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Fix mixtral ONNX Exporter Issue. #29858

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merged 3 commits into from
Apr 5, 2024
Merged

Fix mixtral ONNX Exporter Issue. #29858

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cad84fe
fix mixtral onnx export
AdamLouly Mar 25, 2024
e026d3f
Merge remote-tracking branch 'origin/main' into adamlouly/fix_mixtral…
AdamLouly Apr 3, 2024
d3faf1a
fix qwen model
AdamLouly Apr 3, 2024
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8 changes: 2 additions & 6 deletions src/transformers/models/mixtral/modeling_mixtral.py
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Original file line number Diff line number Diff line change
Expand Up @@ -871,15 +871,11 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
if top_x.shape[0] == 0:
continue

# in torch it is faster to index using lists than torch tensors
top_x_list = top_x.tolist()
idx_list = idx.tolist()

# Index the correct hidden states and compute the expert hidden state for
# the current expert. We need to make sure to multiply the output hidden
# states by `routing_weights` on the corresponding tokens (top-1 and top-2)
current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
current_hidden_states = expert_layer(current_state) * routing_weights[top_x_list, idx_list, None]
current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]

# However `index_add_` only support torch tensors for indexing so we'll use
# the `top_x` tensor here.
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8 changes: 2 additions & 6 deletions src/transformers/models/qwen2_moe/modeling_qwen2_moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -843,15 +843,11 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
if top_x.shape[0] == 0:
continue

# in torch it is faster to index using lists than torch tensors
top_x_list = top_x.tolist()
idx_list = idx.tolist()

# Index the correct hidden states and compute the expert hidden state for
# the current expert. We need to make sure to multiply the output hidden
# states by `routing_weights` on the corresponding tokens (top-1 and top-2)
current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
current_hidden_states = expert_layer(current_state) * routing_weights[top_x_list, idx_list, None]
current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]

# However `index_add_` only support torch tensors for indexing so we'll use
# the `top_x` tensor here.
Expand Down
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