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add fp32 mrope, vision rope kernel

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HimariO 2024-10-29 01:16:21 +08:00
parent 0882f57612
commit 3237bb4614
1 changed files with 220 additions and 3 deletions
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223
ggml/src/ggml-cuda/rope.cu
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@ -4,6 +4,11 @@ struct rope_corr_dims {
float v[2];
};
struct mrope_sections {
int v[4];
};
static __device__ float rope_yarn_ramp(const float low, const float high, const int i0) {
const float y = (i0 / 2 - low) / max(0.001f, high - low);
return 1.0f - min(1.0f, max(0.0f, y));
@ -108,6 +113,114 @@ static __global__ void rope_neox(
dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
}
template<typename T, bool has_ff>
static __global__ void rope_mrope(
const T * x, T * dst, int ne0, int ne2, 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, mrope_sections sections) {
const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
if (i0 >= ne0) {
return;
}
const int row = blockDim.x*blockIdx.x + threadIdx.x;
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 + i0/2;
const int i2 = row/p_delta_rows;
int sect_dims = sections.v[0] + sections.v[1] + sections.v[2] + sections.v[3];
int sec_w = sections.v[1] + sections.v[0];
int sector = (i0 / 2) % sect_dims;
float theta_base = 0.0;
if (sector < sections.v[0]) {
theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
}
else if (sector >= sections.v[0] && sector < sec_w) {
theta_base = pos[i2 + ne2 * 1]*powf(theta_scale, i0/2.0f);
}
else if (sector >= sec_w && sector < sec_w + sections.v[2]) {
theta_base = pos[i2 + ne2 * 2]*powf(theta_scale, i0/2.0f);
}
else if (sector >= sec_w + sections.v[2]) {
theta_base = pos[i2 + ne2 * 3]*powf(theta_scale, i0/2.0f);
}
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 + n_dims/2];
dst[i + 0] = x0*cos_theta - x1*sin_theta;
dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
}
template<typename T, bool has_ff>
static __global__ void rope_vision(
const T * x, T * dst, int ne0, int ne2, 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, mrope_sections sections) {
const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
if (i0 >= ne0) {
return;
}
const int row = blockDim.x*blockIdx.x + threadIdx.x;
// 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 + i0/2;
const int i2 = row/p_delta_rows; // i2-th tokens
int sect_dims = sections.v[0] + sections.v[1];
int sec_w = sections.v[1] + sections.v[0];
int sector = (i0 / 2) % sect_dims;
float theta_base = 0.0;
if (sector < sections.v[0]) {
const int p = sector;
theta_base = pos[i2]*powf(theta_scale, p);
}
else if (sector >= sections.v[0] && sector < sec_w) {
const int p = sector - sections.v[0];
theta_base = pos[i2 + ne2]*powf(theta_scale, p);
}
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 + n_dims];
dst[i + 0] = x0*cos_theta - x1*sin_theta;
dst[i + n_dims] = x0*sin_theta + x1*cos_theta;
}
template<typename T>
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,
@ -156,6 +269,56 @@ static void rope_neox_cuda(
}
}
template<typename T>
static void rope_mrope_cuda(
const T * x, T * dst, int ne0, int ne2, 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, mrope_sections sections, cudaStream_t stream) {
GGML_ASSERT(ne0 % 2 == 0);
const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 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 (freq_factors == nullptr) {
rope_mrope<T, false><<<block_nums, block_dims, 0, stream>>>(
x, dst, ne0, ne2, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
theta_scale, freq_factors, sections
);
} else {
rope_mrope<T, true><<<block_nums, block_dims, 0, stream>>>(
x, dst, ne0, ne2, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
theta_scale, freq_factors, sections
);
}
}
template<typename T>
static void rope_vision_cuda(
const T * x, T * dst, int ne0, int ne2, 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, mrope_sections sections, cudaStream_t stream) {
GGML_ASSERT(ne0 % 2 == 0);
const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 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);
// break down (head_dim, heads, seq) into (CUDA_ROPE_BLOCK_SIZE, x, heads * seq)
// where x ~= ceil(head_dim / CUDA_ROPE_BLOCK_SIZE);
const float theta_scale = powf(freq_base, -2.0f/n_dims);
if (freq_factors == nullptr) {
rope_vision<T, false><<<block_nums, block_dims, 0, stream>>>(
x, dst, ne0, ne2, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
theta_scale, freq_factors, sections
);
} else {
rope_vision<T, true><<<block_nums, block_dims, 0, stream>>>(
x, dst, ne0, ne2, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
theta_scale, freq_factors, sections
);
}
}
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) {
@ -185,6 +348,22 @@ static void rope_neox_cuda_f32(
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);
}
static void rope_mrope_cuda_f32(
const float * x, float * dst, int ne0, int ne2, 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, mrope_sections sections, cudaStream_t stream
) {
rope_mrope_cuda<float>(x, dst, ne0, ne2, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, stream);
}
static void rope_vision_cuda_f32(
const float * x, float * dst, int ne0, int ne2, 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, mrope_sections sections, cudaStream_t stream
) {
rope_vision_cuda<float>(x, dst, ne0, ne2, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, stream);
}
void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
@ -201,8 +380,9 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16);
GGML_ASSERT(src0->type == dst->type);
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne00 = src0->ne[0]; // head dims
const int64_t ne01 = src0->ne[1]; // num heads
const int64_t ne02 = src0->ne[2]; // num heads
const int64_t nr = ggml_nrows(src0);
//const int n_past = ((int32_t *) dst->op_params)[0];
@ -210,6 +390,8 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const int mode = ((int32_t *) dst->op_params)[2];
//const int n_ctx = ((int32_t *) dst->op_params)[3];
const int n_ctx_orig = ((int32_t *) dst->op_params)[4];
// int sections[4];
mrope_sections sections;
// RoPE alteration for extended context
float freq_base;
@ -225,8 +407,15 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float));
memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float));
memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float));
memcpy(&sections.v, (int32_t *) dst->op_params + 11, sizeof(int)*4);
const bool is_neox = mode & GGML_ROPE_TYPE_NEOX;
const bool is_mrope = sections.v[0] > 0 || sections.v[1] > 0 || sections.v[2] > 0;
const bool is_vision = is_mrope && sections.v[3] > 0;
const bool is_neox = (mode & GGML_ROPE_TYPE_NEOX) & !(is_mrope || is_vision); // TODO: fix this with new rope type
if (is_vision) {
GGML_ASSERT(n_dims == ne00/2);
}
const int32_t * pos = (const int32_t *) src1_d;
@ -253,6 +442,34 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
} else {
GGML_ABORT("fatal error");
}
} else if (is_mrope && !is_vision) {
if (src0->type == GGML_TYPE_F32) {
rope_mrope_cuda_f32(
(const float *)src0_d, (float *)dst_d, ne00, ne02, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
attn_factor, corr_dims, freq_factors, sections, stream
);
} else if (src0->type == GGML_TYPE_F16 && false) {
// rope_mrope_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_ABORT("fatal error");
}
} else if (is_vision) {
if (src0->type == GGML_TYPE_F32) {
rope_vision_cuda_f32(
(const float *)src0_d, (float *)dst_d, ne00, ne02, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
attn_factor, corr_dims, freq_factors, sections, stream
);
} else if (src0->type == GGML_TYPE_F16 && false) {
// rope_vision_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_ABORT("fatal error");
}
} else {
if (src0->type == GGML_TYPE_F32) {
rope_norm_cuda_f32(