[1/n] Triton sampling kernel (#3186)

Co-authored-by: Roger Wang <136131678+ywang96@users.noreply.github.com>

tests/kernels/test_rand.py

@@ -0,0 +1,51 @@
+import torch
+import pytest
+import random
+
+from vllm.model_executor.layers.ops.rand import seeded_uniform
+from vllm.model_executor.utils import set_random_seed
+
+
+@pytest.mark.parametrize("dtype",
+                         [torch.float32, torch.float16, torch.bfloat16])
+@pytest.mark.parametrize("use_3d", [True, False])
+def test_seeded_uniform(dtype: torch.dtype, use_3d: bool):
+    device = "cuda"
+    for seed in range(512):
+        set_random_seed(seed)
+        rows = random.randint(1, 512)
+        cols = random.randint(1, 64000)
+        if use_3d:
+            third_dim = random.randint(2, 10)
+            dims = [rows, third_dim, cols]
+        else:
+            dims = [rows, cols]
+        seeds = torch.randint(torch.iinfo(torch.long).min,
+                              torch.iinfo(torch.long).max, (rows, ),
+                              device=device)
+
+        # Test that the same seed produces the same output
+        out = seeded_uniform(*dims, seeds=seeds, dtype=dtype, device=device)
+        out2 = seeded_uniform(*dims, seeds=seeds, dtype=dtype, device=device)
+        torch.testing.assert_close(out, out2)
+        # del to save memory
+        del out2
+
+        out3 = seeded_uniform(*dims, seeds=seeds, dtype=dtype, device=device)
+        torch.testing.assert_close(out, out3)
+        # del to save memory
+        del out3
+
+        # Initialize out tensor with garbage to ensure that it is overwritten
+        out_with_tensor = seeded_uniform(
+            *dims,
+            out=torch.full(
+                (*dims, ),
+                -1,
+                dtype=dtype,
+                device=device,
+            ),
+            seeds=seeds,
+            dtype=dtype,
+        )
+        torch.testing.assert_close(out, out_with_tensor)

tests/kernels/test_sampler.py

@@ -0,0 +1,196 @@
+import gc
+
+import torch
+import pytest
+import triton
+import triton.language as tl
+
+from vllm.model_executor.layers.ops.sample import (
+    _uniform_to_exponential, sample, get_num_triton_sampler_splits,
+    MAX_TRITON_N_COLS)
+from vllm.model_executor.utils import set_random_seed
+from vllm.model_executor.sampling_metadata import SamplingTensors
+
+SINGLE_SPLIT_VOCAB_SIZE = 32000  # llama/mistral/mixtral vocab size
+MULTI_SPLIT_VOCAB_SIZE = MAX_TRITON_N_COLS + 100
+
+
+@pytest.fixture(autouse=True)
+def _cleanup():
+    yield
+    gc.collect()
+    torch.cuda.empty_cache()
+
+
+@triton.jit
+def _uniform_to_exponential_kernel(input, output, n: tl.constexpr):
+    idx = tl.arange(0, n)
+    x = tl.load(input + idx)
+    y = _uniform_to_exponential(x)
+    tl.store(output + idx, y)
+
+
+def test_uniform_to_exponential():
+    """Test that we can convert uniform to exponential without div by 0."""
+    input = torch.tensor([0.0, 1.0 - torch.finfo(torch.float32).eps],
+                         dtype=torch.float32,
+                         device="cuda")
+    output = torch.zeros(input.shape, dtype=torch.float32, device="cuda")
+    _uniform_to_exponential_kernel[(1, )](input, output, 2)
+    assert torch.all(torch.isfinite(output))
+    assert torch.all(output > 0)
+    assert torch.all(torch.isfinite(torch.full_like(output, 1.0) / output))
+
+
+@pytest.mark.parametrize("random_sampling", [True, False, "mixed"])
+@pytest.mark.parametrize("max_best_of", [1, 2, 3, 4, 5])
+@pytest.mark.parametrize("modify_greedy_probs", [True, False])
+@pytest.mark.parametrize("seed", [1337])
+@pytest.mark.parametrize("vocab_size",
+                         [SINGLE_SPLIT_VOCAB_SIZE, MULTI_SPLIT_VOCAB_SIZE])
+@pytest.mark.parametrize("save_logprobs", [True, False])
+def test_sample_decoding_only(random_sampling, max_best_of,
+                              modify_greedy_probs, seed, vocab_size,
+                              save_logprobs):
+    set_random_seed(seed)
+    bs = 8
+    probs = torch.zeros((bs, vocab_size), dtype=torch.float32, device="cuda")
+    for i in range(bs):
+        probs[i, i * (vocab_size // bs)] = 1.0
+    logprobs = torch.rand_like(probs)
+    sample_indices = torch.arange(bs, dtype=torch.long, device="cuda")
+    n_splits = get_num_triton_sampler_splits(probs.shape[1])
+    if random_sampling == "mixed":
+        random_sampling_mask = (torch.rand(
+            (1, bs), device="cuda") < 0.5).expand(n_splits, bs)
+    elif random_sampling:
+        random_sampling_mask = torch.ones((n_splits, bs),
+                                          dtype=torch.bool,
+                                          device="cuda")
+    else:
+        random_sampling_mask = torch.zeros((n_splits, bs),
+                                           dtype=torch.bool,
+                                           device="cuda")
+
+    seeds = torch.randint(1,
+                          torch.iinfo(torch.long).max, (n_splits, bs),
+                          device="cuda").mul_(random_sampling_mask)
+    sampled_tokens, sampled_logprobs, sampled_modified_probs = sample(
+        probs=probs,
+        logprobs=logprobs,
+        sample_indices=sample_indices,
+        seeds=seeds,
+        max_best_of=max_best_of,
+        modify_greedy_probs=modify_greedy_probs,
+        save_logprobs=save_logprobs,
+        _save_modified_probs=True)
+    assert sampled_tokens.shape == (bs, max_best_of)
+    for i in range(bs):
+        assert torch.all(sampled_tokens[i] == i * (vocab_size // bs))
+        request_uses_random_sampling = random_sampling_mask[0, i]
+        if modify_greedy_probs and not request_uses_random_sampling:
+            # If we are modifying greedy probs and the request is greedy,
+            # we want to make sure the probs tensor is modified in place
+            assert torch.allclose(
+                probs[i][sampled_tokens[i]],
+                torch.full_like(probs[i][sampled_tokens[i]], 1.0))
+            assert torch.sum(probs[i]) == 1.0
+            assert torch.allclose(
+                sampled_modified_probs[i][0],
+                torch.full_like(sampled_modified_probs[i][0], 1.0))
+        elif request_uses_random_sampling:
+            # If the request is random, we want to make sure
+            # sampled_modified_probs tensor has noise added
+            # (and thus is different from probs tensor)
+            assert not torch.allclose(sampled_modified_probs[i][0],
+                                      probs[i][sampled_tokens[i]])
+        elif not request_uses_random_sampling:
+            # If the request is greedy and we are not modifying greedy probs,
+            # we want to make sure sampled_modified_probs tensor is the same as
+            # the probs tensor.
+            assert torch.allclose(sampled_modified_probs[i][0],
+                                  probs[i][sampled_tokens[i]])
+
+    if save_logprobs:
+        assert sampled_logprobs.shape == (bs, max_best_of)
+        for i in range(bs):
+            for best_of in range(max_best_of):
+                assert torch.all(sampled_logprobs[i] == logprobs[i][
+                    sampled_tokens[i, best_of]])
+    else:
+        assert sampled_logprobs is None
+
+
+@pytest.mark.parametrize("random_sampling", [True, False, "mixed"])
+@pytest.mark.parametrize("max_best_of", [1, 2, 3, 4, 5])
+@pytest.mark.parametrize("modify_greedy_probs", [True, False])
+@pytest.mark.parametrize("seed", [1337])
+@pytest.mark.parametrize("vocab_size",
+                         [SINGLE_SPLIT_VOCAB_SIZE, MULTI_SPLIT_VOCAB_SIZE])
+def test_sample_prompt_logprobs(random_sampling, max_best_of,
+                                modify_greedy_probs, seed, vocab_size):
+    set_random_seed(seed)
+    prompt_sizes = [16, 32, 64, 128] * 2
+    samples = 8
+    bs = samples + sum(prompt_sizes)
+    probs = torch.zeros((bs, vocab_size), dtype=torch.float32, device="cuda")
+    for i in range(bs):
+        probs[i, i * (vocab_size // bs)] = 1.0
+    logprobs = torch.rand_like(probs)
+    sample_indices = torch.tensor(prompt_sizes,
+                                  dtype=torch.long,
+                                  device="cuda").cumsum_(0)
+    n_splits = get_num_triton_sampler_splits(probs.shape[1])
+    if random_sampling == "mixed":
+        random_sampling_mask = torch.rand(
+            (n_splits, samples), device="cuda") < 0.5
+    elif random_sampling:
+        random_sampling_mask = torch.ones((n_splits, samples),
+                                          dtype=torch.bool,
+                                          device="cuda")
+    else:
+        random_sampling_mask = torch.zeros((n_splits, samples),
+                                           dtype=torch.bool,
+                                           device="cuda")
+
+    seeds = torch.randint(1,
+                          torch.iinfo(torch.long).max, (n_splits, samples),
+                          device="cuda").mul_(random_sampling_mask)
+    sampled_tokens, sampled_logprobs, _ = sample(
+        probs=probs,
+        logprobs=logprobs,
+        sample_indices=sample_indices,
+        seeds=seeds,
+        max_best_of=max_best_of,
+        modify_greedy_probs=modify_greedy_probs,
+        save_logprobs=True)
+    assert sampled_tokens.shape == (samples, max_best_of)
+    assert sampled_logprobs.shape == (samples, max_best_of)
+    for i, t in enumerate(sample_indices):
+        assert torch.all(sampled_tokens[i] == t * (vocab_size // bs))
+        for best_of in range(max_best_of):
+            assert torch.all(sampled_logprobs[i] == logprobs[sample_indices[i]]
+                             [sampled_tokens[i, best_of]])
+
+
+@pytest.mark.parametrize("seed", list(range(16)))
+def test_get_sequence_seeds(seed):
+    """Ensure that we get a different child seed from base 
+    seed + extra entropy"""
+    starting_seed = seed
+    seq_seed = None
+    extra_entropy = 1
+    for i in range(512):
+        new_seq_seed = SamplingTensors._get_sequence_seeds(starting_seed,
+                                                           i,
+                                                           seeds_to_generate=1,
+                                                           is_greedy=False)[0]
+        new_seq_seed_extra_entropy = SamplingTensors._get_sequence_seeds(
+            starting_seed,
+            i,
+            extra_entropy,
+            seeds_to_generate=1,
+            is_greedy=False)[0]
+        assert new_seq_seed_extra_entropy != new_seq_seed
+        assert seq_seed != new_seq_seed
+        seq_seed = new_seq_seed

tests/samplers/test_sampler.py

@@ -302,11 +302,11 @@ def test_sampler_logits_processors(seed: int, device: str):
     batch_size = random.randint(1, 256)
     input_tensor, _, sampler, model_runner = _prepare_test(batch_size)
 
-    # This sample logits processor gives infinite score to the i-th token,
+    # This sample logits processor gives maximum score to the i-th token,
     # where i is the length of the input sequence.
     # We therefore expect the output token sequence to be [0, 1, 2, ...]
     def pick_ith(token_ids, logits):
-        logits[len(token_ids)] = float("inf")
+        logits[len(token_ids)] = torch.finfo(logits.dtype).max
         return logits
 
     seq_group_metadata_list = []
@@ -385,7 +385,7 @@ def test_sampler_top_k_top_p(seed: int, device: str):
 
     sample_probs = None
 
-    def mock_sample(probs, logprobs, sampling_metadata):
+    def mock_sample(probs, *args, **kwargs):
         nonlocal sample_probs
         sample_probs = probs
         return [[prob.topk(1, dim=-1).indices.tolist(), [0]] for prob in probs]

vllm/model_executor/layers/ops/rand.py

@@ -0,0 +1,157 @@
+import torch
+import triton
+import triton.language as tl
+
+from typing import Optional, Union
+
+
+def seeded_uniform(
+    *size,
+    seeds: torch.Tensor,
+    out: Optional[torch.Tensor] = None,
+    dtype: Optional[torch.dtype] = None,
+    device: Optional[Union[torch.device, str]] = None,
+    pin_memory: Optional[bool] = False,
+) -> torch.Tensor:
+    """Similar to torch.rand, but allows for seeds to be set per row.
+
+    seeds must be a 1d tensor. The output tensor may be 1d, 2d, or 3d.
+    If it is 3d, the additional seeds needed will be derived automatically
+    in a deterministic fashion:
+    [
+        row 0: [columns_with_seed_0], [columns_with_seed0^1], ...
+    ]
+    """
+    n_dims = len(size)
+
+    if n_dims > 3:
+        raise ValueError("seeded_uniform only supports up to 3D tensors")
+
+    if out is None:
+        out = torch.empty(*size,
+                          dtype=dtype,
+                          device=device,
+                          pin_memory=pin_memory)
+    elif out.shape != size:
+        raise ValueError("shape of out and size must be the same")
+
+    if n_dims == 3:
+        n_rows, n_3d, n_cols = out.shape
+        stride_row = out.stride(0)
+        stride_3d = out.stride(1)
+    elif n_dims == 2:
+        n_rows, n_cols = out.shape
+        n_3d = 1
+        stride_row = out.stride(0)
+        stride_3d = 1
+    else:
+        n_cols = out.shape[0]
+        n_rows = 1
+        n_3d = 1
+        stride_row = 1
+        stride_3d = 1
+
+    if seeds.ndim != 1:
+        raise ValueError("seeds must be a 1D tensor")
+
+    if seeds.numel() != n_rows:
+        raise ValueError(
+            "seeds must have the same number of elements as out has rows")
+
+    # The philox PRNG Triton uses generates 4 random numbers at once.
+    # Therefore, the most efficient use of it is to divide the
+    # block size by 4, and then save the generated random numbers to
+    # each of the 4 slices of the tensor.
+    full_block_size = triton.next_power_of_2(n_cols)
+    philox_block_size = max(full_block_size // 4, 1)
+    n_slices = full_block_size // philox_block_size
+    num_warps = 4
+    # Manual tuning. This seems to give best performance on A100 for
+    # simple kernels like this.
+    if philox_block_size >= 8192:
+        num_warps = 32
+    elif philox_block_size >= 4096:
+        num_warps = 16
+    elif philox_block_size >= 2048:
+        num_warps = 8
+
+    _seeded_uniform_triton[(n_rows, n_3d)](
+        out,
+        seeds,
+        stride_row,
+        stride_3d,
+        seeds.stride(0),
+        n_rows,
+        n_3d,
+        n_cols,
+        n_slices=n_slices,
+        num_warps=num_warps,
+        block_size=philox_block_size,
+    )
+    return out
+
+
+@triton.jit
+def _seeded_uniform_triton(
+    out_ptr: torch.Tensor,
+    seed_ptr: torch.Tensor,
+    out_row_stride: int,
+    out_3d_stride: int,
+    seed_row_stride: int,
+    n_rows: int,
+    n_3d: int,
+    n_cols: int,
+    n_slices: tl.constexpr,
+    block_size: tl.constexpr,
+):
+    """
+    Generate a random float32 number in [0, 1) for each element in the output
+    tensor. The random numbers in a row generated using the seed for that row.
+
+    Args:
+        out_ptr: The output tensor.
+        seed_ptr: The per-row seeds to use for random number generation.
+        out_row_stride: The stride between rows of the output tensor.
+        out_3d_stride: The stride between 3D slices of the output tensor.
+        seed_row_stride: The stride between rows of the seed tensor.
+        n_rows: The number of rows in the output tensor.
+        n_3d: The size of second dimension of the output tensor,
+            if output tensor is 3D.
+        n_cols: The number of columns in the output tensor.
+        n_slices: The number of philox outputs to use.
+    """
+    tl.static_assert(n_slices > 0 and n_slices <= 4, "0 < n_slices <= 4")
+
+    # Get the row index.
+    row_idx = tl.program_id(axis=0)
+    three_d_idx = tl.program_id(axis=1)
+
+    philox_offsets = tl.arange(0, block_size)
+    # Get the seed for the current element.
+    seed = tl.load(seed_ptr + row_idx * seed_row_stride)
+    if three_d_idx > 0:
+        seed ^= three_d_idx
+    # Generate random numbers in [0, 1).
+    out1, out2, out3, out4 = tl.rand4x(seed, philox_offsets)
+
+    output_row_start_ptr = (out_ptr + row_idx * out_row_stride +
+                            three_d_idx * out_3d_stride)
+    out1_offsets = philox_offsets
+    tl.store(output_row_start_ptr + out1_offsets,
+             out1,
+             mask=out1_offsets < n_cols)
+    if n_slices > 1:
+        out2_offsets = tl.arange(block_size, block_size * 2)
+        tl.store(output_row_start_ptr + out2_offsets,
+                 out2,
+                 mask=out2_offsets < n_cols)
+    if n_slices > 2:
+        out3_offsets = tl.arange(block_size * 2, block_size * 3)
+        tl.store(output_row_start_ptr + out3_offsets,
+                 out3,
+                 mask=out3_offsets < n_cols)
+    if n_slices > 3:
+        out4_offsets = tl.arange(block_size * 3, block_size * 4)
+        tl.store(output_row_start_ptr + out4_offsets,
+                 out4,
+                 mask=out4_offsets < n_cols)

vllm/model_executor/layers/ops/sample.py

@@ -0,0 +1,405 @@
+import math
+from typing import Tuple, Optional
+
+import torch
+import triton
+import triton.language as tl
+
+from vllm.model_executor.layers.ops.rand import seeded_uniform
+
+_EPS = 1e-6
+
+# This is a hardcoded limit in Triton (max block size).
+MAX_TRITON_N_COLS = 131072
+
+
+def get_num_triton_sampler_splits(n_cols: int) -> int:
+    """Get the number of splits to use for Triton sampling.
+
+    Triton has a limit on the number of columns it can handle, so we need to
+    split the tensor and call the kernel multiple times if it's too large.
+    """
+    return math.ceil(n_cols / MAX_TRITON_N_COLS)
+
+
+def _multi_split_sample(
+    probs: torch.Tensor,
+    seeds: torch.Tensor,
+    n_splits: int,
+    sampled_tokens_size: Tuple[int, int],
+    sampled_logprobs_size: Tuple[int, int],
+    sample_indices: torch.Tensor,
+    *,
+    logprobs: Optional[torch.Tensor] = None,
+    modify_greedy_probs: bool = False,
+    save_logprobs: bool = False,
+):
+    """Sample tokens where vocab size is split into multiple parts
+    (too large for Triton otherwise)."""
+    assert seeds.ndim == 2 and seeds.shape[0] == n_splits
+    split_probs = probs.tensor_split(n_splits, 1)
+    split_logprobs = logprobs.tensor_split(n_splits, 1)
+    sampled_tokens_tmp = [
+        torch.empty(sampled_tokens_size, dtype=torch.long, device=probs.device)
+        for _ in range(n_splits)
+    ]
+    sampled_logprobs_tmp = [
+        torch.empty(sampled_logprobs_size,
+                    dtype=probs.dtype,
+                    device=probs.device) for _ in range(n_splits)
+    ]
+    # We are purposefuly using sampled_tokens_size as we need to always
+    # save modified probs in this case.
+    sampled_modified_probs_tmp = [
+        torch.empty(sampled_tokens_size,
+                    dtype=probs.dtype,
+                    device=probs.device) for _ in range(n_splits)
+    ]
+    for i in range(n_splits):
+        n_samples = sample_indices.shape[0]
+        n_cols = split_probs[i].shape[1]
+        n_best = sampled_tokens_tmp[i].shape[1]
+        uniform_noise = seeded_uniform(n_samples,
+                                       n_best,
+                                       n_cols,
+                                       seeds=seeds[i].flatten(),
+                                       device=split_probs[i].device,
+                                       dtype=split_probs[i].dtype)
+        # TODO(yard1): See if we can remove the contiguous() calls.
+        # Will need kernel support.
+        _sample(
+            split_probs[i].contiguous(),
+            split_logprobs[i].contiguous(),
+            sample_indices,
+            sampled_tokens_tmp[i],
+            sampled_logprobs_tmp[i],
+            sampled_modified_probs_tmp[i],
+            seeds[i],
+            uniform_noise,
+            modify_greedy_probs=False,
+            save_logprobs=save_logprobs,
+            save_modified_probs=True,
+        )
+        if i > 0:
+            # Add offset to sampled tokens
+            sampled_tokens_tmp[i].add_(i * split_probs[i - 1].shape[1])
+    sampled_tokens = torch.stack(sampled_tokens_tmp)
+    sampled_modified_probs = torch.stack(sampled_modified_probs_tmp)
+    # Reduce the results from the splits.
+    sampled_modified_probs, indices = torch.max(sampled_modified_probs,
+                                                dim=0,
+                                                keepdim=True)
+    sampled_tokens = sampled_tokens.gather(0, indices).squeeze(0)
+    if save_logprobs:
+        sampled_logprobs = torch.stack(sampled_logprobs_tmp)
+        sampled_logprobs = sampled_logprobs.gather(0, indices).squeeze(0)
+    else:
+        sampled_logprobs = None
+    sampled_modified_probs = sampled_modified_probs.squeeze(0)
+
+    if modify_greedy_probs:
+        # We need to modify the greedy probs for the sampled tokens.
+        # We can't do this in the kernel as we need to know the
+        # sampled tokens.
+        probs.fill_(0.0)
+        probs.scatter_(1, sampled_tokens, 1.0)
+
+    return (sampled_tokens, sampled_logprobs, sampled_modified_probs)
+
+
+def sample(
+    probs: torch.Tensor,
+    seeds: torch.Tensor,
+    *,
+    max_best_of: int = 1,
+    sample_indices: Optional[torch.Tensor] = None,
+    logprobs: Optional[torch.Tensor] = None,
+    modify_greedy_probs: bool = False,
+    save_logprobs: bool = False,
+    _save_modified_probs: bool = False,  # pylint: disable=invalid-name
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]:
+    """Sample tokens from probs. with per-sequence seeds.
+
+    Can sample from a subset of sequences through sample_indices.
+
+    Args:
+        probs: Probabilities to sample from.
+            shape = [batch_size, vocab_size]
+        seeds: Per-sequence seed values.
+            shape = [n, math.ceil(vocab_size / MAX_TRITON_N_COLS)]
+        max_best_of: Number of samples to generate per sequence.
+            Sequence seed will be incremented by 1 each time.
+        sample_indices: Indices of sequences to sample from.
+            If not provided, will sample from all sequences.
+            shape = [n]
+        logprobs: Log-probabilities of the sampled tokens.
+            Only used for saving the logprobs if save_logprobs is True.
+            shape = [batch_size, vocab_size]
+        modify_greedy_probs: Whether to modify the greedy probabilities
+            for speculative sampling (sampled token = 1.0,
+            everything else = 0.0).
+        save_logprobs: Whether to save the log-probabilities of the
+            sampled tokens to a tensor.
+        _save_modified_probs: Whether to save the modified probabilities
+            (including gumbel noise) of the sampled tokens to a tensor.
+            DOES NOT include the modification done by modify_greedy_probs
+            (because we want to use the unmodified probs to pick the best
+            split in case of multi-split sampling).
+            This is exposed only for testing.
+
+    Returns:
+        sampled_tokens: shape = [n, max_best_of]
+        sampled_logprobs: shape = [n, max_best_of] if save_logprobs else None
+        sampled_modified_probs: shape = [n, max_best_of]
+            if save_modified_probs else None
+    """
+    if sample_indices is None:
+        sample_indices = torch.arange(0, probs.shape[0], device=probs.device)
+
+    sampled_tokens_size = (sample_indices.size(0), max_best_of)
+    if save_logprobs:
+        if logprobs is None:
+            raise ValueError(
+                "logprobs tensor must be provided if save_logprobs is True")
+        sampled_logprobs_size = sampled_tokens_size
+    else:
+        # Empty tensors to invoke the kernel
+        sampled_logprobs_size = (0, 0)
+        logprobs = probs
+
+    if _save_modified_probs:
+        sampled_modified_probs_size = sampled_tokens_size
+    else:
+        # Empty tensors to invoke the kernel
+        sampled_modified_probs_size = (0, 0)
+
+    # If the number of columns in probs is too large for Triton to handle,
+    # we split the tensor and sample from each split separately, and then
+    # do an argmax+gather to combine the results.
+    n_splits = get_num_triton_sampler_splits(probs.shape[1])
+    if n_splits > 1:
+        (sampled_tokens, sampled_logprobs,
+         sampled_modified_probs) = _multi_split_sample(
+             probs,
+             seeds,
+             n_splits,
+             sampled_tokens_size,
+             sampled_logprobs_size,
+             sample_indices,
+             logprobs=logprobs,
+             modify_greedy_probs=modify_greedy_probs,
+             save_logprobs=save_logprobs)
+    else:
+        sampled_tokens = torch.empty(sampled_tokens_size,
+                                     dtype=torch.long,
+                                     device=probs.device)
+        sampled_logprobs = torch.empty(sampled_logprobs_size,
+                                       dtype=probs.dtype,
+                                       device=probs.device)
+        sampled_modified_probs = torch.empty(sampled_modified_probs_size,
+                                             dtype=probs.dtype,
+                                             device=probs.device)
+        n_samples = sample_indices.shape[0]
+        n_cols = probs.shape[1]
+        uniform_noise = seeded_uniform(n_samples,
+                                       max_best_of,
+                                       n_cols,
+                                       seeds=seeds.flatten(),
+                                       device=probs.device,
+                                       dtype=probs.dtype)
+
+        _sample(
+            probs,
+            logprobs,
+            sample_indices,
+            sampled_tokens,
+            sampled_logprobs,
+            sampled_modified_probs,
+            seeds,
+            uniform_noise,
+            modify_greedy_probs=modify_greedy_probs,
+            save_logprobs=save_logprobs,
+            save_modified_probs=_save_modified_probs,
+        )
+    return (sampled_tokens, sampled_logprobs if save_logprobs else None,
+            sampled_modified_probs if _save_modified_probs else None)
+
+
+def _sample(probs: torch.Tensor,
+            logprobs: torch.Tensor,
+            sample_indices: torch.Tensor,
+            output_samples: torch.Tensor,
+            output_logprobs: torch.Tensor,
+            output_modified_probs: torch.Tensor,
+            seeds: torch.Tensor,
+            uniform_noise: torch.Tensor,
+            *,
+            modify_greedy_probs: bool = False,
+            save_logprobs: bool = True,
+            save_modified_probs: bool = False) -> torch.Tensor:
+    """Sample tokens from probs.
+
+    Args:
+        probs [batch_size, vocab_size]: probs to sample from.
+        logprobs [batch_size, vocab_size]: logprobs (used when
+            save_logprobsis True).
+        sample_indices [n]: Indices of the samples to use for each row of probs.
+        output_samples [n, n_best]: Output tensor to store samples in.
+        output_logprobs [n, n_best]: Output tensor to store logprobs in.
+        output_modified_probs [n, n_best]: Output tensor to store
+            probs of chosen tokens in (modified with noise).
+        seeds [n]: Seeds to use for sampling. If the seed is 0, we use
+            greedy sampling. Note this is ONLY used for determining
+            whether to use random sampling or not. The actual random
+            noise should be passed as uniform_noise.
+        uniform_noise [batch_size, n_best, vocab_size]: Uniform
+            noise to use for random sampling (will be converted
+            to exponential gumbel noise by the kernel).
+        modify_greedy_probs: If True, we modify the probs tensor in-place
+            to encode the sampling method used for each row. This is used
+            in speculative decoding. Only applies in greedy decoding.
+        save_logprobs: If True, we save the logprobs of the sampled tokens
+            in the output_logprobs tensor.
+        save_modified_probs: If True, we save the modified probs (with noise)
+            of the sampled tokens in the output_modified_probs tensor.
+            DOES NOT include the modification done by modify_greedy_probs
+            (because we want to use the unmodified probs to pick the best
+            split in case of multi-split sampling).
+    """
+    n_samples = sample_indices.shape[0]
+    n_cols = probs.shape[1]
+    n_best = output_samples.shape[1] if len(output_samples.shape) > 1 else 1
+
+    # The block size is the smallest power of two greater than the number of
+    # columns in probs
+    block_size = triton.next_power_of_2(n_cols)
+    num_warps = 4
+    # Manual tuning. This seems to give best performance on A100 for
+    # simple kernels like this.
+    if block_size >= 8192:
+        num_warps = 32
+    elif block_size >= 4096:
+        num_warps = 16
+    elif block_size >= 2048:
+        num_warps = 8
+
+    # Enqueue kernel. The 1D launch grid is simple: we have one kernel
+    # instance per row of the probs matrix
+    _sample_triton[(n_samples, n_best)](
+        sample_indices,
+        output_samples,
+        output_logprobs,
+        output_modified_probs,
+        probs,
+        logprobs,
+        seeds,
+        uniform_noise,
+        output_samples.stride(0),
+        probs.stride(0),
+        uniform_noise.stride(0),
+        uniform_noise.stride(1) if n_best > 1 else 1,
+        n_samples,
+        n_cols,
+        n_best,
+        num_warps=num_warps,
+        block_size=block_size,
+        modify_greedy_probs=modify_greedy_probs,
+        save_logprobs=save_logprobs,
+        save_modified_probs=save_modified_probs,
+    )
+    return output_samples, output_logprobs, output_modified_probs
+
+
+@triton.jit
+def _uniform_to_exponential(uniform_noise):
+    """Convert uniform samples to exponential samples."""
+    # tl.rand returns values in [0, 1), so we clamp lower bound
+    # to _EPS to avoid log(0) and thus division by 0 later
+    lb = tl.full(uniform_noise.shape, _EPS, uniform_noise.dtype)
+    uniform_noise = tl.maximum(uniform_noise, lb)
+    # Use the inversion method to turn uniform samples
+    # into exponential samples
+    exponential_noise = -tl.log(uniform_noise)
+    return exponential_noise
+
+
+@triton.jit
+def _sample_triton(
+        sample_indices_ptr: torch.Tensor, output_ptr: torch.Tensor,
+        output_logprobs_ptr: torch.Tensor,
+        output_modified_probs_ptr: torch.Tensor, probs_ptr: torch.Tensor,
+        logprobs_ptr: torch.Tensor, seeds_ptr: torch.Tensor,
+        uniform_noise_ptr: torch.Tensor, output_row_stride: int,
+        probs_row_stride: int, uniform_noise_row_stride: int,
+        uniform_noise_best_stride: int, n_samples: int, n_cols: int,
+        n_best: int, block_size: tl.constexpr,
+        modify_greedy_probs: tl.constexpr, save_logprobs: tl.constexpr,
+        save_modified_probs: tl.constexpr):
+    # The rows are independent, so we parallelize across those
+    sample_idx = tl.program_id(0)
+    best_idx = tl.program_id(1)
+
+    # Load the row index from DRAM
+    row_idx = tl.load(sample_indices_ptr + sample_idx)
+    seed = tl.load(seeds_ptr + sample_idx)
+    uses_random_sampling = seed != 0
+
+    # The stride represents how much we need to increase the
+    # pointer to advance 1 row
+    row_start_ptr = probs_ptr + row_idx * probs_row_stride
+
+    # The block size is the next power of two greater than n_cols,
+    # so we can fit each row in a single block
+    col_offsets = tl.arange(0, block_size)
+
+    # Load the row into SRAM, using a mask since block_size may be > than n_cols
+    row = tl.load(row_start_ptr + col_offsets,
+                  mask=col_offsets < n_cols,
+                  other=float("-inf"))
+
+    if uses_random_sampling:
+        uniform_noise_start_ptr = (uniform_noise_ptr +
+                                   sample_idx * uniform_noise_row_stride +
+                                   best_idx * uniform_noise_best_stride)
+        uniform_noise = tl.load(uniform_noise_start_ptr + col_offsets,
+                                mask=col_offsets < n_cols,
+                                other=0.5)
+        exponential_noise = _uniform_to_exponential(uniform_noise)
+        row /= exponential_noise
+
+    sampled_value, sampled_token = tl.max(row, axis=0, return_indices=True)
+    # clamp sampled token to n_cols - 1
+    # this should not be necessary, but we do it
+    # just in case
+    if sampled_token >= n_cols:
+        sampled_token = n_cols - 1
+    # Write back output to DRAM
+    output_row_start_ptr = (output_ptr + sample_idx * output_row_stride +
+                            best_idx)
+    tl.store(output_row_start_ptr, sampled_token)
+
+    if modify_greedy_probs:  # noqa
+        if not uses_random_sampling:
+            # Set the probability of the sampled token to 1, all other
+            # tokens to zero. This is used in speculative decoding where
+            # the sampling method must be encoded within the sampled
+            # probability distributions.
+            row = tl.where(col_offsets == sampled_token, 1.0, 0.0)
+            tl.store(row_start_ptr + col_offsets,
+                     row,
+                     mask=col_offsets < n_cols)
+
+    if save_modified_probs:
+        output_row_start_ptr = (output_modified_probs_ptr +
+                                sample_idx * output_row_stride + best_idx)
+        tl.store(output_row_start_ptr, sampled_value)
+
+    if save_logprobs:
+        # Load the row into SRAM, using a mask since block_size
+        # may be > than n_cols
+        sampled_logprob = tl.load(logprobs_ptr + row_idx * probs_row_stride +
+                                  sampled_token)
+        # Write back output to DRAM
+        output_row_start_ptr = (output_logprobs_ptr +
+                                sample_idx * output_row_stride + best_idx)
+        tl.store(output_row_start_ptr, sampled_logprob)

vllm/model_executor/layers/sampler.py

@@ -12,6 +12,7 @@ from vllm.sampling_params import SamplingParams, SamplingType
 from vllm.sequence import (Logprob, PromptLogprobs, SampleLogprobs,
                            SamplerOutput, SequenceData, SequenceGroupOutput,
                            SequenceOutput)
+from vllm.model_executor.layers.ops.sample import (sample as sample_triton)
 from vllm.utils import is_neuron
 
 
@@ -114,7 +115,8 @@ class Sampler(nn.Module):
         logprobs = torch.log_softmax(logits, dim=-1, dtype=torch.float)
 
         # Sample the next tokens.
-        sample_results = _sample(probs, logprobs, sampling_metadata)
+        sample_results = _sample(probs, logprobs, sampling_metadata,
+                                 sampling_tensors)
         # Get the logprobs query results.
         prompt_logprobs, sample_logprobs = _get_logprobs(
             logprobs, sampling_metadata, sample_results)
@@ -375,7 +377,7 @@ def _multinomial(
     return probs.div_(q).argmax(dim=1).view(-1, num_samples)
 
 
-def _sample(
+def _sample_with_torch(
     probs: torch.Tensor,
     logprobs: torch.Tensor,
     sampling_metadata: SamplingMetadata,
@@ -394,7 +396,7 @@ def _sample(
     # Counterintiutively, having two loops here is actually faster.
     # The first loop can run without waiting on GPU<->CPU sync.
     for sampling_type in SamplingType:
-        sample_indices = categorized_sample_indices[sampling_type]
+        sample_indices = categorized_sample_indices[sampling_type][:, 0]
         num_tokens = len(sample_indices)
         if num_tokens == 0:
             continue
@@ -407,17 +409,19 @@ def _sample(
             greedy_samples = torch.argmax(logprobs[sample_indices.long()],
                                           dim=-1)
         elif sampling_type in (SamplingType.RANDOM, SamplingType.RANDOM_SEED):
-            max_best_of = 1
+            max_best_of_in_batch = 1
             for seq_group, is_prompt in zip(seq_groups, is_prompts):
                 if is_prompt:
                     _, sampling_params = seq_group
-                    max_best_of = max(max_best_of, sampling_params.best_of)
+                    max_best_of_in_batch = max(max_best_of_in_batch,
+                                               sampling_params.best_of)
             seeded_args = {} if sampling_type == SamplingType.RANDOM else {
                 "seq_groups": seq_groups,
                 "generators": sampling_metadata.generators,
             }
             multinomial_samples[sampling_type] = _multinomial(
-                probs[sample_indices.long()], max_best_of, **seeded_args)
+                probs[sample_indices.long()], max_best_of_in_batch,
+                **seeded_args)
         elif sampling_type == SamplingType.BEAM:
             beam_search_logprobs = logprobs[sample_indices]
         else:
@@ -448,6 +452,99 @@ def _sample(
     return sample_results
 
 
+def _sample_with_triton_kernel(
+    probs: torch.Tensor,
+    logprobs: torch.Tensor,
+    sampling_metadata: SamplingMetadata,
+    sampling_tensors: SamplingTensors,
+) -> List[Tuple[List[int], List[int]]]:
+    categorized_seq_group_ids = {t: [] for t in SamplingType}
+    categorized_sample_indices = sampling_metadata.categorized_sample_indices
+    for i, seq_group in enumerate(sampling_metadata.seq_groups):
+        _, sampling_params = seq_group
+        sampling_type = sampling_params.sampling_type
+        categorized_seq_group_ids[sampling_type].append(i)
+
+    sample_results_dict: Dict[int, Tuple[List[int], List[int]]] = {}
+    sample_metadata = {}
+    max_best_of_in_batch = 1
+
+    # Counterintiutively, having two loops here is actually faster.
+    # The first loop can run without waiting on GPU<->CPU sync.
+    for sampling_type in SamplingType:
+        sample_indices = categorized_sample_indices[sampling_type][:, 0]
+        sampled_token_indices = categorized_sample_indices[sampling_type][:, 1]
+        num_tokens = len(sample_indices)
+        if num_tokens == 0:
+            continue
+        seq_group_ids = categorized_seq_group_ids[sampling_type]
+        seq_groups = [sampling_metadata.seq_groups[i] for i in seq_group_ids]
+        is_prompts = [i < sampling_metadata.num_prompts for i in seq_group_ids]
+        sample_metadata[sampling_type] = (seq_group_ids, seq_groups,
+                                          is_prompts, sample_indices,
+                                          sampled_token_indices)
+        if sampling_type in (SamplingType.GREEDY, SamplingType.RANDOM,
+                             SamplingType.RANDOM_SEED):
+            for seq_group, is_prompt in zip(seq_groups, is_prompts):
+                if is_prompt:
+                    _, sampling_params = seq_group
+                    max_best_of_in_batch = max(max_best_of_in_batch,
+                                               sampling_params.best_of)
+        elif sampling_type == SamplingType.BEAM:
+            beam_search_logprobs = logprobs[sample_indices]
+        else:
+            raise ValueError(f"Unsupported sampling type: {sampling_type}")
+
+    sampled_tokens, _, _ = sample_triton(
+        probs=probs,
+        seeds=sampling_tensors.sampling_seeds,
+        max_best_of=max_best_of_in_batch,
+        sample_indices=sampling_tensors.sample_indices,
+        logprobs=logprobs,
+        # don't save logprobs because we have logic for that below
+        # TODO: use this instead of the CPU-based logic below
+        save_logprobs=False,
+    )
+
+    # GPU<->CPU sync happens in the loop below.
+
+    for sampling_type in SamplingType:
+        if sampling_type not in sample_metadata:
+            continue
+        (seq_group_ids, seq_groups, is_prompts, sample_indices,
+         sampled_token_indices) = sample_metadata[sampling_type]
+        if sampling_type == SamplingType.GREEDY:
+            sample_results = _greedy_sample(
+                seq_groups, sampled_tokens[sampled_token_indices][:, 0])
+        elif sampling_type in (SamplingType.RANDOM, SamplingType.RANDOM_SEED):
+            sample_results = _random_sample(
+                seq_groups, is_prompts, sampled_tokens[sampled_token_indices])
+        elif sampling_type == SamplingType.BEAM:
+            sample_results = _beam_search_sample(seq_groups, is_prompts,
+                                                 sampling_metadata.seq_data,
+                                                 beam_search_logprobs)
+        sample_results_dict.update(zip(seq_group_ids, sample_results))
+
+    sample_results = [
+        sample_results_dict[i]
+        for i in range(len(sampling_metadata.seq_groups))
+    ]
+    return sample_results
+
+
+def _sample(
+    probs: torch.Tensor,
+    logprobs: torch.Tensor,
+    sampling_metadata: SamplingMetadata,
+    sampling_tensors: SamplingTensors,
+) -> List[Tuple[List[int], List[int]]]:
+    return _sample_with_torch(probs, logprobs, sampling_metadata)
+
+    # TODO: Enable once Triton kernel & associated code is faster.
+    # return _sample_with_triton_kernel(probs, logprobs, sampling_metadata,
+    #                                   sampling_tensors)
+
+
 def _get_logprobs(
     logprobs: torch.Tensor,
     sampling_metadata: SamplingMetadata,

vllm/model_executor/sampling_metadata.py

@@ -2,12 +2,16 @@ from dataclasses import dataclass
 from typing import Dict, List, Optional, Tuple
 
 import torch
+import random
 
 from vllm.sampling_params import SamplingParams, SamplingType
 from vllm.sequence import SequenceData
 from vllm.utils import in_wsl, is_neuron
+from vllm.model_executor.layers.ops.sample import (
+    get_num_triton_sampler_splits)
 
 _SAMPLING_EPS = 1e-5
+_SEED_0_REPLACEMENT = 3403598558
 
 
 class SamplingMetadata:
@@ -67,14 +71,28 @@ class SamplingTensors:
     presence_penalties: torch.Tensor
     frequency_penalties: torch.Tensor
     repetition_penalties: torch.Tensor
+    sampling_seeds: torch.Tensor
+    sample_indices: torch.Tensor
+    extra_seeds: Optional[torch.Tensor]
     prompt_tokens: torch.Tensor
     output_tokens: torch.Tensor
 
     @classmethod
     def from_sampling_metadata(
-            cls, sampling_metadata: "SamplingMetadata", vocab_size: int,
-            device: torch.device,
-            dtype: torch.dtype) -> Tuple["SamplingTensors", bool, bool, bool]:
+        cls,
+        sampling_metadata: "SamplingMetadata",
+        vocab_size: int,
+        device: torch.device,
+        dtype: torch.dtype,
+        *,
+        extra_seeds_to_generate: int = 0,
+        extra_entropy: Optional[Tuple[int, ...]] = None
+    ) -> Tuple["SamplingTensors", bool, bool, bool]:
+        """
+        extra_seeds_to_generate: extra seeds to generate using the
+            user-defined seed for each sequence.
+        extra_entropy: extra entropy to use when generating seeds.
+        """
         prompt_tokens: List[List[int]] = []
         output_tokens: List[List[int]] = []
         top_ks: List[int] = []
@@ -84,9 +102,18 @@ class SamplingTensors:
         presence_penalties: List[float] = []
         frequency_penalties: List[float] = []
         repetition_penalties: List[float] = []
+        sampling_seeds: List[int] = []
+        sample_indices: List[int] = []
+        prompt_best_of: List[int] = []
         do_penalties = False
         do_top_p_top_k = False
         do_min_p = False
+
+        # We need one base seed per Triton slice.
+        seeds_to_generate = (extra_seeds_to_generate +
+                             get_num_triton_sampler_splits(vocab_size))
+
+        sample_indices_start_idx = 0
         for i, seq_group in enumerate(sampling_metadata.seq_groups):
             seq_ids, sampling_params = seq_group
             temperature = sampling_params.temperature
@@ -95,6 +122,10 @@ class SamplingTensors:
             r = sampling_params.repetition_penalty
             top_p = sampling_params.top_p
             min_p = sampling_params.min_p
+            seed = sampling_params.seed
+
+            is_greedy = sampling_params.sampling_type == SamplingType.GREEDY
+
             # k should not be greater than the vocab size.
             top_k = min(sampling_params.top_k, vocab_size)
             top_k = vocab_size if top_k == -1 else top_k
@@ -112,6 +143,7 @@ class SamplingTensors:
                                      or abs(f) >= _SAMPLING_EPS
                                      or abs(r - 1.0) >= _SAMPLING_EPS):
                 do_penalties = True
+
             if (i < sampling_metadata.num_prompts
                     and sampling_params.prompt_logprobs is not None):
                 # For tokens in the prompt that we only need to get
@@ -138,10 +170,34 @@ class SamplingTensors:
             frequency_penalties += [f] * len(seq_ids)
             repetition_penalties += [r] * len(seq_ids)
 
+            is_prompt = i < sampling_metadata.num_prompts
+            if is_prompt:
+                prompt_best_of.append(sampling_params.best_of)
+                prompt_len = sampling_metadata.prompt_lens[i]
+
+                if sampling_params.prompt_logprobs is not None:
+                    # NOTE: the sampling position is the last token
+                    # in the prompt
+                    sample_indices_start_idx += prompt_len - 1
+            for seq_id in seq_ids:
+                seq_data = sampling_metadata.seq_data[seq_id]
+                extra_entropy = extra_entropy or ()
+                seq_seeds = cls._get_sequence_seeds(
+                    seed,
+                    seq_data.get_len(),
+                    *extra_entropy,
+                    seq_id,
+                    seeds_to_generate=seeds_to_generate,
+                    is_greedy=is_greedy)
+                sampling_seeds.append(seq_seeds)
+                sample_indices.append(sample_indices_start_idx)
+                sample_indices_start_idx += 1
+
         sampling_tensors = SamplingTensors.from_lists(
             temperatures, top_ps, top_ks, min_ps, presence_penalties,
-            frequency_penalties, repetition_penalties, prompt_tokens,
-            output_tokens, vocab_size, device, dtype)
+            frequency_penalties, repetition_penalties, sampling_seeds,
+            sample_indices, prompt_tokens, output_tokens, vocab_size,
+            extra_seeds_to_generate, device, dtype)
         return (sampling_tensors, do_penalties, do_top_p_top_k, do_min_p)
 
     @classmethod
@@ -150,9 +206,10 @@ class SamplingTensors:
                    presence_penalties: List[float],
                    frequency_penalties: List[float],
                    repetition_penalties: List[float],
+                   sampling_seeds: List[int], sample_indices: List[int],
                    prompt_tokens: List[List[int]],
                    output_tokens: List[List[int]], vocab_size: int,
-                   device: torch.device,
+                   extra_seeds_to_generate: int, device: torch.device,
                    dtype: torch.dtype) -> "SamplingTensors":
         # Note that the performance will be very bad without
         # pinned memory.
@@ -210,6 +267,12 @@ class SamplingTensors:
             dtype=torch.int,
             pin_memory=pin_memory,
         )
+        sample_indices_t = torch.tensor(
+            sample_indices,
+            device="cpu",
+            dtype=torch.long,
+            pin_memory=pin_memory,
+        )
         prompt_tensor = torch.tensor(
             prompt_padded_tokens,
             device="cpu",
@@ -222,8 +285,28 @@ class SamplingTensors:
             dtype=torch.long,
             pin_memory=pin_memory,
         )
+        # need to transpose and make contiguous to
+        # copy the tensor correctly.
+        # [batch_size, n_seeds] -> [n_seeds, batch_size]
+        sampling_seeds_t = torch.tensor(
+            sampling_seeds,
+            device="cpu",
+            dtype=torch.long,
+            pin_memory=pin_memory,
+        ).T.contiguous()
+
         # Because the memory is pinned, we can do non-blocking
         # transfer to device.
+
+        # How many seeds the sample operation itself will need.
+        num_base_seeds = sampling_seeds_t.shape[0] - extra_seeds_to_generate
+        sampling_seeds_gpu = sampling_seeds_t.to(device=device,
+                                                 non_blocking=True)
+        extra_seeds_gpu = sampling_seeds_gpu[num_base_seeds:]
+        if not extra_seeds_gpu.numel():
+            extra_seeds_gpu = None
+        sampling_seeds_gpu = sampling_seeds_gpu[:num_base_seeds]
+
         return cls(
             temperatures=temperatures_t.to(device=device, non_blocking=True),
             top_ps=top_ps_t.to(device=device, non_blocking=True),
@@ -237,4 +320,38 @@ class SamplingTensors:
                                                            non_blocking=True),
             prompt_tokens=prompt_tensor.to(device=device, non_blocking=True),
             output_tokens=output_tensor.to(device=device, non_blocking=True),
+            sampling_seeds=sampling_seeds_gpu,
+            sample_indices=sample_indices_t.to(device=device,
+                                               non_blocking=True),
+            extra_seeds=extra_seeds_gpu,
         )
+
+    @staticmethod
+    def _get_sequence_seeds(
+        seed: int,
+        *extra_entropy: int,
+        seeds_to_generate: int,
+        is_greedy: bool,
+    ):
+        """Get `seeds_to_generate` child seeds from `seed` and extra entropy."""
+        if not is_greedy:
+            if seed is None:
+                randint_fn = random.randint
+            else:
+                generator = random.Random(str((seed, ) + extra_entropy))
+                randint_fn = generator.randint
+            lo, hi = torch.iinfo(torch.long).min, torch.iinfo(torch.long).max
+            # If the user/random sets seed = 0 but request should
+            # have sampling, we need to change it to something
+            # else. We use a constant in that case.
+            # This way we don't need to create and load a bool
+            # matrix in the sampling kernel, which reduces CPU
+            # overhead and latency.
+            seq_seeds = [
+                randint_fn(lo, hi) or _SEED_0_REPLACEMENT
+                for _ in range(seeds_to_generate)
+            ]
+        else:
+            # For the kernel, seed == 0 means greedy decoding.
+            seq_seeds = [0] * seeds_to_generate
+        return seq_seeds

vllm/sequence.py

@@ -242,6 +242,9 @@ class Sequence:
     def get_token_ids(self) -> List[int]:
         return self.data.get_token_ids()
 
+    def get_prompt_token_ids(self) -> List[int]:
+        return self.data.get_prompt_token_ids()
+
     def get_last_token_id(self) -> int:
         return self.data.get_last_token_id()
 

vllm/worker/model_runner.py

@@ -408,6 +408,7 @@ class ModelRunner:
         selected_token_start_idx = 0
         categorized_sample_indices = {t: [] for t in SamplingType}
         categorized_sample_indices_start_idx = 0
+        categorized_sampled_token_indices_start_idx = 0
         pin_memory = not self.in_wsl and not self.device_config.is_neuron
 
         max_subquery_len = max(subquery_lens) if subquery_lens else 1
@@ -425,9 +426,12 @@ class ModelRunner:
                     categorized_sample_indices_start_idx += subquery_len - 1
 
                 categorized_sample_indices[
-                    sampling_params.sampling_type].append(
-                        categorized_sample_indices_start_idx)
+                    sampling_params.sampling_type].append([
+                        categorized_sample_indices_start_idx,
+                        categorized_sampled_token_indices_start_idx
+                    ])
                 categorized_sample_indices_start_idx += 1
+                categorized_sampled_token_indices_start_idx += 1
 
                 if sampling_params.prompt_logprobs is not None:
                     selected_token_indices.extend(
@@ -449,9 +453,17 @@ class ModelRunner:
 
                 categorized_sample_indices[
                     sampling_params.sampling_type].extend(
-                        range(categorized_sample_indices_start_idx,
-                              categorized_sample_indices_start_idx + num_seqs))
+                        zip(
+                            range(
+                                categorized_sample_indices_start_idx,
+                                categorized_sample_indices_start_idx +
+                                num_seqs),
+                            range(
+                                categorized_sampled_token_indices_start_idx,
+                                categorized_sampled_token_indices_start_idx +
+                                num_seqs)))
                 categorized_sample_indices_start_idx += num_seqs
+                categorized_sampled_token_indices_start_idx += num_seqs
 
             if sampling_params.seed is not None:
                 generators.append(seq_group_metadata.state.generator)
@@ -459,12 +471,14 @@ class ModelRunner:
         selected_token_indices = _async_h2d(selected_token_indices,
                                             dtype=torch.long,
                                             target_device=self.device,
-                                            pin_memory=pin_memory)
+                                            pin_memory=not self.in_wsl)
+
         categorized_sample_indices = {
-            t: _async_h2d(seq_ids,
-                          dtype=torch.int,
-                          target_device=self.device,
-                          pin_memory=pin_memory)
+            t: _maybe_expand_dim(
+                _async_h2d(seq_ids,
+                           dtype=torch.int,
+                           target_device=self.device,
+                           pin_memory=pin_memory), 2, 2)
             for t, seq_ids in categorized_sample_indices.items()
         }
 
@@ -884,3 +898,11 @@ def _async_h2d(
 ) -> torch.Tensor:
     t = torch.tensor(data, dtype=dtype, pin_memory=pin_memory, device="cpu")
     return t.to(device=target_device, non_blocking=True)
+
+
+def _maybe_expand_dim(tensor: torch.Tensor,
+                      target_dims: int,
+                      size: int = 1) -> torch.Tensor:
+    if tensor.ndim < target_dims:
+        tensor = tensor.view(-1, *([size] * (target_dims - tensor.ndim)))
+    return tensor