Merge remote-tracking branch 'mirror/main'

tests/kernels/test_cache.py

@@ -6,12 +6,12 @@ import torch
 from vllm._C import cache_ops
 
 DTYPES = [torch.half, torch.bfloat16, torch.float]
-NUM_TOKENS = [83]  # Arbitrary values for testing
+NUM_TOKENS = [42]  # Arbitrary values for testing
 NUM_LAYERS = [1]  # Arbitrary values for testing
 NUM_HEADS = [8]  # Arbitrary values for testing
 HEAD_SIZES = [64, 80, 96, 112, 128, 256]
 BLOCK_SIZES = [8, 16, 32]
-NUM_BLOCKS = [1024, 36000]  # Arbitrary values for testing
+NUM_BLOCKS = [1024, 3600]  # Arbitrary values for testing
 NUM_MAPPINGS = [256]  # Arbitrary values for testing
 SEEDS = [0]
 DEVICES = [i for i in range(1 if torch.cuda.device_count() == 1 else 2)]

tests/samplers/test_logprobs.py

@@ -30,6 +30,7 @@ def test_get_prompt_logprobs(
                                           temperature=0.0)
     vllm_results = vllm_model.model.generate(
         example_prompts, sampling_params=vllm_sampling_params)
+    del vllm_model
 
     # Test whether logprobs are included in the results.
     for result in vllm_results:

tests/samplers/test_rejection_sampler.py

+"""Tests for rejection sampling."""
+import pytest
+from typing import List, Tuple
+
+import torch
+import torch.nn.functional as F
+
+from vllm.model_executor.utils import set_random_seed
+
+from vllm.model_executor.layers.rejection_sampler import RejectionSampler
+
+
+def mock_causal_accepted_tensor(
+        k: int, last_accepted_indices: torch.Tensor) -> torch.Tensor:
+    """Generate an "accepted" tensor which should yield causally-accepted tokens
+    up to last accepted indices.
+
+    Tokens after last_accepted_indices+1 may also be accepted, although they
+    will not be causally accepted.
+    """
+    batch_size = last_accepted_indices.shape[0]
+
+    accepted = (torch.arange(k).expand(batch_size, k) <=
+                last_accepted_indices.unsqueeze(-1).broadcast_to(
+                    batch_size, k)).to(device="cuda")
+
+    # Sprinkle accepted values after the contiguous initial accepted values.
+    # This replicates the behavior of rejection sampling, which may "accept"
+    # a token that cannot be accepted because of causality.
+    sprinkle_candidates = (
+        torch.arange(k).expand(batch_size, k) >
+        last_accepted_indices.unsqueeze(-1).broadcast_to(batch_size, k) + 1)
+    sprinkle = torch.rand(batch_size, k, device="cuda") > 0.5
+    accepted[sprinkle_candidates] = sprinkle[sprinkle_candidates]
+    return accepted
+
+
+@pytest.mark.parametrize("seed", list(range(10)))
+@pytest.mark.parametrize(
+    "which_tokens_accepted",
+    ["all_tokens_accepted", "no_tokens_accepted", "some_tokens_accepted"])
+@torch.inference_mode()
+def test_correct_output_format(which_tokens_accepted: str, seed: int):
+    """Verify the output has correct format given predetermined accepted matrix.
+    """
+    set_random_seed(seed)
+
+    batch_size = 10
+    k = 5
+    vocab_size = 3000
+
+    if which_tokens_accepted == "all_tokens_accepted":
+        accepted = mock_causal_accepted_tensor(
+            k, -1 + k * torch.ones((batch_size, ), dtype=torch.long))
+    elif which_tokens_accepted == "no_tokens_accepted":
+        accepted = mock_causal_accepted_tensor(
+            k, -torch.ones((batch_size, ), dtype=torch.long))
+    elif which_tokens_accepted == "some_tokens_accepted":
+        last_accepted_indices = torch.randint(low=-1,
+                                              high=k,
+                                              size=(batch_size, ))
+        accepted = mock_causal_accepted_tensor(k, last_accepted_indices)
+    else:
+        raise AssertionError()
+
+    recovered_token_ids = torch.randint(low=0,
+                                        high=vocab_size,
+                                        size=(batch_size, k),
+                                        dtype=torch.int64,
+                                        device="cuda")
+    draft_token_ids = torch.randint(low=0,
+                                    high=vocab_size,
+                                    size=(batch_size, k),
+                                    dtype=torch.int64,
+                                    device="cuda")
+    bonus_token_ids = torch.randint(low=0,
+                                    high=vocab_size,
+                                    size=(batch_size, 1),
+                                    dtype=torch.int64,
+                                    device="cuda")
+
+    rejection_sampler = RejectionSampler()
+    rejection_sampler.init_gpu_tensors(rank=0)
+    output_token_ids = rejection_sampler._create_output(  # pylint: disable=protected-access
+        accepted,
+        recovered_token_ids,
+        draft_token_ids,
+        bonus_token_ids,
+    )
+
+    if which_tokens_accepted == "all_tokens_accepted":
+        # Expect all tokens to be equal to draft tokens.
+        assert torch.equal(output_token_ids[:, :-1], draft_token_ids)
+
+        # Expect all bonus tokens to be included.
+        assert torch.equal(output_token_ids[:, -1:], bonus_token_ids)
+    elif which_tokens_accepted == "no_tokens_accepted":
+        # Expect first token to be equal to recovered tokens.
+        assert torch.equal(output_token_ids[:, 0], recovered_token_ids[:, 0])
+
+        # Expect everything else to be -1.
+        assert torch.equal(output_token_ids[:, 1:],
+                           torch.ones_like(output_token_ids[:, 1:]) * -1)
+    elif which_tokens_accepted == "some_tokens_accepted":
+        recovered_plus_bonus = torch.cat(
+            (recovered_token_ids, bonus_token_ids), dim=-1)
+        # Assert first rejected token is a recovered token or bonus token.
+        assert torch.equal(
+            recovered_plus_bonus[torch.arange(0, batch_size),
+                                 last_accepted_indices + 1],
+            output_token_ids[torch.arange(0, batch_size),
+                             last_accepted_indices + 1])
+
+        # Assert every subsequent token is -1.
+        subsequent_mask = torch.arange(0, k + 1).expand(
+            batch_size, k + 1) >= (last_accepted_indices + 2).unsqueeze(-1)
+        assert torch.all(output_token_ids[subsequent_mask] == -1)
+
+
+@pytest.mark.parametrize("k", list(range(1, 6)))
+@pytest.mark.parametrize("vocab_size", [30_000, 50_000])
+@pytest.mark.parametrize("batch_size", list(range(1, 32)))
+@torch.inference_mode()
+def test_no_crash_with_varying_dims(k: int, vocab_size: int, batch_size: int):
+    rejection_sampler = RejectionSampler()
+    rejection_sampler.init_gpu_tensors(rank=0)
+
+    draft_probs = torch.rand(batch_size,
+                             k,
+                             vocab_size,
+                             dtype=torch.float32,
+                             device="cuda")
+    target_probs = torch.rand(batch_size,
+                              k,
+                              vocab_size,
+                              dtype=torch.float32,
+                              device="cuda")
+    bonus_token_ids = torch.randint(low=0,
+                                    high=vocab_size,
+                                    size=(batch_size, 1),
+                                    dtype=torch.int64,
+                                    device="cuda")
+    draft_token_ids = torch.randint(low=0,
+                                    high=vocab_size,
+                                    size=(batch_size, k),
+                                    dtype=torch.int64,
+                                    device="cuda")
+
+    rejection_sampler(target_probs, bonus_token_ids, draft_probs,
+                      draft_token_ids)
+
+
+@pytest.mark.parametrize("above_or_below_vocab_range", ["above", "below"])
+@pytest.mark.parametrize("which_token_ids",
+                         ["bonus_token_ids", "draft_token_ids"])
+@torch.inference_mode()
+def test_raises_when_vocab_oob(above_or_below_vocab_range: str,
+                               which_token_ids: str):
+    k = 3
+    batch_size = 5
+    vocab_size = 30_000
+
+    rejection_sampler = RejectionSampler(strict_mode=True)
+    rejection_sampler.init_gpu_tensors(rank=0)
+
+    draft_probs = torch.rand(batch_size,
+                             k,
+                             vocab_size,
+                             dtype=torch.float32,
+                             device="cuda")
+    target_probs = torch.rand(batch_size,
+                              k,
+                              vocab_size,
+                              dtype=torch.float32,
+                              device="cuda")
+    bonus_token_ids = torch.randint(low=0,
+                                    high=vocab_size,
+                                    size=(batch_size, 1),
+                                    dtype=torch.int64,
+                                    device="cuda")
+    draft_token_ids = torch.randint(low=0,
+                                    high=vocab_size,
+                                    size=(batch_size, k),
+                                    dtype=torch.int64,
+                                    device="cuda")
+
+    oob_token_ids = None
+    if which_token_ids == "bonus_token_ids":
+        oob_token_ids = bonus_token_ids
+    elif which_token_ids == "draft_token_ids":
+        oob_token_ids = draft_token_ids
+    else:
+        raise AssertionError()
+
+    if above_or_below_vocab_range == "above":
+        rogue_token_id = vocab_size + 1
+    elif above_or_below_vocab_range == "below":
+        rogue_token_id = -1
+    else:
+        raise AssertionError()
+
+    oob_token_ids[0][0] = rogue_token_id
+
+    with pytest.raises(AssertionError):
+        rejection_sampler(target_probs, bonus_token_ids, draft_probs,
+                          draft_token_ids)
+
+
+@pytest.mark.parametrize("draft_and_target_probs_equal", [True, False])
+@pytest.mark.parametrize("seed", list(range(5)))
+@torch.inference_mode()
+def test_rejection_sampling_approximates_target_distribution(
+        seed: int, draft_and_target_probs_equal: bool):
+    """Verify rejection sampling approximates target distribution,
+    despite sampling from a potentially distinct draft distribution.
+
+    This is done by first creating a random target probability
+    distribution and a random draft probability distribution. We then
+    sample token ids from the rejection sampler using these draft
+    and target distributions. The samples are used to estimate
+    the output probability distribution, which we expect to approximate
+    the target distribution.
+
+    A basic distance metric is used to determine similarity between
+    distributions.
+
+    We expect that as we increase the number of samples,
+    the distance between the observed distribution and the target
+    distribution decreases. To measure this, we compare the distance
+    of the observed distribution against both the target distribution
+    and a uniform random distribution. We expect the distance between
+    the observed distribution and the target distribution to improve
+    much more than the distance improvement between the observed
+    distribution and the random distribution.
+
+    When draft_and_target_probs_equal=True, the draft and target
+    probabilities are exactly equal. Rejection sampling should
+    still work without any NaNs or exceptions.
+    """
+    set_random_seed(seed)
+
+    helper = _CorrectnessTestHelper(
+        vocab_size=10,
+        rejection_sampler=RejectionSampler(),
+    )
+
+    draft_probs, target_probs, reference_probs = helper.generate_probs_for_test(
+        draft_and_target_probs_equal)
+
+    sample_sizes = [10, 100, 1_000, 10_000, 100_000]
+    distance_wrt_reference = []
+    distance_wrt_target = []
+
+    for num_samples in sample_sizes:
+        (reference_vs_rejsample_dist,
+         target_vs_rejsample_dist) = helper.run_and_compare_distributions(
+             draft_probs,
+             target_probs,
+             reference_probs,
+             num_samples,
+         )
+
+        distance_wrt_reference.append(reference_vs_rejsample_dist)
+        distance_wrt_target.append(target_vs_rejsample_dist)
+
+        relative_change_in_distance_wrt_target = get_ratio_first_to_last(
+            distance_wrt_target)
+        relative_change_in_distance_wrt_reference = get_ratio_first_to_last(
+            distance_wrt_reference)
+
+        print(f"{num_samples=} {target_vs_rejsample_dist=:.05f} "
+              f"{reference_vs_rejsample_dist=:.05f}")
+        print(f"{num_samples=} {relative_change_in_distance_wrt_target=:.02f} "
+              f"{relative_change_in_distance_wrt_reference=:.02f}")
+
+    relative_change_in_distance_wrt_target = get_ratio_first_to_last(
+        distance_wrt_target)
+    relative_change_in_distance_wrt_reference = get_ratio_first_to_last(
+        distance_wrt_reference)
+
+    expected_improvement_multiplier = 20
+    assert (relative_change_in_distance_wrt_target >
+            relative_change_in_distance_wrt_reference *
+            expected_improvement_multiplier)
+
+
+def get_ratio_first_to_last(elements: List[float]) -> float:
+    return elements[0] / elements[-1]
+
+
+class _CorrectnessTestHelper:
+    """Class that packages together logic required for the unit-level
+    rejection sampling correctness test.
+    """
+
+    def __init__(self, vocab_size: int, rejection_sampler: RejectionSampler):
+        self.rejection_sampler = rejection_sampler
+        self.vocab_size = vocab_size
+        self.vocab_range = (0, vocab_size)
+
+        self.rejection_sampler.init_gpu_tensors(rank=0)
+
+        # Keep test simple, use k=1
+        self.k = 1
+
+        # Bonus tokens not used, but rejection sampler requires
+        # correct shape.
+        self.num_bonus_tokens = 1
+
+    def generate_probs_for_test(
+        self, draft_and_target_probs_equal: bool
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        draft_probs, target_probs = [
+            F.softmax(
+                torch.rand(self.vocab_size, dtype=torch.float32),
+                dim=-1,
+            ) for _ in range(2)
+        ]
+
+        num_reference_probs = 100
+        reference_probs = F.softmax(
+            torch.rand(num_reference_probs,
+                       self.vocab_size,
+                       dtype=torch.float32),
+            dim=-1,
+        )
+
+        if draft_and_target_probs_equal:
+            target_probs = draft_probs.clone()
+
+        return draft_probs, target_probs, reference_probs
+
+    def run_and_compare_distributions(self, draft_probs: torch.Tensor,
+                                      target_probs: torch.Tensor,
+                                      reference_probs: torch.Tensor,
+                                      num_samples: int) -> Tuple[float, float]:
+        # Sample using rejection sampling.
+        rej_sample_probs = self._estimate_rejection_sampling_pdf(
+            draft_probs, target_probs, num_samples)
+
+        # Average distance from reference probs.
+        reference_vs_rejsample_dist = torch.dist(
+            reference_probs,
+            rej_sample_probs).item() / reference_probs.shape[0]
+        target_vs_rejsample_dist = torch.dist(target_probs,
+                                              rej_sample_probs).item()
+
+        return reference_vs_rejsample_dist, target_vs_rejsample_dist
+
+    def _estimate_rejection_sampling_pdf(
+        self,
+        draft_probs: torch.Tensor,
+        target_probs: torch.Tensor,
+        num_samples: int,
+    ) -> torch.Tensor:
+        # Repeat draft probs num_samples times.
+        draft_probs = draft_probs.reshape(1, self.k, self.vocab_size).repeat(
+            num_samples, 1, 1)
+
+        # Repeat target probs num_samples * k times.
+        # Rejection sampler requires bonus token probs, but they aren't used.
+        target_probs = target_probs.reshape(1, 1, self.vocab_size).repeat(
+            num_samples, self.k, 1)
+
+        # Randomly sample draft token ids from draft probs.
+        draft_token_ids = torch.multinomial(draft_probs[:, 0, :],
+                                            num_samples=1,
+                                            replacement=True).reshape(
+                                                num_samples, self.k)
+
+        # Bonus tokens not used but required.
+        bonus_token_ids = torch.zeros((1, self.num_bonus_tokens),
+                                      dtype=torch.int64,
+                                      device="cuda").repeat(num_samples, 1)
+
+        # Get output tokens via rejection sampling.
+        output_token_ids = self.rejection_sampler(target_probs.to("cuda"),
+                                                  bonus_token_ids.to("cuda"),
+                                                  draft_probs.to("cuda"),
+                                                  draft_token_ids.to("cuda"))
+
+        # Remove bonus tokens
+        output_token_ids = output_token_ids[:, :-1].flatten()
+
+        # Estimate probability density function
+        hist = torch.histogram(output_token_ids.to(dtype=torch.float,
+                                                   device="cpu"),
+                               bins=self.vocab_size,
+                               range=self.vocab_range,
+                               density=True)
+
+        return hist.hist

tests/samplers/test_sampler.py

@@ -4,6 +4,7 @@ from unittest.mock import patch
 
 import pytest
 import torch
+from transformers import GenerationConfig, GenerationMixin
 
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.utils import set_random_seed
@@ -74,6 +75,8 @@ def test_sampler_all_greedy(seed: int):
         for nth_output in sequence_output.samples:
             assert nth_output.output_token == expected[i].item()
 
+    del model_runner
+
 
 @pytest.mark.parametrize("seed", RANDOM_SEEDS)
 def test_sampler_all_random(seed: int):
@@ -110,6 +113,8 @@ def test_sampler_all_random(seed: int):
         for nth_output in sequence_output.samples:
             assert nth_output.output_token == i
 
+    del model_runner
+
 
 @pytest.mark.parametrize("seed", RANDOM_SEEDS)
 def test_sampler_all_beam(seed: int):
@@ -143,6 +148,7 @@ def test_sampler_all_beam(seed: int):
     # 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.
+    del model_runner
 
 
 @pytest.mark.parametrize("seed", RANDOM_SEEDS)
@@ -197,6 +203,8 @@ def test_sampler_mixed(seed: int):
         for nth_output in sequence_output.samples:
             assert nth_output.output_token in expected_tokens
 
+    del model_runner
+
 
 @pytest.mark.parametrize("seed", RANDOM_SEEDS)
 def test_sampler_logits_processors(seed: int):
@@ -233,3 +241,69 @@ def test_sampler_logits_processors(seed: int):
     for _, sequence_output in enumerate(sampler_output):
         for idx, nth_output in enumerate(sequence_output.samples):
             assert nth_output.output_token == idx
+
+    del model_runner
+
+
+@pytest.mark.parametrize("seed", RANDOM_SEEDS)
+def test_sampler_top_k_top_p(seed: int):
+    set_random_seed(seed)
+    batch_size = random.randint(1, 256)
+    top_k = random.randint(100, 500)
+    top_p = random.random() * 0.1
+    vocab_size = 32000
+    input_tensor = torch.rand((batch_size, 1024),
+                              device="cuda",
+                              dtype=torch.float16)
+    fake_logits = torch.normal(0,
+                               5,
+                               size=(batch_size, vocab_size),
+                               device=input_tensor.device,
+                               dtype=input_tensor.dtype)
+    sampler = MockLogitsSampler(32000, fake_logits)
+    model_runner = ModelRunner(None, None, None)
+
+    generation_model = GenerationMixin()
+    generation_config = GenerationConfig(top_k=top_k,
+                                         top_p=top_p,
+                                         do_sample=True)
+    warpers = generation_model._get_logits_warper(generation_config)
+    assert len(warpers) == 2  # top_p and top_k
+
+    seq_group_metadata_list = []
+    prompt_lens = []
+    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,
+                    top_k=top_k,
+                    top_p=top_p,
+                ),
+                block_tables={0: [1]},
+            ))
+        prompt_lens.append(seq_group_metadata_list[-1].seq_data[0].get_len())
+
+    sampling_metadata = model_runner._prepare_sample(seq_group_metadata_list,
+                                                     prompt_lens)
+
+    sample_probs = None
+
+    def mock_sample(probs, logprobs, sampling_metadata):
+        nonlocal sample_probs
+        sample_probs = probs
+        return [[prob.topk(1, dim=-1).indices.tolist(), [0]] for prob in probs]
+
+    with patch("vllm.model_executor.layers.sampler._sample", mock_sample):
+        sampler(embedding=None,
+                hidden_states=input_tensor,
+                sampling_metadata=sampling_metadata)
+    hf_probs = warpers(torch.zeros_like(fake_logits), fake_logits.clone())
+    hf_probs = torch.softmax(hf_probs, dim=-1, dtype=torch.float)
+    assert torch.allclose(hf_probs, sample_probs, atol=1e-5)
+    assert torch.equal(hf_probs.eq(0), sample_probs.eq(0))
+
+    del model_runner

vllm/core/policy.py

-from typing import List
+from collections import deque
+from typing import Deque
 
 from vllm.sequence import SequenceGroup
 
@@ -15,13 +16,14 @@ class Policy:
     def sort_by_priority(
         self,
         now: float,
-        seq_groups: List[SequenceGroup],
-    ) -> List[SequenceGroup]:
-        return sorted(
-            seq_groups,
-            key=lambda seq_group: self.get_priority(now, seq_group),
-            reverse=True,
-        )
+        seq_groups: Deque[SequenceGroup],
+    ) -> Deque[SequenceGroup]:
+        return deque(
+            sorted(
+                seq_groups,
+                key=lambda seq_group: self.get_priority(now, seq_group),
+                reverse=True,
+            ))
 
 
 class FCFS(Policy):

vllm/core/scheduler.py

+from collections import deque
 import enum
 import time
-from typing import Dict, Iterable, List, Optional, Tuple, Union
+from typing import Deque, Dict, Iterable, List, Optional, Tuple, Union
 
 from vllm.config import CacheConfig, SchedulerConfig
 from vllm.core.block_manager import AllocStatus, BlockSpaceManager
@@ -29,7 +30,7 @@ class SchedulerOutputs:
 
     def __init__(
         self,
-        scheduled_seq_groups: List[SequenceGroup],
+        scheduled_seq_groups: Iterable[SequenceGroup],
         prompt_run: bool,
         num_batched_tokens: int,
         blocks_to_swap_in: Dict[int, int],
@@ -75,38 +76,52 @@ class Scheduler:
             num_cpu_blocks=self.cache_config.num_cpu_blocks,
             sliding_window=self.cache_config.sliding_window)
 
-        # TODO(zhuohan): Use deque instead of list for better performance.
         # Sequence groups in the WAITING state.
-        self.waiting: List[SequenceGroup] = []
+        self.waiting: Deque[SequenceGroup] = deque()
         # Sequence groups in the RUNNING state.
-        self.running: List[SequenceGroup] = []
+        self.running: Deque[SequenceGroup] = deque()
         # Sequence groups in the SWAPPED state.
-        self.swapped: List[SequenceGroup] = []
+        self.swapped: Deque[SequenceGroup] = deque()
 
     def add_seq_group(self, seq_group: SequenceGroup) -> None:
         # Add sequence groups to the waiting queue.
         self.waiting.append(seq_group)
 
     def abort_seq_group(self, request_id: Union[str, Iterable[str]]) -> None:
+        """Aborts a sequence group with the given ID.
+
+        Check if the sequence group with the given ID
+            is present in any of the state queue.
+        If present, remove the sequence group from the state queue.
+            Also, if any of the sequences in the sequence group is not finished,
+                free the sequence with status `FINISHED_ABORTED`.
+        Otherwise, do nothing.
+
+        Args:
+            request_id: The ID(s) of the sequence group to abort.
+        """
         if isinstance(request_id, str):
             request_id = (request_id, )
         request_ids = set(request_id)
         for state_queue in [self.waiting, self.running, self.swapped]:
-            # We need to reverse the list as we are removing elements
-            # from it as we iterate over it. If we don't do it,
-            # indices will get messed up and we will skip over elements.
-            for seq_group in reversed(state_queue):
+            aborted_groups = []
+            for seq_group in state_queue:
+                if not request_ids:
+                    # Using 'break' here may add two extra iterations,
+                    # but is acceptable to reduce complexity .
+                    break
                 if seq_group.request_id in request_ids:
-                    # Remove the sequence group from the state queue.
-                    state_queue.remove(seq_group)
-                    for seq in seq_group.get_seqs():
-                        if seq.is_finished():
-                            continue
-                        seq.status = SequenceStatus.FINISHED_ABORTED
-                        self.free_seq(seq)
+                    # Appending aborted group into pending list.
+                    aborted_groups.append(seq_group)
                     request_ids.remove(seq_group.request_id)
-                    if not request_ids:
-                        return
+            for aborted_group in aborted_groups:
+                # Remove the sequence group from the state queue.
+                state_queue.remove(aborted_group)
+                for seq in seq_group.get_seqs():
+                    if seq.is_finished():
+                        continue
+                    seq.status = SequenceStatus.FINISHED_ABORTED
+                    self.free_seq(seq)
 
     def has_unfinished_seqs(self) -> bool:
         return self.waiting or self.running or self.swapped
@@ -152,7 +167,7 @@ class Scheduler:
                     for seq in waiting_seqs:
                         seq.status = SequenceStatus.FINISHED_IGNORED
                     ignored_seq_groups.append(seq_group)
-                    self.waiting.pop(0)
+                    self.waiting.popleft()
                     continue
 
                 # If the sequence group cannot be allocated, stop.
@@ -166,7 +181,7 @@ class Scheduler:
                     for seq in waiting_seqs:
                         seq.status = SequenceStatus.FINISHED_IGNORED
                     ignored_seq_groups.append(seq_group)
-                    self.waiting.pop(0)
+                    self.waiting.popleft()
                     continue
 
                 # If the number of batched tokens exceeds the limit, stop.
@@ -188,7 +203,7 @@ class Scheduler:
                     break
                 seq_lens = new_seq_lens
 
-                seq_group = self.waiting.pop(0)
+                seq_group = self.waiting.popleft()
                 self._allocate(seq_group)
                 self.running.append(seq_group)
                 num_curr_seqs += num_new_seqs
@@ -214,14 +229,14 @@ class Scheduler:
         self.running = self.policy.sort_by_priority(now, self.running)
 
         # Reserve new token slots for the running sequence groups.
-        running: List[SequenceGroup] = []
+        running: Deque[SequenceGroup] = deque()
         preempted: List[SequenceGroup] = []
         while self.running:
-            seq_group = self.running.pop(0)
+            seq_group = self.running.popleft()
             while not self.block_manager.can_append_slot(seq_group):
                 if self.running:
                     # Preempt the lowest-priority sequence groups.
-                    victim_seq_group = self.running.pop(-1)
+                    victim_seq_group = self.running.pop()
                     self._preempt(victim_seq_group, blocks_to_swap_out)
                     preempted.append(victim_seq_group)
                 else:
@@ -255,7 +270,7 @@ class Scheduler:
                         self.scheduler_config.max_num_seqs):
                     break
 
-                seq_group = self.swapped.pop(0)
+                seq_group = self.swapped.popleft()
                 self._swap_in(seq_group, blocks_to_swap_in)
                 self._append_slot(seq_group, blocks_to_copy)
                 num_curr_seqs += num_new_seqs
@@ -376,7 +391,7 @@ class Scheduler:
             self.block_manager.free(seq)
         # NOTE: For FCFS, we insert the preempted sequence group to the front
         # of the waiting queue.
-        self.waiting.insert(0, seq_group)
+        self.waiting.appendleft(seq_group)
 
     def _preempt_by_swap(
         self,

vllm/engine/async_llm_engine.py

@@ -253,7 +253,8 @@ class AsyncLLMEngine:
         log_requests: Whether to log the requests.
         start_engine_loop: If True, the background task to run the engine
             will be automatically started in the generate call.
-        *args, *kwargs: Arguments for LLMEngine.
+        *args: Arguments for LLMEngine.
+        *kwargs: Arguments for LLMEngine.
     """
 
     _engine_class: Type[_AsyncLLMEngine] = _AsyncLLMEngine
@@ -428,6 +429,49 @@ class AsyncLLMEngine:
         Yields:
             The output `RequestOutput` objects from the LLMEngine for the
             request.
+
+        Details:
+            - If the engine is not running, start the background loop,
+              which iteratively invokes
+              :meth:`~vllm.engine.async_llm_engine.AsyncLLMEngine.engine_step`
+              to process the waiting requests.
+            - Add the request to the engine's `RequestTracker`.
+              On the next background loop, this request will be sent to
+              the underlying engine.
+              Also, a corresponding `AsyncStream` will be created.
+            - Wait for the request outputs from `AsyncStream` and yield them.
+
+        Example:
+            >>> # Please refer to entrypoints/api_server.py for
+            >>> # the complete example.
+            >>>
+            >>> # initialize the engine and the example input
+            >>> engine = AsyncLLMEngine.from_engine_args(engine_args)
+            >>> example_input = {
+            >>>     "prompt": "What is LLM?",
+            >>>     "stream": False, # assume the non-streaming case
+            >>>     "temperature": 0.0,
+            >>>     "request_id": 0,
+            >>> }
+            >>>
+            >>> # start the generation
+            >>> results_generator = engine.generate(
+            >>>    example_input["prompt"],
+            >>>    SamplingParams(temperature=example_input["temperature"]),
+            >>>    example_input["request_id"])
+            >>>
+            >>> # get the results
+            >>> final_output = None
+            >>> async for request_output in results_generator:
+            >>>     if await request.is_disconnected():
+            >>>         # Abort the request if the client disconnects.
+            >>>         await engine.abort(request_id)
+            >>>         # Return or raise an error
+            >>>         ...
+            >>>     final_output = request_output
+            >>>
+            >>> # Process and return the final output
+            >>> ...
         """
         # Preprocess the request.
         # This should not be used for logging, as it is monotonic time.
@@ -506,3 +550,9 @@ class AsyncLLMEngine:
                      max_log_len=engine_args.max_log_len,
                      start_engine_loop=start_engine_loop)
         return engine
+
+    async def do_log_stats(self) -> None:
+        if self.engine_use_ray:
+            await self.engine.do_log_stats.remote()
+        else:
+            self.engine.do_log_stats()

vllm/engine/llm_engine.py

@@ -257,7 +257,26 @@ class LLMEngine:
         self.cache_config.verify_with_parallel_config(self.parallel_config)
 
     def _init_cache(self) -> None:
-        """Profiles the memory usage and initializes the KV cache."""
+        """Profiles the memory usage and initializes the KV cache.
+
+        The engine will first conduct a profiling of the existing memory usage.
+        Then, it calculate the maximum possible number of GPU and CPU blocks
+        that can be allocated with the remaining free memory.
+        More details can be found in the
+        :meth:`~vllm.worker.worker.Worker.profile_num_available_blocks` method
+        from class :class:`~vllm.worker.Worker`.
+
+        Afterwards, as there may be multiple workers,
+        we take the minimum number of blocks across all workers
+        to ensure this can be applied to all of them.
+
+        Finally, the engine will initialize the KV cache
+        with the calculated number of blocks.
+
+        .. tip::
+            You may limit the usage of GPU memory
+            by adjusting the `gpu_memory_utilization` parameters.
+        """
         # Get the maximum number of blocks that can be allocated on GPU and CPU.
         num_blocks = self._run_workers(
             "profile_num_available_blocks",
@@ -334,6 +353,30 @@ class LLMEngine:
                 use the tokenizer to convert the prompts to token IDs.
             arrival_time: The arrival time of the request. If None, we use
                 the current monotonic time.
+
+        Details:
+            - Set arrival_time to the current time if it is None.
+            - Set prompt_token_ids to the encoded prompt if it is None.
+            - Create `best_of` number of :class:`~vllm.Sequence` objects.
+            - Create a :class:`~vllm.SequenceGroup` object
+              from the list of :class:`~vllm.Sequence`.
+            - Add the :class:`~vllm.SequenceGroup` object to the scheduler.
+
+        Example:
+            >>> # initialize engine
+            >>> engine = LLMEngine.from_engine_args(engine_args)
+            >>> # set request arguments
+            >>> example_prompt = "Who is the president of the United States?"
+            >>> sampling_params = SamplingParams(temperature=0.0)
+            >>> request_id = 0
+            >>>
+            >>> # add the request to the engine
+            >>> engine.add_request(
+            >>>    str(request_id),
+            >>>    example_prompt,
+            >>>    SamplingParams(temperature=0.0))
+            >>> # continue the request processing
+            >>> ...
         """
         if arrival_time is None:
             arrival_time = time.monotonic()
@@ -358,6 +401,17 @@ class LLMEngine:
 
         Args:
             request_id: The ID(s) of the request to abort.
+
+        Details:
+            - Refer to the
+              :meth:`~vllm.core.scheduler.Scheduler.abort_seq_group`
+              from class :class:`~vllm.core.scheduler.Scheduler`.
+
+        Example:
+            >>> # initialize engine and add a request with request_id
+            >>> request_id = str(0)
+            >>> # abort the request
+            >>> engine.abort_request(request_id)
         """
         self.scheduler.abort_seq_group(request_id)
 
@@ -601,8 +655,10 @@ class LLMEngine:
 
         # Create the outputs.
         request_outputs: List[RequestOutput] = []
-        for seq_group in (scheduled_seq_groups +
-                          scheduler_outputs.ignored_seq_groups):
+        for seq_group in scheduled_seq_groups:
+            request_output = RequestOutput.from_seq_group(seq_group)
+            request_outputs.append(request_output)
+        for seq_group in scheduler_outputs.ignored_seq_groups:
             request_output = RequestOutput.from_seq_group(seq_group)
             request_outputs.append(request_output)
 
@@ -615,11 +671,53 @@ class LLMEngine:
     def step(self) -> List[RequestOutput]:
         """Performs one decoding iteration and returns newly generated results.
 
-        This function performs one decoding iteration of the engine. It first
-        schedules the sequences to be executed in the next iteration and the
-        token blocks to be swapped in/out/copy. Then, it executes the model
-        and updates the scheduler with the model outputs. Finally, it decodes
-        the sequences and returns the newly generated results.
+        .. figure:: https://i.imgur.com/sv2HssD.png
+            :alt: Overview of the step function
+            :align: center
+
+            Overview of the step function.
+
+        Details:
+            - Step 1: Schedules the sequences to be executed in the next
+              iteration and the token blocks to be swapped in/out/copy.
+
+                - Depending on the scheduling policy,
+                  sequences may be `preempted/reordered`.
+                - A Sequence Group (SG) refer to a group of sequences
+                  that are generated from the same prompt.
+
+            - Step 2: Calls the workers to execute the model.
+            - Step 3: Processes the model output. This mainly includes:
+
+                - Decodes the relevant outputs.
+                - Updates the scheduled sequence groups with model outputs
+                  based on its `sampling parameters` (`use_beam_search` or not).
+                - Frees the finished sequence groups.
+
+            - Finally, it creates and returns the newly generated results.
+
+        Example:
+            >>> # Please see the example/ folder for more detailed examples.
+            >>>
+            >>> # initialize engine and request arguments
+            >>> engine = LLMEngine.from_engine_args(engine_args)
+            >>> example_inputs = [(0, "What is LLM?",
+            >>>    SamplingParams(temperature=0.0))]
+            >>>
+            >>> # Start the engine with an event loop
+            >>> while True:
+            >>>     if example_inputs:
+            >>>         req_id, prompt, sampling_params = example_inputs.pop(0)
+            >>>         engine.add_request(str(req_id), prompt, sampling_params)
+            >>>
+            >>>     # continue the request processing
+            >>>     request_outputs = engine.step()
+            >>>     for request_output in request_outputs:
+            >>>         if request_output.finished:
+            >>>             # return or show the request output
+            >>>
+            >>>     if not (engine.has_unfinished_requests() or example_inputs):
+            >>>         break
         """
         seq_group_metadata_list, scheduler_outputs = self.scheduler.schedule()
 
@@ -641,6 +739,9 @@ class LLMEngine:
 
         return self._process_model_outputs(output, scheduler_outputs)
 
+    def do_log_stats(self) -> None:
+        self._log_system_stats(False, 0)
+
     def _log_system_stats(
         self,
         prompt_run: bool,

vllm/engine/ray_utils.py

@@ -55,7 +55,7 @@ def initialize_cluster(
     parallel_config: ParallelConfig,
     engine_use_ray: bool = False,
     ray_address: Optional[str] = None,
-) -> Tuple[str, Optional["PlacementGroup"]]:
+) -> Optional["PlacementGroup"]:
     """Initialize the distributed cluster probably with Ray.
 
     Args:

vllm/entrypoints/api_server.py

@@ -74,12 +74,18 @@ if __name__ == "__main__":
     parser.add_argument("--port", type=int, default=8000)
     parser.add_argument("--ssl-keyfile", type=str, default=None)
     parser.add_argument("--ssl-certfile", type=str, default=None)
+    parser.add_argument(
+        "--root-path",
+        type=str,
+        default=None,
+        help="FastAPI root_path when app is behind a path based routing proxy")
     parser = AsyncEngineArgs.add_cli_args(parser)
     args = parser.parse_args()
 
     engine_args = AsyncEngineArgs.from_cli_args(args)
     engine = AsyncLLMEngine.from_engine_args(engine_args)
 
+    app.root_path = args.root_path
     uvicorn.run(app,
                 host=args.host,
                 port=args.port,

vllm/entrypoints/openai/api_server.py

@@ -6,6 +6,7 @@ import asyncio
 import codecs
 import json
 import time
+from contextlib import asynccontextmanager
 from http import HTTPStatus
 from typing import AsyncGenerator, Dict, List, Optional, Tuple, Union
 
@@ -38,11 +39,28 @@ TIMEOUT_KEEP_ALIVE = 5  # seconds
 
 logger = init_logger(__name__)
 served_model = None
-app = fastapi.FastAPI()
+engine_args = None
 engine = None
 response_role = None
 
 
+@asynccontextmanager
+async def lifespan(app: fastapi.FastAPI):
+
+    async def _force_log():
+        while True:
+            await asyncio.sleep(10)
+            await engine.do_log_stats()
+
+    if not engine_args.disable_log_stats:
+        asyncio.create_task(_force_log())
+
+    yield
+
+
+app = fastapi.FastAPI(lifespan=lifespan)
+
+
 def parse_args():
     parser = argparse.ArgumentParser(
         description="vLLM OpenAI-Compatible RESTful API server.")
@@ -88,6 +106,11 @@ def parse_args():
                         type=str,
                         default=None,
                         help="The file path to the SSL cert file")
+    parser.add_argument(
+        "--root-path",
+        type=str,
+        default=None,
+        help="FastAPI root_path when app is behind a path based routing proxy")
 
     parser = AsyncEngineArgs.add_cli_args(parser)
     return parser.parse_args()
@@ -748,6 +771,7 @@ if __name__ == "__main__":
     # Register labels for metrics
     add_global_metrics_labels(model_name=engine_args.model)
 
+    app.root_path = args.root_path
     uvicorn.run(app,
                 host=args.host,
                 port=args.port,

vllm/model_executor/layers/attention.py

@@ -156,20 +156,15 @@ class PagedAttention(nn.Module):
             output = out.view_as(query)
         else:
             # Decoding run.
-            if key_cache is not None and value_cache is not None:
-                output = _paged_attention(
-                    query,
-                    key_cache,
-                    value_cache,
-                    input_metadata,
-                    self.num_kv_heads,
-                    self.scale,
-                    self.alibi_slopes,
-                )
-            else:
-                # This happens during the initial memory profiling run for
-                # CUDA graphs.
-                output = torch.zeros_like(query)
+            output = _paged_attention(
+                query,
+                key_cache,
+                value_cache,
+                input_metadata,
+                self.num_kv_heads,
+                self.scale,
+                self.alibi_slopes,
+            )
 
         # Reshape the output tensor.
         return output.view(batch_size, seq_len, hidden_size)

vllm/model_executor/layers/linear.py

@@ -423,7 +423,10 @@ class QKVParallelLinear(ColumnParallelLinear):
                 shard_offset = shard_offset // param.pack_factor
             param_data = param_data.narrow(output_dim, shard_offset,
                                            shard_size)
-            shard_id = tp_rank // self.num_kv_head_replicas
+            if loaded_shard_id == "q":
+                shard_id = tp_rank
+            else:
+                shard_id = tp_rank // self.num_kv_head_replicas
             start_idx = shard_id * shard_size
             loaded_weight = loaded_weight.narrow(output_dim, start_idx,
                                                  shard_size)

vllm/model_executor/layers/rejection_sampler.py

+from typing import Tuple, Optional
+from functools import cached_property
+
+import torch
+import torch.nn as nn
+import torch.jit
+
+
+class RejectionSampler(nn.Module):
+    """Apply modified rejection sampling as described in "Accelerating Large
+        Language Model Decoding with Speculative Sampling"
+        https://arxiv.org/pdf/2302.01318.pdf.
+    """
+
+    def __init__(self, strict_mode: bool = False):
+        """Create a rejection sampler.
+
+        Args:
+            strict_mode: Whether or not to perform shape/device/dtype checks
+                during sampling. This catches correctness issues but adds
+                nontrivial latency.
+        """
+        super().__init__()
+        self.probs_dtype = torch.float32
+        self.token_id_dtype = torch.int64
+        self._strict_mode = strict_mode
+
+        # NOTE: A "bonus token" is accepted iff all proposal tokens are
+        # accepted. There is always only one possible bonus token. We store this
+        # value in a variable for readability.
+        self._num_bonus_tokens = 1
+
+        self.num_accepted_tokens: Optional[torch.Tensor] = None
+        self.num_emitted_tokens: Optional[torch.Tensor] = None
+        self.num_draft_tokens: int = 0
+
+    def init_gpu_tensors(self, rank: int) -> None:
+        assert self.num_accepted_tokens is None
+        device = f"cuda:{rank}"
+        self.num_accepted_tokens = torch.tensor(0,
+                                                dtype=torch.long,
+                                                device=device)
+        self.num_emitted_tokens = torch.tensor(0,
+                                               dtype=torch.long,
+                                               device=device)
+
+    def forward(
+        self,
+        target_probs: torch.Tensor,
+        bonus_token_ids: torch.Tensor,
+        draft_probs: torch.Tensor,
+        draft_token_ids: torch.Tensor,
+    ) -> torch.Tensor:
+        """Sample token ids using rejection sampling. This accepts or rejects
+        tokens proposed by the draft model using the probability of each token
+        according to the draft and target models.
+
+        In the worst case where all draft tokens are rejected, it is guaranteed
+        one correct token will be emitted.
+
+        In the case where all draft tokens are accepted, a bonus token will be
+        accepted as its cheap to have the target model score this speculative
+        sequence.
+
+        Args:
+            target_probs: The probability distribution over token ids given
+                context according to the target model.
+            shape = [batch_size, num_speculative_tokens, vocab_size]
+
+            bonus_token_ids: The "bonus" token ids that are accepted iff all
+                speculative tokens in a sequence are accepted.
+            shape = [batch_size, num_bonus_tokens]
+
+            draft_probs: The probability distribution over token ids given
+                context according to the draft model.
+            shape = [batch_size, num_speculative_tokens, vocab_size]
+
+            draft_token_ids: The token ids that were sampled from the draft
+                probabilities.
+            shape = [batch_size, num_speculative_tokens]
+
+        Returns:
+            output_token_ids: The token ids sampled via rejection sampling,
+                or -1 if unable to sample a token because the previous token
+                was rejected.
+            shape = [batch_size, num_speculative_tokens + num_bonus_tokens]
+        """
+        # Only perform shape/dtype/device checking in strict mode, as it adds
+        # overhead.
+        if self._strict_mode:
+            self._raise_if_incorrect_shape(target_probs, bonus_token_ids,
+                                           draft_probs, draft_token_ids)
+            self._raise_if_incorrect_dtype(target_probs, bonus_token_ids,
+                                           draft_probs, draft_token_ids)
+            self._raise_if_inconsistent_device(target_probs, bonus_token_ids,
+                                               draft_probs, draft_token_ids)
+            self._raise_if_out_of_bounds_vocab(target_probs.shape[-1],
+                                               bonus_token_ids,
+                                               draft_token_ids)
+
+        accepted, recovered_token_ids = self._batch_modified_rejection_sampling(
+            target_probs,
+            draft_probs,
+            draft_token_ids,
+        )
+
+        output_token_ids = self._create_output(
+            accepted,
+            recovered_token_ids,
+            draft_token_ids,
+            bonus_token_ids,
+        )
+        return output_token_ids
+
+    def _batch_modified_rejection_sampling(
+            self,
+            target_probs: torch.Tensor,  # [batch_size, k, vocab_size]
+            draft_probs: torch.Tensor,  # [batch_size, k, vocab_size]
+            draft_token_ids: torch.Tensor,  # [batch_size, k]
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Perform modified rejection sampling on each sequence.
+
+        Returns:
+            A tuple of two tensors:
+            0: A bool tensor of which tokens in each sequence is accepted.
+                shape = [batch_size, k]
+            1: Token ids sampled from a recovered distribution, to be used
+                when a token is rejected.
+                shape = [batch_size, k]
+        """
+
+        batch_size, k, vocab_size = draft_probs.shape
+
+        # shape [batch_size, k]
+        accepted = self._get_accepted(target_probs, draft_probs,
+                                      draft_token_ids)
+
+        recovered_probs = self._get_recovered_probs(
+            target_probs, draft_probs).reshape(batch_size * k, vocab_size)
+
+        recovered_token_ids = _multinomial(recovered_probs,
+                                           num_samples=1).reshape(
+                                               batch_size, k)
+        return accepted, recovered_token_ids
+
+    def _get_accepted(
+            self,
+            target_probs: torch.Tensor,  # [batch_size, k, vocab_size]
+            draft_probs: torch.Tensor,  # [batch_size, k, vocab_size]
+            draft_token_ids: torch.Tensor,  # [batch_size, k]
+    ) -> torch.Tensor:
+        r"""Create bool matrix over the proposed draft tokens. If
+        True, then a token can be accepted, else it should be
+        rejected.
+
+        Given :math:`q(\hat{x}_{n+1}|x_1, \dots, x_n)`, the probability of
+        :math:`\hat{x}_{n+1}` given context :math:`x_1, \dots, x_n` according
+        to the target model, and :math:`p(\hat{x}_{n+1}|x_1, \dots, x_n)`, the
+        same conditional probability according to the draft model, the token
+        is accepted with probability:
+
+        .. math::
+            \min\left(1, \frac{q(\hat{x}_{n+1}|x_1, \dots, x_n)}
+                           {p(\hat{x}_{n+1}|x_1, \dots, x_n)}\right)
+
+        This implementation does not apply causality. When using the output,
+        if a token is rejected, subsequent tokens should not be used.
+
+        Returns a bool tensor of shape [batch_size, k] specifying which tokens
+        are accepted.
+        """
+        batch_size, k, _ = draft_probs.shape
+        batch_indices = torch.arange(batch_size,
+                                     device=target_probs.device)[:, None]
+        probs_indicies = torch.arange(k, device=target_probs.device)
+
+        # shape [batch_size, k]
+        selected_draft_probs = draft_probs[batch_indices, probs_indicies,
+                                           draft_token_ids]
+
+        # shape [batch_size, k]
+        selected_target_probs = target_probs[batch_indices, probs_indicies,
+                                             draft_token_ids]
+
+        uniform_rand = torch.rand(batch_size,
+                                  k,
+                                  dtype=self.probs_dtype,
+                                  device=target_probs.device)
+        capped_ratio = torch.minimum(
+            selected_target_probs / selected_draft_probs,
+            torch.full((1, ), 1, device=target_probs.device))
+        accepted = uniform_rand < capped_ratio
+
+        return accepted
+
+    def _get_recovered_probs(
+            self,
+            target_probs: torch.Tensor,  # [k, vocab_size]
+            draft_probs: torch.Tensor,  # [k, vocab_size]
+    ) -> torch.Tensor:
+        r"""Create a probability distribution for each proposed token which can
+        be sampled if the proposed token is rejected.
+
+        When this routine is applied sequentially, the true distribution of the
+        target model is recovered (within hardware numerics).
+
+        The probability distribution used in this rejection case is constructed
+        as follows. Given :math:`q(x|x_1, \dots, x_n)`, the probability of
+        :math:`x` given context :math:`x_1, \dots, x_n` according to the target
+        model and :math:`p(x|x_1, \dots, x_n)`, the same conditional probability
+        according to the draft model:
+
+        .. math::
+            x_{n+1} \sim (q(x|x_1, \dots, x_n) - p(x|x_1, \dots, x_n))_+
+
+        where :math:`(f(x))_+` is defined as:
+
+        .. math::
+            (f(x))_+ = \frac{\max(0, f(x))}{\sum_x \max(0, f(x))}
+
+        See https://github.com/vllm-project/vllm/pull/2336 for a visualization
+        of the draft, target, and recovered probability distributions.
+
+        Returns a tensor of shape [batch_size, k, vocab_size].
+
+        Note: This batches operations on GPU and thus constructs the recovered
+        distribution for all tokens, even if they are accepted. This causes
+        division-by-zero errors, so we use self._smallest_positive_value to
+        avoid that. This introduces some drift to the distribution.
+        """
+        _, k, _ = draft_probs.shape
+
+        # shape [batch_size, k, vocab_size]
+        difference = target_probs - draft_probs
+
+        # TODO(cade): Can we use logprobs instead of probs, and avoid the
+        # division-by-zero errors without introducing distribution drift?
+
+        # shape [batch_size, k, vocab_size]
+        f = torch.clamp(difference, min=self._smallest_positive_value)
+
+        # shape [batch_size, k, vocab_size]
+        recovered_probs = f / torch.sum(f, dim=-1).reshape(-1, k, 1)
+
+        return recovered_probs
+
+    @cached_property
+    def _smallest_positive_value(self) -> float:
+        """Return the smallest positive value representable by the probs dtype.
+        This value is used when constructing a distribution from which to sample
+        recovered tokens in the first rejection case.
+
+        See _get_recovered_probs for more details
+
+        Note that this isn't actually the smallest positive value representable
+        by float32, but the smallest positive normal value.
+        See https://en.wikipedia.org/wiki/Subnormal_number for more information.
+        """
+        return torch.finfo(self.probs_dtype).tiny
+
+    def _create_output(
+            self,
+            accepted: torch.Tensor,  # [batch_size, k]
+            recovered_token_ids: torch.Tensor,  # [batch_size, k]
+            draft_token_ids: torch.Tensor,  # [batch_size, k]
+            bonus_token_ids: torch.Tensor,  # [batch_size]
+    ) -> torch.Tensor:
+        """Format output. Returns a matrix of token ids. When
+        a token is rejected via rejection sampling, all subsequent
+        token ids are set to -1 for the sequence.
+
+        shape = [batch_size, k + num_bonus_tokens]
+        """
+        bonus_token_ids = bonus_token_ids.squeeze()
+        batch_size, k = recovered_token_ids.shape
+
+        # Determine the index of the first False value for each row.
+        limits = (accepted == 0).max(1).indices
+        limits[~(accepted == 0).any(1)] = k
+
+        # Create masks using the indices.
+        indices = torch.arange(k, device=accepted.device).unsqueeze(0)
+        accepted_mask = indices < limits.unsqueeze(1)
+        after_false_mask = indices == limits.unsqueeze(1)
+
+        # Create an extended output tensor
+        output_with_bonus_tokens = -torch.ones(
+            (batch_size, k + self._num_bonus_tokens),
+            dtype=self.token_id_dtype,
+            device=accepted.device)
+        output = output_with_bonus_tokens[:, :k]
+
+        # Fill in the first k columns of the output tensor using masks and data
+        # tensors.
+        output[:, :k] = torch.where(accepted_mask, draft_token_ids,
+                                    -torch.ones_like(draft_token_ids))
+
+        # Fill the last column.
+        # We check output directly as accepted may have True values inconsistent
+        # with causal acceptance.
+        output_with_bonus_tokens[:, -1] = torch.where(output[:, -1] != -1,
+                                                      bonus_token_ids, -1)
+
+        # Fill the recovered token ids.
+        output.mul_(~after_false_mask).add_(
+            recovered_token_ids.mul(after_false_mask))
+
+        self.num_accepted_tokens += accepted.sum()
+        self.num_emitted_tokens += (output_with_bonus_tokens != -1).sum()
+        self.num_draft_tokens += batch_size * k
+
+        return output_with_bonus_tokens
+
+    def _raise_if_incorrect_shape(
+        self,
+        target_probs: torch.Tensor,
+        bonus_token_ids: torch.Tensor,
+        draft_probs: torch.Tensor,
+        draft_token_ids: torch.Tensor,
+    ) -> None:
+        (target_batch_size, num_target_probs,
+         target_vocab_size) = target_probs.shape
+        bonus_batch_size, num_bonus_tokens = bonus_token_ids.shape
+        draft_batch_size, num_draft_probs, draft_vocab_size = draft_probs.shape
+        draft_token_ids_batch_size, num_draft_token_ids = draft_token_ids.shape
+
+        assert draft_batch_size == target_batch_size
+        assert num_draft_probs == num_target_probs
+        assert (draft_vocab_size == target_vocab_size
+                ), f"{draft_vocab_size=} {target_vocab_size=}"
+
+        assert draft_token_ids_batch_size == draft_batch_size
+        assert num_draft_token_ids == num_draft_probs
+
+        assert bonus_batch_size == target_batch_size
+        assert num_bonus_tokens == self._num_bonus_tokens
+
+    def _raise_if_incorrect_dtype(
+        self,
+        target_probs: torch.Tensor,
+        bonus_token_ids: torch.Tensor,
+        draft_probs: torch.Tensor,
+        draft_token_ids: torch.Tensor,
+    ) -> None:
+        assert all(probs.dtype == self.probs_dtype
+                   for probs in [target_probs, draft_probs])
+        assert all(token_ids.dtype == self.token_id_dtype
+                   for token_ids in [bonus_token_ids, draft_token_ids])
+
+    def _raise_if_inconsistent_device(
+        self,
+        target_probs: torch.Tensor,
+        bonus_token_ids: torch.Tensor,
+        draft_probs: torch.Tensor,
+        draft_token_ids: torch.Tensor,
+    ) -> None:
+        devices = [
+            t.device for t in
+            [target_probs, bonus_token_ids, draft_probs, draft_token_ids]
+        ]
+        assert all([devices[0] == device for device in devices])
+
+    def _raise_if_out_of_bounds_vocab(
+        self,
+        vocab_size: int,
+        bonus_token_ids: torch.Tensor,
+        draft_token_ids: torch.Tensor,
+    ) -> None:
+        assert torch.all(bonus_token_ids < vocab_size)
+        assert torch.all(bonus_token_ids >= 0)
+        assert torch.all(draft_token_ids < vocab_size)
+        assert torch.all(draft_token_ids >= 0)
+
+
+# torch.multinomial forces a GPU<->CPU sync.
+# Therefore, we use an optimized implementation instead that skips the sync.
+# Note that we always sample with replacement.
+# probs will be modified in place, but this is fine, as we pass
+# in a copy already.
+@torch.jit.script
+def _multinomial(
+    probs: torch.Tensor,
+    num_samples: int,
+) -> torch.Tensor:
+    if num_samples > 1:
+        # This is equivalent to torch.repeat_interleaved (which also
+        # forces a GPU<->CPU sync).
+        probs = probs[:, None, :].expand(probs.shape[0], num_samples,
+                                         probs.shape[1]).contiguous().view(
+                                             -1, probs.shape[1])
+    q = torch.empty_like(probs).exponential_(1.0)
+    return probs.div_(q).argmax(dim=1).view(-1, num_samples)

vllm/model_executor/layers/sampler.py

@@ -76,7 +76,7 @@ class Sampler(nn.Module):
         logits.div_(sampling_tensors.temperatures.unsqueeze_(dim=1))
 
         if do_top_p_top_k:
-            logits = _apply_top_p_top_k(logits, sampling_tensors.top_ps,
+            logits = _apply_top_k_top_p(logits, sampling_tensors.top_ps,
                                         sampling_tensors.top_ks)
 
         if do_min_p:
@@ -185,27 +185,27 @@ def _apply_penalties(logits: torch.Tensor, prompt_tokens_tensor: torch.Tensor,
     return logits
 
 
-def _apply_top_p_top_k(
+def _apply_top_k_top_p(
     logits: torch.Tensor,
     p: torch.Tensor,
     k: torch.Tensor,
 ) -> torch.Tensor:
-    logits_sort, logits_idx = logits.sort(dim=-1, descending=True)
+    logits_sort, logits_idx = logits.sort(dim=-1, descending=False)
+
+    # Apply top-k.
+    top_k_mask = logits_sort.size(1) - k.to(torch.long)
+    # Get all the top_k values.
+    top_k_mask = logits_sort.gather(1, top_k_mask.unsqueeze(dim=1))
+    top_k_mask = logits_sort < top_k_mask
+    logits_sort.masked_fill_(top_k_mask, -float("inf"))
 
     # Apply top-p.
     probs_sort = logits_sort.softmax(dim=-1)
-    probs_sum = probs_sort.cumsum(dim=-1).sub_(probs_sort)
-    top_p_mask = probs_sum > p.unsqueeze_(dim=1)
-
-    # Apply top-k.
-    # Create a mask for the top-k elements.
-    top_k_mask = torch.arange(logits_idx.shape[-1], device=logits_idx.device)
-    top_k_mask = top_k_mask.expand(logits_idx.shape[0], -1)
-    top_k_mask = top_k_mask >= k.unsqueeze_(dim=1)
-
-    # Final mask.
-    mask = (top_p_mask | top_k_mask)
-    logits_sort.masked_fill_(mask, -float("inf"))
+    probs_sum = probs_sort.cumsum(dim=-1)
+    top_p_mask = probs_sum <= 1 - p.unsqueeze(dim=1)
+    # at least one
+    top_p_mask[:, -1] = False
+    logits_sort.masked_fill_(top_p_mask, -float("inf"))
 
     # Re-sort the probabilities.
     src = torch.arange(logits_idx.shape[-1],

vllm/model_executor/models/__init__.py

@@ -33,7 +33,7 @@ _MODELS = {
     "MptForCausalLM": ("mpt", "MPTForCausalLM"),
     "MPTForCausalLM": ("mpt", "MPTForCausalLM"),
     "OPTForCausalLM": ("opt", "OPTForCausalLM"),
-    "PhiForCausalLM": ("phi_1_5", "PhiForCausalLM"),
+    "PhiForCausalLM": ("phi", "PhiForCausalLM"),
     "QWenLMHeadModel": ("qwen", "QWenLMHeadModel"),
     "RWForCausalLM": ("falcon", "FalconForCausalLM"),
     "YiForCausalLM": ("yi", "YiForCausalLM"),

vllm/model_executor/models/phi_1_5.py → vllm/model_executor/models/phi.py

@@ -62,20 +62,6 @@ from vllm.sequence import SamplerOutput
 KVCache = Tuple[torch.Tensor, torch.Tensor]
 
 
-class PhiEmbedding(nn.Module):
-
-    def __init__(self, config: PretrainedConfig):
-        super().__init__()
-
-        self.wte = VocabParallelEmbedding(
-            config.vocab_size,
-            config.hidden_size,
-        )
-
-    def forward(self, input_ids: torch.LongTensor):
-        return self.wte(input_ids)
-
-
 class PhiAttention(nn.Module):
 
     def __init__(self,
@@ -93,27 +79,22 @@ class PhiAttention(nn.Module):
                           tensor_model_parallel_world_size)
 
         # pylint: disable=C0103
-        self.Wqkv = QKVParallelLinear(
-            self.hidden_size,
-            self.head_size,
-            self.total_num_heads,
-            linear_method=linear_method,
-        )
         self.qkv_proj = QKVParallelLinear(
-            config.hidden_size,
+            self.hidden_size,
             self.head_size,
             self.total_num_heads,
-            bias=False,
+            bias=True,
             linear_method=linear_method,
         )
-        self.out_proj = RowParallelLinear(
+        self.dense = RowParallelLinear(
             self.hidden_size,
             self.hidden_size,
             linear_method=linear_method,
         )
 
         scaling = self.head_size**-0.5
-        rotary_dim = config.rotary_dim
+        rotary_dim = int(config.partial_rotary_factor *
+                         (config.hidden_size // config.num_attention_heads))
         assert rotary_dim % 2 == 0
 
         # pylint: disable=C0301
@@ -136,12 +117,12 @@ class PhiAttention(nn.Module):
         kv_cache: KVCache,
         input_metadata: InputMetadata,
     ) -> torch.Tensor:
-        qkv, _ = self.Wqkv(hidden_states)
+        qkv, _ = self.qkv_proj(hidden_states)
         q, k, v = qkv.chunk(chunks=3, dim=-1)
         q, k = self.rotary_emb(position_ids, q, k)
         k_cache, v_cache = kv_cache
         attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
-        output, _ = self.out_proj(attn_output)
+        output, _ = self.dense(attn_output)
         return output
 
 
@@ -166,8 +147,7 @@ class PhiMLP(nn.Module):
             linear_method=linear_method,
         )
         quant_config = getattr(linear_method, "quant_config", None)
-        self.act = get_act_fn(config.activation_function, quant_config,
-                              n_inner)
+        self.act = get_act_fn(config.hidden_act, quant_config, n_inner)
 
     def forward(self, hidden_states):
         hidden_states, _ = self.fc1(hidden_states)
@@ -182,9 +162,9 @@ class PhiLayer(nn.Module):
                  config: PretrainedConfig,
                  linear_method: Optional[LinearMethodBase] = None):
         super().__init__()
-        self.ln = nn.LayerNorm(config.hidden_size,
-                               eps=config.layer_norm_epsilon)
-        self.mixer = PhiAttention(config, linear_method)
+        self.input_layernorm = nn.LayerNorm(config.hidden_size,
+                                            eps=config.layer_norm_eps)
+        self.self_attn = PhiAttention(config, linear_method)
         self.mlp = PhiMLP(config, linear_method)
 
     def forward(
@@ -195,8 +175,8 @@ class PhiLayer(nn.Module):
         input_metadata: InputMetadata,
     ) -> torch.Tensor:
         residual = hidden_states
-        hidden_states = self.ln(hidden_states)
-        attn_outputs = self.mixer(
+        hidden_states = self.input_layernorm(hidden_states)
+        attn_outputs = self.self_attn(
             position_ids=position_ids,
             hidden_states=hidden_states,
             kv_cache=kv_cache,
@@ -215,11 +195,14 @@ class PhiModel(nn.Module):
         super().__init__()
         self.config = config
         self.linear_method = linear_method
-        self.embd = PhiEmbedding(config)
-        self.h = nn.ModuleList([
+        self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
+                                                   config.hidden_size)
+        self.layers = nn.ModuleList([
             PhiLayer(config, linear_method)
             for _ in range(config.num_hidden_layers)
         ])
+        self.final_layernorm = nn.LayerNorm(config.hidden_size,
+                                            eps=config.layer_norm_eps)
 
     def forward(
         self,
@@ -228,27 +211,19 @@ class PhiModel(nn.Module):
         kv_caches: List[KVCache],
         input_metadata: InputMetadata,
     ) -> torch.Tensor:
-        hidden_states = self.embd(input_ids)
+        hidden_states = self.embed_tokens(input_ids)
         for i in range(self.config.num_hidden_layers):
-            layer = self.h[i]
+            layer = self.layers[i]
             hidden_states = layer(
                 positions,
                 hidden_states,
                 kv_caches[i],
                 input_metadata,
             )
-        return hidden_states
-
 
-class PhiCausalLMHead(nn.Module):
+        hidden_states = self.final_layernorm(hidden_states)
 
-    def __init__(self, config: PretrainedConfig):
-        super().__init__()
-        self.ln = nn.LayerNorm(config.hidden_size,
-                               eps=config.layer_norm_epsilon)
-        self.linear = ParallelLMHead(config.vocab_size,
-                                     config.hidden_size,
-                                     bias=True)
+        return hidden_states
 
 
 class PhiForCausalLM(nn.Module):
@@ -260,8 +235,11 @@ class PhiForCausalLM(nn.Module):
         self.config = config
         self.linear_method = linear_method
 
-        self.transformer = PhiModel(config, linear_method)
-        self.lm_head = PhiCausalLMHead(config)
+        self.model = PhiModel(config, linear_method)
+
+        self.lm_head = ParallelLMHead(config.vocab_size,
+                                      config.hidden_size,
+                                      bias=True)
         self.sampler = Sampler(config.vocab_size)
 
     def forward(
@@ -271,9 +249,9 @@ class PhiForCausalLM(nn.Module):
         kv_caches: List[KVCache],
         input_metadata: InputMetadata,
     ) -> torch.Tensor:
-        hidden_states = self.transformer(input_ids, positions, kv_caches,
-                                         input_metadata)
-        hidden_states = self.lm_head.ln(hidden_states)
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata)
+
         return hidden_states
 
     def sample(
@@ -281,7 +259,7 @@ class PhiForCausalLM(nn.Module):
         hidden_states: torch.Tensor,
         sampling_metadata: SamplingMetadata,
     ) -> Optional[SamplerOutput]:
-        head = self.lm_head.linear
+        head = self.lm_head
         next_tokens = self.sampler(head.weight, hidden_states,
                                    sampling_metadata, head.bias)
         return next_tokens
@@ -291,17 +269,37 @@ class PhiForCausalLM(nn.Module):
                      cache_dir: Optional[str] = None,
                      load_format: str = "auto",
                      revision: Optional[str] = None):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v")
+        ]
         params_dict = dict(self.named_parameters())
+
         for name, loaded_weight in hf_model_weights_iterator(
                 model_name_or_path, cache_dir, load_format, revision):
             if "rotary_emb.inv_freq" in name:
                 continue
 
-            # Skip loading extra bias for GPTQ models.
-            if name.endswith(".bias") and name not in params_dict:
-                continue
-            # pylint: disable=E1136
-            param = params_dict[name]
-            weight_loader = getattr(param, "weight_loader",
-                                    default_weight_loader)
-            weight_loader(param, loaded_weight)
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # pylint: disable=E1136
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)

vllm/utils.py

@@ -58,7 +58,9 @@ def in_wsl() -> bool:
 
 
 def get_ip() -> str:
-    return socket.gethostbyname(socket.gethostname())
+    s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
+    s.connect(("8.8.8.8", 80))  # Doesn't need to be reachable
+    return s.getsockname()[0]
 
 
 def get_open_port() -> int:

vllm/worker/model_runner.py

@@ -235,9 +235,11 @@ class ModelRunner:
                     input_block_tables[i, :len(block_table)] = block_table
             block_tables = torch.tensor(input_block_tables, device="cuda")
         else:
+            max_block_table_len = (max_context_len + self.block_size -
+                                   1) // self.block_size
             block_tables = _make_tensor_with_pad(
                 block_tables,
-                max_len=max_context_len,
+                max_len=max_block_table_len,
                 pad=0,
                 dtype=torch.int,
                 device="cuda",
@@ -504,7 +506,9 @@ class ModelRunner:
                     "use '--enforce-eager' in the CLI.")
         logger.info("CUDA graphs can take additional 1~3 GiB memory per GPU. "
                     "If you are running out of memory, consider decreasing "
-                    "`gpu_memory_utilization` or enforcing eager mode.")
+                    "`gpu_memory_utilization` or enforcing eager mode. "
+                    "You can also reduce the `max_num_seqs` as needed "
+                    "to decrease memory usage.")
         start_time = time.perf_counter()
 
         # Prepare dummy inputs. These will be reused for all batch sizes.
@@ -517,9 +521,15 @@ class ModelRunner:
         context_lens = torch.ones(max_batch_size, dtype=torch.int32).cuda()
         block_tables = torch.from_numpy(self.graph_block_tables).cuda()
 
+        graph_batch_size = _get_graph_batch_size(
+            self.scheduler_config.max_num_seqs)
+        batch_size_capture_list = [
+            bs for bs in _BATCH_SIZES_TO_CAPTURE if bs <= graph_batch_size
+        ]
+
         # NOTE: Capturing the largest batch size first may help reduce the
         # memory usage of CUDA graph.
-        for batch_size in reversed(_BATCH_SIZES_TO_CAPTURE):
+        for batch_size in reversed(batch_size_capture_list):
             # Create dummy input_metadata.
             input_metadata = InputMetadata(
                 is_prompt=False,

vllm/worker/worker.py

@@ -87,6 +87,14 @@ class Worker:
         gpu_memory_utilization: float,
         cpu_swap_space: int,
     ) -> Tuple[int, int]:
+        """Profiles the peak memory usage of the model and returns the maximum
+        number of GPU and CPU cache blocks that can be allocated.
+
+        Args:
+            block_size: The size of the cache block.
+            gpu_memory_utilization: The fraction of the total GPU memory to use.
+            cpu_swap_space: The size of the CPU swap space in bytes.
+        """
         # Profile the memory usage of the model and get the maximum number of
         # cache blocks that can be allocated with the remaining free memory.
         torch.cuda.empty_cache()
@@ -231,4 +239,6 @@ def _check_if_gpu_supports_dtype(torch_dtype: torch.dtype):
             raise ValueError(
                 "Bfloat16 is only supported on GPUs with compute capability "
                 f"of at least 8.0. Your {gpu_name} GPU has compute capability "
-                f"{compute_capability[0]}.{compute_capability[1]}.")
+                f"{compute_capability[0]}.{compute_capability[1]}. "
+                "You can use float16 instead by explicitly setting the"
+                "`dtype` flag in CLI, for example: --dtype=half.")