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#include <torch/extension.h>
#include "ATen/cuda/CUDAContext.h"
#include <c10/cuda/CUDAGuard.h>
#include "decoder_masked_multihead_attention.h"
#define CHECK_DEVICE(x) TORCH_CHECK(x.device().type() == torch::kCUDA, #x " must be on CUDA")
#define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")")
#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
#define DISPATCH_FLOAT_AND_HALF_AND_BF16(TYPE, NAME, ...) \
if (TYPE == at::ScalarType::Half) { \
using scalar_t = at::Half; \
__VA_ARGS__(); \
} else if (TYPE == at::ScalarType::BFloat16) { \
using scalar_t = at::BFloat16; \
__VA_ARGS__(); \
} else if (TYPE == at::ScalarType::Float) { \
using scalar_t = float; \
__VA_ARGS__(); \
} else { \
AT_ERROR(#NAME, " not implemented for type '", toString(TYPE), "'"); \
}
template<typename T>
void masked_multihead_attention(const Masked_multihead_attention_params<T>& params,
const cudaStream_t& stream);
template<typename T>
void cross_multihead_attention(const Masked_multihead_attention_params<T>& params,
const cudaStream_t& stream);
template<typename T>
struct SATypeConverter {
using Type = T;
};
template<>
struct SATypeConverter<at::Half> {
using Type = uint16_t;
};
template<>
struct SATypeConverter<at::BFloat16> {
using Type = __nv_bfloat16;
};
template <typename T>
void set_params(Masked_multihead_attention_params<T> ¶ms,
const size_t batch_size,
const size_t nheads,
const size_t nheads_kv,
const size_t memory_max_seqlen,
const size_t headdim,
const int timestep,
const int rotary_embedding_dim,
const float rotary_base,
const bool neox_rotary_style,
const int q_batch_stride,
const int k_batch_stride,
const int v_batch_stride,
const int nnz_heads,
T *q_ptr,
T *k_ptr,
T *v_ptr,
T *k_cache_ptr,
T *v_cache_ptr,
int *length_per_sample,
T *rotary_cos,
T *rotary_sin,
T *out_ptr,
int *nnz_head_idx) {
// Reset the parameters
memset(¶ms, 0, sizeof(params));
params.q = q_ptr;
params.k = k_ptr;
params.v = v_ptr;
params.q_bias = nullptr;
params.k_bias = nullptr;
params.v_bias = nullptr;
params.k_cache = k_cache_ptr;
params.v_cache = v_cache_ptr;
params.out = out_ptr;
params.cache_indir = nullptr;
params.stride_q = q_batch_stride;
params.stride_k = k_batch_stride;
params.stride_v = v_batch_stride;
params.batch_size = batch_size;
params.beam_width = 1;
params.memory_max_len = memory_max_seqlen;
params.num_heads = nheads;
params.num_heads_kv = nheads_kv;
params.num_heads_q_kv_ratio = nheads / nheads_kv;
params.nnz_heads = nnz_heads;
params.hidden_size_per_head = headdim;
params.rotary_embedding_dim = rotary_embedding_dim;
params.rotary_base = rotary_base;
params.neox_rotary_style = neox_rotary_style;
params.timestep = timestep;
params.inv_sqrt_dh = 1.f / sqrt(float(headdim));
params.total_padding_tokens = nullptr;
params.masked_tokens = nullptr;
params.prefix_prompt_lengths = nullptr;
params.max_prefix_prompt_length = 0;
params.relative_attention_bias = nullptr;
params.relative_attention_bias_stride = 0;
params.cross_attention_out = nullptr;
params.max_decoder_seq_len = 0;
params.is_return_cross_attentions = false;
params.finished = nullptr;
params.memory_length_per_sample = nullptr;
params.length_per_sample = length_per_sample;
params.rotary_cos = rotary_cos;
params.rotary_sin = rotary_sin;
params.nnz_head_idx = nnz_head_idx;
}
torch::Tensor single_query_attention(const torch::Tensor q,
const torch::Tensor k,
const torch::Tensor v,
torch::Tensor k_cache,
torch::Tensor v_cache,
c10::optional<const torch::Tensor> length_per_sample_,
c10::optional<const torch::Tensor> rotary_cos_,
c10::optional<const torch::Tensor> rotary_sin_,
c10::optional<const torch::Tensor> nnz_head_idx_,
const int timestep,
int rotary_embedding_dim = 0,
const float rotary_base = 10000.0f,
const bool neox_rotary_style=true) {
CHECK_DEVICE(q); CHECK_DEVICE(k); CHECK_DEVICE(v); CHECK_DEVICE(k_cache); CHECK_DEVICE(v_cache);
int batch_size = v_cache.size(0);
int nheads = q.size(1);
int nheads_kv = v_cache.size(1);
int memory_max_seqlen = v_cache.size(2);
int headdim = v_cache.size(3);
auto input_type = q.scalar_type();
TORCH_CHECK(input_type == at::ScalarType::Float || input_type == at::ScalarType::Half || input_type == at::ScalarType::BFloat16);
CHECK_SHAPE(q, batch_size, nheads, headdim);
CHECK_SHAPE(k, batch_size, nheads_kv, headdim);
CHECK_SHAPE(v, batch_size, nheads_kv, headdim);
CHECK_SHAPE(v_cache, batch_size, nheads_kv, memory_max_seqlen, headdim);
// k_cache shape: [B, H, Dh/x, L, x] where x=8 for fp16 and x=4 for fp32
int packsize = k_cache.dtype() == torch::kFloat32 ? 4 : 8;
CHECK_SHAPE(k_cache, batch_size, nheads_kv, headdim / packsize, memory_max_seqlen, packsize);
TORCH_CHECK(q.stride(2) == 1 && q.stride(1) == headdim);
TORCH_CHECK(k.stride(2) == 1 && k.stride(1) == headdim);
TORCH_CHECK(v.stride(2) == 1 && v.stride(1) == headdim);
CHECK_CONTIGUOUS(v_cache); CHECK_CONTIGUOUS(k_cache);
TORCH_CHECK(q.scalar_type() == input_type);
TORCH_CHECK(k.scalar_type() == input_type);
TORCH_CHECK(v.scalar_type() == input_type);
TORCH_CHECK(k_cache.scalar_type() == input_type);
TORCH_CHECK(v_cache.scalar_type() == input_type);
if (length_per_sample_.has_value()) {
auto length_per_sample = length_per_sample_.value();
CHECK_DEVICE(length_per_sample);
CHECK_SHAPE(length_per_sample, batch_size);
CHECK_CONTIGUOUS(length_per_sample);
TORCH_CHECK(length_per_sample.dtype() == torch::kInt32);
}
if (rotary_cos_.has_value()) {
auto rotary_cos = rotary_cos_.value();
CHECK_DEVICE(rotary_cos);
rotary_embedding_dim = rotary_cos.size(-1) * 2;
CHECK_SHAPE(rotary_cos, batch_size, rotary_embedding_dim / 2);
CHECK_CONTIGUOUS(rotary_cos);
TORCH_CHECK(rotary_cos.scalar_type() == input_type);
TORCH_CHECK(rotary_sin_.has_value());
auto rotary_sin = rotary_sin_.value();
CHECK_DEVICE(rotary_sin);
CHECK_SHAPE(rotary_sin, batch_size, rotary_embedding_dim / 2);
CHECK_CONTIGUOUS(rotary_sin);
TORCH_CHECK(rotary_sin.scalar_type() == input_type);
}
if (nnz_head_idx_.has_value()) {
auto nnz_head_idx = nnz_head_idx_.value();
CHECK_DEVICE(nnz_head_idx);
int nnz_heads = nnz_head_idx.size(0);
CHECK_SHAPE(nnz_head_idx, nnz_heads);
CHECK_CONTIGUOUS(nnz_head_idx);
TORCH_CHECK(nnz_head_idx.dtype() == torch::kInt32);
}
// Otherwise the kernel will be launched from cuda:0 device
// Cast to char to avoid compiler warning about narrowing
at::cuda::CUDAGuard device_guard{(char)q.get_device()};
torch::Tensor out = torch::empty_like(q);
DISPATCH_FLOAT_AND_HALF_AND_BF16(q.scalar_type(), "single_query_attention", [&] {
using DataType = typename SATypeConverter<scalar_t>::Type;
Masked_multihead_attention_params<DataType> params;
set_params(params, batch_size, nheads, nheads_kv, memory_max_seqlen, headdim, timestep,
rotary_embedding_dim, rotary_base, neox_rotary_style,
q.stride(0), k.stride(0), v.stride(0),
nnz_head_idx_.has_value() ? nnz_head_idx_.value().size(0) : 0,
reinterpret_cast<DataType*>(q.data_ptr()),
reinterpret_cast<DataType*>(k.data_ptr()),
reinterpret_cast<DataType*>(v.data_ptr()),
reinterpret_cast<DataType*>(k_cache.data_ptr()),
reinterpret_cast<DataType*>(v_cache.data_ptr()),
length_per_sample_.has_value()
? length_per_sample_.value().data_ptr<int>() : nullptr,
rotary_cos_.has_value()
? reinterpret_cast<DataType*>(rotary_cos_.value().data_ptr()) : nullptr,
rotary_sin_.has_value()
? reinterpret_cast<DataType*>(rotary_sin_.value().data_ptr()) : nullptr,
reinterpret_cast<DataType*>(out.data_ptr()),
nnz_head_idx_.has_value() ? nnz_head_idx_.value().data_ptr<int>() : nullptr
);
auto stream = at::cuda::getCurrentCUDAStream();
masked_multihead_attention(params, stream);
});
return out;
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("single_query_attention", &single_query_attention, "Attention with a single query",
py::arg("q"), py::arg("k"), py::arg("v"), py::arg("k_cache"), py::arg("v_cache"),
py::arg("length_per_sample_"), py::arg("rotary_cos_"),
py::arg("rotary_sin_"), py::arg("nnz_head_idx_"),
py::arg("timestep"), py::arg("rotary_embedding_dim")=0,
py::arg("rotary_base")=10000.0f, py::arg("neox_rotary_style")=true);
}
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