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https://github.com/ggerganov/llama.cpp.git
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ggml: add names to tensors (#1268)
* ggml: add names to tensors * minor improvements to dot file formatting
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slaren
GitHub
parent
f4cef87edf
commit
2d099e5193
56
ggml.c
56
ggml.c
@ -4541,6 +4541,7 @@ struct ggml_tensor * ggml_new_tensor_impl(
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/*.perf_cycles =*/ 0,
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/*.perf_time_us =*/ 0,
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/*.data =*/ (data == NULL && !ctx->no_alloc) ? (void *)(result + 1) : data,
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/*.name =*/ { 0 },
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/*.pad =*/ { 0 },
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};
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@ -4895,6 +4896,15 @@ float * ggml_get_data_f32(const struct ggml_tensor * tensor) {
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return (float *)(tensor->data);
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}
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const char * ggml_get_name(const struct ggml_tensor * tensor) {
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return tensor->name;
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}
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void ggml_set_name(struct ggml_tensor * tensor, const char * name) {
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strncpy(tensor->name, name, sizeof(tensor->name));
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tensor->name[sizeof(tensor->name) - 1] = '\0';
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}
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struct ggml_tensor * ggml_view_tensor(
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struct ggml_context * ctx,
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const struct ggml_tensor * src) {
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@ -5994,6 +6004,7 @@ struct ggml_tensor * ggml_diag_mask_inf(
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//struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
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struct ggml_tensor * result = ggml_view_tensor(ctx, a);
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struct ggml_tensor * b = ggml_new_i32(ctx, n_past);
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ggml_set_name(b, "n_past");
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result->op = GGML_OP_DIAG_MASK_INF;
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result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
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@ -6051,6 +6062,7 @@ struct ggml_tensor * ggml_rope(
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((int32_t *) b->data)[0] = n_past;
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((int32_t *) b->data)[1] = n_dims;
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((int32_t *) b->data)[2] = mode;
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ggml_set_name(b, "n_past, n_dims, mode");
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result->op = GGML_OP_ROPE;
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result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
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@ -12118,10 +12130,16 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph
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snprintf(color, sizeof(color), "white");
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}
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fprintf(fp, " \"%p\" [ \
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style = filled; fillcolor = %s; shape = record; \
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label=\"%d [%" PRId64 ", %" PRId64 "] | <x>%s",
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(void *) node, color,
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fprintf(fp, " \"%p\" [ "
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"style = filled; fillcolor = %s; shape = record; "
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"label=\"",
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(void *) node, color);
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if (strlen(node->name) > 0) {
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fprintf(fp, "%s |", node->name);
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}
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fprintf(fp, "%d [%" PRId64 ", %" PRId64 "] | <x>%s",
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i, node->ne[0], node->ne[1],
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GGML_OP_SYMBOL[node->op]);
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@ -12137,18 +12155,26 @@ label=\"%d [%" PRId64 ", %" PRId64 "] | <x>%s",
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snprintf(color, sizeof(color), "pink");
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if (ggml_nelements(node) == 1) {
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fprintf(fp, " \"%p\" [ \
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style = filled; fillcolor = %s; shape = record; \
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label=\"<x>%.1e\"; ]\n",
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(void *) node, color, (double)ggml_get_f32_1d(node, 0));
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} else {
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fprintf(fp, " \"%p\" [ \
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style = filled; fillcolor = %s; shape = record; \
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label=\"<x>CONST %d [%" PRId64 ", %" PRId64 "]\"; ]\n",
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(void *) node, color,
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i, node->ne[0], node->ne[1]);
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fprintf(fp, " \"%p\" [ "
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"style = filled; fillcolor = %s; shape = record; "
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"label=\"<x>",
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(void *) node, color);
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if (strlen(node->name) > 0) {
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fprintf(fp, "%s | ", node->name);
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}
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if (ggml_nelements(node) == 1) {
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if (node->type == GGML_TYPE_I8 || node->type == GGML_TYPE_I16 || node->type == GGML_TYPE_I32) {
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fprintf(fp, "%d", ggml_get_i32_1d(node, 0));
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}
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else {
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fprintf(fp, "%.1e", (double)ggml_get_f32_1d(node, 0));
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}
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}
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else {
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fprintf(fp, "CONST %d [%" PRId64 ", %" PRId64 "]", i, node->ne[0], node->ne[1]);
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}
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fprintf(fp, "\"; ]\n");
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}
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for (int i = 0; i < gb->n_nodes; i++) {
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8
ggml.h
8
ggml.h
@ -350,7 +350,10 @@ extern "C" {
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int64_t perf_time_us;
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void * data;
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char padding[8];
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char name[32];
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char padding[8]; // TODO: remove and add padding to name?
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};
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// computation graph
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@ -473,6 +476,9 @@ extern "C" {
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GGML_API void * ggml_get_data (const struct ggml_tensor * tensor);
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GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor);
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GGML_API const char * ggml_get_name(const struct ggml_tensor * tensor);
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GGML_API void ggml_set_name(struct ggml_tensor * tensor, const char * name);
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//
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// operations on tensors with backpropagation
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//
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24
llama.cpp
24
llama.cpp
@ -659,6 +659,7 @@ struct llama_model_loader {
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LLAMA_ASSERT(lt.ne.size() == 1);
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tensor = ggml_new_tensor_1d(ggml_ctx, lt.type, lt.ne.at(0));
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}
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ggml_set_name(tensor, lt.name.c_str());
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LLAMA_ASSERT(lt.ggml_tensor == NULL); // if this fails, we called get_tensor twice on the same tensor
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lt.ggml_tensor = tensor;
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num_ggml_tensors_created++;
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@ -798,6 +799,8 @@ static bool kv_cache_init(
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cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
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cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
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ggml_set_name(cache.k, "cache_k");
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ggml_set_name(cache.v, "cache_v");
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return true;
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}
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@ -1084,6 +1087,7 @@ static bool llama_eval_internal(
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gf.n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
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struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
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ggml_set_name(embd, "embd");
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memcpy(embd->data, tokens, N*ggml_element_size(embd));
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struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
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@ -1110,6 +1114,8 @@ static bool llama_eval_internal(
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// compute Q and K and RoPE them
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struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
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struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
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ggml_set_name(Qcur, "Qcur");
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ggml_set_name(Kcur, "Kcur");
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// store key and value to memory
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{
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@ -1130,6 +1136,7 @@ static bool llama_eval_internal(
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ggml_permute(ctx0,
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Qcur,
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0, 2, 1, 3);
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ggml_set_name(Q, "Q");
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struct ggml_tensor * K =
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ggml_permute(ctx0,
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@ -1137,21 +1144,26 @@ static bool llama_eval_internal(
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ggml_view_1d(ctx0, kv_self.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kv_self.k)*n_embd),
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n_embd/n_head, n_head, n_past + N),
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0, 2, 1, 3);
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ggml_set_name(K, "K");
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// K * Q
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struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
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ggml_set_name(KQ, "KQ");
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// KQ_scaled = KQ / sqrt(n_embd/n_head)
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struct ggml_tensor * KQ_scaled =
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ggml_scale(ctx0,
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KQ,
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ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head)));
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struct ggml_tensor * KQ_scale = ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head));
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ggml_set_name(KQ_scale, "1/sqrt(n_embd/n_head)");
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struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
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ggml_set_name(KQ_scaled, "KQ_scaled");
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// KQ_masked = mask_past(KQ_scaled)
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struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
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ggml_set_name(KQ_masked, "KQ_masked");
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// KQ = soft_max(KQ_masked)
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struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
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ggml_set_name(KQ_soft_max, "KQ_soft_max");
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// split cached V into n_head heads
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struct ggml_tensor * V =
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@ -1160,9 +1172,11 @@ static bool llama_eval_internal(
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n_ctx*ggml_element_size(kv_self.v),
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n_ctx*ggml_element_size(kv_self.v)*n_embd/n_head,
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il*n_ctx*ggml_element_size(kv_self.v)*n_embd);
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ggml_set_name(V, "V");
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#if 1
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struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
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ggml_set_name(KQV, "KQV");
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#else
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// make V contiguous in memory to speed up the matmul, however we waste time on the copy
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// on M1 this is faster for the perplexity computation, but ~5% slower for the single-token generation
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@ -1173,11 +1187,13 @@ static bool llama_eval_internal(
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// KQV_merged = KQV.permute(0, 2, 1, 3)
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struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
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ggml_set_name(KQV_merged, "KQV_merged");
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// cur = KQV_merged.contiguous().view(n_embd, N)
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cur = ggml_cpy(ctx0,
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KQV_merged,
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ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
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ggml_set_name(cur, "KQV_merged_contiguous");
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// projection (no bias)
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cur = ggml_mul_mat(ctx0,
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