llama_model_loader: minor, use same variable name for consistency, fix spacing in types cast
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parent
69bdee939a
commit
6df9757ad6
1 changed files with 15 additions and 15 deletions
22
llama.cpp
22
llama.cpp
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@ -3287,9 +3287,9 @@ struct llama_model_loader {
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if (size_done >= size_data) {
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if (size_done >= size_data) {
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// unmap offloaded tensors and metadata
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// unmap offloaded tensors and metadata
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if (use_mmap) {
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if (use_mmap) {
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for (uint32_t file_no = 0; file_no < mappings.size(); file_no++) {
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for (uint32_t idx = 0; idx < mappings.size(); idx++) {
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const auto & mmap_used = mmaps_used[file_no];
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const auto & mmap_used = mmaps_used[idx];
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auto & mapping = mappings.at(file_no);
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auto & mapping = mappings.at(idx);
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mapping->unmap_fragment(0, mmap_used.first);
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mapping->unmap_fragment(0, mmap_used.first);
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if (mmap_used.second != 0) {
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if (mmap_used.second != 0) {
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mapping->unmap_fragment(mmap_used.second, mapping->size);
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mapping->unmap_fragment(mmap_used.second, mapping->size);
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@ -5143,16 +5143,16 @@ static bool llm_load_tensors(
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// this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers
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// this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers
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// this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
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// this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
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if (ml.use_mmap && buft == llama_default_buffer_type_cpu(true)) {
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if (ml.use_mmap && buft == llama_default_buffer_type_cpu(true)) {
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for (uint32_t file_no = 0; file_no < ml.files.size(); file_no++) {
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for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
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void * addr = nullptr;
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void * addr = nullptr;
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size_t first, last;
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size_t first, last;
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ml.get_mapping_range(&first, &last, &addr, file_no, ctx);
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ml.get_mapping_range(&first, &last, &addr, idx, ctx);
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if (first >= last) {
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if (first >= last) {
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continue;
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continue;
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}
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}
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ggml_backend_buffer_t buf = ggml_backend_cpu_buffer_from_ptr((char *) addr + first, last - first);
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ggml_backend_buffer_t buf = ggml_backend_cpu_buffer_from_ptr((char *) addr + first, last - first);
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if (buf != nullptr) {
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if (buf != nullptr) {
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bufs.emplace(file_no, buf);
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bufs.emplace(idx, buf);
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#ifdef GGML_USE_CUBLAS
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#ifdef GGML_USE_CUBLAS
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if (n_layer >= n_gpu_layers) {
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if (n_layer >= n_gpu_layers) {
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ggml_backend_cuda_register_host_buffer(
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ggml_backend_cuda_register_host_buffer(
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@ -5165,17 +5165,17 @@ static bool llm_load_tensors(
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}
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}
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#ifdef GGML_USE_METAL
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#ifdef GGML_USE_METAL
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else if (ml.use_mmap && buft == ggml_backend_metal_buffer_type()) {
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else if (ml.use_mmap && buft == ggml_backend_metal_buffer_type()) {
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for (uint32_t file_no = 0; file_no < ml.files.size(); file_no++) {
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for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
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const size_t max_size = ggml_get_max_tensor_size(ctx);
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const size_t max_size = ggml_get_max_tensor_size(ctx);
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void * addr = nullptr;
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void * addr = nullptr;
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size_t first, last;
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size_t first, last;
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ml.get_mapping_range(&first, &last, &addr, file_no, ctx);
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ml.get_mapping_range(&first, &last, &addr, idx, ctx);
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if (first >= last) {
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if (first >= last) {
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continue;
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continue;
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}
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}
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ggml_backend_buffer_t buf = ggml_backend_metal_buffer_from_ptr((char *) addr + first, last - first, max_size);
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ggml_backend_buffer_t buf = ggml_backend_metal_buffer_from_ptr((char *) addr + first, last - first, max_size);
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if (buf != nullptr) {
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if (buf != nullptr) {
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bufs.emplace(file_no, buf);
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bufs.emplace(idx, buf);
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}
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}
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}
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}
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}
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}
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@ -5189,8 +5189,8 @@ static bool llm_load_tensors(
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mlock_buf->init(ggml_backend_buffer_get_base(buf));
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mlock_buf->init(ggml_backend_buffer_get_base(buf));
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mlock_buf->grow_to(ggml_backend_buffer_get_size(buf));
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mlock_buf->grow_to(ggml_backend_buffer_get_size(buf));
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}
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}
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for (uint32_t file_no = 0; file_no < ml.files.size(); file_no++) {
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for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
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bufs.emplace(file_no, buf);
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bufs.emplace(idx, buf);
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}
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}
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}
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}
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}
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}
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