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cuda : better rope implementation

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ggml-ci
This commit is contained in:
Georgi Gerganov 2024-05-30 13:49:46 +03:00
parent 3035c2db91
commit c989fd06b1
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GPG key ID: 449E073F9DC10735
2 changed files with 106 additions and 109 deletions
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194
ggml-cuda/rope.cu
View file

@ -1,7 +1,7 @@
#include "rope.cuh"
struct rope_corr_dims {
float v[4];
float v[4]; // TODO: is there any reson for this to be 4 instead of 2?
};
static __device__ float rope_yarn_ramp(const float low, const float high, const int i0) {
@ -13,8 +13,7 @@ static __device__ float rope_yarn_ramp(const float low, const float high, const
// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
static __device__ void rope_yarn(
float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale,
float * cos_theta, float * sin_theta
) {
float * cos_theta, float * sin_theta) {
// Get n-d rotational scaling corrected for extrapolation
float theta_interp = freq_scale * theta_extrap;
float theta = theta_interp;
@ -29,27 +28,38 @@ static __device__ void rope_yarn(
*sin_theta = sinf(theta) * mscale;
}
// rope == RoPE == rotary positional embedding
template<typename T, bool has_pos>
static __global__ void rope(
const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
float ext_factor, float attn_factor, rope_corr_dims corr_dims
) {
const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
template<typename T, bool has_ff>
static __global__ void rope_norm(
const T * x, T * dst, int ne0, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors) {
const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
if (col >= ncols) {
if (i0 >= ne0) {
return;
}
const int row = blockDim.x*blockIdx.x + threadIdx.x;
const int i = row*ncols + col;
if (i0 >= n_dims) {
const int i = row*ne0 + i0;
dst[i + 0] = x[i + 0];
dst[i + 1] = x[i + 1];
return;
}
const int i = row*ne0 + i0;
const int i2 = row/p_delta_rows;
const int p = has_pos ? pos[i2] : 0;
const float theta_base = p*powf(freq_base, -float(col)/ncols);
const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
float cos_theta, sin_theta;
rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta);
const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
float cos_theta;
float sin_theta;
rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
const float x0 = x[i + 0];
const float x1 = x[i + 1];
@ -58,23 +68,20 @@ static __global__ void rope(
dst[i + 1] = x0*sin_theta + x1*cos_theta;
}
template<typename T, bool has_pos, bool has_freq_facs>
template<typename T, bool has_ff>
static __global__ void rope_neox(
const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors
) {
const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
const T * x, T * dst, int ne0, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors) {
const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
if (col >= ncols) {
if (i0 >= ne0) {
return;
}
const int row = blockDim.x*blockIdx.x + threadIdx.x;
const int ib = col / n_dims;
const int ic = col % n_dims;
if (ib > 0) {
const int i = row*ncols + ib*n_dims + ic;
if (i0 >= n_dims) {
const int i = row*ne0 + i0;
dst[i + 0] = x[i + 0];
dst[i + 1] = x[i + 1];
@ -82,16 +89,17 @@ static __global__ void rope_neox(
return;
}
const int i = row*ncols + ib*n_dims + ic/2;
const int i = row*ne0 + i0/2;
const int i2 = row/p_delta_rows;
const int p = has_pos ? pos[i2] : 0;
const float freq_factor = has_freq_facs ? freq_factors[ic/2] : 1.0f;
const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
const float theta_base = p*powf(theta_scale, col/2.0f)/freq_factor;
const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
float cos_theta, sin_theta;
rope_yarn(theta_base, freq_scale, corr_dims, ic, ext_factor, attn_factor, &cos_theta, &sin_theta);
float cos_theta;
float sin_theta;
rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
const float x0 = x[i + 0];
const float x1 = x[i + n_dims/2];
@ -100,93 +108,81 @@ static __global__ void rope_neox(
dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
}
template<typename T>
static void rope_cuda(
const T * x, T * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream
) {
GGML_ASSERT(ncols % 2 == 0);
static void rope_norm_cuda(
const T * x, T * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) {
GGML_ASSERT(ne0 % 2 == 0);
const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
const dim3 block_nums(nrows, num_blocks_x, 1);
if (pos == nullptr) {
rope<T, false><<<block_nums, block_dims, 0, stream>>>(
x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims
const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
const dim3 block_nums(nr, n_blocks_x, 1);
const float theta_scale = powf(freq_base, -2.0f/n_dims);
if (freq_factors == nullptr) {
rope_norm<T, false><<<block_nums, block_dims, 0, stream>>>(
x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
theta_scale, freq_factors
);
} else {
rope<T, true><<<block_nums, block_dims, 0, stream>>>(
x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims
rope_norm<T, true><<<block_nums, block_dims, 0, stream>>>(
x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
theta_scale, freq_factors
);
}
}
template<typename T>
static void rope_neox_cuda(
const T * x, T * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream
) {
GGML_ASSERT(ncols % 2 == 0);
const T * x, T * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) {
GGML_ASSERT(ne0 % 2 == 0);
const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
const dim3 block_nums(nrows, num_blocks_x, 1);
const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
const dim3 block_nums(nr, n_blocks_x, 1);
const float theta_scale = powf(freq_base, -2.0f/n_dims);
if (pos == nullptr) {
if (freq_factors == nullptr) {
rope_neox<T, false, false><<<block_nums, block_dims, 0, stream>>>(
x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
rope_neox<T, false><<<block_nums, block_dims, 0, stream>>>(
x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
theta_scale, freq_factors
);
} else {
rope_neox<T, false, true><<<block_nums, block_dims, 0, stream>>>(
x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
rope_neox<T, true><<<block_nums, block_dims, 0, stream>>>(
x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
theta_scale, freq_factors
);
}
} else {
if (freq_factors == nullptr) {
rope_neox<T, true, false><<<block_nums, block_dims, 0, stream>>>(
x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
theta_scale, freq_factors
);
} else {
rope_neox<T, true, true><<<block_nums, block_dims, 0, stream>>>(
x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
theta_scale, freq_factors
);
}
}
}
static void rope_cuda_f16(
const half * x, half * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream) {
static void rope_norm_cuda_f16(
const half * x, half * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) {
rope_cuda<half>(x, dst, ncols, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream);
rope_norm_cuda<half>(x, dst, ne0, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
}
static void rope_cuda_f32(
const float * x, float * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream) {
static void rope_norm_cuda_f32(
const float * x, float * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) {
rope_cuda<float>(x, dst, ncols, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream);
rope_norm_cuda<float>(x, dst, ne0, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
}
static void rope_neox_cuda_f16(
const half * x, half * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
const half * x, half * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) {
rope_neox_cuda<half>(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
rope_neox_cuda<half>(x, dst, ne0, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
}
static void rope_neox_cuda_f32(
const float * x, float * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
const float * x, float * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream
) {
rope_neox_cuda<float>(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
rope_neox_cuda<float>(x, dst, ne0, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
}
void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
@ -207,7 +203,7 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t nrows = ggml_nrows(src0);
const int64_t nr = ggml_nrows(src0);
//const int n_past = ((int32_t *) dst->op_params)[0];
const int n_dims = ((int32_t *) dst->op_params)[1];
@ -216,7 +212,13 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const int n_orig_ctx = ((int32_t *) dst->op_params)[4];
// RoPE alteration for extended context
float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
float freq_base;
float freq_scale;
float ext_factor;
float attn_factor;
float beta_fast;
float beta_slow;
memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float));
memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float));
memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float));
@ -224,20 +226,14 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float));
memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float));
const float * freq_factors = nullptr;
const int32_t * pos = nullptr;
const bool is_neox = mode & 2;
pos = (const int32_t *) src1_d;
const int32_t * pos = (const int32_t *) src1_d;
if (is_neox) {
const float * freq_factors = nullptr;
if (src2 != nullptr) {
freq_factors = (const float *) src2->data;
}
} else {
GGML_ASSERT(src2 == nullptr && "TODO: freq_factors not implemented for !is_neox");
}
rope_corr_dims corr_dims;
ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v);
@ -246,12 +242,12 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
if (is_neox) {
if (src0->type == GGML_TYPE_F32) {
rope_neox_cuda_f32(
(const float *)src0_d, (float *)dst_d, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
(const float *)src0_d, (float *)dst_d, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
attn_factor, corr_dims, freq_factors, stream
);
} else if (src0->type == GGML_TYPE_F16) {
rope_neox_cuda_f16(
(const half *)src0_d, (half *)dst_d, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
(const half *)src0_d, (half *)dst_d, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
attn_factor, corr_dims, freq_factors, stream
);
} else {
@ -259,14 +255,14 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
}
} else {
if (src0->type == GGML_TYPE_F32) {
rope_cuda_f32(
(const float *)src0_d, (float *)dst_d, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
attn_factor, corr_dims, stream
rope_norm_cuda_f32(
(const float *)src0_d, (float *)dst_d, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
attn_factor, corr_dims, freq_factors, stream
);
} else if (src0->type == GGML_TYPE_F16) {
rope_cuda_f16(
(const half *)src0_d, (half *)dst_d, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
attn_factor, corr_dims, stream
rope_norm_cuda_f16(
(const half *)src0_d, (half *)dst_d, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
attn_factor, corr_dims, freq_factors, stream
);
} else {
GGML_ASSERT(false);

1
tests/test-backend-ops.cpp
View file

@ -2256,6 +2256,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
test_cases.emplace_back(new test_rope(type, { 64, 128, 10, 1}, 64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 40B)
}
}
all = false;
}
}