[Frontend] multiple sampling params support (#3570)

tests/entrypoints/test_llm_generate.py

+import pytest
+
+from vllm import LLM, SamplingParams
+
+
+def test_multiple_sampling_params():
+
+    llm = LLM(model="facebook/opt-125m",
+              max_num_batched_tokens=4096,
+              tensor_parallel_size=1)
+
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+
+    sampling_params = [
+        SamplingParams(temperature=0.01, top_p=0.95),
+        SamplingParams(temperature=0.3, top_p=0.95),
+        SamplingParams(temperature=0.7, top_p=0.95),
+        SamplingParams(temperature=0.99, top_p=0.95),
+    ]
+
+    # Multiple SamplingParams should be matched with each prompt
+    outputs = llm.generate(prompts, sampling_params=sampling_params)
+    assert len(prompts) == len(outputs)
+
+    # Exception raised, if the size of params does not match the size of prompts
+    with pytest.raises(ValueError):
+        outputs = llm.generate(prompts, sampling_params=sampling_params[:3])
+
+    # Single SamplingParams should be applied to every prompt
+    single_sampling_params = SamplingParams(temperature=0.3, top_p=0.95)
+    outputs = llm.generate(prompts, sampling_params=single_sampling_params)
+    assert len(prompts) == len(outputs)
+
+    # sampling_params is None, default params should be applied
+    outputs = llm.generate(prompts, sampling_params=None)
+    assert len(prompts) == len(outputs)
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vllm/entrypoints/llm.py

@@ -127,7 +127,8 @@ class LLM:
     def generate(
         self,
         prompts: Optional[Union[str, List[str]]] = None,
-        sampling_params: Optional[SamplingParams] = None,
+        sampling_params: Optional[Union[SamplingParams,
+                                        List[SamplingParams]]] = None,
         prompt_token_ids: Optional[List[List[int]]] = None,
         use_tqdm: bool = True,
         lora_request: Optional[LoRARequest] = None,
@@ -142,7 +143,10 @@ class LLM:
         Args:
             prompts: A list of prompts to generate completions for.
             sampling_params: The sampling parameters for text generation. If
-                None, we use the default sampling parameters.
+                None, we use the default sampling parameters. 
+                When it is a single value, it is applied to every prompt. 
+                When it is a list, the list must have the same length as the 
+                prompts and it is paired one by one with the prompt.
             prompt_token_ids: A list of token IDs for the prompts. If None, we
                 use the tokenizer to convert the prompts to token IDs.
             use_tqdm: Whether to use tqdm to display the progress bar.
@@ -163,27 +167,33 @@ class LLM:
                 and len(prompts) != len(prompt_token_ids)):
             raise ValueError("The lengths of prompts and prompt_token_ids "
                              "must be the same.")
+
+        if prompts is not None:
+            num_requests = len(prompts)
+        else:
+            assert prompt_token_ids is not None
+            num_requests = len(prompt_token_ids)
+
         if sampling_params is None:
             # Use default sampling params.
             sampling_params = SamplingParams()
 
+        elif isinstance(sampling_params,
+                        list) and len(sampling_params) != num_requests:
+            raise ValueError("The lengths of prompts and sampling_params "
+                             "must be the same.")
         if multi_modal_data:
             multi_modal_data.data = multi_modal_data.data.to(torch.float16)
 
         # Add requests to the engine.
-        if prompts is not None:
-            num_requests = len(prompts)
-        else:
-            assert prompt_token_ids is not None
-            num_requests = len(prompt_token_ids)
-
         for i in range(num_requests):
             prompt = prompts[i] if prompts is not None else None
             token_ids = None if prompt_token_ids is None else prompt_token_ids[
                 i]
             self._add_request(
                 prompt,
-                sampling_params,
+                sampling_params[i]
+                if isinstance(sampling_params, list) else sampling_params,
                 token_ids,
                 lora_request=lora_request,
                 # Get ith image while maintaining the batch dim.
@@ -232,4 +242,4 @@ class LLM:
         # This is necessary because some requests may be finished earlier than
         # its previous requests.
         outputs = sorted(outputs, key=lambda x: int(x.request_id))
-        return outputs
+        return outputs
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