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CPU/CUDA: fix (GQA) mul mat back, add CUDA support (llama/11380)

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Johannes Gäßler 2025-01-24 12:38:31 +01:00 committed by Georgi Gerganov
parent 30767b4c4e
commit c262dc80e2
6 changed files with 59 additions and 43 deletions
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4
ggml/src/ggml-cpu/ggml-cpu.c
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@ -7883,7 +7883,7 @@ static void ggml_compute_forward_out_prod_f32(
float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03));
float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13));
float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3));
float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3));
ggml_vec_mad_f32_unroll(ne0, nb01, nb11, d, s0, s1);
}
@ -7892,7 +7892,7 @@ static void ggml_compute_forward_out_prod_f32(
float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03));
float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13));
float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3));
float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3));
ggml_vec_mad_f32(ne0, d, s0, *s1);
}

3
ggml/src/ggml-cpu/ggml-cpu.cpp
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@ -416,7 +416,8 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st
case GGML_OP_IM2COL_BACK:
return src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32;
case GGML_OP_OUT_PROD:
return (src0->type == GGML_TYPE_F32 || ggml_is_quantized(src0->type)) && src1->type == GGML_TYPE_F32;
return (src0->type == GGML_TYPE_F32 || (ggml_is_quantized(src0->type) && src0->ne[2] == src1->ne[2] && src0->ne[3] == src1->ne[3])) &&
src1->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
default:
return true;
}

54
ggml/src/ggml-cuda/binbcast.cu
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@ -93,26 +93,31 @@ static __global__ void k_bin_bcast_unravel(const src0_t * src0, const src1_t * s
template <typename T>
static __global__ void k_repeat_back(
const T * __restrict__ src, T * __restrict__ dst, const int64_t ne00, const int64_t ne01, const int64_t ne02,
const int64_t ne0, const int64_t ne1, const int64_t ne2) {
const T * __restrict__ src, T * __restrict__ dst, const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
const size_t s00, const size_t s01, const size_t s02, const size_t s03,
const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3) {
const int64_t tid0 = (int64_t) blockIdx.x*blockDim.x + threadIdx.x;
const int64_t tid1 = (int64_t) blockIdx.y*blockDim.y + threadIdx.y;
const int64_t tid2 = (int64_t) blockIdx.z*blockDim.z + threadIdx.z;
const int64_t tid0 = int64_t(blockIdx.x)*blockDim.x + threadIdx.x;
const int64_t tid1 = int64_t(blockIdx.y)*blockDim.y + threadIdx.y;
const int64_t tid23 = int64_t(blockIdx.z)*blockDim.z + threadIdx.z;
const int64_t tid2 = tid23 % ne2;
const int64_t tid3 = tid23 / ne2;
if (tid0 >= ne0) {
return;
}
T sum = 0;
for (int64_t i2 = tid2; i2 < ne02; i2 += ne2) {
for (int64_t i1 = tid1; i1 < ne01; i1 += ne1) {
for (int64_t i0 = tid0; i0 < ne00; i0 += ne0) {
sum += src[i2*ne01*ne00 + i1*ne00 + i0];
for (int64_t i3 = tid3; i3 < ne03; i3 += ne3) {
for (int64_t i2 = tid2; i2 < ne02; i2 += ne2) {
for (int64_t i1 = tid1; i1 < ne01; i1 += ne1) {
for (int64_t i0 = tid0; i0 < ne00; i0 += ne0) {
sum += src[i3*s03 + i2*s02 + i1*s01 + i0*s00];
}
}
}
}
dst[tid2*ne1*ne0 + tid1*ne0 + tid0] = sum;
dst[tid3*ne2*ne1*ne0 + tid2*ne1*ne0 + tid1*ne0 + tid0] = sum;
}
template<float (*bin_op)(const float, const float)>
@ -274,12 +279,14 @@ struct bin_bcast_cuda {
template <typename T>
static void repeat_back_cuda(
const T * src, T * dst, const int64_t ne00, const int64_t ne01, const int64_t ne02,
const int64_t ne0, const int64_t ne1, const int64_t ne2, cudaStream_t stream) {
const T * src, T * dst, const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
const size_t s00, const size_t s01, const size_t s02, const size_t s03,
const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3, cudaStream_t stream) {
const dim3 block_dims(WARP_SIZE, 1, 1);
const dim3 block_nums((ne0 + WARP_SIZE - 1) / WARP_SIZE, ne1, ne2);
k_repeat_back<T><<<block_nums, block_dims, 0, stream>>>(src, dst, ne00, ne01, ne02, ne0, ne1, ne2);
const dim3 block_nums((ne0 + WARP_SIZE - 1) / WARP_SIZE, ne1, ne2*ne3);
k_repeat_back<T><<<block_nums, block_dims, 0, stream>>>
(src, dst, ne00, ne01, ne02, ne03, s00, s01, s02, s03, ne0, ne1, ne2, ne3);
}
template<class op>
@ -326,27 +333,26 @@ void ggml_cuda_op_repeat_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst
const ggml_tensor * src0 = dst->src[0];
GGML_ASSERT(src0->type == dst->type);
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(dst));
GGML_ASSERT(ggml_can_repeat(dst, src0));
cudaStream_t stream = ctx.stream();
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
GGML_ASSERT(src0->ne[3] == 1);
GGML_TENSOR_UNARY_OP_LOCALS;
const int64_t ne0 = dst->ne[0];
const int64_t ne1 = dst->ne[1];
const int64_t ne2 = dst->ne[2];
GGML_ASSERT(dst->ne[3] == 1);
GGML_ASSERT(ne2*ne3 <= (1 << 15));
const size_t ts = ggml_type_size(src0->type);
const size_t s00 = nb00 / ts;
const size_t s01 = nb01 / ts;
const size_t s02 = nb02 / ts;
const size_t s03 = nb03 / ts;
switch (dst->type) {
case GGML_TYPE_F32: {
const float * src0_d = (const float *) src0->data;
float * dst_d = (float *) dst->data;
repeat_back_cuda<float>(src0_d, dst_d, ne00, ne01, ne02, ne0, ne1, ne2, stream);
repeat_back_cuda(src0_d, dst_d, ne00, ne01, ne02, ne03, s00, s01, s02, s03, ne0, ne1, ne2, ne3, stream);
} break;
default: {
GGML_ASSERT(false);

2
ggml/src/ggml-cuda/ggml-cuda.cu
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@ -3002,7 +3002,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
} break;
case GGML_OP_REPEAT_BACK:
return op->type == GGML_TYPE_F32 && op->src[0]->ne[3] == 1;
return op->type == GGML_TYPE_F32 && (op->src[0]->ne[2]*op->src[0]->ne[3]) <= (1 << 15);
case GGML_OP_CONCAT:
{
ggml_type src0_type = op->src[0]->type;

7
ggml/src/ggml-cuda/out-prod.cu
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@ -34,6 +34,9 @@ void ggml_cuda_out_prod(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
CUBLAS_CHECK(cublasSetStream(handle, stream));
const int64_t lda = nb01 / sizeof(float);
const int64_t ldc = nb1 / sizeof(float);
const bool src1_T = ggml_is_transposed(src1);
const cublasOperation_t src1_cublas_op = src1_T ? CUBLAS_OP_N : CUBLAS_OP_T;
const int64_t ldb = (src1_T ? nb10 : nb11) / sizeof(float);
@ -57,9 +60,9 @@ void ggml_cuda_out_prod(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
CUBLAS_CHECK(
cublasSgemm(handle, CUBLAS_OP_N, src1_cublas_op,
ne0, ne1, ne01,
&alpha, src0_d + (i3/dps3)*s03 + (i2/dps2)*s02, ne00,
&alpha, src0_d + (i3/dps3)*s03 + (i2/dps2)*s02, lda,
src1_d + i3 *s13 + i2 *s12, ldb,
&beta, dst_d + i3 *s3 + i2 *s2, ne0));
&beta, dst_d + i3 *s3 + i2 *s2, ldc));
}
}
}

32
ggml/src/ggml.c
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@ -5343,7 +5343,7 @@ static void ggml_compute_backward(
} break;
case GGML_OP_MUL: {
if (src0_needs_grads) {
ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, src1, grad));
ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, grad, src1));
}
if (src1_needs_grads) {
struct ggml_tensor * tmp = ggml_mul(ctx, src0, grad);
@ -5435,21 +5435,25 @@ static void ggml_compute_backward(
// src1.shape [n,p,qq,rr]
if (src0_needs_grads) {
struct ggml_tensor * s1_tg =
GGML_ASSERT(grad->ne[2] == src1->ne[2]);
GGML_ASSERT(grad->ne[3] == src1->ne[3]);
struct ggml_tensor * tmp =
ggml_out_prod(ctx, // [n,m,qq,rr]
src1, // [n,p,qq,rr]
grad); // [m,p,qq,rr]
const int64_t qq = s1_tg->ne[2];
const int64_t rr = s1_tg->ne[3];
const int64_t q1 = src0->ne[2];
const int64_t r1 = src0->ne[3];
const bool ne2_broadcasted = qq > q1;
const bool ne3_broadcasted = rr > r1;
if (ne2_broadcasted || ne3_broadcasted) {
// sum broadcast repetitions of s1_tg into shape of src0
s1_tg = ggml_repeat_back(ctx, s1_tg, src0);
if (!ggml_are_same_shape(tmp, src0)) {
GGML_ASSERT(tmp->ne[0] == src0->ne[0]);
GGML_ASSERT(tmp->ne[1] == src0->ne[1]);
GGML_ASSERT(tmp->ne[3] == 1);
const int64_t nr2 = tmp->ne[2] / src0->ne[2];
const size_t nb2 = tmp->nb[2] * nr2;
const size_t nb3 = tmp->nb[2];
tmp = ggml_view_4d(ctx, tmp, src0->ne[0], src0->ne[1], src0->ne[2], nr2, tmp->nb[1], nb2, nb3, 0);
tmp = ggml_repeat_back(ctx, tmp, src0);
}
ggml_add_or_set(ctx, cgraph, isrc0, s1_tg /*= [n,m,q1,r1]*/);
ggml_add_or_set(ctx, cgraph, isrc0, tmp);
}
if (src1_needs_grads) {
ggml_add_or_set(ctx, cgraph, isrc1,
@ -5518,7 +5522,9 @@ static void ggml_compute_backward(
if (src0_needs_grads) {
GGML_ASSERT(!cgraph->grads[isrc0] || ggml_is_contiguous(cgraph->grads[isrc0]));
GGML_ASSERT(ggml_is_contiguous(grad));
ggml_add_or_set(ctx, cgraph, isrc0, grad);
GGML_ASSERT(ggml_nelements(tensor) == ggml_nelements(src0));
ggml_add_or_set(ctx, cgraph, isrc0,
ggml_are_same_shape(tensor, src0) ? grad : ggml_reshape(ctx, grad, src0));
}
} break;
case GGML_OP_RESHAPE: {