[Frontend][Core] Move guided decoding params into sampling params (vl…

…lm-project#8252)

Signed-off-by: Joe Runde <Joseph.Runde@ibm.com>
Co-authored-by: Nick Hill <nickhill@us.ibm.com>

tests/entrypoints/llm/test_guided_generate.py

@@ -7,7 +7,7 @@
 
 from vllm.entrypoints.llm import LLM
 from vllm.outputs import RequestOutput
-from vllm.sampling_params import SamplingParams
+from vllm.sampling_params import GuidedDecodingParams, SamplingParams
 
 from ...conftest import cleanup
 
@@ -31,14 +31,12 @@ def test_guided_regex(sample_regex, llm):
     sampling_params = SamplingParams(
         temperature=0.8,
         top_p=0.95,
-    )
-    outputs = llm.generate(
-        prompts=[
-            f"Give an example IPv4 address with this regex: {sample_regex}"
-        ] * 2,
-        sampling_params=sampling_params,
-        use_tqdm=True,
-        guided_options_request=dict(guided_regex=sample_regex))
+        guided_decoding=GuidedDecodingParams(regex=sample_regex))
+    outputs = llm.generate(prompts=[
+        f"Give an example IPv4 address with this regex: {sample_regex}"
+    ] * 2,
+                           sampling_params=sampling_params,
+                           use_tqdm=True)
 
     assert outputs is not None
     for output in outputs:
@@ -57,15 +55,13 @@ def test_guided_json_completion(sample_json_schema, llm):
     sampling_params = SamplingParams(
         temperature=1.0,
         max_tokens=1000,
-    )
-    outputs = llm.generate(
-        prompts=[
-            f"Give an example JSON for an employee profile "
-            f"that fits this schema: {sample_json_schema}"
-        ] * 2,
-        sampling_params=sampling_params,
-        use_tqdm=True,
-        guided_options_request=dict(guided_json=sample_json_schema))
+        guided_decoding=GuidedDecodingParams(json=sample_json_schema))
+    outputs = llm.generate(prompts=[
+        f"Give an example JSON for an employee profile "
+        f"that fits this schema: {sample_json_schema}"
+    ] * 2,
+                           sampling_params=sampling_params,
+                           use_tqdm=True)
 
     assert outputs is not None
 
@@ -86,12 +82,11 @@ def test_guided_choice_completion(sample_guided_choice, llm):
     sampling_params = SamplingParams(
         temperature=0.8,
         top_p=0.95,
-    )
+        guided_decoding=GuidedDecodingParams(choice=sample_guided_choice))
     outputs = llm.generate(
         prompts="The best language for type-safe systems programming is ",
         sampling_params=sampling_params,
-        use_tqdm=True,
-        guided_options_request=dict(guided_choice=sample_guided_choice))
+        use_tqdm=True)
 
     assert outputs is not None
     for output in outputs:
@@ -112,13 +107,13 @@ def test_guided_grammar(sample_sql_statements, llm):
         temperature=0.8,
         top_p=0.95,
         max_tokens=1000,
-    )
+        guided_decoding=GuidedDecodingParams(grammar=sample_sql_statements))
     outputs = llm.generate(
         prompts=("Generate a sql state that select col_1 from "
                  "table_1 where it is equals to 1"),
         sampling_params=sampling_params,
         use_tqdm=True,
-        guided_options_request=dict(guided_grammar=sample_sql_statements))
+    )
 
     assert outputs is not None
     for output in outputs:
@@ -140,3 +135,28 @@ def test_guided_grammar(sample_sql_statements, llm):
         assert generated_text.strip() == ground_truth
 
         print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+
+@pytest.mark.skip_global_cleanup
+def test_guided_options_request_deprecation_warning(sample_regex, llm):
+    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+    with pytest.warns(DeprecationWarning, match="guided_options_request"):
+        llm.generate(prompts="This should fail",
+                     sampling_params=sampling_params,
+                     use_tqdm=True,
+                     guided_options_request=dict(guided_regex=sample_regex))
+
+
+@pytest.mark.skip_global_cleanup
+def test_validation_against_both_guided_decoding_options(sample_regex, llm):
+    sampling_params = SamplingParams(
+        temperature=0.8,
+        top_p=0.95,
+        guided_decoding=GuidedDecodingParams(regex=sample_regex))
+
+    with pytest.raises(ValueError, match="Cannot set both"):
+        llm.generate(prompts="This should fail",
+                     sampling_params=sampling_params,
+                     use_tqdm=True,
+                     guided_options_request=dict(guided_regex=sample_regex))

tests/model_executor/conftest.py

@@ -0,0 +1,49 @@
+import pytest
+
+
+@pytest.fixture
+def sample_regex():
+    return (r"((25[0-5]|(2[0-4]|1\d|[1-9]|)\d)\.){3}"
+            r"(25[0-5]|(2[0-4]|1\d|[1-9]|)\d)")
+
+
+@pytest.fixture
+def sample_json_schema():
+    return {
+        "type": "object",
+        "properties": {
+            "name": {
+                "type": "string"
+            },
+            "age": {
+                "type": "integer"
+            },
+            "skills": {
+                "type": "array",
+                "items": {
+                    "type": "string",
+                    "maxLength": 10
+                },
+                "minItems": 3
+            },
+            "work_history": {
+                "type": "array",
+                "items": {
+                    "type": "object",
+                    "properties": {
+                        "company": {
+                            "type": "string"
+                        },
+                        "duration": {
+                            "type": "number"
+                        },
+                        "position": {
+                            "type": "string"
+                        }
+                    },
+                    "required": ["company", "position"]
+                }
+            }
+        },
+        "required": ["name", "age", "skills", "work_history"]
+    }

tests/entrypoints/openai/test_guided_processors.py → tests/model_executor/test_guided_processors.py

@@ -1,14 +1,12 @@
-# This unit test should be moved to a new
-# tests/test_guided_decoding directory.
 import pytest
 import torch
 from transformers import AutoTokenizer
 
-from vllm.entrypoints.openai.protocol import CompletionRequest
 from vllm.model_executor.guided_decoding import (
     get_guided_decoding_logits_processor)
 from vllm.model_executor.guided_decoding.outlines_logits_processors import (
     JSONLogitsProcessor, RegexLogitsProcessor)
+from vllm.sampling_params import GuidedDecodingParams
 
 
 def test_guided_logits_processors(sample_regex, sample_json_schema):
@@ -44,11 +42,9 @@ async def test_guided_logits_processor_black_box(backend: str, sample_regex,
     tokenizer = AutoTokenizer.from_pretrained('HuggingFaceH4/zephyr-7b-beta')
     token_ids = tokenizer.encode(
         f"Give an example IPv4 address with this regex: {sample_regex}")
-    regex_request = CompletionRequest(model='test',
-                                      prompt=token_ids,
-                                      guided_regex=sample_regex)
+    regex_request = GuidedDecodingParams(regex=sample_regex, backend=backend)
     regex_lp = await get_guided_decoding_logits_processor(
-        backend, regex_request, tokenizer)
+        regex_request, tokenizer)
     assert regex_lp is not None
     tensor = torch.rand(32000)
     original_tensor = torch.clone(tensor)
@@ -59,14 +55,31 @@ async def test_guided_logits_processor_black_box(backend: str, sample_regex,
     token_ids = tokenizer.encode(
         f"Give an employee profile that fits this schema: {sample_json_schema}"
     )
-    json_request = CompletionRequest(model='test',
-                                     prompt=token_ids,
-                                     guided_json=sample_json_schema)
+    json_request = GuidedDecodingParams(json=sample_json_schema,
+                                        backend=backend)
     json_lp = await get_guided_decoding_logits_processor(
-        backend, json_request, tokenizer)
+        json_request, tokenizer)
     assert json_lp is not None
     tensor = torch.rand(32000)
     original_tensor = torch.clone(tensor)
     tensor = json_lp(token_ids, tensor)
     assert tensor.shape == original_tensor.shape
     assert not torch.allclose(tensor, original_tensor)
+
+
+def test_multiple_guided_options_not_allowed(sample_json_schema, sample_regex):
+    with pytest.raises(ValueError,
+                       match="You can only use one kind of guided"):
+        GuidedDecodingParams(json=sample_json_schema, regex=sample_regex)
+
+    with pytest.raises(ValueError,
+                       match="You can only use one kind of guided"):
+        GuidedDecodingParams(json=sample_json_schema, json_object=True)
+
+    with pytest.raises(ValueError,
+                       match="You can only use one kind of guided"):
+        GuidedDecodingParams(json=sample_json_schema, choice=["a", "b"])
+
+    with pytest.raises(ValueError,
+                       match="You can only use one kind of guided"):
+        GuidedDecodingParams(json=sample_json_schema, grammar="test grammar")

vllm/engine/async_llm_engine.py

@@ -20,6 +20,8 @@
 from vllm.inputs import PromptType
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
+from vllm.model_executor.guided_decoding import (
+    get_guided_decoding_logits_processor)
 from vllm.model_executor.layers.sampler import SamplerOutput
 from vllm.outputs import EmbeddingRequestOutput, RequestOutput
 from vllm.pooling_params import PoolingParams
@@ -477,6 +479,18 @@ async def add_request_async(
         )
         processed_inputs = self.input_processor(preprocessed_inputs)
 
+        if isinstance(params, SamplingParams) and \
+            params.guided_decoding is not None:
+            # Guided decoding has an async implementation for building logits
+            # processors in a separate threadpool.
+            # We want to invoke that here instead of using the blocking
+            # implementation in the LLMEngine
+            params = await build_guided_decoding_logits_processor_async(
+                sampling_params=params,
+                tokenizer=self.get_tokenizer(lora_request),
+                default_guided_backend=self.decoding_config.
+                guided_decoding_backend)
+
         self._add_processed_request(
             request_id=request_id,
             processed_inputs=processed_inputs,
@@ -494,6 +508,36 @@ async def check_health_async(self) -> None:
         self.model_executor.check_health()
 
 
+async def build_guided_decoding_logits_processor_async(
+        sampling_params: SamplingParams, tokenizer: AnyTokenizer,
+        default_guided_backend: str) -> SamplingParams:
+    """Constructs logits processors based on the guided_decoding,
+    logits_bias, and allowed_token_ids fields in sampling_params. Deletes
+    those fields and adds the constructed logits processors to the
+    logits_processors field. Modifies sampling params in-place and returns
+    the modified sampling params."""
+    if (guided_decoding := sampling_params.guided_decoding) is None:
+        return sampling_params
+
+    logger.debug("Building guided decoding logits processor. "
+                 "Params: %s", guided_decoding)
+
+    guided_decoding.backend = guided_decoding.backend or default_guided_backend
+
+    processor = await get_guided_decoding_logits_processor(
+        guided_params=guided_decoding, tokenizer=tokenizer)
+
+    if processor:
+        if sampling_params.logits_processors is None:
+            sampling_params.logits_processors = []
+        sampling_params.logits_processors.append(processor)
+
+    # Unset guided decoding params after constructing the lp from them
+    sampling_params.guided_decoding = None
+
+    return sampling_params
+
+
 class AsyncLLMEngine:
     """An asynchronous wrapper for :class:`LLMEngine`.
 

vllm/engine/llm_engine.py

@@ -25,6 +25,7 @@
     SequenceGroupOutputProcessor)
 from vllm.engine.output_processor.stop_checker import StopChecker
 from vllm.engine.output_processor.util import create_output_by_sequence_group
+from vllm.entrypoints.openai.logits_processors import get_logits_processors
 from vllm.executor.executor_base import ExecutorBase
 from vllm.executor.gpu_executor import GPUExecutor
 from vllm.executor.ray_utils import initialize_ray_cluster
@@ -33,6 +34,8 @@
 from vllm.inputs.preprocess import InputPreprocessor
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
+from vllm.model_executor.guided_decoding import (
+    get_local_guided_decoding_logits_processor)
 from vllm.model_executor.layers.sampler import SamplerOutput
 from vllm.outputs import (EmbeddingRequestOutput, RequestOutput,
                           RequestOutputFactory)
@@ -843,6 +846,9 @@ def _create_sequence_group_with_sampling(
             raise ValueError(f"Cannot request more than "
                              f"{max_logprobs} logprobs.")
 
+        sampling_params = self._build_logits_processors(
+            sampling_params, lora_request)
+
         # Defensive copy of SamplingParams, which are used by the sampler,
         # this doesn't deep-copy LogitsProcessor objects
         sampling_params = sampling_params.clone()
@@ -1895,3 +1901,51 @@ def _validate_model_inputs(self, inputs: Union[LLMInputs,
             # TODO: Find out how many placeholder tokens are there so we can
             # check that chunked prefill does not truncate them
             # max_batch_len = self.scheduler_config.max_num_batched_tokens
+
+    def _build_logits_processors(
+            self, sampling_params: SamplingParams,
+            lora_request: Optional[LoRARequest]) -> SamplingParams:
+        """Constructs logits processors based on the guided_decoding,
+        logits_bias, and allowed_token_ids fields in sampling_params. Deletes
+        those fields and adds the constructed logits processors to the
+        logits_processors field. Returns the modified sampling params."""
+
+        logits_processors = []
+        if (guided_decoding := sampling_params.guided_decoding) is not None:
+
+            logger.debug(
+                "Building guided decoding logits processor in "
+                "LLMEngine. Params: %s", guided_decoding)
+
+            tokenizer = self.get_tokenizer(lora_request=lora_request)
+            guided_decoding.backend = guided_decoding.backend or \
+                self.decoding_config.guided_decoding_backend
+
+            processor = get_local_guided_decoding_logits_processor(
+                guided_params=guided_decoding, tokenizer=tokenizer)
+            if processor:
+                logits_processors.append(processor)
+
+            # Unset so this doesn't get passed down to the model
+            sampling_params.guided_decoding = None
+
+        if (sampling_params.logit_bias or sampling_params.allowed_token_ids):
+            tokenizer = self.get_tokenizer(lora_request=lora_request)
+
+            processors = get_logits_processors(
+                logit_bias=sampling_params.logit_bias,
+                allowed_token_ids=sampling_params.allowed_token_ids,
+                tokenizer=tokenizer)
+            logits_processors.extend(processors)
+
+            # Unset so these don't get passed down to the model
+            sampling_params.logit_bias = None
+            sampling_params.allowed_token_ids = None
+
+        if logits_processors:
+            if sampling_params.logits_processors is None:
+                sampling_params.logits_processors = logits_processors
+            else:
+                sampling_params.logits_processors.extend(logits_processors)
+
+        return sampling_params