Removed the unused options from GroupedLinear docs and fixed the bug with offsets (#1220)

* Removing the unused options from GroupedLinear docs and fixing the bug
with offsets
Signed-off-by: Przemyslaw Tredak <ptredak@nvidia.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci



* offsets -> fp8_meta_offsets
Signed-off-by: Przemyslaw Tredak <ptredak@nvidia.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci



---------
Signed-off-by: Przemyslaw Tredak <ptredak@nvidia.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

transformer_engine/pytorch/module/grouped_linear.py

@@ -44,18 +44,6 @@ from ..export import is_in_onnx_export_mode
 
 __all__ = ["GroupedLinear"]
 
-"""
-The offset for fp8_meta_index.
-_GEMM_INPUT = 0
-_GEMM_WEIGHT = num_gemms
-_GEMM_OUTPUT = 2 * num_gemms
-Must be properly set in GroupedLinear's initialization.
-"""
-_GEMM_INPUT = 0
-_GEMM_WEIGHT = 0
-_GEMM_OUTPUT = 0
-_GRAD_OUTPUT = 0
-
 
 class _GroupedLinear(torch.autograd.Function):
     """GroupedLinear semi-top level module
@@ -74,12 +62,9 @@ class _GroupedLinear(torch.autograd.Function):
         fp8_meta: Dict[str, Any],
         fuse_wgrad_accumulation: bool,
         cpu_offloading: bool,
-        tp_group: Union[dist_group_type, None],
-        tp_size: int,
         sequence_parallel: bool,
-        tensor_parallel: bool,
         activation_dtype: torch.dtype,
-        parallel_mode: Union[str, None],
+        fp8_meta_offsets: Dict[str, int],
         is_grad_enabled: bool,
         weights_fp8: List[Union[Float8Tensor, None]],
         *weights_and_biases: Union[Float8Tensor, torch.Tensor, None],
@@ -103,7 +88,6 @@ class _GroupedLinear(torch.autograd.Function):
         inputmats_t = []
         inputmat_scale_inv = None
 
-        global _GEMM_INPUT, _GEMM_WEIGHT, _GEMM_OUTPUT
         if fp8:
             fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
             inputmat_scale_inv = torch.empty([num_gemms], dtype=torch.float32, device=inp.device)
@@ -114,7 +98,9 @@ class _GroupedLinear(torch.autograd.Function):
                 and not sequence_parallel
             ):
                 # FP8 input for forward, FP8 input transpose for backward wgrad
-                indices = list(range(_GEMM_INPUT, _GEMM_INPUT + num_gemms))
+                indices = list(
+                    range(fp8_meta_offsets["input"], fp8_meta_offsets["input"] + num_gemms)
+                )
                 inputmats, inputmats_t = fp8_multi_cast_transpose_fused(
                     inputmats_no_fp8,
                     fp8_meta["scaling_fwd"],
@@ -130,7 +116,7 @@ class _GroupedLinear(torch.autograd.Function):
                     cast_to_fp8(
                         inputmats_no_fp8[i],
                         fp8_meta["scaling_fwd"],
-                        _GEMM_INPUT + i,
+                        fp8_meta_offsets["input"] + i,
                         fp8_dtype_forward,
                         scale_inv=inputmat_scale_inv,
                     )
@@ -194,14 +180,14 @@ class _GroupedLinear(torch.autograd.Function):
                 for i in range(num_gemms):
                     # amax of input
                     amin, amax = inputmats[i].aminmax()
-                    fp8_meta["scaling_fwd"].amax_history[0][_GEMM_INPUT + i] = torch.max(
-                        -amin, amax
-                    ).float()
+                    fp8_meta["scaling_fwd"].amax_history[0][fp8_meta_offsets["input"] + i] = (
+                        torch.max(-amin, amax).float()
+                    )
                     # amax of weight
                     amin, amax = weights[i].aminmax()
-                    fp8_meta["scaling_fwd"].amax_history[0][_GEMM_WEIGHT + i] = torch.max(
-                        -amin, amax
-                    ).float()
+                    fp8_meta["scaling_fwd"].amax_history[0][fp8_meta_offsets["weight"] + i] = (
+                        torch.max(-amin, amax).float()
+                    )
 
             out = torch.empty(
                 [sum(m_splits), weights[0].size(0)],
@@ -266,11 +252,8 @@ class _GroupedLinear(torch.autograd.Function):
             ctx.is_first_microbatch = is_first_microbatch
             ctx.use_bias = use_bias
             ctx.sequence_parallel = sequence_parallel
-            ctx.tensor_parallel = tensor_parallel
             ctx.inp_shape = inp.shape
-            ctx.parallel_mode = parallel_mode
-            ctx.tp_group = tp_group
-            ctx.tp_size = tp_size
+            ctx.fp8_meta_offsets = fp8_meta_offsets
             ctx.requires_dgrad = inp.requires_grad
             ctx.reduce_and_update_bwd_fp8_tensors = False
             if ctx.fp8 and requires_grad(inp, weights[0], biases[0]):
@@ -300,7 +283,6 @@ class _GroupedLinear(torch.autograd.Function):
                     w.main_grad = main_grads[i]
                     weights[i] = w
 
-            global _GEMM_INPUT, _GEMM_WEIGHT, _GRAD_OUTPUT
             # preprocess grad_output
             grad_output = grad_output.contiguous()
             grad_output_mats = torch.split(
@@ -318,13 +300,18 @@ class _GroupedLinear(torch.autograd.Function):
                             fp8_cast_transpose_bgrad_fused(
                                 grad_output_mats[i],
                                 ctx.fp8_meta["scaling_bwd"],
-                                _GRAD_OUTPUT + i,
+                                ctx.fp8_meta_offsets["grad_output"] + i,
                                 fp8_dtype_backward,
                             )
                         )
                 else:
                     if not ctx.fp8_meta["recipe"].override_linear_precision.wgrad:
-                        indices = list(range(_GRAD_OUTPUT, _GRAD_OUTPUT + ctx.num_gemms))
+                        indices = list(
+                            range(
+                                ctx.fp8_meta_offsets["grad_output"],
+                                ctx.fp8_meta_offsets["grad_output"] + ctx.num_gemms,
+                            )
+                        )
                         grad_output_c, grad_output_t = fp8_multi_cast_transpose_fused(
                             grad_output_mats,
                             ctx.fp8_meta["scaling_bwd"],
@@ -338,7 +325,7 @@ class _GroupedLinear(torch.autograd.Function):
                             grad_output_c[i] = cast_to_fp8(
                                 grad_output_mats[i],
                                 ctx.fp8_meta["scaling_bwd"],
-                                _GRAD_OUTPUT + i,
+                                ctx.fp8_meta_offsets["grad_output"] + i,
                                 fp8_dtype_backward,
                             )
 
@@ -363,7 +350,7 @@ class _GroupedLinear(torch.autograd.Function):
                         weights_fp8[0]._fp8_dtype,
                         grad_output_c,
                         ctx.fp8_meta["scaling_bwd"].scale_inv,
-                        _GRAD_OUTPUT,
+                        ctx.fp8_meta_offsets["grad_output"],
                         fp8_dtype_backward,
                         [dgrad],
                         ctx.activation_dtype,
@@ -416,7 +403,7 @@ class _GroupedLinear(torch.autograd.Function):
                             fp8_dtype_forward,
                             grad_output_t,
                             ctx.fp8_meta["scaling_bwd"].scale_inv,
-                            _GRAD_OUTPUT,
+                            ctx.fp8_meta_offsets["grad_output"],
                             fp8_dtype_backward,
                             wgrad_list,
                             ctx.activation_dtype,
@@ -497,12 +484,9 @@ class _GroupedLinear(torch.autograd.Function):
             None,  # fp8_meta
             None,  # fuse_wgrad_accumulation
             None,  # cpu_offloading
-            None,  # tp_group
-            None,  # tp_size
             None,  # sequence_parallel
-            None,  # tensor_parallel
             None,  # activation_dtype
-            None,  # parallel_mode
+            None,  # fp8_meta_offsets
             None,  # is_grad_enabled
             None,  # weights_fp8
             *wgrad_list,
@@ -536,23 +520,6 @@ class GroupedLinear(TransformerEngineBaseModule):
           responsibility to ensure all parameters are moved to the GPU before running the
           forward pass.
 
-    Parallelism parameters
-    ----------------------
-    sequence_parallel : bool, default = `False`
-                       if set to `True`, uses sequence parallelism.
-    tp_group : ProcessGroup, default = `None`
-              tensor parallel process group.
-    tp_size : int, default = 1
-             used as TP (tensor parallel) world size when TP groups are not formed during
-             initialization. In this case, users must call the
-             `set_tensor_parallel_group(tp_group)` method on the initialized module before the
-             forward pass to supply the tensor parallel group needed for tensor and sequence
-             parallel collectives.
-    parallel_mode : {None, 'column', 'row'}, default = `None`
-                   used to decide whether this GroupedLinear 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.
-
     Optimization parameters
     -----------------------
     fuse_wgrad_accumulation : bool, default = 'False'
@@ -613,8 +580,7 @@ class GroupedLinear(TransformerEngineBaseModule):
         self.get_rng_state_tracker = get_rng_state_tracker
         self.rng_tracker_name = rng_tracker_name
 
-        global _GEMM_INPUT, _GEMM_WEIGHT, _GEMM_OUTPUT
-        _GEMM_INPUT, _GEMM_WEIGHT, _GEMM_OUTPUT = 0, num_gemms, 2 * num_gemms
+        self._offsets = {"input": 0, "weight": num_gemms, "output": 2 * num_gemms, "grad_output": 0}
 
         if tp_group is None:
             self.tp_size = tp_size
@@ -651,7 +617,7 @@ class GroupedLinear(TransformerEngineBaseModule):
                 ),
                 init_fn=init_method,
                 get_rng_state_tracker=get_rng_state_tracker,
-                fp8_meta_index=_GEMM_WEIGHT + i,
+                fp8_meta_index=self._offsets["weight"] + i,
             )
 
             # Construct bias parameters if needed
@@ -774,7 +740,7 @@ class GroupedLinear(TransformerEngineBaseModule):
                         weight_tensors_fp8[i] = self.get_fp8_workspace(
                             tensor=weight_tensors[i],
                             fp8_meta_forward=True,
-                            fp8_meta_index=_GEMM_WEIGHT + i,
+                            fp8_meta_index=self._offsets["weight"] + i,
                             cache_name=(None if is_first_microbatch is None else f"weight{i}"),
                             update_workspace=update_workspace,
                             skip_update_flag=skip_fp8_weight_update,
@@ -798,12 +764,9 @@ class GroupedLinear(TransformerEngineBaseModule):
                 self.fp8_meta,
                 self.fuse_wgrad_accumulation,
                 CPUOffloadEnabled,
-                self.tp_group,
-                self.tp_size,
                 self.sequence_parallel,
-                self.tp_size > 1,
                 self.activation_dtype,
-                self.parallel_mode,
+                self._offsets,
                 torch.is_grad_enabled(),
                 weight_tensors_fp8,
                 *weight_tensors,