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| 1 | +#include "ggml-backend-impl.h" |
| 2 | +#include "ggml-impl.h" |
| 3 | +#include "shared/apir_cs_rpc.h" |
| 4 | + |
| 5 | +#include <cinttypes> |
| 6 | +#include <unordered_map> |
| 7 | +#include <unordered_set> |
| 8 | +#include <vector> |
| 9 | + |
| 10 | +std::unordered_set<ggml_backend_buffer_t> backend_buffers; |
| 11 | + |
| 12 | +void apir_track_backend_buffer(ggml_backend_buffer_t buffer) { |
| 13 | + backend_buffers.insert(buffer); |
| 14 | +} |
| 15 | + |
| 16 | +bool apir_untrack_backend_buffer(ggml_backend_buffer_t buffer) { |
| 17 | + auto it = backend_buffers.find(buffer); |
| 18 | + if (it == backend_buffers.end()) { |
| 19 | + return false; |
| 20 | + } |
| 21 | + |
| 22 | + backend_buffers.erase(it); |
| 23 | + return true; |
| 24 | +} |
| 25 | + |
| 26 | +std::unordered_set<ggml_backend_buffer_t> apir_get_track_backend_buffers() { |
| 27 | + return backend_buffers; |
| 28 | +} |
| 29 | + |
| 30 | +ggml_tensor * apir_deserialize_tensor(ggml_context * ctx, const apir_rpc_tensor * tensor) { |
| 31 | + ggml_tensor * result = |
| 32 | + ggml_new_tensor_4d(ctx, (ggml_type) tensor->type, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); |
| 33 | + for (uint32_t i = 0; i < GGML_MAX_DIMS; i++) { |
| 34 | + result->nb[i] = tensor->nb[i]; |
| 35 | + } |
| 36 | + result->buffer = reinterpret_cast<ggml_backend_buffer_t>(tensor->buffer); |
| 37 | + if (result->buffer && backend_buffers.find(result->buffer) == backend_buffers.end()) { |
| 38 | + printf("WARNING: HOST BUFFER NOT FOUND | %p\n", (void *) result->buffer); |
| 39 | + result->buffer = nullptr; |
| 40 | + } |
| 41 | + |
| 42 | + uint64_t tensor_data = tensor->data; |
| 43 | + if (result->buffer) { |
| 44 | + // require that the tensor data does not go beyond the buffer end |
| 45 | + uint64_t tensor_size = (uint64_t) ggml_nbytes(result); |
| 46 | + uint64_t buffer_start = (uint64_t) ggml_backend_buffer_get_base(result->buffer); |
| 47 | + uint64_t buffer_size = (uint64_t) ggml_backend_buffer_get_size(result->buffer); |
| 48 | + |
| 49 | + // tensor->data is serialized as an offset to the buffer base address |
| 50 | + tensor_data += buffer_start; |
| 51 | + |
| 52 | + GGML_ASSERT(tensor_data + tensor_size >= tensor_data); // check for overflow |
| 53 | + GGML_ASSERT(tensor_data >= buffer_start && tensor_data + tensor_size <= buffer_start + buffer_size); |
| 54 | + } |
| 55 | + |
| 56 | + result->op = (ggml_op) tensor->op; |
| 57 | + for (uint32_t i = 0; i < GGML_MAX_OP_PARAMS / sizeof(int32_t); i++) { |
| 58 | + result->op_params[i] = tensor->op_params[i]; |
| 59 | + } |
| 60 | + result->flags = tensor->flags; |
| 61 | + result->data = reinterpret_cast<void *>(tensor_data); |
| 62 | + ggml_set_name(result, tensor->name); |
| 63 | + return result; |
| 64 | +} |
| 65 | + |
| 66 | +ggml_tensor * apir_create_node(uint64_t id, |
| 67 | + ggml_context * ctx, |
| 68 | + const std::unordered_map<uint64_t, const apir_rpc_tensor *> & tensor_ptrs, |
| 69 | + std::unordered_map<uint64_t, ggml_tensor *> & tensor_map) { |
| 70 | + if (id == 0) { |
| 71 | + return nullptr; |
| 72 | + } |
| 73 | + if (tensor_map.find(id) != tensor_map.end()) { |
| 74 | + return tensor_map[id]; |
| 75 | + } |
| 76 | + const apir_rpc_tensor * tensor = tensor_ptrs.at(id); |
| 77 | + ggml_tensor * result = apir_deserialize_tensor(ctx, tensor); |
| 78 | + if (result == nullptr) { |
| 79 | + return nullptr; |
| 80 | + } |
| 81 | + tensor_map[id] = result; |
| 82 | + for (int i = 0; i < GGML_MAX_SRC; i++) { |
| 83 | + result->src[i] = apir_create_node(tensor->src[i], ctx, tensor_ptrs, tensor_map); |
| 84 | + } |
| 85 | + result->view_src = apir_create_node(tensor->view_src, ctx, tensor_ptrs, tensor_map); |
| 86 | + result->view_offs = tensor->view_offs; |
| 87 | + return result; |
| 88 | +} |
| 89 | + |
| 90 | +ggml_cgraph * apir_deserialize_graph(uint32_t n_nodes, |
| 91 | + uint32_t n_tensors, |
| 92 | + const apir_rpc_tensor * tensors, |
| 93 | + const uint64_t * nodes) { |
| 94 | + size_t buf_size = ggml_tensor_overhead() * (n_nodes + n_tensors) + ggml_graph_overhead_custom(n_nodes, false); |
| 95 | + ggml_init_params params = { |
| 96 | + /*.mem_size =*/buf_size, |
| 97 | + /*.mem_buffer =*/NULL, |
| 98 | + /*.no_alloc =*/true, |
| 99 | + }; |
| 100 | + ggml_context * ctx = ggml_init(params); |
| 101 | + ggml_cgraph * graph = ggml_new_graph_custom(ctx, n_nodes, false); |
| 102 | + graph->n_nodes = n_nodes; |
| 103 | + std::unordered_map<uint64_t, const apir_rpc_tensor *> tensor_ptrs; |
| 104 | + for (uint32_t i = 0; i < n_tensors; i++) { |
| 105 | + tensor_ptrs[tensors[i].id] = &tensors[i]; |
| 106 | + } |
| 107 | + std::unordered_map<uint64_t, ggml_tensor *> tensor_map; |
| 108 | + for (uint32_t i = 0; i < n_nodes; i++) { |
| 109 | + int64_t id; |
| 110 | + memcpy(&id, &nodes[i], sizeof(id)); |
| 111 | + graph->nodes[i] = apir_create_node(id, ctx, tensor_ptrs, tensor_map); |
| 112 | + } |
| 113 | + |
| 114 | + return graph; |
| 115 | +} |
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