Introduction of CUDA Graphs to LLama.cpp (#6766)

* DRAFT: Introduction of CUDA Graphs to LLama.cpp

* FIx issues raised in comments

* Tidied to now only use CUDA runtime (not mixed with driver calls)

* disable for multi-gpu and batch size > 1

* Disable CUDA graphs for old GPU arch and with env var

* added missing CUDA_CHECKs

* Addressed comments

* further addressed comments

* limit to GGML_ALLOW_CUDA_GRAPHS defined in llama.cpp cmake

* Added more comprehensive graph node checking

* With mechanism to fall back if graph capture fails

* Revert "With mechanism to fall back if graph capture fails"

This reverts commit eb9f15fb6f.

* Fall back if graph capture fails and address other comments

* - renamed GGML_ALLOW_CUDA_GRAPHS to GGML_CUDA_USE_GRAPHS

- rename env variable to disable CUDA graphs to GGML_CUDA_DISABLE_GRAPHS

- updated Makefile build to enable CUDA graphs

- removed graph capture failure checking in ggml_cuda_error
  using a global variable to track this is not thread safe, but I am also not safistied with checking an error by string
  if this is necessary to workaround some issues with graph capture with eg. cuBLAS, we can pass the ggml_backend_cuda_context to the error checking macro and store the result in the context

- fixed several resource leaks

- fixed issue with zero node graphs

- changed fixed size arrays to vectors

- removed the count of number of evaluations before start capturing, and instead changed the capture mode to relaxed

- removed the check for multiple devices so that it is still possible to use a single device, instead checks for split buffers to disable cuda graphs with -sm row

- changed the op for checking batch size to GGML_OP_ADD, should be more reliable than GGML_OP_SOFT_MAX

- code style fixes

- things to look into
  - VRAM usage of the cudaGraphExec_t, if it is significant we may need to make it optional
  - possibility of using cudaStreamBeginCaptureToGraph to keep track of which ggml graph nodes correspond to which cuda graph nodes

* fix build without cuda graphs

* remove outdated comment

* replace minimum cc value with a constant

---------

Co-authored-by: slaren <slarengh@gmail.com>
This commit is contained in:
agray3 2024-05-08 21:55:49 +01:00 committed by GitHub
parent c12452c7ae
commit bc4bba364f
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GPG Key ID: B5690EEEBB952194
11 changed files with 372 additions and 44 deletions

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@ -405,6 +405,7 @@ if (LLAMA_CUDA)
list(APPEND GGML_SOURCES_CUDA "ggml-cuda.cu") list(APPEND GGML_SOURCES_CUDA "ggml-cuda.cu")
add_compile_definitions(GGML_USE_CUDA) add_compile_definitions(GGML_USE_CUDA)
add_compile_definitions(GGML_CUDA_USE_GRAPHS)
if (LLAMA_CUDA_FORCE_DMMV) if (LLAMA_CUDA_FORCE_DMMV)
add_compile_definitions(GGML_CUDA_FORCE_DMMV) add_compile_definitions(GGML_CUDA_FORCE_DMMV)
endif() endif()

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@ -433,7 +433,7 @@ ifdef LLAMA_CUDA
else else
CUDA_PATH ?= /usr/local/cuda CUDA_PATH ?= /usr/local/cuda
endif endif
MK_CPPFLAGS += -DGGML_USE_CUDA -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include MK_CPPFLAGS += -DGGML_USE_CUDA -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include -DGGML_CUDA_USE_GRAPHS
MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L/usr/lib/wsl/lib MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L/usr/lib/wsl/lib
OBJS += ggml-cuda.o OBJS += ggml-cuda.o
OBJS += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu)) OBJS += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu))

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@ -2410,11 +2410,184 @@ GGML_CALL static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
GGML_UNUSED(backend); GGML_UNUSED(backend);
} }
static void set_ggml_graph_node_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
graph_node_properties->node_address = node->data;
graph_node_properties->node_op = node->op;
for (int i = 0; i < GGML_MAX_DIMS; i++) {
graph_node_properties->ne[i] = node->ne[i];
graph_node_properties->nb[i] = node->nb[i];
}
for (int i = 0; i < GGML_MAX_SRC; i++) {
graph_node_properties->src_address[i] = node->src[i] ? node->src[i]->data : nullptr;
}
}
static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
if (node->data != graph_node_properties->node_address &&
node->op != GGML_OP_CPY &&
node->op != GGML_OP_VIEW) {
return false;
}
if (node->op != graph_node_properties->node_op) {
return false;
}
for (int i = 0; i < GGML_MAX_DIMS; i++) {
if (node->ne[i] != graph_node_properties->ne[i]) {
return false;
}
if (node->nb[i] != graph_node_properties->nb[i]) {
return false;
}
}
for (int i = 0; i < GGML_MAX_SRC; i++) {
if (node->src[i] &&
node->src[i]->data != graph_node_properties->src_address[i] &&
node->op != GGML_OP_CPY &&
node->op != GGML_OP_VIEW
) {
return false;
}
}
return true;
}
GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
ggml_cuda_set_device(cuda_ctx->device); ggml_cuda_set_device(cuda_ctx->device);
#ifdef USE_CUDA_GRAPH
static const bool disable_cuda_graphs_due_to_env = (getenv("GGML_CUDA_DISABLE_GRAPHS") != nullptr);
// Objects required for CUDA Graph
if (cuda_ctx->cuda_graph == nullptr) {
cuda_ctx->cuda_graph.reset(new ggml_cuda_graph());
}
bool use_cuda_graph = true;
bool cuda_graph_update_required = false;
// pointer to CUDA cpy kernel, which is required to identify
// kernel parameters which need updated in the graph for each token
void * ggml_cuda_cpy_fn_ptr = nullptr;
if (cuda_ctx->cuda_graph->graph == nullptr) {
if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) {
cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true;
#ifndef NDEBUG
fprintf(stderr, "%s: disabling CUDA graphs due to GPU architecture\n", __func__);
#endif
}
}
// Disable CUDA graphs in presence of env var, old GPU, use-case which is changing too rapidly,
// or previous graph capture failure.
// Also disable for multi-gpu for now. TO DO investigate
if (disable_cuda_graphs_due_to_env
|| cuda_ctx->cuda_graph->disable_due_to_gpu_arch
|| cuda_ctx->cuda_graph->disable_due_to_too_many_updates
|| cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture) {
use_cuda_graph = false;
}
if (use_cuda_graph) {
if (cuda_ctx->cuda_graph->instance == nullptr) {
cuda_graph_update_required = true;
}
// Check if the graph size has changed
if (cuda_ctx->cuda_graph->ggml_graph_properties.size() != (size_t)cgraph->n_nodes) {
cuda_graph_update_required = true;
cuda_ctx->cuda_graph->ggml_graph_properties.resize(cgraph->n_nodes);
}
// Loop over nodes in GGML graph to determine if CUDA graph update is required
// and store properties to allow this comparison for the next token
for (int i = 0; i < cgraph->n_nodes; i++) {
bool has_matching_properties = true;
if (!cuda_graph_update_required) {
has_matching_properties = ggml_graph_node_has_matching_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]);
}
if (!has_matching_properties) {
cuda_graph_update_required = true;
}
set_ggml_graph_node_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]);
}
// Loop over nodes in GGML graph to obtain info needed for CUDA graph
cuda_ctx->cuda_graph->updated_kernel_arg.clear();
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
if (node->src[0] && ggml_backend_buffer_is_cuda_split(node->src[0]->buffer)) {
use_cuda_graph = false; // Split buffers are not supported by CUDA graph capture
#ifndef NDEBUG
fprintf(stderr, "%s: disabling CUDA graphs due to split buffer\n", __func__);
#endif
}
if (node->op == GGML_OP_MUL_MAT_ID) {
use_cuda_graph = false; // This node type is not supported by CUDA graph capture
#ifndef NDEBUG
fprintf(stderr, "%s: disabling CUDA graphs due to mul_mat_id\n", __func__);
#endif
}
if (node->op == GGML_OP_ADD && node->src[1] && node->src[1]->ne[1] > 1) {
// disable CUDA graphs for batch size > 1 for now.
// Changes in batch size or context size can cause changes to the grid size of some kernels.
use_cuda_graph = false;
#ifndef NDEBUG
fprintf(stderr, "%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]);
#endif
}
if (node->op == GGML_OP_CPY) {
// store the copy op parameter which changes with each token.
cuda_ctx->cuda_graph->updated_kernel_arg.push_back((char **) &(node->src[1]->data));
if (ggml_cuda_cpy_fn_ptr == nullptr) {
// store a pointer to the copy op CUDA kernel to identify it later
ggml_cuda_cpy_fn_ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]);
}
}
if (!use_cuda_graph) {
break;
}
}
// Disable CUDA graphs (from the next token) if the use-case is demanding too many consecutive graph updates.
if (cuda_graph_update_required) {
cuda_ctx->cuda_graph->number_consecutive_updates++;
} else {
cuda_ctx->cuda_graph->number_consecutive_updates = 0;
}
if (cuda_ctx->cuda_graph->number_consecutive_updates >= 4) {
cuda_ctx->cuda_graph->disable_due_to_too_many_updates = true;
#ifndef NDEBUG
fprintf(stderr, "%s: disabling CUDA graphs due to too many consecutive updates\n", __func__);
#endif
}
}
if (use_cuda_graph && cuda_graph_update_required) { // Start CUDA graph capture
CUDA_CHECK(cudaStreamBeginCapture(cuda_ctx->stream(), cudaStreamCaptureModeRelaxed));
}
#else
bool use_cuda_graph = false;
bool cuda_graph_update_required = false;
#endif // USE_CUDA_GRAPH
bool graph_evaluated_or_captured = false;
while (!graph_evaluated_or_captured) {
// Only perform the graph execution if CUDA graphs are not enabled, or we are capturing the graph.
// With the use of CUDA graphs, the execution will be performed by the graph launch.
if (!use_cuda_graph || cuda_graph_update_required) {
for (int i = 0; i < cgraph->n_nodes; i++) { for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i]; ggml_tensor * node = cgraph->nodes[i];
@ -2437,6 +2610,105 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
} }
GGML_ASSERT(ok); GGML_ASSERT(ok);
} }
}
#ifdef USE_CUDA_GRAPH
if (use_cuda_graph && cuda_graph_update_required) { // End CUDA graph capture
if (cuda_ctx->cuda_graph->graph != nullptr) {
CUDA_CHECK(cudaGraphDestroy(cuda_ctx->cuda_graph->graph));
cuda_ctx->cuda_graph->graph = nullptr;
}
CUDA_CHECK(cudaStreamEndCapture(cuda_ctx->stream(), &cuda_ctx->cuda_graph->graph));
#if 0
if (disable_cuda_graphs_due_to_failed_capture) {
use_cuda_graph = false;
cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture = true;
#ifndef NDEBUG
fprintf(stderr, "%s: disabling CUDA graphs due to failed graph capture\n", __func__);
#endif
} else {
graph_evaluated_or_captured = true; // CUDA graph has been captured
}
#endif
graph_evaluated_or_captured = true; // CUDA graph has been captured
} else {
graph_evaluated_or_captured = true; // ggml graph has been directly evaluated
}
}
if (use_cuda_graph) {
if (cuda_ctx->cuda_graph->instance == nullptr) { // Create executable graph from captured graph.
CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0));
}
// Perform update to graph (if required for this token), and change copy parameter (required for every token)
if (cuda_graph_update_required) {
// Extract nodes from graph
if (cuda_ctx->cuda_graph->num_nodes == 0) {
// First call with null argument gets number of nodes in graph
CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, nullptr, &cuda_ctx->cuda_graph->num_nodes));
}
// Subsequent call with non-null argument gets nodes
cuda_ctx->cuda_graph->nodes.resize(cuda_ctx->cuda_graph->num_nodes);
cuda_ctx->cuda_graph->params.resize(cuda_ctx->cuda_graph->num_nodes);
if (cuda_ctx->cuda_graph->num_nodes > 0) {
CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, cuda_ctx->cuda_graph->nodes.data(), &cuda_ctx->cuda_graph->num_nodes));
// Loop over nodes, and extract kernel parameters from each node
for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
cudaGraphNodeType node_type;
CUDA_CHECK(cudaGraphNodeGetType(cuda_ctx->cuda_graph->nodes[i], &node_type));
if (node_type == cudaGraphNodeTypeKernel) {
cudaError_t stat = cudaGraphKernelNodeGetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]); // Get params using runtime
if (stat == cudaErrorInvalidDeviceFunction) {
// Fails due to incorrect handling by CUDA runtime of CUDA BLAS node.
// We don't need to update blas nodes, so clear error and move on.
cudaGetLastError();
} else {
GGML_ASSERT(stat == cudaSuccess);
}
}
}
}
}
// One of the arguments to the copy kernel is updated for each token, hence we need to
// replace that argument with the updated value in the CUDA graph
if (!cuda_graph_update_required) { // on update steps, the live parameters will already be captured
int k = 0;
for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
if (cuda_ctx->cuda_graph->params[i].func == ggml_cuda_cpy_fn_ptr) {
char ** updated_kernel_arg_ptr = cuda_ctx->cuda_graph->updated_kernel_arg.at(k++);
cuda_ctx->cuda_graph->params[i].kernelParams[1] = updated_kernel_arg_ptr;
CUDA_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]));
}
}
}
// Update graph executable
cudaGraphExecUpdateResultInfo result_info;
cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info);
if (stat == cudaErrorGraphExecUpdateFailure) {
#ifndef NDEBUG
fprintf(stderr, "%s: CUDA graph update failed\n", __func__);
#endif
// The pre-existing graph exec cannot be updated due to violated constraints
// so instead clear error and re-instantiate
cudaGetLastError();
CUDA_CHECK(cudaGraphExecDestroy(cuda_ctx->cuda_graph->instance));
cuda_ctx->cuda_graph->instance = nullptr;
CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0));
} else {
GGML_ASSERT(stat == cudaSuccess);
}
// Launch graph
CUDA_CHECK(cudaGraphLaunch(cuda_ctx->cuda_graph->instance, cuda_ctx->stream()));
#else
graph_evaluated_or_captured = true;
#endif // USE_CUDA_GRAPH
}
return GGML_STATUS_SUCCESS; return GGML_STATUS_SUCCESS;
} }

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@ -31,5 +31,4 @@ void ggml_cuda_op_clamp(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); memcpy(&max, (float *) dst->op_params + 1, sizeof(float));
clamp_f32_cuda(src0_d, dst_d, min, max, ggml_nelements(src0), stream); clamp_f32_cuda(src0_d, dst_d, min, max, ggml_nelements(src0), stream);
CUDA_CHECK(cudaGetLastError());
} }

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@ -19,6 +19,7 @@
#include <cassert> #include <cassert>
#include <cfloat> #include <cfloat>
#include <string> #include <string>
#include <vector>
#if defined(GGML_USE_HIPBLAS) #if defined(GGML_USE_HIPBLAS)
#include <hip/hip_runtime.h> #include <hip/hip_runtime.h>
@ -526,6 +527,43 @@ struct ggml_tensor_extra_gpu {
cudaEvent_t events[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS]; // events for synchronizing multiple GPUs cudaEvent_t events[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS]; // events for synchronizing multiple GPUs
}; };
#if (CUDART_VERSION >= 12000) && defined(GGML_CUDA_USE_GRAPHS)
#define USE_CUDA_GRAPH
#endif
struct ggml_graph_node_properties {
void * node_address;
ggml_op node_op;
int64_t ne[GGML_MAX_DIMS];
size_t nb[GGML_MAX_DIMS];
void * src_address[GGML_MAX_SRC];
};
struct ggml_cuda_graph {
#ifdef USE_CUDA_GRAPH
~ggml_cuda_graph() {
if (instance != nullptr) {
CUDA_CHECK(cudaGraphExecDestroy(instance));
}
if (graph != nullptr) {
CUDA_CHECK(cudaGraphDestroy(graph));
}
}
cudaGraph_t graph = nullptr;
cudaGraphExec_t instance = nullptr;
size_t num_nodes = 0;
std::vector<cudaGraphNode_t> nodes;
std::vector<cudaKernelNodeParams> params;
bool disable_due_to_gpu_arch = false;
bool disable_due_to_too_many_updates = false;
bool disable_due_to_failed_graph_capture = false;
int number_consecutive_updates = 0;
std::vector<ggml_graph_node_properties> ggml_graph_properties;
std::vector<char **> updated_kernel_arg;
#endif
};
struct ggml_backend_cuda_context { struct ggml_backend_cuda_context {
int device; int device;
std::string name; std::string name;
@ -534,6 +572,8 @@ struct ggml_backend_cuda_context {
cudaStream_t streams[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS] = { { nullptr } }; cudaStream_t streams[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS] = { { nullptr } };
cublasHandle_t cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr}; cublasHandle_t cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr};
std::unique_ptr<ggml_cuda_graph> cuda_graph;
explicit ggml_backend_cuda_context(int device) : explicit ggml_backend_cuda_context(int device) :
device(device), device(device),
name(GGML_CUDA_NAME + std::to_string(device)) { name(GGML_CUDA_NAME + std::to_string(device)) {

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@ -727,7 +727,6 @@ static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict_
} }
to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
int id;
switch (type) { switch (type) {
case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_0:
return dequantize_row_q4_0_cuda; return dequantize_row_q4_0_cuda;
@ -738,8 +737,7 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
case GGML_TYPE_Q5_1: case GGML_TYPE_Q5_1:
return dequantize_block_cuda<QK5_1, QR5_1, dequantize_q5_1>; return dequantize_block_cuda<QK5_1, QR5_1, dequantize_q5_1>;
case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_0:
CUDA_CHECK(cudaGetDevice(&id)); if (ggml_cuda_info().devices[ggml_cuda_get_device()].cc >= CC_PASCAL) {
if (ggml_cuda_info().devices[id].cc >= CC_PASCAL) {
return dequantize_block_q8_0_f16_cuda; return dequantize_block_q8_0_f16_cuda;
} }
return dequantize_block_cuda<QK8_0, QR8_0, dequantize_q8_0>; return dequantize_block_cuda<QK8_0, QR8_0, dequantize_q8_0>;

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@ -459,3 +459,32 @@ void ggml_cuda_dup(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0]; const ggml_tensor * src0 = dst->src[0];
ggml_cuda_cpy(ctx, src0, dst); ggml_cuda_cpy(ctx, src0, dst);
} }
void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) {
if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
return (void*) cpy_f32_f16<cpy_1_f32_f32>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
return (void*) cpy_f32_f16<cpy_1_f32_f16>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
return (void*) cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
return (void*) cpy_f32_q<cpy_blck_f32_q4_0, QK4_0>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
return (void*) cpy_f32_q<cpy_blck_f32_q4_1, QK4_1>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_0) {
return (void*) cpy_f32_q<cpy_blck_f32_q5_0, QK5_0>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_IQ4_NL) {
return (void*) cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_1) {
return (void*) cpy_f32_q<cpy_blck_f32_q5_1, QK5_1>;
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
return (void*) cpy_f32_f16<cpy_1_f32_f16>;
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
return (void*) cpy_f32_f16<cpy_1_f16_f32>;
} else {
fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
ggml_type_name(src0->type), ggml_type_name(src1->type));
GGML_ASSERT(false);
}
}

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@ -5,3 +5,5 @@
void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1); void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1);
void ggml_cuda_dup(ggml_backend_cuda_context & ctx, ggml_tensor * dst); void ggml_cuda_dup(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1);

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@ -1735,8 +1735,7 @@ static void ggml_mul_mat_q4_0_q8_1_cuda(
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
int id; int id = ggml_cuda_get_device();
CUDA_CHECK(cudaGetDevice(&id));
const int compute_capability = ggml_cuda_info().devices[id].cc; const int compute_capability = ggml_cuda_info().devices[id].cc;
int mmq_x, mmq_y, nwarps; int mmq_x, mmq_y, nwarps;
@ -1780,8 +1779,7 @@ static void ggml_mul_mat_q4_1_q8_1_cuda(
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
int id; int id = ggml_cuda_get_device();
CUDA_CHECK(cudaGetDevice(&id));
const int compute_capability = ggml_cuda_info().devices[id].cc; const int compute_capability = ggml_cuda_info().devices[id].cc;
int mmq_x, mmq_y, nwarps; int mmq_x, mmq_y, nwarps;
@ -1825,8 +1823,7 @@ static void ggml_mul_mat_q5_0_q8_1_cuda(
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
int id; int id = ggml_cuda_get_device();
CUDA_CHECK(cudaGetDevice(&id));
const int compute_capability = ggml_cuda_info().devices[id].cc; const int compute_capability = ggml_cuda_info().devices[id].cc;
int mmq_x, mmq_y, nwarps; int mmq_x, mmq_y, nwarps;
@ -1870,8 +1867,7 @@ static void ggml_mul_mat_q5_1_q8_1_cuda(
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
int id; int id = ggml_cuda_get_device();
CUDA_CHECK(cudaGetDevice(&id));
const int compute_capability = ggml_cuda_info().devices[id].cc; const int compute_capability = ggml_cuda_info().devices[id].cc;
int mmq_x, mmq_y, nwarps; int mmq_x, mmq_y, nwarps;
@ -1915,8 +1911,7 @@ static void ggml_mul_mat_q8_0_q8_1_cuda(
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
int id; int id = ggml_cuda_get_device();
CUDA_CHECK(cudaGetDevice(&id));
const int compute_capability = ggml_cuda_info().devices[id].cc; const int compute_capability = ggml_cuda_info().devices[id].cc;
int mmq_x, mmq_y, nwarps; int mmq_x, mmq_y, nwarps;
@ -1960,8 +1955,7 @@ static void ggml_mul_mat_q2_K_q8_1_cuda(
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
int id; int id = ggml_cuda_get_device();
CUDA_CHECK(cudaGetDevice(&id));
const int compute_capability = ggml_cuda_info().devices[id].cc; const int compute_capability = ggml_cuda_info().devices[id].cc;
int mmq_x, mmq_y, nwarps; int mmq_x, mmq_y, nwarps;
@ -2007,8 +2001,7 @@ static void ggml_mul_mat_q3_K_q8_1_cuda(
#if QK_K == 256 #if QK_K == 256
int id; int id = ggml_cuda_get_device();
CUDA_CHECK(cudaGetDevice(&id));
const int compute_capability = ggml_cuda_info().devices[id].cc; const int compute_capability = ggml_cuda_info().devices[id].cc;
int mmq_x, mmq_y, nwarps; int mmq_x, mmq_y, nwarps;
@ -2053,8 +2046,7 @@ static void ggml_mul_mat_q4_K_q8_1_cuda(
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
int id; int id = ggml_cuda_get_device();
CUDA_CHECK(cudaGetDevice(&id));
const int compute_capability = ggml_cuda_info().devices[id].cc; const int compute_capability = ggml_cuda_info().devices[id].cc;
int mmq_x, mmq_y, nwarps; int mmq_x, mmq_y, nwarps;
@ -2098,8 +2090,7 @@ static void ggml_mul_mat_q5_K_q8_1_cuda(
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
int id; int id = ggml_cuda_get_device();
CUDA_CHECK(cudaGetDevice(&id));
const int compute_capability = ggml_cuda_info().devices[id].cc; const int compute_capability = ggml_cuda_info().devices[id].cc;
int mmq_x, mmq_y, nwarps; int mmq_x, mmq_y, nwarps;
@ -2143,8 +2134,7 @@ static void ggml_mul_mat_q6_K_q8_1_cuda(
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
int id; int id = ggml_cuda_get_device();
CUDA_CHECK(cudaGetDevice(&id));
const int compute_capability = ggml_cuda_info().devices[id].cc; const int compute_capability = ggml_cuda_info().devices[id].cc;
int mmq_x, mmq_y, nwarps; int mmq_x, mmq_y, nwarps;

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@ -89,8 +89,7 @@ static void mul_mat_vec_q_cuda(
GGML_ASSERT(ncols_x % qk == 0); GGML_ASSERT(ncols_x % qk == 0);
GGML_ASSERT(ncols_y <= MMVQ_MAX_BATCH_SIZE); GGML_ASSERT(ncols_y <= MMVQ_MAX_BATCH_SIZE);
int id; int id = ggml_cuda_get_device();
CUDA_CHECK(cudaGetDevice(&id));
int64_t nwarps = 1; int64_t nwarps = 1;
int64_t rows_per_cuda_block = 1; int64_t rows_per_cuda_block = 1;
@ -328,8 +327,7 @@ void ggml_cuda_op_mul_mat_vec_q(
const int64_t ne0 = dst->ne[0]; const int64_t ne0 = dst->ne[0];
int id; int id = ggml_cuda_get_device();
CUDA_CHECK(cudaGetDevice(&id));
// the main device has a larger memory buffer to hold the results from all GPUs // the main device has a larger memory buffer to hold the results from all GPUs
// nrows_dst == nrows of the matrix that the kernel writes into // nrows_dst == nrows of the matrix that the kernel writes into

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@ -28,5 +28,4 @@ void ggml_cuda_op_scale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
memcpy(&scale, dst->op_params, sizeof(float)); memcpy(&scale, dst->op_params, sizeof(float));
scale_f32_cuda(src0_d, dst_d, scale, ggml_nelements(src0), stream); scale_f32_cuda(src0_d, dst_d, scale, ggml_nelements(src0), stream);
CUDA_CHECK(cudaGetLastError());
} }