[Model] Initialize Florence-2 language backbone support (vllm-project…

…#9555)

Signed-off-by: NickLucche <nlucches@redhat.com>

examples/florence2_inference.py

@@ -0,0 +1,44 @@
+'''
+Demonstrate prompting of text-to-text
+encoder/decoder models, specifically Florence-2
+'''
+# TODO(Isotr0py):
+# Move to offline_inference_vision_language.py after porting vision backbone
+from vllm import LLM, SamplingParams
+
+dtype = "float"
+
+# Create a Florence-2 encoder/decoder model instance
+llm = LLM(
+    model="microsoft/Florence-2-base",
+    tokenizer="facebook/bart-base",
+    dtype=dtype,
+    trust_remote_code=True,
+)
+
+prompts = [
+    "<CAPTION>", "<DETAILED_CAPTION>", "<MORE_DETAILED_CAPTION>",
+    "<CAPTION_TO_PHRASE_GROUNDING>", "<OD>", "<DENSE_REGION_CAPTION>",
+    "<REGION_PROPOSAL>", "<OCR>", "<OCR_WITH_REGION>"
+]
+# Create a sampling params object.
+sampling_params = SamplingParams(
+    temperature=0,
+    top_p=1.0,
+    min_tokens=0,
+    max_tokens=20,
+)
+
+# Generate output tokens from the prompts. The output is a list of
+# RequestOutput objects that contain the prompt, generated
+# text, and other information.
+outputs = llm.generate(prompts, sampling_params)
+
+# Print the outputs.
+for output in outputs:
+    prompt = output.prompt
+    encoder_prompt = output.encoder_prompt
+    generated_text = output.outputs[0].text
+    print(f"Encoder prompt: {encoder_prompt!r}, "
+          f"Decoder prompt: {prompt!r}, "
+          f"Generated text: {generated_text!r}")

tests/conftest.py

@@ -253,7 +253,9 @@ def __init__(
         dtype: str = "half",
         *,
         model_kwargs: Optional[Dict[str, Any]] = None,
+        is_embedding_model: bool = False,
         is_sentence_transformer: bool = False,
+        skip_tokenizer_init: bool = False,
         auto_cls: Type[_BaseAutoModelClass] = AutoModelForCausalLM,
         postprocess_inputs: Callable[[BatchEncoding],
                                      BatchEncoding] = identity,
@@ -281,11 +283,12 @@ def __init__(
                     **model_kwargs,
                 ))
 
-        self.tokenizer = AutoTokenizer.from_pretrained(
-            model_name,
-            torch_dtype=torch_dtype,
-            trust_remote_code=True,
-        )
+        if not skip_tokenizer_init:
+            self.tokenizer = AutoTokenizer.from_pretrained(
+                model_name,
+                torch_dtype=torch_dtype,
+                trust_remote_code=True,
+            )
 
         # don't put this import at the top level
         # it will call torch.cuda.device_count()
@@ -295,6 +298,8 @@ def __init__(
             torch_dtype=torch_dtype,
             trust_remote_code=True,
         )
+        if skip_tokenizer_init:
+            self.tokenizer = self.processor.tokenizer
 
         self.postprocess_inputs = postprocess_inputs
 
@@ -535,6 +540,7 @@ def generate_encoder_decoder_greedy_logprobs_limit(
         encoder_decoder_prompts: List[ExplicitEncoderDecoderPrompt[str, str]],
         max_tokens: int,
         num_logprobs: int,
+        images: Optional[PromptImageInput] = None,
         **kwargs: Any,
     ) -> List[TokensTextLogprobs]:
         '''
@@ -545,11 +551,17 @@ def generate_encoder_decoder_greedy_logprobs_limit(
         all_output_ids: List[List[int]] = []
         all_output_strs: List[str] = []
 
-        for (encoder_prompt,
-             decoder_prompt) in to_enc_dec_tuple_list(encoder_decoder_prompts):
+        for i, (encoder_prompt, decoder_prompt) in enumerate(
+                to_enc_dec_tuple_list(encoder_decoder_prompts)):
+            processor_kwargs: Dict[str, Any] = {
+                "text": encoder_prompt,
+                "return_tensors": "pt",
+            }
+            if images is not None and images[i] is not None:
+                processor_kwargs["images"] = images[i]
 
             encoder_input_ids = self.wrap_device(
-                self.tokenizer(encoder_prompt, return_tensors="pt").input_ids,
+                self.processor(**processor_kwargs).input_ids,
                 device=self.model.device.type,
             )
 

tests/models/encoder_decoder/vision_language/test_florence2.py

@@ -0,0 +1,102 @@
+from functools import partial
+from typing import List, Optional, Tuple, Type
+
+import pytest
+from PIL import Image
+
+from vllm.inputs.data import ExplicitEncoderDecoderPrompt
+from vllm.sequence import SampleLogprobs
+
+from ....conftest import HfRunner, VllmRunner
+from ...utils import check_logprobs_close
+
+Florence2Prompt = partial(ExplicitEncoderDecoderPrompt,
+                          decoder_prompt=None,
+                          mm_processor_kwargs=None)
+
+MODELS = ["microsoft/Florence-2-base"]
+# Florence-2 uses BartFastTokenizer which can't be loaded from AutoTokenizer
+# Therefore, we borrow the BartTokenizer from the original Bart model
+TOKENIZER = "facebook/bart-base"
+PROMPTS = [
+    Florence2Prompt(encoder_prompt="<CAPTION>"),
+    Florence2Prompt(encoder_prompt="<DETAILED_CAPTION>"),
+    Florence2Prompt(encoder_prompt="<MORE_DETAILED_CAPTION>"),
+    Florence2Prompt(encoder_prompt="<CAPTION_TO_PHRASE_GROUNDING>"),
+    Florence2Prompt(encoder_prompt="<DENSE_REGION_CAPTION>"),
+    Florence2Prompt(encoder_prompt="<REGION_PROPOSAL>"),
+    Florence2Prompt(encoder_prompt="<OCR_WITH_REGION>"),
+    Florence2Prompt(encoder_prompt="<OCR>"),
+    Florence2Prompt(encoder_prompt="<OD>"),
+]
+
+
+def vllm_to_hf_output(vllm_output: Tuple[List[int], str,
+                                         Optional[SampleLogprobs]], ):
+    """Sanitize vllm output to be comparable with hf output."""
+    output_ids, output_str, out_logprobs = vllm_output
+
+    hf_output_str = "</s><s>" + output_str + "</s>"
+
+    return output_ids, hf_output_str, out_logprobs
+
+
+def run_test(
+    hf_runner: Type[HfRunner],
+    vllm_runner: Type[VllmRunner],
+    prompts: List[ExplicitEncoderDecoderPrompt],
+    model: str,
+    *,
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+    tensor_parallel_size: int,
+    distributed_executor_backend: Optional[str] = None,
+) -> None:
+    with vllm_runner(model,
+                     tokenizer_name=TOKENIZER,
+                     dtype=dtype,
+                     tensor_parallel_size=tensor_parallel_size,
+                     distributed_executor_backend=distributed_executor_backend,
+                     enforce_eager=True) as vllm_model:
+        vllm_outputs = vllm_model.generate_encoder_decoder_greedy_logprobs(
+            prompts, max_tokens, num_logprobs)
+
+    # Florence-2 processors require image inputs
+    dummy_image = Image.new(mode="RGB", size=(2, 2))
+    with hf_runner(model, dtype=dtype, skip_tokenizer_init=True) as hf_model:
+        hf_model.model.get_output_embeddings = lambda: \
+            hf_model.model.language_model.lm_head
+        hf_outputs = (hf_model.generate_encoder_decoder_greedy_logprobs_limit(
+            prompts,
+            max_tokens,
+            num_logprobs,
+            images=[dummy_image] * len(prompts),
+        ))
+
+    check_logprobs_close(
+        outputs_0_lst=hf_outputs,
+        outputs_1_lst=[
+            vllm_to_hf_output(vllm_output) for vllm_output in vllm_outputs
+        ],
+        name_0="hf",
+        name_1="vllm",
+    )
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+@pytest.mark.parametrize("max_tokens", [64])
+@pytest.mark.parametrize("num_logprobs", [5])
+def test_models(hf_runner, vllm_runner, model, dtype, max_tokens,
+                num_logprobs) -> None:
+    run_test(
+        hf_runner,
+        vllm_runner,
+        PROMPTS,
+        model,
+        dtype=dtype,
+        max_tokens=max_tokens,
+        num_logprobs=num_logprobs,
+        tensor_parallel_size=1,
+    )