[Sampler] Vectorized sampling (simplified) (#1048)

Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>

tests/samplers/test_sampler.py

+import pytest
+import random
+from typing import Tuple
+from unittest.mock import patch
+
+import torch
+
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.utils import set_random_seed
+from vllm.sequence import SamplingParams, SequenceData, SequenceGroupMetadata
+from vllm.worker.worker import Worker
+
+
+class MockLogitsSampler(Sampler):
+
+    def __init__(self, vocab_size: int, fake_logits: torch.Tensor):
+        super().__init__(vocab_size=vocab_size)
+        self.fake_logits = fake_logits
+
+    def forward(self, *args, **kwargs):
+        with patch("vllm.model_executor.layers.sampler._prune_hidden_states",
+                   lambda x, y: x):
+            with patch("vllm.model_executor.layers.sampler._get_logits",
+                       lambda *args, **kwargs: self.fake_logits):
+                return super().forward(*args, **kwargs)
+
+
+def _prepare_test(
+    batch_size: int
+) -> Tuple[torch.Tensor, torch.Tensor, MockLogitsSampler, Worker]:
+    vocab_size = 32000
+    input_tensor = torch.rand((batch_size, 1024),
+                              device="cuda",
+                              dtype=torch.float16)
+    fake_logits = torch.full((batch_size, vocab_size),
+                             1e-2,
+                             device=input_tensor.device,
+                             dtype=input_tensor.dtype)
+    sampler = MockLogitsSampler(32000, fake_logits)
+    worker = Worker(None, None, None)
+    worker.block_size = 16
+    return input_tensor, fake_logits, sampler, worker
+
+
+RANDOM_SEEDS = list(range(128))
+
+
+@pytest.mark.parametrize("seed", RANDOM_SEEDS)
+def test_sampler_all_greedy(seed: int):
+    set_random_seed(seed)
+    batch_size = random.randint(1, 256)
+    input_tensor, fake_logits, sampler, worker = _prepare_test(batch_size)
+
+    seq_group_metadata_list = []
+    for i in range(batch_size):
+        seq_group_metadata_list.append(
+            SequenceGroupMetadata(
+                request_id=f"test_{i}",
+                is_prompt=True,
+                seq_data={0: SequenceData([1, 2, 3])},
+                sampling_params=SamplingParams(temperature=0, ),
+                block_tables={0: [1]},
+            ))
+
+    _, _, input_metadata = worker._prepare_inputs(seq_group_metadata_list)
+    sampler_output = sampler(embedding=None,
+                             hidden_states=input_tensor,
+                             input_metadata=input_metadata)
+    expected = torch.argmax(fake_logits, dim=-1)
+    for i, sequence_output in enumerate(sampler_output):
+        for nth_output in sequence_output:
+            assert nth_output.output_token == expected[i].item()
+
+
+@pytest.mark.parametrize("seed", RANDOM_SEEDS)
+def test_sampler_all_random(seed: int):
+    set_random_seed(seed)
+    batch_size = random.randint(1, 256)
+    input_tensor, fake_logits, sampler, worker = _prepare_test(batch_size)
+
+    for i in range(batch_size):
+        fake_logits[i, i] = 1e2
+
+    seq_group_metadata_list = []
+    for i in range(batch_size):
+        seq_group_metadata_list.append(
+            SequenceGroupMetadata(
+                request_id=f"test_{i}",
+                is_prompt=True,
+                seq_data={0: SequenceData([1, 2, 3])},
+                sampling_params=SamplingParams(
+                    temperature=1.0,
+                    n=random.randint(1, 10),
+                ),
+                block_tables={0: [1]},
+            ))
+
+    _, _, input_metadata = worker._prepare_inputs(seq_group_metadata_list)
+    sampler_output = sampler(embedding=None,
+                             hidden_states=input_tensor,
+                             input_metadata=input_metadata)
+    for i, sequence_output in enumerate(sampler_output):
+        for nth_output in sequence_output:
+            assert nth_output.output_token == i
+
+
+@pytest.mark.parametrize("seed", RANDOM_SEEDS)
+def test_sampler_all_beam(seed: int):
+    set_random_seed(seed)
+    batch_size = random.randint(1, 256)
+    input_tensor, fake_logits, sampler, worker = _prepare_test(batch_size)
+
+    seq_group_metadata_list = []
+    for i in range(batch_size):
+        seq_group_metadata_list.append(
+            SequenceGroupMetadata(
+                request_id=f"test_{i}",
+                is_prompt=True,
+                seq_data={0: SequenceData([1, 2, 3])},
+                sampling_params=SamplingParams(
+                    temperature=0,
+                    best_of=2,
+                    use_beam_search=True,
+                ),
+                block_tables={0: [1]},
+            ))
+
+    _, _, input_metadata = worker._prepare_inputs(seq_group_metadata_list)
+    sampler(embedding=None,
+            hidden_states=input_tensor,
+            input_metadata=input_metadata)
+    # no assertion here as I am not sure how to determine whether
+    # the outputs are expected - in other words, this just tests
+    # whether there are no exceptions in the sampler
+    # when handling an all-beam search case.
+
+
+@pytest.mark.parametrize("seed", RANDOM_SEEDS)
+def test_sampler_mixed(seed: int):
+    set_random_seed(seed)
+    batch_size = random.randint(1, 256)
+    input_tensor, fake_logits, sampler, worker = _prepare_test(batch_size)
+
+    seq_group_metadata_list = []
+    expected_tokens = []
+    for i in range(batch_size):
+        n = 1
+        sampling_type = random.randint(0, 2)
+        if sampling_type == 0:
+            sampling_params = SamplingParams(temperature=0)
+        elif sampling_type == 1:
+            n = random.randint(1, 10)
+            sampling_params = SamplingParams(
+                temperature=random.random() + 0.1,
+                top_p=min(random.random() + 0.1, 1),
+                top_k=random.randint(0, 10) or -1,
+                n=n,
+                presence_penalty=random.randint(0, 1),
+            )
+        else:
+            sampling_params = SamplingParams(temperature=0,
+                                             use_beam_search=True,
+                                             best_of=2)
+        for idx in range(n):
+            fake_logits[i, i + idx] = 1e2
+            expected_tokens.append(i + idx)
+        seq_group_metadata_list.append(
+            SequenceGroupMetadata(
+                request_id=f"test_{i}",
+                is_prompt=True,
+                seq_data={0: SequenceData([1, 2, 3])},
+                sampling_params=sampling_params,
+                block_tables={0: [1]},
+            ))
+
+    _, _, input_metadata = worker._prepare_inputs(seq_group_metadata_list)
+    sampler_output = sampler(embedding=None,
+                             hidden_states=input_tensor,
+                             input_metadata=input_metadata)
+    for i, sequence_output in enumerate(sampler_output):
+        if seq_group_metadata_list[i].sampling_params.use_beam_search:
+            continue
+        for nth_output in sequence_output:
+            assert nth_output.output_token in expected_tokens

vllm/model_executor/layers/sampler.py

 """A layer that samples the next tokens from the model's outputs."""
-from typing import Dict, List, Tuple, Optional
+from typing import Dict, List, Optional, Tuple
 
-import numpy as np
 import torch
 import torch.nn as nn
 
 from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.parallel_utils.tensor_parallel import (
     gather_from_tensor_model_parallel_region)
-from vllm.sampling_params import SamplingParams
-from vllm.sequence import SamplerOutput, SequenceOutputs
+from vllm.sampling_params import SamplingParams, SamplingType
+from vllm.sequence import SamplerOutput, SequenceData, SequenceOutputs
 
 _SAMPLING_EPS = 1e-5
 
@@ -44,12 +43,8 @@ class Sampler(nn.Module):
         hidden_states = _prune_hidden_states(hidden_states, input_metadata)
 
         # Get the logits for the next tokens.
-        logits = torch.matmul(hidden_states, embedding.t())
-        if embedding_bias is not None:
-            logits += embedding_bias
-        logits = gather_from_tensor_model_parallel_region(logits)
-        # Remove paddings in vocab (if any).
-        logits = logits[:, :self.vocab_size]
+        logits = _get_logits(hidden_states, embedding, embedding_bias,
+                             self.vocab_size)
 
         # Apply presence and frequency penalties.
         output_tokens = _get_output_tokens(input_metadata)
@@ -59,7 +54,7 @@ class Sampler(nn.Module):
         assert len(presence_penalties) == logits.shape[0]
         assert len(frequency_penalties) == logits.shape[0]
         logits = _apply_penalties(logits, output_tokens, presence_penalties,
-                                  frequency_penalties, self.vocab_size)
+                                  frequency_penalties)
 
         # Apply temperature scaling.
         temperatures = _get_temperatures(input_metadata)
@@ -90,19 +85,47 @@ class Sampler(nn.Module):
         return _sample(probs, logprobs, input_metadata)
 
 
+def _get_logits(hidden_states: torch.Tensor, embedding: torch.Tensor,
+                embedding_bias: Optional[torch.Tensor],
+                vocab_size: int) -> torch.Tensor:
+    # Get the logits for the next tokens.
+    logits = torch.matmul(hidden_states, embedding.t())
+    if embedding_bias is not None:
+        logits += embedding_bias
+    logits = gather_from_tensor_model_parallel_region(logits)
+    # Remove paddings in vocab (if any).
+    logits = logits[:, :vocab_size]
+    return logits
+
+
 def _prune_hidden_states(
     hidden_states: torch.Tensor,
     input_metadata: InputMetadata,
 ) -> torch.Tensor:
+    last_token_indices = {t: [] for t in SamplingType}
     start_idx = 0
-    last_token_indicies: List[int] = []
-    for prompt_len in input_metadata.prompt_lens:
-        last_token_indicies.append(start_idx + prompt_len - 1)
-        start_idx += prompt_len
-    last_token_indicies.extend(
-        range(start_idx, start_idx + input_metadata.num_generation_tokens))
-    return hidden_states.index_select(
-        0, torch.tensor(last_token_indicies, device=hidden_states.device))
+    for i, seq_group in enumerate(input_metadata.seq_groups):
+        seq_ids, sampling_params = seq_group
+        sampling_type = sampling_params.sampling_type
+        if i < input_metadata.num_prompts:
+            assert len(seq_ids) == 1, "Prompt input should have only one seq."
+            prompt_len = input_metadata.prompt_lens[i]
+            last_token_indices[sampling_type].append(start_idx + prompt_len -
+                                                     1)
+            start_idx += prompt_len
+        else:
+            num_seqs = len(seq_ids)
+            last_token_indices[sampling_type].extend(
+                range(start_idx, start_idx + num_seqs))
+            start_idx += num_seqs
+
+    all_last_token_indices = []
+    for sampling_type in SamplingType:
+        all_last_token_indices.extend(last_token_indices[sampling_type])
+    all_last_token_indices = torch.tensor(all_last_token_indices,
+                                          dtype=torch.long,
+                                          device=hidden_states.device)
+    return hidden_states.index_select(0, all_last_token_indices)
 
 
 def _get_penalties(
@@ -149,11 +172,8 @@ def _apply_penalties(
     output_tokens: List[List[int]],
     presence_penalties: List[float],
     frequency_penalties: List[float],
-    vocab_size: int,
 ) -> torch.Tensor:
-    num_seqs = logits.shape[0]
-    # Collect the indices of sequences that have non-zero penalties.
-    indices = []
+    num_seqs, vocab_size = logits.shape
     for i in range(num_seqs):
         if not output_tokens[i]:
             continue
@@ -161,33 +181,40 @@ def _apply_penalties(
         f = frequency_penalties[i]
         if abs(p) < _SAMPLING_EPS and abs(f) < _SAMPLING_EPS:
             continue
-        indices.append(i)
-
-    # Return early if all sequences have zero penalties.
-    if not indices:
+        break
+    else:
+        # Return early if all sequences have zero penalties.
         return logits
 
-    bin_counts = []
-    for i in indices:
-        bin_counts.append(np.bincount(output_tokens[i], minlength=vocab_size))
-    bin_counts = np.stack(bin_counts, axis=0)
-    bin_counts = torch.from_numpy(bin_counts).to(dtype=logits.dtype,
-                                                 device=logits.device)
+    max_output_len = max(len(tokens) for tokens in output_tokens)
+    padded_output_tokens = [
+        tokens + [vocab_size] * (max_output_len - len(tokens))
+        for tokens in output_tokens
+    ]
+    output_tokens_tensor = torch.tensor(padded_output_tokens,
+                                        dtype=torch.long,
+                                        device=logits.device)
+
+    # Compute the bin counts for the output tokens.
+    # vocab_size + 1 for padding.
+    bin_counts = torch.zeros((num_seqs, vocab_size + 1),
+                             dtype=torch.long,
+                             device=logits.device)
+    bin_counts.scatter_add_(1, output_tokens_tensor,
+                            torch.ones_like(output_tokens_tensor))
+    bin_counts = bin_counts[:, :vocab_size]  # Remove the padding bin.
 
-    frequency_penalties = [frequency_penalties[i] for i in indices]
     frequency_penalties = torch.tensor(frequency_penalties,
                                        dtype=logits.dtype,
                                        device=logits.device)
-    presence_penalties = [presence_penalties[i] for i in indices]
     presence_penalties = torch.tensor(presence_penalties,
                                       dtype=logits.dtype,
                                       device=logits.device)
 
     # We follow the definition in OpenAI API.
     # Refer to https://platform.openai.com/docs/api-reference/parameter-details
-    logits[indices] -= frequency_penalties.unsqueeze(dim=1) * bin_counts
-    presence_mask = (bin_counts > 0.0).to(dtype=logits.dtype)
-    logits[indices] -= presence_penalties.unsqueeze(dim=1) * presence_mask
+    logits -= frequency_penalties.unsqueeze(dim=1) * bin_counts
+    logits -= presence_penalties.unsqueeze(dim=1) * (bin_counts > 0)
     return logits
 
 
@@ -268,95 +295,154 @@ def _apply_top_p_top_k(
 def _get_topk_logprobs(
     logprobs: torch.Tensor,
     num_logprobs: Optional[int],
-) -> Dict[int, float]:
+) -> List[Dict[int, float]]:
+    num_seqs = logprobs.size(0)
     if num_logprobs is None or num_logprobs == 0:
-        return {}
-
-    topk_logprobs, topk_ids = torch.topk(logprobs, num_logprobs)
-    if num_logprobs == 1:
-        topk_logprobs = [topk_logprobs.item()]
-        topk_ids = [topk_ids.item()]
-    else:
-        topk_logprobs = topk_logprobs.tolist()
-        topk_ids = topk_ids.tolist()
-
-    token_to_logprob: Dict[int, float] = {}
-    for token_id, logprob in zip(topk_ids, topk_logprobs):
-        token_to_logprob[token_id] = logprob
-    return token_to_logprob
+        return [{} for _ in range(num_seqs)]
+
+    all_topk_logprobs, all_topk_ids = torch.topk(logprobs,
+                                                 num_logprobs,
+                                                 dim=-1)
+    all_topk_logprobs = all_topk_logprobs.cpu()
+    all_topk_ids = all_topk_ids.cpu()
+    all_token_to_logprob = []
+    for topk_logprobs, topk_ids in zip(all_topk_logprobs, all_topk_ids):
+        token_to_logprob: Dict[int, float] = {}
+        for token_id, logprob in zip(topk_ids, topk_logprobs):
+            token_to_logprob[token_id.item()] = logprob.item()
+        all_token_to_logprob.append(token_to_logprob)
+    return all_token_to_logprob
+
+
+def _build_sequence_outputs(
+    parent_ids: List[int],
+    next_token_ids: List[int],
+    selected_token_logprobs: torch.Tensor,
+    parent_seq_ids: List[int],
+    parent_logprobs: torch.Tensor,
+    num_output_logprobs: Optional[int],
+) -> List[SequenceOutputs]:
+    # Get top-k log probabilities for the next tokens.
+    next_logprobs = _get_topk_logprobs(parent_logprobs, num_output_logprobs)
+    seq_outputs: List[SequenceOutputs] = []
+    for parent_id, next_token_id, token_logprob in zip(
+            parent_ids, next_token_ids, selected_token_logprobs):
+        output_logprobs = next_logprobs[parent_id].copy()
+        output_logprobs[next_token_id] = token_logprob
+        seq_outputs.append(
+            SequenceOutputs(parent_seq_ids[parent_id], next_token_id,
+                            output_logprobs))
+    return seq_outputs
 
 
-def _sample_from_prompt(
-    prob: torch.Tensor,
-    sampling_params: SamplingParams,
-) -> List[int]:
-    if sampling_params.use_beam_search:
-        # Beam search.
-        beam_width = sampling_params.best_of
-        # Sample 2 * beam_width candidates to make sure that with high
-        # probability we can get `beam_width` candidates in addition to
-        # the finished sequences for the next iteration. See
-        # https://github.com/tensorflow/tensor2tensor/blob/bafdc1b67730430d38d6ab802cbd51f9d053ba2e/tensor2tensor/utils/beam_search.py#L557-L563
-        # for details. See also HF reference:
-        # https://github.com/huggingface/transformers/blob/a4dd53d88e4852f023332d284ff07a01afcd5681/src/transformers/generation/utils.py#L3063-L3065
-        _, next_token_ids = torch.topk(prob, 2 * beam_width)
-        next_token_ids = next_token_ids.tolist()
-    elif sampling_params.temperature < _SAMPLING_EPS:
-        # Greedy sampling.
-        assert sampling_params.best_of == 1
-        next_token_id = torch.argmax(prob)
-        next_token_ids = [next_token_id.item()]
-    else:
-        # Random sampling.
-        # Sample `best_of` tokens for the prompt.
-        num_seqs = sampling_params.best_of
-        next_token_ids = torch.multinomial(prob,
-                                           num_samples=num_seqs,
-                                           replacement=True)
-        next_token_ids = next_token_ids.tolist()
-    return next_token_ids
-
-
-def _sample_from_generation_tokens(
-    seq_ids: List[int],
+def _greedy_sample(
+    selected_seq_groups: List[Tuple[List[int], SamplingParams]],
+    logprobs: torch.Tensor,
+) -> List[Tuple[List[int], List[int]]]:
+    samples = torch.argmax(logprobs, dim=-1).cpu()
+    sample_idx = 0
+    results = []
+    for seq_group in selected_seq_groups:
+        seq_ids, _ = seq_group
+        num_parent_seqs = len(seq_ids)
+        assert num_parent_seqs == 1, (
+            "Greedy sampling should have only one seq.")
+        parent_ids = list(range(num_parent_seqs))
+        next_token_ids = [samples[sample_idx].item()]
+        results.append((next_token_ids, parent_ids))
+        sample_idx += num_parent_seqs
+    assert sample_idx == logprobs.size(0)
+    return results
+
+
+def _random_sample(
+    selected_seq_groups: List[Tuple[List[int], SamplingParams]],
+    is_prompts: List[bool],
     probs: torch.Tensor,
+) -> List[Tuple[List[int], List[int]]]:
+    # Find the maximum best_of value of the prompt phase requests.
+    max_best_of = 1
+    for seq_group, is_prompt in zip(selected_seq_groups, is_prompts):
+        if is_prompt:
+            seq_ids, sampling_params = seq_group
+            max_best_of = max(max_best_of, sampling_params.best_of)
+    random_samples = torch.multinomial(probs,
+                                       num_samples=max_best_of,
+                                       replacement=True).cpu()
+    sample_idx = 0
+    results = []
+    for seq_group, is_prompt in zip(selected_seq_groups, is_prompts):
+        seq_ids, sampling_params = seq_group
+        num_parent_seqs = len(seq_ids)
+        if is_prompt:
+            # Prompt phase.
+            assert num_parent_seqs == 1, (
+                "Prompt input should have only one seq.")
+            parent_ids = [0] * sampling_params.best_of
+            next_token_ids = random_samples[
+                sample_idx, :sampling_params.best_of].tolist()
+        else:
+            # Generation phase.
+            parent_ids = list(range(num_parent_seqs))
+            next_token_ids = random_samples[sample_idx:sample_idx +
+                                            num_parent_seqs, 0].tolist()
+        results.append((next_token_ids, parent_ids))
+        sample_idx += num_parent_seqs
+    assert sample_idx == probs.size(0)
+    return results
+
+
+def _beam_search_sample(
+    selected_seq_groups: List[Tuple[List[int], SamplingParams]],
+    is_prompts: List[bool],
+    seq_data: Dict[int, SequenceData],
     logprobs: torch.Tensor,
-    seq_logprobs: List[float],
-    sampling_params: SamplingParams,
-) -> Tuple[List[int], List[int]]:
-    # NOTE(woosuk): sampling_params.best_of can be greater than
-    # len(seq_ids) because some sequences in the group might have
-    # been already terminated.
-    if sampling_params.use_beam_search:
-        # Beam search.
-        # Add cumulative logprobs for the sequences in the group.
-        seq_logprobs = torch.tensor(seq_logprobs,
-                                    dtype=torch.float,
-                                    device=logprobs.device)
-        logprobs = logprobs + seq_logprobs.unsqueeze(dim=1)
-
-        vocab_size = logprobs.size(-1)
-        beam_width = len(seq_ids)
-        _, topk_ids = torch.topk(logprobs.flatten(), 2 * beam_width)
-        topk_ids = topk_ids.tolist()
-        seq_idx = [i // vocab_size for i in topk_ids]
-        parent_seq_ids = [seq_ids[i] for i in seq_idx]
-        next_token_ids = [i % vocab_size for i in topk_ids]
-    elif sampling_params.temperature < _SAMPLING_EPS:
-        # Greedy sampling.
-        assert len(seq_ids) == 1
-        next_token_id = torch.argmax(probs, dim=-1)
-        next_token_ids = [int(next_token_id.item())]
-        parent_seq_ids = seq_ids
-    else:
-        # Random sampling.
-        # Sample 1 token for each sequence in the group.
-        next_token_ids = torch.multinomial(probs,
-                                           num_samples=1,
-                                           replacement=True)
-        next_token_ids = next_token_ids.squeeze(dim=-1).tolist()
-        parent_seq_ids = seq_ids
-    return parent_seq_ids, next_token_ids
+) -> List[Tuple[List[int], List[int]]]:
+    # We sample 2 * beam_width candidates to make sure that with high
+    # probability we can get `beam_width` candidates in addition to
+    # the finished sequences for the next iteration. See
+    # https://github.com/tensorflow/tensor2tensor/blob/bafdc1b67730430d38d6ab802cbd51f9d053ba2e/tensor2tensor/utils/beam_search.py#L557-L563
+    # for details. See also HF reference:
+    # https://github.com/huggingface/transformers/blob/a4dd53d88e4852f023332d284ff07a01afcd5681/src/transformers/generation/utils.py#L3063-L3065
+    #
+    # Note: Beam search is not vectorized, so its speed can be slower than
+    # other sampling methods.
+    sample_idx = 0
+    results = []
+    for seq_group, is_prompt in zip(selected_seq_groups, is_prompts):
+        seq_ids, sampling_params = seq_group
+        num_parent_seqs = len(seq_ids)
+        beam_width = sampling_params.best_of
+        seq_group_logprobs = logprobs[sample_idx:sample_idx + num_parent_seqs]
+        if is_prompt:
+            # Prompt phase.
+            assert num_parent_seqs == 1, (
+                "Prompt input should have only one seq.")
+            parent_ids = [0] * (2 * beam_width)
+            _, next_token_ids = torch.topk(seq_group_logprobs[0],
+                                           2 * beam_width)
+            next_token_ids = next_token_ids.tolist()
+        else:
+            # Generation phase.
+            cumulative_logprobs = [
+                seq_data[seq_id].cumulative_logprob for seq_id in seq_ids
+            ]
+            cumulative_logprobs = torch.tensor(
+                cumulative_logprobs,
+                dtype=torch.float,
+                device=seq_group_logprobs.device)
+            seq_group_logprobs = (seq_group_logprobs +
+                                  cumulative_logprobs.unsqueeze(dim=1))
+            _, topk_ids = torch.topk(seq_group_logprobs.flatten(),
+                                     2 * beam_width)
+            topk_ids = topk_ids.tolist()
+            vocab_size = seq_group_logprobs.size(-1)
+            parent_ids = [i // vocab_size for i in topk_ids]
+            next_token_ids = [i % vocab_size for i in topk_ids]
+        results.append((next_token_ids, parent_ids))
+        sample_idx += num_parent_seqs
+    assert sample_idx == logprobs.size(0)
+    return results
 
 
 def _sample(
@@ -364,65 +450,80 @@ def _sample(
     logprobs: torch.Tensor,
     input_metadata: InputMetadata,
 ) -> SamplerOutput:
-    seq_outputs: SamplerOutput = []
-
-    # TODO(woosuk): Optimize.
-    idx = 0
+    categorized_seq_group_ids = {t: [] for t in SamplingType}
+    category_num_tokens = {t: 0 for t in SamplingType}
     for i, seq_group in enumerate(input_metadata.seq_groups):
-        seq_group_outputs: List[SequenceOutputs] = []
         seq_ids, sampling_params = seq_group
-        if i < input_metadata.num_prompts:
-            # Generate the next tokens for a prompt input.
-            assert len(seq_ids) == 1, "Prompt input should have only one seq."
-            parent_seq_id = seq_ids[0]
-            prob = probs[idx]
-            logprob = logprobs[idx]
-            idx += 1
-
-            # Sample the next tokens.
-            next_token_ids = _sample_from_prompt(prob, sampling_params)
-            # Get top-k log probabilities for the next tokens.
-            next_logprobs = _get_topk_logprobs(logprob,
-                                               sampling_params.logprobs)
-
-            # Build the output.
-            for next_token_id in next_token_ids:
-                output_logprobs = next_logprobs.copy()
-                output_logprobs[next_token_id] = logprob[next_token_id].item()
-                seq_group_outputs.append(
-                    SequenceOutputs(parent_seq_id, next_token_id,
-                                    output_logprobs))
+        sampling_type = sampling_params.sampling_type
+        categorized_seq_group_ids[sampling_type].append(i)
+        num_seqs = len(seq_ids)
+        category_num_tokens[sampling_type] += num_seqs
+
+    seq_outputs_dict: Dict[int, List[SequenceOutputs]] = {}
+    category_start_idx = 0
+    for sampling_type in SamplingType:
+        seq_group_ids = categorized_seq_group_ids[sampling_type]
+        seq_groups = [input_metadata.seq_groups[i] for i in seq_group_ids]
+        is_prompts = [i < input_metadata.num_prompts for i in seq_group_ids]
+        num_tokens = category_num_tokens[sampling_type]
+        if num_tokens == 0:
+            continue
+        category_logprobs = logprobs[category_start_idx:category_start_idx +
+                                     num_tokens]
+        category_probs = probs[category_start_idx:category_start_idx +
+                               num_tokens]
+        if sampling_type == SamplingType.GREEDY:
+            sample_results = _greedy_sample(seq_groups, category_logprobs)
+        elif sampling_type == SamplingType.RANDOM:
+            sample_results = _random_sample(seq_groups, is_prompts,
+                                            category_probs)
+        elif sampling_type == SamplingType.BEAM:
+            sample_results = _beam_search_sample(seq_groups, is_prompts,
+                                                 input_metadata.seq_data,
+                                                 category_logprobs)
         else:
-            # Generate the next tokens for generation tokens.
+            raise ValueError(f"Unsupported sampling type: {sampling_type}")
+
+        # Batched query for logprobs of selected token
+        batched_logprobs_query_seq_indices: List[int] = []
+        batched_logprobs_query_token_indices: List[int] = []
+        sample_idx = 0
+        for seq_group_id, seq_group, sample_result in zip(
+                seq_group_ids, seq_groups, sample_results):
+            seq_ids, sampling_params = seq_group
+            next_token_ids, parent_ids = sample_result
             num_parent_seqs = len(seq_ids)
-            prob = probs[idx:idx + num_parent_seqs]
-            logprob = logprobs[idx:idx + num_parent_seqs]
-            idx += num_parent_seqs
-
-            # Sample the next tokens.
-            seq_logprobs = [
-                input_metadata.seq_data[seq_id].cumulative_logprob
-                for seq_id in seq_ids
-            ]
-            parent_seq_ids, next_token_ids = _sample_from_generation_tokens(
-                seq_ids, prob, logprob, seq_logprobs, sampling_params)
-
-            # Get top-k log probabilities for the next tokens.
-            next_logprobs: Dict[int, Dict[int, float]] = {}
-            for j, seq_id in enumerate(seq_ids):
-                next_logprobs[seq_id] = _get_topk_logprobs(
-                    logprob[j], sampling_params.logprobs)
-
-            # Build the output.
-            for parent_seq_id, next_token_id in zip(parent_seq_ids,
-                                                    next_token_ids):
-                j = seq_ids.index(parent_seq_id)
-                output_logprobs = next_logprobs[parent_seq_id].copy()
-                output_logprobs[next_token_id] = logprob[j,
-                                                         next_token_id].item()
-                seq_group_outputs.append(
-                    SequenceOutputs(parent_seq_id, next_token_id,
-                                    output_logprobs))
-        seq_outputs.append(seq_group_outputs)
-
-    return seq_outputs
+            batched_logprobs_query_seq_indices.extend(
+                [sample_idx + parent_id for parent_id in parent_ids])
+            batched_logprobs_query_token_indices.extend(next_token_ids)
+            sample_idx += num_parent_seqs
+        assert sample_idx == num_tokens
+        batched_logprobs_query_result = category_logprobs[[
+            batched_logprobs_query_seq_indices,
+            batched_logprobs_query_token_indices
+        ]].tolist()
+
+        # Build the sequence outputs.
+        sample_idx = 0
+        result_idx = 0
+        for seq_group_id, seq_group, sample_result in zip(
+                seq_group_ids, seq_groups, sample_results):
+            seq_ids, sampling_params = seq_group
+            next_token_ids, parent_ids = sample_result
+            num_results = len(next_token_ids)
+            num_parent_seqs = len(seq_ids)
+            parent_logprobs = category_logprobs[sample_idx:sample_idx +
+                                                num_parent_seqs]
+            selected_token_logprobs = batched_logprobs_query_result[
+                result_idx:result_idx + num_results]
+            seq_output = _build_sequence_outputs(parent_ids, next_token_ids,
+                                                 selected_token_logprobs,
+                                                 seq_ids, parent_logprobs,
+                                                 sampling_params.logprobs)
+            seq_outputs_dict[seq_group_id] = seq_output
+            sample_idx += num_parent_seqs
+            result_idx += num_results
+        assert sample_idx == num_tokens
+        category_start_idx += num_tokens
+
+    return [seq_outputs_dict[i] for i in range(len(input_metadata.seq_groups))]

vllm/sampling_params.py

 """Sampling parameters for text generation."""
+from enum import IntEnum
+from functools import cached_property
 from typing import List, Optional, Union
 
 _SAMPLING_EPS = 1e-5
 
 
+class SamplingType(IntEnum):
+    GREEDY = 0
+    RANDOM = 1
+    BEAM = 2
+
+
 class SamplingParams:
     """Sampling parameters for text generation.
 
@@ -166,6 +174,14 @@ class SamplingParams:
         if self.top_k != -1:
             raise ValueError("top_k must be -1 when using greedy sampling.")
 
+    @cached_property
+    def sampling_type(self) -> SamplingType:
+        if self.use_beam_search:
+            return SamplingType.BEAM
+        if self.temperature < _SAMPLING_EPS:
+            return SamplingType.GREEDY
+        return SamplingType.RANDOM
+
     def __repr__(self) -> str:
         return (f"SamplingParams(n={self.n}, "
                 f"best_of={self.best_of}, "