straightforward /v1/models endpoint

.devops/tools.sh

@@ -13,8 +13,6 @@ elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
     ./quantize "$@"
 elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then
     ./main "$@"
-elif [[ "$arg1" == '--finetune' || "$arg1" == '-f' ]]; then
-    ./finetune "$@"
 elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
     echo "Converting PTH to GGML..."
     for i in `ls $1/$2/ggml-model-f16.bin*`; do
@@ -36,8 +34,6 @@ else
     echo "              ex: --outtype f16 \"/models/7B/\" "
     echo "  --quantize (-q): Optimize with quantization process ggml"
     echo "              ex: \"/models/7B/ggml-model-f16.bin\" \"/models/7B/ggml-model-q4_0.bin\" 2"
-    echo "  --finetune (-f): Run finetune command to create a lora finetune of the model"
-    echo "              See documentation for finetune for command-line parameters"
     echo "  --all-in-one (-a): Execute --convert & --quantize"
     echo "              ex: \"/models/\" 7B"
     echo "  --server (-s): Run a model on the server"

.github/workflows/build.yml

@@ -498,17 +498,6 @@ jobs:
           path: |
             cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip
 
-  ios-xcode-build:
-    runs-on: macos-latest
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v3
-
-      - name: Build Xcode project
-        run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' build
-
-
 #  freeBSD-latest:
 #    runs-on: macos-12
 #    steps:

.gitignore

@@ -47,7 +47,6 @@ models-mnt
 /libllama.so
 /llama-bench
 /llava-cli
-/lookahead
 /main
 /metal
 /perplexity
@@ -88,16 +87,15 @@ poetry.lock
 poetry.toml
 
 # Test binaries
-/tests/test-grammar-parser
-/tests/test-llama-grammar
-/tests/test-double-float
-/tests/test-grad0
-/tests/test-opt
-/tests/test-quantize-fns
-/tests/test-quantize-perf
-/tests/test-sampling
-/tests/test-tokenizer-0-llama
-/tests/test-tokenizer-0-falcon
-/tests/test-tokenizer-1-llama
-/tests/test-tokenizer-1-bpe
-/tests/test-rope
+tests/test-grammar-parser
+tests/test-llama-grammar
+tests/test-double-float
+tests/test-grad0
+tests/test-opt
+tests/test-quantize-fns
+tests/test-quantize-perf
+tests/test-sampling
+tests/test-tokenizer-0-llama
+tests/test-tokenizer-0-falcon
+tests/test-tokenizer-1-llama
+tests/test-tokenizer-1-bpe

CMakeLists.txt

@@ -43,7 +43,6 @@ else()
 endif()
 
 # general
-option(BUILD_SHARED_LIBS                "build shared libraries"                                OFF)
 option(LLAMA_STATIC                     "llama: static link libraries"                          OFF)
 option(LLAMA_NATIVE                     "llama: enable -march=native flag"                      ON)
 option(LLAMA_LTO                        "llama: enable link time optimization"                  OFF)
@@ -101,9 +100,6 @@ option(LLAMA_BUILD_TESTS                "llama: build tests"    ${LLAMA_STANDALO
 option(LLAMA_BUILD_EXAMPLES             "llama: build examples" ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_SERVER               "llama: build server example"                           ON)
 
-# Required for relocatable CMake package
-include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
-
 #
 # Compile flags
 #
@@ -116,11 +112,6 @@ set(THREADS_PREFER_PTHREAD_FLAG ON)
 find_package(Threads REQUIRED)
 include(CheckCXXCompilerFlag)
 
-# enable libstdc++ assertions for debug builds
-if (CMAKE_SYSTEM_NAME MATCHES "Linux")
-    add_compile_definitions($<$<CONFIG:Debug>:_GLIBCXX_ASSERTIONS>)
-endif()
-
 if (NOT MSVC)
     if (LLAMA_SANITIZE_THREAD)
         add_compile_options(-fsanitize=thread)
@@ -170,7 +161,7 @@ if (LLAMA_METAL)
     #add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/")
 
     # copy ggml-metal.metal to bin directory
-    configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY)
+    configure_file(ggml-metal.metal bin/ggml-metal.metal COPYONLY)
 
     set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}
         ${FOUNDATION_LIBRARY}

Makefile

@@ -2,13 +2,13 @@
 BUILD_TARGETS = \
 	main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
 	simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search  \
-	speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead tests/test-c.o
+	speculative infill tokenize benchmark-matmult parallel finetune export-lora tests/test-c.o
 
 # Binaries only useful for tests
 TEST_TARGETS = \
 	tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt \
 	tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama          \
-	tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe tests/test-rope
+	tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe
 
 # Code coverage output files
 COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report
@@ -30,7 +30,7 @@ ifeq '' '$(findstring clang,$(shell $(CC) --version))'
 	CC_VER := $(shell $(CC) -dumpfullversion -dumpversion | awk -F. '{ printf("%02d%02d%02d", $$1, $$2, $$3) }')
 else
 	CC_IS_CLANG=1
-	ifeq '' '$(findstring Apple,$(shell $(CC) --version))'
+	ifeq '' '$(findstring Apple LLVM,$(shell $(CC) --version))'
 		CC_IS_LLVM_CLANG=1
 	else
 		CC_IS_APPLE_CLANG=1
@@ -174,10 +174,6 @@ ifdef LLAMA_DEBUG
 	MK_CFLAGS   += -O0 -g
 	MK_CXXFLAGS += -O0 -g
 	MK_LDFLAGS  += -g
-
-	ifeq ($(UNAME_S),Linux)
-		MK_CXXFLAGS += -Wp,-D_GLIBCXX_ASSERTIONS
-	endif
 else
 	MK_CPPFLAGS += -DNDEBUG
 endif
@@ -652,7 +648,7 @@ beam-search: examples/beam-search/beam-search.cpp ggml.o llama.o $(COMMON_DEPS)
 finetune: examples/finetune/finetune.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-export-lora: examples/export-lora/export-lora.cpp ggml.o common/common.h $(OBJS)
+export-lora: examples/export-lora/export-lora.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
@@ -661,9 +657,6 @@ speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS)
 parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-
 ifdef LLAMA_METAL
 metal: examples/metal/metal.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
@@ -705,28 +698,28 @@ vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
 q8dot: pocs/vdot/q8dot.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
 
-tests/test-llama-grammar: tests/test-llama-grammar.cpp ggml.o grammar-parser.o $(OBJS)
+tests/test-llama-grammar: tests/test-llama-grammar.cpp ggml.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o grammar-parser.o $(OBJS)
+tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-double-float: tests/test-double-float.cpp ggml.o $(OBJS)
+tests/test-double-float: tests/test-double-float.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-grad0: tests/test-grad0.cpp ggml.o $(OBJS)
+tests/test-grad0: tests/test-grad0.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-opt: tests/test-opt.cpp ggml.o $(OBJS)
+tests/test-opt: tests/test-opt.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-quantize-fns: tests/test-quantize-fns.cpp ggml.o $(OBJS)
+tests/test-quantize-fns: tests/test-quantize-fns.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o $(OBJS)
+tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(OBJS)
+tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
@@ -741,8 +734,5 @@ tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMM
 tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-rope: tests/test-rope.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-
 tests/test-c.o: tests/test-c.c llama.h
 	$(CC) $(CFLAGS) -c $(filter-out %.h,$^) -o $@

README.md

@@ -10,8 +10,6 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
 
 ### Hot topics
 
-- Using `llama.cpp` with AWS instances: https://github.com/ggerganov/llama.cpp/discussions/4225
-- Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216
 - Collecting Apple Silicon performance stats: https://github.com/ggerganov/llama.cpp/discussions/4167
 
 ----
@@ -116,8 +114,6 @@ as the main playground for developing new features for the [ggml](https://github
 - [nat/openplayground](https://github.com/nat/openplayground)
 - [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui)
 - [withcatai/catai](https://github.com/withcatai/catai)
-- [semperai/amica](https://github.com/semperai/amica)
-- [psugihara/FreeChat](https://github.com/psugihara/FreeChat)
 
 ---
 
@@ -324,7 +320,7 @@ mpirun -hostfile hostfile -n 3 ./main -m ./models/7B/ggml-model-q4_0.gguf -n 128
 
 ### BLAS Build
 
-Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS and CLBlast. There are currently several different BLAS implementations available for build and use:
+Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). BLAS doesn't affect the normal generation performance. There are currently three different implementations of it:
 
 - #### Accelerate Framework:
 
@@ -896,7 +892,7 @@ Additionally, there the following images, similar to the above:
 - `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
 - `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
 
-The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now).
+The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the Gitlab Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now).
 
 #### Usage
 

common/CMakeLists.txt

@@ -11,12 +11,7 @@ if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git")
     if(NOT IS_DIRECTORY "${GIT_DIR}")
         file(READ ${GIT_DIR} REAL_GIT_DIR_LINK)
         string(REGEX REPLACE "gitdir: (.*)\n$" "\\1" REAL_GIT_DIR ${REAL_GIT_DIR_LINK})
-        string(FIND "${REAL_GIT_DIR}" "/" SLASH_POS)
-        if (SLASH_POS EQUAL 0)
-            set(GIT_DIR "${REAL_GIT_DIR}")
-        else()
-            set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../${REAL_GIT_DIR}")
-        endif()
+        set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../${REAL_GIT_DIR}")
     endif()
 
     set(GIT_INDEX "${GIT_DIR}/index")
@@ -31,7 +26,7 @@ add_custom_command(
     COMMENT "Generating build details from Git"
     COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION}
             -DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME}
-            -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/gen-build-info-cpp.cmake"
+            -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/build-info.cmake"
     WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/.."
     DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in" ${GIT_INDEX}
     VERBATIM

convert-hf-to-gguf.py

@@ -10,7 +10,7 @@ import re
 import sys
 from enum import IntEnum
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast, Optional
+from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast
 
 import numpy as np
 import torch
@@ -59,7 +59,7 @@ class Model:
                 from safetensors import safe_open
                 ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu"))
             else:
-                ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True))
+                ctx = contextlib.nullcontext(torch.load(self.dir_model / part_name, map_location="cpu"))
 
             with ctx as model_part:
                 for name in model_part.keys():
@@ -168,8 +168,6 @@ class Model:
             return PersimmonModel
         if model_architecture in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
             return StableLMModel
-        if model_architecture == "QWenLMHeadModel":
-            return QwenModel
         return Model
 
     def _is_model_safetensors(self) -> bool:
@@ -205,8 +203,6 @@ class Model:
             return gguf.MODEL_ARCH.PERSIMMON
         if arch in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
             return gguf.MODEL_ARCH.STABLELM
-        if arch == "QWenLMHeadModel":
-            return gguf.MODEL_ARCH.QWEN
 
         raise NotImplementedError(f'Architecture "{arch}" not supported!')
 
@@ -836,131 +832,6 @@ class StableLMModel(Model):
         self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
         self.gguf_writer.add_layer_norm_eps(1e-5)
 
-
-class QwenModel(Model):
-    @staticmethod
-    def token_bytes_to_string(b):
-        from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
-        byte_encoder = bytes_to_unicode()
-        return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
-
-    @staticmethod
-    def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: Optional[int] = None) -> list[bytes]:
-        parts = [bytes([b]) for b in token]
-        while True:
-            min_idx = None
-            min_rank = None
-            for i, pair in enumerate(zip(parts[:-1], parts[1:])):
-                rank = mergeable_ranks.get(pair[0] + pair[1])
-                if rank is not None and (min_rank is None or rank < min_rank):
-                    min_idx = i
-                    min_rank = rank
-            if min_rank is None or (max_rank is not None and min_rank >= max_rank):
-                break
-            assert min_idx is not None
-            parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:]
-        return parts
-
-    def set_vocab(self):
-        dir_model = self.dir_model
-        hparams = self.hparams
-        tokens: list[bytearray] = []
-        toktypes: list[int] = []
-
-        from transformers import AutoTokenizer  # type: ignore[attr-defined]
-        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
-        vocab_size = hparams["vocab_size"]
-        assert max(tokenizer.get_vocab().values()) < vocab_size
-
-        merges = []
-        vocab = {}
-        mergeable_ranks = tokenizer.mergeable_ranks
-        for token, rank in mergeable_ranks.items():
-            vocab[self.token_bytes_to_string(token)] = rank
-            if len(token) == 1:
-                continue
-            merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
-            assert len(merged) == 2
-            merges.append(' '.join(map(self.token_bytes_to_string, merged)))
-
-        reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in vocab.items()}
-        added_vocab = tokenizer.special_tokens
-
-        for i in range(vocab_size):
-            if i not in reverse_vocab:
-                pad_token = f"[PAD{i}]".encode("utf-8")
-                tokens.append(bytearray(pad_token))
-                toktypes.append(gguf.TokenType.USER_DEFINED)
-            elif reverse_vocab[i] in added_vocab:
-                tokens.append(reverse_vocab[i])
-                toktypes.append(gguf.TokenType.CONTROL)
-            else:
-                tokens.append(reverse_vocab[i])
-                toktypes.append(gguf.TokenType.NORMAL)
-
-        self.gguf_writer.add_tokenizer_model("gpt2")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(dir_model, load_merges=False)
-        special_vocab.merges = merges
-        special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"])
-        special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"])
-        special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"])
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def set_gguf_parameters(self):
-        self.gguf_writer.add_name("Qwen")
-        self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
-        self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
-        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
-        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
-        self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
-        self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
-        self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
-
-    def write_tensors(self):
-        block_count = self.hparams["num_hidden_layers"]
-        model_kv = dict(self.get_tensors())
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
-        for name, data_torch in model_kv.items():
-            # we don't need these
-            if name.endswith(".rotary_emb.inv_freq"):
-                continue
-
-            old_dtype = data_torch.dtype
-
-            # convert any unsupported data types to float32
-            if data_torch.dtype not in (torch.float16, torch.float32):
-                data_torch = data_torch.to(torch.float32)
-
-            data = data_torch.squeeze().numpy()
-
-            # map tensor names
-            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
-            if new_name is None:
-                print(f"Can not map tensor {name!r}")
-                sys.exit()
-
-            n_dims = len(data.shape)
-            data_dtype = data.dtype
-
-            # if f32 desired, convert any float16 to float32
-            if self.ftype == 0 and data_dtype == np.float16:
-                data = data.astype(np.float32)
-
-            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
-            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
-                data = data.astype(np.float32)
-
-            # if f16 desired, convert any float32 2-dim weight tensors to float16
-            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
-                data = data.astype(np.float16)
-
-            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
-            self.gguf_writer.add_tensor(new_name, data)
-
 ###### CONVERSION LOGIC ######
 
 
@@ -1009,21 +880,20 @@ print(f"Loading model: {dir_model.name}")
 
 hparams = Model.load_hparams(dir_model)
 
-with torch.inference_mode():
-    model_class = Model.from_model_architecture(hparams["architectures"][0])
-    model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian)
+model_class = Model.from_model_architecture(hparams["architectures"][0])
+model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian)
 
-    print("Set model parameters")
-    model_instance.set_gguf_parameters()
+print("Set model parameters")
+model_instance.set_gguf_parameters()
 
-    print("Set model tokenizer")
-    model_instance.set_vocab()
+print("Set model tokenizer")
+model_instance.set_vocab()
 
-    if args.vocab_only:
-        print(f"Exporting model vocab to '{fname_out}'")
-        model_instance.write_vocab()
-    else:
-        print(f"Exporting model to '{fname_out}'")
-        model_instance.write()
+if args.vocab_only:
+    print(f"Exporting model vocab to '{fname_out}'")
+    model_instance.write_vocab()
+else:
+    print(f"Exporting model to '{fname_out}'")
+    model_instance.write()
 
-    print(f"Model successfully exported to '{fname_out}'")
+print(f"Model successfully exported to '{fname_out}'")

convert.py

@@ -267,7 +267,7 @@ class Params:
             n_ctx = 2048
 
         return Params(
-            n_vocab          = model["tok_embeddings.weight"].shape[0],
+            n_vocab          = config.get("vocab_size", model["tok_embeddings.weight"].shape[0]),
             n_embd           = config["dim"],
             n_layer          = config["n_layers"],
             n_ctx            = n_ctx,

examples/CMakeLists.txt

@@ -32,7 +32,6 @@ else()
     add_subdirectory(save-load-state)
     add_subdirectory(simple)
     add_subdirectory(speculative)
-    add_subdirectory(lookahead)
     add_subdirectory(train-text-from-scratch)
     if (LLAMA_METAL)
         add_subdirectory(metal)

examples/batched-bench/batched-bench.cpp

@@ -155,7 +155,7 @@ int main(int argc, char ** argv) {
     }
 
     LOG_TEE("\n");
-    LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d, n_threads = %d, n_threads_batch = %d\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq, ctx_params.n_threads, ctx_params.n_threads_batch);
+    LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq);
     LOG_TEE("\n");
 
     LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP",     "TG",     "B",    "N_KV",     "T_PP s",   "S_PP t/s", "T_TG s",   "S_TG t/s", "T s",      "S t/s");

examples/batched.swift/README.md

@@ -1,4 +1,4 @@
 This is a swift clone of `examples/batched`.
 
 $ `make`
-$ `./batched_swift MODEL_PATH [PROMPT] [PARALLEL]`
+$ `./swift MODEL_PATH [PROMPT] [PARALLEL]`

examples/batched.swift/Sources/main.swift

@@ -153,7 +153,7 @@ while n_cur <= n_len {
         // const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
 
         // is it an end of stream? -> mark the stream as finished
-        if new_token_id == llama_token_eos(model) || n_cur == n_len {
+        if new_token_id == llama_token_eos(context) || n_cur == n_len {
             i_batch[i] = -1
             // print("")
             if n_parallel > 1 {
@@ -230,15 +230,18 @@ private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String
     var result = [CChar](repeating: 0, count: 8)
     let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count))
     if nTokens < 0 {
-        let actualTokensCount = -Int(nTokens)
-        result = .init(repeating: 0, count: actualTokensCount)
+        if result.count >= -Int(nTokens) {
+            result.removeLast(-Int(nTokens))
+        } else {
+            result.removeAll()
+        }
         let check = llama_token_to_piece(
             model,
             token,
             &result,
             Int32(result.count)
         )
-        assert(check == actualTokensCount)
+        assert(check == nTokens)
     } else {
         result.removeLast(result.count - Int(nTokens))
     }
@@ -256,4 +259,5 @@ private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String
         buffer = []
         return bufferString
     }
+    return nil
 }

examples/llama.swiftui/.gitignore

@@ -1 +0,0 @@
-xcuserdata

examples/llama.swiftui/README.md

@@ -1,7 +0,0 @@
-# llama.swiftui
-
-Local inference of llama.cpp on an iPhone.
-So far I only tested with starcoder 1B model, but it can most likely handle 7B models as well.
-
-https://github.com/bachittle/llama.cpp/assets/39804642/e290827a-4edb-4093-9642-2a5e399ec545
-

examples/llama.swiftui/llama.cpp.swift/LibLlama.swift

@@ -1,184 +0,0 @@
-import Foundation
-
-// import llama
-
-enum LlamaError: Error {
-    case couldNotInitializeContext
-}
-
-actor LlamaContext {
-    private var model: OpaquePointer
-    private var context: OpaquePointer
-    private var batch: llama_batch
-    private var tokens_list: [llama_token]
-
-    var n_len: Int32 = 512
-    var n_cur: Int32 = 0
-    var n_decode: Int32 = 0
-
-    init(model: OpaquePointer, context: OpaquePointer) {
-        self.model = model
-        self.context = context
-        self.tokens_list = []
-        self.batch = llama_batch_init(512, 0, 1)
-    }
-
-    deinit {
-        llama_free(context)
-        llama_free_model(model)
-        llama_backend_free()
-    }
-
-    static func createContext(path: String) throws -> LlamaContext {
-        llama_backend_init(false)
-        let model_params = llama_model_default_params()
-
-        let model = llama_load_model_from_file(path, model_params)
-        guard let model else {
-            print("Could not load model at \(path)")
-            throw LlamaError.couldNotInitializeContext
-        }
-        var ctx_params = llama_context_default_params()
-        ctx_params.seed = 1234
-        ctx_params.n_ctx = 2048
-        ctx_params.n_threads = 8
-        ctx_params.n_threads_batch = 8
-
-        let context = llama_new_context_with_model(model, ctx_params)
-        guard let context else {
-            print("Could not load context!")
-            throw LlamaError.couldNotInitializeContext
-        }
-
-        return LlamaContext(model: model, context: context)
-    }
-
-    func get_n_tokens() -> Int32 {
-        return batch.n_tokens;
-    }
-
-    func completion_init(text: String) {
-        print("attempting to complete \"\(text)\"")
-
-        tokens_list = tokenize(text: text, add_bos: true)
-
-        let n_ctx = llama_n_ctx(context)
-        let n_kv_req = tokens_list.count + (Int(n_len) - tokens_list.count)
-
-        print("\n n_len = \(n_len), n_ctx = \(n_ctx), n_kv_req = \(n_kv_req)")
-
-        if n_kv_req > n_ctx {
-            print("error: n_kv_req > n_ctx, the required KV cache size is not big enough")
-        }
-
-        for id in tokens_list {
-            print(token_to_piece(token: id))
-        }
-
-        // batch = llama_batch_init(512, 0) // done in init()
-        batch.n_tokens = Int32(tokens_list.count)
-
-        for i1 in 0..<batch.n_tokens {
-            let i = Int(i1)
-            batch.token[i] = tokens_list[i]
-            batch.pos[i] = i1
-            batch.n_seq_id[Int(i)] = 1
-            batch.seq_id[Int(i)]![0] = 0
-            batch.logits[i] = 0
-        }
-        batch.logits[Int(batch.n_tokens) - 1] = 1 // true
-
-        if llama_decode(context, batch) != 0 {
-            print("llama_decode() failed")
-        }
-
-        n_cur = batch.n_tokens
-    }
-
-    func completion_loop() -> String {
-        var new_token_id: llama_token = 0
-
-        let n_vocab = llama_n_vocab(model)
-        let logits = llama_get_logits_ith(context, batch.n_tokens - 1)
-
-        var candidates = Array<llama_token_data>()
-        candidates.reserveCapacity(Int(n_vocab))
-
-        for token_id in 0..<n_vocab {
-            candidates.append(llama_token_data(id: token_id, logit: logits![Int(token_id)], p: 0.0))
-        }
-        candidates.withUnsafeMutableBufferPointer() { buffer in
-            var candidates_p = llama_token_data_array(data: buffer.baseAddress, size: buffer.count, sorted: false)
-
-            new_token_id = llama_sample_token_greedy(context, &candidates_p)
-        }
-
-        if new_token_id == llama_token_eos(context) || n_cur == n_len {
-            print("\n")
-            return ""
-        }
-
-        let new_token_str = token_to_piece(token: new_token_id)
-        print(new_token_str)
-        // tokens_list.append(new_token_id)
-
-        batch.n_tokens = 0
-
-        batch.token[Int(batch.n_tokens)] = new_token_id
-        batch.pos[Int(batch.n_tokens)] = n_cur
-        batch.n_seq_id[Int(batch.n_tokens)] = 1
-        batch.seq_id[Int(batch.n_tokens)]![0] = 0
-        batch.logits[Int(batch.n_tokens)] = 1 // true
-        batch.n_tokens += 1
-
-        n_decode += 1
-
-        n_cur += 1
-
-        if llama_decode(context, batch) != 0 {
-            print("failed to evaluate llama!")
-        }
-
-        return new_token_str
-    }
-
-    func clear() {
-        tokens_list.removeAll()
-    }
-
-    private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
-        let n_tokens = text.count + (add_bos ? 1 : 0)
-        let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
-        let tokenCount = llama_tokenize(model, text, Int32(text.count), tokens, Int32(n_tokens), add_bos, false)
-
-        var swiftTokens: [llama_token] = []
-        for i in 0..<tokenCount {
-            swiftTokens.append(tokens[Int(i)])
-        }
-
-        tokens.deallocate()
-
-        return swiftTokens
-    }
-
-    private func token_to_piece(token: llama_token) -> String {
-        let result = UnsafeMutablePointer<Int8>.allocate(capacity: 8)
-        result.initialize(repeating: Int8(0), count: 8)
-        defer {
-            result.deallocate()
-        }
-        let nTokens = llama_token_to_piece(model, token, result, 8)
-
-        if nTokens < 0 {
-            let newResult = UnsafeMutablePointer<Int8>.allocate(capacity: Int(-nTokens))
-            newResult.initialize(repeating: Int8(0), count: Int(-nTokens))
-            defer {
-                newResult.deallocate()
-            }
-            _ = llama_token_to_piece(model, token, newResult, -nTokens)
-            return String(cString: newResult)
-        } else {
-            return String(cString: result)
-        }
-    }
-}

examples/llama.swiftui/llama.cpp.swift/bridging-header.h

@@ -1,5 +0,0 @@
-//
-//  Use this file to import your target's public headers that you would like to expose to Swift.
-//
-
-#import "llama.h"

examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj

@@ -1,481 +0,0 @@
-// !$*UTF8*$!
-{
-    archiveVersion = 1;
-    classes = {
-    };
-    objectVersion = 56;
-    objects = {
-
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examples/llama.swiftui/llama.swiftui.xcodeproj/project.xcworkspace/contents.xcworkspacedata

@@ -1,7 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<Workspace
-   version = "1.0">
-   <FileRef
-      location = "self:">
-   </FileRef>
-</Workspace>

examples/llama.swiftui/llama.swiftui.xcodeproj/project.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist

@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-    <key>IDEDidComputeMac32BitWarning</key>
-    <true/>
-</dict>
-</plist>

examples/llama.swiftui/llama.swiftui/Assets.xcassets/AccentColor.colorset/Contents.json

@@ -1,11 +0,0 @@
-{
-  "colors" : [
-    {
-      "idiom" : "universal"
-    }
-  ],
-  "info" : {
-    "author" : "xcode",
-    "version" : 1
-  }
-}

examples/llama.swiftui/llama.swiftui/Assets.xcassets/AppIcon.appiconset/Contents.json

@@ -1,13 +0,0 @@
-{
-  "images" : [
-    {
-      "idiom" : "universal",
-      "platform" : "ios",
-      "size" : "1024x1024"
-    }
-  ],
-  "info" : {
-    "author" : "xcode",
-    "version" : 1
-  }
-}

examples/llama.swiftui/llama.swiftui/Assets.xcassets/Contents.json

@@ -1,6 +0,0 @@
-{
-  "info" : {
-    "author" : "xcode",
-    "version" : 1
-  }
-}

examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift

@@ -1,45 +0,0 @@
-import Foundation
-
-@MainActor
-class LlamaState: ObservableObject {
-    @Published var messageLog = ""
-
-    private var llamaContext: LlamaContext?
-    private var modelUrl: URL? {
-        Bundle.main.url(forResource: "q8_0", withExtension: "gguf", subdirectory: "models")
-        // Bundle.main.url(forResource: "llama-2-7b-chat", withExtension: "Q2_K.gguf", subdirectory: "models")
-    }
-    init() {
-        do {
-            try loadModel()
-        } catch {
-            messageLog += "Error!\n"
-        }
-    }
-
-    private func loadModel() throws {
-        messageLog += "Loading model...\n"
-        if let modelUrl {
-            llamaContext = try LlamaContext.createContext(path: modelUrl.path())
-            messageLog += "Loaded model \(modelUrl.lastPathComponent)\n"
-        } else {
-            messageLog += "Could not locate model\n"
-        }
-    }
-
-    func complete(text: String) async {
-        guard let llamaContext else {
-            return
-        }
-        messageLog += "Attempting to complete text...\n"
-        await llamaContext.completion_init(text: text)
-        messageLog += "\(text)"
-
-        while await llamaContext.n_cur <= llamaContext.n_len {
-            let result = await llamaContext.completion_loop()
-            messageLog += "\(result)"
-        }
-        await llamaContext.clear()
-        messageLog += "\n\ndone\n"
-    }
-}

examples/llama.swiftui/llama.swiftui/Preview Content/Preview Assets.xcassets/Contents.json

@@ -1,6 +0,0 @@
-{
-  "info" : {
-    "author" : "xcode",
-    "version" : 1
-  }
-}

examples/llama.swiftui/llama.swiftui/UI/ContentView.swift

@@ -1,42 +0,0 @@
-import SwiftUI
-
-struct ContentView: View {
-    @StateObject var llamaState = LlamaState()
-
-    @State private var multiLineText = ""
-
-    var body: some View {
-        VStack {
-            ScrollView(.vertical) {
-                Text(llamaState.messageLog)
-            }
-
-            TextEditor(text: $multiLineText)
-                .frame(height: 200)
-                .padding()
-                .border(Color.gray, width: 0.5)
-            Button(action: {
-                sendText()
-            }) {
-                Text("Send")
-                    .padding()
-                    .background(Color.blue)
-                    .foregroundColor(.white)
-                    .cornerRadius(8)
-            }
-        }
-        .padding()
-    }
-
-    func sendText() {
-        Task {
-            await llamaState.complete(text: multiLineText)
-            multiLineText = ""
-        }
-    }
-}
-/*
-#Preview {
-    ContentView()
-}
-*/

examples/llama.swiftui/llama.swiftui/llama_swiftuiApp.swift

@@ -1,10 +0,0 @@
-import SwiftUI
-
-@main
-struct llama_swiftuiApp: App {
-    var body: some Scene {
-        WindowGroup {
-            ContentView()
-        }
-    }
-}

examples/llava/convert-image-encoder-to-gguf.py

@@ -5,7 +5,7 @@ import json
 import torch
 import numpy as np
 from gguf import *
-from transformers import CLIPModel, CLIPProcessor, CLIPVisionModel
+from transformers import CLIPModel, CLIPProcessor
 
 TEXT = "clip.text"
 VISION = "clip.vision"
@@ -78,19 +78,11 @@ ap.add_argument("--text-only", action="store_true", required=False,
                 help="Save a text-only model. It can't be used to encode images")
 ap.add_argument("--vision-only", action="store_true", required=False,
                 help="Save a vision-only model. It can't be used to encode texts")
-ap.add_argument("--clip_model_is_vision", action="store_true", required=False,
-                help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
 ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
 ap.add_argument("--image-mean", nargs=3, type=float, required=False, help="Override image mean values")
 ap.add_argument("--image-std", nargs=3, type=float, required=False, help="Override image std values")
 ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
-# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
-default_image_mean = [0.48145466, 0.4578275, 0.40821073]
-default_image_std = [0.26862954, 0.26130258, 0.27577711]
-ap.add_argument('--image_mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
-ap.add_argument('--image_std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
 
-# with proper
 args = ap.parse_args()
 
 
@@ -104,22 +96,15 @@ if args.use_f32:
 # output in the same directory as the model if output_dir is None
 dir_model = args.model_dir
 
-if args.clip_model_is_vision:
-    vocab = None
-    tokens = None
-else:
-    with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
-        vocab = json.load(f)
-        tokens = [key for key in vocab]
+
+with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
+    vocab = json.load(f)
+    tokens = [key for key in vocab]
 
 with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
     config = json.load(f)
-    if args.clip_model_is_vision:
-        v_hparams = config
-        t_hparams = None
-    else:
-        v_hparams = config["vision_config"]
-        t_hparams = config["text_config"]
+    v_hparams = config["vision_config"]
+    t_hparams = config["text_config"]
 
 # possible data types
 #   ftype == 0 -> float32
@@ -132,12 +117,9 @@ ftype = 1
 if args.use_f32:
     ftype = 0
 
-if args.clip_model_is_vision:
-    model = CLIPVisionModel.from_pretrained(dir_model)
-    processor = None
-else:
-    model = CLIPModel.from_pretrained(dir_model)
-    processor = CLIPProcessor.from_pretrained(dir_model)
+
+model = CLIPModel.from_pretrained(dir_model)
+processor = CLIPProcessor.from_pretrained(dir_model)
 
 fname_middle = None
 has_text_encoder = True
@@ -146,13 +128,13 @@ has_llava_projector = False
 if args.text_only:
     fname_middle = "text-"
     has_vision_encoder = False
+elif args.vision_only:
+    fname_middle = "vision-"
+    has_text_encoder = False
 elif args.llava_projector is not None:
     fname_middle = "mmproj-"
     has_text_encoder = False
     has_llava_projector = True
-elif args.vision_only:
-    fname_middle = "vision-"
-    has_text_encoder = False
 else:
     fname_middle = ""
 
@@ -200,12 +182,8 @@ if has_vision_encoder:
     block_count = v_hparams["num_hidden_layers"] - 1 if has_llava_projector else v_hparams["num_hidden_layers"]
     fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), block_count)
 
-    if processor is not None:
-        image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean
-        image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std
-    else:
-        image_mean = args.image_mean if args.image_mean is not None else default_image_mean
-        image_std = args.image_std if args.image_std is not None else default_image_std
+    image_mean = processor.image_processor.image_mean if args.image_mean is None else args.image_mean
+    image_std = processor.image_processor.image_std if args.image_std is None else args.image_std
     fout.add_array("clip.vision.image_mean", image_mean)
     fout.add_array("clip.vision.image_std", image_std)
 

examples/lookahead/CMakeLists.txt

@@ -1,5 +0,0 @@
-set(TARGET lookahead)
-add_executable(${TARGET} lookahead.cpp)
-install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_11)

examples/lookahead/README.md

@@ -1,7 +0,0 @@
-# llama.cpp/examples/lookahead
-
-Demonstartion of lookahead decoding technique:
-
-https://lmsys.org/blog/2023-11-21-lookahead-decoding/
-
-More info: https://github.com/ggerganov/llama.cpp/pull/4207

examples/lookahead/lookahead.cpp

@@ -1,487 +0,0 @@
-#include "common.h"
-#include "llama.h"
-
-#include <cmath>
-#include <cstdio>
-#include <string>
-#include <vector>
-
-struct ngram_data {
-    bool active = false;
-
-    llama_seq_id seq_id = -1;
-
-    std::vector<int> i_batch;
-
-    std::vector<llama_token> tokens;
-};
-
-// n-gram container
-struct ngram_container {
-    ngram_container(int n_vocab, int N, int G) {
-        cnt.resize(n_vocab);
-        head.resize(n_vocab);
-        tokens.resize(n_vocab * G * (N - 1));
-    }
-
-    int n_total = 0;
-
-    std::vector<int> cnt;
-    std::vector<int> head;
-
-    // [n_vocab][G][N - 1]
-    // for each token of the vocab, keep a ring-buffer of capacity G of n-grams of size N - 1
-    std::vector<llama_token> tokens;
-};
-
-int main(int argc, char ** argv) {
-    gpt_params params;
-
-    if (gpt_params_parse(argc, argv, params) == false) {
-        return 1;
-    }
-
-    const int W = 15; // lookahead window
-    const int N = 5;  // n-gram size
-    const int G = 15; // max verification n-grams
-
-    const bool dump_kv_cache = params.dump_kv_cache;
-
-#ifndef LOG_DISABLE_LOGS
-    log_set_target(log_filename_generator("lookahead", "log"));
-    LOG_TEE("Log start\n");
-    log_dump_cmdline(argc, argv);
-#endif // LOG_DISABLE_LOGS
-
-    // init llama.cpp
-    llama_backend_init(params.numa);
-
-    llama_model * model = NULL;
-    llama_context * ctx = NULL;
-
-    // load the target model
-    std::tie(model, ctx) = llama_init_from_gpt_params(params);
-
-    // Tokenize the prompt
-    const bool add_bos = llama_should_add_bos_token(model);
-    LOG("add_bos tgt: %d\n", add_bos);
-
-    std::vector<llama_token> inp;
-    std::vector<llama_token> all;
-
-    inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
-    all = inp;
-
-    const int max_context_size     = llama_n_ctx(ctx);
-    const int max_tokens_list_size = max_context_size - 4;
-
-    if ((int) inp.size() > max_tokens_list_size) {
-        fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
-        return 1;
-    }
-
-    fprintf(stderr, "\n\n");
-
-    for (auto id : inp) {
-        fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
-    }
-
-    fflush(stderr);
-
-    const int n_input = inp.size();
-
-    const auto t_enc_start = ggml_time_us();
-
-    // eval the prompt
-    llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0,           0));
-    llama_decode(ctx, llama_batch_get_one(&inp.back(),           1, n_input - 1, 0));
-
-    for (int s = 1; s < W + G + 1; ++s) {
-        llama_kv_cache_seq_cp(ctx, 0, s, -1, -1);
-    }
-
-    const auto t_enc_end = ggml_time_us();
-
-    int n_predict = 0;
-    int n_accept  = 0;
-
-    int n_past = inp.size();
-
-    llama_token id = 0;
-
-    // used to determine end of generation
-    bool has_eos = false;
-
-    // for each decoded batch, we have at most W + G + 1 distinct sequences:
-    // seq_id == 0           : the current input token
-    // seq_id [1, W]         : tokens from the past N - 1 Jacobi iterations
-    // seq_id [W + 1, W + G] : verification n-grams
-    llama_batch batch = llama_batch_init(params.n_ctx, 0, W + G + 1);
-
-    // target model sampling context
-    struct llama_sampling_context * ctx_sampling = llama_sampling_init(params.sparams);
-
-    // verification n-grams
-    std::vector<ngram_data> ngrams_cur(G);
-
-    // tokens for the past N - 1 Jacobi iterations
-    std::vector<llama_token> tokens_j_prev(W);
-    std::vector<std::vector<llama_token>> tokens_j(N - 1);
-    for (int j = 0; j < N - 1; j++) {
-        tokens_j[j].resize(W);
-
-        for (int i = 0; i < W; i++) {
-            // there are different ways to init these tokens
-            if (0) {
-                // initialize randomly from the prompt tokens
-                tokens_j[j][i] = all[1 + rand() % (all.size() - 1)];
-            } else {
-                // initialize with a sequence of increasing numbers
-                tokens_j[j][i] = 100 + i;
-            }
-        }
-    }
-
-    std::vector<llama_seq_id> seq_id_look;
-
-    // the input token belongs both to all sequences
-    std::vector<llama_seq_id> seq_id_all(W + G + 1);
-    for (int i = 0; i < W + G + 1; i++) {
-        seq_id_all[i] = i;
-    }
-
-    // here we keep adding new n-grams as we go
-    ngram_container ngrams_observed(llama_n_vocab(model), N, G);
-
-    // debug
-    struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, W + G + 1);
-
-    const auto t_dec_start = ggml_time_us();
-
-    // sample first token
-    {
-        id = llama_sampling_sample(ctx_sampling, ctx, NULL, 0);
-
-        llama_sampling_accept(ctx_sampling, ctx, id, true);
-
-        {
-            const std::string token_str = llama_token_to_piece(ctx, id);
-
-            printf("%s", token_str.c_str());
-            fflush(stdout);
-        }
-    }
-
-    while (true) {
-        // debug
-        if (dump_kv_cache) {
-            llama_kv_cache_view_update(ctx, &kvc_view);
-            dump_kv_cache_view_seqs(kvc_view, 40);
-        }
-
-        // build the mask from https://lmsys.org/blog/2023-11-21-lookahead-decoding/
-        //
-        // Example for W = 5, N = 4, G = 2:
-        // (I = input, L = lookahead, V = verification)
-        //
-        // Batch:  0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
-        // T:        -2 -2 -2 -2 -1 -1 -1 -1 -1  0  0  0  0  0  0
-        // Info:   I  L  L  L  L  L  L  L  L  L  L  L  L  L  L  V  V  V  V  V  V
-        // Pos:    0  1  2  3  4  1  2  3  4  5  2  3  4  5  6  1  2  3  1  2  3   (+ n_past)
-        // Logits: 1  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1
-        // ---------------------------------------------------------------------
-        // Seq:    0
-        //         1              1              1
-        //         2  2              2              2
-        //         3  3  3              3              3
-        //         4  4  4  4              4              4
-        //         5  5  5  5  5              5              5
-        //         6                                            6  6  6
-        //         7                                                     7  7  7
-        // ---------------------------------------------------------------------
-        //                                       |  |  |  |  |  |  |  |  |  |  |
-        //                                       V  V  V  V  V  |  |  |  |  |  |
-        //                                         j_tokens     |  |  |  |  |  |
-        //                                                      V  V  V  V  V  V
-        //                                                             id
-        {
-            llama_batch_clear(batch);
-
-            // current token - first token of the first level
-            llama_batch_add(batch, id, n_past, seq_id_all, true);
-
-            // verification n-grams - queue this before the lookahead tokens for less KV cache fragmentation
-            {
-                const int g_cur = ngrams_observed.cnt[id];
-
-                ngrams_cur.resize(g_cur);
-                for (int g = 0; g < g_cur; g++) {
-                    ngrams_cur[g].active = true;
-                    ngrams_cur[g].tokens.resize(N);
-                    ngrams_cur[g].i_batch.resize(N);
-                    ngrams_cur[g].seq_id = W + 1 + g;
-                    ngrams_cur[g].i_batch[0] = 0;
-                    ngrams_cur[g].tokens [0] = id;
-                }
-
-                for (int j = 0; j < N - 1; j++) {
-                    for (int g = 0; g < g_cur; g++) {
-                        const int idx = id*(N - 1)*G + g*(N - 1);
-
-                        const llama_token t = ngrams_observed.tokens[idx + j];
-
-                        ngrams_cur[g].tokens [j + 1] = t;
-                        ngrams_cur[g].i_batch[j + 1] = batch.n_tokens;
-
-                        llama_batch_add(batch, t, n_past + j + 1, { W + 1 + g }, true);
-                    }
-                }
-            }
-
-            // fill the remaining W - 1 tokens for the first level
-            for (int i = 1; i < W; i++) {
-                seq_id_look.resize(W - i);
-                for (int j = 0; j < W - i; j++) {
-                    seq_id_look[j] = i + j + 1;
-                }
-
-                llama_batch_add(batch, tokens_j[0][i], n_past + i, seq_id_look, false);
-            }
-
-            // fill the rest of the levels
-            for (int j = 1; j < N - 1; j++) {
-                for (int i = 0; i < W; i++) {
-                    llama_batch_add(batch, tokens_j[j][i], n_past + j + i, { i + 1 }, j == N - 2);
-                }
-            }
-        }
-
-        if (llama_decode(ctx, batch) != 0) {
-            fprintf(stderr, "\n\n%s: error: llama_decode failed - increase KV cache size\n", __func__);
-            return 1;
-        }
-
-        int seq_id_best = 0;
-
-        for (int v = 0; v < N; ++v) {
-            int i_batch = 0;
-
-            // if no active ngrams are left, it means the sampled token does not pass the verification
-            if (v > 0) {
-                for (int g = 0; g < (int) ngrams_cur.size(); g++) {
-                    if (ngrams_cur[g].active) {
-                        i_batch = ngrams_cur[g].i_batch[v];
-                        seq_id_best = ngrams_cur[g].seq_id;
-
-                        ++n_accept;
-                        break;
-                    }
-                }
-
-                // no more matches -> create a new batch
-                if (i_batch == 0) {
-                    break;
-                }
-            }
-
-            // sample the next token
-            id = llama_sampling_sample(ctx_sampling, ctx, NULL, i_batch);
-
-            llama_sampling_accept(ctx_sampling, ctx, id, true);
-
-            // print
-            {
-                const std::string token_str = llama_token_to_piece(ctx, id);
-
-                if (v == 0) {
-                    printf("%s", token_str.c_str());
-                } else {
-                    // print light cyan
-                    printf("\033[0;96m%s\033[0m", token_str.c_str());
-                }
-                fflush(stdout);
-
-                if (id == llama_token_eos(model)) {
-                    has_eos = true;
-                }
-
-                all.push_back(id);
-            }
-
-            ++n_predict;
-            ++n_past;
-
-            if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
-                break;
-            }
-
-            // verify across active n-grams
-            for (int g = 0; g < (int) ngrams_cur.size(); g++) {
-                if (ngrams_cur[g].active) {
-                    if (v == N - 1) {
-                        ngrams_cur[g].active = false;
-                    } else {
-                        if (id != ngrams_cur[g].tokens[v + 1]) {
-                            ngrams_cur[g].active = false;
-                        }
-                    }
-                }
-            }
-
-            // print known n-grams starting with token id (debug)
-            if (0 && v == 0) {
-                if (ngrams_observed.cnt[id] > 0) {
-                    printf("\n - %d n-grams starting with '%s'\n", ngrams_observed.cnt[id], llama_token_to_piece(ctx, id).c_str());
-                }
-
-                for (int i = 0; i < ngrams_observed.cnt[id]; i++) {
-                    printf("   - ngram %2d: ", i);
-
-                    const int idx = id*(N - 1)*G + i*(N - 1);
-
-                    for (int j = 0; j < N - 1; j++) {
-                        const std::string token_str = llama_token_to_piece(ctx, ngrams_observed.tokens[idx + j]);
-
-                        printf("%s", token_str.c_str());
-                    }
-
-                    printf("\n");
-                }
-            }
-
-            // update lookahead tokens
-            {
-                for (int i = 0; i < W; i++) {
-                    tokens_j_prev[i] = tokens_j[0][i];
-                }
-
-                for (int j = 0; j < N - 2; j++) {
-                    tokens_j[j] = tokens_j[j + 1];
-                }
-
-                if (v == 0) {
-                    // sample from the last level
-                    for (int i = 0; i < W; i++) {
-                        tokens_j[N - 2][i] = llama_sampling_sample(ctx_sampling, ctx, NULL, ngrams_cur.size()*(N-1) + W*(N - 2) + i);
-                    }
-                } else {
-                    for (int i = 0; i < W; i++) {
-                        // there are different ways to init these tokens
-                        if (0) {
-                            // random init
-                            tokens_j[N - 2][i] = all[1 + rand() % (all.size() - 1)];
-                        } else {
-                            // init from the previous level
-                            tokens_j[N - 2][i] = tokens_j[0][i];
-                        }
-                    }
-                }
-            }
-
-            // update observed ngrams
-            if (v == 0) {
-                // the first token of the n-gram is determined by the index in the container so it is not stored
-                std::vector<llama_token> ngram(N - 1);
-
-                // n-gram generation
-                // ref: https://github.com/hao-ai-lab/LookaheadDecoding/issues/14#issuecomment-1826198518
-                for (int f = 0; f < W; ++f) {
-                    const int ft = tokens_j_prev[f]; // first token of the n-gram
-
-                    for (int j = 0; j < N - 1; ++j) {
-                        ngram[j] = tokens_j[j][f];
-                    }
-
-                    // filter-out repeating n-grams
-                    {
-                        bool is_unique = true;
-
-                        for (int k = 0; k < ngrams_observed.cnt[ft]; ++k) {
-                            const int idx = ft*(N - 1)*G + k*(N - 1);
-
-                            bool is_match = true;
-                            for (int j = 0; j < N - 1; ++j) {
-                                if (ngrams_observed.tokens[idx + j] != ngram[j]) {
-                                    is_match = false;
-                                    break;
-                                }
-                            }
-
-                            if (is_match) {
-                                is_unique = false;
-                                break;
-                            }
-                        }
-
-                        if (!is_unique) {
-                            continue;
-                        }
-                    }
-
-                    const int head = ngrams_observed.head[ft];
-                    const int idx  = ft*(N - 1)*G + head*(N - 1);
-
-                    for (int i = 0; i < N - 1; i++) {
-                        ngrams_observed.tokens[idx + i] = ngram[i];
-                    }
-
-                    ngrams_observed.cnt[ft]  = std::min(G, ngrams_observed.cnt[ft] + 1);
-                    ngrams_observed.head[ft] = (head + 1) % G;
-
-                    ngrams_observed.n_total++;
-                }
-            }
-        }
-
-        if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
-            break;
-        }
-
-        // KV cache management
-        // if no verification token matched, we simply remove all cells from this batch -> no fragmentation
-        llama_kv_cache_seq_rm(ctx, -1, n_past, -1);
-
-        if (seq_id_best != 0) {
-            // if a verification token matched, we keep the best sequence and remove the rest
-            // this leads to some KV cache fragmentation
-            llama_kv_cache_seq_keep(ctx, seq_id_best);
-            llama_kv_cache_seq_cp  (ctx, seq_id_best, 0, -1, -1);
-            llama_kv_cache_seq_rm  (ctx, seq_id_best,    -1, -1);
-
-            for (int s = 1; s < W + G + 1; ++s) {
-                llama_kv_cache_seq_cp(ctx, 0, s, -1, -1);
-            }
-        }
-    }
-
-    auto t_dec_end = ggml_time_us();
-
-    LOG_TEE("\n\n");
-
-    LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input,   (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
-    LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict  / ((t_dec_end - t_dec_start) / 1e6f));
-
-    LOG_TEE("\n");
-    LOG_TEE("W = %2d\n", W);
-    LOG_TEE("N = %2d\n", N);
-    LOG_TEE("G = %2d\n", G);
-    LOG_TEE("\n");
-    LOG_TEE("n_predict = %d\n", n_predict);
-    LOG_TEE("n_accept  = %d\n", n_accept);
-
-    llama_print_timings(ctx);
-
-    llama_kv_cache_view_free(&kvc_view);
-    llama_sampling_free(ctx_sampling);
-
-    llama_batch_free(batch);
-
-    llama_free(ctx);
-    llama_free_model(model);
-
-    llama_backend_free();
-
-    fprintf(stderr, "\n\n");
-
-    return 0;
-}

examples/main/main.cpp

@@ -100,12 +100,6 @@ static void sigint_handler(int signo) {
 }
 #endif
 
-static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
-    (void) level;
-    (void) user_data;
-    LOG_TEE("%s", text);
-}
-
 int main(int argc, char ** argv) {
     gpt_params params;
     g_params = &params;
@@ -119,7 +113,6 @@ int main(int argc, char ** argv) {
     log_set_target(log_filename_generator("main", "log"));
     LOG_TEE("Log start\n");
     log_dump_cmdline(argc, argv);
-    llama_log_set(llama_log_callback_logTee, nullptr);
 #endif // LOG_DISABLE_LOGS
 
     // TODO: Dump params ?

examples/server/api_like_OAI.py

@@ -11,10 +11,10 @@ app = Flask(__name__)
 slot_id = -1
 
 parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.")
-parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.')
-parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: 'USER: ')", default="USER: ")
-parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: 'ASSISTANT: ')", default="ASSISTANT: ")
-parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: 'ASSISTANT's RULE: ')", default="ASSISTANT's RULE: ")
+parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n')
+parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: '\\nUSER: ')", default="\\nUSER: ")
+parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: '\\nASSISTANT: ')", default="\\nASSISTANT: ")
+parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: '\\nASSISTANT's RULE: ')", default="\\nASSISTANT's RULE: ")
 parser.add_argument("--stop", type=str, help="the end of response in chat completions(default: '</s>')", default="</s>")
 parser.add_argument("--llama-api", type=str, help="Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)", default='http://127.0.0.1:8080')
 parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="")
@@ -34,19 +34,19 @@ def is_present(json, key):
 
 #convert chat to prompt
 def convert_chat(messages):
+    prompt = "" + args.chat_prompt.replace("\\n", "\n")
 
-    system_n = args.system_name
-    user_n = args.user_name
-    ai_n = args.ai_name
-    stop = args.stop
+    system_n = args.system_name.replace("\\n", "\n")
+    user_n = args.user_name.replace("\\n", "\n")
+    ai_n = args.ai_name.replace("\\n", "\n")
+    stop = args.stop.replace("\\n", "\n")
 
-    prompt = "" + args.chat_prompt + stop
 
     for line in messages:
         if (line["role"] == "system"):
-            prompt += f"{system_n}{line['content']}{stop}"
+            prompt += f"{system_n}{line['content']}"
         if (line["role"] == "user"):
-            prompt += f"{user_n}{line['content']}{stop}"
+            prompt += f"{user_n}{line['content']}"
         if (line["role"] == "assistant"):
             prompt += f"{ai_n}{line['content']}{stop}"
     prompt += ai_n.rstrip()
@@ -130,7 +130,7 @@ def make_resData_stream(data, chat=False, time_now = 0, start=False):
             }
         ]
     }
-    slot_id = data.get("slot_id")
+    slot_id = data["slot_id"]
     if (chat):
         if (start):
             resData["choices"][0]["delta"] =  {
@@ -150,13 +150,11 @@ def make_resData_stream(data, chat=False, time_now = 0, start=False):
     return resData
 
 
-@app.route('/chat/completions', methods=['POST', 'OPTIONS'])
-@app.route('/v1/chat/completions', methods=['POST', 'OPTIONS'])
+@app.route('/chat/completions', methods=['POST'])
+@app.route('/v1/chat/completions', methods=['POST'])
 def chat_completions():
     if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
         return Response(status=403)
-    if request.method == 'OPTIONS':
-        return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
     body = request.get_json()
     stream = False
     tokenize = False
@@ -179,22 +177,20 @@ def chat_completions():
             data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
             time_now = int(time.time())
             resData = make_resData_stream({}, chat=True, time_now=time_now, start=True)
-            yield 'data: {}\n\n'.format(json.dumps(resData))
+            yield 'data: {}\n'.format(json.dumps(resData))
             for line in data.iter_lines():
                 if line:
                     decoded_line = line.decode('utf-8')
                     resData = make_resData_stream(json.loads(decoded_line[6:]), chat=True, time_now=time_now)
-                    yield 'data: {}\n\n'.format(json.dumps(resData))
-        return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
+                    yield 'data: {}\n'.format(json.dumps(resData))
+        return Response(generate(), mimetype='text/event-stream')
 
 
-@app.route('/completions', methods=['POST', 'OPTIONS'])
-@app.route('/v1/completions', methods=['POST', 'OPTIONS'])
+@app.route('/completions', methods=['POST'])
+@app.route('/v1/completions', methods=['POST'])
 def completion():
     if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
         return Response(status=403)
-    if request.method == 'OPTIONS':
-        return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
     body = request.get_json()
     stream = False
     tokenize = False
@@ -220,8 +216,8 @@ def completion():
                 if line:
                     decoded_line = line.decode('utf-8')
                     resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now)
-                    yield 'data: {}\n\n'.format(json.dumps(resData))
-        return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
+                    yield 'data: {}\n'.format(json.dumps(resData))
+        return Response(generate(), mimetype='text/event-stream')
 
 if __name__ == '__main__':
     app.run(args.host, port=args.port)

examples/server/server.cpp

@@ -155,23 +155,15 @@ struct task_server {
     json data;
     bool infill_mode = false;
     bool embedding_mode = false;
-    int multitask_id = -1;
 };
 
 struct task_result {
     int id;
-    int multitask_id = -1;
     bool stop;
     bool error;
     json result_json;
 };
 
-struct task_multi {
-    int id;
-    std::set<int> subtasks_remaining{};
-    std::vector<task_result> results{};
-};
-
 // TODO: can become bool if we can't find use of more states
 enum slot_state
 {
@@ -414,9 +406,6 @@ struct llama_client_slot
     double t_prompt_processing; // ms
     double t_token_generation; // ms
 
-    // multitasks
-    int multitask_id = -1;
-
     void reset() {
         num_prompt_tokens      = 0;
         generated_text         = "";
@@ -540,8 +529,7 @@ struct llama_server_context
 
     std::vector<task_server> queue_tasks;
     std::vector<task_result> queue_results;
-    std::vector<task_multi>  queue_multitasks;
-    std::mutex mutex_tasks; // also guards id_gen, and queue_multitasks
+    std::mutex mutex_tasks;
     std::mutex mutex_results;
 
     ~llama_server_context()
@@ -1124,40 +1112,17 @@ struct llama_server_context
         return slot.images.size() > 0;
     }
 
-    void send_error(task_server& task, std::string error)
+    void send_error(int id, std::string error)
     {
         std::lock_guard<std::mutex> lock(mutex_results);
         task_result res;
-        res.id = task.id;
-        res.multitask_id = task.multitask_id;
+        res.id = id;
         res.stop = false;
         res.error = true;
         res.result_json = { { "content", error } };
         queue_results.push_back(res);
     }
 
-    void add_multi_task(int id, std::vector<int>& sub_ids)
-    {
-        std::lock_guard<std::mutex> lock(mutex_tasks);
-        task_multi multi;
-        multi.id = id;
-        std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end()));
-        queue_multitasks.push_back(multi);
-    }
-
-    void update_multi_task(int multitask_id, int subtask_id, task_result& result)
-    {
-        std::lock_guard<std::mutex> lock(mutex_tasks);
-        for (auto& multitask : queue_multitasks)
-        {
-            if (multitask.id == multitask_id)
-            {
-                multitask.subtasks_remaining.erase(subtask_id);
-                multitask.results.push_back(result);
-            }
-        }
-    }
-
     json get_model_props()
     {
         return get_formated_generation(slots[0]);
@@ -1202,7 +1167,6 @@ struct llama_server_context
         std::lock_guard<std::mutex> lock(mutex_results);
         task_result res;
         res.id = slot.task_id;
-        res.multitask_id = slot.multitask_id;
         res.error = false;
         res.stop = false;
 
@@ -1242,7 +1206,6 @@ struct llama_server_context
         std::lock_guard<std::mutex> lock(mutex_results);
         task_result res;
         res.id = slot.task_id;
-        res.multitask_id = slot.multitask_id;
         res.error = false;
         res.stop = true;
 
@@ -1288,12 +1251,6 @@ struct llama_server_context
             res.result_json["model"] = slot.oaicompat_model;
         }
 
-        // parent multitask, if any, needs to be updated
-        if (slot.multitask_id != -1)
-        {
-            update_multi_task(slot.multitask_id, slot.task_id, res);
-        }
-
         queue_results.push_back(res);
     }
 
@@ -1302,7 +1259,6 @@ struct llama_server_context
         std::lock_guard<std::mutex> lock(mutex_results);
         task_result res;
         res.id = slot.task_id;
-        res.multitask_id = slot.multitask_id;
         res.error = false;
         res.stop = true;
 
@@ -1329,9 +1285,9 @@ struct llama_server_context
         queue_results.push_back(res);
     }
 
-    int request_completion(json data, bool infill, bool embedding, int multitask_id)
+    int request_completion(json data, bool infill, bool embedding)
     {
-        std::unique_lock<std::mutex> lock(mutex_tasks);
+        std::lock_guard<std::mutex> lock(mutex_tasks);
         task_server task;
         task.id = id_gen++;
         task.target_id = 0;
@@ -1339,16 +1295,6 @@ struct llama_server_context
         task.infill_mode = infill;
         task.embedding_mode = embedding;
         task.type = COMPLETION_TASK;
-        task.multitask_id = multitask_id;
-
-        // when a completion task's prompt array is not a singleton, we split it into multiple requests
-        if (task.data.at("prompt").size() > 1)
-        {
-            lock.unlock(); // entering new func scope
-            return split_multiprompt_task(task);
-        }
-
-        // otherwise, it's a single-prompt task, we actually queue it
         queue_tasks.push_back(task);
         return task.id;
     }
@@ -1367,17 +1313,8 @@ struct llama_server_context
 
             for (int i = 0; i < (int) queue_results.size(); i++)
             {
-                // for now, tasks that have associated parent multitasks just get erased once multitask picks up the result
-                if (queue_results[i].multitask_id == task_id)
-                {
-                    update_multi_task(task_id, queue_results[i].id, queue_results[i]);
-                    queue_results.erase(queue_results.begin() + i);
-                    continue;
-                }
-
                 if (queue_results[i].id == task_id)
                 {
-                    assert(queue_results[i].multitask_id == -1);
                     task_result res = queue_results[i];
                     queue_results.erase(queue_results.begin() + i);
                     return res;
@@ -1467,27 +1404,6 @@ struct llama_server_context
         queue_tasks.push_back(task);
     }
 
-    int split_multiprompt_task(task_server& multiprompt_task)
-    {
-        auto prompt_count = multiprompt_task.data.at("prompt").size();
-        assert(prompt_count > 1);
-
-        int multitask_id = id_gen++;
-        std::vector<int> subtask_ids(prompt_count);
-        for (int i = 0; i < prompt_count; i++)
-        {
-            json subtask_data = multiprompt_task.data;
-            subtask_data["prompt"] = subtask_data["prompt"][i];
-
-            // subtasks inherit everything else (infill mode, embedding mode, etc.)
-            subtask_ids[i] = request_completion(subtask_data, multiprompt_task.infill_mode, multiprompt_task.embedding_mode, multitask_id);
-        }
-
-        // queue up the multitask so we can track its subtask progression
-        add_multi_task(multitask_id, subtask_ids);
-        return multitask_id;
-    }
-
     void process_tasks()
     {
         std::lock_guard<std::mutex> lock(mutex_tasks);
@@ -1503,7 +1419,7 @@ struct llama_server_context
                     {
                         LOG_TEE("slot unavailable\n");
                         // send error result
-                        send_error(task, "slot unavailable");
+                        send_error(task.id, "slot unavailable");
                         return;
                     }
 
@@ -1517,12 +1433,11 @@ struct llama_server_context
                     slot->infill = task.infill_mode;
                     slot->embedding = task.embedding_mode;
                     slot->task_id = task.id;
-                    slot->multitask_id = task.multitask_id;
 
                     if (!launch_slot_with_data(slot, task.data))
                     {
                         // send error result
-                        send_error(task, "internal_error");
+                        send_error(task.id, "internal_error");
                         break;
                     }
                 } break;
@@ -1538,38 +1453,6 @@ struct llama_server_context
                 } break;
             }
         }
-
-        // remove finished multitasks from the queue of multitasks, and add the corresponding result to the result queue
-        auto queue_iterator = queue_multitasks.begin();
-        while (queue_iterator != queue_multitasks.end())
-        {
-            if (queue_iterator->subtasks_remaining.empty())
-            {
-                // all subtasks done == multitask is done
-                task_result aggregate_result;
-                aggregate_result.id = queue_iterator->id;
-                aggregate_result.stop = true;
-                aggregate_result.error = false;
-
-                // collect json results into one json result
-                std::vector<json> result_jsons;
-                for (auto& subres : queue_iterator->results)
-                {
-                    result_jsons.push_back(subres.result_json);
-                    aggregate_result.error = aggregate_result.error && subres.error;
-                }
-                aggregate_result.result_json = json{ "results", result_jsons };
-
-                std::lock_guard<std::mutex> lock(mutex_results);
-                queue_results.push_back(aggregate_result);
-
-                queue_iterator = queue_multitasks.erase(queue_iterator);
-            }
-            else
-            {
-                ++queue_iterator;
-            }
-        }
     }
 
     bool update_slots() {
@@ -1961,7 +1844,6 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     printf("    -spf FNAME, --system-prompt-file FNAME\n");
     printf("                        Set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n");
     printf("  --mmproj MMPROJ_FILE  path to a multimodal projector file for LLaVA.\n");
-    printf("  --log-disable         disables logging to a file.\n");
     printf("\n");
 }
 
@@ -2316,11 +2198,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
             }
             params.mmproj = argv[i];
         }
-        else if (arg == "--log-disable")
-        {
-            log_set_target(stdout);
-            LOG_INFO("logging to file is disabled.", {});
-        }
         else
         {
             fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
@@ -2719,7 +2596,7 @@ int main(int argc, char **argv)
     svr.Post("/completion", [&llama](const httplib::Request &req, httplib::Response &res)
             {
                 json data = json::parse(req.body);
-                const int task_id = llama.request_completion(data, false, false, -1);
+                const int task_id = llama.request_completion(data, false, false);
                 if (!json_value(data, "stream", false)) {
                     std::string completion_text;
                     task_result result = llama.next_result(task_id);
@@ -2808,7 +2685,7 @@ int main(int argc, char **argv)
             {
                 json data = oaicompat_completion_params_parse(json::parse(req.body));
 
-                const int task_id = llama.request_completion(data, false, false, -1);
+                const int task_id = llama.request_completion(data, false, false);
 
                 if (!json_value(data, "stream", false)) {
                     std::string completion_text;
@@ -2877,7 +2754,7 @@ int main(int argc, char **argv)
     svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res)
             {
                 json data = json::parse(req.body);
-                const int task_id = llama.request_completion(data, true, false, -1);
+                const int task_id = llama.request_completion(data, true, false);
                 if (!json_value(data, "stream", false)) {
                     std::string completion_text;
                     task_result result = llama.next_result(task_id);
@@ -2981,7 +2858,7 @@ int main(int argc, char **argv)
                 {
                     prompt = "";
                 }
-                const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false, true, -1);
+                const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false, true);
                 task_result result = llama.next_result(task_id);
                 return res.set_content(result.result_json.dump(), "application/json");
             });

examples/speculative/README.md

@@ -1,8 +0,0 @@
-# llama.cpp/examples/speculative
-
-Demonstartion of speculative decoding and tree-based speculative decoding techniques
-
-More info:
-
-- https://github.com/ggerganov/llama.cpp/pull/2926
-- https://github.com/ggerganov/llama.cpp/pull/3624

ggml-alloc.c

@@ -137,7 +137,7 @@ void ggml_tallocr_alloc(ggml_tallocr_t alloc, struct ggml_tensor * tensor) {
 
 #ifdef GGML_ALLOCATOR_DEBUG
     add_allocated_tensor(alloc, tensor);
-    size_t cur_max = (char*)addr - (char*)alloc->base + size;
+    size_t cur_max = (char*)addr - (char*)alloc->data + size;
     if (cur_max > alloc->max_size) {
         printf("max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
         for (int i = 0; i < 1024; i++) {

ggml-cuda.cu

@@ -443,7 +443,6 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_
 #define CUDA_SCALE_BLOCK_SIZE 256
 #define CUDA_CLAMP_BLOCK_SIZE 256
 #define CUDA_ROPE_BLOCK_SIZE 256
-#define CUDA_SOFT_MAX_BLOCK_SIZE 1024
 #define CUDA_ALIBI_BLOCK_SIZE 32
 #define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32
 #define CUDA_QUANTIZE_BLOCK_SIZE 256
@@ -502,31 +501,6 @@ static size_t g_scratch_offset = 0;
 
 static cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr};
 
-static __device__ __forceinline__ float warp_reduce_sum(float x) {
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        x += __shfl_xor_sync(0xffffffff, x, mask, 32);
-    }
-    return x;
-}
-
-static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32);
-        a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32);
-    }
-    return a;
-}
-
-static __device__ __forceinline__ float warp_reduce_max(float x) {
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
-    }
-    return x;
-}
-
 static __global__ void add_f32(const float * x, const float * y, float * dst, const int kx, const int ky) {
     const int i = blockDim.x*blockIdx.x + threadIdx.x;
 
@@ -603,6 +577,15 @@ static __global__ void sqr_f32(const float * x, float * dst, const int k) {
     dst[i] = x[i] * x[i];
 }
 
+static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32);
+        a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32);
+    }
+    return a;
+}
+
 template <int block_size>
 static __global__ void norm_f32(const float * x, float * dst, const int ncols) {
     const int row = blockIdx.x*blockDim.y + threadIdx.y;
@@ -641,6 +624,14 @@ static __global__ void norm_f32(const float * x, float * dst, const int ncols) {
     }
 }
 
+static __device__ __forceinline__ float warp_reduce_sum(float x) {
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        x += __shfl_xor_sync(0xffffffff, x, mask, 32);
+    }
+    return x;
+}
+
 template <int block_size>
 static __global__ void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps) {
     const int row = blockIdx.x*blockDim.y + threadIdx.y;
@@ -4619,8 +4610,8 @@ static __global__ void rope(
 
 template<typename T, bool has_pos>
 static __global__ void rope_neox(
-    const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims
+    const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
+    float ext_factor, float attn_factor, rope_corr_dims corr_dims
 ) {
     const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
@@ -4629,25 +4620,23 @@ static __global__ void rope_neox(
     }
 
     const int row = blockDim.x*blockIdx.x + threadIdx.x;
-    const int ib = col / n_dims;
-    const int ic = col % n_dims;
-
-    const int i = row*ncols + ib*n_dims + ic/2;
+    const int i = row*ncols + col/2;
     const int i2 = row/p_delta_rows;
 
-    float cur_rot = inv_ndims * ic - ib;
+    // simplified from `(ib * ncols + col) * (-1 / ncols)`, where ib is assumed to be zero
+    const float cur_rot = -float(col)/ncols;
 
     const int p = has_pos ? pos[i2] : 0;
-    const float theta_base = p*freq_scale*powf(theta_scale, col/2.0f);
+    const float theta_base = p*powf(freq_base, cur_rot);
 
     float cos_theta, sin_theta;
     rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta);
 
     const float x0 = x[i + 0];
-    const float x1 = x[i + n_dims/2];
+    const float x1 = x[i + ncols/2];
 
-    dst[i + 0]        = x0*cos_theta - x1*sin_theta;
-    dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
+    dst[i + 0]       = x0*cos_theta - x1*sin_theta;
+    dst[i + ncols/2] = x0*sin_theta + x1*cos_theta;
 }
 
 static __global__ void rope_glm_f32(
@@ -4726,74 +4715,45 @@ static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int
     dst[i] = x[i] - (col > n_past + row % rows_per_channel) * INT_MAX; // equivalent within rounding error but slightly faster on GPU
 }
 
-static __global__ void soft_max_f32(const float * x, const float * y, float * dst, const int ncols, const int nrows_y, const float scale) {
-    const int tid  = threadIdx.x;
-    const int rowx = blockIdx.x;
-    const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension
-
-    const int block_size = blockDim.x;
-
-    const int warp_id = threadIdx.x / WARP_SIZE;
-    const int lane_id = threadIdx.x % WARP_SIZE;
-
-    __shared__ float buf[CUDA_SOFT_MAX_BLOCK_SIZE/WARP_SIZE];
+// the CUDA soft max implementation differs from the CPU implementation
+// instead of doubles floats are used
+static __global__ void soft_max_f32(const float * x, float * dst, const int ncols) {
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+    const int block_size = blockDim.y;
+    const int tid = threadIdx.y;
 
     float max_val = -INFINITY;
 
     for (int col = tid; col < ncols; col += block_size) {
-        const int ix = rowx*ncols + col;
-        const int iy = rowy*ncols + col;
-        max_val = max(max_val, x[ix]*scale + (y ? y[iy] : 0.0f));
+        const int i = row*ncols + col;
+        max_val = max(max_val, x[i]);
     }
 
     // find the max value in the block
-    max_val = warp_reduce_max(max_val);
-    if (block_size > WARP_SIZE) {
-        if (warp_id == 0) {
-            buf[lane_id] = -INFINITY;
-        }
-        __syncthreads();
-
-        if (lane_id == 0) {
-            buf[warp_id] = max_val;
-        }
-        __syncthreads();
-
-        max_val = buf[lane_id];
-        max_val = warp_reduce_max(max_val);
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        max_val = max(max_val, __shfl_xor_sync(0xffffffff, max_val, mask, 32));
     }
 
     float tmp = 0.f;
 
     for (int col = tid; col < ncols; col += block_size) {
-        const int ix = rowx*ncols + col;
-        const int iy = rowy*ncols + col;
-        const float val = expf((x[ix]*scale + (y ? y[iy] : 0.0f)) - max_val);
+        const int i = row*ncols + col;
+        const float val = expf(x[i] - max_val);
         tmp += val;
-        dst[ix] = val;
+        dst[i] = val;
     }
 
-    // find the sum of exps in the block
-    tmp = warp_reduce_sum(tmp);
-    if (block_size > WARP_SIZE) {
-        if (warp_id == 0) {
-            buf[lane_id] = 0.f;
-        }
-        __syncthreads();
-
-        if (lane_id == 0) {
-            buf[warp_id] = tmp;
-        }
-        __syncthreads();
-
-        tmp = buf[lane_id];
-        tmp = warp_reduce_sum(tmp);
+    // sum up partial sums
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
     }
 
     const float inv_tmp = 1.f / tmp;
 
     for (int col = tid; col < ncols; col += block_size) {
-        const int i = rowx*ncols + col;
+        const int i = row*ncols + col;
         dst[i] *= inv_tmp;
     }
 }
@@ -5779,26 +5739,20 @@ static void rope_cuda(
 
 template<typename T>
 static void rope_neox_cuda(
-    const T * x, T * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
+    const T * x, T * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
     float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream
 ) {
     GGML_ASSERT(ncols % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
     const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
     const dim3 block_nums(nrows, num_blocks_x, 1);
-
-    const float theta_scale = powf(freq_base, -2.0f/n_dims);
-    const float inv_ndims = -1.0f / n_dims;
-
     if (pos == nullptr) {
         rope_neox<T, false><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
-            theta_scale, inv_ndims
+            x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims
         );
     } else {
         rope_neox<T, true><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
-            theta_scale, inv_ndims
+            x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims
         );
     }
 }
@@ -5830,12 +5784,10 @@ static void diag_mask_inf_f32_cuda(const float * x, float * dst, const int ncols
     diag_mask_inf_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols_x, rows_per_channel, n_past);
 }
 
-static void soft_max_f32_cuda(const float * x, const float * y, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, cudaStream_t stream) {
-    int nth = WARP_SIZE;
-    while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2;
-    const dim3 block_dims(nth,     1, 1);
+static void soft_max_f32_cuda(const float * x, float * dst, const int ncols_x, const int nrows_x, cudaStream_t stream) {
+    const dim3 block_dims(1, WARP_SIZE, 1);
     const dim3 block_nums(nrows_x, 1, 1);
-    soft_max_f32<<<block_nums, block_dims, 0, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
+    soft_max_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols_x);
 }
 
 static void im2col_f32_f16_cuda(const float * x, half * dst,
@@ -6755,14 +6707,15 @@ inline void ggml_cuda_op_rope(
         GGML_ASSERT(false);
         rope_glm_f32_cuda(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, n_ctx, main_stream);
     } else if (is_neox) {
+        GGML_ASSERT(ne00 == n_dims && "ne00 != n_dims is not implemented for CUDA yet");
         if (src0->type == GGML_TYPE_F32) {
             rope_neox_cuda(
-                (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
+                (const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
                 attn_factor, corr_dims, main_stream
             );
         } else if (src0->type == GGML_TYPE_F16) {
             rope_neox_cuda(
-                (const half *)src0_dd, (half *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
+                (const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
                 attn_factor, corr_dims, main_stream
             );
         } else {
@@ -6886,18 +6839,14 @@ inline void ggml_cuda_op_soft_max(
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
-    GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
-
     const int64_t ne00 = src0->ne[0];
-    const int64_t nrows_x = ggml_nrows(src0);
-    const int64_t nrows_y = src1 ? ggml_nrows(src1) : 1;
+    const int64_t nrows = ggml_nrows(src0);
 
-    float scale = 1.0f;
-    memcpy(&scale, dst->op_params, sizeof(float));
-
-    soft_max_f32_cuda(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, main_stream);
+    soft_max_f32_cuda(src0_dd, dst_dd, ne00, nrows, main_stream);
 
+    (void) src1;
     (void) dst;
+    (void) src1_dd;
 }
 
 inline void ggml_cuda_op_scale(

ggml-metal.m

@@ -1028,27 +1028,20 @@ void ggml_metal_graph_compute(
                             int nth = 32; // SIMD width
 
                             if (ne00%4 == 0) {
-                                while (nth < ne00/4 && nth < 256) {
-                                    nth *= 2;
-                                }
                                 [encoder setComputePipelineState:ctx->pipeline_soft_max_4];
                             } else {
-                                while (nth < ne00 && nth < 1024) {
+                                do {
                                     nth *= 2;
-                                }
+                                } while (nth <= ne00 && nth <= 1024);
+                                nth /= 2;
                                 [encoder setComputePipelineState:ctx->pipeline_soft_max];
                             }
-
-                            const float scale = ((float *) dst->op_params)[0];
-
-                            [encoder setBuffer:id_src0 offset:offs_src0   atIndex:0];
-                            [encoder setBuffer:id_src1 offset:offs_src1   atIndex:1];
-                            [encoder setBuffer:id_dst  offset:offs_dst    atIndex:2];
-                            [encoder setBytes:&ne00  length:sizeof(ne00)  atIndex:3];
-                            [encoder setBytes:&ne01  length:sizeof(ne01)  atIndex:4];
-                            [encoder setBytes:&ne02  length:sizeof(ne02)  atIndex:5];
-                            [encoder setBytes:&scale length:sizeof(scale) atIndex:6];
-                            [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];
+                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+                            [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
+                            [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
+                            [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
+                            [encoder setThreadgroupMemoryLength:GGML_PAD(nth/32*sizeof(float), 16) atIndex:0];
 
                             [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                         } break;
@@ -1358,19 +1351,15 @@ void ggml_metal_graph_compute(
                             float eps;
                             memcpy(&eps, dst->op_params, sizeof(float));
 
-                            int nth = 32; // SIMD width
-
-                            while (nth < ne00/4 && nth < 1024) {
-                                nth *= 2;
-                            }
+                            const int nth = MIN(512, ne00);
 
                             [encoder setComputePipelineState:ctx->pipeline_rms_norm];
-                            [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
-                            [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
-                            [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
-                            [encoder setBytes:&nb01    length:sizeof(uint64_t) atIndex:3];
-                            [encoder setBytes:&eps     length:sizeof(   float) atIndex:4];
-                            [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];
+                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+                            [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
+                            [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3];
+                            [encoder setBytes:&eps  length:sizeof(   float) atIndex:4];
+                            [encoder setThreadgroupMemoryLength:GGML_PAD(nth/32*sizeof(float), 16) atIndex:0];
 
                             const int64_t nrows = ggml_nrows(src0);
 
@@ -1444,8 +1433,7 @@ void ggml_metal_graph_compute(
                             const int n_past     = ((int32_t *) dst->op_params)[0];
                             const int n_dims     = ((int32_t *) dst->op_params)[1];
                             const int mode       = ((int32_t *) dst->op_params)[2];
-                            // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal
-                            const int n_orig_ctx = ((int32_t *) dst->op_params)[4];
+                            const int n_orig_ctx = ((int32_t *) dst->op_params)[3];
 
                             float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
                             memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));

ggml-metal.metal

@@ -39,8 +39,6 @@ typedef struct {
     int8_t  qs[QK8_0]; // quants
 } block_q8_0;
 
-#define N_SIMDWIDTH 32 // assuming SIMD group size is 32
-
 // general-purpose kernel for addition of two tensors
 // pros: works for non-contiguous tensors, supports broadcast across dims 1, 2 and 3
 // cons: not very efficient
@@ -182,12 +180,10 @@ kernel void kernel_gelu(
 
 kernel void kernel_soft_max(
         device const float * src0,
-        device const float * src1,
         device       float * dst,
         constant   int64_t & ne00,
         constant   int64_t & ne01,
         constant   int64_t & ne02,
-        constant     float & scale,
         threadgroup float  * buf [[threadgroup(0)]],
         uint  tgpig[[threadgroup_position_in_grid]],
         uint  tpitg[[thread_position_in_threadgroup]],
@@ -198,77 +194,73 @@ kernel void kernel_soft_max(
     const int64_t i02 = (tgpig - i03*ne02*ne01) / ne01;
     const int64_t i01 = (tgpig - i03*ne02*ne01 - i02*ne01);
 
-    device const float * psrc0 =        src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
-    device const float * pmask = src1 ? src1                                      + i01*ne00 : nullptr;
-    device       float * pdst  =        dst  + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+    device const float * psrc0 = src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+    device       float * pdst  = dst  + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
 
     // parallel max
-    float lmax = -INFINITY;
+    float lmax = tpitg < ne00 ? psrc0[tpitg] : -INFINITY;
 
-    for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
-        lmax = MAX(lmax, psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f));
+    for (int i00 = tpitg + ntg; i00 < ne00; i00 += ntg) {
+        lmax = MAX(lmax, psrc0[i00]);
     }
 
-    // find the max value in the block
-    float max_val = simd_max(lmax);
-    if (ntg > N_SIMDWIDTH) {
-        if (sgitg == 0) {
-            buf[tiisg] = -INFINITY;
-        }
-
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        if (tiisg == 0) {
-            buf[sgitg] = max_val;
-        }
-
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        max_val = buf[tiisg];
-        max_val = simd_max(max_val);
+    float max = simd_max(lmax);
+    if (tiisg == 0) {
+        buf[sgitg] = max;
     }
 
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // broadcast, simd group number is ntg / 32
+    for (uint i = ntg / 32 / 2; i > 0; i /= 2) {
+       if (tpitg < i) {
+           buf[tpitg] = MAX(buf[tpitg], buf[tpitg + i]);
+       }
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    max = buf[0];
+
     // parallel sum
     float lsum = 0.0f;
     for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
-        const float exp_psrc0 = exp((psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f)) - max_val);
+        const float exp_psrc0 = exp(psrc0[i00] - max);
         lsum += exp_psrc0;
+        // Remember the result of exp here. exp is expensive, so we really do not
+        // wish to compute it twice.
         pdst[i00] = exp_psrc0;
     }
 
     float sum = simd_sum(lsum);
-    if (ntg > N_SIMDWIDTH) {
-        if (sgitg == 0) {
-            buf[tiisg] = 0.0f;
-        }
-
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        if (tiisg == 0) {
-            buf[sgitg] = sum;
-        }
-
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        sum = buf[tiisg];
-        sum = simd_sum(sum);
+    if (tiisg == 0) {
+        buf[sgitg] = sum;
     }
 
-    const float inv_sum = 1.0f/sum;
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // broadcast, simd group number is ntg / 32
+    for (uint i = ntg / 32 / 2; i > 0; i /= 2) {
+       if (tpitg < i) {
+           buf[tpitg] += buf[tpitg + i];
+       }
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    sum = buf[0];
 
     for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
-        pdst[i00] *= inv_sum;
+        pdst[i00] /= sum;
     }
 }
 
 kernel void kernel_soft_max_4(
         device const float * src0,
-        device const float * src1,
         device       float * dst,
         constant   int64_t & ne00,
         constant   int64_t & ne01,
         constant   int64_t & ne02,
-        constant     float & scale,
         threadgroup float  * buf [[threadgroup(0)]],
         uint  tgpig[[threadgroup_position_in_grid]],
         uint  tpitg[[thread_position_in_threadgroup]],
@@ -279,68 +271,64 @@ kernel void kernel_soft_max_4(
     const int64_t i02 = (tgpig - i03*ne02*ne01) / ne01;
     const int64_t i01 = (tgpig - i03*ne02*ne01 - i02*ne01);
 
-    device const float4 * psrc4 =        (device const float4 *)(src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
-    device const float4 * pmask = src1 ? (device const float4 *)(src1 +                                      i01*ne00) : nullptr;
-    device       float4 * pdst4 =        (device       float4 *)(dst  + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
+    device const float4 * psrc4 = (device const float4 *)(src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
+    device       float4 * pdst4 = (device       float4 *)(dst  + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
 
     // parallel max
-    float4 lmax4 = -INFINITY;
+    float4 lmax4 = tpitg < ne00/4 ? psrc4[tpitg] : -INFINITY;
 
-    for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) {
-        lmax4 = fmax(lmax4, psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f));
+    for (int i00 = tpitg + ntg; i00 < ne00/4; i00 += ntg) {
+        lmax4 = fmax(lmax4, psrc4[i00]);
     }
 
     const float lmax = MAX(MAX(lmax4[0], lmax4[1]), MAX(lmax4[2], lmax4[3]));
-
-    float max_val = simd_max(lmax);
-    if (ntg > N_SIMDWIDTH) {
-        if (sgitg == 0) {
-            buf[tiisg] = -INFINITY;
-        }
-
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        if (tiisg == 0) {
-            buf[sgitg] = max_val;
-        }
-
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        max_val = buf[tiisg];
-        max_val = simd_max(max_val);
+    float max = simd_max(lmax);
+    if (tiisg == 0) {
+        buf[sgitg] = max;
     }
 
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // broadcast, simd group number is ntg / 32
+    for (uint i = ntg / 32 / 2; i > 0; i /= 2) {
+       if (tpitg < i) {
+           buf[tpitg] = MAX(buf[tpitg], buf[tpitg + i]);
+       }
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    max = buf[0];
+
     // parallel sum
     float4 lsum4 = 0.0f;
     for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) {
-        const float4 exp_psrc4 = exp((psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f)) - max_val);
+        const float4 exp_psrc4 = exp(psrc4[i00] - max);
         lsum4 += exp_psrc4;
         pdst4[i00] = exp_psrc4;
     }
 
     const float lsum = lsum4[0] + lsum4[1] + lsum4[2] + lsum4[3];
     float sum = simd_sum(lsum);
-    if (ntg > N_SIMDWIDTH) {
-        if (sgitg == 0) {
-            buf[tiisg] = 0.0f;
-        }
-
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        if (tiisg == 0) {
-            buf[sgitg] = sum;
-        }
-
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        sum = buf[tiisg];
-        sum = simd_sum(sum);
+    if (tiisg == 0) {
+        buf[sgitg] = sum;
     }
 
-    const float inv_sum = 1.0f/sum;
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // broadcast, simd group number is ntg / 32
+    for (uint i = ntg / 32 / 2; i > 0; i /= 2) {
+       if (tpitg < i) {
+           buf[tpitg] += buf[tpitg + i];
+       }
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    sum = buf[0];
 
     for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) {
-        pdst4[i00] *= inv_sum;
+        pdst4[i00] /= sum;
     }
 }
 
@@ -447,13 +435,14 @@ kernel void kernel_rms_norm(
         constant   int64_t & ne00,
         constant  uint64_t & nb01,
         constant     float & eps,
-        threadgroup float  * buf [[threadgroup(0)]],
+        threadgroup float  * sum [[threadgroup(0)]],
         uint tgpig[[threadgroup_position_in_grid]],
         uint tpitg[[thread_position_in_threadgroup]],
         uint sgitg[[simdgroup_index_in_threadgroup]],
         uint tiisg[[thread_index_in_simdgroup]],
         uint   ntg[[threads_per_threadgroup]]) {
-    device const float4 * x = (device const float4 *) ((device const char *) src0 + tgpig*nb01);
+    device const float4 * x        = (device const float4 *) ((device const char *) src0 + tgpig*nb01);
+    device const float  * x_scalar = (device const float  *) x;
 
     float4 sumf = 0;
     float all_sum = 0;
@@ -464,30 +453,40 @@ kernel void kernel_rms_norm(
     }
     all_sum = sumf[0] + sumf[1] + sumf[2] + sumf[3];
     all_sum = simd_sum(all_sum);
-    if (ntg > N_SIMDWIDTH) {
-        if (sgitg == 0) {
-            buf[tiisg] = 0.0f;
-        }
-
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        if (tiisg == 0) {
-            buf[sgitg] = all_sum;
-        }
-
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        all_sum = buf[tiisg];
-        all_sum = simd_sum(all_sum);
+    if (tiisg == 0) {
+        sum[sgitg] = all_sum;
     }
 
-    const float mean  = all_sum/ne00;
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // broadcast, simd group number is ntg / 32
+    for (uint i = ntg / 32 / 2; i > 0; i /= 2) {
+       if (tpitg < i) {
+           sum[tpitg] += sum[tpitg + i];
+       }
+    }
+    if (tpitg == 0) {
+        for (int i = 4 * (ne00 / 4); i < ne00; i++) {
+            sum[0] += x_scalar[i];
+        }
+        sum[0] /= ne00;
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    const float mean  = sum[0];
     const float scale = 1.0f/sqrt(mean + eps);
 
     device float4 * y = (device float4 *) (dst + tgpig*ne00);
+    device float * y_scalar = (device float *) y;
     for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) {
         y[i00] = x[i00] * scale;
     }
+    if (tpitg == 0) {
+        for (int i00 = 4 * (ne00 / 4); i00 < ne00; i00++) {
+            y_scalar[i00] = x_scalar[i00] * scale;
+        }
+    }
 }
 
 // function for calculate inner product between half a q4_0 block and 16 floats (yl), sumy is SUM(yl[i])
@@ -577,6 +576,7 @@ inline float block_q_n_dot_y(device const block_q5_1 * qb_curr, float sumy, thre
 // putting them in the kernel cause a significant performance penalty
 #define N_DST 4        // each SIMD group works on 4 rows
 #define N_SIMDGROUP 2  // number of SIMD groups in a thread group
+#define N_SIMDWIDTH 32 // assuming SIMD group size is 32
 //Note: This is a template, but strictly speaking it only applies to
 //      quantizations where the block size is 32. It also does not
 //      giard against the number of rows not being divisible by

ggml-opencl.cpp

@@ -1,18 +1,20 @@
-#include "ggml.h"
 #include "ggml-opencl.h"
 
 #include <array>
 #include <atomic>
-#include <cstdio>
-#include <cstdlib>
-#include <cstring>
-#include <limits>
 #include <sstream>
 #include <vector>
+#include <limits>
 
 #define CL_TARGET_OPENCL_VERSION 110
 #include <clblast.h>
 
+#include <stdlib.h>
+#include <stdio.h>
+#include <string.h>
+
+#include "ggml.h"
+
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif

ggml.c

@@ -4826,17 +4826,7 @@ struct ggml_tensor * ggml_diag_mask_zero_inplace(
 static struct ggml_tensor * ggml_soft_max_impl(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
-        struct ggml_tensor  * mask,
-        float                 scale,
         bool                  inplace) {
-    GGML_ASSERT(ggml_is_contiguous(a));
-    if (mask) {
-        GGML_ASSERT(ggml_is_contiguous(mask));
-        GGML_ASSERT(mask->ne[2] == 1);
-        GGML_ASSERT(mask->ne[3] == 1);
-        GGML_ASSERT(ggml_can_repeat_rows(mask, a));
-    }
-
     bool is_node = false;
 
     if (a->grad) {
@@ -4845,13 +4835,9 @@ static struct ggml_tensor * ggml_soft_max_impl(
 
     struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
 
-    float params[] = { scale };
-    ggml_set_op_params(result, params, sizeof(params));
-
     result->op   = GGML_OP_SOFT_MAX;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
     result->src[0] = a;
-    result->src[1] = mask;
 
     return result;
 }
@@ -4859,21 +4845,13 @@ static struct ggml_tensor * ggml_soft_max_impl(
 struct ggml_tensor * ggml_soft_max(
         struct ggml_context * ctx,
         struct ggml_tensor  * a) {
-    return ggml_soft_max_impl(ctx, a, NULL, 1.0f, false);
+    return ggml_soft_max_impl(ctx, a, false);
 }
 
 struct ggml_tensor * ggml_soft_max_inplace(
         struct ggml_context * ctx,
         struct ggml_tensor  * a) {
-    return ggml_soft_max_impl(ctx, a, NULL, 1.0f, true);
-}
-
-struct ggml_tensor * ggml_soft_max_ext(
-        struct ggml_context * ctx,
-        struct ggml_tensor  * a,
-        struct ggml_tensor  * mask,
-        float                 scale) {
-    return ggml_soft_max_impl(ctx, a, mask, scale, false);
+    return ggml_soft_max_impl(ctx, a, true);
 }
 
 // ggml_soft_max_back
@@ -9395,7 +9373,7 @@ static bool ggml_compute_forward_mul_mat_use_blas(
     // TODO: find the optimal values for these
     if (ggml_is_contiguous(src0) &&
         ggml_is_contiguous(src1) &&
-      //src0->type == GGML_TYPE_F32 &&
+        src0->type == GGML_TYPE_F32 &&
         src1->type == GGML_TYPE_F32 &&
         (ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) {
 
@@ -10573,25 +10551,20 @@ static void ggml_compute_forward_diag_mask_zero(
 static void ggml_compute_forward_soft_max_f32(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
-              struct ggml_tensor * dst) {
-    assert(ggml_is_contiguous(dst));
-    assert(ggml_are_same_shape(src0, dst));
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguous(dst));
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
     if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
         return;
     }
 
-    float scale = 1.0f;
-    memcpy(&scale, (float *) dst->op_params + 0, sizeof(float));
-
     // TODO: handle transposed/permuted matrices
 
     const int ith = params->ith;
     const int nth = params->nth;
 
-    const int64_t ne11 = src1 ? src1->ne[1] : 1;
-
     const int nc = src0->ne[0];
     const int nr = ggml_nrows(src0);
 
@@ -10602,40 +10575,29 @@ static void ggml_compute_forward_soft_max_f32(
     const int ir0 = dr*ith;
     const int ir1 = MIN(ir0 + dr, nr);
 
-    float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith;
-
     for (int i1 = ir0; i1 < ir1; i1++) {
-        float * sp = (float *)((char *) src0->data + i1*src0->nb[1]);
-        float * dp = (float *)((char *)  dst->data +  i1*dst->nb[1]);
-
-        // broadcast the mask across rows
-        float * mp = src1 ? (float *)((char *) src1->data + (i1%ne11)*src1->nb[1]) : NULL;
-
-        ggml_vec_cpy_f32  (nc, wp, sp);
-        ggml_vec_scale_f32(nc, wp, scale);
-        if (mp) {
-            ggml_vec_acc_f32(nc, wp, mp);
-        }
+        float *sp = (float *)((char *) src0->data + i1*src0->nb[1]);
+        float *dp = (float *)((char *)  dst->data +  i1*dst->nb[1]);
 
 #ifndef NDEBUG
         for (int i = 0; i < nc; ++i) {
             //printf("p[%d] = %f\n", i, p[i]);
-            assert(!isnan(wp[i]));
+            assert(!isnan(sp[i]));
         }
 #endif
 
         float max = -INFINITY;
-        ggml_vec_max_f32(nc, &max, wp);
+        ggml_vec_max_f32(nc, &max, sp);
 
         ggml_float sum = 0.0;
 
         uint16_t scvt;
         for (int i = 0; i < nc; i++) {
-            if (wp[i] == -INFINITY) {
+            if (sp[i] == -INFINITY) {
                 dp[i] = 0.0f;
             } else {
-                // const float val = (wp[i] == -INFINITY) ? 0.0 : exp(wp[i] - max);
-                ggml_fp16_t s = GGML_FP32_TO_FP16(wp[i] - max);
+                // const float val = (sp[i] == -INFINITY) ? 0.0 : exp(sp[i] - max);
+                ggml_fp16_t s = GGML_FP32_TO_FP16(sp[i] - max);
                 memcpy(&scvt, &s, sizeof(scvt));
                 const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt]);
                 sum += (ggml_float)val;
@@ -10660,12 +10622,11 @@ static void ggml_compute_forward_soft_max_f32(
 static void ggml_compute_forward_soft_max(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
-              struct ggml_tensor * dst) {
+        struct ggml_tensor * dst) {
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_soft_max_f32(params, src0, src1, dst);
+                ggml_compute_forward_soft_max_f32(params, src0, dst);
             } break;
         default:
             {
@@ -13902,7 +13863,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             } break;
         case GGML_OP_SOFT_MAX:
             {
-                ggml_compute_forward_soft_max(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_soft_max(params, tensor->src[0], tensor);
             } break;
         case GGML_OP_SOFT_MAX_BACK:
             {
@@ -15728,14 +15689,13 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
             {
                 n_tasks = 1;
             } break;
+        case GGML_OP_COUNT:
+            {
+                GGML_ASSERT(false);
+            } break;
         default:
             {
-                fprintf(stderr, "%s: op not implemented: ", __func__);
-                if (node->op < GGML_OP_COUNT) {
-                    fprintf(stderr, "%s\n", ggml_op_name(node->op));
-                } else {
-                    fprintf(stderr, "%d\n", node->op);
-                }
+                printf("%s: op %s not implemented\n", __func__, ggml_op_name(node->op));
                 GGML_ASSERT(false);
             } break;
     }
@@ -15938,12 +15898,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
                         cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
                     }
                 } break;
-            case GGML_OP_SOFT_MAX:
-                {
-                    n_tasks = MIN(MIN(4, n_threads), ggml_nrows(node->src[0]));
-
-                    cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
-                } break;
             case GGML_OP_CONV_TRANSPOSE_1D:
                 {
                     GGML_ASSERT(node->src[0]->ne[3] == 1);

ggml.h

@@ -244,10 +244,11 @@
 #define GGML_ASSERT(x) \
     do { \
         if (!(x)) { \
-            fflush(stdout); \
             fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
+            fflush(stderr); \
+            fflush(stdout); \
             ggml_print_backtrace(); \
-            abort(); \
+            exit(1); \
         } \
     } while (0)
 
@@ -1282,14 +1283,6 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
 
-    // fused soft_max(a*scale + mask)
-    // mask is optional
-    GGML_API struct ggml_tensor * ggml_soft_max_ext(
-            struct ggml_context * ctx,
-            struct ggml_tensor  * a,
-            struct ggml_tensor  * mask,
-            float                 scale);
-
     GGML_API struct ggml_tensor * ggml_soft_max_back(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,

gguf-py/gguf/constants.py

@@ -92,7 +92,6 @@ class MODEL_ARCH(IntEnum):
     BERT      = auto()
     BLOOM     = auto()
     STABLELM  = auto()
-    QWEN      = auto()
 
 
 class MODEL_TENSOR(IntEnum):
@@ -133,7 +132,6 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.BERT:           "bert",
     MODEL_ARCH.BLOOM:          "bloom",
     MODEL_ARCH.STABLELM:       "stablelm",
-    MODEL_ARCH.QWEN:           "qwen",
 }
 
 TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@@ -319,20 +317,6 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
-    MODEL_ARCH.QWEN: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.ROPE_FREQS,
-        MODEL_TENSOR.ATTN_NORM,
-        MODEL_TENSOR.ATTN_QKV,
-        MODEL_TENSOR.ATTN_OUT,
-        MODEL_TENSOR.ATTN_ROT_EMBD,
-        MODEL_TENSOR.FFN_NORM,
-        MODEL_TENSOR.FFN_GATE,
-        MODEL_TENSOR.FFN_DOWN,
-        MODEL_TENSOR.FFN_UP,
-    ],
     MODEL_ARCH.GPT2: [
         # TODO
     ],
@@ -352,10 +336,6 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
     MODEL_ARCH.PERSIMMON: [
         MODEL_TENSOR.ROPE_FREQS,
     ],
-    MODEL_ARCH.QWEN: [
-        MODEL_TENSOR.ROPE_FREQS,
-        MODEL_TENSOR.ATTN_ROT_EMBD,
-    ],
 }
 
 #

gguf-py/gguf/tensor_mapping.py

@@ -10,7 +10,7 @@ class TensorNameMap:
         # Token embeddings
         MODEL_TENSOR.TOKEN_EMBD: (
             "gpt_neox.embed_in",                         # gptneox
-            "transformer.wte",                           # gpt2 gpt-j mpt refact qwen
+            "transformer.wte",                           # gpt2 gpt-j mpt refact
             "transformer.word_embeddings",               # falcon
             "word_embeddings",                           # bloom
             "model.embed_tokens",                        # llama-hf
@@ -38,7 +38,7 @@ class TensorNameMap:
         # Output
         MODEL_TENSOR.OUTPUT: (
             "embed_out",                 # gptneox
-            "lm_head",                   # gpt2 mpt falcon llama-hf baichuan qwen
+            "lm_head",                   # gpt2 mpt falcon llama-hf baichuan
             "output",                    # llama-pth bloom
             "word_embeddings_for_head",  # persimmon
         ),
@@ -51,7 +51,7 @@ class TensorNameMap:
             "norm",                                    # llama-pth
             "embeddings.LayerNorm",                    # bert
             "transformer.norm_f",                      # mpt
-            "ln_f",                                    # refact bloom qwen
+            "ln_f",                                    # refact bloom
             "language_model.encoder.final_layernorm",  # persimmon
         ),
 
@@ -65,7 +65,7 @@ class TensorNameMap:
         # Attention norm
         MODEL_TENSOR.ATTN_NORM: (
             "gpt_neox.layers.{bid}.input_layernorm",                # gptneox
-            "transformer.h.{bid}.ln_1",                             # gpt2 gpt-j refact qwen
+            "transformer.h.{bid}.ln_1",                             # gpt2 gpt-j refact
             "transformer.blocks.{bid}.norm_1",                      # mpt
             "transformer.h.{bid}.input_layernorm",                  # falcon7b
             "h.{bid}.input_layernorm",                              # bloom
@@ -85,7 +85,7 @@ class TensorNameMap:
         # Attention query-key-value
         MODEL_TENSOR.ATTN_QKV: (
             "gpt_neox.layers.{bid}.attention.query_key_value",                     # gptneox
-            "transformer.h.{bid}.attn.c_attn",                                     # gpt2 qwen
+            "transformer.h.{bid}.attn.c_attn",                                     # gpt2
             "transformer.blocks.{bid}.attn.Wqkv",                                  # mpt
             "transformer.h.{bid}.self_attention.query_key_value",                  # falcon
             "h.{bid}.self_attention.query_key_value",                              # bloom
@@ -119,7 +119,7 @@ class TensorNameMap:
         # Attention output
         MODEL_TENSOR.ATTN_OUT: (
             "gpt_neox.layers.{bid}.attention.dense",                     # gptneox
-            "transformer.h.{bid}.attn.c_proj",                           # gpt2 refact qwen
+            "transformer.h.{bid}.attn.c_proj",                           # gpt2 refact
             "transformer.blocks.{bid}.attn.out_proj",                    # mpt
             "transformer.h.{bid}.self_attention.dense",                  # falcon
             "h.{bid}.self_attention.dense",                              # bloom
@@ -139,7 +139,7 @@ class TensorNameMap:
         # Feed-forward norm
         MODEL_TENSOR.FFN_NORM: (
             "gpt_neox.layers.{bid}.post_attention_layernorm",                # gptneox
-            "transformer.h.{bid}.ln_2",                                      # gpt2 refact qwen
+            "transformer.h.{bid}.ln_2",                                      # gpt2 refact
             "h.{bid}.post_attention_layernorm",                              # bloom
             "transformer.blocks.{bid}.norm_2",                               # mpt
             "model.layers.{bid}.post_attention_layernorm",                   # llama-hf
@@ -161,20 +161,18 @@ class TensorNameMap:
             "encoder.layer.{bid}.intermediate.dense",                 # bert
             "transformer.h.{bid}.mlp.fc_in",                          # gpt-j
             "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h",  # persimmon
-            "transformer.h.{bid}.mlp.w1",                             # qwen
         ),
 
         # Feed-forward gate
         MODEL_TENSOR.FFN_GATE: (
             "model.layers.{bid}.mlp.gate_proj",  # llama-hf refact
             "layers.{bid}.feed_forward.w1",      # llama-pth
-            "transformer.h.{bid}.mlp.w2",        # qwen
         ),
 
         # Feed-forward down
         MODEL_TENSOR.FFN_DOWN: (
             "gpt_neox.layers.{bid}.mlp.dense_4h_to_h",                # gptneox
-            "transformer.h.{bid}.mlp.c_proj",                         # gpt2 refact qwen
+            "transformer.h.{bid}.mlp.c_proj",                         # gpt2 refact
             "transformer.blocks.{bid}.ffn.down_proj",                 # mpt
             "transformer.h.{bid}.mlp.dense_4h_to_h",                  # falcon
             "h.{bid}.mlp.dense_4h_to_h",                              # bloom

llama.cpp

@@ -46,6 +46,7 @@
     #endif
     #include <windows.h>
     #include <io.h>
+    #include <stdio.h> // for _fseeki64
 #endif
 
 #include <algorithm>
@@ -192,7 +193,6 @@ enum llm_arch {
     LLM_ARCH_REFACT,
     LLM_ARCH_BLOOM,
     LLM_ARCH_STABLELM,
-    LLM_ARCH_QWEN,
     LLM_ARCH_UNKNOWN,
 };
 
@@ -209,7 +209,6 @@ static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
     { LLM_ARCH_REFACT,          "refact"    },
     { LLM_ARCH_BLOOM,           "bloom"     },
     { LLM_ARCH_STABLELM,        "stablelm"  },
-    { LLM_ARCH_QWEN,            "qwen"      },
 };
 
 enum llm_kv {
@@ -520,22 +519,6 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
-    {
-        LLM_ARCH_QWEN,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
 
     {
         LLM_ARCH_UNKNOWN,
@@ -1135,12 +1118,6 @@ static std::string llama_token_to_piece(const struct llama_context * ctx, llama_
 //
 
 struct llama_state {
-    llama_state() {
-#ifdef GGML_USE_METAL
-        ggml_metal_log_set_callback(log_callback, log_callback_user_data);
-#endif
-    }
-
     // We save the log callback globally
     ggml_log_callback log_callback = llama_log_callback_default;
     void * log_callback_user_data = nullptr;
@@ -1266,9 +1243,6 @@ struct llama_layer {
     struct ggml_tensor * wqkv;
 
     // attention bias
-    struct ggml_tensor * bq;
-    struct ggml_tensor * bk;
-    struct ggml_tensor * bv;
     struct ggml_tensor * bo;
     struct ggml_tensor * bqkv;
 
@@ -2368,15 +2342,6 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                }
             } break;
-        case LLM_ARCH_QWEN:
-            {
-                GGUF_GET_KEY(ctx, hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
-                switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 40: model.type = e_model::MODEL_13B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
 
         default: (void)0;
     }
@@ -2674,15 +2639,15 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     }
 
     // general kv
-    LLAMA_LOG_INFO("%s: general.name     = %s\n",    __func__, model.name.c_str());
+    LLAMA_LOG_INFO("%s: general.name   = %s\n",    __func__, model.name.c_str());
 
     // special tokens
-    if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token        = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); }
-    if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token        = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); }
-    if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token        = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); }
-    if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token        = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); }
-    if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token        = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); }
-    if (vocab.linefeed_id    != -1) { LLAMA_LOG_INFO( "%s: LF token         = %d '%s'\n", __func__, vocab.linefeed_id,    vocab.id_to_token[vocab.linefeed_id].text.c_str() );    }
+    if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); }
+    if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); }
+    if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); }
+    if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); }
+    if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); }
+    if (vocab.linefeed_id    != -1) { LLAMA_LOG_INFO( "%s: LF token  = %d '%s'\n", __func__, vocab.linefeed_id,    vocab.id_to_token[vocab.linefeed_id].text.c_str() );    }
 }
 
 static void llm_load_tensors(
@@ -2812,30 +2777,6 @@ static void llm_load_tensors(
                         layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, backend_split);
                         layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},     backend_split);
 
-                        try {
-                            layer.bq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, backend);
-                        } catch (const std::runtime_error& e) {
-                            if (std::string(e.what()).find("not found") != std::string::npos) layer.bq = NULL; else throw;
-                        }
-
-                        try {
-                            layer.bk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, backend);
-                        } catch (const std::runtime_error& e) {
-                            if (std::string(e.what()).find("not found") != std::string::npos) layer.bk = NULL; else throw;
-                        }
-
-                        try {
-                            layer.bv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, backend);
-                        } catch (const std::runtime_error& e) {
-                            if (std::string(e.what()).find("not found") != std::string::npos) layer.bv = NULL; else throw;
-                        }
-
-                        try {
-                            layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend);
-                        } catch (const std::runtime_error& e) {
-                            if (std::string(e.what()).find("not found") != std::string::npos) layer.bo = NULL; else throw;
-                        }
-
                         layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
 
                         layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, backend_split);
@@ -2844,14 +2785,9 @@ static void llm_load_tensors(
 
                         if (backend == GGML_BACKEND_GPU) {
                             vram_weights +=
-                                ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
-                                ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) +
-                                (layer.bq ? ggml_nbytes(layer.bq) : 0) +
-                                (layer.bk ? ggml_nbytes(layer.bk) : 0) +
-                                (layer.bv ? ggml_nbytes(layer.bv) : 0) +
-                                (layer.bo ? ggml_nbytes(layer.bo) : 0) +
-                                ggml_nbytes(layer.ffn_norm) + ggml_nbytes(layer.ffn_gate) +
-                                ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
+                                ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq)       + ggml_nbytes(layer.wk)       +
+                                ggml_nbytes(layer.wv)        + ggml_nbytes(layer.wo)       + ggml_nbytes(layer.ffn_norm) +
+                                ggml_nbytes(layer.ffn_gate)  + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
                         }
                     }
                 } break;
@@ -3369,71 +3305,6 @@ static void llm_load_tensors(
                         }
                     }
                 } break;
-            case LLM_ARCH_QWEN:
-                {
-                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
-                    {
-                        ggml_backend_type backend_norm;
-                        ggml_backend_type backend_output;
-
-                        if (n_gpu_layers > int(n_layer)) {
-                            // norm is not performance relevant on its own but keeping it in VRAM reduces data copying
-                            // on Windows however this is detrimental unless everything is on the GPU
-#ifndef _WIN32
-                            backend_norm = llama_backend_offload;
-#else
-                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
-#endif // _WIN32
-
-                            backend_output = llama_backend_offload_split;
-                        } else {
-                            backend_norm   = GGML_BACKEND_CPU;
-                            backend_output = GGML_BACKEND_CPU;
-                        }
-
-                        model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
-                        model.output      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
-
-                        if (backend_norm == GGML_BACKEND_GPU) {
-                            vram_weights += ggml_nbytes(model.output_norm);
-                        }
-                        if (backend_output == GGML_BACKEND_GPU_SPLIT) {
-                            vram_weights += ggml_nbytes(model.output);
-                        }
-                    }
-
-                    const uint32_t n_ff = hparams.n_ff / 2;
-
-                    const int i_gpu_start = n_layer - n_gpu_layers;
-
-                    model.layers.resize(n_layer);
-
-                    for (uint32_t i = 0; i < n_layer; ++i) {
-                        const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
-                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
-
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
-
-                        layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd * 3}, backend_split);
-                        layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd * 3},         backend);
-                        layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},     backend_split);
-
-                        layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
-
-                        layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, backend_split);
-                        layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
-                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
-
-                        if (backend == GGML_BACKEND_GPU) {
-                            vram_weights +=
-                                ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wqkv)     + ggml_nbytes(layer.bqkv)     +
-                                ggml_nbytes(layer.wo)        + ggml_nbytes(layer.ffn_norm) + ggml_nbytes(layer.ffn_gate) +
-                                ggml_nbytes(layer.ffn_down)  + ggml_nbytes(layer.ffn_up);
-                        }
-                    }
-                } break;
 
             default:
                 throw std::runtime_error("unknown architecture");
@@ -3598,7 +3469,7 @@ static void llm_build_k_shift(
        struct ggml_cgraph * graph,
             llm_rope_type   type,
                   int64_t   n_ctx,
-                  int       n_rot,
+                  int64_t   n_rot,
                   float     freq_base,
                   float     freq_scale,
        const llm_build_cb & cb) {
@@ -3630,7 +3501,7 @@ static void llm_build_k_shift(
             // we rotate only the first n_rot dimensions
             ggml_rope_custom_inplace(ctx,
                     ggml_view_3d(ctx, kv.k,
-                        n_embd_head, n_head_kv, n_ctx,
+                        n_rot, n_head_kv, n_ctx,
                         ggml_element_size(kv.k)*n_embd_head,
                         ggml_element_size(kv.k)*n_embd_gqa,
                         ggml_element_size(kv.k)*n_embd_gqa*n_ctx*il),
@@ -3828,29 +3699,23 @@ static struct ggml_tensor * llm_build_kqv(
     struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
     cb(kq, "kq", il);
 
+    kq = ggml_scale(ctx, kq, kq_scale);
+    cb(kq, "kq_scaled", il);
+
     if (max_alibi_bias > 0.0f) {
-        // temporary branch until we figure out how to handle ggml_alibi through ggml_add
-        kq = ggml_scale(ctx, kq, kq_scale);
-        cb(kq, "kq_scaled", il);
-
-        if (max_alibi_bias > 0.0f) {
-            // TODO: n_head or n_head_kv
-            // TODO: K-shift is likely not working
-            // TODO: change to ggml_add
-            kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, max_alibi_bias);
-            cb(kq, "kq_scaled_alibi", il);
-        }
-
-        kq = ggml_add(ctx, kq, kq_mask);
-        cb(kq, "kq_masked", il);
-
-        kq = ggml_soft_max(ctx, kq);
-        cb(kq, "kq_soft_max", il);
-    } else {
-        kq = ggml_soft_max_ext(ctx, kq, kq_mask, 1.0f/sqrtf(float(n_embd_head)));
-        cb(kq, "kq_soft_max_ext", il);
+        // TODO: n_head or n_head_kv
+        // TODO: K-shift is likely not working
+        // TODO: change to ggml_add
+        kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, max_alibi_bias);
+        cb(kq, "kq_scaled_alibi", il);
     }
 
+    kq = ggml_add(ctx, kq, kq_mask);
+    cb(kq, "kq_masked", il);
+
+    kq = ggml_soft_max(ctx, kq);
+    cb(kq, "kq_soft_max", il);
+
     // split cached v into n_head heads
     struct ggml_tensor * v =
         ggml_view_3d(ctx, kv.v,
@@ -4015,24 +3880,12 @@ struct llm_build_context {
                 // compute Q and K and RoPE them
                 struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
-                if (model.layers[il].bq) {
-                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
-                    cb(Qcur, "Qcur", il);
-                }
 
                 struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
-                if (model.layers[il].bk) {
-                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
-                    cb(Kcur, "Kcur", il);
-                }
 
                 struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
-                if (model.layers[il].bv) {
-                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
-                    cb(Vcur, "Vcur", il);
-                }
 
                 Qcur = ggml_rope_custom(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
@@ -4051,7 +3904,7 @@ struct llm_build_context {
                 llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
 
                 cur = llm_build_kqv(ctx0, hparams, kv_self,
-                        model.layers[il].wo, model.layers[il].bo,
+                        model.layers[il].wo, NULL,
                         Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
                 cb(cur, "kqv_out", il);
             }
@@ -5044,121 +4897,6 @@ struct llm_build_context {
 
         return gf;
     }
-
-    struct ggml_cgraph * build_qwen() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
-
-        struct ggml_tensor * cur;
-        struct ggml_tensor * inpL;
-
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
-        cb(inpL, "inp_embd", -1);
-
-        // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-        cb(inp_pos, "inp_pos", -1);
-
-        // KQ_scale
-        struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
-        cb(KQ_scale, "KQ_scale", -1);
-
-        // KQ_mask (mask for 1 head, it wil be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // shift the entire K-cache if needed
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
-        }
-
-        for (int il = 0; il < n_layer; ++il) {
-            struct ggml_tensor * inpSA = inpL;
-
-            cur = llm_build_norm(ctx0, inpL, hparams,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, cb, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
-                cb(cur, "wqkv", il);
-
-                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-                cb(cur, "bqkv", il);
-
-                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
-                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
-                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 2*sizeof(float)*(n_embd)));
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
-                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
-
-                // using mode = 2 for neox mode
-                Qcur = ggml_rope_custom(
-                    ctx0, Qcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx,
-                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
-                );
-                cb(Qcur, "Qcur", il);
-
-                Kcur = ggml_rope_custom(
-                    ctx0, Kcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx,
-                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
-                );
-                cb(Kcur, "Kcur", il);
-
-                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
-
-                cur = llm_build_kqv(ctx0, hparams, kv_self,
-                        model.layers[il].wo, NULL,
-                        Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
-                cb(cur, "kqv_out", il);
-            }
-
-            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward forward
-            {
-                cur = llm_build_norm(ctx0, ffn_inp, hparams,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, cb, il);
-                cb(cur, "ffn_norm", il);
-
-                cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   NULL,
-                        model.layers[il].ffn_gate, NULL,
-                        model.layers[il].ffn_down, NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = llm_build_norm(ctx0, cur, hparams,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, cb, -1);
-        cb(cur, "result_norm", -1);
-
-        // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
-        cb(cur, "result_output", -1);
-
-        ggml_build_forward_expand(gf, cur);
-
-        return gf;
-    }
 };
 
 //
@@ -5298,7 +5036,6 @@ static const std::unordered_map<const char *, llm_offload_func_e> k_offload_map
     { "kq_scaled_alibi",            OFFLOAD_FUNC_KQ  },
     { "kq_masked",                  OFFLOAD_FUNC_KQ  },
     { "kq_soft_max",                OFFLOAD_FUNC_V   },
-    { "kq_soft_max_ext",            OFFLOAD_FUNC_V   },
     { "v",                          OFFLOAD_FUNC_V   },
     { "kqv",                        OFFLOAD_FUNC_V   },
     { "kqv_merged",                 OFFLOAD_FUNC_V   },
@@ -5633,10 +5370,6 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_stablelm();
             } break;
-        case LLM_ARCH_QWEN:
-            {
-                result = llm.build_qwen();
-            } break;
         default:
             GGML_ASSERT(false);
     }
@@ -5811,8 +5544,18 @@ static int llama_decode_internal(
         n_threads = std::min(4, n_threads);
     }
 
+    // If all tensors can be run on the GPU then using more than 1 thread is detrimental.
+    const bool full_offload_supported =
+        model.arch == LLM_ARCH_LLAMA      ||
+        model.arch == LLM_ARCH_BAICHUAN   ||
+        model.arch == LLM_ARCH_FALCON     ||
+        model.arch == LLM_ARCH_REFACT     ||
+        model.arch == LLM_ARCH_MPT        ||
+        model.arch == LLM_ARCH_STARCODER  ||
+        model.arch == LLM_ARCH_STABLELM;
+
     const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3;
-    if (ggml_cpu_has_cublas() && fully_offloaded) {
+    if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) {
         n_threads = 1;
     }
 
@@ -6671,13 +6414,10 @@ struct llama_grammar_candidate {
 // pointer. If an invalid sequence is encountered, returns `llama_partial_utf8.n_remain == -1`.
 static std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
         const char         * src,
-        size_t               n_src,
         llama_partial_utf8   partial_start) {
     static const int      lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 3, 4 };
     const char          * pos      = src;
     std::vector<uint32_t> code_points;
-    // common english strings have the same number of codepoints and bytes. `+ 1` for the terminating 0.
-    code_points.reserve(n_src + 1);
     uint32_t              value    = partial_start.value;
     int                   n_remain = partial_start.n_remain;
 
@@ -6728,13 +6468,6 @@ static std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
     return std::make_pair(std::move(code_points), llama_partial_utf8{ value, n_remain });
 }
 
-static std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
-        std::string src,
-        llama_partial_utf8 partial_start
-) {
-    return decode_utf8(src.c_str(), src.size(), partial_start);
-}
-
 // returns true iff pos points to the end of one of the definitions of a rule
 static bool llama_grammar_is_end_of_sequence(const llama_grammar_element * pos) {
     switch (pos->type) {
@@ -7288,7 +7021,6 @@ void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * c
     // Replace the data in candidates with the new_candidates data
     std::copy(new_candidates.begin(), new_candidates.end(), candidates->data);
     candidates->size = new_candidates.size();
-    candidates->sorted = false;
 
     if (ctx) {
         ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
@@ -7385,7 +7117,7 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
         } else if (piece.empty() || piece[0] == 0) {
             candidates->data[i].logit = -INFINITY;
         } else {
-            candidates_decoded.push_back(decode_utf8(piece, grammar->partial_utf8));
+            candidates_decoded.push_back(decode_utf8(piece.c_str(), grammar->partial_utf8));
             candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second });
         }
     }
@@ -7592,7 +7324,7 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
     const std::string piece = llama_token_to_piece(ctx, token);
 
     // Note terminating 0 in decoded string
-    const auto   decoded     = decode_utf8(piece, grammar->partial_utf8);
+    const auto   decoded     = decode_utf8(piece.c_str(), grammar->partial_utf8);
     const auto & code_points = decoded.first;
     for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
         grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it);
@@ -7910,21 +7642,18 @@ static void llama_convert_tensor_internal(
         return;
     }
 
-    size_t block_size = tensor->type == GGML_TYPE_F16 ? 1 : (size_t)ggml_blck_size(tensor->type);
-    size_t block_size_bytes = ggml_type_size(tensor->type);
+    auto block_size = tensor->type == GGML_TYPE_F16 ? 1 : (size_t)ggml_blck_size(tensor->type);
+    auto block_size_bytes = ggml_type_size(tensor->type);
 
     GGML_ASSERT(nelements % block_size == 0);
-    size_t nblocks = nelements / block_size;
-    size_t blocks_per_thread = nblocks / nthread;
-    size_t spare_blocks = nblocks - (blocks_per_thread * nthread); // if blocks aren't divisible by thread count
+    auto nblocks = nelements / block_size;
+    auto blocks_per_thread = nblocks / nthread;
+    auto spare_blocks = nblocks - (blocks_per_thread * nthread); // if blocks aren't divisible by thread count
 
-    size_t in_buff_offs = 0;
-    size_t out_buff_offs = 0;
-
-    for (int tnum = 0; tnum < nthread; tnum++) {
-        size_t thr_blocks = blocks_per_thread + (tnum == nthread - 1 ? spare_blocks : 0); // num blocks for this thread
-        size_t thr_elems = thr_blocks * block_size; // number of elements for this thread
-        size_t thr_block_bytes = thr_blocks * block_size_bytes; // number of input bytes for this thread
+    for (auto tnum = 0, in_buff_offs = 0, out_buff_offs = 0; tnum < nthread; tnum++) {
+        auto thr_blocks = blocks_per_thread + (tnum == nthread - 1 ? spare_blocks : 0); // num blocks for this thread
+        auto thr_elems = thr_blocks * block_size; // number of elements for this thread
+        auto thr_block_bytes = thr_blocks * block_size_bytes; // number of input bytes for this thread
 
         auto compute = [qtype] (ggml_type typ, uint8_t * inbuf, float * outbuf, int nels) {
             if (typ == GGML_TYPE_F16) {
@@ -8840,6 +8569,8 @@ struct llama_context * llama_new_context_with_model(
 
 #ifdef GGML_USE_METAL
             if (model->n_gpu_layers > 0) {
+                ggml_metal_log_set_callback(llama_log_callback_default, NULL);
+
                 ctx->ctx_metal = ggml_metal_init(1);
                 if (!ctx->ctx_metal) {
                     LLAMA_LOG_ERROR("%s: ggml_metal_init() failed\n", __func__);
@@ -9975,9 +9706,6 @@ const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal
 void llama_log_set(ggml_log_callback log_callback, void * user_data) {
     g_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
     g_state.log_callback_user_data = user_data;
-#ifdef GGML_USE_METAL
-    ggml_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
-#endif
 }
 
 static void llama_log_internal_v(ggml_log_level level, const char * format, va_list args) {

llama.h

@@ -185,7 +185,7 @@ extern "C" {
         // ref: https://github.com/ggerganov/llama.cpp/pull/2054
         float    rope_freq_base;   // RoPE base frequency, 0 = from model
         float    rope_freq_scale;  // RoPE frequency scaling factor, 0 = from model
-        float    yarn_ext_factor;  // YaRN extrapolation mix factor, negative = from model
+        float    yarn_ext_factor;  // YaRN extrapolation mix factor, NaN = from model
         float    yarn_attn_factor; // YaRN magnitude scaling factor
         float    yarn_beta_fast;   // YaRN low correction dim
         float    yarn_beta_slow;   // YaRN high correction dim

prompts/chat-with-qwen.txt

@@ -1 +0,0 @@
-You are a helpful assistant.

requirements-hf-to-gguf.txt

@@ -1,3 +0,0 @@
--r requirements.txt
-torch==2.1.1
-transformers==4.35.2

scripts/build-info.cmake

@@ -1,3 +1,5 @@
+set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp.in")
+set(OUTPUT_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp")
 set(BUILD_NUMBER 0)
 set(BUILD_COMMIT "unknown")
 set(BUILD_COMPILER "unknown")
@@ -56,3 +58,23 @@ else()
     )
     set(BUILD_TARGET ${OUT})
 endif()
+
+# Only write the build info if it changed
+if(EXISTS ${OUTPUT_FILE})
+    file(READ ${OUTPUT_FILE} CONTENTS)
+    string(REGEX MATCH "LLAMA_COMMIT = \"([^\"]*)\";" _ ${CONTENTS})
+    set(OLD_COMMIT ${CMAKE_MATCH_1})
+    string(REGEX MATCH "LLAMA_COMPILER = \"([^\"]*)\";" _ ${CONTENTS})
+    set(OLD_COMPILER ${CMAKE_MATCH_1})
+    string(REGEX MATCH "LLAMA_BUILD_TARGET = \"([^\"]*)\";" _ ${CONTENTS})
+    set(OLD_TARGET ${CMAKE_MATCH_1})
+    if (
+        NOT OLD_COMMIT   STREQUAL BUILD_COMMIT   OR
+        NOT OLD_COMPILER STREQUAL BUILD_COMPILER OR
+        NOT OLD_TARGET   STREQUAL BUILD_TARGET
+    )
+        configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
+    endif()
+else()
+    configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
+endif()

scripts/gen-build-info-cpp.cmake

@@ -1,24 +0,0 @@
-include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
-
-set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp.in")
-set(OUTPUT_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp")
-
-# Only write the build info if it changed
-if(EXISTS ${OUTPUT_FILE})
-    file(READ ${OUTPUT_FILE} CONTENTS)
-    string(REGEX MATCH "LLAMA_COMMIT = \"([^\"]*)\";" _ ${CONTENTS})
-    set(OLD_COMMIT ${CMAKE_MATCH_1})
-    string(REGEX MATCH "LLAMA_COMPILER = \"([^\"]*)\";" _ ${CONTENTS})
-    set(OLD_COMPILER ${CMAKE_MATCH_1})
-    string(REGEX MATCH "LLAMA_BUILD_TARGET = \"([^\"]*)\";" _ ${CONTENTS})
-    set(OLD_TARGET ${CMAKE_MATCH_1})
-    if (
-        NOT OLD_COMMIT   STREQUAL BUILD_COMMIT   OR
-        NOT OLD_COMPILER STREQUAL BUILD_COMPILER OR
-        NOT OLD_TARGET   STREQUAL BUILD_TARGET
-    )
-        configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
-    endif()
-else()
-    configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
-endif()