[Ops][Misc] Refactor and optimize CausalConv1d for Ascend (#7495)
### What this PR does / why we need it?
During the prefill phase of Qwen3-Next and Qwen3.5, the
`torch.ops._C_ascend.causal_conv1d_fn` operator exhibits significant
performance bottlenecks. To address this, we have re-implemented the
optimization using `torch.ops._C_ascend.npu_causal_conv1d_custom`.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
1 accuracy test
```
[2026-03-20 16:44:22,961] [ais_bench] [INFO] Start launch task state board ...
+-----------------------------+-----------+------------+-------------+----------+-------------------------------------------+---------------------+
| Task Name | Process | Progress | Time Cost | Status | Log Path | Extend Parameters |
+=============================+===========+============+=============+==========+===========================================+=====================+
| vllm-api-general-chat/gsm8k | 2918978 | NA | 0:00:01 | finish | logs/eval/vllm-api-general-chat/gsm8k.out | None |
+-----------------------------+-----------+------------+-------------+----------+-------------------------------------------+---------------------+
[2026-03-20 16:44:34,284] [ais_bench] [INFO] Evaluation tasks completed.
[2026-03-20 16:44:34,287] [ais_bench] [INFO] Summarizing evaluation results...
dataset version metric mode vllm-api-general-chat
--------- --------- -------- ------ -----------------------
gsm8k 271d0b accuracy gen 96.21
```
2 ut modify test
`pytest -sv
/home/c30006096/vllm-ascend/tests/e2e/nightly/single_node/ops/singlecard_ops/triton/test_causal_conv1d.py::test_ascend_causal_conv1d`
- vLLM version: v0.17.0
- vLLM main:
8b6325758c
Signed-off-by: wenba0 <3054239545@qq.com>
Signed-off-by: jiaojiao <56385650+wenba0@users.noreply.github.com>
This commit is contained in:
@@ -485,19 +485,21 @@ npu_copy_and_expand_eagle_inputs_meta(
|
||||
out_new_token_indices, out_hidden_state_mapping};
|
||||
}
|
||||
|
||||
at::Tensor causal_conv1d_fn_meta(
|
||||
const at::Tensor& mixed_qkv_non_spec_T,
|
||||
const at::Tensor& conv_weights,
|
||||
const c10::optional<at::Tensor>& bias_opt,
|
||||
c10::string_view activation,
|
||||
at::Tensor npu_causal_conv1d_custom_meta(
|
||||
const at::Tensor& x,
|
||||
const at::Tensor& weight,
|
||||
const at::Tensor& conv_state,
|
||||
const at::Tensor& has_initial_state,
|
||||
const at::Tensor& non_spec_state_indices_tensor,
|
||||
const at::Tensor& non_spec_query_start_loc,
|
||||
int64_t pad_slot_id)
|
||||
const c10::optional<at::Tensor>& bias_opt,
|
||||
at::IntArrayRef query_start_loc_opt,
|
||||
at::IntArrayRef cache_indices_opt,
|
||||
at::IntArrayRef initial_state_mode_opt,
|
||||
at::IntArrayRef num_accepted_tokens_opt,
|
||||
int64_t activation_mode,
|
||||
int64_t pad_slot_id,
|
||||
int64_t run_mode)
|
||||
{
|
||||
|
||||
at::Tensor output = at::empty_symint(mixed_qkv_non_spec_T.sym_sizes(), mixed_qkv_non_spec_T.options());
|
||||
at::Tensor output = at::empty_symint(x.sym_sizes(), x.options());
|
||||
return output;
|
||||
}
|
||||
|
||||
@@ -611,7 +613,7 @@ TORCH_LIBRARY_IMPL_EXPAND(CONCAT(_C, _ascend), Meta, ops) {
|
||||
// CopyAndExpandEagleInputs
|
||||
ops.impl("npu_copy_and_expand_eagle_inputs", &vllm_ascend::meta::npu_copy_and_expand_eagle_inputs_meta);
|
||||
// causal_conv1d_fn
|
||||
ops.impl("causal_conv1d_fn", &vllm_ascend::meta::causal_conv1d_fn_meta);
|
||||
ops.impl("npu_causal_conv1d_custom", &vllm_ascend::meta::npu_causal_conv1d_custom_meta);
|
||||
// moe_grouped_matmul
|
||||
ops.impl("moe_grouped_matmul", &vllm_ascend::meta::moe_grouped_matmul_meta);
|
||||
// Lightning indexer quant
|
||||
|
||||
Reference in New Issue
Block a user