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[Lint]Style: Convert vllm-ascend/ to ruff format(Batch #12) (#6177)

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### What this PR does / why we need it?
**Scope of Changes**:
| File Path |
| :--- |
| `vllm_ascend/ops/triton/activation/swiglu_quant.py` |
| `vllm_ascend/ops/triton/batch_invariant/matmul.py` |
| `vllm_ascend/ops/triton/batch_invariant/mean.py` |
| `vllm_ascend/ops/triton/batch_invariant/rmsnorm.py` |
| `vllm_ascend/ops/triton/fla/chunk.py` |
| `vllm_ascend/ops/triton/fla/chunk_delta_h.py` |
| `vllm_ascend/ops/triton/fla/chunk_o.py` |
| `vllm_ascend/ops/triton/fla/chunk_scaled_dot_kkt.py` |
| `vllm_ascend/ops/triton/fla/cumsum.py` |
| `vllm_ascend/ops/triton/fla/fused_qkvzba_split_reshape.py` |
| `vllm_ascend/ops/triton/fla/l2norm.py` |
| `vllm_ascend/ops/triton/fla/layernorm_guard.py` |
| `vllm_ascend/ops/triton/fla/sigmoid_gating.py` |
| `vllm_ascend/ops/triton/fla/solve_tril.py` |
| `vllm_ascend/ops/triton/fla/utils.py` |
| `vllm_ascend/ops/triton/fla/wy_fast.py` |
| `vllm_ascend/ops/triton/fused_gdn_gating.py` |
| `vllm_ascend/ops/triton/layernorm_gated.py` |
| `vllm_ascend/ops/triton/linearnorm/split_qkv_rmsnorm_rope.py` |
| `vllm_ascend/ops/triton/mamba/causal_conv1d.py` |
| `vllm_ascend/ops/triton/reject_sample.py` |
| `vllm_ascend/ops/triton/rope.py` |
| `vllm_ascend/ops/triton/spec_decode/utils.py` |
| `vllm_ascend/ops/triton/triton_utils.py` |

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.14.0
- vLLM main:
d68209402d

Signed-off-by: MrZ20 <2609716663@qq.com>
This commit is contained in:
SILONG ZENG
2026-01-23 14:59:19 +08:00
committed by GitHub
parent 193acc2c19
commit 78af0c30a3
25 changed files with 760 additions and 996 deletions
Download Patch File Download Diff File Expand all files Collapse all files

56
vllm_ascend/ops/triton/fla/sigmoid_gating.py
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@@ -14,7 +14,7 @@ import os
import torch
from vllm.triton_utils import tl, tldevice, triton
if os.environ.get('FLA_USE_FAST_OPS', '0') == '1':
if os.environ.get("FLA_USE_FAST_OPS", "0") == "1":
div = tldevice.fast_dividef
exp = tldevice.fast_expf
log = tldevice.fast_logf
@@ -31,17 +31,15 @@ else:
log2 = tl.log2
@triton.heuristics({
'USE_INITIAL_STATE':
lambda args: args['h0'] is not None,
'IS_VARLEN':
lambda args: args['cu_seqlens'] is not None,
"IS_CONTINUOUS_BATCHING":
lambda args: args['ssm_state_indices'] is not None,
"IS_SPEC_DECODING":
lambda args: args['num_accepted_tokens'] is not None,
})
@triton.jit(do_not_specialize=['N', 'T'])
@triton.heuristics(
{
"USE_INITIAL_STATE": lambda args: args["h0"] is not None,
"IS_VARLEN": lambda args: args["cu_seqlens"] is not None,
"IS_CONTINUOUS_BATCHING": lambda args: args["ssm_state_indices"] is not None,
"IS_SPEC_DECODING": lambda args: args["num_accepted_tokens"] is not None,
}
)
@triton.jit(do_not_specialize=["N", "T"])
def fused_recurrent_gated_delta_rule_fwd_kernel(
q,
k,
@@ -70,8 +68,7 @@ def fused_recurrent_gated_delta_rule_fwd_kernel(
stride_indices_tok: tl.constexpr,
USE_INITIAL_STATE: tl.constexpr, # whether to use initial state
INPLACE_FINAL_STATE: tl.constexpr, # whether to store final state inplace
IS_BETA_HEADWISE: tl.
constexpr, # whether beta is headwise vector or scalar,
IS_BETA_HEADWISE: tl.constexpr, # whether beta is headwise vector or scalar,
USE_QK_L2NORM_IN_KERNEL: tl.constexpr,
IS_VARLEN: tl.constexpr,
IS_CONTINUOUS_BATCHING: tl.constexpr,
@@ -82,8 +79,7 @@ def fused_recurrent_gated_delta_rule_fwd_kernel(
i_n, i_hv = i_nh // HV, i_nh % HV
i_h = i_hv // (HV // H)
if IS_VARLEN:
bos, eos = tl.load(cu_seqlens + i_n).to(
tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64)
bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64)
all = T
T = eos - bos
else:
@@ -108,8 +104,9 @@ def fused_recurrent_gated_delta_rule_fwd_kernel(
i_t = tl.load(num_accepted_tokens + i_n).to(tl.int64) - 1
else:
i_t = 0
p_h0 = h0 + tl.load(ssm_state_indices + i_n * stride_indices_seq +
i_t).to(tl.int64) * stride_init_state_token
p_h0 = (
h0 + tl.load(ssm_state_indices + i_n * stride_indices_seq + i_t).to(tl.int64) * stride_init_state_token
)
else:
p_h0 = h0 + bos * HV * K * V
p_h0 = p_h0 + i_hv * K * V + o_k[:, None] * V + o_v[None, :]
@@ -164,18 +161,21 @@ def fused_recurrent_gated_delta_rule_fwd_kernel(
# keep the states for multi-query tokens
if INPLACE_FINAL_STATE:
p_ht = ht + tl.load(ssm_state_indices + i_n * stride_indices_seq +
i_t).to(tl.int64) * stride_final_state_token
p_ht = (
ht + tl.load(ssm_state_indices + i_n * stride_indices_seq + i_t).to(tl.int64) * stride_final_state_token
)
else:
p_ht = ht + (bos + i_t) * stride_final_state_token
p_ht = p_ht + i_hv * K * V + o_k[:, None] * V + o_v[None, :]
tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), mask=mask_h)
@triton.heuristics({
"USE_INITIAL_STATE": lambda args: args["h0_source"] is not None,
"IS_VARLEN": lambda args: args["cu_seqlens"] is not None,
})
@triton.heuristics(
{
"USE_INITIAL_STATE": lambda args: args["h0_source"] is not None,
"IS_VARLEN": lambda args: args["cu_seqlens"] is not None,
}
)
@triton.jit(do_not_specialize=["T"])
def fused_sigmoid_gating_delta_rule_update_kernel(
A_log,
@@ -245,8 +245,7 @@ def fused_sigmoid_gating_delta_rule_update_kernel(
idx = tl.load(h0_indices + i_n)
# if idx >= 0:
tmp0 = tl.where(idx < 0, 0, idx)
p_h0 = (h0_source + tmp0 * HV * K * V + i_hv * K * V +
o_k[:, None] * V + o_v[None, :])
p_h0 = h0_source + tmp0 * HV * K * V + i_hv * K * V + o_k[:, None] * V + o_v[None, :]
temp1 = tl.load(p_h0, mask=mask_h, other=0).to(tl.float32)
temp2 = tl.zeros_like(temp1)
value0 = tl.where(idx < 0, temp2, temp1)
@@ -314,8 +313,7 @@ def fused_sigmoid_gating_delta_rule_update_kernel(
if USE_INITIAL_STATE:
idx = tl.load(h0_indices + i_n)
if idx >= 0:
p_h0 = (h0_source + idx * HV * K * V + i_hv * K * V +
o_k[:, None] * V + o_v[None, :])
p_h0 = h0_source + idx * HV * K * V + i_hv * K * V + o_k[:, None] * V + o_v[None, :]
tl.store(p_h0, b_h.to(p_h0.dtype.element_ty), mask=mask_h)
@@ -350,7 +348,7 @@ def fused_sigmoid_gating_delta_rule_update(
num_warps = 1
if scale is None:
scale = k.shape[-1]**-0.5
scale = k.shape[-1] ** -0.5
else:
assert scale > 0, "scale must be positive"