[docs] fixed links with 404 (#27327)

* fixed links with 404

* make style

docs/source/en/perf_train_gpu_one.md

@@ -394,7 +394,7 @@ Choose which backend to use by specifying it via `torch_compile_backend` in the
 
 **Inference-only backend**s:
 * `dynamo.optimize("ofi")` -  Uses Torchscript optimize_for_inference.  [Read more](https://pytorch.org/docs/stable/generated/torch.jit.optimize_for_inference.html)
-* `dynamo.optimize("fx2trt")` -  Uses Nvidia TensorRT for inference optimizations.  [Read more](https://github.com/pytorch/TensorRT/blob/master/docsrc/tutorials/getting_started_with_fx_path.rst)
+* `dynamo.optimize("fx2trt")` -  Uses NVIDIA TensorRT for inference optimizations.  [Read more](https://pytorch.org/TensorRT/tutorials/getting_started_with_fx_path.html)
 * `dynamo.optimize("onnxrt")` -  Uses ONNXRT for inference on CPU/GPU.  [Read more](https://onnxruntime.ai/)
 * `dynamo.optimize("ipex")` -  Uses IPEX for inference on CPU.  [Read more](https://github.com/intel/intel-extension-for-pytorch)
 
@@ -505,7 +505,7 @@ Most related papers and implementations are built around Tensorflow/TPUs:
 - [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://arxiv.org/abs/2101.03961)
 - [GLaM: Generalist Language Model (GLaM)](https://ai.googleblog.com/2021/12/more-efficient-in-context-learning-with.html)
 
-And for Pytorch DeepSpeed has built one as well: [DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale](https://arxiv.org/abs/2201.05596), [Mixture of Experts](https://www.deepspeed.ai/tutorials/mixture-of-experts/) - blog posts:  [1](https://www.microsoft.com/en-us/research/blog/deepspeed-powers-8x-larger-moe-model-training-with-high-performance/), [2](https://www.microsoft.com/en-us/research/publication/scalable-and-efficient-moe-training-for-multitask-multilingual-models/) and specific deployment with large transformer-based natural language generation models: [blog post](https://www.deepspeed.ai/news/2021/12/09/deepspeed-moe-nlg.html), [Megatron-Deepspeed branch](Thttps://github.com/microsoft/Megatron-DeepSpeed/tree/moe-training).
+And for Pytorch DeepSpeed has built one as well: [DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale](https://arxiv.org/abs/2201.05596), [Mixture of Experts](https://www.deepspeed.ai/tutorials/mixture-of-experts/) - blog posts:  [1](https://www.microsoft.com/en-us/research/blog/deepspeed-powers-8x-larger-moe-model-training-with-high-performance/), [2](https://www.microsoft.com/en-us/research/publication/scalable-and-efficient-moe-training-for-multitask-multilingual-models/) and specific deployment with large transformer-based natural language generation models: [blog post](https://www.deepspeed.ai/2021/12/09/deepspeed-moe-nlg.html), [Megatron-Deepspeed branch](https://github.com/microsoft/Megatron-DeepSpeed/tree/moe-training).
 
 ## Using PyTorch native attention and Flash Attention