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xc-llm-ascend/csrc/torch_binding.cpp

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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/torch.h>
#include <torch_npu/csrc/core/npu/NPUStream.h>
#include <torch_npu/csrc/framework/OpCommand.h>
#include "torch_npu/csrc/core/npu/NPUGuard.h"
#include <torch_npu/csrc/npu/Module.h>
#include "acl/acl.h"
#include "acl/acl_rt.h"
#include "ops.h"
#include "utils.h"
#include "mla_preprocess/op_host/mla_preprocess.h"
#include "batch_matmul_transpose/op_host/batch_matmul_transpose.h"
#include "aclnn_torch_adapter/op_api_common.h"
#include <c10/core/Device.h>
#include <c10/util/Exception.h>
#include <c10/util/Logging.h>
namespace vllm_ascend {
const int64_t INT4_NUMS_IN_INT32 = 8;
void swap_blocks_impl(torch::Tensor& src, torch::Tensor& dst,
const torch::Tensor& block_mapping, aclrtStream stream)
{
torch::Device src_device = src.device();
torch::Device dst_device = dst.device();
aclrtMemcpyKind memcpy_type;
if ((!src_device.is_cpu()) && (!dst_device.is_cpu())) {
TORCH_CHECK(src_device.index() == dst_device.index(),
"src and dst must be on the same npu");
memcpy_type = ACL_MEMCPY_DEVICE_TO_DEVICE;
} else if ((!src_device.is_cpu()) && dst_device.is_cpu()) {
memcpy_type = ACL_MEMCPY_DEVICE_TO_HOST;
} else if (src_device.is_cpu() && (!dst_device.is_cpu())) {
memcpy_type = ACL_MEMCPY_HOST_TO_DEVICE;
} else {
TORCH_CHECK(false, "Invalid device combination, src tensor device: ", src_device, ", dst tensor device: ", dst_device);
}
TORCH_CHECK(block_mapping.device().is_cpu(), "block_mapping must be on CPU");
char* src_ptr = static_cast<char*>(src.data_ptr());
char* dst_ptr = static_cast<char*>(dst.data_ptr());
const int64_t block_size_in_bytes = src.element_size() * src.stride(0);
const int64_t num_blocks = block_mapping.size(0);
const int64_t max_src_block = src.size(0);
const int64_t max_dst_block = dst.size(0);
for (size_t i = 0; i < num_blocks; i++) {
int64_t src_block_number = block_mapping[i][0].item<int64_t>();
int64_t dst_block_number = block_mapping[i][1].item<int64_t>();
TORCH_CHECK(src_block_number >= 0 && src_block_number <= max_src_block,
"src block index ", src_block_number, " out of range (max: ", max_src_block, ")");
TORCH_CHECK(dst_block_number >= 0 && dst_block_number <= max_dst_block,
"dst block index ", dst_block_number, " out of range (max: ", max_dst_block, ")");
int64_t src_offset = src_block_number * block_size_in_bytes;
int64_t dst_offset = dst_block_number * block_size_in_bytes;
aclrtMemcpyAsync(dst_ptr + dst_offset, block_size_in_bytes,
src_ptr + src_offset, block_size_in_bytes,
memcpy_type, stream);
}
}
void swap_blocks(torch::Tensor &x, torch::Tensor &y, const torch::Tensor &z)
{
const c10_npu::OptionalNPUGuard npuGuard(
(!x.device().is_cpu()) ? x.device() : y.device()
);
aclrtStream stream = c10_npu::getCurrentNPUStream().stream();
swap_blocks_impl(x, y, z, stream);
return;
}
AscendType get_dtype_from_torch(at::ScalarType scalarType)
{
if (scalarType == at::ScalarType::Float) {
return AscendType::FP32;
} else if (scalarType == at::ScalarType::BFloat16) {
return AscendType::BF16;
} else {
return AscendType::FP16;
}
}
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);
int device_id = 0;
int64_t aiv_num = 0;
TORCH_CHECK(aclGetDeviceCapability(device_id, ACL_DEVICE_INFO_VECTOR_CORE_NUM, &aiv_num) == ACL_SUCCESS);
uint32_t loop_cnt = (num_tokens + aiv_num - 1) / aiv_num;
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, aiv_num);
return 0;
});
cmd.Run();
return {query_dst, key_dst};
}
std::tuple<at::Tensor &, at::Tensor &, at::Tensor &, at::Tensor &, at::Tensor &> mla_preprocess(
const at::Tensor &hiddenState, const at::Tensor &wdqkv,
const at::Tensor &descale0, const at::Tensor &gamma1, const at::Tensor &beta1, const at::Tensor &wuq,
const at::Tensor &descale1, const at::Tensor &gamma2, const at::Tensor &cos, const at::Tensor &sin,
const at::Tensor &wuk, const at::Tensor &kv_cache, const at::Tensor &kv_cache_rope, const at::Tensor &slotmapping,
const at::Tensor &quant_scale0, const at::Tensor &quant_offset0, const at::Tensor &bias0,
const at::Tensor &quant_scale1, const at::Tensor &quant_offset1, const at::Tensor &bias1,
const c10::optional<at::Tensor> &ctkv_scale, const c10::optional<at::Tensor> &q_nope_scale,
c10::optional<c10::string_view> cache_mode, c10::optional<c10::string_view> quant_mode, c10::optional<bool> enable_inner_out, at::Tensor &q_out0,
at::Tensor &kv_cache_out0, at::Tensor &q_out1, at::Tensor &kv_cache_out1, at::Tensor &inner_out)
{
at::Tensor CtkvScale =
ctkv_scale.has_value()
? ctkv_scale.value()
: at::empty({1}, at::TensorOptions().dtype(at::kHalf).device(hiddenState.options().device()));
at::Tensor QnopeScale =
q_nope_scale.has_value()
? q_nope_scale.value()
: at::empty({1}, at::TensorOptions().dtype(at::kHalf).device(hiddenState.options().device()));
bool enableInnerOut =
enable_inner_out.has_value()
? enable_inner_out.value()
: false;
auto [workspace_tensor, tiling, block_dim] = mlapo::mla_preprocess_tiling(
hiddenState,
wuk,
cache_mode,
quant_mode,
enableInnerOut
);
void *hidden_state_ptr = hiddenState.data_ptr();
void *quant_scale0_ptr = quant_scale0.data_ptr();
void *quant_offset0_ptr = quant_offset0.data_ptr();
void *wdqkv_ptr = wdqkv.data_ptr();
void *bias0_ptr = bias0.data_ptr();
void *gamma1_ptr = gamma1.data_ptr();
void *beta1_ptr = beta1.data_ptr();
void *quant_scale1_ptr = quant_scale1.data_ptr();
void *quant_offset1_ptr = quant_offset1.data_ptr();
void *gamma2_ptr = gamma2.data_ptr();
void *sin_ptr = sin.data_ptr();
void *cos_ptr = cos.data_ptr();
void *kv_cache_ptr = kv_cache.data_ptr();
void *slotmapping_ptr = slotmapping.data_ptr();
void *wuq_ptr = wuq.data_ptr();
void *bias1_ptr = bias1.data_ptr();
void *wuk_ptr = wuk.data_ptr();
void *descale0_ptr = descale0.data_ptr();
void *descale1_ptr = descale1.data_ptr();
void *ctkv_scale_ptr = CtkvScale.data_ptr();
void *qnope_scale_ptr = QnopeScale.data_ptr();
void *q_out0_ptr = q_out0.data_ptr();
void *kv_cache_out0_ptr = kv_cache_out0.data_ptr();
void *q_out1_ptr = q_out1.data_ptr();
void *kv_cache_out1_ptr = kv_cache_out1.data_ptr();
void *inner_out_ptr = inner_out.data_ptr();
void *workspace_ptr = workspace_tensor.data_ptr();
void *tiling_ptr = tiling.data_ptr();
aclrtStream stream = c10_npu::getCurrentNPUStream().stream();
at_npu::native::OpCommand cmd;
cmd.Name("mla_preprocess");
cmd.SetCustomHandler([stream, hidden_state_ptr, quant_scale0_ptr, quant_offset0_ptr, wdqkv_ptr, bias0_ptr,
gamma1_ptr, beta1_ptr, quant_scale1_ptr, quant_offset1_ptr, gamma2_ptr, sin_ptr, cos_ptr,
kv_cache_ptr, slotmapping_ptr, wuq_ptr, bias1_ptr, wuk_ptr, descale0_ptr, descale1_ptr, ctkv_scale_ptr,
qnope_scale_ptr, q_out0_ptr, kv_cache_out0_ptr, q_out1_ptr, kv_cache_out1_ptr, inner_out_ptr, workspace_ptr,
tiling_ptr, block_dim]() -> int {
mla_preprocess_impl(stream, hidden_state_ptr, quant_scale0_ptr, quant_offset0_ptr, wdqkv_ptr, bias0_ptr,
gamma1_ptr, beta1_ptr, quant_scale1_ptr, quant_offset1_ptr, gamma2_ptr, sin_ptr, cos_ptr, sin_ptr, cos_ptr,
kv_cache_ptr, slotmapping_ptr, wuq_ptr, bias1_ptr, wuk_ptr, descale0_ptr, descale1_ptr, ctkv_scale_ptr,
qnope_scale_ptr, q_out0_ptr, kv_cache_out0_ptr, q_out1_ptr, kv_cache_out1_ptr, inner_out_ptr, workspace_ptr,
tiling_ptr, block_dim);
return 0;
});
cmd.Run();
return std::forward_as_tuple(q_out0, kv_cache_out0, q_out1, kv_cache_out1, inner_out);
}
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 {
int device_id = 0;
int64_t aiv_num = 0;
TORCH_CHECK(aclGetDeviceCapability(device_id, ACL_DEVICE_INFO_VECTOR_CORE_NUM, &aiv_num) == ACL_SUCCESS);
uint32_t loop_cnt = (size + aiv_num - 1) / aiv_num;
// 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,
aiv_num);
return 0;
});
cmd.Run();
return {masked_input, mask};
}
void bgmv_shrink(at::Tensor &x, at::Tensor &weight, at::Tensor &indices, at::Tensor &y, double scale)
{
at::ScalarType scalar_type = x.scalar_type();
TORCH_CHECK(scalar_type == torch::kHalf || scalar_type == torch::kBFloat16, "only support half and bf16");
TORCH_CHECK(x.dim() == 2, "x should be [batch_size, hidden_in]");
TORCH_CHECK(weight.dim() == 3 || weight.dim() == 4,
"weight should be [num_loras, hidden_out, hidden_in] or [num_loras, 1, hidden_out, hidden_in]");
TORCH_CHECK(y.dim() == 2, "y should be [batch_size, hidden_out]");
TORCH_CHECK(indices.dim() == 1, "indices should be [batch_size]");
TORCH_CHECK(x.size(0) == y.size(0) && x.size(0) == indices.size(0),
"the first dimension of x, y, indices should be same");
TORCH_CHECK(x.size(1) > y.size(1), "hidden in should be greater than hidden out");
void* x_ptr = x.data_ptr();
void* weight_ptr = weight.data_ptr();
void* indices_ptr = indices.data_ptr();
int indices_size = indices.size(0);
void* y_ptr = y.data_ptr();
int batch_size = x.size(0);
int input_hidden_token = x.size(1);
uint32_t lora_rank = y.size(1);
float scale_f = static_cast<float>(scale);
aclrtStream stream = c10_npu::getCurrentNPUStream().stream();
at_npu::native::OpCommand cmd;
cmd.Name("bgmv_shrink");
cmd.SetCustomHandler([scalar_type, stream, x_ptr, weight_ptr, indices_ptr, indices_size, y_ptr, batch_size, input_hidden_token,
lora_rank, scale_f]() -> int {
auto dtype = get_dtype_from_torch(scalar_type);
int device_id = 0;
int64_t aiv_num = 0;
TORCH_CHECK(aclGetDeviceCapability(device_id, ACL_DEVICE_INFO_VECTOR_CORE_NUM, &aiv_num) == ACL_SUCCESS);
int num_tokens_per_core = (batch_size + aiv_num - 1) / aiv_num;
TORCH_CHECK("num_tokens_per_core != 0", "num_tokens_per_core should not be 0");
bgmv_shrink_impl(dtype, stream, x_ptr, weight_ptr, indices_ptr, indices_size, y_ptr, batch_size, num_tokens_per_core,
input_hidden_token, lora_rank, scale_f);
return 0;
});
cmd.Run();
return;
}
at::Tensor bgmv_expand(at::Tensor &x, at::Tensor &weight, at::Tensor &indices, at::Tensor &y,
int64_t slice_offset, int64_t slice_size)
{
at::ScalarType scalar_type = y.scalar_type();
TORCH_CHECK(scalar_type == torch::kHalf || scalar_type == torch::kBFloat16, "only support half and bf16");
TORCH_CHECK(x.dim() == 2, "x should be [batch_size, hidden_in]");
TORCH_CHECK(weight.dim() == 3 || weight.dim() == 4,
"weight should be [num_loras, hidden_out, hidden_in] or [num_loras, 1, hidden_out, hidden_in]");
TORCH_CHECK(y.dim() == 2, "y should be [batch_size, hidden_out]");
TORCH_CHECK(indices.dim() == 1, "indices should be [batch_size]");
TORCH_CHECK(x.size(0) == y.size(0) && x.size(0) == indices.size(0),
"the first dimension of x, y, indices should be same");
TORCH_CHECK(x.size(1) <= slice_size, "hidden in should be smaller than hidden out");
TORCH_CHECK(slice_offset >= 0, "slice offset should be no smaller than 0");
TORCH_CHECK((slice_size + slice_offset) <= y.size(1),
"slice_size + slice_offset should be smaller than the second dimension of y")
at::Tensor y_out = y;
void* x_ptr = x.data_ptr();
void* weight_ptr = weight.data_ptr();
void* indices_ptr = indices.data_ptr();
int indices_size = indices.size(0);
void* y_ptr = y.data_ptr();
void* y_out_ptr = y_out.data_ptr();
int batch_size = x.size(0);
int lora_rank = x.size(1);
int output_full_dim = y.size(1);
aclrtStream stream = c10_npu::getCurrentNPUStream().stream();
at_npu::native::OpCommand cmd;
cmd.Name("bgmv_expand");
cmd.SetCustomHandler([scalar_type, stream, x_ptr, weight_ptr, indices_ptr, indices_size, y_ptr, y_out_ptr, batch_size, lora_rank,
slice_offset, slice_size, output_full_dim]() -> int {
auto dtype = get_dtype_from_torch(scalar_type);
int device_id = 0;
int64_t aiv_num = 0;
TORCH_CHECK(aclGetDeviceCapability(device_id, ACL_DEVICE_INFO_VECTOR_CORE_NUM, &aiv_num) == ACL_SUCCESS);
int num_tokens_per_core = (batch_size + aiv_num - 1) / aiv_num;
TORCH_CHECK("num_tokens_per_core != 0", "num_tokens_per_core should not be 0");
bgmv_expand_impl(dtype, stream, x_ptr, weight_ptr, indices_ptr, indices_size, y_ptr, y_out_ptr, batch_size,
num_tokens_per_core, lora_rank, slice_size, slice_offset, output_full_dim);
return 0;
});
cmd.Run();
return y_out;
}
void sgmv_shrink(at::Tensor &x, at::Tensor &weight, at::Tensor &lora_indices, at::Tensor &seq_len,
at::Tensor &y, double scale)
{
at::ScalarType scalar_type = x.scalar_type();
TORCH_CHECK(scalar_type == torch::kHalf || scalar_type == torch::kBFloat16, "only support half and bf16");
TORCH_CHECK(x.dim() == 2, "x should be [batch_size, hidden_in]");
TORCH_CHECK(weight.dim() == 3 || weight.dim() == 4,
"weight should be [num_loras, hidden_out, hidden_in] or [num_loras, 1, hidden_out, hidden_in]");
TORCH_CHECK(y.dim() == 2, "y should be [batch_size, hidden_out]");
TORCH_CHECK(x.size(1) > y.size(1), "hidden in should be greater than hidden out");
void* x_ptr = x.data_ptr();
void* weight_ptr = weight.data_ptr();
void* lora_indices_ptr = lora_indices.data_ptr();
void* seq_len_ptr = seq_len.data_ptr();
int lora_indices_size = lora_indices.size(0);
int seq_len_size = seq_len.size(0);
void* y_ptr = y.data_ptr();
int batch_size = x.size(0);
int input_hidden_token = x.size(1);
uint32_t lora_rank = y.size(1);
float scale_f = static_cast<float>(scale);
aclrtStream stream = c10_npu::getCurrentNPUStream().stream();
at_npu::native::OpCommand cmd;
cmd.Name("sgmv_shrink");
cmd.SetCustomHandler([scalar_type, stream, x_ptr, weight_ptr, lora_indices_ptr, lora_indices_size,
seq_len_ptr, seq_len_size, y_ptr,
batch_size, input_hidden_token, lora_rank, scale_f]() -> int {
auto dtype = get_dtype_from_torch(scalar_type);
int device_id = 0;
int64_t aiv_num = 0;
TORCH_CHECK(aclGetDeviceCapability(device_id, ACL_DEVICE_INFO_VECTOR_CORE_NUM, &aiv_num) == ACL_SUCCESS);
int num_tokens_per_core = (batch_size + aiv_num - 1) / aiv_num;
TORCH_CHECK("num_tokens_per_core != 0", "num_tokens_per_core should not be 0");
sgmv_shrink_impl(dtype, stream, x_ptr, weight_ptr, lora_indices_ptr, lora_indices_size, seq_len_ptr, seq_len_size,
y_ptr, batch_size,
num_tokens_per_core, input_hidden_token, lora_rank, scale_f);
return 0;
});
cmd.Run();
return;
}
at::Tensor sgmv_expand(at::Tensor &x, at::Tensor &weight, at::Tensor &lora_indices, at::Tensor &seq_len,
at::Tensor &y, int64_t slice_offset, int64_t slice_size)
{
at::ScalarType scalar_type = y.scalar_type();
TORCH_CHECK(scalar_type == torch::kHalf || scalar_type == torch::kBFloat16, "only support half and bf16");
TORCH_CHECK(x.dim() == 2, "x should be [batch_size, hidden_in]");
TORCH_CHECK(weight.dim() == 3 || weight.dim() == 4,
"weight should be [num_loras, hidden_out, hidden_in] or [num_loras, 1, hidden_out, hidden_in]");
TORCH_CHECK(y.dim() == 2, "y should be [batch_size, hidden_out]");
TORCH_CHECK(x.size(1) <= slice_size, "hidden in should be smaller than hidden out");
TORCH_CHECK(slice_offset >= 0, "slice offset should be no smaller than 0");
TORCH_CHECK((slice_size + slice_offset) <= y.size(1),
"slice_size + slice_offset should be smaller than the second dimension of y")
at::Tensor y_out = y;
void* x_ptr = x.data_ptr();
void* weight_ptr = weight.data_ptr();
void* lora_indices_ptr = lora_indices.data_ptr();
void* seq_len_ptr = seq_len.data_ptr();
int lora_indices_size = lora_indices.size(0);
int seq_len_size = seq_len.size(0);
void* y_ptr = y.data_ptr();
void* y_out_ptr = y_out.data_ptr();
int batch_size = x.size(0);
int lora_rank = x.size(1);
int output_full_dim = y.size(1);
aclrtStream stream = c10_npu::getCurrentNPUStream().stream();
at_npu::native::OpCommand cmd;
cmd.Name("sgmv_expand");
cmd.SetCustomHandler([scalar_type, stream, x_ptr, weight_ptr, lora_indices_ptr, lora_indices_size, seq_len_ptr, seq_len_size, y_ptr, y_out_ptr,
batch_size, lora_rank, slice_offset, slice_size, output_full_dim]() -> int {
auto dtype = get_dtype_from_torch(scalar_type);
int device_id = 0;
int64_t aiv_num = 0;
TORCH_CHECK(aclGetDeviceCapability(device_id, ACL_DEVICE_INFO_VECTOR_CORE_NUM, &aiv_num) == ACL_SUCCESS);
int num_tokens_per_core = (batch_size + aiv_num - 1) / aiv_num;
TORCH_CHECK("num_tokens_per_core != 0", "num_tokens_per_core should not be 0");
sgmv_expand_impl(dtype, stream, x_ptr, weight_ptr, lora_indices_ptr, lora_indices_size, seq_len_ptr, seq_len_size, y_ptr, y_out_ptr,
batch_size, num_tokens_per_core, lora_rank, slice_size, slice_offset, output_full_dim);
return 0;
});
cmd.Run();
return y_out;
}
std::tuple<at::Tensor, at::Tensor, at::Tensor> grouped_matmul_swiglu_quant(
const at::Tensor &x, const at::Tensor &weight, const at::Tensor &weight_scale, const at::Tensor &x_scale,
const at::Tensor &group_list, const c10::optional<at::Tensor> &bias, const c10::optional<at::Tensor> &offset)
{
int m = x.sizes()[0];
int n = weight.sizes()[2];
bool is_a8w4 = x.dtype() == at::kChar && weight.dtype() == at::kInt;
if (is_a8w4) {
n *= INT4_NUMS_IN_INT32;
}
at::Tensor output = at::empty({m, n/2}, x.options().dtype(c10::ScalarType::Char));
at::Tensor output_scale = at::empty({m}, x.options().dtype(c10::ScalarType::Float));
at::Tensor output_offset = at::empty({}, x.options().dtype(c10::ScalarType::Float));
EXEC_NPU_CMD(
aclnnGroupedMatmulSwigluQuantWeightNZ,
x,
weight,
bias,
offset,
weight_scale,
x_scale,
group_list,
output,
output_scale,
output_offset);
return std::tuple<at::Tensor, at::Tensor, at::Tensor>(output, output_scale, output_offset);
}
std::tuple<at::Tensor, at::Tensor, at::Tensor> grouped_matmul_swiglu_quant_weight_nz_tensor_list(
const at::Tensor & x,
const at::TensorList & weight,
const at::TensorList & weight_scale,
const at::Tensor & x_scale,
const at::Tensor & group_list,
const c10::optional<at::Tensor> & bias,
const c10::optional<at::Tensor> & offset)
{
auto x_size = x.sizes();
int n = weight[0].sizes()[1];
int m = x_size[0];
int k = x_size[1];
at::Tensor output = at::empty({m, n/2}, x.options().dtype(at::kChar));
at::Tensor output_scale = at::empty({m}, x.options().dtype(at::kFloat));
at::Tensor output_offset = at::empty({m}, x.options().dtype(at::kFloat));
EXEC_NPU_CMD(
aclnnGroupedMatmulSwigluQuantWeightNzTensorList,
x,
weight,
bias,
offset,
weight_scale,
x_scale,
group_list,
output,
output_scale,
output_offset);
return std::tuple<at::Tensor, at::Tensor, at::Tensor>(output, output_scale, output_offset);
}
std::tuple<at::Tensor, at::Tensor> dispatch_gmm_combine_decode(
const at::Tensor &x,
const at::Tensor &expert_ids,
const at::Tensor &gmm1_permuted_weight,
const at::Tensor &gmm1_permuted_weight_scale,
const at::Tensor &gmm2_weight,
const at::Tensor &gmm2_weight_scale,
const c10::optional<at::Tensor> &expert_smooth_scales,
const c10::optional<at::Tensor> &expert_scales,
c10::string_view group_ep,
int64_t ep_rank_size,
int64_t ep_rank_id,
int64_t moe_expert_num,
int64_t shared_expert_num,
int64_t shared_expert_rank_num,
int64_t quant_mode,
int64_t global_bs)
{
auto x_shape = x.sizes();
int bs = x_shape[0];
int h = x_shape[1];
at::Tensor output = at::empty({bs, h}, x.options());
bool is_shared_expert = (ep_rank_id < shared_expert_rank_num);
int64_t num_local_experts = is_shared_expert ? 1 : moe_expert_num / (ep_rank_size - shared_expert_rank_num);
at::Tensor ep_recv_count = at::empty({num_local_experts * ep_rank_size}, expert_ids.options());
vector<char> group_ep_chrs(group_ep.begin(), group_ep.end());
group_ep_chrs.push_back('\0');
char *group_ep_ptr = &group_ep_chrs[0];
EXEC_NPU_CMD(
// op api
aclnnDispatchGmmCombineDecode,
// input tensors
x,
expert_ids,
gmm1_permuted_weight,
gmm1_permuted_weight_scale,
gmm2_weight,
gmm2_weight_scale,
expert_smooth_scales,
expert_scales,
//input attrs
group_ep_ptr,
ep_rank_size,
ep_rank_id,
moe_expert_num,
shared_expert_num,
shared_expert_rank_num,
quant_mode,
global_bs,
// output tensors
output,
ep_recv_count);
return {output, ep_recv_count};
}
void batch_matmul_transpose(const at::Tensor &tensor_a, const at::Tensor &tensor_b, at::Tensor &tensor_c,
c10::optional<c10::string_view> format_mode,
c10::optional<c10::string_view> quant_mode)
{
auto [tiling_tensor, block_dim] = bmm_trans::batch_matmul_transpose_tiling(
tensor_a,
tensor_b,
tensor_c,
format_mode,
quant_mode
);
void *gm_a = tensor_a.data_ptr();
void *gm_b = tensor_b.data_ptr();
void *gm_c = tensor_c.data_ptr();
void *gm_tiling_data = tiling_tensor.data_ptr();
aclrtStream stream = c10_npu::getCurrentNPUStream().stream();
at_npu::native::OpCommand cmd;
cmd.Name("batch_matmul_transpose");
cmd.SetCustomHandler([stream, gm_a, gm_b, gm_c, gm_tiling_data,
block_dim]() -> int {
batch_matmul_transpose_impl(stream, gm_a, gm_b, gm_c, gm_tiling_data,
block_dim);
return 0;
});
cmd.Run();
return;
}
at::Tensor& dispatch_ffn_combine(
const at::Tensor& x,
const at::Tensor& weight1,
const at::Tensor& weight2,
const at::Tensor& expert_idx,
const at::Tensor& scale1,
const at::Tensor& scale2,
const at::Tensor& probs,
c10::string_view group,
int64_t max_output_size,
at::Tensor& out
) {
char *group_ep_ptr = const_cast<char *>(group.data());
EXEC_NPU_CMD(aclnnDispatchFFNCombine,
x,
weight1,
weight2,
expert_idx,
scale1,
scale2,
probs,
group_ep_ptr,
max_output_size,
out);
return out;
}
at::Tensor npu_lightning_indexer(
const at::Tensor &query, const at::Tensor &key, const at::Tensor &weights,
const c10::optional<at::Tensor> &actual_seq_lengths_query,
const c10::optional<at::Tensor> &actual_seq_lengths_key,
const c10::optional<at::Tensor> &block_table, c10::string_view layout_query,
c10::string_view layout_key, int64_t sparse_count, int64_t sparse_mode)
{
// npu tensor max size
constexpr int32_t SIZE = 8;
constexpr int32_t DIM_0 = 0;
constexpr int32_t DIM_1 = 1;
constexpr int32_t DIM_2 = 2;
constexpr int32_t DIM_3 = 3;
TORCH_CHECK(query.numel() > 0, "Query is empty.");
TORCH_CHECK(key.numel() > 0, "Key is empty.");
TORCH_CHECK(weights.numel() > 0, "Weights is empty.");
for (size_t i = 0; i < query.sizes().size(); i++) {
TORCH_CHECK(query.size(i) > 0, "All values within query's shape should be greater "
"than 0, but shape[", i, "] is ", query.size(i));
}
TORCH_CHECK(sparse_count > 0, "sparse count should be greater than 0, but now is ", sparse_count);
at::SmallVector<int64_t, SIZE> output_size;
std::string query_layout_str = std::string(layout_query);
std::string key_layout_str = std::string(layout_key);
if (query_layout_str == "BSND") {
output_size = {query.size(DIM_0), query.size(DIM_1), key.size(DIM_2), sparse_count};
} else {
int n_dim_index = 0;
n_dim_index = (key_layout_str == "TND") ? DIM_1 : DIM_2;
output_size = {query.size(DIM_0), key.size(n_dim_index), sparse_count};
}
at::Tensor lightning_indexer_output = at::empty(output_size, query.options().dtype(at::kInt));
// convert str
char *query_layout_ptr = const_cast<char *>(query_layout_str.c_str());
char *key_layout_ptr = const_cast<char *>(key_layout_str.c_str());
EXEC_NPU_CMD(
aclnnLightningIndexer,
query,
key,
weights,
actual_seq_lengths_query,
actual_seq_lengths_key,
block_table,
query_layout_ptr,
key_layout_ptr,
sparse_count,
sparse_mode,
lightning_indexer_output);
return lightning_indexer_output;
}
at::Tensor npu_sparse_flash_attention(
const at::Tensor &query, const at::Tensor &key, const at::Tensor &value,
const at::Tensor &sparse_indices, double scale_value, int64_t sparse_block_size,
const c10::optional<at::Tensor> &block_table,
const c10::optional<at::Tensor> &actual_seq_lengths_query,
const c10::optional<at::Tensor> &actual_seq_lengths_kv,
const c10::optional<at::Tensor> &query_rope,
const c10::optional<at::Tensor> &key_rope, c10::string_view layout_query,
c10::string_view layout_kv,
int64_t sparse_mode)
{
std::string layout_query_str = std::string(layout_query);
std::string layout_kv_str = std::string(layout_kv);
for (size_t i = 0; i < query.sizes().size(); i++) {
TORCH_CHECK(query.size(i) > 0, "All values within query's shape should be greater "
"than 0, but shape[", i, "] is ", query.size(i));
}
// construct the output tensor
at::Tensor output = at::empty(query.sizes(), query.options().dtype(query.dtype()));
// convert str
char *layout_query_ptr = const_cast<char *>(layout_query_str.c_str());
char *layout_kv_ptr = const_cast<char *>(layout_kv_str.c_str());
EXEC_NPU_CMD(
aclnnSparseFlashAttention,
query,
key,
value,
sparse_indices,
block_table,
actual_seq_lengths_query,
actual_seq_lengths_kv,
query_rope,
key_rope,
scale_value,
sparse_block_size,
layout_query_ptr,
layout_kv_ptr,
sparse_mode,
output);
return output;
}
} // namespace vllm_ascend
TORCH_LIBRARY_EXPAND(CONCAT(_C, _ascend), 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("bgmv_shrink(Tensor! x, Tensor! weight, Tensor! indices, Tensor! y, float scale) -> ()");
ops.impl("bgmv_shrink", torch::kPrivateUse1, &vllm_ascend::bgmv_shrink);
ops.def(
"bgmv_expand(Tensor! x, Tensor! weight, Tensor! indices, Tensor! y,"
" int slice_offset, int slice_size) -> Tensor");
ops.impl("bgmv_expand", torch::kPrivateUse1, &vllm_ascend::bgmv_expand);
ops.def("sgmv_shrink(Tensor! x, Tensor! weight, Tensor! lora_indices, Tensor! seq_len, Tensor! y, float scale) -> ()");
ops.impl("sgmv_shrink", torch::kPrivateUse1, &vllm_ascend::sgmv_shrink);
ops.def(
"sgmv_expand(Tensor! x, Tensor! weight, Tensor! lora_indices, Tensor! seq_len, Tensor! y,"
" int slice_offset, int slice_size) -> Tensor");
ops.impl("sgmv_expand", torch::kPrivateUse1, &vllm_ascend::sgmv_expand);
ops.def(
"mla_preprocess(Tensor hiddenState, Tensor wdqkv,"
" Tensor descale0, Tensor gamma1, Tensor beta1, Tensor wuq, Tensor descale1,"
" Tensor gamma2, Tensor cos, Tensor sin, Tensor wuk, Tensor kv_cache,"
" Tensor kv_cache_rope, Tensor slotmapping, Tensor quant_scale0,"
" Tensor quant_offset0, Tensor bias0, Tensor quant_scale1, Tensor quant_offset1,"
" Tensor bias1, Tensor? ctkv_scale, Tensor? q_nope_scale, str? cache_mode,"
" str? quant_mode, bool? enable_inner_out, Tensor! q_out0, Tensor! kv_cache_out0, Tensor! q_out1,"
" Tensor! kv_cache_out1, Tensor! inner_out) -> (Tensor q_out0, Tensor kv_cache_out0,"
" Tensor q_out1, Tensor kv_cache_out1, Tensor inner_out)"
);
ops.impl("mla_preprocess", torch::kPrivateUse1, &vllm_ascend::mla_preprocess);
//batch_matmul ops refer to sgl-kernel-npu
ops.def(
"batch_matmul_transpose(Tensor tensor_a, Tensor tensor_b, Tensor tensor_c, str? format_mode=None, str? quant_mode=None) -> ()");
ops.impl("batch_matmul_transpose", torch::kPrivateUse1, &vllm_ascend::batch_matmul_transpose);
ops.def("swap_blocks(Tensor! x, Tensor! y, Tensor z) -> ()");
ops.impl("swap_blocks", torch::kPrivateUse1, &vllm_ascend::swap_blocks);
ops.def(
"grouped_matmul_swiglu_quant(Tensor x, Tensor weight, Tensor weight_scale, Tensor x_scale,"
" Tensor group_list, *, Tensor? bias=None,"
" Tensor? offset=None) -> (Tensor output, Tensor output_scale, Tensor output_offset)");
ops.impl("grouped_matmul_swiglu_quant", torch::kPrivateUse1, &vllm_ascend::grouped_matmul_swiglu_quant);
ops.def(
"dispatch_gmm_combine_decode(Tensor x, Tensor expert_ids, Tensor gmm1_permuted_weight,"
" Tensor gmm1_permuted_weight_scale,"
" Tensor gmm2_weight, Tensor gmm2_weight_scale,"
" Tensor? expert_smooth_scales=None, Tensor? expert_scales=None,"
" str group_ep='',"
" int ep_rank_size=0, int ep_rank_id=0, int moe_expert_num=0,"
" int shared_expert_num=1, int shared_expert_rank_num=0,"
" int quant_mode=0,"
" int global_bs=0) -> (Tensor output, Tensor ep_recv_count)"
);
ops.impl("dispatch_gmm_combine_decode", torch::kPrivateUse1, &vllm_ascend::dispatch_gmm_combine_decode);
ops.def(
"grouped_matmul_swiglu_quant_weight_nz_tensor_list(Tensor x, Tensor[] weight, Tensor[] weight_scale, Tensor x_scale,"
" Tensor group_list, *,"
" Tensor? bias=None, Tensor? offset=None) ->"
" (Tensor output, Tensor output_scale, Tensor output_offset)"
);
ops.impl("grouped_matmul_swiglu_quant_weight_nz_tensor_list", torch::kPrivateUse1, &vllm_ascend::grouped_matmul_swiglu_quant_weight_nz_tensor_list);
ops.def(
"npu_lightning_indexer(Tensor query, Tensor key, Tensor weights, *,"
" Tensor? actual_seq_lengths_query=None, Tensor? actual_seq_lengths_key=None,"
" Tensor? block_table=None, str layout_query='BSND', str layout_key='BSND',"
" int sparse_count=2048, int sparse_mode=3) -> Tensor"
);
ops.impl("npu_lightning_indexer", torch::kPrivateUse1, &vllm_ascend::npu_lightning_indexer);
ops.def(
"npu_sparse_flash_attention(Tensor query, Tensor key, Tensor value,"
" Tensor sparse_indices, float scale_value, int sparse_block_size, *,"
" Tensor? block_table=None, Tensor? actual_seq_lengths_query=None,"
" Tensor? actual_seq_lengths_kv=None, Tensor? query_rope=None,"
" Tensor? key_rope=None, str layout_query='BSND', str layout_kv='BSND',"
" int sparse_mode=3) -> Tensor"
);
ops.impl("npu_sparse_flash_attention", torch::kPrivateUse1, &vllm_ascend::npu_sparse_flash_attention);
ops.def(
"dispatch_ffn_combine(Tensor x, Tensor weight1, Tensor weight2, Tensor expert_idx,"
" Tensor scale1, Tensor scale2, Tensor probs, str group,"
" int max_output_size, Tensor! out) -> Tensor"
);
ops.impl("dispatch_ffn_combine", torch::kPrivateUse1, &vllm_ascend::dispatch_ffn_combine);
}
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