Merge tag 'v0.7.1' into v0.7.1-dev

csrc/quantization/marlin/qqq/marlin_qqq_gemm_kernel.cu

@@ -141,8 +141,8 @@ __device__ inline FragB dequant_per_group(int q, FragS_GROUP& frag_s, int i) {
   static constexpr uint32_t HI = 0x00f000f0;
   static constexpr uint32_t EX = 0x64006400;
   // Guarantee that the `(a & b) | c` operations are LOP3s.
-  uint32_t t0 = lop3<(0xf0 & 0xcc) | 0xaa>(q, LO, EX);
-  uint32_t t1 = lop3<(0xf0 & 0xcc) | 0xaa>(q, HI, EX);
+  uint32_t t0 = lop3 < (0xf0 & 0xcc) | 0xaa > (q, LO, EX);
+  uint32_t t1 = lop3 < (0xf0 & 0xcc) | 0xaa > (q, HI, EX);
   // We want signed int4 outputs, hence we fuse the `-8` symmetric zero point
   // directly into `SUB` and `ADD`.
   static constexpr uint32_t SUB = 0x64086408;

csrc/quantization/marlin/sparse/common/mma.h

@@ -127,8 +127,8 @@ __device__ inline FragB dequant_4bit(int q) {
   const int HI = 0x00f000f0;
   const int EX = 0x64006400;
   // Guarantee that the `(a & b) | c` operations are LOP3s.
-  int lo = lop3<(0xf0 & 0xcc) | 0xaa>(q, LO, EX);
-  int hi = lop3<(0xf0 & 0xcc) | 0xaa>(q, HI, EX);
+  int lo = lop3 < (0xf0 & 0xcc) | 0xaa > (q, LO, EX);
+  int hi = lop3 < (0xf0 & 0xcc) | 0xaa > (q, HI, EX);
   // We want signed int4 outputs, hence we fuse the `-8` symmetric zero point
   // directly into `SUB` and `ADD`.
   const int SUB = 0x64086408;

csrc/rocm/attention.cu

@@ -218,7 +218,7 @@ __global__ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_kernel(
     scalar_t* __restrict__ out,  // [num_seqs, num_heads, max_num_partitions,
                                  // head_size]
     scalar_t* __restrict__ final_out,  // [num_seqs, num_heads, head_size]
-    int max_ctx_blocks, float k_scale, float v_scale) {
+    int max_ctx_blocks, const float* k_scale_ptr, const float* v_scale_ptr) {
   constexpr int NWARPS = NUM_THREADS / WARP_SIZE;
   const int warpid = threadIdx.x / WARP_SIZE;
   const int laneid = threadIdx.x % WARP_SIZE;
@@ -406,7 +406,7 @@ __global__ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_kernel(
             // Vlocalb8[h][b * BLOCK_SIZE / 8 + d] = v_ptrh8be[d];
             const _B8x8 Vlocalb8 = v_ptrh8be[d];
             Vlocal[h][b * BLOCK_SIZE / 8 + d] =
-                scaled_convert_b8x8<scalar_t, KV_DTYPE>(Vlocalb8, v_scale);
+                scaled_convert_b8x8<scalar_t, KV_DTYPE>(Vlocalb8, *v_scale_ptr);
           }
         }
       }
@@ -416,7 +416,7 @@ __global__ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_kernel(
   #pragma unroll
       for (int d = 0; d < KHELOOP; d++) {
         Klocal[d] =
-            scaled_convert_b8x8<scalar_t, KV_DTYPE>(Klocalb8[d], k_scale);
+            scaled_convert_b8x8<scalar_t, KV_DTYPE>(Klocalb8[d], *k_scale_ptr);
       }
     }
 
@@ -890,7 +890,7 @@ __global__ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_kernel(
     scalar_t* __restrict__ out,  // [num_seqs, num_heads, max_num_partitions,
                                  // head_size]
     scalar_t* __restrict__ final_out,  // [num_seqs, num_heads, head_size]
-    int max_ctx_blocks, float k_scale, float v_scale) {
+    int max_ctx_blocks, const float* k_scale, const float* v_scale) {
   UNREACHABLE_CODE
 }
 
@@ -907,7 +907,9 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_reduce_kernel(
     const scalar_t* __restrict__ tmp_out,  // [num_seqs, num_heads,
                                            // max_num_partitions, head_size]
     const int* __restrict__ context_lens,  // [num_seqs]
-    const int max_num_partitions){UNREACHABLE_CODE}
+    const int max_num_partitions) {
+  UNREACHABLE_CODE
+}
 
 #endif  // defined(__HIP__MI300_MI250__) TODO: Add NAVI support
 
@@ -919,7 +921,7 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_reduce_kernel(
           block_tables_ptr, context_lens_ptr, max_num_blocks_per_seq,         \
           alibi_slopes_ptr, q_stride, kv_block_stride, kv_head_stride,        \
           exp_sums_ptr, max_logits_ptr, tmp_out_ptr, out_ptr, max_ctx_blocks, \
-          k_scale, v_scale);
+          k_scale_ptr, v_scale_ptr);
 
 template <typename T, typename KVT, vllm::Fp8KVCacheDataType KV_DTYPE,
           int BLOCK_SIZE, int HEAD_SIZE, int PARTITION_SIZE = 512>
@@ -928,8 +930,8 @@ void paged_attention_custom_launcher(
     torch::Tensor& tmp_out, torch::Tensor& query, torch::Tensor& key_cache,
     torch::Tensor& value_cache, const int num_kv_heads, float scale,
     torch::Tensor& block_tables, torch::Tensor& context_lens,
-    int max_context_len, const c10::optional<torch::Tensor>& alibi_slopes,
-    float k_scale, float v_scale) {
+    int max_context_len, const std::optional<torch::Tensor>& alibi_slopes,
+    torch::Tensor& k_scale, torch::Tensor& v_scale) {
   int num_seqs = query.size(0);
   int num_heads = query.size(1);
   int head_size = query.size(2);
@@ -953,6 +955,8 @@ void paged_attention_custom_launcher(
   KVT* value_cache_ptr = reinterpret_cast<KVT*>(value_cache.data_ptr());
   int* block_tables_ptr = block_tables.data_ptr<int>();
   int* context_lens_ptr = context_lens.data_ptr<int>();
+  const float* k_scale_ptr = reinterpret_cast<const float*>(k_scale.data_ptr());
+  const float* v_scale_ptr = reinterpret_cast<const float*>(v_scale.data_ptr());
 
   const int max_ctx_blocks = DIVIDE_ROUND_UP(max_context_len, BLOCK_SIZE);
   const int max_num_partitions =
@@ -1086,8 +1090,9 @@ void paged_attention(
     torch::Tensor& block_tables,  // [num_seqs, max_num_blocks_per_seq]
     torch::Tensor& context_lens,  // [num_seqs]
     int64_t block_size, int64_t max_context_len,
-    const c10::optional<torch::Tensor>& alibi_slopes,
-    const std::string& kv_cache_dtype, double k_scale, double v_scale) {
+    const std::optional<torch::Tensor>& alibi_slopes,
+    const std::string& kv_cache_dtype, torch::Tensor& k_scale,
+    torch::Tensor& v_scale) {
   const int head_size = query.size(2);
   if (kv_cache_dtype == "auto") {
     if (query.dtype() == at::ScalarType::Half) {

csrc/rocm/ops.h

@@ -9,6 +9,6 @@ void paged_attention(torch::Tensor& out, torch::Tensor& exp_sums,
                      double scale, torch::Tensor& block_tables,
                      torch::Tensor& context_lens, int64_t block_size,
                      int64_t max_context_len,
-                     const c10::optional<torch::Tensor>& alibi_slopes,
-                     const std::string& kv_cache_dtype, double k_scale,
-                     double v_scale);
+                     const std::optional<torch::Tensor>& alibi_slopes,
+                     const std::string& kv_cache_dtype, torch::Tensor& k_scale,
+                     torch::Tensor& v_scale);

csrc/rocm/torch_bindings.cpp

@@ -27,7 +27,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, rocm_ops) {
       "                int max_context_len,"
       "                Tensor? alibi_slopes,"
       "                str kv_cache_dtype,"
-      "                float k_scale, float v_scale) -> ()");
+      "                Tensor k_scale, Tensor v_scale) -> ()");
   rocm_ops.impl("paged_attention", torch::kCUDA, &paged_attention);
 }
 

csrc/sparse/cutlass/sparse_scaled_mm_c3x.cu

@@ -286,7 +286,7 @@ void cutlass_scaled_sparse_mm_sm90(torch::Tensor& out, torch::Tensor const& a,
                                    torch::Tensor const& bt_meta,
                                    torch::Tensor const& a_scales,
                                    torch::Tensor const& b_scales,
-                                   c10::optional<torch::Tensor> const& bias) {
+                                   std::optional<torch::Tensor> const& bias) {
   TORCH_CHECK(a_scales.dtype() == torch::kFloat32);
   TORCH_CHECK(b_scales.dtype() == torch::kFloat32);
   if (bias) {

csrc/sparse/cutlass/sparse_scaled_mm_entry.cu

@@ -22,7 +22,7 @@ void cutlass_scaled_sparse_mm_sm90(torch::Tensor& c, torch::Tensor const& a,
                                    torch::Tensor const& e,
                                    torch::Tensor const& a_scales,
                                    torch::Tensor const& b_scales,
-                                   c10::optional<torch::Tensor> const& bias);
+                                   std::optional<torch::Tensor> const& bias);
 #endif
 
 void cutlass_scaled_sparse_mm(torch::Tensor& c, torch::Tensor const& a,
@@ -30,7 +30,7 @@ void cutlass_scaled_sparse_mm(torch::Tensor& c, torch::Tensor const& a,
                               torch::Tensor const& bt_meta,
                               torch::Tensor const& a_scales,
                               torch::Tensor const& b_scales,
-                              c10::optional<torch::Tensor> const& bias) {
+                              std::optional<torch::Tensor> const& bias) {
   // Checks for conformality
   TORCH_CHECK(a.dim() == 2 && bt_nzs.dim() == 2 && c.dim() == 2);
   TORCH_CHECK(c.size(1) == bt_nzs.size(0) && bt_nzs.size(1) * 2 == a.size(1) &&

csrc/torch_bindings.cpp

@@ -30,7 +30,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "    Tensor value_cache, int num_kv_heads, float scale,"
       "    Tensor block_tables, Tensor seq_lens, int block_size,"
       "    int max_seq_len, Tensor? alibi_slopes,"
-      "    str kv_cache_dtype, float k_scale, float v_scale,"
+      "    str kv_cache_dtype, Tensor k_scale, Tensor v_scale,"
       "    int tp_rank, int blocksparse_local_blocks,"
       "    int blocksparse_vert_stride, int blocksparse_block_size,"
       "    int blocksparse_head_sliding_step) -> ()");
@@ -44,7 +44,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "    Tensor value_cache, int num_kv_heads, float scale,"
       "    Tensor block_tables, Tensor seq_lens, int block_size,"
       "    int max_seq_len, Tensor? alibi_slopes,"
-      "    str kv_cache_dtype, float k_scale, float v_scale,"
+      "    str kv_cache_dtype, Tensor k_scale, Tensor v_scale,"
       "    int tp_rank, int blocksparse_local_blocks,"
       "    int blocksparse_vert_stride, int blocksparse_block_size,"
       "    int blocksparse_head_sliding_step) -> ()");
@@ -208,6 +208,9 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def("silu_and_mul(Tensor! out, Tensor input) -> ()");
   ops.impl("silu_and_mul", torch::kCUDA, &silu_and_mul);
 
+  ops.def("mul_and_silu(Tensor! out, Tensor input) -> ()");
+  ops.impl("mul_and_silu", torch::kCUDA, &mul_and_silu);
+
   // Activation function used in GeGLU with `none` approximation.
   ops.def("gelu_and_mul(Tensor! out, Tensor input) -> ()");
   ops.impl("gelu_and_mul", torch::kCUDA, &gelu_and_mul);
@@ -511,6 +514,13 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def("cutlass_scaled_mm_supports_fp8(int cuda_device_capability) -> bool");
   ops.impl("cutlass_scaled_mm_supports_fp8", &cutlass_scaled_mm_supports_fp8);
 
+  // Check if cutlass scaled_mm supports block quantization (used by DeepSeekV3)
+  ops.def(
+      "cutlass_scaled_mm_supports_block_fp8(int cuda_device_capability) -> "
+      "bool");
+  ops.impl("cutlass_scaled_mm_supports_block_fp8",
+           &cutlass_scaled_mm_supports_fp8);
+
   // Check if cutlass sparse scaled_mm is supported for CUDA devices of the
   // given capability
   ops.def(
@@ -636,7 +646,7 @@ TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _cache_ops), cache_ops) {
       "                  Tensor! key_cache, Tensor! value_cache,"
       "                  Tensor slot_mapping,"
       "                  str kv_cache_dtype,"
-      "                  float k_scale, float v_scale) -> ()");
+      "                  Tensor k_scale, Tensor v_scale) -> ()");
   cache_ops.impl("reshape_and_cache", torch::kCUDA, &reshape_and_cache);
 
   // Reshape the key and value tensors and cache them.
@@ -646,7 +656,7 @@ TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _cache_ops), cache_ops) {
       "                        Tensor! value_cache,"
       "                        Tensor slot_mapping,"
       "                        str kv_cache_dtype,"
-      "                        float k_scale, float v_scale) -> ()");
+      "                        Tensor k_scale, Tensor v_scale) -> ()");
   cache_ops.impl("reshape_and_cache_flash", torch::kCUDA,
                  &reshape_and_cache_flash);
 
@@ -666,6 +676,15 @@ TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _cache_ops), cache_ops) {
       "                  str kv_cache_dtype) -> ()");
   cache_ops.impl("write_cache_multi_layers", torch::kCUDA, &write_cache_multi_layers);
 
+  // Concat kv_c and k_pe and cache them.
+  cache_ops.def(
+      "concat_and_cache_mla(Tensor kv_c, Tensor k_pe,"
+      "                     Tensor! kv_cache,"
+      "                     Tensor slot_mapping,"
+      "                     str kv_cache_dtype,"
+      "                     Tensor scale) -> ()");
+  cache_ops.impl("concat_and_cache_mla", torch::kCUDA, &concat_and_cache_mla);
+
   // Convert the key and value cache to fp8 data type.
   cache_ops.def(
       "convert_fp8(Tensor! dst_cache, Tensor src_cache, float scale, "

docs/Makefile

@@ -18,3 +18,7 @@ help:
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
 	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+clean:
+	@$(SPHINXBUILD) -M clean "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+	rm -rf "$(SOURCEDIR)/getting_started/examples"

docs/README.md

@@ -16,4 +16,5 @@ make html
 ```bash
 python -m http.server -d build/html/
 ```
+
 Launch your browser and open localhost:8000.

docs/requirements-docs.txt

 sphinx==6.2.1
+sphinx-argparse==0.4.0
 sphinx-book-theme==1.0.1
 sphinx-copybutton==0.5.2
+sphinx-design==0.6.1
+sphinx-togglebutton==0.3.2
 myst-parser==3.0.1
-sphinx-argparse==0.4.0
 msgspec
 cloudpickle
 
@@ -19,3 +21,4 @@ openai # Required by docs/source/serving/openai_compatible_server.md's vllm.entr
 fastapi # Required by docs/source/serving/openai_compatible_server.md's vllm.entrypoints.openai.cli_args
 partial-json-parser # Required by docs/source/serving/openai_compatible_server.md's vllm.entrypoints.openai.cli_args
 requests
+zmq

docs/source/_static/custom.js

+// Add RunLLM widget
 document.addEventListener("DOMContentLoaded", function () {
     var script = document.createElement("script");
     script.type = "module";
@@ -15,4 +16,23 @@ document.addEventListener("DOMContentLoaded", function () {
   
     script.async = true;
     document.head.appendChild(script);
-  });
+  });
\ No newline at end of file
+
+// Update URL search params when tab is clicked
+  document.addEventListener("DOMContentLoaded", function () {
+    const tabs = document.querySelectorAll(".sd-tab-label");
+
+    function updateURL(tab) {
+      const syncGroup = tab.getAttribute("data-sync-group");
+      const syncId = tab.getAttribute("data-sync-id");
+      if (syncGroup && syncId) {
+          const url = new URL(window.location);
+          url.searchParams.set(syncGroup, syncId);
+          window.history.replaceState(null, "", url);
+      }
+    }
+
+    tabs.forEach(tab => {
+        tab.addEventListener("click", () => updateURL(tab));
+    });
+});

docs/source/dev/engine/engine_index.md → docs/source/api/engine/index.md

@@ -8,10 +8,10 @@
 .. currentmodule:: vllm.engine
 ```
 
-```{toctree}
+:::{toctree}
 :caption: Engines
 :maxdepth: 2
 
 llm_engine
 async_llm_engine
-```
+:::

docs/source/api/inference_params.md

+# Inference Parameters
+
+Inference parameters for vLLM APIs.
+
+(sampling-params)=
+
+## Sampling Parameters
+
+```{eval-rst}
+.. autoclass:: vllm.SamplingParams
+    :members:
+```
+
+(pooling-params)=
+
+## Pooling Parameters
+
+```{eval-rst}
+.. autoclass:: vllm.PoolingParams
+    :members:
+```

docs/source/api/model/adapters.md

+# Model Adapters
+
+## Module Contents
+
+```{eval-rst}
+.. automodule:: vllm.model_executor.models.adapters
+    :members:
+    :member-order: bysource
+```

docs/source/api/model/index.md

+# Model Development
+
+## Submodules
+
+:::{toctree}
+:maxdepth: 1
+
+interfaces_base
+interfaces
+adapters
+:::

docs/source/api/model/interfaces.md

+# Optional Interfaces
+
+## Module Contents
+
+```{eval-rst}
+.. automodule:: vllm.model_executor.models.interfaces
+    :members:
+    :member-order: bysource
+```

docs/source/api/model/interfaces_base.md

+# Base Model Interfaces
+
+## Module Contents
+
+```{eval-rst}
+.. automodule:: vllm.model_executor.models.interfaces_base
+    :members:
+    :member-order: bysource
+```