Skip to content

delete useless code #7959

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

Open
zhoutianzi666 wants to merge 2 commits into
base: develop
Choose a base branch
from
+61 −168
Open

delete useless code #7959

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
00bb3da
commit
zhoutianzi666 May 29, 2026
de30baf
commit
zhoutianzi666 May 29, 2026
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
211 changes: 49 additions & 162 deletions custom_ops/gpu_ops/append_attention.cu
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -177,59 +177,59 @@ void AppendAttentionKernel(
sink_size);
};

int encoder_num_blocks_data = encoder_num_blocks.data<int>()[0];
int kv_num_blocks_data = kv_num_blocks.data<int>()[0];

auto dispatch_EncoderWriteCacheWithRopeKernel = [&](auto temp_args) -> void {
DISPATCH_BOOL_DTYPE(enforce_fmul_rn, EnforceFmulRN, {
EncoderWriteCacheWithRopeKernel<data_t,
decltype(temp_args),
EnforceFmulRN>(
meta_data,
qkv,
seq_lens_this_time,
seq_lens_this_time,
seq_lens_decoder,
batch_id_per_token,
cu_seqlens_q,
block_tables,
kv_batch_ids,
kv_tile_ids_per_batch,
rotary_embs,
qkv_out_scales,
qkv_bias,
cache_k_quant_scales,
cache_v_quant_scales,
cache_k_zp,
cache_v_zp,
kv_signal_data,
cache_quant_type_str,
kv_num_blocks_data,
max_input_length,
use_neox_rotary_style,
rope_3d,
main_stream,
&qkv_out,
const_cast<paddle::Tensor*>(&key_cache),
const_cast<paddle::Tensor*>(&value_cache),
q_norm_weight,
k_norm_weight,
rms_norm_eps);
})
};

if (qkv_out_scales) {
int tmp;
dispatch_EncoderWriteCacheWithRopeKernel(tmp);
} else {
data_t tmp;
dispatch_EncoderWriteCacheWithRopeKernel(tmp);
}

if (max_enc_len_this_time > 0) {
if (max_just_dec_len_this_time > 0) {
cudaEventRecord(main_event, main_stream);
}
int encoder_num_blocks_data = encoder_num_blocks.data<int>()[0];
int kv_num_blocks_data = kv_num_blocks.data<int>()[0];

auto dispatch_EncoderWriteCacheWithRopeKernel =
[&](auto temp_args) -> void {
DISPATCH_BOOL_DTYPE(enforce_fmul_rn, EnforceFmulRN, {
EncoderWriteCacheWithRopeKernel<data_t,
decltype(temp_args),
EnforceFmulRN>(
meta_data,
qkv,
seq_lens_this_time,
seq_lens_encoder,
seq_lens_decoder,
batch_id_per_token,
cu_seqlens_q,
block_tables,
kv_batch_ids,
kv_tile_ids_per_batch,
rotary_embs,
qkv_out_scales,
qkv_bias,
cache_k_quant_scales,
cache_v_quant_scales,
cache_k_zp,
cache_v_zp,
kv_signal_data,
cache_quant_type_str,
kv_num_blocks_data,
max_input_length,
use_neox_rotary_style,
rope_3d,
main_stream,
&qkv_out,
const_cast<paddle::Tensor*>(&key_cache),
const_cast<paddle::Tensor*>(&value_cache),
q_norm_weight,
k_norm_weight,
rms_norm_eps);
})
};

if (qkv_out_scales) {
int tmp;
dispatch_EncoderWriteCacheWithRopeKernel(tmp);
} else {
data_t tmp;
dispatch_EncoderWriteCacheWithRopeKernel(tmp);
}
if (out_linear_in_scale > 0.0) {
switch (fmha_out.dtype()) {
case paddle::DataType::INT8: {
Expand Down Expand Up @@ -289,119 +289,6 @@ void AppendAttentionKernel(
} else {
exec_stream = main_stream;
}
DISPATCH_BOOL_DTYPE(enforce_fmul_rn, EnforceFmulRN, {
if (speculate_decoder) {
if (qkv_out_scales) {
SpeculateWriteCacheWithRoPEKernel<data_t, int, EnforceFmulRN>(
meta_data,
qkv, // [token_num, num_heads, head_dim]
seq_lens_decoder,
seq_lens_encoder,
batch_id_per_token,
cu_seqlens_q,
block_tables,
rotary_embs,
qkv_out_scales,
qkv_bias,
cache_k_quant_scales,
cache_v_quant_scales,
cache_k_zp,
cache_v_zp,
cache_quant_type_str,
use_neox_rotary_style,
rope_3d,
max_input_length,
exec_stream,
&qkv_out,
const_cast<paddle::Tensor*>(&key_cache),
const_cast<paddle::Tensor*>(&value_cache),
q_norm_weight,
k_norm_weight,
rms_norm_eps);
} else {
SpeculateWriteCacheWithRoPEKernel<data_t, data_t, EnforceFmulRN>(
meta_data,
qkv_out, // [token_num, num_heads, head_dim]
seq_lens_decoder,
seq_lens_encoder,
batch_id_per_token,
cu_seqlens_q,
block_tables,
rotary_embs,
qkv_out_scales,
qkv_bias,
cache_k_quant_scales,
cache_v_quant_scales,
cache_k_zp,
cache_v_zp,
cache_quant_type_str,
use_neox_rotary_style,
rope_3d,
max_input_length,
exec_stream,
&qkv_out,
const_cast<paddle::Tensor*>(&key_cache),
const_cast<paddle::Tensor*>(&value_cache),
q_norm_weight,
k_norm_weight,
rms_norm_eps);
}
} else {
if (qkv_out_scales) {
DecoderWriteCacheWithRoPEKernel<data_t, int, EnforceFmulRN>(
meta_data,
qkv, // [token_num, num_heads, head_dim]
seq_lens_decoder,
seq_lens_encoder,
cu_seqlens_q,
block_tables,
rotary_embs,
qkv_out_scales,
qkv_bias,
cache_k_quant_scales,
cache_v_quant_scales,
cache_k_zp,
cache_v_zp,
cache_quant_type_str,
use_neox_rotary_style,
rope_3d,
max_input_length,
exec_stream,
&qkv_out,
const_cast<paddle::Tensor*>(&key_cache),
const_cast<paddle::Tensor*>(&value_cache),
q_norm_weight,
k_norm_weight,
rms_norm_eps);
} else {
DecoderWriteCacheWithRoPEKernel<data_t, data_t, EnforceFmulRN>(
meta_data,
qkv_out, // [token_num, num_heads, head_dim]
seq_lens_decoder,
seq_lens_encoder,
cu_seqlens_q,
block_tables,
rotary_embs,
qkv_out_scales,
qkv_bias,
cache_k_quant_scales,
cache_v_quant_scales,
cache_k_zp,
cache_v_zp,
cache_quant_type_str,
use_neox_rotary_style,
rope_3d,
max_input_length,
exec_stream,
&qkv_out,
const_cast<paddle::Tensor*>(&key_cache),
const_cast<paddle::Tensor*>(&value_cache),
q_norm_weight,
k_norm_weight,
rms_norm_eps);
}
}
})

if (out_linear_in_scale > 0.0) {
switch (fmha_out.dtype()) {
Expand Down
18 changes: 12 additions & 6 deletions custom_ops/gpu_ops/append_attn/get_block_shape_and_split_kv_block.cu
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -383,9 +383,8 @@ void GetBlockShapeAndSplitKVBlock(
}
// mla_backend not need run the following code.
if (mla_backend) return;

// encoder
if (max_enc_len_this_time > 0) {
// deal with write cache kv!
{
const uint32_t max_tile_size_per_bs_kv =
div_up(max_enc_dec_len_this_time, block_size);
const uint32_t kv_batch_shape = bsz * max_tile_size_per_bs_kv;
Expand All @@ -400,16 +399,23 @@ void GetBlockShapeAndSplitKVBlock(

split_kv_block<<<1, 32, 0, seq_lens_encoder.stream()>>>(
seq_lens_decoder.data<int>(),
seq_lens_encoder.data<int>(),
seq_lens_this_time.data<int>(),
kv_batch_ids.data<int>(),
kv_tile_ids_per_batch.data<int>(),
kv_num_blocks_x.data<int>(),
bsz,
block_size,
block_size);

kv_num_blocks_x_cpu.copy_(
kv_num_blocks_x, kv_num_blocks_x_cpu.place(), true);
#ifndef PADDLE_WITH_CUSTOM_DEVICE_METAX_GPU
if (!phi::backends::gpu::IsCUDAGraphCapturing())
#endif
kv_num_blocks_x_cpu.copy_(
kv_num_blocks_x, kv_num_blocks_x_cpu.place(), true);
}

// encoder
if (max_enc_len_this_time > 0) {
// Clear buffer
const uint32_t encoder_max_tile_size_per_bs_q =
div_up((max_enc_dec_len_this_time * group_size), encoder_block_shape_q);
Expand Down
Loading