vllm-quantization: import in kernels-community again

scripts/check_kernel_freshness.py

@@ -43,6 +43,7 @@
     "trimul-gpumode": "https://github.com/davidberard98/gpumode-trimul",
     "triton-kernels": "https://github.com/triton-lang/triton.git",
     "vllm-flash-attn3": "https://github.com/Dao-AILab/flash-attention",
+    "vllm-quantization": "",
     "yoso": "https://github.com/mlpen/YOSO",
     "cv-utils": "",
     "liger-kernels": "https://github.com/linkedin/Liger-Kernel",

vllm-quantization/attention/attention_dtypes.h

@@ -0,0 +1,7 @@
+#pragma once
+
+#include "attention_generic.cuh"
+#include "dtype_float16.cuh"
+#include "dtype_float32.cuh"
+#include "dtype_bfloat16.cuh"
+#include "dtype_fp8.cuh"

vllm-quantization/attention/attention_generic.cuh

@@ -0,0 +1,65 @@
+/*
+ * Adapted from
+ * https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention_utils.h
+ * Copyright (c) 2023, The vLLM team.
+ * Copyright (c) 2020-2023, 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.
+ */
+#pragma once
+
+#include <stdint.h>
+
+namespace vllm {
+
+// A vector type to store Q, K, V elements.
+template <typename T, int VEC_SIZE>
+struct Vec {};
+
+// A vector type to store FP32 accumulators.
+template <typename T>
+struct FloatVec {};
+
+// Template vector operations.
+template <typename Acc, typename A, typename B>
+inline __device__ Acc mul(A a, B b);
+
+template <typename T>
+inline __device__ float sum(T v);
+
+template <typename T>
+inline __device__ float dot(T a, T b) {
+  return sum(mul<T, T, T>(a, b));
+}
+
+template <typename A, typename T>
+inline __device__ float dot(T a, T b) {
+  return sum(mul<A, T, T>(a, b));
+}
+
+template <typename T>
+inline __device__ void zero(T& dst) {
+  constexpr int WORDS = sizeof(T) / 4;
+  union {
+    T raw;
+    uint32_t words[WORDS];
+  } tmp;
+
+#pragma unroll
+  for (int ii = 0; ii < WORDS; ++ii) {
+    tmp.words[ii] = 0u;
+  }
+  dst = tmp.raw;
+}
+
+}  // namespace vllm