[Paddle] Add main_grad (#779)

* support main_grad
Signed-off-by: Shijie Wang <jaywan@nvidia.com>

* update main_grad and fuse_wgrad_accumulation
Signed-off-by: Shijie Wang <jaywan@nvidia.com>

* fix ci errors
Signed-off-by: Shijie Wang <jaywan@nvidia.com>

* minor change
Signed-off-by: Shijie Wang <jaywan@nvidia.com>

---------
Signed-off-by: Shijie Wang <jaywan@nvidia.com>

tests/paddle/test_master_grad.py

+# Copyright (c) 2022-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# See LICENSE for license information.
+"""Test TransformerLayer encoder main_grad"""
+
+import numpy as np
+import pytest
+
+import paddle
+from paddle.distributed.fleet.utils import mix_precision_utils
+
+import transformer_engine.paddle as te
+from transformer_engine.paddle.fp8 import is_fp8_available
+
+is_fp8_supported, reason = is_fp8_available()
+
+
+def create_optimizer(model, use_pure_bf16, use_main_grad):
+    '''Create optimizer'''
+    if use_main_grad:
+        assert use_pure_bf16
+        model = mix_precision_utils.MixPrecisionLayer(model, dtype="bfloat16")
+    optimizer = paddle.optimizer.AdamW(
+        parameters=model.parameters(),
+        learning_rate=0.0001,
+        multi_precision=use_pure_bf16,
+    )
+    if use_main_grad:
+        optimizer = mix_precision_utils.MixPrecisionOptimizer(optimizer)
+
+    return optimizer
+
+
+class Net(paddle.nn.Layer):
+    '''Network use for main_grad testing'''
+
+    def __init__(self, fuse_wgrad_accumulation):
+        super().__init__()
+        self.layer = te.TransformerLayer(
+            4096,
+            16384,
+            32,
+            layer_type='encoder',
+            fuse_wgrad_accumulation=fuse_wgrad_accumulation,
+        )
+
+    def forward(self, inp):
+        out = self.layer(inp)
+        return out
+
+
+def train(enable_master_grad, fuse_wgrad_accumulation=False):
+    '''Train function'''
+    paddle.seed(10)
+
+    accumulate_steps = 4
+
+    if fuse_wgrad_accumulation:
+        assert enable_master_grad, "fuse_wgrad_accumulation requires enable_master_grad"
+
+    model = Net(fuse_wgrad_accumulation)
+
+    optimizer = create_optimizer(model, use_pure_bf16=True, use_main_grad=enable_master_grad)
+
+    loss_list = []
+    for step_id in range(16):
+        inp = paddle.uniform([2, 1024, 4096], dtype='float32')
+        inp.stop_gradient = False
+        with te.fp8_autocast(enabled=True):
+            out = model(inp)
+        loss = out.mean()
+        loss_list.append(loss)
+        loss.backward()
+
+        # gradient accumulation
+        if (step_id + 1) % accumulate_steps == 0:
+            optimizer.step()
+            optimizer.clear_grad()
+
+    return loss_list
+
+
+@pytest.mark.skipif(not is_fp8_supported, reason=reason)
+def test_master_grad():
+    '''Test main_grad'''
+    paddle.set_default_dtype('float32')
+    loss1 = train(enable_master_grad=False)
+    loss2 = train(enable_master_grad=True)
+    loss3 = train(enable_master_grad=True, fuse_wgrad_accumulation=True)
+
+    np.testing.assert_allclose(loss1, loss2, rtol=1e-5, atol=1e-5)
+    np.testing.assert_allclose(loss1, loss3, rtol=1e-5, atol=1e-5)

tests/paddle/test_operators.py

@@ -417,9 +417,9 @@ class TestGemm:
         workspace = paddle.zeros(shape=[33_554_432], dtype='uint8')
 
         ref_out = paddle.matmul(a, b.T)
-        actual_out = fp8_gemm(b_casted, fp8_meta.scale_inv, FP8FwdTensors.GEMM1_WEIGHT, fp8_dtype,
-                              a_casted, fp8_meta.scale_inv, FP8FwdTensors.GEMM1_INPUT, fp8_dtype,
-                              out_dtype, workspace)
+        actual_out, _ = fp8_gemm(b_casted, fp8_meta.scale_inv, FP8FwdTensors.GEMM1_WEIGHT,
+                                 fp8_dtype, a_casted, fp8_meta.scale_inv, FP8FwdTensors.GEMM1_INPUT,
+                                 fp8_dtype, out_dtype, workspace)
 
         assert_allclose(actual_out, ref_out)
 

transformer_engine/paddle/cpp_extensions.py

@@ -26,6 +26,7 @@ def gemm(
     accumulate: bool = False,
     layout: str = "TN",
     out: Optional[paddle.Tensor] = None,
+    out_dtype: Optional[paddle.dtype] = None,
     bias: Optional[paddle.Tensor] = None,
     use_bias: bool = False,
 ) -> Tuple[Union[paddle.Tensor, None], ...]:
@@ -35,16 +36,23 @@ def gemm(
     transa = layout[0] == "T"
     transb = layout[1] == "T"
 
-    return_output = False
     if out is None:
+        if accumulate:
+            out = paddle.zeros(
+                shape=[
+                    B.shape[1] if transb else B.shape[0],
+                    A.shape[0] if transa else A.shape[1],
+                ],
+                dtype=out_dtype if out_dtype is not None else dtype,
+            )
+        else:
             out = paddle.empty(
                 shape=[
                     B.shape[1] if transb else B.shape[0],
                     A.shape[0] if transa else A.shape[1],
                 ],
-            dtype=dtype,
+                dtype=out_dtype if out_dtype is not None else dtype,
             )
-        return_output = True
 
     if gelu and not grad:
         gelu_input = paddle.empty_like(out, dtype=dtype)
@@ -94,9 +102,7 @@ def gemm(
         0,    # math_sm_count
     )
 
-    if return_output:
     return out, grad_bias, gelu_input
-    return None, grad_bias, gelu_input
 
 
 def fp8_gemm(
@@ -125,8 +131,16 @@ def fp8_gemm(
     if D_dtype is not None and D_dtype in [tex.DType.kFloat8E4M3, tex.DType.kFloat8E5M2]:
         assert fp8_meta_tensor is not None and out_index is not None
 
-    return_output = False
     if out is None:
+        if accumulate:
+            out = paddle.zeros(
+                shape=[
+                    B.shape[0],
+                    A.shape[0],
+                ],
+                dtype=out_dtype,
+            )
+        else:
             out = paddle.empty(
                 shape=[
                     B.shape[0],
@@ -134,7 +148,7 @@ def fp8_gemm(
                 ],
                 dtype=out_dtype,
             )
-        return_output = True
+
     # Use bfloat16 as default bias_dtype
     bias_dtype = paddle.bfloat16 if bias is None else bias.dtype
     if gelu:
@@ -172,13 +186,7 @@ def fp8_gemm(
         0,    # math_sm_count
     )
 
-    if return_output:
-        if gelu:
     return out, gelu_input
-        return out
-    if gelu:
-        return gelu_input
-    return None
 
 
 def cast_to_fp8(

transformer_engine/paddle/layer/attention.py

@@ -562,6 +562,16 @@ class MultiHeadAttention(paddle.nn.Layer):
                    name should be registered through
                    `paddle.distributed.fleet.meta_parallel.get_rng_state_tracker()
                    .add(rng_state_name, seed)`.
+
+    Optimization parameters
+    -----------------------
+    fuse_wgrad_accumulation : bool, default = 'False'
+                             if set to `True`, enables fusing of creation and accumulation of
+                             the weight gradient. When enabled, it is assumed that the weights
+                             have an additional `main_grad` attribute (used instead of the
+                             regular `grad`) which is a pre-allocated buffer of the correct
+                             size to accumulate gradients in.
+
     """
 
     def __init__(
@@ -584,6 +594,7 @@ class MultiHeadAttention(paddle.nn.Layer):
         sequence_parallel: bool = False,
         tp_group: Optional[dist_group_type] = None,
         num_gqa_groups: Optional[int] = None,
+        fuse_wgrad_accumulation: bool = False,
         rng_state_name: str = 'local_seed',
         backend: str = 'transformer_engine',
     ) -> None:
@@ -633,6 +644,7 @@ class MultiHeadAttention(paddle.nn.Layer):
                     parallel_mode=qkv_parallel_mode,
                     sequence_parallel=self.sequence_parallel,
                     tp_group=self.tp_group,
+                    fuse_wgrad_accumulation=fuse_wgrad_accumulation,
                     backend=self.backend,
                 )
             else:
@@ -644,6 +656,7 @@ class MultiHeadAttention(paddle.nn.Layer):
                     parallel_mode=qkv_parallel_mode,
                     sequence_parallel=self.sequence_parallel,
                     tp_group=self.tp_group,
+                    fuse_wgrad_accumulation=fuse_wgrad_accumulation,
                     backend=self.backend,
                 )
 
@@ -661,6 +674,7 @@ class MultiHeadAttention(paddle.nn.Layer):
                     parallel_mode=qkv_parallel_mode,
                     sequence_parallel=self.sequence_parallel,
                     tp_group=self.tp_group,
+                    fuse_wgrad_accumulation=fuse_wgrad_accumulation,
                     backend=self.backend,
                 )
             else:
@@ -672,6 +686,7 @@ class MultiHeadAttention(paddle.nn.Layer):
                     parallel_mode=qkv_parallel_mode,
                     sequence_parallel=self.sequence_parallel,
                     tp_group=self.tp_group,
+                    fuse_wgrad_accumulation=fuse_wgrad_accumulation,
                     backend=self.backend,
                 )
             self.key_value = Linear(
@@ -682,6 +697,7 @@ class MultiHeadAttention(paddle.nn.Layer):
                 parallel_mode=qkv_parallel_mode,
                 sequence_parallel=self.sequence_parallel,
                 tp_group=self.tp_group,
+                fuse_wgrad_accumulation=fuse_wgrad_accumulation,
                 backend=self.backend,
             )
 
@@ -706,6 +722,7 @@ class MultiHeadAttention(paddle.nn.Layer):
             parallel_mode="row" if set_parallel_mode else None,
             sequence_parallel=self.sequence_parallel,
             tp_group=self.tp_group,
+            fuse_wgrad_accumulation=fuse_wgrad_accumulation,
             backend=self.backend,
         )
 

transformer_engine/paddle/layer/layernorm_linear.py

@@ -202,6 +202,7 @@ class _LayerNormLinear(paddle.autograd.PyLayer):
         sequence_parallel: bool,
         tp_group: Union[dist_group_type, None],
         tp_size: int,
+        fuse_wgrad_accumulation: bool,
         is_first_microbatch: bool,
     ) -> Union[Tuple[paddle.Tensor, ...], paddle.Tensor]:
         if normalization == "RMSNorm":
@@ -285,6 +286,7 @@ class _LayerNormLinear(paddle.autograd.PyLayer):
             ctx.sequence_parallel = sequence_parallel
             ctx.tp_group = tp_group
             ctx.tp_size = tp_size
+            ctx.fuse_wgrad_accumulation = fuse_wgrad_accumulation
             ctx.requires_dgrad = not inp.stop_gradient
             ctx.requires_wgrad = not weight.stop_gradient
             ctx.requires_bgrad = use_bias and not bias.stop_gradient
@@ -329,6 +331,12 @@ class _LayerNormLinear(paddle.autograd.PyLayer):
             ) = TransformerEngineBaseLayer.grad_output_preprocess(ctx, grad_outputs[0],
                                                                   ctx.parallel_mode == "row")
 
+            if ctx.is_first_microbatch is not None:
+                accumulate_wgrad_into_param_main_grad = (ctx.fuse_wgrad_accumulation
+                                                         and not ctx.is_first_microbatch)
+            else:
+                accumulate_wgrad_into_param_main_grad = ctx.fuse_wgrad_accumulation
+
             # Prepare ln_out for Linear bwd
             linear_inputmat = ln_out
             if ctx.fp8_enabled:
@@ -365,6 +373,8 @@ class _LayerNormLinear(paddle.autograd.PyLayer):
                 ctx.tensor_parallel,
                 ctx.sequence_parallel,
                 ctx.tp_group,
+                ctx.fuse_wgrad_accumulation,
+                accumulate_wgrad_into_param_main_grad,
             )
 
             if not ctx.fp8_enabled:
@@ -396,6 +406,8 @@ class _LayerNormLinear(paddle.autograd.PyLayer):
                 # weight_fp8 and weight_t_fp8 are stop_gradient tensors
                 weight_cache_grad = (None, None)
 
+        if ctx.requires_wgrad and ctx.fuse_wgrad_accumulation:
+            wgrad = None
         return (
             dxmat.reshape(ctx.inp_shape) if ctx.requires_dgrad else None,
             dgamma if ctx.requires_ln_wgrad else None,
@@ -449,6 +461,15 @@ class LayerNormLinear(TransformerEngineBaseLayer):
                    When set to `None`, no communication is performed.
     sequence_parallel : bool, default = `False`
                        if set to `True`, uses sequence parallelism.
+
+    Optimization parameters
+    -----------------------
+    fuse_wgrad_accumulation : bool, default = 'False'
+                             if set to `True`, enables fusing of creation and accumulation of
+                             the weight gradient. When enabled, it is assumed that the weights
+                             have an additional `main_grad` attribute (used instead of the
+                             regular `grad`) which is a pre-allocated buffer of the correct
+                             size to accumulate gradients in.
     """
 
     def __init__(
@@ -464,6 +485,7 @@ class LayerNormLinear(TransformerEngineBaseLayer):
         parallel_mode: Optional[str] = None,
         sequence_parallel: bool = False,
         tp_group: Union[dist_group_type, None] = None,
+        fuse_wgrad_accumulation: bool = False,
         backend: str = 'transformer_engine',
     ) -> None:
         super().__init__()
@@ -497,6 +519,8 @@ class LayerNormLinear(TransformerEngineBaseLayer):
 
         self.sequence_parallel = self.tensor_parallel and sequence_parallel
 
+        self.fuse_wgrad_accumulation = fuse_wgrad_accumulation
+
         # LayerNorm weights
         self.ln_weight = self.create_parameter(
             shape=[self.in_features],
@@ -610,6 +634,7 @@ class LayerNormLinear(TransformerEngineBaseLayer):
                 self.sequence_parallel,
                 self.tp_group,
                 self.tp_size,
+                self.fuse_wgrad_accumulation,
                 is_first_microbatch,
             )
 

transformer_engine/paddle/layer/layernorm_mlp.py

@@ -211,6 +211,8 @@ def _mlp_backward(
     tensor_parallel: bool,
     sequence_parallel: bool,
     tp_group: Union[dist_group_type, None],
+    fuse_wgrad_accumulation: bool,
+    accumulate_wgrad_into_param_main_grad: bool,
 ):
     (
         fc1_dgrad,
@@ -238,6 +240,7 @@ def _mlp_backward(
             fc2_input,
             None,
             fc2_input_fp8_index,
+            fc2_weight,
             fc2_weight_t_fp8,
             fc2_weight_fp8_index,
             grad_output,
@@ -253,6 +256,8 @@ def _mlp_backward(
             tensor_parallel,
             sequence_parallel,
             tp_group,
+            fuse_wgrad_accumulation,
+            accumulate_wgrad_into_param_main_grad,
         )
 
         if activation == "gelu":
@@ -299,6 +304,7 @@ def _mlp_backward(
             fc1_input,
             None,
             fc1_input_fp8_index,
+            fc1_weight,
             fc1_weight_t_fp8,
             fc1_weight_fp8_index,
             dgelu_no_fp8,
@@ -314,6 +320,8 @@ def _mlp_backward(
             tensor_parallel,
             sequence_parallel,
             tp_group,
+            fuse_wgrad_accumulation,
+            accumulate_wgrad_into_param_main_grad,
         )
     else:
         dgelu, fc2_wgrad, fc2_bgrad = _linear_bwd_non_fp8(
@@ -328,6 +336,8 @@ def _mlp_backward(
             tensor_parallel,
             sequence_parallel,
             tp_group,
+            fuse_wgrad_accumulation=fuse_wgrad_accumulation,
+            accumulate_wgrad_into_param_main_grad=accumulate_wgrad_into_param_main_grad,
             gelu_input=fc1_out,
             activation=activation,
         )
@@ -347,6 +357,8 @@ def _mlp_backward(
             tensor_parallel,
             sequence_parallel,
             tp_group,
+            fuse_wgrad_accumulation=fuse_wgrad_accumulation,
+            accumulate_wgrad_into_param_main_grad=accumulate_wgrad_into_param_main_grad,
         )
     return (
         fc1_dgrad,
@@ -393,6 +405,7 @@ class _LayerNormMLP(paddle.autograd.PyLayer):
         sequence_parallel: bool,
         tp_group: Union[dist_group_type, None],
         tp_size: int,
+        fuse_wgrad_accumulation: bool,
         is_first_microbatch: bool,
     ) -> Union[Tuple[paddle.Tensor, ...], paddle.Tensor]:
         if normalization == "RMSNorm":
@@ -498,6 +511,7 @@ class _LayerNormMLP(paddle.autograd.PyLayer):
             ctx.sequence_parallel = sequence_parallel
             ctx.tp_group = tp_group
             ctx.tp_size = tp_size
+            ctx.fuse_wgrad_accumulation = fuse_wgrad_accumulation
             ctx.requires_dgrad = not inp.stop_gradient
             ctx.requires_fc1_wgrad = not fc1_weight.stop_gradient
             ctx.requires_fc2_wgrad = not fc2_weight.stop_gradient
@@ -548,6 +562,12 @@ class _LayerNormMLP(paddle.autograd.PyLayer):
                 fc2_bgrad,
             ) = TransformerEngineBaseLayer.grad_output_preprocess(ctx, grad_outputs[0], True)
 
+            if ctx.is_first_microbatch is not None:
+                accumulate_wgrad_into_param_main_grad = (ctx.fuse_wgrad_accumulation
+                                                         and not ctx.is_first_microbatch)
+            else:
+                accumulate_wgrad_into_param_main_grad = ctx.fuse_wgrad_accumulation
+
             (
                 fc1_dgrad,
                 fc1_wgrad,
@@ -585,6 +605,8 @@ class _LayerNormMLP(paddle.autograd.PyLayer):
                 ctx.tensor_parallel,
                 ctx.sequence_parallel,
                 ctx.tp_group,
+                ctx.fuse_wgrad_accumulation,
+                accumulate_wgrad_into_param_main_grad,
             )
             if not ctx.fp8_enabled:
                 # fc2_bias is fused with gemm for non-FP8 path
@@ -619,6 +641,11 @@ class _LayerNormMLP(paddle.autograd.PyLayer):
                 fc1_weight_cache_grad = (None, None)
                 fc2_weight_cache_grad = (None, None)
 
+        if ctx.requires_fc1_wgrad and ctx.fuse_wgrad_accumulation:
+            fc1_wgrad = None
+        if ctx.requires_fc2_wgrad and ctx.fuse_wgrad_accumulation:
+            fc2_wgrad = None
+
         return (
             dxmat.reshape(ctx.inp_shape) if ctx.requires_dgrad else None,
             dgamma if ctx.requires_ln_wgrad else None,
@@ -679,6 +706,14 @@ class LayerNormMLP(TransformerEngineBaseLayer):
     tp_group : paddle.distributed.collective.Group, default = `None`
                tensor parallel process group.
 
+    Optimization parameters
+    -----------------------
+    fuse_wgrad_accumulation : bool, default = 'False'
+                             if set to `True`, enables fusing of creation and accumulation of
+                             the weight gradient. When enabled, it is assumed that the weights
+                             have an additional `main_grad` attribute (used instead of the
+                             regular `grad`) which is a pre-allocated buffer of the correct
+                             size to accumulate gradients in.
     """
 
     def __init__(
@@ -695,6 +730,7 @@ class LayerNormMLP(TransformerEngineBaseLayer):
         set_parallel_mode: bool = False,
         sequence_parallel: bool = False,
         tp_group: Optional[dist_group_type] = None,
+        fuse_wgrad_accumulation: bool = False,
         backend: str = 'transformer_engine',
     ) -> None:
         super().__init__()
@@ -720,6 +756,8 @@ class LayerNormMLP(TransformerEngineBaseLayer):
         self.set_parallel_mode = set_parallel_mode
         self.sequence_parallel = self.tensor_parallel and sequence_parallel
 
+        self.fuse_wgrad_accumulation = fuse_wgrad_accumulation
+
         if self.set_parallel_mode:
             self.size_per_partition = divide(self.ffn_hidden_size, self.tp_size)
         else:
@@ -876,6 +914,7 @@ class LayerNormMLP(TransformerEngineBaseLayer):
                 self.sequence_parallel,
                 self.tp_group,
                 self.tp_size,
+                self.fuse_wgrad_accumulation,
                 is_first_microbatch,
             )
 

transformer_engine/paddle/layer/linear.py

@@ -96,7 +96,7 @@ def _linear_fwd_fp8(
                 out=weight_fp8,
             )
 
-    out = fp8_gemm(
+    out, _ = fp8_gemm(
         weight_fp8,
         fp8_meta["scaling_fwd"].scale_inv,
         weight_fp8_index,
@@ -245,6 +245,7 @@ def _linear_bwd_fp8(
     inputmat: paddle.Tensor,
     inputmat_t: paddle.Tensor,
     inputmat_fp8_index: FP8FwdTensors,
+    weight: paddle.Tensor,
     weight_t_fp8: paddle.Tensor,
     weight_fp8_index: FP8FwdTensors,
     grad_output: paddle.Tensor,
@@ -260,6 +261,8 @@ def _linear_bwd_fp8(
     tensor_parallel: bool,
     sequence_parallel: bool,
     tp_group: Union[dist_group_type, None],
+    fuse_wgrad_accumulation: bool,
+    accumulate_wgrad_into_param_main_grad: bool,
 ):
     dgrad, wgrad, handle = None, None, None
 
@@ -275,7 +278,7 @@ def _linear_bwd_fp8(
     fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
     fp8_dtype_backward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=False)
     if requires_dgrad:
-        dgrad = fp8_gemm(
+        dgrad, _ = fp8_gemm(
             weight_t_fp8,
             fwd_scale_inverses,
             weight_fp8_index,
@@ -303,7 +306,8 @@ def _linear_bwd_fp8(
             if inputmat_t_total is None:
                 inputmat_t_total = transpose(inputmat_total, fp8_dtype_backward)
                 clear_tensor_data(inputmat_total)
-            wgrad = fp8_gemm(
+
+            wgrad, _ = fp8_gemm(
                 inputmat_t_total,
                 fwd_scale_inverses,
                 inputmat_fp8_index,
@@ -312,8 +316,10 @@ def _linear_bwd_fp8(
                 fp8_meta["scaling_bwd"].scale_inv,
                 grad_output_fp8_index,
                 fp8_dtype_backward,
-                activation_dtype,
+                "float32" if fuse_wgrad_accumulation else activation_dtype,
                 get_workspace(),
+                accumulate=accumulate_wgrad_into_param_main_grad,
+                out=weight.main_grad if fuse_wgrad_accumulation else None,
                 use_split_accumulator=_2X_ACC_WGRAD,
             )
             clear_tensor_data(inputmat_t_total, grad_output_t)
@@ -323,11 +329,17 @@ def _linear_bwd_fp8(
                 grad_output,
                 activation_dtype,
                 get_workspace(),
-                layout="NT",
                 grad=True,
+                accumulate=accumulate_wgrad_into_param_main_grad,
+                layout="NT",
+                out=weight.main_grad if fuse_wgrad_accumulation else None,
+                out_dtype="float32" if fuse_wgrad_accumulation else None,
             )
             clear_tensor_data(inputmat_total)
 
+        if fuse_wgrad_accumulation:
+            weight.main_grad = wgrad
+
     if parallel_mode == "column" and tensor_parallel and handle is not None:
         handle.wait()
 
@@ -346,6 +358,8 @@ def _linear_bwd_non_fp8(
     tensor_parallel: bool,
     sequence_parallel: bool,
     tp_group: Union[dist_group_type, None],
+    fuse_wgrad_accumulation: bool,
+    accumulate_wgrad_into_param_main_grad: bool,
     gelu_input: Union[paddle.Tensor, None] = None,
     activation: str = "",
 ):
@@ -386,10 +400,16 @@ def _linear_bwd_non_fp8(
             grad_output,
             activation_dtype,
             get_workspace(),
-            layout="NT",
             grad=True,
+            accumulate=accumulate_wgrad_into_param_main_grad,
+            layout="NT",
+            out=weight.main_grad if fuse_wgrad_accumulation else None,
+            out_dtype="float32" if fuse_wgrad_accumulation else None,
             use_bias=requires_bgrad,
         )
+        if fuse_wgrad_accumulation:
+            weight.main_grad = wgrad
+
     elif requires_bgrad:
         bgrad = grad_output.sum(axis=0)
 
@@ -421,6 +441,8 @@ def _linear_bwd(
     tensor_parallel: bool,
     sequence_parallel: bool,
     tp_group: Union[dist_group_type, None],
+    fuse_wgrad_accumulation: bool,
+    accumulate_wgrad_into_param_main_grad: bool,
 ):
     dgrad, wgrad, bgrad = None, None, None
     if fp8_enabled:
@@ -428,6 +450,7 @@ def _linear_bwd(
             inputmat,
             inputmat_t,
             inputmat_fp8_index,
+            weight,
             weight_t_fp8,
             weight_fp8_index,
             grad_output,
@@ -443,6 +466,8 @@ def _linear_bwd(
             tensor_parallel,
             sequence_parallel,
             tp_group,
+            fuse_wgrad_accumulation=fuse_wgrad_accumulation,
+            accumulate_wgrad_into_param_main_grad=accumulate_wgrad_into_param_main_grad,
         )
     else:
         dgrad, wgrad, bgrad = _linear_bwd_non_fp8(
@@ -457,6 +482,8 @@ def _linear_bwd(
             tensor_parallel,
             sequence_parallel,
             tp_group,
+            fuse_wgrad_accumulation=fuse_wgrad_accumulation,
+            accumulate_wgrad_into_param_main_grad=accumulate_wgrad_into_param_main_grad,
         )
     return dgrad, wgrad, bgrad
 
@@ -483,6 +510,7 @@ class _Linear(paddle.autograd.PyLayer):
         sequence_parallel: bool,
         tp_group: Union[dist_group_type, None],
         tp_size: int,
+        fuse_wgrad_accumulation: bool,
         is_first_microbatch: bool,
     ) -> paddle.Tensor:
         # Make sure input dimensions are compatible
@@ -561,6 +589,7 @@ class _Linear(paddle.autograd.PyLayer):
             ctx.sequence_parallel = sequence_parallel
             ctx.tp_group = tp_group
             ctx.tp_size = tp_size
+            ctx.fuse_wgrad_accumulation = fuse_wgrad_accumulation
             ctx.requires_dgrad = not inp.stop_gradient
             ctx.requires_wgrad = not weight.stop_gradient
             ctx.requires_bgrad = use_bias and not bias.stop_gradient
@@ -591,6 +620,11 @@ class _Linear(paddle.autograd.PyLayer):
                 bgrad,
             ) = TransformerEngineBaseLayer.grad_output_preprocess(ctx, grad_output,
                                                                   ctx.parallel_mode == "row")
+            if ctx.is_first_microbatch is not None:
+                accumulate_wgrad_into_param_main_grad = (ctx.fuse_wgrad_accumulation
+                                                         and not ctx.is_first_microbatch)
+            else:
+                accumulate_wgrad_into_param_main_grad = ctx.fuse_wgrad_accumulation
 
             dgrad, wgrad, bgrad_ = _linear_bwd(
                 inputmat,
@@ -614,6 +648,8 @@ class _Linear(paddle.autograd.PyLayer):
                 ctx.tensor_parallel,
                 ctx.sequence_parallel,
                 ctx.tp_group,
+                ctx.fuse_wgrad_accumulation,
+                accumulate_wgrad_into_param_main_grad,
             )
 
             if not ctx.fp8_enabled:
@@ -626,18 +662,22 @@ class _Linear(paddle.autograd.PyLayer):
                 # weight_fp8 and weight_t_fp8 are stop_gradient tensors
                 weight_cache_grad = (None, None)
 
+            dgrad_return = dgrad.reshape(ctx.inp_shape) if ctx.requires_dgrad else None
             if not ctx.use_bias:
-                return (
-                    wgrad if ctx.requires_wgrad else None,
-                    *weight_cache_grad,
-                    dgrad.reshape(ctx.inp_shape) if ctx.requires_dgrad else None,
-                )
+                bgrad_return = ()
+            elif ctx.requires_bgrad:
+                bgrad_return = (bgrad,)
+            else:
+                bgrad_return = (None,)
+
+        if ctx.requires_wgrad and ctx.fuse_wgrad_accumulation:
+            wgrad = None
 
         return (
             wgrad if ctx.requires_wgrad else None,
             *weight_cache_grad,
-                dgrad.reshape(ctx.inp_shape) if ctx.requires_dgrad else None,
-                bgrad if ctx.requires_bgrad else None,
+            dgrad_return,
+            *bgrad_return,
         )
 
 
@@ -668,6 +708,16 @@ class Linear(TransformerEngineBaseLayer):
                    When set to `None`, no communication is performed.
     sequence_parallel : bool, default = `False`
                        if set to `True`, uses sequence parallelism.
+
+    Optimization parameters
+    -----------------------
+    fuse_wgrad_accumulation : bool, default = 'False'
+                             if set to `True`, enables fusing of creation and accumulation of
+                             the weight gradient. When enabled, it is assumed that the weights
+                             have an additional `main_grad` attribute (used instead of the
+                             regular `grad`) which is a pre-allocated buffer of the correct
+                             size to accumulate gradients in.
+
     """
 
     def __init__(
@@ -679,6 +729,7 @@ class Linear(TransformerEngineBaseLayer):
         parallel_mode: Optional[str] = None,
         sequence_parallel: bool = False,
         tp_group: Union[dist_group_type, None] = None,
+        fuse_wgrad_accumulation: bool = False,
         backend: str = 'transformer_engine',
     ) -> None:
         super().__init__()
@@ -705,6 +756,8 @@ class Linear(TransformerEngineBaseLayer):
 
         self.sequence_parallel = self.tensor_parallel and sequence_parallel
 
+        self.fuse_wgrad_accumulation = fuse_wgrad_accumulation
+
         # Initialize weight parameter
         with track_rng_state(enable=self.tensor_parallel):
             # TE linear weight is in column major
@@ -779,6 +832,7 @@ class Linear(TransformerEngineBaseLayer):
                 self.sequence_parallel,
                 self.tp_group,
                 self.tp_size,
+                self.fuse_wgrad_accumulation,
                 is_first_microbatch,
             )
 

transformer_engine/paddle/layer/transformer.py

@@ -100,6 +100,16 @@ class TransformerLayer(paddle.nn.Layer):
                    specified name should be registered through
                    `paddle.distributed.fleet.meta_parallel.get_rng_state_tracker()
                    .add(rng_state_name, seed)`.
+
+    Optimization parameters
+    -----------------------
+    fuse_wgrad_accumulation : bool, default = 'False'
+                             if set to `True`, enables fusing of creation and accumulation of
+                             the weight gradient. When enabled, it is assumed that the weights
+                             have an additional `main_grad` attribute (used instead of the
+                             regular `grad`) which is a pre-allocated buffer of the correct
+                             size to accumulate gradients in.
+
     """
 
     def __init__(self,
@@ -124,6 +134,7 @@ class TransformerLayer(paddle.nn.Layer):
                  set_parallel_mode: bool = False,
                  sequence_parallel: bool = False,
                  tp_group: Optional[dist_group_type] = None,
+                 fuse_wgrad_accumulation: bool = False,
                  attention_dropout_rng_state_name: str = 'local_seed',
                  hidden_dropout_rng_state_name: str = 'global_seed',
                  backend: str = 'transformer_engine') -> None:
@@ -168,6 +179,7 @@ class TransformerLayer(paddle.nn.Layer):
             'max_sequence_length': max_sequence_length,
             "tp_group": tp_group,
             "num_gqa_groups": num_gqa_groups,
+            "fuse_wgrad_accumulation": fuse_wgrad_accumulation,
             "rng_state_name": attention_dropout_rng_state_name,
             "backend": backend,
         }
@@ -202,6 +214,7 @@ class TransformerLayer(paddle.nn.Layer):
             set_parallel_mode=set_parallel_mode,
             sequence_parallel=self.sequence_parallel,
             tp_group=tp_group,
+            fuse_wgrad_accumulation=fuse_wgrad_accumulation,
             backend=backend,
         )