[Frontend][Core] Move guided decoding params into sampling params (#8252)

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

tests/model_executor/test_guided_processors.py

@@ -0,0 +1,85 @@
+import pytest
+import torch
+from transformers import AutoTokenizer
+
+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):
+    """Basic unit test for RegexLogitsProcessor and JSONLogitsProcessor."""
+    tokenizer = AutoTokenizer.from_pretrained('HuggingFaceH4/zephyr-7b-beta')
+    regex_LP = RegexLogitsProcessor(sample_regex, tokenizer)
+    json_LP = JSONLogitsProcessor(sample_json_schema,
+                                  tokenizer,
+                                  whitespace_pattern=None)
+
+    token_ids = tokenizer.encode(
+        f"Give an example IPv4 address with this regex: {sample_regex}")
+    tensor = torch.rand(32000)
+    original_tensor = torch.clone(tensor)
+    regex_LP(token_ids, tensor)
+    assert tensor.shape == original_tensor.shape
+    assert not torch.allclose(tensor, original_tensor)
+
+    token_ids = tokenizer.encode(
+        f"Give an employee profile that fits this schema: {sample_json_schema}"
+    )
+    tensor = torch.rand(32000)
+    original_tensor = torch.clone(tensor)
+    json_LP(token_ids, tensor)
+    assert tensor.shape == original_tensor.shape
+    assert not torch.allclose(tensor, original_tensor)
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("backend", ["outlines", "lm-format-enforcer"])
+async def test_guided_logits_processor_black_box(backend: str, sample_regex,
+                                                 sample_json_schema):
+    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 = GuidedDecodingParams(regex=sample_regex, backend=backend)
+    regex_lp = await get_guided_decoding_logits_processor(
+        regex_request, tokenizer)
+    assert regex_lp is not None
+    tensor = torch.rand(32000)
+    original_tensor = torch.clone(tensor)
+    tensor = regex_lp(token_ids, tensor)
+    assert tensor.shape == original_tensor.shape
+    assert not torch.allclose(tensor, original_tensor)
+
+    token_ids = tokenizer.encode(
+        f"Give an employee profile that fits this schema: {sample_json_schema}"
+    )
+    json_request = GuidedDecodingParams(json=sample_json_schema,
+                                        backend=backend)
+    json_lp = await get_guided_decoding_logits_processor(
+        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")