[Bug] onnxruntime-gpu 1.16.3 not thread-safe with BERT onnx model in fp16 using CUDA provider

### Describe the issue

I am working with language models, and I encountered this specific use-case when working with a BERT model I exported from PyTorch (https://huggingface.co/dicta-il/dictabert).
I initialize an `InferenceSession` object with my model, and then try to run multiple inputs through in parallel. When I try to initialize the full version of the model it works just fine, but when I initialize the fp16 version of the model (created using `onnxconverter_common.float16.convert_float_to_float16`) then the multi-threading crashes with various errors, all having to do with illegal memory access. Here are a few example errors:

```
[E:onnxruntime:, sequential_executor.cc:514 onnxruntime::ExecuteKernel] Non-zero status code returned while running Attention node. Name:'Attention_0' Status Message: CUBLAS failure 14: CUBLAS_STATUS_INTERNAL_ERROR ; GPU=0 ; hostname=DESKTOP-9FIMDJR ; file=D:\a\_work\1\s\onnxruntime\contrib_ops\cuda\bert\attention.cc ; line=249 ; expr=cublasGemmHelper( cublas, CUBLAS_OP_N, CUBLAS_OP_N, n, m, k, &one, reinterpret_cast(weights->Data()), n, reinterpret_cast(input->Data()), k, &zero, reinterpret_cast(gemm_buffer.get()), n, device_prop);
```

```
[E:onnxruntime:, sequential_executor.cc:514 onnxruntime::ExecuteKernel] Non-zero status code returned while running MatMul node. Name:'/encoder/layer.0/intermediate/dense/MatMul' Status Message: CUBLAS failure 14: CUBLAS_STATUS_INTERNAL_ERROR ; GPU=0 ; hostname=DESKTOP-9FIMDJR ; file=D:\a\_work\1\s\onnxruntime\core\providers\cuda\math\matmul.cc ; line=169 ; expr=cublasGemmHelper( GetCublasHandle(ctx), transB, transA, static_cast(helper.N()), static_cast(helper.M()), static_cast(helper.K()), &alpha, reinterpret_cast(right_X->Data()), ldb, reinterpret_cast(left_X->Data()), lda, &zero, reinterpret_cast(Y->MutableData()), ldc, device_prop);
```

### To reproduce

You can download the onnx model from [here](https://www.dropbox.com/scl/fi/vyxnknulralskuzevtcqn/model.fp16.onnx?rlkey=ftk96zuzqwwk4xb8f883w5w2y&dl=1).

```cs
// Initialize the session
var options = new SessionOptions {
ExecutionMode = ExecutionMode.ORT_SEQUENTIAL,
LogVerbosityLevel = (int)OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO,
};
options.AppendExecutionProvider_CUDA(0);
var session = new InferenceSession("model.fp16.onnx", options);

// Create sample inputs (vocabulary size is 128,000)
var inputIds = Enumerable.Range(0, 27 * 52).Select(_ => Random.Shared.NextInt64(3, 128000)).ToArray();
var mask = Enumerable.Repeat(1L, 27 * 52).ToArray();
var tokenTypeIds = Enumerable.Repeat(0L, 27 * 52).ToArray();

// Try running multiple inputs through in parallel
Enumerable.Range(0, 500).AsParallel().ForAll(_ => {
var output = session.Run(new[] {
NamedOnnxValue.CreateFromTensor("input_ids", new DenseTensor(inputIds, new[] { 27, 52 })),
NamedOnnxValue.CreateFromTensor("token_type_ids", new DenseTensor(mask, new[] { 27, 52 })),
NamedOnnxValue.CreateFromTensor("attention_mask", new DenseTensor(tokenTypeIds, new[] { 27, 52 })),
});
});
```

### Urgency

We are counting on this feature for deploying a product in our organization. It is not overly urgent, but it would be very helpful to be able to execute inference runs in parallel with the same model.

### Platform

Windows

### OS Version

19045.2965

### ONNX Runtime Installation

Released Package

### ONNX Runtime Version or Commit ID

1.16.3

### ONNX Runtime API

C#

### Architecture

X64

### Execution Provider

CUDA

### Execution Provider Library Version

CUDA 11.4