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[package]
name = "candle-rotary"
version = "0.0.1"
edition = "2021"
description = "Rotary layer for the candle ML framework."
keywords = ["tensor", "machine-learning"]
categories = ["science"]
license = "MIT OR Apache-2.0"
readme = "README.md"
[dependencies]
candle = { version = "*", package = "candle-core", features = ["cuda"]}
half = { version = "2.3.1", features = ["num-traits"] }
[build-dependencies]
anyhow = { version = "1", features = ["backtrace"] }
bindgen_cuda = "0.1.1"
[dev-dependencies]
anyhow = { version = "1", features = ["backtrace"] }
candle-nn = { version = "0.3.0", features = ["cuda"] }
LICENSE-APACHE 0 → 100644
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LICENSE-MIT 0 → 100644
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Permission is hereby granted, free of charge, to any
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DEALINGS IN THE SOFTWARE.
README.md 0 → 100644
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# Candle Rotary
All files in `kernels` are adapted from https://github.com/vllm-project/vllm/tree/main/csrc and are under the vLLM
Project copyright.
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build.rs 0 → 100644
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// Build script to run nvcc and generate the C glue code for launching the flash-attention kernel.
// The cuda build time is very long so one can set the CANDLE_FLASH_ATTN_BUILD_DIR environment
// variable in order to cache the compiled artifacts and avoid recompiling too often.
use anyhow::{Context, Result};
use std::path::PathBuf;
const KERNEL_FILES: [&str; 1] = ["kernels/rotary.cu"];
fn main() -> Result<()> {
println!("cargo:rerun-if-changed=build.rs");
for kernel_file in KERNEL_FILES.iter() {
println!("cargo:rerun-if-changed={kernel_file}");
}
let out_dir = PathBuf::from(std::env::var("OUT_DIR").context("OUT_DIR not set")?);
let build_dir = match std::env::var("CANDLE_ROTARY_BUILD_DIR") {
Err(_) =>
{
#[allow(clippy::redundant_clone)]
out_dir.clone()
}
Ok(build_dir) => {
let path = PathBuf::from(build_dir);
path.canonicalize().expect(&format!(
"Directory doesn't exists: {} (the current directory is {})",
&path.display(),
std::env::current_dir()?.display()
))
}
};
let kernels : Vec<_>= KERNEL_FILES.iter().collect();
let builder = bindgen_cuda::Builder::default().kernel_paths(kernels).out_dir(build_dir.clone())
.arg("-std=c++17")
.arg("-O3")
.arg("-U__CUDA_NO_HALF_OPERATORS__")
.arg("-U__CUDA_NO_HALF_CONVERSIONS__")
.arg("-U__CUDA_NO_HALF2_OPERATORS__")
.arg("-U__CUDA_NO_BFLOAT16_CONVERSIONS__")
.arg("--expt-relaxed-constexpr")
.arg("--expt-extended-lambda")
.arg("--use_fast_math")
.arg("--ptxas-options=-v")
.arg("--verbose")
.arg("-fPIC")
.arg("--gpu-max-threads-per-block=256");
let out_file = build_dir.join("librotary.a");
builder.build_lib(out_file);
println!("cargo:rustc-link-search={}", build_dir.display());
println!("cargo:rustc-link-lib=rotary");
println!("cargo:rustc-link-lib=dylib=cudart");
println!("cargo:rustc-link-lib=dylib=stdc++");
Ok(())
}
kernels/cuda_compat.h 0 → 100644
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#pragma once
#ifndef USE_ROCM
#define VLLM_LDG(arg) __ldg(arg)
#else
#define VLLM_LDG(arg) *(arg)
#endif
#ifndef USE_ROCM
#define VLLM_SHFL_XOR_SYNC(var, lane_mask) __shfl_xor_sync(uint32_t(-1), var, lane_mask)
#else
#define VLLM_SHFL_XOR_SYNC(var, lane_mask) __shfl_xor(var, lane_mask)
#endif
#ifndef USE_ROCM
#define VLLM_SHFL_SYNC(var, src_lane) __shfl_sync(uint32_t(-1), var, src_lane)
#else
#define VLLM_SHFL_SYNC(var, src_lane) __shfl(var, src_lane)
#endif
#ifndef USE_ROCM
#define VLLM_DevFuncAttribute_SET_MaxDynamicSharedMemorySize(FUNC, VAL) \
cudaFuncSetAttribute(FUNC, cudaFuncAttributeMaxDynamicSharedMemorySize, VAL)
#else
#define VLLM_DevFuncAttribute_SET_MaxDynamicSharedMemorySize(FUNC, VAL) \
hipFuncSetAttribute(FUNC, hipFuncAttributeMaxDynamicSharedMemorySize, VAL)
#endif
kernels/rotary.cu 0 → 100644
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#include <cuda_fp16.h>
#include <cuda_bf16.h>
#include <stdint.h>
#include "cuda_compat.h"
namespace vllm {
template<typename scalar_t, bool IS_NEOX>
inline __device__ void apply_rotary_embedding(
scalar_t* __restrict__ arr,
const scalar_t* __restrict__ cos_ptr,
const scalar_t* __restrict__ sin_ptr,
int rot_offset,
int rot_dim)
{
int x_index, y_index;
scalar_t cos, sin;
if (IS_NEOX) {
// GPT-NeoX style rotary embedding.
x_index = rot_offset;
y_index = rot_dim + rot_offset;
cos = VLLM_LDG(cos_ptr + x_index);
sin = VLLM_LDG(sin_ptr + x_index);
} else {
// GPT-J style rotary embedding.
x_index = 2 * rot_offset;
y_index = 2 * rot_offset + 1;
cos = VLLM_LDG(cos_ptr + x_index / 2);
sin = VLLM_LDG(sin_ptr + x_index / 2);
}
const scalar_t x = arr[x_index];
const scalar_t y = arr[y_index];
arr[x_index] = x * cos - y * sin;
arr[y_index] = y * cos + x * sin;
}
template<typename scalar_t, bool IS_NEOX>
__global__ void rotary_embedding_kernel(
scalar_t* __restrict__ query, // [num_tokens, num_heads, head_size]
scalar_t* __restrict__ key, // [num_tokens, num_heads, head_size]
const scalar_t* __restrict__ cos_cache, // [num_tokens, rot_dim]
const scalar_t* __restrict__ sin_cache, // [num_tokens, rot_dim]
const int rot_dim,
const int64_t query_stride,
const int64_t key_stride,
const int num_heads,
const int num_kv_heads,
const int head_size) {
// Each thread block is responsible for one token.
const int token_idx = blockIdx.x;
const scalar_t* cos_ptr = cos_cache + token_idx * rot_dim;
const scalar_t* sin_ptr = sin_cache + token_idx * rot_dim;
const int nq = num_heads * rot_dim;
for (int i = threadIdx.x; i < nq; i += blockDim.x) {
const int head_idx = i / rot_dim;
const int64_t token_head = token_idx * query_stride + head_idx * head_size;
const int rot_offset = i % rot_dim;
apply_rotary_embedding<scalar_t, IS_NEOX>(query + token_head, cos_ptr,
sin_ptr, rot_offset, rot_dim);
}
const int nk = num_kv_heads * rot_dim;
for (int i = threadIdx.x; i < nk; i += blockDim.x) {
const int head_idx = i / rot_dim;
const int64_t token_head = token_idx * key_stride + head_idx * head_size;
const int rot_offset = i % rot_dim;
apply_rotary_embedding<scalar_t, IS_NEOX>(key + token_head, cos_ptr,
sin_ptr, rot_offset, rot_dim);
}
}
} // namespace vllm
#define CALL_ROTARY(T, IS_NEOX) \
vllm::rotary_embedding_kernel<T, IS_NEOX><<<grid, block, 0, stream>>>( \
reinterpret_cast<T*>(query), \
reinterpret_cast<T*>(key), \
reinterpret_cast<T*>(cos_cache), \
reinterpret_cast<T*>(sin_cache), \
rot_dim, \
query_stride, \
key_stride, \
num_heads, \
num_kv_heads, \
head_size);
extern "C" void rotary_embedding(
void *query, // [num_tokens, num_heads, head_size]
void *key, // [num_tokens, num_kv_heads, head_size]
void *cos_cache, // [num_tokens, rot_dim]
void *sin_cache, // [num_tokens, rot_dim]
int32_t is_neox,
int32_t head_size,
int64_t num_tokens,
int32_t rot_dim,
int32_t num_heads,
int32_t num_kv_heads,
int64_t query_stride,
int64_t key_stride,
uint32_t dtype // 0 => f16; 1 => bf16; 2 => f32
) {
dim3 grid(num_tokens);
dim3 block(std::min(num_heads * rot_dim, 512));
const cudaStream_t stream = 0;
const bool is_neox_bool = is_neox;
if (is_neox_bool) {
if (dtype == 0){
CALL_ROTARY(half, true);
} else if (dtype == 1) {
CALL_ROTARY(__nv_bfloat16, true);
} else if (dtype == 2) {
CALL_ROTARY(float, true);
}
} else {
if (dtype == 0){
CALL_ROTARY(half, false);
} else if (dtype == 1) {
CALL_ROTARY(__nv_bfloat16, false);
} else if (dtype == 2) {
CALL_ROTARY(float, false);
}
}
}
src/ffi.rs 0 → 100644
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use core::ffi::{c_int, c_long, c_void};
extern "C" {
pub(crate) fn rotary_embedding(
query: *const c_void,
key: *const c_void,
cos_cache: *const c_void,
sin_cache: *const c_void,
is_neox: c_int,
head_size: c_int,
num_tokens: c_long,
rot_dim: c_int,
num_heads: c_int,
num_kv_heads: c_int,
query_stride: c_long,
key_stride: c_long,
dtype: u32,
);
}
src/lib.rs 0 → 100644
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mod ffi;
use candle::cuda_backend::cudarc::driver::DevicePtr;
use candle::{DType, Device, Result, Storage, Tensor};
use half::{bf16, f16};
use std::ffi::{c_int, c_long};
fn apply_rotary_<
T: candle::cuda_backend::CudaDType + candle::cuda_backend::cudarc::driver::DeviceRepr,
>(
query: &Tensor,
key: &Tensor,
cos_cache: &Tensor,
sin_cache: &Tensor,
is_neox: bool,
) -> Result<()> {
let dtype = query.dtype();
if key.dtype() != dtype || cos_cache.dtype() != dtype || sin_cache.dtype() != dtype {
candle::bail!("apply-rotary expects all tensors to have the same dtype");
}
let internal_type = match dtype {
DType::F16 => 0,
DType::BF16 => 1,
DType::F32 => 2,
dtype => candle::bail!("dtype {dtype:?} is not supported"),
};
let (q, q_l) = query.storage_and_layout();
let q = match &*q {
Storage::Cuda(q) => q,
_ => candle::bail!("query must be a cuda tensor"),
};
let (k, k_l) = key.storage_and_layout();
let k = match &*k {
Storage::Cuda(k) => k,
_ => candle::bail!("key must be a cuda tensor"),
};
let (cc, cc_l) = cos_cache.storage_and_layout();
let cc = match &*cc {
Storage::Cuda(cc) => cc,
_ => candle::bail!("cos_cache must be a cuda tensor"),
};
let (sc, sc_l) = sin_cache.storage_and_layout();
let sc = match &*sc {
Storage::Cuda(sc) => sc,
_ => candle::bail!("sin_cache must be a cuda tensor"),
};
let q_rank = q_l.stride().len();
let k_rank = k_l.stride().len();
let cc_rank = cc_l.stride().len();
let sc_rank = sc_l.stride().len();
if q_rank != 3 || k_rank != 3 {
candle::bail!("apply-rotary expects input tensors of rank 3 (k: {q_l:?}, v: {k_l:?})")
}
if cc_rank != 2 || sc_rank != 2 {
candle::bail!("apply-rotary expects cache tensors of rank 2 (k: {cc_l:?}, v: {sc_l:?})")
}
// Get cuda slices for all tensors
let q = q.as_cuda_slice::<T>()?;
let k = k.as_cuda_slice::<T>()?;
let cc = cc.as_cuda_slice::<T>()?;
let sc = sc.as_cuda_slice::<T>()?;
// Get cuda views for all tensors
let q = q.slice(q_l.start_offset()..);
let k = k.slice(k_l.start_offset()..);
let cc = cc.slice(cc_l.start_offset()..);
let sc = sc.slice(sc_l.start_offset()..);
let (num_tokens, num_heads, head_size) = q_l.shape().dims3()?;
let (num_tokens_kv, num_kv_heads, head_size_kv) = k_l.shape().dims3()?;
if (num_tokens, head_size) != (num_tokens_kv, head_size_kv) {
candle::bail!("shape mismatch q {:?} and k {:?}", q_l.shape(), k_l.shape())
}
let rot_dim = cc_l.dims()[1];
if (num_tokens, rot_dim) != cc_l.shape().dims2()? {
candle::bail!(
"shape mismatch cos_cache {:?}, expected {:?}",
cc_l.shape(),
(num_tokens, rot_dim)
)
}
if (num_tokens, rot_dim) != sc_l.shape().dims2()? {
candle::bail!(
"shape mismatch sin_cache {:?}, expected {:?}",
sc_l.shape(),
(num_tokens, rot_dim)
)
}
let query_stride = q_l.stride()[0];
let key_stride = k_l.stride()[0];
let q_ptr = *q.device_ptr() as *const core::ffi::c_void;
let k_ptr = *k.device_ptr() as *const core::ffi::c_void;
let cc_ptr = *cc.device_ptr() as *const core::ffi::c_void;
let sc_ptr = *sc.device_ptr() as *const core::ffi::c_void;
let neox = if is_neox { 1 } else { 0 };
unsafe {
ffi::rotary_embedding(
q_ptr,
k_ptr,
cc_ptr,
sc_ptr,
neox,
head_size as c_int,
num_tokens as c_long,
rot_dim as c_int,
num_heads as c_int,
num_kv_heads as c_int,
query_stride as c_long,
key_stride as c_long,
internal_type,
)
}
Ok(())
}
pub fn inv_freqs(dim: usize, base: f32, device: &Device) -> Result<Tensor> {
let inv_freq: Vec<_> = (0..dim)
.step_by(2)
.map(|i| 1f32 / base.powf(i as f32 / dim as f32))
.collect();
let inv_freq_len = inv_freq.len();
Tensor::from_vec(inv_freq, (1, inv_freq_len), device)
}
pub fn cos_sin(length: usize, inv_freqs: &Tensor, dtype: DType) -> Result<(Tensor, Tensor)> {
let t = Tensor::arange(0u32, length as u32, inv_freqs.device())?
.to_dtype(DType::F32)?
.reshape((length, 1))?;
let freqs = t.matmul(&inv_freqs)?;
let cos = freqs.cos()?.to_dtype(dtype)?;
let sin = freqs.sin()?.to_dtype(dtype)?;
Ok((cos, sin))
}
/// Apply Rotary position encoding inplace
///
/// # Arguments
///
/// * `query` - Query tensor of shape `(num_tokens, num_heads, head_size)`.
/// * `key` - Key tensor of shape `(num_tokens, num_kv_heads, head_size)`.
/// * `cos_cache` - Aligned cache of shape `(num_tokens, rot_dim)`
/// * `sin_cache` - Aligned cache of shape `(num_tokens, rot_dim)`
/// * `is_neox` - Use neox encoding instead of gpt-j style rotary
pub fn apply_rotary_inplace(
query: &Tensor,
key: &Tensor,
cos_cache: &Tensor,
sin_cache: &Tensor,
is_neox: bool,
) -> Result<()> {
match key.dtype() {
DType::F16 => apply_rotary_::<f16>(query, key, cos_cache, sin_cache, is_neox),
DType::BF16 => apply_rotary_::<bf16>(query, key, cos_cache, sin_cache, is_neox),
DType::F32 => apply_rotary_::<f32>(query, key, cos_cache, sin_cache, is_neox),
dt => {
candle::bail!("apply_rotary is only supported for f32, f16 and bf16 ({dt:?})")
}
}
}
tests/rotary_tests.rs 0 → 100644
View file @ 1deb3fe4
use anyhow::Result;
use candle::{DType, Device, Tensor, D};
fn to_vec3_round(t: Tensor, digits: i32) -> Result<Vec<Vec<Vec<f32>>>> {
let b = 10f32.powi(digits);
let t = t.to_vec3::<f32>()?;
let t = t
.iter()
.map(|t| {
t.iter()
.map(|t| t.iter().map(|t| f32::round(t * b) / b).collect())
.collect()
})
.collect();
Ok(t)
}
fn rotate_half(xs: &Tensor) -> candle::error::Result<Tensor> {
let last_dim = xs.dim(D::Minus1)?;
let xs1 = xs.narrow(D::Minus1, 0, last_dim / 2)?;
let xs2 = xs.narrow(D::Minus1, last_dim / 2, last_dim / 2)?;
Tensor::cat(&[&xs2.neg()?, &xs1], D::Minus1)
}
fn freqs(rot_dim: usize, seqlen: usize, dev: &Device) -> candle::error::Result<Tensor> {
let inv_freq: Vec<_> = (0..rot_dim)
.step_by(2)
.map(|i| 1f32 / 10000f32.powf(i as f32 / rot_dim as f32))
.collect();
let inv_freq_len = inv_freq.len();
let inv_freq = Tensor::from_vec(inv_freq, (1, inv_freq_len), dev)?;
let t = Tensor::arange(0u32, seqlen as u32, dev)?
.to_dtype(DType::F32)?
.reshape((seqlen, 1))?;
t.matmul(&inv_freq)
}
fn apply_rotary_emb_qkv(
q: &Tensor,
k: &Tensor,
cos: &Tensor,
sin: &Tensor,
) -> Result<(Tensor, Tensor)> {
let cos = cos.unsqueeze(1)?; // (seq_len, 1, dim)
let sin = sin.unsqueeze(1)?; // (seq_len, 1, dim)
let q_embed = (q.broadcast_mul(&cos)? + rotate_half(q)?.broadcast_mul(&sin))?;
let k_embed = (k.broadcast_mul(&cos)? + rotate_half(k)?.broadcast_mul(&sin))?;
Ok((q_embed, k_embed))
}
#[test]
fn rotary() -> Result<()> {
let device = Device::new_cuda(0)?;
let seqlen = 12;
let num_heads = 8;
let rot_dim = 64;
let q = Tensor::randn(0.0, 1.0, (seqlen, num_heads, rot_dim), &device)?.to_dtype(DType::F32)?;
let k = Tensor::randn(0.0, 1.0, (seqlen, num_heads, rot_dim), &device)?.to_dtype(DType::F32)?;
let (expected_q, expected_k) = {
let freqs = freqs(rot_dim, seqlen, &device)?;
let freqs = Tensor::cat(&[&freqs, &freqs], D::Minus1)?;
apply_rotary_emb_qkv(&q, &k, &freqs.cos()?, &freqs.sin()?)
}?;
// Create inv freqs
let inv_freqs = candle_rotary::inv_freqs(rot_dim, 10000f32, &device)?;
// Create an over-sized cos sin cache like you would usually do
let (cos, sin) = candle_rotary::cos_sin(32, &inv_freqs, DType::F32)?;
// Positions for seqlen
let position_ids = Tensor::arange(0, seqlen as u32, &device)?;
// Filter cos and sin
let cos = cos.index_select(&position_ids, 0)?;
let sin = sin.index_select(&position_ids, 0)?;
// Inplace
candle_rotary::apply_rotary_inplace(&q, &k, &cos, &sin, true)?;
assert_eq!(to_vec3_round(expected_q, 3)?, to_vec3_round(q, 3)?);
assert_eq!(to_vec3_round(expected_k, 3)?, to_vec3_round(k, 3)?);
Ok(())
}
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