[CPU] SparseAttention op (#21110)

Add SparseAttention cpu implementation.
- [x] Refactoring GQAAttentionBase
- [x] Add SparseAttention implementation
- [x] Add test cases

This is unfused version. Flash attention version will be added later.

docs/ContribOperators.md

@@ -5646,7 +5646,7 @@ This version of the operator has been available since version 1 of the 'com.micr
 #### Type Constraints
 
 <dl>
-<dt><tt>T</tt> : tensor(float16), tensor(bfloat16)</dt>
+<dt><tt>T</tt> : tensor(float), tensor(float16), tensor(bfloat16)</dt>
 <dd>Constrain input and output to float tensors.</dd>
 <dt><tt>M</tt> : tensor(int32)</dt>
 <dd>Constrain integer type.</dd>

docs/OperatorKernels.md

@@ -512,6 +512,7 @@ Do not modify directly.*
 |Sampling|*in* input_ids:**I**<br> *in* max_length:**I**<br> *in* min_length:**I**<br> *in* repetition_penalty:**T**<br> *in* vocab_mask:**I**<br> *in* prefix_vocab_mask:**I**<br> *in* attention_mask:**I**<br> *in* presence_mask:**I**<br> *in* seed:**I**<br> *out* sequences:**I**<br> *out* filtered_logits:**T**|1+|**T** = tensor(float)|
 |SkipLayerNormalization|*in* input:**T**<br> *in* skip:**T**<br> *in* gamma:**T**<br> *in* beta:**T**<br> *in* bias:**T**<br> *out* output:**T**<br> *out* mean:**U**<br> *out* inv_std_var:**U**<br> *out* input_skip_bias_sum:**T**|1+|**T** = tensor(double), tensor(float)|
 |SkipSimplifiedLayerNormalization|*in* input:**T**<br> *in* skip:**T**<br> *in* gamma:**T**<br> *in* bias:**T**<br> *out* output:**T**<br> *out* mean:**U**<br> *out* inv_std_var:**U**<br> *out* input_skip_bias_sum:**T**|1+|**T** = tensor(double), tensor(float)|
+|SparseAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *in* block_row_indices:**M**<br> *in* block_col_indices:**M**<br> *in* total_sequence_length:**M**<br> *in* key_total_sequence_lengths:**M**<br> *in* cos_cache:**T**<br> *in* sin_cache:**T**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**|1+|**M** = tensor(int32)<br/> **T** = tensor(float)|
 |SparseToDenseMatMul|*in* A:**T**<br> *in* B:**T1**<br> *out* Y:**T1**|1+|**T** = sparse_tensor(double), sparse_tensor(float), sparse_tensor(int32), sparse_tensor(int64), sparse_tensor(uint32), sparse_tensor(uint64)<br/> **T1** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
 |Tokenizer|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(string)|
 |TransposeMatMul|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|

onnxruntime/contrib_ops/cpu/bert/attention_base.h

@@ -68,7 +68,6 @@ class AttentionBase {
                      const Tensor* past_seq_len = nullptr) const;
 
   int num_heads_;                          // number of attention heads
-  int kv_num_heads_;                       // different for k and v for group query attention
   bool is_unidirectional_;                 // whether every token can only attend to previous tokens.
   std::vector<int64_t> qkv_hidden_sizes_;  // Q, K, V hidden sizes parsed from the qkv_hidden_sizes attribute.
   bool require_same_hidden_size_;          // whether the implementation supports different hidden sizes of Q/K/V.

onnxruntime/contrib_ops/cpu/bert/attention_cpu_base.h

@@ -3,9 +3,8 @@
 
 #pragma once
 
-#include "attention_base.h"
-#include "attention_helper.h"
-
+#include "contrib_ops/cpu/bert/attention_base.h"
+#include "contrib_ops/cpu/bert/attention_helper.h"
 #include "core/common/common.h"
 #include "core/common/safeint.h"
 #include "core/framework/op_kernel.h"

onnxruntime/contrib_ops/cpu/bert/gqa_attention_base.h

@@ -3,8 +3,8 @@
 
 #pragma once
 
-#include "attention_base.h"
-#include "attention_helper.h"
+#include "contrib_ops/cpu/bert/attention_base.h"
+#include "contrib_ops/cpu/bert/attention_helper.h"
 
 #include "core/common/common.h"
 #include "contrib_ops/cpu/bert/attention_common.h"
@@ -14,14 +14,31 @@
 namespace onnxruntime {
 namespace contrib {
 
-class GQAAttentionBase : public AttentionBase {
+class GQAAttentionBase {
  protected:
-  GQAAttentionBase(const OpKernelInfo& info, bool require_same_hidden_size)
-      : AttentionBase(info, require_same_hidden_size) {}
+  GQAAttentionBase(const OpKernelInfo& info, bool has_local) {
+    int64_t num_heads = 0;
+    ORT_ENFORCE(info.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0);
+    num_heads_ = static_cast<int>(num_heads);
 
-  int local_window_size_;
-  bool do_rotary_;
+    int64_t kv_num_heads = 0;
+    ORT_ENFORCE(info.GetAttr("kv_num_heads", &kv_num_heads).IsOK() && kv_num_heads > 0);
+    kv_num_heads_ = static_cast<int>(kv_num_heads);
+
+    scale_ = info.GetAttrOrDefault<float>("scale", 0.0f);
+
+    do_rotary_ = info.GetAttrOrDefault<int64_t>("do_rotary", 0) == 1;
+    rotary_interleaved_ = info.GetAttrOrDefault<int64_t>("rotary_interleaved", 0) == 1;
+
+    local_window_size_ = has_local ? static_cast<int>(info.GetAttrOrDefault<int64_t>("local_window_size", -1)) : -1;
+  }
+
+  int num_heads_;     // number of attention heads of Q
+  int kv_num_heads_;  // number of attention heads of K or V
+  float scale_;       // the scaling factor applied before softmax
+  bool do_rotary_;    // whether or not to use rotary embeddings
   bool rotary_interleaved_;
+  int local_window_size_;
 
   template <typename T>
   Status ApplyAttention(const T* Q,                                 // Q data with shape BxNxSxH

onnxruntime/contrib_ops/cpu/bert/group_query_attention.cc

@@ -1,11 +1,12 @@
 // Copyright (c) Microsoft Corporation. All rights reserved.
 // Licensed under the MIT License.
 
-#include "group_query_attention.h"
-#include "group_query_attention_helper.h"
-#include "attention_utils.h"
-#include "rotary_embedding.h"
-#include "rotary_embedding_helper.h"
+#include "contrib_ops/cpu/bert/group_query_attention.h"
+#include "contrib_ops/cpu/bert/group_query_attention_helper.h"
+#include "contrib_ops/cpu/bert/rotary_helper.h"
+#include "contrib_ops/cpu/bert/attention_utils.h"
+#include "contrib_ops/cpu/bert/rotary_embedding.h"
+#include "contrib_ops/cpu/bert/rotary_embedding_helper.h"
 
 #include "core/framework/tensorprotoutils.h"
 #include "core/graph/onnx_protobuf.h"
@@ -33,19 +34,8 @@ ONNX_OPERATOR_TYPED_KERNEL_EX(
     GroupQueryAttention<float>);
 
 template <typename T>
-GroupQueryAttention<T>::GroupQueryAttention(const OpKernelInfo& info) : OpKernel(info), GQAAttentionBase(info, false) {
-  int64_t num_heads = 0;
-  int64_t kv_num_heads = 0;
-  ORT_ENFORCE(info.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0);
-  ORT_ENFORCE(info.GetAttr("kv_num_heads", &kv_num_heads).IsOK() && kv_num_heads > 0);
-  num_heads_ = static_cast<int>(num_heads);
-  kv_num_heads_ = static_cast<int>(kv_num_heads);
-
-  mask_filter_value_ = info.GetAttrOrDefault<float>("mask_filter_value", -10000.0f);
-  local_window_size_ = static_cast<int>(info.GetAttrOrDefault<int64_t>("local_window_size", -1));
-  do_rotary_ = info.GetAttrOrDefault<int64_t>("do_rotary", 0) == 1;
-  rotary_interleaved_ = info.GetAttrOrDefault<int64_t>("rotary_interleaved", 0) == 1;
-}
+GroupQueryAttention<T>::GroupQueryAttention(const OpKernelInfo& info)
+    : OpKernel(info), GQAAttentionBase(info, true) {}
 
 template <typename T>
 Status GroupQueryAttention<T>::Compute(OpKernelContext* context) const {
@@ -174,14 +164,14 @@ Status GroupQueryAttention<T>::Compute(OpKernelContext* context) const {
     if (packed_qkv) {
       const T* v_input = k_input + kv_num_heads_ * sequence_length * head_size;
       T* v_rotary = k_rotary + kv_num_heads_ * sequence_length * head_size;
-      ORT_RETURN_IF_ERROR(group_query_attention_helper::PackVIntoRotaryQKV<T>(tp,
-                                                                              parameters.batch_size,
-                                                                              parameters.sequence_length,
-                                                                              parameters.num_heads,
-                                                                              parameters.kv_num_heads,
-                                                                              parameters.head_size,
-                                                                              v_input,
-                                                                              v_rotary));
+      ORT_RETURN_IF_ERROR(rotary_helper::PackVIntoRotaryQKV<T>(tp,
+                                                               parameters.batch_size,
+                                                               parameters.sequence_length,
+                                                               parameters.num_heads,
+                                                               parameters.kv_num_heads,
+                                                               parameters.head_size,
+                                                               v_input,
+                                                               v_rotary));
     }
   }
 

onnxruntime/contrib_ops/cpu/bert/group_query_attention_helper.h

@@ -263,38 +263,6 @@ Status CheckInputs(const Tensor* query,
 
   return CheckInputs(query, key, value, past_key, past_value, cos_cache, sin_cache, parameters, num_heads, kv_num_heads, seqlens_k, total_seqlen, scale);
 }
-
-template <typename T>
-Status PackVIntoRotaryQKV(concurrency::ThreadPool* tp,
-                          int batch_size,
-                          int sequence_length,
-                          int num_heads,
-                          int kv_num_heads,
-                          int head_size,
-                          const T* input,
-                          T* output) {
-  int seq_stride = head_size;
-  int head_stride = sequence_length * seq_stride;
-  int batch_stride = (num_heads + 2 * kv_num_heads) * head_stride;
-
-  const int loop_len = batch_size * sequence_length * kv_num_heads;
-  const double cost = static_cast<double>(head_size);
-  ThreadPool::TryParallelFor(tp, loop_len, cost, [&](std::ptrdiff_t begin, std::ptrdiff_t end) {
-    for (std::ptrdiff_t ptr = begin; ptr != end; ++ptr) {
-      const int b = static_cast<int>((ptr / kv_num_heads) / sequence_length);
-      const int s = static_cast<int>((ptr / kv_num_heads) % sequence_length);
-      const int n = static_cast<int>(ptr % kv_num_heads);
-      const int block_offset = b * batch_stride + s * seq_stride + n * head_stride;
-      const T* input_data = input + block_offset;
-      T* output_data = output + block_offset;
-      for (int i = 0; i < head_size; i++) {
-        output_data[i] = input_data[i];
-      }
-    }
-  });
-  return Status::OK();
-}
-
 }  // namespace group_query_attention_helper
 }  // namespace contrib
 }  // namespace onnxruntime

onnxruntime/contrib_ops/cpu/bert/rotary_helper.h

@@ -0,0 +1,47 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#pragma once
+
+#include "core/common/common.h"
+#include "core/providers/common.h"
+#include "contrib_ops/cpu/bert/attention_common.h"
+
+namespace onnxruntime {
+namespace contrib {
+namespace rotary_helper {
+
+template <typename T>
+Status PackVIntoRotaryQKV(concurrency::ThreadPool* tp,
+                          int batch_size,
+                          int sequence_length,
+                          int num_heads,
+                          int kv_num_heads,
+                          int head_size,
+                          const T* input,
+                          T* output) {
+  int seq_stride = head_size;
+  int head_stride = sequence_length * seq_stride;
+  int batch_stride = (num_heads + 2 * kv_num_heads) * head_stride;
+
+  const int loop_len = batch_size * sequence_length * kv_num_heads;
+  const double cost = static_cast<double>(head_size);
+  ThreadPool::TryParallelFor(tp, loop_len, cost, [&](std::ptrdiff_t begin, std::ptrdiff_t end) {
+    for (std::ptrdiff_t ptr = begin; ptr != end; ++ptr) {
+      const int b = static_cast<int>((ptr / kv_num_heads) / sequence_length);
+      const int s = static_cast<int>((ptr / kv_num_heads) % sequence_length);
+      const int n = static_cast<int>(ptr % kv_num_heads);
+      const int block_offset = b * batch_stride + s * seq_stride + n * head_stride;
+      const T* input_data = input + block_offset;
+      T* output_data = output + block_offset;
+      for (int i = 0; i < head_size; i++) {
+        output_data[i] = input_data[i];
+      }
+    }
+  });
+  return Status::OK();
+}
+
+}  // namespace rotary_helper
+}  // namespace contrib
+}  // namespace onnxruntime

onnxruntime/contrib_ops/cpu/cpu_contrib_kernels.cc

@@ -21,6 +21,7 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1,
 class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, GreedySearch);
 class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, MultiHeadAttention);
 class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, GroupQueryAttention);
+class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, SparseAttention);
 class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, RotaryEmbedding);
 class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, Sampling);
 class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, AttnLSTM);
@@ -281,6 +282,7 @@ Status RegisterCpuContribKernels(KernelRegistry& kernel_registry) {
     BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, GreedySearch)>,
     BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, MultiHeadAttention)>,
     BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, GroupQueryAttention)>,
+    BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, SparseAttention)>,
     BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, RotaryEmbedding)>,
     BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, Sampling)>,
     BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, AttnLSTM)>,