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xc-llm-ascend/csrc/torch_binding.cpp
ttanzhiqiang 2498d297ae add custom ascendc kernel vocabparallelembedding (#796) 
This PR add custom ascendc kernel vocabparallelembedding support in
vllm-ascend, related CMakeLists and setuptools is also added in this PR.

pytest -s benchmarks/ops/ben_vocabparallelembedding.py
pytest -s tests/ops/test_vocabparallelembedding.py

---------

Signed-off-by: ttanzhiqiang <389825161@qq.com>
2025-06-12 10:44:33 +08:00

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/*
* Copyright (c) Huawei Technologies Co., Ltd. 2024. 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 <torch/extension.h>
#include <torch/library.h>
#include <torch/version.h>
#include <torch_npu/csrc/core/npu/NPUStream.h>
#include <torch_npu/csrc/framework/OpCommand.h>
#include <torch_npu/csrc/npu/Module.h>
#include <pybind11/pybind11.h>
#include "acl/acl.h"
#include "tiling/platform/platform_ascendc.h"
#include "aclnn/opdev/platform.h"
#include "ops.h"
#include "utils.h"
namespace vllm_ascend {
std::tuple<at::Tensor, at::Tensor> rotary_embedding(at::Tensor &positions, at::Tensor &query, at::Tensor &key,
int64_t head_size, at::Tensor &cos_sin_cache, bool is_neox)
{
int32_t deviceId = 0;
int64_t num_tokens = positions.numel();
int positions_ndim = positions.dim();
TORCH_CHECK(
positions_ndim == 1 || positions_ndim == 2,
"positions must have shape [num_tokens] or [batch_size, seq_len]");
if (positions_ndim == 1) {
TORCH_CHECK(
query.size(0) == positions.size(0) && key.size(0) == positions.size(0),
"query, key and positions must have the same number of tokens");
}
if (positions_ndim == 2) {
TORCH_CHECK(
query.size(0) == positions.size(0) &&
key.size(0) == positions.size(0) &&
query.size(1) == positions.size(1) &&
key.size(1) == positions.size(1),
"query, key and positions must have the same batch_size and seq_len");
}
TORCH_CHECK(head_size % 32 == 0, "rotary_embedding: headSize should be divisible by 32");
int query_hidden_size = query.numel() / num_tokens;
int key_hidden_size = key.numel() / num_tokens;
TORCH_CHECK(query_hidden_size % head_size == 0);
TORCH_CHECK(key_hidden_size % head_size == 0);
TORCH_CHECK(is_neox == true, "rotary_embedding: neox=false is not supported as custom kernel in vllm-ascend");
// Make sure query and key have consistent number of heads
int num_heads = query_hidden_size / head_size;
int num_kv_heads = key_hidden_size / head_size;
TORCH_CHECK(num_heads % num_kv_heads == 0);
at::Tensor query_dst = at::empty({num_tokens, num_heads, head_size}, query.options());
at::Tensor key_dst = at::empty({num_tokens, num_kv_heads, head_size}, key.options());
int rot_dim = cos_sin_cache.size(1);
int seq_dim_idx = positions_ndim - 1;
int64_t *position_ids_ptr = positions.data_ptr<int64_t>();
void *query_dst_ptr = query_dst.data_ptr();
void *key_dst_ptr = key_dst.data_ptr();
void *query_ptr = query.data_ptr();
void *key_ptr = key.data_ptr();
void *cos_sin_cache_ptr = cos_sin_cache.data_ptr();
int64_t query_stride = query.stride(seq_dim_idx);
int64_t key_stride = key.stride(seq_dim_idx);
int64_t dst_query_stride = query_dst.stride(0);
int64_t dst_key_stride = key_dst.stride(0);
at::ScalarType scalar_type = query.scalar_type();
aclrtStream stream = c10_npu::getCurrentNPUStream().stream();
at_npu::native::OpCommand cmd;
cmd.Name("rotary_embedding");
cmd.SetCustomHandler([scalar_type, is_neox, num_tokens, stream, position_ids_ptr, query_dst_ptr, key_dst_ptr,
query_ptr, key_ptr, cos_sin_cache_ptr, rot_dim, query_stride, key_stride,
dst_query_stride, dst_key_stride, num_heads, num_kv_heads, head_size]() -> int {
auto dtype_num = get_dtype_from_torch(scalar_type);
fe::PlatFormInfos platform_infos;
int device_id = 0;
fe::PlatformInfoManager::GeInstance().GetRuntimePlatformInfosByDevice(device_id, platform_infos);
uint32_t aivNum = platform_infos.GetCoreNumByType("aiv");
uint32_t loop_cnt = (num_tokens + aivNum - 1) / aivNum;
rotary_embedding_impl(dtype_num, is_neox, stream, position_ids_ptr, query_dst_ptr, key_dst_ptr, query_ptr,
key_ptr, cos_sin_cache_ptr, rot_dim, query_stride, key_stride, dst_query_stride,
dst_key_stride, num_heads, num_kv_heads, head_size, num_tokens, loop_cnt, aivNum);
return 0;
});
cmd.Run();
return {query_dst, key_dst};
}
std::tuple<at::Tensor, at::Tensor> get_masked_input_and_mask(
at::Tensor &input,
const int64_t org_vocab_start_index,
const int64_t org_vocab_end_index,
const int64_t num_org_vocab_padding,
const int64_t added_vocab_start_index,
const int64_t added_vocab_end_index)
/*
https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/vocab_parallel_embedding.py#L161-L198
Embedding parallelized in the vocabulary dimension.
Adapted from torch.nn.Embedding, note that we pad the vocabulary size to
make sure it is divisible by the number of model parallel GPUs.
In order to support various loading methods, we ensure that LoRA-added
embeddings are always at the end of TP-sharded tensors. In other words,
we shard base embeddings and LoRA embeddings separately (both padded),
and place them in the same tensor.
In this example, we will have the original vocab size = 1010,
added vocab size = 16 and padding to 64. Therefore, the total
vocab size with padding will be 1088 (because we first pad 1010 to
1024, add 16, and then pad to 1088).
Therefore, the tensor format looks like the following:
TP1, rank 0 (no sharding):
|< --------BASE-------- >|< -BASE PADDING-- >|< -----LORA------ >|< -LORA PADDING-- >|
corresponding token_id: | 0 | 1 | ... | 1009 | -1 | ... | -1 | 1010 | ... | 1015 | -1 | ... | -1 |
index: | 0 | 1 | ... | 1009 | 1010 | ... | 1023 | 1024 | ... | 1039 | 1040 | ... | 1087 |
TP2, rank 0:
|< --------------------BASE--------------------- >|< -----LORA------ >|< -LORA PADDING- >|
corresponding token_id: | 0 | 1 | 2 | ... | 497 | 498 | ... | 511 | 1000 | ... | 1015 | -1 | ... | -1 |
index: | 0 | 1 | 2 | ... | 497 | 498 | ... | 511 | 512 | ... | 527 | 520 | ... | 543 |
TP2, rank 1:
|< -----------BASE----------- >|< -BASE PADDING- >|< -----------LORA PADDING----------- >|
corresponding token_id: | 512 | 513 | 514 | ... | 1009 | -1 | ... | -1 | -1 | ... | -1 | -1 | ... | -1 |
index: | 0 | 1 | 2 | ... | 497 | 498 | ... | 511 | 512 | ... | 519 | 520 | ... | 543 |
Parameters:
org_vocab_start_index //base embeddings start
org_vocab_end_index //base embeddings end
num_org_vocab_padding //base embeddings padding
added_vocab_start_index //LoRA embeddings start
added_vocab_end_index //LoRA embeddings end
*/
{
// Input validation
TORCH_CHECK(input.dim() >= 1, "input must have at least 1 dimension");
TORCH_CHECK(org_vocab_start_index >= 0, "org_vocab_start_index must be non-negative");
TORCH_CHECK(org_vocab_end_index >= org_vocab_start_index, "org_vocab_end_index must be greater than org_vocab_start_index");
TORCH_CHECK(num_org_vocab_padding >= 0, "num_org_vocab_padding must be non-negative");
TORCH_CHECK(added_vocab_start_index >= org_vocab_end_index, "added_vocab_start_index must be greater than org_vocab_end_index");
TORCH_CHECK(added_vocab_end_index >= added_vocab_start_index, "added_vocab_end_index must be greater than added_vocab_start_index");
// Get total number of elements
int64_t size = input.numel();
// Create output tensors
at::Tensor masked_input = at::empty_like(input);
at::Tensor mask = at::empty_like(input).to(at::kBool);
// Get data pointers
void *input_ptr = input.data_ptr();
void *masked_input_ptr = masked_input.data_ptr();
void *mask_ptr = mask.data_ptr();
// Get current stream
aclrtStream stream = c10_npu::getCurrentNPUStream().stream();
// Get scalar type
at::ScalarType scalar_type = input.scalar_type();
// Create and configure OpCommand
at_npu::native::OpCommand cmd;
cmd.Name("get_masked_input_and_mask");
cmd.SetCustomHandler([scalar_type, size, stream,
input_ptr, masked_input_ptr, mask_ptr,
org_vocab_start_index, org_vocab_end_index,
num_org_vocab_padding, added_vocab_start_index,
added_vocab_end_index]() -> int {
// Get platform info
fe::PlatFormInfos platform_infos;
int device_id = 0;
fe::PlatformInfoManager::GeInstance().GetRuntimePlatformInfosByDevice(device_id, platform_infos);
uint32_t aivNum = platform_infos.GetCoreNumByType("aiv");
uint32_t loop_cnt = (size + aivNum - 1) / aivNum;
// Call implementation
get_masked_input_and_mask_impl(
stream,
input_ptr,
masked_input_ptr,
mask_ptr,
org_vocab_start_index,
org_vocab_end_index,
num_org_vocab_padding,
added_vocab_start_index,
added_vocab_end_index,
size,
loop_cnt,
aivNum);
return 0;
});
cmd.Run();
return {masked_input, mask};
}
void verify_tensor(std::string const& name, at::Tensor const& t,
int64_t const size_0, int64_t const size_1,
c10::ScalarType const type) {
bool size_0_cond = true;
if (size_0 != -1) {
size_0_cond = t.size(0) == size_0;
}
bool size_1_cond = true;
if (size_1 != -1) {
size_1_cond = t.size(1) == size_1;
}
bool is_contiguous = t.is_contiguous();
bool same_type = t.dtype() == type;
bool pass = size_0_cond && size_1_cond && is_contiguous && same_type;
if (!pass) {
TORCH_CHECK(false, "tensor: name = ", name, ", shape = ", t.sizes(),
" is_cont = ", t.is_contiguous(), ", type = ", t.dtype(),
" is not as expected: shape = [", size_0, ", ", size_1,
"], type = ", type);
}
}
void advance_step_flashattn_ascendc(
int64_t num_seqs, int64_t num_queries, int64_t block_size,
at::Tensor& input_tokens,
at::Tensor& sampled_token_ids,
at::Tensor& input_positions,
at::Tensor& seq_lens,
at::Tensor& slot_mapping,
at::Tensor& block_tables
){
// Verify all tensors
verify_tensor("input_tokens", input_tokens, num_seqs, -1, at::kLong);
verify_tensor("sampled_token_ids", sampled_token_ids, num_queries, 1,at::kLong);
verify_tensor("input_positions", input_positions, num_seqs, -1, at::kLong);
verify_tensor("seq_lens", seq_lens, num_seqs, -1, at::kInt);
verify_tensor("slot_mapping", slot_mapping, num_seqs, -1, at::kInt);
verify_tensor("block_tables", block_tables, num_seqs, -1, at::kInt);
int64_t* input_tokens_ptr = input_tokens.data_ptr<int64_t>();
int64_t* sampled_token_ids_ptr = sampled_token_ids.data_ptr<int64_t>();
int64_t* input_positions_ptr = input_positions.data_ptr<int64_t>();
int32_t* seq_lens_ptr = seq_lens.data_ptr<int32_t>();
int32_t* slot_mapping_ptr = slot_mapping.data_ptr<int32_t>();
int32_t* block_tables_ptr = block_tables.data_ptr<int32_t>();
int32_t device_id;
aclrtGetDevice(&device_id);
auto npu_stream = c10_npu::getCurrentNPUStream(device_id);
aclrtStream stream = npu_stream.stream();
// aclrtStream stream = c10_npu::getCurrentNPUStream().stream();
at_npu::native::OpCommand cmd;
cmd.Name("advance_step_flashattn_ascendc");
cmd.SetCustomHandler([stream, num_seqs, num_queries,
block_size, input_tokens_ptr, sampled_token_ids_ptr,
input_positions_ptr, seq_lens_ptr, slot_mapping_ptr,
block_tables_ptr, block_tables]() -> int {
launch_advance_step_flashattn(stream,
num_seqs,
num_queries,
block_size,
input_tokens_ptr,
sampled_token_ids_ptr,
input_positions_ptr,
seq_lens_ptr,
slot_mapping_ptr,
block_tables_ptr,
block_tables.stride(0));
return 0;
});
cmd.Run();
return ;
}
} // namespace vllm_ascend
TORCH_LIBRARY_EXPAND(_C, ops)
{
// vLLM-Ascend custom ops
ops.def("weak_ref_tensor(Tensor input) -> Tensor");
ops.impl("weak_ref_tensor", torch::kPrivateUse1, &vllm_ascend::weak_ref_tensor);
// Rotary embedding
// Apply GPT-NeoX style rotary embedding to query and key.
ops.def(
"rotary_embedding(Tensor positions, Tensor! query,"
" Tensor! key, int head_size,"
" Tensor cos_sin_cache, bool is_neox) -> (Tensor query, Tensor key)");
ops.impl("rotary_embedding", torch::kPrivateUse1, &vllm_ascend::rotary_embedding);
ops.def(
"get_masked_input_and_mask(Tensor input, "
" int org_vocab_start_index, "
" int org_vocab_end_index, "
" int num_org_vocab_padding, "
" int added_vocab_start_index, "
" int added_vocab_end_index) -> (Tensor masked_input, Tensor mask)");
ops.impl("get_masked_input_and_mask", torch::kPrivateUse1, &vllm_ascend::get_masked_input_and_mask);
ops.def(
"advance_step_flashattn_ascendc(int num_seqs, int num_queries, int block_size,"
" Tensor! input_tokens, Tensor! sampled_token_ids, Tensor! input_positions,"
" Tensor! seq_lens, Tensor! slot_mapping, Tensor! block_tables) -> ()");
ops.impl("advance_step_flashattn_ascendc", torch::kPrivateUse1, &vllm_ascend::advance_step_flashattn_ascendc);
}
REGISTER_EXTENSION(_C)
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