feat: add EAGLE3 speculative decoding support

EAGLE3 is an encoder-decoder based speculative decoding method:
- Extracts features from target model at specific layers
- Uses feature fusion layer to compress target features
- Generates draft tokens with single-layer decoder
- Maps draft vocabulary to target vocabulary via d2t tensor

Key changes:
- Add LLM_ARCH_EAGLE3 architecture
- Add EAGLE3 encoder/decoder graph (src/models/eagle3.cpp)
- Add feature extraction from target model layers
- Add g_embeddings handling for decoder input
- Add GGML_TENSOR_FLAG_SYNC for GPU synchronization
- Add --eagle3 flag for speculative-simple example
- Add EAGLE3 model conversion in convert_hf_to_gguf.py

common/arg.cpp

@@ -3007,6 +3007,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.speculative.p_min = std::stof(value);
         }
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_DRAFT_P_MIN"));
+    add_opt(common_arg(
+        {"--eagle3"},
+        "use EAGLE3 speculative decoding with the draft model",
+        [](common_params & params) {
+            params.speculative.eagle3 = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
     add_opt(common_arg(
         {"-cd", "--ctx-size-draft"}, "N",
         string_format("size of the prompt context for the draft model (default: %d, 0 = loaded from model)", params.speculative.n_ctx),