furyhawk/ik_llama.cpp
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Per model CUDA contexts

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Kawrakow
2026-06-24 07:12:07 +00:00
parent 3530b65869
commit 75a5f6d079
10 changed files with 120 additions and 104 deletions
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examples/cvector-generator/pca.hpp
+1 -1
View File
@@ -66,7 +66,7 @@ struct pca_model {
pca_model(struct ggml_tensor * t_input) {
#ifdef GGML_USE_CUDA
fprintf(stderr, "%s: using CUDA backend\n", __func__);
backend = ggml_backend_cuda_init(0, nullptr); // init device 0
backend = ggml_backend_cuda_init(0, nullptr, nullptr); // init device 0
if (!backend) {
fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
}
ggml/include/ggml-cuda.h
+2 -2
View File
@@ -21,7 +21,7 @@ extern "C" {
#define GGML_CUDA_MAX_DEVICES 16
// backend API
GGML_API GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device, const void * params);
GGML_API GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device, const void * params, const void * model);
GGML_API GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend);
@@ -43,7 +43,7 @@ GGML_API GGML_CALL void ggml_backend_cuda_unregister_host_buffer(void * buffer);
GGML_API GGML_CALL void ggml_backend_cuda_log_set_callback(ggml_log_callback log_callback, void * user_data);
GGML_API GGML_CALL void ggml_backend_cuda_invalidate_graphs(void);
GGML_API GGML_CALL void ggml_backend_cuda_invalidate_graphs(const void * model);
#ifdef __cplusplus
}
#endif
ggml/src/ggml-cuda.cu
+26 -28
View File
@@ -298,13 +298,22 @@ const ggml_cuda_device_info & ggml_cuda_info() {
}
/* ---------- hot-swap: invalidate all cached CUDA graphs ---------- */
extern "C" void ggml_backend_cuda_invalidate_graphs(void) {
extern "C" void ggml_backend_cuda_invalidate_graphs(const void * model) {
auto & info = const_cast<ggml_cuda_device_info &>(ggml_cuda_info());
for (int i = 0; i < info.device_count; ++i) {
if (info.all_ctx[i]) {
info.all_ctx[i]->cuda_graphs.clear();
if (auto it = info.all_ctx.find(model); it != info.all_ctx.end()) {
for (auto ctx : it->second) {
if (ctx) {
ctx->cuda_graphs.clear();
}
}
} else {
fprintf(stderr, "================================= %s: did not find entry for model at %p\n", __func__, model);
}
//for (int i = 0; i < info.device_count; ++i) {
// if (info.all_ctx[i]) {
// info.all_ctx[i]->cuda_graphs.clear();
// }
//}
}
// #define DEBUG_CUDA_MALLOC
@@ -517,13 +526,14 @@ static std::condition_variable ggml_cuda_lock_cv;
//static std::atomic<int> ggml_cuda_lock_counter;
static int ggml_cuda_lock_counter = 0;
ggml_backend_cuda_context::ggml_backend_cuda_context(int device) :
device(device), name(GGML_CUDA_NAME + std::to_string(device)) {
ggml_backend_cuda_context::ggml_backend_cuda_context(int device, const void * model) :
device(device), name(GGML_CUDA_NAME + std::to_string(device)), model(model) {
auto info = const_cast<ggml_cuda_device_info*>(&ggml_cuda_info());
if (info->all_ctx[device]) {
auto & all_ctx = info->all_ctx[model];
if (all_ctx[device]) {
GGML_CUDA_LOG_WARN("%s: a context for device %d already exists?\n", __func__, device);
} else{
info->all_ctx[device] = this;
all_ctx[device] = this;
}
}
@@ -555,9 +565,12 @@ ggml_backend_cuda_context::~ggml_backend_cuda_context() {
}
}
auto info = const_cast<ggml_cuda_device_info*>(&ggml_cuda_info());
if (info->all_ctx[device] == this) {
info->all_ctx[device] = nullptr;
if (auto it = info->all_ctx.find(model); it != info->all_ctx.end() && it->second[device] == this) {
it->second[device] = nullptr;
}
//if (info->all_ctx[device] == this) {
// info->all_ctx[device] = nullptr;
//}
}
@@ -4247,23 +4260,8 @@ GGML_CALL static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_
needs_f16_f32_copy = true;
} else {
#ifdef GGML_USE_NCCL__
auto & info = ggml_cuda_info();
auto nbytes = ggml_nbytes(src);
ncclGroupStart();
ggml_cuda_set_device(cuda_ctx_src->device);
auto status1 = ncclSend(src->data, nbytes, ncclUint8, cuda_ctx_dst->device, info.nccl_coms[cuda_ctx_src->device],
info.all_ctx[cuda_ctx_src->device]->stream());
ggml_cuda_set_device(cuda_ctx_dst->device);
auto status2 = ncclRecv(dst->data, nbytes, ncclUint8, cuda_ctx_src->device, info.nccl_coms[cuda_ctx_dst->device],
info.all_ctx[cuda_ctx_dst->device]->stream());
ncclGroupEnd();
GGML_ASSERT(status1 == ncclSuccess && status2 == ncclSuccess);
return true;
#else
ggml_cuda_set_device(cuda_ctx_src->device);
CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, cuda_ctx_dst->device, src->data, cuda_ctx_src->device, ggml_nbytes(dst), cuda_ctx_src->stream()));
#endif
}
#endif
}
@@ -5249,13 +5247,13 @@ static cuda_params ggml_cuda_parse_params(const char * params_string) {
return params;
}
GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device, [[maybe_unused]] const void * param_string) {
GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device, [[maybe_unused]] const void * param_string, const void * model) {
if (device < 0 || device >= ggml_backend_cuda_get_device_count()) {
GGML_CUDA_LOG_ERROR("%s: invalid device %d\n", __func__, device);
return nullptr;
}
ggml_backend_cuda_context * ctx = new ggml_backend_cuda_context(device);
ggml_backend_cuda_context * ctx = new ggml_backend_cuda_context(device, model);
if (ctx == nullptr) {
GGML_CUDA_LOG_ERROR("%s: failed to allocate context\n", __func__);
return nullptr;
@@ -5370,7 +5368,7 @@ GGML_CALL void ggml_backend_cuda_unregister_host_buffer(void * buffer) {
// backend registry
GGML_CALL static ggml_backend_t ggml_backend_reg_cuda_init(const char * params, void * user_data) {
ggml_backend_t cuda_backend = ggml_backend_cuda_init((int) (intptr_t) user_data, nullptr);
ggml_backend_t cuda_backend = ggml_backend_cuda_init((int) (intptr_t) user_data, nullptr, nullptr);
return cuda_backend;
GGML_UNUSED(params);
ggml/src/ggml-cuda/common.cuh
+4 -2
View File
@@ -762,7 +762,8 @@ struct ggml_cuda_device_info {
std::array<float, GGML_CUDA_MAX_DEVICES> default_tensor_split = {};
ggml_backend_cuda_context * all_ctx[GGML_CUDA_MAX_DEVICES] = { nullptr };
std::unordered_map<const void *, std::array<ggml_backend_cuda_context *, GGML_CUDA_MAX_DEVICES>> all_ctx;
//ggml_backend_cuda_context * all_ctx[GGML_CUDA_MAX_DEVICES] = { nullptr };
#ifdef GGML_USE_NCCL
ncclComm_t nccl_coms[GGML_CUDA_MAX_DEVICES];
bool have_nccl;
@@ -864,10 +865,11 @@ struct ggml_backend_cuda_context {
#endif
const void * model;
void * copy_buffer = nullptr;
size_t copy_size = 0;
explicit ggml_backend_cuda_context(int device);
explicit ggml_backend_cuda_context(int device, const void * model);
~ggml_backend_cuda_context();
ggml/src/ggml-cuda/reduce.cu
+76 -66
View File
@@ -94,6 +94,11 @@ static void copy_missing_tensors(ggml_backend_cuda_context & ctx, ggml_tensor *
if (ncopy < 1) return;
auto & info = ggml_cuda_info();
auto it = info.all_ctx.find(ctx.model);
if (it == info.all_ctx.end()) {
GGML_ABORT("Fatal error");
}
auto & all_ctx = it->second;
auto size = ggml_nbytes(dst);
int isrc = 0;
for (int ii = 0; ii < ncopy; ++ii) {
@@ -102,9 +107,9 @@ static void copy_missing_tensors(ggml_backend_cuda_context & ctx, ggml_tensor *
isrc = (isrc + 1)%nhave;
//printf("%s: copying from device %d to device %d: %p -> %p\n", __func__, j, i, dst->src[j]->data, dst->src[i]->data);
ggml_cuda_set_device(j);
CUDA_CHECK(cudaMemcpyPeerAsync(dst->src[i]->data, info.all_ctx[i]->device, dst->src[j]->data, info.all_ctx[j]->device,
size, info.all_ctx[j]->stream()));
CUDA_CHECK(cudaEventRecord(info.all_ctx[j]->copy_event, info.all_ctx[j]->stream()));
CUDA_CHECK(cudaMemcpyPeerAsync(dst->src[i]->data, all_ctx[i]->device, dst->src[j]->data, all_ctx[j]->device,
size, all_ctx[j]->stream()));
CUDA_CHECK(cudaEventRecord(all_ctx[j]->copy_event, all_ctx[j]->stream()));
}
isrc = 0;
for (int ii = 0; ii < ncopy; ++ii) {
@@ -112,7 +117,7 @@ static void copy_missing_tensors(ggml_backend_cuda_context & ctx, ggml_tensor *
int j = idx[isrc];
isrc = (isrc + 1)%nhave;
ggml_cuda_set_device(i);
CUDA_CHECK(cudaStreamWaitEvent(info.all_ctx[i]->stream(), info.all_ctx[j]->copy_event, 0));
CUDA_CHECK(cudaStreamWaitEvent(all_ctx[i]->stream(), all_ctx[j]->copy_event, 0));
}
ggml_cuda_set_device(ctx.device);
}
@@ -133,6 +138,11 @@ void ggml_cuda_op_reduce([[maybe_unused]] ggml_backend_cuda_context & ctx, ggml_
}
auto & info = ggml_cuda_info();
auto it = info.all_ctx.find(ctx.model);
if (it == info.all_ctx.end()) {
GGML_ABORT("Fatal error");
}
auto & all_ctx = it->second;
#ifdef GGML_USE_NCCL
// Somehow I'm not able to figure out how to use NCCL correctly.
// It does not work at all if not all GPUs participate in the reduce op, and we
@@ -153,7 +163,7 @@ void ggml_cuda_op_reduce([[maybe_unused]] ggml_backend_cuda_context & ctx, ggml_
ggml_cuda_set_device(i);
auto status = ncclAllReduce(dst->src[i] ? dst->src[i]->data : nullptr,
dst->src[i] ? dst->src[i]->data : nullptr,
ggml_nelements(dst), data_type, ncclSum, info.nccl_coms[i], info.all_ctx[i]->stream());
ggml_nelements(dst), data_type, ncclSum, info.nccl_coms[i], all_ctx[i]->stream());
if (status != ncclSuccess) {
fprintf(stderr, "%s: ncclAllReduce failed with status %d\n", __func__, (int)status);
GGML_ABORT("Fatal error");
@@ -275,7 +285,7 @@ void ggml_cuda_op_reduce([[maybe_unused]] ggml_backend_cuda_context & ctx, ggml_
auto size_per_device = nblocks_per_device * tt.type_size;
for (int ii = 0; ii < nhave; ++ii) {
int i = idx[ii];
auto this_ctx = info.all_ctx[i];
auto this_ctx = all_ctx[i];
if (!this_ctx->copy_event || !this_ctx->compute_event || size_per_device > this_ctx->copy_size) {
ggml_cuda_set_device(this_ctx->device);
if (!this_ctx->copy_event) {
@@ -300,14 +310,14 @@ void ggml_cuda_op_reduce([[maybe_unused]] ggml_backend_cuda_context & ctx, ggml_
int peer = idx[(ii+1)%nhave];
auto this_nelem = std::min(nelem_per_device, nelem - ichunk*nelem_per_device);
auto this_size = (this_nelem / tt.blck_size) * tt.type_size;
ggml_cuda_set_device(info.all_ctx[peer]->device);
ggml_cuda_set_device(all_ctx[peer]->device);
if (stage > 0) {
CUDA_CHECK(cudaStreamWaitEvent(info.all_ctx[peer]->stream(), info.all_ctx[i]->compute_event, 0));
CUDA_CHECK(cudaStreamWaitEvent(all_ctx[peer]->stream(), all_ctx[i]->compute_event, 0));
}
CUDA_CHECK(cudaMemcpyPeerAsync(info.all_ctx[i]->copy_buffer, info.all_ctx[i]->device,
(const char *)dst->src[peer]->data + ichunk*size_per_device, info.all_ctx[peer]->device,
this_size, info.all_ctx[peer]->stream()));
CUDA_CHECK(cudaEventRecord(info.all_ctx[peer]->copy_event, info.all_ctx[peer]->stream()));
CUDA_CHECK(cudaMemcpyPeerAsync(all_ctx[i]->copy_buffer, all_ctx[i]->device,
(const char *)dst->src[peer]->data + ichunk*size_per_device, all_ctx[peer]->device,
this_size, all_ctx[peer]->stream()));
CUDA_CHECK(cudaEventRecord(all_ctx[peer]->copy_event, all_ctx[peer]->stream()));
ichunk = (ichunk + 1)%nhave;
}
ichunk = stage;
@@ -315,24 +325,24 @@ void ggml_cuda_op_reduce([[maybe_unused]] ggml_backend_cuda_context & ctx, ggml_
int i = idx[ii];
int peer = idx[(ii+1)%nhave];
auto this_nelem = std::min(nelem_per_device, nelem - ichunk*nelem_per_device);
ggml_cuda_set_device(info.all_ctx[i]->device);
CUDA_CHECK(cudaStreamWaitEvent(info.all_ctx[i]->stream(), info.all_ctx[peer]->copy_event, 0));
ggml_cuda_set_device(all_ctx[i]->device);
CUDA_CHECK(cudaStreamWaitEvent(all_ctx[i]->stream(), all_ctx[peer]->copy_event, 0));
int num_blocks = (this_nelem + CUDA_REDUCE_BLOCK_SIZE - 1)/CUDA_REDUCE_BLOCK_SIZE;
if (dst->type == GGML_TYPE_F16) {
k_add<half, CUDA_REDUCE_BLOCK_SIZE><<<num_blocks, CUDA_REDUCE_BLOCK_SIZE, 0, info.all_ctx[i]->stream()>>>(this_nelem,
(const half *)info.all_ctx[i]->copy_buffer, (half *)dst->src[i]->data + ichunk*nelem_per_device);
k_add<half, CUDA_REDUCE_BLOCK_SIZE><<<num_blocks, CUDA_REDUCE_BLOCK_SIZE, 0, all_ctx[i]->stream()>>>(this_nelem,
(const half *)all_ctx[i]->copy_buffer, (half *)dst->src[i]->data + ichunk*nelem_per_device);
} else if (dst->type == GGML_TYPE_Q8_0) {
k_add<CUDA_REDUCE_BLOCK_SIZE><<<num_blocks, CUDA_REDUCE_BLOCK_SIZE, 0, info.all_ctx[i]->stream()>>>(this_nelem,
(const block_q8_0 *)info.all_ctx[i]->copy_buffer, (block_q8_0 *)dst->src[i]->data + ichunk*nelem_per_device/tt.blck_size);
k_add<CUDA_REDUCE_BLOCK_SIZE><<<num_blocks, CUDA_REDUCE_BLOCK_SIZE, 0, all_ctx[i]->stream()>>>(this_nelem,
(const block_q8_0 *)all_ctx[i]->copy_buffer, (block_q8_0 *)dst->src[i]->data + ichunk*nelem_per_device/tt.blck_size);
} else if (dst->type == GGML_TYPE_BF16) {
k_add<nv_bfloat16, CUDA_REDUCE_BLOCK_SIZE><<<num_blocks, CUDA_REDUCE_BLOCK_SIZE, 0, info.all_ctx[i]->stream()>>>(
this_nelem, (const nv_bfloat16 *)info.all_ctx[i]->copy_buffer,
k_add<nv_bfloat16, CUDA_REDUCE_BLOCK_SIZE><<<num_blocks, CUDA_REDUCE_BLOCK_SIZE, 0, all_ctx[i]->stream()>>>(
this_nelem, (const nv_bfloat16 *)all_ctx[i]->copy_buffer,
(nv_bfloat16 *)dst->src[i]->data + ichunk*nelem_per_device);
} else {
k_add<float, CUDA_REDUCE_BLOCK_SIZE><<<num_blocks, CUDA_REDUCE_BLOCK_SIZE, 0, info.all_ctx[i]->stream()>>>(this_nelem,
(const float *)info.all_ctx[i]->copy_buffer, (float *)dst->src[i]->data + ichunk*nelem_per_device);
k_add<float, CUDA_REDUCE_BLOCK_SIZE><<<num_blocks, CUDA_REDUCE_BLOCK_SIZE, 0, all_ctx[i]->stream()>>>(this_nelem,
(const float *)all_ctx[i]->copy_buffer, (float *)dst->src[i]->data + ichunk*nelem_per_device);
}
CUDA_CHECK(cudaEventRecord(info.all_ctx[i]->compute_event, info.all_ctx[i]->stream()));
CUDA_CHECK(cudaEventRecord(all_ctx[i]->compute_event, all_ctx[i]->stream()));
ichunk = (ichunk + 1)%nhave;
}
}
@@ -343,21 +353,21 @@ void ggml_cuda_op_reduce([[maybe_unused]] ggml_backend_cuda_context & ctx, ggml_
int peer = idx[(ii+1)%nhave];
auto this_nelem = std::min(nelem_per_device, nelem - ichunk*nelem_per_device);
auto this_size = (this_nelem / tt.blck_size) * tt.type_size;
ggml_cuda_set_device(info.all_ctx[peer]->device);
ggml_cuda_set_device(all_ctx[peer]->device);
if (stage == 0) {
CUDA_CHECK(cudaStreamWaitEvent(info.all_ctx[peer]->stream(), info.all_ctx[i]->compute_event, 0));
CUDA_CHECK(cudaStreamWaitEvent(all_ctx[peer]->stream(), all_ctx[i]->compute_event, 0));
}
CUDA_CHECK(cudaMemcpyPeerAsync((char *)dst->src[i]->data + ichunk*size_per_device, info.all_ctx[i]->device,
(const char *)dst->src[peer]->data + ichunk*size_per_device, info.all_ctx[peer]->device,
this_size, info.all_ctx[peer]->stream()));
CUDA_CHECK(cudaEventRecord(info.all_ctx[peer]->copy_event, info.all_ctx[peer]->stream()));
CUDA_CHECK(cudaMemcpyPeerAsync((char *)dst->src[i]->data + ichunk*size_per_device, all_ctx[i]->device,
(const char *)dst->src[peer]->data + ichunk*size_per_device, all_ctx[peer]->device,
this_size, all_ctx[peer]->stream()));
CUDA_CHECK(cudaEventRecord(all_ctx[peer]->copy_event, all_ctx[peer]->stream()));
ichunk = (ichunk + 1)%nhave;
}
for (int ii = 0; ii < nhave; ++ii) {
int i = idx[ii];
int peer = idx[(ii+1)%nhave];
ggml_cuda_set_device(info.all_ctx[i]->device);
CUDA_CHECK(cudaStreamWaitEvent(info.all_ctx[i]->stream(), info.all_ctx[peer]->copy_event, 0));
ggml_cuda_set_device(all_ctx[i]->device);
CUDA_CHECK(cudaStreamWaitEvent(all_ctx[i]->stream(), all_ctx[peer]->copy_event, 0));
}
}
ggml_cuda_set_device(ctx.device);
@@ -372,8 +382,8 @@ void ggml_cuda_op_reduce([[maybe_unused]] ggml_backend_cuda_context & ctx, ggml_
GGML_ASSERT(dst->src[i]->type == dst->type);
GGML_ASSERT(ggml_are_same_shape(dst, dst->src[i]));
ggml_cuda_set_device(i);
if (!info.all_ctx[i]->copy_event) {
CUDA_CHECK(cudaEventCreateWithFlags(&info.all_ctx[i]->copy_event, cudaEventDisableTiming));
if (!all_ctx[i]->copy_event) {
CUDA_CHECK(cudaEventCreateWithFlags(&all_ctx[i]->copy_event, cudaEventDisableTiming));
}
}
auto nelem = ggml_nelements(dst);
@@ -386,20 +396,20 @@ void ggml_cuda_op_reduce([[maybe_unused]] ggml_backend_cuda_context & ctx, ggml_
task.ptrs[0] = (char *)dst->src[i]->data;
int j = idx[2*ii+1];
ggml_cuda_set_device(j);
CUDA_CHECK(cudaEventRecord(info.all_ctx[j]->copy_event, info.all_ctx[j]->stream()));
CUDA_CHECK(cudaEventRecord(all_ctx[j]->copy_event, all_ctx[j]->stream()));
task.ptrs[1] = (char *)dst->src[j]->data;
ggml_cuda_set_device(i);
CUDA_CHECK(cudaStreamWaitEvent(info.all_ctx[i]->stream(), info.all_ctx[j]->copy_event));
CUDA_CHECK(cudaStreamWaitEvent(all_ctx[i]->stream(), all_ctx[j]->copy_event));
if (dst->type == GGML_TYPE_F16) {
k_reduce_add_T<half, CUDA_REDUCE_BLOCK_SIZE, 2><<<nblocks, CUDA_REDUCE_BLOCK_SIZE, 0, info.all_ctx[i]->stream()>>>(task);
k_reduce_add_T<half, CUDA_REDUCE_BLOCK_SIZE, 2><<<nblocks, CUDA_REDUCE_BLOCK_SIZE, 0, all_ctx[i]->stream()>>>(task);
} else {
k_reduce_add_T<float, CUDA_REDUCE_BLOCK_SIZE, 2><<<nblocks, CUDA_REDUCE_BLOCK_SIZE, 0, info.all_ctx[i]->stream()>>>(task);
k_reduce_add_T<float, CUDA_REDUCE_BLOCK_SIZE, 2><<<nblocks, CUDA_REDUCE_BLOCK_SIZE, 0, all_ctx[i]->stream()>>>(task);
}
}
for (int ii = 0; ii < nhave/2; ++ii) {
int i = idx[2*ii+0];
ggml_cuda_set_device(i);
CUDA_CHECK(cudaEventRecord(info.all_ctx[i]->copy_event, info.all_ctx[i]->stream()));
CUDA_CHECK(cudaEventRecord(all_ctx[i]->copy_event, all_ctx[i]->stream()));
}
for (int ii = 0; ii < nhave/2; ++ii) {
int i = idx[2*ii+1];
@@ -411,23 +421,23 @@ void ggml_cuda_op_reduce([[maybe_unused]] ggml_backend_cuda_context & ctx, ggml_
int j = idx[(2*ii+2)%nhave];
task.ptrs[1] = (char *)dst->src[j]->data;
ggml_cuda_set_device(i);
CUDA_CHECK(cudaStreamWaitEvent(info.all_ctx[i]->stream(), info.all_ctx[j]->copy_event));
CUDA_CHECK(cudaStreamWaitEvent(all_ctx[i]->stream(), all_ctx[j]->copy_event));
if (dst->type == GGML_TYPE_F16) {
k_reduce_add_T<half, CUDA_REDUCE_BLOCK_SIZE, 2><<<nblocks, CUDA_REDUCE_BLOCK_SIZE, 0, info.all_ctx[i]->stream()>>>(task);
k_reduce_add_T<half, CUDA_REDUCE_BLOCK_SIZE, 2><<<nblocks, CUDA_REDUCE_BLOCK_SIZE, 0, all_ctx[i]->stream()>>>(task);
} else {
k_reduce_add_T<float, CUDA_REDUCE_BLOCK_SIZE, 2><<<nblocks, CUDA_REDUCE_BLOCK_SIZE, 0, info.all_ctx[i]->stream()>>>(task);
k_reduce_add_T<float, CUDA_REDUCE_BLOCK_SIZE, 2><<<nblocks, CUDA_REDUCE_BLOCK_SIZE, 0, all_ctx[i]->stream()>>>(task);
}
}
for (int ii = 0; ii < nhave/2; ++ii) {
int i = idx[2*ii+1];
ggml_cuda_set_device(i);
CUDA_CHECK(cudaEventRecord(info.all_ctx[i]->copy_event, info.all_ctx[i]->stream()));
CUDA_CHECK(cudaEventRecord(all_ctx[i]->copy_event, all_ctx[i]->stream()));
}
for (int ii = 0; ii < nhave/2; ++ii) {
int i = idx[(2*ii+2)%nhave];
ggml_cuda_set_device(i);
int j = idx[2*ii+1];
CUDA_CHECK(cudaStreamWaitEvent(info.all_ctx[i]->stream(), info.all_ctx[j]->copy_event));
CUDA_CHECK(cudaStreamWaitEvent(all_ctx[i]->stream(), all_ctx[j]->copy_event));
}
ggml_cuda_set_device(ctx.device);
if (ncopy > 0) {
@@ -442,10 +452,10 @@ void ggml_cuda_op_reduce([[maybe_unused]] ggml_backend_cuda_context & ctx, ggml_
GGML_ASSERT(dst->src[i]->type == dst->type);
GGML_ASSERT(ggml_are_same_shape(dst, dst->src[i]));
ggml_cuda_set_device(i);
if (!info.all_ctx[i]->copy_event) {
CUDA_CHECK(cudaEventCreateWithFlags(&info.all_ctx[i]->copy_event, cudaEventDisableTiming));
if (!all_ctx[i]->copy_event) {
CUDA_CHECK(cudaEventCreateWithFlags(&all_ctx[i]->copy_event, cudaEventDisableTiming));
}
CUDA_CHECK(cudaEventRecord(info.all_ctx[i]->copy_event, info.all_ctx[i]->stream()));
CUDA_CHECK(cudaEventRecord(all_ctx[i]->copy_event, all_ctx[i]->stream()));
}
//printf("Recorded events\n");
auto nelem = ggml_nelements(dst);
@@ -465,37 +475,37 @@ void ggml_cuda_op_reduce([[maybe_unused]] ggml_backend_cuda_context & ctx, ggml_
for (int jj = 0; jj < nhave; ++jj) {
if (jj == ii) continue;
int j = idx[jj];
CUDA_CHECK(cudaStreamWaitEvent(info.all_ctx[i]->stream(), info.all_ctx[j]->copy_event));
CUDA_CHECK(cudaStreamWaitEvent(all_ctx[i]->stream(), all_ctx[j]->copy_event));
task.ptrs[k++] = (char *)dst->src[j]->data + ii*nelem_per_device*elem_size;
}
int nblock = (this_nelem + CUDA_REDUCE_BLOCK_SIZE - 1)/CUDA_REDUCE_BLOCK_SIZE;
if (dst->type == GGML_TYPE_F16) {
switch (nhave) {
case 2:
k_reduce_add_T<half, CUDA_REDUCE_BLOCK_SIZE, 2><<<nblock, CUDA_REDUCE_BLOCK_SIZE, 0, info.all_ctx[i]->stream()>>>(task);
k_reduce_add_T<half, CUDA_REDUCE_BLOCK_SIZE, 2><<<nblock, CUDA_REDUCE_BLOCK_SIZE, 0, all_ctx[i]->stream()>>>(task);
break;
case 3:
k_reduce_add_T<half, CUDA_REDUCE_BLOCK_SIZE, 3><<<nblock, CUDA_REDUCE_BLOCK_SIZE, 0, info.all_ctx[i]->stream()>>>(task);
k_reduce_add_T<half, CUDA_REDUCE_BLOCK_SIZE, 3><<<nblock, CUDA_REDUCE_BLOCK_SIZE, 0, all_ctx[i]->stream()>>>(task);
break;
case 4:
k_reduce_add_T<half, CUDA_REDUCE_BLOCK_SIZE, 4><<<nblock, CUDA_REDUCE_BLOCK_SIZE, 0, info.all_ctx[i]->stream()>>>(task);
k_reduce_add_T<half, CUDA_REDUCE_BLOCK_SIZE, 4><<<nblock, CUDA_REDUCE_BLOCK_SIZE, 0, all_ctx[i]->stream()>>>(task);
break;
default:
k_reduce_add<half, CUDA_REDUCE_BLOCK_SIZE><<<nblock, CUDA_REDUCE_BLOCK_SIZE, 0, info.all_ctx[i]->stream()>>>(task);
k_reduce_add<half, CUDA_REDUCE_BLOCK_SIZE><<<nblock, CUDA_REDUCE_BLOCK_SIZE, 0, all_ctx[i]->stream()>>>(task);
}
} else {
switch (nhave) {
case 2:
k_reduce_add_T<float, CUDA_REDUCE_BLOCK_SIZE, 2><<<nblock, CUDA_REDUCE_BLOCK_SIZE, 0, info.all_ctx[i]->stream()>>>(task);
k_reduce_add_T<float, CUDA_REDUCE_BLOCK_SIZE, 2><<<nblock, CUDA_REDUCE_BLOCK_SIZE, 0, all_ctx[i]->stream()>>>(task);
break;
case 3:
k_reduce_add_T<float, CUDA_REDUCE_BLOCK_SIZE, 3><<<nblock, CUDA_REDUCE_BLOCK_SIZE, 0, info.all_ctx[i]->stream()>>>(task);
k_reduce_add_T<float, CUDA_REDUCE_BLOCK_SIZE, 3><<<nblock, CUDA_REDUCE_BLOCK_SIZE, 0, all_ctx[i]->stream()>>>(task);
break;
case 4:
k_reduce_add_T<float, CUDA_REDUCE_BLOCK_SIZE, 4><<<nblock, CUDA_REDUCE_BLOCK_SIZE, 0, info.all_ctx[i]->stream()>>>(task);
k_reduce_add_T<float, CUDA_REDUCE_BLOCK_SIZE, 4><<<nblock, CUDA_REDUCE_BLOCK_SIZE, 0, all_ctx[i]->stream()>>>(task);
break;
default:
k_reduce_add<float, CUDA_REDUCE_BLOCK_SIZE><<<nblock, CUDA_REDUCE_BLOCK_SIZE, 0, info.all_ctx[i]->stream()>>>(task);
k_reduce_add<float, CUDA_REDUCE_BLOCK_SIZE><<<nblock, CUDA_REDUCE_BLOCK_SIZE, 0, all_ctx[i]->stream()>>>(task);
}
}
}
@@ -503,7 +513,7 @@ void ggml_cuda_op_reduce([[maybe_unused]] ggml_backend_cuda_context & ctx, ggml_
for (int ii = 0; ii < nhave; ++ii) {
int i = idx[ii];
ggml_cuda_set_device(i);
CUDA_CHECK(cudaEventRecord(info.all_ctx[i]->copy_event, info.all_ctx[i]->stream()));
CUDA_CHECK(cudaEventRecord(all_ctx[i]->copy_event, all_ctx[i]->stream()));
}
//printf("Recorded events again\n");
for (int ii = 0; ii < nhave; ++ii) {
@@ -512,7 +522,7 @@ void ggml_cuda_op_reduce([[maybe_unused]] ggml_backend_cuda_context & ctx, ggml_
for (int jj = 0; jj < nhave; ++jj) {
if (jj == ii) continue;
int j = idx[jj];
CUDA_CHECK(cudaStreamWaitEvent(info.all_ctx[i]->stream(), info.all_ctx[j]->copy_event));
CUDA_CHECK(cudaStreamWaitEvent(all_ctx[i]->stream(), all_ctx[j]->copy_event));
}
}
ggml_cuda_set_device(ctx.device);
@@ -536,11 +546,11 @@ void ggml_cuda_op_reduce([[maybe_unused]] ggml_backend_cuda_context & ctx, ggml_
GGML_ASSERT(ggml_are_same_shape(dst, dst->src[i]));
if (i == ctx.device) continue;
ggml_cuda_set_device(i);
CUDA_CHECK(cudaMemcpyPeerAsync(ptr, ctx.device, dst->src[i]->data, i, nbytes, info.all_ctx[i]->stream()));
if (!info.all_ctx[i]->copy_event) {
CUDA_CHECK(cudaEventCreateWithFlags(&info.all_ctx[i]->copy_event, cudaEventDisableTiming));
CUDA_CHECK(cudaMemcpyPeerAsync(ptr, ctx.device, dst->src[i]->data, i, nbytes, all_ctx[i]->stream()));
if (!all_ctx[i]->copy_event) {
CUDA_CHECK(cudaEventCreateWithFlags(&all_ctx[i]->copy_event, cudaEventDisableTiming));
}
CUDA_CHECK(cudaEventRecord(info.all_ctx[i]->copy_event, info.all_ctx[i]->stream()));
CUDA_CHECK(cudaEventRecord(all_ctx[i]->copy_event, all_ctx[i]->stream()));
ptr += nbytes;
}
auto nelem = ggml_nelements(dst);
@@ -550,7 +560,7 @@ void ggml_cuda_op_reduce([[maybe_unused]] ggml_backend_cuda_context & ctx, ggml_
for (int ii = 0; ii < nhave; ++ii) {
int i = idx[ii];
if (i == ctx.device) continue;
CUDA_CHECK(cudaStreamWaitEvent(ctx.stream(), info.all_ctx[i]->copy_event, 0));
CUDA_CHECK(cudaStreamWaitEvent(ctx.stream(), all_ctx[i]->copy_event, 0));
if (dst->type == GGML_TYPE_F16) {
k_add<half, CUDA_REDUCE_BLOCK_SIZE><<<num_blocks, CUDA_REDUCE_BLOCK_SIZE, 0, ctx.stream()>>>(nelem, (const half *)ptr, (half *)dst->data);
} else if (dst->type == GGML_TYPE_BF16) {
@@ -572,15 +582,15 @@ void ggml_cuda_op_reduce([[maybe_unused]] ggml_backend_cuda_context & ctx, ggml_
int i = idx[ii];
if (i == ctx.device) continue;
ggml_cuda_set_device(i);
CUDA_CHECK(cudaStreamWaitEvent(info.all_ctx[i]->stream(), ctx.copy_event, 0));
CUDA_CHECK(cudaMemcpyPeerAsync(dst->src[i]->data, i, dst->data, ctx.device, nbytes, info.all_ctx[i]->stream()));
CUDA_CHECK(cudaEventRecord(info.all_ctx[i]->copy_event, info.all_ctx[i]->stream()));
CUDA_CHECK(cudaStreamWaitEvent(all_ctx[i]->stream(), ctx.copy_event, 0));
CUDA_CHECK(cudaMemcpyPeerAsync(dst->src[i]->data, i, dst->data, ctx.device, nbytes, all_ctx[i]->stream()));
CUDA_CHECK(cudaEventRecord(all_ctx[i]->copy_event, all_ctx[i]->stream()));
}
ggml_cuda_set_device(ctx.device);
for (int ii = 0; ii < nhave; ++ii) {
int i = idx[ii];
if (i == ctx.device) continue;
CUDA_CHECK(cudaStreamWaitEvent(ctx.stream(), info.all_ctx[i]->copy_event, 0));
CUDA_CHECK(cudaStreamWaitEvent(ctx.stream(), all_ctx[i]->copy_event, 0));
}
if (ncopy > 0) {
copy_missing_tensors(ctx, dst, nhave, ncopy, idx, copy_idx);
src/graphs/build_gemma4.cpp
+4
View File
@@ -626,6 +626,7 @@ ggml_cgraph * llm_build_context::build_gemma4_mtp() {
ggml_tensor * KQ_mask_l = is_sliding ? KQ_mask_swa : KQ_mask;
const int target_il = gemma4_mtp_target_kv_layer(hparams, target_hparams, il);
printf("--- Layer %d: is_sliding = %d, freq_base_l = %g, freq_scale_l = %g, n_rot_l = %d, n_swa = %d, n_embd_head = %d\n", il, is_sliding, freq_base_l, freq_scale_l, n_rot_l, n_swa, n_embd_head);
auto split_kl = (const ggml_split_tensor_t *)target_kv.k_l[target_il]->extra;
auto split_vl = (const ggml_split_tensor_t *)target_kv.v_l[target_il]->extra;
@@ -690,10 +691,13 @@ ggml_cgraph * llm_build_context::build_gemma4_mtp() {
auto v = ggml_view_3d(ctx0, split_vl->splits[id], n_embd_head, target_n_kv, n_head_kv,
ggml_row_size(split_vl->splits[id]->type, n_embd_head)*n_head_kv,
ggml_row_size(split_vl->splits[id]->type, n_embd_head), 0);
printf("id = %d: q = %ld x %ld x %ld, k = %ld x %ld x %ld, v = %ld x %ld x %ld\n", id, q->ne[0], q->ne[1], q->ne[2], k->ne[0], k->ne[1], k->ne[2], v->ne[0], v->ne[1], v->ne[2]);
cur = ggml_flash_attn_ext(ctx0, q, k, v, KQ_mask_l, hparams.f_attention_scale, 0.0f, 0.0f);
cur->op_params[4] = n_swa;
cb(cur, "fa", il_cb);
printf(" -> %ld x %ld x %ld", cur->ne[0], cur->ne[1], cur->ne[2]);
cur = ggml_reshape_2d(ctx0, cur, split_ol->splits[id]->ne[0], ggml_nelements(cur)/split_ol->splits[id]->ne[0]);
printf(" -> %ld x %ld x %ld\n", cur->ne[0], cur->ne[1], cur->ne[2]);
cur = llm_build_lora_mm(lctx, ctx0, split_ol->splits[id], cur);
cb(cur, "qkv", il_cb);
ggml_build_forward_expand(gf, cur);
src/llama-model-loader.cpp
+2 -1
View File
@@ -1053,6 +1053,7 @@ void llama_model_loader::load_data_for(struct ggml_tensor * cur) const {
// Returns false if cancelled by progress_callback
bool llama_model_loader::load_all_data(
struct ggml_context * ctx,
struct llama_model * model,
llama_buf_map & bufs_mmap,
llama_mlocks * lmlocks,
llama_progress_callback progress_callback,
@@ -1083,7 +1084,7 @@ bool llama_model_loader::load_all_data(
for (int i = 0; i < ggml_backend_cuda_get_device_count(); ++i) {
auto * cuda_buffer_type = ggml_backend_cuda_buffer_type(i);
if (buffer_type == cuda_buffer_type) {
cuda_backend = ggml_backend_cuda_init(i, nullptr);
cuda_backend = ggml_backend_cuda_init(i, nullptr, model);
break;
}
}
src/llama-model-loader.h
+1
View File
@@ -184,6 +184,7 @@ struct llama_model_loader {
// Returns false if cancelled by progress_callback
bool load_all_data(
struct ggml_context * ctx,
struct llama_model * model,
llama_buf_map & bufs_mmap,
llama_mlocks * lmlocks,
llama_progress_callback progress_callback,
src/llama-reload.cpp
+1 -1
View File
@@ -939,7 +939,7 @@ bool reload_info::reload_changed_tensors(llama_model & model) {
if (r) {
#ifdef GGML_USE_CUDA
ggml_backend_cuda_invalidate_graphs();
ggml_backend_cuda_invalidate_graphs(&model);
#endif
}
return r;
src/llama.cpp
+3 -3
View File
@@ -4016,7 +4016,7 @@ static bool llm_load_tensors(
for (auto & it : ctx_bufs) {
ggml_context * ctx = it.first;
auto & bufs = it.second;
if (!ml.load_all_data(ctx, bufs, use_mlock ? &model.mlock_mmaps : NULL, progress_callback, progress_callback_user_data)) {
if (!ml.load_all_data(ctx, &model, bufs, use_mlock ? &model.mlock_mmaps : NULL, progress_callback, progress_callback_user_data)) {
return false;
}
}
@@ -7164,7 +7164,7 @@ struct llama_context * llama_init_from_model(
#elif defined(GGML_USE_CUDA)
if (model->split_mode == LLAMA_SPLIT_MODE_NONE) {
// with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_GRAPH, only the main GPU backend is used
ggml_backend_t backend = ggml_backend_cuda_init(main_gpu_id, cparams.cuda_params);
ggml_backend_t backend = ggml_backend_cuda_init(main_gpu_id, cparams.cuda_params, model);
if (backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, main_gpu_id);
llama_free(ctx);
@@ -7183,7 +7183,7 @@ struct llama_context * llama_init_from_model(
params = new_params.data();
}
for (int device = 0; device < ggml_backend_cuda_get_device_count(); ++device) {
ggml_backend_t backend = ggml_backend_cuda_init(device, params);
ggml_backend_t backend = ggml_backend_cuda_init(device, params, model);
if (backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, device);
llama_free(ctx);