[CUDA] FusedMHARunnerFP16v2 thread-safe (#21420)

### Description
- [x] Rewrite FusedMHARunnerFP16v2 to make it thread-safe. 
- [x] Add multi-threading tests

Previously, the kernel parameters params is stored as a member of mha
runner, which means that different threads might change the params at
the same time and impacts the other threads.

For example, if batch_size and seq_len was changed by another thread to
larger values in setup(...), buffer overrun might happen in run(...)
because a kernel could read/write memory out of range of allocated
buffers.

In new implementation, I change the api and remove mutable member
variables to make it thread safe. Below is summary of change:

Before:
```
class FusedMHARunnerFP16v2::mhaImpl {
   void setup(int seq_len, int batch_size) {
      // change scalar params
   }

   void run(input, output) {
      // change params for input and output pointers
      // launch kernel using params
   }

   Fused_multihead_attention_params_v2 params; // mutable, not thread-safe
}
```

After:
```
class FusedMHARunnerFP16v2::FmhaImpl {
   void setup(int seq_len, int batch_size, Fused_multihead_attention_params_v2& params) {
      // change params
   }

   void run(params, input, output) {
      // change params with input and output pointers
      // launch kernel using params
   }
}
```

### Motivation and Context
#18854
#21413

onnxruntime/contrib_ops/cuda/bert/attention.cc

@@ -149,8 +149,8 @@ Status Attention<T>::ComputeInternal(OpKernelContext* context) const {
                                        nullptr == relative_position_bias &&
                                        parameters.past_sequence_length == 0 &&
                                        parameters.hidden_size == parameters.v_hidden_size &&
-                                       FusedMHARunnerFP16v2::is_supported(sm, parameters.head_size, sequence_length,
-                                                                          enable_trt_flash_attention_, true);
+                                       FusedMHARunnerFP16v2::IsSupported(sm, parameters.head_size, sequence_length,
+                                                                         enable_trt_flash_attention_, true);
         if (use_causal_fused_runner) {
           // Here we assume that num_heads, head_size and is_unidirectional does not change for an Attention node.
           if (nullptr == fused_fp16_runner_.get()) {
@@ -171,8 +171,8 @@ Status Attention<T>::ComputeInternal(OpKernelContext* context) const {
                               nullptr == present &&
                               nullptr == relative_position_bias &&
                               parameters.hidden_size == parameters.v_hidden_size &&
-                              FusedMHARunnerFP16v2::is_supported(sm, parameters.head_size, sequence_length,
-                                                                 enable_trt_flash_attention_, false);
+                              FusedMHARunnerFP16v2::IsSupported(sm, parameters.head_size, sequence_length,
+                                                                enable_trt_flash_attention_, false);
 
       if (use_fused_runner) {
         // Here we assume that num_heads, head_size and is_unidirectional does not change for an Attention node.
@@ -184,8 +184,8 @@ Status Attention<T>::ComputeInternal(OpKernelContext* context) const {
         }
 
         // In case some kernel not loaded due to shared memory limit, we need to double check here.
-        const int S = fused_fp16_runner_->getSFromMaxSeqLen(sequence_length);
-        if (fused_fp16_runner_->isValid(S)) {
+        const int normalized_seq_len = fused_fp16_runner_->NormalizeSequenceLength(sequence_length);
+        if (fused_fp16_runner_->IsValid(normalized_seq_len)) {
           fused_runner = fused_fp16_runner_.get();
         }
       }

onnxruntime/contrib_ops/cuda/bert/attention_impl.cu

@@ -245,12 +245,10 @@ Status FusedTrtSelfAttention(
 
   FusedMHARunnerFP16v2* fused_fp16_runner = reinterpret_cast<FusedMHARunnerFP16v2*>(data.fused_runner);
 
-  const int S = causal ? sequence_length : fused_fp16_runner->getSFromMaxSeqLen(sequence_length);
+  const int s = causal ? sequence_length : fused_fp16_runner->NormalizeSequenceLength(sequence_length);
 
   // B = 2 * batch_size when there is padding in input, and B = batch_size when padding is removed.
-  const int B = (nullptr == data.mask_index ? batch_size : 2 * batch_size);
-
-  fused_fp16_runner->setup(S, B);
+  const int b = (nullptr == data.mask_index ? batch_size : 2 * batch_size);
 
   if (!causal) {
     assert(data.qkv_format == AttentionQkvFormat::QKV_BSN3H);
@@ -261,12 +259,12 @@ Status FusedTrtSelfAttention(
       packed_qkv = data.query;
     }
 
-    fused_fp16_runner->run(packed_qkv, sequence_offset, data.output, stream);
+    fused_fp16_runner->Run(b, s, packed_qkv, sequence_offset, data.output, stream);
     DUMP_TENSOR("fused output", data.output,
                 batch_size, sequence_length, parameters.num_heads, parameters.v_head_size);
   } else {
     assert(data.qkv_format == AttentionQkvFormat::Q_K_V_BNSH_QKV_BS3NH);
-    fused_fp16_runner->run(data.gemm_buffer, sequence_offset, data.output, stream);
+    fused_fp16_runner->Run(b, s, data.gemm_buffer, sequence_offset, data.output, stream);
     DUMP_TENSOR("fused causal output", data.output,
                 batch_size, sequence_length, parameters.num_heads, parameters.v_head_size);
   }

onnxruntime/contrib_ops/cuda/bert/multihead_attention.cc

@@ -193,8 +193,8 @@ Status MultiHeadAttention<T>::ComputeInternal(OpKernelContext* context) const {
                           (nullptr == key_padding_mask || is_mask_1d_seq_len) &&
                           parameters.hidden_size == parameters.v_hidden_size &&
                           parameters.sequence_length == parameters.kv_sequence_length &&
-                          FusedMHARunnerFP16v2::is_supported(sm, parameters.head_size, sequence_length,
-                                                             enable_trt_flash_attention_, false);
+                          FusedMHARunnerFP16v2::IsSupported(sm, parameters.head_size, sequence_length,
+                                                            enable_trt_flash_attention_, false);
   if (use_fused_runner) {
     // Here we assume that num_heads and head_size does not change for a MultiHeadAttention node.
     if (nullptr == fused_fp16_runner_.get()) {
@@ -206,8 +206,8 @@ Status MultiHeadAttention<T>::ComputeInternal(OpKernelContext* context) const {
     }
 
     // In case some kernel not loaded due to shared memory limit, we need to double check here.
-    const int S = fused_fp16_runner_->getSFromMaxSeqLen(sequence_length);
-    if (fused_fp16_runner_->isValid(S)) {
+    const int normalized_seq_len = fused_fp16_runner_->NormalizeSequenceLength(sequence_length);
+    if (fused_fp16_runner_->IsValid(normalized_seq_len)) {
       fused_runner = fused_fp16_runner_.get();
     }
   }

onnxruntime/contrib_ops/cuda/bert/packed_attention.cc

@@ -55,11 +55,11 @@ MHARunner* TrtFusedAttention<T>::GetFusedRunner(const cudaDeviceProp& device_pro
 
   // Check whether we can use fused kernel
   int sm = device_prop.major * 10 + device_prop.minor;
-  bool is_fMHA_supported = FusedMHARunnerFP16v2::is_supported(sm,
-                                                              parameters.head_size,
-                                                              parameters.sequence_length,
-                                                              enable_trt_flash_attention_,
-                                                              false /*causal*/);
+  bool is_fMHA_supported = FusedMHARunnerFP16v2::IsSupported(sm,
+                                                             parameters.head_size,
+                                                             parameters.sequence_length,
+                                                             enable_trt_flash_attention_,
+                                                             false /*causal*/);
 
   if (!is_fMHA_supported) {
     return fused_runner;
@@ -72,8 +72,8 @@ MHARunner* TrtFusedAttention<T>::GetFusedRunner(const cudaDeviceProp& device_pro
   }
 
   // In case some kernel not loaded due to shared memory limit, we need to double check here.
-  const int S = fused_fp16_runner_->getSFromMaxSeqLen(parameters.sequence_length);
-  if (fused_fp16_runner_->isValid(S)) {
+  const int normalized_seq_len = fused_fp16_runner_->NormalizeSequenceLength(parameters.sequence_length);
+  if (fused_fp16_runner_->IsValid(normalized_seq_len)) {
     fused_runner = fused_fp16_runner_.get();
   }
 

onnxruntime/contrib_ops/cuda/bert/packed_attention_impl.cu

@@ -459,10 +459,9 @@ Status FusedScaledDotProductAttention(
                          parameters.token_count, stream);
 
   FusedMHARunnerFP16v2* fused_fp16_runner = reinterpret_cast<FusedMHARunnerFP16v2*>(fused_runner);
-  const int S = fused_fp16_runner->getSFromMaxSeqLen(sequence_length);
-  fused_fp16_runner->setup(S, batch_size);
-
-  fused_fp16_runner->run(data.workspace, data.cumulative_sequence_length, data.output, stream);
+  const int normalized_seq_len = fused_fp16_runner->NormalizeSequenceLength(sequence_length);
+  fused_fp16_runner->Run(batch_size, normalized_seq_len,
+                         data.workspace, data.cumulative_sequence_length, data.output, stream);
   return Status::OK();
 }
 

onnxruntime/contrib_ops/cuda/bert/packed_multihead_attention_impl.cu

@@ -575,10 +575,8 @@ Status FusedAttentionTrt(
   }
 
   FusedMHARunnerFP16v2* fused_fp16_runner = reinterpret_cast<FusedMHARunnerFP16v2*>(fused_runner);
-  const int S = fused_fp16_runner->getSFromMaxSeqLen(sequence_length);
-  fused_fp16_runner->setup(S, batch_size);
-
-  fused_fp16_runner->run(qkv, data.cumulative_sequence_length, data.output, stream);
+  const int normalized_seq_len = fused_fp16_runner->NormalizeSequenceLength(sequence_length);
+  fused_fp16_runner->Run(batch_size, normalized_seq_len, qkv, data.cumulative_sequence_length, data.output, stream);
   return Status::OK();
 }