feat: fully async _select_generated_logits with tests

Signed-off-by: ixlmar <[email protected]>

Author: ixlmar

tensorrt_llm/_torch/pyexecutor/sampler.py

@@ -1366,6 +1366,7 @@ def _select_generated_logits(
         req_num_generation_steps: torch.Tensor,
         num_context_logits_prefix_sum: list[int],
         generation_requests_total_steps: int,
+        num_logits_to_keep: int,
     ) -> torch.Tensor:
         # raw_logits should contain only the generated logits.
         # If return context logits is requested, select only the generated logits.
@@ -1394,9 +1395,10 @@ def _select_generated_logits(
                 req_num_steps_fictitious_cuda = req_num_generation_steps_cuda[
                     : (len(scheduled_requests.context_requests) + 1)
                 ].clone()
-                req_num_steps_fictitious_cuda[-1] = generation_requests_total_steps
-                next_context_req_offsets_cuda[-1] = (
-                    next_context_req_offsets_cuda[-2] + req_num_steps_fictitious_cuda[-1]
+                req_num_steps_fictitious_cuda[-1].fill_(generation_requests_total_steps)
+                next_context_req_offsets_cuda[-1].copy_(
+                    next_context_req_offsets_cuda[-2] + req_num_steps_fictitious_cuda[-1],
+                    non_blocking=True,
                 )
             else:
                 req_num_steps_fictitious_cuda = req_num_generation_steps_cuda[
@@ -1412,6 +1414,7 @@ def _select_generated_logits(
             indices_to_keep_cuda = torch_multi_arange(
                 starts=(next_context_req_offsets_cuda - req_num_steps_fictitious_cuda),
                 ends=next_context_req_offsets_cuda,
+                output_length=num_logits_to_keep,
             )
 
             raw_logits_cuda = raw_logits_cuda[indices_to_keep_cuda]
@@ -1455,6 +1458,7 @@ def _process_requests(
                 if scheduled_requests.generation_requests
                 else 0
             ),
+            num_logits_to_keep=sum_steps,
         )
 
         # Handle embedding bias

tensorrt_llm/_torch/pyexecutor/sampling_utils.py

@@ -350,18 +350,28 @@ def torch_multi_arange(
     *,
     starts: Optional[torch.Tensor] = None,
     steps: Optional[torch.Tensor] = None,
+    output_length: Optional[int] = None,
 ) -> torch.Tensor:
     """Efficiently compute torch.cat([torch.arange(b, e, d) for b, e, d in zip(starts, ends, steps)]).
 
     Starts, ends, steps need to share dtype and shape. Invalid ranges like range(1, 2, -1) are
     silently discarded. 'steps' defaults to 1 and 'starts' defaults to 0.
+
+    Provide 'output_length' to avoid synchronization when using device tensors.
     """
     if steps is not None:
         assert ends.dtype == steps.dtype
         assert ends.shape == steps.shape
+        assert ends.device == steps.device
     if starts is not None:
         assert ends.dtype == starts.dtype
         assert ends.shape == starts.shape
+        assert ends.device == starts.device
+    if ends.device != torch.device("cpu") and output_length is None:
+        raise ValueError("Device tensors require 'output_length'")
+
+    if ends.numel() == 0:
+        return ends.clone()
 
     # This algorithm combines torch.repeat_interleaved() and torch.cumsum() to
     # construct the result.
@@ -378,29 +388,37 @@ def torch_multi_arange(
         repeats = repeats.clone()
         repeats -= starts
     if steps is not None:
-        repeats = (repeats + steps - 1).div(steps, rounding_mode="floor")
-    repeats = repeats.clip(0)  # ignore invalid ranges
+        repeats *= steps.sign()
+        steps_abs = steps.abs()
+        repeats = (repeats + steps_abs - 1).div(steps_abs, rounding_mode="floor")
+    repeats = repeats.clip(min=0)  # ignore invalid ranges
     range_ends = repeats - 1  # last element in each range
     if steps is not None:
         range_ends *= steps
     if starts is not None:
         range_ends += starts
     prev_range_ends = range_ends.roll(1)  # last element in preceding range (or 0)
-    prev_range_ends[0] = 0
-    ones = (
-        torch.tensor(1, dtype=ends.dtype, pin_memory=True)
-        .to(device=ends.device, non_blocking=True)
-        .broadcast_to(ends.shape)
-    )
+    prev_range_ends[0].fill_(0)
+    ones = torch.ones((), dtype=ends.dtype, device=ends.device)
+    zeros = torch.zeros((), dtype=ends.dtype, device=ends.device)
     if steps is None:
-        steps = ones
+        steps = ones.broadcast_to(ends.shape)
     jumps = -prev_range_ends  # delta from one range to the next
     if starts is not None:
         jumps += starts
+    #     NB: Apply correction for empty ranges
+    jumps_corrections = torch.where(repeats == 0, jumps, zeros).cumsum(0, dtype=ends.dtype)
+    jumps += jumps_corrections
     seq = torch.cat((jumps.unsqueeze(-1), steps.unsqueeze(-1)), dim=1).view(-1)
     #
     # 2. Construct output via torch.repeat_interleave() and torch.cumsum()
-    seq_repeats = torch.cat((ones.unsqueeze(-1), (repeats - 1).unsqueeze(-1)), dim=1).view(-1)
-    seq = seq.repeat_interleave(seq_repeats)
-    seq = seq.cumsum(0)
+    #     NB: For a resulting empty range, repeats - 1 == -1. In this case, we
+    #         should set repeats for delta and increment both to 0 instead.
+    jump_repeats = torch.where(repeats == 0, zeros, ones)
+    step_repeats = torch.where(repeats == 0, zeros, repeats - 1)
+    seq_repeats = torch.cat((jump_repeats.unsqueeze(-1), step_repeats.unsqueeze(-1)), dim=1).view(
+        -1
+    )
+    seq = seq.repeat_interleave(seq_repeats, output_size=output_length)
+    seq = seq.cumsum(0, dtype=ends.dtype)
     return seq

tests/integration/test_lists/test-db/l0_a10.yml

@@ -15,6 +15,8 @@ l0_a10:
   tests:
   # ------------- PyTorch tests ---------------
   - unittest/_torch/sampler/test_torch_sampler.py
+  - unittest/_torch/sampler/test_torch_multi_arange.py
+  - unittest/utils/test_util.py
   - unittest/_torch/modeling/test_modeling_mistral.py
   - unittest/_torch/modeling/test_modeling_pixtral.py
   # NOTE: this is a CPU-only test, but we do not have a dedicated job for this (and therefore no

tests/unittest/_torch/sampler/test_torch_multi_arange.py

@@ -0,0 +1,132 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from itertools import product
+from typing import Iterable, Optional
+
+import numpy as np
+import pytest
+import torch
+from utils.util import assert_no_cuda_sync, force_ampere
+
+from tensorrt_llm._torch.pyexecutor.sampling_utils import torch_multi_arange
+
+BASE_CASES = [
+    (None, [], None, []),
+    ([], [], None, []),
+    (None, [], [], []),
+    ([], [], [], []),
+    (None, [1], None, [0]),
+    (None, [-1], None, []),
+    (None, [3], None, [0, 1, 2]),
+    (None, [-3], None, []),
+    ([-5], [-3], None, [-5, -4]),
+    ([-5], [-2], [2], [-5, -3]),
+    ([-5], [-1], [2], [-5, -3]),
+    ([-5], [-3], [3], [-5]),
+    ([-3], [-5], None, []),
+    ([-3], [-5], [-1], [-3, -4]),
+    ([-3], [-5], [-3], [-3]),
+    ([-3], [-5], [1], []),
+    ([-5], [-3], [-2], []),
+    ([-3], [2], None, [-3, -2, -1, 0, 1]),
+    ([-3], [2], [2], [-3, -1, 1]),
+    ([-3], [3], [2], [-3, -1, 1]),
+    ([2], [5], None, [2, 3, 4]),
+    ([2], [5], [2], [2, 4]),
+    ([2], [6], [2], [2, 4]),
+]
+
+
+def _build_multi_arange_case() -> tuple[Iterable, Iterable, Iterable, Iterable]:
+    gen = np.random.default_rng(seed=42)
+    cases = [
+        BASE_CASES[i] for i in gen.choice(len(BASE_CASES), 128)
+        if len(BASE_CASES[i][3]) > 0
+    ]
+    starts = [
+        val for case in cases
+        for val in (case[0] if case[0] is not None else [0] * len(case[1]))
+    ]
+    ends = [val for case in cases for val in case[1]]
+    steps = [
+        val for case in cases
+        for val in (case[2] if case[2] is not None else [1] * len(case[1]))
+    ]
+    expected = [val for case in cases for val in case[3]]
+    return starts, ends, steps, expected
+
+
+@force_ampere
+@pytest.mark.parametrize(
+    "device, dtype, starts, ends, steps, expected",
+    [
+        pytest.param(device, dtype, starts, ends, steps, expected)
+        for (dtype, (starts, ends, steps, expected), device) in product(
+            [
+                torch.int32,
+                torch.int64,
+            ],
+            BASE_CASES + [_build_multi_arange_case()],
+            [
+                "cpu",
+                "cuda",
+            ],
+        )
+    ],
+)
+def test_torch_multi_arange(
+    device: str,
+    dtype: torch.dtype,
+    starts: Optional[Iterable],
+    ends: Iterable,
+    steps: Optional[Iterable],
+    expected: Iterable,
+):
+    torch_device = torch.device(device)
+
+    def _make_tensor(data: Iterable) -> torch.Tensor:
+        return torch.tensor(data, device=torch_device, dtype=dtype)
+
+    def _maybe_make_tensor(data: Optional[Iterable]) -> Optional[torch.Tensor]:
+        if data is None:
+            return None
+        return _make_tensor(data)
+
+    starts_tensor = _maybe_make_tensor(starts)
+    ends_tensor = _make_tensor(ends)
+    steps_tensor = _maybe_make_tensor(steps)
+    expected_tensor = _make_tensor(expected)
+
+    extra_args = {}
+    if device != "cpu":
+        # Pre-allocates a large chunk of memory, because PyTorch caching memory allocator
+        # can sync otherwise.
+        buf = torch.ones((2**30, ), device=device)
+        del buf
+        extra_args["output_length"] = expected_tensor.numel()
+        # Warmup to avoid syncs due to lazy loading of kernels
+        _ = torch_multi_arange(ends_tensor,
+                               starts=starts_tensor,
+                               steps=steps_tensor,
+                               **extra_args)
+
+    with torch.cuda.Stream():
+        with assert_no_cuda_sync():
+            result = torch_multi_arange(ends_tensor,
+                                        starts=starts_tensor,
+                                        steps=steps_tensor,
+                                        **extra_args)
+
+    torch.testing.assert_close(result, expected_tensor)