[Feature][Model] Add DeepSeek V4.1 serving support

.github/workflows/scripts/estimated_times.yaml

@@ -49,6 +49,7 @@ estimated_times:
   tests/e2e/pull_request/one_card/test_completion_with_prompt_embeds.py: 170
   tests/e2e/pull_request/one_card/test_cpu_offloading.py: 30
   tests/e2e/pull_request/one_card/test_cpu_weight_offload.py: 890
+  tests/e2e/pull_request/one_card/test_deepseek_v4_vision_precision.py: 600
   tests/e2e/pull_request/one_card/test_guided_decoding.py: 670
   tests/e2e/pull_request/one_card/test_minicpm.py: 300
   tests/e2e/pull_request/one_card/test_minimax_m3_sparse_attn.py: 440

benchmarks/prepare_indexer_indices.py

@@ -0,0 +1,112 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Compare fused indexer postprocessing in an NPU graph.
+
+Run: python benchmarks/prepare_indexer_indices.py
+Times exclude compilation, graph capture and host tensor allocation. Repetition
+counts adapt to a warmup measurement so slow INT32-sort baselines stay bounded.
+"""
+
+import argparse
+import json
+import statistics
+from functools import partial
+
+import torch
+import torch_npu  # noqa: F401
+
+from vllm_ascend.ops.triton.prepare_indexer_indices import prepare_indexer_indices
+from vllm_ascend.ops.triton.triton_utils import init_device_properties_triton
+
+
+def reference_indices(selected, positions, compress_ratio):
+    visible = ((positions + 1) // compress_ratio).unsqueeze(-1)
+    valid = (selected >= 0) & (selected < visible)
+    sentinel = torch.iinfo(torch.int32).max
+    selected = torch.where(valid, selected, sentinel).sort(dim=-1).values
+    return torch.where(selected == sentinel, -1, selected)
+
+
+def graph_latency_us(fn, value):
+    for _ in range(3):
+        fn(value)
+    torch.npu.synchronize()
+    start = torch.npu.Event(enable_timing=True)
+    end = torch.npu.Event(enable_timing=True)
+    start.record()
+    fn(value)
+    end.record()
+    end.synchronize()
+    estimate_ms = max(start.elapsed_time(end), 0.001)
+    # Capture at most about 10 ms of work and measure about 50 ms per sample.
+    batch = min(32, max(1, int(10 / estimate_ms)))
+    repeats = min(20, max(1, int(50 / (batch * estimate_ms))))
+    graph = torch.npu.NPUGraph()
+    with torch.npu.graph(graph, capture_error_mode="thread_local", auto_dispatch_capture=True):
+        for _ in range(batch):
+            output = fn(value)
+    for _ in range(3):
+        graph.replay()
+    torch.npu.synchronize()
+    samples = []
+    for _ in range(5):
+        start = torch.npu.Event(enable_timing=True)
+        end = torch.npu.Event(enable_timing=True)
+        start.record()
+        for _ in range(repeats):
+            graph.replay()
+        end.record()
+        end.synchronize()
+        samples.append(start.elapsed_time(end) * 1000 / (batch * repeats))
+    # Keep the captured outputs alive until timing completes.
+    del output
+    return statistics.median(samples)
+
+
+def benchmark(stage, value, reference, fused, **shape):
+    expected, actual = reference(value), fused(value)
+    if isinstance(expected, torch.Tensor):
+        expected, actual = (expected,), (actual,)
+    for output, ref in zip(actual, expected):
+        torch.testing.assert_close(output, ref, rtol=0, atol=0)
+    original_us = graph_latency_us(reference, value)
+    fused_us = graph_latency_us(fused, value)
+    print(
+        json.dumps(
+            {
+                "stage": stage,
+                **shape,
+                "reference_us": original_us,
+                "triton_us": fused_us,
+                "speedup": original_us / fused_us,
+            }
+        ),
+        flush=True,
+    )
+
+
+@torch.inference_mode()
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--tokens", type=int, nargs="+", default=[1, 32, 256, 4096])
+    parser.add_argument("--topk", type=int, nargs="+", default=[128, 2048])
+    args = parser.parse_args()
+    torch.npu.set_device(0)
+    init_device_properties_triton()
+    torch.manual_seed(41)
+    for tokens in args.tokens:
+        for topk in args.topk:
+            selected = torch.randint(-1, 4096, (tokens, topk), dtype=torch.int32, device="npu")
+            positions = torch.full((tokens,), 4095, dtype=torch.int64, device="npu")
+            benchmark(
+                "indices",
+                selected,
+                partial(reference_indices, positions=positions, compress_ratio=2),
+                partial(prepare_indexer_indices, positions=positions, compress_ratio=2),
+                tokens=tokens,
+                topk=topk,
+            )
+
+
+if __name__ == "__main__":
+    main()

benchmarks/quantize_indexer_query.py

@@ -0,0 +1,83 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Compare indexer query quantization latency inside an NPU graph.
+
+Run: python benchmarks/quantize_indexer_query.py
+Times exclude compilation, graph capture and host tensor allocation.
+"""
+
+import argparse
+import json
+import statistics
+
+import torch
+import torch_npu  # noqa: F401
+
+from vllm_ascend.ops.triton.quantize_indexer_query import quantize_indexer_query
+from vllm_ascend.ops.triton.triton_utils import init_device_properties_triton
+
+
+def reference(query):
+    scale = (query.float().abs().amax(-1) / 127.0).half().clamp_min_(2.0**-24)
+    quantized = (query.float() / scale.float().unsqueeze(-1)).round().clamp(-127, 127).to(torch.int8)
+    return quantized, scale
+
+
+def graph_latency_us(fn, query, batch=32, repeats=20):
+    for _ in range(3):
+        fn(query)
+    torch.npu.synchronize()
+    graph = torch.npu.NPUGraph()
+    with torch.npu.graph(graph, capture_error_mode="thread_local", auto_dispatch_capture=True):
+        for _ in range(batch):
+            output = fn(query)
+    for _ in range(3):
+        graph.replay()
+    torch.npu.synchronize()
+    samples = []
+    for _ in range(5):
+        start = torch.npu.Event(enable_timing=True)
+        end = torch.npu.Event(enable_timing=True)
+        start.record()
+        for _ in range(repeats):
+            graph.replay()
+        end.record()
+        end.synchronize()
+        samples.append(start.elapsed_time(end) * 1000 / (batch * repeats))
+    # Keep the captured outputs alive until timing completes.
+    assert output[0].shape == query.shape
+    return statistics.median(samples)
+
+
+@torch.inference_mode()
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--tokens", type=int, nargs="+", default=[1, 32, 256, 4096])
+    parser.add_argument("--heads", type=int, nargs="+", default=[32, 64])
+    args = parser.parse_args()
+    torch.npu.set_device(0)
+    init_device_properties_triton()
+    torch.manual_seed(41)
+    for tokens in args.tokens:
+        for heads in args.heads:
+            query = torch.randn(tokens, heads, 128, dtype=torch.bfloat16, device="npu")
+            for actual, expected in zip(quantize_indexer_query(query), reference(query)):
+                torch.testing.assert_close(actual, expected, rtol=0, atol=0)
+            original_us = graph_latency_us(reference, query)
+            fused_us = graph_latency_us(quantize_indexer_query, query)
+            print(
+                json.dumps(
+                    {
+                        "tokens": tokens,
+                        "heads": heads,
+                        "reference_us": original_us,
+                        "triton_us": fused_us,
+                        "speedup": original_us / fused_us,
+                    }
+                ),
+                flush=True,
+            )
+
+
+if __name__ == "__main__":
+    main()

csrc/attention/common/op_kernel/aicpu_common.h

@@ -16,6 +16,7 @@
 #ifndef AICPU_COMMON_H
 #define AICPU_COMMON_H
 
+#include <algorithm>
 #include <cstdint>
 #include <vector>
 #include "log.h"
@@ -103,7 +104,7 @@ inline bool IsTensorExists(const Tensor *tensor)
 
 inline std::vector<int64_t> GetTensorDataAsInt64(const Tensor *tensor)
 {
-    std::vector<int64_t> result {};
+    std::vector<int64_t> result{};
 
     if (!IsTensorExists(tensor)) {
         return result;