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Nov 24, 2024
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misc: remove duplicate norm cuda kernels #631

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171 changes: 16 additions & 155 deletions include/flashinfer/norm.cuh
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,7 @@ namespace norm {

template <uint32_t VEC_SIZE, typename T>
__global__ void RMSNormKernel(T* __restrict__ input, T* __restrict__ weight, T* __restrict__ output,
const uint32_t d, float eps) {
const uint32_t d, float weight_bias, float eps) {
const uint32_t bx = blockIdx.x;
const uint32_t tx = threadIdx.x, ty = threadIdx.y;
constexpr uint32_t warp_size = 32;
Expand Down Expand Up @@ -87,7 +87,7 @@ __global__ void RMSNormKernel(T* __restrict__ input, T* __restrict__ weight, T*
}
#pragma unroll
for (uint32_t j = 0; j < VEC_SIZE; j++) {
output_vec[j] = float(input_vec[j]) * rms_rcp * float(weight_vec[j]);
output_vec[j] = float(input_vec[j]) * rms_rcp * (weight_bias + float(weight_vec[j]));
}
if ((i * num_threads + thread_id) * VEC_SIZE < d) {
output_vec.store(output + bx * d + i * num_threads * VEC_SIZE + thread_id * VEC_SIZE);
Expand All @@ -105,7 +105,8 @@ cudaError_t RMSNorm(T* input, T* weight, T* output, uint32_t batch_size, uint32_
dim3 nblks(batch_size);
dim3 nthrs(32, num_warps);
const uint32_t smem_size = num_warps * sizeof(float);
void* args[] = {&input, &weight, &output, &d, &eps};
float weight_bias = 0.f;
void* args[] = {&input, &weight, &output, &d, &weight_bias, &eps};

DISPATCH_ALIGNED_VEC_SIZE(vec_size, VEC_SIZE, {
auto kernel = RMSNormKernel<VEC_SIZE, T>;
Expand All @@ -116,7 +117,8 @@ cudaError_t RMSNorm(T* input, T* weight, T* output, uint32_t batch_size, uint32_

template <uint32_t VEC_SIZE, typename T>
__global__ void FusedAddRMSNormKernel(T* __restrict__ input, T* __restrict__ residual,
T* __restrict__ weight, const uint32_t d, float eps) {
T* __restrict__ weight, const uint32_t d, float weight_bias,
float eps) {
const uint32_t bx = blockIdx.x;
const uint32_t tx = threadIdx.x, ty = threadIdx.y;
constexpr uint32_t warp_size = 32;
Expand Down Expand Up @@ -187,7 +189,7 @@ __global__ void FusedAddRMSNormKernel(T* __restrict__ input, T* __restrict__ res
}
#pragma unroll
for (uint32_t j = 0; j < VEC_SIZE; j++) {
input_vec[j] = x_vec[j] * rms_rcp * float(weight_vec[j]);
input_vec[j] = x_vec[j] * rms_rcp * (weight_bias + float(weight_vec[j]));
}
if ((i * num_threads + thread_id) * VEC_SIZE < d) {
input_vec.store(input + bx * d + i * num_threads * VEC_SIZE + thread_id * VEC_SIZE);
Expand All @@ -205,7 +207,8 @@ cudaError_t FusedAddRMSNorm(T* input, T* residual, T* weight, uint32_t batch_siz
dim3 nblks(batch_size);
dim3 nthrs(32, num_warps);
const uint32_t smem_size = (num_warps + d) * sizeof(float);
void* args[] = {&input, &residual, &weight, &d, &eps};
float weight_bias = 0.f;
void* args[] = {&input, &residual, &weight, &d, &weight_bias, &eps};

DISPATCH_ALIGNED_VEC_SIZE(vec_size, VEC_SIZE, {
auto kernel = FusedAddRMSNormKernel<VEC_SIZE, T>;
Expand All @@ -215,73 +218,6 @@ cudaError_t FusedAddRMSNorm(T* input, T* residual, T* weight, uint32_t batch_siz
return cudaSuccess;
}

template <uint32_t VEC_SIZE, typename T>
__global__ void GemmaRMSNormKernel(T* __restrict__ input, T* __restrict__ weight,
T* __restrict__ output, const uint32_t d, float eps) {
const uint32_t bx = blockIdx.x;
const uint32_t tx = threadIdx.x, ty = threadIdx.y;
constexpr uint32_t warp_size = 32;
const uint32_t num_warps = blockDim.y;
const uint32_t thread_id = tx + ty * warp_size;
const uint32_t num_threads = num_warps * warp_size;
const uint32_t rounds = ceil_div(d, VEC_SIZE * num_threads);
extern __shared__ float smem[];

float sum_sq = 0.f;

for (uint32_t i = 0; i < rounds; i++) {
vec_t<T, VEC_SIZE> input_vec;
input_vec.fill(0.f);
if ((i * num_threads + thread_id) * VEC_SIZE < d) {
input_vec.load(input + bx * d + i * num_threads * VEC_SIZE + thread_id * VEC_SIZE);
}
#pragma unroll
for (uint32_t j = 0; j < VEC_SIZE; j++) {
sum_sq += float(input_vec[j]) * float(input_vec[j]);
}
}

// first, warp reduce sum
#pragma unroll
for (uint32_t offset = warp_size / 2; offset > 0; offset /= 2) {
sum_sq += math::shfl_xor_sync(sum_sq, offset);
}

smem[ty] = sum_sq;
__syncthreads();
// then, cross warp reduce sum using only the first warp
if (ty == 0) {
sum_sq = (tx < num_warps) ? smem[tx] : 0.f;
#pragma unroll
for (uint32_t offset = warp_size / 2; offset > 0; offset /= 2) {
sum_sq += math::shfl_xor_sync(sum_sq, offset);
}
smem[0] = sum_sq;
}
__syncthreads();

float rms_rcp = math::rsqrt(smem[0] / (float(d) + eps));

for (uint32_t i = 0; i < rounds; i++) {
vec_t<T, VEC_SIZE> input_vec;
vec_t<T, VEC_SIZE> weight_vec;
vec_t<T, VEC_SIZE> output_vec;
input_vec.fill(0.f);
weight_vec.fill(0.f);
if ((i * num_threads + thread_id) * VEC_SIZE < d) {
input_vec.load(input + bx * d + i * num_threads * VEC_SIZE + thread_id * VEC_SIZE);
weight_vec.load(weight + i * num_threads * VEC_SIZE + thread_id * VEC_SIZE);
}
#pragma unroll
for (uint32_t j = 0; j < VEC_SIZE; j++) {
output_vec[j] = float(input_vec[j]) * rms_rcp * (1.0f + float(weight_vec[j]));
}
if ((i * num_threads + thread_id) * VEC_SIZE < d) {
output_vec.store(output + bx * d + i * num_threads * VEC_SIZE + thread_id * VEC_SIZE);
}
}
}

template <typename T>
cudaError_t GemmaRMSNorm(T* input, T* weight, T* output, uint32_t batch_size, uint32_t d,
float eps = 1e-5, cudaStream_t stream = 0) {
Expand All @@ -292,92 +228,16 @@ cudaError_t GemmaRMSNorm(T* input, T* weight, T* output, uint32_t batch_size, ui
dim3 nblks(batch_size);
dim3 nthrs(32, num_warps);
const uint32_t smem_size = num_warps * sizeof(float);
void* args[] = {&input, &weight, &output, &d, &eps};
float weight_bias = 1.f;
void* args[] = {&input, &weight, &output, &d, &weight_bias, &eps};

DISPATCH_ALIGNED_VEC_SIZE(vec_size, VEC_SIZE, {
auto kernel = GemmaRMSNormKernel<VEC_SIZE, T>;
auto kernel = RMSNormKernel<VEC_SIZE, T>;
FLASHINFER_CUDA_CALL(cudaLaunchKernel((void*)kernel, nblks, nthrs, args, smem_size, stream));
});
return cudaSuccess;
}

template <uint32_t VEC_SIZE, typename T>
__global__ void GemmaFusedAddRMSNormKernel(T* __restrict__ input, T* __restrict__ residual,
T* __restrict__ weight, const uint32_t d, float eps) {
const uint32_t bx = blockIdx.x;
const uint32_t tx = threadIdx.x, ty = threadIdx.y;
constexpr uint32_t warp_size = 32;
const uint32_t num_warps = blockDim.y;
const uint32_t thread_id = tx + ty * warp_size;
const uint32_t num_threads = num_warps * warp_size;
const uint32_t rounds = ceil_div(d, VEC_SIZE * num_threads);
extern __shared__ float smem[];

float sum_sq = 0.f;

for (uint32_t i = 0; i < rounds; i++) {
vec_t<T, VEC_SIZE> input_vec;
input_vec.fill(0.f);
vec_t<T, VEC_SIZE> residual_vec;
residual_vec.fill(0.f);
if ((i * num_threads + thread_id) * VEC_SIZE < d) {
input_vec.load(input + bx * d + i * num_threads * VEC_SIZE + thread_id * VEC_SIZE);
residual_vec.load(residual + bx * d + i * num_threads * VEC_SIZE + thread_id * VEC_SIZE);
}
#pragma unroll
for (uint32_t j = 0; j < VEC_SIZE; j++) {
float x = float(input_vec[j]);
x += float(residual_vec[j]);
sum_sq += x * x;
residual_vec[j] = (T)x;
}
if ((i * num_threads + thread_id) * VEC_SIZE < d) {
residual_vec.store(residual + bx * d + i * num_threads * VEC_SIZE + thread_id * VEC_SIZE);
}
}

// first, warp reduce sum
#pragma unroll
for (uint32_t offset = warp_size / 2; offset > 0; offset /= 2) {
sum_sq += math::shfl_xor_sync(sum_sq, offset);
}

smem[ty] = sum_sq;
__syncthreads();
// then, cross warp reduce sum using only the first warp
if (ty == 0) {
sum_sq = (tx < num_warps) ? smem[tx] : 0.f;
#pragma unroll
for (uint32_t offset = warp_size / 2; offset > 0; offset /= 2) {
sum_sq += math::shfl_xor_sync(sum_sq, offset);
}
smem[0] = sum_sq;
}
__syncthreads();

float rms_rcp = math::rsqrt(smem[0] / (float(d) + eps));

for (uint32_t i = 0; i < rounds; i++) {
vec_t<T, VEC_SIZE> input_vec;
vec_t<T, VEC_SIZE> weight_vec;
vec_t<T, VEC_SIZE> residual_vec;
input_vec.fill(0.f);
weight_vec.fill(0.f);
residual_vec.fill(0.f);
if ((i * num_threads + thread_id) * VEC_SIZE < d) {
weight_vec.load(weight + i * num_threads * VEC_SIZE + thread_id * VEC_SIZE);
residual_vec.load(residual + bx * d + i * num_threads * VEC_SIZE + thread_id * VEC_SIZE);
}
#pragma unroll
for (uint32_t j = 0; j < VEC_SIZE; j++) {
input_vec[j] = float(residual_vec[j]) * rms_rcp * (1.0f + float(weight_vec[j]));
}
if ((i * num_threads + thread_id) * VEC_SIZE < d) {
input_vec.store(input + bx * d + i * num_threads * VEC_SIZE + thread_id * VEC_SIZE);
}
}
}

template <typename T>
cudaError_t GemmaFusedAddRMSNorm(T* input, T* residual, T* weight, uint32_t batch_size, uint32_t d,
float eps = 1e-5, cudaStream_t stream = 0) {
Expand All @@ -387,11 +247,12 @@ cudaError_t GemmaFusedAddRMSNorm(T* input, T* residual, T* weight, uint32_t batc
const uint32_t num_warps = ceil_div(block_size, 32);
dim3 nblks(batch_size);
dim3 nthrs(32, num_warps);
const uint32_t smem_size = num_warps * sizeof(float);
void* args[] = {&input, &residual, &weight, &d, &eps};
const uint32_t smem_size = (num_warps + d) * sizeof(float);
float weight_bias = 1.f;
void* args[] = {&input, &residual, &weight, &d, &weight_bias, &eps};

DISPATCH_ALIGNED_VEC_SIZE(vec_size, VEC_SIZE, {
auto kernel = GemmaFusedAddRMSNormKernel<VEC_SIZE, T>;
auto kernel = FusedAddRMSNormKernel<VEC_SIZE, T>;
FLASHINFER_CUDA_CALL(cudaLaunchKernel((void*)kernel, nblks, nthrs, args, smem_size, stream));
});

Expand Down
18 changes: 10 additions & 8 deletions tests/test_norm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,19 +21,21 @@


def llama_rms_norm(x, w, eps=1e-6):
def _norm(x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + eps)

output = _norm(x.float()).type_as(x)
return output * w
orig_dtype = x.dtype
x = x.float()
variance = x.pow(2).mean(dim=-1, keepdim=True)
x = x * torch.rsqrt(variance + eps)
x = x * w.float()
x = x.to(orig_dtype)
return x


def gemma_rms_norm(x, w, eps=1e-6):
orig_dtype = x.dtype
x = x.float()
variance = x.pow(2).mean(dim=-1, keepdim=True)
x = x * torch.rsqrt(variance + eps)
x = x * (1.0 + w)
x = x * (1.0 + w.float())
x = x.to(orig_dtype)
return x

Expand All @@ -45,7 +47,7 @@ def gemma_fused_add_rms_norm(x, residual, w, eps=1e-6):
x = x.float()
variance = x.pow(2).mean(dim=-1, keepdim=True)
x = x * torch.rsqrt(variance + eps)
x = x * (1.0 + w)
x = x * (1.0 + w.float())
x = x.to(orig_dtype)
return x, residual

Expand All @@ -58,7 +60,7 @@ def fused_add_rms_norm(x, residual, weight, eps):

variance = x.pow(2).mean(dim=-1, keepdim=True)
x = x * torch.rsqrt(variance + eps)
x = x.to(orig_dtype) * weight
x = (x * weight.float()).to(orig_dtype)
return x, residual


Expand Down