{diff_val:<15.6f}{hf_val:<15.6f}{sg_val:.6f}")
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np.testing.assert_allclose(hf_np, sg_np)
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def get_completion_request(self) -> ChatCompletionRequest:
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json_str = f"""
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{{
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"model": "{self.model_path}",
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"messages": [
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{{
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"role": "user",
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"content": [
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{{
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"type": "image_url",
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"image_url": {{
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"url": "{self.image_url}"
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}}
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}},
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{{
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"type": "text",
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"text": "What's in this picture?"
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}}
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]
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}}
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]
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}}
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"""
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return ChatCompletionRequest.model_validate_json(json_str)
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def get_processor_output(self, req: Optional[ChatCompletionRequest] = None):
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if req is None:
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req = self.get_completion_request()
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conv = generate_chat_conv(req, template_name=self.chat_template)
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text = conv.get_prompt()
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# Process inputs using processor
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# FIXME: the formal arguments may differ
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inputs = self.processor(
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text=[text],
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images=[self.main_image],
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return_tensors="pt",
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).to(self.device)
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return inputs
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def get_sglang_model(self):
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self.model_runner = ModelRunner(
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model_config=ModelConfig(self.model_path, model_override_args="{}"),
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mem_fraction_static=0.8,
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gpu_id=0,
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tp_rank=0,
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tp_size=1,
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pp_rank=0,
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pp_size=1,
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nccl_port=12435,
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server_args=ServerArgs(
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model_path=self.model_path,
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disable_cuda_graph=True,
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),
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)
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return self.model_runner.model
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# TODO: MiniCPMV is not compatible with transformers==4.52.3, temporarily disabled
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# class TestMiniCPMVLogits(VisionLLMLogitsBase):
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# @classmethod
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# def setUpClass(cls):
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# super().setUpClass()
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# cls.model_path = "openbmb/MiniCPM-V-2_6"
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# cls.tokenizer = AutoTokenizer.from_pretrained(
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# cls.model_path, trust_remote_code=True
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# )
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# cls.processor = AutoProcessor.from_pretrained(
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# cls.model_path, trust_remote_code=True
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# )
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# cls.chat_template = "minicpmv"
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#
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# cls.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# cls.hf_model = (
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# AutoModel.from_pretrained(
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# cls.model_path, torch_dtype=torch.bfloat16, trust_remote_code=True
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# )
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# .eval()
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# .to(cls.device)
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# )
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# init_embedding_cache(0)
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#
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# async def test_vlm_embedding_output(self):
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# """
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# Compares the embedding output of vlm
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# """
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# inputs = self.get_processor_output()
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#
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# with torch.no_grad():
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# # hf
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# model_inputs = {
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# "input_ids": inputs.input_ids,
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# "image_bound": inputs.image_bound,
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# "pixel_values": inputs.pixel_values,
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# "tgt_sizes": inputs.tgt_sizes,
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# }
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# (hf_output, _) = self.hf_model.get_vllm_embedding(
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# model_inputs,
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# )
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# hf_output = hf_output.squeeze(0)
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#
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# # sglang
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# model = self.get_sglang_model()
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# input_ids = inputs["input_ids"].to(self.device).flatten()
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#
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# pixel_values = inputs["pixel_values"]
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# tgt_sizes = inputs["tgt_sizes"]
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# pixel_values_flat: List[torch.Tensor] = []
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# tgt_sizes_flat: List[torch.Tensor] = []
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# for pixel_b, tgt_b in zip(pixel_values, tgt_sizes):
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# # per image
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# if len(pixel_b) != len(tgt_b):
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# raise ValueError(
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# "Inconsistent N lengths, found: "
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# f"{len(pixel_b)} vs {len(tgt_b)}"
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# )
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# for pixel_n, tgt_n in zip(pixel_b, tgt_b):
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# pixel_values_flat += [pixel_n]
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# tgt_sizes_flat += [tgt_n]
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#
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# im_start_id, im_end_id = (
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# self.tokenizer.im_start_id,
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# self.tokenizer.im_end_id,
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# )
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# slice_start_id, slice_end_id = (
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# self.tokenizer.slice_start_id,
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# self.tokenizer.slice_end_id,
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# )
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#
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# image_offsets = BaseMultimodalProcessor.get_mm_items_offset_by_pair(
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# input_ids=input_ids, mm_start_id=im_start_id, mm_end_id=im_end_id
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# )
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# slice_offsets = BaseMultimodalProcessor.get_mm_items_offset_by_pair(
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# input_ids=input_ids, mm_start_id=slice_start_id, mm_end_id=slice_end_id
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# )
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# image_offsets.extend(slice_offsets)
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# image_offsets = sorted(image_offsets)
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#
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# sglang_output = embed_mm_inputs(
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# mm_inputs_list=[
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# MultimodalInputs(
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# mm_items=[
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# MultimodalDataItem(
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# pixel_values=pixel_values_flat,
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# image_offsets=image_offsets,
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# tgt_size=tgt_sizes_flat,
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# modality=Modality.IMAGE,
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# pad_value=self.processor.tokenizer.unk_token_id,
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# )
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# ]
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# ),
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# ],
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# extend_prefix_lens=[0],
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# extend_seq_lens=[input_ids.shape[0]],
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# input_ids=input_ids,
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# input_embedding=model.get_input_embeddings(),
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# image_data_embedding_func=model.get_image_feature,
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# placeholder_tokens={
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# Modality.IMAGE: self.processor.tokenizer.unk_token_id,
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# },
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# )
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#
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# self.compare_outputs(sglang_output, hf_output)
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