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FastDeploy/custom_ops/gpu_ops/append_attention.cu
周周周 aa76085d1f
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[Attention] remove cum_offsets from atten, and use cu_seqlens_q (#2870) 
[Attention] remove cum_offsets from atten, and use cu_seqlens_q (#2870)
2025-07-16 20:10:57 +08:00

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// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "append_attn/append_attention_kernel.h"
#include "append_attn/decoder_write_cache_with_rope_kernel.h"
#include "append_attn/speculate_write_cache_with_rope_kernel.h"
#include "append_attn/encoder_write_cache_with_rope_kernel.h"
#ifndef PD_BUILD_STATIC_OP
#define PD_BUILD_STATIC_OP(name) PD_BUILD_OP(static_op_##name)
#endif
template <typename T>
class type2value;
template <>
class type2value<phi::dtype::bfloat16> {
public:
static constexpr paddle::DataType value = paddle::DataType::BFLOAT16;
};
template <>
class type2value<phi::dtype::float16> {
public:
static constexpr paddle::DataType value = paddle::DataType::FLOAT16;
};
template <paddle::DataType D>
std::vector<paddle::Tensor> AppendAttentionKernel(
const AppendAttnMetaData& meta_data,
const paddle::Tensor& qkv,
const paddle::Tensor& key_cache,
const paddle::Tensor& value_cache,
const paddle::Tensor& seq_lens_encoder,
const paddle::Tensor& seq_lens_decoder,
const paddle::Tensor& seq_lens_this_time,
const paddle::Tensor& padding_offsets,
const paddle::Tensor& cu_seqlens_q,
const paddle::Tensor& block_tables,
const paddle::Tensor& encoder_batch_ids,
const paddle::Tensor& encoder_tile_ids_per_batch,
const paddle::Tensor& encoder_num_blocks,
const paddle::Tensor& kv_batch_ids,
const paddle::Tensor& kv_tile_ids_per_batch,
const paddle::Tensor& kv_num_blocks,
const paddle::Tensor& decoder_batch_ids,
const paddle::Tensor& decoder_tile_ids_per_batch,
const paddle::Tensor& decoder_num_blocks,
const paddle::Tensor& set_max_lengths,
const paddle::Tensor& max_len_kv,
const paddle::optional<paddle::Tensor>& rotary_embs,
const paddle::optional<paddle::Tensor>& attn_mask,
const paddle::optional<paddle::Tensor>& qkv_bias,
const paddle::optional<paddle::Tensor>& qkv_out_scales,
const paddle::optional<paddle::Tensor>& cache_k_quant_scales,
const paddle::optional<paddle::Tensor>& cache_v_quant_scales,
const paddle::optional<paddle::Tensor>& cache_k_dequant_scales,
const paddle::optional<paddle::Tensor>& cache_v_dequant_scales,
const paddle::optional<paddle::Tensor>& cache_k_zp,
const paddle::optional<paddle::Tensor>& cache_v_zp,
const paddle::optional<paddle::Tensor>& out_linear_shifts,
const paddle::optional<paddle::Tensor>& out_linear_smooths,
const paddle::optional<paddle::Tensor>& kv_signal_data,
const std::string& cache_quant_type_str,
const bool use_neox_rotary_style,
const bool rope_3d,
const int max_input_length,
const float quant_max_bound,
const float quant_min_bound,
const float out_linear_in_scale,
const int encoder_block_shape_q,
const int decoder_block_shape_q,
const int max_partition_size,
const int encoder_max_partition_size,
const int speculate_max_draft_token_num,
const bool causal,
const bool speculate_decoder) {
typedef PDTraits<D> traits_;
typedef typename traits_::DataType DataType_;
typedef typename traits_::data_t data_t;
// set_max_lengths: max_len_this_time, max_enc_len_this_time, max_dec_len_this_time, max_enc_dec_len_this_time,
// max_just_dec_len_this_time, max_just_dec_merged_len_this_time, max_system_len, max_just_dec_len_without_system
int max_len_this_time = set_max_lengths.data<int>()[0];
int max_enc_len_this_time =set_max_lengths.data<int>()[1];
int max_dec_len_this_time = set_max_lengths.data<int>()[2];
int max_enc_dec_len_this_time = set_max_lengths.data<int>()[3];
int max_just_dec_len_this_time = set_max_lengths.data<int>()[4];
auto main_stream = qkv.stream();
static cudaEvent_t main_event;
static cudaEvent_t decoder_event;
static cudaStream_t decoder_stream;
static bool init_flag = false;
if (max_just_dec_len_this_time > 0 && max_enc_len_this_time > 0 &&
!init_flag) {
cudaEventCreateWithFlags(&main_event, cudaEventDisableTiming);
cudaEventCreateWithFlags(&decoder_event, cudaEventDisableTiming);
cudaStreamCreateWithFlags(&decoder_stream, cudaStreamNonBlocking);
init_flag = true;
}
paddle::Tensor qkv_out;
if (qkv_out_scales) {
qkv_out = GetEmptyTensor(qkv.dims(), D, qkv.place());
} else {
qkv_out = qkv;
}
paddle::Tensor fmha_out;
if (out_linear_in_scale > 0.0) {
if (fabs(quant_max_bound - 127.0f) < 0.000001) {
fmha_out = GetEmptyTensor(
{meta_data.token_nums, meta_data.q_num_heads * meta_data.head_dims},
paddle::DataType::INT8,
qkv.place());
} else if (fabs(quant_max_bound - 448.0f) < 0.000001) {
fmha_out = GetEmptyTensor(
{meta_data.token_nums, meta_data.q_num_heads * meta_data.head_dims},
paddle::DataType::FLOAT8_E4M3FN,
qkv.place());
}else{
PD_THROW("Only supported attr of quant_max_bound in ['127', '448'].");
}
} else {
fmha_out = GetEmptyTensor(
{meta_data.token_nums, meta_data.q_num_heads * meta_data.head_dims},
D,
qkv.place());
}
auto dispatch_CascadeAppendAttentionKernel = [&](auto temp_args,
const paddle::Tensor& lambda_batch_ids,
const paddle::Tensor& lambda_tile_ids_per_batch,
const int lambda_num_blocks_data,
const int lambda_block_shape_q,
const int lambda_max_dec_len,
const bool lambda_is_decoder,
const bool lambda_enable_prefill,
cudaStream_t& lambda_stream
) -> void {
CascadeAppendAttentionKernel<data_t, decltype(temp_args)>(
meta_data,
qkv_out,
key_cache,
value_cache,
attn_mask,
cache_k_dequant_scales,
cache_v_dequant_scales,
cache_k_zp,
cache_v_zp,
out_linear_shifts,
out_linear_smooths,
seq_lens_this_time,
seq_lens_decoder,
seq_lens_encoder,
padding_offsets,
cu_seqlens_q,
block_tables,
lambda_batch_ids,
lambda_tile_ids_per_batch,
cache_quant_type_str,
lambda_num_blocks_data,
lambda_block_shape_q,
max_input_length,
lambda_max_dec_len,
quant_max_bound,
quant_min_bound,
out_linear_in_scale,
max_partition_size,
encoder_max_partition_size,
speculate_max_draft_token_num,
causal,
lambda_is_decoder,
lambda_enable_prefill,
lambda_stream,
&fmha_out);
};
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 {
EncoderWriteCacheWithRopeKernel<data_t, decltype(temp_args)>(
meta_data,
qkv,
seq_lens_this_time,
seq_lens_encoder,
seq_lens_decoder,
padding_offsets,
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));
};
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:{
int8_t tmp;
dispatch_CascadeAppendAttentionKernel(tmp, encoder_batch_ids, encoder_tile_ids_per_batch, encoder_num_blocks_data, encoder_block_shape_q, max_enc_dec_len_this_time, false, true, main_stream);
break;
}
case paddle::DataType::FLOAT8_E4M3FN:{
phi::dtype::float8_e4m3fn tmp;
dispatch_CascadeAppendAttentionKernel(tmp, encoder_batch_ids, encoder_tile_ids_per_batch, encoder_num_blocks_data, encoder_block_shape_q, max_enc_dec_len_this_time, false, true, main_stream);
break;
}
default:{
PD_THROW("Only supported output fmha_out of quant dtype in ['int8', 'FLOAT8_E4M3FN'].");
break;
}
}
} else {
data_t tmp;
dispatch_CascadeAppendAttentionKernel(tmp, encoder_batch_ids, encoder_tile_ids_per_batch, encoder_num_blocks_data, encoder_block_shape_q, max_enc_dec_len_this_time, false, true, main_stream);
}
}
if (max_just_dec_len_this_time > 0) {
int decoder_num_blocks_data = decoder_num_blocks.data<int>()[0];
int max_len_kv_data = max_len_kv.data<int>()[0];
cudaStream_t exec_stream;
if (max_enc_len_this_time > 0) {
cudaStreamWaitEvent(decoder_stream, main_event);
exec_stream = decoder_stream;
} else {
exec_stream = main_stream;
}
if (speculate_decoder) {
if (qkv_out_scales) {
SpeculateWriteCacheWithRoPEKernel<data_t, int>(
meta_data,
qkv, // [token_num, num_heads, head_dim]
seq_lens_decoder,
seq_lens_encoder,
padding_offsets,
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,
max_input_length,
exec_stream,
&qkv_out,
const_cast<paddle::Tensor*>(&key_cache),
const_cast<paddle::Tensor*>(&value_cache));
} else {
SpeculateWriteCacheWithRoPEKernel<data_t, data_t>(
meta_data,
qkv_out, // [token_num, num_heads, head_dim]
seq_lens_decoder,
seq_lens_encoder,
padding_offsets,
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,
max_input_length,
exec_stream,
&qkv_out,
const_cast<paddle::Tensor*>(&key_cache),
const_cast<paddle::Tensor*>(&value_cache));
}
} else {
if (qkv_out_scales) {
DecoderWriteCacheWithRoPEKernel<data_t, int>(
meta_data,
qkv, // [token_num, num_heads, head_dim]
seq_lens_decoder,
seq_lens_encoder,
padding_offsets,
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));
} else {
DecoderWriteCacheWithRoPEKernel<data_t, data_t>(
meta_data,
qkv_out, // [token_num, num_heads, head_dim]
seq_lens_decoder,
seq_lens_encoder,
padding_offsets,
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));
}
}
if (out_linear_in_scale > 0.0) {
switch (fmha_out.dtype()) {
case paddle::DataType::INT8:{
int8_t tmp;
dispatch_CascadeAppendAttentionKernel(tmp, decoder_batch_ids, decoder_tile_ids_per_batch, decoder_num_blocks_data,
decoder_block_shape_q, max_len_kv_data, !speculate_decoder, !speculate_decoder, exec_stream);
break;
}
case paddle::DataType::FLOAT8_E4M3FN:{
phi::dtype::float8_e4m3fn tmp;
dispatch_CascadeAppendAttentionKernel(tmp, decoder_batch_ids, decoder_tile_ids_per_batch, decoder_num_blocks_data,
decoder_block_shape_q, max_len_kv_data, !speculate_decoder, !speculate_decoder, exec_stream);
break;
}
}
} else {
data_t tmp;
dispatch_CascadeAppendAttentionKernel(tmp, decoder_batch_ids, decoder_tile_ids_per_batch, decoder_num_blocks_data,
decoder_block_shape_q, max_len_kv_data, !speculate_decoder, !speculate_decoder, exec_stream);
}
if (max_enc_len_this_time > 0) {
cudaEventRecord(decoder_event, exec_stream);
cudaStreamWaitEvent(main_stream, decoder_event);
}
}
return {fmha_out, qkv_out};
}
std::vector<paddle::Tensor> AppendAttention(
const paddle::Tensor& qkv,
const paddle::Tensor& key_cache,
const paddle::Tensor& value_cache,
const paddle::Tensor& seq_lens_encoder,
const paddle::Tensor& seq_lens_decoder,
const paddle::Tensor& seq_lens_this_time,
const paddle::Tensor& padding_offsets,
const paddle::Tensor& cu_seqlens_q,
const paddle::Tensor& block_tables,
const paddle::Tensor& encoder_batch_ids,
const paddle::Tensor& encoder_tile_ids_per_batch,
const paddle::Tensor& encoder_num_blocks,
const paddle::Tensor& kv_batch_ids,
const paddle::Tensor& kv_tile_ids_per_batch,
const paddle::Tensor& kv_num_blocks,
const paddle::Tensor& decoder_batch_ids,
const paddle::Tensor& decoder_tile_ids_per_batch,
const paddle::Tensor& decoder_num_blocks,
const paddle::Tensor& set_max_lengths,
const paddle::Tensor& max_len_kv,
const paddle::optional<paddle::Tensor>& rotary_embs,
const paddle::optional<paddle::Tensor>& attn_mask,
const paddle::optional<paddle::Tensor>& qkv_bias,
const paddle::optional<paddle::Tensor>& qkv_out_scales,
const paddle::optional<paddle::Tensor>& cache_k_quant_scales,
const paddle::optional<paddle::Tensor>& cache_v_quant_scales,
const paddle::optional<paddle::Tensor>& cache_k_dequant_scales,
const paddle::optional<paddle::Tensor>& cache_v_dequant_scales,
const paddle::optional<paddle::Tensor>& cache_k_zp,
const paddle::optional<paddle::Tensor>& cache_v_zp,
const paddle::optional<paddle::Tensor>& out_linear_shifts,
const paddle::optional<paddle::Tensor>& out_linear_smooths,
const paddle::optional<paddle::Tensor>& kv_signal_data,
const std::string& compute_dtype,
const std::string& cache_quant_type_str,
const bool use_neox_rotary_style,
const bool rope_3d,
const int max_input_length,
const float quant_max_bound,
const float quant_min_bound,
const float out_linear_in_scale,
const int encoder_block_shape_q,
const int decoder_block_shape_q,
const int max_partition_size,
const int encoder_max_partition_size,
const int speculate_max_draft_token_num,
const bool causal,
const bool speculate_decoder) {
AppendAttnMetaData meta_data;
const auto& qkv_dims = qkv.dims();
const auto& key_cache_dims = key_cache.dims();
meta_data.token_nums = qkv_dims[0];
meta_data.kv_num_heads = key_cache_dims[1];
meta_data.head_dims = key_cache_dims[3];
// TODO: trick method support c4, add attr head_dims in the future
if (cache_quant_type_str == "cache_int4_zp") {
meta_data.head_dims *= 2;
}
const int total_num_head =
qkv_dims[qkv_dims.size() - 1] / meta_data.head_dims;
meta_data.q_num_heads = total_num_head - 2 * meta_data.kv_num_heads;
meta_data.max_blocks_per_seq = block_tables.dims()[1];
meta_data.block_size = key_cache.dims()[2];
meta_data.batch_size = seq_lens_this_time.dims()[0];
auto dispatch_by_template = [&](auto temp_args) -> std::vector<paddle::Tensor> {
return AppendAttentionKernel<type2value<decltype(temp_args)>::value>(
meta_data,
qkv,
key_cache,
value_cache,
seq_lens_encoder,
seq_lens_decoder,
seq_lens_this_time,
padding_offsets,
cu_seqlens_q,
block_tables,
encoder_batch_ids,
encoder_tile_ids_per_batch,
encoder_num_blocks,
kv_batch_ids,
kv_tile_ids_per_batch,
kv_num_blocks,
decoder_batch_ids,
decoder_tile_ids_per_batch,
decoder_num_blocks,
set_max_lengths,
max_len_kv,
rotary_embs,
attn_mask,
qkv_bias,
qkv_out_scales,
cache_k_quant_scales,
cache_v_quant_scales,
cache_k_dequant_scales,
cache_v_dequant_scales,
cache_k_zp,
cache_v_zp,
out_linear_shifts,
out_linear_smooths,
kv_signal_data,
cache_quant_type_str,
use_neox_rotary_style,
rope_3d,
max_input_length,
quant_max_bound,
quant_min_bound,
out_linear_in_scale,
encoder_block_shape_q,
decoder_block_shape_q,
max_partition_size,
encoder_max_partition_size,
speculate_max_draft_token_num,
causal,
speculate_decoder);
};
phi::dtype::float16 fp16_dtype;
phi::dtype::bfloat16 bp16_dtype;
switch (qkv.dtype()) {
case paddle::DataType::FLOAT16: return dispatch_by_template(fp16_dtype);
case paddle::DataType::BFLOAT16: return dispatch_by_template(bp16_dtype);
case paddle::DataType::INT32: {
if (compute_dtype == "bf16") {
return dispatch_by_template(bp16_dtype);
} else if (compute_dtype == "fp16") {
return dispatch_by_template(fp16_dtype);
} else {
PD_THROW("Only supported attr of compute_dtype in ['fp16', 'bf16'].");
break;
}
}
default: {
PD_THROW(
"NOT supported data type. "
"Only float16 and bfloat16 are supported. ");
break;
}
}
return {paddle::Tensor{}};
}
std::vector<std::vector<int64_t>> AppendAttentionInferShape(
const std::vector<int64_t>& qkv_shape,
const std::vector<int64_t>& key_cache_shape,
const std::vector<int64_t>& value_cache_shape,
const std::vector<int64_t>& seq_lens_encoder_shape,
const std::vector<int64_t>& seq_lens_decoder_shape,
const std::vector<int64_t>& seq_lens_this_time_shape,
const std::vector<int64_t>& padding_offsets_shape,
const std::vector<int64_t>& cu_seqlens_q_shape,
const std::vector<int64_t>& block_tables_shape,
const std::vector<int64_t>& encoder_batch_ids_shape,
const std::vector<int64_t>& encoder_tile_ids_per_batch_shape,
const std::vector<int64_t>& encoder_num_blocks_shape,
const std::vector<int64_t>& kv_batch_ids_shape,
const std::vector<int64_t>& kv_tile_ids_per_batch_shape,
const std::vector<int64_t>& kv_num_blocks_shape,
const std::vector<int64_t>& decoder_batch_ids_shape,
const std::vector<int64_t>& decoder_tile_ids_per_batch_shape,
const std::vector<int64_t>& decoder_num_blocks_shape,
const std::vector<int64_t>& set_max_lengths_shape,
const std::vector<int64_t>& max_len_kv_shape,
const paddle::optional<std::vector<int64_t>>& rotary_embs_shape,
const paddle::optional<std::vector<int64_t>>& attn_mask_shape,
const paddle::optional<std::vector<int64_t>>& qkv_bias_shape,
const paddle::optional<std::vector<int64_t>>& qkv_out_scales_shape,
const paddle::optional<std::vector<int64_t>>& cache_k_quant_scales_shape,
const paddle::optional<std::vector<int64_t>>& cache_v_quant_scales_shape,
const paddle::optional<std::vector<int64_t>>& cache_k_dequant_scales_shape,
const paddle::optional<std::vector<int64_t>>& cache_v_dequant_scales_shape,
const paddle::optional<std::vector<int64_t>>& cache_k_zp_shape,
const paddle::optional<std::vector<int64_t>>& cache_v_zp_shape,
const paddle::optional<std::vector<int64_t>>& out_linear_shifts_shape,
const paddle::optional<std::vector<int64_t>>& out_linear_smooths_shape,
const paddle::optional<std::vector<int64_t>>& kv_signal_data_shape,
const std::string& compute_dtype,
const std::string& cache_quant_type_str,
const bool use_neox_rotary_style,
const bool rope_3d,
const int max_input_length,
const float quant_max_bound,
const float quant_min_bound,
const float out_linear_in_scale,
const int encoder_block_shape_q,
const int decoder_block_shape_q,
const int max_partition_size,
const int encoder_max_partition_size,
const int speculate_max_draft_token_num,
const bool causal,
const bool speculate_decoder) {
const int token_num = qkv_shape[0];
const int kv_num_heads = key_cache_shape[1];
int head_dim = key_cache_shape[3];
if (cache_quant_type_str == "cache_int4_zp") {
head_dim *= 2;
}
const int total_num_head = qkv_shape[qkv_shape.size() - 1] / head_dim;
const int num_heads = total_num_head - 2 * kv_num_heads;
return {{token_num, num_heads * head_dim}, qkv_shape};
}
std::vector<paddle::DataType> AppendAttentionInferDtype(
const paddle::DataType& qkv_dtype,
const paddle::DataType& key_cache_dtype,
const paddle::DataType& value_cache_dtype,
const paddle::DataType& seq_lens_encoder_dtype,
const paddle::DataType& seq_lens_decoder_dtype,
const paddle::DataType& seq_lens_this_time_dtype,
const paddle::DataType& padding_offsets_dtype,
const paddle::DataType& cu_seqlens_q_dtype,
const paddle::DataType& block_tables_dtype,
const paddle::DataType& encoder_batch_ids_dtype,
const paddle::DataType& encoder_tile_ids_per_batch_dtype,
const paddle::DataType& encoder_num_blocks_dtype,
const paddle::DataType& kv_batch_ids_dtype,
const paddle::DataType& kv_tile_ids_per_batch_dtype,
const paddle::DataType& kv_num_blocks_dtype,
const paddle::DataType& decoder_batch_ids_dtype,
const paddle::DataType& decoder_tile_ids_per_batch_dtype,
const paddle::DataType& decoder_num_blocks_dtype,
const paddle::DataType& set_max_lengths_dtype,
const paddle::DataType& max_len_kv_dtype,
const paddle::optional<paddle::DataType>& rotary_embs_dtype,
const paddle::optional<paddle::DataType>& attn_mask_dtype,
const paddle::optional<paddle::DataType>& qkv_bias_dtype,
const paddle::optional<paddle::DataType>& qkv_out_scales_dtype,
const paddle::optional<paddle::DataType>& cache_k_quant_scales_dtype,
const paddle::optional<paddle::DataType>& cache_v_quant_scales_dtype,
const paddle::optional<paddle::DataType>& cache_k_dequant_scales_dtype,
const paddle::optional<paddle::DataType>& cache_v_dequant_scales_dtype,
const paddle::optional<paddle::DataType>& cache_k_zp_dtype,
const paddle::optional<paddle::DataType>& cache_v_zp_dtype,
const paddle::optional<paddle::DataType>& out_linear_shifts_dtype,
const paddle::optional<paddle::DataType>& out_linear_smooths_dtype,
const paddle::optional<paddle::DataType>& kv_signal_data_dtype,
const std::string& compute_dtype,
const std::string& cache_quant_type_str,
const bool use_neox_rotary_style,
const bool rope_3d,
const int max_input_length,
const float quant_max_bound,
const float quant_min_bound,
const float out_linear_in_scale,
const int encoder_block_shape_q,
const int decoder_block_shape_q,
const int max_partition_size,
const int encoder_max_partition_size,
const int speculate_max_draft_token_num,
const bool causal,
const bool speculate_decoder) {
if (compute_dtype == "bf16") {
if (out_linear_in_scale > 0.0) {
if (fabs(quant_max_bound - 127.0f) < 0.000001) {
return {paddle::DataType::INT8, paddle::DataType::BFLOAT16};
} else if (fabs(quant_max_bound - 448.0f) < 0.000001) {
return {paddle::DataType::FLOAT8_E4M3FN, paddle::DataType::BFLOAT16};
}else{
PD_THROW("Only supported attr of quant_max_bound in ['127.0', '448.0'].");
}
} else {
return {paddle::DataType::BFLOAT16, paddle::DataType::BFLOAT16};
}
} else if (compute_dtype == "fp16") {
if (out_linear_in_scale > 0.0) {
if (fabs(quant_max_bound - 127.0f) < 0.000001) {
return {paddle::DataType::INT8, paddle::DataType::FLOAT16};
} else if (fabs(quant_max_bound - 448.0f) < 0.000001) {
return {paddle::DataType::FLOAT8_E4M3FN, paddle::DataType::FLOAT16};
}else{
PD_THROW("Only supported attr of quant_max_bound in ['127.0', '448.0'].");
}
} else {
return {paddle::DataType::FLOAT16, paddle::DataType::FLOAT16};
}
} else {
PD_THROW("Only supported attr of compute_dtype in ['fp16', 'bf16'].");
}
}
PD_BUILD_STATIC_OP(append_attention)
.Inputs({"qkv",
"key_cache",
"value_cache",
"seq_lens_encoder",
"seq_lens_decoder",
"seq_lens_this_time",
"padding_offsets",
"cu_seqlens_q",
"block_tables",
"encoder_batch_ids",
"encoder_tile_ids_per_batch",
"encoder_num_blocks",
"kv_batch_ids",
"kv_tile_ids_per_batch",
"kv_num_blocks",
"decoder_batch_ids",
"decoder_tile_ids_per_batch",
"decoder_num_blocks",
"set_max_lengths",
"max_len_kv",
paddle::Optional("rotary_embs"),
paddle::Optional("attn_mask"),
paddle::Optional("qkv_bias"),
paddle::Optional("qkv_out_scales"),
paddle::Optional("cache_k_quant_scales"),
paddle::Optional("cache_v_quant_scales"),
paddle::Optional("cache_k_dequant_scales"),
paddle::Optional("cache_v_dequant_scales"),
paddle::Optional("cache_k_zp"),
paddle::Optional("cache_v_zp"),
paddle::Optional("out_linear_shifts"),
paddle::Optional("out_linear_smooths"),
paddle::Optional("kv_signal_data")})
.Outputs({"fmha_out", "qkv_out", "key_cache_out", "value_cache_out"})
.SetInplaceMap({{"key_cache", "key_cache_out"},
{"value_cache", "value_cache_out"}})
.Attrs({"compute_type: std::string",
"cache_quant_type: std::string",
"use_neox_rotary_style: bool",
"rope_3d: bool",
"max_input_length: int",
"quant_max_bound: float",
"quant_min_bound: float",
"out_linear_in_scale: float",
"encoder_block_shape_q: int",
"decoder_block_shape_q: int",
"max_partition_size: int",
"encoder_max_partition_size: int",
"speculate_max_draft_token_num: int",
"causal: bool",
"speculate_decoder: bool"})
.SetKernelFn(PD_KERNEL(AppendAttention))
.SetInferShapeFn(PD_INFER_SHAPE(AppendAttentionInferShape))
.SetInferDtypeFn(PD_INFER_DTYPE(AppendAttentionInferDtype));
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