docs fix

Signed-off-by: Pawel Gadzinski <pgadzinski@nvidia.com>

transformer_engine/pytorch/module/grouped_linear.py

@@ -528,11 +528,11 @@ class GroupedLinear(TransformerEngineBaseModule):
                  used for initializing weights in the following way: `init_method(weight)`.
                  When set to `None`, defaults to `torch.nn.init.normal_(mean=0.0, std=0.023)`.
     get_rng_state_tracker : Callable, default = `None`
-                 used to get the random number generator state tracker for initilizeing weights.
+                 used to get the random number generator state tracker for initializing weights.
     rng_tracker_name : str, default = `None`
                  the param passed to get_rng_state_tracker to get the specific rng tracker.
     device : Union[torch.device, str], default = "cuda"
-          The device on which the parameters of the model will allocated. It is the user's
+          The device on which the parameters of the model will be allocated. It is the user's
           responsibility to ensure all parameters are moved to the GPU before running the
           forward pass.
 

transformer_engine/pytorch/module/layernorm.py

@@ -110,7 +110,7 @@ class LayerNorm(torch.nn.Module):
                             y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \varepsilon}} *
                             (1 + \gamma) + \beta
     device : Union[torch.device, str], default = "cuda"
-          The device on which the parameters of the model will allocated. It is the user's
+          The device on which the parameters of the model will be allocated. It is the user's
           responsibility to ensure all parameters are moved to the GPU before running the
           forward pass.
     """

transformer_engine/pytorch/module/layernorm_linear.py

@@ -816,7 +816,7 @@ class LayerNormLinear(TransformerEngineBaseModule):
                             y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \varepsilon}} *
                             (1 + \gamma) + \beta
     device : Union[torch.device, str], default = "cuda"
-          The device on which the parameters of the model will allocated. It is the user's
+          The device on which the parameters of the model will be allocated. It is the user's
           responsibility to ensure all parameters are moved to the GPU before running the
           forward pass.
 

transformer_engine/pytorch/module/layernorm_mlp.py

@@ -1193,7 +1193,7 @@ class LayerNormMLP(TransformerEngineBaseModule):
                             y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \varepsilon}} *
                             (1 + \gamma) + \beta
     device : Union[torch.device, str], default = "cuda"
-          The device on which the parameters of the model will allocated. It is the user's
+          The device on which the parameters of the model will be allocated. It is the user's
           responsibility to ensure all parameters are moved to the GPU before running the
           forward pass.
 

transformer_engine/pytorch/module/linear.py

@@ -650,7 +650,7 @@ class Linear(TransformerEngineBaseModule):
                  used for initializing weights in the following way: `init_method(weight)`.
                  When set to `None`, defaults to `torch.nn.init.normal_(mean=0.0, std=0.023)`.
     get_rng_state_tracker : Callable, default = `None`
-                 used to get the random number generator state tracker for initilizeing weights.
+                 used to get the random number generator state tracker for initializing weights.
     rng_tracker_name : str, default = `None`
                  the param passed to get_rng_state_tracker to get the specific rng tracker.
     parameters_split : Optional[Union[Tuple[str, ...], Dict[str, int]]], default = None
@@ -662,7 +662,7 @@ class Linear(TransformerEngineBaseModule):
                       names that end in `_weight` or `_bias`, so trailing underscores are
                       stripped from any provided names.
     device : Union[torch.device, str], default = "cuda"
-          The device on which the parameters of the model will allocated. It is the user's
+          The device on which the parameters of the model will be allocated. It is the user's
           responsibility to ensure all parameters are moved to the GPU before running the
           forward pass.
 

transformer_engine/pytorch/module/rmsnorm.py

@@ -120,7 +120,7 @@ class RMSNorm(torch.nn.Module):
                          .. math::
                             y = \frac{x}{RMS_\varepsilon(x)} * (1 + \gamma)
     device : Union[torch.device, str], default = "cuda"
-          The device on which the parameters of the model will allocated. It is the user's
+          The device on which the parameters of the model will be allocated. It is the user's
           responsibility to ensure all parameters are moved to the GPU before running the
           forward pass.
     """