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fix vpp overlap precision with norm_topk_prob #449

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12 changes: 7 additions & 5 deletions src/paddlefleet/transformer/moe/moe_layer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -533,17 +533,19 @@ def compute_gate(self, hidden_states):
return self.gate(hidden_states)

def dispatch_preprocess(self, args):
hidden_states, token_probs, token_indices = args
hidden_states, token_indices, token_weights, gates_masked, mask = args
assert isinstance(self.token_dispatcher, MoEFlexTokenDispatcher)
hidden_states = self.token_dispatcher.dispatch_preprocess_overlap(
hidden_states, token_probs, token_indices
hidden_states, token_indices, token_weights, gates_masked, mask = (
self.token_dispatcher.dispatch_preprocess_overlap(
hidden_states, token_indices, token_weights, gates_masked, mask
)
)
token_probs = self.token_dispatcher._comm_manager.token_probs
token_indices = self.token_dispatcher._comm_manager.token_indices
return hidden_states, token_indices, token_probs
return hidden_states, token_indices, token_probs, gates_masked, mask

def compute_dispatch(self, args, async_finish=False):
hidden_states, token_indices, token_weights = args
hidden_states, token_indices, token_weights, gates_masked, mask = args
if self.moe_use_fusion_node:
dispatched_hidden_states, fp8_dispatched_handle = (
self.token_dispatcher.token_dispatch_overlap(
Expand Down
9 changes: 5 additions & 4 deletions src/paddlefleet/transformer/moe/token_dispatcher.py
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Original file line number Diff line number Diff line change
Expand Up @@ -380,14 +380,15 @@ def dispatch_preprocess(
def dispatch_preprocess_overlap(
self,
hidden_states: paddle.Tensor,
token_probs: paddle.Tensor,
token_indices: paddle.Tensor,
token_weights: paddle.Tensor,
probs: paddle.Tensor,
routing_map: paddle.Tensor,
):
self.hidden_shape = hidden_states.shape
hidden_states = hidden_states.view([-1, self.hidden_shape[-1]])
self._comm_manager.token_probs = token_probs
self._comm_manager.token_indices = token_indices
return hidden_states
self._comm_manager.setup_metadata(routing_map, probs)
return hidden_states, token_indices, token_weights, probs, routing_map

def token_dispatch_overlap(
self,
Expand Down
116 changes: 86 additions & 30 deletions src/paddlefleet/transformer/transformer_layer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -656,18 +656,26 @@ def pre_process_compute(self, hidden_states):
residuals,
topk_weights,
topk_indices,
gates_masked,
mask,
aux_loss,
)

def dispatch_preprocess_compute(self, args):
hidden_states, topk_weights, topk_indices = args
hidden_states, token_indices, token_weights, gates_masked, mask = args

hidden_states, token_indices, token_weights = (
hidden_states, token_indices, token_weights, gates_masked, mask = (
self.mlp.dispatch_preprocess(
(hidden_states, topk_weights, topk_indices)
(
hidden_states,
token_indices,
token_weights,
gates_masked,
mask,
)
)
)
return hidden_states, token_indices, token_weights
return hidden_states, token_indices, token_weights, gates_masked, mask

def post_process_compute(self, args, is_first_fwd=False):
mlp_output, residual = args
Expand Down Expand Up @@ -743,19 +751,33 @@ def forward(self, inputs):
residual,
hidden_states,
residuals,
topk_weights,
topk_indices,
token_weights,
token_indices,
gates_masked,
mask,
aux_loss,
) = self.pre_process_node.forward(hidden_states)

hidden_states, token_indices, token_weights = (
hidden_states, token_indices, token_weights, gates_masked, mask = (
self.dispatch_preprocess_node.forward(
(hidden_states, topk_weights, topk_indices)
(
hidden_states,
token_indices,
token_weights,
gates_masked,
mask,
)
)
)

hidden_states = self.dispatch_node.forward(
(hidden_states, token_indices, token_weights),
(
hidden_states,
token_indices,
token_weights,
gates_masked,
mask,
),
async_finish=True,
)
dispatch_fw_event = deep_ep.get_event_from_comm_stream(
Expand Down Expand Up @@ -832,29 +854,39 @@ def backward(self, output_grad):
combine_bw_event.calc_stream_wait(self.group_id)
output_grad = self.mlp_node.backward(output_grad)

(output_grad, token_indices_grad, token_weights_grad) = (
self.dispatch_node.backward(output_grad)
)
(
output_grad,
token_indices_grad,
token_weights_grad,
gates_masked_grad,
) = self.dispatch_node.backward(output_grad)
dispatch_bw_event = deep_ep.get_event_from_comm_stream(
self.group_id
)
dispatch_bw_event.calc_stream_wait(self.group_id)

(
output_grad,
topk_weights_grad,
topk_indices_grad,
token_indices_grad,
token_weights_grad,
gates_masked_grad,
) = self.dispatch_preprocess_node.backward(
(output_grad, token_indices_grad, token_weights_grad)
(
output_grad,
token_indices_grad,
token_weights_grad,
gates_masked_grad,
)
)

output_grad = self.pre_process_node.backward(
(
residual_grad,
output_grad,
residuals_grad,
topk_weights_grad,
topk_indices_grad,
token_weights_grad,
token_indices_grad,
gates_masked_grad,
aux_loss_grad,
)
)
Expand Down Expand Up @@ -943,20 +975,34 @@ def forward_backward(self, inputs, output_grad, split_bw=False):
residual,
hidden_states,
residuals,
topk_weights,
topk_indices,
token_weights,
token_indices,
gates_masked,
mask,
aux_loss,
) = self.forward_node.pre_process_node.forward(hidden_states)

hidden_states, token_indices, token_weights = (
hidden_states, token_indices, token_weights, gates_masked, mask = (
self.forward_node.dispatch_preprocess_node.forward(
(hidden_states, topk_weights, topk_indices)
(
hidden_states,
token_indices,
token_weights,
gates_masked,
mask,
)
)
)

# 4. DISPATCH(F)
hidden_states = self.forward_node.dispatch_node.forward(
(hidden_states, token_indices, token_weights),
(
hidden_states,
token_indices,
token_weights,
gates_masked,
mask,
),
async_finish=True,
)
dispatch_fw_event = deep_ep.get_event_from_comm_stream(
Expand All @@ -968,9 +1014,12 @@ def forward_backward(self, inputs, output_grad, split_bw=False):
output_grad = self.backward_node.mlp_node.backward(output_grad)

# 6. DISPATCH(B)
output_grad, token_indices_grad, token_weights_grad = (
self.backward_node.dispatch_node.backward(output_grad)
)
(
output_grad,
token_indices_grad,
token_weights_grad,
gates_masked_grad,
) = self.backward_node.dispatch_node.backward(output_grad)
dispatch_bw_event = deep_ep.get_event_from_comm_stream(
self.backward_node.group_id
)
Expand All @@ -996,19 +1045,26 @@ def forward_backward(self, inputs, output_grad, split_bw=False):
dispatch_bw_event.calc_stream_wait(self.backward_node.group_id)
(
output_grad,
topk_weights_grad,
topk_indices_grad,
token_indices_grad,
token_weights_grad,
gates_masked_grad,
) = self.backward_node.dispatch_preprocess_node.backward(
(output_grad, token_indices_grad, token_weights_grad)
(
output_grad,
token_indices_grad,
token_weights_grad,
gates_masked_grad,
)
)

output_grad = self.backward_node.pre_process_node.backward(
(
residual_grad,
output_grad,
residuals_grad,
topk_weights_grad,
topk_indices_grad,
token_weights_grad,
token_indices_grad,
gates_masked_grad,
aux_loss_grad,
)
)
Expand Down
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