vbatts/llama.cpp
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remove debug prints and function to compute tensor data hash

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xaedes 2023-08-18 16:24:13 +02:00
parent 011f47f972
commit a0c2752ba7
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GPG key ID: 30030EDD817EA2B1
2 changed files with 1 additions and 75 deletions
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25
ggml-alloc.c
View file

@ -162,22 +162,12 @@ void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor)
printf("\n");
}
#endif
if ((char*)addr - (char*)alloc->data + size > alloc->max_size) {
printf("%s: op=%s name=%s max_size=%zu\n", __func__, ggml_op_name(tensor->op), ggml_get_name(tensor), (char*)addr - (char*)alloc->data + size);
}
alloc->max_size = MAX(alloc->max_size, (char*)addr - (char*)alloc->data + size);
}
// this is a very naive implementation, but for our case the number of free blocks should be very small
static void ggml_allocator_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
// static int counter = 0;
// counter++;
// if (counter > 2) {
// printf("%s: counter=%d OMIT\n", __func__, counter);
// return;
// } else {
// printf("%s: counter=%d\n", __func__, counter);
// }
void * ptr = tensor->data;
if (ptr < alloc->data || (char*)ptr >= (char*)alloc->data + alloc->max_size) {
@ -189,7 +179,6 @@ static void ggml_allocator_free_tensor(struct ggml_allocr * alloc, struct ggml_t
size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
size = aligned_offset(NULL, size, alloc->alignment);
// printf("%s: free data=[%p..%p] op=%s name=%s n_free_blocks=%d\n", __func__, tensor->data, (char*) tensor->data + size, ggml_op_name(tensor->op), ggml_get_name(tensor), alloc->n_free_blocks);
AT_PRINTF("%s: freeing %s (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, size, alloc->n_free_blocks);
#ifdef GGML_ALLOCATOR_DEBUG
@ -489,23 +478,11 @@ static size_t ggml_allocator_alloc_graph_tensors_n(
if (parent == NULL) {
break;
}
bool was_null = parent->data == NULL;
allocate_node(alloc, parent);
// if (was_null) {
// printf("%s: alloc n[%02d] %d data=[%p..%p] %s %s\n", __func__, i, j, parent->data, (char*) parent->data + ggml_nbytes(parent), ggml_op_name(parent->op), ggml_get_name(parent));
// } else {
// printf("%s: exist n[%02d] %d data=[%p..%p] %s %s\n", __func__, i, j, parent->data, (char*) parent->data + ggml_nbytes(parent), ggml_op_name(parent->op), ggml_get_name(parent));
// }
}
// allocate node
bool was_null = node->data == NULL;
allocate_node(alloc, node);
// if (was_null) {
// printf("%s: alloc node[%02d] data=[%p..%p] %s %s\n", __func__, i, node->data, (char*) node->data + ggml_nbytes(node), ggml_op_name(node->op), ggml_get_name(node));
// } else {
// printf("%s: exist node[%02d] data=[%p..%p] %s %s\n", __func__, i, node->data, (char*) node->data + ggml_nbytes(node), ggml_op_name(node->op), ggml_get_name(node));
// }
AT_PRINTF("exec: %s (%s) <= ", ggml_op_name(node->op), node->name);
for (int j = 0; j < GGML_MAX_SRC; j++) {

51
ggml.c
View file

@ -17548,48 +17548,6 @@ static void ggml_opt_get_grad(int np, struct ggml_tensor * const ps[], float * g
// ref: https://arxiv.org/pdf/1412.6980.pdf
//
uint32_t compute_data_checksum(struct ggml_tensor * tensor) {
const int n3 = (tensor->n_dims >= 3) ? tensor->ne[3] : 1;
const int n2 = (tensor->n_dims >= 2) ? tensor->ne[2] : 1;
const int n1 = (tensor->n_dims >= 1) ? tensor->ne[1] : 1;
const int n0 = (tensor->n_dims >= 0) ? tensor->ne[0] : 1;
const size_t nb0 = tensor->nb[0];
const size_t nb1 = tensor->nb[1];
const size_t nb2 = tensor->nb[2];
const size_t nb3 = tensor->nb[3];
const size_t nb = ggml_element_size(tensor);
uint32_t result = 0;
for (int i3 = 0; i3 < n3; ++i3) {
for (int i2 = 0; i2 < n2; ++i2) {
for (int i1 = 0; i1 < n1; ++i1) {
for (int i0 = 0; i0 < n0; ++i0) {
char * ptr = ((char *) tensor->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3);
uint32_t val;
memcpy(&val, ptr, nb);
result = result ^ val;
result = (((result << 1u) | ((result >> 31u) & 0x1u)) + 1u) & 0xffffffffu;
}
}
}
}
return result;
}
void print_data_checksums(struct ggml_cgraph * g) {
for (int i = 0; i < g->n_nodes; ++i) {
struct ggml_tensor * node = g->nodes[i];
for (int j = 0; j<GGML_MAX_SRC; ++j) {
if (node->src[j]) {
struct ggml_tensor * src = node->src[j];
uint32_t chk = compute_data_checksum(src);
printf("%s: node[%3d]->src[%d] chk=[%08x] data=[%p] op=%s name=%s\n", __func__, i, j, chk, src->data, ggml_op_name(src->op), ggml_get_name(src));
}
}
uint32_t chk = compute_data_checksum(node);
printf("%s: node[%3d] chk=[%08x] data=[%p] op=%s name=%s\n", __func__, i, chk, node->data, ggml_op_name(node->op), ggml_get_name(node));
}
}
static enum ggml_opt_result ggml_opt_adam(
struct ggml_context * ctx,
struct ggml_opt_context * opt,
@ -17651,8 +17609,6 @@ static enum ggml_opt_result ggml_opt_adam(
cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;
ggml_graph_compute(gb, &cplan);
print_data_checksums(gb);
opt->adam.fx_prev = ggml_get_f32_1d(f, 0);
opt->adam.fx_best = opt->adam.fx_prev;
if (pf) {
@ -17714,8 +17670,6 @@ static enum ggml_opt_result ggml_opt_adam(
const float beta2h = 1.0f/(1.0f - powf(beta2, opt->iter));
int64_t i = 0;
for (int p = 0; p < np; ++p) {
printf("%s: para[%3d] chk=[%08x] op=%s name=%s\n", __func__, p, compute_data_checksum(ps[p]), ggml_op_name(ps[p]->op), ggml_get_name(ps[p]));
printf("%s: para[%3d]->grad chk=[%08x] op=%s name=%s\n", __func__, p, compute_data_checksum(ps[p]->grad), ggml_op_name(ps[p]->grad->op), ggml_get_name(ps[p]->grad));
const int64_t ne = ggml_nelements(ps[p]);
const float p_decay = ((ps[p]->n_dims >= decay_min_ndim) ? decay : 0.0) * sched;
for (int64_t j = 0; j < ne; ++j) {
@ -17794,11 +17748,6 @@ static enum ggml_opt_result ggml_opt_adam(
}
}
print_data_checksums(gb);
for (int p = 0; p < np; ++p) {
printf("%s: para[%3d] chk=[%08x] op=%s name=%s\n", __func__, p, compute_data_checksum(ps[p]), ggml_op_name(ps[p]->op), ggml_get_name(ps[p]));
printf("%s: para[%3d]->grad chk=[%08x] op=%s name=%s\n", __func__, p, compute_data_checksum(ps[p]->grad), ggml_op_name(ps[p]->grad->op), ggml_get_name(ps[p]->grad));
}
return GGML_OPT_DID_NOT_CONVERGE;
}