change PyTorch version in README

Signed-off-by: Sung Hyun Cho <hope5487@gmail.com>

Author: roycho96

README.md

@@ -114,7 +114,7 @@ dynamically link them at runtime.
 
 ## Requirements
 * [PyTorch](https://pytorch.org/) must be installed _before_ installing DeepSpeed.
-* For full feature support we recommend a version of PyTorch that is >= 1.9 and ideally the latest PyTorch stable release.
+* For full feature support we recommend a version of PyTorch that is >= 2.0 and ideally the latest PyTorch stable release.
 * A CUDA or ROCm compiler such as [nvcc](https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/#introduction) or [hipcc](https://github.com/ROCm-Developer-Tools/HIPCC) used to compile C++/CUDA/HIP extensions.
 * Specific GPUs we develop and test against are listed below, this doesn't mean your GPU will not work if it doesn't fall into this category it's just DeepSpeed is most well tested on the following:
   * NVIDIA: Pascal, Volta, Ampere, and Hopper architectures

tests/unit/v1/zero/test_zero_functorch_linear.py

@@ -7,7 +7,7 @@
 ZeRO Stage 3 uses ``LinearFunctionForZeroStage3`` (via ``zero3_linear_wrap``) as
 the memory-efficient linear path. After ``deepspeed.initialize``, global
 ``torch.nn.functional.linear`` is often the built-in again, so tests call
-``zero3_linear_wrap`` directly—the same ``autograd.Function`` as when the patch
+``zero3_linear_wrap`` directly-the same ``autograd.Function`` as when the patch
 is active. Legacy ``forward(ctx, ...)`` + ``ctx.save_for_backward`` in forward
 raises on strict functorch builds::