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Merged
merged 4 commits into from
Mar 10, 2025
Merged

Faster MoE token generation on CUDA #248

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adf8e2a
This gives us ~20% TG speedup for DeepSeek on CUDA
Mar 9, 2025
461c319
Slightly better
Mar 9, 2025
90ab066
Also do it for plain (not fused) mul_mat_id
Mar 9, 2025
cfec338
Guard against numerical precision issues for MLA on CUDA
Mar 9, 2025
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310 changes: 278 additions & 32 deletions ggml/src/ggml-cuda.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -1765,6 +1765,93 @@ static void ggml_cuda_mul_mat_vec_p021(ggml_backend_cuda_context & ctx, const gg
ggml_mul_mat_p021_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream);
}

/*
static void ggml_cuda_op_gemv_id(
ggml_backend_cuda_context & ctx,
const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src0_ids, ggml_tensor * dst, ggml_cuda_op_mul_mat_t op,
quantize_cuda_t quantize_src1) {

GGML_ASSERT(src0->ne[3] == 1);
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(src1));
GGML_ASSERT(ggml_is_contiguous(dst));
GGML_ASSERT(ggml_nrows(src1) == 1);
GGML_ASSERT(src0_ids->ne[1] == 1);
GGML_ASSERT(src0_ids->ne[0] <= dst->ne[2]);
GGML_ASSERT(dst->ne[1] == 1);
GGML_ASSERT(src0->ne[0] == src1->ne[0]);

GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer));
GGML_ASSERT(ggml_backend_buffer_is_cuda(dst->buffer));
GGML_ASSERT(ggml_backend_buffer_is_cuda(src1->buffer));

ggml_backend_cuda_buffer_context * src0_ctx = (ggml_backend_cuda_buffer_context *) src0->buffer->context;
ggml_backend_cuda_buffer_context * src1_ctx = (ggml_backend_cuda_buffer_context *) src1->buffer->context;
ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *) dst->buffer->context;

int device_id = ctx.device;
GGML_ASSERT(src0_ctx->device == device_id);
GGML_ASSERT(src1_ctx->device == device_id);
GGML_ASSERT(dst_ctx->device == device_id);

const bool split = ggml_backend_buffer_is_cuda_split(src0->buffer);
GGML_ASSERT(!split);

const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];

const int64_t ne10 = src1->ne[0];
const int64_t nrows1 = 1;

const int64_t ne0 = dst->ne[0];
const int64_t ne2 = dst->ne[2];

const int64_t nb2 = dst->nb[2];

// Why?
GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1));

const size_t src0_rs = ggml_row_size(src0->type, ne00);
const size_t q8_1_ts = sizeof(block_q8_1);
const size_t q8_1_bs = QK8_1;

const int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING);

ggml_cuda_pool_alloc<char> src0_dd_alloc;
ggml_cuda_pool_alloc<float> src1_ddf_alloc;
ggml_cuda_pool_alloc<char> src1_ddq_alloc;
ggml_cuda_pool_alloc<float> dst_dd_alloc;

char * src0_dd = nullptr;
float * src1_ddf = (float *)src1->data;
char * src1_ddq = nullptr; // q8_1
float * dst_dd = (float *)dst->data;

bool quantization_done = false;

const bool src1_on_device = device_id == src1_ctx->device;
const bool dst_on_device = device_id == dst_ctx->device;

ggml_cuda_set_device(device_id);
cudaStream_t stream = ctx.stream(device_id, 0);

src0_dd = (char *) src0->data;

if (quantize_src1) {
size_t src_1_ddq_size = nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs;
src1_ddq = src1_ddq_alloc.alloc(ctx.pool(device_id), src_1_ddq_size);
quantize_src1(src1_ddf, src1_ddq, ne10, 1, 1, src1_padded_col_size, src0->type, stream);
}

ggml_cuda_op_mul_mat_vec_q_id(ctx, src0, src1, src0_ids, dst,
(const char *)src0->data, (const float *)src1->data, src1_ddq, (float *)dst->data,
0, ne01, 1, src1_padded_col_size, stream);
CUDA_CHECK(cudaGetLastError());

}
*/

static void ggml_cuda_mul_mat_vec_nc(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(!ggml_is_transposed(src0));
GGML_ASSERT(!ggml_is_transposed(src1));
Expand Down Expand Up @@ -2090,6 +2177,52 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
const ggml_tensor * src1 = dst->src[1];
const ggml_tensor * ids = dst->src[2];

if (src1->ne[1] == 1 && src1->ne[2] == 1 && src1->ne[3] == 1 &&
ggml_is_quantized(src0->type) &&
ggml_backend_buffer_is_cuda(src0->buffer) &&
ggml_backend_buffer_is_cuda(src1->buffer) &&
ggml_backend_buffer_is_cuda(dst->buffer) &&
!ggml_backend_buffer_is_cuda_split(src0->buffer) &&
src1->type == GGML_TYPE_F32) {
int device_id = ctx.device;
ggml_backend_cuda_buffer_context * src0_ctx = (ggml_backend_cuda_buffer_context *) src0->buffer->context;
ggml_backend_cuda_buffer_context * src1_ctx = (ggml_backend_cuda_buffer_context *) src1->buffer->context;
ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *) dst->buffer->context;
if (src0_ctx->device == device_id &&
src1_ctx->device == device_id &&
dst_ctx->device == device_id) {
GGML_ASSERT(src1->ne[0] % QK8_1 == 0);
// Fast TG path
const int64_t n_ids = ids->ne[0];
auto stream = ctx.stream(device_id, 0);

auto local_dst = *dst;
local_dst.ne[2] = n_ids;
local_dst.ne[1] = local_dst.ne[3] = 1;
local_dst.nb[2] = local_dst.nb[1];

auto local_src1 = *src1;
local_src1.nb[2] = local_src1.nb[3] = 0;

const int64_t src1_padded_col_size = GGML_PAD(src1->ne[0], MATRIX_ROW_PADDING);
ggml_cuda_pool_alloc<char> src1_quantized(ctx.pool());
auto src_1_ddq_size = src1_padded_col_size*sizeof(block_q8_1)/QK8_1;
local_src1.data = src1_quantized.alloc(src_1_ddq_size);
quantize_row_q8_1_cuda((const float *)src1->data, (void *)src1_quantized.get(), src1->ne[0], 1, 1, src1_padded_col_size,
src0->type, stream);
CUDA_CHECK(cudaGetLastError());

local_src1.nb[1] = src_1_ddq_size;

ggml_cuda_op_mul_mat_vec_q_id(ctx, src0, &local_src1, ids, &local_dst,
(const char *)src0->data, nullptr, src1_quantized.get(), (float *)dst->data,
0, src0->ne[1], 1, src1_padded_col_size, stream);
CUDA_CHECK(cudaGetLastError());

return;
}
}

GGML_TENSOR_BINARY_OP_LOCALS

GGML_ASSERT(!ggml_backend_buffer_is_cuda_split(src0->buffer) && "mul_mat_id does not support split buffers");
Expand Down Expand Up @@ -2232,6 +2365,121 @@ static bool ggml_cuda_up_gate_unary(ggml_backend_cuda_context & ctx, ggml_tensor
const ggml_tensor * src1 = dst->src[2];
const ggml_tensor * ids = dst->src[3];

if (src1->ne[1] == 1 && src1->ne[2] == 1 && src1->ne[3] == 1 &&
ggml_is_quantized(src0_1->type) &&
ggml_is_quantized(src0_2->type) &&
ggml_backend_buffer_is_cuda(src0_1->buffer) &&
ggml_backend_buffer_is_cuda(src0_2->buffer) &&
ggml_backend_buffer_is_cuda(src1->buffer) &&
ggml_backend_buffer_is_cuda(dst->buffer) &&
!ggml_backend_buffer_is_cuda_split(src0_1->buffer) &&
!ggml_backend_buffer_is_cuda_split(src0_2->buffer) &&
src1->type == GGML_TYPE_F32) {
int device_id = ctx.device;
ggml_backend_cuda_buffer_context * src0_1_ctx = (ggml_backend_cuda_buffer_context *) src0_1->buffer->context;
ggml_backend_cuda_buffer_context * src0_2_ctx = (ggml_backend_cuda_buffer_context *) src0_2->buffer->context;
ggml_backend_cuda_buffer_context * src1_ctx = (ggml_backend_cuda_buffer_context *) src1->buffer->context;
ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *) dst->buffer->context;
if (src0_1_ctx->device == device_id &&
src0_2_ctx->device == device_id &&
src1_ctx->device == device_id &&
dst_ctx->device == device_id) {
// Fast TG path
const int64_t n_ids = ids->ne[0];
auto stream = ctx.stream(device_id, 0);
ggml_cuda_pool_alloc<char> dst_up_contiguous(ctx.pool(), sizeof(float)*dst->ne[0]*n_ids);
ggml_cuda_pool_alloc<char> dst_gate_contiguous(ctx.pool(), sizeof(float)*dst->ne[0]*n_ids);

auto local_dst = *dst;
local_dst.ne[2] = n_ids;
local_dst.ne[1] = local_dst.ne[3] = 1;
local_dst.nb[1] = local_dst.nb[2] = local_dst.nb[3] = local_dst.ne[0]*sizeof(float);

auto local_src1 = *src1;
local_src1.nb[2] = local_src1.nb[3] = 0;

const int64_t src1_padded_col_size = GGML_PAD(src1->ne[0], MATRIX_ROW_PADDING);
ggml_cuda_pool_alloc<char> src1_quantized(ctx.pool());
if (ggml_is_quantized(src0_1->type) || ggml_is_quantized(src0_2->type)) {
GGML_ASSERT(src1->ne[0] % QK8_1 == 0);
auto src_1_ddq_size = src1_padded_col_size*sizeof(block_q8_1)/QK8_1;
local_src1.data = src1_quantized.alloc(src_1_ddq_size);
// Note: no use is currently made of the quantization type passed into quantize_row_q8_1_cuda.
// If that were to change, we would need to adjust the code to handle src0_1->type != src0_2->type
quantize_row_q8_1_cuda((const float *)src1->data, (void *)src1_quantized.get(), src1->ne[0], 1, 1, src1_padded_col_size,
src0_1->type, stream);
CUDA_CHECK(cudaGetLastError());

local_src1.nb[1] = src_1_ddq_size;
}

local_dst.data = dst_up_contiguous.get();
ggml_cuda_op_mul_mat_vec_q_id(ctx, src0_1, &local_src1, ids, &local_dst,
(const char *)src0_1->data, (const float *)src1->data, src1_quantized.get(), (float *)dst_up_contiguous.get(),
0, src0_1->ne[1], 1, src1_padded_col_size, stream);
CUDA_CHECK(cudaGetLastError());

local_dst.data = dst_gate_contiguous.get();
ggml_cuda_op_mul_mat_vec_q_id(ctx, src0_2, &local_src1, ids, &local_dst,
(const char *)src0_2->data, (const float *)src1->data, src1_quantized.get(), (float *)dst_gate_contiguous.get(),
0, src0_2->ne[1], 1, src1_padded_col_size, stream);
CUDA_CHECK(cudaGetLastError());

if (next && next->op == GGML_OP_MUL_MAT_ID && ggml_is_quantized(next->src[0]->type) &&
ggml_backend_buffer_is_cuda(next->src[0]->buffer) &&
!ggml_backend_buffer_is_cuda_split(next->src[0]->buffer) &&
((ggml_backend_cuda_buffer_context *)next->src[0]->buffer->context)->device == device_id &&
ggml_backend_buffer_is_cuda(next->buffer) &&
((ggml_backend_cuda_buffer_context *)next->buffer->context)->device == device_id) {

ggml_fused_mul_unary(ctx, (ggml_unary_op)dst->op_params[0], dst->ne[0]*n_ids,
(const float *)dst_gate_contiguous.get(), (const float *)dst_up_contiguous.get(), (float *)dst_gate_contiguous.get());
CUDA_CHECK(cudaGetLastError());

const int64_t dst_padded_col_size = GGML_PAD(dst->ne[0], MATRIX_ROW_PADDING);
GGML_ASSERT(dst->ne[0] % QK8_1 == 0);
auto dst_row_size = dst_padded_col_size*sizeof(block_q8_1)/QK8_1;
auto dst_ddq_size = n_ids*dst_row_size;
ggml_cuda_pool_alloc<char> dst_quantized(ctx.pool(), dst_ddq_size);
quantize_row_q8_1_cuda((const float *)dst_gate_contiguous.get(), (void *)dst_quantized.get(), dst->ne[0], n_ids, 1,
dst_padded_col_size, next->src[0]->type, stream);
CUDA_CHECK(cudaGetLastError());

std::vector<char> ids_host(ggml_nbytes(ids));
const char * ids_dev = (const char *) ids->data;
CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream));
CUDA_CHECK(cudaStreamSynchronize(stream));

local_dst.ne[2] = 1;

auto local_next = *next;
local_next.ne[2] = local_next.ne[1];
local_next.ne[1] = local_next.ne[3] = 1;
local_next.nb[2] = local_next.nb[1];

local_src1 = *next->src[1];
local_src1.ne[1] = local_src1.ne[2] = local_src1.ne[3] = 1;
local_src1.nb[1] = local_src1.nb[2] = local_src1.nb[3] = dst_row_size;

auto local_src0 = *next->src[0];
local_src0.ne[2] = local_src0.ne[3] = 1;

ggml_cuda_op_mul_mat_vec_q_id(ctx, &local_src0, &local_src1, ids, &local_next,
(const char *)next->src[0]->data, nullptr, dst_quantized.get(), (float *)next->data,
0, next->src[0]->ne[1], 1, dst_padded_col_size, stream);
CUDA_CHECK(cudaGetLastError());

return true;
} else {
ggml_fused_mul_unary(ctx, (ggml_unary_op)dst->op_params[0], ggml_nelements(dst),
(const float *)dst_gate_contiguous.get(), (const float *)dst_up_contiguous.get(), (float *)dst->data);
CUDA_CHECK(cudaGetLastError());
return false;
}
}
}


GGML_TENSOR_BINARY_OP_LOCALS

GGML_ASSERT(!ggml_backend_buffer_is_cuda_split(src0_1->buffer) && "mul_mat_id does not support split buffers");
Expand Down Expand Up @@ -2299,49 +2547,47 @@ static bool ggml_cuda_up_gate_unary(ggml_backend_cuda_context & ctx, ggml_tensor
if (fuse_down) {
final_dst.src[1] = &dst_row;
}
for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) {
for (int64_t id = 0; id < n_ids; id++) {
const int32_t i02 = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]);
for (int64_t id = 0; id < n_ids; id++) {
const int32_t i02 = *(const int32_t *) (ids_host.data() + id*ids->nb[0]);

if (i02 < 0 || i02 >= n_as) continue;
//GGML_ASSERT(i02 >= 0 && i02 < n_as);
if (i02 < 0 || i02 >= n_as) continue;
//GGML_ASSERT(i02 >= 0 && i02 < n_as);

const int64_t i11 = id % ne11;
const int64_t i12 = iid1;
const int64_t i11 = id % ne11;
const int64_t i12 = 0;

const int64_t i1 = id;
const int64_t i2 = i12;
const int64_t i1 = id;
const int64_t i2 = i12;

src0_1_row.data = src0_1_original + i02*nb02;
src0_2_row.data = src0_2_original + i02*nb02;
src1_row.data = src1_original + i11*nb11 + i12*nb12;
//dst_row.data = dst_original + i1*nb1 + i2*nb2;
src0_1_row.data = src0_1_original + i02*nb02;
src0_2_row.data = src0_2_original + i02*nb02;
src1_row.data = src1_original + i11*nb11 + i12*nb12;
//dst_row.data = dst_original + i1*nb1 + i2*nb2;

dst_row.data = dst_up_contiguous.get();
ggml_cuda_mul_mat(ctx, &src0_1_row, &src1_row, &dst_row);
CUDA_CHECK(cudaGetLastError());
dst_row.data = dst_up_contiguous.get();
ggml_cuda_mul_mat(ctx, &src0_1_row, &src1_row, &dst_row);
CUDA_CHECK(cudaGetLastError());

dst_row.data = dst_gate_contiguous.get();
ggml_cuda_mul_mat(ctx, &src0_2_row, &src1_row, &dst_row);
CUDA_CHECK(cudaGetLastError());
dst_row.data = dst_gate_contiguous.get();
ggml_cuda_mul_mat(ctx, &src0_2_row, &src1_row, &dst_row);
CUDA_CHECK(cudaGetLastError());

if (fuse_down) {
ggml_fused_mul_unary(ctx, (ggml_unary_op)dst->op_params[0], dst_row.ne[0],
(const float *)dst_gate_contiguous.get(), (const float *)dst_up_contiguous.get(), (float *)dst_gate_contiguous.get());
CUDA_CHECK(cudaGetLastError());
if (fuse_down) {
ggml_fused_mul_unary(ctx, (ggml_unary_op)dst->op_params[0], dst_row.ne[0],
(const float *)dst_gate_contiguous.get(), (const float *)dst_up_contiguous.get(), (float *)dst_gate_contiguous.get());
CUDA_CHECK(cudaGetLastError());

final_src.data = (char *)next->src[0]->data + i02*next->src[0]->nb[2];
final_dst.data = (char *)next->data + i1*next->nb[1] + i2*next->nb[2];
ggml_cuda_mul_mat(ctx, &final_src, &dst_row, &final_dst);
CUDA_CHECK(cudaGetLastError());
final_src.data = (char *)next->src[0]->data + i02*next->src[0]->nb[2];
final_dst.data = (char *)next->data + i1*next->nb[1] + i2*next->nb[2];
ggml_cuda_mul_mat(ctx, &final_src, &dst_row, &final_dst);
CUDA_CHECK(cudaGetLastError());

} else {
} else {

ggml_fused_mul_unary(ctx, (ggml_unary_op)dst->op_params[0], dst_row.ne[0],
(const float *)dst_gate_contiguous.get(), (const float *)dst_up_contiguous.get(), (float *)(dst_original + i1*nb1 + i2*nb2));
CUDA_CHECK(cudaGetLastError());
ggml_fused_mul_unary(ctx, (ggml_unary_op)dst->op_params[0], dst_row.ne[0],
(const float *)dst_gate_contiguous.get(), (const float *)dst_up_contiguous.get(), (float *)(dst_original + i1*nb1 + i2*nb2));
CUDA_CHECK(cudaGetLastError());

}
}
}
} else {
Expand Down
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