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[main] moe(perf): Refactor GDN A2A helper flow #5392

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[main] moe(perf): Refactor GDN A2A helper flow #5392

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132 changes: 79 additions & 53 deletions megatron/core/ssm/gated_delta_net.py
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Original file line number Diff line number Diff line change
Expand Up @@ -346,42 +346,9 @@ def forward(
qkvzba, _ = self.in_proj(hidden_states)
nvtx_range_pop(suffix="in_proj")

# CP All to All: CP to HP
if self.cp_size > 1:
# # Pre-permute head dim so a single unsectioned a2a is equivalent to per-section a2a.
head_perm = _build_head_perm_for_split_sections(
(
self.qk_dim_local_tp,
self.qk_dim_local_tp,
self.v_dim_local_tp,
self.v_dim_local_tp,
self.num_value_heads // self.tp_size,
self.num_value_heads // self.tp_size,
),
self.pg_collection.cp.size(),
torch.cuda.current_device(),
)
qkvzba = qkvzba.index_select(-1, head_perm)
if packed_seq_params is not None and packed_seq_params.qkv_format == 'thd':
qkvzba = tensor_a2a_cp2hp(
qkvzba,
seq_dim=0,
head_dim=-1,
cp_group=self.pg_collection.cp,
undo_attention_load_balancing=False,
)
if self.cp_size > 1:
# Permute at the seq dim so that a single unsectioned a2a
# is equivalent to per-sequence a2a.
# This also folds the ``_undo_attention_load_balancing`` step.
thd_cp_a2a_idx, thd_cp_a2a_inv = _build_thd_cp_a2a_perm(
cu_seqlens_q, self.cp_size, seq_len
)
qkvzba = qkvzba.index_select(0, thd_cp_a2a_idx)
else:
qkvzba = tensor_a2a_cp2hp(
qkvzba, seq_dim=0, head_dim=-1, cp_group=self.pg_collection.cp
)
qkvzba, thd_cp_a2a_inv = self._a2a_cp_to_hp(
qkvzba, self.cp_size, self.pg_collection.cp, cu_seqlens_q, seq_len, packed_seq_params
)

# Transpose: s b x --> b s x
# From sbhd to bshd format
Expand Down Expand Up @@ -493,23 +460,9 @@ def _gated_norm_and_a2a(core_attn_out: torch.Tensor, gate: torch.Tensor):
norm_out_hp = norm_out_hp.reshape(batch, seq_len, -1)
norm_out_hp = norm_out_hp.transpose(0, 1).contiguous()

# CP all to all: HP to CP
if packed_seq_params is not None and packed_seq_params.qkv_format == 'thd':
if self.cp_size > 1:
norm_out_hp = norm_out_hp.index_select(0, thd_cp_a2a_inv)
norm_out = tensor_a2a_hp2cp(
norm_out_hp,
seq_dim=0,
head_dim=-1,
cp_group=self.pg_collection.cp,
redo_attention_load_balancing=False,
)
else:
norm_out = tensor_a2a_hp2cp(
norm_out_hp, seq_dim=0, head_dim=-1, cp_group=self.pg_collection.cp
)

return norm_out
return self._a2a_hp_to_cp(
norm_out_hp, self.cp_size, self.pg_collection.cp, packed_seq_params, thd_cp_a2a_inv
)

if self.recompute_norm_out:
self.norm_out_checkpoint = tensor_parallel.CheckpointWithoutOutput()
Expand All @@ -527,6 +480,79 @@ def _gated_norm_and_a2a(core_attn_out: torch.Tensor, gate: torch.Tensor):

return out, out_bias

def _a2a_cp_to_hp(
self,
qkvzba: torch.Tensor,
cp_size: int,
cp_group: torch.distributed.ProcessGroup,
cu_seqlens_q: Optional[torch.Tensor],
seq_len: int,
packed_seq_params: Optional[PackedSeqParams],
) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
"""Run GDN context-parallel to hidden-parallel A2A and return its inverse context."""
if cp_size > 1:
# Pre-permute head dim so a single unsectioned a2a is equivalent to per-section a2a.
head_perm = _build_head_perm_for_split_sections(
(
self.qk_dim_local_tp,
self.qk_dim_local_tp,
self.v_dim_local_tp,
self.v_dim_local_tp,
self.num_value_heads // self.tp_size,
self.num_value_heads // self.tp_size,
),
cp_size,
qkvzba.device,
)
qkvzba = qkvzba.index_select(-1, head_perm)

thd_cp_a2a_inv = None
if packed_seq_params is not None and packed_seq_params.qkv_format == 'thd':
qkvzba = tensor_a2a_cp2hp(
qkvzba,
seq_dim=0,
head_dim=-1,
cp_group=cp_group,
undo_attention_load_balancing=False,
)
if cp_size > 1:
# Permute at the seq dim so that a single unsectioned a2a
# is equivalent to per-sequence a2a.
# This also folds the ``_undo_attention_load_balancing`` step.
thd_cp_a2a_idx, thd_cp_a2a_inv = _build_thd_cp_a2a_perm(
cu_seqlens_q, cp_size, seq_len
)
qkvzba = qkvzba.index_select(0, thd_cp_a2a_idx)
else:
qkvzba = tensor_a2a_cp2hp(qkvzba, seq_dim=0, head_dim=-1, cp_group=cp_group)

return qkvzba, thd_cp_a2a_inv

def _a2a_hp_to_cp(
self,
norm_out: torch.Tensor,
cp_size: int,
cp_group: torch.distributed.ProcessGroup,
packed_seq_params: Optional[PackedSeqParams],
thd_cp_a2a_inv: Optional[torch.Tensor],
) -> torch.Tensor:
"""Run GDN hidden-parallel to context-parallel A2A using CP-to-HP context."""
if packed_seq_params is not None and packed_seq_params.qkv_format == 'thd':
if cp_size > 1:
assert thd_cp_a2a_inv is not None
norm_out = norm_out.index_select(0, thd_cp_a2a_inv)
norm_out = tensor_a2a_hp2cp(
norm_out,
seq_dim=0,
head_dim=-1,
cp_group=cp_group,
redo_attention_load_balancing=False,
)
else:
norm_out = tensor_a2a_hp2cp(norm_out, seq_dim=0, head_dim=-1, cp_group=cp_group)

return norm_out

@jit_fuser
def _apply_gated_norm(self, x, gate):
# Output Norm
Expand Down
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