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[XPU] bind some OPs for VL model with pybind (#4522)

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Lucas
2025-10-27 10:50:08 +08:00
committed by GitHub
parent cdc40cdc2a
commit 5c6105f4a2
8 changed files with 1789 additions and 1087 deletions
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custom_ops/gpu_ops/update_inputs_v1.cu
+133 -129
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@@ -15,146 +15,150 @@
#include "helper.h"
template <int THREADBLOCK_SIZE>
__global__ void update_inputs_kernel_v1(bool *not_need_stop,
int *seq_lens_this_time,
int *seq_lens_encoder,
int *seq_lens_decoder,
int *step_seq_lens_decoder,
int64_t *prompt_lens,
int64_t *topk_ids,
int64_t *input_ids,
int *block_tables,
const int64_t *stop_nums,
bool *stop_flags,
bool *is_block_step,
const int64_t *next_tokens,
const int bsz,
const int max_bsz,
const int input_ids_stride,
const int block_num_per_seq,
const int block_size,
bool prefill_one_step_stop) {
int thread_idx = threadIdx.x;
typedef cub::BlockReduce<int64_t, THREADBLOCK_SIZE> BlockReduce;
__shared__ typename BlockReduce::TempStorage temp_storage;
__global__ void update_inputs_kernel_v1(bool* not_need_stop,
int* seq_lens_this_time,
int* seq_lens_encoder,
int* seq_lens_decoder,
int* step_seq_lens_decoder,
int64_t* prompt_lens,
int64_t* topk_ids,
int64_t* input_ids,
int* block_tables,
const int64_t* stop_nums,
bool* stop_flags,
bool* is_block_step,
const int64_t* next_tokens,
const int bsz,
const int max_bsz,
const int input_ids_stride,
const int block_num_per_seq,
const int block_size,
bool prefill_one_step_stop) {
int thread_idx = threadIdx.x;
typedef cub::BlockReduce<int64_t, THREADBLOCK_SIZE> BlockReduce;
__shared__ typename BlockReduce::TempStorage temp_storage;
bool stop_flag_now = false;
int64_t stop_flag_now_int = 0;
if (thread_idx < max_bsz) {
if (thread_idx < bsz) {
stop_flag_now = stop_flags[thread_idx];
stop_flag_now_int = static_cast<int64_t>(stop_flag_now);
} else {
stop_flag_now_int = 1;
}
}
bool stop_flag_now = false;
int64_t stop_flag_now_int = 0;
if (thread_idx < max_bsz) {
if (thread_idx < bsz) {
if(stop_flag_now) {
seq_lens_this_time[thread_idx] = 0; // stop at next step
seq_lens_decoder[thread_idx] = 0;
seq_lens_encoder[thread_idx] = 0;
stop_flag_now = stop_flags[thread_idx];
stop_flag_now_int = static_cast<int64_t>(stop_flag_now);
} else {
stop_flag_now_int = 1;
}
}
if (thread_idx < bsz) {
if (stop_flag_now) {
seq_lens_this_time[thread_idx] = 0; // stop at next step
seq_lens_decoder[thread_idx] = 0;
seq_lens_encoder[thread_idx] = 0;
} else {
if (seq_lens_this_time[thread_idx] + seq_lens_decoder[thread_idx] >=
prompt_lens[thread_idx]) {
if (prefill_one_step_stop) {
// prefill done, stop
stop_flags[thread_idx] = true;
seq_lens_this_time[thread_idx] = 0;
seq_lens_decoder[thread_idx] = 0;
seq_lens_encoder[thread_idx] = 0;
stop_flag_now_int = 1;
} else {
if (seq_lens_this_time[thread_idx] + seq_lens_decoder[thread_idx] >= prompt_lens[thread_idx]) {
if (prefill_one_step_stop) {
// prefill done, stop
stop_flags[thread_idx] = true;
seq_lens_this_time[thread_idx] = 0;
seq_lens_decoder[thread_idx] = 0;
seq_lens_encoder[thread_idx] = 0;
stop_flag_now_int = 1;
} else{
// decoding
seq_lens_decoder[thread_idx] += seq_lens_this_time[thread_idx];
seq_lens_this_time[thread_idx] = 1;
seq_lens_encoder[thread_idx] = 0;
int64_t *input_ids_now = input_ids + thread_idx * input_ids_stride;
input_ids_now[0] = next_tokens[thread_idx];
// decoding
seq_lens_decoder[thread_idx] += seq_lens_this_time[thread_idx];
seq_lens_this_time[thread_idx] = 1;
seq_lens_encoder[thread_idx] = 0;
int64_t* input_ids_now = input_ids + thread_idx * input_ids_stride;
input_ids_now[0] = next_tokens[thread_idx];
// to judge whether block is not enough
int *block_table_now = block_tables + thread_idx * block_num_per_seq;
if (seq_lens_this_time[thread_idx] != 0 && block_table_now[seq_lens_decoder[thread_idx] / block_size] == -1) {
// should be scheduled by server
is_block_step[thread_idx] = true;
seq_lens_this_time[thread_idx]= 0;
stop_flags[thread_idx] = true;
step_seq_lens_decoder[thread_idx] = seq_lens_decoder[thread_idx];
seq_lens_decoder[thread_idx] = 0;
stop_flag_now_int = 1;
}
}
} else
{
stop_flags[thread_idx] = true;
seq_lens_this_time[thread_idx] = 0;
seq_lens_decoder[thread_idx] = 0;
seq_lens_encoder[thread_idx] = 0;
topk_ids[thread_idx] = -1;
stop_flag_now_int = 1;
}
// to judge whether block is not enough
int* block_table_now = block_tables + thread_idx * block_num_per_seq;
if (seq_lens_this_time[thread_idx] != 0 &&
block_table_now[seq_lens_decoder[thread_idx] / block_size] ==
-1) {
// should be scheduled by server
is_block_step[thread_idx] = true;
seq_lens_this_time[thread_idx] = 0;
stop_flags[thread_idx] = true;
step_seq_lens_decoder[thread_idx] = seq_lens_decoder[thread_idx];
seq_lens_decoder[thread_idx] = 0;
stop_flag_now_int = 1;
}
}
} else {
stop_flags[thread_idx] = true;
seq_lens_this_time[thread_idx] = 0;
seq_lens_decoder[thread_idx] = 0;
seq_lens_encoder[thread_idx] = 0;
topk_ids[thread_idx] = -1;
stop_flag_now_int = 1;
}
}
__syncthreads();
int64_t stop_sum = BlockReduce(temp_storage).Sum(stop_flag_now_int);
if (thread_idx == 0) {
not_need_stop[0] = stop_sum < stop_nums[0];
}
}
__syncthreads();
int64_t stop_sum = BlockReduce(temp_storage).Sum(stop_flag_now_int);
if (thread_idx == 0) {
not_need_stop[0] = stop_sum < stop_nums[0];
}
}
void UpdateInputesV1(const paddle::Tensor &stop_flags,
const paddle::Tensor &not_need_stop, // only on cpu
const paddle::Tensor &seq_lens_this_time,
const paddle::Tensor &seq_lens_encoder,
const paddle::Tensor &seq_lens_decoder,
const paddle::Tensor &step_seq_lens_decoder,
const paddle::Tensor &prompt_lens,
const paddle::Tensor &topk_ids,
const paddle::Tensor &input_ids,
const paddle::Tensor &block_tables,
const paddle::Tensor &stop_nums,
const paddle::Tensor &next_tokens,
const paddle::Tensor &is_block_step,
const int block_size) {
void UpdateInputsV1(const paddle::Tensor& stop_flags,
const paddle::Tensor& not_need_stop, // only on cpu
const paddle::Tensor& seq_lens_this_time,
const paddle::Tensor& seq_lens_encoder,
const paddle::Tensor& seq_lens_decoder,
const paddle::Tensor& step_seq_lens_decoder,
const paddle::Tensor& prompt_lens,
const paddle::Tensor& topk_ids,
const paddle::Tensor& input_ids,
const paddle::Tensor& block_tables,
const paddle::Tensor& stop_nums,
const paddle::Tensor& next_tokens,
const paddle::Tensor& is_block_step,
const int block_size) {
#ifdef PADDLE_WITH_CUSTOM_DEVICE
auto dev_ctx = static_cast<const phi::CustomContext*>(paddle::experimental::DeviceContextPool::Instance().Get(input_ids.place()));
auto cu_stream = dev_ctx->stream();
auto dev_ctx = static_cast<const phi::CustomContext*>(
paddle::experimental::DeviceContextPool::Instance().Get(
input_ids.place()));
auto cu_stream = dev_ctx->stream();
#else
auto cu_stream = input_ids.stream();
auto cu_stream = input_ids.stream();
#endif
bool prefill_one_step_stop = false;
if (const char *env_p = std::getenv("PREFILL_NODE_ONE_STEP_STOP_V1")) {
if (env_p[0] == '1') {
prefill_one_step_stop = true;
}
bool prefill_one_step_stop = false;
if (const char* env_p = std::getenv("PREFILL_NODE_ONE_STEP_STOP_V1")) {
if (env_p[0] == '1') {
prefill_one_step_stop = true;
}
const int max_bsz = stop_flags.shape()[0];
const int now_bsz = seq_lens_this_time.shape()[0];
const int input_ids_stride = input_ids.shape()[1];
const int block_num_per_seq = block_tables.shape()[1];
auto not_need_stop_gpu = not_need_stop.copy_to(stop_flags.place(), false);
update_inputs_kernel_v1<1024><<<1, 1024, 0, cu_stream>>>(
const_cast<bool *>(not_need_stop_gpu.data<bool>()),
const_cast<int *>(seq_lens_this_time.data<int>()),
const_cast<int *>(seq_lens_encoder.data<int>()),
const_cast<int *>(seq_lens_decoder.data<int>()),
const_cast<int *>(step_seq_lens_decoder.data<int>()),
const_cast<int64_t *>(prompt_lens.data<int64_t>()),
const_cast<int64_t *>(topk_ids.data<int64_t>()),
const_cast<int64_t *>(input_ids.data<int64_t>()),
const_cast<int *>(block_tables.data<int>()),
stop_nums.data<int64_t>(),
const_cast<bool *>(stop_flags.data<bool>()),
const_cast<bool *>(is_block_step.data<bool>()),
next_tokens.data<int64_t>(),
now_bsz,
max_bsz,
input_ids_stride,
block_num_per_seq,
block_size,
prefill_one_step_stop);
auto not_need_stop_cpu =
not_need_stop_gpu.copy_to(not_need_stop.place(), false);
bool *not_need_stop_data = const_cast<bool *>(not_need_stop.data<bool>());
not_need_stop_data[0] = not_need_stop_cpu.data<bool>()[0];
}
const int max_bsz = stop_flags.shape()[0];
const int now_bsz = seq_lens_this_time.shape()[0];
const int input_ids_stride = input_ids.shape()[1];
const int block_num_per_seq = block_tables.shape()[1];
auto not_need_stop_gpu = not_need_stop.copy_to(stop_flags.place(), false);
update_inputs_kernel_v1<1024><<<1, 1024, 0, cu_stream>>>(
const_cast<bool*>(not_need_stop_gpu.data<bool>()),
const_cast<int*>(seq_lens_this_time.data<int>()),
const_cast<int*>(seq_lens_encoder.data<int>()),
const_cast<int*>(seq_lens_decoder.data<int>()),
const_cast<int*>(step_seq_lens_decoder.data<int>()),
const_cast<int64_t*>(prompt_lens.data<int64_t>()),
const_cast<int64_t*>(topk_ids.data<int64_t>()),
const_cast<int64_t*>(input_ids.data<int64_t>()),
const_cast<int*>(block_tables.data<int>()),
stop_nums.data<int64_t>(),
const_cast<bool*>(stop_flags.data<bool>()),
const_cast<bool*>(is_block_step.data<bool>()),
next_tokens.data<int64_t>(),
now_bsz,
max_bsz,
input_ids_stride,
block_num_per_seq,
block_size,
prefill_one_step_stop);
auto not_need_stop_cpu =
not_need_stop_gpu.copy_to(not_need_stop.place(), false);
bool* not_need_stop_data = const_cast<bool*>(not_need_stop.data<bool>());
not_need_stop_data[0] = not_need_stop_cpu.data<bool>()[0];
}
PD_BUILD_STATIC_OP(update_inputs_v1)
@@ -190,4 +194,4 @@ PD_BUILD_STATIC_OP(update_inputs_v1)
{"stop_flags", "stop_flags_out"},
{"step_seq_lens_decoder", "step_seq_lens_decoder_out"},
{"is_block_step", "is_block_step_out"}})
.SetKernelFn(PD_KERNEL(UpdateInputesV1));
.SetKernelFn(PD_KERNEL(UpdateInputsV1));