Deprecate unused APIs (#321)

* Deprecate unused APIs
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* review comments
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Review
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

---------
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

transformer_engine/pytorch/module/layernorm_linear.py

@@ -4,6 +4,7 @@
 
 """LayerNormLinear API"""
 import os
+import warnings
 from typing import Union, Optional, Callable, Tuple, List, Dict, Any
 
 
@@ -538,6 +539,11 @@ class LayerNormLinear(TransformerEngineBaseModule):
     r"""
     Applies layer normalization followed by linear transformation to the incoming data.
 
+    .. warning::
+
+        Argument :attr:`skip_weight_param_allocation` is deprecated and will
+        be fully removed in future releases.
+
     Parameters
     ----------
     in_features : int
@@ -585,9 +591,6 @@ class LayerNormLinear(TransformerEngineBaseModule):
                    used to decide whether this Linear layer is Column Parallel Linear or Row
                    Parallel Linear as described `here <https://arxiv.org/pdf/1909.08053.pdf>`_.
                    When set to `None`, no communication is performed.
-    skip_weight_param_allocation: bool, default = `False`
-                                 if set to `True`, weight parameter is not allocated and must be
-                                 passed as a keyword argument `weight` during the forward pass.
 
     Optimization parameters
     -----------------------
@@ -633,6 +636,14 @@ class LayerNormLinear(TransformerEngineBaseModule):
     ) -> None:
         super().__init__()
 
+        if skip_weight_param_allocation:
+            warnings.warn(
+                "Argument `skip_weight_param_allocation` is deprecated and"
+                "will be fully removed in future releases. It is ignored"
+                "starting from v0.11.",
+                category=DeprecationWarning,
+            )
+
         params_dtype = torch.get_default_dtype() if params_dtype is None else params_dtype
         self.in_features = in_features
         self.out_features = out_features
@@ -695,7 +706,6 @@ class LayerNormLinear(TransformerEngineBaseModule):
         setattr(self.layer_norm_bias, "sequence_parallel", self.sequence_parallel)
         self.reset_layer_norm_parameters()
 
-        if not skip_weight_param_allocation:
         self.weight_tensor = torch.empty(
             self.out_features, self.in_features,
             device=torch.cuda.current_device(),
@@ -821,17 +831,15 @@ class LayerNormLinear(TransformerEngineBaseModule):
         """
         Apply layer normalization to the input followed by a linear transformation.
 
+        .. warning::
+
+            Arguments :attr:`weight` and :attr:`bias` are deprecated and will
+            be fully removed in future releases.
+
         Parameters
         ----------
         inp : torch.Tensor
              Input tensor.
-        weight : torch.Tensor, default = None
-                An optional weight tensor for the module. This argument is compulsory if module
-                is initialized with `skip_weight_param_allocation=True`
-        bias : torch.Tensor, default = None
-              An optional bias tensor for the module. This argument is compulsory if module
-              is initialized with `skip_weight_param_allocation=True` and one of `use_bias`
-              or `return_bias`
         is_first_microbatch : {True, False, None}, default = None
                              During training using either gradient accumulation or
                              pipeline parallelism a minibatch of data is further split
@@ -847,16 +855,20 @@ class LayerNormLinear(TransformerEngineBaseModule):
                                produced)
         """
 
+        if weight is not None or bias is not None:
+            raise RuntimeError(
+                "Arguments `weight` and `bias` are deprecated and "
+                "will be fully removed in future releases."
+            )
+
         with self.prepare_forward(inp, is_first_microbatch) as inp:
             bias_tensor = (
-                bias if bias is not None
-                else self.bias if self.parameters_split is None
+                self.bias if self.parameters_split is None
                 else self.bias_tensor if not torch.is_grad_enabled()
                 else self.noop_cat("bias_tensor", self.bias_names)
             )
             weight_tensor = (
-                weight if weight is not None
-                else self.weight if self.parameters_split is None
+                self.weight if self.parameters_split is None
                 else self.weight_tensor if not torch.is_grad_enabled()
                 else self.noop_cat("weight_tensor", self.weight_names)
             )

transformer_engine/pytorch/module/linear.py

@@ -3,6 +3,7 @@
 # See LICENSE for license information.
 
 """Linear API"""
+import warnings
 from typing import Union, Optional, Callable, Tuple, List, Dict, Any
 
 import torch
@@ -441,6 +442,11 @@ class Linear(TransformerEngineBaseModule):
 
     On NVIDIA GPUs it is a drop-in replacement for `torch.nn.Linear`.
 
+    .. warning::
+
+        Argument :attr:`skip_weight_param_allocation` is deprecated and will
+        be fully removed in future releases.
+
     Parameters
     ----------
     in_features : int
@@ -474,9 +480,6 @@ class Linear(TransformerEngineBaseModule):
                    used to decide whether this Linear layer is Column Parallel Linear or Row
                    Parallel Linear as described `here <https://arxiv.org/pdf/1909.08053.pdf>`_.
                    When set to `None`, no communication is performed.
-    skip_weight_param_allocation: bool, default = `False`
-                                 if set to `True`, weight parameter is not allocated and must be
-                                 passed as a keyword argument `weight` during the forward pass.
 
     Optimization parameters
     -----------------------
@@ -518,6 +521,14 @@ class Linear(TransformerEngineBaseModule):
     ) -> None:
         super().__init__()
 
+        if skip_weight_param_allocation:
+            warnings.warn(
+                "Argument `skip_weight_param_allocation` is deprecated and"
+                "will be fully removed in future releases. It has ignored"
+                "starting from v0.11.",
+                category=DeprecationWarning,
+            )
+
         params_dtype = torch.get_default_dtype() if params_dtype is None else params_dtype
         self.in_features = in_features
         self.out_features = out_features
@@ -558,7 +569,6 @@ class Linear(TransformerEngineBaseModule):
 
         self.sequence_parallel = (self.tp_size > 1) and sequence_parallel
 
-        if not skip_weight_param_allocation:
         self.weight_tensor = torch.empty(
             self.out_features, self.in_features,
             device=torch.cuda.current_device(),
@@ -668,17 +678,15 @@ class Linear(TransformerEngineBaseModule):
         """
         Apply the linear transformation to the input.
 
+        .. warning::
+
+            Arguments :attr:`weight` and :attr:`bias` are deprecated and will
+            be fully removed in future releases.
+
         Parameters
         ----------
         inp : torch.Tensor
              Input tensor.
-        weight : torch.Tensor, default = None
-                An optional weight tensor for the module. This argument is compulsory if module
-                is initialized with `skip_weight_param_allocation=True`
-        bias : torch.Tensor, default = None
-              An optional bias tensor for the module. This argument is compulsory if module
-              is initialized with `skip_weight_param_allocation=True` and one of `use_bias`
-              or `return_bias`
         is_first_microbatch : {True, False, None}, default = None
                              During training using either gradient accumulation or
                              pipeline parallelism a minibatch of data is further split
@@ -694,16 +702,20 @@ class Linear(TransformerEngineBaseModule):
                                produced)
         """
 
+        if weight is not None or bias is not None:
+            raise RuntimeError(
+                "Arguments `weight` and `bias` are deprecated and "
+                "will be fully removed in future releases."
+            )
+
         with self.prepare_forward(inp, is_first_microbatch) as inp:
             bias_tensor = (
-                bias if bias is not None
-                else self.bias if self.parameters_split is None
+                self.bias if self.parameters_split is None
                 else self.bias_tensor if not torch.is_grad_enabled()
                 else self.noop_cat("bias_tensor", self.bias_names)
             )
             weight_tensor = (
-                weight if weight is not None
-                else self.weight if self.parameters_split is None
+                self.weight if self.parameters_split is None
                 else self.weight_tensor if not torch.is_grad_enabled()
                 else self.noop_cat("weight_tensor", self.weight_names)
             )