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ggml : sync (im2col, GPU conv, 32-bit arm compat) (#4060)

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ggml-ci
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Georgi Gerganov 2023-11-13 16:55:52 +02:00 • committed by GitHub
parent c049b37d7b
commit 3d68f364f1
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8 changed files with 586 additions and 1075 deletions
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106
ggml-cuda.cu
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@ -4489,6 +4489,13 @@ static __device__ void cpy_1_f32_f16(const char * cxi, char * cdsti) {
*dsti = __float2half(*xi);
}
static __device__ void cpy_1_f16_f16(const char * cxi, char * cdsti) {
const half * xi = (const half *) cxi;
half * dsti = (half *) cdsti;
*dsti = *xi;
}
template <cpy_kernel_t cpy_1>
static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne,
const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
@ -4742,6 +4749,25 @@ static __global__ void clamp_f32(const float * x, float * dst, const float min,
dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]);
}
static __global__ void im2col_f32_f16(
const float * x, half * dst,
int ofs0, int ofs1, int IW, int IH, int CHW,
int s0, int s1, int p0, int p1, int d0, int d1) {
const int iiw = blockIdx.z * s0 + threadIdx.z * d0 - p0;
const int iih = blockIdx.y * s1 + threadIdx.y * d1 - p1;
const int offset_dst =
(threadIdx.x * gridDim.y * gridDim.z + blockIdx.y * gridDim.z + blockIdx.z) * CHW +
(blockIdx.x * (blockDim.y * blockDim.z) + threadIdx.y * blockDim.z + threadIdx.z);
if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
dst[offset_dst] = __float2half(0.0f);
} else {
const int offset_src = threadIdx.x * ofs0 + blockIdx.x * ofs1;
dst[offset_dst] = __float2half(x[offset_src + iih * IW + iiw]);
}
}
template<int qk, int qr, dequantize_kernel_t dq>
static void get_rows_cuda(const void * x, const int32_t * y, float * dst, const int nrows, const int ncols, cudaStream_t stream) {
const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
@ -5642,6 +5668,16 @@ static void ggml_cpy_f32_f16_cuda(
(cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12);
}
static void ggml_cpy_f16_f16_cuda(
const char * cx, char * cdst, const int ne,
const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) {
const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
cpy_f32_f16<cpy_1_f16_f16><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
(cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12);
}
static void scale_f32_cuda(const float * x, float * dst, const float scale, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE;
scale_f32<<<num_blocks, CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, k);
@ -5725,6 +5761,15 @@ static void soft_max_f32_cuda(const float * x, float * dst, const int ncols_x, c
soft_max_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols_x);
}
static void im2col_f32_f16_cuda(const float * x, half * dst,
int OH, int IW, int IH, int OW, int IC,
int KH, int KW, int N, int ofs0, int ofs1,
int s0, int s1, int p0, int p1, int d0, int d1, cudaStream_t stream) {
dim3 block_nums(IC, OH, OW);
dim3 block_dims(N, KH, KW);
im2col_f32_f16<<<block_nums, block_dims, 0, stream>>>(x, dst, ofs0, ofs1, IW, IH, (IC * KH * KW), s0, s1, p0, p1, d0, d1);
}
// buffer pool for cuda
#define MAX_CUDA_BUFFERS 256
@ -6522,8 +6567,7 @@ inline void ggml_cuda_op_mul_mat_cublas(
src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src1_as);
to_fp16_cuda(src1_ddf_i, src1_as_f16, ne, stream);
}
const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddq_i : src1_as_f16;
const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddf_i : src1_as_f16;
size_t dst_as = 0;
half * dst_f16 = (half *) ggml_cuda_pool_malloc(row_diff*src1_ncols * sizeof(half), &dst_as);
@ -6698,6 +6742,45 @@ inline void ggml_cuda_op_alibi(
(void) src1_dd;
}
inline void ggml_cuda_op_im2col(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
GGML_ASSERT(src0->type == GGML_TYPE_F16);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F16);
const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
const int32_t s1 = ((const int32_t*)(dst->op_params))[1];
const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
const int32_t p1 = ((const int32_t*)(dst->op_params))[3];
const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
const int32_t d1 = ((const int32_t*)(dst->op_params))[5];
const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1;
const int64_t N = src1->ne[is_2D ? 3 : 2];
const int64_t IC = src1->ne[is_2D ? 2 : 1];
const int64_t IH = is_2D ? src1->ne[1] : 1;
const int64_t IW = src1->ne[0];
const int64_t KH = is_2D ? src0->ne[1] : 1;
const int64_t KW = src0->ne[0];
const int64_t OH = is_2D ? dst->ne[2] : 1;
const int64_t OW = dst->ne[1];
const size_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4; // nb is byte offset, src is type float32
const size_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
im2col_f32_f16_cuda(src1_dd, (half*) dst_dd,
OH, IW, IH, OW, IC, KH, KW, N,
ofs0, ofs1, s0, s1, p0, p1, d0, d1, main_stream);
(void) src0;
(void) src0_dd;
}
inline void ggml_cuda_op_diag_mask_inf(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
@ -7610,6 +7693,9 @@ static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, gg
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
ggml_cpy_f32_f16_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02,
ne10, ne11, nb10, nb11, nb12, main_stream);
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
ggml_cpy_f16_f16_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02,
ne10, ne11, nb10, nb11, nb12, main_stream);
} else {
fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
ggml_type_name(src0->type), ggml_type_name(src1->type));
@ -7641,6 +7727,10 @@ static void ggml_cuda_alibi(const ggml_tensor * src0, const ggml_tensor * src1,
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_alibi);
}
void ggml_cuda_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_im2col);
}
static void ggml_cuda_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
(void) src0;
(void) src1;
@ -7934,6 +8024,15 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
return false;
}
if (tensor->op == GGML_OP_MUL_MAT) {
if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) {
#ifndef NDEBUG
fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = %d, src1->ne[3] = %d - fallback to CPU\n", __func__, tensor->name, tensor->src[0]->ne[3], tensor->src[1]->ne[3]);
#endif
return false;
}
}
switch (tensor->op) {
case GGML_OP_REPEAT:
func = ggml_cuda_repeat;
@ -8012,6 +8111,9 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
case GGML_OP_ALIBI:
func = ggml_cuda_alibi;
break;
case GGML_OP_IM2COL:
func = ggml_cuda_im2col;
break;
default:
return false;
}