Fix Attention Runtime Error for CLIP model (microsoft#17729)

### Description
The condition check is not correct
```
if (is_unidirectional_ && enable_fused_causal_attention_) {  // GPT
}
else { // BERT
}
```

Change it to 
```
if (is_unidirectional_) {  // GPT
}
else { // BERT
}
```

Another walkaround is to enable fused causal attention by adding an
environment variable `ORT_ENABLE_FUSED_CAUSAL_ATTENTION=1` before
running stable diffusion.

### Motivation and Context

Without the fix, optimized CLIP model of stable diffusion will encounter
error in running Attention node:

2023-09-24 16:15:31.206037898 [E:onnxruntime:,
sequential_executor.cc:514 ExecuteKernel] Non-zero status code returned
while running Attention node. Name:'Attention_0' Status Message:
/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/tensorrt_fused_multihead_attention/mha_runner.cu:207
bool
onnxruntime::contrib::cuda::FusedMHARunnerFP16v2::mhaImpl::is_flash_attention(int)
const interface->mHasCausalMask == false was false.

Note that the bug has been there for a long time. It is just surfaced
since we recently added a fusion for CLIP, which will trigger the error.

We will add a comprehensive test for causal attention later to avoid
such corner cases.

onnxruntime/contrib_ops/cuda/bert/attention.cc

@@ -140,27 +140,29 @@ Status Attention<T>::ComputeInternal(OpKernelContext* context) const {
 #endif
 
   if (!use_flash_attention) {
-    if (is_unidirectional_ && enable_fused_causal_attention_) {  // GPT
-      // GPT fused kernels requires left side padding. mask can be:
-      //     none (no padding), 1D sequence lengths or 2d mask.
-      // Fused kernels don't support different sequence lengths of q and kv, so only apply to the first token
-      // where past state is empty.
-      bool is_mask_2d_key_padding = parameters.mask_type == AttentionMaskType::MASK_2D_KEY_PADDING;
-      bool use_causal_fused_runner = (nullptr == mask_index || is_mask_1d_seq_len || is_mask_2d_key_padding) &&
-                                     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);
-      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()) {
-          fused_fp16_runner_ = FusedMHARunnerFP16v2::Create(num_heads_, parameters.head_size, sm, is_unidirectional_,
-                                                            enable_trt_flash_attention_, parameters.scale);
+    if (is_unidirectional_) {  // GPT
+      if (enable_fused_causal_attention_) {
+        // GPT fused kernels requires left side padding. mask can be:
+        //     none (no padding), 1D sequence lengths or 2d mask.
+        // Fused kernels don't support different sequence lengths of q and kv, so only apply to the first token
+        // where past state is empty.
+        bool is_mask_2d_key_padding = parameters.mask_type == AttentionMaskType::MASK_2D_KEY_PADDING;
+        bool use_causal_fused_runner = (nullptr == mask_index || is_mask_1d_seq_len || is_mask_2d_key_padding) &&
+                                       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);
+        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()) {
+            fused_fp16_runner_ = FusedMHARunnerFP16v2::Create(num_heads_, parameters.head_size, sm, is_unidirectional_,
+                                                              enable_trt_flash_attention_, parameters.scale);
+          }
+
+          // Here we assume all causal kernels can be loaded into shared memory. TODO: add a function to check.
+          fused_runner = fused_fp16_runner_.get();
         }
-
-        // Here we assume all causal kernels can be loaded into shared memory. TODO: add a function to check.
-        fused_runner = fused_fp16_runner_.get();
       }
     } else {  // BERT
       bool use_fused_runner = !disable_fused_self_attention_ &&