init v0.23.0

Signed-off-by: Sun Ruoxi <sunruoxi@4paradigm.com>

vllm_ascend/worker/v2/sample/min_p.py

@@ -0,0 +1,91 @@
+# Adapt from https://github.com/vllm-project/vllm/blob/main/vllm/v1/worker/gpu/sample/min_p.py.
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. 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.
+# This file is a part of the vllm-ascend project.
+
+import torch
+from vllm.triton_utils import tl, triton
+
+from vllm_ascend.ops.triton.triton_utils import get_vectorcore_num
+
+
+@triton.jit(do_not_specialize=["num_tokens"])
+def _min_p_kernel(
+    in_logits_ptr,
+    out_logits_ptr,
+    logits_stride,
+    expanded_idx_mapping_ptr,
+    min_p_ptr,
+    vocab_size,
+    num_tokens,
+    BLOCK_SIZE: tl.constexpr,
+):
+    pid = tl.program_id(0)
+    core_num = tl.num_programs(0)
+
+    tokens_per_block = (num_tokens + core_num - 1) // core_num
+    start_token = pid * tokens_per_block
+    end_token = tl.minimum(start_token + tokens_per_block, num_tokens)
+
+    for token_idx in tl.range(start_token, end_token):
+        req_state_idx = tl.load(expanded_idx_mapping_ptr + token_idx)
+        min_p = tl.load(min_p_ptr + req_state_idx).to(tl.float32)
+        if min_p != 0.0:
+            max_val = float("-inf")
+            for i in range(0, vocab_size, BLOCK_SIZE):
+                block = i + tl.arange(0, BLOCK_SIZE)
+                mask = block < vocab_size
+                logits = tl.load(
+                    in_logits_ptr + token_idx * logits_stride + block,
+                    mask=mask,
+                    other=float("-inf"),
+                )
+                max_val = tl.max(tl.maximum(logits, max_val))
+            max_val = max_val.to(tl.float32)  # type: ignore
+
+            threshold = max_val + tl.log(min_p)
+            for i in range(0, vocab_size, BLOCK_SIZE):
+                block = i + tl.arange(0, BLOCK_SIZE)
+                mask = block < vocab_size
+                logits = tl.load(
+                    in_logits_ptr + token_idx * logits_stride + block,
+                    mask=mask,
+                    other=float("-inf"),
+                )
+                logits = tl.where(logits < threshold, float("-inf"), logits)
+                tl.store(out_logits_ptr + token_idx * logits_stride + block, logits, mask=mask)
+
+
+def apply_min_p(logits: torch.Tensor, expanded_idx_mapping: torch.Tensor, min_p: torch.Tensor) -> None:
+    num_tokens, vocab_size = logits.shape
+
+    vec_core = get_vectorcore_num()
+    core_nums = min(num_tokens, vec_core)
+
+    BLOCK_SIZE = min(triton.next_power_of_2(vocab_size), 8192)
+
+    _min_p_kernel[(core_nums,)](
+        logits,
+        logits,
+        logits.stride(0),
+        expanded_idx_mapping,
+        min_p,
+        vocab_size,
+        num_tokens,
+        BLOCK_SIZE=BLOCK_SIZE,
+        multibuffer=False,
+    )