Merge branch 'master' into compilade/lazy-convert-hf

Author: compilade

.flake8

@@ -1,4 +1,17 @@
 [flake8]
 max-line-length = 125
 ignore = E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503
-exclude = examples/*,examples/*/**,*/**/__init__.py
+exclude =
+    # Do not traverse examples
+    examples,
+    # Do not include package initializers
+    __init__.py,
+    # No need to traverse our git directory
+    .git,
+    # There's no value in checking cache directories
+    __pycache__,
+    # No need to include the build path
+    build,
+    # This contains builds that we don't want to check
+    dist  # This is generated with `python build .` for package releases
+# max-complexity = 10

.github/workflows/bench.yml

@@ -52,7 +52,19 @@ jobs:
             ftype: q4_0
             pr_comment_enabled: "true"
 
-    if: ${{ github.event.inputs.gpu-series == 'Standard_NC4as_T4_v3' || github.event.schedule || github.event.pull_request || github.head_ref == 'master' || github.ref_name == 'master' || github.event.push.ref == 'refs/heads/master' }}
+    if: |
+      inputs.gpu-series == 'Standard_NC4as_T4_v3'
+      || (
+        github.event_name == 'schedule'
+        && github.ref_name == 'master'
+        && github.repository_owner == 'ggerganov'
+      )
+      || github.event_name == 'pull_request_target'
+      || (
+        github.event_name == 'push'
+        && github.event.ref == 'refs/heads/master'
+        && github.repository_owner == 'ggerganov'
+      )
     steps:
       - name: Clone
         id: checkout

Makefile

@@ -77,11 +77,10 @@ test: $(TEST_TARGETS)
 			./$$test_target $(CURDIR)/models/ggml-vocab-llama-bpe.gguf; \
 			./$$test_target $(CURDIR)/models/ggml-vocab-phi-3.gguf; \
 			./$$test_target $(CURDIR)/models/ggml-vocab-falcon.gguf; \
-			./$$test_target $(CURDIR)/models/ggml-vocab-deepseek-coder.gguf; \
-			./$$test_target $(CURDIR)/models/ggml-vocab-deepseek-llm.gguf; \
 			./$$test_target $(CURDIR)/models/ggml-vocab-bert-bge.gguf; \
 			./$$test_target $(CURDIR)/models/ggml-vocab-starcoder.gguf; \
 			./$$test_target $(CURDIR)/models/ggml-vocab-gpt-2.gguf; \
+			./$$test_target $(CURDIR)/models/ggml-vocab-refact.gguf; \
 		elif [ "$$test_target" = "tests/test-tokenizer-1-spm" ]; then \
 			continue; \
 		elif [ "$$test_target" = "tests/test-tokenizer-1-bpe" ]; then \

README.md

@@ -712,6 +712,8 @@ Building the program with BLAS support may lead to some performance improvements
 
 To obtain the official LLaMA 2 weights please see the <a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a> section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face.
 
+Note: `convert.py` does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face.
+
 ```bash
 # obtain the official LLaMA model weights and place them in ./models
 ls ./models
@@ -977,48 +979,20 @@ Here is a demo of an interactive session running on Pixel 5 phone:
 
 https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
 
-#### Building the Project using Termux (F-Droid)
-Termux from F-Droid offers an alternative route to execute the project on an Android device. This method empowers you to construct the project right from within the terminal, negating the requirement for a rooted device or SD Card.
-
-Outlined below are the directives for installing the project using OpenBLAS and CLBlast. This combination is specifically designed to deliver peak performance on recent devices that feature a GPU.
-
-If you opt to utilize OpenBLAS, you'll need to install the corresponding package.
-```
-apt install libopenblas
-```
-
-Subsequently, if you decide to incorporate CLBlast, you'll first need to install the requisite OpenCL packages:
-```
-apt install ocl-icd opencl-headers opencl-clhpp clinfo
-```
-
-In order to compile CLBlast, you'll need to first clone the respective Git repository, which can be found at this URL: https://github.com/CNugteren/CLBlast. Alongside this, clone this repository into your home directory. Once this is done, navigate to the CLBlast folder and execute the commands detailed below:
+#### Build on Android using Termux
+[Termux](https://github.com/termux/termux-app#installation) is an alternative to execute `llama.cpp` on an Android device (no root required).
 ```
-cmake .
-make
-cp libclblast.so* $PREFIX/lib
-cp ./include/clblast.h ../llama.cpp
+apt update && apt upgrade -y
+apt install git
 ```
 
-Following the previous steps, navigate to the LlamaCpp directory. To compile it with OpenBLAS and CLBlast, execute the command provided below:
+It's recommended to move your model inside the `~/` directory for best performance:
 ```
-cp /data/data/com.termux/files/usr/include/openblas/cblas.h .
-cp /data/data/com.termux/files/usr/include/openblas/openblas_config.h .
-make LLAMA_CLBLAST=1 //(sometimes you need to run this command twice)
-```
-
-Upon completion of the aforementioned steps, you will have successfully compiled the project. To run it using CLBlast, a slight adjustment is required: a command must be issued to direct the operations towards your device's physical GPU, rather than the virtual one. The necessary command is detailed below:
+cd storage/downloads
+mv model.gguf ~/
 ```
-GGML_OPENCL_PLATFORM=0
-GGML_OPENCL_DEVICE=0
-export LD_LIBRARY_PATH=/vendor/lib64:$LD_LIBRARY_PATH
-```
-
-(Note: some Android devices, like the Zenfone 8, need the following command instead - "export LD_LIBRARY_PATH=/system/vendor/lib64:$LD_LIBRARY_PATH". Source: https://www.reddit.com/r/termux/comments/kc3ynp/opencl_working_in_termux_more_in_comments/ )
-
-For easy and swift re-execution, consider documenting this final part in a .sh script file. This will enable you to rerun the process with minimal hassle.
 
-Place your desired model into the `~/llama.cpp/models/` directory and execute the `./main (...)` script.
+[Follow the Linux build instructions](https://github.com/ggerganov/llama.cpp#build) to build `llama.cpp`.
 
 ### Docker
 

common/common.cpp

@@ -76,7 +76,7 @@ int32_t get_num_physical_cores() {
     // enumerate the set of thread siblings, num entries is num cores
     std::unordered_set<std::string> siblings;
     for (uint32_t cpu=0; cpu < UINT32_MAX; ++cpu) {
-        std::ifstream thread_siblings("/sys/devices/system/cpu"
+        std::ifstream thread_siblings("/sys/devices/system/cpu/cpu"
             + std::to_string(cpu) + "/topology/thread_siblings");
         if (!thread_siblings.is_open()) {
             break; // no more cpus

convert-hf-to-gguf-update.py

@@ -1,3 +1,5 @@
+#!/usr/bin/env python3
+
 # This script downloads the tokenizer models of the specified models from Huggingface and
 # generates the get_vocab_base_pre() function for convert-hf-to-gguf.py
 #
@@ -31,6 +33,7 @@
 from enum import IntEnum, auto
 from transformers import AutoTokenizer
 
+logging.basicConfig(level=logging.DEBUG)
 logger = logging.getLogger("convert-hf-to-gguf-update")
 
 
@@ -62,6 +65,8 @@ class TOKENIZER_TYPE(IntEnum):
     {"name": "mpt",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
     {"name": "starcoder",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
     {"name": "gpt-2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", },
+    {"name": "refact",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", },
+    {"name": "command-r",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", },
 ]
 
 # make directory "models/tokenizers" if it doesn't exist
@@ -102,6 +107,14 @@ def download_file_with_auth(url, token, save_path):
     save_path = f"models/tokenizers/{name}/tokenizer.json"
     download_file_with_auth(url, token, save_path)
 
+    # if downloaded file is less than 1KB, we likely need to download an LFS instead
+    if os.path.getsize(save_path) < 1024:
+        # remove the file
+        os.remove(save_path)
+        url = f"{repo}/resolve/main/tokenizer.json"
+        save_path = f"models/tokenizers/{name}/tokenizer.json"
+        download_file_with_auth(url, token, save_path)
+
     if tokt == TOKENIZER_TYPE.SPM:
         url = f"{repo}/resolve/main/tokenizer.model"
         save_path = f"models/tokenizers/{name}/tokenizer.model"
@@ -158,8 +171,8 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         chktok = tokenizer.encode(chktxt)
         chkhsh = sha256(str(chktok).encode()).hexdigest()
 
-        print(f"chktok: {{chktok}}")
-        print(f"chkhsh: {{chkhsh}}")
+        logger.debug(f"chktok: {{chktok}}")
+        logger.debug(f"chkhsh: {{chkhsh}}")
 
         res = None
 
@@ -168,22 +181,22 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         #       don't edit the hashes manually!
 {src_ifs}
         if res is None:
-            print("\\n")
-            print("**************************************************************************************")
-            print("** WARNING: The BPE pre-tokenizer was not recognized!")
-            print("**          There are 2 possible reasons for this:")
-            print("**          - the model has not been added to convert-hf-to-gguf-update.py yet")
-            print("**          - the pre-tokenization config has changed upstream")
-            print("**          Check your model files and convert-hf-to-gguf-update.py and update them accordingly.")
-            print("** ref:     https://github.com/ggerganov/llama.cpp/pull/6920")
-            print("**")
-            print(f"** chkhsh:  {{chkhsh}}")
-            print("**************************************************************************************")
-            print("\\n")
+            logger.warning("\\n")
+            logger.warning("**************************************************************************************")
+            logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!")
+            logger.warning("**          There are 2 possible reasons for this:")
+            logger.warning("**          - the model has not been added to convert-hf-to-gguf-update.py yet")
+            logger.warning("**          - the pre-tokenization config has changed upstream")
+            logger.warning("**          Check your model files and convert-hf-to-gguf-update.py and update them accordingly.")
+            logger.warning("** ref:     https://github.com/ggerganov/llama.cpp/pull/6920")
+            logger.warning("**")
+            logger.warning(f"** chkhsh:  {{chkhsh}}")
+            logger.warning("**************************************************************************************")
+            logger.warning("\\n")
             raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()")
 
-        print(f"tokenizer.ggml.pre: {{repr(res)}}")
-        print(f"chkhsh: {{chkhsh}}")
+        logger.debug(f"tokenizer.ggml.pre: {{repr(res)}}")
+        logger.debug(f"chkhsh: {{chkhsh}}")
 
         return res
 """
@@ -197,6 +210,8 @@ def get_vocab_base_pre(self, tokenizer) -> str:
 # generate tests for each tokenizer model
 
 tests = [
+    "ied 4 ½ months",
+    "Führer",
     "",
     " ",
     "  ",
@@ -281,6 +296,6 @@ def get_vocab_base_pre(self, tokenizer) -> str:
 for model in models:
     name = model["name"]
 
-    logger.info(f"python3 convert-hf-to-gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only")
+    print(f"python3 convert-hf-to-gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only") # noqa: NP100
 
 logger.info("\n")

convert-hf-to-gguf.py

@@ -391,6 +391,12 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         if chkhsh == "3ce83efda5659b07b1ad37ca97ca5797ea4285d9b9ab0dc679e4a720c9da7454":
             # ref: https://huggingface.co/openai-community/gpt2
             res = "gpt-2"
+        if chkhsh == "6221ad2852e85ce96f791f476e0b390cf9b474c9e3d1362f53a24a06dc8220ff":
+            # ref: https://huggingface.co/smallcloudai/Refact-1_6-base
+            res = "refact"
+        if chkhsh == "9c2227e4dd922002fb81bde4fc02b0483ca4f12911410dee2255e4987644e3f8":
+            # ref: https://huggingface.co/CohereForAI/c4ai-command-r-v01
+            res = "command-r"
 
         if res is None:
             logger.warning("\n")
@@ -407,7 +413,7 @@ def get_vocab_base_pre(self, tokenizer) -> str:
             logger.warning("\n")
             raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()")
 
-        logger.debug(f"tokenizer.ggml.pre: {res}")
+        logger.debug(f"tokenizer.ggml.pre: {repr(res)}")
         logger.debug(f"chkhsh: {chkhsh}")
 
         return res

convert-lora-to-ggml.py

@@ -16,6 +16,7 @@
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
 import gguf
 
+logging.basicConfig(level=logging.DEBUG)
 logger = logging.getLogger("lora-to-gguf")
 
 NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1}

examples/gguf-split/gguf-split.cpp

@@ -32,6 +32,7 @@ struct split_params {
     int n_split_tensors = 128;
     std::string input;
     std::string output;
+    bool no_tensor_first_split = false;
     bool dry_run = false;
 };
 
@@ -49,6 +50,7 @@ static void split_print_usage(const char * executable) {
     printf("  --merge                 merge multiple GGUF to a single GGUF\n");
     printf("  --split-max-tensors     max tensors in each split (default: %d)\n", default_params.n_split_tensors);
     printf("  --split-max-size N(M|G) max size per split\n");
+    printf("  --no-tensor-first-split do not add tensors to the first split (disabled by default)\n");
     printf("  --dry-run               only print out a split plan and exit, without writing any new files\n");
     printf("\n");
 }
@@ -100,6 +102,10 @@ static void split_params_parse_ex(int argc, const char ** argv, split_params & p
             arg_found = true;
             params.dry_run = true;
         }
+        if (arg == "--no-tensor-first-split") {
+            arg_found = true;
+            params.no_tensor_first_split = true;
+        }
 
         if (is_op_set) {
             throw std::invalid_argument("error: either --split or --merge can be specified, but not both");
@@ -200,10 +206,10 @@ struct split_strategy {
         // because we need to know list of tensors for each file in advance, we will build all the ctx_out for all output splits
         int i_split = -1;
         struct gguf_context * ctx_out = NULL;
-        auto new_ctx_out = [&]() {
+        auto new_ctx_out = [&](bool allow_no_tensors) {
             i_split++;
             if (ctx_out != NULL) {
-                if (gguf_get_n_tensors(ctx_out) == 0) {
+                if (gguf_get_n_tensors(ctx_out) == 0 && !allow_no_tensors) {
                     fprintf(stderr, "error: one of splits have 0 tensors. Maybe size or tensors limit is too small\n");
                     exit(EXIT_FAILURE);
                 }
@@ -220,7 +226,12 @@ struct split_strategy {
         };
 
         // initialize ctx_out for the first split
-        new_ctx_out();
+        new_ctx_out(false);
+
+        // skip first split if no_tensor_first_split is set
+        if (params.no_tensor_first_split) {
+            new_ctx_out(true);
+        }
 
         // process tensors one by one
         size_t curr_tensors_size = 0; // current size by counting only tensors size (without metadata)
@@ -230,7 +241,7 @@ struct split_strategy {
             size_t n_bytes = GGML_PAD(ggml_nbytes(t), GGUF_DEFAULT_ALIGNMENT);
             size_t next_tensors_size = curr_tensors_size + n_bytes;
             if (should_split(i, next_tensors_size)) {
-                new_ctx_out();
+                new_ctx_out(false);
                 curr_tensors_size = n_bytes;
             } else {
                 curr_tensors_size = next_tensors_size;

examples/gguf-split/tests.sh

@@ -55,15 +55,15 @@ $MAIN --model $WORK_PATH/ggml-model-merge.gguf --random-prompt --n-predict 32
 echo PASS
 echo
 
-# 4. Split with no tensor in metadata
-#$SPLIT --split-max-tensors 32 --no-tensor-in-metadata $WORK_PATH/ggml-model-merge.gguf $WORK_PATH/ggml-model-split-32-tensors
-#echo PASS
-#echo
+# 4. Split with no tensors in the first split
+$SPLIT --split-max-tensors 32 --no-tensor-first-split $WORK_PATH/ggml-model-merge.gguf $WORK_PATH/ggml-model-split-32-tensors
+echo PASS
+echo
 
 # 4b. Test the sharded model is loading properly
-#$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00006.gguf --random-prompt --n-predict 32
-#echo PASS
-#echo
+$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00007.gguf --random-prompt --n-predict 32
+echo PASS
+echo
 
 # 5. Merge
 #$SPLIT --merge $WORK_PATH/ggml-model-split-32-tensors-00001-of-00006.gguf $WORK_PATH/ggml-model-merge-2.gguf