diff --git a/deepspeed/compression/basic_layer.py b/deepspeed/compression/basic_layer.py
index 679e10920940..bc2b54951bbe 100644
--- a/deepspeed/compression/basic_layer.py
+++ b/deepspeed/compression/basic_layer.py
@@ -618,7 +618,7 @@ def fix_channel_pruning_helper(self, mask, dim_reduction=True):
 
 
 def _reduce(input_):
-    """All-reduce the the input tensor across model parallel group."""
+    """All-reduce the input tensor across model parallel group."""
     group = g_mpu.get_model_parallel_group()
 
     # Bypass the function if we are using only 1 GPU.
diff --git a/deepspeed/module_inject/containers/features/meta_tensor.py b/deepspeed/module_inject/containers/features/meta_tensor.py
index c36925a242ba..5fb55bc74339 100644
--- a/deepspeed/module_inject/containers/features/meta_tensor.py
+++ b/deepspeed/module_inject/containers/features/meta_tensor.py
@@ -52,7 +52,7 @@ def load_params(self, module, sd, weight_quantizer, mp_replace, prefix):
         """
         Load all the transformer parameter from the checkpoint file (sd).
         In addition to the parameter names, we require two
-        more parameters to help read the the data correctly
+        more parameters to help read the data correctly
         from the checkpoint and split the qkv heads in the
         right order:
             1. `use_load_prefix` (Default: False): this specifies
diff --git a/deepspeed/runtime/zero/contiguous_memory_allocator.py b/deepspeed/runtime/zero/contiguous_memory_allocator.py
index 79f01e5466b9..632f68ca1718 100644
--- a/deepspeed/runtime/zero/contiguous_memory_allocator.py
+++ b/deepspeed/runtime/zero/contiguous_memory_allocator.py
@@ -78,7 +78,7 @@ def allocate_tensor(self, size):
         return ret_tensor
 
     #assigns the tensor data to the param data and keeps track of the assignment
-    #any change the the underlying buffer from defragmentation will cause a
+    #any change the underlying buffer from defragmentation will cause a
     #reassignment of the param data
     def assign_to_param(self, tensor, param, numel, shape):
         tensor_id = id(tensor)
diff --git a/deepspeed/runtime/zero/stage_1_and_2.py b/deepspeed/runtime/zero/stage_1_and_2.py
index 38823f024e2b..bb578218b93c 100755
--- a/deepspeed/runtime/zero/stage_1_and_2.py
+++ b/deepspeed/runtime/zero/stage_1_and_2.py
@@ -257,7 +257,7 @@ def __init__(self,
         # These are the parameters that will be updated by this process directly
         self.params_in_partition = []
 
-        # Offset from the first parameter in the the self.params_in_partition
+        # Offset from the first parameter in the self.params_in_partition
         # the parameter boundaries may not align with partition boundaries
         # so we need to keep track of the offset
         self.first_offset = []
diff --git a/docs/_pages/config-json.md b/docs/_pages/config-json.md
index f920b4ba74dd..e6ad26ca67fd 100755
--- a/docs/_pages/config-json.md
+++ b/docs/_pages/config-json.md
@@ -1144,7 +1144,7 @@ Deepspeed's Monitor module can log training details into a [Tensorboard](https:/
 | `Train/Eigenvalues/ModelBlockParam_{i}`   | Eigen values per param block. | `eigenvalue` must be enabled. |
 | `Train/Samples/elapsed_time_ms_forward`   | The global duration of the forward pass. | `flops_profiler.enabled` or `wall_clock_breakdown`. |
 | `Train/Samples/elapsed_time_ms_backward`   | The global duration of the forward pass. | `flops_profiler.enabled` or `wall_clock_breakdown`.  |
-| `Train/Samples/elapsed_time_ms_backward_inner`   | The backward time that does not include the the gradient reduction time. Only in cases where the gradient reduction is not overlapped, if it is overlapped then the inner time should be about the same as the entire backward time. | `flops_profiler.enabled` or `wall_clock_breakdown`.  |
+| `Train/Samples/elapsed_time_ms_backward_inner`   | The backward time that does not include the gradient reduction time. Only in cases where the gradient reduction is not overlapped, if it is overlapped then the inner time should be about the same as the entire backward time. | `flops_profiler.enabled` or `wall_clock_breakdown`.  |
 | `Train/Samples/elapsed_time_ms_backward_allreduce`   | The global duration of the allreduce operation. | `flops_profiler.enabled` or `wall_clock_breakdown`.  |
 | `Train/Samples/elapsed_time_ms_step`   | The optimizer step time | `flops_profiler.enabled` or `wall_clock_breakdown`.  |
 
diff --git a/docs/_tutorials/gan.md b/docs/_tutorials/gan.md
index 1389c91617dd..09572a439eb0 100755
--- a/docs/_tutorials/gan.md
+++ b/docs/_tutorials/gan.md
@@ -101,7 +101,7 @@ deepspeed gan_deepspeed_train.py --dataset celeba --cuda --deepspeed_config gan_
 
 ## Performance Comparison
 
-We use a total batch size of 64 and perform the training on 16 GPUs for 1 epoch on a DGX-2 node which leads to 3x speed-up. The summary of the the results is given below:
+We use a total batch size of 64 and perform the training on 16 GPUs for 1 epoch on a DGX-2 node which leads to 3x speed-up. The summary of the results is given below:
 
 - Baseline total wall clock time for 1 epochs is 393 secs
 
diff --git a/docs/_tutorials/onebit-adam.md b/docs/_tutorials/onebit-adam.md
index a64439018db4..932bb355cf26 100644
--- a/docs/_tutorials/onebit-adam.md
+++ b/docs/_tutorials/onebit-adam.md
@@ -46,7 +46,7 @@ cd DeepSpeedExamples/
 In 1-bit Adam v2, we introduce a new system implementation for compressed communication using the NCCL backend of PyTorch distributed. This significantly improves the usability due to NCCL’s integration with PyTorch distributed. The performance of our new NCCL-based implementation is also better than our earlier MPI-based implementation for Ethernet-based systems and on-par for InfiniBand-based systems. Thus we highly recommend users to choose this implementation.
 
 **Watch out!**
-This NCCL-based implementation requires PyTorch >= 1.8. It also requires NCCL >= 2.8.3 when you have 64 or more GPUs to avoid certain NCCL runtime bugs. Currently (2021/03/16) NCCL 2.8.3 is not officially supported by PyTorch. The solution we used is by hacking in NCCL 2.8.3 via `LD_PRELOAD`: 1) Install NCCL 2.8.3. This works for us on a CUDA 11 system: `apt-get install -y libnccl2=2.8.3-1+cuda11.0 libnccl-dev=2.8.3-1+cuda11.0`. 2) Set `LD_PRELOAD` to the the library path. This works for us: `LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libnccl.so.2.8.3`. To confirm `LD_PRELOAD` is working you can see the version it uses in the NCCL logs if you have `NCCL_DEBUG=INFO`, it should say: NCCL version 2.8.3+cuda11.0.
+This NCCL-based implementation requires PyTorch >= 1.8. It also requires NCCL >= 2.8.3 when you have 64 or more GPUs to avoid certain NCCL runtime bugs. Currently (2021/03/16) NCCL 2.8.3 is not officially supported by PyTorch. The solution we used is by hacking in NCCL 2.8.3 via `LD_PRELOAD`: 1) Install NCCL 2.8.3. This works for us on a CUDA 11 system: `apt-get install -y libnccl2=2.8.3-1+cuda11.0 libnccl-dev=2.8.3-1+cuda11.0`. 2) Set `LD_PRELOAD` to the library path. This works for us: `LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libnccl.so.2.8.3`. To confirm `LD_PRELOAD` is working you can see the version it uses in the NCCL logs if you have `NCCL_DEBUG=INFO`, it should say: NCCL version 2.8.3+cuda11.0.
 {: .notice--warning}
 
 #### 1.2.2 MPI-based implementation
diff --git a/docs/_tutorials/onebit-lamb.md b/docs/_tutorials/onebit-lamb.md
index 822f79e61740..4873f1f35c17 100644
--- a/docs/_tutorials/onebit-lamb.md
+++ b/docs/_tutorials/onebit-lamb.md
@@ -32,7 +32,7 @@ cd DeepSpeedExamples/
 In DeepSpeed, we introduce a system implementation for compressed communication using the NCCL backend of PyTorch distributed. This implementation provides better performance and usability than the MPI-based implementation below. Thus we highly recommend users to choose this implementation.
 
 **Watch out!**
-This NCCL-based implementation requires PyTorch >= 1.8. It also requires NCCL >= 2.8.3 when you have 64 or more GPUs to avoid certain NCCL runtime bugs. Currently (2021/03/16) NCCL 2.8.3 is not officially supported by PyTorch. The solution we used is by hacking in NCCL 2.8.3 via `LD_PRELOAD`: 1) Install NCCL 2.8.3. This works for us on a CUDA 11 system: `apt-get install -y libnccl2=2.8.3-1+cuda11.0 libnccl-dev=2.8.3-1+cuda11.0`. 2) Set `LD_PRELOAD` to the the library path. This works for us: `LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libnccl.so.2.8.3`. To confirm `LD_PRELOAD` is working you can see the version it uses in the NCCL logs if you have `NCCL_DEBUG=INFO`, it should say: NCCL version 2.8.3+cuda11.0.
+This NCCL-based implementation requires PyTorch >= 1.8. It also requires NCCL >= 2.8.3 when you have 64 or more GPUs to avoid certain NCCL runtime bugs. Currently (2021/03/16) NCCL 2.8.3 is not officially supported by PyTorch. The solution we used is by hacking in NCCL 2.8.3 via `LD_PRELOAD`: 1) Install NCCL 2.8.3. This works for us on a CUDA 11 system: `apt-get install -y libnccl2=2.8.3-1+cuda11.0 libnccl-dev=2.8.3-1+cuda11.0`. 2) Set `LD_PRELOAD` to the library path. This works for us: `LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libnccl.so.2.8.3`. To confirm `LD_PRELOAD` is working you can see the version it uses in the NCCL logs if you have `NCCL_DEBUG=INFO`, it should say: NCCL version 2.8.3+cuda11.0.
 {: .notice--warning}
 
 #### 1.2.2 MPI-based implementation
diff --git a/docs/_tutorials/progressive_layer_dropping.md b/docs/_tutorials/progressive_layer_dropping.md
index b3da9ba807a3..1054e91b24b2 100755
--- a/docs/_tutorials/progressive_layer_dropping.md
+++ b/docs/_tutorials/progressive_layer_dropping.md
@@ -95,7 +95,7 @@ Note that the above configuration assumes training on 64 X 32GB V100 GPUs. Each
 
 Table 1. Pre-training hyperparameters
 
-**Note:** DeepSpeed now supports PreLayerNorm as the default way for training BERT, because of its ability to avoid vanishing gradient, stabilize optimization, and performance gains, as described in our fastest BERT training [blog post](https://www.deepspeed.ai/2020/05/27/fastest-bert-training.html). We therefore support the switchable Transformer block directly on the the BERT with PreLayerNorm. The implementation can be found at "example\bing_bert\nvidia\modelingpreln_layerdrop.py".
+**Note:** DeepSpeed now supports PreLayerNorm as the default way for training BERT, because of its ability to avoid vanishing gradient, stabilize optimization, and performance gains, as described in our fastest BERT training [blog post](https://www.deepspeed.ai/2020/05/27/fastest-bert-training.html). We therefore support the switchable Transformer block directly on the BERT with PreLayerNorm. The implementation can be found at "example\bing_bert\nvidia\modelingpreln_layerdrop.py".
 
 ## Fine-tuning with DeepSpeed on GLUE Tasks
 
diff --git a/docs/_tutorials/zero-one-adam.md b/docs/_tutorials/zero-one-adam.md
index 751fce597e9f..2dd956e802fd 100644
--- a/docs/_tutorials/zero-one-adam.md
+++ b/docs/_tutorials/zero-one-adam.md
@@ -33,7 +33,7 @@ cd DeepSpeedExamples/
 In DeepSpeed, we introduce a system implementation for compressed communication using the NCCL backend of PyTorch distributed. This implementation provides better performance and usability than the MPI-based implementation below. Thus we highly recommend users to choose this implementation.
 
 **Watch out!**
-This NCCL-based implementation requires PyTorch >= 1.8. It also requires NCCL >= 2.8.3 when you have 64 or more GPUs to avoid certain NCCL runtime bugs. Currently (2021/03/16) NCCL 2.8.3 is not officially supported by PyTorch. The solution we used is by hacking in NCCL 2.8.3 via `LD_PRELOAD`: 1) Install NCCL 2.8.3. This works for us on a CUDA 11 system: `apt-get install -y libnccl2=2.8.3-1+cuda11.0 libnccl-dev=2.8.3-1+cuda11.0`. 2) Set `LD_PRELOAD` to the the library path. This works for us: `LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libnccl.so.2.8.3`. To confirm `LD_PRELOAD` is working you can see the version it uses in the NCCL logs if you have `NCCL_DEBUG=INFO`, it should say: NCCL version 2.8.3+cuda11.0.
+This NCCL-based implementation requires PyTorch >= 1.8. It also requires NCCL >= 2.8.3 when you have 64 or more GPUs to avoid certain NCCL runtime bugs. Currently (2021/03/16) NCCL 2.8.3 is not officially supported by PyTorch. The solution we used is by hacking in NCCL 2.8.3 via `LD_PRELOAD`: 1) Install NCCL 2.8.3. This works for us on a CUDA 11 system: `apt-get install -y libnccl2=2.8.3-1+cuda11.0 libnccl-dev=2.8.3-1+cuda11.0`. 2) Set `LD_PRELOAD` to the library path. This works for us: `LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libnccl.so.2.8.3`. To confirm `LD_PRELOAD` is working you can see the version it uses in the NCCL logs if you have `NCCL_DEBUG=INFO`, it should say: NCCL version 2.8.3+cuda11.0.
 {: .notice--warning}
 
 #### 1.2.2 MPI-based implementation
diff --git a/docs/code-docs/source/autotuning.rst b/docs/code-docs/source/autotuning.rst
index 8019ce3d0c26..23ae090e2ceb 100644
--- a/docs/code-docs/source/autotuning.rst
+++ b/docs/code-docs/source/autotuning.rst
@@ -6,7 +6,7 @@ The DeepSpeed Autotuner mitigates this pain point and automatically discovers th
 The Autotuner uses model information, system information, and heuristics to efficiently tune system knobs that affect compute and memory efficiencies, such as ZeRO optimization stages, micro-batch sizes, and many other ZeRO optimization configurations.
 It not only reduces the time and resources users spend on tuning, but also can discover configurations better than hand-tuned methods.
 
-Please see the the `Autotuning tutorial <https://www.deepspeed.ai/tutorials/autotuning/>`_ for usage details.
+Please see the `Autotuning tutorial <https://www.deepspeed.ai/tutorials/autotuning/>`_ for usage details.
 
 Autotuner
 ---------------------------------------------------
diff --git a/docs/code-docs/source/monitor.rst b/docs/code-docs/source/monitor.rst
index a0ab894a97e4..d286af23f09e 100644
--- a/docs/code-docs/source/monitor.rst
+++ b/docs/code-docs/source/monitor.rst
@@ -15,7 +15,7 @@ overview of what DeepSpeed will log automatically.
     `Train/Eigenvalues/ModelBlockParam_{i}`,Eigen values per param block.,`eigenvalue` must be enabled.
     `Train/Samples/elapsed_time_ms_forward`,The global duration of the forward pass.,`flops_profiler.enabled` or `wall_clock_breakdown`.
     `Train/Samples/elapsed_time_ms_backward`,The global duration of the forward pass.,`flops_profiler.enabled` or `wall_clock_breakdown`.
-    `Train/Samples/elapsed_time_ms_backward_inner`,The backward time that does not include the the gradient reduction time. Only in cases where the gradient reduction is not overlapped, if it is overlapped then the inner time should be about the same as the entire backward time.,`flops_profiler.enabled` or `wall_clock_breakdown`.
+    `Train/Samples/elapsed_time_ms_backward_inner`,The backward time that does not include the gradient reduction time. Only in cases where the gradient reduction is not overlapped, if it is overlapped then the inner time should be about the same as the entire backward time.,`flops_profiler.enabled` or `wall_clock_breakdown`.
     `Train/Samples/elapsed_time_ms_backward_allreduce`,The global duration of the allreduce operation.,`flops_profiler.enabled` or `wall_clock_breakdown`.
     `Train/Samples/elapsed_time_ms_step`,The optimizer step time,`flops_profiler.enabled` or `wall_clock_breakdown`.