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CUDA: batched+noncont MMQ, refactor bs>1 MoE code (#13199)

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This commit is contained in:
Johannes Gäßler
2025-04-30 23:12:59 +02:00
committed by GitHub
parent 6f67cf1f48
commit e1e8e0991f
9 changed files with 869 additions and 440 deletions
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181
ggml/src/ggml-cuda/getrows.cu
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@@ -33,8 +33,8 @@ static __global__ void k_get_rows(
dfloat2 v;
dequantize_kernel(src0_row, ib, iqs, v);
dst_row[iybs + iqs + 0] = v.x;
dst_row[iybs + iqs + y_offset] = v.y;
dst_row[iybs + iqs + 0] = float(v.x);
dst_row[iybs + iqs + y_offset] = float(v.y);
}
template<typename src0_t, typename dst_t>
@@ -60,7 +60,7 @@ static __global__ void k_get_rows_float(
dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
const src0_t * src0_row = (const src0_t *)((const char *) src0 + i01*nb01 + i11*nb02 + i12*nb03);
dst_row[i00] = src0_row[i00];
dst_row[i00] = float(src0_row[i00]);
}
template<typename grad_t, typename dst_t>
@@ -86,120 +86,159 @@ static __global__ void k_get_rows_back_float(
dst[dst_row*ncols + col] = sum;
}
template<int qk, int qr, dequantize_kernel_t dq>
static void get_rows_cuda(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
const void * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) {
GGML_TENSOR_BINARY_OP_LOCALS
template<int qk, int qr, dequantize_kernel_t dq, typename dst_t>
static void get_rows_cuda_q(
const void * src0_d, const int32_t * src1_d, dst_t * dst_d,
const int64_t ne00, const size_t nb01, const size_t nb02, const size_t nb03,
const int64_t ne10, const int64_t ne11, const int64_t ne12, const size_t nb10, const size_t nb11, const size_t nb12,
const size_t nb1, const size_t nb2, const size_t nb3,
cudaStream_t stream) {
const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
const int block_num_x = (ne00 + 2*CUDA_GET_ROWS_BLOCK_SIZE - 1) / (2*CUDA_GET_ROWS_BLOCK_SIZE);
const dim3 block_nums(block_num_x, ne10, ne11*ne12);
// strides in elements
//const size_t s0 = nb0 / ggml_element_size(dst);
const size_t s1 = nb1 / ggml_element_size(dst);
const size_t s2 = nb2 / ggml_element_size(dst);
const size_t s3 = nb3 / ggml_element_size(dst);
// const size_t s0 = nb0 / sizeof(dst_t);
const size_t s1 = nb1 / sizeof(dst_t);
const size_t s2 = nb2 / sizeof(dst_t);
const size_t s3 = nb3 / sizeof(dst_t);
const size_t s10 = nb10 / ggml_element_size(src1);
const size_t s11 = nb11 / ggml_element_size(src1);
const size_t s12 = nb12 / ggml_element_size(src1);
//const size_t s13 = nb13 / ggml_element_size(src1);
const size_t s10 = nb10 / sizeof(int32_t);
const size_t s11 = nb11 / sizeof(int32_t);
const size_t s12 = nb12 / sizeof(int32_t);
// const size_t s13 = nb13 / sizeof(int32_t);
GGML_ASSERT(ne00 % 2 == 0);
k_get_rows<qk, qr, dq><<<block_nums, block_dims, 0, stream>>>(
src0_dd, src1_dd, dst_dd,
src0_d, src1_d, dst_d,
ne00, /*ne01, ne02, ne03,*/
/*ne10, ne11,*/ ne12, /*ne13,*/
/* s0,*/ s1, s2, s3,
/* nb00,*/ nb01, nb02, nb03,
s10, s11, s12/*, s13*/);
GGML_UNUSED(dst);
}
template<typename src0_t>
template<typename src0_t, typename dst_t>
static void get_rows_cuda_float(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
const src0_t * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) {
GGML_TENSOR_BINARY_OP_LOCALS
GGML_ASSERT(ne13 == 1);
const src0_t * src0_d, const int32_t * src1_d, dst_t * dst_d,
const int64_t ne00, const size_t nb01, const size_t nb02, const size_t nb03,
const int64_t ne10, const int64_t ne11, const int64_t ne12, const size_t nb10, const size_t nb11, const size_t nb12,
const size_t nb1, const size_t nb2, const size_t nb3,
cudaStream_t stream) {
const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
const int block_num_x = (ne00 + CUDA_GET_ROWS_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BLOCK_SIZE;
const dim3 block_nums(block_num_x, ne10, ne11*ne12);
// strides in elements
//const size_t s0 = nb0 / ggml_element_size(dst);
const size_t s1 = nb1 / ggml_element_size(dst);
const size_t s2 = nb2 / ggml_element_size(dst);
const size_t s3 = nb3 / ggml_element_size(dst);
// const size_t s0 = nb0 / sizeof(dst_t);
const size_t s1 = nb1 / sizeof(dst_t);
const size_t s2 = nb2 / sizeof(dst_t);
const size_t s3 = nb3 / sizeof(dst_t);
const size_t s10 = nb10 / ggml_element_size(src1);
const size_t s11 = nb11 / ggml_element_size(src1);
const size_t s12 = nb12 / ggml_element_size(src1);
//const size_t s13 = nb13 / ggml_element_size(src1);
const size_t s10 = nb10 / sizeof(int32_t);
const size_t s11 = nb11 / sizeof(int32_t);
const size_t s12 = nb12 / sizeof(int32_t);
// const size_t s13 = nb13 / sizeof(int32_t);
k_get_rows_float<<<block_nums, block_dims, 0, stream>>>(
src0_dd, src1_dd, dst_dd,
src0_d, src1_d, dst_d,
ne00, /*ne01, ne02, ne03,*/
/*ne10, ne11,*/ ne12, /*ne13,*/
/* s0,*/ s1, s2, s3,
/* nb00,*/ nb01, nb02, nb03,
s10, s11, s12/*, s13*/);
}
GGML_UNUSED(dst);
template <typename dst_t>
static void ggml_cuda_get_rows_switch_src0_type(
const void * src0_d, const ggml_type src0_type, const int32_t * src1_d, dst_t * dst_d,
const int64_t ne00, const size_t nb01, const size_t nb02, const size_t nb03,
const int64_t ne10, const int64_t ne11, const int64_t ne12, const size_t nb10, const size_t nb11, const size_t nb12,
const size_t nb1, const size_t nb2, const size_t nb3,
cudaStream_t stream) {
switch (src0_type) {
case GGML_TYPE_F16:
get_rows_cuda_float((const half *) src0_d, src1_d, dst_d,
ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
break;
case GGML_TYPE_F32:
get_rows_cuda_float((const float *) src0_d, src1_d, dst_d,
ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
break;
case GGML_TYPE_BF16:
get_rows_cuda_float((const nv_bfloat16 *) src0_d, src1_d, dst_d,
ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
break;
case GGML_TYPE_Q4_0:
get_rows_cuda_q<QK4_0, QR4_0, dequantize_q4_0>(src0_d, src1_d, dst_d,
ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
break;
case GGML_TYPE_Q4_1:
get_rows_cuda_q<QK4_1, QR4_1, dequantize_q4_1>(src0_d, src1_d, dst_d,
ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
break;
case GGML_TYPE_Q5_0:
get_rows_cuda_q<QK5_0, QR5_0, dequantize_q5_0>(src0_d, src1_d, dst_d,
ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
break;
case GGML_TYPE_Q5_1:
get_rows_cuda_q<QK5_1, QR5_1, dequantize_q5_1>(src0_d, src1_d, dst_d,
ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
break;
case GGML_TYPE_Q8_0:
get_rows_cuda_q<QK8_0, QR8_0, dequantize_q8_0>(src0_d, src1_d, dst_d,
ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
break;
default:
// TODO: k-quants
GGML_ABORT("%s: unsupported src0 type: %s\n", __func__, ggml_type_name(src0_type));
break;
}
}
void get_rows_cuda(
const void * src0_d, ggml_type src0_type, const int32_t * src1_d, void * dst_d, ggml_type dst_type,
int64_t ne00, size_t nb01, size_t nb02, size_t nb03,
int64_t ne10, int64_t ne11, int64_t ne12, size_t nb10, size_t nb11, size_t nb12,
size_t nb1, size_t nb2, size_t nb3,
cudaStream_t stream) {
switch (dst_type) {
case GGML_TYPE_F32:
ggml_cuda_get_rows_switch_src0_type(src0_d, src0_type, src1_d, (float *) dst_d,
ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
break;
case GGML_TYPE_F16:
ggml_cuda_get_rows_switch_src0_type(src0_d, src0_type, src1_d, (half *) dst_d,
ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
break;
case GGML_TYPE_BF16:
ggml_cuda_get_rows_switch_src0_type(src0_d, src0_type, src1_d, (nv_bfloat16 *) dst_d,
ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
break;
default:
GGML_ABORT("%s: unsupported dst type: %s\n", __func__, ggml_type_name(dst_type));
break;
}
}
void ggml_cuda_op_get_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
const void * src0_d = (const void *) src0->data;
const int32_t * src1_d = (const int32_t *) src1->data;
float * dst_d = (float *) dst->data;
cudaStream_t stream = ctx.stream();
GGML_TENSOR_BINARY_OP_LOCALS
GGML_ASSERT(src1->type == GGML_TYPE_I32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
GGML_ASSERT(ne13 == 1);
GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));
GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type));
GGML_ASSERT(dst->nb[0] == ggml_type_size(dst->type));
switch (src0->type) {
case GGML_TYPE_F16:
get_rows_cuda_float(src0, src1, dst, (const half *) src0_d, src1_d, dst_d, stream);
break;
case GGML_TYPE_F32:
get_rows_cuda_float(src0, src1, dst, (const float *) src0_d, src1_d, dst_d, stream);
break;
case GGML_TYPE_Q4_0:
get_rows_cuda<QK4_0, QR4_0, dequantize_q4_0>(src0, src1, dst, src0_d, src1_d, dst_d, stream);
break;
case GGML_TYPE_Q4_1:
get_rows_cuda<QK4_1, QR4_1, dequantize_q4_1>(src0, src1, dst, src0_d, src1_d, dst_d, stream);
break;
case GGML_TYPE_Q5_0:
get_rows_cuda<QK5_0, QR5_0, dequantize_q5_0>(src0, src1, dst, src0_d, src1_d, dst_d, stream);
break;
case GGML_TYPE_Q5_1:
get_rows_cuda<QK5_1, QR5_1, dequantize_q5_1>(src0, src1, dst, src0_d, src1_d, dst_d, stream);
break;
case GGML_TYPE_Q8_0:
get_rows_cuda<QK8_0, QR8_0, dequantize_q8_0>(src0, src1, dst, src0_d, src1_d, dst_d, stream);
break;
default:
// TODO: k-quants
GGML_ABORT("%s: unsupported type: %s\n", __func__, ggml_type_name(src0->type));
break;
}
get_rows_cuda(src0->data, src0->type, (const int32_t *) src1->data, dst->data, dst->type,
ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
}
void ggml_cuda_op_get_rows_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {