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backend: use ggml_new_graph for GGML backend v2 (#1719)
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@ -317,7 +317,7 @@ void bert_eval(
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};
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struct ggml_context *ctx0 = ggml_init(params);
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struct ggml_cgraph gf = {};
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struct ggml_cgraph *gf = ggml_new_graph(ctx0);
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// Embeddings. word_embeddings + token_type_embeddings + position_embeddings
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struct ggml_tensor *token_layer = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
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@ -448,10 +448,10 @@ void bert_eval(
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ggml_tensor *output = inpL;
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// run the computation
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ggml_build_forward_expand(&gf, output);
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ggml_build_forward_expand(gf, output);
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//ggml_graph_compute_g4a()
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ggml_graph_compute_g4a(ctx->work_buf, &gf, n_threads);
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//ggml_graph_compute(ctx0, &gf);
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ggml_graph_compute_g4a(ctx->work_buf, gf, n_threads);
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//ggml_graph_compute(ctx0, gf);
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// float *dat = ggml_get_data_f32(output);
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@ -460,7 +460,7 @@ void bert_eval(
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#ifdef GGML_PERF
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// print timing information per ggml operation (for debugging purposes)
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// requires GGML_PERF to be defined
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ggml_graph_print(&gf);
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ggml_graph_print(gf);
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#endif
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if (!mem_req_mode) {
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@ -343,7 +343,7 @@ bool gptj_eval(
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};
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struct ggml_context * ctx0 = ggml_init(params);
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struct ggml_cgraph gf = {};
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struct ggml_cgraph * gf = ggml_new_graph(ctx0);
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// KQ_pos - contains the positions
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struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
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@ -395,8 +395,8 @@ bool gptj_eval(
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( n_ctx)*ggml_element_size(model.kv_self.v),
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(il*n_ctx)*ggml_element_size(model.kv_self.v)*n_embd + n_past*ggml_element_size(model.kv_self.v));
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ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
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ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
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ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
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ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
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}
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// Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
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@ -515,22 +515,22 @@ bool gptj_eval(
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// logits -> probs
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//inpL = ggml_soft_max(ctx0, inpL);
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ggml_build_forward_expand(&gf, inpL);
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ggml_build_forward_expand(gf, inpL);
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// run the computation
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{
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std::unique_ptr<uint8_t []> data;
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auto plan = ggml_graph_plan(&gf, n_threads);
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auto plan = ggml_graph_plan(gf, n_threads);
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if (plan.work_size > 0) {
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data.reset(new uint8_t[plan.work_size]);
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plan.work_data = data.get();
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}
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ggml_graph_compute(&gf, &plan);
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ggml_graph_compute(gf, &plan);
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}
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//if (n_past%100 == 0) {
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// ggml_graph_print (&gf);
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// ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
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// ggml_graph_print (gf);
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// ggml_graph_dump_dot(gf, NULL, "gpt-2.dot");
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//}
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//embd_w.resize(n_vocab*N);
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