[Cherry-Pick][CI]Support different inferseed in speculate decoding(#5568) (#5597)

* fix mtp entropy drop in RL

* optimize usage and fix unit test

* optimize padding_sampling_params speed(vectorized)

tests/layers/test_speculative_sampler.py

@@ -30,6 +30,7 @@ from fastdeploy.model_executor.layers.sample.meta_data import SamplingMetadata
 from fastdeploy.model_executor.layers.sample.sampler import (
     MTPSampler,
     SpeculativeSampler,
+    padding_sampling_params,
 )
 
 
@@ -72,7 +73,7 @@ def _create_default_sampling_metadata(
         bad_words_token_ids=paddle.full(shape=[batch_size], fill_value=-1, dtype="int64"),
         eos_token_ids=paddle.full(shape=[batch_size], fill_value=-2, dtype="int64"),
         min_p=paddle.randn([batch_size]),
-        seed=paddle.to_tensor([[2025]]),
+        seed=paddle.full(shape=[batch_size], fill_value=0, dtype="int64"),
     )
     if max_num_logprobs is not None:
         fake_sampling_metadata.max_num_logprobs = max_num_logprobs
@@ -143,6 +144,19 @@ def _create_share_inputs(max_num_seqs, max_draft_token_num, max_model_len, vocab
     return share_inputs
 
 
+def _create_padding_inputs():
+    # batch_size = 3
+    top_p = paddle.to_tensor([[0.9], [0.8], [0.7], [1.0]], dtype="float32")
+    top_k = paddle.to_tensor([[10], [20], [30], [40]], dtype="int32")
+    infer_seed = paddle.to_tensor([[100], [200], [300], [400]], dtype="int64")
+
+    # decoder, encoder, decoder
+    seq_lens_encoder = paddle.to_tensor([[0], [5], [0], [0]], dtype="int32")
+    seq_lens_this_time = paddle.to_tensor([[3], [2], [0], [2]], dtype="int32")
+
+    return top_p, top_k, infer_seed, seq_lens_this_time, seq_lens_encoder
+
+
 def test_speculative_sampler():
     batch_size = 32
     vocab_size = 1024
@@ -220,8 +234,52 @@ def test_mtp_sampler_logprobs():
         sampler(logits, sampling_metadata, max_model_len, share_inputs)
 
 
+def test_padding_sampling_params_basic():
+    top_p, top_k, infer_seed, seq_lens_this_time, seq_lens_encoder = _create_padding_inputs()
+
+    top_p_pad, top_k_pad, seed_pad = padding_sampling_params(
+        top_p, top_k, infer_seed, seq_lens_this_time, seq_lens_encoder
+    )
+
+    # decoder(3) + encoder(1) + decoder(2) = 6
+    assert top_p_pad.shape == [6, 1]
+    assert top_k_pad.shape == [6, 1]
+    assert seed_pad.shape == [6, 1]
+
+    # top_p padding check
+    expected_top_p = [0.9, 0.9, 0.9, 0.8, 1.0, 1.0]
+    assert paddle.allclose(top_p_pad.squeeze(), paddle.to_tensor(expected_top_p, dtype="float32"))
+
+    # top_k padding check
+    expected_top_k = [10, 10, 10, 20, 40, 40]
+    assert paddle.equal_all(top_k_pad.squeeze(), paddle.to_tensor(expected_top_k, dtype="int32"))
+
+
+def test_padding_sampling_params_seed_offset():
+    top_p, top_k, infer_seed, seq_lens_this_time, seq_lens_encoder = _create_padding_inputs()
+
+    _, _, seed_pad = padding_sampling_params(top_p, top_k, infer_seed, seq_lens_this_time, seq_lens_encoder)
+
+    # decoder(0): 100 + 4*k
+    # encoder(1): 200 (no offset)
+    # null
+    # decoder(3): 400 + 4*k
+    expected_seed = [
+        100,
+        104,
+        108,  # first decoder seq (len=3)
+        200,  # encoder
+        400,
+        404,  # second decoder seq (len=2)
+    ]
+
+    assert paddle.equal_all(seed_pad.squeeze(), paddle.to_tensor(expected_seed, dtype="int64"))
+
+
 if __name__ == "__main__":
     test_speculative_sampler()
     test_speculative_sampler_logprobs()
     test_mtp_sampler()
     test_mtp_sampler_logprobs()
+    test_padding_sampling_params_basic()
+    test_padding_sampling_params_seed_offset()