[cpu][perf] Accelerate unquantized-linear for AArch64 through oneDNN/ACL and...

[cpu][perf] Accelerate unquantized-linear for AArch64 through oneDNN/ACL and weight prepack (#25948)
Signed-off-by: Fadi Arafeh <fadi.arafeh@arm.com>
Co-authored-by: Li, Jiang <jiang1.li@intel.com>

cmake/cpu_extension.cmake

@@ -213,6 +213,7 @@ if ((AVX512_FOUND AND NOT AVX512_DISABLED) OR (ASIMD_FOUND AND NOT APPLE_SILICON
         endif()
         set(ONEDNN_AARCH64_USE_ACL "ON")
         set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wl,-rpath,$ENV{ACL_ROOT_DIR}/build/")
+        add_compile_definitions(VLLM_USE_ACL)
     endif()
 
     set(ONEDNN_LIBRARY_TYPE "STATIC")
@@ -226,7 +227,7 @@ if ((AVX512_FOUND AND NOT AVX512_DISABLED) OR (ASIMD_FOUND AND NOT APPLE_SILICON
     set(ONEDNN_ENABLE_ITT_TASKS "OFF")
     set(ONEDNN_ENABLE_MAX_CPU_ISA "OFF")
     set(ONEDNN_ENABLE_CPU_ISA_HINTS "OFF")
-    set(ONEDNN_VERBOSE "OFF")
+    set(ONEDNN_VERBOSE "ON")
     set(CMAKE_POLICY_DEFAULT_CMP0077 NEW)
 
     FetchContent_MakeAvailable(oneDNN)

csrc/cpu/dnnl_helper.cpp

@@ -137,9 +137,8 @@ DNNLMatMulPrimitiveHandler::DNNLMatMulPrimitiveHandler(
 }
 
 void DNNLMatMulPrimitiveHandler::prepack_weight(
-    void* original_b_ptr, dnnl::memory::desc b_target_mem_desc) {
-  dnnl::memory::desc original_b_md({b_k_size_, b_n_size_}, b_type_,
-                                   {b_k_stride_, b_n_stride_});
+    void* original_b_ptr, dnnl::memory::desc original_b_md,
+    dnnl::memory::desc b_target_mem_desc) {
   dnnl::memory original_weight(original_b_md, default_engine(), original_b_ptr);
   dnnl::memory packed_weight(b_target_mem_desc, default_engine());
   {
@@ -250,7 +249,9 @@ W8A8MatMulPrimitiveHandler::W8A8MatMulPrimitiveHandler(const Args& args)
   if (a_qs_ == QuantizationStrategy::PER_TOKEN) {
     assert(!use_azp_);
   };
-  prepack_weight(args.b_ptr,
+  dnnl::memory::desc original_b_md({b_k_size_, b_n_size_}, b_type_,
+                                   {b_k_stride_, b_n_stride_});
+  prepack_weight(args.b_ptr, original_b_md,
                  create_primitive_desc(
                      MSizeCacheKey{.a_m_size = DNNL_RUNTIME_DIM_VAL,
                                    .use_bias = false,
@@ -412,12 +413,25 @@ MatMulPrimitiveHandler::MatMulPrimitiveHandler(const Args& args)
   assert(ab_type_ == dnnl::memory::data_type::f32 ||
          ab_type_ == dnnl::memory::data_type::bf16 ||
          ab_type_ == dnnl::memory::data_type::f16);
-  prepack_weight(args.b_ptr,
+
+  dnnl::memory::desc original_b_md({b_k_size_, b_n_size_}, b_type_,
+                                   {b_k_stride_, b_n_stride_});
+
+  prepack_weight(args.b_ptr, original_b_md,
                  create_primitive_desc(
-                     MSizeCacheKey{.a_m_size = DNNL_RUNTIME_DIM_VAL,
-                                   .a_m_stride = DNNL_RUNTIME_DIM_VAL,
-                                   .use_bias = false,
-                                   .bias_type = dnnl::memory::data_type::undef},
+                     MSizeCacheKey{
+#ifdef VLLM_USE_ACL
+                         // Arm Compute Library (ACL) backend for oneDNN does
+                         // not support runtime
+                         // dimensions, so we set M to a default value
+                         .a_m_size = 128,
+                         .a_m_stride = b_k_size_,
+#else
+                         .a_m_size = DNNL_RUNTIME_DIM_VAL,
+                         .a_m_stride = DNNL_RUNTIME_DIM_VAL,
+#endif
+                         .use_bias = false,
+                         .bias_type = dnnl::memory::data_type::undef},
                      true)
                      .weights_desc());
   init_runtime_memory_cache(args);
@@ -443,13 +457,31 @@ void MatMulPrimitiveHandler::execute(ExecArgs& args) {
   c_storage->set_data_handle((void*)args.c_ptr);
   c_mem_desc->dims[0] = args.a_m_size;
 
+#ifndef VLLM_USE_ACL
+  // We do not support in ACL backend of oneDNN, we handle bias by:
+  // 1. copying it into the result tensor
+  // 2. attaching a fused-sum post-op to the matmul primitive
   if (args.use_bias) {
     auto&& [bias_storage, bias_mem_desc] = get_runtime_memory_ptr(2);
     bias_storage->set_data_handle((void*)args.bias_ptr);
   }
-
+#endif
   dnnl::matmul matmul = get_matmul_cache(args);
 
+// With ACL backend of oneDNN, the required memory format might change when the
+// source tensor dims change. This does not really happen in practice, so isn't
+// a performance hit, but we need to support it because the API allows for it.
+#ifdef VLLM_USE_ACL
+  auto new_expected_wei_desc =
+      dnnl::matmul::primitive_desc(
+          const_cast<dnnl_primitive_desc_t>(matmul.get_primitive_desc()))
+          .weights_desc();
+  if (new_expected_wei_desc != b_target_mem_desc_) {
+    prepack_weight(memory_cache_[DNNL_ARG_WEIGHTS].get_data_handle(),
+                   b_target_mem_desc_, new_expected_wei_desc);
+  }
+#endif
+
   auto&& [scratchpad_storage, scratchpad_mem_desc] = get_runtime_memory_ptr(3);
   scratchpad_storage->set_data_handle(
       DNNLScratchPadManager::get_dnnl_scratchpad_manager()->get_data<void>());
@@ -484,7 +516,13 @@ dnnl::matmul::primitive_desc MatMulPrimitiveHandler::create_primitive_desc(
   } else {
     a_md = dnnl::memory::desc({key.a_m_size, b_k_size_}, b_type_,
                               {key.a_m_stride, 1});
+#ifdef VLLM_USE_ACL
+    // ACL's backend of oneDNN always expects the weight format to be "any"
+    b_md = dnnl::memory::desc({b_k_size_, b_n_size_}, b_type_,
+                              dnnl::memory::format_tag::any);
+#else
     b_md = b_target_mem_desc_;
+#endif
   }
   dnnl::memory::desc c_md({key.a_m_size, b_n_size_}, c_type_,
                           dnnl::memory::format_tag::ab);
@@ -494,8 +532,18 @@ dnnl::matmul::primitive_desc MatMulPrimitiveHandler::create_primitive_desc(
 
   if (key.use_bias) {
     dnnl::memory::desc bias_md({1, b_n_size_}, key.bias_type, {b_n_size_, 1});
+// Since ACL's matmuls don't support passing a bias_md, we apply the bias
+// through a fused-sum post-op
+#ifdef VLLM_USE_ACL
+    dnnl::post_ops post_ops;
+    post_ops.append_sum();
+    attr.set_post_ops(post_ops);
+    return dnnl::matmul::primitive_desc(default_engine(), a_md, b_md, c_md,
+                                        attr);
+#else
     return dnnl::matmul::primitive_desc(default_engine(), a_md, b_md, bias_md,
                                         c_md, attr);
+#endif
   } else {
     return dnnl::matmul::primitive_desc(default_engine(), a_md, b_md, c_md,
                                         attr);
@@ -511,13 +559,23 @@ void MatMulPrimitiveHandler::init_runtime_memory_cache(const Args& args) {
                    default_engine(), nullptr);
   set_runtime_memory_ptr(1, memory_cache_[DNNL_ARG_DST].get());
 
+// ACL matmuls don't support bias_md, so we don't need these
+#ifndef VLLM_USE_ACL
   memory_cache_[DNNL_ARG_BIAS] =
       dnnl::memory({{b_n_size_}, dnnl::memory::data_type::f32, {1}},
                    default_engine(), nullptr);
   set_runtime_memory_ptr(2, memory_cache_[DNNL_ARG_BIAS].get());
-
+#endif
   memory_cache_[DNNL_ARG_SCRATCHPAD] =
       dnnl::memory({{b_n_size_}, dnnl::memory::data_type::f32, {1}},
                    default_engine(), nullptr);
   set_runtime_memory_ptr(3, memory_cache_[DNNL_ARG_SCRATCHPAD].get());
 }
+
+bool is_onednn_acl_supported() {
+#ifdef VLLM_USE_ACL
+  return true;
+#else
+  return false;
+#endif
+}

csrc/cpu/dnnl_helper.h

@@ -101,7 +101,7 @@ class DNNLMatMulPrimitiveHandler {
  protected:
   DNNLMatMulPrimitiveHandler(const Args& args, dnnl::memory::data_type b_type);
 
-  void prepack_weight(void* original_b_ptr,
+  void prepack_weight(void* original_b_ptr, dnnl::memory::desc original_b_md,
                       dnnl::memory::desc b_target_mem_desc);
 
   void set_runtime_memory_ptr(size_t index, dnnl_memory* memory_ptr);

csrc/cpu/dnnl_kernels.cpp

@@ -527,21 +527,42 @@ void onednn_mm(torch::Tensor& c,        // [M, OC], row-major
   MatMulPrimitiveHandler* ptr =
       reinterpret_cast<MatMulPrimitiveHandler*>(handler);
 
+// ACL matmuls expect contiguous source tensors
+#ifdef VLLM_USE_ACL
+  torch::Tensor a_contig = a.contiguous();
+#endif
+
   MatMulPrimitiveHandler::ExecArgs exec_args;
+
+#ifdef VLLM_USE_ACL
+  exec_args.a_m_size = a_contig.size(0);
+  exec_args.a_m_stride = a_contig.stride(0);
+#else
   exec_args.a_m_size = a.size(0);
   exec_args.a_m_stride = a.stride(0);
-
+#endif
   VLLM_DISPATCH_FLOATING_TYPES(a.scalar_type(), "onednn_mm", [&] {
     if (bias.has_value()) {
       exec_args.use_bias = true;
       exec_args.bias_type = get_dnnl_type<scalar_t>();
+#ifdef VLLM_USE_ACL
+      // ACL matmuls in oneDNN do not support a bias.
+      // We handle a matmul with bias by doing: c = bias; c += matmul(a, b)
+      c.copy_(bias.value());
+#else
       exec_args.bias_ptr = bias->data_ptr<scalar_t>();
+#endif
     } else {
       exec_args.use_bias = false;
       exec_args.bias_type = get_dnnl_type<void>();
       exec_args.bias_ptr = nullptr;
     }
+#ifdef VLLM_USE_ACL
+    exec_args.a_ptr = a_contig.data_ptr<scalar_t>();
+#else
     exec_args.a_ptr = a.data_ptr<scalar_t>();
+
+#endif
     exec_args.c_ptr = c.data_ptr<scalar_t>();
 
     ptr->execute(exec_args);

csrc/cpu/torch_bindings.cpp

@@ -27,6 +27,8 @@ int64_t create_onednn_mm_handler(const torch::Tensor& b,
 void onednn_mm(torch::Tensor& c, const torch::Tensor& a,
                const std::optional<torch::Tensor>& bias, int64_t handler);
 
+bool is_onednn_acl_supported();
+
 void mla_decode_kvcache(torch::Tensor& out, torch::Tensor& query,
                         torch::Tensor& kv_cache, double scale,
                         torch::Tensor& block_tables, torch::Tensor& seq_lens);
@@ -181,6 +183,9 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "int handler) -> ()");
   ops.impl("onednn_mm", torch::kCPU, &onednn_mm);
 
+  // Check if oneDNN was built with ACL backend
+  ops.def("is_onednn_acl_supported() -> bool", &is_onednn_acl_supported);
+
   // Create oneDNN W8A8 handler
   ops.def(
       "create_onednn_scaled_mm_handler(Tensor b, Tensor b_scales, ScalarType "

setup.py

@@ -205,6 +205,11 @@ class cmake_build_ext(build_ext):
         # Make sure we use the nvcc from CUDA_HOME
         if _is_cuda():
             cmake_args += [f'-DCMAKE_CUDA_COMPILER={CUDA_HOME}/bin/nvcc']
+
+        other_cmake_args = os.environ.get("CMAKE_ARGS")
+        if other_cmake_args:
+            cmake_args += other_cmake_args.split()
+
         subprocess.check_call(
             ['cmake', ext.cmake_lists_dir, *build_tool, *cmake_args],
             cwd=self.build_temp)

vllm/_custom_ops.py

@@ -1926,6 +1926,10 @@ else:
     _supports_onednn = False
 
 
+def is_onednn_acl_supported():
+    return torch.ops._C.is_onednn_acl_supported()
+
+
 def create_onednn_mm(
     weight: torch.Tensor,  # [K, N]
     primitive_cache_size: int = 128,

vllm/model_executor/layers/utils.py

@@ -165,8 +165,9 @@ def dispatch_cpu_unquantized_gemm(
         if remove_weight:
             layer.weight = torch.nn.Parameter(torch.empty(0),
                                               requires_grad=False)
-    elif (ops._supports_onednn
-          and current_platform.get_cpu_architecture() == CpuArchEnum.X86):
+    elif ops._supports_onednn and (current_platform.get_cpu_architecture()
+                                   == CpuArchEnum.X86
+                                   or ops.is_onednn_acl_supported()):
         origin_weight = layer.weight
         if remove_weight:
             layer.weight = torch.nn.Parameter(torch.empty(0),