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Sun Ruoxi 7f8a1b1f7a init v0.23.0 
Signed-off-by: Sun Ruoxi <sunruoxi@4paradigm.com>
2026-08-27 15:11:51 +08:00
..
op_host
init v0.23.0
2026-08-27 15:11:51 +08:00
op_kernel
init v0.23.0
2026-08-27 15:11:51 +08:00
CMakeLists.txt
init v0.23.0
2026-08-27 15:11:51 +08:00
README.md
init v0.23.0
2026-08-27 15:11:51 +08:00

README.md

Compressor

产品支持情况

产品 是否支持
Ascend 950PR/Ascend 950DT √
Atlas A3 训练系列产品/Atlas A3 推理系列产品 √
Atlas A2 训练系列产品/Atlas A2 推理系列产品 ×
Atlas 200I/500 A2 推理产品 ×
Atlas 推理系列加速卡产品 ×
Atlas 训练系列产品 ×

功能说明

  • API功能：Compressor是推理场景下SAS和QLI的前处理算子，用于将每4或128个token的KV cache压缩成一个，然后每个token与这些压缩的KV cache进行DSA计算。在长序列的情况下，Compressor可以有效地减少计算开销。

  • 计算公式：

    压缩阶段：

    1. 计算矩阵乘法：
      • C4A: \left[kv\_state^a, score\_state^a\right] = X @ \left[W^{aKV}, W^{aGate}\right], \left[kv\_state^b, score\_state^b\right] = X @ \left[W^{bKV}, W^{bGate}\right];
      • C128A: \left[kv\_state, score\_state\right] = X @ \left[W^{KV}, W^{Gate}\right]
    2. 计算分组加法：
      • C4A: score\_state_i^\prime = \left[score\_state_{\left[4(i-1)+1:4i,:\right]}^a; score\_state_{\left[4i+1:4(i+1),:\right]}^b\right] + Ape,~i=1,2,\cdots, \frac{s}{4};
      • C128A: score\_state_i^\prime = score\_state_{\left[128(i-1)+1:128i,:\right]} + Ape,~i=1,2,\cdots, \frac{s}{128};
    3. 计算分组Softmax：
      • C4A: S_i^\prime = softmax(score\_state_i^\prime),~i=1,2,\cdots, \frac{s}{4};
      • C128A: S_i^\prime = softmax(score\_state_i^\prime),~i=1,2,\cdots, \frac{s}{128};
    4. 计算Hadamard乘积：
      • C4A: (S_H)_i = S_i^\prime \odot \left[kv\_state^a_{\left[4(i-1)+1:4i,:\right]} ; kv\_state^b_{\left[4i+1:4(i+1),:\right]}\right],~i=1,2,\cdots, \frac{s}{4};
      • C128A: S_H = S_i^\prime \odot kv\_state;
    5. 沿着压缩轴分组求和：
      • C4A: C_{i}^{\text{Comp}} = \left[1\right]_{1\times8} @ (S_H)_i, ~i=1,2,\cdots, \frac{s}{4};
      • C128A: C_{i}^{\text{Comp}} = \left[1\right]_{1\times128} @ (S_H)_i, ~i=1,2,\cdots, \frac{s}{128};

    后处理阶段：

    1. 计算RMSNorm：
      • \text{RMS}(C^{\text{Comp}}) = \sqrt{\frac{1}{N} \sum_{i=j* N}^{(j+1)* N} {(C_{i}^{\text{Comp}})}^{\text{2}} + norm\_eps} ,N=head\_dim, ~j=1,2,\cdots, \frac{s}{cmp\_ratio}
      • \text{RmsNorm}(C^{\text{Comp}}) = norm\_weight \cdot \frac{C_{i}^{\text{Comp}}}{\text{RMS}(C^{\text{Comp}})}
    2. 计算Rope；

  • 主要计算过程为：

    1. 将输入$X$与$W^{KV}$做Matmul运算得到$kv_state$，将输入$X$与$W^{Gate}$做Matmul运算后再与$Ape$做Add运算得到$score_state$，$kv_state$与$score_state$根据输入的start_pos及cu_seqlens完成更新。
    2. 在coff为2的情况下对$kv_state$和$score_state$进行数据重排。
    3. 对$score_state$进行softmax运算将softmax结果与$kv_state$做Mul计算，后进行ReduceSum运算。
    4. 根据输入数据norm_weight、rope_sin、rope_cos，进行RMSNorm和Rope运算，得到$cmp_kv$结果输出。

参数说明

参数名 输入/输出/属性 描述 数据类型 数据格式
x 输入 公式中的$X$，表示原始不经压缩的数据。 FLOAT16、BFLOAT16 ND
wkv 输入 公式中的$W^{KV}$，表示kv压缩权重。 FLOAT16、BFLOAT16 ND
wgate 输入 公式中的$W^{Gate}$，表示gate压缩权重。 FLOAT16、BFLOAT16 ND
kv_state 输入 公式中的$kv_state$，表示kv_state的历史数据。 FLOAT32 ND
score_state 输入 公式中的$score_state$，表示score_state中的历史数据。 FLOAT32 ND
ape 输入 公式中的$Ape$，表示positional biases。 FLOAT32 ND
norm_weight 输入 表示计算RmsNorm时的权重系数。 FLOAT16、BFLOAT16 ND
rope_sin 输入 表示Rope计算时sin的权重系数。 FLOAT16、BFLOAT16 ND
rope_cos 输入 表示Rope计算时cos的权重系数。 FLOAT16、BFLOAT16 ND
rope_head_dim 属性 表示rope_cos和rope_sin的hidden层最小单元大小，当前仅支持64。 INT32 -
cmp_ratio 属性 用于稀疏计算，表示数据压缩率。 INT32 -
kv_block_table 可选输入 表示kv_state存储使用的block映射表。当其中元素的值为0时，表示当前位置无需进行更新kv_state操作。 INT32 ND
score_block_table 可选输入 表示score_state存储使用的block映射表。当其中元素的值为0时，表示当前位置无需进行更新score_state操作。 INT32 ND
cu_seqlens 可选输入 表示不同Batch中的有效token数。 INT32 ND
seqused 可选输入 表示不同Batch中实际参与压缩的token数，如果指定为None时，表示和每个Batch上的Sequence Length长度相同。 INT32 ND
start_pos 可选输入 表示计算起始位置。 INT32 ND
coff 可选属性 默认值1，支持1/2。当coff=1时，无需进行overlap数据重排。当coff=2时，需要进行overlap数据重排。 INT32 -
norm_eps 可选属性 表示RmsNorm计算的权重系数。默认值1e-6。 FLOAT32 -
rotary_mode 可选属性 表示Rop计算的模式。默认值1，支持1/2。rotary_mode为1时，代表half模式。rotary_mode为2时，代表interleave模式。 INT32 -
enabled_grad 可选属性 训练场景使用，表示是否参与反向更新。默认值false，支持false/true。目前暂不支持输入true。 BOOL -
cmp_kv 输出 表示压缩后的数据。 FLOAT16、BFLOAT16 ND
wkv_proj 可选输出 训练反向使用，表示wkv权重Matmul的计算结果，目前暂不支持返回wkv_proj。 FLOAT16、BFLOAT16 ND
softmax_res 可选输出 训练反向使用，表示Softmax计算结果，目前暂不支持返回softmax_res。 FLOAT16、BFLOAT16 ND
norm_x 可选输出 训练反向使用，表示Rms计算的输入，目前暂不支持返回norm_x。 FLOAT16、BFLOAT16 ND
norm_rstd 可选输出 训练反向使用，表示Rms计算的中间结果，目前暂不支持返回norm_rstd。 FLOAT16、BFLOAT16 ND

约束说明

  • x参数维度含义：B（Batch Size）表示输入样本批量大小、S（Sequence Length）表示输入样本序列长度、H（Head Size）表示hidden层的大小、D（Head Dim）表示hidden层的最小单元大小、T表示所有Batch输入样本序列长度的累加和。
  • 输入shape限制：
    • wkv支持输入shape[coff* D,H]
    • wgate支持输入shape[coff* D,H]
    • kv_state、score_state支持输入shape[block_num,block_size,coff* D]，要求block_num>0。
    • ape支持输入shape[cmp_ratio,coff* D]
    • norm_weight支持输入shape[D,]
    • start_pos支持输入shape[B,]
    • 若x的维度采用BS合轴，即x的输入shape为[T,H]
      • rope_sin、rope_cos要求输入shape为[min(T,T//cmp_ratio+B),rope_head_dim]。
      • cu_seqlens输入shape必须为[B+1,]。该参数中每个元素的值表示当前batch与之前所有batch的token数总和，即前缀和，因此后一个元素的值必须大于等于前一个元素的值，且第一位必须位0。
      • seqused，支持输入shape[B,]，要求每个Batch的有效token数要求小于等于对应Sequence Length长度，即seqused[n] <= cu_seqlens[n+1] - cu_seqlens[n]，且不小于0。
      • kv_block_table、score_block_table支持输入shape[B,ceil(Smax/block_size)]。Smax为每个Batch中最大的Sequence Length，即Smax=max(start_pos)+max(cu_seqlens[n+1] - cu_seqlens[n])。
      • cmp_kv，输出shape为[min(T,T//cmp_ratio+B),D]：compressed_tokens + compressed_tokens + ... + compressed_tokens + pad。
      • wkv_proj，输出shape为[T,coff* D]。
      • norm_x，输出shape为[min(T,T//cmp_ratio+B),D]。
      • norm_rstd，输出shape为[min(T,T//cmp_ratio+B)]。
    • 若x的维度不采用BS合轴，即x的输入shape为[B,S,H]
      • rope_sin、rope_cos要求输入shape为[B,ceil(S/cmp_ratio),rope_head_dim]。
      • cu_seqlens，参数必须为空。
      • seqused，支持输入shape[B,]，要求每个Batch的有效token数要求小于等于对应Sequence Length长度，即要求seqused[n] <= S，且不小于0。
      • kv_block_table、score_block_table支持输入shape[B,ceil(Smax/block_size)]。Smax为每个Batch中最大的Sequence Length，即Smax=max(start_pos)+S。
      • cmp_kv，输出shape为[B,ceil(S/cmp_ratio),D]：(compressed_tokens+pad0) + (compressed_tokens+pad1) + ... + (compressed_tokens+padN)。
      • wkv_proj，输出shape为[B,S,coff* D]。
      • norm_x，输出shape为[B,ceil(S/cmp_ratio),D]。
      • norm_rstd，输出shape为[B,ceil(S/cmp_ratio)]。
  • 输入值域限制：
    • 该接口支持B、S泛化，且存在如下场景限制：
      • 部分长序列场景下，如果计算量过大可能会导致出现超过NPU内存的报错，注：这里计算量会受x输入shape的影响，值越大计算量越大。典型的长序列（即B、S的乘积或T较大）场景包括但不限于：
      B S H
      100 65525 4096
      25 261120 4096
      100 131072 4096
      100 261120 4096
  • 输入属性限制：
    • 支持D为128/512。
    • 支持H为1K~10K，512对齐。
    • 泛化支持block_size小于等于1024，16对齐。
    • 支持cmp_ratio为4/128。支持如下三种情况：
      • C4A: D=512, coff=2, cmp_ratio=4；
      • C4Li: D=128, coff=2, cmp_ratio=4；
      • C128A: D=512, coff=1, cmp_ratio=128。
    • 支持rotary_mode为2，Rope计算模式为interleave。

Atlas A3 推理系列产品 调用说明

  • 单算子模式调用

    import torch
import torch_npu
import numpy as np
import custom_ops
import torch.nn as nn
import math

def get_seq_used_by_batch(batch_idx, S, seqused, cu_seqlens):
    if seqused is not None:
        return seqused[batch_idx]
    else:
        if cu_seqlens is not None:
            return cu_seqlens[batch_idx + 1] - cu_seqlens[batch_idx]
        else:
            return S

data_type = torch.bfloat16
hidden_size = 4096
rope_head_dim = 64
norm_eps = 1e-6
coff = 1 # 1:no overlap 2:overlap
cmp_ratio = 128
rotary_mode = 2
head_dim = 512
cu_seqlens = [0, 1]
# -------------
B = 1
S = 1
S_max = 0
block_size = 128
start_pos = [8191] * B # (B,)
start_p=8191
seqused = None # (B,), None时cu_seqlens的数据全部参与计算，否则按传参实际值计算

# BS是否合轴
bs_combine_flag = True
update_flag = 1

if seqused is not None:
    seqused = torch.tensor(seqused).to(torch.int32)
if start_pos is not None:
    start_pos = torch.tensor(start_pos).to(torch.int32)
else:
    start_pos = torch.full((B,), start_p, dtype=torch.int32)

if bs_combine_flag:
    if cu_seqlens is None:
        T = B * S
        if T !=0:
            cu_seqlens = torch.arange(0, T + 1, S, dtype=torch.int32)
        else:
            cu_seqlens = torch.zeros((B+1), dtype=torch.int32)
    else:
        cu_seqlens = torch.tensor(cu_seqlens).to(torch.int32)
    for i in range(B):
        if start_pos[i] + cu_seqlens[i + 1] - cu_seqlens[i] > S_max:
            S_max = start_pos[i] + cu_seqlens[i + 1] - cu_seqlens[i]
else:
    cu_seqlens = None
    S_max = max(start_pos) + S
### ======================== gen input data start =============================
# page state
max_block_num_per_batch = (S_max + block_size - 1) // block_size
block_num = B * max_block_num_per_batch
next_block_id = 1
print(f"max_block_num_per_batch: {max_block_num_per_batch}")
block_table = torch.zeros(size=(B, max_block_num_per_batch), dtype=torch.int32)
for i in range(B):
    # 需要读取state的范围
    cur_start = start_pos[i] // cmp_ratio * cmp_ratio - cmp_ratio
    cur_end = start_pos[i] // cmp_ratio * cmp_ratio + cmp_ratio
    if start_pos[i] % cmp_ratio == 0:
        cur_end = start_pos[i]
    cur_end = min(cur_end, start_pos[i] + S)
    cur_start_block_id = (cur_start // block_size) if cur_start >= 0 else 0
    cur_end_block_id = (cur_end - 1) // block_size
    for j in range(cur_start_block_id, cur_end_block_id + 1):
        block_table[i][j] = next_block_id
        next_block_id = next_block_id + 1
    # 需要写入state的范围
    end_pos = get_seq_used_by_batch(i, S, seqused, cu_seqlens)
    next_start = (start_pos[i] + end_pos) // cmp_ratio * cmp_ratio - cmp_ratio
    next_end = (start_pos[i] + end_pos) // cmp_ratio * cmp_ratio + cmp_ratio
    if (start_pos[i] + end_pos) % cmp_ratio == 0:
        next_end = start_pos[i] + end_pos
    next_end = min(next_end, start_pos[i] + end_pos)
    next_start_block_id = (next_start // block_size) if next_start >= 0 else 0
    next_end_block_id = (next_end - 1) // block_size
    for j in range(next_start_block_id, next_end_block_id + 1):
        if block_table[i][j] == 0:
            block_table[i][j] = next_block_id
            next_block_id = next_block_id + 1

if B==0:
    kv_state = torch.tensor(np.random.uniform(-10, 10, (0, block_size, coff * head_dim))).to(torch.float32)
    score_state = torch.tensor(np.random.uniform(-10, 10, (0, block_size, coff * head_dim))).to(torch.float32)
else:
    kv_state = torch.tensor(np.random.uniform(-10, 10, (torch.max(block_table) + 1, block_size, coff * head_dim))).to(torch.float32)
    score_state = torch.tensor(np.random.uniform(-10, 10, (torch.max(block_table) + 1, block_size, coff * head_dim))).to(torch.float32)

# other input
if bs_combine_flag:
    x_shape = (cu_seqlens[-1], hidden_size)
    rope_sin_shape = (min(x_shape[0], x_shape[0] // cmp_ratio + B), rope_head_dim)
    rope_cos_shape = rope_sin_shape
else:
    x_shape = (B, S, hidden_size)
    rope_sin_shape = (B, (S + cmp_ratio - 1) // cmp_ratio, rope_head_dim)
    rope_cos_shape = rope_sin_shape

x = torch.tensor(np.random.uniform(-10.0, 10.0, x_shape)).to(data_type).npu()
wkv = torch.tensor(np.random.uniform(-10, 10, (coff * head_dim, hidden_size))).to(data_type).npu()
wgate = torch.tensor(np.random.uniform(-10, 10, (coff * head_dim, hidden_size))).to(data_type).npu()
ape = torch.tensor(np.random.uniform(-10, 10, (cmp_ratio, coff * head_dim))).to(torch.float32).npu()
norm_weight = torch.tensor(np.random.uniform(-10, 10, (head_dim))).to(data_type).npu()
rope_sin = torch.tensor(np.random.uniform(-1, 1, rope_sin_shape)).to(data_type).npu()
rope_cos = torch.tensor(np.random.uniform(-1, 1, rope_cos_shape)).to(data_type).npu()
kv_state = kv_state.npu()
score_state = score_state.npu()
block_table = block_table.npu()
start_pos = torch.tensor(start_pos).to(torch.int32).npu()
if cu_seqlens is not None:
    cu_seqlens = torch.tensor(cu_seqlens).to(torch.int32).npu()
if seqused is not None:
    seqused = torch.tensor(seqused).to(torch.int32).npu()

cmp_kv,_ ,_ ,_ ,_ = (
    torch.ops.custom.compressor(
        x,
        wkv,
        wgate,
        kv_state,
        score_state,
        ape,
        norm_weight,
        rope_sin,
        rope_cos,
        kv_block_table = block_table,
        score_block_table = block_table,
        cu_seqlens = cu_seqlens,
        seqused = seqused,
        start_pos = start_pos,
        rope_head_dim = rope_head_dim,
        cmp_ratio = cmp_ratio,
        coff = coff,
        norm_eps = norm_eps,
        rotary_mode = rotary_mode
    )
)

  • aclgraph调用

    import torch
import torch_npu
import numpy as np
import torch.nn as nn
import torchair
import custom_ops
import math

def get_seq_used_by_batch(batch_idx, S, seqused, cu_seqlens):
    if seqused is not None:
        return seqused[batch_idx]
    else:
        if cu_seqlens is not None:
            return cu_seqlens[batch_idx + 1] - cu_seqlens[batch_idx]
        else:
            return S

data_type = torch.bfloat16
hidden_size = 4096
rope_head_dim = 64
norm_eps = 1e-6
coff = 1 # 1:no overlap 2:overlap
cmp_ratio = 128
rotary_mode = 2
head_dim = 512
cu_seqlens = [0, 1]
# -------------
B = 1
S = 1
S_max = 0
block_size = 128
start_pos = [8191] * B # (B,)
start_p=8191
seqused = None # (B,), None时cu_seqlens的数据全部参与计算，否则按传参实际值计算

# BS是否合轴
bs_combine_flag = True
update_flag = 1

if seqused is not None:
    seqused = torch.tensor(seqused).to(torch.int32)
if start_pos is not None:
    start_pos = torch.tensor(start_pos).to(torch.int32)
else:
    start_pos = torch.full((B,), start_p, dtype=torch.int32)

if bs_combine_flag:
    if cu_seqlens is None:
        T = B * S
        if T !=0:
            cu_seqlens = torch.arange(0, T + 1, S, dtype=torch.int32)
        else:
            cu_seqlens = torch.zeros((B+1), dtype=torch.int32)
    else:
        cu_seqlens = torch.tensor(cu_seqlens).to(torch.int32)
    for i in range(B):
        if start_pos[i] + cu_seqlens[i + 1] - cu_seqlens[i] > S_max:
            S_max = start_pos[i] + cu_seqlens[i + 1] - cu_seqlens[i]
else:
    cu_seqlens = None
    S_max = max(start_pos) + S
### ======================== gen input data start =============================
# page state
max_block_num_per_batch = (S_max + block_size - 1) // block_size
block_num = B * max_block_num_per_batch
next_block_id = 1
print(f"max_block_num_per_batch: {max_block_num_per_batch}")
block_table = torch.zeros(size=(B, max_block_num_per_batch), dtype=torch.int32)
for i in range(B):
    # 需要读取state的范围
    cur_start = start_pos[i] // cmp_ratio * cmp_ratio - cmp_ratio
    cur_end = start_pos[i] // cmp_ratio * cmp_ratio + cmp_ratio
    if start_pos[i] % cmp_ratio == 0:
        cur_end = start_pos[i]
    cur_end = min(cur_end, start_pos[i] + S)
    cur_start_block_id = (cur_start // block_size) if cur_start >= 0 else 0
    cur_end_block_id = (cur_end - 1) // block_size
    for j in range(cur_start_block_id, cur_end_block_id + 1):
        block_table[i][j] = next_block_id
        next_block_id = next_block_id + 1
    # 需要写入state的范围
    end_pos = get_seq_used_by_batch(i, S, seqused, cu_seqlens)
    next_start = (start_pos[i] + end_pos) // cmp_ratio * cmp_ratio - cmp_ratio
    next_end = (start_pos[i] + end_pos) // cmp_ratio * cmp_ratio + cmp_ratio
    if (start_pos[i] + end_pos) % cmp_ratio == 0:
        next_end = start_pos[i] + end_pos
    next_end = min(next_end, start_pos[i] + end_pos)
    next_start_block_id = (next_start // block_size) if next_start >= 0 else 0
    next_end_block_id = (next_end - 1) // block_size
    for j in range(next_start_block_id, next_end_block_id + 1):
        if block_table[i][j] == 0:
            block_table[i][j] = next_block_id
            next_block_id = next_block_id + 1

if B==0:
    kv_state = torch.tensor(np.random.uniform(-10, 10, (0, block_size, coff * head_dim))).to(torch.float32)
    score_state = torch.tensor(np.random.uniform(-10, 10, (0, block_size, coff * head_dim))).to(torch.float32)
else:
    kv_state = torch.tensor(np.random.uniform(-10, 10, (torch.max(block_table) + 1, block_size, coff * head_dim))).to(torch.float32)
    score_state = torch.tensor(np.random.uniform(-10, 10, (torch.max(block_table) + 1, block_size, coff * head_dim))).to(torch.float32)

# other input
if bs_combine_flag:
    x_shape = (cu_seqlens[-1], hidden_size)
    rope_sin_shape = (min(x_shape[0], x_shape[0] // cmp_ratio + B), rope_head_dim)
    rope_cos_shape = rope_sin_shape
else:
    x_shape = (B, S, hidden_size)
    rope_sin_shape = (B, (S + cmp_ratio - 1) // cmp_ratio, rope_head_dim)
    rope_cos_shape = rope_sin_shape

x = torch.tensor(np.random.uniform(-10.0, 10.0, x_shape)).to(data_type).npu()
wkv = torch.tensor(np.random.uniform(-10, 10, (coff * head_dim, hidden_size))).to(data_type).npu()
wgate = torch.tensor(np.random.uniform(-10, 10, (coff * head_dim, hidden_size))).to(data_type).npu()
ape = torch.tensor(np.random.uniform(-10, 10, (cmp_ratio, coff * head_dim))).to(torch.float32).npu()
norm_weight = torch.tensor(np.random.uniform(-10, 10, (head_dim))).to(data_type).npu()
rope_sin = torch.tensor(np.random.uniform(-1, 1, rope_sin_shape)).to(data_type).npu()
rope_cos = torch.tensor(np.random.uniform(-1, 1, rope_cos_shape)).to(data_type).npu()
kv_state = kv_state.npu()
score_state = score_state.npu()
block_table = block_table.npu()
start_pos = torch.tensor(start_pos).to(torch.int32).npu()
if cu_seqlens is not None:
    cu_seqlens = torch.tensor(cu_seqlens).to(torch.int32).npu()
if seqused is not None:
    seqused = torch.tensor(seqused).to(torch.int32).npu()

class CompressorNetwork(nn.Module):
    def __init__(self):
        super(CompressorNetwork, self).__init__()

    def forward(self, x, wkv, wgate, kv_state, score_state, ape, norm_weight, rope_sin,
                rope_cos, rope_head_dim, cmp_ratio, kv_block_table = None, score_block_table = None, cu_seqlens = None,
                seqused = None, start_pos = None, coff = 1, norm_eps = 1e-6, rotary_mode = 1):
        cmp_kv,_ ,_ ,_ ,_ = (
            torch.ops.custom.compressor(
                x,
                wkv,
                wgate,
                kv_state,
                score_state,
                ape,
                norm_weight,
                rope_sin,
                rope_cos,
                kv_block_table = kv_block_table,
                score_block_table = score_block_table,
                cu_seqlens = cu_seqlens,
                seqused = seqused,
                start_pos = start_pos,
                rope_head_dim = rope_head_dim,
                cmp_ratio = cmp_ratio,
                coff = coff,
                norm_eps = norm_eps,
                rotary_mode = rotary_mode
            )
        )
        return cmp_kv

from torchair.configs.compiler_config import CompilerConfig
config = CompilerConfig()
npu_backend = torchair.get_npu_backend(compiler_config=config)
torch._dynamo.reset()
npu_mode = torch.compile(CompressorNetwork(), fullgraph=True, backend=npu_backend, dynamic=False)
cmp_kv = npu_mode(
                x,
                wkv,
                wgate,
                kv_state,
                score_state,
                ape,
                norm_weight,
                rope_sin,
                rope_cos,
                kv_block_table = block_table,
                score_block_table = block_table,
                cu_seqlens = cu_seqlens,
                seqused = seqused,
                start_pos = start_pos,
                rope_head_dim = rope_head_dim,
                cmp_ratio = cmp_ratio,
                coff = coff,
                norm_eps = norm_eps,
                rotary_mode = rotary_mode)

更多使用示例见pytest示例。