Skip to content

[main] remove torch.cat and replace it by List[0] #2153

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Aug 7, 2025
Merged

[main] remove torch.cat and replace it by List[0] #2153

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion tests/ut/ops/test_fused_ops.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -112,7 +112,7 @@ def mock_moe_env(mocker: MockerFixture):
torch.randn(16, 2)
)), \
patch("torch_npu.npu_grouped_matmul", return_value=(
(torch.randn(8, 2), torch.randn(8, 2))
[torch.randn(16, 2)]
)), \
patch("torch_npu.npu_swiglu", return_value=(
torch.randn(16, 2)
Expand Down
38 changes: 12 additions & 26 deletions vllm_ascend/ops/fused_moe.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -204,11 +204,9 @@ def fused_experts_with_mc2(
group_list_type=1,
group_type=0,
group_list=group_list,
)
)[0]

# TODO: Remove this in the future.
gate_up_out = torch.cat(gate_up_out_list, dim=0)
gate_up_out = torch_npu.npu_swiglu(gate_up_out)
gate_up_out = torch_npu.npu_swiglu(gate_up_out_list)

w2 = w2.transpose(1, 2)
down_out_list = torch_npu.npu_grouped_matmul(
Expand All @@ -218,9 +216,7 @@ def fused_experts_with_mc2(
group_list_type=1,
group_type=0,
group_list=group_list,
)

down_out_list = torch.cat(down_out_list, dim=0)
)[0]

# moeCombine
kwargs_mc2 = {
Expand Down Expand Up @@ -311,9 +307,8 @@ def apply_mlp(
group_list_type=group_list_type,
group_type=0,
group_list=group_list,
)
)[0]

hidden_states = torch.cat(hidden_states, dim=0)
hidden_states = torch_npu.npu_swiglu(hidden_states)

w2 = w2.transpose(1, 2)
Expand All @@ -324,9 +319,8 @@ def apply_mlp(
group_list_type=group_list_type,
group_type=0,
group_list=group_list,
)
)[0]

hidden_states = torch.cat(hidden_states, dim=0)
return hidden_states


Expand Down Expand Up @@ -416,23 +410,19 @@ def fused_experts_with_all2all(
group_list_type=0,
group_type=0,
group_list=expert_tokens,
)
)[0]

# TODO: Remove this in the future.
hidden_states = torch.cat(gate_up_out_list, dim=0)
hidden_states = torch_npu.npu_swiglu(hidden_states)
hidden_states = torch_npu.npu_swiglu(gate_up_out_list)

w2 = w2.transpose(1, 2)
down_out_list = torch_npu.npu_grouped_matmul(
hidden_states = torch_npu.npu_grouped_matmul(
x=[hidden_states],
weight=[w2],
split_item=2,
group_list_type=0,
group_type=0,
group_list=expert_tokens,
)

hidden_states = torch.cat(down_out_list, dim=0)
)[0]

if expert_map is not None:
resorted_idx = torch.argsort(sorted_idx)
Expand Down Expand Up @@ -822,11 +812,9 @@ def fused_experts(
group_list_type=0,
group_type=0,
group_list=expert_tokens,
)
)[0]

# TODO: Remove this in the future.
gate_up_out = torch.cat(gate_up_out_list, dim=0)
gate_up_out = torch_npu.npu_swiglu(gate_up_out)
gate_up_out = torch_npu.npu_swiglu(gate_up_out_list)

w2 = w2.transpose(1, 2)
down_out_list = torch_npu.npu_grouped_matmul(
Expand All @@ -836,9 +824,7 @@ def fused_experts(
group_list_type=0,
group_type=0,
group_list=expert_tokens,
)

down_out_list = torch.cat(down_out_list, dim=0)
)[0]

if expert_map is not None:
weighted_down_out = down_out_list * sorted_weights.unsqueeze(1)
Expand Down
Loading