[V1][Core] min_p sampling support (#13191)

Signed-off-by: Aoyu <aoyuzhan@amazon.com>
Co-authored-by: Aoyu <aoyuzhan@amazon.com>

tests/v1/sample/test_sampler.py

@@ -81,6 +81,8 @@ def _create_default_sampling_metadata(
         top_k=torch.empty(batch_size, ),
         no_top_p=True,
         no_top_k=True,
+        min_p=torch.empty(batch_size, ),
+        no_min_p=True,
         generators={},
         max_num_logprobs=0,
         prompt_token_ids=_create_prompt_tokens_tensor(prompt_token_ids,
@@ -336,6 +338,46 @@ def test_sampler_repetition_penalty(device: str, batch_size: int,
                 non_penalized_token_id in output_tokens)
 
 
+@pytest.mark.parametrize("device", CUDA_DEVICES)
+@pytest.mark.parametrize("batch_size", [1, 2, 32])
+@pytest.mark.parametrize("min_p", [0.0, 0.1])
+def test_sampler_min_p(device: str, batch_size: int, min_p: float):
+    """
+    Tests that when min_p is applied, tokens with probability below 
+    min_p * max_prob are masked with -inf.
+    """
+    torch.set_default_device(device)
+    fake_logits = _create_fake_logits(batch_size, VOCAB_SIZE)
+
+    # Create one dominant token per batch
+    for i in range(batch_size):
+        fake_logits[i, 0] = 10.0  # High logit for first token
+        fake_logits[i, 1:] = 1e-2  # Others remain low
+
+    sampling_metadata = _create_default_sampling_metadata(
+        NUM_OUTPUT_TOKENS, batch_size, VOCAB_SIZE, torch.device(device))
+
+    # Configure min_p parameters
+    sampling_metadata.min_p = torch.full((batch_size, ), min_p, device=device)
+
+    sampler = Sampler()
+    logits = sampler.apply_min_p(fake_logits, sampling_metadata.min_p)
+    logits = logits.cpu()
+
+    for batch_idx in range(batch_size):
+        for token_id in range(VOCAB_SIZE):
+            if token_id == 0:
+                # Dominant token should always be unmasked
+                assert logits[batch_idx][token_id] != -float("inf")
+            else:
+                if min_p > 0.0:
+                    # Non-dominant tokens should be masked when min_p > 0
+                    assert logits[batch_idx][token_id] == -float("inf")
+                else:
+                    # No masking when min_p is 0
+                    assert logits[batch_idx][token_id] != -float("inf")
+
+
 @pytest.mark.parametrize("device", CUDA_DEVICES)
 @pytest.mark.parametrize("batch_size", [1, 2, 32])
 @pytest.mark.parametrize("bias_value", [-0.1, 1.2])