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ggml-opt: fix data corruption (ggml/1022)
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@JohannesGaessler @ggerganov
JohannesGaessler authored and ggerganov committed Nov 21, 2024
1 parent 9abe9ee commit 02e4eaf
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Showing 5 changed files with 129 additions and 118 deletions.
2 changes: 2 additions & 0 deletions ggml/src/ggml-backend.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -252,6 +252,7 @@ void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_ten
}

void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
GGML_ASSERT(tensor);
ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;

if (size == 0) {
Expand All @@ -266,6 +267,7 @@ void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, siz
}

void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
GGML_ASSERT(tensor);
ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;

if (size == 0) {
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3 changes: 3 additions & 0 deletions ggml/src/ggml-impl.h
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Original file line number Diff line number Diff line change
Expand Up @@ -295,6 +295,9 @@ struct ggml_cgraph {
enum ggml_cgraph_eval_order order;
};

// returns a slice of cgraph with nodes [i0, i1)
// the slice does not have leafs or gradients
// if you need the gradients, get them from the original graph
struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph, int i0, int i1);

// Memory allocation
Expand Down
147 changes: 67 additions & 80 deletions ggml/src/ggml-opt.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -14,51 +14,51 @@
#include <vector>

struct ggml_opt_dataset {
struct ggml_context * ctx;
ggml_backend_buffer_t buf;
struct ggml_tensor * data;
struct ggml_tensor * labels;
struct ggml_context * ctx = nullptr;
ggml_backend_buffer_t buf = nullptr;
struct ggml_tensor * data = nullptr;
struct ggml_tensor * labels = nullptr;

int64_t ndata;
int64_t ndata_shard;
size_t nbs_data;
size_t nbs_labels;
int64_t ndata = -1;
int64_t ndata_shard = -1;
size_t nbs_data = -1;
size_t nbs_labels = -1;

std::vector<int64_t> permutation;
};

struct ggml_opt_context {
ggml_backend_sched_t backend_sched;
ggml_cgraph * allocated_graph;
ggml_cgraph * allocated_graph_copy;
struct ggml_context * ctx_static;
struct ggml_context * ctx_static_cpu;
struct ggml_context * ctx_compute;
struct ggml_context * ctx_copy;
ggml_backend_buffer_t buf_static;
ggml_backend_buffer_t buf_static_cpu;
ggml_backend_sched_t backend_sched = nullptr;
ggml_cgraph * allocated_graph = nullptr;
ggml_cgraph * allocated_graph_copy = nullptr;
struct ggml_context * ctx_static = nullptr;
struct ggml_context * ctx_static_cpu = nullptr;
struct ggml_context * ctx_compute = nullptr;
struct ggml_context * ctx_copy = nullptr;
ggml_backend_buffer_t buf_static = nullptr;
ggml_backend_buffer_t buf_static_cpu = nullptr;
std::mt19937 rng;

struct ggml_tensor * inputs;
struct ggml_tensor * outputs;
struct ggml_tensor * labels;
struct ggml_tensor * inputs = nullptr;
struct ggml_tensor * outputs = nullptr;
struct ggml_tensor * labels = nullptr;

struct ggml_tensor * loss;
struct ggml_tensor * pred;
struct ggml_tensor * ncorrect;
struct ggml_tensor * loss = nullptr;
struct ggml_tensor * pred = nullptr;
struct ggml_tensor * ncorrect = nullptr;

struct ggml_cgraph * gf;
struct ggml_cgraph * gb_grad;
struct ggml_cgraph * gb_opt;
struct ggml_cgraph * gf = nullptr;
struct ggml_cgraph * gb_grad = nullptr;
struct ggml_cgraph * gb_opt = nullptr;

int64_t iter;
int32_t opt_period;
int32_t opt_i;
bool loss_per_datapoint;
int64_t iter = 1;
int32_t opt_period = 1;
int32_t opt_i = 0;
bool loss_per_datapoint = false;

ggml_opt_get_optimizer_params get_opt_pars;
void * get_opt_pars_ud;
struct ggml_tensor * adamw_params;
ggml_opt_get_optimizer_params get_opt_pars = nullptr;
void * get_opt_pars_ud = nullptr;
struct ggml_tensor * adamw_params = nullptr;
};

struct ggml_opt_result {
Expand All @@ -67,8 +67,8 @@ struct ggml_opt_result {
std::vector<int32_t> pred;
int64_t ncorrect = 0;

bool loss_per_datapoint = false;
int64_t opt_period = -1;
int64_t opt_period = -1;
bool loss_per_datapoint = false;
};

// ====== Dataset ======
Expand Down Expand Up @@ -188,11 +188,11 @@ struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * us
}

struct ggml_opt_params ggml_opt_default_params(
ggml_backend_sched_t backend_sched,
struct ggml_context * ctx_compute,
struct ggml_tensor * inputs,
struct ggml_tensor * outputs,
enum ggml_opt_loss_type loss_type) {
ggml_backend_sched_t backend_sched,
struct ggml_context * ctx_compute,
struct ggml_tensor * inputs,
struct ggml_tensor * outputs,
enum ggml_opt_loss_type loss_type) {
return {
/*backend_sched =*/ backend_sched,
/*ctx_compute =*/ ctx_compute,
Expand Down Expand Up @@ -237,25 +237,33 @@ static ggml_tensor * map_tensor(std::map<ggml_tensor *, ggml_tensor *> & tensor_
return new_tensor;
}

static ggml_cgraph * dup_graph(ggml_context * ctx, ggml_cgraph * graph) {
static ggml_cgraph * dup_graph(ggml_context * ctx, ggml_cgraph * src) {
std::map<ggml_tensor *, ggml_tensor *> tensor_map;

ggml_cgraph * new_graph = ggml_new_graph_custom(ctx, GGML_DEFAULT_GRAPH_SIZE, /*grads =*/ true);
ggml_cgraph * dst = ggml_new_graph_custom(ctx, src->size, /*grads =*/ true);

for (int i = 0; i < graph->n_leafs; i++) {
ggml_build_forward_expand(new_graph, map_tensor(tensor_map, ctx, graph->leafs[i]));
for (int i = 0; i < src->n_leafs; i++) {
ggml_build_forward_expand(dst, map_tensor(tensor_map, ctx, src->leafs[i]));
}
for (int i = 0; i < graph->n_nodes; i++) {
ggml_build_forward_expand(new_graph, map_tensor(tensor_map, ctx, graph->nodes[i]));
GGML_ASSERT(dst->n_leafs == src->n_leafs);
for (int i = 0; i < src->n_nodes; i++) {
ggml_build_forward_expand(dst, map_tensor(tensor_map, ctx, src->nodes[i]));
}
for (int i = 0; i < graph->n_nodes; ++i) {
const size_t igrad_src = ggml_hash_find(&graph->visited_hash_set, graph->nodes[i]);
const size_t igrad_dst = ggml_hash_find(&new_graph->visited_hash_set, new_graph->nodes[i]);
graph->grads[igrad_dst] = new_graph->grads[igrad_src];
graph->grad_accs[igrad_dst] = new_graph->grad_accs[igrad_src];
GGML_ASSERT(dst->n_nodes == src->n_nodes);
for (int i = 0; i < src->n_nodes; ++i) {
const size_t igrad_src = ggml_hash_find(&src->visited_hash_set, src->nodes[i]);
const size_t igrad_dst = ggml_hash_find(&dst->visited_hash_set, dst->nodes[i]);

GGML_ASSERT(igrad_src != GGML_HASHSET_FULL);
GGML_ASSERT(ggml_bitset_get(src->visited_hash_set.used, igrad_src));
GGML_ASSERT(igrad_dst != GGML_HASHSET_FULL);
GGML_ASSERT(ggml_bitset_get(dst->visited_hash_set.used, igrad_dst));

dst->grads[igrad_dst] = src->grads[igrad_src];
dst->grad_accs[igrad_dst] = src->grad_accs[igrad_src];
}

return new_graph;
return dst;
}

static void ggml_opt_alloc_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph) {
Expand Down Expand Up @@ -284,18 +292,13 @@ static void ggml_opt_alloc_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph

ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
ggml_opt_context_t result = new struct ggml_opt_context;
result->backend_sched = params.backend_sched;
result->allocated_graph = nullptr;
result->allocated_graph_copy = nullptr;
result->ctx_compute = params.ctx_compute;
result->ctx_copy = nullptr;
result->inputs = params.inputs;
result->outputs = params.outputs;
result->iter = 1;
result->opt_period = params.opt_period;
result->opt_i = 0;
result->get_opt_pars = params.get_opt_pars;
result->get_opt_pars_ud = params.get_opt_pars_ud;
result->backend_sched = params.backend_sched;
result->ctx_compute = params.ctx_compute;
result->inputs = params.inputs;
result->outputs = params.outputs;
result->opt_period = params.opt_period;
result->get_opt_pars = params.get_opt_pars;
result->get_opt_pars_ud = params.get_opt_pars_ud;

GGML_ASSERT(result->inputs->data && "the inputs must be allocated statically");
GGML_ASSERT(result->opt_period >= 1);
Expand Down Expand Up @@ -348,7 +351,6 @@ ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {

switch (params.loss_type) {
case GGML_OPT_LOSS_TYPE_MEAN: {
result->labels = nullptr;
result->loss = ggml_sum(result->ctx_static, result->outputs);
ggml_set_name(result->loss, "loss_sum");
const float scale = 1.0f / (result->opt_period * ggml_nelements(result->outputs));
Expand All @@ -358,7 +360,6 @@ ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
break;
}
case GGML_OPT_LOSS_TYPE_SUM: {
result->labels = nullptr;
result->loss = ggml_sum(result->ctx_static, result->outputs);
ggml_set_name(result->loss, "loss_sum");
result->loss_per_datapoint = false;
Expand Down Expand Up @@ -413,14 +414,7 @@ ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
}

if (params.build_type == GGML_OPT_BUILD_TYPE_FORWARD) {
result->gb_grad = nullptr;
result->gb_opt = nullptr;

result->buf_static = ggml_backend_alloc_ctx_tensors(result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0));
result->buf_static_cpu = nullptr;

ggml_opt_alloc_graph(result, result->gf);

return result;
}

Expand All @@ -429,14 +423,8 @@ ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
ggml_build_backward_expand(result->ctx_static, result->ctx_compute, result->gb_grad, accumulate);

if (params.build_type == GGML_OPT_BUILD_TYPE_GRAD) {
result->gb_opt = nullptr;

result->buf_static = ggml_backend_alloc_ctx_tensors(result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0));
result->buf_static_cpu = nullptr;

ggml_opt_alloc_graph(result, result->gb_grad);
ggml_graph_reset(result->gb_grad);

return result;
}

Expand Down Expand Up @@ -466,7 +454,6 @@ ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {

result->buf_static_cpu = ggml_backend_alloc_ctx_tensors_from_buft(result->ctx_static_cpu, ggml_backend_cpu_buffer_type());

ggml_opt_alloc_graph(result, result->gb_opt);
ggml_graph_reset(result->gb_opt);

return result;
Expand Down
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